Chapter 8 Air Quality Prediction
153
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
Chapter 8 Air Quality Prediction
154
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.
Chapter 8 Air Quality Prediction
155
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.
Chapter 8 Air Quality Prediction
156
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.
Chapter 8 Air Quality Prediction
157
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
Chapter 8 Air Quality Prediction
158
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”
Chapter 8 Air Quality Prediction
159
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).
Chapter 8 Air Quality Prediction
160
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
Chapter 8 Air Quality Prediction
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
Chapter 8 Air Quality Prediction
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)
6.00-9.00 hours 120 126 2,668 2,100 3,665 8679 9.00-12.00 hours 162 245 2,997 3,245 6,956 13605 12.00-15.00 hours 139 298 2,796 2,954 4,389 10576 15.00-18.00 hours 126 189 2,651 3,134 5,656 11756 18.00-21.00 hours 131 173 2,221 2,689 6,321 11535 Total 678 1031 13333 14122 26987 56151
� Karkend
(R3-T15)
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
Chapter 8 Air Quality Prediction
163
� Loyabad (R3-
T13)
6.00-9.00 hours 126 138 2,776 2,110 3,697 8847 9.00-12.00 hours 168 245 3,110 3,335 6,868 13726 12.00-15.00 hours 137 320 2,791 2,960 4,492 10700 15.00-18.00 hours 129 204 2,787 3,114 5,569 11803 18.00-21.00 hours 110 210 2,332 2,566 5,142 10360 Total 670 1117 13796 14085 25768 55436
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)
6.00-9.00 hours 125 131 2,448 2,107 3,697 8508 9.00-12.00 hours 162 312 2,887 3,248 6,991 13600 12.00-15.00 hours 138 244 2,496 2,854 5,289 11021 15.00-18.00 hours 123 288 2,751 3,236 5,660 12058 18.00-21.00 hours 131 325 2,000 2,580 6,313 11349 Total 679 1300 12582 14025 27950 56536
� 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)
Already mentioned in R1-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
Chapter 8 Air Quality Prediction
164
� 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)
Already mentioned in R4-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
Chapter 8 Air Quality Prediction
165
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
Chapter 8 Air Quality Prediction
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
Chapter 8 Air Quality Prediction
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
Chapter 8 Air Quality Prediction
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
Chapter 8 Air Quality Prediction
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
Chapter 8 Air Quality Prediction
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
Chapter 8 Air Quality Prediction
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
Chapter 8 Air Quality Prediction
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)
Chapter 8 Air Quality Prediction
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
Chapter 8 Air Quality Prediction
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.
Chapter 8 Air Quality Prediction
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
Chapter 8 Air Quality Prediction
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.
Chapter 8 Air Quality Prediction
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
Chapter 8 Air Quality Prediction
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
Chapter 8 Air Quality Prediction
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)
Chapter 8 Air Quality Prediction
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)
Chapter 8 Air Quality Prediction
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.