Analysis of the impact of multiple green space patterns and key meteorological factors on particulate matter pollution: a case study in the Zhengzhou metropolitan area.

Abstract

Atmospheric particulate matter (PM) is a primary pollutant affecting urban air quality, posing increasing threats to public health and ecological environments. While urban green spaces and meteorological conditions individually influence PM pollution, the mechanisms by which meteorological indicators mediate the relationship between green space patterns and PM concentrations remain unclear. We used daily PM concentration data in the Zhengzhou Metropolitan Area (ZMA) in 2021, combined with high-resolution satellite imagery and climate monitoring data. By employing Generalized Linear Models (GLMs) and Partial Least Squares Structural Equation Modeling (PLS-SEM), we investigated the effects of green spaces and meteorological conditions on PM, highlighting the significant mediating role of key meteorological indicators in the process by which green spaces mitigate PM pollution. Results indicated that PM_2.5 concentrations were more sensitive to green space patterns and meteorological conditions at 1-6 km scales compared to PM₁₀. Significant scale-dependent differences were observed in the coupling between PM concentrations and green spaces. PLS-SEM revealed that key meteorological indicators, particularly wind speed and humidity, significantly mediated the impact of green spaces on PM pollution, with mediation effects peaking at the 4 km scale. The percentage of largest green space patches had the most pronounced mediated effect on PM_2.5 and PM₁₀ through climate factors. Conclusively, to maximize ecological benefits, it is essential to consider wind speed and humidity around green spaces. The findings emphasize the importance of optimizing green space patterns at multiple scales and incorporating local microclimate considerations in future PM pollution management within the ZMA.