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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Land |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-445X/14/1/7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|---|
| ISSN: | 2073-445X |