Research Journal of Recent Sciences ______ ______________________________ ______ ____ ___ ISSN 2277 - 2502 Vol. 3 ( ISC - 2013 ), 24 - 27 (201 4 ) Res. J. Recent . Sci. International Science Congress Association 24 Review Paper Air pollution Modeling for Human Exposure Predictions : A Review Anita Dubey Govt. M.L.B. College Bhopal, M.P. , INDIA Available online at: www.isca.in , www.isca. me Received 2 01 3 , revised 201 4 , accepted 201 4 Abstract The aim of this study is to provide a comprehensive review with regards to recent developments, major research, computational methods and air quality models applications. The development of models for air pol lution assessment has been identified as an important area for future research. Air pollution due to massive use of motor vehicles in urban areas of India is one of the most serious and fast growing problem to solve. These motor vehicles emit significant q uantities of CO2, CO, hydrocarbons, oxides of nitrogen, SPM and other toxic substances in the atmosphere which adversely affect the environmental and the health. The objective of this study is to understand the chemistry of air pollution with its precise e stimation through modeling. The behavior and relation between emission and deposition of pollutants can explain with the help of air quality models. Modeling is a set of different scientific methods that are helpful to analyze nature and behavior of pollut ants in the atmosphere. On the basis of source of pollutants air quality models are classified as point, area or line source models. Various Gaussian based line source models are commonly used in India to assess the impact of vehicular pollution along the roads or highways. Sources of air pollution, chemistry of pollutants and computational methods for dispersion modeling are discussed and reviewed with respect to various literature and corresponding methods. The paper includes comparative study of various air quality models and study of complex phenomenon of air pollution. Recent modified air quality models and their future scope are also discussed in the paper which help for scientists who work in the same field. Keywords : Air pollution, air quality model s, pollutants, computational methods. Introduction The problem of air pollution because of continuous development and increase of population in the urban areas has become so remarkable that there is a urgency for timely information about changes in the pollution level. The transport and diffusion of pollutants in the atmosphere depend on nature as well as meteorological and emission conditions of pollutants. Respiratory difficulties, heart disease, loss of agricultural products and damage to aquatic and terrestrial ecosystems are main adverse effects of air pollution. Photochemical reactions taking place within the atmosphere are responsible for emission of many of pollutants that cause serious health hazards to human. Main traffic related pollutants like CO reduces oxygen carrying capacity of blood, benzene pollutants cause cancer and SO2 and particulates can cause respiratory diseases (UNEP, 2009) . Air quality dispersion models act as a valuable tool to predict the quality of air against the National Amb ient Air Quality Standards and are useful in the air pollution management. Modeling of air pollution is based on various models like Gaussian modes, box models, narrow plume models and complex computational fluid dynamic models . Gaussian models are based o n a set of empirical equations that is mainly applied to coal burning electricity producing plants and to exhaust from automobiles in the cities 1, 2 . Figure - 1 Visualization of a buoyant Gaussian air pollutant dispersion plume The standard algorithm used in the Gaussian plume model (O.G. Sutton, 1932) is as follows : Where , i. C(x,y,z,)is the concentration of the emission (in microgram per cubic meter) at any point x meters downwind of the source, y meters laterally from the centre line of the plume and z meter above ground level. ii. Q is the quantity or mass of Research Journal of Recent Sciences _ _____ _ _ _______________________________ ______________ _ ________ ISSN 2277 - 2502 Vol. 3 ( ISC - 2013 ), 24 - 27 (201 4 ) Res. J. Recent. Sci. International Science Congress Association 25 the emission in grams per unit of time (second) . iii. u is the wind speed (in meters per second) . iv. H is the height of the source above ground level(in meters) . v. and are the standard deviat ions of a statistically normal plume in the lateral and vertical dimensions respectively. Gaussian plume models have following features: They , i. do not require significant computer resources, th ey can be run on any desktop PC. ii. are easy to use and com paratively small number of input variables are required . iii. are widely used, easy to study the results which can easily be compared with others. iv. can give significant results for short level sources . Some common types of models are : Dispersion mode ls 3 : which use equations to represent the path that pollutants travel in the air in order to calculate the downwind air concentrations Receptor models 4 : which use properties of air pollutants to identify and quantify the sources of air pollutants, Meteo rological models 5 : which use equations that represent the behavior of atmosphere in order to predict the meteorologi cal conditions at specific area. Physical models : small used in wind tunnels to simulate actual conditions and Statistical models: whe re statistics are used to relate emissions and the resulting concentrations. Result s and Discussion The modern science of air pollution modeling introduced in the year 1920 when the dispersion of toxic chemicals released in the battlefield under differen t conditions was trying to estimate by military scientists in England 6 . Fossil fuel power generation is continuously expanding in India with the growth of population and industrialization. Combustion of fossil fuels thus produces air pollutants considerabl y. To address this issue, Ministry of Environment and Forests (MEF), Government of India decided to put forward dispersion models to evaluate the adverse effect of power plant operations on the ambient air quality in terms of concentration level of differe nt pollutants. A dispersion model is a computational method for predicting concentrations downwind of a pollutant source on the basis of knowledge of the emissions properties, surface structure, wind speed, stability, local topography. AIRVIRO, a regional scale dispersion model, developed by the Swedish scientists was used to analyze the impact of emission of oxides of nitrogen from automobiles to the air quality in Singapore 7 . Jiang et al 8 described an approach to analyze air quality assessment of a power plant in Hongkong Shenzhen area. Borrego et al 9 studied dispersion modeling for the assessment of air pollution in Lisbon city. The Transport Emission Model for line sources (TREM) and the Local Scale Dispersion Model (VADIS) were described by them 10,11 . T he Central Pollution Control Board (CPCB) of India put forward general guidelines for EIA studies that are listed in EIA Notification 2006 12,13 to obtain environmental clearance. The major elements of an AQM are depicted schematically in the following fig ure: Figure - 2 Major elements of air quality model Figure - 3 Shows the four stages of modeling 14 Research Journal of Recent Sciences _ _____ _ _ _______________________________ ______________ _ ________ ISSN 2277 - 2502 Vol. 3 ( ISC - 2013 ), 24 - 27 (201 4 ) Res. J. Recent. Sci. International Science Congress Association 26 Figure - 4 Flow chart showing air pollution processing module 15 Research Journal of Recent Sciences _ _____ _ _ _______________________________ ______________ _ ________ ISSN 2277 - 2502 Vol. 3 ( ISC - 2013 ), 24 - 27 (201 4 ) Res. J. Recent. Sci. International Science Congress Association 27 An air dispersion model has five main stages: input data co llection, processing of dispersion model, output data, interpretation of dispersion modeling results and preparation of the assessment report. Stages of modeling and processing of basic environmental data and details of it are shown in figure 3 and 4 as f low charts. The pollution control regulation can be represented by a predictive model to gain ambient standards. Therefore an air quality management system is to prepare for monitoring the ambient air quality. There are two general types of dispersion mod els, Gaussian plume models such as AUSPLUME, ISCST3, AERMOD and CTDMPLUS and advanced models such as CALPUFF and TAPM. A numbers of air pollution modeling software are available such as AUSPLUME, it is very easy to apply and quick to run, AERMOD, it is use d to define variation of turbulence with height and dispersion coefficients as a continuum, CTDMPLUS, it is developed for tall point sources in complex terrain areas, CALPUFF, ( a puff model) is used in regulating long range transport of pollutants 16 and T APM, which is developed to stimulate three dimensional meteorology and pollution dispersion 17 . There are still many open points to the improvement of the meteorological models and of the emission inventory. It is keeping in mind that the models of intermed iate algorithms are of great interest. Conclusion Gaussian models, narrow plume models, box models, trajectory models and gradient transport models are the basic models of air pollution modeling. A three dimensional axis system is set up within the crossw ind, downwind and vertical direction. By Gaussian distribution, pollutant concentrations crosswind and vertically are analyzed. Uniform mixing throughout the volume of a three dimensional box is described by box models. Pollutant concentrations upwind are described by narrow plume hypothesis based models 18 - 21 . References 1. Briggs G.A., Plume Rise, USAEC Critical Review Series , (1969) 2. Briggs, G.A., Some recent analysis of plume rise observ ation, Proc. Second Inter nat’l , Clean air congress, Academic press , Ne w York, (1971) 3. http://www.bcairquality.ca/assessment/dispersion - modeling. html (2014) 4. http://www.bcair quality.ca/assessment/receptor - modeling. html (2014) 5. http://www.bcairquality.ca/assessment/meteorological - modeling. html (2014) 6. Theory and objectives of air dispersion mo deling, Robert Macdonald, Ph.D., P.Eng. Department of Mechanical Engineering University of Waterloo. Modeling Air Emissions for Compliance MME 474A Wind Engineering December ( 2003 ) 7. Mukherjee P., Viswanathan S. and Choon L.C., Modeling mobile source emissi ons in presence of stationary sources , J of Haz. Mat., 76(1), 23 – 37 (2000) 8. Jiang W., Hu F. and Wang W., A non - hydrostatic dispersion modeling system and its application to air pollution assessments over coastal complex terrain , J of Wind Eng. and Ind.l Aer odyn ., 87(1), 15 – 43 (2000) 9. Borrego C., Tchepel O., Costa A.M., Amorim J. H. and Miranda A. I., Emission and dispersion modeling of Lisbon air quality at local scale , Atm. Env ., 37(37), 5197 – 5205 (2003) 10. Rama Krishna T.V.B.P.S., Reddy M.K., Reddy R.C. and Si ngh R.N., Impact of an industrial complex on the ambient air quality: Case study using a dispersion model , Atm. Env ., 39(29), 5395 – 5407 (2005) 11. 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Builtjes P., The Problem – Air Pollution. Chapter 1 of air quality modeling ( http ://www.envirocomp.org/ ) and ( http://www.awma.org/ ) (2003) 19. Shah J.J., Nagpal T. and Brandon C., Urban Air Quality Management Strategy ea Guidebook. The World Bank, Washington DC, USA (1997) 20. Census - India, Census of India, The Government of India, New Delhi, India (2001) 21. Census - India, Census of India, The Government of India, New Delhi, India (2011) 22. International Journal of Energy and Environment (IJEE), 1(1), 97 - 112 (2010)