Estimations of ambient fine particle and ozone level at a suburban site of Beijing in winter

Abstract

Estimates of PM2.5 and O3 in suburban areas are of importance for assessing exposure risk and epidemiological studies of air pollution where large scale and long-term measurements network are absent. To fulfill this goal, our study develops a flexible approach to predict levels of PM2.5 and O3 at a suburban site of Beijing using multilayer perceptron (MLP) neural network analysis with the inputs of gaseous air pollutants (CO, SO2, NO, and NO2) and meteorological parameters (wind direction, wind speed, temperature, pressure and humidity). Daily ambient data of PM2.5, O3, PM10, CO, SO2, NO, and NO2 were estimated using hourly data collected from January 20 to March 10 in the years from 2016–2020 at a suburban site of Beijing, respectively. Ambient measured levels of PM2.5 and O3 were compared with the output estimates of PM2.5 and O3 through MLP neural network analysis with limited input variables. Overall, MLP neural network analysis could explain 97% of measured PM2.5 mass and 82% of measured O3 level with R2 values of 0.983 and 0.905, respectively. This approach could be helpful for reconstruct historical PM2.5 and O3 levels in suburban areas.