How do greenspace landscapes affect PM2.5 exposure in Wuhan? Linking spatial-nonstationary, annual varying, and multiscale perspectives

As an ambient atmospheric pollutant, fine particulate matter (PM2.5) has posed significant adverse impacts on public health around the world. To attenuate population exposure risk to PM2.5 pollution, greenspace been considered as a promising approach. Little is known, however, about attenuating of landscapes risks at various locations, scales, and levels. This study employed hotspot analysis, weighted barycenter, time-series clustering investigate spatiotemporal dynamics across Wuhan. In addition, multi-scale geographically regression (MGWR) was used determine relationships between landscape patterns yearly over four years (2000, 2005, 2010, 2015). Results revealed that, 2000 2016, variations in coverages within Wuhan showed inverse U-shape trend. The K-DTW differentiated area into seven spatial clusters with homogeneous temporal dynamics. general, there were three stages fluctuations Wuhan: 2000–2005, 2006–2011, 2012–2016. MGWR also disclosed associations parameters (AI, ED, SI, PLAND). PLAND green spaces can mitigate broader scale (the average bandwidth 1391), while AI, SI are generally associated reduction local scales. Wuhan, we confirmed such metrics varying levels risks. results indicate that attenuation effectiveness toward by not only site- scale-dependent but affected findings this contribute planning management for mitigating PM2.5-attributable impacts.