Insight into Secondary Inorganic Aerosol (SIA) production enhanced by domestic ozone using a machine learning technique

Abstract

Taiwan has prioritized management and air pollution control of ozone in the past decade. Atmospheric ozone reacts with gaseous precursors to produce secondary inorganic aerosols (SIAs), which worsen air quality to a great extent. The present study applied a bivariate polar plot, K-means clustering, and a nonlinear autoregressive network with exogenous input neural network (NARX-NN) to identify the sources of O3 and the formation mechanism of SIAs in an urban area, aiming to facilitate the development of exposure risk assessment methods and an O3 pollution early warning system for human health protection. Results show that the present NARX-NN model could be used to capture 72 h ahead of water-soluble inorganic salts (WIS) and O3 concentrations accurately with R2 of 0.71 for NO3-, 0.72 for SO42-, 0.80 for NH4+, and 0.79 for O3 concentrations. Based on the statistical and K-means clustering analysis, the domestic high O3 hours in a year (O3 concentrations>60 ppb) account for up to 76% and 99% of the total high O3 hours in northern and southern Taiwan, respectively. Under volatile organic compounds (VOCs)-limited regime, an increase in O3 concentrations by 57.4% was found to enhance heterogeneous oxidation reaction to produce SIAs by 48.0% of NO3- and 56.6% of SO42- under prevailing northeast monsoon during winter and spring seasons. It is evident that strict control measures are still necessary for local ozone sources to maintain air quality. The source tracing framework developed in the present study is applicable for policymakers to develop early warning systems and maintain air quality during the decision-making process.