Spatial Autocorrelation and Temporal Convergence of PM2.5 Concentrations in Chinese Cities

Abstract

Scientific study of the temporal and spatial distribution characteristics of haze is important for the governance of haze pollution and the formulation of environmental policies. This study used panel data of the concentrations of particulate matter sized < 2.5 μm (PM2.5) in 340 major cities from 1999 to 2016 to calculate the spatial distribution correlation by the spatial analysis method and test the temporal convergence of the urban PM2.5 concentration distribution using an econometric model. It found that the spatial autocorrelation of PM2.5 seemed positive, and this trend increased over time. The yearly concentrations of PM2.5 were converged, and the temporal convergence fluctuated under the influence of specific historical events and economic backgrounds. The spatial agglomeration effect of PM2.5 concentrations in adjacent areas weakened the temporal convergence of PM2.5 concentrations. This paper introduced policy implications for haze prevention and control.

A Moderation effect of atmospheric conditions on PM2.5traffic related concentration

“…The dispersion/dilution of the particulate matter occurs in time, and therefore spatial autocorrelation, temporal convergence, and lag-time autocorrelation in time series are present [39,40].…”

Spatiotemporal characteristic analysis of PM2.5 in central China and modeling of driving factors based on MGWR: a case study of Henan Province

“…The goal of pertinent study should be to investigate the spatiotemporal distribution properties of PM 2.5 and the underlying causes of its existence, according to present research findings. From the studies of the spatiotemporal distribution characteristics of PM 2.5 , scholars have mainly explored PM 2.5 spatiotemporal variation patterns and spatial aggregation characteristics through spatial center of gravity shift evaluation, cold and hot spot analysis, and spatial autocorrelation analysis (29)(30)(31)(32)(33). Among them, the spatial center of gravity shift can be realized by the standard deviation ellipsoid (SDE) model (34,35), which can not only calculate the center of gravity of the PM 2.5 concentration spatial distribution but also effectively reflect the spatial aggregation trend of PM 2.5 .…”

PM2.5 Estimation in the Czech Republic using Extremely Randomized Trees: A Comprehensive Data Analysis

“…Inspired by the first law of geography which proposes that all features present on a geographic surface have a connection with each other, and that geographic entities have a stronger association with nearby entities as compared to those that are located far away [19]. In a study spanning from 1999 to 2016, the yearly average PM2.5 levels in Chinese cities exhibited a typical autocorrelation [20]. In another study, including SA improved the performance of the Random Forest (RF) model and decreased the Root Mean Square Error (RMSE) by ~18% when estimating PM2.5 over Sichuan Basin in 2019 [21].…”