Spatio-Temporal Change Pattern Investigation of PM2.5 in Jiangsu Province with MODIS Time Series Products

Abstract

In the last decade, the spatio-temporal patterns of PM2.5 on various scales, ranging from global, continent, and country to regional levels, has been the focus of considerable studies. However, these studies on spatio-temporal variability have concentrated primarily on changes in the spatial distribution patterns of regional PM2.5 concentrations and ignored temporal characteristics at a local site from a heterogeneous surface, such as local variability, persistence, and stability of PM2.5 exposure. Understanding the temporal characteristics of PM2.5 concentration changes at the local scale will help determine the local impacts of PM2.5, such as local exposure risk and vulnerability to PM2.5. This study aims to reveal the local characteristics of temporal variation at the scale of a prefecture-level city and its distinct-varying patterns from those at the provincial scale by using the annual satellite-derived PM2.5 concentration product from 2000 to 2015. The evolutionary trends, stability, and persistence of annual changes were discovered with a set of time series analysis methods, such as linear regression analysis + F-test, coefficient of variation method, and Hurst index. This study uses PM2.5 product data for a total of 16 years, from 2000 to 2015, and uses time series analysis methods, such as Theil–Sen median trend analysis + Mann–Kendall test, one-dimensional linear regression analysis + F-test, coefficient of variation method, and Hurst index, to reveal the temporal variation characteristics and spatial patterns of PM2.5 in Jiangsu Province. The results show that the increasing trends or slopes of annual averaged PM2.5 concentrations in Jiangsu Province are not consistent at the prefecture-level city scale, but they are consistent in northern, central and southern Jiangsu at a larger upward trend since 2000. The areas with significant increasing trends are concentrated in Xuzhou and Lianyungang and other northern cities. From the viewpoint of variability, the areas in medium and high variability are mainly aggregated in the areas north of the Yangtze River. According to the combination of persistence analysis and trend analysis, future variation in PM2.5 concentrations indicates an inverse persistence for an increasing trend, meaning the air quality decline in Jiangsu will slow.