quantification of aerosol properties in four locations in indo-gangetic plain...

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QUANTIFICATION OF AEROSOL PROPERTIES IN FOUR LOCATIONS

IN INDO-GANGETIC PLAIN: IMPLICATIONS FOR CLIMATIC IMPACTS

By

HUMERA BIBI

DEPARTMENT OF PHYSICS

UNIVERSITY OF PESHAWAR

SESSION 2012-2013

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This work has been submitted by

Humera Bibi

as thesis in partial fulfillment for the requirement of the

Degree

of

DOCTOR OF PHILOSOPHY

in

PHYSICS

Environmental aerosol Physics Group

Department of Physics

University of Peshawar

Peshawar, Pakistan

Session 2012-13

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Certificate

It is certified the work contained in this thesis entitled “QUANTIFICATION OF AEROSOL

PROPERTIES IN FOUR LOCATIONS IN INDO-GANGETIC PLAIN:

IMPLICATIONSFOR CLIMATIC IMPACTS” has been carried out By Ms. Humera Bibi. In

all the publications, her name is written as Humera Bibi.

Supervisor Submitted through:

Dr. Khan Alam Chairman

Assistant Professor Department of Physics

Department of Physics University of Peshawar

University of Peshawar Peshawar, Pakistan

Peshawar, Pakistan

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Approval

This thesis entitled “QUANTIFICATION OF AEROSOL PROPERTIES IN FOUR

LOCATIONS IN INDO-GANGETIC PLAIN: IMPLICATIONS FOR CLIMATIC

IMPACTS” prepared by Humera Bibi in Partial fulfillment of the requirement for the degree

Doctor of Philosophy in Physics, has been approved and accepted by the following:

_______________ _______________

Supervisor Chairman

Dr. Khan Alam Department of Physics

Assistant Professor University of Peshawar

Department of Physics Peshawar, Pakistan

University of Peshawar

Peshawar, Pakistan

_______________

External examiner

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Dedicated to

my parents, husband and kids

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Acknowledgement

To start and complete thesis, is not only a difficult but is also an uphill task, however Allah

almighty made this task easy for me and today I have completed this thesis with the blessing

of Allah almighty who bestowed upon me the strength and wisdom to complete this tough task.

Today I am going to acknowledge the able and timely guidance and advise of Dr. Khan Alam,

my supervisor and mentor who fully supported and guided me in completion of this thesis, here

I am frankly conceded that without the active support, guidance and advise of Dr. Khan Alam

the present task was not possible, here I also acknowledge the support of my dearest and closest

friend Ms. Samina Bibi PhD scholar of my department who whole heartedly supported me and

always boosted my courage for the completion of this task. I also whole heartedly extend my

thanks and gratitude to my family members who always supported and co-operated with me

and always appreciated and acknowledged my hard work, struggle and efforts towards the

present task.

I am grateful to the Multi-sensor Aerosol Product Sampling System (MAPSS) teams at NASA

for the provision of satellite data and would like to thank NASA for providing the AERONET

data for four observational sites (Karachi, Lahore, Jaipur and Kanpur). I also grateful to

CALIPSO mission scientists for the provision of data utilized in this research work. We

gratefully acknowledge the working team for the global HYSPLIT data set.

Humera Bibi

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List of publications

1. Intercomparison of MODIS, MISR, OMI, and CALIPSO aerosol optical depth

retrievals for four locations on the Indo-Gangetic plains and validation against

AERONET data, Atmospheric Environment 111, 113-126 (2015) by Humera Bibi,

Khan Alam, Farrukh Chishtie, Samina Bibi, Imran Shahid, Thomas Blaschke,

2. In-depth discrimination of aerosol types using multiple clustering techniques over four

locations in Indo-Gangetic plains, Atmospheric Research 181, 106–114 (2016) by

Humera Bibi, Khan Alam, Samina Bibi.

3. Long-term (2007–2013) analysis of aerosol optical properties over four locations in

the Indo-Gangetic plains, Applied Optics 55, 6199-6211 (2016) by Humera Bibi, Khan

Alam, Thomas Blaschke, Samina Bibi, Muhammad Jawed Iqbal.

4. Long-term analysis of shortwave direct aerosol radiative forcing for four locations on

the Indo-Gangetic plains: Implications to climate forcing, (submitted) by Humera Bibi,

Khan Alam, Samina Bibi.

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Abstract

This thesis provides an intercomparison of aerosol optical depth (AOD) retrievals from

satellite-based MODerate resolution Imaging Spectroradiometer (MODIS), Multiangle

Imaging Spectroradiometer (MISR), Ozone Monitoring Instrument (OMI), and Cloud-Aerosol

Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) instrumentation at four sites

with different aerosol environments (Karachi, Lahore, Jaipur, and Kanpur) in Indo-Gangetic

Plain (IGP) during 2007-2013, with validation against AOD observations from the ground-

based AERosol RObotic NETwork (AERONET). Both MODIS Deep Blue (MODISDB) and

MODIS Standard (MODISSTD) products were compared with the AERONET products. The

MODISSTD-AERONET comparisons revealed a high degree of correlation for the four

investigated sites at Karachi, Lahore, Jaipur, and Kanpur, the MODISDB-AERONET

comparisons revealed even better correlations, and the MISR-AERONET comparisons also

indicated strong correlations, as did the OMI-AERONET comparisons, while the CALIPSO-

AERONET comparisons revealed only poor correlations due to the limited number of data

points available. We also computed the Root Mean Square error (RMSE), Mean Absolute Error

(MAE) and Root Mean Bias (RMB). Using AERONET data to validate MODISSTD, MODISDB,

MISR, OMI, and CALIPSO data revealed that MODISSTD data was more accurate over

vegetated locations than over un-vegetated locations, while MISR data was more accurate over

areas closer to the ocean than over other areas. The MISR instrument performed better than the

other instruments over Karachi and Kanpur, while the MODISSTD AOD retrievals were better

than those from the other instruments over Lahore and Jaipur. We also computed the Expected

Error Bounds (EEBs) for both MODIS retrievals and found that MODISSTD consistently

outperformed MODISDB in all the investigated areas. High AOD values were observed by the

MODISSTD, MODISDB, MISR, and OMI instruments during the summer months (April-

August); these ranged from 0.32 to 0.78, possibly due to human activity and biomass burning.

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In contrast, high AOD values were observed by the CALIPSO instrument between September

and December, due to high concentrations of smoke and soot aerosols. The variable monthly

AOD figures obtained with different sensors indicate overestimation by MODISSTD,

MODISDB, OMI, and CALIPSO instruments over Karachi, Lahore, Jaipur and Kanpur, relative

to the AERONET data, but underestimation by the MISR instrument.

The examination of the distribution and spectral behavior of the optical properties of

atmospheric aerosols in the IGP were also being performed using an AERONET. The AOD

and Angstrom Exponent (AE) results revealed a high AOD with a low AE value over Karachi

and Jaipur in July, while a high AOD with a high AE value was reported over Lahore and

Kanpur during October and December. The pattern of the aerosol Volume Size Distribution

(VSD) was similar across all four sites, with a prominent peak in coarse mode at a radius of

4.0–5.0 μm, and in fine mode at a radius of 0.1–4.0 μm, for all seasons. On the other hand,

during the winter months, the fine-mode peaks were comparable to the coarse mode, which

was not the case during the other seasons. The Single Scattering Albedo (SSA) was found to

be strongly wavelength dependent during all seasons and for all sites, except for Kanpur, where

the SSA decreases with increasing wavelength during winter and post-monsoon. It was found

that the phase function of the atmospheric aerosol was high at a small angle and stable around

a scattering angle of 90°–180° at all sites and during all seasons. Spectral variation of the

Asymmetry Parameter (AP) revealed a decreasing trend with increasing wavelength, and this

decreasing trend was more pronounced during the summer, winter, and post-monsoon as

compared to pre-monsoon. Furthermore, extensive measurements suggest that both Real

Refractive Index (RRI) and imaginary Refractive Index (IRI) show contrasting spectral

behavior during all seasons. The analysis of the National Oceanic and Atmospheric

Administration (NOAA) Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT)

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model back trajectory revealed that the seasonal variation in aerosol types was influenced by a

contribution of air masses from multiple source locations.

Discrimination of aerosol types is essential over the IGP because several aerosol types

originate from different sources having different atmospheric impacts. We analyzed a seasonal

discrimination of aerosol types by multiple clustering techniques using AERONET data sets

for the study period over observational sites. We discriminated the aerosols into three major

types; dust, biomass burning and urban/industrial. The discrimination was carried out by

analyzing different aerosol optical properties such as AOD, AE, SSA, RRI, Extinction

Angstrom Exponent (EAE), Absorption Angstrom Exponent (AAE) and their interrelationship

to investigate the dominant aerosol types and to examine the variation in their seasonal

distribution. The results revealed that during summer and pre-monsoon, dust aerosols were

dominant while during winter and post-monsoon prevailing aerosols were biomass burning and

urban industrial, and the mixed type of aerosols were present in all seasons. These types of

aerosol discriminated from AERONET were in good agreement with CALIPSO measurement.

Finally, the long term radiative impacts of aerosol on regional climate of IGP were

studied. For this purpose, the spatio-temporal variations of Shortwave Direct Aerosol Radiative

Forcing (SDARF) and Shortwave Direct Aerosol Radiative Forcing Efficiency (SDARFE) at

the Top Of Atmosphere (TOA), SURface (SUR) and within ATMosphere (ATM) along with

atmospheric Heating Rate (HR) were calculated using Santa Barbara DISORT Atmospheric

Radiative Transfer (SBDART) model. It was observed that the monthly averaged SDARFTOA

was either positive or negative, whereas SDARFSUR were found to be negative leading to

positive ATM during all the months over all sites. The seasonal analysis of SDARF revealed

that SDARFTOA and SDARFSUR were negative during all the seasons. The increment in the net

atmosphere forcing lead to maximum HR in November-December and May, presenting the

strongest atmospheric absorption. Similar to SDARF, the monthly averaged SDARFETOA were

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found to be either positive or negative, while SDARFESUR were negative throughout the

months resulting in the more enhanced positive SDARFEATM. Generally, the highest values of

SDARFETOA were observed during the winter and lowest during the summer over all the sites

except for Karachi where the lowest TOA efficiency was noted in pre-monsoon. Accordingly,

the highest values of SDARFESUR were observed during the winter and minimum during the

summer over the rest of the sites. The SDARFE at ATM were observed to be maximum during

pre-monsoon and lowest during the summer over Karachi, Lahore and Kanpur, while over

Jaipur, maximum SDARFE at ATM were noted during winter and minimum during summer.

Additionally, to compare the model estimated forcing against AERONET derived forcing, the

regression analysis of AERONET-SBDART forcing were carried out. It was observed that

SDARF at the SUR and TOA showed relatively higher correlation over Lahore, moderate over

Jaipur and Kanpur and lower over Karachi.

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Table of Contents

Introduction ................................................................................................................................ 1

1.1 Aerosols ....................................................................................................................... 1

1.2 Classification of aerosols ............................................................................................ 2

1.3 Life span and removal mechanism of aerosol ............................................................. 2

1.4 Aerosols over Indo Gangetic plain .............................................................................. 3

1.5 Aerosol physical and optical properties ...................................................................... 4

1.5.1 Scattering ............................................................................................................. 4

1.5.2 Absorption............................................................................................................ 4

1.5.3 Aerosol optical depth ........................................................................................... 5

1.5.4 Angstrom exponent .............................................................................................. 5

1.5.5 Volume size distribution ...................................................................................... 5

1.5.6 Single scattering albedo ....................................................................................... 6

1.5.7 Phase function ...................................................................................................... 6

1.5.8 Asymmetry parameter .......................................................................................... 6

1.5.9 Refractive indices................................................................................................. 7

1.6 Clustering analysis ...................................................................................................... 7

1.7 Remote sensing instruments ........................................................................................ 8

1.8 Aerosol radiative effects.............................................................................................. 9

1.9 Motivation of research work ..................................................................................... 10

1.10 Layout of study.......................................................................................................... 11

Review of the literature ............................................................................................................ 13

2.1 Validation of satellite AOD retrievals against ground based AOD retrievals .......... 13

2.2 Discrimination of aerosol types using multiple clustering techniques ...................... 17

2.3 Analysis of aerosol properties ................................................................................... 20

2.4 Estimation of aerosol radiative forcing ..................................................................... 23

Site description, instrumentation and methodology................................................................. 30

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3.1 Site description and meteorology .............................................................................. 30

3.2 Instrumentation.......................................................................................................... 33

3.2.1 Aerosol robotic network .................................................................................... 33

3.2.2 Moderate resolution imaging spectroradiometer ............................................... 35

3.2.3 Ozone monitoring instrument ............................................................................ 36

3.2.4 Multiangle imaging Spectroradiometer ............................................................. 37

3.2.5 Cloud-aerosol lidar and infrared pathfinder satellite observations .................... 37

3.2.6 Hybrid single particle lagrangian integrated trajectory ..................................... 39

3.3 Methodology ............................................................................................................. 39

3.3.1 AOD retrieval algorithms: The Dark Target and the Deep Blue approaches .... 39

3.3.2 Comparisons between satellite-based and ground-based AODs ....................... 40

3.3.3 Aerosol classification through cluster analysis .................................................. 43

3.3.4 Estimation of aerosol radiative forcing .............................................................. 44

Intercomparison of MODIS, MISR, OMI, and CALIPSO aerosol optical depth retrievals for

four locations on the Indo-Gangetic plains and validation against AERONET data .............. 47

4.1 Intercomparison of satellite and ground-based aerosol optical depth ....................... 47

4.2 Monthly aerosol optical depth variability ................................................................. 58

Long-term (2007–2013) analysis of aerosol optical properties over four locations in the Indo-

Gangetic plains......................................................................................................................... 71

5.1 Variability of aerosol optical properties .................................................................... 71

5.1.1 Aerosol optical depth and angstrom exponent ................................................... 71

5.1.2 Volume size distribution .................................................................................... 75

5.1.3 Single scattering albedo ..................................................................................... 77

5.1.4 Phase function .................................................................................................... 80

5.1.5 Asymmetry parameter ........................................................................................ 82

5.1.6 Refractive indices............................................................................................... 85

5.2 Hybrid single particle lagrangian integrated trajectory ............................................. 89

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In-depth discrimination of aerosol types using multiple clustering techniques over four

locations in Indo-Gangetic plains ............................................................................................ 93

6.1 Multiple clustering techniques .................................................................................. 93

6.1.1 Aerosol optical depth versus angstrom exponent .............................................. 93

6.1.2 Extinction angstrom exponent versus absorption angstrom exponent ............... 96

6.1.3 Extinction angstrom exponent versus single scattering albedo ......................... 99

6.1.4 Extinction angstrom exponent versus real refractive index ............................. 102

6.2 Vertical profile of aerosol from CALIPSO ............................................................. 104

Estimation of shortwave direct aerosol radiative forcing for four locations on the Indo-

Gangetic plains: Model results and ground measurement ..................................................... 106

7.1 Monthly and seasonal aerosol radiative forcing ...................................................... 106

7.2 Atmospheric heating rate ........................................................................................ 114

7.3 Monthly and seasonal aerosol radiative forcing efficiency ..................................... 117

7.4 Validation ................................................................................................................ 122

7.5 HYSPLIT back trajectory analysis .......................................................................... 125

Conclusions and Future work ................................................................................................ 128

8.1 Summary, conclusions and future work .................................................................. 128

8.2 Future work .................................................................................................................. 134

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List of abbreviations

IGP Indo Gangetic plain

SACOL Climate and environmental observatory of Lanzhou University

AOD Aerosol Optical Depth

AE Angstrom Exponent

VSD Volume Size Distribution

SSA Single Scattering Albedo

AP Asymmetry Parameter

RRI Real Refractive Index

IRI Imaginary Refractive Index

AAE Absorption Angstrom Exponent

EAE Extinction Angstrom Exponent

AERONET Aerosol Robotic Network

MODIS Moderate Resolution Imagining Spectroradiometer

OMI Ozone Monitoring Instrument

MISR Multi-angle Imaging Spectroradiometer

CALIPSO Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations

HYSPLIT Hybrid Single Particle Lagrangian Integrated Trajectory

MWR Multi-Wavelength solar Radiometer

NIVR Netherlands Agency for Aerospace Programs

DT Dark Target

LUT Look-Up Table

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DB Deep Blue

RMSE Root Mean Square error

MAE Mean Absolute Error

RMB Root Mean Bias

EE Expected Error

EEB Expected Error Bound

AMSL Above Mean Sea level

AGL Above Ground Level

SBDART Santa Barbara DISORT Atmospheric Radiative Transfer

SDARF Shortwave Direct Aerosol Radiative Forcing

SDARFE Shortwave Direct Aerosol Radiative Forcing Efficiency

TOA Top Of Atmosphere

SUR SURface

ATM ATMosphere

HR Heating Rate

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List of figures

1.1 Atmospheric cycling of aerosols.

1.2 Estimated annual and global average energy balance.

3.1 Map of the study area.

3.2 Aerosol robotic network.

3.3 Moderate resolution imaging spectro-radiometer.

3.4 Ozone monitoring instrument.

3.5 Multi-angle imaging spectroradiometer.

3.6 Cloud-aerosol lidar and infrared pathfinder satellite observations.

3.7 Schematic diagram of Shortwave Radiative Forcing using SBDART model.

4.2 Scatter plots for AERONET AOD vs. MODIS Deep Blue product over different cities

(Karachi, Lahore, Kanpur and Jaipur).

4.1 Scatter plots for AERONET AOD vs. MODIS Standard product over different cities


4.3 Scatter plots for AERONET AOD vs. MISR over different cities (Karachi, Lahore, Kanpur

and Jaipur).

4.4 Scatter plots for AERONET AOD vs. OMI over different cities (Karachi, Lahore, Kanpur

and Jaipur).

4.5 Scatter plots for AERONET AOD vs. CALIPSO over different cities (Karachi, Lahore,

Kanpur and Jaipur).

4.6 Variability in monthly mean AOD values from AERONET and MODIS Standard product

over different cities (Karachi, Lahore, Kanpur and Jaipur).

4.7 Variability in monthly mean AOD values from AERONET and MODIS Deep Blue product


xviii

4.8 Variability in monthly mean AOD values from AERONET and MISR over different cities


4.9 Variability in monthly mean AOD values from AERONET and OMI over different cities


4.10 Variability in monthly mean AOD values from AERONET and CALIPSO over different

cities (Karachi, Lahore, Kanpur and Jaipur).

5.1 Time series of monthly averaged AOD at 500 nm and AE in the range of 440-870 nm for

Karachi, Lahore, Jaipur and Kanpur during the 2007-2013 period.

5.2 Seasonal average variation of aerosol volume size distribution (a) Summer, (b) Winter, (c)

Pre-monsoon, and (d) Post-monsoon over Karachi, Lahore, Jaipur and Kanpur during

the 2007-2013 period.

5.3 The average seasonal variation of Single Scattering Albedo (a) Summer, (b) Winter, (c)



5.4 Seasonal variation of the phase function at 440 nm (a) Summer, (b) Winter, (c) Pre-

monsoon, and (d) Post-monsoon over Karachi, Lahore, Jaipur and Kanpur during the

2007-2013 period.

5.5 Average seasonal variation of the Asymmetry Parameter (a) Summer, (b) Winter, (c) Pre-

monsoon, and (d) Post-monsoon over Karachi, Lahore, Jaipur and Kanpur during the

2007-2013 period.

5.6 Average seasonal variation of Real Refractive Index during (a) Summer, (b) Winter, (c)



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5.7 Average seasonal variation of the Imaginary Refractive Index for (a) Summer, (b) Winter,

(c) Pre-monsoon, and (d) Post-monsoon over Karachi, Lahore, Jaipur, and Kanpur

during the 2007-2013 period.

5.8 72-hour HYSPLIT back trajectories representing the air masses’ origins and pathways at

500, 1000, and 1500 m Above Ground Level (AGL) in each season during the 2007-

2013 period over Karachi, Lahore, Jaipur, and Kanpur.

6.1 Scattered plot between AOD500 nm and AE440-870 nm showing the clusters of aerosol types

during summer, winter, pre-monsoon and post-monsoon over a) Karachi, b) Lahore, c)

Jaipur and d) Kanpur.

6.2 Scattered plot between EAE440-870 and AAE440-870 nm showing the clusters of aerosol types



6.3 Scattered plot between EAE440-870 nm and SSA440 nm showing the clusters of aerosol types



6.4 Scattered plot between EAE440-870 nm and RRI440 nm showing the clusters of aerosol types



6.5 Classification of aerosol subtypes derived from CALIPSO data, for the selected days

representing a) Summer, b) Winter, c) Pre-monsoon and d)Post-monsoon over the

Karachi, Lahore, Jaipur and Kanpur.

7.1 Monthly variation of SDARF at TOA, SUR and ATM over a) Karachi, b) Lahore, c) Jaipur

and d) Kanpur for the period 2007-2013.

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7.2 Seasonal variation of SDARF at TOA, SUR and ATM over a) Karachi, b) Lahore, c) Jaipur


7.3 Monthly variation of SDARF at ATM and Atmospheric HR over a) Karachi, b) Lahore, c)

Jaipur and d) Kanpur for the period 2007-2013.

7.4 Monthly variation of SDARFE at TOA, SUR and ATM over a) Karachi, b) Lahore, c)


7.5 Seasonal variation of SDARFE at TOA, SUR and ATM over a) Karachi, b) Lahore, c)


7.6 Scatter plots of AERONET vs SBDART SDARF at TOA over a) Karachi, b) Lahore, c)


7.7 Scatter plots of AERONET vs SBDART SDARF at SUR over a) Karachi, b) Lahore, c)


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List of tables

Table 3.1: Measurement period and site information including coordinate and altitude for each site.

Table 4.1: Parameters of AERONET derived AOD vs. MODISSTD, MODISDB, MISR, OMI and

CALIPSO AODs over four locations in India and Pakistan.

Table 4.2: RMSE, MAE of AERONET, MODIS, MISR, OMI and CALIPSO.

Table 4.3: RMB and % of AOD values within EEB.

Table 4.4: Monthly average AOD values of AERONET, MODIS, MISR, OMI, and CALIPSO.

Table 5.1: Monthly averaged values and standard deviation of AOD at a wavelength of 500 nm and AE

(440-870 nm) for the measurement period during 2007-2013, over Karachi, Lahore, Jaipur and

Kanpur.

Table 5.2: Annual averaged values and Standard deviation of SSA at 440, 675, 870 and 1020 nm during

the period from 2007-2013 over Karachi, Lahore, Jaipur and Kanpur.

Table 5.3: Average annual values and standard deviations of the AP at 440, 675, 870 and 1020 nm

during the 2007-2013 period over Karachi, Lahore, Jaipur, and Kanpur.

Table 5.4: Averaged annual values and standard deviations of RRI and IRI at 440, 675, 870, and 1020

nm for the period 2007-2013 over Karachi, Lahore, Jaipur, and Kanpur.

Table 6.1: Threshold values of aerosol properties for different types of aerosol over each site.

Table 7.1: The annual averaged SDARF, SDARFE, and associated atmospheric HR over each site.

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Chapter 1

Introduction

1.1 Aerosols

Atmosphere comprises a complex mixture of solid particles and liquid droplets that are jointly

called as aerosols. Aerosols are an important component of the earth-ocean-atmosphere system

with great differences in their nature, size, emission sources and optical properties. Aerosols

have short life span (few hours or days) and differ in diameter from few nanometers to few

hundred micrometers. They are mostly produced either by natural process or by anthropogenic

activities and dispersed horizontally and vertically through widespread atmospheric circulation

[1]. The most common natural aerosol components are soil dust, sea salt, volcanic aerosols,

natural sulfates and the main anthropogenic aerosols are soot sulfate and organics [2, 3].

Natural aerosols are significant because they establish base level aerosols and there is no

successful control on them unlike anthropogenic aerosols. On the global level, natural aerosols

are several times more than anthropogenic aerosols [4, 5]. Although this rate is only valid on a

global level and the information on regional scales is rather different due to the contribution

from transport and industrial areas. Atmospheric aerosols can contribute to several local,

regional and global processes. On local and regional scales, they can have possible adverse

effects on health including premature deaths, allergies, respiratory problems, such as influenza,

pneumonia and many more as well as harmful cardiovascular effects such as heart attacks and

strokes [6]. They can also affect human health by degrading air quality and climate by cooling

or heating the lower atmosphere. Consequently, better understanding about sources,

composition, formation and transformation of atmospheric aerosol particles attain critical

importance in order to better quantify these problems.

2

1.2 Classification of aerosols

According to their composition and origination process, aerosols are classified into two classes:

i) primary aerosols ii) secondary aerosols. Primary and secondary aerosols are identified by

their size, shape and chemical composition. Primary aerosol particles are directly emitted in

the atmosphere due to incomplete combustion, biomass burning and fragmentation processes

[7]. Secondary aerosols are produced due to gas to particle conversion and play an important

role in global processes like formation of clouds and heterogeneous nucleation of water vapour

[8]. The size allotment of aerosols depends on their production method, and the chemical nature

of aerosols is mostly determined by their production source.

According to size, aerosols are divided into three categories namely: i) nucleation mode

particles (with radius ranged from 0.001-0.1µ𝑚 which are produced by gas to particle

conversion processes), ii) accumulation mode particles (with radius ranged from 0.1-1.0 µ𝑚

which are formed either by coagulation of smaller particles or by heterogeneous condensation

and iii) coarse mode particles (having radius greater than 1.0 µ𝑚 which are mostly formed by

construction and demolition of building and through long ranged transportation) [9].

Nucleation and accumulation are chemical and mechanical processes during which aerosols of

given sizes are produced. On the other hand, coarse mode particles are mostly formed by

mechanical processes.

1.3 Life span and removal mechanism of aerosol

The life time of small size aerosols is about a week in lower troposphere but it increases with

increasing altitude. On the other side, larger particles with higher settling velocity have also

shorter residence time in the atmosphere. The residence time of aerosols can be affected by

number of factors like their processes of formation, transformation and coagulation [10].

Generally, the residence time of aerosol is greater (up to two years) in the stratosphere as

compared to troposphere (up to week or month) [1]. Once aerosols are produced in the

3

atmosphere, then they move long or short distances in the air, they may also change their

composition before they are finally removed from the atmosphere. Removal processes of

aerosols are carried out by two important methods; dry deposition and wet deposition [11].

During dry deposition, aerosols settle on the earth surface which is due to gravitational force

or due to their Brownian motion. While, in wet deposition the falling rain drops captured the

aerosols, and removed those from the atmosphere (see Fig. 1.1). Although in various cases

when water vapour condenses on the aerosols, which led to the formation of droplets when

large sized droplets become properly then precipitation begins.

Figure 1.1: Atmospheric cycling of aerosols [12].

1.4 Aerosols over Indo Gangetic plain

The Indo Gangetic plain (IGP) is influenced by various natural and anthropogenic aerosols due

to its densely population and high pollution emission resulting in spatio-temporal variation.

Therefore, it is challenging to categorize atmospheric aerosols into different types (dust,

biomass burning, urban/industrial and mixture of these) because of difference in regional

climate, topography, nature and lifetime [13]. Each type of particles has a different climatic

effect due to its size and absorptive nature such as dust particles having large size which can

4

absorb shortwave radiation and carbonaceous aerosols of small size having a strong absorbing

nature, whereas, sulfate particles have small size and reflect some solar radiation back to space

[14]. The diversity in spatio-temporal distribution also leads to the existence of aerosol and

their impact on global and regional climate. It is further predicted that relative prevalence of

each aerosol type changes with season [15]. Atmospheric aerosols and their properties are

responsible for the uncertainty in estimating the global climate change.

1.5 Aerosol physical and optical properties

1.5.1 Scattering

Scattering is a process by which an electromagnetic radiation impinges on a particle and causes

electric charges in the particle to arrange in one or more dipoles which in turn re-radiate

secondary spherical waves in all direction. The angular distribution of the scattered radiation

will depend on the size of the particle although the total energy is conserved [16]. Scattering

will be elastic if the secondary waves have the same frequency as the primary waves which are

further classified into two criteria: Rayleigh scattering and Mie scattering. When the

wavelength of incoming radiation is larger than the particle size the scattering is termed as

Rayleigh scattering, due to which the electric dipoles setup inside the particle and air molecule

in the atmosphere act as Rayleigh centers. Rayleigh scattering is symmetric as electric dipoles

radiate equal quantities of fluxes in forward and backward direction. When the particle size

becomes larger as compared to the wavelength then multi-poles are induced and the scattering

pattern becomes complex which is classified as Mie scattering. In Mie scattering forward

scattering becomes more prominent as compared to backward scattering [17].

1.5.2 Absorption

Absorption is the process in which the incoming radiation losses its energy when it passes

through the material resulting in an increase of their internal energy. The absorbed energy is

5

re-emitted at the other wavelengths or as a thermal energy. The incoming light will be absorbed

or scattered by aerosols producing a net warming or cooling effect in the atmosphere. Black

carbon is the big absorber of light passing in the atmosphere, although in remote clean areas its

absorption is low. To study the changes in climate, the knowledge of absorption properties of

aerosol is important, but still the absorption by atmospheric aerosols is indecisive. The sum of

absorption and total light scattered in all directions is termed as extinction, it also depends upon

the wavelength of the incoming radiation nature of the prevailing medium.

1.5.3 Aerosol optical depth

Aerosol Optical Depth (AOD) is the measurement that represents the total attenuation of solar

radiation caused by aerosol via scattering or absorption processes and also tells us how much

direct sunlight is prevented from reaching the ground by these aerosol particles. AOD is the

total extinction integrated over a vertical column, and is the most important optical parameter

used for radiative forcing calculation, expressed as:

AOD(λ) = ∫ βext,λ(h)dhh2

h1 (1.1)

where λ is the wavelength, βext is the extinction coefficient of a particle and h1 is lower and h2

is the higher altitude in units of length.

1.5.4 Angstrom exponent

Angstrom Exponent (AE) is the indirect measure of aerosol size distribution and is computed

by the spectral dependence of the AOD according to the Ångström equation [18]:

AOD(λ)~ λ−AE (1.2)

where λ is the wavelength and AE is the range of 440-870 nm [19]. It can be defined in term of

Absorption Angstrom Exponent (AAE) and Extinction Angstrom Exponent (EAE).

1.5.5 Volume size distribution

The aerosol Volume Size Distribution (VSD) has a two-mode structure that can be

characterized by the sum of two log-normal distributions:

6

dV(r)

d ln r= ∑

Cv,i

√2πσi exp [−

(ln r−ln rv,i)2

2σi2 ]2

i=1 (1.3)

where Cv,i denotes the particle volume concentration, rv,i is the median radius, and σi is the

standard deviation [20-23]. The particles with smaller radii (r < 0.6 μm) belong to the fine

mode and all particles with larger radii (r > 0.6 μm) belong to the coarse mode [24].

1.5.6 Single scattering albedo

Single Scattering Albedo (SSA) is the ratio of the scattering efficiency (Qsca) to the total

extinction efficiency (Qext), and describes the effect of both the scattering and absorption

properties of aerosols. The SSA is given by:

SSA =Qscat

Qext=

Qscat

Qscat+Qabs (1.4)

SSA is a function of aerosol size, composition, concentration of absorbing components and

their mixing with non-absorbing components.

1.5.7 Phase function

The phase function defines the angular distribution of the scattered radiation by a particle at a

given wavelength; it is the scattered intensity relative to the incident beam at a particular angle

θ and normalized by the integral of the scattered intensity at all angles:

P(θ) =F(θ)

∫ F (θ)π

0 sin θ dθ (1.5)

where F(θ) is the intensity of scattered radiation and θ is the scattering angle (angle between

the incident and scattered radiation) and P(θ) is the phase function [11]. The P(θ) of aerosol

particles is a function of the size distribution, index of refraction, internal structure and shape

of particles [25]. The P(θ) of the atmospheric aerosol depends on the mode (fine or coarse) of

the particles [26].

1.5.8 Asymmetry parameter

The Asymmetry Parameter (AP) is defined as the intensity weighted average cosine of the

scattering angle:

7

AP = ∫ cosθP(θ)sinθdθπ

0 (1.6)

where θ is the angle between the incident and scattering light and P(θ) is the phase function.

The value of AP ranges from -1 to +1; AP = -1 shows entirely back-scattered radiation, AP =

+1 shows entirely forward-scattered radiation and AP = 0 denotes symmetric scattering. Like

the SSA, this parameter also depends on the wavelength, size, and composition of particles [27,

28].

1.5.9 Refractive indices

The index of refraction can be obtained by combining real and imaginary parts of Refractive

Index (RI). Mathematically it can be expressed as:

RI = n + ik (1.7)

where n represents the Real part of RI (RRI) and k represents the Imaginary part of RI (IRI).

The RRI indicates the reduction in the velocity of light when passing through medium while

compared to its velocity in a vacuum and it contains only positive value. The magnitude of

total scattering increases with increase in RRI [29-31]. The IRI indicates extinction coefficient

or absorption coefficient. It quantifies the nature of the absorption, as a higher IRI indicates

higher absorption. When there is no absorption the IRI is equal to zero and RI will stay with

the only real component. However, positive value of IRI indicates the absorption capacity of

the material.

1.6 Clustering analysis

Cluster analysis is an important technique used for classification of aerosol. Such analysis is

used for classification of huge datasets into numerous groups using predefined aerosol

parameters. The AERONET dataset based on several optical and physical characteristics of the

aerosols, can be categorized into several groups for the discrimination of aerosol types [32].

The discrimination of aerosol types was carried out by analyzing the scattered graph between

8

AOD and AE [33, 34], EAE and AAE [35], EAE and SSA [13] and RRI and AAE [36]. Some

other clustering techniques were also used by earlier researchers [32, 37, 38].

1.7 Remote sensing instruments

Satellite-based remote sensing measurements allow systematic retrievals of aerosol optical

properties on both local and global scales [39, 40]. Ground-based measurements of aerosols

also fulfill a vital role in characterizing the optical and microphysical properties of aerosols, as

well as in determining aerosol loadings and the radiative effects that aerosols have over specific

locations [41, 42].

The ability to examine the aerosol optical properties in the atmosphere using automatic,

ground-based remote sensing techniques, has improved significantly over recent decades with

the development of the AErosol RObotic NETwork (AERONET) [43]. Continuous AOD time

series with a very high temporal resolution are now available for selected stations through this

global network of ground-based radiometers [44]. For global coverage, however, polar-orbiting

sun-synchronous satellite sensors are used to achieve a seasonal characterization of aerosols

[45]. Besides providing global coverage, observations from satellites have the additional

advantage of allowing complete mapping of large areas in a single snapshot. Over the last

decade satellite sensors such as the MODerate resolution Imaging Spectroradiometer

(MODIS), the Multiangle Imaging Spectroradiometer (MISR), the Ozone Monitoring

Instrument (OMI), and the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation

(CALIPSO) instrument have investigated the atmosphere by characterizing physical and

chemical properties of aerosols using observations and retrieval algorithms [8, 46-48]. Global

aerosol properties from satellite observations are very useful for constraining aerosol

parameterization in atmospheric models. The reliability of different datasets, however,

becomes an important question in the interpretation of global and regional aerosol variability

[49]. To determine the spatial and temporal variability of aerosol parameters, and to validate

9

the models used, satellite-based measurements of aerosol parameters need to be checked

against ground-based measurements. Our understanding of the distribution of aerosols in the

atmosphere has been greatly enhanced by the availability of satellite data [50], which has

allowed a global coverage to be achieved. Emphasis has been placed on retrieving aerosol

properties from satellite data, with data from the MODIS and MISR dedicated satellite sensors

being widely used in aerosol research [51].

1.8 Aerosol radiative effects

The radiative effects of aerosols remain a significant source of uncertainty in atmospheric

studies [52]. A variety of aerosols can influence the atmosphere by absorbing and scattering

the electromagnetic radiation through direct effect or acting as cloud condensation nuclei

through indirect effect thereby modifying the cloud properties [40]. Both (direct and indirect)

effects of aerosol have large regional variations due to short life time of aerosols which give

the high spatial and temporal variability in aerosol loading, also in their chemical composition

and optical properties. Aerosol Radiative Forcing (ARF) is the net change in energy balance of

the earth system due to forced perturbation in atmospheric layers [53]. A precise estimation of

the aerosol mixing condition is necessary for an exact measurement of ARF because rough

assumptions can lead to high uncertainties in the aerosol climatic effect [54]. Impacts on the

radiation balance are very unusual, a comprehensive examination of aerosol optical and

radiative properties on a regional level is thus necessary [55]. ARF are mainly dependent on

AOD, SSA, AP, surface reflectance, vertical distribution of aerosols and ozone content [56].

The average flux reaching the earth surface per square meter is ~ 342 Wm-2, out of this about

30 % (~103 Wm-2) is reflected to space mainly due to scattering type of aerosols or cloud. To

maintain the radiative balance, about 240 Wm-2 is reflected to space through longwave

radiations (see Fig. 1.2).

10

Figure 1.2: Estimated annual and global average energy balance [57].

1.9 Motivation of research work

The IGP region is facing severe air quality threats due to the large quantity of air pollutants

during the growing urbanization and industrialization, which make aerosols over the region

highly complex in nature [33, 58]. This region is dominated by the complicated mixture of

anthropogenic aerosol loading, urban/industrial aerosols, and natural aerosol emissions that are

jointly responsible for the significant seasonal variability [59]. These aerosols are an important

component in the earth's climatic system and are likely to affect our understanding of radiative

forcing. However, due to our limited understanding of the aerosols properties, there remain

significant uncertainties in their temporal and spatial variations and the effects that they have

on the earth's climate [4]. The understanding of the heterogeneity in the seasonal aerosol types

supports the more precise optical properties by both satellite and ground based sensors [14, 15,

33]. Therefore, it is essential to discriminate the prominent aerosol types in different seasons

11

to improve the understanding about the impact of these aerosols on climate and monsoon

circulation.

1.10 Layout of study

This study provides detail analysis of satellite and ground observation to asses and quantify the

optical and radiative effect of aerosol over Karachi, Lahore, Jaipur and Kanpur of IGP for the

period 2007–2013.

After the introduction chapter, the rest of the thesis is structured in the following manner:

Chapter 2: This chapter provides the relevant background about aerosol physical, optical and

radiative properties used throughout the thesis.

Chapter 3: This chapter presents information about instrument and methodology adopted in

this study.

Chapter 4: In this chapter the AERONET, MODIS, MISR, OMI and CALIPSO AOD

retrievals were analyzed. The satellite AOD retrievals were independently validated by

comparing them with the relevant ground-based AERONET AOD observations. The work

presented in this chapter has been published in journal of Atmospheric Environment.

Chapter 5: This chapter is devoted to the aerosol optical properties which were measured using

the AERONET. We analyzed the annual and monthly averaged spatio-temporal variability of

the AOD and AE. We also examined the seasonal characteristics of the aerosol VSD, SSA,

phase function, AP, RRI, and IRI. The work displayed in this chapter has been published in

Applied Optics.

Chapter 6: This chapter presents a detailed seasonal classification of aerosol types by multiple

clustering techniques using AERONET datasets. Such classification is carried out by analyzing

different aerosol properties such as AOD vs. AE, EAE vs. AAE, EAE vs. SSA and EAE vs.

RRI in order to investigate the dominant aerosol types and to examine the variation in their

seasonal distribution. Furthermore, the confirmation of AERONET derived aerosol types

12

analyzed in the studied sites were compared with CALIPSO retrieved subtypes. This work has

been published in journal of Atmospheric Research.

Chapter 7: This chapter contains the analysis of spatial and temporal distribution of SDARF

and SDARFE at TOA, SUR and ATM. In addition, the atmospheric HR was also examined

over the observational sites. Furthermore, SBDART estimated SDARF was compared with

AERONET derived SDARF at TOA and SUR. This work has also been submitted to the journal

of Atmospheric Environment.

Chapter 8: This chapter summarizes the research work and provides recommendation and

suggestions for future work.

13

Chapter 2

Review of the literature

The validation of satellite AOD against ground based observations is essential to identify the

uncertainties of these datasets as well as for the development of improved algorithms.

Likewise, discrimination of aerosol types is necessary because different aerosol types having

different atmospheric impacts. The analysis of aerosol and radiative properties is very

important to understand the recent climate change phenomena. In the view of this, a systematic

review of the published literature concerning the comparison of satellite and ground-based

AODs, discrimination of aerosol types and assessment of aerosol optical and radiative

properties is compiled in the following sub-sections.

2.1 Validation of satellite AOD retrievals against ground based

AOD retrievals

To examine the accuracy of datasets, numerous studies have also used ground-measured AODs

to validate satellite derived AODs worldwide. They reported positive correlation between the

satellite and ground-based AODs. The major finding of the previous are mentioned below:

Ichoku et al. [60] performed an intercomparison between MODIS AODs and

AERONET AODs during October 2000 and found an excellent agreement over the ocean,

while poor correlation over land due to surface variability. Likewise, Chu et al. [61] reported

a good correlation between MODIS AODs and AERONET AODs at two wavelength (470 nm

at 660 nm) during July to September 2000.

Abdou et al. [62] observed larger values of AOD over land obtained from the MODIS

instrument than that from the MISR instrument during March, June and September 2002.

14

Prasad et al. [31] published an intercomparison of MODIS and MISR over Kanpur for

the period 2001 to 2005. They found a good correlation between MODIS and AERONET

during winter but a poor correlation in summer. In contrast, relatively good correlation between

MISR and AERONET during summer while a poor correlation in winter was observed. AOD

data measured from the MISR instrument were compared with AERONET AOD

measurements by Jiang et al. [63] over the Beijing urban area during 2002-2004, they observed

very good correlation between the two data sets.

Curier et al. [64] obtained a comparison between AOD retrieved from OMI with ground

measurement data for the duration from May to June 2005 over different sites in western

Europe and found a relatively good correlation for sites such as Paris and Lille, respectively in

France, over El Arenosillo in Spain, and poor correlation over Ispra (Italy); very poor

correlation was also observed for the other sites. During the same year, results obtained by

Christopher et al. [65] confirmed that the MISR is a suitable sensor for retrieving the AOD

data in desert regions during 2005-2006 and showed a high correlation between MISR and

AERONET AOD data in comparison over different other sites.

Hoelzemann et al. [66] provided a validation of MODIS AODs when intercompared

with AERONET data over numerous sites in South America for the period 2001 to 2005 and

obtained a good correlation between the two sensors.

Liu et al. [67] carried out the comparison between AODs retrieved from MISR with

those from ground-based measurements during July 2004 to July 2006 in most northern,

southwestern and eastern parts of China and presented high correlations. They also found a

reasonable agreement between MISR retrieved AODs and ground measurements at desert

locations in China. Whereas, MISR AOD values over Beijing were found to be lower than

AERONET AOD values.

15

Alam et al. [68] validated MODIS and MISR AODs against AERONET AODs for the

period 2010-2011 over Karachi and Lahore. They found the best correlation for MODIS-

AERONET over Lahore, with relatively poor correlation over Karachi, while good correlation

for MISR-AERONET data over Karachi and poor correlation over Lahore. They concluded

that MISR showed good performance for area close to ocean, whereas MODIS functioned

remarkably for vegetated areas. They also noted a maximum AERONET AOD value in the

month of July 2007 over Karachi. Marey et al. [69] utilized MODIS and OMI data for the 10

year period of 2000 to 2009, observed high AODs in April and May and low AODs in

December and January over Niledelta.

Choudhry et al. [70] carried out a comparison between MODIS and AERONET AODs

over Kanpur (2006-2010), Gandhi College (2006-2010), and Nainital (2008-2010) and

reported good correlations for Kanpur and Nainital and a weak correlation for Gandhi College.

They also found increment in AOD values over various sites in India during the pre-monsoon

season, and decrement during the post-monsoon season. Cheng et al. [71] carried out an

intercomparison between MODIS, MISR and GOCART aerosol products with AERONET

data over China for the long duration of 2001-2011 and observed that MISR AODs showed

higher correlation with AERONET AODs, which can be endorsed to its better viewing and

spectral capabilities.

Gupta et al. [72] also compared MODIS-derived AODs with AERONET-derived

AODs over Lahore and Karachi for the 10 year period 2001 to 2010 and found a similarly good

correlation between the MODIS-AERONET. They also found a seasonal cycle of higher AOD

values in summer and lower AOD values in winter. During the same year, in another study

over Pune, India, More et al. [42] compared AOD data from MODIS and MICROTOPS with

AERONET data of 2008-2010, and noticed good correlations on the seasonal scale. Similarly,

Qi et al. [51] validated MODIS and MISR AOD with AERONET observations over four sites

16

in northern China during 2006 to 2009. They showed that MISR measured AODs were more

accurate than MODIS AODs at the SACOL (Climate and environmental observatory of

Lanzhou University) and Beijing sites while, at the Xianghe and Xinglong sites, MODIS AODs

were better than MISR AODs. Likewise, Ramachandran and Kedia, [1] showed comparison

between MISR and MODIS satellite-based AOD retrievals with ground-based MICROTOPS

and AERONET sun photometer AOD retrievals during 2006 and 2008 over Karachi, Kanpur,

Ahmadabad, Gurushikhar, and Gandhi College. They noticed that the correlations were not

strong due to the large differences between satellite and ground based retrieval. The research

work made by Wong et al. [73] compared the AOD retrievals from MODIS, MISR, OMI and

CALIPSO against AERONET AOD data over Hong Kong during the years 2000-2011. They

suggested that AOD retrievals, particularly those from MODIS provide more reliable and

accurate measurements for daily air quality monitoring over various land surfaces. They noted

that correlation was highest for MODIS, followed by MISR and OMI and low for CALIPSO.

Alam et al. [74] yielded the comparison between MODIS and AERONET AODs over

Lahore in pre-monsoon and post-monsoon seasons for the period of 2009-2010 and obtained

comparable correlation coefficients for both the seasons. Similarly, Alam et al., [23] also

intercompared the MODIS and AERONET AODs over Lahore during a dust storm that

occurred in March 2010 and found a good correlation between them. Ahn et al. [75]

intercompared satellite observations using the MODISDB, MISR, OMAERUV, and OMAERO

algorithms with AERONET AOD observations over 44 selected locations for 8 years during

2005-2012 and found that the overall retrieval algorithms showed good correlation with

AERONET data over all sites. Ma and Yu, [76] observed that CALIPSO AOD were

considerably lower than MODIS AOD over dusty regions during their study period of 5 years

(2007-2011).

17

Misra et al. [77] have intercompared MODIS AOD obtained from the DB algorithm

with ground-based sun photometer (MICROTOPS) measurements over Ahmadabad for the

period 2002-2005 and found only a poor correlation between the two datasets. Payra et al. [78]

also performed an intercomparison between MODIS AOD and AERONET AOD with different

spatial and temporal time scales over Jaipur for the years 2009-2012. They showed that MODIS

AOD overestimates AERONET AOD during pre-monsoon, whereas it underestimates the

AERONET AOD during dry and post-monsoon seasons.

Kumar et al. [34] utilized MISR and MODIS AODs and intercompared against

AERONET AODs for the period of 2003-2013 over Durban. They presented that the

correlation between MISR and AERONET was stronger than that of MODIS and AERONET.

They also showed that AOD was highest during pre-monsoon, followed by summer and post-

monsoon and lowest during winter.

Adesina et al. [79] developed a comparison of MISR and MODIS with AERONET for

the 10-year study period during 2004 to 2013 over two different environments namely,

Skukuza and Richards Bay. They implied that MISR was better correlated with AERONET as

compared to MODIS.

2.2 Discrimination of aerosol types using multiple clustering

techniques

Cluster analysis is one of the important techniques used for discrimination of aerosols. In this

analysis, huge datasets segregate into various groups using predefined aerosol parameters by

assigning some threshold values. Previously, the discrimination of aerosol types can be

achieved through different aerosol optical properties such as AOD and AE, AAE EAE, SSA

and RRI and their interrelationship to investigate the dominant aerosol types and to examine

the variation in their seasonal distribution.

18

Goloub et al. [80] characterized the aerosols into Saharan dust, urban/industrial and

biomass burning, based on AOD-AE relationship during 1996 over three oceanic sites by mean

of POLDER/ADEOS measurements.

Masmoudi et al. [81] characterized the aerosol into different types via AOD-AE

scattered plots, for the period April-June 2011 over Ouagadougou, Banizoumbou, Thala, IMC

Oristano and Rome Tor Vergata.

Pace et al. [82] classified the aerosol into different types such as desert dust and biomass

burning/urban industrial aerosols through AOD-AE cluster analysis during the period July

2001 to September 2003 at the island of Lampedusa, in Central Mediterranean.

Kaskaoutis et al. [83] categorized the aerosol into different types such as

urban/industrial, clean maritime, desert dust during 2000-2005 over Athens using MODIS

AOD and fine mode data. In another study, Kaskaoutis et al. [84] segregated the aerosols into

biomass burning/urban mixed type and desert dust, through AOD-AE relationship, during

2003-2004 over four AERONET sites.

Toledano et al. [85] categorized the aerosols into five types such as desert, mixed,

biomass, marine and continental over El Arenosillo during2000-2004 using AERONET data.

Russell et al. [86] clustered the aerosol in three different types such as desert dust,

biomass burning and urban industrial using the cluster techniques of EAE-AAE over

worldwide locations at different time period for each observational site.

Giles et al. [37] carried out the classification of three types of aerosol: dust, black

carbon and mixed by using a cluster technique of EAE and AAE over IGP during 2002-2008.

In similar way, during 2012, using the AOD-AE clustering technique, Pathak et al. [15]

classified the aerosol into five categories (continental average, marine continental average,

urban/industrial and biomass burning, desert dust and mixed type) over Dibrugarh for the years

19

2002-2010 using Multi-Wavelength solar Radiometer (MWR) measurements and the seasonal

variation of aerosol type showing the contribution of urban/industrial and biomass burning

during the winter and pre-monsoon and mixed type during monsoon and post-monsoon. Mishra

and Shibata, [35] adopted the seasonal classification of aerosol by investigating the scattered

plot of EAE against AAE over Kanpur during October and November of 2009 and grouped the

aerosol in dust, biomass burning and urban/industrial. During the same year, Alam et al. [87]

classified the aerosol into mineral dust and urban/industrial using the EAE-AAE technique

during summer and winter over Karachi and Lahore for the period 2010-2011. Similarly, Giles

et al. [13] established the relationship between SSA and EAE to classify the aerosol into dust,

mixed, urban/industrial and biomass burning during 1999-2010 at different AERONET sites.

Sharma et al. [33] documented the seasonal distribution of aerosols and differentiated

them into clean marine, anthropogenic, biomass burning, mostly dust and mixed aerosol over

Greater Noida for the period 2010-2013 using ground sunphotometer. Kedia et al. [38]

presented the categorization of the absorbing aerosol into mostly dust, mostly black carbon and

mixed dust and black carbon over IGP using scatter plots of EAE and AAE based on

AERONET dataset covering the period 2006 to 2010. Russell et al. [36] portioned the aerosols

into seven specified classes such as pure dust, polluted dust, biomass burning, dark smoke,

biomass burning white, urban-industrial, developed economy, urban-industrial, developing

economy, pure marine using Mahalanobis SSA-EAE clustering technique. In their work, they

also implemented EAE-RRI clustering and categorized aerosols into pure dust, polluted dust,

light biomass smoke, dark biomass smoke, urban-industrial, and pure marine over the Island

of Crete.

Tan et al. [88] separated different types of aerosol such as biomass burning,

urban/industrial, marine and dust by means of AOD and AE over Penang and Kuching during

2012. By using a similar AOD-AE clustering technique, Kumar et al. [34] classified aerosols

20

into clean marine, continental clean, biomass burning/urban industrial and desert dust over

Durban for the years 2003-2013. Similarly, five pronounced aerosols types such as desert dust,

maritime, biomass burning, mixed and arid background aerosols were identified using AOD-

AE clustering method based on AERONET in Jaipur during 2009-2012 by Verma et al. [89].

Che et al. [90] plotted AAE versus EAE to sort the aerosols into mixed, urban/industrial and

biomass burning during the heavy haze in January 2013 over Beijing. They also confirmed the

contribution of dust or polluted dust during haze events through CALIPSO aerosol subtypes.

Lately, Tiwari et al. [91] showed the four prevailing aerosol clusters like biomass

burning, anthropogenic, mostly dust and mixed aerosol through AOD-AE over New Delhi via

Sun/Sky radiometer POM-02 during April 2011-March 2013. Yu et al. [92] used the AOD-AE

cluster analysis and showed clean marine, clean continental, biomass burning/urban industrial,

desert dust and mixed type aerosols during haze-fog and no haze-fog days in January 2013 over

an urban environment of Beijing. They also establish the AAE vs. EAE relationship to

distinguish the dominant aerosol types such as mixed type aerosol and biomass burning/urban

industrial. Further, they analyzed the CALIPSO images and recorded the major contribution

of clean continental with some contribution of smoke and polluted dust during the non-haze

episode, whereas, smoke, dust and polluted dust were prominent during a haze episode. Tariq

et al. [93] identified the dominant aerosol types as mixed type, and urban industrial

and/biomass burning during haze events over Lahore using AERONET data in October 2013.

They verified these aerosol types during a haze episode through CALIPSO subtypes of aerosol.

2.3 Analysis of aerosol properties

The assessment of aerosol properties is very important to understand the recent climate change

phenomena. Various researchers emphasized on examining the spectral behavior of associated

aerosol optical properties throughout the world and they confirmed the reasonability and

21

reliability of spectral aerosol optical properties from AERONET. This section is based on

previous work related to aerosol properties reported by the numerous researchers.

Dubovik et al. [24] conducted a study related to aerosol peroperties for different type

of particles over woldwide location for a period from 1993 to 2000. They analyzed AOD, AE,

VSD, SSA, phase function, RI and reported the significant variations in aerosol properties for

each type of particles.

Dey et al. [59] presented the impacts of dust events on aerosol properties over Kanpur

during the period of 2001 to 2003. For this, they examined AOD, AE, VSD, SSA, RRI and IRI

and observed the contrasting spectral variations of aerosol optical properties.

Singh et al. [94] studied the distribution of AOD and AE along with SSA over Delhi

during pre-monsoon of 2004. They suggested the abundance of desert dust aerosol during the

observational period.

Toledano et al. [95] studied the AOD and AE variations over El Arenosillo during

2000-2004 and implied that only dust particles are responsible for the very large AOD values

and low AE values. Similar results were found by Xin et al. [96] for the period during August

2004 to September 2005 over Tibetan plateau.

Zheng et al. [22] published a study on aerosol properties such as AOD, AE, VSD, SSA

and RI during the period from January 1999 to March 2001 over Dunhuang. They suggested

that these optical properties varied seasonal in areas located near dust dominated regions.

Eck et al. [97] monitored the aerosol properties over Alaska, performed from 1994 to

2008 except winter season. They examined the variations in AOD, AE, VSD, SSA and IRI

over the study site during the observational period.

22

Eck et al. [98] studied the climatological aspects of aerosol optical properties related to

fine/coarse mode aerosol mixture over Bijing, Kanpur and Ilorin for mutilyears study durations.

Towards this, they analyzed AOD, SSA and other optical properties for their analysis.

Alam et al. [20] analyzed the aerosol optical properties over Karachi during the period

of August 2006 to July 2007. They revealed a significant variation in AOD, AE, SSA, AP, RRI

and IRI during their study. Guleria et al. [99] noticed the high AOD with corresponding low

AE during the summer, which is attributed to the relative abundance of coarse size particles

while studying the aerosol optical characteristics during April- March, 2006 over Mohal.

During the same time Gautam et al. [100] examined the distribution of aerosol and associated

optical properties during pre-monsoon season of 2009 over IGP and Southern slope of

Himalayas. They analyzed AOD, AE, VSD and SSA variations and observed the enhanced

dust loading over southern Asia and Western IGP, while, over Eastern IGP and slope of

Himalayas, strongly absorbing haze was dominant. Furthermore, the aerosol optical properties

was reported by Bi et al. [101] over SACOL Loess Plateau of the Northwestern China during

August 2006 to October 2008. They investigated the notable variations in aerosol optical

properties like AOD, AE, VSD, SSA, AP, phase function, RRI and IRI for their study period.

Alam et al. [87] studied the variability of aerosol optical properties during summer and

winter seasons of 2010-2011 and suggested that high values of AE indicate a dominance of

fine particles, while low values specify the domination of coarse particles. They also analyzed

SSA, AP, RRI and IRI during their observational period.

Zhuravleva et al. [102] carried out an analysis in which they focused on aerosol optical

characteristics over Tomsk during summer time in 2003-2009. They achieved the results by

examining the spectral variation in SSA and AP together with VSD.

Alam et al. [74] estimated the aerosol properties over Lahore during pre-monsoon and

post-monsoon for the years 2009-2010. In their analysis, they investigated AOD, VSD, SSA,

23

AP, RRI and IRI and found the significant aerosol characteristics over the study region. Dumka

et al. [103] analyzed the latitudinal variability of aerosol optical properties over IGP to central

Himalayas during pre-monsoon of 2008-2009 over the Himalayan foothills. For this purpose,

they studied the spectral variations in AOD, AE and SSA and bimodal log-normal size

distribution in term of VSD during their study period.

Wang et al. [58] investigated the aerosol optical properties for the long period of time

(2007-2013) over China and found a significant variation in the AOD and AE. From AOD and

AE analysis they observed the domination of fine particles. They also analyzed the VSD, SSA,

real and imaginary parts of refractive index and noticed the significant variations throughout

the study period. Another study conducted over Gorongosa by Adesina et al. [104] during July

2012 to December 2012 regarding the aerosol properties. They reported the remarkable results

while analyzing the aerosol properties in term of AOD, AE, VSD, SSA, AP, RRI and IRI over

the experimental sites.

Kang et al. [105] conducted a study to examine the aerosol optical properties over

Nanjing for the period September 2007 to August 2008. They analyzed AOD, AE and SSA and

noticed the variability in seasonal distribution of aerosol. Yu et al. [92] investigated the aerosol

optical properties during dust events that took place over Beijing between 2001 and 2014 and

observed the high AOD and low AE. Further, they also studied SSA, AP, RRI and IRI during

the haze-fog events.

2.4 Estimation of aerosol radiative forcing

The most serious problems facing the global science community are the environmental and

climatic issues of atmospheric aerosols, obtained from both natural and anthropogenic emission

sources, affecting the global radiation budget, air quality, human health and hydrologic cycles

[54, 106, 107]. Aerosols and anthropogenic greenhouse gases play an important role in climate

change among other factors [108]. During last century, the earth’s surface temperature increase

24

by 0.6 οC was the highest in the last millennium, is due to a shift in energy balance between

absorption of incoming solar radiation and emission of outgoing thermal radiation on the earth

surface [109, 110]. The variety of aerosols can influence the atmosphere by absorbing and

scattering electromagnetic radiation (direct effect) or acting as cloud condensation nuclei

(indirect effect) thereby modifying the cloud properties [111]. There are numerous studies

reported in the literature, focused on aerosol radiative impacts during high pollution episodes

and several studies focused on radiative forcing calculations for a limited interval of time

period. Generally, ARF at TOA values were either positive or negative; negative values of

ARF at TOA show the increment in the backscattering due to scattering type of aerosols leads

to cooling the atmosphere or earth’s system, while positive values due to absorption of solar

radiations by absorbing type of aerosols contribute to heating the atmosphere. ARF at SUR

was found to be negative during all months, which is due to the attenuation of solar flux at the

surface by atmospheric aerosols implying a net cooling effect. Finally, the difference between

ARF at TOA and ARF at SUR leading to the atmospheric ARF, indicating the net cooling

(negative) or heating (positive) effect of the atmosphere. In this section, a number of literature

dealing with ARF are presented.

Satheesh and Ramanathan, [112] conducted a study over Indian Ocean and found that

ARF at both TOA and SUR were found to be negative. Takemura et al. [113] yielded that

carbonaceous and soil dust are responsible for positive atmospheric forcing because of their

absorption capability while sulfate and sea salt aerosols reflect the solar radiations leading to a

negative forcing. Gadhavi and Jayaraman, [114] reported a positive forcing at TOA and

negative (-0.83 Wm-2) at the SUR over Maitri during the study period of January-February

2001.

Ramana et al. [115] found that atmospheric ARF was positive which translates into HR

of 1 Kday−1 within 2 km during winter 2003 showing a strong impact on winter time inversion

25

and residual time of aerosol in Himalayan regions. They also estimated Aerosol Radiative

Forcing Efficiency (ARFE) over Kathmandu and found that the monthly averaged ARFE at

SUR were found to be negative, showing the dominance of strongly absorbing aerosol in the

region.

Pant et al. [116] reported that during December, the ARF at TOA and ATM were

positive, whereas at SUR was negative during December 2004 over Manora Peak. Assessment

of seasonal averaged ARF at the surface over Ahmedabad showed a negative forcing at SUR

over all seasons [117].

Dey and Tripathi, [118] calculated the ARF over Kanpur during December 2004 to

January 2005 and noticed that the absorption was highest during December to January and May

to June with HR of ~1 Kday-1.

Dey and Tripathi, [119] also documented the negative values of ARF at SUR having

highest values during pre-monsoon over Kanpur during the observation period of 2001-2005.

Similarly, in other study Pandithurai et al. [120] performed a study on ARF over Delhi and

reported increased cooling at SUR by obtaining more negative value during pre-monsoon, and

a positive value of atmospheric ARF with HR of 0.6 to 2.5 Kday-1 which is attributed to the

abundance of dust particles.

Singh et al. [121] worked on ARF over Delhi during January 2006 to January 2007 and

displayed that the TOA ARF was highest (positive) during June and lowest (negative) during

November. Whereas, SUR ARF was highest (maximum negative) during May and lowest (least

negative) during August leading to atmospheric ARF with highest positive in June. The low

values obtained in August suggested that the strongest cooling effect was at the surface and

heating effect was in the atmosphere. Li et al. [106] carried out the analysis of ARF over 25

station in China during 2006 and found that the overall the annual and diurnal averaged ARF

were negative at SUR, while positive at the TOA and within the ATM showing the significantly

26

heating of atmosphere and cooling the earth surface. They also showed seasonal ARF at TOA,

SUR and ATM were highest during summer and lowest during winter. Furthermore, they

showed comparison of downward and upward forcing at TOA and SUR from AERONET and

SBDART and noted a good correlation. Pathak et al. [122] carried out the study on seasonal

ARF and ARFE over Dibrugarh during the period June 2008-May 2009 and reported that ARF

at SUR was nearly zero in monsoon (summer) and was negative during all other seasons over

Dibrugarh. They also reported that ARFE at ATM showed highest positive value in winter and

lowest positive value during monsoon. However, the estimated HR over Bay of Bengal was

~0.3 Kday-1, higher than that of Arabian Sea during pre-monsoon (March to May, 2006) [123].

Alam et al. [20] performed a study on ARF over mega city Karachi for the period

August 2006 to July 2007 and found the negative ARF at TOA and SUR, while positive

atmospheric ARF with highest during the month of November 2006. They also showed good

correlation by comparing ARF retrieved from AERONET with estimated ARF from SBDART

at SUR and TOA.

Similarly, Alam et al. [87] analyzed ARF during summer and winter for the years 2012-

2011 over Karachi and Lahore. They noted the negative ARF at TOA and SUR whereas,

positive ARF within ATM translate to HR of 1.1 and 1.8 Kday−1 during winter and 1.2 and 2.3

Kday−1 during summer, over Karachi and Lahore respectively. Likewise, the comparison

between AERONET retrieved and SBDART calculated ARF at SUR showed good correlation.

During the same year, while comparing model and AERONET ARF during selected desert dust

episodes over Granada from 2005 to 2010, Valenzuela et al. [124] confirmed that there were

small differences between the input data used for calculating SBDART and AERONET ARF

by finding the small differences in output data and indicate high correlation at SUR between

these two data sets. Kumar and Devara, [125] calculated the ARF at SUR, TOA and in ATM

with corresponding HR over Pune during five years of their study period (2004-2009) and

27

found the negative ARF at SUR and TOA, while within ATM, it was observed to be positive

during all the seasons. They also observed the highest HR in Pre- monsoon followed by the

post-monsoon and winter in descending order having the values of 0.95, 0.86 and 0.84 Kday-1

respectively. Cherian et al. [126] noted that the averaged ARF were negative at TOA and SUR

and positive in ATM, over Bay of Bengal and Arabian Sea during 2006.

Ramachandran and Kedia, [52] reported that ARFE at TOA and the SUR display strong

seasonal differences due to the variations in aerosol characteristics and surface reflectance.

They found that during pre-monsoon, the values of ARFE at TOA was less negative and at

SUR was more negative due to higher surface reflectance over Kanpur and Gandhi College.

Srivastava and Ramachandran, [107] established a research on ARF, ARFE and

validation between SBDART ARF and AERONET ARF during 2007-2009 over IGP. They

noticed that the ARFE was observed to be maximum at SUR and ATM, whereas, minimum at

TOA during pre-monsoon than other seasons for probable mixing state. They observed good

correlation by plotting SBDART ARF versus AERONET ARF at the SUR and TOA. HR

during pre-monsoon and summer were 0.75 and 0.5 Kday−1 over Kanpur and college [107].

Esteve et al. [127] performed a study on monthly ARF and ARFE over Burjassot during

the years 2003-2011 and found that monthly ARF and ARFE revealed a significant variation

during the study years at TOA (negative), SUR (negative) and ATM (Positive). They

demonstrated that ARFE lower negative values in February and higher negative in May at SUR

and lower negative values in May and higher negative in December at TOA during the entire

period of observation. Likewise, an another study related to ARF, conducted over New Delhi

by Taneja et al. [128] in which they concluded that ARF at TOA and SUR was observed to be

negative while that in ATM was found to be positive during all the months of the year 2013.

Analogously, Dumka et al. [103] established a research on estimating ARF at TOA, SUR and

ATM over Kanpur, Bareilly, Pantnagar and Nainital during premonsoon of 2008 and 2009.

28

Analogous to the previous finding they presented that averaged ARF were observed to be

negative at TOA, SUR and positive within ATM with estimated HR over Kanpur, Bareilly,

Pantnagar and Nainital of 1.56, 1.58, 1.41 and 1.07 Kday−1, respectively. Similarly, Srivastava

et al. [129] also observed the strong heating in atmosphere of 2.0 Kday-1 during dust events

occurred in March 2012 over Jodhpur.

Bhaskar et al. [130] also performed an effort to estimate the ARF, ARFE and HR during

the period 2004-2012 over Jodhpur. They reported that ARF at TOA and ARF at SUR were

negative during all the seasons, while ARF in ATM were positive in all the seasons presenting

net heating of the atmosphere and was found to be highest during pre- monsoon than rest of

the year. They also found that HR were in the range of 0.49 to 1.13 Kday−1 with minimum in

post-monsoon and maximum in pre-monsoon. Furthermore, they concluded that ARFE at

TOA, SUR and ATM represented the net cooling effect at SUR and TOA while warming effect

in ATM. Lately, Wu et al. [131] showed that the averaged seasonal variation in ARF at SUR

and TOA were negative during summer, winter, spring and autumn resulting in cooling on

surface, but warming in the atmosphere (positive atmospheric forcing) at Tongyu during their

observational period (March 2010-Febraury 2014). Che et al. [90] also observed negative ARF

at TOA and SUR over Shenyang, Anshan, Benxi, and Fushun, respectively during 2009-2013.

Similarly, Adesina et al. [104] have documented the negative value of TOA ARF and SUR

ARF, leading to a positive value of ARF over Gorongosa during July to December 2012. They

noted the good correlation of ARF at SUR and TOA by comparing AERONET against

SBDART. Guleria and Kuniyal, [132] calculated atmospheric ARF and corresponding HR over

Mohal during April 2006 to March 2010 and observed that HR during winter, pre-monsoon,

summer and post-monsoon were 0.66, 0.47, 0.46 and 0.56 Kday-1, respectively. Similarly, Kant

et al. [56] investigated ARF during pre-monsoon season of 2013 over Dehradun and Patiala

and noted positive atmospheric ARF as well as HR of 1.0 and 1.5 Kday−1, respectively. Patel

29

and Kumar, [133] have reported maximum positive ARF in ATM in May with corresponding

HR of 1.06 Kday−1 over Dehradun during March to June 2012. While comparing SBDART

ARF against AERONET ARF, Kumar et al. [134] noticed a good agreement between the two

datasets during pre-monsoon of 2010 over Kanpur.

Recently, Kalluri et al. [135] reported that the values of ARF and ARFE in ATM were

observed to be positive and at SUR were found to be negative during winter, summer, monsoon

and post-monsoon seasons for the period January 2013-December 2014 over Anantapur.

Similarly, Singh et al. [53] conducted a study related to ARF with associated HR over dust

events over Patiala during March 2012 and reported the negative ARF at TOA and SUR, while

positive in ATM translating to the maximum HR of 2.2 Kday−1 during dust events over Patiala

showing the significant atmospheric heating due to dust particles.

30

Chapter 3

Site description, instrumentation and methodology

This chapter presents an overview of the study areas and the datasets from both ground and

satellite based observations that are used this thesis.

3.1 Site description and meteorology

IGP is supposed to be one heavily polluted and densely populated region of the world [136]

covering a huge area between 23.0°N, 68.0°E and 30.0°N, 93.50°E. It is surrounded from east

to Bay of Bengal, from west to Thar Desert and Arabian Sea, from the north to the Himalaya

and from south to the Vindyana Satpura range. The IGP is a heavily aerosol-loaded region with

different seasonal characteristics due to the distinctive nature of its topography and dense

population. Since last decade, it is one of the popular regions for high aerosol loading due to

vehicular emissions, coal burning, and long range transport of dust coupled with abrupt

variation in meteorological conditions [89, 91]. These variations occur from natural and

anthropogenic sources [137]. This region is suffering with high aerosol loading originating

from the complex combination of natural and anthropogenic sources showing strong seasonal

variation [138]. Understanding and quantifying the effects of atmospheric aerosols over IGP

region are difficult due to its complex nature. Atmospheric aerosols are distinguished as a

crucial parameter in climate change studies over IGP due to which significant changes in

atmospheric temperature and ARF are observed [139, 140]. Aerosols over IGP exhibit strong

seasonal and annual variability. This region experiences four distinct seasons every year. In

this study the long-term data were seasonally arranged into summer (June-August), winter

(December-February), pre-monsoon (March-May) and post-monsoon (September-November).

In IGP, the monsoon starts from July and continue till the month of September with heavy rain

followed by lower wind. During summer and pre-monsoon due to high temperature and low

31

pressure, dust particles (coarse) are lifted upward from the arid and semi-arid areas along with

strong surface winds, resulting storms which can affect the IGP [141-143]. This area is robustly

affected by regular and strong dust storms during pre-monsoon and in the beginning of

monsoon due to high aerosol loading [139]. While during winter, low temperature and high

pressure resulting in low surface convection which causes haze and dense fogs within the lower

atmospheric layer [93, 144], which leads to poor air quality, reduction in visibility and also is

unfavorable to human health [100]. Furthermore, during this season biomass burning aerosols

(fine) are also dominant due to fossil fuel burning. During post-monsoon agriculture activities

are prominent due to which smoke plumes can sheathe the whole IGP. In this season, harvesting

generates high concentrations of fine particles. This study focused on four sites in IGP as shown

in Fig. 3.1. Table 3.1 presents the measurement period and site information including

coordinate and altitude for each site. These sites are situated in the monsoon region of Pakistan

and India playing crucial role not only in monsoon circulation over IGP but also affecting the

global climate [145].

Table 3.1: Measurement period and site information including coordinate and altitude for each site.

Site Location Coordinates Altitude (AMSL) Measurement period

Karachi 24.8° N, 67.0° E 8 m Jan 2007-Mar 2013

Lahore 31.5° N, 74.3° E 210 m Jan 2007-Oct 2012

Jaipur 26.9° N, 75.8° E 431 m Apr 2009-Aug 2012

Kanpur 26.5° N, 80.3° E 142 m Mar 2007-Mar 2013

Karachi (24.8°N and 67.0°E) having an altitude of 8 m Above Mean Sea level (AMSL)

with an area of 3500 km2 located on the Arabian Sea, one of the largest and most populous

metropolitan cities (16 million) in Pakistan. The atmosphere of Karachi is characterized as arid

and semi-arid with warm and dry winter, while hot and humid summer that dominates the warm

32

pre-monsoon. The average temperature varies from 33 to 36 oC during summer, while

temperature lies between 12 to 22 oC during winter.

Lahore (31.5°N, 74.3°E) with high altitude 210 m AMSL is situated on the eastern bank

of the Ravi River covering area of 404 km2 with population of about 10 million. Lahore is

characterized by a semi-arid climate having hot summer and cold winter with dense fog in the

month of January. The average temperature during the hot summer ranges between 33 and 39

oC. Whereas, the average temperature in winter ranges from 7 to 12 oC.

Jaipur (26.9°N, 75.8°E) is located at an altitude of 431 m covering an area of about

1484 km2 AMSL having population of approximately 2.3 million. It is located in the western

part of IGP near the western edge of the Thar Desert. The climate of Jaipur is defined with

semi-arid having a very hot summer with an average temperature varies from 25 to 30 oC, while

pleasant and mild winter having temperature of range of 10 - 15 oC. During monsoon, heavy

rains as well as thunderstorms are also frequent.

Kanpur (26.5°N, 80.3°E) positioned at an altitude of 142 m AMSL covering an area of

1642 km2. It is considered as one of the densely polluted city having population 2.5 million,

located in the central part of IGP. It experiences very hot and long summer with an average of

temperature ranges from 25 to 39 oC, while relatively short winter season having temperature

range of 7 to 13 oC. Dust storms are frequent in pre-monsoon [59, 142] and severe fog is

common in winter.

33

Figure 3.1: Map of the study area.

3.2 Instrumentation

This section offers details about ground based and satellite based instruments used during my

study period.

3.2.1 Aerosol robotic network

AERONET is a ground-based remote sensing aerosol network established by NASA. It uses

CIMEL sun/sky radiometers that take measurements of the direct sun and diffuse sky radiances

within the 340-1020 nm and 440-1020 nm spectral ranges, respectively [43]. AERONET data

is available at three levels: Level 1.0 (unscreened), Level 1.5 (cloud screened; [146]), and Level

2.0 (cloud screened and quality assured; [43]). AERONET provides columnar AODs over both

land and ocean but is restricted to point observations [20, 74]. Although such ground-based

aerosol remote sensing has a limited spatial coverage, wide angular and spectral measurements

of solar and sky radiation provide reliable and continuous data on aerosol optical properties at

34

particular locations [24]. The uncertainty in retrieval under clear sky conditions for AOD is

usually less than ±0.01 for the longer wavelength (> 440 nm), less than ±0.02 for the shorter

wavelength (440 nm), and ±0.05 for sky radiance measurements [24, 147].

Figure 3.2: Aerosol robotic network [148].

In this study, AERONET all point level 2.0 data of direct product (AOD500 nm, AE440–

870 nm and water vapour) and inversion product (SSA440 nm, RRI440 nm, EAE440–870 nm and

AAE440– 870 nm) were used. The aerosol distribution patterns in each season were quantitatively

identified according to scattered plots of aerosol optical properties. Therefore, the variations in

aerosol optical properties were seasonally investigated to determine the aerosol types over

studied sites. Additionally, AERONET retrieved SSA, AP, phase function, and RI at four

different wavelengths of 440, 670, 870, and 1020 nm, respectively, were utilized for the

analysis of the seasonal variation of the aerosol optical properties. Furthermore, the AERONET

also retrieved the VSD (μm3∕μm2) using 22 radius size bins ranged from 0.05 to 15 μm. The

data can be downloaded from the AERONET website (http://aeronet.gsfc.nasa.gov/) and

detailed information about the instrument is documented by Dubovik et al. (2002).

http://aeronet.gsfc.nasa.gov/

35

3.2.2 Moderate resolution imaging spectroradiometer

MODIS is a satellite instrument that is carried on both the Terra (EOS AM) and Aqua (EOS

PM) satellites. Terra's orbit around the earth is timed so that it passes from north to south across

the equator in the morning, while Aqua passes south to north over the equator in the afternoon.

The MODIS instrument has 36 spectral bands that provide abundant information on

atmospheric, terrestrial, and oceanic environments. MODIS uses different methods for data

retrieval over land [8] and over oceans [149]. The MODIS instrument provides observations at

moderate spatial resolutions (1-250 km) and temporal resolutions (1-2 days), over different

portions of the electromagnetic spectrum. In order to improve the accuracy and quality of

retrieved data, the MODIS algorithms have been updated to make use of improved cloud-

masking processes, aerosol models, and the surface reflectance database [150, 151]. The

introduction of the Deep-Blue algorithm has improved the MODIS Level 2 observations over

bright land surfaces such as the Sahara Desert. In this study, Aqua-MODIS (MYD04) level 2.0

collection 5.1 (C051) AOD data from DB and DT algorithms at 550 nm with a spatial resolution

of 10 km were used. Moreover, Aqua-MODIS (MYD04) level 2.0 collection 6 (C006) surface

reflectance data from DB algorithm at 660 nm at 10 km spatial resolution were also utilized.

Detailed information on algorithms for the retrieval of aerosol and cloud parameters is available

from http://modisatmos. gsfc.nasa.gov/.

Figure 3.3: Moderate resolution imaging Spectroradiometer [148].

36

3.2.3 Ozone monitoring instrument

The OMI satellite (EOS-Aura) was launched in July 2004 by the Netherlands Agency for

Aerospace Programs (NIVR), in collaboration with the Finnish Meteorological Institute (FMI).

The OMI has a nadir-viewing imaging spectrometer that measures the Top Of the Atmosphere

(TOA) upwelling radiances in the ultraviolet and visible regions of the solar spectrum (270-

500 nm), with a spectral resolution of approximately 0.5 nm in the ultraviolet and 0.63 nm in

the visible wavelength ranges [152]. Spatial resolution is approximately 13 x 24 km2 taking

almost 1 day for overall global coverage. The OMI was originally designed to retrieve data on

trace gases such as O3, NO2, SO2, etc., but it also provides valuable information on atmospheric

aerosols. It has a wavelength range around 400 nm that can be used to detect elevated layers of

absorbing aerosols such as those resulting from biomass burning and desert dust plumes.

An improved OMI AOD (final AOD) Level-2 version 3 data product at 500 nm were

used in this study. Moreover, columnar ozone version 3 data at spatial resolution of 0.25 x 0.25ᵒ

were also utilized in this study. The data used in the present work is downloaded from

http://disc.gsfc.nasa.gov/Aura/OMI/omaero_v003.shtml.

Figure 3.4: Ozone monitoring instrument [148].

http://disc.gsfc.nasa.gov/Aura/OMI/omaero_v003.shtml

37

3.2.4 Multiangle imaging Spectroradiometer

The MISR instrument was launched in 1999 on a polar orbiting sun-synchronous satellite

(TERRA), which has an altitude of 705 km. The MISR has a temporal resolution of 16 days

and nominal spatial resolutions of 250 m, 275 m, and 1 km, but radiances at 1.1 km resolution

are processed to yield the standard Level-2 MISR aerosol product at a 17.6 km x 17.6 km pixel

size. A heterogeneous land algorithm was developed by Martonchik et al. [46]. The MISR

instrument continuously acquires daytime data over most parts of the world, but with a

frequency that is dependent on latitude. Due to the overlap of the swathes (paths) near the poles

and their broad separation at the equator, coverage intervals vary between 2 and 9 days,

respectively. The MISR Level-2 global data product (AOD) at 550 nm used in this study during

2007-2013, is available on a daily basis from https://www-misr.jpl.nasa.gov/ [68, 73].

Figure 3.5: Multiangle imaging Spectroradiometer [148].

3.2.5 Cloud-aerosol lidar and infrared pathfinder satellite observations

The CALIPSO satellite was launched on April 28, 2006, with equator crossing times of about

13:30 and 01:30 and a 16-day repeating cycle [153, 154]. Observations from the CALIPSO

satellite have resulted in a remarkable improvement in our understanding of the radiative

effects of aerosols. Unlike other satellite-based passive remote sensing instruments, CALIPSO

https://www-misr.jpl.nasa.gov/

38

can detect aerosols both in clear sky conditions and beneath thin cloud layers, as well as over

bright surfaces [76, 155-159]. The CALIPSO satellite gives the distribution of aerosols and

clouds in vertical atmospheric profiles on the global/regional scale [160]. The main aim of the

CALIPSO satellite is to provide a global, multi-year data sets of cloud and aerosol optical and

spatial properties from which, the uncertainties of aerosol direct and indirect effects on climate

forcing and cloud climate feedback are evaluated [76]. The CALIPSO satellite carries a Cloud-

Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument that operates at two

wavelengths (532 nm and 1064 nm) and provides continuous observations with attenuated

backscatter during day and night, covering the entire globe. Uncertainties in the CALIPSO

calibration are reported by Powell et al. [161] and the CALIOP calibration is probable to be <

6%. The high uncertainty is due to the assumption of negligible aerosol scattering in the

calibration region. Measurements from the CALIPSO satellite provide the significant

improvement in our knowledge of discrimination of different aerosol types including clean

marine, dust, polluted continental, clean continental, polluted dust and smoke. To date,

CALIPSO data have been mainly used to analyze dust aerosols, and in particular, to study the

presence and distribution of aerosols associated with Asian dust plumes [155, 156].

Figure 3.6: Cloud-aerosol lidar and infrared pathfinder satellite observations [148].

39

In the present study level 2 version 3 AOD data at 532 nm with spatial resolution of 05

km (05kmALay v.3) were used. This study also utilized the level 2 version 3.01 attenuated

backscatter data to classify the aerosols for the selected days corresponding to different seasons

in order to explain the seasonal variation of aerosol types in total atmospheric column over

IGP. The data downloaded from the website (http://www.calipso.larc.nasa.gov/).

3.2.6 Hybrid single particle lagrangian integrated trajectory

National Oceanic and Atmospheric Administration (NOAA) HYbrid Single Particle

Lagrangian Integrated Trajectory (HYSPLIT) model can be used to calculate the particular

forward and backward air masses trajectories [162]. This model was used to compute the

possible backward trajectories of air masses from the source regions to the studied sites. We

ran this model for 72 h (3 days) at 500, 1000, and 1500 m above the ground level (AGL) for

particular days representing each season during the entire studied period through the website

(http://ready.arl.noaa.gov/HYSPLIT.php).

3.3 Methodology

Detailed description of methodology used in the current thesis is mentioned in the following

subsections.

3.3.1 AOD retrieval algorithms: The Dark Target and the Deep Blue

approaches

Since 1999 there have been several product updates to both the Aqua and the Terra MODIS

AOD retrieval algorithms. Two significant new approaches have been introduced. The first

approach involves the “Dark Target” (DT) retrieval algorithm [8, 150], which is limited to

surface reflectance up to 0.15 and assumes transparency of aerosols in the middle infrared (mid-

IR) spectral range. The first step in the DT approach uses empirical relationships in the visible

and mid-IR parts of the spectra and calculates surface reflectance at 470 and 660 nm. The

http://www.calipso.larc.nasa.gov/

40

aerosol is then classified using the ratios between the path radiance at the two wavelengths

upon which the surface reflectance is based. In the final step of the algorithm (following

corrections that include removing cloud screening effects, gas absorption effects, and radiances

obtained from the satellite data together with those simulated from the ground data) the

radiative transfer is determined and then final AOD values are determined using a Look-Up

Table (LUT) method [149, 150]. The DT approach breaks down at surface reflectance above

0.15 or with coarse size particles; these problems were first addressed in a major product update

by Levy et al. [151] Using the DT approach, this improved product takes polarization into

account when computing the radiative transfer, thereby improving the AOD retrieval for desert-

like surfaces and for coarser particles. The second approach involves the “Deep Blue” (DB)

algorithm [163], which differs from the DT algorithm. As the name implies, the major physical

assumption is that there is lower surface reflectance in the blue part of the visible spectra than

in the red part, and this is used to retrieve AOD values for geographical regions with surface

reflectance greater than 0.15. Such reflectance typically occurs in desert, arid, semi-arid, and

urban geographical regions.

3.3.2 Comparisons between satellite-based and ground-based AODs

Comparisons between satellite-based and ground-based AODs are crucial for radiative forcing

calculations, for estimating uncertainties in satellite measurements, for data assimilation, and

for the development of improved algorithms. In this study, we have compared data from

MODIS, MISR, OMI, and CALIPSO satellite borne sensors with ground-based (AERONET)

AOD data for four different locations in India and Pakistan (Karachi, Lahore, Jaipur, and

Kanpur) between 2007 and 2013. For such a comparison, it is necessary to adjust the AOD

values from each sensor to a common wavelength. The AERONET AOD wavelength was

therefore converted to the MODISSTD, MODISDDB, OMI, MISR, and CALIPSO AOD

wavelengths using:

41

AODa = AODb (a/b)−AE (3.1)

where a=550 nm, 558 nm, and 532 nm for MODIS (Standard and Deep Blue products), MISR,

and CALIPSO, respectively, b=500 nm for AERONET, and AE is the (440-870 nm) angstrom

exponent [23, 141, 158, 159].

Therefore, for comparison of the AERONET AOD with that of satellite AOD at the

same wavelength above interpolation (Equation (3.1)) is required [19, 150]. The investigation

into the correlation between satellite based remote sensing AOD data and ground-based AOD

data have suggested that the correlation between retrieved AODs would be enhanced by taking

into account surface reflectance and aerosol properties [1]. In order to calculate the correlation

coefficient, we used datasets from MODIS (Standard and Deep Blue products) MISR, OMI,

and CALIPSO sensors that matched the AERONET data at same wavelength. These datasets

were used to compare AOD values obtained from MODIS (Standard and Deep Blue products),

MISR, OMI, and CALIPSO sensors with the corresponding values from AERONET. Linear

regression analysis was performed for MODISSTD, MODISDB, MISR, OMI and CALIPSO

AODs with respect to AERONET AODs using:

AODsatellite = m × AODAERONET + c (3.2)

where m (slope), c (intercept), AODAERONET represents AERONET AOD and AODsatellite

represents AODs from MODIS, MISR, OMI and CALIPSO satellites. The regression

coefficient (R2), which is the square of the correlation coefficient, indicates the correlation

between satellite and AERONET AODs [164]. All of these quantities (m, c, and R2) serve as

useful indicators of the local spatial characteristics of the aerosol parameter (AOD) at a

particular location and time [60]. The slope (m) of the linear regression equation (Equation

(3.2)) reveals how close the assumed aerosol model over a particular region is to the local

aerosol type, and the intercept indicates the error caused by surface reflectance [164, 165]. The

linear regression equation therefore provides information concerning the factors that affect the

42

correlation [166]. If there was a perfect correlation between ground-based AOD and satellite-

based measurements then the value of c would be 0 and of m would be 1 [165]. Large intercepts

are due to large errors in surface reflectance and at ground surface reflection the retrieval

algorithm is biased towards low AOD values, which are indicated by non-zero intercepts that

may be associated with an inappropriate assumption or with calibration error [61, 165]. In

contrast to real situations, where the slope in the retrieval algorithm is other than unity, this

may indicate some irregularities between the optical properties and the aerosol microphysical

properties used in the retrieval algorithm [167]. In addition to using linear regression, we also

computed the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) between the

satellite and AERONET observations. The RMSE is defined as

RMSE = √1

n∑ (AOD(satellite)i − AOD(AERONET)i)2n

i=1 (3.3)

and the MAE as

MAE =1

n∑ |AOD(satellite)i − AOD(AERONET)i|

ni=1 (3.4)

where n is the number of observations.

Overestimation or underestimation of retrievals can be quantified by calculating the Root Mean

Bias (RMB), which is defined as:

RMB =AOD̅̅ ̅̅ ̅̅ ̅sattelite

AOD̅̅ ̅̅ ̅̅ ̅AERONET (3.5)

If RMB < 1, then this represents an underestimation, and if RMB > 1 this indicates

overestimation.

The accuracy of algorithms used in this study, in particular the MODIS algorithms, can

be further assessed using the expected error (EE) measure, which is determined as confidence

envelopes for each retrieval algorithm over land and are used in particular to evaluate the

quality of Deep Blue Collection 5 (C005) AOD product. The expected error has been defined

[168, 169] as:

43

EE = ±(0.05 + AODAERONET) (3.6)

For good quality matches the MODIS-retrieved AOD is expected to fall within the Expected

Error Bound (EEB), which is:

AODAERONET − |EE| ≤ AODsatellite ≤ AODAERONET + |EE| (3.7)

where |EE| is the absolute value of the Expected Error defined previously.

3.3.3 Aerosol classification through cluster analysis

Identification of aerosol types is essential because different aerosol types are originated from

several sources having diverse physical, chemical and optical properties also showing different

atmospheric impacts [24]. Seasonal variation of aerosol types and their optical characteristics

over four different locations having different meteorological conditions in IGP can be helpful

to evaluate the ARF as well as to improve the climate models. Several methods can be adopted

to distinguish aerosol types such as dust, biomass burning, urban/industrial and mixed type

aerosol, which include polluted dust, polluted continental, clean continental etc. originating

from the mixture of natural and anthropogenic pollutants. The most used clustering technique

for discrimination of aerosols into different types is to correlate AOD with AE as both are

wavelengths dependent which were adopted recently by numerous researches for selected

region [33, 34, 89, 91, 93]. Moreover, variation in AE indicates the change in particle size. The

AOD-AE clustering method sorts the aerosol types into dust, anthropogenic and marine

aerosols but cannot have the potential to further classify anthropogenic into absorbing and non-

absorbing [170]. Similarly, the classification of aerosol can be carried out by other clustering

technique by correlating the different optical properties. In all these techniques, for better

assessment of aerosol types, some particular threshold values are selected to categorize the

aerosols depending on the composition of aerosols in different seasons [34, 35, 37]. Dominant

aerosol types were computed by correlation between absorption and size relationship [37]

which may be distinguished from one another depending on seasons. AAE of aerosols is a

44

function of their composition [35] and EAE is an indicator of particle size [86]. Hence AAE is

a key to distinguish aerosol types when accompanied by EAE, however, it cannot have the

potential to discriminate biomass burning from urban/industrial alone [35]. Furthermore, to

verify identification of aerosol types, it is useful to correlate EAE with sensible parameter like

SSA and RRI, which can better separate biomass burning from urban/industrial [36]. As SSA

can be helpful to discriminate the aerosols according to absorbing and non-absorbing nature in

different size range due to spectral absorption feature of different types of aerosol [38, 170]

and RRI is also used to differentiate the scattering behavior of aerosol [30]. These types of

aerosol retrieved from AERONET can further validate with the aerosol types retrieved from

the CALIPSO satellite.

3.3.4 Estimation of aerosol radiative forcing

Aerosol modify incoming solar and outgoing infrared radiation, this modification in radiation

fluxes is termed as ARF. The ARF is considered for both the shortwave radiation (0.3-4.0 μ𝑚)

and long wave radiation (4-100 μ𝑚). The radiative fluxes with aerosol and aerosol free cases

were calculated using SBDART model developed by Ricchiazzi et al. [171]. SBDART model

is a FORTRAN code that computes plane parallel radiative transfer in clear and cloudy

conditions within and at the surface of the earth’s atmosphere. This model is well appropriate

for a broad range of problems in atmospheric radiative energy balance and remote sensing. In

SBDART radiative transfer equations are numerically integrated using DISORT radiative

transfer model by Stamnes et al. [172]. SBDART has adopted six standard atmospheric profiles

that suit for tropics, mid latitude summer, mid latitude winter, sub-arctic summer, sub-arctic

winter and US62. Fig. 3.7 shows the flow chart presenting the method to estimate SDARF,

SDARFE and atmospheric HR. According to the meteorological conditions in IGP, March to

September months was integrated as mid latitude summer and rest of the months was adopted

as mid latitude winter while estimating ARF using SBDART model. The other important input

45

parameters needed in SBDART include AOD, SSA, AP and columnar water vapour derived

from AERONET while surface albedo from MODIS and columnar ozone from OMI.

Figure 3.7: Schematic diagram of Shortwave Radiative Forcing using SBDART model.

In this study, daily averaged SDARF calculations at TOA and SUR were performed

using SBDART model for the study period. The radiative forcing calculations were performed

using eight radiation streams to obtain the TOA and the SUR downward and upward fluxes at

1-hr interval for a 24-h period with and without aerosol conditions separately. The radiative

forcing at the TOA and the surface is obtained as the difference between the down and up

fluxes for both with and without aerosols. The averaged SDARF is often expressed as:

SDARFTOA,SUR=∫ [ Flux(net)with aerosol TOA,SUR −Flux(net)wihout aerosol TOA,SUR ]dh

240

∫ dh24

0

(3.8)

The resultant SDARFATM was estimated as the difference between the radiative forcing at TOA

and SUR given as:

SDARFATM = SDARFTOA − SDARFSUR (3.9)

where SDARFTOA, SDARFSUR and SDARFATM were SDARF at TOA, SUR and ATM,

respectively. If the sign of radiative forcing is negative, the aerosol causes a net loss of radiative

SDARF

SBDART MODEL

AERONET

AOD, SSA, AP and Columnar water

MODIS

Surface reflectance

SDARFTOA

SDARFETOA

SDARFATM

SDARFEATM

SDARFSUR

SDARFESUR

OMI

Ozone

Atmospheric HR

46

flux to the atmosphere and leads to a cooling effect; however, if it is positive, it leads to a

warming effect [131].

The SDARFATM in Wm-2 represents the amount of solar radiations trapped in the

atmosphere by aerosols. The greater the amount of SDARFATM the greater is the amount of

trapped energy. The HR due to absorption of aerosols (ΔSDARFATM) is computed from the

first law of thermodynamics and hydrostatic equilibrium suggested by Liou, [173] given as:

∂T

∂t =

g

Cp ΔSDARFATM

Δ𝑃 (3.10)

where ∂T

∂t is the HR in Kday-1, g is the acceleration due to gravity (9.8 ms-2), Cp is the specific

heat capacity of air at constant pressure (i.e. 1006 Jkg-1 K-1) and Δ𝑃 is the atmospheric pressure

difference between top and bottom layers of the atmosphere where mostly aerosols are present.

The pressure was taken 300 hPa because large amount of aerosols which contribute to the local

heating are present in the lower atmosphere [122]. Further the AOD at 500 nm from AERONET

was converted into AOD at 550 nm by using equation 3.1. Then, SDARFE is obtained by

dividing the value of forcing by AOD at 550 nm given by:

SDARFE = SDARF/AOD500 (3.11)

where SDARF (Wm-2) is the forcing at TOA, SUR and ATM. Generally, the net radiative effect

is still facing some uncertainties in ARF calculation due to the model atmosphere, assumption

in input parameters (i.e. SSA and AP), surface reflectance, location and seasons [126]. Finally,

the reliability of SBDART model was analyzed by validating the SDARF estimated from

SBDART model with SDARF retrieved from AERONET. For such validation purpose, the

validation of daily averaged SBDART estimated SDARF were compared with AERONET

retrieved SDARF at TOA and SUR individually. Additionally, RMSE between estimated and

retrieved forcing was also computed. The RMSE is defined as:

RMSE = √1

n ∑ (SDARF(SBDART)i−SDARF(AERONET)i)2n

i=1 (3.12)

where n is the no of observations.

47

Chapter 4

Intercomparison of MODIS, MISR, OMI, and CALIPSO

aerosol optical depth retrievals for four locations on the

Indo-Gangetic plains and validation against AERONET

data

This chapter provides an intercomparison of AOD retrievals from satellite-based MODIS,

MISR, OMI, and CALIPSO instrumentation over Karachi, Lahore, Jaipur, and Kanpur between

2007 and 2013, with validation against AOD observations from the ground-based AERONET.

Both MODIS Deep Blue (MODISDB) and MODIS STanDard (MODISSTD) products were

compared with the AERONET products. We also computed RMSE, MAE and RMB.

Additionally, the monthly variability of AOD derived from different sensors were examined,

to obtain the overestimation or underestimation of different satellite instruments relative to the

AERONET data.

4.1 Intercomparison of satellite and ground-based aerosol

optical depth

The regression approach revealed the correlation between MODISSTD and AERONET at 550

nm over Karachi, Lahore, Jaipur, and Kanpur. Fig. 4.1 shows a very good overall agreement

between MODISSTD data and AERONET data, with m ~1.14, 1.09, 1.17 and 1.06, c ~0.02,

0.09, -0.07 and 0.08, and R2 ~ 0.71, 0.67, 0.76 and 0.61 over Karachi, Lahore, Jaipur and

Kanpur, respectively. The best correlation was for Jaipur (R2 ~ 0.76), which is consistent with

the correlation (R2 ~ 0.72) found by Tripathi et al. [165] over the Ganga Basin in India.

48

Figure 4.1: Scatter plots for AERONET AOD vs. MODIS Standard product over different cities


49

Previous studies have also used AERONET-measured AODs to validate MODIS

derived AODs. Alam et al. [68] found the best correlation between MODIS and AERONET

AODs over Lahore (R2 = 0.72), with relatively poor correlation over Karachi (R2 = 0.58). Gupta

et al. [72] also compared MODIS-derived AODs with AERONET-derived AODs over Lahore

and found a similarly good correlation between the two (R2 = 0.72). More et al. [42] compared

AOD data from MODIS and MICROTOPS with AERONET data over Pune, India, and found

good correlations (0.62-0.93) on the seasonal scale.

Excellent agreement has been found between MODIS and AERONET measurements

over the ocean (R2 ~ 0.84), while MODIS performed less well over land (R2 ~ 0.53) due to

surface variability [60]. Chu et al. [61] reported a good correlation between MODIS and

AERONET (R2 = 0.88 at 470 nm and R2 = 0.72 at 660 nm). Prasad et al. [31] found a good

correlation (R2 = 0.47) between MODIS and AERONET over Kanpur during winter but a poor

correlation (R2 = 0.29) in summer. Alam et al. [74] compared the MODIS and AERONET

AODs over Lahore in pre-monsoon and post-monsoon seasons, obtaining correlation

coefficients of 0.66 and 0.68, respectively. Similarly, Alam et al. [23] compared the MODIS

and AERONET AODs over Lahore during dust storm and found a highest correlation (R2 >

0.92) between the two datasets. Choudhry et al. [70] provided a comparison between MODIS

and AERONET AODs over Kanpur, Gandhi College, and Nainital, and found good

correlations for Kanpur and Nainital (R2 > 0.7, 0.68, respectively) and a poor correlation for

Gandhi College (R2 ~ 0.5). Parameters (m, c, and R2) for correlations between MODISSTD,

MODISDB, MISR, OMI, and CALIPSO measurements of AOD and AERONET-derived AOD

are presented in Table 4.1.

Fig. 4.2 shows the regression results for MODISDB AOD vs. AERONET AOD at 550

nm over Karachi, Lahore, Jaipur and Kanpur. The R2 values for MODISDB-AERONET are

0.54, 0.64, 0.73 and 0.61 over Karachi, Lahore, Jaipur and Kanpur, respectively, with

50

corresponding intercepts of -0.13, -0.06, -0.21 and -0.15 (as shown in Table 4.1). The intercepts

for the MODISDB-AERONET comparisons are thus negative for all sites, implying a slight

overcorrection for surface reflectance. The R2 value for the MODISDB-AERONET comparison

over Jaipur is relatively high (0.73) compared to the other sites. Misra et al. [77] compared

MODIS AOD values derived from the DB algorithm with ground-based sun photometer

(MICROTOPS) observations over Ahmadabad for the period from 2002 to 2005 and reported

only a poor correlation (R2 = 0.43). Hoelzemann et al. [66] carried out a validation of MODIS

AODs using AERONET data over numerous sites in South America and found a correlation

between the two of R2 > 0.5.

Table 4.1: Parameters of AERONET derived AOD vs. MODISSTD, MODISDB, MISR, OMI and

CALIPSO AODs over four locations in India and Pakistan.

Sites

AERONET vs.

MODISSTD MODISDB MISR OMI CALIPSO

N R2 M c N R2 m c N R2 m c N R2 m c N R2 m c

Karachi 635 0.71 1.14 0.02 635 0.54 0.91 -0.13 122 0.79 0.78 0.04 757 0.55 1.11 0.09 81 0.47 0.97 0.03

Lahore 597 0.67 1.09 0.09 597 0.64 1.09 -0.06 89 0.57 0.54 0.11 277 0.51 0.77 0.04 59 0.23 0.72 0.08

Jaipur 400 0.76 1.17 -0.07 400 0.73 1.55 -0.21 90 0.63 0.68 0.06 182 0.62 1.05 -0.13 33 0.23 1.20 -0.01

Kanpur 887 0.61 1.06 0.08 876 0.61 1.36 -0.15 175 0.73 0.60 0.12 511 0.45 0.85 -0.08 51 0.36 0.83 0.07

51

Figure 4.2: Scatter plots for AERONET AOD vs. MODIS Deep Blue product over different cities


52

The MISR-derived AOD was compared to the AERONET AOD at 558 nm over the

four investigated sites: the R2 values for the MISR-AERONET comparison at 558 nm were

0.79, 0.57, 0.63 and 0.73 over Karachi, Lahore, Jaipur and Kanpur, respectively, as shown in

Fig. 4.3.

The poor MISR retrievals over Lahore and Jaipur can be attributed to the scarcity of

matched data points during the study period. The high R2 value over Karachi (0.79) indicates

a significant correlation between the MISR and AERONET retrievals.

Alam et al. [68] observed good correlation (R2 = 0.67) between MISR and AERONET

data over Karachi and poor correlation over Lahore (R2 = 0.10). Prasad et al. [31] found a

relatively good MISR-AERONET correlation over Kanpur during summer (R2 = 0.64) and a

poor correlation in winter (R2 = 0.33). Results obtained by Christopher et al. [65] confirmed

that the MISR sensor is a reliable sensor for the retrieval of AOD data in desert regions and

indicated a high correlation (R2 = 0.89) for comparisons between MISR and AERONET AOD

data over different sites.

AOD data retrieved from the MISR instrument over the Beijing urban area between

2002 and 2004 were compared with AERONET AOD measurements by Jiang et al. [63] who

noted that, over all, the correlation between MISR and AERONET AOD was very good (R2 =

0.86). Cheng et al. [71] compared MODIS, MISR and GOCART aerosol products over China

with AERONET data and found that MISR AODs showed higher correlation (R2 = 0.77) with

AERONET measurements, which can be attributed to its better viewing and spectral

capabilities.

53

Figure 4.3: Scatter plots for AERONET AOD vs. MISR over different cities (Karachi, Lahore, Kanpur

and Jaipur).

54

In highly reflective areas AOD retrievals from the MISR sensor are better than those

from other sensors because of its multiangular capability, which enables it to differentiate

between sunlight reflected at the earth's surface and sunlight reflected by aerosols [51].

Ramachandran and Kedia, [1] compared MISR and MODIS satellite-based AOD data with

ground-based MICROTOPS and AERONET sun photometer AOD data from between 2006

and 2008 over Karachi, Kanpur, Ahmadabad, Gurushikhar, and Gandhi College. They found

that the correlations were not strong (R2 = 0.25) due to the differences between satellite-

retrieved AODs and ground-based measurements. Liu et al. [67] noted that AODs retrieved

from MISR showed a good correlation (R2 > 0.86) with those from ground-based

measurements in most northern, southwestern and eastern and parts of China.

Fig. 4.4 shows scatter plots of OMI AOD vs. AERONET AOD at 500 nm over Karachi,

Lahore, Jaipur and Kanpur, where the R2 values were 0.55, 0.51, 0.62 and 0.45, respectively.

The best correlation (R2 = 0.62) is observed for Jaipur.

Curier et al. [64] compared AOD retrievals from OMI over western Europe with ground

measurement data and observed a correlation (R2) of 0.37 and 0.41 for sites such as Paris and

Lille, respectively in France, 0.39 for El Arenosillo in Spain, and 0.17 for Ispra (Italy); very

poor correlation (R2 < 0.08) was also observed for the other sites. Ahn et al. [75] compared

satellite retrievals by the MODISDB, MISR, OMAERUV, and OMAERO algorithms to

AERONET AOD retrievals over 44 selected locations and found that the retrieval algorithms

had a correlation (R2) of 0.65 with AERONET data over all sites.

55

Figure 4.4: Scatter plots for AERONET AOD vs. OMI over different cities (Karachi, Lahore, Kanpur

and Jaipur).

56

Fig. 4.5 shows scatter plots of CALIPSO retrievals against AERONET data at 532 nm,

for the four stations in this study. The plots reveal R2 values of 0.47, 0.23, 0.23 and 0.36 for

Karachi, Lahore, Jaipur and Kanpur, respectively, with the best correlation being for Karachi.

The poor correlation between CALIPSO and AERONET AODs over these sites is due to an

insufficient number of data points. The low c-values in Fig. 4.5 (0.03, 0.08, -0.01, and 0.08 for

Karachi, Lahore, Jaipur and Kanpur, respectively) indicate that vegetated areas (deciduous

forest, evergreen forest, cropland) provide the best estimates of surface reflectance, as has

previously been noted by Chu et al. [61].

Table 4.2 shows the computed RMSE and MAE values for the different satellites

sensors. The smallest RMSE and MAE values were 0.1051 and 0.0671, respectively, for MISR

observations over Karachi, while the largest errors (0.3531 and 0.2624, respectively) were

found for CALIPSO retrievals over Jaipur. Table 4.3 shows the RMB values, which indicate

overestimation of MODISSTD retrievals at all sites except Jaipur, and underestimation of

MODISDB retrievals at all sites except Kanpur. The percentage of AOD measurements that fall

within the EEB also indicates that MODISSTD consistently outperformed MODISDB at all sites.

Table 4.2: RMSE, MAE of AERONET, MODIS, MISR, OMI and CALIPSO.

Sites MODISSTD MODISDB MISR OMI CALIPSO

RMSE MAE RMSE MAE RMSE MAE RMSE MAE RMSE MAE

Karachi 0.1734 0.1168 0.2414 0.2012 0.1015 0.0671 0.3051 0.1899 0.2148 0.1527

Lahore 0.2624 0.1797 0.2339 0.1764 0.1731 0.1101 0.2555 0.1731 0.3209 0.2309

Jaipur 0.1299 0.0862 0.2122 0.1573 0.1038 0.0682 0.1948 0.1534 0.3531 0.2624

Kanpur 0.2538 0.1693 0.3119 0.2425 0.1850 0.1270 0.2122 0.1573 0.3147 0.2293

Table 4.3: RMB and % of AOD values within EEB.

Sites MODISSTD MODISDB MISR OMI CALIPSO

RMB % RMB % RMB % RMB % RMB %

Karachi 1.1931 59.7 0.5951 21.4 0.8974 N/A 1.3098 N/A 1.0522 N/A

Lahore 1.2454 49.4 0.9798 42 0.7707 N/A 0.8401 N/A 0.8609 N/A

Jaipur 0.9901 69 0.9908 39.5 0.8280 N/A 0.7642 N/A 1.1843 N/A

Kanpur 1.2002 55.9 1.0939 31.2 0.8024 N/A 0.7290 N/A 0.9475 N/A

57

Figure 4.5: Scatter plots for AERONET AOD vs. CALIPSO over different cities (Karachi, Lahore,

Kanpur and Jaipur).

58

Using AERONET AOD as the standard the correlation coefficient between

AERONET-MODISSTD and AERONET AODs was found to be relatively high over Lahore

and Jaipur and lower over Karachi and Kanpur. In contrast, the MISR data showed a good

correlation with AERONET data over Karachi and Kanpur and was less well correlated over

Lahore and Jaipur. The CALIPSO sensor yielded too few data points for a statistical analysis,

making it difficult to obtain a valid comparison between CALIPSO data and AERONET data.

Abdou et al. [62] reported larger AODs over land from the MODIS instrument than from the

MISR instrument. Qi et al. [51] compared MODIS and MISR AOD values with AERONET

measurements over four sites in northern China: their results showed that MISR AODs were

more accurate than MODIS AODs at the SACOL (Climate and environmental observatory of

Lanzhou University) and Beijing sites but that MODIS AOD retrievals were better than MISR

retrievals at the Xianghe and Xinglong sites. Good agreement between MISR retrieved AODs

and ground measurements were observed at the desert location in China by Liu et al. [67].

In summary, our comparisons of MODISSTD, MODISDB, MISR, OMI, and CALIPSO

AODs with AERONET AODs indicated that the MISR sensor performed better over Karachi

and Kanpur than over the two sites, while the MODISSTD sensor performed better over Lahore

and Jaipur. MODISSTD AOD values over Lahore and Jaipur were larger than corresponding

AOD values from all other satellite sensors. The MISR data were shown to be in better

agreement with AERONET data over Karachi and Kanpur than data from any of the other

satellite sensors.

4.2 Monthly aerosol optical depth variability

As part of our study we derived time series of monthly mean AOD values from AERONET,

MODIS, MISR, OMI and CALIPSO sensors. The observations in Fig. 4.6 show that the

monthly mean MODISSTD AOD values ranged from 0.22-1.92, 0.18-1.56, 0.21-0.92, and 0.35-

1.76 over Karachi, Lahore, Jaipur and Kanpur, respectively, compared to monthly mean

59

AERONET values that ranged from 0.18-1.36, 0.18-0.91, 0.21-0.74, and 0.25-0.97,

respectively. These results show an overestimation of MODISSTD AOD relative to AERONET

AOD.

Fig. 4.6 shows that, for Karachi, the maximum AERONET AOD value (1.36) was

recorded during the month of July 2008 and the maximum MODISSTD AOD value (1.92) was

also reported for the same month in 2008; for Lahore, the maximum AERONET AOD value

(0.91) was observed during the month of October 2010 and the maximum MODISSTD AOD

value (1.56) in July 2011; for Jaipur, the maximum AERONET AOD value (0.74) occurred

during the month of June 2011 and the maximum MODISSTD AOD value (0.92) was also in

June 2011; for Kanpur, the maximum AERONET AOD value (0.97) was recorded in December

2009 and the maximum MODISSTD AOD value (1.76) in July 2011. These results are similar

to those obtained by other researchers [31, 87, 121, 174].

Alam et al. [68] noted a maximum AERONET AOD value (0.92) in the month of July

2007 over Karachi. Singh et al. [142] reported the largest AERONET AOD value (1.05) as

occurring in July in both 2003 and 2004 over Kanpur. The smallest AOD values are generally

observed in winter [142, 175, 176]. Prasad et al. [31] found that MISR yielded better results

than MODIS (using AERONET as a standard) in both summer and winter.

60

Figure 4.6: Variability in monthly mean AOD values from AERONET and MODIS Standard product


61

Fig. 4.7 shows that the monthly mean MODISDB AOD values ranged from 0.06-1.3,

0.20-1.64, 0.05-1.07, and 0.06-1.53 over Karachi, Lahore, Jaipur and Kanpur, respectively,

compared to monthly mean AERONET AOD values that ranged from 0.18-1.36, 0.18-0.91,

0.21-0.74, and 0.25-0.97, respectively, indicating an overestimation of MODISDB values

relative to AERONET values. Fig. 4.7 shows that the maximum AERONET and MODISDB

AOD values during the study period were 0.91 (recorded in October 2010) and 1.64 (recorded

in August 2009), respectively. The figure shows that the maximum AERONET and MODISDB

AOD values for Jaipur were 0.74 (June 2011) and 1.07 (July 2011). The maximum AERONET

and MODISDB AOD values for Kanpur in December 2009 were 0.97 and 1.53, respectively,

which are similar to the results obtained by Lyamani et al. [177] during a period of high

temperatures.

High AOD values have been recorded in almost all major cities of Pakistan during the

summer months [178]. Wang and Yang, [179] found that the AOD in northern and eastern

China increased during spring and summer and decreased in autumn and winter. The high AOD

in summer was found by Balakrishnaiah et al. [180] to be associated with coarse particles and

the lower AOD in winter with fine particles. A seasonal pattern of higher AOD values in spring

and summer and lower AOD values in autumn has also been reported by Jiang et al. [63]. Gupta

et al. [72] found a seasonal cycle of higher AOD values in summer and lower AOD values in

winter. In contrast, Kaskaoutis et al. [181] reported high AOD values during winter and the

post-monsoon season, with low AOD values during the pre-monsoon and monsoon seasons

over Kanpur. Alam et al. [74] reported that AOD values for MODIS were compatible with

AERONET values during the pre-monsoon and post-monsoon seasons. High pre-monsoon

MODIS AOD values were observed in Pune, Visakhapatnam, and Hyderabad by

Balakrishnaiah et al. [182].

62

Figure 4.7: Variability in monthly mean AOD values from AERONET and MODIS Deep Blue product


63

Fig. 4.8 shows that the monthly mean MISR AOD values ranged from 0.08-0.86, 0.08-

0.72, 0.20-0.56, and 0.18-0.78 over Karachi, Lahore, Jaipur and Kanpur, respectively,

compared to monthly mean AERONET values that ranged from 0.07-0.82, 0.18-1.25, 0.22-

0.70, and 0.15-1.17, respectively. The MISR values indicate an underestimation of the AOD

relative to the AERONET AOD values. The maximum AERONET and MISR AOD values for

Karachi were found to be 0.82 and 0.86, respectively, in May 2009. During May 2009, which

are similar to the values reported by Liu et al. [158, 159] from atmospheric dust particles in

China following dust events. The maximum AERONET and MISR AOD values for Lahore

were 1.25 and 0.72, respectively, in March 2008; the maximum AERONET and MISR AOD

values for Jaipur were 0.70 in November 2011 and 0.72 in May 2007, respectively; and finally,

the maximum AERONET and MISR AOD values for Kanpur were 1.17 and 0.78 in June 2010,

respectively.

MISR AOD values over Beijing were found by Liu et al. [67] to be lower than

AERONET AOD values. Qi et al. [51] noted that MISR yielded more accurate results than

MODIS at the SACOL site and over Beijing but that, in contrast, MODIS AOD retrievals were

more accurate than MISR retrievals at Xianghe and Xinglong. High AOD values in India have

been found to be related to dust events [183]. High AOD values were noted in spring and

autumn by Fotiadi et al. [44] in the Eastern Mediterranean Basin, but Alam et al. [68] found

that AOD values over various cities in Pakistan were higher in summer than in spring, autumn,

or winter.

64

Figure 4.8: Variability in monthly mean AOD values from AERONET and MISR over different cities


65

Fig. 4.9 shows that monthly average OMI AOD values ranged from 0.25-2.01, 0.18-

1.83, 0.11-0.92, and 0.15-1.73 over Karachi, Lahore, Jaipur and Kanpur, respectively,

compared to monthly average AERONET AOD values that ranged from 0.23-1.07, 0.23-1.00,

0.20-0.87, and 0.32-1.24, respectively. The figure shows that the maximum AERONET and

OMI AOD values were 1.07 and 2.01, respectively, over Karachi during July 2011. The

maximum AERONET AOD values for Lahore, Jaipur, and Kanpur were 1.0, 0.87, and 1.24,

respectively, during the study period, while the maximum OMI AOD values were 1.83, 0.92,

and 1.73, respectively.

El-Metwally et al. [184] reported that maximum AOD values in April and October were

due to a combination of natural processes and human activities over Cairo. Using data from

MODIS and OMI, Marey et al. [69] observed high AODs in April and May and low AODs in

December and January over Niledelta. AODs in Southeast Asia have been reported to be high

in spring due to biomass burning [185]. The AOD over India has been reported to be increasing

rapidly since 2000 [186]. Frank et al. [187] noted high AOD values in summer and low AOD

values in winter over the Mojave Desert in southern California. AOD is reported to be higher

in pre-monsoon seasons that post monsoon due to dust storms and convective activity over

Pune, India [188].

66

Figure 4.9: Variability in monthly mean AOD values from AERONET and OMI over different cities


67

Fig. 4.10 shows the variability in AERONET and CALIPSO AOD values. Monthly

averaged AERONET AOD values ranged from 0.14-1.32, 0.24-0.85, 0.11-0.66 and 0.27-1.20

over Karachi, Lahore, Jaipur and Kanpur, respectively, while monthly average CALIPSO AOD

values ranged from 0.07-1.53, 0.10-1.07, 0.07-1.13 and 0.01-1.46, respectively. The maximum

monthly average AERONET AOD values over Karachi, Lahore, Jaipur, and Kanpur were 1.32,

0.85, 0.66 and 1.20, respectively, while the maximum monthly average CALIPSO AOD values

were 1.53, 1.07, 1.13, and 1.46, respectively.

Ma et al. [76] noted that CALIPSO AOD values were considerably lower than MODIS

AOD values over dusty regions during their study period. The seasonal variations in AOD

values that we noted in our study (high in summer and low in winter) are similar to those noted

in previous studies by other authors [189]. Choudhry et al. [70] found increases in AOD values

over various sites in India during the pre-monsoon season, and decreases during the post-

monsoon season. The monthly averaged AOD values and standard deviations for AERONET,

MODIS, MISR, OMI, and CALIPSO are given in Table 4.4.

Alam et al. [178] and Munir and Zareen, [190] also noted high AOD values over

Karachi and Lahore in summer, due to fact that both are industrialized, urbanized locations.

Alam et al. [68] suggested that AOD is increasing as a result of regular and ongoing

anthropogenic activities (such as industrial activity, traffic, and cooking). Higher air

temperatures also tend to hold more water vapour, which in turn encourages the growth of

aerosols [81].

Table 4.4: Monthly average AOD values of AERONET, MODIS, MISR, OMI, and CALIPSO.

Sites AERONET MODISSTD MODISDB MISR OMI

Avg±SD N Avg±SD N Avg±SD N Avg±SD N Avg±SD N

Karachi 0.47± 0.27 1456 0.51± 0.34 1095 0.59±0.41 1848 0.36±0.18 175 0.72±0.56 1221

Lahore 0.66± 0.31 599 0.76± 0.43 1851 0.57±0.39 1675 0.41±0.19 270 0.58±0.46 968

Jaipur 0.48± 0.23 781 0.38± 0.27 1278 0.46±0.39 1828 0.32±0.16 285 0.31±0.28 342

Kanpur 0.66± 0.31 1382 0.70± 0.36 1388 0.73± 0.51 1671 0.47±0.18 246 0.47±0.38 838

68

Figure 4.10: Variability in monthly mean AOD values from AERONET and CALIPSO over different

cities (Karachi, Lahore, Kanpur and Jaipur).

69

Our own results are in strong agreement with those obtained by Ramachandran and

Kedia, [1] who found that AOD values over Kanpur were high during winter (December)

because of the dominance of fine aerosols from the burning of fossil fuels and biomass, while

the higher AOD values during summer (July) than winter were attributed to the dominance of

coarse dust and sea salt particles. El-Metwally et al. [191] reported maximum AOD values

over Cairo (> 0.2) in October due to farmers burning residues following the rice harvest, with

plumes associated with biomass-burning leading to increased AOD values. The AOD values

retrieved over Kanpur by Ramachandran et al. [52] were mostly in the range 0.5-1. The highest

AOD values obtained over Kanpur by Dey et al. [59] and Singh et al. [142] occurred during

the summer months.

Sarkar et al. [48] and Ranjan et al. [174] analyzed variations in AOD and reported high

AOD values during the summer, increasing from March, and reaching a maximum in June,

with high AOD values persisting until August over India, neighbouring Pakistan. The

atmospheric boundary layer is narrow during the post-monsoon (October and November) and

winter (December, January and February), which restricts pollutants to a smaller volume close

to the earth's surface, resulting in increased AOD values [1]. Dust activity has been found to

increase in spring (March-May) over the Indian subcontinent [2]. AOD values in general

depend on seasonal cycles and are higher during the pre-monsoon (March, April, and May)

due to higher winds speeds from the southwest and monsoon (June, July, August), which

increase the sea salt and dust concentrations in the atmosphere [1, 65].

Our analysis shows seasonal variations in AOD values, with maximum values in

June/July from all of the sensors. High AOD values were observed by the MODISSTD,

MODISDB, MISR, and OMI instruments during the summer months (April-August); these

ranged from 0.32 to 0.78, possibly due to human activity and biomass burning. In contrast,

high AOD values were observed by the CALIPSO instrument between September and

70

December, due to high concentrations of smoke and soot aerosols. The variable monthly AOD

figures obtained with different sensors indicate overestimation by MODISSTD, MODISDB,

OMI, and CALIPSO instruments over Karachi, Lahore, Jaipur and Kanpur, relative to the

AERONET data, but underestimation by the MISR instrument.

71

Chapter 5

Long-term (2007–2013) analysis of aerosol optical

properties over four locations in the Indo-Gangetic plains

The work presented in this chapter emphasizes on examining the distribution and the spectral

behavior of associated optical properties of atmospheric aerosols in IGP. Measurements were

performed using AERONET Sun photometer at four sites (Karachi, Lahore, Jaipur and Kanpur)

with different aerosol environments during the period 2007-2013. Monthly averaged variability

in AOD and AE were measured. Moreover, the seasonal spectral behavior of SSA, phase

function, AP and RI were also examined. Finally, the analysis of the HYSPLIT model back

trajectory revealed that the seasonal variation in aerosol types was influenced by a contribution

of air masses from multiple source locations.

5.1 Variability of aerosol optical properties

The variations of aerosol optical properties (i.e. AOD, AE, SSA, phase function, AP, and RI)

over the observational sites were analyzed in the present study. These are discussed in the

following subsections.

5.1.1 Aerosol optical depth and angstrom exponent

The AOD is the total extinction integrated over a vertical column and is the most important

optical parameter used for SDARF calculation. The AOD-AE graph is a basic tool to analyze

aerosol characteristics. Furthermore, the AOD provides detailed information about the aerosol

loading, and AE is associated with the aerosol size or type. The combined analysis of both

parameters allows a better interpretation of the retrieved data [95]. High values of AE indicate

a dominance of fine particles, while low values specify the domination of coarse particles [87].

In order to understand the long-term aerosol optical properties over four different environments

72

within the IGP, we have examined the AOD and AE at these sites over the seven-year period.

The AODs were reasonably high, with values ranging from 0.25 to 1.02, 0.20 to 1.01, 0.26 to

0.79, and 0.33 to 1.14, while AE varies from 0.19 to 1.23, 0.40 to 1.44, 0.20 to 1.54, and 0.23

to 1.38 for Karachi, Lahore, Jaipur, and Kanpur, respectively. The annual averaged AOD and

AE values at wavelengths of 500 nm and 440-870 nm, respectively, are listed in Table 5.1.

Fig. 5.1(a-d) shows the monthly averaged variations of the AOD at 500 nm and AE in

the 440-870 nm range. The AOD values were generally high in July 2008 (1.02), October 2010

(1.01), July 2011 (0.79), and December 2009 (1.14), with corresponding AE values of 0.19,

0.96, 0.78, and 1.29 for Karachi, Lahore, Jaipur, and Kanpur, respectively. It was observed that

the values of AOD were higher in Karachi and Kanpur than in Lahore and Jaipur. Fig. 5.1(a)

and 5.1(c) depict an opposite trend (high AOD with low AE) that was observed in July over

Karachi and Jaipur, representing the coarse mode aerosol-like dust particles that result from

dust events. This further confirmed that only dust particles are responsible for the very large

AOD values and low AE values observed in other studies over Spain [95] and over the Tibetan

plateau [96]. Recently, Yu et al. [92] also investigated aerosol optical properties (high AOD

and low AE) during dust events that took place over Beijing between 2001 and 2014.

Table 5.1: Monthly averaged values and standard deviation of AOD at a wavelength of 500 nm and AE

(440-870 nm) for the measurement period during 2007-2013, over Karachi, Lahore, Jaipur and Kanpur.

Site AOD(500nm)±Standard deviation AE(440-870nm)±Standard deviation

Karachi 0.48 ± 0.19 0.60 ± 0.30

Lahore 0.64 ± 0.16 0.88 ± 0.27

Jaipur 0.48 ± 0.12 0.73± 0.38

Kanpur 0.67 ± 0.17 0.97 ± 0.33

Guleria et al. [99] noted that the AOD was high with corresponding low AE during the

summer over Mohal, which is attributed to the relative abundance of coarse size particles.

73

Wang et al. [58] found a similar variation in the AOD and AE for the same period of time

(2007-2013) over China. Similar behavior of the AOD versus AE have previously been

reported [87, 100]. Dey et al. [59] also found the same inverse relationship between the AOD

and AE during dust storm over the IGP. Likewise, Alam et al. [20] reported that a high AOD

corresponds to a low AE over Karachi in the summer.

The situation was significantly different during October and December, during which

time a high AOD with a high AE was observed over Lahore and Kanpur, respectively, as shown

in Fig. 5.1(b) and 5.1(d). This is an indication of fine-mode pollution aerosols due to

vehicular/industrial emissions and biomass burning, as documented by Eck et al. [98]. A

probable explanation for the presence of these types of aerosol in the atmosphere during these

months is the burning of agricultural wastes after harvesting and the winds transporting the

biomass-burning plumes over the studied region [184]. Kang et al. [105] also observed a

sudden increase in the AOD during October over Nanjing from sunphotometer measurements

which is attributed to agricultural residue burning to clear the harvest and also biomass burning

from the nearby surroundings.

74

Figure 5.1: Time series of monthly averaged AOD at 500 nm and AE in the range of 440-870 nm for

Karachi, Lahore, Jaipur and Kanpur during the 2007-2013 period.

75

5.1.2 Volume size distribution

The size distribution of aerosols is an important parameter in understanding their effect on the

climate. The VSD has a two-mode structure that can be characterized by the sum of two log-

normal distributions; fine and coarse mode [20-23]. Numerous studies revealed that a bimodal

log-normal function is the most suitable model for aerosol particle size distributions [23, 24,

98, 100, 103]. Fig. 5.2(a-d) illustrate the seasonal aerosol VSD during the summer, winter, pre-

monsoon, and post-monsoon, representing two modes (fine and coarse) over four sites for the

studied period. The volume concentrations were different for fine and coarse modes, but the

shapes of these distributions were analogous across all seasons. The monthly averaged patterns

of VSD were similar across all four sites, with a prominent peak in the coarse mode associated

with the dust dominated atmosphere, as documented by previous authors [59, 103, 142].

The coarse-mode peak values were 0.32, 0.07, 0.24, and 0.14 μm3∕μm2 centered on 4.0-

5.0 μm during summer, pre-monsoon, and post-monsoon seasons, respectively. The noticeable

peak in the coarse mode indicates the domination of coarse-mode aerosol particles over the

IGP [103]. Similar patterns of the VSD for desert dust aerosols were obtained by Dubovik et

al. [24]. On the other hand, the concentration of coarse-mode aerosols decreases during the

winter and a prominent peak in fine mode was observed, as shown in Fig. 5.2(b). The log-

normal aerosol size distribution showed that the fine-mode peak values were 0.06, 0.07, 0.07,

and 0.08 μm3∕μm2, centered on radii of 0.1, 0.3, 0.3, and 0.4 μm during summer, winter, pre-

monsoon, and post-monsoon seasons, respectively. The higher value of the fine-mode

concentration signifies a build-up of aerosol particles mostly produced by anthropogenic

sources over the IGP [192, 193].

76

Figure 5.2: Seasonal average variation of aerosol volume size distribution: (a) Summer, (b) Winter, (c)

Pre-monsoon, and (d) Post-monsoon over Karachi, Lahore, Jaipur and Kanpur during the 2007-2013

period.

77

In general, a greater influence of dust loading was observed in the coarse-mode peak in

the summer and pre-monsoon that is comparatively higher than in the winter at all sites. Higher

relative values in the coarse mode were observed during the post-monsoon period compared to

the winter period at all investigated sites. The bimodal patterns of the VSD resulted from a

number of factors, including the mixing of air masses with different aerosol pollutants,

nucleation of fine particles, and nucleation and growth of the larger particles in the atmosphere

[142]. Moreover, the VSD of the aerosol particles in fine modes were found to be high during

the summer and pre-monsoon seasons in Lahore, while in the winter and post-monsoon the

fine modes were found in higher concentrations over Kanpur as compared to other sites. This

type of VSD has been observed by Dumka et al. [103] over the Himalayan foothills.

Furthermore, the aerosol volume concentration shows a higher coarse mode value in

Karachi in the summer as compared to other sites, which was attributed to the coarse mode

dust aerosol particles. Adesina et al. [104]analyzed the bimodal structure of VSD and reported

that the aerosol has a mixture of coarse particles (sea salt or mineral dust) over Gorongosa in

Mozambique.

5.1.3 Single scattering albedo

The SSA is an important parameter for calculating DARF. The SSA is the ratio of the scattering

coefficient to the total extinction coefficient (scattering + absorption), and describes the effect

of both the scattering and absorption properties of aerosols. The SSA is a function of aerosol

size composition [194]. Table 5.2 depicts the annually averaged values and standard deviations

of the SSA at 440, 675, 870, and 1020 nm for the period 2007-2013 over Karachi, Lahore,

Jaipur, and Kanpur. The spectral variations of the monthly averaged SSA values are shown in

Fig. 5.3(a-d). The SSA was found to be strongly wavelength dependent due to the influence of

dust and anthropogenic activities during both the summer and pre-monsoon seasons. In the

summer, the averaged SSA values at all wavelengths were found to be more than 0.94 for

78

Karachi, Lahore, and Jaipur, representing the dominance of dust aerosol particles. An increase

in SSA with increasing wavelength was also found to be common during dust events, as

analyzed by previous authors [13, 23, 86, 98]. On the other hand, compared to the other sites,

the relatively lower values of SSA were found to be moderately wavelength dependent at

Kanpur, thus indicating the minor contribution of fine particles, such as urban pollution. A

similar wavelength dependency was reported by Barnard et al. [195] over Mexico.

In the pre-monsoon season, the SSA increases with increasing wavelength for all sites

due to coarse aerosol particles, which are predominantly dust aerosols. This is due to the fact

that higher absorption is caused by dust aerosols at shorter wavelengths rather than longer

wavelengths [86, 103, 119, 194]. This increasing trend with wavelength is analogous to

previously reported results over the IGP during the pre-monsoon period [37, 120, 142].

Table 5.2: Annual averaged values and Standard deviation of SSA at 440, 675, 870 and 1020 nm

during the period from 2007-2013 over Karachi, Lahore, Jaipur and Kanpur.

Site SSA ± Standard deviation

440 nm 675 nm 870 nm 1020 nm

Karachi 0.90±0.02 0.92 ±0.02 0.93±0.03 0.94±0.03

Lahore 0.88±0.02 0.90±0.02 0.91±0.03 0.91±0.03

Jaipur 0.90±0.02 0.92±0.03 0.93±0.03 0.93±0.03

Kanpur 0.89±0.02 0.90±0.02 0.90±0.03 0.90±0.04

During the winter, when dust is not a major contributor to the aerosols, the SSA values

decreased with increasing wavelength and the lowest value was reached in Kanpur. A similar

type of spectral dependency was reported by Bi et al. [101] over SACOL Loess Plateau of the

Northwestern China. Zheng et al. [22] also found that the SSA decreased with increasing

wavelength during the winter in China, when local fine pollution aerosols are dominant.

79

Figure 5.3: The average seasonal variation of SSA (a) Summer, (b) Winter, (c) Pre-monsoon, and (d)

Post-monsoon over Karachi, Lahore, Jaipur and Kanpur during the 2007-2013 period.

80

A similar decreasing spectral trend was observed for fine aerosol (urban pollution and

smoke) particles [24, 194]. While the SSA slightly increases with increasing wavelength during

the winter over Karachi and Jaipur, a steep increase in SSA values between 440 and 675 nm

and a decrease in SSA values between 675 and 1020 nm were found over Lahore. Barnard et

al. [195] observed a sharp increase in SSA between 400 and 500 nm, and a decrease in SSA

from 500 to 870 nm, which were attributed to the significant absorption at shorter wavelengths

due to organic matter [196]. During the post-monsoon season, the SSA was found to be strongly

wavelength dependent, with lower values than in the summer and pre-monsoon over all sites

except Kanpur, reflecting the presence of coarse particles with a smaller amount of fine

particles. Alam et al. [74] found the same pattern of increasing SSA values with increasing

wavelength during the post-monsoon period. A flat spectrum for Kanpur showed the

dominance of fine aerosol particles like biomass-burning aerosols and aerosols from fossil fuel

combustion.

5.1.4 Phase function

The phase function defines the angular distribution of the scattered radiation by a particle at a

given wavelength; it is the scattered intensity relative to the incident beam at a particular angle

θ and normalized by the integral of the scattered intensity at all angles [11]. The phase function

of aerosol particles is a function of the size distribution, index of refraction, internal structure,

and the shape of the particles [25]. It also depends on the mode of the particles, such as fine

and coarse [26]. Fig. 5.4(a-d) illustrate the variability of the phase function of aerosol particles

versus the scattering angle (θ) for summer, winter, pre-monsoon, and post-monsoon seasons

over Karachi, Lahore, Jaipur, and Kanpur. At small angles (θ = 0°) the phase functions reached

a maximum and ranged from 260-432, 104-257, 388-450, and 140-324 during the summer,

winter, pre-monsoon, and post-monsoon, respectively, for all sites.

81

Figure 5.4: Seasonal variation of the phase function at 440 nm (a) Summer, (b) Winter, (c) Pre-

monsoon, and (d) Post-monsoon over Karachi, Lahore, Jaipur and Kanpur during the 2007-2013 period.

82

It was noted that the phase function was higher during the summer and pre-monsoon,

moderate during post-monsoon, and relatively low during the winter. The maximum phase

function at minimum scattering angles is mostly due to the coarse particles because of their

larger size. In contrast, the fine particles are responsible for the low phase function behavior at

higher scattering angles (θ > 10°). As shown in Fig. 5.4, there was a monotonic decrease in the

phase function at the scattering angles between 0° and 10°, followed by an increasingly flat

and featureless trend with increasing scattering angles, centred at around 90°-180°, most likely

remaining neutral over all seasons. Bi et al. [101] reported that the phase function is highly

sensitive for both coarse and fine-grained particles for all seasons.

The uniform phase function at θ > 90° can be explained by mixtures of different types

of particles. The phase function of the aerosol particles at scattering angles greater than 90° is

essential for satellite remote sensing, estimating climate forcing, as well as for resolving

atmospheric correction problems [25]. Mishchenko et al. [197] observed smooth scattering

behavior of the phase function measured for mixed particles. Bohren and Singham, [29]

documented the flat side scattering and backscattering at higher scattering angles for

nonspherical particles in the presence of mineral dust. The phase functions of coarse particles

are larger than those of fine particles for both forward and backward directions [101].

5.1.5 Asymmetry parameter

AP, i.e. angular distribution of light scattering by the aerosol particles, is an important

parameter for estimating DARF. Like the SSA, this parameter also depends on the wavelength,

size, and composition of particles [27, 28]. Fig. 5.5(a-d) depicts the spectral variations in

averaged AP at wavelengths of 440, 675, 870, and 1020 nm during the summer, winter, pre-

monsoon, and post-monsoon seasons over the studied sites.

83

Figure 5.5: Average seasonal variation of the AP (a) Summer, (b) Winter, (c) Pre-monsoon, and (d)

Post-monsoon over Karachi, Lahore, Jaipur and Kanpur during the 2007-2013 period.

84

During summer, the AP values were high (∼0.74) at shorter wavelengths and decreased

from 440 to 870 nm, followed by a marginal increase up to 1020 nm, as discussed in afore-

mentioned properties expressing the abundance of coarse particle except in Kanpur, in which

fine particles were present to some extent. The AP values at 440 nm were lower than (0.72) for

the winter over all sites, and decreased with increasing wavelengths, reaching the lowest value

(0.61) at 1020 nm, representing the abundance of fine particles. A similar decrease in AP values

with increasing wavelength was reported by Alam et al. [87] over Lahore and Karachi.

Zhuravleva et al. [102] reported that AP decreases with an increase in wavelength over western

Siberia. Table 5.3 depicts the annual averaged values and standard deviations of AP at 440,

675, 870, and 1020 nm during the studied period over Karachi, Lahore, Jaipur, and Kanpur.

During the pre-monsoon season, the AP values decreased at wavelengths between 440

and 675 nm, remained neutral up to wavelengths of 780 nm, and then increased for wavelengths

up to 1020 nm for all sites displaying coarse particles. During the post-monsoon season, the

AP was found to decrease with increasing wavelengths in the visible and infrared spectra at

Karachi, Lahore, and Jaipur, an observation that has been attributed to the presence of coarse

mode (dust-like) particles. In Kanpur, on the other hand, an overall decrease in the AP value

was observed, suggesting a relative abundance of fine aerosol particles.

Table 5.3: Average annual values and standard deviations of the AP at 440, 675, 870 and 1020

nm during the 2007-2013 period over Karachi, Lahore, Jaipur, and Kanpur.

Site AP ± standard deviation

440 nm 675 nm 870 nm 1020 nm

Karachi 0.72±0.01 0.86 ±0.02 0.67±0.03 0.68±0.03

Lahore 0.71±0.01 0.66±0.02 0.65±0.03 0.65±0.03

Jaipur 0.73±0.01 0.69±0.02 0.68±0.03 0.68±0.05

Kanpur 0.72±0.01 0.67±0.02 0.65±0.03 0.65±0.04

A similar variation in AP values, which depend on the aerosol type as well as on

seasonal variability, was also documented by pervious researchers [87, 104]. Our results are

85

comparable to those obtained by Srivastava et al. [198] over Kanpur and Gandhi College in

India. A rapid decrease in AP with respect to wavelength was found by Alam et al. [74] during

the post-monsoon and pre-monsoon seasons over Lahore.

5.1.6 Refractive indices

The optical properties of the aerosols are defined in terms of the index of refraction, obtained

by combining real and imaginary parts of the RI. The RI depends on the chemical composition

of aerosols and provides information regarding the nature of the aerosols. The imaginary part

quantifies the nature of the absorption, as a higher IRI indicates a higher absorption. On the

other hand, the magnitude of total scattering increases with an increase in the real part of the

refractive index [30, 31, 199]. Table 5.4 lists the annual averaged values and standard

deviations of the RRI and IRI at 440, 675, 870, and 1020 nm wavelengths measured during the

studied period over the four sites. The seasonal pattern of RRI values at wavelengths of 440,

675, 870, and 1020 nm over Karachi, Lahore, Jaipur, and Kanpur are shown in Fig. 5.6(a-d).

An increasing trend was noted from wavelengths between 440 and 670 nm, reaching the highest

value at 870 nm, and then decreasing at 1020 nm for all seasons over all sites. The RRI was

found to be highest at larger wavelengths (λ < 670 nm) during all of the seasons, indicating

higher scattering, as also reported by Singh et al. [142]. Our results are consistent with the

results documented by Alam et al. [87]. The monthly averaged RRI values vary within 1.45-

1.49, 1.43-1.48, 1.48-1.51, and 1.45-1.47 at shorter wavelength (440 nm) and 1.47-1.50, 1.46-

1.51, 1.50-1.54, and 1.48-1.52 at longer wavelength (1020 nm) for the summer, winter, pre-

monsoon, and post-monsoon seasons, respectively.

86

Figure 5.6: Average seasonal variation of RRI during (a) Summer, (b) Winter, (c) Pre-monsoon, and

(d) Post-monsoon over Karachi, Lahore, Jaipur and Kanpur during the 2007-2013 period.

87

The relatively higher values of RRI during the summer and pre-monsoon were

attributed to the coarse particles, and the lower values during the winter and post-monsoon

periods were attributable to anthropogenic fine particles in the atmosphere, since the RRI

values of dust aerosols were greater than those of anthropogenic aerosols [23] . The RRI

displayed a strong spectral dependence during the pre-monsoon and monsoon seasons, where

dust is the major component, as observed by Singh et al. [142] at Kanpur. Alam et al. [23]

found that the RRI values were higher for shorter wavelengths in the presence of dust in the

Middle East and Southwest Asia. Similar results for the RRI were reported over Kanpur during

the summer season [141, 165].

Table 5.4: Averaged annual values and standard deviations of RRI and IRI at 440, 675, 870, and 1020

nm for the period 2007-2013 over Karachi, Lahore, Jaipur, and Kanpur.

Site 440 nm 675 nm 870 nm 1020 nm

RRI IRI RRI IRI RRI IRI RRI IRI

Karachi 1.48±0.03 0.006±0.001 1.51 ±0.03 0.004 ±0.002 1.52±0.02 0.004±0.001 1.51±0.02 0.004±0.001

Lahore 1.47±0.03 0.010±0.003 1.50 ±0.03 0.007±0.002 1.51±0.03 0.006±0.003 1.51±0.03 0.006±0.001

Jaipur 1.46±0.03 0.007±0.002 1.48 ±0.03 0.005±0.002 1.49±0.03 0.005±0.002 1.48±0.03 0.004±0.002

Kanpur 1.47±0.03 0.010±0.004 1.49 ±0.04 0.014±0.001 1.50±0.03 0.009±0.003 1.49±0.03 0.008±0.004

The IRI was found to decrease with increasing wavelengths with varying magnitudes

during all the seasons over Karachi, Lahore, and Jaipur, as shown in Fig. 5.7(a-d). On the other

hand, Kanpur revealed a similar systematic spectral variation during pre-monsoon and post-

monsoon, whereas it was almost neutral to the spectral variation during the summer and winter

seasons. Singh et al. [142] reported that the IRI decreases with the wavelength during the post-

monsoon season over Kanpur. The averaged values of the IRI were relatively low, ranging

from 0.003 to 0.008 and 0.002 to 0.007 at 440 nm and 1020 nm, respectively, in the summer

and thus indicating coarse dust particles. Similar results were found by Adisena et al. [104]

over Gorongosa for the summer period. The values ranged between 0.009 and 0.015 at 440 nm,

88

and 0.007 and 0.005 at 1020 nm for the winter, suggesting that the fine absorbing aerosols are

present. Our recorded values are analogous to the results of Eck et al. [200] for southern Africa.

Figure 5.7: Average seasonal variation of the IRI for (a) Summer, (b) Winter, (c) Pre-monsoon, and (d)

Post-monsoon over Karachi, Lahore, Jaipur, and Kanpur during the 2007-2013 period.

89

Relatively low values of the IRI were calculated by Levin et al. [201]. The spectral

behavior of the IRI remains similar for all sites during the pre-monsoon period during which

the values of the IRI reached a high value of 0.012 at 440 nm and a low of 0.002 at 1020 nm,

which is attributed to the higher dust loading. Similar results were recorded by Prasad and

Singh et al. [141] during the pre-monsoon over the IGP. The averaged value of the IRI was

higher (0.01) at shorter wavelengths than at longer wavelengths (0.004) during the post-

monsoon period, which was attributed to the absorption of fine particles (organic/black carbon)

[202]. The high values resulted from the absorption of fine aerosols, and the low values

indicated the existence of coarse dust aerosols [87]. In fact, the IRI values in the visible

spectrum during this season are similar to the values obtained by Alam et al. [74] during the

post-monsoon season.

5.2 Hybrid single particle lagrangian integrated trajectory

In order to investigate the probable pathways and source regions of air masses and in order to

determine a link between the air masses and the seasonal variation of aerosol particles over the

studied sites, 72 h back trajectories at 500, 1000, and 1500 m were computed using the

HYSPLIT model during each season for the entire studied period. Fig. 5.8(a-d) reveal that

different types of air masses originating from multiple source regions arrived at the studied

sites.

During summer and pre-monsoon, the air mass trajectories bringing coarse mode

particles through long range transport originated in South Asia (India, Rajasthan, Nepal,

Afghanistan), Central Asia (Turkmenistan), the Middle East (Iran, Syria, Iraq), and the Arabian

Sea and Caspian Sea. In addition, some of the air masses reaching the studied sites also came

from the local areas of Pakistan and India.

91

Figure 5.8: 72-hour HYSPLIT back trajectories representing the air masses’ origins and pathways at

500, 1000, and 1500 m Above Ground Level (AGL) in each season during the 2007-2013 period over

Karachi, Lahore, Jaipur, and Kanpur.

On the other hand, during the winter and post-monsoon seasons, the fine aerosol

particles mainly originated from local anthropogenic activities such as biomass burning, and

vehicular and industrial emissions, along with a smaller contribution from long range

transportation from Iran, Uzbekistan, and Afghanistan. Alam et al. [87] calculated similar back

92

trajectories of air masses that reached Lahore and Karachi from Iran, Afghanistan, and India,

as well as from the Arabian Sea. Our results are consistent with the findings of Singh et al.

[142] and Verma et al. [203] over Kanpur and Jaipur, respectively.

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Chapter 6

In-depth discrimination of aerosol types using multiple

clustering techniques over four locations in Indo-Gangetic

plains

Discrimination of aerosol types is essential over the IGP because several aerosol types originate

from different sources having different atmospheric impacts. In this paper, we analyzed a

seasonal discrimination of aerosol types by multiple clustering techniques using AERONET

datasets for the period 2007–2013 over Karachi, Lahore, Jaipur and Kanpur. These types of

aerosol discriminated from AERONET were compared with CALIPSO (the Cloud-Aerosol

Lidar and Infrared Pathfinder Satellite Observation) measurement.

6.1 Multiple clustering techniques

Cluster analysis is an important technique used for classification of aerosol. Such analysis is

used for classification of huge datasets into numerous groups using predefined aerosol

parameters. The AERONET dataset based on several optical and physical characteristics of the

aerosols can be categorized into several groups for the discrimination of aerosol types [32].

The discrimination of aerosol types was carried out by analyzing the scattered graph between

AOD and AE [33], EAE and AAE [35], EAE and SSA [13] and RRI and AAE [36]. Some

other clustering techniques were also used by earlier researchers [32, 37, 38].

6.1.1 Aerosol optical depth versus angstrom exponent

The distribution of aerosol types in different seasons depends on production mechanism and

the lifetime of aerosols in the atmosphere as well as on geographical locations, creating a

different seasonal circulation of AOD and AE [82]. Variances in the relationship between the

AOD and AE provide a potential approach to classify and asses the effects of different sources

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on the seasonal aerosol concentration and size of aerosol particles [204]. For the classification

of aerosol types the selected seasonal threshold values of AOD and AE were compiled as, dust;

2.9 < AOD > 0.5 and 0.4 < AE > 0.01 and Biomass burning & urban/industrial; 1.7 < AOD >

0.01 and 1.7 < AE > 0.7 for each site. To date, numerous researchers categorized different

aerosol types using the AOD-AE clustering technique over different regions [92, 205, 206].

The classification of aerosol types can be obtained by the seasonal scattered plot of AOD500 nm

against AE440–870 nm as shown in the Fig. 6.1(a–d), in which specific clusters indicate the

different types of aerosols. It was noted that dust particle concentrations were low in Lahore as

compared to other sites throughout the studied period. The cluster points associated with high

AOD and a low AE represent dust aerosols (coarse particles) mainly coming from the arid

regions of IGP during summer and pre-monsoon. While during winter and post-monsoon, the

presence of biomass burning and urban/industrial were observed, which corresponds to the

absorbing aerosols (fine particles) over all sites. During winter (dry) seasons, the biomass

burning aerosols increases due to the combustion processes which causes high AOD than that

in the summer (wet) season [207].

It should be mentioned here that clean marine aerosols (not circled) were also present

during winter season in Karachi (see Fig. 6.1a) as it is a large coastal site located near Arabian

Sea which is in agreement with the observation made by CALIPSO. The leftover scattered

points (not circled) corresponding to a wide range of AOD and AE over all seasons were

classified as mixed type of aerosol resulting from the mixture of natural and anthropogenic

aerosol [92]. The mixed type aerosol concentrations were high over Karachi and Kanpur as

compared to Lahore and Jaipur. Characterization of aerosol into different types (Saharan dust,

urban/industrial and biomass burning) was carried out based on AOD-AE relationship over

three oceanic sites using POLEDER/ADEOS measurements [80].

95

Figure 6.1: Scattered plot between AOD500 nm and AE440-870 nm showing the clusters of aerosol types

during summer, winter, pre-monsoon and post-monsoon over a) Karachi, b) Lahore, c) Jaipur and d)

Kanpur.

96

AOD-AE scattered plots were analyzed in order to discriminate aerosol into different

types through the cluster region at several locations [81-84]. By using same AOD-AE

clustering technique, Kumar et al. [34] categorized four types of aerosols such as: clean marine,

continental clean, biomass burning/urban industrial and desert dust over Durban, South Africa.

In similar way, Sharma et al. [33] was reported the seasonal distribution of aerosol and

discriminated them into five groups (clean marine, anthropogenic, biomass burning, mostly

dust and mixed aerosol) over Greater Noida using Ground Sunphotometer. Tan et al. [88]

identified different types of aerosol like biomass burning, urban/industrial, marine and dust by

analyzing AOD and AE over Penang and Kuching, Malaysia. Four prevailing aerosol clusters

(biomass burning, anthropogenic, mostly dust and mixed aerosol) based on AOD-AE were

recognized in New Delhi via Sun/Sky radiometer POM-02 [91]. Further, using the similar

clustering technique, Pathak et al. [15] classified the aerosol into five categories (continental

average, marine continental average, urban/industrial and biomass burning, desert dust and

mixed type) over Dibrugarh using MWR measurements and the seasonal variation of aerosol

type showing the contribution of urban/industrial and biomass burning during the winter and

pre-monsoon and mixed type during monsoon and post-monsoon. Moreover, Toledano et al.

[85] characterized the aerosols into five types (desert, mixed, biomass, marine and continental)

over El Arenosillo using AERONET data. Five dominant aerosols (desert dust, maritime,

biomass burning, mixed and arid background aerosols) were found using AOD versus AE

clustering method based on AERONET in Jaipur [89]. Tariq et al. [93] found that the

pronounced aerosol type during haze events was biomass burning over Lahore using

AERONET data.

6.1.2 Extinction angstrom exponent versus absorption angstrom exponent

Several techniques can be adopted to distinguish aerosol types by assigning some threshold

values. Table 6.1 summarizes the threshold values of aerosol properties for different types of

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aerosol over each site. Fig. 6.2(a–d) depicts seasonal scatter plots of AAE440–870 nm against

EAE440–870 nm to characterize different types of aerosol over Karachi, Lahore, Jaipur and

Kanpur. The cluster analysis represents three significant types of aerosol which were classified

as: dust (high AAE and low EAE), biomass burning (high AAE and high EAE), and

urban/industrial aerosol (low AAE and high EAE).

Table 6.1: Threshold values of aerosol properties for different types of aerosol over each site.

Aerosol types EAE vs AAE EAE vs SSA EAE vs RRI

Dust 0.4<EAE>0.01 3.0<AAE>1.0 0.4<EAE>0.1 0.96<SSA>0.88 0.41<EAE>0.01 1.59<RRI>1.44

Biomass

burning

1.7<EAE>0.8 2.3<AAE>1.1 1.7<EAE>0.9 0.91<SSA>0.82 1.50<EAE>1.00 1.57<RRI>1.43

Urban/Industrial 1.6<EAE>0.8 1.3<AAE>0.6 1.7<EAE>0.9 0.96<SSA>0.89 1.74<EAE>0.70 1.43<RRI>1.35

The theoretical values of AAE are close to 1 for black carbon [86]. The urban/industrial

and biomass burning types aerosol tend to overlap each other as documented by previous

authors [13, 35]. The intermediate values of AAE and EAE were observed overall sites, which

indicate the existence of mixed type aerosol. However, the scatter graph shows clear cluster

separation over Karachi and Jaipur as compared to Lahore and Kanpur, where mixed type

aerosols concentrations were high, resulting from the mixture of different size particles

especially in the summer and pre-monsoon.

It is clear from the figure that dust aerosols were dominant during summer and pre-

monsoon, originating from the desert or from the long-range transport activities, while during

winter and post-monsoon biomass burning and urban industrial aerosols were dominant which

highlight the significant anthropogenic activities.

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Figure 6.2: Scattered plot between EAE440-870 and AAE440-870 nm showing the clusters of aerosol types


Kanpur.

99

Mishra and Shibata [35] carried out similar seasonal classification of aerosols by

analyzing the scattered plot of EAE against AAE over Kanpur and grouped the aerosol in dust,

biomass burning and urban/industrial. Some clustering analysis techniques were adopted by

numerous authors to categorize the different aerosol types [208-211]. On the basis of above

mentioned discrimination technique, during summer and pre-monsoon, coarse particles were

enhanced which specify the presence of dust over Karachi, Jaipur and Kanpur, while in post-

monsoon and winter experiences fine particles due to biomass burning accompanied by urban

industrial. Whereas, over Lahore during summer and pre-monsoon mixed type and fine aerosol

were also observed. The distributions and types of anthropogenic aerosols are rather complex

because of the variety of their sources with respect to location and season [170]. Giles et al.

[37] categorized three types of aerosol (dust, black carbon and mixed) by using a similar cluster

technique of EAE and AAE over IGP. Che et al. [90] adopted AAE with respect to EAE

clustering technique to sort the aerosol types into mixed, urban/industrial and biomass burning

during the heavy haze period in Beijing. Russell et al. [86] grouped the aerosol in three types

(desert dust, biomass burning and urban industrial) using the cluster analysis of EAE and AAE

over worldwide locations. Alam et al. [87] classified the aerosol into two categories (mineral

dust and urban/industrial) using the same technique over Karachi and Lahore. Kedia et al. [38]

seasonally categorized the absorbing aerosol into mostly dust, mostly black carbon and mixed

dust and black carbon over IGP using scatter plots of EAE versus AAE via AERONET dataset.

6.1.3 Extinction angstrom exponent versus single scattering albedo

To categorize the aerosols into different types, SSA as a function of EAE can be plotted. The

SSA values may vary from 0 (completely absorbing) to 1 (completely scattering). Basically,

the SSA is an important parameter to classify the aerosols into different types including desert

dust, urban/industrial, biomass burning due to their spectral absorption nature of aerosol

mixture [24] and EAE is an indicator of particle size [86]. The analyses of both parameters

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(EAE and SSA) provide the better categorization of aerosol types [13, 36]. Lee et al. [170]

discriminated the aerosols into absorbing and non-absorbing by using SSA at 440 nm. The fine

particle can also be categorized into non-absorbing, moderately absorbing and strongly

absorbing depending on their SSA values [212].

Fig. 6.3(a-d) reveals the seasonal scattered plot between EAE440–870 nm and SSA440 over

all sites, pursuing the cluster technique as suggested by Russell et al. [36]. SSA versus EAE,

categorized the aerosol into dust having high SSA and low EAE, biomass burning with

moderate SSA and high EAE, and urban/industrial were associated with high SSA and high

EAE. The remaining scattered points were discriminated as a mixed type of aerosol.

It was evident from the figure that dust type aerosols were predominant during summer

and pre-monsoon, while, biomass burning and urban/industrial aerosol were in abundant during

winter and post-monsoon over all sites except Lahore, where some biomass burning and

urban/industrial aerosols were also noted in summer which matched with the above result as

discussed in Section 3.2. Similar partitioning of aerosols were carried out by Russell et al. [36]

into seven specified classes such as i) pure dust, ii) polluted dust, iii) biomass burning, dark

smoke, iv) biomass burning white, v) urban-industrial, developed economy, vi) urban-

industrial, developing economy, vii) pure marine using Mahalanobis SSA-EAE clustering

technique over the island of Crete, Greece. Similarly, Giles et al. [13] established the

relationship between SSA and EAE to categorize between dust, mixed, urban/industrial and

biomass burning using similar techniques at different AERONET sites.

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Figure 6.3: Scattered plot between EAE440-870 nm and SSA440 nm showing the clusters of aerosol types


Kanpur.

102

6.1.4 Extinction angstrom exponent versus real refractive index

The complex refractive index is an optical property of great importance to achieve reliable

results for identification of aerosol. It is not independent of SSA and size of aerosol, but its

values may vary from type to type, due to the chemical composition of aerosol [24]. The

classification of aerosols into different types over IGP was carried out by correlating the EAE

with RRI, following the approach provided by Russell et al. [36]. Fig. 6.4(a–d) reveals the

seasonal scattered plot of EAE440–870 nm against RRI440 nm and observed that during summer

and pre-monsoon the dust particles were prominent while during winter and post-monsoon the

biomass burning and urban/industrial particles were prominent over all sites. The three

significant types of aerosol which were identified as dust having low EAE and high RRI,

biomass burning having high EAE and high RRI, and urban/industrial aerosol having high EAE

and low EAE over all sites. The remaining scattered points were not included in any of the

above-mentioned aerosol types can be identified as a mixed type aerosol.

The overlapping between the clusters (Biomass burning and Urban/Industrial) can be

reduced by using different properties against EAE such as by replacing SSA with RRI versus

EAE. Such type of clustering techniques are implemented by Russell et al. [36], they

categorized aerosols into different types (e.g. pure dust, polluted dust, light biomass smoke,

dark biomass smoke, urban-industrial, and pure marine) over the Island of Crete, Greece. Our

values for biomass burning are comparable with the findings of Raut and Chazette, [213] for

soot aerosols over Paris.

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Figure 6.4: Scattered plot between EAE440-870 nm and RRI440 nm showing the clusters of aerosol types


Kanpur.

104

We discriminated the aerosols into three major types; dust, biomass burning and

urban/industrial. The discrimination was carried out by analyzing different aerosol optical

properties such as AOD, AE, EAE, AAE, SSA and RRI and their interrelationship to

investigate the dominant aerosol types and to examine the variation in their seasonal

distribution. The results revealed that during summer and pre-monsoon, dust aerosols were

dominant while during winter and post-monsoon prevailing aerosols were biomass burning and

urban industrial, and the mixed type of aerosols were present in all seasons.

6.2 Vertical profile of aerosol from CALIPSO

The measurement of aerosol subtype profiles from CALIPSO close to the studied sites during

selected days representing summer, winter, pre-monsoon and post-monsoon seasons were

shown in Fig. 6.5. It is clearly evident from the Fig. 6.5(a and c) that the presence of dust and

polluted dust layers reached up to a height of 7 km during summer and pre-monsoon over all

sites, while these layers reached up to 11 km during pre-monsoon over Lahore.

These dust aerosols are prominent due the long range transport and dust storms while

polluted dust are attributed to anthropogenic activities in IGP [92]. Kumar et al. [214] observed

the dominancy of dust and polluted dust during monsoon and pre-monsoon over central India.

Che et al. [90] found the contribution of dust or polluted dust during haze events over Beijing.

Fig. 6.5(b) reveals that during winter aerosol layer reached up to 5 km were frequently consist

of polluted continental, smoke, and polluted dust while seldom distribution of dust was

extending to an altitude of 10 km from the surface for all sites, along with a minor contribution

of clean marine below 1 km over Karachi. These aerosol types may be due the biomass burning,

anthropogenic activities and from long range transportation [214].

105

Figure 6.5: Classification of aerosol subtypes derived from CALIPSO data, for the selected days

representing a) Summer, b) Winter, c) Pre-monsoon and d) Post-monsoon over the studied sites.

Yu et al. [215] recorded the major contribution of clean continental with some

contribution of smoke and polluted dust during the non-haze episode, whereas, smoke, dust

and polluted dust were prominent during a haze episode over Beijing. Fig. 6.5(d) shows a

mixed aerosol layer consisting of dust, polluted dust, polluted continental and smoke, lies

below an altitude of 5 km during post-monsoon. It was observed that dust and polluted dust

were prominent relative to smoke and polluted continental. Similar subtypes of aerosol were

noted by Tariq et al. [93] during a haze episode in October over Lahore. In conclusion, the

types of aerosol discriminated from AERONET (as discussed in section 6.1) were in good

agreement with CALIPSO measurement.

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Chapter 7

Estimation of shortwave direct aerosol radiative forcing

for four locations on the Indo-Gangetic plains: Model

results and ground measurement

This chapter aims to understand the long term radiative impacts of aerosol on regional climate

of IGP. For this purpose, the spatio-temporal variations of SDARF and SDARFE at the TOA,

SUR and within the ATM along with atmospheric HR were calculated using SBDART model.

Furthermore, the SDARFE at the TOA, SUR and within the ATM over Karachi, Lahore, Jaipur

and Kanpur. Additionally, to compare the model estimated forcing against AERONET derived

forcing, the regression analysis of AERONET-SBDART forcing were carried out. The annual

averaged SDARF, SDARFE, and associated atmospheric HR over Karachi, Lahore, Jaipur and

Kanpur are listed in the table 7.1. These are discussed in the following sub-sections.

7.1 Monthly and seasonal aerosol radiative forcing

ARF is defined as the net (upward minus downward) change in TOA/surface solar flux `due to

atmospheric aerosols. Atmospheric aerosols are mainly a combination of scattering and

absorbing types and their impacts in the form of cooling and heating the atmosphere is a

function of different optical properties. In this study, SDARF was calculated separately for the

TOA and SUR using SBDART model. However, the difference between SDARFTOA and

SDARFSUR provides the SDARFATM representing the amount of energy trapped within the

atmosphere by absorbing of the aerosol and get transformed into heat energy. The monthly

averaged values of SDARFTOA and SDARFSUR and SDARFATM were calculated using daily

averaged ARF for the period 2007–2013. Fig. 7.1(a-d) reveals monthly averaged SDARF at

TOA, SUR and ATM over Karachi, Lahore, Jaipur and Kanpur, respectively. The monthly

averaged computed values of SDARFTOA ranged from -20 (January 2011) to 2 (July 2009), -24

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(February 2010) to 1 (May 2007), -14 (November 2013) to 4 (June 2010) and -20 (December)

to 4 (May 2008) Wm-2 over Karachi, Lahore, Jaipur and Kanpur, respectively. SDARFTOA

values were either positive or negative; negative values of SDARFTOA show the increment in

the backscattering due to scattering type of aerosols leads to cooling the atmosphere or earth’s

system, while positive values due to absorption of solar radiations by absorbing type of aerosols

contribute to heating the atmosphere.

Satheesh and Srinivasan, [216] reported the impact of different surface reflectance on

ARF and observed that the response of land surface to the changes in solar flux is more

significant as compared to the ocean because of its low heat capacities. They also found that

the sign of ARF at TOA changes from negative to positive due to high surface reflectance. The

TOA forcing is strongly sensitive to the atmospheric aerosol loading along with surface

reflectance; however, the surface or atmospheric forcing is highly sensitive to only aerosol

loading [217]. The ARFTOA due to biofuel can be differentiated from fossil fuel and natural

sources in term of sign. The negative ARFTOA is due to the emission of fossil fuel combustion

and natural processes., while positive due to the emission of biofuel over Bay of Bengal,

Arabian Sea and central India [218].

Table 7.1: The annual averaged SDARF, SDARFE, and associated atmospheric HR over each site.

Site

N SDARFTOA

Wm-2

SDARFSUR

Wm-2

SDARFATM

Wm-2

SDARFETOA

Wm-2/AOD

SDARFSUR

Wm-2/AOD

SDARFATM

Wm-2/AOD

HR

Wm-2/AOD

Karachi 435 -6±4 -33±7 27±6 -13±9 -72±23 59±21 0.8±0.2

Lahore 676 -11±5 -48±9 37±10 -17±8 -78±16 61±16 1.0±9.3

Jaipur 389 -5±4 -29±6 24±6 -11±8 -64±15 53±15 0.7±0.2

Kanpur 647 -10±5 -45±11 35±10 -14±7 -70±15 55±15 1.0±0.3

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Figure 7.1: Monthly variation of SDARF at TOA, SUR and ATM over a) Karachi, b) Lahore, c) Jaipur


109

The monthly averaged SDARFSUR values were varied from -20 (December 2008) to -

54 (November 2009) Wm-2 over Karachi, -31 (September 2012) to -81 (November 2007) Wm-

2 over Lahore, -17 (August 2011) to -44 Wm-2 (November 2013) over Jaipur and -15

(September 2013) to -69 (November 2012) Wm-2 over Kanpur. It is noticed that the values of

SDARFSUR were largest (negative) in the month of November over all sites implying the

strongest cooling at the surface. Generally, SDARFSUR were found to be negative during all

months, which is due to the attenuation of solar flux at the surface by atmospheric aerosols

implying a net cooling effect over all sites. Finally, the difference between SDARFTOA and

SDARFSUR leading to the SDARFATM which contributing in net cooling or heating effect of the

atmosphere. The monthly averaged SDARFATM were lie between 16 (December2012) and 49

(November 2009), 20 (July 2011) and 73 (May 2007), 10 (August 2011) and 38 (December

2010) and 9 (September 2012) and 59 (December 2013) Wm-2 over Karachi, Lahore, Jaipur

and Kanpur, respectively. SDARFATM were observed to be positive showing a net heating

effect during the studied period for all observational sites. Karachi is coastal site as compared

to other sites and SDARFATM was observed to be highest in November due to increment in dust

plums and black carbon concentration [20] while low in December which may due to the

washout processes due to precipitation. On the other side, it was observed that the value of

SDARFATM was highest over Lahore as compared with other sites implying the strongest

heating in the atmosphere as huge amount of energy was trapped inside the atmosphere. Alam

et al. [20] also found the highest ARFATM (61 Wm-2) during the month of November 2006 over

Karachi. The low values of SDARFATM during July, September and August over Lahore, Jaipur

and Kanpur were mainly due to washout of aerosols during these months. The high values of

SDARFATM during the month of May over Lahore is due to high aerosol loading in the

atmosphere from long range transportation of mineral dust. The high SDARFATM over Jaipur

and Kanpur, was strongly influenced by the biomass burning activities. Generally, the

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magnitude of SDARFSUR and SDARFATM are very large during all the months with varying

magnitude and opposite sign. Takemura et al. [113] reported that carbonaceous and soil dust

are responsible for positive atmospheric forcing due to its absorption potential while sulfate

and sea salt aerosols that do not absorb the solar radiations leads to a negative forcing.

Previously, Singh et al. [121] reported that the DARFTOA was highest during June (21 Wm-2)

and lowest (-1.4 Wm-2) during November over Delhi. Likewise, DARFSUR was highest

(maximum negative) during May (-110 Wm-2) and lowest (least negative) during August (-46

Wm-2) leading to atmospheric forcing having highest in June (115 Wm-2) and lowest in August

(46 Wm-2) implying strongest cooling at the surface and heating in the atmosphere. Pant et al.

[116] found that during December, the SDARFTOA, SDARFSUR and SDARFATM were 0.7, -4.2

and 4.9 Wm-2, respectively over Manora Peak. Alam et al. [20] noticed that the SDARFTOA,

SDARFSUR and SDARFATM ranged from -7 to -35, -56 to -96 and 38 to 61 Wm-2, respectively

over Karachi which are much higher than our estimated SADARF. Assessment of ARF over

Maitri showed a positive forcing (0.95 Wm-2) at TOA and negative (–0.83 Wm-2) at the SUR

[114]. Esteve et al. [127] found that monthly ARF exhibited a significant variation during the

study year ranging from -6 to -29 at SUR and -1.5 to 3.9 at TOA having an average of -17 and

-2.2 Wm-2. Taneja et al. [128] conducted a study related to ARF over New Delhi and concluded

that DARFSUR was observed to be lowest (-33.6 Wm-2) during March and highest (-117 Wm-2)

during June, while DARFTOA was lowest (- 14.8 Wm-2) during March and highest (-48.8 Wm-

2) during September and DARFATM was lowest (18.8 Wm-2) during March and highest (90.6

Wm-2) during June.

Li et al. [106] found that the overall the annual and diurnal averaged ARF were 0.3 at

the TOA, -15.7 at SUR and 16.0 Wm-2 within the ATM over 25 station in China, suggesting

the significantly heat up the atmosphere and cooling the earth surface. Analogously, Adesina

et al. [104] have estimated the high negative value of SDARFTOA (-22.36 Wm-2) and

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SDARFSUR (-89.22 Wm-2), producing the high positive value of SDARFATM (66.87 Wm-2) over

Gorongosa. The averaged ARF were observed to be -17.63, -45.75 and 38.14 at TOA, SUR

and within ATM, respectively while less negative values were observed over Kanpur than that

over Pantnagar and Bareilly having values of -17.63, -73.06 and 55.43 Wm-2 at TOA, SUR and

within ATM, respectively [103]. Cherian et al. [126] noticed that the averaged DARF were -

8,6, -21.4 and 12.9 Wm-2 at TOA, SUR and ATM, respectively over Bay of Bengal, whereas

-6.8, -12.8 and 6 Wm-2, respectively over Arabian Sea. In addition to monthly variation,

SDARF at TOP, SUR and ATM also shows robust seasonal variations which were estimated

from monthly averaged values for the study period. Fig. 7.2(a-d) illustrates the seasonal

variation of SDARFTOA, SDARFSUR and SDARFATM with HR in parenthesis over Karachi,

Lahore, Jaipur and Kanpur.

It is observed that over Karachi all the three forcing values, SDARFTOA, SDARFSUR

and SDARFATM were maximum during summer, while moderate during winter and post-

monsoon while minimum during pre-monsoon with the highest heating rate of 0.8 Kday-1

during summer. In contrast, the highest SDARFSUR was noted during pre-monsoon over

Lahore, while the maximum SDARFSUR during winter over Jaipur and Kanpur. However

maximum values of SDARFATM during pre-monsoon with maximum heating rate of 1.2, 0.7

and 1.1 Kday-1 over Lahore, Jaipur and Kanpur, respectively. The SDARFTOA and SDARFSUR

values were negative during all the seasons over all the sites indicating a net cooling effect due

to atmospheric aerosols. The smaller negative values of TOA were due to the higher fraction

of absorption by aerosol particles at the surface, decreasing the solar radiations backscattered

to TOA [90]. The highest SDARFATM during summer and pre-monsoon showing significant

heating at ATM and cooling at SUR due to dust aerosol.

112

Figure 7.2: Seasonal variation of SDARF at TOA, SUR and ATM with HR in parenthesis over a)

Karachi, b) Lahore, c) Jaipur and d) Kanpur for the period 2007-2013.

113

The high SDARFSUR during winter is due to the strong accumulation of aerosols in the

lower atmosphere as a result of low mixing height with corresponding weak winds and longer

aerosol life time as well as due to the absence of precipitation [54]. The seasonal heating rate

varies from one place to another depending on various factors like loading of aerosols, seasons

changes, geographical locations, underlying surface etc., [125]. Recently, [130] have reported

that ARFTOA and DARFSUR values were negative during all the seasons presenting the loading

of scattering type particles like dust particles whereas DARFATM values were positive in all the

seasons presenting heating of the atmosphere, which was found to be highest during pre-

monsoon (40.5 Wm-2) than rest of the year (19.5 Wm-2). Ganguly and Jayaraman, [117] noticed

that the seasonal averaged ARF at the surface during winter, monsoon, pre-monsoon and post-

monsoon were -54, -41, -41 and -63 Wm-2, respectively over Ahmedabad. Similarly, Dey and

Tripathi, [119] documented the negative values of SDARFSUR (> -20 Wm-2) having highest

values (> -30 Wm-2) during pre-monsoon over Kanpur. Li et al. [106] conducted the study on

ARF and reported the values of ARF at TOA, SUR and ATM were highest during summer and

lowest during winter over China. Similarly, Kumar and Devara, [125] observed the highest HR

in Pre-monsoon followed by the post-monsoon and winter in descending order having the

values of 0.949, 0.86 and 0.84 Kday-1 over Pune. They found the larger SDARFSUR during pre-

monsoon which were analogous for winter and post-monsoon while the SDARFTOA was

observed to be negative during all the seasons.

Guleria and Kuniyal, [132] observed that DARFATM calculated over Mohal transforms

to HR of 0.66, 0.47, 0.46 and 0.56 Kday-1 winter, pre-monsoon, summer and post-monsoon,

respectively. The domination of coarse mode aerosols was observed primary due a greater

reduction in surface arriving shortwave solar radiations by 20.8 Wm-2, which get transformed

into atmospheric heating rate of 0.47 Kday-1. Pathak et al. [122] shows that SDARFSUR was

nearly zero in monsoon (summer) and was negative during all other seasons over Dibrugarh.

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Pandithurai et al. [120] conducted a study on ARF over Delhi and reported increased in cooling

at SUR from -39 to -99 Wm-2 during pre-monsoon, whereas the varied between 27 to 123 Wm-

2 which is attributed to the abundance of dust particles. Singh et al. [121] carried out the analysis

of ARF over Delhi and concluded that DARFATM was maximum during summer and winter.

Lately, Wu et al. [131] showed that the averaged seasonal variation in ADRFSUR were -34.19,

-18.94, -30.52 and -22.44 Wm-2 during summer, winter, spring and autumn, respectively and

ADRFTOA were -12.81, -8.78, -9.60 and -8.03, Wm-2, respectively. Maximum (negative) values

of DARFSUR and minimum (negative) at TOA were estimated to be -26.28 and -9.42 Wm-2,

respectively resulting in a strong cooling on surface, but warming in the atmosphere causing

significant impact on regional climate of China.

7.2 Atmospheric heating rate

The radiative and consequently the climatic implications of atmospheric aerosols are analyzed

in the form of atmospheric heating rate. In order to analyze the month to month HR variations,

the averaged SDARFATM and associated HR over Karachi, Lahore, Jaipur and Kanpur are

displayed in Fig. 7.3(a-d). The maximum HR in November-December and May is attributed to

the strongest atmospheric absorption. During winter months, the highest concentration of black

carbon (strong absorbing aerosol), while during May dust (moderately absorbing aerosols) get

mixed with black carbon causing high atmospheric warming [119].

The variation in the amount of solar energy trapped by the atmosphere have strong

implications for temperature changes and thermal structure of the atmosphere as well as

important consequences for many processes, like cloud formation, precipitation, and monsoon

circulation.

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Figure 7.3: Monthly variation of SDARF at ATM and Atmospheric HR over a) Karachi, b) Lahore, c)


116

Recently, Dumka et al. [103] showed that estimated HR over Kanpur, Bareilly, Pantnagar

and Nainital were 1.56, 1.58, 1.41 and 1.07 Kday−1, respectively which are higher than HR

calculated for this study. Similarly, Srivastava et al. [129] also reported the significant heating

in atmosphere during dust events occurred in March 2012 having the estimated HR of 2.0 Kday-

1 over Jodhapur. Previously, Dey and Tripathi, [118] found the highest absorption during

December to January and May to June with HR of ~1 Kday-1 over Kanpur. In other study,

Pandithurai et al. [120] documented the enhanced atmospheric absorption by dust aerosol in

pre-monsoon season with HR ranged from 0.6 to 2.5 Kday-1. During pre-monsoon (March to

May, 2006), the averaged HR over Bay of Bengal was ~0.3, higher than that of Arabian Sea

[123]. Pathak et al. [122] found the maximum (1.0 Kday−1) HR during pre-monsoon and lowest

(0.35 Kday−1) during monsoon over Dibrugarh. Recently, Kant et al. [56] investigated ARF

over Dehradun and Patiala and reported that ARFATM was 37.34 and 54.8 Wm-2 with respective

HR of 1.0 and 1.5 Kday−1, respectively. Patel and Kumar, [133] have reported maximum

ARFATM (38.79 Wm-2) in May with associated HR of 1.06 Kday−1 over Dehradun during pre-

monsoon. The estimated SDARFATM was 25 Wm-2 and HR of 1 Kday−1 within 2 km have a

strong impact on winter time inversion and residual time of aerosol in Himalayan region

Ramana et al. [115]. The HR reaches up to 2.2 Kday−1 during dust events over Patiala showing

the significant atmospheric heating due to dust particles Singh et al. [53]. HR during pre-

monsoon and summer were 0.75 and 0.5 Kday−1 over Kanpur and Gandhi College [107].

Bhaskar et al. [130] found that HR were in the range of 0.49 to 1.13 Kday−1 with minimum in

post-monsoon and maximum in pre-monsoon over Jodhpur. Alam et al. [87] analyzed the HR

over Karachi and Lahore and concluded that HR was 1.1 and 1.8 Kday−1 during winter and 1.2

and 2.3 Kday−1 during summer, respectively which were higher than our result.

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7.3 Monthly and seasonal aerosol radiative forcing efficiency

Variations in the magnitude ARF not only show strong dependency on types of the aerosol but

also on its column abundance that is ARF is strong function of AOD. This result in SDARFE,

which is defined as the change in the net flux per unit of AOD Kazadzis et al. [219]. It can also

be defined by the linear regression slope between SDARF and AOD [220]. It segregates the

impacts of aerosol optical properties on radiation from AOD and shows the sensitive of SDARF

to AOD [126]. SDARFE defines the efficiency of aerosol particles to interact with radiations

and identify the aerosols types along with their absorption efficiency [221]. The daily averaged

AOD500 retrieved from AERONET and the SDARF and ATM were used to derive the

SDARFE at TOA, SUR and ATM.

Fig. 7.4(a-d) shows the SDARFETOA, SDARFESUR and SDARFEATM calculated using

AOD at the four observational sites. From the figure, it was observed that overall the variation

in SDARFE was similar to that of SDARF but with relatively higher values. The monthly

averaged values of SDARFETOA were found to be maximum June 2010, May 2007, June 2010

and May 2008 while minimum January 2013, February 2010, January 2011and January 2012

over Karachi, Lahore, Jaipur and Kanpur, respectively. Likewise, the monthly averaged

SDARFESUR values were ranged from minimum (negative) to maximum (negative) October

2011 to January 2013 over Karachi, July 2011 to December 2013 over Lahore, June 2013 to

December 2012 over Jaipur and September to November over Kanpur. Finally, the

SDARFEATM varied from October 2011 to February 2010, July 2011 to May 2007, August

2011 to June 2010 and September 2013 to December 2012 over Karachi, Lahore, Jaipur and

Kanpur, respectively.

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Figure 7.4: Monthly variation of SDARFE at TOA, SUR and ATM over a) Karachi, b) Lahore, c) Jaipur


119

A large negative values of SDARFE at SUR show a decrement of solar energy reaching

the earth surface producing a cooling effect, whereas at TOA show an increment in radiations

which are reflected back to space resulting in cooling the earth-atmosphere system and positive

value in ATM indicate a significant amount of radiation trapped in the atmosphere and cannot

reach the surface [127]. Recently, Wu et al. [131] documented that the monthly averaged ARF

at SUR varied in the range −30 to −57 Wm-2 and at TOA was found to be in the range −11 to

23 Wm-2 whereas in ATM it was in the range 19 to 38 Wm-2 over Northeast China. Though,

irrespective of aerosol loading at the SUR, the ADRFE decreases with increase in AOD,

signifying a decreased aerosol absorbing ability. When AOD was low then there were large

deviations in the SDARFE i.e. at SUR there was significantly decreasing trend as AOD

increased which may be associated with increase in SSA as AOD increases [222]. Srivastava

and Ramachandran, [107] found that forcing efficiency is independent of AOD and related to

the SSA and surface reflectance. They noticed that due to lower SSA and higher surface

reflectance, the SDARFE was observed to be maximum at SUR and ATM whereas minimum

at TOA during pre-monsoon than other seasons for probable mixing state over IGP. Esteve et

al. [127] performed a study on ARFE over Burjassot in Spain and documented that ARFE

ranged between -78 Wm-2 in February and -168 Wm-2 in May at SUR and between -13.1 Wm-

2 in May and -26.9 Wm-2 in December at TOA having the averaged value of -128 and -20.8

Wm-2 at SUR and TOA, respectively during the entire period of observation. The ARFE at

TOA, SUR and ATM were -4.2, -63.2 and 59 Wm-2, respectively representing the net cooling

effect at SUR and TOA while warming effect in ATM over Jodhpur [130]. The ARFE were

ranged from -22 to -25 Wm-2 at TOA and -70 to -75 Wm-2 at SUR over Indian Ocean [112].

Kalluri et al. [135] documented that annual averaged ARFE were -19.7, - 89.5 and 69.8 Wm-2

at TOA, SUR and ATM, respectively over Anantapur. Ramana et al. [115] calculated the

ARFE Kathmandu and found that during January and February, the monthly averaged

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ARFESUR were -76 and -73 Wm-2, respectively with an average value of -73 Wm-2 showing the

dominance of strongly absorbing aerosol in the region. The averaged value of ARFESUR and

ARFETOA were -267.77, -208.45, -337.65, -232.86 Wm-2 and -7.83, -48.91, -40.59, -27.81 Wm-

2 over Shenyang, Anshan, Benxi, and Fushun, respectively [90].

Seasonal variation of SDARFE was observed by averaging the monthly averaged

SDARFE. Fig. 7.5(a-d) shows the SDARFETOA, SDARFESUR and SDARFEATM during winter,

summer, pre-monsoon and post-monsoon over the study sites for the selected period of

observation. SDARFE at TOA was observed to be highest during winter and lowest during

summer over all the sites except for Karachi where the lowest efficiency was observed in pre-

monsoon. Similarly, the maximum SDARFE at SUR was also found during winter and

minimum during summer over all the observational sites.

The values of SDARFE at ATM were highest during pre-monsoon and lowest during

summer over Karachi, Lahore and Kanpur while were highest during winter and lowest during

summer over Jaipur. In this respect, Ramachandran and Kedia, [52] documented that ARFE at

TOA and at the SUR display strong seasonal differences due to the variations in aerosol

characteristics and surface reflectance. They found that during pre-monsoon, the values of

ARFE at TOA was low negative and at SUR was high due to higher surface reflectance over

Kanpur and Gandhi College. However, Garcıa et al. [222] suggest that higher efficiency at

surface is due to the presence of more absorbent aerosol particles.

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Figure 7.5: Seasonal variation of SDARFE at TOA, SUR and ATM over a) Karachi, b) Lahore, c) Jaipur


122

Recently, Kalluri et al. [135] reported that the values of ARFE in ATM and at SUR were

found to be 78.5, 78.1, 61.0, 61.6 Wm-2 and -93.6, -95.5, -85.2, -83.6 Wm-2 during winter,

summer, monsoon and post-monsoon, respectively over Anantapur. Previously, Pathak et al.

[122] reported that ARFE at ATM showed highest value (88.6 Wm-2) in winter and lowest

value (74.4 Wm-2) during monsoon over Dibrugarh which are higher than our result for Jaipur.

7.4 Validation

In this section the reliability of this model was analyzed by comparing the SDARF estimated

from SBDART model with SDARF retrieved from AERONET. For this purpose, the validation

of daily averaged SDARF at TOA and SUR estimated from SBDART and retrieved from

AERONET were carried out separately as seen from Fig. 7.6(a-d) and Fig. 7.7(a-d). The

correlation coefficient (R2) for the entire study period between ARONET and SBDART at SUR

and TOA were 0.51 and 0.46 over Karachi; 0.81 and 0.73 over Lahore; 0.66 and 0.57 over

Jaipur and 0.69 and 0.55 over Kanpur, respectively. From the regression analysis of

AERONET-SBDART, it was observed that SDARF showed relatively higher correlation over

Lahore, moderate over Jaipur and Kanpur, whereas lower over Karachi at the SUR and TOA.

Overall the comparison between two data sets was convincing even with the uncertainties in

the retrieval of input parameters.

In spite of the different methodology and algorithms of radiative transfer, a good agreement

between AERONET and SBDART radiation indicates that the approach taken to assess the

optical properties of atmospheric aerosol and the estimated SDARF is appropriate [107].

Similarly, Adesina et al. [104] noticed the good correlation at ARFSUR (0.95) and at ARFTOA

(0.97) by comparing AERONET against SBDART over Gorongosa for the period of July to

December, 2012. Similar comparison between AERONET retrieved and SBDART calculated

ARF at SUR was performed by Alam et al. [87] and reported good correlation of 0.98 and

0.99 over Karachi and Lahore from 2010 to 2011.

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Figure 7.6: Scatter plots of AERONET vs. SBDART SDARF at TOA over a) Karachi, b) Lahore, c)


124

Figure 7.7: Scatter plots of AERONET vs. SBDART SDARF at SUR over a) Karachi, b) Lahore, c)


125

Previously, Li et al. [106] carried out the comparison of downward and upward forcing at TOA

and SUR from AERONET and SBDART and observed a good correlation ranged from 0.92 to

0.99 over 25 different stations across China during 2006. The R2 for the whole study of

observation (2007-2009) by plotting SBDART ARF versus AERONET ARF at the SUR and

TOA were 0.98 and 0.92, respectively over Kanpur [107].

Alam et al. [20] compared ARF retrieved from AERONET with estimated ARF from

SBDART at SUR (0.92) and TOA (0.82) over Karachi from August 2006 to July 2007 showing

good correlation. Previously, Kumar et al. [134] noticed a good correlation (0.89 at SUR and

0.78 at TOA) between AERONET and SBDART ARF during pre-monsoon over Kanpur.

Likewise, Valenzuela et al. [124] confirmed that there were small differences between in the

input data used for calculating SBDART and AERONET ARF by finding the small differences

in output data and indicate high correlation (> 0.98) at SUR between these two data sets during

selected desert dust episodes over Granada from 2005 to 2010.

7.5 HYSPLIT back trajectory analysis

The transport routes and directions of trajectories indicate the potential source areas from where

the air masses originated and arrived at the receptor sites. In this regard, the cluster trajectories

of air mass and their percentage contribution were calculated using the HYSPLIT model [162].

The 3-day back trajectories were separated into three clusters based on their pathways during

winter, summer, pre-monsoon and post-monsoon at 500 and 1000 m over Karachi, Lahore,

Jaipur and Kanpur during the observational period (see Fig. 7.8(a-d)).

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Figure. 7.8. 3-day back trajectories at 500 and 1000 m above ground level during (a) Winter, (b)

Summer, (c) Pre-monsoon and (d) Post-monsoon over Karachi, Lahore, Jaipur and Kanpur using

HYSPLIT model.

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During winter and post-monsoon, the trajectories were mostly of local origin carrying fine

particles and less contribution of coarse particles from Arabian Sea, Turkmenistan, Afghanistan

and Iran at both heights over the studied sites. Whereas, during summer and pre-monsoon,

most of the air masses were arriving from Caspian Sea, Arabian Sea, Iran, Bangladesh, Yamen

and Oman covering long distances bringing coarse particles to the studied sites except over

Lahore where majority of the trajectories were originating from local sources. To investigate

the source regions and the possible transport of air masses, similar cluster trajectories were also

conducted by previous researchers [53, 125, 133].

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Chapter 8

Conclusions and Future work

8.1 Summary, conclusions and future work

Atmospheric aerosols over South Asia have received a growing attention in the last few

decades due to their effects on human health, air quality, ecosystems, visibility, and radiation

balance. They also affect the seasonal variations, variability of the summer monsoon, and

distribution of rainfall. Aerosols have a very strong spatial and temporal variation in their

concentration and constitute a very vital component in the global climate system. Therefore, it

is challenging to categorize atmospheric aerosols into different types (dust, biomass burning,

urban/industrial and mixture of these) because of differences in regional climate, topography,

nature and lifetime duration. It is also crucial to globally analyze the optical properties of

aerosols due to their large spatial and temporal variability. Accurate and reliable measurements

of aerosol distributions and properties are required in order to reduce these large uncertainties,

since aerosols play a vital role in current analysis and predictions of the global climate. This

doctoral thesis focused on four different environments in IGP including Karachi, Lahore, Jaipur

and Kanpur using long-term ground and satellite based data-sets for the period 2007-2013

covering the following major objectives:

1. To obtained the accuracy and coverage of satellite and ground sensors, validation of

aerosol properties will be carried out

2. To classify urban/industrial and mineral dust aerosols using satellite data and ground-

based data-sets

3. To analyze aerosol physical and optical properties. These properties will be utilized in

radiative transfer modelling

4. To investigate the climatic effects of aerosol

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We successfully achieve the above-mentioned objectives and concluded the whole thesis with

the following conspicuous findings:

Firstly, we have compared AOD values derived from MODISBD, MODISSTD, MISR,

OMI and CALIPSO satellite-borne instruments with those from four AERONET

ground-based sites in order to validate the satellite retrievals against ground

measurements. Over all, the MODISSTD retrievals showed an excellent agreement with

AERONET observations over bright surfaces such as desert or coastal sites (e.g. over

Jaipur or Karachi), while MISR retrievals showed a high degree of correlation with

AERONET observations close to the ocean (e.g. over Karachi and Kanpur). MODISDB

retrievals showed a reasonable agreement with AERONET observations over all sites,

as did OMI retrievals. There are numerous important aerosol sources in southern Asia,

including human activities that emit both fine and coarse particles, smoke from biomass

burning, and sea salt from the ocean. High AOD values have been recorded by

MODISSTD, MODISDB, MISR, and OMI over Karachi, Lahore, Jaipur and Kanpur,

although there are also seasonal variations recorded by CALIPSO. AOD values were

observed to be higher in summer (June to August) than during the rest of the year due

a predominance of coarse dust and sea salt particles and possibly also due to the higher

water vapour content of the atmosphere due to high summer temperatures, which

encourages the growth of aerosols. It was also noted that high AOD values occurred in

October, associated with the harvesting of crops and subsequent plumes associated with

biomass-burning. High AOD values in southern Asia during the month of December

may be due the predominance of fine aerosol emissions from smoke and fossil fuels. In

March and April high wind speeds cause increased dust activity which also leads to

higher AOD values.

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Secondly, the aerosol optical properties have different temporal and spatial variability

due to different types and emission sources over the IGP. Before moving toward last

objective, we investigated the aerosol source locations and the seasonal variation of the

aerosol optical properties over Karachi, Lahore, Jaipur, and Kanpur using AERONET

data for the period 2007-2013. Over the studied sites, the monthly averaged variations

of the AOD at 500 nm and AE in the range of 440-870 nm have two characteristic

features: (a) the value of the AOD was negatively correlated with AE (high AOD with

low AE) in July over Karachi and Jaipur, representing the presence of coarse mode

particles resulting from dust events, and (b) during October and December the situation

is significantly different, with the AOD positively correlated with AE (high AOD with

high AE) over Lahore and Kanpur, respectively, representing fine-mode aerosols

emitted from vehicles, industries, and biomass-burning activities. The overall pattern

of size distribution was similar across all four sites and characterized by two peaks, one

between 0.1 and 0.4 μm and the other between 4.0 and 5.0 μm, revealing two modes

(fine and coarse). In general, it was observed that the coarse mode peaks were

comparatively higher in the summer and pre-monsoon than in the winter at all sites.

During the post-monsoon period, relatively higher values in the coarse mode were

observed in comparison to the winter season. The SSA was found to increase with

increasing wavelengths due to the influence of dust and anthropogenic activities during

both the summer and pre-monsoon seasons. During the winter, the SSA values

decreased with increasing wavelengths when dust is not a major contributor to the

atmospheric aerosols. During the post-monsoon, the SSA was found to be strongly

wavelength dependent and to have relatively lower values than in the summer and pre-

monsoon period over all sites except Kanpur, reflecting the existence of coarse particles

with smaller amounts of fine particles. The phase function was higher during the

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summer and pre-monsoon, moderate during post-monsoon, and relatively low during

the winter. The relatively higher phase function at low scattering angles is mostly due

to the coarse particles, while the fine particles are responsible for the low phase function

at higher scattering angles (θ > 10°). The AP was inversely related to the wavelength

during all seasons over all sites. For all seasons, the AP values were found to be greater

than (0.70) and decreased with increasing wavelengths, reaching its lowest value (0.61)

during the winter. Additionally, during pre-monsoon the AP values were found to

decrease with increasing wavelengths in the visible region and increase in the infrared

region at Karachi, Lahore, and Jaipur, pointing to the presence of coarse particles, while

in Kanpur the AP were found to decrease as wavelengths increased, revealing the

presence of fine particles. The relatively higher values of RRI during the summer and

pre-monsoon were attributed to the coarse particles, and the lower values during the

winter and post-monsoon were attributable to anthropogenic fine particles in the

atmosphere. The IRI was found to decrease with the wavelength during all seasons over

Karachi, Lahore, and Jaipur. On the other hand, Kanpur reveals similar systematic

spectral variations during the pre-monsoon and post-monsoon seasons, whereas it was

almost neutral to the spectral variation during the summer and winter, implying the

absorbing nature of the atmospheric aerosol. It is observed from the HYSPLIT back

trajectory analysis that the air masses originated in South Asia, Central Asia, the Middle

East, and the Arabian Sea and Caspian Sea, arriving at the studied sites as a result of

long-range transport together with some air masses from local sources.

As, the aerosol optical properties retrieved from AERONET were utilized to categorize

aerosols into different dominant groups over four observational sites in IGP. These

dominant aerosol groups were identified by correlating the aerosol properties associated

to the dominant size and radiation absorptivity and can be distinguished from one

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another by specific physical interpretable cluster region. To investigate seasonal

variation of aerosol types, it is essential to classify the complex mixture of aerosols into

different categories. First of all, the most common AOD-AE clustering technique was

carried out to distinguished dust from biomass burning and urban industrial. To further

differentiate between biomass burning and urban/industrial EAE versus AAE cluster

techniques were adopted because AAE is a key to distinguish aerosol types when

accompanied by EAE. Furthermore, to verify identification of aerosol types it is useful

to correlate EAE with other sensible parameters like SSA and RRI, which can better

separate biomass burning from urban/industrial. From all these clustering techniques,

it was concluded that during summer and pre-monsoon, dust particles were dominant

while during winter and post-monsoon prevailing aerosols were biomass burning, urban

industrial and the mixed type of aerosols were present in all seasons. Additionally,

AERONET classified aerosol types were further compared with the CALIPSO

retrieved aerosol subtypes. From the seasonal classification of aerosol subtypes derived

from CALIPSO data, it was observed that, during summer and pre-monsoon, dust and

polluted dust layers reached up to a height of 10 km. While during winter aerosol

loading up to 5 km were mostly consist of polluted continental, smoke, and polluted

dust, however, seldom distribution of dust particles was extended to an altitude of 10

km from the surface along with a minor contribution of clean marine below 1 km.

Whereas, during post-monsoon a mixed aerosol layer consisting of dust, polluted dust,

polluted continental and smoke, lies below an altitude of 5 km was noted. From both

satellite and ground based observations, it was concluded that during the summer and

pre-monsoon dust particles were dominant, while during post-monsoon, biomass

burning particles (smoke) were the dominant type of aerosols.

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Finally, in order to understand the radiative impacts of aerosol on regional climate of

IGP, the SDARF and SDARFE at TOA, SUR and ATM and associated atmospheric

HR were calculated using SBDART model. The analysis was carried out using different

time scale (Monthly, seasonal and annual) over Karachi, Lahore, Jaipur and Kanpur .

The monthly averaged values of SDARFTOA were observed to be either positive or

negative; negative values of SDARFTOA showing the cooling of the atmosphere or

earth’s system, while positive values were contributed to heating of the atmosphere. It

was noted that the values of SDARFSUR were largest (negative) in the month of

November over all sites indicating the strongest cooling at the surface. Generally,

SDARFSUR were found to be negative during all months. From the seasonal analysis of

SDARF, it was found that SDARFTOA and SDARFSUR were negative during all the

seasons over all the sites indicating a net cooling effect due to atmospheric aerosols.

The increment in the net atmosphere forcing to an averaged heating rate of 0.8, 1.0, 0.7

and 1.0 Kday−1 over Karachi, Lahore, Jaipur and Kanpur, respectively. The maximum

HR in November-December and May is attributed to the strongest atmospheric

absorption. Similar to SDARFTOA, the monthly averaged values of SDARFETOA were

found to be either positive or negative. Likewise, the monthly averaged SDARFESUR

values were negative throughout the months. Finally, the more enhanced positive

values of SDARFEATM were found throughout the months. Seasonally averaged

SDARFE at TOA was observed to be highest during winter and lowest during summer

over all the sites except for Karachi where the lowest efficiency was observed in pre-

monsoon. Consequently, the maximum SDARFE at SUR was noticed during the winter

and minimum during summer over all sites. The SDARFE at ATM were maximum

during pre-monsoon and lowest during the summer over Karachi, Lahore and Kanpur

while maximum during winter and minimum during summer over Jaipur. The

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regression analysis of AERONET-SBDART at the SUR and TOA revealed that

SDARF showed relatively higher correlation over Lahore, moderate over Jaipur and

Kanpur and lower over Karachi.

8.2 Future work

Though the work presented in this thesis advances the current knowledge of aerosol properties

over four different locations in IGP for the seven-year period by analyzing the intercomparison

of MODIS, MISR, OMI and CALIPSO AOD against AERONET AOD, classification of

aerosol types and variation in aerosol optical properties and shortwave aerosol direct effect.

Further analysis is needed over the different environments, particularly over the glacier region

as well as over the world wide locations, in order to better understand the reliability of satellite

sensors. Furthermore, the others satellite aerosol optical properties such as SSA and AE also

need to be validated against ground retrievals for the long period of observations. Additionally,

classification of aerosol through satellite based observations is of vital importance and need to

be performed. Furthermore, to improve the knowledge about the variation in atmospheric

circulations due to variations in cloud optical properties, the investigation of indirect radiative

impacts of aerosol on regional climate of IGP is also required.

135

References

1. Ramachandran, S.; Kedia, S. Climate. 2013, 1 (3), 84-119.

2. Prospero, J. M.; Ginoux, P.; Torres, O.; Nicholson, S. E.; Gill, T. E. Rev. Geophys.

2002, 40 (1).

3. Habib, G.; Venkataraman, C.; Chiapello, I.; Ramachandran, S.; Boucher, O.; Reddy,

M. S. Atmos. Environ. 2006, 40 (11), 1909-1921.

4. Solomon, S.; Qin, D.; Manning, M.; Chen, Z.; Marquis, M.; Averyt, K.; Tignor, M.;

Miller, H., Climate change 2007: the physical science basis. Cambridge, New York,

Melbourne, Madrid, Cape Town. In Singapore, Sao Paulo, Delhi: Cambridge

University Press: 2008.

5. Charlson, R. J.; Schwartz, S. Science. 1992, 255 (5043), 423.

6. Nemmar, A.; Hoet, P. H.; Dinsdale, D.; Vermylen, J.; Hoylaerts, M. F.; Nemery, B.

Circulation. 2003, 107 (8), 1202-1208.

7. Jonathan, A., Vegetation–Climate Interaction: How Vegetation Makes the Global

Environment. In New York: Springer: 2003.

8. Kaufman, Y.; Tanré, D.; Remer, L. A.; Vermote, E.; Chu, A.; Holben, B. J. Geophys.

Res. Atmos. 1997, 102 (D14), 17051-17067.

9. Pelletier, B.; Santer, R.; Vidot, J. J. Geophys. Res. Atmos. 2007, 112 (D6).

10. Jaenicke, R. Geophys. J. Int. 1993, 54, 1-31.

11. Seinfeld, J. H.; Pandis, S. N., Atmospheric chemistry and physics: from air pollution to

climate change. John Wiley & Sons: 2016.

12. Pöschl, U. Angew. Chem. Int. Ed. 2005, 44 (46), 7520-7540.

13. Giles, D. M.; Holben, B. N.; Eck, T. F.; Sinyuk, A.; Smirnov, A.; Slutsker, I.;

Dickerson, R.; Thompson, A.; Schafer, J. J. Geophys. Res. Atmos. 2012, 117 (D17).

14. Higurashi, A.; Nakajima, T. Geophys. Res. Lett. 2002, 29 (17).

136

15. Pathak, B.; Bhuyan, P. K.; Gogoi, M.; Bhuyan, K. Atmos. Environ. 2012, 47, 307-315.

16. McCartney, E. J. New York, John Wiley and Sons, Inc. 421 p. 1976.

17. Reist, P., Aerosol science and technology. 1993. In New York: McGraw-Hill.

18. Ångström, A. Tellus. 1964, 16 (1), 64-75.

19. Eck, T. F.; Holben, B. N.; Reid, J. S.; Dubovik; Smirnov, A.; O'Neill, N. T.; Slutsker,

I.; Kinne, S. J. Geophys. Res. 1999, 104 (3133331349), 00093-5.

20. Alam, K.; Trautmann, T.; Blaschke, T. Atmos. Res. 2011, 101 (3), 773-782.

21. Xia, X.-a.; Wang, P.-c.; Chen, H.-b.; Gouloub, P.; Zhang, W.-x. J. Rmote Sens. Beijing

2005, 9 (4), 429.

22. Zheng, Y.; Liu, J.; Wu, R.; Li, Z.; Wang, B.; Tamio, T. J. Geophys. Res. Atmos. 2008,

113 (D16).

23. Alam, K.; Trautmann, T.; Blaschke, T.; Subhan, F. Remote Sens. Environ. 2014, 143,

216-227.

24. Dubovik, O.; Holben, B.; Eck, T. F.; Smirnov, A.; Kaufman, Y. J.; King, M. D.; Tanré,

D.; Slutsker, I. J. Atmos. Sci. 2002, 59 (3), 590-608.

25. Kokhanovsky, A. J. Atmos. Sci. 1998, 55 (3), 314-320.

26. Shifrin, K., Scattering of light in a turbid medium. National Aeronautics and Space

Administration: 1968; Vol. 477.

27. Andrews, E.; Sheridan, P. J.; Fiebig, M.; McComiskey, A.; Ogren, J. A.; Arnott, P.;

Covert, D.; Elleman, R.; Gasparini, R.; Collins, D. J. Geophys. Res. Atmos. 2006, 111

(D5).

28. Ramachandran, S.; Rajesh, T. J. Geophys. Res. Atmos. 2008, 113 (D16).

29. Bohren, C. F.; Singham, S. B. J. Geophys. Res. Atmos. 1991, 96 (D3), 5269-5277.

30. Sinyuk, A.; Torres, O.; Dubovik, O. Geophys. Res. Lett. 2003, 30 (2).

137

31. Prasad, A. K.; Singh, S.; Chauhan, S.; Srivastava, M. K.; Singh, R. P.; Singh, R. Atmos.

Environ. 2007, 41 (29), 6289-6301.

32. Omar, A. H.; Won, J. G.; Winker, D. M.; Yoon, S. C.; Dubovik, O.; McCormick, M. P.

J. Geophys. Res. Atmos. 2005, 110 (D10).

33. Sharma, M.; Kaskaoutis, D. G.; Singh, R. P.; Singh, S. Aerosol Air Qual. Res. 2014, 14

(3), 608-622.

34. Kumar, K. R.; Yin, Y.; Sivakumar, V.; Kang, N.; Yu, X.; Diao, Y.; Adesina, A. J.;

Reddy, R. Atmos. Environ. 2015, 117, 9-18.

35. Mishra, A. K.; Shibata, T. Atmos. Environ. 2012, 57, 205-218.

36. Russell, P. B.; Kacenelenbogen, M.; Livingston, J. M.; Hasekamp, O. P.; Burton, S. P.;

Schuster, G. L.; Johnson, M. S.; Knobelspiesse, K. D.; Redemann, J.; Ramachandran,

S. J. Geophys. Res. Atmos. 2014, 119 (16), 9838-9863.

37. Giles, D. M.; Holben, B. N.; Tripathi, S. N.; Eck, T. F.; Newcomb, W. W.; Slutsker, I.;

Dickerson, R. R.; Thompson, A. M.; Mattoo, S.; Wang, S. H. J. Geophys. Res. Atmos.

2011, 116 (D18).

38. Kedia, S.; Ramachandran, S.; Holben, B. N.; Tripathi, S. Atmos. Environ. 2014, 98,

607-619.

39. Kahn, R. A.; Gaitley, B. J.; Garay, M. J.; Diner, D. J.; Eck, T. F.; Smirnov, A.; Holben,

B. N. J. Geophys. Res. Atmos. 2010, 115 (D23).

40. Kaufman, Y. J.; Remer, L. A.; Tanré, D.; Li, R.-R.; Kleidman, R.; Mattoo, S.; Levy, R.

C.; Eck, T. F.; Holben, B. N.; Ichoku, C. IEEE. T. Geosc. Remot. 2005, 43 (12), 2886-

2897.

41. Jethva, H.; Satheesh, S.; Srinivasan, J. Geophys. Res. Lett. 2007, 34 (19).

42. More, S.; Pradeep Kumar, P.; Gupta, P.; Devara, P.; Aher, G. Aerosol Air Qual. Res.

2013, 13 (1), 107-121.

138

43. Holben, B. N.; Eck, T.; Slutsker, I.; Tanre, D.; Buis, J.; Setzer, A.; Vermote, E.; Reagan,

J. A.; Kaufman, Y.; Nakajima, T. Remote Sens. Environ. 1998, 66 (1), 1-16.

44. Fotiadi, A.; Hatzianastassiou, N.; Drakakis, E.; Matsoukas, C.; Pavlakis, K.;

Hatzidimitriou, D.; Gerasopoulos, E.; Mihalopoulos, N.; Vardavas, I. Atmos. Chem.

Phys. 2006, 6 (12), 5399-5413.

45. Chu, D. A.; Kaufman, Y.; Zibordi, G.; Chern, J.; Mao, J.; Li, C.; Holben, B. J. Geophys.

Res. Atmos. 2003, 108 (D21).

46. Martonchik, J. V.; Diner, D. J.; Kahn, R. A.; Ackerman, T. P.; Verstraete, M. M.; Pinty,

B.; Gordon, H. R. IEEE. T. Geosc. Remot. 1998, 36 (4), 1212-1227.

47. Zhang, J.; Christopher, S. A. Geophys. Res. Lett. 2003, 30 (23).

48. Winker, D. M.; Hostetler, C.; Vaughan, M.; Omar, A. Release 2006, 2, 29.

49. Li, J.; Carlson, B. E.; Lacis, A. A. J. Geophys. Res. Atmos. 2014, 119 (1), 153-166.

50. Myhre, G.; Stordal, F.; Johnsrud, M.; Diner, D. J.; Geogdzhayev, I. V.; Haywood, J.

M.; Holben, B.; Holzer-Popp, T.; Ignatov, A.; Kahn, R. Atmos. Chem. Phys. 2005, 5

(6), 1697-1719.

51. Qi, Y.; Ge, J.; Huang, J. Chin. Sci. Bull. 2013, 58 (20), 2497-2506.

52. Ramachandran, S.; Ghosh, S.; Verma, A.; Panigrahi, P. K. Environ. Res. Lett. 2013, 8

(1), 014034.

53. Singh, A.; Tiwari, S.; Sharma, D.; Singh, D.; Tiwari, S.; Srivastava, A. K.; Rastogi, N.;

Singh, A. Meteorol. Atmos. Phys. 2016, 1-14.

54. Kaskaoutis, D.; Sinha, P.; Vinoj, V.; Kosmopoulos, P.; Tripathi, S.; Misra, A.; Sharma,

M.; Singh, R. Atmos. Environ. 2013, 79, 7-19.

55. Rajeev, K.; Ramanathan, V. J. Geophys. Res. 2001, 106 (17,221), e17.

56. Kant, Y.; Singh, A.; Mitra, D.; Singh, D.; Srikanth, P.; Madhusudanacharyulu, A.;

Murthy, Y. Advan. Meteorol. 2015, 2015.

139

57. Kiehl, J. T.; Trenberth, K. E. Bull. Am. Meteorol. Soc. 1997, 78 (2), 197-208.

58. Wang, L.; Gong, W.; Xia, X.; Zhu, J.; Li, J.; Zhu, Z. Atmos. Environ. 2015, 101, 94-

102.

59. Dey, S.; Tripathi, S. N.; Singh, R. P.; Holben, B. J. Geophys. Res. Atmos. 2004, 109

(D20).

60. Ichoku, C.; Chu, D. A.; Mattoo, S.; Kaufman, Y. J.; Remer, L. A.; Tanré, D.; Slutsker,

I.; Holben, B. N. Geophys. Res. Lett. 2002, 29 (12).

61. Chu, D.; Kaufman, Y.; Ichoku, C.; Remer, L.; Tanré, D.; Holben, B. Geophys. Res.

Lett. 2002, 29 (12).

62. Abdou, W. A.; Diner, D. J.; Martonchik, J. V.; Bruegge, C. J.; Kahn, R. A.; Gaitley, B.

J.; Crean, K. A.; Remer, L. A.; Holben, B. J. Geophys. Res. Atmos. 2005, 110 (D10).

63. Jiang, X.; Liu, Y.; Yu, B.; Jiang, M. Remote Sens. Environ. 2007, 107 (1), 45-53.

64. Curier, R.; Veefkind, J.; Braak, R.; Veihelmann, B.; Torres, O.; De Leeuw, G. J.

Geophys. Res. Atmos. 2008, 113 (D17).

65. Christopher, S. A.; Gupta, P.; Haywood, J.; Greed, G. J. Geophys. Res. Atmos. 2008,

113 (D23).

66. Hoelzemann, J. J.; Longo, K. M.; Fonseca, R. M.; Do RosáRio, N. M.; Elbern, H.;

Freitas, S. R.; Pires, C. J. Geophys. Res. Atmos. 2009, 114 (D13).

67. Liu, J.; Xia, X.; Li, Z.; Wang, P.; Min, M.; Hao, W.; Wang, Y.; Xin, J.; Li, X.; Zheng,

Y. Tellus B. 2010, 62 (2), 117-124.

68. Alam, K.; Qureshi, S.; Blaschke, T. Atmos. Environ. 2011, 45 (27), 4641-4651.

69. Marey, H.; Gille, J.; El-Askary, H.; Shalaby, E.; El-Raey, M. Atmos. Chem. Phys. 2011,

11 (20), 10637-10648.

70. Choudhry, P.; Misra, A.; Tripathi, S. Ann. Geophys. 2012, 30, 1479-1493.

140

71. Cheng, T.; Chen, H.; Gu, X.; Yu, T.; Guo, J.; Guo, H. J. Quant. Spectrosc. Radiat.

Transfer. 2012, 113 (16), 2135-2145.

72. Gupta, P.; Khan, M. N.; da Silva, A.; Patadia, F. Atmos. Pollut. Res. 2013, 4 (1), 43-52.

73. Wong, M. S.; Shahzad, M. I.; Nichol, J. E.; Lee, K. H.; Chan, P. Int. J. Remote Sens.

2013, 34 (3), 897-918.

74. Alam, K.; Sahar, N.; Iqbal, Y. Aerosol Air Qual. Res. 2014, 14 (1), 99-107.

75. Ahn, C.; Torres, O.; Jethva, H. J. Geophys. Res. Atmos. 2014, 119 (5), 2457-2473.

76. Ma, X.; Yu, F. Atmos. Res. 2014, 140, 28-37.

77. Misra, A.; Jayaraman, A.; Ganguly, D. Aerosol Air Qual. Res. 2015, 15 (1), 252-262.

78. Payra, S.; Soni, M.; Kumar, A.; Prakash, D.; Verma, S. Environmental science &

technology 2015, 49 (15), 9237-9246.

79. Adesina, A. J.; Kumar, K. R.; Sivakumar, V.; Piketh, S. J. Journal of Atmospheric and

Solar-Terrestrial Physics 2016, 148, 82-95.

80. Goloub, P.; Tanre, D.; Deuze, J.-L.; Herman, M.; Marchand, A.; Bréon, F.-M. IEEE. T.

Geosc. Remot. 1999, 37 (3), 1586-1596.

81. Masmoudi, M.; Chaabane, M.; Tanré, D.; Gouloup, P.; Blarel, L.; Elleuch, F. Atmos.

Res. 2003, 66 (1), 1-19.

82. Pace, G.; Sarra, A. d.; Meloni, D.; Piacentino, S.; Chamard, P. Atmos. Chem. Phys.

2006, 6 (3), 697-713.

83. Kaskaoutis, D.; Kosmopoulos, P.; Kambezidis, H.; Nastos, P. Atmos. Environ. 2007,

41 (34), 7315-7329.

84. Kaskaoutis, D.; Kambezidis, H.; Hatzianastassiou, N.; Kosmopoulos, P.; Badarinath,

K. Atmos. Chem. Phys. Discuss. 2007, 7 (3), 6357-6411.

85. Toledano, C.; Cachorro, V.; De Frutos, A.; Torres, B.; Berjon, A.; Sorribas, M.; Stone,

R. J. Appl. Meteor. Climatol. 2009, 48 (5), 962-981.

141

86. Russell, P.; Bergstrom, R.; Shinozuka, Y.; Clarke, A.; DeCarlo, P.; Jimenez, J.;

Livingston, J.; Redemann, J.; Dubovik, O.; Strawa, A. Atmos. Chem. Phys. 2010, 10

(3), 1155-1169.

87. Alam, K.; Trautmann, T.; Blaschke, T.; Majid, H. Atmos. Environ. 2012, 50, 234-245.

88. Tan, F.; San Lim, H.; Abdullah, K.; Yoon, T. L.; Holben, B. Atmos. Pollut. Res. 2015,

6 (4), 682-695.

89. Verma, S.; Prakash, D.; Ricaud, P.; Payra, S.; Attié, J.-L.; Soni, M. Aerosol Air Qual.

Res. 2015, 15, 985-993.

90. Che, H.; Xia, X.; Zhu, J.; Wang, H.; Wang, Y.; Sun, J.; Zhang, X.; Shi, G. Environ. Sci.

Pollut. Res. 2015, 22 (2), 1043-1053.

91. Tiwari, S.; Tiwari, S.; Hopke, P.; Attri, S.; Soni, V.; Singh, A. K. Environ. Sci. Pollut.

Res. 2016, 23 (9), 8781-8793.

92. Yu, X.; Lu, R.; Kumar, K. R.; Ma, J.; Zhang, Q.; Jiang, Y.; Kang, N.; Yang, S.; Wang,

J.; Li, M. Res. Environ. Sci. Pollut. 2016, 23, 15432-15442.

93. Tariq, S.; Ali, M. Asia-Pac. J. Atmos. Sci. 2016, 52 (1), 25-33.

94. Singh, S.; Nath, S.; Kohli, R.; Singh, R. Geophys. Res. Lett. 2005, 32 (13).

95. Toledano, C.; Cachorro, V.; Berjon, A.; De Frutos, A.; Sorribas, M.; De la Morena, B.;

Goloub, P. Q. J. R. Meteorol. Soc 2007, 133 (624), 795-807.

96. Xin, J.; Wang, Y.; Li, Z.; Wang, P.; Hao, W. M.; Nordgren, B. L.; Wang, S.; Liu, G.;

Wang, L.; Wen, T. J. Geophys. Res. Atmos. 2007, 112 (D5).

97. Eck, T.; Holben, B.; Reid, J.; Sinyuk, A.; Hyer, E.; O'Neill, N.; Shaw, G.; Vande Castle,

J.; Chapin, F.; Dubovik, O. J. Geophys. Res. Atmos. 2009, 114 (D11).

98. Eck, T. F.; Holben, B.; Sinyuk, A.; Pinker, R.; Goloub, P.; Chen, H.; Chatenet, B.; Li,

Z.; Singh, R. P.; Tripathi, S. N. J. Geophys. Res. Atmos. 2010, 115 (D19).

142

99. Guleria, R. P.; Kuniyal, J. C.; Rawat, P. S.; Thakur, H. K.; Sharma, M.; Sharma, N. L.;

Singh, M.; Chand, K.; Sharma, P.; Thakur, A. K. Atmos. Res. 2011, 101 (3), 726-738.

100. Gautam, R.; Hsu, N.; Tsay, S.; Lau, K.; Holben, B.; Bell, S.; Smirnov, A.; Li, C.;

Hansell, R.; Ji, Q. Atmos. Chem. Phys. 2011, 11 (24), 12841-12863.

101. Bi, J.; Huang, J.; Fu, Q.; Wang, X.; Shi, J.; Zhang, W.; Huang, Z.; Zhang, B. J. Quan.

Spectrosc. Radiat. Transfer. 2011, 112 (2), 346-360.

102. Zhuravleva, T. B.; Bedareva, T. V.; Sviridenkov, M. A. In Proceedings of the

International Radiation Symposium (IRC/IAMAS), AIP Publishing: 147-150, 2013.

103. Dumka, U.; N Tripathi, S.; Misra, A.; Giles, D.; Eck, T.; Sagar, R.; Holben, B. J.

Geophys. Res. Atmos. 2014, 119 (8), 4750-4769.

104. Adesina, A. J.; Kumar, K. R.; Sivakumar, V. Meteorol. Atmos. Phy. 2015, 127 (2), 217-

228.

105. Kang, N.; Kumar, K. R.; Yu, X.; Yin, Y. Environ. Sci. Pollut. Res. 2016, 1-21.

106. Li, Z.; Lee, K. H.; Wang, Y.; Xin, J.; Hao, W. M. J. Geophys. Res. Atmos. 2010, 115

(D7).

107. Srivastava, R.; Ramachandran, S. Q. J. Meteorol. Soc. 2013, 139 (670), 137-151.

108. Michael, M.; Yadav, A.; Tripathi, S.; Kanawade, V.; Gaur, A.; Sadavarte, P.;

Venkataraman, C. Geosci Model Dev. Dis. 2014, 7 (1), 431-482.

109. IPCC. International Panel on Climate Change. 2007, 6 (07), 333.

110. Kaufman, Y. J.; Tanré, D.; Boucher, O. Nature. 2002, 419 (6903), 215-223.

111. Kaufman, Y.; Koren, I.; Remer, L.; Tanré, D.; Ginoux, P.; Fan, S. J. Geophys. Res.

Atmos. 2005, 110 (D10).

112. Satheesh, S.; Ramanathan, V. Nature. 2000, 405 (6782), 60-63.

113. Takemura, T.; Nakajima, T.; Dubovik, O.; Holben, B. N.; Kinne, S. J. Climate. 2002,

15 (4), 333-352.

114. Gadhavi, H.; Jayaraman, A. Curr. Sci. Bangalore. 2004, 86 (2), 296-304.

143

115. Ramana, M.; Ramanathan, V.; Podgorny, I.; Pradhan, B. B.; Shrestha, B. Geophys. Res.

Lett. 2004, 31 (5).

116. Pant, P.; Hegde, P.; Dumka, U.; Sagar, R.; Satheesh, S.; Moorthy, K. K.; Saha, A.;

Srivastava, M. J. Geophys. Res. Atmos. 2006, 111 (D17).

117. Ganguly, D.; Jayaraman, A. J. Geophys. Res. Atmos. 2006, 111 (D24).

118. Dey, S.; Tripathi, S. J. Geophys. Res. Atmos. 2007, 112 (D3).

119. Dey, S.; Tripathi, S. J. Geophys. Res. Atmos. 2008, 113 (D4).

120. Pandithurai, G.; Dipu, S.; Dani, K.; Tiwari, S.; Bisht, D.; Devara, P.; Pinker, R. J.

Geophys. Res. Atmos. 2008, 113 (D13).

121. Singh, S.; Soni, K.; Bano, T.; Tanwar, R.; Nath, S.; Arya, B. Ann. Geophys. 2010, 28

1157-1166.

122. Pathak, B.; Kalita, G.; Bhuyan, K.; Bhuyan, P.; Moorthy, K. K. J. Geophys. Res. Atmos.

2010, 115 (D19).

123. Kedia, S.; Ramachandran, S.; Kumar, A.; Sarin, M. J. Geophys. Res. Atmos. 2010, 115

(D7).

124. Valenzuela, A.; Olmo, F.; Lyamani, H.; Antón, M.; Quirantes, A.; Alados-Arboledas,

L. Atmos. Chem. Phys. 2012, 12 (21), 10331-10351.

125. Kumar, S.; Devara, P. Tellus B 2012, 64.

126. Cherian, R.; Venkataraman, C.; Ramachandran, S.; Quaas, J.; Kedia, S. Atmos. Chem.

Phys. 2012, 12 (3), 1287-1305.

127. Esteve, A. R.; Estellés, V.; Utrillas, M. P.; Martínez-Lozano, J. A. Atmos. Res. 2014,

137, 195-204.

128. Taneja, K.; Soni, V.; Attri, S.; Peshin, S.; Mor, V.; Sateesh, M. Glob. Sustain. Transit.

Impacts Innov. 2014, 124-130.

144

129. Srivastava, P.; Dey, S.; Agarwal, P.; Basil, G. Int. J. Remote Sens. 2014, 35 (13), 5036-

5052.

130. Bhaskar, V. V.; Safai, P.; Raju, M. Atmos. Environ. 2015, 114, 66-74.

131. Wu, Y.; Zhu, J.; Che, H.; Xia, X.; Zhang, R. Atmos. Res. 2015, 157, 56-65.

132. Guleria, R. P.; Kuniyal, J. C. Air Qual. Atmos. Health. 2015, 1-14.

133. Patel, P. N.; Kumar, R. Aerosol Air Qual. Res. 2015, 15 (5), 2082-2093.

134. Kumar, S.; Kumar, S.; Kaskaoutis, D.; Singh, R. P.; Singh, R. K.; Mishra, A. K.;

Srivastava, M. K.; Singh, A. K. Aeolian Res. 2015, 17, 15-31.

135. Kalluri, R. O. R.; Gugamsetty, B.; Kotalo, R. G.; Nagireddy, S. K. R.; Tandule, C. R.;

Thotli, L. R.; Ramakrishna, R. R.; Surendranair, S. B. Sci. Total Environ. 2016, 566,

1002-1013.

136. Singh, A.; Rastogi, N.; Sharma, D.; Singh, D. Aerosol Air Qual. Res. 2015, 15, 376-

386.

137. Lodhi, N. K.; Beegum, S. N.; Singh, S.; Kumar, K. J. Geophys. Res. Atmos. 2013, 118

(3), 1361-1375.

138. Srivastava, A. K.; Singh, S.; Tiwari, S.; Bisht, D. Environ. Sci. Pollut. Res. 2012, 19

(4), 1144-1158.

139. Gautam, R.; Hsu, N.; Lau, K. M.; Tsay, S. C.; Kafatos, M. Geophys. Res. Lett. 2009,

36 (7).

140. Gautam, R.; Hsu, N. C.; Lau, K. M. J. Geophys. Res. Atmos. 2010, 115 (D17).

141. Prasad, A. K.; Singh, R. P. J. Geophys. Res. Atmos. 2007, 112 (D9).

142. Singh, R.; Dey, S.; Tripathi, S.; Tare, V.; Holben, B. J. Geophys. Res. Atmos. 2004, 109

(D23).

143. Pandithurai, G.; Pinker, R.; Devara, P.; Takamura, T.; Dani, K. J. Geophys. Res. Atmos.

2007, 112 (D8).

145

144. Gautam, R.; Hsu, N. C.; Kafatos, M.; Tsay, S. C. J. Geophys. Res. Atmos. 2007, 112

(D5).

145. Tiwari, S.; Srivastava, A.; Singh, A.; Singh, S. Environ. Sci. Pollut. Res. 2015, 22 (16),

12246-12260.

146. Smirnov, A.; Holben, B.; Eck, T.; Dubovik, O.; Slutsker, I. Remote Sens. Environ.

2000, 73 (3), 337-349.

147. Holben, B.; Tanre, D.; Smirnov, A.; Eck, T.; Slutsker, I.; Abuhassan, N.; Newcomb,

W.; Schafer, J.; Chatenet, B.; Lavenu, F. J. Geophys. Res. Atmos. 2001, 106 (D11),

12067-12097.

148. https://www.google.com.pk/.

149. Tanré, D.; Kaufman, Y.; Herman, M.; Mattoo, S. J. Geophys. Res. Atmos. 1997, 102

(D14), 16971-16988.

150. Remer, L. A.; Kaufman, Y.; Tanré, D.; Mattoo, S.; Chu, D.; Martins, J. V.; Li, R.-R.;

Ichoku, C.; Levy, R.; Kleidman, R. J. Atmos. Sci. 2005, 62 (4), 947-973.

151. Levy, R. C.; Remer, L. A.; Mattoo, S.; Vermote, E. F.; Kaufman, Y. J. J. Geophys. Res.

Atmos. 2007, 112 (D13).

152. Levelt, P. F.; Hilsenrath, E.; Leppelmeier, G. W.; van den Oord, G. H.; Bhartia, P. K.;

Tamminen, J.; de Haan, J. F.; Veefkind, J. P. IEEE. T. Geosc. Remot. 2006, 44 (5),

1199-1208.

153. Winker, D. M.; Vaughan, M. A.; Omar, A.; Hu, Y.; Powell, K. A.; Liu, Z.; Hunt, W.

H.; Young, S. A. J. Atmos. Oceanic Technol. 2009, 26 (11), 2310-2323.

154. Winker, D.; Pelon, J.; Coakley Jr, J.; Ackerman, S.; Charlson, R.; Colarco, P.; Flamant,

P.; Fu, Q.; Hoff, R.; Kittaka, C. Bull. Am. Meteorol. Soc. 2010, 91 (9), 1211.

155. Huang, J.; Ge, J.; Weng, F. Remote Sens. Environ. 2007, 110 (2), 186-191.

http://www.google.com.pk/

146

156. Huang, J.; Minnis, P.; Yi, Y.; Tang, Q.; Wang, X.; Hu, Y.; Liu, Z.; Ayers, K.; Trepte,

C.; Winker, D. Geophys. Res. Lett. 2007, 34 (18).

157. Winker, D. M.; Hunt, W. H.; McGill, M. J. Geophys. Res. Lett. 2007, 34 (19).

158. Liu, J.; Zheng, Y.; Li, Z.; Wu, R. Atmos. Res. 2008, 89 (1), 194-205.

159. Liu, Z.; Liu, D.; Huang, J.; Vaughan, M.; Uno, I.; Sugimoto, N.; Kittaka, C.; Trepte,

C.; Wang, Z.; Hostetler, C. Atmos. Chem. Phys. 2008, 8 (16), 5045-5060.

160. Winker, D. M.; Pelon, J. R.; McCormick, M. P. In Third International Asia-Pacific

Environmental Remote Sensing Remote Sensing of the Atmosphere, International

Society for Optics and Photonics. 2003, p 1-11.

161. Powell, K. A.; Hostetler, C. A.; Vaughan, M. A.; Lee, K.-P.; Trepte, C. R.; Rogers, R.

R.; Winker, D. M.; Liu, Z.; Kuehn, R. E.; Hunt, W. H. J. Atmos. Oceanic Technol.

2009, 26 (10), 2015-2033.

162. Draxler, R. R.; Rolph, G., HYSPLIT (HYbrid Single-Particle Lagrangian Integrated

Trajectory) model access via NOAA ARL READY website (http://www. arl. noaa.

gov/ready/hysplit4. html). NOAA Air Resources Laboratory, Silver Spring. In Md:

2003.

163. Hsu, N. C.; Tsay, S.-C.; King, M. D.; Herman, J. R. IEEE. T. Geosc. Remot. 2004, 42

(3), 557-569.

164. Hyer, E.; Reid, J.; Zhang, J. Atmos. Meas. Tech. 2011, 4 (3), 379-408.

165. Tripathi, S.; Dey, S.; Chandel, A.; Srivastava, S.; Singh, R. P.; Holben, B. In

Comparison of MODIS and AERONET derived aerosol optical depth over the Ganga

Basin, India, Ann. Geophys., 2005; 2005; pp 1093-1101.

166. Misra, A.; Tripathi, S.; Kaul, D.; Welton, E. J. J. Atmos. Oceanic Technol. 2012, 29 (9),

1285-1294.

http://www/

147

167. Zhao, T. X.; Stowe, L. L.; Smirnov, A.; Crosby, D.; Sapper, J.; McClain, C. R. J. Atmos.

Sci. 2002, 59 (3), 294-312.

168. Remer, L. A.; Kleidman, R. G.; Levy, R. C.; Kaufman, Y. J.; Tanré, D.; Mattoo, S.;

Martins, J. V.; Ichoku, C.; Koren, I.; Yu, H. J. Geophys. Res. Atmos. 2008, 113 (D14).

169. Levy, R. C.; Remer, L. A.; Kleidman, R. G.; Mattoo, S.; Ichoku, C.; Kahn, R.; Eck, T.

Atmos. Chem. Phys. 2010, 10 (21), 10399-10420.

170. Lee, J.; Kim, J.; Song, C.; Kim, S.; Chun, Y.; Sohn, B.; Holben, B. Atmos. Environ.

2010, 44 (26), 3110-3117.

171. Ricchiazzi, P.; Yang, S.; Gautier, C.; Sowle, D. Bull. Am. Meteorol. Soc. 1998, 79 (10),

2101.

172. Stamnes, K.; Tsay, S.-C.; Wiscombe, W.; Jayaweera, K. Appl. Opt 1988, 27 (12), 2502-

2509.

173. Liou, K.-N., An introduction to atmospheric radiation. Academic press: 2002; Vol. 84.

174. Ranjan, R. R.; Joshi, H.; Iyer, K. Aerosol Air Qual. Res. 2007, 7 (1), 33-45.

175. Alam, K.; Khan, R.; Ali, S.; Ajmal, M.; Khan, G.; Muhammad, W.; Ali, M. A. Arab. J.

Geosci 2015, 8 (1), 547-555.

176. Alam, K.; Khan, R.; Blaschke, T.; Mukhtiar, A. J. Atmos. Sol.-Terr. Phys. 2014, 107,

104-112.

177. Lyamani, H.; Olmo, F.; Alcantara, A.; Alados-Arboledas, L. Atmos. Environ. 2006, 40

(33), 6453-6464.

178. Alam, K.; Iqbal, M. J.; Blaschke, T.; Qureshi, S.; Khan, G. Adv. Space. Res. 2010, 46

(9), 1162-1176.

179. Wang, Y.; Yang, C. J. Opt. Soc. Am. 2008, 25 (2), 348-356.

148

180. Balakrishnaiah, G.; Reddy, B. S. K.; Gopal, K. R.; Reddy, R.; Reddy, L.; Ahammed,

Y. N.; Narasimhulu, K.; Moorthy, K. K.; Babu, S. S. Atmos. Environ. 2011, 45 (6),

1275-1285.

181. Kaskaoutis, D. G.; Singh, R. P.; Gautam, R.; Sharma, M.; Kosmopoulos, P. G.; Tripathi,

S. Environ. Res. Lett. 2012, 7 (2), 024003.

182. Balakrishnaiah, G.; Reddy, B. S. K.; Gopal, K. R.; Reddy, R.; Reddy, L.; Swamulu, C.;

Ahammed, Y. N.; Narasimhulu, K.; KrishnaMoorthy, K.; Babu, S. S. Atmos. Environ.

2012, 47, 435-445.

183. Sarkar, S.; Chokngamwong, R.; Cervone, G.; Singh, R.; Kafatos, M. Adv. Space. Res.

2006, 37 (12), 2153-2159.

184. El-Metwally, M.; Alfaro, S.; Wahab, M. A.; Zakey, A.; Chatenet, B. Atmos. Res. 2010,

97 (1), 14-25.

185. Tang, Y.; Carmichael, G. R.; Woo, J. H.; Thongboonchoo, N.; Kurata, G.; Uno, I.;

Streets, D. G.; Blake, D. R.; Weber, R. J.; Talbot, R. W. J. Geophys. Res. Atmos. 2003,

108 (D21).

186. Prasad, A. K.; Singh, R. P.; Singh, A. J. Indian Soc. Remote Sens. 2004, 32 (4), 313-

316.

187. Frank, T. D.; Di Girolamo, L.; Geegan, S. Remote Sens. Environ. 2007, 107 (1), 54-64.

188. Devara, P.; Saha, S.; Raj, P. E.; Sonbawne, S.; Dani, K.; Tiwari, Y.; Maheskumar, R.

Aerosol Air Qual. Res. 2005, 5 (1), 103-114.

189. Pandithurai, G.; Devara, P.; Raj, P.; Sharma, S. Aerosol Sci. Ttech. 1997, 26 (2), 154-

162.

190. Munir, S.; Zareen, R. In Study of MODIS-derived cloud top temperature/pressure and

aerosol optical thickness, 36th COSPAR Scientific Assembly, 2006, 911.

149

191. El‐Metwally, M.; Alfaro, S.; Abdel Wahab, M.; Chatenet, B. J. Geophys. Res. Atmos.

2008, 113 (D14).

192. Hegde, P.; Pant, P.; Naja, M.; Dumka, U.; Sagar, R. Geophys. Res. Lett. 2007, 34 (23).

193. Dumka, U.; Moorthy, K. K.; Satheesh, S.; Sagar, R.; Pant, P. J. Appl. Meteor. Climatol.

2008, 47 (5), 1467-1475.

194. Bergstrom, R. W.; Pilewskie, P.; Russell, P.; Redemann, J.; Bond, T.; Quinn, P.; Sierau,

B. Atmos. Chem. Phys. 2007, 7 (23), 5937-5943.

195. Barnard, J. C.; Volkamer, R.; Kassianov, E. I. Atmos. Chem. Phys. 2008, 8 (22), 6665-

6679.

196. Marley, N. A.; Gaffney, J. S.; Tackett, M.; Sturchio, N. C.; Heraty, L.; Martinez, N.;

Hardy, K. D.; Marchany-Rivera, A.; Guilderson, T.; MacMillan, A. Atmos. Chem.

Phys. 2009, 9 (5), 1537-1549.

197. Mishchenko, M. I.; Travis, L. D.; Kahn, R. A.; West, R. A. J. Geophys. Res. Atmos.

1997, 102 (D14), 16831-16847.

198. Srivastava, A.; Tiwari, S.; Devara, P.; Bisht, D.; Srivastava, M. K.; Tripathi, S.; Goloub,

P.; Holben, B. Ann. Geophy. 2011, 789-804.

199. Bohren, C. F.; Huffman, D. R. Absorption and Scattering of Light by Small Particles

1983, 82-129.

200. Eck, T.; Holben, B.; Ward, D.; Mukelabai, M.; Dubovik, O.; Smirnov, A.; Schafer, J.;

Hsu, N.; Piketh, S.; Queface, A. J. Geophys. Res. Atmos. 2003, 108 (D13).

201. Levin, Z.; Joseph, J. H.; Mekler, Y. J. Sci. 1980, 37 (4), 882-891.

202. Arola, A.; Schuster, G.; Myhre, G.; Kazadzis, S.; Dey, S.; Tripathi, S. Atmos. Chem.

Phys. 2011, 11 (1), 215-225.

203. Verma, S.; Payra, S.; Gautam, R.; Prakash, D.; Soni, M.; Holben, B.; Bell, S. Environ.

Monit. Assess. 2013, 185 (9), 7327-7342.

150

204. Wang, Z.; Liu, D.; Wang, Z.; Wang, Y.; Khatri, P.; Zhou, J.; Takamura, T.; Shi, G. J.

Geophys. Res. Atmos. 2014, 119 (10), 6128-6139.

205. Kaskaoutis, D. G.; Kumar Kharol, S.; Sinha, P.; Singh, R. P.; Kambezidis, H.; Rani

Sharma, A.; Badarinath, K. Atmos. Chem. Phys. 2011, 11 (14), 7097-7117.

206. Kumar, K. R.; Sivakumar, V.; Reddy, R. R.; Gopal, K. R.; Adesina, A. J. Aerosol Air

Qual. Res. 2014, 14, 108-123.

207. Ma, Y.; Xin, J.; Zhang, W.; Wang, Y. Atmos. Res. 2016, 178, 187-195.

208. Dubovik, O.; King, M. D. J. Geophys. Res. 2000, 105 (D16), 20673-20696.

209. Cattrall, C.; Reagan, J.; Thome, K.; Dubovik, O. J. Geophys. Res. Atmos. 2005, 110

(D10).

210. Mielonen, T.; Arola, A.; Komppula, M.; Kukkonen, J.; Koskinen, J.; de Leeuw, G.;

Lehtinen, K. Geophys. Res. Lett. 2009, 36 (18).

211. Qin, Y.; Mitchell, R. Atmos. Chem. Phys. 2009, 9 (6), 1943-1956.

212. Levy, R. C.; Remer, L. A.; Dubovik, O. J. Geophys. Res. Atmos. 2007, 112 (D13).

213. Raut, J.-C.; Chazette, P. Atmos. Chem. Phys. 2007, 7 (11), 2797-2815.

214. Kumar, G. S.; Padmakumari, B.; Bhadram, C. J. Ind. Geophys. Union. 2012, 16 (4), 18.

215. Yu, X.; Kumar, K. R.; Lü, R.; Ma, J. Environ. Pollut. 2016, 210, 217-226.

216. Satheesh, S.; Srinivasan, J. Geophys. Res. Lett. 2002, 29 (18).

217. Meier, J.; Tegen, I.; Heinold, B.; Wolke, R. Geophys. Res. Lett. 2012, 39 (9).

218. Verma, S.; Venkataraman, C.; Boucher, O. Atmos. environ. 2011, 45 (26), 4398-4407.

219. Kazadzis, S.; Kouremeti, N.; Bais, A.; Kazantzidis, A.; Meleti, C. In Aerosol forcing

efficiency in the UVA region from spectral solar irradiance measurements at an urban

environment, Annales geophysicae: atmospheres, hydrospheres and space sciences,

2009; 2009; p 2515.

151

220. Palancar, G. G.; Olcese, L. E.; Lanzaco, B. L.; Achad, M.; López, M. L.; Toselli, B. M.

Atmos. Res. 2016, 176, 1-9.

221. Moorthy, K. K.; Nair, V. S.; Babu, S. S.; Satheesh, S. Q. J. R. Meteorol. Soc. 2009, 135

(645), 2131-2145.

222. Garcıa, O.; Dıaz, J.; Expósito, F.; Dıaz, A.; Dubovik, O.; Derimian, Y.; Dubuisson, P.;

Roger, J. Atmos. Chem. Phys. 2012, 12, 5129-5145.

quantification of aerosol properties in four locations in indo-gangetic plain...

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