Improving imputation of missing PM _2.5 speciation data using PMF-informed source-receptor relationships

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.