PREDICTING OZONE LEVELS - A STATISTICAL-MODEL FOR PREDICTING OZONE LEVELS IN THE SHUAIBA INDUSTRIAL-AREA, KUWAIT

This paper presents a statistical model that is capable of predicting ozone levels from precursor concentrations and meteorological conditio ns during daylight hours in the Shuaiba Industrial Area (SIA) of Kuwai t. The model has been developed from ambient air quality data that was recorded for one year starting from December 1994 using an air pollut ion mobile monitoring station. The functional relationship between ozo ne level and the various independent variables has been determined by using a stepwise multiple regression modelling procedure. The model co ntains two terms that describe the dependence of ozone on nitrogen oxi des (NOx) and non-methane hydrocarbon precursor concentrations, and ot her terms that relate to wind direction, wind speed, sulphur dioxide ( SO2) and solar energy. In the model, the levels of the precursors are inversely related to ozone concentration, whereas SO2 concentration, w ind speed and solar radiation are positively correlated. Typically, 63 % of the variation in ozone levels can be explained by the levels of NOx. The model is shown to be statistically significant and model pred ictions and experimental observations are shown to be consistent. A de tailed analysis of the ozone-temperature relationship is also presente d; at temperatures less than 27 degrees C there is a positive correlat ion between temperature and ozone concentration whereas at temperature s greater than 27 degrees C a negative correlation is seen. This is th e first time a non-monotonic relationship between ozone levels and tem perature has been reported and discussed.