air quality prediction in study areashodhganga.inflibnet.ac.in/bitstream/10603/6306/11/11_chapter...

Air quality prediction in study area

Korba, district in the state of Chattisgarh, which is in the electricity deficit western

grid, and is linked to the southern and northern grids, has been chosen as an

illustration to estimate the health impact due to air pollution caused by coal based

power generation.

In order to assess the health impact, it is necessary to know the pollutant

concentration distribution over the study domain. However, due to financial and

technical constraints, it is not possible to monitor the ambient concentration at many

locations. Currently, Chattisgarh Pollution Control Board is monitoring air quality at

two locations in Korba. Therefore, air quality modelling is used as a tool to assess the

concentration distribution over the entire study domain. Also, the ambient

concentration at a particular receptor is impacted by emissions from various sources.

And, in order to quantify the impact of each source a source contribution analysis is

required. For this, dispersion models are well suited for estimating quantitative

source relationship, because the effects of individual emission sources or source

regions on predicted ambient concentration could be studied (Blanchard, 1999).

Accordingly, air quality modelling has been carried out for the study area using a

dispersion model.

4.1 Description of study area1 Korba is situated in central India in the northern half of Chattisgarh state (Figure 4.1).

Korba was assigned the district status on 25th May 1998 (Dhagamwar et al., 2003).

The district has an area of 7145 sq km. About 40% of the area is covered with forest.

The population of Korba is 10 11 823 in which 6 44 860 (63.7%) is rural and 3 66 963

(36.3%) is urban. The average population density is 153 per sq km. Tribal population

constitutes 51.67% of the total population. The main occupation of the people is

agriculture. The district is divided into four tehsils (Korba, Katghora, Kartala and Pali)

and five development blocks (Korba, Kartala, Katghora, Pali, Podi Uproda). Koriya

and Sarguja districts surround it in the north; Janjgir- Champa district in the south

and Bilaspur borders it on the western side.

1 Information accessed from official website of Korba district http://korba.nic.in and

http://web.archive.org/web/20070816063955/powercitykorba.com as on 17 January 2008

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TERI University-Ph.D. Thesis, 2011

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4.1.1 Geographic location and topography Korba district is located between the latitude of 22°01' to 23°01' north and longitude

of 82°08' to 83°09' east at the height of 304.8m from the sea level. The main source

of water in the district is the River Hasdeo a tributary of Mahanadi.

The industries are located in Korba city which has an area of 215sq km. Korba city has

an average elevation of 252 m. The terrain is gently undulating around Korba which

becomes medium to highly undulating towards north. The elevation of the terrain is

275m to 335m above mean sea level (MSL) in gently undulating area whereas in

northeastern hilly region it is 918m above MSL (NTPC, 2005 and CSEB, 2004).

However, the rise of elevation is gradual.

4.1.2 Climate of Korba

The climatic conditions are represented on the basis of the prolonged meteorological

data available from India Meteorological Department’s weather station operating at

Champa. This station is approximately 50 km from Korba city. The data is available

from 1975-1984. The climate of Korba district is hot and dry as it falls in the hot

temperature climate zone. 2 The region receives good rainfall from the South-West

Monsoon. The rainy season is from mid June till September end, winter is from

December to February and summer is from April to mid June. Based on the data, the

average rainfall in the district is 1118 mm. More than 75 % of the rainfall is received

during monsoon season. The maximum number of rainy days is observed in the

month of August. On average there are about 65 rainy days (i.e. days with rainfall of

2.5mm or more). During pre monsoon light clouds are observed during in the

evenings (NTPC, 2005). The skies are clear except in monsoon.

The predominant wind direction in all the seasons is northerly except in pre monsoon

and monsoon. During pre monsoon stronger westerly winds are observed whereas in

winters wind direction is mainly northerly during morning hours. In southwest

monsoon wind directions are mainly westerly and southwesterly. Generally light

winds prevail throughout the year. Winds are stronger during pre monsoon season

and monsoon. Summer season is characterised by extreme heat and hot dust storms

blows all over the region. The humidity level drops to less than 20% during the

summer, and this is the driest period of the year. However during monsoon humidity

reaches up to 98%. During January and February, sometimes-dense fog covers the

area.

2 http://korbacity.com/korba/korba.htm#



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4.1.3 Air Quality Status in Korba

The CPCB has designated Korba city as critically polluted area. The daily average

concentration of SPM and RSPM for the month of Feb –May 2004 at the two

monitoring station operated by the pollution control board are shown in Figure 4.2

and 4.3. At HIG 21, 22 and Pragati Nagar monitoring stations, the 24-hourly

concentration of RSPM is above the prescribed National Ambient Air Quality

Standards (NAAQS)3. The SPM concentration at HIG 21, 22 exceeds the standard

value on many days while that at Pragati Nagar is always within the standard value.

4.2 Air quality Modelling “Air pollution dispersion models are mathematical formulations of the fundamental

processes of emissions, transport, diffusion, chemical reaction and deposition of

pollutants. They predict ambient pollutant concentrations as a function of time,

location and emissions levels” (Blanchard, 1999). They are based on the principles of

conservation of mass, heat, motion, water and chemical species (Pielke, 1984 cited in

Blanchard, 1999).

4.2.1 Spatial extent of modelling

SCREEN 3 model has been used to decide the spatial extent of modelling for the

present study. Screen3 is a screening model developed by USEPA (1995). It gives the

preliminary and conservative estimates by examining a full range of meteorological

conditions, including all stability classes and wind speeds to find maximum

concentration. It can account for only single emission source. Therefore, NTPC plant

that has the maximum capacity and the highest stack is taken as a source to define

the extent. The result showed that maximum concentration of SPM (79 µgm-3) under

stability A (very unstable atmospheric conditions) was observed at a distance of 1.3

km from the source and concentration levels of above 10µgm-3 were observed till a

distance of 30 Km under stability C (slightly unstable atmospheric conditions)

around the NTPC Plant. Screen estimates maximum hourly average concentration.

To estimate the daily average concentrations from hourly average, it has been

recommended to multiply with a factor of 0.4. (USEPA, 1995). Therefore, a

maximum hourly concentration of 10µg m-3 is equal to daily average concentration of

4 µg m-3 of SPM. Assuming 90% in this as PM10 and 50% of PM10 as PM4

2.5, the

3 RSPM standard is based on revised National Ambient Air quality Standards (NAAQS) according to MoEF notification dated November 16th 2009 while the SPM standards corresponds to the earlier NAAQS. 4 It was observed that the PM10/SPM =0.9 approx at monitoring stations in the study area. Whereas, PM2.5 was not being monitored in the region therefore PM 2.5 has been assumed that 50% of PM10. This value is reported for Delhi (CPCB,2002) and has been recommended for developing nation in case local PM2.5 values are not available (WHO, 2005 cited in Beig et al.2010 )



45

concentration of PM 2.5 would be 1.8 µg m-3 and in context of annual average it will be

even lesser than this. . The annual average air quality guideline according to WHO for

PM2.5 is 10µg m-3. Therefore, broadly an area corresponding 35 km around the NTPC

plant was chosen as the study domain. This will comprise an area of 68km×68km.

The study area is located in the catchments area of Hasdeo River. The study area is

mostly plain with elevation ranging from 250-300m above MSL. It comprises of

Korba city in the centre and surrounding villages. The study area is shown in Figure

4.4a.

N

CHHATTISGARH

INDIA

KORBA

Figure 4.1 Map showing district Korba and its location in India



46

HIG 21, 22

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Figure 4.2 SPM and RSPM concentration (in µg m-3) at HIG 21, 22 national ambient air

quality station



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Figure 4.3 SPM and RSPM concentration (in µg m-3) at Pragati Nagar national

ambient air quality station

Pragati Nagar

0

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Pragati Nagar

020406080

100120140160180

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4.2.2 Temporal extent of modelling

Based on the limited availability of meteorological data, modelling has been

performed for the months of February, March, April and May 2004. These cover the

months of both winter and summer season. Hence in this study, the worst-case

scenario could be captured.

4.2.3 Pollutant to be modelled

Particulate matter is selected as the pollutant of concern to perform air quality

modelling. This is because particulate matter has shown the strongest association

with morbidity and mortality (Pour& Anderstani, 2007, Wang & Mauzerall,2006).

Particulate matter also happens to be the main pollutant emitted by power plants

because of high ash content in coal. Since SPM is monitored by all power plants and

the industries in the region. SPM is chosen as indicator of particulate pollution to

perform air quality modelling rather PM10.

The impacts of SO2 is not considered in this study because ambient concentration of

SO2 in the area is very low. As per data collected from Korba Pollution Control Board

daily average concentrations of SO2 at Korba were 13-14 µg m-3 and the

corresponding WHO ambient air quality standard for SO2 is 20µg m-3. Therefore, the

level of SO2 is well within limits. Moreover, pollutants are correlated with each other

therefore inclusion of SO2 can overestimate the health damage (Wang & Mauzerall,

2006)

4.2.4 Selection of appropriate model

Air quality models can be categorized as gaussian, numerical and empirical,

according to the emissions modeling procedure they implement. By far, Gaussian

models are most widely used because they are computationally efficient and can

simulate the transport of non-reactive primary or linear secondary species. However,

for some applications such as complex meteorological or topographical conditions, it

may be appropriate to use a model that applies numerical or empirical modeling

procedures.

The principal issues to choose a model are: the complexity of dispersion (e.g. terrain

and meteorology effects) and the scale of potential effects, including the sensitivity of

receiving environment (NIWAR, 2004). Gaussian plume model can produce reliable

results in medium complex atmospheric and topographical conditions with relatively

simple effects. However, for more complex atmospheric and topographical conditions,

significant changes in meteorological conditions can occur over short distances. In

such cases, advanced puff and particle models and meteorological modeling may be

required to maintain a similar degree of accuracy. EPA recommends the use of

CALPUFF, a Gaussian puff model, for long-range (> 100km) and/or non-steady-state



49

conditions. But the major drawback with the advanced models is that they are

resource intensive and requires more detail topographical and meteorological data,

which is not easily available in developing country context.

Gaussian models treat pollutant emissions as dispersing from a source as a plume,

traveling downwind, and spreading horizontally and vertically. With a Gaussian

distribution, pollutants are more concentrated near the centerline and less toward

the edges; this is also referred to as a “normal” distribution. The most frequently used

models in Gaussian plume category are AERMOD and ISCST3. They have undergone

extensive peer review followed by field testing (Bowers et al., 1979 a, b and Perry,

Cimorelli et al., 1994). AERMOD and ISCST3 are capable of modeling all source types,

under nearly all atmospheric and terrain conditions. ISCST3 and AERMOD are

appropriate for following applications.

1. Industrial source complexes

2. Rural or urban areas

3. Flat or rolling terrain

4. Transport distances less than 50 kilometers

5. 1 hour to annual averaging times

6. Continuous toxic air emissions.

AERMOD uses boundary –layer similarity theory to define turbulence and dispersion

coefficient as continuum, rather than discrete set of stability classes like ISCST3.

Variation of turbulence with height allows a better treatment of dispersion from

different release height. Therefore, it can predict the high concentration that can be

observed close to a stack under convective conditions when dispersion coefficients for

unstable conditions are non Gaussian. Hence, its prediction is nearly unbiased for

complex terrains. The present case study area of Korba is having medium complex atmospheric and

topographic conditions and the modeling domain is about 35km. Moreover, the

pollutant to be modeled is Suspended Particulate Matter (SPM). Therefore, a

Gaussian plume model is suitable for this study. Further, to choose between ISCST3

and AERMOD, ISCST3 has been finally chosen to perform air quality modeling, as

the data required for AERMOD modeling is not available (i.e. meteorology data of

upper air station). The justification for the choice of ISCST3 model is provided below:

1) It is a multiple source dispersion model i.e. it can model emissions from point,

line and area sources.

2) It is recommended for Industrial complexes. Korba too is an Industrial

complex.



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3) ISC3 is a relatively sophisticated regulatory model. It incorporates features of

previously developed models, including the Fugitive Dust Model (FDM).

4) It is evaluated for Indian condition as well (Bhanarkar et al., 2005, Manju et.

al., 2002, Goyal and Rao 2007, Goyal et al., 2006, Ramakrishna et al., 2004)

5) Modelling domain is less than 50 Km as a radius around the source

6) Unlike AERMOD it is less resource intensive.

7) It is possible to use ISC3 to approximate a line source using elongated area or

volume sources in close proximity to one another

4.2.5 Inputs required for ISCST3

The ISCST3 requires input of receptor locations, location of source of emissions,

emission from each source (in g s-1 for point source and in g s-1 m-2 for area sources),

emission source characteristics (stack height, top inner diameter, exit stack velocity

and temperature for point sources and dimensions of area in context of area sources)

and hourly meteorological data (wind direction, wind speed category, average

temperature, stability, mixing height) and surface roughness/ terrain characteristics

(Rural /urban ). The equations used in ISCST3 model have been specified in

Annexure II.

4.2.6 Data Description 4.2.6.1 Meteorological data

Meteorological data is an important input for air quality modelling as meteorology of

the area is responsible for dispersion of the pollutant. Meteorology data has been

collected from BALCO industry that conducts continuous monitoring of met data.

However, many times the instruments were not in working condition. Therefore, data

was available for February 2004 (later half), March 2004, April 2004 and May 2004.

Hence, modelling has been performed for these four months. These months covers

months of winter and summer. The hourly meteorological inputs required by the

model are wind speed, wind direction, stability and mixing height.

Wind Speed and wind direction The hourly meteorological data of wind speed and wind direction was sourced from

BALCO for the above mentioned months. Overall, only a few values were missing.

These values were, however, substituted as per the method suggested by Atkinson

and Lee (1992). The frequency distribution of wind speed and direction is presented

in the form of wind rose (Figure 4.5). In month of February and March, wind

direction is predominantly from North and North-North West (60-64%) with

maximum wind speed of 3.6 ms-1. 38-40% 0f the hours wind speed was less than 2.1

ms-1 in the winter months. The summer months of April and May 2004 show a wider

spread in terms of wind direction. The westerly winds start blowing in this season.



51

The prominent wind direction is from West North West. However, maximum wind

speed reaches up to 5.7 ms-1, though 50-58% 0f the hours times wind speed was less

than 2.1 ms-1. Also the calm conditions were more prevalent during February (16 days)

and March with approximately 55% of the time, it as calm.

Mixing Height A CPCB publication provides the mixing height contours for different months for the

entire country (CPCB, 2002). Based on this document, mixing height values have

been estimated over study domain for winter and summer months.

Atmospheric stability Pasquil (1961) categorises the extent of atmospheric turbulence in terms of stability.

The six stability classes named A, B, C, D, E and F have been used to categorise the

atmospheric turbunce. A being the most unstable or most turbulent class, and class F

the most stable or least turbulent class. Different methods are available in literature

to estimate the atmospheric stability i.e. Pasquil Gifford method, Vertical

temperature gradient (DT/DZ), Sigma theta method and modified sigma method

(MST) (Mitchell, 1982) . Any of these methods could be used to estimate the stability.

However, the most common method to estimate the stability is Pasquill Gifford

stability classification method. This method incorporates both mechanical and

buoyant turbulence. This method requires hourly wind speed, solar insolation at

daytime, and cloud cover at nighttime to know the hourly stability of the atmosphere.

In the present study, daytime, stability has been estimated using the Pasquill Gifford

method. In this method, the insolation in watt m-2 or Langley hr-1 is required to be

categorised as strong, week and moderate. This categorisation is done using

classification provided by Ludwig and Dabbert (1972) cited in Ludwig and Dabbert

(1976).

However, for the night time, considering the unavailability of cloud cover data

required to determine the stability as per Pasquill Gifford method, the stability has

been estimated using the MST. The MST yields reasonable dispersion estimates

compared to those produced using methods of vertical temperature gradient and

Pasquill-Turner (Mitchell, 1982). This method requires hourly horizontal fluctuation

in wind direction along with wind speed to determine the stability of atmosphere.

Further, irrespective of any sky condition during the transition from day to night or

from night to day, stability is taken as “D” as suggested by Pasquill (Turner, 1994).



52

Figure 4.4a Map showing Korba district and the spatial extent of the area considered for this study.

KORBA DISTRICT STUDY DOMAIN



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Figure 4.4b: Enlarged view showing details of pollution sources



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4.2.6.2 Emission Inventory Emission inventory comprises of the description and listing of air pollutant emission

sources, including pollutant emission quantification and their spatial distribution.

Emission inventory is a critical input to the modelling process because

concentrations are directly proportional to the emissions. In this study, monthly

average emissions have been estimated for the months of Feb (15 days of Feb) to May

2004. This section describes the methodology used to estimate the emissions from

different emission sources in Korba.

The main polluting sources in the Korba regions are:

1. Chattisgarh State Electricity Board Power plant-East (CSEB E)

2. Chattisgarh State Electricity Board Power plant-West (CSEB W)

3. NTPC power Plant (NTPC)

4. Coal mining activity

5. Ash Ponds

6. Bharat Aluminium Corporation Limited (BALCO) plant

7. BALCO Captive Power Plant (BCPP)

8. Domestic Fuel Burning

The location of the pollution sources in study domain is presented in Figure 4.4b. All

the point sources are within a distance of 10km in the centre of the study domain.

The mining area is in south of study domain.

Point Source Emissions The point sources in korba include stacks of the industries and power plants. The

emissions data of industries has been collected from the District Collectorate, the

Regional Pollution Control Board and from the industries. The emission data (in mg

Nm-3) is monitored by the Pollution Control Board and industries.

Considering the reliability of results, the emissions data available from the Pollution

Control Board or district collectorate has been utilised for this study. In case, data for

a particular industry for any month is not available, then the industry specified

emissions are used.

Besides the emissions, information about stacks characteristics is also required as an

input to ISCST3 model. Details of point source are provided in Table 4.1.



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Mining Korba has 10 568 ha of leasehold area in opencast coal mining. Cowherd (1982) and

USEPA (1995) have derived empirical formulae to determine the emission rates from

western surface coal mining activities but these are not applicable for Indian

conditions due to different site practices, geo mining conditions and micro

metrological conditions. Therefore, empirical formula developed for Indian mines by

Chakraborty et al. (2002) have been used. These formulae estimate emissions from

various opencast mining activities as well as for the overall mine. They have also been

validated by comparing against the field observations. The formula developed to

estimate SPM emissions from the whole mine area is given below.

bbpauE 3.04/01.07.92.04.0 E =emission rate gs-1

u = wind speed (ms-1)

a =area (Km2)

b =OB handling (Mm3yr-1)

p = Coal/mineral production (MtYr-1)

The data required to estimate the emissions from overall mine has been collected

from district collect orate except the meteorological data, which is collected from the

BALCO industry. The details of the mining area and estimated emissions from these

areas are provided in Table 4.2. It provides the information of opencast mines

existing in the study domain. For each mine, SPM emissions are estimated using the

above formula. These are subsequently distributed in various grids in proportion to

the ration of the mining area in that grid.

Vehicular emission inventory In this study, vehicular emissions have been estimated considering the road network

lying in the study domain. There are three kinds of paved roads in the Korba: village

road, district roads and state highway. The length of these roads in Korba district is

351 km, 41 km and 535 km respectively. There can be two sources of emissions on

paved roads.

1) Tail pipe emissions

2) Road dust resuspension



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Emission inventory from tail pipe Goyal and Ramakrishna (1998) discussed four different methodologies of emission

estimation from vehicular sources. In this study, based on data availability, the

method used is as follows:

The emission rate of air pollutants on a reasonably straight highway from continuous

line source, i.e. q (g s-1) can be determined as the product of the emission factor and

traffic density

RLEDq iijji ,,

ijD = traffic density of i type of vehicle on j type of roads (Vehicle h-1)

iE = Emission factor (g vehicle-1Km-1)

jiq , = source strength per unit length (g s-1)

RL =Road length in each grid

Density of the vehicles in each grid is multiplied with the road length in that

particular grid. This gives the vehicle kilometrer travelled (VKT) in each grid, which

is then multiplied by the emission factor to get the emissions in that grid due to

different types of vehicles.

Traffic count data of three kinds of road has been collected from the Executive

Engineer Public Works Department (PWD) Korba. PWD Korba carried out traffic

count surveys for 7 consecutive days to estimate the total 24 hr average vehicle count

of different categories of vehicle. The survey was carried out in May 2004 for the

village roads, in December 2004 for district roads and for state highway in July 2000.

The emission factors for different type of vehicles have been taken from CPCB (2000).

Details of input to vehicular sources inventory is provided in Table 4.3.

Road dust resuspension The grid wise emissions from the paved roads have been calculated using the

following equation

extijji ERLDq ,,

ijD = traffic density of i type of vehicle on j type of roads (Vehicle h-1)

extE = Emission factor (g vehicle-1km-1)

jiq , = source strength per unit length in a grid (g s-1)

RL =Road length in each grid



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The emission factor extE due to road dust resuspension varies with “silt loadings” as

well as the average weight of vehicles travelling on the road. Emission factors to

estimate the dust emissions from paved roads have been based on the equation

derived by USEPA (2006).

NPCWsLkE ext 4

132

5.165.0

k = particle size multiplier for particle size range and unit of interest

(For TSP=24 g/VKT)

sL = road surface silt loading (gm-2)

extE = annual or other long term average emission factor in the same unit as k

W = Average weight (tons) of vehicle

C = emission factor for 1980’s vehicle fleet exhaust, break wear and tyre wear

P = number of wet days with at least 0.254 mm (.01 in) of precipitation during the

averaging period, and

N = number of days in the averaging period (e.g. 365 for annual, 91 for seasonal, 30

for monthly)

k , C and sL is adopted from USEPA (2006). K is taken as 24 g/VKT, and C is taken

as 0.1317 g/VKT, for particle size greater than 30.The values of sL is 0.6 g m-2 and

0.2 g m-2 if average density of vehicle on road is less than 500 and 500-5000

respectively. P is taken from met data available for the Korba region. W , Average

weight of the vehicles has been estimated using the available category-wise vehicular

data for Korba and the weight of each kind of vehicle for Korba from PWD, Korba.

Number of days in averaging period has been taken 30, for monthly average

emissions.



58

Figure 4.5: Monthly wind roses in the study area

Wind Rose March 2004 Wind Rose February 2004

Wind Rose May 2004 Wind Rose April 2004



59

Domestic Source Inventory Fuel consumption pattern of Korba is rather primitive (Table 4.4). 66% of the

household in Korba use fuel wood followed by coal, charcoal and lignite (which

accounts for 15%) for the purpose of domestic fuel burning. Gridded emission

inventory due to domestic fuel burning has been prepared using the following

equation:

n

iij PEFiFE

02592000/

jE =Emission of TSP in grid J (gs-1)

iF = monthly per capita fuel consumption of fuel type i (in Kg)

EFi =Emission factor per unit of fuel in gkg-1

P =population in each grid (the population in each grid has been distributed

according to the proportion of area of particular village in that grid.)

The data of monthly per capita fuel consumption has been taken from MOSPI (2007)

(Table 4.5 and 4.6). The data presented in the MOSPI (2007) was surveyed during

July 2004- July 2005. The population data is available at village and city level from

Census of India (2001) for the year 2001. This population data has been projected for

the year 2004 based on growth rates as specified for urban and rural area of

Chattisgarh state (COI, 2006). Gridded emission inventory is prepared taking into

account the population of area of the village in that grid. The estimated emissions and

the emission factors used to estimate those emissions have been provided in the

tables 4.7 and 4.8 respectively.

Emissions from Ash Ponds The methodology developed by USEPA (1989), as cited in WRAP (2006), has been

used to estimate emissions from ash ponds. The annual emission factor is calculated

by using the following formula:

15/235/3653655.1/7.1 fpsTSP f = percentage of time the unobstructed wind speed is greater than 12mph at the

mean pile height

s = silt content of material (weight %)

p = number of days per year with at least 0.01 inch of precipitation Silt content of the ash ponds has been taken from Prasun et al. (2005). Based on site

observations and discussions with pollution control board officials, it is assumed that



60

approximately 25 % of the ash pond area remains wet because of the disposal of fresh

ash slurry. Therefore, emissions from 75% of the total area of ash ponds have been

considered for this study. Also, industries are spraying water on the ash ponds. Thus,

a reduction of 35% emission (for periodic spraying) is assumed based on USEPA

(1995) cited in NPI (1999). To prepare gridded emission inventory from ash ponds,

the emission factor is multiplied with the area of ash pond in that grid. Details about

the ash ponds and their respective emissions are presented in Table 4.9.

4.2.6.3 Emission contribution from different sources

Based on the methodology described above, SPM emissions have been estimated

from various sources. The contribution of different sources to the total SPM

emissions is shown in Figure 4.6. The power plants stacks have the maximum share

of SPM emissions (58%). In addition, ash ponds contribute to 7% of total SPM

emission in the area. Thus the total contribution of emissions from power sector is

65%.

It is not necessary that source contributing to maximum emission will have

maximum impacts. The ambient concentration at a particular receptor depends on

the source characteristics as well as the source receptor distance. Therefore, a

modelling exercise is conducted in order to establish the contribution of different

sources to the ambient concentration at any given receptor.

4.2.7 Model run

Using the emissions and meteorological data inputs, ISCST3 model run was

performed in the default mode for “rural conditions”. The ISC model implements the

following regulatory options:

1. Use stack-tip downwash (except for Schulman-Scire downwash);

2. Use buoyancy-induced dispersion (except for Schulman-Scire downwash);

3. Do not use gradual plume rise (except for building downwash); 4. Use the calms processing routines;

5. Use upper-bound concentration estimates for sources influenced by building

downwash from super-squat buildings;

6. Use default wind profile exponents; and

7. Use default vertical potential temperature gradients.

The model predicts the concentration at different receptors due to emissions from

various sources that have been quantified and included in the input file. However,

observed air quality includes pollutant concentration due to natural sources, sources

other than the ones currently considered and unidentified sources. This is termed as

background concentration (USEPA, 2005). These background sources cannot be



61

accounted in the modelling process. Therefore, background concentration should be

added before model evaluation. TERI (2000) estimated the background

concentration for Korba (Table 4.10). Nonbirra village, about 15km west of Gevra

mines (Figure 4.2b), was considered as the background site. The average RSPM value

at Nonbirra was found to be varying from 29 to 61 µgm-3 . The ration of RSPM to

SPM was found to be ranging from 54% -65% with an average of 60%. The average

background concentration was within 100 µgm-3 for all the four seasons. In this study,

78 µgm-3 and 100µgm-3 SPM concentrations has been adopted as background

concentration for winter and summer respectively.

4.3 Results The ISCST-3 model has been used to predict the 24-h averaged ground level SPM

concentrations due to various point and area sources in Korba. The resultant

concentration includes the concentration due to contribution from all sources as well

as the background concentration. First, the 24-h averaged resultant concentrations

are compared against the corresponding observed concentrations at two receptors

locations for the days on which the Regional Pollution Control Board, Korba, had

monitored the air quality. The model performance is further evaluated using various

statistical parameters. The spatial distribution of SPM concentrations is analysed by

plotting the isopleths. Finally, the source contribution to the concentration at the two

selected receptors is analyzed.



62

Table 4.1 Details of emissions from point sources and stack characteristics Industry Stack no. Feb 2004 March 2004 April 2004 May 2004 Top inner

Diameter# (m)

Stack height # (m)

Emission* (g/s)

Temp# (K)

Velocity# (m/s)

Emission (g/s)

Temp (K)

Velocity (m/s)

Emission (g/s)

Temp (K)

Velocity (m/s)

Emission (g/s)

Temp (K)

Velocity (m/s)

1 046.24 402.0 18.86 046.006 404.3 19.15 044.484 405.0 18.87 042.17 402.5 18.87 3.831 198 2 042.38 402.5 18.60 045.037 403.0 19.42 044.538 405.0 19.29 045.03 401.0 18.91 3.831 198 3 043.97 400.5 18.50 041.625 402.3 18.15 040.780 405.0 18.18 035.91 400.5 18.32 3.831 198 4 168.76 409.0 19.86 181.982 411.0 21.40 171.400 413.5 20.01 155.94 403.6 19.50 7.670 220. 5 184.76 413.5 21.02 182.997 412.0 20.60 174.016 413.5 20.67 159.58 405.5 19.48 7.670 220

National Thermal Power Corporation (NTPC) 6 177.32 409.5 21.00 180.567 410.0 21.00 167.141 411.5 20.27 192.42 408.5 20.61 7.670 220

1 018.71 430.0 21.00 023.928 430.0 21.00 026.055 430.0 21.00 025.58 430.0 21.00 3.200 180 2 020.76 430.0 21.00 026.942 430.0 21.00 028.231 430.0 21.00 027.05 430.0 21.00 3.200 180 3 028.01 430.0 21.00 027.048 430.0 21.00 027.555 430.0 21.00 020.79 430.0 21.00 3.200 180

Balco captive power plant

4 018.34 430.0 21.00 018.542 430.0 21.00 020.871 430.0 21.00 023.95 430.0 21.00 3.200 180 Calcination 010.90 422.0 06.96 010.557 422.0 06.96 006.100 422.0 06.96 011.05 422.0 06.96 2.730 030

AnodePlant 000.60 311.0 14.40 000.600 311.0 14.40 000.600 311.0 14.40 000.60 311.0 14.40 0.690 045 HP boiler 006.53 406.0 13.24 005.894 406.0 13.24 002.569 406.0 13.24 002.62 406.0 13.24 2.730 080 LP boiler 001.13 406.0 13.52 001.488 406.0 13.52 001.000 406.0 13.52 001.44 406.0 13.52 1.790 070

5 005.05 300.0 17.27 006.152 300.0 17.27 003.250 300.0 17.27 003.25 300.0 17.27 1.790 080 6 003.47 300.0 17.27 003.470 300.0 17.27 003.470 300.0 17.27 003.33 300.0 17.27 1.790 080 7 003.47 300.0 17.27 003.470 300.0 17.27 003.120 300.0 17.27 003.25 300.0 17.27 1.790 080

BALCO

8 003.47 300.0 17.27 003.470 300.0 17.27 003.470 300.0 17.27 003.33 300.0 17.27 1.790 080 1 223.77 417.0 15.48 117.757 417.0 15.47 261.554 417.0 15.48 197.36 417.0 15.48 6.500 160 CSEB West

2 071.81 418.0 13.32 185.535 418.0 13.32 191.917 418.0 13.32 074.80 418.0 13.32 6.500 160 1 022.03 421.0 21.00 024.731 421.0 21.00 016.616 421.0 21.00 023.01 421.0 21.00 1.604 120 CSEB East

2 051.55 412.0 17.50 021.286 412.0 17.50 015.867 412.0 17.50 012.79 412.0 17.50 2.760 120 Source: *District collectorate Korba # Industries in Korba



63

Table 4.2 Mining details in the study domain

Source: District collectorate Korba

Major mining areas (opencast) Sub area Coillery name

Lease hold area (ha) till 25-11-2005

Production in 2003-04 (Mt year-1)

OB handling Mm3yr-1

Area under mining (ha)

stripping ratio

(m3/t) Emissions g/s

(Feb 2004)

Emissions gs-1 (March

2004 )

Emissions ( gs-1 ) April

2004

Emissions ( gs-1 ) may

2004) Korba Manikpur Manikpur 2105 2.34 3.272 19.57 17.45 16.23 18.91

Kusmunda Kusmunda Kusmunda 2659 7.59 9.85 419 1 : 1.41 23.82 21.23 19.76 23.01

2-Laxman 420 0.98 0.98 151 1 : .91 13.37 11.92 11.09 12.92 Gevra Gevra 1-Gevra 2945 21.89 11.69 1081 1 : 1.08 25.51 22.74 21.16 24.65

2-Dipka(Dipka + Dipka aug) 2437 13.66 8.57 360 1 : 0.96 22.91 20.43 19.01 22.14



64

Table 4.3 Vehicular density per day and emission factors used to estimate emissions from vehicular sources

Vehicle density per day* Emission factors# gkm-1 Deterioration factor#

Village

Road

Main District

Road State

Highway

Sakti Korba Road (May

2004)

Seepat Baloda

road (Dec 2004)

Korba Champa

Road (2000)

Vehicles registered

till 2000

Vehicles registered after 2000

0-5 years

5-10 years

2 wheelers 239 477 979 0.101 0.050 1.200 1.300 3 wheelers 2 29 8 0.151 0.080 1.475 1.700

Cars (PCG) 15 82 449 0.050 0.030 1.097 1.280 Jeeps n car

(PCD) 3 18 99 0.438 0.073 1.187 1.263 Light

Commercial Vehicles 3 108 44 0.521 0.208 1.190 1.255

Trucks 2 100 621 0.833 0.292 1.350 1.595 Buses 20 45 42 0.583 1.667 1.190 1.355

Tractor trailor and tractor 15 316 31 0.438 0.073 1.095 1.307

Source:* Public Works Department Korba: personal communication #CPCB (2000) Table 4.4 Fuel use according to the number of household in Korba district

Number of households

% of house

hold Rural Urban

% of household

Rural

% of household

urban Firewood 135,582 66.87 121,076 14,506 0.927 0.201 Crop residue 2,473 1.22 1,807 666 0.014 0.009 Cow dung cake 795 0.39 479 316 0.004 0.004 Coal, Lignite, Charcoal 31,586 15.58 4,265 27,321 0.033 0.379 Kerosene 1,920 0.947 543 1,377 0.0042 0.019 LPG 25,522 12.592 1,887 23,635 0.0142 0.327 Electricity 4,040 1.99 231 3,809 0.002 0.0528 Biogas 292 0.14 83 209 0.0006 0.0028 Any other 135 0.06 20 115 0.00015 0.00158 No cooking 413 0.20 185 228 0.0014 0.0032 Total 202,758 130,576 72,182

Source: Census (2001)



65

Source: MoSPI (2007) Source: MoSPI (2007)

Table 4.5 Monthly per capita quantity and value of consumption for non food items for Madhya Pradesh (Urban)

Quantity (kg) Value(Rs) per thousand

household sample

households Coke (kg) 0.039 0.09 3 4 Firewood and chips (kg) 9.472 14.2 407 381 Electricity (std. unit) 21.933 37.76 887 696 Dung cake 1.25 196 142 Kerosene PDS (liter) 0.314 2.66 410 398 Kerosene other sources (litre) 0.114 1.5 167 140 Coal (kg) 0.495 0.83 29 47 L.P.G (kg) 1.441 29.84 511 360 charcoal (kg) 0.009 0.08 3 5

Table 4.6Monthly per capita quantity and value of consumption for non food items for Madhya Pradesh (Rural)

Quantity Value

(Rs) per thousand

household sample

households Coke (kg) 0.072 0.05 2 6 Firewood and chips (kg) 24.075 25.6 960 1896

Electricity (std. unit) 5.567 9.1 583 1302 Dung cake 3.93 563 1083

Kerosene PDS (litre) 0.453 4.47 860 1713 Kerosene other sources (litre) 0.049 0.63 105 210 Coal (kg) 0.084 0.12 7 22 L.P.G (kg) 0.044 0.94 20 98

charcoal (kg) 0.091 0.1 6 14



66

Table 4. 7 Emission factors used to estimate emissions from domestic fuel burning

Sources a: USEPA,2000 has been considered b: URBAIR,1992 Table 4.8 Estimated emission from domestic fuel burning over the study domain

Table 4.9 Details about existing ash ponds in Korba

Ash pond name Area in ha Emissions g s-1 Height of ash

pond ( m) Dhanrash 176 28.017 6 Lotlotta 89 14.168 9 Danganiakhar1 81 12.894 9 Danganiakhar2 63 10.029 9 Risda 20 3.188 15 Risdi 16 2.547 15 Charpara 220 35.021 15 Redmud 111 4.796 15 Balgikhar 27 4.298 9 Porimaar 20 3.183 9 Risda Previous 60 9.551 6

Fuel TSP (g kg-1) Firewood and chips 0.92 a Kerosene .605 a LPG .51a Coal 10b

Charcoal 2.375 a

Month Emission (g s-1) Feb 2004 9.710 March 2004 9.727 April 2004 9.744 May 2004 9.762



67

Figure 4.6 % Share of different sources to total SPM emissions in Korba Table 4.10 Background concentration (in µg m-3) at Korba Pollutant Summer Winter Monsoon Post Monsoon

Maximum 181 105 77 147 Average 100 78 49 90

SPM

Minimum 62 41 22 37 Maximum 114 81 32 89 Average 61 51 29 49

RSPM

Minimum 39 35 26 28 SOURCE: TERI 2000

4.3.1 Comparison of 24-h average concentrations

Figures 4.7 and 4.8 show the comparison of 24-h averaged predicted concentrations

computed by using the ISCST-3 model and the corresponding observed concentration

at the monitoring stations i.e. HIG 21, 22 and Pragati Nagar, respectively. A total of

23 and 24 observations were available at HIG 21, 22 and Pragati Nagar, respectively.

These values include at least three days from each of the four months. It is noticed

that the predicted concentration match reasonably well with the observed

concentration at both the stations. However, there is a slight under prediction at HIG

21, 22 monitoring station. The average predicted concentration at HIG 21, 22 is 169.7

µgm-3 compared to the average observed concentration value of 207.8 µgm-3. At

Pragati Nagar station, the average predicted concentration is 143.4µgm-3

comparisons to the average observed concentration of 142.8 µgm-3. This could

probably be due to the construction activity underway for a new power plant near to

HIG 21, 22 monitoring station that is not accounted in the emission inventory. At

HIG 21, 22 monitoring station, the concentrations exceed the 24-hourly average

standard of 200µgm-3 whereas, at Pragati Nagar station, the concentrations are

Percentage emissions of SPM from different sectors

5%

27%

58%

2% 1%7% Mining

VehiclesDomestic Ash pondsBALCOPower plants



68

within the limits of ambient air quality standards as prescribed by CPCB. The reason

for high concentration at HIG 21, 22 is due to the fact that it is located in the city and

is in the downwind direction of major polluting sources. However, Pragati Nagar

station is broadly in the upwind direction of major sources.

4.3.2 Model performance evaluation

Although air quality models are powerful tools to predict the concentration at

receptor locations but the predictions of air quality models are never perfect because

of uncertainties as a result of errors in input data, model physics, numerical

representation and natural turbulence in atmospheric boundary layer, which

determines the dispersion. Thus, it is imperative to verify the model results, as these

are utilized to assess the public health and environmental impacts.

According to Oreskes et al. (1994) in context of natural systems, models can be

evaluated rather than verified or validated because the natural system is never closed

and model solution is always non-unique, as a result of certain irreducible inherent

uncertainties. According to him, a model can be evaluated by comparing the several

set of observation and predictions. The model evaluation is comprised of three

components: scientific, statistical and operational. Scientific evaluation aims to

examine the model algorithms, physics and assumptions to check its accuracy,

efficiency and sensitivity. Statistical evaluation procedure examines the model

performance by comparing the predicted and observed concentration. The observed

concentration can be directly measured by instruments, or are themselves products

of other models or analysis procedures. Thus, it is important to recognize that

different degrees of uncertainty are associated with different types of observations.

The third component tries to check the user friendliness of the model (Chang and

Hanna, 2004). In this present study, model has been evaluated based on statistical

evaluation method, which is the main component of model evaluation process.



69

Figure 4.7 Comparison of observed and predicted concentration at monitoring

station at HIG 21,22

Figure 4.8 Comparison of observed and predicted concentration at monitoring

station at Pragati Nagar.

0

50

100

150

200

250

300

16/2

/200

423

/2/2

004

25/2

/200

41/

3/20

043/

3/20

0410

/3/2

004

17/3

/200

422

/3/2

004

24/3

/200

429

/3/2

004

31/3

/200

45/

4/20

047/

4/20

0412

/4/2

004

19/4

/200

421

/4/2

004

26/4

/200

428

/4/2

004

5/5/

2005

12/5

/200

519

/5/2

004

24/5

/200

426

/5/2

004

Date

Conc

entr

atio

n (m

icro

gram

per

cub

ic m

eter

)

PredictedObserved

0

20

40

60

80

100

120

140

160

180

200

15/2

/200

4

17/2

/200

4

19/2

/200

4

24/2

/200

4

26/2

/200

4

4/3/

2004

9/3/

2004

11/3

/200

4

16/3

/200

4

18/3

/200

4

1/4/

2004

6/4/

2004

8/4/

2004

13/4

/200

4

15/4

/200

4

22/4

/200

4

27/4

/200

4

29/4

/200

4

6/5/

2004

11/5

/200

4

13/5

/200

4

18/5

/200

4

20/5

/200

4

25/5

/200

4

Date

Conc

entra

tion

(mic

rogr

am p

er c

ubic

met

er)

PredictedObserved



70

For statistical evaluation it is important to decide whether observations and

predictions be paired in time, in space, or in both time and space? Also, it is

necessary to decide whether highest concentration or average concentration should

be compared. To decide on these factors, the goal of a model evaluation study must

first be well defined. For example, for regulatory applications, it is important to know

the high and second high concentration rather than the location of highest

concentration. Therefore, air quality models are evaluated to find out whether they

can correctly predict the high end of the concentration distribution. On other hand,

for environmental justice applications, 24 hour averaged PM at specific locations

such as heavily populated areas is of more importance. In such cases average

modelled concentration should be compared with the average observed concentration.

Similar is the case of the present study; hence 24-hour average predicted

concentration is compared with the observed concentration. And the observations

and predictions are paired in both time and space.

A variety of model performance measures are available in the literature. There is not

a single best performance measure or best evaluation methodology. It is

recommended that a suite of different performance measure be applied. Hanna et al

(1991; 1993) suggested various model performance measures. These performance

measures are fractional Bias (FB), the geometric mean bias (MG), the normalised

mean square error (NMSE), the geometric variance (VG), the correlation coefficient

(R), and the fraction of predictions within a factor of two of observation (FAC2).

Willmott (1982) mentioned that correlation coefficient is not a robust measure as it is

sensitive to few aberrant data pairs i.e. a scatter plot might show generally poor

agreement despite of good match between several data pairs. On the other hand, the

presence of a good match for a few extreme pairs will greatly improve correlation

coefficient. Therefore, instead of Correlation coefficient, index of agreement has been

as suggested by Willmott (1982).

FB= OP

PO

5.0

NMSE = 2

ii

ii

OPPO



71

IOA =

n

iii

n

iii

OOOP

OP

1

2

1

2

1

FAC2= 25.0 i

i

OP

MG = )lnlnexp( PO

VG = 2lnlnexp PO

Where O is mean monitored concentration, P is mean modelled concentration, iO is

ith monitored concentration, iP is ith modelled concentration.

For the current work, all the statistical measure mentioned above were taken into

consideration to assess the agreement between estimated and observed

concentrations. The distribution of variable of interest is the determining factor to

choose the performance measure. If the distribution resembles a lognormal

distribution for atmospheric pollutant concentration then linear measures FB and

NMSE are strongly influenced by infrequently occurring high observed and predicted

concentrations whereas logarithmic measures MG and VG provide a more balanced

treatment of extremely low and high values. FB and MG are measures of mean

relative bias and indicate only systematic errors whereas NMSE and VG are measures

of mean relative scatter and reflect both systematic and unsystematic (random)

errors. For FB, which is based on a linear scale systematic bias, refers to the

arithmetic difference between iP and iO and for MG, which is based on logarithmic

scale; the systematic bias refers to the ratio of iP to iO . Because FB is based on the

mean bias, it is possible for a model whose prediction are completely out of phase

with observation to still have a FB=0 because of the compensating errors. Factor 2 is

the more robust measure because high and low outliers do not overly influence it.

A perfect model would have MG, VG, R, and FAC2 equal to 1.0; and FB and NMSE

equal to 0.0. However, as noted earlier, because of the influence of random

atmospheric processes, there is no such thing as a perfect model in air quality

modelling. Therefore, to determine the suitability of an air quality model, Chang and

Hanna, 2004 and Park and Seok (2007) summarised the acceptance criteria (Table

4.9).

The model performance is assessed with 23 and 24 pairs of 24 hr average observed

and predicted values at the two monitoring locations i.e., HIG 21, 22 and Pragati

Nagar, respectively.



72

The model performance with respect to the acceptable performance criteria has been

presented in Table 4.11. It can be seen that model performance is good with respect to

all parameters. The results show reasonable agreement between the predicted and

observed values. All the predicted 24-hourly average concentrations are within a

factor of two of the observed values. The fractional bias or the relative bias in the

predictions of SPM concentrations is –0.01 at Pragati Nagar and 0.20 at HIG 21, 22,

both of which indicate good performance. Normalised mean square error is

significantly low with a value of 0.06 at HIG 21, 22 and 0.01 at Pragati Nagar. The

parameters such as Index of agreement as well as MG and VG show good

performance. Though the numbers of paired data are limited, the evaluations of the

results show that the performance of ISCST3 model in simulating the dispersion of

SPM in Korba is reasonably good. Thus, the model results could be utilised for

further study in terms of health impacts.

4.3.3 Concentration distribution over the study domain

The spatial distribution of the SPM concentration has been examined over an area of

68km×68km with NTPC Plant at its centre. The ISCST3 model is used to predict the

ground level concentrations of SPM over the study domain with 4 km grid spacing.

The contours of the monthly averaged predicted SPM concentrations over the study

region are shown in Figures 4.9 to 4.12. It is observed that concentrations of SPM are

exceeding the Indian standards in Korba city in both summer (April and May 2004)

and winter (February 2004) seasons. It is further observed that the peak

concentrations are higher in winter than in summer. The maximum concentrations

of SPM are found to be 430 µg m−3in February, 418 µg m−3 in March, 331 µg m−3 April

and 342 µg m−3 in May. This could be due to the fact that the percentages of calm

winds are more during February and March 2004. Though the maximum

concentration values recorded vary from one season to another, the contours of SPM

show bunching effects in five to six locations. The presence of these pockets is site

specific i.e. due to the emissions from area sources such as mining and ash ponds.

Figure 4.13 represents the SPM concentration distribution (in µgm-3) averaged over

four months representing winter and summer months. For this work, this average is

assumed to be annual average over the study domain. The peak predicted average

value of SPM for 2004 is 380 µg m−3. It is also noticed that the isopleths of SPM are

found to have distributed to large distances. The concentration of SPM is found to be

more in the south and southeast of the sources. As a result Korba ward that is

downwind of the sources experiences high concentration level. The concentration is



73

considerably high in Korba city ranging from 140-380 µg m−3. The city inhabits about

31% of the total district population. The maximum concentration is observed in and

around the mining area. As can be observed from figure 4.13, concentration is

exceeding the standards of 200 µg m−3 in this region. The main contribution of SPM

is from mining and ash ponds. It is also clearly visible that monitoring station HIG 21,

22 is downwind and therefore concentration is high at this station as compared to

Pragati Nagar station. The concentration distribution as a result of different sources

in the area is depicted in Figure 4.14 to 4.18. The maximum concentration due to

area sources such as mining and ash ponds is significantly higher than the tall stacks.

The maximum concentration as a result of mining is 180 µg m−3, ash ponds are 219

µg m−3 and power plant stacks is 6 µg m−3. Fig 4.14 shows the concentration

distribution as a result of all the chimneys of power plants in the area. The emissions

from power plant stacks are contributing to negligible concentration compared to the

area sources. The reason is that good stack height leads to dispersion of the

pollutants over a wide area. The annual average increments in concentration

obtained through local dispersion modelling with ISCST3 for NTPC power plant

shows a maximum of 3 µg m−3. For CSEB East power plant increment in

concentration values shows a maximum of 0.9 µg m−3where as CSEB West power

plant show upto increment values present a maximum of 10 µg m−3. Captive Power

plant of BALCO shows maximum increment of 1 µg m−3. On an average, the plumes

follow South–southeast direction in all cases.

Therefore, the concentration distribution is skewed towards the south and southeast

direction. This behaviour is consistent with the wind roses of these months. The

resultant isopleths due to mining is shown in Figure 5.15. Since, mining is an area

source, therefore it leads to high concentration in the surrounding region. The

maximum concentration is observed in East and South direction. The impact due to

mining in Korba city is low compared to the village area surrounding mining region

as Korba city is upwind of mining area during the months of summer and winter

season.

The concentration as a result of ash ponds is shown in Figure 5.16. The concentration

is high in the south direction. The maximum concentration due to ash ponds is 220

µg m−3. As per modelling results, ash ponds are the highest contributor to

concentration in the city region after the background concentration.

The concentration distribution due to vehicular sources is varying as per the location

of sources. The grid having maximum road length or dense network is having

maximum concentration. The concentration due to domestic fuel burning is varying



74

according to the population density of the grid. The maximum incremental

concentration as a result of domestic sources is 5 µg m−3. The incremental

concentration as a result of vehicular sources and domestic sources is slightly

underestimated. In case of vehicular sources, emission from unpaved roads has not

been accounted because of unavailability of detailed information on unpaved roads.

However, the emissions from unpaved roads have actually been taken care in

background concentration. For domestic sources, non-inclusion of unaccounted coal

burning from stolen coal is leading to the underestimation in concentration.

Table 4.11 Model performance with respect to selected statistical parameters

Evaluation parameters

Formulas Ideal value

Monitoring Station Interpretation (source: Park and Seok, 2004)

Pragati Nagar

HIG 21, 22

Fractional Bias

OP

PO

5.0

0 - 0.01 .20 -0.3 < FB < 0.3 Good 1 < FB <1.2 Fair underestimation -1.2 < FB <-1 Overestimation FB >1.33 or FB< -1.33 Poor

NMSE

2

ii

ii

OPPO

0 .0130 .0627 NMSE<4 Good 9<NMSE<16 Fair 25<NMSE Poor

Index of agreement

n

iii

n

iii

OOOP

OP

1

2

1

2

1

Range 0 to 1

.60

.50

0.5<IOA Good 0.3<IOA<0.4 Fair IOA< 0.2 Poor

Factor within two

25.0 i

i

OP

1 1 1 0.5< FAC2 Good 0.3<FAC2<0.4 Fair FAC2<0.2 Poor

Geometric Mean Bias (MG)

)lnlnexp( PO

1 1.0 1.21 0.7<MG<1.3 Good 3<MG<4 Fair underestimation 0.25<MG<0.33 Overestimation MG >5 or MG <0.2 Poor

Geometric Variance (VG)

2lnlnexp PO

1 1.01 1.06 VG < 1.6 Good 3.34 < VG <6.82 Fair 12 < VG Poor

Figure 4.9 SPM contour plot showing concentration distribution over study

domain for the month of February 2004



76


domain for the month of March 2004



77


domain for the month of April 2004



78


domain for the month of May 2004



79


domain for average of four months.



80


domain as a result of point sources.



81


domain as a result of mining activities



82


domain as a result of vehicular sources



83


domain as a result of domestic sources



84


domain as a result of ash ponds



85

4.3.4 Source contribution analysis

Relative contribution of the sources in terms of concentration at two locations i.e.

at HIG 21, 22 and at Pragati Nagar is shown in Figure 4.19. It can be seen that

area sources are contributing more to the pollutants. The emissions from stacks

are high but contribution of stacks in concentration is significantly less as higher

stacks are leading to better dispersion of pollutants. The relative contribution of

ash ponds in total concentration is maximum at both the stations. At HIG 21,22

mining is second major contributor whereas at Pragati Nagar vehicular sources

are contributing towards concentration more than the mining. Figure 4.19 SPM concentration contributions from different sources at two

ambient air quality monitoring locations

Monitoring station Pragati Nagar

8%

73%

5%12% 2%

Mining Ash pondsDomesticVehicularPoint sources

Monitoring station HIG 21,22

17%

59%

7%8%

9%Mining Ash pondsDomesticVehicularPoint sources

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