Vertical Profiling of PM1 and PM2.5 Dynamics: UAV-Based Observations in Seasonal Urban Atmosphere

Urban particulate matter (PM) pollution critically impacts public health and climate. However, traditional ground-based monitoring fails to resolve vertical PM distribution, limiting understanding of transport and stratification-coupled mechanisms. Vertical profiles collected by an unmanned aerial vehicle (UAV) over Hangzhou, a core megacity in China’s Yangtze River Delta, reveal the spatiotemporal heterogeneity and multi-scale drivers of regional PM pollution during two intensive ten-day campaigns capturing peak pollution scenarios (winter: 17–26 January 2019; summer: 21–30 August 2019). Results show stark seasonal differences: winter PM1 and PM2.5 averages were 2.6- and 2.7-fold higher (p < 0.0001) than summer. Diurnal patterns were bimodal in winter and unimodal (single valley) in summer. Vertically consistent PM1 and PM2.5 distributions featured sharp morning (08:00) concentration increases within specific layers (winter: 250–325 m; summer: 350–425 m). Analysis demonstrates multi-scale coupling of synoptic systems, boundary layer processes, and vertical wind structure governing pollution. Key mechanisms include a winter “Transport-Accumulation-Reactivation” cycle driven by cold air, and summer typhoon circulation influences. We identify hygroscopic growth triggered by inversion-high humidity coupling and sea-breeze-driven secondary aerosol formation. Leveraging UAV-based vertical profiling over Hangzhou, this study pioneers a three-dimensional dissection of layer-coupled PM dynamics in the Yangtze River Delta, offering a scalable paradigm for aerial–ground networks to achieve precision stratified control strategies in megacities.

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Publication Details

Journal
Atmosphere
Published
2025-08-15
DOI
https://doi.org/10.3390/atmos16080968
Citations
4
Primary Topic
Air Quality Monitoring and Forecasting
Type
article
Field-Weighted Citation Impact
3.20

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article

Vertical Profiling of PM1 and PM2.5 Dynamics: UAV-Based Observations in Seasonal Urban Atmosphere

Ming Yang, Yuting Pang, Bing Qi, Xuezhu Ye et al.
4 citations
Atmosphere
Air Quality Monitoring and Forecasting
3.20
article

Vertical Profiling of PM1 and PM2.5 Dynamics: UAV-Based Observations in Seasonal Urban Atmosphere

Ming Yang, Yuting Pang, Bing Qi, Xuezhu Ye, Chi Zhang, Zhen Zhao
article en
4 citations

Abstract

Urban particulate matter (PM) pollution critically impacts public health and climate. However, traditional ground-based monitoring fails to resolve vertical PM distribution, limiting understanding of transport and stratification-coupled mechanisms. Vertical profiles collected by an unmanned aerial vehicle (UAV) over Hangzhou, a core megacity in China’s Yangtze River Delta, reveal the spatiotemporal heterogeneity and multi-scale drivers of regional PM pollution during two intensive ten-day campaigns capturing peak pollution scenarios (winter: 17–26 January 2019; summer: 21–30 August 2019). Results show stark seasonal differences: winter PM1 and PM2.5 averages were 2.6- and 2.7-fold higher (p < 0.0001) than summer. Diurnal patterns were bimodal in winter and unimodal (single valley) in summer. Vertically consistent PM1 and PM2.5 distributions featured sharp morning (08:00) concentration increases within specific layers (winter: 250–325 m; summer: 350–425 m). Analysis demonstrates multi-scale coupling of synoptic systems, boundary layer processes, and vertical wind structure governing pollution. Key mechanisms include a winter “Transport-Accumulation-Reactivation” cycle driven by cold air, and summer typhoon circulation influences. We identify hygroscopic growth triggered by inversion-high humidity coupling and sea-breeze-driven secondary aerosol formation. Leveraging UAV-based vertical profiling over Hangzhou, this study pioneers a three-dimensional dissection of layer-coupled PM dynamics in the Yangtze River Delta, offering a scalable paradigm for aerial–ground networks to achieve precision stratified control strategies in megacities.

AtmosphereVol. 16(8)
ZheJiang Academy of Agricultural Sciences (CN), Zhejiang Meteorological Bureau (CN)
National Key Research and Development Program of China
Openalex Percentile: Top 8%
Air Quality Monitoring and Forecasting
3.20
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