Air--Pollution--Mathematical models

The purpose of this study was to develop a user-friendly mathematical model for prediction of daily, ground level ozone concentration in Palm Beach County, Florida. The focus of this project was to investigate the correlation between hourly ozone concentrations and pre-existing pollutant levels and meteorological data. An artificial neural network model was applied, involving a backpropagation algorithm and the tangent sigmoid as the transfer function. Surface meteorological data and upper air data such as pressure, temperature, dew point temperature, wind speed and wind direction were included in the model, along with the ozone concentration in the hour previous to the forecast. Based on the model results, the 8-hour average ozone concentration is to be forecasted. This will assist state and local air pollution officials in providing the general public with early notice of an impending air quality problem.

Available air quality data has been analyzed using the Industrial Source Complex Dispersion Model (ISC3). The FORTRAN program Mobile 5a was utilized to obtain the emission factors. Concerning the toxic volatile organic compounds, the simulations indicate that benzene exceeded the previous permissible air quality standard. Toluene, ethylbenzene and xylene values remained within allowable concentration levels. Long-term annual benzene emissions from stationary sources exceeded the former reference concentration of 0.12 mug/m 3 about five times per year, whereas the data for 8 and 24 hours were relatively low. Simulation results for the mobile sources demonstrated that the emission factor is a highly sensitive parameter. Possible realistic scenarios at Port Everglades were obtained through varying benzene concentrations. The results are plotted as concentration contours, in order to visualize areas of suspected health risk associated to air pollution. Compared to the Reference Concentration for Chronic Inhalation Exposure, EPA, the modeled results appear significantly less.