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

ABSTRACT

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

KEYWORDS:

• PM2.5 exposure
• greenspace
• spatiotemporal variations
• multi-scale geographically weighted regression
• time-series clustering

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The annual ground-level PM2.5 concentration dataset can be accessed from the Socioeconomic Data and Application Center (https://beta.sedac.ciesin.columbia.edu/data/set/sdei-global-annual-gwr-pm2-5-modis-misr-seawifs-aod). The Landscan^TM population distribution data is provided by Oak Ridge National Laboratory (https://landscan.ornl.gov/). Annual land cover products in China (CLCD) are freely available at http://doi.org/10.5281/zenodo.4417810.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/10095020.2022.2085187.

Additional information

Funding

This work is supported by the National Natural Science Foundation of China [grant numbers 51878515, 51378399, and 41331175].

Notes on contributors

Qingming Zhan

Qingming Zhan is currently a professor with School of Urban Design, Wuhan University. Prof. Zhan received his Ph.D. degree in geo-information science from Wageningen University - ITC, the Netherlands, in 2003. His research interests include digital and smart cities, planning support systems, object-based analysis of remote sensing images, etc.

Chen Yang

Chen Yang is a Ph.D. candidate in College of Urban and Environmental Sciences, Peking University. He received his master’s degree from School of Urban Design, Wuhan University in 2020. His research interests include urban system sciences, remote sensing, climate change, and urban sustainability

Huimin Liu

Huimin Liu is an associate research fellow of School of Urban Design, Wuhan University. Her research interests include urban sustainability, environmental patterns and dynamics, health cities, climate change, and remote sensing.