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CHAPTER 4

MATERIALS AND METHODS

4.1 Description of sampling sites

Agra (27°10′N, 78°05′E, and 169 m.s.l.) is located in the north central part of India. It is the

home of world famous heritage monument Taj Mahal. Two thirds of its peripheral boundaries

(SE, W and NW) are bounded by the Thar Desert of Rajasthan and therefore is a semiarid area

characterized by loose, sandy, and calcareous soil containing an excess of salts. Agra’s climate is

tropical and strongly influenced by the aeolian dust blown from the Asian subcontinent and Thar

Desert of Rajasthan. Meteorologically the year is divisible into three distinct seasons; summer

(March–June), monsoon (July–September) and winter (October – February). Summer season is

associated with strong hot dry westerly winds and high temperature ranging between 38 - 48°C.

Relative humidity in the summer ranges between 18 and 48%. The monsoon season is hot and

humid, temperature ranges from 24 to 36°C and the relative humidity ranges from 70 to 90%,

while in winter season temperature drops ranges from 2 to 31°C and the relative humidity ranges

from 41 to 82%. The major industrial activities are ferrous and non-ferrous metal casting, rubber

processing, chrome and nickel plating units, electroplating industry, tanneries, lime oxidation,

pulverization, engineering works and chemicals. Agra is famous for Petha (famous Indian

confectionary) and shoe industries which contribute to aerosol loading through their solid waste

dumping and incineration. Apart from local sources, Mathura refinery, Firozabad glass industries

and brick kiln factories are also situated within 40 km from Agra.

PM2.5 samples were collected at traffic (National Highway II), rural (Lal Gadi) and suburban

sites (Dayalbagh Educational Institute) of Agra (Fig 4.1). On the other hand, PM10 samples were

collected at suburban site (Dayalbagh Educational Institute) in Agra. The site descriptions are as

follows:

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National Highway II (NH II): It is the busiest highway of Agra. This site is influenced by heavy

traffic flow with an average of 105 vehicles per day (Satsangi et al., 2012). It lies about 3 km

away from the industrial area (Nunhai). The sampler was mounted on the rooftop of a roadside

house (12m away from road) about 15m height above the ground.

Lal Gadi (LG): This is small village situated at the northern outskirts of Agra city. This site is

surrounded by agricultural fields with minimal traffic/industrial activity. Coal, wood, crop

residues and cowdung cakes are mainly used as fuel for cooking purpose. Agricultural activities

predominate throughout the year (Pachauri et al., 2013b). The sampler was installed on the

rooftop of a small single storey house about 12m above the ground levels.

Fig 4.1 Location of Agra indicating traffic, rural and campus sites

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Dayalbagh University campus (suburban): This campus site is about 10 km away from the

industrial sector of the city, where due to agricultural practices, vegetation predominates.

Agriculture is the major activity and cereals and pulses are grown. In summers, uncultivated

fields lie barren and dry. This site is surrounded by small residential community. The Institute

campus lies by the side of the road that carries mixed vehicular traffic on the order of 103

vehicles in a day (Satsangi et al., 2012). The sampler was placed on the roof of Science Faculty

building about 12m above the ground level.

4.2 Sample collection

PM2.5 and PM10 samples were collected using Fine Particulate Sampler (Envirotech APM 550;

Fig 4.2) operated at a constant flow rate of 16.6 Lmin-1

on pre-weighed 47 mm quartz fibre

filters (Pallflex, Tissuquartz). Sampling was done for 24 h with frequency of once a week from

April 2010 to September 2012. Day and night sampling was also done for 12 hours. Daytime

samples were collected from 7:00 am - 7:00 pm while nighttime samples were collected from

7:00 pm - 7:00 am. Before exposure, the quartz fiber filters were pre-heated in a muffle furnace

at 800°C for 3 h to remove organic impurities. Filter papers were weighed thrice before and after

sampling using four digit balance (Mettler, Toledo). Before weighing the samples were

equilibrated in desiccators at 20–30 °C and relative humidity of 20–35% in humidity controlled

room for 24 h. The conditioned and weighed PM2.5 and PM10 filters were placed in cassettes and

were placed in polyethylene zip-lock bags and taken to the field for sampling to avoid

contamination of the quartz filter on the way. Filters were handled only with tweezers coated

with Teflon tape to reduce the possibility of contamination. After weighing the samples were

wrapped in aluminum foil and sealed in polyethylene zip-lock bags and stored in deep freezer at

-4 °C until the time of analysis to prevent the degradation of organic compounds due to photo-

oxidation.

The fine particulate sampler is designed to work at a constant flow rate of 16.67 ± 0.83 L/min.

The flow rate of the sampler was calibrated before every sample through Gas Flow Meter for

“Leak Test” in order to avoid any air leakage and to check accurate flow of air to the sample.

Daily flow rate calculations (gas meter reading/timer reading) were made to make sure that the

fluctuations in flow rate are within the range. Glass fibre filter in the “wins impactor” was

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changed after 48 h of sampling or when the filter gets clogged. The filter in the “wins impactor”

was rinsed with 3–4 drops of silicon oil at regular intervals as per the need. Periodic cleaning of

the sampler was done to make the sampler dust free so that the dust on the sampler may not be

counted with the mass concentration of the sample.

Blank test was also done by using operational blanks (unexposed filters), which were processed

with field samples. The blank filters were taken once a month. They were exposed in the field

when the field-sampling box was opened to remove and replace field samples. Field blank values

were very low (0.2 ± 0.1 µg), typically below or around the method detection limits (0.28 ± 0.1

µg/m3, using 3σ values of total procedural blank concentrations of the filter).

Fig 4.2 Fine Particulate Sampler (Envirotech APM 550)

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4.3 Chemical Analysis

In the present study, the following chemical constituents of particulate matter were analyzed:

1. Carbonaceous species using Transmission OCEC Carbon Analyzer (Sunset Laboratories,

Forest Grove, USA).

2. Water soluble ionic species (WSIS; cations and anions) using Dionex ICS 1100 Ion

Chromatograph.

4.3.1 Analysis of Carbonaceous Species

PM2.5 and PM10 samples were analyzed for OC and EC with thermal–optical transmission (TOT)

method using a Carbon Analyzer (Fig 4.3) developed by Sunset Laboratory Inc., (Birch and

Cary, 1996) which is based on National Institute for Occupational Safety and Health protocol

(NIOSH, 1996). The method uses a 13-min measurement cycle where a 1.5 cm2 punch sample is

heated in a He-only flow with fixed hold times during the temperature ramp up to 850 °C to

determine OC by measuring the evolved CO2 gas.

Fig 4.3 Thermal/optical Sunset OCEC Analyzer

In this thermal-optical method, speciation of organic, carbonate, and elemental carbon is

accomplished through temperature and atmosphere control. A schematic of the instrument is

shown in Fig 4.4. An optical feature corrects for pyrolytically generated EC, or "char," which is

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formed during the analysis of some materials (e.g., cigarette smoke, pollen). He-Ne laser light

passed through the filter allows continuous monitoring of filter transmittance. Because of the

high temperatures employed during the analysis, quartz-fiber filters are required for sample

collection. Normally, a 1.54 cm2

rectangular portion (taken with a punch) of the filter deposit is

analyzed, and organic and elemental carbon are reported as µg cm-2

of deposit area. Total EC

and OC on the filter are calculated by multiplying reported values by the sample deposit area. In

this approach, a homogeneous filter deposit is assumed. A flame ionization detector (FID) is

used for quantification (as CH4) of evolved carbon, and instrument calibration is achieved

through injection of a known volume of methane into the sample oven.

Fig 4.4 Schematic of Sunset OCEC Analyzer

The analysis proceeds essentially in two stages. In the first, organic and carbonate carbon (if

present) are volatilized from the sample in a pure helium atmosphere as the temperature is

stepped to about 820°C. Evolved carbon is catalytically oxidized to CO, in a bed of granular

MnO, (held at about 900°C), reduced to CH4 in a Ni/firebrick methanator (at 450°0, and

quantified as CH4 by FID. During the second stage of the analysis, pyrolysis correction and EC

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measurement are made. The oven temperature is reduced, oxygen (10%)-helium mix is

introduced, and the oven temperature is then raised to about 860°C. As oxygen enters the oven,

pyrolytically generated EC is oxidized and a concurrent increase in filter transmittance occurs.

Correction for the char contribution to EC is accomplished by measuring the amount of char

oxidation required to return the filter to its initial transmittance value. The point at which the

filter transmittance reaches its initial value (vertical solid line) is defined as the "split" between

organic and elemental carbon. Carbon evolved prior to the split is considered "organic"

(including carbonate), and carbon volatilized after the split and prior to the peak used for

instrument calibration (final peak) is considered "elemental." If desired, the presence of

carbonate can be verified through analysis of a second portion (punch) of the filter after its

exposure to HCl vapor. In the second analysis, the absence of the suspect peak (typically the

fourth peak) is indicative of carbonate in the original sample. Table 4.1 shows the different

temperature and purge gas conditions during the analysis of the sample.

Table 4.1 Temperature and purge gas conditions for the analysis of EC and OC using

thermal/optical EC-OC Analyzer

An example of the instrument output, called a "thermogram," is shown in Fig 4.5. The three

traces appearing in the thermogram correspond to temperature, filter transmittance, and detector

response (FID). Thermal-optical analysis defines OC as optically transparent carbon removed

STEP GAS HOLD TIME (s) TEMP ( ºC)

1 He 60 310

2 He 60 475

3 He 60 615

4 He 90 870

5 He 50 Oven off

6 He/O2 45 550

7 He/O2 45 625

8 He/O2 45 700

9 He/O2 45 775

10 He/O2 45 850

11 He/O2 120 870

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(through thermal desorption or pyrolysis) and char deposited when heating a filter sample to a

preset maximum (850 °C or 900°C) in a non-oxidizing (helium) carrier gas while EC is defined

as carbon (e.g., in soot particle cores) that can only be removed from the filter under an oxidizing

carrier gas (He/O2). Optically absorbing carbon removed at high temperatures (e.g. 850°C) in a

non-oxidizing carrier gas when internal (sample matrix) oxidants are present.

Fig 4.5 Thermogram showing OC and EC

The carbon removed to bring the transmittance back to its pre-pyrolysis level is considered to be

equal to the pyrolytically-generated EC. Thus, all carbon evolved before this point is reported as

OC, and after is EC. The pyrolysis correction assumes either the original and pyrolytically

generated EC have the same absorptivity or pyrolytically generated EC evolves first. While

neither assumption is likely to be completely true, the error introduced is likely to be small

relative to the size of the pyrolysis correction. High purity gases and zero grade air (Matheson

Gas Products, Montgomeryville, PA) were used. Fig 4.6 show the thermogram of a sample as

obtained from the instrument.

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Fig 4.6 Thermogram of a sample obtained from Sunset OCEC Analyzer

4.3.1.1 Quality Control Measures for Carbonaceous analysis

For quality control, the OCEC analyzer was calibrated using a blank punch of pre-heated Quartz

Fiber Filter and standard sucrose solutions every day. Analytical uncertainties were estimated by

calibrating the analyzer with a fresh working standard solution of sucrose (3.2 µg µl-1

) before

every analysis. A solution of 10 µl gives 32.0 ± 0.2 µg OC, with no measurable signal for EC.

On duplicate analysis, the variation in measurements was found to be less than 5%. A series of

standard sucrose solution of different concentrations (1.6, 3.2, 4.8, 6.4 and 8.0 µg µl-1

) were also

prepared, analyzed and the concentrations obtained were plotted for regression analysis. A good

correlation coefficient (R) of 0.96 was obtained between the known and observed concentrations

of standard sucrose solution. Fig 4.7 shows the thermogram of a standard sucrose solution.

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Sampled quartz filters were also analyzed similarly for blank corrections. The overall blank

concentrations from the quartz filters for OC and EC were 0.5 ± 0.2 and 0.0±0.02 µg cm-2

,

respectively. These were subtracted from the measured OC and EC concentrations in the aerosol

samples. Field blanks for particulate samples were collected by mounting the filter paper in the

sampler and putting the system on just for one minute. The concentrations of OC and EC for

field blanks were found to be below the detection limits. The detection limit for OC was

calculated as three times the standard deviation of the blank concentration, whereas the detection

limit for EC was assumed to be equal to the minimum signal (0.2 μg m−3

) measurable on the

instrument.

The term ‘Precision’ is used to describe the reproducibility of results. Precision is the degree of

refinement in the performance of an operation, or the degree of perfection in the instruments and

methods used to obtain a result. It relates to the quality of an operation by which a result is

obtained. To calculate the precision a standard of solution of sucrose (3.2 µg µl-1

) was run for

nine times and the precision reported as deviation from the mean in terms of percentage.

Calibration accuracy was continuously monitored by analyses of calibration verification

standards; the uncertainty was at maximum 5%. The term ‘Accuracy’ denotes the nearness of a

measurement to its accepted value and is expressed in terms of error (%). Accuracy is the degree

of conformity with a standard (the "truth"). It relates to the quality of a result, and is

distinguished from precision, which relates to the quality of the operation by which the result is

obtained. The accuracy was calculated by the difference between observed value Xo and the

accepted value Xa.

A = Xo – Xa

In this expression the accepted value may itself be subjected to considerable uncertainty, so the

more realistic term is relative error, which is error in terms of percentage. The accuracy has been

calculated in terms of relative errors (%). Detection limits, precision and accuracy of OC and EC

are presented in Table 4.2.

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Fig 4.7 Thermogram of Sucrose Standard

Table 4.2 Method Detection Limit, precision, accuracy and number of field blanks below

detection limits of carbonaceous species

Species

Method

Detection

Limit

Precision

(%)

Accuracy

(%)

No. of Field Blanks Below

Detection Limit (n = 8)

OC (µg/cm2) 0.2 1.7 1.0 6

EC (µg/cm2) 0.01 1.5 1.0 8

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4.3.2 Analysis of Water Soluble Ionic Species (WSIS)

4.3.2.1 Extraction of Samples

Prior to the analysis of water soluble ionic species, each filter was extracted by ultrasonic

agitation in 100 mL deionized water (Conductivity=18 Mmho cm-1

) for 45 minutes and then

filtered through Whatman 41 filter paper into two pre-washed polyethylene bottles. Filtrate was

divided into two parts; one part was treated with chloroform and refrigerated for anionic analysis

while the other part was treated with dil. HNO3 and preserved for cation analysis.

4.3.2.2 Instrumentation (Ion Chromatograph Dionex ICS 1100)

The concentrations of major anions (F-, Cl

-, NO3

- and SO4

2-) and cations (Na

+, NH4

+, K

+, Mg

2+

and Ca2+

) were analyzed by Ion Chromatography using Dionex (Sunnyvale, CA, USA) ICS 1100

Ion Chromatograph (Fig 4.8) with Dionex Chromeleon chromatography software. The cationic

concentrations were analyzed by the system equipped with guard column (CG12A), analytical

column (CS12A) and cation self-regenerating suppressor (CSRS 300 4mm) using 20mM

Methane Sulfonic Acid as an eluent while the major anions were separated by guard column

(AS11A), analytical column (AS11) and anion self-regenerating suppressor (ASRS- ULTRA

4mm) using 6 mM 50% NaOH as an eluent.

Fig 4.8 Ion Chromatograph Dionex ICS 1100

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Ion Exchange Chromatography relies on charge-charge interactions between the ions in the

sample and the charges immobilized on the resin of your choice. Ion exchange chromatography

can be subdivided into cation exchange chromatography, in which positively charged ions bind

to a negatively charged resin; and anion exchange chromatography, in which the binding ions are

negative, and the immobilized functional group is positive. Once the solutes are bound, the

column is washed to equilibrate it in the starting buffer, which should be of low ionic strength;

then the bound molecules are eluted off using a gradient of a second buffer which steadily

increases the ionic strength of the eluent solution. Alternatively, the pH of the eluent buffer can

be modified as to give the ions or the matrix a charge at which they will not interact and the ions

of interest elute from the resin.

As the eluent flows through the column, based on the selectivity and retention time each ion gets

separated. Each peak represents a separate ion from the sample solution. The elution time, or

time it takes for the ion to move through the column, varies for each ion species as they elute

from the column separately as the pH and/or ionic strength of the eluent is increased. The

concentration of ions moving through the column at a particular time is represented by the height

and the breadth of the peaks and can be correlated to the concentration of a particular species in

the sample solution. Ion concentrations can be calculated using the area under each peak, where

a larger area correlates with a higher concentration of a particular ion species. Most ion

chromatography machines provide software that calculates this area, which users can convert to

ppm or other quantity using calibration standard solutions. Schematic of Dionex Ion

Chromatograph has been represented in Fig 4.9. The Ion Chromatograph (Dionex ICS 1100)

used in the present study is provided with a software, Chromeleon 6.0, which directly converts

peak height to ppm concentrations that can be recorded through the software.

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Fig 4.9 Schematic of Dionex Ion Chromatograph

4.3.2.3 Quality Control Measures for WSIS analysis

For quality control, unloaded filter paper was extracted as described above and analyzed for blank

corrections and subtracted from the measured ionic concentrations in the aerosol samples.

4.3.2.3.1 Regression Analysis

The method of determining water soluble cationic and anionic species by Dionex Ion

Chromatograph was standardized by running a series of cationic (Na+, NH4

+, K

+, Mg

2+ and Ca

2+)

and anionic (F-, Cl

-, NO3

- and SO4

2-) standards in order to quantify the resulting peak from the ion

chromatography. Calibration curves between peak areas obtained and concentration of a series of

standards in the range 0.25 to 10 ppm were constructed through regression analysis. Regression

analysis yielded a mathematical relationship between peak area (Y) and concentration (X)

according to the regression model Y = mX + c, where c is the intercept and m is the slope of the

line. The calibration curves constructed were linear over the range of interest with good R2 values

ranging from 0.97 to 0.99. Fig 4.10 shows the standard anion and cation chromatograph using

Dionex ion chromatograph.

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Anion Chromatograph

Cation Chromatograph

Fig 4.10 Standard Ion Chromatographs obtained from Dionex ICS 1100

102

4.3.2.3.2 Uncertainty

Analytical uncertainties were estimated by calibrating the Dionex ICS 1100 Ion chromatograph

with a fresh working standard solution of 1 ppm, prepared from a 1000 ppm stock solution. The

variation in peak area was found to be less than 5%. The detection limit for every ion was

calculated as three times the standard deviation of the blank.

To calculate the precision a standard of 1 ppm was run for nine times and the precision reported

as deviation from the mean in terms of percentage while the accuracy has been calculated in

terms of relative errors (%). Detection limits, precision and accuracy for various cationic and

anionic species are presented in Table 4.3.

Table 4.3 Method Detection Limit, precision and accuracy of different ionic species

Na NH4 K Mg Ca F Cl NO3 SO4

Detection Limit

(µg m-3

)

0.1 0.1 0.2 0.1 0.2 0.01 0.20 0.30 0.20

Precision (%) 2.0 4.7 0.7 2.9 3.8 0.4 5.6 1.1 5.2

Accuracy (%) 4.6 1.4 1.6 4.5 3.8 0.8 0.3 0.7 2.1

4.4 Statistical Analysis

All statistical analysis was performed with SPSS 16.0 software package.

4.4.1 Correlation Analysis

The concept of ‘correlation’ is a statistical tool which studies the relationship between two

variables. Correlation Analysis involves various methods and techniques used for studying and

measuring the extent of the relationship between the two variables. Two variables are said to be

in correlation if the change in one of the variables results in a change in the other variable.

Correlation between two variables is said to be negative or inverse if the variables deviate in

opposite direction. That is, if the increase in the variables deviate in opposite direction. That is, if

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increase (or decrease) in the values of one variable results on an average, in corresponding

decrease (or increase) in the values of other variable.

The Coefficient of Correlation

One of the most widely used statistics is the coefficient of correlation ‘r’ which measures the

degree of association between the two values of related variables given in the data set. It takes

values from + 1 to – 1. If two sets or data have r = +1, they are said to be perfectly correlated

positively if r = -1 they are said to be perfectly correlated negatively; and if r = 0 they are

uncorrelated.

The square of the r value, known as the coefficient of determination or r2, describes the

proportion of change in the dependent variable Y which is said to be explained by a change in

the independent variable X. If two variables have an r value of 0.40, for example, the coefficient

of determination is 0.16 and it may be stated that only 16% of the change in Y can be explained

by a change in X. The larger the correlation coefficient, the larger the coefficient of

determination, and the more influence changes in the independent variable have on the

dependent variable.

4.4.2 Independent samples t Test

t-test compares sample means by calculating Student’s t and displays the two-tailed probability

of the difference between the means. Statistics are available for one-sample (tested against a

specified value), independent samples (different groups of cases), or paired samples (different

variables).

A one-sample t-test helps determine whether (the population mean) is equal to a hypothesized

value (the test mean). The test uses the standard deviation of the sample to estimate (the

population standard deviation). If the difference between the sample mean and the test mean is

large relative to the variability of the sample mean, then is unlikely to be equal to the test mean.

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4.5 Meteorological Parameters

Meteorological data such as ambient temperature, rainfall, relative humidity, wind speed and

wind direction were recorded through an automatic weather monitoring system (Envirotech’s

Wind Monitor WM271; Fig 4.11) mounted on the roof 8m above the ground level at the

sampling site. The data logger acquires data from the sensors for air temperature, solar radiation,

rainfall, relative humidity, wind speed and wind direction and store the data in its memory for

later retrieval. It was programmed to collect data at 1 min. interval and store them in memory to

be downloaded to a computer. The wind speed varied in a narrow range of 0.4 to 12 m/s. The

winter months showed very clam weather conditions (81.4 % calm) while during summer period

the wind speed was very high reaching to its maximum value (10 – 12 m/s) especially during

dust storms. The temperature varied from 3 to 48.7°C whereas relative humidity varied between

14.4 to 91.4%. The weather was found to be cold and calm during winter months especially

during fog/haze events with lowest temperature (2°C), relative humidity (83.4%) and wind speed

(0.45 m/s) while during summer period the highest temperature was recorded as 48.7°C with

lowest relative humidity (14.4%). Fig 4.12 shows the average temperature, solar radiation,

relative humidity and wind speed observed during the study period and Fig 4.13 shows the

prevailing wind direction at the sampling site.

Fig 4.11 Envirotech WM 271 Wind Monitor

105

Fig 4.12 Averaged monthly variation of meteorological parameters during the sampling

period

106

Summer Winter

Monsoon

Fig 4.13 Wind rose plots for summer, winter and monsoon seasons

4.6 Air Mass Backward Trajectory Analysis

In order to identify the source and transport pathways of the airborne particles arriving at the

sampling site, the air mass backward trajectory analysis was carried out. These air mass back-

trajectories were obtained from the final run data archive of Global Data Assimilation System

model using NOAA (National Oceanic and Atmospheric Administration) Air Resource

107

Laboratory (ARL) Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) Model

(http://www.arl.noaa.gov/ready/hysplit4.html (accessed via NOAA ARL Realtime

Environmental Applications and Display sYstem (READY) website http://ready.arl.noaa.gov).

The five-day back trajectory analysis of winter months was simulated at 12:00 hrs (local time) at

500, 1000 and 1500 m above the ground level and has been represented in Figure 4.14.

Fig 4.14 Five-day air-mass back trajectories during calculated at 500, 1000 and 1500 m

4.7 SEM- EDX Analysis

PM2.5 samples collected from different sites were analyzed by Scanning electron microscopy

coupled to energy- dispersive X- ray spectroscopy (SEM/EDX) at National Institute of

Oceanography, Goa. The SEM – EDX analysis was carried out with the help of computer

controlled field emission scanning electron microscope SEM (JSM – 5800 LV) equipped with an

energy dispersive X – ray system (Oxford 6841). The dry and loaded quartz fiber filters were

punched in 1 mm2 from the centre of each sample. All the samples were mounted on plastic stubs

for gold coating. A very thin film of gold (Au) was deposited on the surface of each sample

108

using vacuum coating unit called Gold Sputter Coater (SPI – MODULE) which can prepare 6

samples at a time. The fine coating of gold makes the samples electrically conductive. The

samples were placed in the corner of SEM – EDX chamber. The working conditions were set at

an accelerating voltage of 20 kV, a beam current of 40 – 50 µA and a Si (Li) detector 10mm

away from the samples to be analyzed. X- Ray detection limit is ~0.1%. The Oxford ISIS EDS

system with 133 eV resolutions is capable of collecting spectrum from multiple points, lines

across the interface and elemental mapping.

Fig 4.15 Scanning electron microscopy coupled to energy- dispersive X- ray spectroscope

EDX analysis was carried out at each analysis point and the elements present were both

qualitatively and quantitatively measured. Approximately 100 particles were analyzed on each

filter. The EDX spectra of blank Quartz fiber filter was also obtained and their composition was

manually subtracted during the evaluation of the EDX spectra of individual aerosol particles.