An Integrated Air Quality Modeling Package for Economic Evaluation of Health Impacts for an Industrialized Region in Mumbai

Rapid growth in population, industries and vehicular traffic has resulted in severe air pollution concentration in urban areas, imposing a direct economic cost of damage to human health. An air quality dispersion model is a tool which can be used efficiently for development of rational and objective management strategies to mitigate the air pollution problem. It quantifies the simulation capacity of the atmosphere and relates various control inputs to the resultant change in air pollution concentration. Further, the predicted change in air pollution concentration can be converted into health benefits by integrating the air quality simulation model with a concentration response module and an economic module of health benefits. Such an air quality modeling system can be used as an effective decision making tool for evaluation of air quality management strategies.

An integrated air quality model for an airshed is presented in this paper, which consists of the following modules, viz. (i) an emission module to determine emissions from various types of sources in the airshed, (ii) a dispersion module to relate pollutant emissions to ambient concentration and (iii) an assessment module for evaluating the health benefits for various sets of control techniques subject to some other constraint such as technological or economic feasibility. The rational method of decision making by assessing economic values of health damages due to air pollution and relating it to alternate control techniques using air quality models, is presented in this paper.

At present, a large number of air quality dispersion models are available, but each has its own advantages and limitations. In this study, two recent, popular and well validated dispersion models viz. Industrial Source Complex (ISC-3) and Atmospheric Dispersion Modeling system (ADMS-3) have been applied. ISC-3 has been recommended by USEPA and is also widely used in India. ADMS-3 has been developed by CERC, UK. In order to economically evaluate the health exposure due to the estimated air pollution concentration Environmental Benefits Mapping and Application Program (BenMAP) software, developed by USEPA has been used. It estimates health benefits associated with air quality changes by creating population level exposure surfaces and places an economic value on the reduced incidences of a wide range of health outcomes associated with ambient air pollution. BenMAP is a tool for estimating benefits associated with air pollution reduction strategies. The special features of BenMAP are as follows: „XIt spatially interpolates air quality data using Krigging and Voronoi Neighbor Averaging. „XEstimates health effect incidences based on concentration-response functions. „XEstimates economic value of avoided incidences. „XGenerates uncertainty distributions around incidence changes and valuation estimates using Monte Carlo methods.

The Chembur - Trombay Region has been selected for application of the integrated model because it has severe air pollution problems. It is located on the North-East part of Mumbai (latitude 18¢X 54' N, longitude 72¢X 49' E), Capital of Maharashtra State (India), which is also known as the "Commercial Capital of India". The whole study-domain area is about 110 km2. It has a mixed terrain. Most of the part is located at an elevation of 11 m above the mean sea level, but in SE direction, it has Trombay Hills, whose highest peak is 302 m above the MSL. It is predominantly an industrial region housing heavy industries, like refineries, fertilizer industry, thermal power plant and other chemical industries. This region also has several small scale industries. Many residential colonies and slums are also located in this region.

The study domain has been divided into square grids of 500m and the terrain effect is also taken into consideration. A detailed emission inventory has been compiled for the four major industries, viz. two petrochemical refineries, one thermal power plant and one fertilizer plant with total of 84 stacks. SO2, NOx and particulate matter (PM) emissions have been estimated from stack monitoring data as well as using data like fuel consumption and control techniques. A three hourly sequential meteorological data has been collected from the nearest meteorological station, while the upper air data has been taken from a website for the same station. PCRAMMET meteorological preprocessor has been used for processing of meteorological data for ISCST. The Holzworth method is used for estimation of morning and afternoon mixing heights. Diurnal variation of micro-meteorological parameters has also been plotted for four seasons, viz. winter, summer, monsoon and post-monsoon. Estimation of assimilative capacity of the atmosphere with the help of ventilation coefficient has been done. Concentration mapping of SO2, NOx and PM has been done for 8hrs, 24hrs, monthly and seasonal averaging time with ISCST-3 and ADMS-3.

It is observed that the computed concentrations of pollutants are most of the time higher from ISCST-3 than ADMS-3 model predictions. But, both models give the same trends in concentration variation for all the seasons. Subsequent to modeling of air pollution concentration, BenMAP has been utilized for estimation of population exposure and health effects due to ambient air pollution by using appropriate concentration-response function for different diseases derived from epidemiological studies. This model gives a visual display of the spatial distribution of health damages in the region, so that the hot spots due to air pollution can be identified for priority remedial action.

The integrated package can be used to display changes in health risk as a function of input control variables like change in fuel quality and advanced control technology. It can economically evaluate the environmental benefits of the control strategies and mitigation measures. Thus, the integrated package of an air quality dispersion model and a health benefits evaluation model provides a powerful decision support for air quality management.