Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 19, issue 12
Articles | Volume 19, issue 12
https://doi.org/10.5194/amt-19-4219-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-19-4219-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 12
Research article
 | 
26 Jun 2026
Research article |  | 26 Jun 2026

Improving imputation of missing PM2.5 speciation data using PMF-informed source-receptor relationships

Wubin Zhu, Mingjie Xie, Qili Dai, Xiaohui Bi, Yufen Zhang, and Yinchang Feng

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Short summary
Missing values are common in air quality measurements and can lead to biased environmental conclusions if not properly addressed. We developed a new method to reconstruct missing data by using inherent physical relationships between emission sources and measured concentrations. Unlike existing statistical imputation approaches, improves particulate matter speciation accuracy and preserves physically meaningful source information, providing a robust foundation for atmospheric research.
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