Spatio-Temporal Distribution Characteristics and Drivers of PM[sub.2.5] Pollution in Henan Province, Central China, before and during the COVID-19 Epidemic

• Published in: International journal of environmental research and public health Vol. 20; no. 6
• Main Authors: Lv, Pengcheng, Zhang, Haoyu, Li, Xiaodong
• Format: Journal Article
• Language: English
• Published: MDPI AG 01-03-2023
• Subjects: Automobile drivers; China; Epidemics; Pollution; China
• ISSN: 1660-4601
• DOI: 10.3390/ijerph20064788

PM[sub.2.5] is the main cause of haze pollution, and studying its spatio-temporal distribution and driving factors can provide a scientific basis for prevention and control policies. Therefore, this study uses air quality monitoring information and socioeconomic data before and during the COVID-19 outbreak in 18 prefecture-level cities in Henan Province from 2017 to 2020, using spatial autocorrelation analysis, ArcGIS mapping, and the spatial autocorrelation analysis. ArcGIS mapping and the Durbin model were used to reveal the characteristics of PM[sub.2.5] pollution in Henan Province in terms of spatial and temporal distribution characteristics and analyze its causes. The results show that: (1) The annual average PM[sub.2.5] concentration in Henan Province fluctuates, but decreases from 2017 to 2020, and is higher in the north and lower in the south. (2) The PM[sub.2.5] concentrations in Henan Province in 2017-2020 are positively autocorrelated spatially, with an obvious spatial spillover effect. Areas characterized by a high concentration saw an increase between 2017 and 2019, and a decrease in 2020; values in low-concentration areas remained stable, and the spatial range showed a decreasing trend. (3) The coefficients of socio-economic factors that increased the PM[sub.2.5] concentration were construction output value > industrial electricity consumption > energy intensity; those with negative effects were: environmental regulation > green space coverage ratio > population density. Lastly, PM[sub.2.5] concentrations were negatively correlated with precipitation and temperature, and positively correlated with humidity. Traffic and production restrictions during the COVID-19 epidemic also improved air quality.