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Improving imputation of missing PM<sub>2.5</sub> speciation data using PMF-informed source-receptor relationships
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DOI：10.5194/amt-19-4219-2026.png)
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Abstract
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Abstract. Missing values are ubiquitous in atmospheric monitoring due to instrument drift; calibration cycles; operational interruptions; and other random malfunctions. Such gaps can undermine the reliability of subsequent analyses and introduce systematic biases. Conventional imputation methods; such as geometric mean substitution; K-nearest neighbor (KNN); Bayesian principal component analysis (BPCA); and deep learning models often rely primarily on statistical correlations; may require auxiliary inputs; and offer limited physical interpretability. To address this issue; we propose a novel source-receptor-informed Positive Matrix Factorization Reconstruction (PMFr) method that leverages PMF-derived source-receptor relationships; rather than purely statistical interpolation; to impute missing PM2.5 speciation data without requiring auxiliary data. Benchmarking on a two-month dataset against commonly used imputation techniques; including KNN; BPCA; and a deep learning predictive model; demonstrates that PMFr achieves superior accuracy and robustness across real-world missing scenarios; with a mean coefficient of determination (R2) of 0.81; index of agreement (IoA) of 0.92; and mean absolute percentage error (MAPE) of 22.8 %; reducing MAPE by 25.5 %–29.1 %; particularly for key PM2.5 species. Further PMF-based validation shows that PMFr better preserves source-profile composition and source-contribution temporal features; indicating that the completed dataset retains more physically meaningful source information and is more suitable for source apportionment. These results highlight PMFr as a robust and physically interpretable approach for reconstructing reliable PM2.5 speciation data.
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