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- Publisher Website: 10.1016/j.apgeog.2025.103644
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Article: Mapping the distribution of pedestrian exposure to air pollution on urban road segments based on mobile monitoring and street view images
| Title | Mapping the distribution of pedestrian exposure to air pollution on urban road segments based on mobile monitoring and street view images |
|---|---|
| Authors | |
| Keywords | Geographically weighted regression Pedestrian exposure to air pollution Street view images |
| Issue Date | 1-Jun-2025 |
| Publisher | Elsevier |
| Citation | Applied Geography, 2025, v. 179 How to Cite? |
| Abstract | Urban air pollution poses a significant global environmental challenge, with pedestrians being particularly vulnerable due to their proximity to road traffic and limited protection. This study investigated the spatial distribution of pedestrian exposure to PM2.5 in Central London and identified high-exposure road segments using air pollution mobile monitoring data and street view images. Influential factors were analyzed through a geographically weighted regression model. The results revealed that pedestrian exposure was spatially clustered, with two high-exposure hot spots identified. Commercial land use, traffic and transport facilities, points of interest (POIs), building height, and street aspect ratio were positively associated with exposure levels, while urban greenness exhibited a negative correlation. The effects of these factors varied across road segments. Based on these results and existing literature, the study also proposed a framework for green infrastructure planning to mitigate pedestrian exposure to air pollution in the study area. |
| Persistent Identifier | http://hdl.handle.net/10722/360744 |
| ISSN | 2023 Impact Factor: 4.0 2023 SCImago Journal Rankings: 1.204 |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Yu, Xujing | - |
| dc.contributor.author | Ma, Jun | - |
| dc.date.accessioned | 2025-09-13T00:36:09Z | - |
| dc.date.available | 2025-09-13T00:36:09Z | - |
| dc.date.issued | 2025-06-01 | - |
| dc.identifier.citation | Applied Geography, 2025, v. 179 | - |
| dc.identifier.issn | 0143-6228 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/360744 | - |
| dc.description.abstract | <p>Urban air pollution poses a significant global environmental challenge, with pedestrians being particularly vulnerable due to their proximity to road traffic and limited protection. This study investigated the spatial distribution of pedestrian exposure to PM2.5 in Central London and identified high-exposure road segments using air pollution mobile monitoring data and street view images. Influential factors were analyzed through a geographically weighted regression model. The results revealed that pedestrian exposure was spatially clustered, with two high-exposure hot spots identified. Commercial land use, traffic and transport facilities, points of interest (POIs), building height, and street aspect ratio were positively associated with exposure levels, while urban greenness exhibited a negative correlation. The effects of these factors varied across road segments. Based on these results and existing literature, the study also proposed a framework for green infrastructure planning to mitigate pedestrian exposure to air pollution in the study area.</p> | - |
| dc.language | eng | - |
| dc.publisher | Elsevier | - |
| dc.relation.ispartof | Applied Geography | - |
| dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
| dc.subject | Geographically weighted regression | - |
| dc.subject | Pedestrian exposure to air pollution | - |
| dc.subject | Street view images | - |
| dc.title | Mapping the distribution of pedestrian exposure to air pollution on urban road segments based on mobile monitoring and street view images | - |
| dc.type | Article | - |
| dc.identifier.doi | 10.1016/j.apgeog.2025.103644 | - |
| dc.identifier.scopus | eid_2-s2.0-105003688972 | - |
| dc.identifier.volume | 179 | - |
| dc.identifier.eissn | 1873-7730 | - |
| dc.identifier.issnl | 0143-6228 | - |
