Effects of Weather and Anthropogenic Precursors on Ground-Level Ozone Concentrations in Malaysian Cities

Abstract

Ground-level ozone (O3) is a significant source of air pollution, mainly in most urban areas across the globe. Ground-level O3 is not emitted directly into the atmosphere. It results from photo-chemical reactions between precursors and is influenced by weather factors such as temperature. This study investigated the spatial and temporal analysis of ground-level ozone and analyzed the significant anthropogenic precursors and the weather parameters associated with ground-level ozone during daytime and nighttime at three cities in peninsular Malaysia, namely, Kuala Terengganu, Perai, and Seremban from 2016 to 2020. Secondary data were acquired from the Department of Environment (DOE), Malaysia, including hourly data of O3 with trace gases and weather parameters. The secondary data were analyzed using temporal analysis such as descriptive statistics, box plot, and diurnal plot as well as spatial analysis such as contour plot and wind rose diagram. Spearman correlation was used to identify the association of O3 with its precursors and weather parameters. The results show that a higher concentration of O3 during the weekend due to “ozone weekend effects” was pronounced, however, a slightly significant effect was observed in Perai. The two monsoonal seasons in Malaysia had a minimal effect on the study areas except for Kuala Terengganu due to the geographical location. The diurnal pattern of O3 concentration indicates bimodal peaks of O3 precursors during the peak traffic hours in the morning and evening with the highest intensity of O3 precursors detected in Perai. Spearman correlation analysis determined that the variations in O3 concentrations during day and nighttime generally coincide with the influence of nitrogen oxides (NO) and temperature. Lower NO concentration will increase the amount of O3 concentration and an increasing amount of O3 concentration is influenced by the higher temperature of its surroundings. Two predictive models, i.e., linear (multiple linear regression) and nonlinear models (artificial neural network) were developed and evaluated to predict the next day and nighttime O3 levels. ANN resulted in better prediction for all areas with better prediction identified for daytime O3 levels.