Positive matrix factorization outperforms machine learning in imputing missing PM2.5 and further identifying spatial patterns in multi-sites without external data

Missing observations of fine particulate matter (PM2.5) distort air pollution studies by reducing the available concentration information. While machine learning (ML) and statistical methods are commonly used for imputation, they typically rely on external datasets, limiting reproducibility. This st