Evaluation of the impact of intensive PM2.5 reduction policy in Seoul, South Korea using machine learning

Abstract

The average PM2.5 concentration in South Korea decreased steadily, but the monthly average PM2.5 concentration in January–March increased over time. The Seasonal Fine Dust Management System (hereafter, seasonal management) was implemented from December 2019 to March 2020 in order to reduce high PM2.5 concentrations. This study evaluated the effect of seasonal management using machine learning algorithms, long–short term memory (LSTM), convolutional neural network–LSTM (CNN–LSTM), and ensemble models (Random forest and extreme gradient boosting), and the business–as–usual (BAU) approach. The CNN–LSTM model was defined as four models (multi-headed model 1 and model 2; single-headed model 1 and model 2) according to the method of handling independent variables and the composition of layers. All models performed well; however, the single-headed CNN–LSTM model 1 outperformed the other models. In the BAU period, the observed PM2.5 concentration was 31.98 μg/m3 and the seasonal management reduced PM2.5 concentrations to between 1.09 μg/m3 and 2.73 μg/m3. Overall, the change in PM2.5 concentrations through the seasonal management was apparent during the BAU period, and the machine learning algorithms and BAU approach can be used for the evaluation of policy impact.