RESUMO
Particulate matter (PM) is a major source of urban air pollution that poses a serious threat to the environment and human health. This study quantified the dry deposition effect of PM2.5 and PM10 on vegetation using a mathematical model to overcome the limitations of traditional site-scale research. Additionally, multi-source satellite remote sensing products were combined to form a raster dataset to estimate the effect of dry deposition on PM2.5 and PM10 in China's urban green spaces from 2000 to 2020. The spatial and temporal changes in the long-term series were analyzed, and the influence of environmental factors on dry deposition was analyzed in combination with wavelet changes. The experimental results showed that: 1) from 2000 to 2020, the dry deposition effect of PM2.5 and PM10 on vegetation showed an initial increasing and then decreasing trend caused by the sudden drop in atmospheric pollutant particle concentration driven by local policies; 2) broad-leaved forests provided the main dry deposition effects in urban spaces, accounting for 89.22 %, indicating a need to increase the density of these forest types in urban development planning to improve air quality; and 3) PM2.5, PM10, and environmental impact factors have time-frequency scale coherences, and the coherence between PM2.5 reduction and these factors is more complex than that of PM10, with precipitation being the best variable to explain the change in PM2.5 and PM10. These findings are important for the prevention and control of urban air pollution, regional planning of green spaces, and sustainable development of cities.
