New insight into the urban PM2.5 pollution island effect enabled by the Gaussian surface fitting model: A case study in a mega urban agglomeration region of China
The phenomenon of the urban PM2.5 pollution island (UPI, the PM2.5 concentration difference along the urban–rural gradient) has been a growing concern due to its pernicious impacts on both residential and environmental health. To date, however, studies on the spatialization and indexation of the UPI...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-09-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S156984322200173X |
| _version_ | 1811183381277310976 |
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| author | Lei Yao Shuo Sun Yixu Wang Chaoxue Song Ying Xu |
| author_facet | Lei Yao Shuo Sun Yixu Wang Chaoxue Song Ying Xu |
| author_sort | Lei Yao |
| collection | DOAJ |
| description | The phenomenon of the urban PM2.5 pollution island (UPI, the PM2.5 concentration difference along the urban–rural gradient) has been a growing concern due to its pernicious impacts on both residential and environmental health. To date, however, studies on the spatialization and indexation of the UPI effect and what is might be attributed to are still lacking. In this study, the Gaussian surface fitting model was innovatively used to depict the spatial morphology of the UPI effect for 13 cities in the Beijing–Tianjin–Hebei urban agglomeration region of China. Multiple indicators were introduced from the fitted Gaussian surface to portray the UPI intensity (magnitude), footprint (impact extent), and capacity (cumulative risk load) characteristics. Then, their potential relationships with several representative natural and anthropogenic factors were examined based on panel, cross-section, and time-series analysis. The main findings can be summarized as follows: 1) The intensity, footprint, and capacity indicators based on the fitted Gaussian model complementarily depict the spatial characteristics of the UPI effect. The spatiotemporal analysis showed that most of the case cities experienced remarkably intensified but heterogeneous UPI effects from 2000 to 2015. 2) The panel, cross-section, and time-series analysis enriched the attribution portrayal of the UPI effect. Anthropogenic factors assigned to urbanization generally had more impact on the UPI effect than natural factors. However, the specific contributors varied across cities and times. Based on the Gaussian surface fitting model, multidimensional indicators, and multi-perspective analysis, the results of this study offer new insight into our understanding of the UPI effect and its potential associations, which is useful to broaden both the research methodology and the perspectives in the current study and thus benefit future environmental regulations by providing a worthwhile scientific reference. |
| first_indexed | 2024-04-11T09:46:44Z |
| format | Article |
| id | doaj.art-bf92210035e247b3a1350fed1acd7dcd |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-11T09:46:44Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-bf92210035e247b3a1350fed1acd7dcd2022-12-22T04:30:57ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322022-09-01113102982New insight into the urban PM2.5 pollution island effect enabled by the Gaussian surface fitting model: A case study in a mega urban agglomeration region of ChinaLei Yao0Shuo Sun1Yixu Wang2Chaoxue Song3Ying Xu4College of Geography and Environment, Shandong Normal University, Jinan 250014, China; Corresponding author.College of Geography and Environment, Shandong Normal University, Jinan 250014, ChinaCollege of Geography and Environment, Shandong Normal University, Jinan 250014, ChinaCollege of Geography and Environment, Shandong Normal University, Jinan 250014, China; School of Geographical Sciences, Northeast Normal University, Changchun 130024, ChinaSchool of Civil Engineering, Shandong Jiaotong University, Jinan 250023, ChinaThe phenomenon of the urban PM2.5 pollution island (UPI, the PM2.5 concentration difference along the urban–rural gradient) has been a growing concern due to its pernicious impacts on both residential and environmental health. To date, however, studies on the spatialization and indexation of the UPI effect and what is might be attributed to are still lacking. In this study, the Gaussian surface fitting model was innovatively used to depict the spatial morphology of the UPI effect for 13 cities in the Beijing–Tianjin–Hebei urban agglomeration region of China. Multiple indicators were introduced from the fitted Gaussian surface to portray the UPI intensity (magnitude), footprint (impact extent), and capacity (cumulative risk load) characteristics. Then, their potential relationships with several representative natural and anthropogenic factors were examined based on panel, cross-section, and time-series analysis. The main findings can be summarized as follows: 1) The intensity, footprint, and capacity indicators based on the fitted Gaussian model complementarily depict the spatial characteristics of the UPI effect. The spatiotemporal analysis showed that most of the case cities experienced remarkably intensified but heterogeneous UPI effects from 2000 to 2015. 2) The panel, cross-section, and time-series analysis enriched the attribution portrayal of the UPI effect. Anthropogenic factors assigned to urbanization generally had more impact on the UPI effect than natural factors. However, the specific contributors varied across cities and times. Based on the Gaussian surface fitting model, multidimensional indicators, and multi-perspective analysis, the results of this study offer new insight into our understanding of the UPI effect and its potential associations, which is useful to broaden both the research methodology and the perspectives in the current study and thus benefit future environmental regulations by providing a worthwhile scientific reference.http://www.sciencedirect.com/science/article/pii/S156984322200173XAirborne PM2.5 pollutionIsland effectGaussian surfaceIntensity-Footprint-CapacityPartial Least Squares regressionBeijing-Tianjin-Hebei |
| spellingShingle | Lei Yao Shuo Sun Yixu Wang Chaoxue Song Ying Xu New insight into the urban PM2.5 pollution island effect enabled by the Gaussian surface fitting model: A case study in a mega urban agglomeration region of China International Journal of Applied Earth Observations and Geoinformation Airborne PM2.5 pollution Island effect Gaussian surface Intensity-Footprint-Capacity Partial Least Squares regression Beijing-Tianjin-Hebei |
| title | New insight into the urban PM2.5 pollution island effect enabled by the Gaussian surface fitting model: A case study in a mega urban agglomeration region of China |
| title_full | New insight into the urban PM2.5 pollution island effect enabled by the Gaussian surface fitting model: A case study in a mega urban agglomeration region of China |
| title_fullStr | New insight into the urban PM2.5 pollution island effect enabled by the Gaussian surface fitting model: A case study in a mega urban agglomeration region of China |
| title_full_unstemmed | New insight into the urban PM2.5 pollution island effect enabled by the Gaussian surface fitting model: A case study in a mega urban agglomeration region of China |
| title_short | New insight into the urban PM2.5 pollution island effect enabled by the Gaussian surface fitting model: A case study in a mega urban agglomeration region of China |
| title_sort | new insight into the urban pm2 5 pollution island effect enabled by the gaussian surface fitting model a case study in a mega urban agglomeration region of china |
| topic | Airborne PM2.5 pollution Island effect Gaussian surface Intensity-Footprint-Capacity Partial Least Squares regression Beijing-Tianjin-Hebei |
| url | http://www.sciencedirect.com/science/article/pii/S156984322200173X |
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