Spatiotemporally Heterogeneous Effects of Urban Landscape Pattern on PM2.5: Seasonal Mechanisms in Urumqi, China

PM2.5 pollution presents a significant risk to urban habitability. The urban landscape pattern (ULP) serves as a crucial regulator that profoundly influences the spatiotemporal distribution features of PM2.5. Analysis of the driving mechanisms of the ULP is therefore essential for optimizing urban ecological spatial planning. However, the driving mechanism is dynamic and exhibits seasonal variations. This study selected four landscape metrics and four control variables, developed a geographically and temporally weighted regression (GTWR) model, and examined the spatiotemporal and seasonal effects of ULP on PM2.5 concentrations in the central urban area of Urumqi (CUA) from 2003 to 2023. The results show the following: (1) Over the past two decades, the four ULP metrics have shown an increasing trend in the CUA. (2) The spatial distribution of PM2.5 concentrations follows a latitudinal gradient, with higher concentrations observed in the northern regions and lower concentrations in the southern regions, initially increasing and then declining over time. (3) The driving mechanisms of ULP on PM2.5 exhibited significant variations across different locations and time scales. (4) Seasonal variations arise from pronounced meteorological contrasts and intensified pollution from central heating, which is particularly evident in central CUA.

Authors

Institutions

Publication Details

Journal
Land
Published
2025-05-30
DOI
https://doi.org/10.3390/land14061184
Primary Topic
Urban Heat Island Mitigation
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Spatiotemporally Heterogeneous Effects of Urban Landscape Pattern on PM2.5: Seasonal Mechanisms in Urumqi, China

Xingchi Zhou, Yantao Xi, Shuangqiao Wang, Yuanfan Zhang
Land
Urban Heat Island Mitigation
article

Spatiotemporally Heterogeneous Effects of Urban Landscape Pattern on PM2.5: Seasonal Mechanisms in Urumqi, China

Xingchi Zhou, Yantao Xi, Shuangqiao Wang, Yuanfan Zhang
article en

Abstract

PM2.5 pollution presents a significant risk to urban habitability. The urban landscape pattern (ULP) serves as a crucial regulator that profoundly influences the spatiotemporal distribution features of PM2.5. Analysis of the driving mechanisms of the ULP is therefore essential for optimizing urban ecological spatial planning. However, the driving mechanism is dynamic and exhibits seasonal variations. This study selected four landscape metrics and four control variables, developed a geographically and temporally weighted regression (GTWR) model, and examined the spatiotemporal and seasonal effects of ULP on PM2.5 concentrations in the central urban area of Urumqi (CUA) from 2003 to 2023. The results show the following: (1) Over the past two decades, the four ULP metrics have shown an increasing trend in the CUA. (2) The spatial distribution of PM2.5 concentrations follows a latitudinal gradient, with higher concentrations observed in the northern regions and lower concentrations in the southern regions, initially increasing and then declining over time. (3) The driving mechanisms of ULP on PM2.5 exhibited significant variations across different locations and time scales. (4) Seasonal variations arise from pronounced meteorological contrasts and intensified pollution from central heating, which is particularly evident in central CUA.

LandVol. 14(6)
China University of Mining and Technology (CN)
Openalex Percentile: Top 89%
Urban Heat Island Mitigation
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.