chapter 8 air quality prediction 8.0...

Chapter 8 Air Quality Prediction

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8.0 INTRODUCTION

Particulate matter (PM) is one of the key constituents of the pollutants in

ambient air of JCF and surrounding area. The problem of PM pollution mainly arises

due to emissions from coal mining and allied activities, coal-based industries, biomass

incineration, vehicles, re-suspension of traffic dust and commercial and domestic use

of fuels (Mohan et. al., 2011). Thus, air quality management is a growing concern in

the study area. As such, it is quite necessary to have regular assessment and prediction

of air quality. This will help in providing database to prevent and minimize the

deterioration of air quality in the study area.

Air quality models and exposure assessment studies provide a tool to

understand the implications of pollutant emissions and aid in deciding control and

management strategies. Air dispersion models have been widely used to address this

issue by providing invaluable information for better and more efficient air quality

planning (Kho et al., 2007). They are computer-based models that calculate the

distribution of the pollutants in the atmosphere from specified emission sources and

meteorological scenario. The present chapter, thus, evaluates the application of two

models CALINE 4 and ISC-AERMOD in predicting the concentrations of Particulate

Matter (PM), carbon-mono-oxide (CO) and oxides of nitrogen (NOx) for area and line

sources, respectively in the study area.

8.1 METHODOLOGY

8.1.1 Model Used

Modelling of air pollution is accomplished with the aid of Gaussian dispersion

plume models that accounted for the dispersion characteristics of the pollutants.The

two models were used for pollution sources. For line source CALINE 4 and ISC-

AERMOD were used, while for area source ISC-AERMOD is used. The Description

of these two models is given below-

a) CALINE4:

The CALINE 4 model has better performance than other line source models

and is widely used to predict near road vehicle emissions (Benson, 1992; Loranger et

al., 1995; Nagendra and Khare, 2002). Its purpose is to help planners protect public


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health from the adverse effects of excessive CO exposure. It embeds the concept of

mixing zone and uses modified Gaussian plume distributions (Benson, 1984).

The ability of the CALINE 4 model to predict air quality reliably up to 500 m

from the roadway and the special options for modeling air quality near street canyons,

intersections and parking facilities bestows a great advantage to the model (Majumdar

et. al., 2010) with respect to given meteorological condition (e.g., wind speed, wind

direction, mixing height, stability class, temperature, background concentrations),

source strength (e.g., vehicular density and vehicle emission factor) and road-

geometry (e.g., roadway height, receptor locations and heights, number of links,

surface roughness, mixing zone width, etc.). The source strength is the amount of

pollutant emitted into the air per meter per second (g/m/s), and calculated as the sum

of emission factors multiplied by number of vehicles for all classes of vehicles. Total

line source strength is the amount of pollutant emitted into the air per second (g/s),

and calculated as by multiplying the emission source strength with length of the road.

CALINE 4 uses a series of equivalent finite line sources to represent the road

segment. The total road networks is divided into finite number of elements, then each

element is modeled as an equivalent finite line source positioned normal to the wind

direction and centered at the element midpoint. A local X-Y coordinate system

aligned with wind direction and originating at the element midpoint is defined for

each element (Majumdar et al., 2008).

The emissions occurring within an element are assumed to be released along

finite line sources representing element and it follows the Gaussian dispersion pattern

of downwind from the element. Incremental concentration from each link is computed

and then summed up to get the total concentration estimate for a definite receptor

position. The perpendicular connecting the midpoint of the link and receptor location

is known as receptor distance. To estimate the worst case pollutant scenario, worst

case wind angle is deployed during the model run. The user defines the proposed

roadway geometry, worst-case meteorological parameters, anticipated traffic volumes,

and receptor positions.


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b) ISC-AERMOD:

The AERMOD was developed from the Industrial Sources Complex Short

Term Model (ISCST3) by incorporating more complex algorithms and concepts, i.e.,

planetary boundary layer (PBL) theory and advanced methods for complex terrains. It

was introduced by the American Meteorological Society/EPA Regulatory Model

Improvement Committee to provide a state–of–the–art dispersion model for routine

regulatory applications (EPA, 2004; Zou et al., 2009). As with ISCST3, the

AERMOD is considered accurate for dispersion modeling at distances not exceeding

50 km from the emission source.

AERMOD incorporates air dispersion based on planetary boundary layer

turbulence structure and scaling concepts, including treatment of both surface and

elevated sources, and both simple and complex terrains (Cohan et al., 2011; Cimorelli

et al., 2005). It is used for the assessment of pollutant concentrations from a variety of

sources viz., point, area and line (Kesarkar et al., 2007; Cimorelli et al., 2005; EPA,

2005). It assumes a Gaussian plume distribution in the vertical and horizontal for

stable boundary layers and Gaussian in the horizontal and bi–Gaussian in the vertical

for convective boundary layers (Cohan, 2011). This model is typically used for large

areas (Faulkner et al., 2007; Touma et al., 2007; Stein et al., 2007; Kumar et al., 2006;

Jampana et al., 2004) or stationary sources (Orloff et al., 2006). It is a steady-state

plume model that assumes that concentrations at all distances during a modeled hour

are governed by the temporally averaged meteorology of the hour.

The model is composed of three parts: AERMOD Meteorological

Preprocessor (AERMET), AERMOD Terrain Preprocessor (AERMAP) and

AERMOD Gaussian Plume Model with the PBL modules. The model utilizes a file of

surface boundary layer parameters and a file of profile variables including wind

speed, wind direction, and turbulence parameters. These two types of meteorological

inputs are generated by meteorological preprocessor for AERMOD. Both the

meteorological input files are sequential ASCII files, and the model automatically

recognizes the format generated as the default format. The model processes all

available meteorological data in the specified input file by default, but the user can

easily specify selected days to process.


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The AERMOD meteorological preprocessor makes use of the surface

characteristics of the land surrounding the site along with the hourly surface

meteorological data to produce more realistic estimates of parameters that effect

dispersion, such as albedo, bowen ratio and surface roughness. A steady state, unit

emission rate of g/s is used for all volume sources in modeling runs. Use of the unit

emission rate allows the air modeling output to be expressed on a unit emission rate

basis, µg/m3. However, model air concentrations are based on a unit emission rate of

g/s; they are expressed as µg/m3 per g/s. The model air concentrations are multiplied

by the source specific emission rate to generate the predicted air concentrations. The

model has considerable flexibility in the specification of receptor locations. The user

has the capability of specifying multiple receptor networks in a single run and may

also mix Cartesian grid receptor networks and polar grid receptor networks in the

same run. The model has flexibility in specifying the location of the origin for polar

receptors, other than the default origin at (0, 0) in x, y coordinates.

8.2 RESULTS AND DISCUSSION OF VEHICULAR MODELING (LINE

SOURCE)

8.2.1 Pollution due to Vehicular activity (road traffic)

The vehicular emissions are one of the major sources of air pollution in the

study area. Unlike industrial emissions, vehicular pollutants are released at ground

level and hence their impacts on the recipient population are likely to be significant.

Figure 8.1 shows the existing mines, road network in the study area and form the

basis of air quality modeling. Figure 8.2 displays the dominant road networks and

traffic junction points considered for the air quality prediction at the respective road

networks.


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Figure 8.1: Existing Mines and Road Network in the Map of the Study Area

Figure 8.2: Vehicular Junction Points and Dominant Road Networks in the

Study Area


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In order to assess vehicular movement and associated air pollution in different roads,

consideration was confined to the road networks and traffic junctions only as shown

in Table 8.1.

Table 8.1: Road Networks and Traffic Junctions

Index No Road Networks and

Traffic junctions

Latitude Longitude

R1 Steel Gate to Chas

(National Highway-32)

(36 Km)

T1 � Steel Gate 23049’19.09” 86028’58.49”

T2 � ISM 23048’35.09” 86026’32.57”

T3 � Railway

Station

23047’46.19” 86025’50.29”

T4 � Bank More 23047’19.93” 86025’11.49”

T5 � Karkend 23045’15.81” 86023’15.74”

T6 � Mohuda More 23044’57.30” 86015’01.52”

T7 � Chas 23039’52.97” 86012’09.50”

R2 Dhanbad to Sindri

(20Km)

T4 � Bank More 23047’19.93” 86025’11.49”

T8 � RSP

College/Katras

More

23045’09.36” 86024’44.67”

T9 � Jharia 23044’38.21” 86024’35.90”

T10 � Lodna More 23043’08.60” 86024’38.35”

T11 � Patherdih 23040’10.65” 86026’08.76”

T12 � Sindri 23039’12.14” 86028’20.88”

R3 Jharia to Katras

(18km)

T9 � Jharia 23044’38.21” 86024’35.90”

T8 � Katras More 23047’52.74” 86018’07.49”

T5 � Karkend 23045’15.81” 86023’15.74”

T13 � Loyabad 23048’15.22” 86021’52.19”

T14 � Sijua 23047’19.93” 86020’11.35”

T15 � Katras 23047’52.74” 86018’07.49”

R4 Sijua to Rajganj

(11 Km)

T14 � Sijua 23047’19.93” 86020’11.35”

T16 � Tetulmari 23049’15.22” 86020’52.19”

T17 � Rajganj 23053’27.11” 86020’58.54”

R5 Mohuda More to

Rajganj

(18Km.)

T6 � Mohuda More 23044’57.30” 86015’01.52”

T18 � Kako More 23051’21.94”; 86019’06.74”

T19 � Tilatanr 23049’27.11” 86017’58.54”

T17 � Rajganj 23053’27.11” 86020’58.54”

R6 Jharia to Baliapur

(11 Km)

T9 � Jharia 23044’38.21” 86024’35.90”

T20 � Bandhdih 23047’27.11” 86028’58.54”

T21 � Baliapur 23043’30.85” 86031’27.56”


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8.2.2 Traffic counting at different road networks

Total six road link/networks viz., R1, R2, R3, R4, R5 and R6 with twenty one

traffic junction points were selected for vehicular pollution source modeling which

covers the entire study area. Three hourly average traffic counting was done for

different category of vehicles (Light duty, heavy duty) at different junctions through

road networks during 6.00 to 21.00 hours during 2008-2009 and is shown in Table

8.2. This gives a picture of total number of vehicles plying on each road link. Field

motor vehicle counts were continuously monitored for different categories of vehicles

(i.e. heavy duty, light duty petrol and diesel, etc.) for each road network. Based on

this database, average daily vehicular movement on the different road networks of the

study area were evaluated as shown in Table 8.3.

Receptors were located close to the roadway where pollutant concentrations

caused by mobile sources are to be measured. Receptors can exist as part of a grid or

as discrete receptors. A receptor was located outside the “mixing zone” of a roadway,

which is the total width of the travel lanes of a roadway plus 3 meters (10 feet) on

either side. A total of 20 discrete receptors (monitoring site) were assessed in terms of

potential exposure of CO by comparing with the National Ambient Air Quality

Standard of 4 mg/m3 (1hour). Potential exposure that exceeds the ambient standard

may be of greater concern to public health because they increase the total body burden

for CO (USEPA, 1999).


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Table 8.2: Traffic Counting of Different Category of Vehicles Plying in Selected Junctions/Nodes of Different Road Network

Road Network/

Junctions

(Index No)

Time

(Hours) Heavy vehicles Light vehicles

(Cars/Taxis, etc) Three

wheelers

(Tempo,

Auto-

Rickshaw)

Two

wheelers

(Motor cycle,

Scooter,

moped)

Total

Vehicles (Bus,

Trekkers)

Trucks/Trac

tors/

Tailors/

Goods

vehicles

1)NH-32

� Steel Gate

(R1-T1)

6.00-9.00 hours 130 242 354 522 1,566 2814

9.00-12.00 hours 115 234 367 540 1,587 2843

12.00-15.00 hours 60 90 1,338 1,242 3,624 6354

15.00-18.00 hours 59 130 1,395 1,104 3,822 6510

18.00-21.00 hours 102 123 1,428 1,152 4,092 6897

Total 466 819 4882 4560 14691 25418

� ISM

(R1-T2)

6.00-9.00 hours 186 93 795 801 1,704 3579

9.00-12.00 hours 82 175 800 834 1,734 3625

12.00-15.00 hours 113 151 816 828 1,908 3816

15.00-18.00 hours 101 132 800 816 1,834 3683

18.00-21.00 hours 72 101 1,092 720 2,928 4913

Total 554 652 4303 3999 10108 19616

� Railway Station

(R1-T3)

6.00-9.00 hours 529 422 892 320 3,233 5396

9.00-12.00 hours 669 450 906 327 3,495 5847

12.00-15.00 hours 576 483 663 219 3,195 5136

15.00-18.00 hours 393 333 603 183 2,451 3963

18.00-21.00 hours 353 383 587 144 3,309 4776

Total 25118

� Bank More

(R1-T4)

6.00-9.00 hours 183 513 1,500 2400 2,652 7783

9.00-12.00 hours 113 630 1,634 3500 3,430 11603

12.00-15.00 hours 185 427 2,304 3,420 2,380 7672

15.00-18.00 hours 112 530 2,312 3,620 2,314 9970

18.00-21.00 hours 101 500 3,134 3,750 3,870 6831

Total 2520 2071 3651 1193 15683 43859


161

� Karkend

(R1-T5)

6.00-9.00 hours 122 131 2,760 2,119 3,675 8807

9.00-12.00 hours 160 250 3,100 3,345 6,756 13611

12.00-15.00 hours 131 312 2,896 2,984 4,481 10804

15.00-18.00 hours 121 197 2,751 3,124 5,552 11745

18.00-21.00 hours 129 176 2,328 2,589 5,342 10564

Total 663 1066 13835 14161 25806 55531 � Mohuda-More

(R1-T6)

6.00-9.00 hours 324 367 589 327 2,169 3776

9.00-12.00 hours 460 410 1,038 475 2,844 5227

12.00-15.00 hours 487 425 817 467 3,212 5408

15.00-18.00 hours 527 524 1,129 539 5,126 7845

18.00-21.00 hours 315 522 942 372 4,317 6468

Total 2113 2248 4515 2180 17668 28724

� Chas

(R1-T7)

6.00-9.00 hours 334 345 593 321 2,189 3782 9.00-12.00 hours 470 425 1,238 485 2,954 5572 12.00-15.00 hours 489 435 822 461 3,312 5519 15.00-18.00 hours 522 518 1,229 529 5,246 8044 18.00-21.00 hours 413 532 923 363 4,343 6574 Total 2228 2255 4805 2159 18044 29491

2) Dhanbad To Sindri � BankMore

(R2-T4)

Already mentioned in R1-T4

� RSP College/Katras

More

(R2-T8)

6.00-9.00 hours 99 106 2,451 1,980 3,265 7901

9.00-12.00 hours 110 220 2,934 2,224 6,656 12144

12.00-15.00 hours 103 273 2,782 1,712 3,282 8152

15.00-18.00 hours 123 163 2,543 2,113 5,456 10398

18.00-21.00 hours 109 179 2,321 1,589 5,123 9321

Total 544 941 13031 9618 23782 47916 � Jharia

(R2-T9)

6.00-9.00 hours 109 112 2,551 1,976 3,465 8213

9.00-12.00 hours 132 229 2,956 2,856 6,756 12929 12.00-15.00 hours 113 289 2,790 2,954 4,289 10435 15.00-18.00 hours 143 178 2,643 2,934 5,456 11354 18.00-21.00 hours 129 169 2,121 2,589 6,123 11131 Total 626 977 13061 13309 26089 54062


162

� Lodna more

(R1-T10)

6.00-9.00 hours 128 124 2,693 1,998 3,276 8219 9.00-12.00 hours 165 232 2,978 2,524 6,798 12697 12.00-15.00 hours 103 263 2,782 1,734 3,450 8332 15.00-18.00 hours 135 233 2,643 2,113 5,572 10696 18.00-21.00 hours 127 189 2,221 1,681 5,323 9541 Total 658 1041 13317 10050 24419 49458

� Patherdih

(R2-T11)

6.00-9.00 hours 123 134 2,451 1,989 3,651 8348

9.00-12.00 hours 142 289 2, 790 2,999 6,856 10286 12.00-15.00 hours 113 221 2, 556 3,421 4,297 8052 15.00-18.00 hours 163 187 2,436 3,436 5,466 11688 18.00-21.00 hours 121 179 2,101 3,189 6,743 12333 Total 662 1010 6988 15034 27013 50707

� Sindri (R2-T12) 6.00-9.00 hours 98 111 2,451 1,876 3,578 8114 9.00-12.00 hours 112 221 2,893 2,898 6,996 13120 12.00-15.00 hours 146 275 2,824 2,864 4,889 10998 15.00-18.00 hours 112 224 2,669 2,934 5,856 11795 18.00-21.00 hours 129 161 1,780 2,389 6,463 10922 Total 597 992 12617 12961 27782 54949

3)Jharia to Katras � Jharia(R3-T9) Already mentioned in R2-T9

� RSP

college/Katras

More

(R3-T8)


� Karkend

(R3-T15)



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� Loyabad (R3-

T13)


Sijua(R3-T14) 6.00-9.00 hours 119 136 2,648 2,207 3,690 8800 9.00-12.00 hours 152 298 2,987 3,341 6,997 13775 12.00-15.00 hours 132 254 2,696 2,854 5,189 11125 15.00-18.00 hours 120 173 2,551 3,230 5,656 11730 18.00-21.00 hours 130 189 2,021 2,580 6,301 11221 Total 653 1050 12903 14212 27833 56651

� Katras More (R3-T15) Already mentioned in R2-T8

4) Sijua To Rajganj � Sijua (R4-T14) Already mentioned in R3-T13

� Tetulmari

(R4-T16)


� Rajganj (R4-

T17)

6.00-9.00 hours 101 106 2,451 1,960 3,165 7783

9.00-12.00 hours 89 220 2,834 2,004 6,456 11603

12.00-15.00 hours 123 273 2,682 1,512 3,082 7672

15.00-18.00 hours 75 173 2,443 2,123 5,156 9970

18.00-21.00 hours 50 213 1,679 1,886 3,003 6831

Total 438 985 12089 9485 20862 43859

5)Mohuda More To Rajganj � Mohuda-

More(R5-T6)


� Kako- More

(R5-T18)

6.00-9.00 hours 345 367 579 329 2,379 3999

9.00-12.00 hours 410 480 1,238 445 5,512 8085

12.00-15.00 hours 448 425 823 409 2,987 5092

15.00-18.00 hours 427 524 1,334 549 4,226 7060

18.00-21.00 hours 315 522 850 380 4,056 6123

Total 1945 2318 4824 2112 19160 30359


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� Tilatanr

(R5-T19)

6.00-9.00 hours 340 360 589 319 2,269 3877 9.00-12.00 hours 459 475 1,038 435 5,212 7619 12.00-15.00 hours 415 421 827 410 2,887 4960 15.00-18.00 hours 427 520 1,329 549 4,126 6951 18.00-21.00 hours 316 542 934 382 4,022 6196 Total 1957 2318 4717 2095 18516 29603

� Rajganj(R5-

T17)


6) Jharia To Baliapur � Jharia(R6-T9) Already mentioned in R2-T9

� Bandh dih(R6-

T20)

6.00-9.00 hours 130 156 2,351 1,979 3,651 8267 9.00-12.00 hours 146 324 2, 680 3,400 6,850 10720 12.00-15.00 hours 110 221 2, 356 2,990 4,297 7618 15.00-18.00 hours 169 187 2,436 3,436 5,461 11689 18.00-21.00 hours 119 179 2,119 3,189 6,740 12346

Total 674 1067 6906 14994 26999 50640 � Baliapur(R6-

T21)

6.00-9.00 hours 130 176 2,241 1,679 3,621 7847 9.00-12.00 hours 146 290 2, 580 3,620 5,900 9956 12.00-15.00 hours 110 221 2, 136 2,678 4,676 7685 15.00-18.00 hours 169 204 2,296 3,226 5,497 11392 18.00-21.00 hours 115 290 2,116 3,169 6,721 12411

Total 670 1181 6653 14372 26415 49291


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Table 8.3: Average Vehicular Count on different Road Networks

Sl

No

Road

Networks

Heavy vehicles Light vehicles

(cars/taxis,

etc)

Three

wheeler

(Tempo,

Auto-

Rickshaw)

Two wheeler

(Motor cycle,

Scooter,

moped)

Total

vehicles (Bus,

Trekk

ers)

Trucks/

Tractors/

Tailors/

Goods

vehicles

1 NH-32 257 288 1374 1078 3510 6507

2 Dhanbad

To Sindri

126 252 2496 2589 4791

10254

3 Jharia to

Katras

133 213 2693 2829 5320

11188

4 Sijua To

Rajganj

136 260 2516 2805 5590

11307

5 Mohuda

More To

Rajganj

390 464 954 421 3768

5997

6 Jharia To

Baliapur

135 213 2302 2999 5400

11049

8.2.3 Vehicular source modeling

The line source modeling was done by using CALINE 4 and ISC-AERMOD.

Deterioration in the condition and emission factors for different categories of vehicles

was collected from CPCB Publication (Transport Fuel Quality for Year, 2005) as

shown in Table 8.4. Deterioration factor and emission factor for different categories

of vehicles were calculated. Due to low sulphur content in diesel and petrol, the SO2

emission due to vehicular movements was not considered.

Table 8.4: Deterioration and Emission factors for different Categories of

Vehicles

Category of Vehicles Deterioration factor Emission factor (g/km)

SPM NOx CO SPM NOx CO

Heavy vehicles (Bus, Goods vehicle)

1.35 1.0 1.18 0.56 12.0 3.6

Trucks/Tractors

1.59 1.0 1.33 0.28 6.3 3.6

Light vehicles

(cars/taxis, etc)

1.28 1.0 1.14 0.07 0.5 0.9

Three wheeler (Tempo,

Auto-Rickshaw)

1.70 1.7 1.70 0.08 0.11 4.3

Two wheeler (Motor

cycle, Scooter, moped)

1.30 1.3 1.30 0.05 0.3 2.2

Source: Transport Fuel Quality for Year 2005, CPCB


166

Pollution load of different category of vehicles on each road network was

determined using equation 8.1. The total number of vehicles for each category was

used to estimate emission as daily average. That time period was selected because the

minimum averaging period for the dispersion modeling is 24 hours for particulate

matter.

Pollution load=Tn x Xi x T(0-5)xLxR1………(Equation 8.1)

Where, Xi – Pollutant parameter

Tn – Number of vehicles during 24 hours

T (0-5) - Deterioration factor in five years

L – Road length in km

R1 – Factor representing intermediate road link

Accordingly the vehicular pollution loads of six different road networks were

evaluated as shown in Table 8.5(A sample calculation has shown in annexure III).

Table 8.5: Vehicular Pollution Load at different Road Networks (g/m/s)

Sl

No Road networks SPM NOx CO

(g/m/s)

1 NH-32(36km) 1.25E-02 1.09E-01 3.35E-01

2 Dhanbad to Sindri (20km) 1.50E-03 4.60E-02 2.55E-01

3 Jharia to Katras(18km) 5.80E-03 3.50E-02 1.99E-01

4 Sijua to Rajganj (11km) 2.10E-03 1.25E-02 7.10E-02

5 Mohuda More to

Rajganj(18km) 2.78E-03

3.02E-02

5.79E-02

6 Jharia to Baliapur(11km) 2.10E-03 1.22E-02 7.50E-02

This generated database was used to run both CALINE 4 and ISC-AERMOD

models for line sources.

Carbon Monoxide Concentration (Based on 1 hour modeling)

Maximum one hour average CO concentrations at each link/road network

were modeled through CALINE 4 and AERMOD. Both the models were run

separately to get the predicted CO concentration at each of the receptor locations.

Model was run with 20 receptor points. Model output (CALINE-4) as shown in Table


167

8.6 revealed maximum concentrations of 1.70 ,1.56, 1.46 ppm at Bank More (T4) and

1.54, 1.48 and 1.40 ppm at Railway station (T3) junction followed by 1.43, 1.31 and

1.37 ppm at Mohuda (T6) during three peak traffic situations (9.00 to 10.00 hours,

12.00 to 13.00 hours and 17.00 to 18.00 hours, respectively).

However, the AERMOD Model output as isopleths is depicted through

Figures 8.3. These indicated maximum concentration zone in NH-32 (T1 and T2)

during all the peak hours of vehicle movement. In all other road networks maximum

values 0.18-0.23 ppm were found during morning 9.00-10.00 hours and afternoon

12.00 to 13.00 hours.

Table 8.6: Predicted CO Concentration (CALINE4) at Selected Strategic

Locations

Traffic

Junction

points

Code Average Predicted CO in ppm

9.00-10.00

hours

12.00 to13.00

hours

17.00 to 18.00

hours

Steel Gate RC1 1.12 1.10 1.00

ISM-Main Gate RC2 0.90 0.98 0.88

Railway Station RC3 1.54 1.48 1.40

Bank More RC4 1.70 1.56 1.46

Karkend RC5 1.09 0.92 1.00

Mohuda RC6 1.43 1.31 1.37

Chas RC7 1.34 1.23 1.10

RSP college RC8 0.85 0.73 0.78

Jharia RC9 1.10 1.04 1.05

Patherdih RC10 0.98 0.92 0.90

Sindri RC11 0.54 0.48 0.50

Loyabad RC12 0.78 0.67 0.73

Sijua RC13 0.87 0.76 0.80

Katras RC14 0.89 0.71 0.83

Tetulmari RC15 0.77 0.67 0.68

Kako More RC16 0.76 0.64 0.71

Tilatanr RC17 0.81 0.76 0.73

Rajganj RC18 0.76 0.64 0.72

Bandhdih RC19 0.64 0.54 0.60

Baliapur RC20 0.70 0.61 0.67


168

Figure 8.3: Isopleths Showing Dispersion of CO during 9.00-10.00 hours, 12.00 to

13.00 hours and 17.00 to 18.00 hours on Road Networks through ISC-AERMOD

9.00 to10.00

hours

12.00 to

13.00 hours

17.00 to

18.00 hours


169

8.2.4 Performance of the Models

The results of the above mentioned models were validated with observed ambient CO

monitoring data (Table 8.7). Necessary statistical analysis was also carried out to

assess the performance of the models based on a set measured and predicted CO

concentration data as shown in Table 8.8. It is evident that CALINE 4 model is over

predicting whereas AERMOD line source model is under predicting the concentration

of CO pollutant. However, higher value of ‘r’ in case of CALINE4 indicates that

model is more appropriate in application of predicting CO dispersion. Scatter plots of

observed and predicted concentration (Figure 8.4 and Figure 8.5) along with

regression coefficients of the two models have found a good agreement of above

statement.

Table 8.7: Average Monitored CO Concentration at Selected Strategic Locations

Traffic

Junction

points

Code Average Monitored CO in ppm 9.00 to10.00

hours

12.00 to 13.00

hours

17.00 to 18.00

hours

Steel gate RC1 0.90 0.92 0.86

ISM-Main gate RC2 0.80 0.88 0.78

Railway Station RC3 1.34 0.98 0.88

Bank More RC4 1.50 1.43 1.30

Karkend RC5 0.87 0.84 0.78

Mohuda RC6 1.12 1.10 1.00

Chas RC7 1.09 0.96 0.92

RSP college RC8 0.70 0.67 0.60

Jharia RC9 0.94 0.90 0.85

Patherdih RC10 0.87 0.85 0.78

Sindri RC11 0.34 0.30 0.24

Loyabad RC12 0.55 0.54 0.50

Sijua RC13 0.65 0.55 0.49

Katras RC14 0.69 0.65 0.60

Tetulmari RC15 0.67 0.61 0.55

Kako More RC16 0.58 0.56 0.51

Tilatanr RC17 0.60 0.58 0.53

Rajganj RC18 0.56 0.53 0.48

Bandhdih RC19 0.49 0.47 0.43

Baliapur RC20 0.53 0.50 0.45

Table 8.8: Statistical Evaluation of Models

Sl.No. Average Predicted

Concentration

(ppm)

Average Observed Concentration

(ppm)

CALINE-4 ISC-AERMOD 0.735

1 0.92 0.60

2 0.91* 0.67*

*Correlation Coefficient


170

Figure 8.4:Correlation Analysis between Observed and Predicted CO by

CALINE-4

Figure 8.5: Correlation Analysis between Observed and Predicted CO by ISC-

AERMOD


171

(b) AERMOD results for 24 hourly predicted SPM and NOX, concentration

Twenty four hourly averages of SPM and NOx concentrations at each link/road

network have been modeled through AERMOD. Model outputs are depicted as

isopleths in Figures 8.6 and 8.7 respectively.

Suspended particulate matter:

Figure 8.6 depicts the isopleths for 24 hourly SPM concentrations at each road

network. It consists of six road network which covers entire study area, viz., R1, R2,

R3, R4, R5 and R6. The model output predicted higher concentration (749µg/m3) at

Chasnala (A21) followed by A10 (Sijua) and lowest (194µg/m3) at BIT-Sindri (A23).

The locations were situated under R2 and R3 road networks, respectively. Thus, it

may be imperative to say that road network adjacent to coal mining activities produce

higher suspended particulate matter due to frequent movement of vehicles.

Oxides of Nitrogen: 24 hourly average NOx concentration model output is shown in

Figure 8.7. The isopleths of predicted NOX concentration on road network reveals that

the maximum concentration of 144 µg/m3 at A6 (Bank More) followed by 137 µg/m

3

at A7 (Kusunda). On the other hand, lowest concentration 14 µg/m3 was recorded at

A20 (Barari). As location A6 (Bank-more) is getting higher pollution load from

vehicular exhausts.

Performance of the model:

The results of the model were validated with observed ambient SPM and NOx

monitoring data as shown in Table 8.9. Higher value of ‘r’ in both cases (SPM and

NOx) indicates that model applicability in predicting SPM and Nox dispersion.

Scatter plots of observed and predicted concentration (Figure 8.8 and Figure 8.9)

along with regression coefficients have found a good agreement of above statement.

Table 8.9 Statistical Analysis of Model Performance for 24 hourly average SPM

and Nox

Pollutants Average observed

Concentration

(ppm)

Average Predicted

Concentration

(ppm)

Correlation

Coefficient

SPM 521 530 0.995

NOx 49 57 0.970


172

Figure 8.6: Isopleths for 24 hourly SPM in the Study Area (Line source)

Figure 8.7: Isopleths for 24 hourly NOx in the Study Area (Line source)


173

Figure 8.8 :Correlation Analysis between Observed and Predicted SPM by ISC-

AERMOD

Figure 8.9:Correlation Analysis between Observed and Predicted NOx by ISC-

AERMOD


174

8.3 RESULTS AND DISCUSSION OF AREA SOURCE MODELING

8.3.1 Pollution due to Mining activities

The huge reserve of prime coking coal in JCF have attracted several coal

mining activities along with other ancillary industries thereby leading to considerable

degradation of air environment, especially due to generation of huge amount of

Particulate Matter. It is generally accepted that the contribution of gaseous pollutants,

e.g., SO2 and NOx is relatively insignificant in comparison to PM in coal mining

areas. Thus, PM is considered as major threat to public health in coal mining area. In

view of the above, the study was taken up for SPM (including PM10) emission for

modeling.

The preferred approach to evaluate quantum of air pollution due to mining

operations is to use emission factors. Emission factors have been widely used in

different countries for developing emission control strategies and environmental

assessment. Here, emission factors are taken from the primary international sources,

namely, the USEPA’s compilation of air pollutant emission factors for stationary and

mobile sources (1995). Wherever necessary, the factors were adjusted to be more

consistent with the on-site experience in JCF. In making the adjustments in the

emission factors, it was recognized that the emission control equipment and the

manufacturing or production processes are generally not as advanced in the sector of

India as in USA and Canada. Therefore, emission factors from USEPA’s compilation

of air pollutant emission factors and engineering judgment were used to make these

factors more representatives for JCF.

Table 8.10 shows the emission factors used for coal mining operations such as

drilling, overburden loading and unloading, coal loading and unloading, coal handling

or screening plant, exposed overburden dump, stock yard, workshop, exposed pit

surface, transport road and haul road, etc.


175

Table 8.10: Emission Factors for Coal Mining Operations of JCF

Sl No Source Type Emission Factors of SPM

(kg/1000 t of coal/overburden)

1 Underground mines

� Coal dumping 0.2

� Waste material transport 28.1

� Waste material dumping 2.2

� Waste material bulldozing 4.5

� Wind erosion: exposed dump areas 4.4

� Sand use on mine site 2.3

� Coal truck loading 0.3

� On-site truck haul 36.0

78.0

2 Opencast mine – coal operations

� Coal excavation and loading 0.3

� Coal bulldozing 0.3

� Coal truck haul 54.0

� Wind erosion: exposed pit area 12.4

� Wind erosion: exposed dump area 4.4

� Mine grading 11.3

� Light and medium duty vehicle travel 25.0

107.8

3 Open cast mines – overburden removal

� Rock drilling 8.4

� Rock blasting 61.0

� Rock (overburden) loading 5.4

� Rock (overburden) truck haul 421.0

� Rock (overburden) dumper movement 10.9

� Rock bulldozing at excavation point 0.7

� Rock bulldozing at rock dumps 3.0

� Rock bulldozing at reclamation areas 1.5

� Rock bulldozing at pit extension 0.7

� Rock bulldozing at haul road extension 0.7

513.5

4 Coal storage/coal stacks/CHP

Coal unloading/dumping 29.9

Coal storage/bulldozer handling 0.5

Wind erosion 0.4

Coal load out to truck/train 0.6

31.3

Source: USEPA (2000)

8.3.2 Total emission evaluation

Except for ventilation shafts, underground coal mines are relatively minor

sources of particulate emissions. Underground mining can result in emissions from

the mine exhausts, handling of coal on the surface and wind erosion from coal stacks.

Road dust emission from vehicular movements in opencast projects is

frequently the most significant mining related particulate emission from ambient air


176

quality perspective. In developing emission rates for the opencast mines the following

factors were considered in the evaluation.

� The ratio of coal to overburden, i.e., stripping ratio, is generally assumed to be

a factor between 2 and 2.5 on the basis of cubic meter of overburden removed

per tone of coal mined.

� Transporting overburden and coal on haul roads in and around the mines

represents the most significant particulates emission in opencast mines.

� JCF is large and equipped with high capacity heavy earth moving machineries

(HEMM). The vehicular exhaust, while significant in area immediately

adjacent to haul roads in the mines, and are less significant from regional

particulate emission perspective than particulate emission generated by the

movement of the vehicles over the haul roads.

� While blasting is a significant short-term source of particulate emissions.

These emissions are less significant when a 24 hour averaging period is

considered for the air quality (particulate matter) evaluation.

� The emission from other sources, specifically, overburden and coal loading by

shovels, formation of dumps and stacks, contour grading using dozers, and

wind erosion of coal piles and overburden were considered in the analysis.

� The total particulate emissions are assumed to be uniform and proportional to

the total quantity of coal/overburden produced by each mine, while this is a

simplified assumption, the alternative would be to consider detailed mine

plans for future mines. The future and, in some cases, the current coal

production rates from JCF have been used in this evaluation.

As per the above considerations, the total particulate emissions from different

mines in the coalfield were evaluated as shown in Table 8.11.

The coal fires are also a major source of particulate matter (along with gaseous

pollutants as well) in coal mining area. As such, the additional emission of

particulate matter due to coal fires for the mines, active mine fire were evaluated

as per the guideline of USEPA and the results are presented in Table 8.11.

8.3.3 Input data for air pollution modeling

The modeling exercise was carried out to assess the environmental status of the study

area.


177

Table 8.11: Emission Rate of SPM at Various Mines of Jharia Coalfield S.NO. Name of Mines Type Location Emission Rate( g/s/m2)

Latitude (North) Longitude (East ) Due to normal mining

activities

Due to mine fire Total Emission

1 Madhubandh UG 23045’17.10”N 86012’18.91”E 5.2E-8 - 5.2E-8

2 Shatabdi OC 23047’25.91”N 86014’20.03”E 2.2E-6 1.53E-9 2.2E-6

3 Muraidih OC 23047’10.33”N 86013’04.11”E 1.3E-6 - 1.3E-6

4 Jamunia OC 23046’38.13”N 86011’08.12”E 5.7E-6 - 5.7E-6

5 Block-II OC 23046’22.11”N 86012’21.12”E 1.0E-6 - 1.0E-6

6 Govindpur UG 23048’09.23”N 86016’29.21”E 6.2E-7 - 6.2E-7

7 New Akashkinaree OC 23048’21.15”N 86017’18.23”E 7.1E-7 1.53E-9 7.1E-7

8 Block- IV OC 23048’08.05”N 86016’07.11”E 3.3E-6 - 3.3E-6

9 Jogidih UG 23048’14.09”N 86015’43.14”E 4.9E-7 - 4.9E-7

10 Kharkharee UG 23046’10.47”N 86014’48.25”E 9.1E-8 - 9.1E-8

11 Katras Chaulidih UG 23047’14.88”N 86018’05.07”E 1.4E-7 1.51E-9 1.5E-7

12 Salanpur UG 23048’21.16”N 86018’39.24”E 3.7E-7 - 3.7E-7

13 Ramkanali UG 23048’31.06”N 86019’06.06”E 1.2E-6 - 1.2E-6

14 Angarpathra colliery UG 23048’09.28”N 86018’56.19”E 5.3E-7 - 5.3E-7

15 Basdeopur colliery OC 23046’47.68”N 86021’50.63”E 2.1E-6 7.65E-10 2.1E-6

16 Mudidih OC 23048’10.17”N 86020’40.23”E 1.2E-7 - 1.2E-7

17 Tetulmari OC 23048’24.15”N 86020’50.13”E 1.8E-7 2.55E-9 1.8E-7

18 Nichitpur OC 23047’56.71”N 86021’24.34”E 1.5E-6 2.65E-9 1.5E-6

19 Bassuriya UG 23047’27.68”N 86022’24.37”E 1.4E-7 1.27E-9 1.4E-7

20 Godhur UG 23047’01.53”N 86023’24.54”E 6.1E-7 - 6.1E-7

21 Dhansar OC 23046’19.07”N 86023’51.04”E 3.2E-7 - 3.2E-7

22 Bhagaband UG 23044’52.36”N 86021’56.09”E 5.6E-8 - 5.6E-8

23 Aluska UG 23045’57.79”N 86023’16.74”E 7.2E-7 1.91E-9

24 Burragarh/Simlabahal/Honilidih UG 23044’10.61”N 86023’20.71”E 4.4E-7 - 4.4E-7

25 Ena OC 23045’20.04”N 86024’26.84”E 7.4E-7 1.53E-9 7.5E-8

26 Bastacola OC 23045’57.51”N 86025’14.60”E 4.4E-7 - 4.4E-7

27 Bera OC 23045’31.02”N 86025’38.35”E 1.9E-7 - 1.9E-7

28 Kuya OC 23044’56.22”N 86026’34.52”E 3.0E-7 - 3.0E-7

29 Ghanoodih OC 23044’36.17”N 86026’19.04”E 7.5E-7 1.44E-9 7.5E-7

30 Bagdigi OC 23042’27.67”N 86025’32.52”E 1.3E-7 3.83E-10 1.3E-7

31 North Tisra UG 23043’46.23”N 86026’47.09”E 4.2E-7 6.12E-10 4.2E-7

32 Jairampur UG 23043’19.13”N 86026’40.14”E 8.8E-7 2.54E-9 8.8E-7

33 Bararee OC 23041’51.42”N 86025’30.90”E 4.2E-8 4.37E-10 4.2E-8

34 Lodna UG 23043’24.98”N 86025’13.69”E 1.4E-7 3.06E-10 1.4E-7

35 Bhowra South OC 23040’26.43”N 86023’39.16”E 7.0E-8 - 7.0E-8

36 Bhowra North OC 23041’09.81”N 86023’43.73”E 1.4E-7 - 1.4E-7

37 Patherdih OC 23040’08.62”N 86025’56.61”E 6.4E-8 - 1.4E-8

38 Sudamdih UG 23040’00.86”N 86024’53.14”E 2.2E-7 - 6.2E-7

39 Lohapatti UG 23044’30.51”N 86012’53.77”E 1.4E-8 - 1.4E-8

40 Moonidih UG 23044’19.33”N 86020’49.11”E 6.2E-7 - 6.2E-7


178

The preparation of input data files for the model was undertaken as per the specified

format. This included appropriate estimation of the emissions from the various

sources based on the activity data and the emission factors taken into consideration in

the present study. The emission data were input to the model along with

meteorological data for assessing the concentration levels of the pollutant which will

be compared with the observed concentrations. A total of 40 sources were considered,

which consists of area sources (out of which 18 are mixed, 17 are underground and 5

are opencast mines.

8.3.4 Model output of area source modeling

The model output of isopleths of SPM due to operating coal mines as well as

integrated activities (mines and road traffic) are shown in Figures 8.10 and 8.11,

respectively. Figure 8.10 depicts maximum predicted concentration of 753 µg/m3

at

A21 (Chasnala), followed by 751 µg/m3 at A10 (Sijua). Similarly, modeling output

for integrated activities (i.e., mining and road traffic) depicted highest SPM

concentration of the order of 758µg/m3

at A21 (Chasnala) and lowest concentration of

196 µg/m3

at A23 (BIT- Sindri).

8.3.5 Model Performance

Scatter plot of observed and predicted SPM concentrations (Figure 9.12 and

9.13) along with regression coefficients provide good validation of the model.

Furthermore, different statistical parameters e.g., Normalizes Mean Square Error

(NMSE), Fractional Bias (FB) and Correlation Coefficient (CC) were also evaluated

as shown in Table 8.12. Values of NMSE and FB satisfy the model reliability criteria

which have been used in some earlier studies (Chang and Hanna, 2004; Kumar and

Luo, 1993). Besides, low NMSE values and higher value of ‘r’ indicate its suitability

for the application in area source modeling of SPM in the study area.

Table 8.12 Statistical Analysis of Model Performance

Pollutant Average concentration

(µg/m3)

Statistical analysis

Observed Predicted NMSE FB CC

SPM 522 521 0.0014 0.038 0.99

SPM* 522 541 0.0013 0.036 0.98

*Integrated activities


179

Figure 8.10: Isopleths for 24 hour SPM for Operating Coal Mines

Figure 8.11: Isopleths for 24 hour SPM for Integrated Activities

(Mines and Road traffic)


180

Figure 8.12: Correlation analysis between Observed and Predicted SPM

Concentration by ISC-AERMOD

Figure 8.13: Correlation analysis between Observed and Predicted SPM

Concentration by ISC-AERMOD( Integrated situation)


181

8.4 SUMMARY

This chapter is devoted to evaluate emission levels of pollutants due to coal

mines and road traffic situations of the study area. These data base were used in

CALINE 4 and ISC-AERMOD models for evaluating isopleths profile for respective

pollutants.

With respect to one hour modeling of CO for vehicular traffic, CALINE 4

indicates more appropriate. Besides, 24 hour modeling of SPM by ISC-AERMOD for

both mining and road traffic were used for evaluating isopleths profile of SPM in the

study area as isolated manner as well as integrated manner.

chapter 8 air quality prediction 8.0...

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