Positive matrix factorization (PMF) has been used by atmospheric scientists to extract underlying factors present in large datasets. This paper presents a new technique for error-weighted PMF that drastically reduces the computational costs of previously developed algorithms. We use this technique to deliver interpretable factors and solution diagnostics from an atmospheric chemistry dataset.