Environmental Justice in the 15-Minute City: Assessing Air Pollution Exposure Inequalities Through Machine Learning and Spatial Network Analysis
The intersection of environmental justice and urban accessibility presents a critical challenge in sustainable city planning. While the “15-minute city” concept has emerged as a prominent framework for promoting walkable neighborhoods, its implications for environmental exposure inequalities remain...
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MDPI AG
2025-03-01
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| Series: | Smart Cities |
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| Online Access: | https://www.mdpi.com/2624-6511/8/2/53 |
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| author | Feifeng Jiang Jun Ma |
| author_facet | Feifeng Jiang Jun Ma |
| author_sort | Feifeng Jiang |
| collection | DOAJ |
| description | The intersection of environmental justice and urban accessibility presents a critical challenge in sustainable city planning. While the “15-minute city” concept has emerged as a prominent framework for promoting walkable neighborhoods, its implications for environmental exposure inequalities remain underexplored. This study introduces an innovative methodology for assessing air pollution exposure disparities within the context of 15-minute activity zones in New York City. By integrating street-level PM2.5 predictions with spatial network analysis, this research evaluates exposure patterns that more accurately reflect residents’ daily mobility experiences. The results reveal significant socioeconomic and racial disparities in air pollution exposure, with lower-income areas and Black communities experiencing consistently higher PM2.5 levels within their 15-minute walking ranges. A borough-level analysis further underscores the influence of localized urban development patterns and demographic distributions on environmental justice outcomes. A comparative analysis demonstrates that traditional census tract-based approaches may underestimate these disparities by failing to account for actual pedestrian mobility patterns. These findings highlight the necessity of integrating high-resolution environmental justice assessments into urban planning initiatives to foster more equitable and sustainable urban development. |
| format | Article |
| id | doaj-art-0e0f84455f9d42eb95b01d6748b04172 |
| institution | OA Journals |
| issn | 2624-6511 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Smart Cities |
| spelling | doaj-art-0e0f84455f9d42eb95b01d6748b041722025-08-20T02:25:11ZengMDPI AGSmart Cities2624-65112025-03-01825310.3390/smartcities8020053Environmental Justice in the 15-Minute City: Assessing Air Pollution Exposure Inequalities Through Machine Learning and Spatial Network AnalysisFeifeng Jiang0Jun Ma1Department of Urban Planning and Design, The University of Hong Kong, Hong Kong, ChinaDepartment of Urban Planning and Design, The University of Hong Kong, Hong Kong, ChinaThe intersection of environmental justice and urban accessibility presents a critical challenge in sustainable city planning. While the “15-minute city” concept has emerged as a prominent framework for promoting walkable neighborhoods, its implications for environmental exposure inequalities remain underexplored. This study introduces an innovative methodology for assessing air pollution exposure disparities within the context of 15-minute activity zones in New York City. By integrating street-level PM2.5 predictions with spatial network analysis, this research evaluates exposure patterns that more accurately reflect residents’ daily mobility experiences. The results reveal significant socioeconomic and racial disparities in air pollution exposure, with lower-income areas and Black communities experiencing consistently higher PM2.5 levels within their 15-minute walking ranges. A borough-level analysis further underscores the influence of localized urban development patterns and demographic distributions on environmental justice outcomes. A comparative analysis demonstrates that traditional census tract-based approaches may underestimate these disparities by failing to account for actual pedestrian mobility patterns. These findings highlight the necessity of integrating high-resolution environmental justice assessments into urban planning initiatives to foster more equitable and sustainable urban development.https://www.mdpi.com/2624-6511/8/2/53air pollution exposureenvironmental justice15-minute citymachine learninggraph networkspatial disparity |
| spellingShingle | Feifeng Jiang Jun Ma Environmental Justice in the 15-Minute City: Assessing Air Pollution Exposure Inequalities Through Machine Learning and Spatial Network Analysis Smart Cities air pollution exposure environmental justice 15-minute city machine learning graph network spatial disparity |
| title | Environmental Justice in the 15-Minute City: Assessing Air Pollution Exposure Inequalities Through Machine Learning and Spatial Network Analysis |
| title_full | Environmental Justice in the 15-Minute City: Assessing Air Pollution Exposure Inequalities Through Machine Learning and Spatial Network Analysis |
| title_fullStr | Environmental Justice in the 15-Minute City: Assessing Air Pollution Exposure Inequalities Through Machine Learning and Spatial Network Analysis |
| title_full_unstemmed | Environmental Justice in the 15-Minute City: Assessing Air Pollution Exposure Inequalities Through Machine Learning and Spatial Network Analysis |
| title_short | Environmental Justice in the 15-Minute City: Assessing Air Pollution Exposure Inequalities Through Machine Learning and Spatial Network Analysis |
| title_sort | environmental justice in the 15 minute city assessing air pollution exposure inequalities through machine learning and spatial network analysis |
| topic | air pollution exposure environmental justice 15-minute city machine learning graph network spatial disparity |
| url | https://www.mdpi.com/2624-6511/8/2/53 |
| work_keys_str_mv | AT feifengjiang environmentaljusticeinthe15minutecityassessingairpollutionexposureinequalitiesthroughmachinelearningandspatialnetworkanalysis AT junma environmentaljusticeinthe15minutecityassessingairpollutionexposureinequalitiesthroughmachinelearningandspatialnetworkanalysis |