Refining green space renewal for PM10 mitigation across heterogeneous built environments

Urban green space renewal is essential for improving air quality in rapidly urbanizing areas. Understanding the interactive effects of building-green space on PM 10 is key to developing effective mitigation strategies. This study compared the seasonal distribution patterns of PM 10 , investigated the interactive effects of building-green space configurations on PM 10 , and identified the nonlinear response relationships and threshold effects between urban spatial patterns and PM 10 concentrations. The results indicated that floor area ratio (FAR) dominated PM 10 regulation in spring and summer. When 0.8 < FAR <1.6, combinations with moderate-to-low NDVI (0.25–0.5), high vegetation surface area (VSA: 400,000–780,000 m 2 ), and high building-vegetation volume ratio (BVVR: 7.5–12.5) significantly reduced PM 10 concentrations. In contrast, the building shape coefficient (BSC) dominated PM 10 regulation in autumn and winter. When BSC approached or exceeded 0.2, its interactions with lower NDVI and higher VSA consistently produced lower SHAP values, and nonlinear fitting confirmed that combinations with NDVI <0.15 or 0.3 < NDVI<0.48 and 400,000 < VSA < 780,000 m 2 were effective in mitigating PM 10 . The findings highlight threshold effects of green space under heterogeneous built environments and provide a scientific basis for green space renewal to enhance PM 10 mitigation.

Authors

Institutions

Publication Details

Journal
Urban Climate
Published
2026-10-06
DOI
https://doi.org/10.1016/j.uclim.2026.103174
Primary Topic
Urban Green Space and Health
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Refining green space renewal for PM10 mitigation across heterogeneous built environments

Jiulong Wang, Hongzhou He, Chunping Miao
Urban Climate
Urban Green Space and Health
article

Refining green space renewal for PM10 mitigation across heterogeneous built environments

Jiulong Wang, Hongzhou He, Chunping Miao
article en

Abstract

Urban green space renewal is essential for improving air quality in rapidly urbanizing areas. Understanding the interactive effects of building-green space on PM 10 is key to developing effective mitigation strategies. This study compared the seasonal distribution patterns of PM 10 , investigated the interactive effects of building-green space configurations on PM 10 , and identified the nonlinear response relationships and threshold effects between urban spatial patterns and PM 10 concentrations. The results indicated that floor area ratio (FAR) dominated PM 10 regulation in spring and summer. When 0.8 < FAR <1.6, combinations with moderate-to-low NDVI (0.25–0.5), high vegetation surface area (VSA: 400,000–780,000 m 2 ), and high building-vegetation volume ratio (BVVR: 7.5–12.5) significantly reduced PM 10 concentrations. In contrast, the building shape coefficient (BSC) dominated PM 10 regulation in autumn and winter. When BSC approached or exceeded 0.2, its interactions with lower NDVI and higher VSA consistently produced lower SHAP values, and nonlinear fitting confirmed that combinations with NDVI <0.15 or 0.3 < NDVI<0.48 and 400,000 < VSA < 780,000 m 2 were effective in mitigating PM 10 . The findings highlight threshold effects of green space under heterogeneous built environments and provide a scientific basis for green space renewal to enhance PM 10 mitigation.

Urban ClimateVol. 70
Chang'an University (CN)
Sustainable cities and communities, Climate action
Openalex Percentile: Top 17%
Urban Green Space and Health
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.