Unveiling PM2.5 Transport Pathways: A Trajectory-Channel Model Framework for Spatiotemporally Quantitative Source Apportionment

In this study, we introduced a novel Trajectory-Channel Transport Model (TCTM) to unravel spatiotemporal dynamics of PM2.5 pollution. By integrating high-resolution simulations from the Weather Research and Forecasting (WRF) model with the Nested Air-Quality Prediction Modeling System (WRF-NAQPMS) and 72 h backward-trajectory analysis, TCTM enables the precise identification of source regions, the delineation of key transport corridors, and a quantitative assessment of regional contributions to receptor sites. Focusing on four Yangtze River Delta cities (Hangzhou, Shanghai, Nanjing, Hefei) during a January 2020 pollution event, the results demonstrate that TCTM’s Weighted Concentration Source (WCS) and Source Pollution Characteristic Index (SPCI) outperform traditional PSCF and CWT methods in source-attribution accuracy and resolution. Unlike receptor-based statistical approaches, TCTM reconstructs pollutant transport processes, quantifies spatial decay, and assigns contributions via physically interpretable metrics. This innovative framework offers actionable insights for targeted air-quality management strategies, highlighting its potential as a robust tool for pollution mitigation planning.

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Publication Details

Journal
Atmosphere
Published
2025-07-18
DOI
https://doi.org/10.3390/atmos16070883
Citations
2
Primary Topic
Atmospheric chemistry and aerosols
Type
article
Field-Weighted Citation Impact
2.60
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article

Unveiling PM2.5 Transport Pathways: A Trajectory-Channel Model Framework for Spatiotemporally Quantitative Source Apportionment

F. Fang, Yong Pan, Hang Xiao, Jie Zheng et al.
2 citations
Atmosphere
Atmospheric chemistry and aerosols
2.60
article

Unveiling PM2.5 Transport Pathways: A Trajectory-Channel Model Framework for Spatiotemporally Quantitative Source Apportionment

F. Fang, Yong Pan, Hang Xiao, Jie Zheng, Mengrong Yang, Lei Tong, Fanghui Liang
article en
2 citations

Abstract

In this study, we introduced a novel Trajectory-Channel Transport Model (TCTM) to unravel spatiotemporal dynamics of PM2.5 pollution. By integrating high-resolution simulations from the Weather Research and Forecasting (WRF) model with the Nested Air-Quality Prediction Modeling System (WRF-NAQPMS) and 72 h backward-trajectory analysis, TCTM enables the precise identification of source regions, the delineation of key transport corridors, and a quantitative assessment of regional contributions to receptor sites. Focusing on four Yangtze River Delta cities (Hangzhou, Shanghai, Nanjing, Hefei) during a January 2020 pollution event, the results demonstrate that TCTM’s Weighted Concentration Source (WCS) and Source Pollution Characteristic Index (SPCI) outperform traditional PSCF and CWT methods in source-attribution accuracy and resolution. Unlike receptor-based statistical approaches, TCTM reconstructs pollutant transport processes, quantifies spatial decay, and assigns contributions via physically interpretable metrics. This innovative framework offers actionable insights for targeted air-quality management strategies, highlighting its potential as a robust tool for pollution mitigation planning.

AtmosphereVol. 16(7)
University of Nottingham Ningbo China (CN), Chinese Academy of Sciences (CN), Ningbo Municipal Bureau of Ecology and Environment (CN), Institute of Urban Environment (CN), Beijing Municipal Ecological and Environmental Monitoring Center (CN), University of Chinese Academy of Sciences (CN), Ningbo Institute of Industrial Technology (CN), Imperial College London (GB)
Openalex Percentile: Top 9%
Atmospheric chemistry and aerosols
2.60
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