Using Machine Learning Approach to Evaluate the PM2.5 Concentrations in China from 1998 to 2016

摘要

Pollution is one of the main negative outcomes for rapid economic growth without sustainable development in China. Different types of pollutions are harming people's health and the impacts of pollution on environment and people's health could last for decades. Fine particulate matter(PM2.5), which is one of most common types of air pollutions in China, could penetrate and sediment in human's respiratory system and cause different kind of respiratory diseases. Research has shown the strong association between Aerosol Optical Depth (AOD) and PM2.5. For this reason, remote sensing imagery could be used to estimate the level of PM2.5 concentration near ground. With utilizing PM2.5 dataset estimated by Socioeconomic Data and Applications Center (SEDAC) and machine learning approach, this paper is aimed to provide spatiotemporal comparison of PM2.5 concentrations in China. Result from this analysis could help people to better understand the recent history and current status of PM2.5 pollution in China.