Deciphering wintertime air pollution upon the West Lake of Hangzhou, China

Abstract

The West Lake of Hangzhou, a world famous landscape and cultural symbol of China, suffered from severe air quality degradation in January 2015. In this work, Random Forest (RF) and Recurrent Neural Networks (RNN) are used to analyze and predict air pollutants on the central island of the West Lake. We quantitatively demonstrate that the PM2.5 and PM10 were chiefly associated by the ups and downs of the gaseous air pollutants (SO2, NO2 and CO). Compared with the gaseous air pollutants, meteorological circumstances and regional transport played trivial roles in shaping PM. The predominant meteorological factor for SO2, NO2 and surface O3 was dew-point deficit. The proportion of sulfate in PM10 was higher than that in PM2.5. CO was strongly positively linked with PM. We discover that machine learning can accurately predict daily average wintertime SO2, NO2, PM2.5 and PM10, casting new light on the forecast and early warning of the high episodes of air pollutants in the future.