Impact of Urban Neighborhood Morphology on PM2.5 Concentration Distribution at Different Scale Buffers
PM2.5 air pollution is a critical global health issue. This paper introduces an innovative framework to explore the multi-scale relationship between urban morphology and PM2.5 concentrations. An enhanced Land Use Regression (LUR) model integrates geographic, architectural, and visual factors, enabli...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Land |
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| Online Access: | https://www.mdpi.com/2073-445X/14/1/7 |
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| author | Zhen Wang Kexin Hu Zheyu Wang Bo Yang Zhiyu Chen |
| author_facet | Zhen Wang Kexin Hu Zheyu Wang Bo Yang Zhiyu Chen |
| author_sort | Zhen Wang |
| collection | DOAJ |
| description | PM2.5 air pollution is a critical global health issue. This paper introduces an innovative framework to explore the multi-scale relationship between urban morphology and PM2.5 concentrations. An enhanced Land Use Regression (LUR) model integrates geographic, architectural, and visual factors, enabling analysis from neighborhood to regional scales. A stratified sampling strategy, combined with standardized mobile monitoring and fixed-site data, establishes a robust and verifiable data collection methodology. Cross-validation (CV R<sup>2</sup> > 0.70) further confirms the model’s reliability and robustness. The nested buffer analysis reveals scale-dependent effects of urban morphology on PM2.5 concentrations, providing quantitative evidence for planning interventions. Quantitative analysis shows land use (β = 0.42, <i>p</i> < 0.01), visual factors (β = 0.38, <i>p</i> < 0.01), and building density (β = 0.35, <i>p</i> < 0.01) in descending order of influence. Geographic factors are significant at the regional scale (2000–3000 m) while architectural parameters dominate at the neighborhood scale (50–500 m), informing both macro-scale spatial optimization and micro-scale design. This framework, through standardized parameters and reproducible procedures, supports cross-regional and cross-scale air quality assessments, providing quantitative metrics for urban planning, neighborhood optimization, and public space design. |
| format | Article |
| id | doaj-art-64c41bb9a51a4bb8b5264480a646eb04 |
| institution | Kabale University |
| issn | 2073-445X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj-art-64c41bb9a51a4bb8b5264480a646eb042025-01-24T13:37:31ZengMDPI AGLand2073-445X2024-12-01141710.3390/land14010007Impact of Urban Neighborhood Morphology on PM2.5 Concentration Distribution at Different Scale BuffersZhen Wang0Kexin Hu1Zheyu Wang2Bo Yang3Zhiyu Chen4School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Architecture and Urban Planning, University of Arizona, Tucson, AZ 85721, USAHubei Engineering and Technology Research Center of Urbanization, Wuhan 430074, ChinaPM2.5 air pollution is a critical global health issue. This paper introduces an innovative framework to explore the multi-scale relationship between urban morphology and PM2.5 concentrations. An enhanced Land Use Regression (LUR) model integrates geographic, architectural, and visual factors, enabling analysis from neighborhood to regional scales. A stratified sampling strategy, combined with standardized mobile monitoring and fixed-site data, establishes a robust and verifiable data collection methodology. Cross-validation (CV R<sup>2</sup> > 0.70) further confirms the model’s reliability and robustness. The nested buffer analysis reveals scale-dependent effects of urban morphology on PM2.5 concentrations, providing quantitative evidence for planning interventions. Quantitative analysis shows land use (β = 0.42, <i>p</i> < 0.01), visual factors (β = 0.38, <i>p</i> < 0.01), and building density (β = 0.35, <i>p</i> < 0.01) in descending order of influence. Geographic factors are significant at the regional scale (2000–3000 m) while architectural parameters dominate at the neighborhood scale (50–500 m), informing both macro-scale spatial optimization and micro-scale design. This framework, through standardized parameters and reproducible procedures, supports cross-regional and cross-scale air quality assessments, providing quantitative metrics for urban planning, neighborhood optimization, and public space design.https://www.mdpi.com/2073-445X/14/1/7air qualitypollutant dispersionurban neighborhood morphologyLand Use Regression (LUR)different scale buffers |
| spellingShingle | Zhen Wang Kexin Hu Zheyu Wang Bo Yang Zhiyu Chen Impact of Urban Neighborhood Morphology on PM2.5 Concentration Distribution at Different Scale Buffers Land air quality pollutant dispersion urban neighborhood morphology Land Use Regression (LUR) different scale buffers |
| title | Impact of Urban Neighborhood Morphology on PM2.5 Concentration Distribution at Different Scale Buffers |
| title_full | Impact of Urban Neighborhood Morphology on PM2.5 Concentration Distribution at Different Scale Buffers |
| title_fullStr | Impact of Urban Neighborhood Morphology on PM2.5 Concentration Distribution at Different Scale Buffers |
| title_full_unstemmed | Impact of Urban Neighborhood Morphology on PM2.5 Concentration Distribution at Different Scale Buffers |
| title_short | Impact of Urban Neighborhood Morphology on PM2.5 Concentration Distribution at Different Scale Buffers |
| title_sort | impact of urban neighborhood morphology on pm2 5 concentration distribution at different scale buffers |
| topic | air quality pollutant dispersion urban neighborhood morphology Land Use Regression (LUR) different scale buffers |
| url | https://www.mdpi.com/2073-445X/14/1/7 |
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