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<p>This study evaluates an innovative system that can be used for Near Real-Time Source Apportionment (NRT model) providing results within minutes after the measurements across six Chinese cities: Beijing, Langfang, Shijiazhuang, Xi’an, Wuhan, and Chongqing during 2020–22. The system leverages the AXA instrumental setup (ACSM, Xact, Aethalometer) to integrate high–time-resolution data and provide detailed insights into major particulate matter (PM) sources and their contributions. Secondary PM components dominated across all sites, accounting for up to 66% of the total PM2.5 mass in some cities. Primary sources such as solid fuel combustion contributed approximately 10–30%, while episodic dust events were a major source in Langfang during specific periods. The system’s performance was validated by strong correlations (R<sup>2</sup> &gt; 0.82) with results from optimized source apportionment analyses. Furthermore, robustness tests using reduced datasets (two thirds for training and one third for validation) confirmed the system’s reliability and adaptability under dynamic monitoring conditions. In these tests, high correlations with the optimized source apportionment were achieved, indicating the operational reliability of the model. These findings underscore the NRT model’s potential as a critical tool for real-time air quality management, enabling rapid identification of pollution sources and informing timely mitigation strategies to improve urban air-quality.</p>

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Evaluation of real-time source apportionment approaches in six Chinese cities using the AXA (ACSM, Xact, Aethalometer) instrumental set-up

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月19日

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  • Manousos I. Manousakas,
  • Tianqu Cui,
  • Qiyuan Wang,
  • Lu Qi,
  • Markus Furger,
  • Rico K. Y. Cheung,
  • Lubna Dada,
  • Yufang Hao,
  • Peeyush Khare,
  • Jie Tian,
  • Yuemei Han,
  • Yang Chen,
  • Shaofei Kong,
  • Yunfei Wu,
  • Yele Sun,
  • Renjian Zhang,
  • Jay G. Slowik,
  • Junji Cao,
  • Kaspar R. Daellenbach,
  • André S. H. Prévôt

摘要

This study evaluates an innovative system that can be used for Near Real-Time Source Apportionment (NRT model) providing results within minutes after the measurements across six Chinese cities: Beijing, Langfang, Shijiazhuang, Xi’an, Wuhan, and Chongqing during 2020–22. The system leverages the AXA instrumental setup (ACSM, Xact, Aethalometer) to integrate high–time-resolution data and provide detailed insights into major particulate matter (PM) sources and their contributions. Secondary PM components dominated across all sites, accounting for up to 66% of the total PM2.5 mass in some cities. Primary sources such as solid fuel combustion contributed approximately 10–30%, while episodic dust events were a major source in Langfang during specific periods. The system’s performance was validated by strong correlations (R2 > 0.82) with results from optimized source apportionment analyses. Furthermore, robustness tests using reduced datasets (two thirds for training and one third for validation) confirmed the system’s reliability and adaptability under dynamic monitoring conditions. In these tests, high correlations with the optimized source apportionment were achieved, indicating the operational reliability of the model. These findings underscore the NRT model’s potential as a critical tool for real-time air quality management, enabling rapid identification of pollution sources and informing timely mitigation strategies to improve urban air-quality.