A Machine-Learning-Based Classification Method for Meteorological Conditions of Ozone Pollution

Abstract

Ozone pollution is harmful to human health and ecosystem, which occurs in ecosystems and has occurred frequently in China in recent years, especially during the warm seasons. Meteorological conditions are among the important factors affecting the occurrence of ozone pollution. In this study, a classification method for meteorological conditions of ozone pollution levels based on a back propagation (BP) neural network was proposed to reflect the impact of meteorological conditions on the occurrence of ozone pollution. Ozone pollution was divided into three levels according to surface hourly ozone (O 3 ) concentrations and thus into three groups of meteorological conditions. The input physical parameters for the BP neural network were determined by evaluating the relationship between surface O 3 concentrations and meteorological parameters and precursors, including relative humidity, temperature, mixing layer height, precipitation