File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.3390/smartcities8020053
- Scopus: eid_2-s2.0-105003483546
- Find via

Supplementary
-
Citations:
- Scopus: 0
- Appears in Collections:
Article: Environmental Justice in the 15-Minute City: Assessing Air Pollution Exposure Inequalities Through Machine Learning and Spatial Network Analysis
| Title | Environmental Justice in the 15-Minute City: Assessing Air Pollution Exposure Inequalities Through Machine Learning and Spatial Network Analysis |
|---|---|
| Authors | |
| Keywords | 15-minute city air pollution exposure environmental justice graph network machine learning spatial disparity |
| Issue Date | 1-Apr-2025 |
| Publisher | MDPI |
| Citation | Smart Cities, 2025, v. 8, n. 2 How to Cite? |
| Abstract | Highlights: What are the main findings? The study uncovers significant socioeconomic and racial disparities in air pollution exposure within 15-minute walking ranges, with lower-income and Black communities facing higher PM2.5 levels. Traditional exposure assessments often underestimate disparities by failing to account for residents’ daily mobility patterns and activity spaces. What is the implication of the main finding? The findings emphasize the need for dynamic, accessibility-based environmental justice assessments that reflect real-world mobility and exposure patterns. The study calls for tailored urban planning strategies to address localized pollution inequalities and ensure equitable outcomes in 15-minute city initiatives. 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. |
| Persistent Identifier | http://hdl.handle.net/10722/359465 |
| ISSN | 2023 Impact Factor: 7.0 2023 SCImago Journal Rankings: 1.326 |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Jiang, Feifeng | - |
| dc.contributor.author | Ma, Jun | - |
| dc.date.accessioned | 2025-09-07T00:30:33Z | - |
| dc.date.available | 2025-09-07T00:30:33Z | - |
| dc.date.issued | 2025-04-01 | - |
| dc.identifier.citation | Smart Cities, 2025, v. 8, n. 2 | - |
| dc.identifier.issn | 2624-6511 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/359465 | - |
| dc.description.abstract | Highlights: What are the main findings? The study uncovers significant socioeconomic and racial disparities in air pollution exposure within 15-minute walking ranges, with lower-income and Black communities facing higher PM2.5 levels. Traditional exposure assessments often underestimate disparities by failing to account for residents’ daily mobility patterns and activity spaces. What is the implication of the main finding? The findings emphasize the need for dynamic, accessibility-based environmental justice assessments that reflect real-world mobility and exposure patterns. The study calls for tailored urban planning strategies to address localized pollution inequalities and ensure equitable outcomes in 15-minute city initiatives. 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. | - |
| dc.language | eng | - |
| dc.publisher | MDPI | - |
| dc.relation.ispartof | Smart Cities | - |
| dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
| dc.subject | 15-minute city | - |
| dc.subject | air pollution exposure | - |
| dc.subject | environmental justice | - |
| dc.subject | graph network | - |
| dc.subject | machine learning | - |
| dc.subject | spatial disparity | - |
| dc.title | Environmental Justice in the 15-Minute City: Assessing Air Pollution Exposure Inequalities Through Machine Learning and Spatial Network Analysis | - |
| dc.type | Article | - |
| dc.identifier.doi | 10.3390/smartcities8020053 | - |
| dc.identifier.scopus | eid_2-s2.0-105003483546 | - |
| dc.identifier.volume | 8 | - |
| dc.identifier.issue | 2 | - |
| dc.identifier.eissn | 2624-6511 | - |
| dc.identifier.issnl | 2624-6511 | - |
