Spatiotemporal Evolution Analysis of PM 2.5 Concentrations in Central China Using the Random Forest Algorithm

Gang Fang (), Yin Zhu () and Junnan Zhang
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Gang Fang: School of Environment and Surveying Engineering, Suzhou University, Suzhou 234000, China
Yin Zhu: School of Environment and Surveying Engineering, Suzhou University, Suzhou 234000, China
Junnan Zhang: School of Environment and Surveying Engineering, Suzhou University, Suzhou 234000, China

Sustainability, 2024, vol. 16, issue 19, 1-22

Abstract: This study focuses on Central China (CC), including Shanxi, Henan, Anhui, Hubei, Jiangxi, and Hunan provinces. The 2019 average annual precipitation (PRE), average annual temperature (TEM), average annual wind speed (WS), population density (POP), normalized difference vegetation index (NDVI), aerosol optical depth (AOD), gross domestic product (GDP), and elevation (DEM) data were used as explanatory variables to predict the average annual PM 2.5 concentrations (PM 2.5 Cons) in CC. The average annual PM 2.5 Cons were predicted using different models, including multiple linear regression (MLR), back propagation neural network (BPNN), and random forest (RF) models. The results showed higher prediction accuracy and stability of the RF algorithm (RFA) than those of the other models. Therefore, it was used to analyze the contributions of the explanatory factors to the PM 2.5 concentration (PM 2.5 Con) prediction in CC. Subsequently, the spatiotemporal evolution of the PM 2.5 Cons from 2010 to 2021 was systematically analyzed. The results indicated that (1) PRE and AOD had the most significant impacts on the PM 2.5 Cons. Specifically, the PRE and AOD values exhibited negative and positive correlations with the PM 2.5 Cons, respectively. The NDVI and WS were negatively correlated with the PM 2.5 Cons; (2) the southern and northern parts of Shanxi and Henan provinces, respectively, experienced the highest PM 2.5 Cons in the 2010–2013 period, indicating severe air pollution. However, the PM 2.5 Cons in the 2014–2021 period showed spatial decreasing trends, demonstrating the effectiveness of the implemented air pollution control measures in reducing pollution and improving air quality in CC. The findings of this study provide scientific evidence for air pollution control and policy making in CC. To further advance atmospheric sustainability in CC, the study suggested that the government enhance air quality monitoring, manage pollution sources, raise public awareness about environmental protection, and promote green lifestyles.

Keywords: random forest algorithm; PM 2.5; spatiotemporal evolution analysis; atmospheric monitoring and sustainable development; Central China (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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