Green space coverage versus air pollution: a cloud-based remote sensing data analysis in Sichuan, Western China

Abstract

Air pollution is the greatest health risk to human health, as acknowledged in several Sustainable Development Goals (SDG), such as SDG-1, SDG-3, SDG-7, and SDG-11. Despite global comprehension of the positive effect of Green Space Coverage (GSC) on mitigating air pollution, investigating the impact of different GSC types has received little attention. Here, we utilized multiple air pollution data and a cloud-computing platform to examine the role of different GSC types in mitigating NO2 and PM2.5 pollutants across 2019 and 2022 in Sichuan Province, China. We classified GSC areas into tall GSC and short GSC classes, taking into account the recognized importance of vegetation height in prior studies. Our analysis revealed that tall GSCs exhibit lower pollutant levels across all areas studied, indicating a potential correlation between GSC height and pollution mitigation efficacy. Furthermore, in high human activity areas, while tall GSC emerged as effective sinks for PM 2.5 compared to short GSC (25% and 20% lower average annual in 2019 and 2022, respectively), their performance in reducing NO2 pollutant levels was relatively limited (9% and 4% lower average annual in 2019 and 2022, respectively). These findings can contribute to urban planning and environmental management.

Keywords

• Air pollution; green coverage; sustainable development; remote sensing
• Sichuan
• China