How to apply O_3 and PM_(2.5) collaborative control to practical management in China: A study based on meta-analysis and machine learning

摘要

Constant increase of atmospheric O_3 concentration is a barrier for the further air quality improvement in China. Given that PM_(2.5) is still controlled as a key pollutant, managements for the collaborative reduction of O_3 and PM2.5 are urgently required in China. In the current work, monitoring data of O_3 and PM_(2.5) from 2015 to 2016 in 1464 monitoring sites (MS) was collected and cleaned. Additionally, 7 anthropogenic emission reductions were jointed with the corresponding monitoring data. According to the O_3 and PM_(2.5) variation, a meta-analysis was conducted and divided regions into 4 categories via the effect size, region Ⅰ: O_3 and PM_(2.5) collaborative reduction, region Ⅱ: PM_(2.5) decreased and O_3 increased, region Ⅲ: O_3 decreased and PM_(2.5) increased, regions Ⅳ: both O_3 and PM_(2.5) increased. Then, based on the region labels, machine learning was used to identify the pattern between region label and its precursor reductions. The findings were as follows: (1) Principal component analysis showed that NH_3 was not focused on. (2) Random forest had a well performance on region classification with the accuracy of 80.40% and the importance of the 7 precursors was in the sequence of VOCs＞NH_3 ＞ PM_(2.5) ＞ OC ＞ SO_2 ＞ NO_x ＞ Coarse PM. (3) Polytomous logistic regression evaluated the critical factors that influenced the region label, which showed that the reductions of VOCs, NH_3 and PM_(2.5) could achieve the collaborative reduction in a short time in most of cities in China. Based on the statistical results above, a kinetic management system including evaluation and policy-making sections was finally established, which filled the gap of the collaborative reduction in environmental management in China.