Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China

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dc.contributor.authorLi, Xuan
dc.contributor.authorWu, Chaofan
dc.contributor.authorMeadows, Michael E
dc.contributor.authorZhang, Zhaoyang
dc.contributor.authorLin, Xingwen
dc.contributor.authorZhang, Zhenzhen
dc.contributor.authorChi, Yonggang
dc.contributor.authorFeng, Meili
dc.contributor.authorLi, Enguang
dc.contributor.authorHu, Yuhong
dc.date.accessioned2021-10-07T17:16:52Z
dc.date.available2021-10-07T17:16:52Z
dc.date.issued2021-07-30
dc.date.updated2021-08-06T15:19:29Z
dc.description.abstractFine particulate matter in the lower atmosphere (PM<sub>2.5</sub>) continues to be a major public health problem globally. Identifying the key contributors to PM<sub>2.5</sub> pollution is important in monitoring and managing atmospheric quality, for example, in controlling haze. Previous research has been aimed at quantifying the relationship between PM<sub>2.5</sub> values and their underlying factors, but the spatial and temporal dynamics of these factors are not well understood. Based on random forest and Shapley additive explanation (SHAP) algorithms, this study analyses the spatiotemporal variations in selected key factors influencing PM<sub>2.5</sub> in Zhejiang Province, China, for the period 2000–2019. The results indicate that, while factors influencing PM<sub>2.5</sub> varied significantly during the period studied, SHAP values suggest that there is consistency in their relative importance as follows: meteorological factors (e.g., atmospheric pressure) &gt; socioeconomic factors (e.g., gross domestic product, GDP) &gt; topography and land cover factors (e.g., elevation). The contribution of GDP and transportation factors initially increased but has declined in the recent past, indicating that economic and infrastructural development does not necessarily result in increased PM<sub>2.5</sub> concentrations. Vegetation productivity, as indicated by changes in NDVI, is demonstrated to have become more important in improving air quality, and the area of the province over which it constrains PM<sub>2.5</sub> concentrations has increased between 2000 and 2019. Mapping of SHAP values suggests that, although the relative importance of industrial emissions has declined during the period studied, the actual area positively impacted by such emissions has actually increased. Despite developments in government policy, greater efforts to conserve energy and reduce emissions are still needed. The study further demonstrates that the combination of random forest and SHAP methods provides a valuable means to identify regional differences in key factors affecting atmospheric PM<sub>2.5</sub> values and offers a reliable reference for pollution control strategies.en_US
dc.identifierdoi: 10.3390/rs13153011
dc.identifier.apacitationLi, X., Wu, C., Meadows, M. E., Zhang, Z., Lin, X., Zhang, Z., ... Hu, Y. (2021). Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China. <i>Remote Sensing</i>, 13(15), 3011. http://hdl.handle.net/11427/34203en_ZA
dc.identifier.chicagocitationLi, Xuan, Chaofan Wu, Michael E Meadows, Zhaoyang Zhang, Xingwen Lin, Zhenzhen Zhang, Yonggang Chi, Meili Feng, Enguang Li, and Yuhong Hu "Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China." <i>Remote Sensing</i> 13, 15. (2021): 3011. http://hdl.handle.net/11427/34203en_ZA
dc.identifier.citationLi, X., Wu, C., Meadows, M.E., Zhang, Z., Lin, X., Zhang, Z., Chi, Y. & Feng, M. et al. 2021. Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China. <i>Remote Sensing.</i> 13(15):3011. http://hdl.handle.net/11427/34203en_ZA
dc.identifier.ris TY - Journal Article AU - Li, Xuan AU - Wu, Chaofan AU - Meadows, Michael E AU - Zhang, Zhaoyang AU - Lin, Xingwen AU - Zhang, Zhenzhen AU - Chi, Yonggang AU - Feng, Meili AU - Li, Enguang AU - Hu, Yuhong AB - Fine particulate matter in the lower atmosphere (PM<sub>2.5</sub>) continues to be a major public health problem globally. Identifying the key contributors to PM<sub>2.5</sub> pollution is important in monitoring and managing atmospheric quality, for example, in controlling haze. Previous research has been aimed at quantifying the relationship between PM<sub>2.5</sub> values and their underlying factors, but the spatial and temporal dynamics of these factors are not well understood. Based on random forest and Shapley additive explanation (SHAP) algorithms, this study analyses the spatiotemporal variations in selected key factors influencing PM<sub>2.5</sub> in Zhejiang Province, China, for the period 2000–2019. The results indicate that, while factors influencing PM<sub>2.5</sub> varied significantly during the period studied, SHAP values suggest that there is consistency in their relative importance as follows: meteorological factors (e.g., atmospheric pressure) &gt; socioeconomic factors (e.g., gross domestic product, GDP) &gt; topography and land cover factors (e.g., elevation). The contribution of GDP and transportation factors initially increased but has declined in the recent past, indicating that economic and infrastructural development does not necessarily result in increased PM<sub>2.5</sub> concentrations. Vegetation productivity, as indicated by changes in NDVI, is demonstrated to have become more important in improving air quality, and the area of the province over which it constrains PM<sub>2.5</sub> concentrations has increased between 2000 and 2019. Mapping of SHAP values suggests that, although the relative importance of industrial emissions has declined during the period studied, the actual area positively impacted by such emissions has actually increased. Despite developments in government policy, greater efforts to conserve energy and reduce emissions are still needed. The study further demonstrates that the combination of random forest and SHAP methods provides a valuable means to identify regional differences in key factors affecting atmospheric PM<sub>2.5</sub> values and offers a reliable reference for pollution control strategies. DA - 2021-07-30 DB - OpenUCT DP - University of Cape Town IS - 15 J1 - Remote Sensing LK - https://open.uct.ac.za PY - 2021 T1 - Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China TI - Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China UR - http://hdl.handle.net/11427/34203 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/34203
dc.identifier.vancouvercitationLi X, Wu C, Meadows ME, Zhang Z, Lin X, Zhang Z, et al. Factors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, China. Remote Sensing. 2021;13(15):3011. http://hdl.handle.net/11427/34203.en_ZA
dc.language.isoenen_US
dc.publisher.departmentDepartment of Environmental and Geographical Scienceen_US
dc.publisher.facultyFaculty of Scienceen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.sourceRemote Sensingen_US
dc.source.journalissue15en_US
dc.source.journalvolume13en_US
dc.source.pagination3011en_US
dc.source.urihttps://www.mdpi.com/journal/remotesensing
dc.titleFactors Underlying Spatiotemporal Variations in Atmospheric PM2.5 Concentrations in Zhejiang Province, Chinaen_US
dc.typeJournal Articleen_US
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