Exploration of spatial heterogeneity in the effect of woodland proportion on PM2.5: An empirical study from the Yangtze River economic Belt, China

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Woodland is the most important land cover type for enhancing the environmental governance capacity and ecological protection benefits. Woodland proportion is crucial in reducing the quality and concentration of atmospheric fine particulate matter (PM2.5) and improving the atmospheric environment. Existing studies have mostly examined the connection between the woodland proportion and PM2.5 on a global scale, while ignoring the geographical variability and differences in the scope and intensity of the effect. Therefore, this study investigated the regional features of the effect from the perspective of spatial heterogeneity, using a spatial agglomeration analysis method, and global and local regression analyses models with 1049 counties in the Yangtze River Economic Belt (YREB). The studies reveal that woodland proportion has a considerable impact on PM2.5, and the effect’s direction is identical to that of the expectation at the global scale. The PM2.5 concentration falls by 0.017% for every 1% increase in the woodland proportion. The influence of woodland proportion on PM2.5 showed noticeable spatial heterogeneity, and the woodland proportion in 794 counties is strongly associated with PM2.5 concentration in local areas, with 66.44% of counties having the most obvious negative effect, and the areas with the greatest effect intensity are mostly distributed in mountainous areas. The concentration of PM2.5 in the YREB exhibiting a prominent characteristics of local spatial correlation and agglomeration, and a spatial characteristics of eastward peak and an westward low. Contrastingly, the distribution of the woodland proportion were the opposite—eastward high and westward low—with noticeable spatial variability. The findings of this study offer a foundation for decision-making and theoretical support for national or regional implementation of regionally differentiated and precise measures to prevent and reduce air pollution.