Air Pollution Dispersion Modeling Performance for Mining Complex

Transcription

1 Available online at ISSN: (Print) ISSN: (Online) Environ. We Int. J. Sci. Tech. 5 (2010) Environment & We An International Journal of Science & Technology Air Pollution Dispersion Modeling Performance for Mining Complex Jaiprakash *, Gurdeep singh #, A. K. Pal! Department of Environmental Science and Engineering, Indian School of Mines University, Dhanbad *jaipism@gmail.com, # s_gurdeep@yahoo.com,! palasim2003@yahoo.co.in Abstract Dhanbad, the coal capital of India, is a rapidly growing urban center. There has been an increasing trend of population growth since the early 2006, along with the process of industrialization and motorization. The combustion of fuel by vehicles and mining industries are producing significant to condemnable air pollution load. The impact of SPM and PM 10 emissions resulting from various air pollution sources in mining complex, industries, and vehicles, was estimated using the AMS/EPA Regulatory Model (AERMOD). It is a fairly recent and promising model for estimating concentrations of air pollutants, SPM and PM 10 concentrations from mining, industries and vehicular was found to be 73, 20 and 7% respectively. Further statistical analysis was carried out to evaluate the model performance by comparing measured and predicted SPM/PM 10 concentrations. The model performance was found good with an accuracy of about 64.9%. Keywords: Emission inventory; mining industries; Air quality; AERMOD model; Statistical analysis Introduction Air pollution has become a serious problem in urban cities of India, as a result of industrialization and motorization. Control of air pollutants is necessary to provide a better and safe environment for future generation. The studies conducted by National Environmental Engineering Research Institute (NEERI, 1990, 1993, 1996 a,b) resulted in-sight view of the causes of air pollution problems, based on which it was possible to adopt suitable mitigative measures for combating air pollution problems in a significant

2 manner. Dispersion model can be used for calculating pollutant concentrations at any point in the impact area under study. However, concentration of pollutant is influenced by the meteorology of the region. Hence, Emission inventory of different sources in various urban cities (Bencala et al., 1979; Bhanarkar et al., 2005; Gujar et al., 2004; Mittal and Sharma 2003a; Sharma et al., 2002; Garg et al., 2001a,b; Gargave and Aggarwal, 1999; ). In this connection, air pollution dispersion modeling can serve as important tools in arriving at and prioritizing the control measures to be implemented both with respect to pollution sources and pollutants as part of air quality management. Source contribution analysis based on emission inventory and air quality modeling using AERMOD model has been reported in the area of Delhi (Manju et al., 2009;). A limited number of studies were carried out in India using AERMOD model with the observed concentration of pollutants in ambient air, but do not include pollution potential due to various polluting sources. Present study evaluates the performance of USEPA model AERMOD for modeling concentrations of SPM and PM 10 in Dhanbad during winter ( ) as the adverse meteorological status during winter favours accumulation of more pollution level in comparison the other seasons. The predicted concentrations by model has been compared with obserevd value and their performance has been eavluated based on various statistical parameters. Materials and Methods Study area Dhanbad is the heart of Jharia coal field (JCF). JCF contains the only remaining reserves of prime coking coal in India. The total coal reserves of Bharat Coking Coal Ltd (BCCL, a subsidiary of CIL) of JCF are estimates to be 17,077 million tones. Further, 2340 million tones of coal lie within jurisdiction of IISCO and TISCO. It is located in the eastern part of India in the state of Jharkhand with longitude between E and E and latitude between 23º37 3 N and 24 4 N. The urban and rural population of the region as per 2001 census as 2,394,434, is spread in an area of about 6078 hectares and has experienced a phenomenal industrial growth in the last few decades. Maximum and minimum temperature recorded in the region is 43.0 and 14.0 C, respectively, and the average rainfall is around 1331 mm. Two important rivers, Damodar and Barakar flow in this region. Major open cast coal mines and many other small industries (beehive coke plant, bricks plant, etc.) are located in the region. As a result, the air environment of the region has been deteriorated over the years. AERMOD model was applied for impact assessment from these sources. This will facilitate an in-depth investigation for evaluating assimilative capacity of the air environment so as to devise appropriate air pollution control policy for the study area. Emission inventory In order to determine the contribution of different air pollution sources, emission inventory dataset was developed during winter ( ) and also used in air quality modelling. Three major sources, e.g., opencast mines, other industries and road traffic, were considered to prepare emission inventory of SPM and PM 10 in the study area. 35 major emission sources ( point, line and area sources) as shown in Figure 1 were 206

3 Jaiprakash et al., / Environ. We Int. J. Sci. Tech. 5 (2010) conisdered in the study area. The Emission factor of each sources has been carried out seperately. N 23⁰ 54 Prepared Emission Inventory of Study Area Barwa Adda Govindpur 23⁰ 51 Katras Bank More Baliapur 23⁰ 48 23⁰ 45 23⁰ 42 Road network (L1-L6) Point sources (S1-S20) 23⁰ 39 Sindri Open cast mines (M1-M9) 86⁰ 05 86⁰ 11 86⁰ 14 86⁰ 17 86⁰ 20 23⁰ 36 86⁰ 23 Figure 1 List of pollution source in the study area (Dhanbad) ⁰ 26 86⁰ 29 86⁰ 32 23⁰ 33

4 Industrial point sources There were 20 industrial point sources which include thermal power plants, beehive coke plants, hard coke plants, brick manufactures and other miscellaneous industries. To estimate emission load from these industrial point sources, industrial inventory was prepared with respect to production capacities, stack emission characteristics including stack height, stack diameter, flue gas temperature, and exit gas velocity from the different industrial sources. A field survey was conducted for ground truthing of the different industrial sources. Calculation of emission rate for the industrial point source was determined as per the SPCB guideline. Table 1 displays necessary stack details. Table 1 Stack details for different industrial point sources Stack Height (m) Gas Temp ( K) Diameter (m) Exit Velocity (m/s) S S S S S S S S S S S S S S S S S S S S Vehicular emission source In order to assess actual vehicular movement along the road networks, traffic counts were carried out during Winter, at different junctions/intersections including National Highway (NH), State Highway (SH) and intra-city roads around Dhanbad. Based on this database, average daily vehicular movement on the different road 208

5 networks of the Dhanbad area was evaluated (Table 2) and accordingly the vehicular pollution loads of six different road networks were evaluated (Sivacoumar and Thanasekaran, 1999). Emission factors for different categories of vehicles were collected from CPCB Publication (Transport Fuel Quality for Year 2005) and were as shown in Table 3. Table 2 Average vehicular count on different road networks SI. No Road Network /Traffic junction Heavy Vehicles (Bus, Trekkers) Trucks/Tractors & Goods Vehicles Light Vehicles Three wheelers Two wheelers Total Vehicles L1 L2 Govindpur to ISMU Main Gate - 8km ISMU Main Gate to Rajendra Market -5km L3 Rajendra Market to Sindri -10 km L4 Rajendra Market to Katras -14 km L5 Shramik Chowk to Barawa Adda 10 km L6 Jharia to Baliapur -10 km Table 3 Emission factors for different categories of vehicles Category of Vehicles Deterioration factor Emission Factor (g/km) SPM PM 10 SPM PM 10 Heavy vehicles (Bus, Goods vehicle) Trucks/Tractors Light vehicles (cars/taxis, etc) Three wheeler (Tempo, Auto-Rickshaw) Two wheeler (Motor cycle, Scooter, moped)

6 Pollution load of different category of vehicles on each road network was determined using equation (1). Pollution load = T n x X i xt (0-5 year) x L x R 1.. (1) Where, X i 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 Open cast mining In order to assess the emission inventory of open cast mining, field survey was conducted during winter and necessary details are shown in Table 4. Emission factors from USEPA s Compilation of Air Pollutant Emission Factors and engineering judgment were used to make these factors more representatives for Jharia coalfield (BCCL Dhanbad, ). Table 5 lists the emission factors used for coal mining industries. Table 4 List of the mines with salient features SI. No. Name of the mines Active Area (sq. m) Production ( ) (10 6 tones/y) Latitude (North) Longitude (East) M1 Barora Area Damoda M2 Muraidih Muraidih M3 Block II Block II M4 Sijua Area Sijua, Nichitpur, Tetulmari M5 Kusunda Dhansar, Ganga OCP M6 Kustore Rajpura OCP M7 Bastakolla M8 Lodna Barare, S.Tisra M9 East Jharia (Patherdih ) COCP,Patherdih

7 Table 5 Emission Factors for coal mining operations of Jharia Coalfield Sl No Source Type Emission Factors of SPM (kg/1000 t of coal/overburden) Emission Factors of PM 10 (kg/1000 t of coal/overburden) 1 Opencast mine coal operations Open cast mines overburden removal Coal storage/coal stacks/chp Meteorological data collection Meteorological data were collected along with an air quality monitoring program during of the study area. A Weather Monitor Station (WM-231) instrument was installed at the roof-top of the building of Department of Environmental Science and Engineering, Indian School of Mines University, Dhanbad, to monitor wind speed and direction to the upwind and downwind directions. A mono-static SODAR system was operated round the clock during winter to estimate the mixing height and hourly stability class variations. The antenna of SODAR emits sound pulse and records subsequent back-scattered echo intensity caused by the small-scale temperature fluctuations of the atmospheric layers. The Doppler frequency shifts of the echo signal recorded by antenna were used for estimating stability class and mixing height (Singal et al., 1965). These parameters were used as meteorological input to AERMOD model along with source and receptor data. Air quality monitoring In order to assess air quality status, 20 monitoring stations were set as shown in Figure 2. The monitoring stations were categorized as residential/commercial areas or industrial areas. For assessing the air quality (SPM and PM 10 ) status the air quality standard for industrial (coal mining) area (as per GSR 742 (E), Ministry of Environment & Forests, New Delhi, Notification dated ), given in Table 6 was taken into consideration. The concentration of SPM and PM 10 was measured in each monitoring stations during December 2008 to February 2009 by using Respirable Dust Sampler (Envirotech Model APM 460). Dust laden ambient air enters the Respirable Dust Sampler through inlet pipe. As the air enters the cyclone, coarse and non-respirable dust is separated from the air by centrifugal forces acting on the solid particles. These coarse particulates fall through the cyclone and get collected in the sampling bottle fitted at its bottom. The air stream passing through the filter paper, which was clamped between the 211

8 Jaiprakash et al., / Environ. We Int. J. Sci. Tech. 5 (2010) top cover and filter adopter assembly, carries the fine dust forming the respirable fraction (PM10). The instrument was operated at a flow rate of m3/min. Monitoring of PM10 was carried out twice a week in each monitoring site for a period of 24 hour. SPM and PM10 were collected on cm Whatman Quartz Microfibre filters and cup adopter. The particulate mass concentrations were measured gravimetrically by weighing the particles collected and knowing the total volume of air sampled. Filter papers were kept in a desiccator for 24 hour before and after the sample collection. Field and laboratory blank filter samples were routinely analyzed for PM10 to evaluate analytical bias and precision. It is assumed that the PM10 deposited on quartz microfiber filter papers were uniformly distributed over the entire area. Flow rate and mass concentration has been calculated Air volume sampled (V) = ൫ + ൯, m3 (2) Where Q1= initial flow rate in m3/min Q2= final flow rate in m3/min T= sampling time in min. Mass concentration of SPM and PM10 can be calculated as given formula. ሺ ሻ ^ SPM /PM10 (µg/m3) =... (3) Where M1= Initial weight of filter paper (g) M2 = final weight of filter paper (g) V = volume of air sample, (m3) 23⁰ 54 Ambient Air Monitoring locations of study area Govindpur (A1) Hirak Point (A15) Muraidih (A19) 23⁰ 51 Katras (A16) Tetulmari (A17) Steel Gate (A2) Bus Stand (A6) Block II (A20) 23⁰ 48 ISMU Main Gate (A3) 23⁰ 45 Court More (A4) Sijua (A18) Bank More (A7) Railway Station (A5) Bastacolla (A8) South Tisra (A13) Jamadoba (A9) Patherdih (A10) 23⁰ 36 BIT Sindri (A14) 86⁰ 11 86⁰ 14 86⁰ 17 86⁰ 20 86⁰ 23 86⁰ 26 86⁰ 29 Figure 2 Ambient monitoring locations in the study area ⁰ 39 Chasnalla (A12) Sudamdih (A11) 86⁰ 05 23⁰ 42 86⁰ 32 23⁰ 33

9 Table 6 Air Quality Standards (24-hour) for Existing Areas of Coal mining Area Category SPM RPM SO 2 NOx Concentration in µg/m 3 A Industrial area (Jharia Coalfield) Air quality prediction Keeping in view industrial, vehicular and open cast mining sources, meteorology, topography and data availability, ISC-AERMOD model (5.80 version) was identified as an appropriate model for air quality prediction (EPA, 1998). The model is capable of handling multiple sources, including point, volume, and area source types. Several source groups may be specified in a single run, with the source contributions combined for each group. The AERMOD 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 AERMOD 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 the meteorological preprocessor for AERMOD, which is called AERMET (EPA, 1998b). Both of these meteorological input files are sequential ASCII files, and the model automatically recognizes the format generated by AERMET as the default format. The model will process all available meteorological data in the specified input file by default, but the user can easily specify selected days or ranges of days to process. AERMOD calculates the convective and mixing height. Plume rise is determined by turbulence profile that varies with height. During unstable condition plume displacement is caused by random convective velocities. AERMOD is capable of estimating pollutant concentration from point line and area sources. The model incorporates the effect of increased surface heating from an urban area on pollutant dispersion under stable condition. There were 20 elevated stacks in various industries in the study area continuously emitting air pollutants which were considered for modeling purposes. Required input database included: detailed characteristics of source emission and dimension (pollutant emission rate, flue gas exit velocity and temperature, stack height and top inner diameter) and hourly boundary layer meteorological data (wind speed and direction, ambient temperature, atmospheric stability class, mixing height).the 471 receptor locations in the study area were arranged in a grid configuration to cover all important sites for estimating SPM and PM 10 concentration. The model was applied to predict SPM and PM 10 concentration on a 24- hourly average basis to facilitate comparison with national ambient air quality standards prescribed by Central Pollution Control Board, India (CPCB, 1995). In coal mining complex, main pollutant contibuted by open cast mining activities are SPM and PM 10. The SPM and PM 10 concentration has been considering 700 µg/m 3 and 300 µg/m 3 respectively which was specified by MOEF (Vide MOEF notification 25 th september 2000). 213

10 Statistical analysis In this work the performance of modeling has been done through some statistical approach. The parameters considered for the study are model bias, Normalized mean square error (NMSE), correlation coefficient, fraction bias and index of agreement. Model bias indicates that whether or not a model is over predicting or under predicting. Model Bias = NMSE is a fundamental statistical performance parameter, since it gives on the actual value of the error produced by the model. It emphasizes the scatter in the entire data set. (Kumar et al, 2006) NMSE= C O C P C O C P The Correlation analysis involves statistical performance obtained by liner square regression. The value of coefficient of correlation close to 1 indicates perfect correlation between observed and predicted values which is a sign of good model performance. The coefficient of correlation is given by: r = Where: Cp: Predicted values, Co: Observed values, σ CP: Predicted Standard deviation and σ CO : Observed Standard deviation The fraction bias (FB) is a nonlinear operator which is used to represent the relative difference between model and observation in bound range (± 2) and has an ideal value of zero for an ideal model. (Cooper, 1999). FB= 2 + Willmot recommended the use of index of agreement denoted as d, which depicts the accuracy in the predictions. (Willmot et al., 1980 ;) Index of agreement was calculated using the following formula: d = 1 n i = 1 n ( P i O i ) 2 i = 1 ( P i O + O i O ) 2 Where, Pi is the predicted value, Oi is the observed value and over-bar shows the average over the dataset. The value of d should vary between 0 and

11 Result and Discussion Emission load In order to assess air quality status in Dhanbad region, emission loads of 35 major air pollution sources (point, line and area sources) has been computed as emission load. This emission load showed that about 73, 20 and 7 % of the total SPM and PM 10 emissions were generated from the open cast mines, small scale industries and vehicular moment. Meteorological status Prevailing wind direction was found to be north with average wind speed of 2.0 m/s during daytime, whereas during night time prevailing wind direction and average speed were north-west and 0.9 m/s respectively. Wind rose diagram is shown in Figure 3 during winter The maximum mixing height was about 1100 m during afternoon hours (12:00 to 14:00) and minimum is about 100 m during early morning and late evening hours. Diurnal variations in the atmospheric stability determined based on SODAR data indicate that stable conditions prevail during the night hours, whereas the atmosphere becomes unstable during noon hours. Figure 3 Wind rosé diagram of the study area during winter

12 Ambient air quality status Ambient air quality monitoring for SPM and PM 10 was done as per CPCB guide lines at 20 locations in the study area during winter ( ). SPM and PM 10 concentrations are shown in Figures 4 and 5 respectively. 24 hour average maximum SPM concentrations at Bastakolla, Sudamdih, South Tisra, Sijua, Katras Block II were within the range of µg/m 3. At Sindri (273.6 µg/m3) and ISMU main gate (263.4 µg/m 3 ), comparatively lower concentration level were recorded. Similarly, the 24 hour average maximum concentrations of PM 10 at Bank More Bastakolla, Sudamdih, South Tisra, Tetulmari, Sijua were within the range of µg/m 3. Sindri (136.4 µg/m 3 ) and ISMU main gate (144.8 µg/m 3 ) registered lowest concentration levels. Concentration of SPM (in µg/m3 ) SPM concentration at different locations A1 A2 A3 A4 A5 A6 A7 A8 A9 A10A11A12A13A14A15A16A17A18A19A20 Locations of the study area Figure 4 SPM concentration at different locations NAAQ S Concentration of PM 10 (in µg/m 3 ) A1 A2 A3 A4 A5 A6 A7 A8 A9 A10A11A12A13A14A15A16A17A18A19A20 Locations o f the study area Figure 5 PM 10 concentration at different location 216

13 Model performance evaluation The concentration of SPM and PM 10 has been computed using ISC-AERMOD model. The model uses the hourly meteorological, emission and receptor data. Computation was done for predicting the existing air quality status with respect to (a) existing mines, (b) beehive coke plants/small scale industries, (c) road traffic and (d) integrated activities which include both mines and industries along with vehicular movement through the major road networks in Dhanbad. The outputs are presented in Figures 6 and 7 for SPM and PM 10 respectively. For computer run with run, 5000 m contour interval was kept for computation. For the validation of the model results, SPM and PM 10 concentrations at receptor locations (ambient air quality monitoring locations) have been predicted through the model. The predicted values at certain receptor locations have been corrected by considering local background sources. The performance of the AERMOD model has then been examined with the help of scatter plot diagram as shown in Figure 8 for SPM and Figure 9 for PM 10. A perusal of the figures of both SPM and PM 10 revealed similar trend for both predicted and observed values. The correlation coefficient, for SPM and for PM 10 also indicates moderate to high association. Model accuracy The model accuracy has been carried out computing several statistical errors, i.e., Model Bias (MB), Normalized Mean Square Error (NMSE), Correlation coefficient (r 2 ), Fractional Bias (FB) and index of agreement. The numerical values of statistical errors are shown in Table 7. To determine the reliability of a model the criteria as suggested by Kumar et al. (2006), have been used. These parameters were calculated using observed and predicted concentration of SPM and PM 10 at different locations. Statistical analysis has been observed and predicted concentrations indicated good accuracy for both SPM and PM 10. The Model bias of SPM (51.7) and PM 10 (63.78) showed over predicting situations for both SPM and PM 10. The NMSE values for SPM ( ) and PM 10 (0.1771) indicated strength of association. Similarly, the correlation coefficients for SPM (0.84) and PM 10 (0.82) indicated moderate to high association. The values of FB for SPM (0.312) and PM 10 (0.334) were within ±2, indicating less deviation between observed and predicted values. However, the value of Index of agreement indicates the moderate association. Thus, it is to be concluded that the performance of the model is reasonably good. Table 7 Statistical errors computed for the model Statistical error Ideal value SPM PM 10 Model Bias Normal mean square error Least value Correlation coefficient Fraction bias ± Index of agreement

14 Jaiprakash et al., / Enviro Environ. We Int. J. Sci. Tech. 5 (2010) Figure 6 Isopleths of predicted SPM concentratio concentrations (µg/m3) 218

15 Jaiprakash et al al., / Environ. We Int. J. Sci. Tech. 5 (2010) Figure 7 Isopleths of predicted PM10 concentrations (µg/m3) 219

16 Predicted SPM Concentration (µg/m 3 ) y = 1.226x R² = Observed SPM Concentration (µg/m 3 ) Figure 8 Correlation between predicted vs. observed SPM concentration Predicted PM 10 Concentration (µg/m 3 ) y = 1.136x R² = Observed PM 10 Concentration (µg/m 3 ) Figure 9 Correlation between predicted vs. observed PM 10 concentration Conclusion Isopleths of predicted SPM and PM 10 concentrations revealed maximum SPM concentrations at Bastakolla, Sudamdih, South Tisra, Sijua, Katras and Block II localities, whereas Sindri and ISMU main gate registered comparatively lower concentration levels. Similarly, maximum predicted PM 10 concentrations were observed at Bank More, Bastakolla, Sudamdih, South Tisra, Tetulmari, and Sijua localities due to different mining 220

17 activities, plying of higher number of vehicles, etc. Sindri and ISMU main gate, however, registered comparatively lower concentration levels for both SPM and PM 10. It has also been seen that open cast mines contributed 73 % of total SPM/PM 10 emission load in the study area, whereas small scale industries and vehicular traffic contributed about 20 % and 7 % of the total SPM/PM 10 emission load respectively. The predicted values from ISC-AERMOD model was compared with monitored values which showed more or less good accuracy. The statistical error tests also justified the same. The study also provides some guidelines for understanding the complexity of air pollution problems in Dhanbad region. However, it is also felt to initiate in-depth investigations for evaluating the assimilative capacity of the air environment so as to devise appropriate air pollution control policy for the study area. References BCCL Dhanbad, Annual Report: Environment Division, Lakes Environment Software, ISC-AERMOD View, 2008, URL: Bencala, K.E., and Seinfeld, J.H., An Air Quality Model Performance Assessment Package. Atmospheric Environment 13, Bhanarkar, A.D., Gajghate, D.G., and Hasan, M.Z., Assessment of Impacts of Fossil Fuel based Power Plant. International Journal of Environmental Studies 60, CPCB 2005 Transport Fuel Quality for Year Central Pollution Control Board Publication, New Delhi. CPCB, 1995 National Ambient Air Quality Standards. Central Pollution Control Board Notification, New Delhi. EPA, User's Guide for the ISC-AERMOD Dispersion Models (EPA-450/ b). Environmental Protection Agency, North Carolina Garg, A., Shukla, P.R., Bhattacharya, S., Dhadhwal, V.K., Subregion (district) and Sectoral Level SO2 and NOx Emission in India: assessment of inventories and mitigation.atmospheric Environment 35, Gargava, P., Aggarwal, A.L., Emission Inventory for an Industrial Area of India. International Journal of Environmental Studies 55, Gujar, B.R., Aardenne van, J.A., Lelieveld, J., Mohan, M., Emission Estimates and Trends ( ) for megacity Delhi and implications. Atmospheric Environment 38, Kumar A., Dixit S., Varadarajan C., Vijayan A., and Masuraha A., 2006 Evaluation of the AERMOD Dispersion Model as a function of Atmospheric Stability for Urban Area, Environmental Progress Vol. 25 pp Ministry of Environmental Forests, MOEF (Vide MOEF notification 25 th september 2000), Air Quality Standards (24-hour) for Existing Coal Mining Areas, Mittal, M.L., Sharma, C., 2003a. Anthropogenic Emissions from Energy Activities in India: Generation and Source characterization (Part I. Emissions from thermal power generation in India). URL: pcrm/emissions/india.pdf. 221

18 Mittal, M.L., Sharma, C., 2003b. Anthropogenic emissions from energy activities in India: generation and source characterization (Part II. Emissions from vehicular transport in India). URL: emissions/india_report_1pagelayout.pdf. Mohan, M., Bhati, S., Marrapu, P., Performance Evaluation of AERMOD and ADMS Urban Models in a Tropical Urban Environment. Indian Journal of Air Pollution Control Vol. 9 (I), NEERI, National Ambient Air Quality Monitoring Report ( ). National Environmental Engineering Research Institute,Nagpur, India. NEERI, Air Pollution Studies to Redefne Taj Trapezium Coordinates. National Environmental Engineering Research Institute, Nagpur, India. NEERI, Regional Environmental Impact Assessment Studies for Jamshedpur Region, report Vols. I and II. National Environmental Engineering Research Institute, Nagpur, India. NEERI, Carrying Capacity based Developmental Planning of Doon Valley. National Environmental Engineering Research Institute, Nagpur, India. NEERI, Carrying Capacity Based Developmental Planning of National Capital Region. National Environmental Engineering Research Institute, Nagpur, India. Sharma, C., Dasgupta, A., Mitra, A.P., Inventory of GHGs and other Urban Pollutants from Transport Sector in Delhi and Calcutta. In: Proceedings of Workshop of IGES/ APN Mega-city Project, January 2002, Kitakyushu, Japan. Singal, S.P., Lewthwaite, E.W.D, Wratt, D.S., Estimating atmospheric stability from mono static acoustic sounder records. Atmospheric Environment 19 (2), Sivacoumar, R., Thanasekaran, K., Line source model for vehicular pollution prediction near roadways and model evaluation through statistical analysis. Environmental Pollution 104, Willmot, C., Wicks, D.E., An empirical modeling for the spatial interpolation of monthly precipitation within California. Physical Geography 1,