Pollution Dispersion Modeling for Concentrations of PM, SO X and NO X around Manali Region, India

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1 Indexed in Scopus Compendex and Geobase Elsevier, Geo-Ref Information Services-USA, List B of Scientific Journals, Poland, Directory of Research Journals ISSN , Volume 09, No. 03 June 2016, P.P Pollution Dispersion Modeling for Concentrations of PM, SO X and NO X around Manali Region, India EDWIN D THANGAM 1, NARAYANAN RM 1 AND RAJU AEDLA 2 1 Department of Civil Engineering, Dr. M.G.R. Educational and Research Institute University, Maduravoyal, Chennai , India 2 GSST, Kumamoto University, Kurokami, Kumamoto, Japan edwinthangam@yahoo.com, narayanan.rm@gmail.com, rajuaedla.nitk@gmail.com Abstract: The Manali industrial area is one of the largest petrochemical complexes in India and the only petrochemical complex in the state of Tamil Nadu which generates huge quantities of pollutants such as Sox, NOx and PM. The study region spreads over an aerial extent of 800 hectares intersected by villages and inhabited areas, which is located at Latitude 13 o N and Longitude 80 o E. The total population of Manali is 58,174 as per Census, In this study maximum ground level concentrations of air pollution around Manali region were modeled for four different industries using AERMOD dispersion modeling software. The study characterizes the emission concentration of SOx, NOx and PM individually for different sources. Further a complex model was generated towards estimating the maximum ground level concentrations around Manali region. The results revealed that the annual concentrations of pollutants SOx (148 to 170 µg/m 3 ) and NOx (74 to 93 µg/m 3 ) exceed the prescribed Central Pollution Control Board (CPCB) annual standards of 50 µg/ m 3 and 40 µg/m 3. The study concluded with the identification of hotspot area around Manali region like TKS Nagar, Eastern part of Thiruvottiyur village for excess concentration of SOx and NOx. The individual analysis of industrial sources revealed that the excess concentration observed around Manali region is due to the uncontrolled stack emission of M/s.TPL and is a major threat to the sustainable livelihood of human population. Keywords: Air pollution, plume dispersion, Ground level concentration, air quality standards, Hotspots 1. Introduction According to the recent estimates of World Health Organization (WHO), every year approximately 2.7 million deaths are attributed through air pollution. Chronic exposure to air pollutants is a worldwide problem. Long-term exposure of people to nonlethal air pollutants and their effects on global and regional atmospheric cycles were studied by various authors over the past decades. Especially the investigations are focused on Total suspended particulates (TSP), particulate matter (PM), nitrogen dioxide, sulfur dioxide, due to their health impact (Kirk-Othmer 2007). Abdul-Wahab (2004) found that the air emissions in the Mina Al-Fahal refinery in Oman was caused mainly by the quantities of fuel gas burned, which results in the emission of various pollutants to the atmosphere, including the SO 2 that is the main subject of his paper. The composition of the fuel gas used in the combustion contains 78.9 mol % hydrogen (H 2 ), 3.7 mol % methane(ch 4 ), 5.1 mol % ethane (C 2 H 6 ), 7.9 mol % propane(c 3 H 8 ), 4.1 mol % butane (C 4 H 10 ), 0.1 mol % pentane(c 5 H 12 ), and 0.2 mol % hydrogen sulfide (H 2 S). In the combustion, air is used as a source of oxygen (O 2 ). When this fuel is burned, carbon (C) in the fuel reacts to form either carbon monoxide (CO) or CO 2, H 2 forms H 2 O, and S forms SO 2. At temperatures greater than1800 C, some of the nitrogen (N 2 ) in the air reacts to form NO. PM 10 is the portion of particulate in the air having an aerodynamic equivalent diameter (AED) less than or equal to 10mm. Emissions that cannot reasonably pass through a stack, chimney, vent, or other functionally equivalent opening are considered fugitive emissions (40 CFR Part ). EPA defines emission factors as typical values that attempt to narrate the quantity of a pollutant released to the atmosphere with an activity associated with the release of that pollutant (USEPA 1995). For PM 10, the current NAAQS is 150mgm 3 (24-h average). Decision-making concerned to pollution control strategies requires reliable information on the likely future air quality for post implementation scenarios. Air quality modeling has the potential to provide this information in a relatively simplistic yet fairly reliable manner and is increasingly becoming an essential component of all urban air quality management programs (Fedra 2000; McHugh et al. 2007). Ambient air pollution has become a matter of grave concern (Banerjee 2010). The air pollution models are used to support laws and regulations intended to protect air quality. The models have been the topics of comprehensive assessment to determine their performance under a variety of meteorological conditions (Riswadkar and Kumar 1994; Kumar 1993; Patel and Kumar 1998; Paine et al 1998). Depending on the meteorological conditions and source type, number of dispersion models was #SPL Copyright 2016 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.

2 Pollution Dispersion Modeling for Concentrations of PM, SO X and NO X around Manali Region, India 585 available in the United States Environmental Protection Agency (US EPA) for different applications. The US EPA has issued strategies on choosing specific air quality models (40 CFR Part ). The dispersion models are classified into two types 1) screening and 2) refined models. Almost the regulatory models represented by EPA use either Gaussian Plume Algorithm or a variation of Gaussian Plume model. Gaussian models are very effective, practical, and used extensively used even today. The results obtained through gaussian models provides outcomes that closely matches with the experimental data, integrate instability in an ad-hoc manner, quicker than numerical models, simple in mathematics and no supercomputers are required. Air Quality Modeling is an effort to forecast or mimic the ambient concentrations of contaminants in the atmosphere. These regulatory models are used mainly as a quantifiable tool to correlate the causes and effects of pollutant concentration levels found in an area. The model also stimulates the flow of air pollutants in the atmosphere and thus predicts the associated health and environmental impacts. In India, the application of air quality models is mainly limited to regulatory purposes with the Industrial Source Complex (ISC) of United States Environmental Protection Agency (USEPA) (ISCST3 1998) being the model of choice. Industrial Source Complex (ISC) is one of the widely used air quality model for short term emissions characterization. The ISC models provide an opportunity to estimate emissions from a wide range of sources which might be encountered around typical industrial sources. Very few cases of application of these models in urban air quality management are currently available (TERI 2005; IES 2005; Athalye et al. 2006). United States Environmental Protection Agency has proposed several models to simulate air quality under different scenarios. One such recent proposal is AERMOD model for industrial sources. Presently AERMOD acts as EPA preferred regulatory model (USEPA 2003). 2. Aim and Objectives The main aim of the study is focused towards finding maximum ground level concentration of pollutants (SOx, NOx and PM) emitted from the following industries around Manali region using AERMOD model. M/s.Chennai Petroleum Corporation Limited (CPCL), M/s. Manali Petro Products Limited (MPL), M/s. Tamilnadu Petro Products Limited (TPL) and M/s. Madras Fertilisers Limited (MFL) industries, 3. Study Area Description The Manali industrial area is one of the largest petrochemical complexes in India and the only petrochemical complex in the state of Tamil Nadu which generates huge quantities of pollutants such as PM, NOx, Sox and spread over an area of 800 hectares intersected by villages and is inside the inhabited area which is located at Latitude 13 o N and Longitude 80 o E. The total population of Manali is 58,174 as per Census, Manali region averages mm of rainfall. The people living in these villages are employment dependents on these industries and are exposed to the air pollutants released by the industries. This industrial area is well connected by road, rail and ports.

3 586 EDWIN D THANGAM, NARAYANAN RM AND RAJU AEDLA 4. Aermod Model and its Characteristics As cited by Ashok Kumar (2004), AERMOD is a steady-state Gaussian plume model useful for the computation of pollutant dispersion applicable to rural and urban; flat and complex terrain; surface and elevated release; and multiple sources (point, area and volume) of emissions (Cimorelli et al. 2005; Perry et al. 2005; Cimorelli et al. 2004) Gaussian and bi- Gaussian approaches are adopted in AERMOD dispersion models (USEPA 2002). It produces day-today, monthly as well as annual pollutants concentrations in ambient air. The model handles variety of pollutant sources in a wide range of settings like rural, urban flat and complex terrains. It is a modernized version of the Industrial Source Complex (ISCST3) model proposed by USEPA for assessing air quality impacts from various industries. One of the major advancements that AERMOD offer is its ability of surface and mixed layer scaling to depict the planetary boundary layer (PBL). When compared to the ISCST3 model the advanced AERMOD model comprises improved algorithms. Some of them are listed below as cited by (Ashok Kumar 2004): Dispersion in both the convective and stable boundary layers, Plume rise and buoyancy, Plume penetration into elevated inversions, Treatment of elevated, near-surface, and surface level sources, Computation of vertical profiles of wind, turbulence, and temperature, Treatment of receptors on all types of terrain (from the surface up to and above the plume height) and complex terrain modeling computations, and Incorporation of the Plume Rise Model Enhancements (PRIME) building downwash algorithms Some of the primary features and capabilities or AERMOD are: Source types : Multiple point, area and volume sources Source releases: Surface, near surface and elevated sources Source locations: Urban or rural locations. Urban effects are scaled by population. Plume types: Continuous, buoyant plumes Plume deposition: Dry or wet deposition of particulates and/or gases Plume dispersion treatment: Gaussian model treatment in horizontal and in vertical for stable atmospheres. Non-Gaussian treatment in vertical for unstable atmospheres Terrain types: Simple or Complex terrain Building effects : Handled by PRIME downwash algorithms Meteorology data height levels: Accepts meteorology data from multiple heights Meteorological data profiles: Vertical profiles or wind, turbulence and temperature are created. 5. Materials and Methods The methodology adopted in the study has been given in Figure (1) The primary data (Emission inventory) and Secondary data (Micro meteorology) were obtained from the respective industries and meteorological monitoring stations. The data were supplied as input to AERMOD model. The listing and description of pollutant sources, including estimated pollutant emission quantification comprise of emission inventory (Bhanarkar 2005; Gargava and Aggarwal 1999). 5.1 Emission Inventory Emission data for pollutants SOx, NOx and PM for Manali region were collected from 42 stacks for M/s. CPCL, 2 stacks for M/s. MPL, 4 Stacks for M/s. MFL and 15 stacks of M/s. TPL during the year Data were obtained continuously at an interval of 10 minutes. The following source characteristics for each individual stacks were obtained and used in AERMOD model. The parameters considered in this study includes stack emission characteristics, Point Source, Stack ID, Stack attached to emission source, base elevation, height, diameter, exit gas velocity, flue gas exit temperature, release type, emission rate (g/sec), X - coordinate, Y - coordinate through field studies. 5.2 Micro-Meteorology Meteorological data were collected along with an air quality monitoring program during the study period (2012). The following micro meteorological conditions were collected from the portable weather forecasting instrument velocity of wind direction of wind Ambient temperature

4 Pollution Dispersion Modeling for Concentrations of PM, SO X and NO X around Manali Region, India 587 Atmospheric stability Mixing height The collected data were plotted in Wind rose plots below to address the role of wind direction in the study area. WIND ROSE 2012 ANNUAL carried out under a complex terrain with grid spacing of 0.5 km for the domain. For each source and every hour, the origin of the source s coordinate system is positioned at the ground surface at the base of the stack. The downwind direction is presented along x axis as positive, the crosswind is plotted as y axis which is normal to the x axis and the z axis extends to the vertical direction. For a steady-state Gaussian plume, the hourly concentration at downwind distance x (meters) and crosswind distance y (meters) is given by: 5.3 Aermod Modeling The AERMOD was developed by integrating complex algorithms and concepts, i.e., planetary boundary layer (PBL) theory from the Industrial Sources Complex Short Term Model (ISCST3). Similar to ISCST3, the AERMOD is considered to be accurate for dispersion modeling for the distances not exceeding 50 km from the emission source (US EPA, 2005). In fact AERMOD consist of three separate modeling components 1) AERMOD (American meteorological Society/Environmental protection agency Regulatory Model Improvement Committee AERMIC Dispersion Model), 2) AERMAP (AERMOD Terrain Preprocessor), and 3) AERMET (AERMOD Meteorological Preprocessor). The features of the AERMOD module includes: i) perception of plume penetration, ii) assessment of dispersion coefficients, iii) estimation of plume rise, iv) prediction of pollutant concentrations in convective and stable layers v) management of downwash, and vi) treatment of complex and simple terrains. The AERMET components include processing of hourly surface and meteorological data pertaining to upper atmosphere. The surface parameter coefficients for the AERMET module were set to summer conditions for model simulations of the climate in Manali region. The AERMAP module is used in processing the terrain data in concurrence with a layout of receptors and emission sources which is further used in the AERMOD control files. The AERMOD modeling system used in this work was run with a commercial interface, AERMOD View (Version 7.5.0) (Lakes Environmental Software, Waterloo, Ontario, Canada). The simulation was Where: - is the air pollution concentration (g/m 3 ) in mass per volume, Q pollutant emission rate (mass per unit time); K a scaling coefficient to convert calculated concentrations to desired units (default value of 10 6 for Qin g/s and concentration in µg/m 3 ) V Vertical term D Decay term mean wind speed (m/s) at release height Standard deviation of lateral and vertical concentration distribution (m) Equation (1) is the basic formulation to model the plume impacts from point releases, flare releases, and volume releases in AERMOD. The AERMOD modeling involves five steps to predict the maximum ground level concentration of SOx, NOx and PM Stage I: Pre-processing Meteorology Data Using AERMET Stage II: Importing Meteorology Data To AERMOD From AERMET Stage III : AERMOD- Feeding Source Information Stage IV: Receptors (Ncart) Stage V: Running AERMOD for determining the maximum ground level concentration of various pollutant parameters During the preprocessing surficial data sector has been assigned with urban the land use parameter with the conditions was given as average as inputs. The baseline elevation is provided with 9m MSL. The profile meteorological data were given for a period of one year using the surface stations. Various source location parameters were assigned like type of source, source id, description of the source, X coordinate, Y coordinate, Base elevation, Release height. Source release parameters such us emission rates, gas exit temperature in K, stack inside diameter, gas exit velocity and gas exit flow rate were assigned with fixed mode. The grids were developed with 5 Km radius for M/s. CPCL, M/s.TPL, M/s.MPL &

5 588 EDWIN D THANGAM, NARAYANAN RM AND RAJU AEDLA M/s.MFL and 7 Km radius for Manali overall average. The receptor pathways were created with 21x21 grids covering each 500m for the output concentration maps. In order to simulate the SO 2, NOx, PM concentrations at three air quality monitoring sites in the study area three discrete receptors located at these sites were set. Then, we used the AERMOD model to simulate SO 2, NO X, PM concentrations at different time scales at 1, 3, 8 h, daily, monthly, and annual intervals. The simulations were carried out under the model configurations of urban dispersion coefficients and elevated terrain without considering downwash effects. To evaluate the coherence between Petroleum Industry (Oil Refining), Minimum Emission Standards and the Ambient Air Quality Standards, emission rates were then adjusted to comply with the regulated emission standards, and modelestimated ambient concentrations were compared with the 1 hour, 24 hour and annual average Air Quality Standards. 6. Results and Discussion The maximum ground level concentrations of pollutants around Manali region were modeled using AERMOD Dispersion Modeling Software. The study illustrates the annual predicted emission concentration (long term exposure) of SOx, NOx and PM, for individual and complex models around Manali region as indicated in the Figures (1-16). All the figures show that, the maximum predicted average pollutant concentrations and corresponding distances from the source at which these occur are a function of the year. Based on the observed results the following two areas around Manali region were highly affected by excess emissions of SOx and NOx. 1. TKS Nagar (SOx emission) 2. Part of Thiruvottiyur (NOx emission) Close inspection of the annual Wind rose plot reveals that the majority of the prevailing wind direction is NNE to ENE during the study period (2012).The predominant wind speed i.e. 60% of the wind speed varied from knots and 20% of the wind speed ranged from 7-11 knots. The individual analysis of M/s. CPCL stacks and AERMOD model predicted emission data revealed that the annual concentration of SOx is in the range of 36.9 to 57 µg/m 3. This is somewhat exceeding the permissible norms of (CPCB) 50 µg/m 3 at a corresponding distance of 0.3 to 2.5 km. Similarly the other parameters like NOx and PM were evaluated. The NOx value projected for the region varied between 1 to 22 µg/m 3 and the PM varied between 1.5 and 27 µg/m 3. The predictions of NOx and PM revealed that the controlled emissions were adhered to in the M/s.CPCL stacks. The individual analysis of predicted emissions from M/s.MPL stacks revealed that the SOx value projected for the region varied between 4.1 and 5.7 µg/m 3, the NOx value projected for the region varied between 1.6 and 1.9 µg/m 3, the PM varied from 5.3 to 6.15 µg/m 3 at a corresponding distance of ~ 0.6 km indicating that there is no harm arising from stack emissions. The individual analysis of predicted model data of M/s.MFL stacks revealed SOx value projected for the region varied between 9 and 14 µg/m 3, the NOx value projected for the region varied between 0.5 and 0.92 µg/m 3, the PM varied between 3.3 to 5.4 µg/m 3 at a corresponding distance range of 1.0 to 3.0 km indicating that the controlled emission norms were adopted by M/s MFL. From the individual analysis of AERMOD model output of M/s.TPL stacks, the annual concentration of SOx showed higher range of 148 to 170 µg/m 3 which exceeds the annual average prescribed by CPCB standards (50 µg/m 3 ) at a corresponding distance of 0.5 to 0.7 km. Similarly, the NOx value projected for the region varied between 74 and 93 µg/m 3 and also exceeds the annual average of permissible standards of CPCB (40 µg/m 3 ) at a corresponding distance of 1.0 km. However the projected PM varied between 6.9 and 7.2 µg/m 3 indicating that there is no harm arising due to PM emissions from the TPL stacks. From the predicted emissions of M/s TPL, it is observed that the combustion emissions are mainly released through fifteen stacks/flares. Flare systems in petrochemical industries are designed to provide safe disposal of gases/vapors released from process equipment. The steam assisted flare system uses fuel gas to burn any unprocessed hydrocarbons and H 2 S from the entire petro products, generating SO 2 in the process. Unfortunately, the combustion process in these flares is intermittent and is less than 100% efficient, resulting in the emission of unburnt hydrocarbons, carbon monoxide and soot. In addition, the high temperature of combustion would also result in the formation of carbon dioxide, NO X and SO X. These pollutants may have a severe influence on the industrial area and surrounding urban localities (Cairncross 2007; Saqer and Al-Haddad 2008). To validate the modeling, model-predicted ambient concentrations based on actual refinery emissions were compared with corresponding monitored data from two local monitoring stations. The validity of AERMOD simulation has been studied by various authors, where, SOx, NOx and PM 10 simulation were tested to be credible both in different time and area scales (Kesarkar et al. 2007; Vijay Bhaskar et al. 2008; O Shaughnessy and Altmaier 2011; Seangkiatiyuth et al. 2011). The previous published literature backed up the consistency of our study using AERMOD modeling system. In addition to the

6 Pollution Dispersion Modeling for Concentrations of PM, SO X and NO X around Manali Region, India 589 literature on input data of our simulation were collected from personal observations of emissions from stack and flares. The combined concentrations for various industries evaluated through AERMOD complex model revealed that the concentrations for SOx and NOx around the specific areas in Manali region exceeded the prescribed standards. In general, the results showed that the highest pollutant concentrations were found to occur relatively close to the sources of emissions. The excess emissions of SOx and NOx in the above specified area may cause health impacts to the exposed communities. 7. Conclusion Air Quality Modeling is an effort to forecast or mimic the ambient concentrations of contaminants in the atmosphere. These regulatory models are used mainly as a quantifiable tool to correlate the causes and effects of pollutant concentration levels found in an area. The study characterizes the emission concentration of SOx, NOx and PM individually for different sources. Further, a complex model was generated towards estimating the maximum ground level concentrations around Manali region. The study concluded with the identification of hotspot areas around Manali region like TKS Nagar, Eastern part of Thiruvottiyur village for excess concentration of SOx and NOx. The individual analysis of industrial sources revealed that the excess concentration observed around Manali region is due to the uncontrolled stack emission of M/s.TPL and is a major threat to the sustainable livelihood of the human population. The study suggests that periodic monitoring and pollution control methods have to be strictly followed to minimize the emission at M/s. TPL stacks. Acknowledgements The authors wish to record their thanks to Tamilnadu Pollution Control Board for arranging the AERMOD software to predict the air quality around Manali region. The authors record their thanks to Er. A.C.S Arunkumar, President, Dr. M.G.R Educational and Research Institute, for his continuous encouragement to conduct this research. References [1] 40 CFR Part 51., (2003). Code of Federal Regulations Title 40, Part 51, State Operating Permit Program. USEPA, Washington, DC April 15. [2] 40 CFR Part 70., (1999). Code of Federal Regulations. Title 40, Part 70., State Operating Permit Program. USEPA, Washington, DC [3] Abdul-Wahab Sabah A., (2004). Evaluation of the Industrial Source Complex Short-Term Model: Dispersion over Terrain. Journal of the Air & Waste Management Association., 54: [4] Ashok Kumar., (2004). Understanding the USEPA s AERMOD Modeling System, University of Toledo. Web. mod/aermod_drm_course %20 notes.doc [5] Athalye, V.N., Patil, R.S and Sethi, V., (2006). Modeling and mapping of air pollution health benefits for an urban region. In National Air Quality Workshop, Indian Institute of Technology, Roorkee, India, February. [6] Banerjee, T., (2010). Assessment and model performance evaluation of air quality at Integrated Industrial Estate-Pantnagar. Ph.D. Dissertation, G.B. Pant University of Agriculture and Technology, India. [7] Bhanarkar, A.D., Goyal, S.K., Sivacoumar, R. and Rao, C.V.C., (2005). Assessment of contribution of SO 2 and NO 2 from different sources in Jamshedpur region, India. Atmospheric Environment., 39, [8] Cairncross E., (2007). Report and Technical Protocol for the Monitoring and Regulation of Flaring from Oil Refineries in South Africa. CONTRACT: UEM-STTA-M Web. ualitymanagement/brownhaze/pages/brownhaz estudy.asp [9] Cimorelli, A.J., Perry, S.G., Venkatram, A., Weil, J., Paine, R., Wilson, R.B., Lee, R.F., Peters, E.D. and Brode, R.W., (2005). AERMOD: a dispersion model for industrial source applications Part I: general model formulation and boundary layer characterization. Journal of Applied Meteorology., 44, [10] Cimorelli, A.J., Perry, S.G., Venkatram, A., Weil, J.C., Paine, R.J., Wilson, R.B., Lee, R.F., Peters, W.D., Brode, R.W. and Paumier, J.O., (2004). AERMOD: Description of model formulation. US Environmental Protection Agency EPA Report No. 454/R d, 85. [11] Fedra, K., (2000). Model-based decision support for integrated urban air quality management. In: Longhurst, J.W., Elsom, D.M., Power, H. (Eds.), Air Quality Management. WIT Press, Southampton, [12] Gargava, P. and Aggarwal, A.L., (1999). Emission inventory for an industrial area of India. International Journal of Environmental Studies., 55, [13] IES (2005). Integrated Environmental Strategies (IES) study for city of Hyderabad, INDIA, Final Report prepared by EPTRI, Hyderabad, April Web. STRATEGIES.pdf [14] ISCST3 (1998). User Guide for Industrial Source Complex ISCST3 model, USEPA. [15] Kesarkar A P., Dalvi M., Kaginalkar A., Ojha A., (2007). Coupling of the Weather Research and Forecasting Model with AERMOD for pollutant dispersion modeling. A case study for PM 10

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8 Pollution Dispersion Modeling for Concentrations of PM, SO X and NO X around Manali Region, India 591 Fig 2.Predicted annual average concentration contours of PM (µg/m 3 ) in year 2012 (CPCL) Fig 3.Predicted annual average concentration contours of PM (µg/m 3 ) in year 2012 (MPL) Fig 4.Predicted annual average concentration contours of PM (µg/m 3 ) in year 2012 (TPL)

9 592 EDWIN D THANGAM, NARAYANAN RM AND RAJU AEDLA Fig 5.Predicted annual average concentration contours of PM (µg/m 3 ) in year 2012 (MFL) Fig 6.Predicted annual average concentration contours of SOx (µg/m 3 ) in year 2012 (CPCL) Fig 7 Predicted annual average concentration contours of SOx (µg/m 3 ) in year 2012 (MPL)

10 Pollution Dispersion Modeling for Concentrations of PM, SO X and NO X around Manali Region, India 593 Fig 8.Predicted annual average concentration contours of SOx (µg/m 3 ) in year 2012 (TPL) Fig 9.Predicted annual average concentration contours of SOx (µg/m 3 ) in year 2012 (MFL) Fig 10.Predicted annual average concentration contours of NOx (µg/m 3 ) in year 2012 (CPCL)

11 594 EDWIN D THANGAM, NARAYANAN RM AND RAJU AEDLA Fig 11.Predicted annual average concentration contours of NOx (µg/m 3 ) in year 2012 (MPL) Fig 12.Predicted annual average concentration contours of NOx (µg/m 3 ) in year 2012 (TPL) Fig 13.Predicted annual average concentration contours of NOx (µg/m 3 ) in year 2012 (MFL)

12 Pollution Dispersion Modeling for Concentrations of PM, SO X and NO X around Manali Region, India 595 Fig 14.Predicted annual average concentration contours of PM (µg/m 3 ) in year 2012 (Combined Sources) Fig 15.Predicted annual average concentration contours of SOx (µg/m 3 ) in year 2012 (Combined Sources) Fig 16.Predicted annual average concentration contours of NOx (µg/m 3 ) in year 2012 (Combined sources)