Factors Underlying Spatiotemporal Variations in Atmospheric PM<sub>2.5</sub> Concentrations in Zhejiang Province, China
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 h...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-07-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/15/3011 |
| _version_ | 1797525187904667648 |
|---|---|
| author | Xuan Li Chaofan Wu Michael E. Meadows Zhaoyang Zhang Xingwen Lin Zhenzhen Zhang Yonggang Chi Meili Feng Enguang Li Yuhong Hu |
| author_facet | Xuan Li Chaofan Wu Michael E. Meadows Zhaoyang Zhang Xingwen Lin Zhenzhen Zhang Yonggang Chi Meili Feng Enguang Li Yuhong Hu |
| author_sort | Xuan Li |
| collection | DOAJ |
| description | 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) > socioeconomic factors (e.g., gross domestic product, GDP) > 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. |
| first_indexed | 2024-03-10T09:09:14Z |
| format | Article |
| id | doaj.art-7f0c85c1428c4dc695bea66a4d4360ad |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T09:09:14Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-7f0c85c1428c4dc695bea66a4d4360ad2023-11-22T06:07:32ZengMDPI AGRemote Sensing2072-42922021-07-011315301110.3390/rs13153011Factors Underlying Spatiotemporal Variations in Atmospheric PM<sub>2.5</sub> Concentrations in Zhejiang Province, ChinaXuan Li0Chaofan Wu1Michael E. Meadows2Zhaoyang Zhang3Xingwen Lin4Zhenzhen Zhang5Yonggang Chi6Meili Feng7Enguang Li8Yuhong Hu9College of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, ChinaSchool of Geographical Sciences, University of Nottingham Ningbo China, Ningbo 315100, ChinaCollege of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, ChinaCollege of Geography and Environmental Sciences, Zhejiang Normal University, Jinhua 321004, ChinaFine 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) > socioeconomic factors (e.g., gross domestic product, GDP) > 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.https://www.mdpi.com/2072-4292/13/15/3011atmospheric pollutionrandom forest regressionSHAPinfluencing factors |
| spellingShingle | Xuan Li Chaofan Wu Michael E. Meadows Zhaoyang Zhang Xingwen Lin Zhenzhen Zhang Yonggang Chi Meili Feng Enguang Li Yuhong Hu Factors Underlying Spatiotemporal Variations in Atmospheric PM<sub>2.5</sub> Concentrations in Zhejiang Province, China Remote Sensing atmospheric pollution random forest regression SHAP influencing factors |
| title | Factors Underlying Spatiotemporal Variations in Atmospheric PM<sub>2.5</sub> Concentrations in Zhejiang Province, China |
| title_full | Factors Underlying Spatiotemporal Variations in Atmospheric PM<sub>2.5</sub> Concentrations in Zhejiang Province, China |
| title_fullStr | Factors Underlying Spatiotemporal Variations in Atmospheric PM<sub>2.5</sub> Concentrations in Zhejiang Province, China |
| title_full_unstemmed | Factors Underlying Spatiotemporal Variations in Atmospheric PM<sub>2.5</sub> Concentrations in Zhejiang Province, China |
| title_short | Factors Underlying Spatiotemporal Variations in Atmospheric PM<sub>2.5</sub> Concentrations in Zhejiang Province, China |
| title_sort | factors underlying spatiotemporal variations in atmospheric pm sub 2 5 sub concentrations in zhejiang province china |
| topic | atmospheric pollution random forest regression SHAP influencing factors |
| url | https://www.mdpi.com/2072-4292/13/15/3011 |
| work_keys_str_mv | AT xuanli factorsunderlyingspatiotemporalvariationsinatmosphericpmsub25subconcentrationsinzhejiangprovincechina AT chaofanwu factorsunderlyingspatiotemporalvariationsinatmosphericpmsub25subconcentrationsinzhejiangprovincechina AT michaelemeadows factorsunderlyingspatiotemporalvariationsinatmosphericpmsub25subconcentrationsinzhejiangprovincechina AT zhaoyangzhang factorsunderlyingspatiotemporalvariationsinatmosphericpmsub25subconcentrationsinzhejiangprovincechina AT xingwenlin factorsunderlyingspatiotemporalvariationsinatmosphericpmsub25subconcentrationsinzhejiangprovincechina AT zhenzhenzhang factorsunderlyingspatiotemporalvariationsinatmosphericpmsub25subconcentrationsinzhejiangprovincechina AT yonggangchi factorsunderlyingspatiotemporalvariationsinatmosphericpmsub25subconcentrationsinzhejiangprovincechina AT meilifeng factorsunderlyingspatiotemporalvariationsinatmosphericpmsub25subconcentrationsinzhejiangprovincechina AT enguangli factorsunderlyingspatiotemporalvariationsinatmosphericpmsub25subconcentrationsinzhejiangprovincechina AT yuhonghu factorsunderlyingspatiotemporalvariationsinatmosphericpmsub25subconcentrationsinzhejiangprovincechina |