IDENTIFYING THE DRIVING FACTORS OF POPULATION EXPOSURE TO FINE PARTICULATE MATTER (PM2.5) IN WUHAN, CHINA
Keywords: fine particulate matter, population exposure, driving factors, machine learning, inter-annual dynamics, variable importance
Abstract. Characterizing the spatiotemporal dynamics of population exposure to fine particulate matter (PM2.5) and the underlying external forcing can provide proactive implication for public health precautions. In this study, satellite-derived surface-level PM2.5 concentration as well as landscape factors and socioeconomic data are collected to identify the inter-annual variations and potential driving forces of population exposure to fine particulate matter (PM2.5) in Wuhan, China from 2000 to 2015. The fine-scale PM2.5 exposures in 2000, 2005, 2010 and 2015 were first estimated. Then the contributions of landscape factors and socioeconomic forcing are quantified by a machine learning method (i.e. Random Forest). The results revealed that the population in Wuhan faced increasing and more clustering PM2.5 threats from 2000 to 2010. Then a weakened and dispersed health threat of PM2.5 was witnessed in 2015. In general, the Gross Domestic Product (GDP) contributed the most to high-level PM2.5 exposure in the period of 2000–2015, i.e. variable importance (VIM) equalled to xxx. Among all the biophysical and landscape characteristics, the percentage of urban landscape (PLAND_UA) and urban area fraction were attributed the most to the PM2.5 population exposure. In parallel, precipitation played a crucial part in the mitigation of PM2.5 exposure. The identification of inter-annual dynamics of population PM2.5 exposure and the underlying forcing can facilitate the decision making and epidemiological precautions in the evaluation and alleviation of population exposure risks.