Development and evaluation of an integrated method using distance- and probability-based profile matching approaches in receptor modeling

To identify sources of pollutants, source apportionment studies are conducted using receptor models. Although a receptor model such as Positive Matrix Factorization (PMF) can effectively retrieve factor profiles, the necessary step to link these profiles with actual sources is rather subjective and might be inconsistent due to lack of harmonization in the literatures or databases of source fingerprints. To address this gap, this study developed a numerical method integrating distance- and probability-based profile matching approaches to objectively identify apportioned factor profiles, thus facilitating source identification and enhancing the comparability of the interpretation results. The applicability of this method was evaluated with profile data derived from the U.S. Environmental Protection Agency's SPECIATE database. Results showed that the matching accuracy of each category using the probability-based profile matching approach was greater than 80% except for diesel emissions (61%), demonstrating the potential of using numerical methods for factor identification. In addition, the integrated multi-step method is feasible for identifying mixed source profiles with the matching accuracy >85% after post-hoc grouping. This approach would be more effective with a harmonized database of source fingerprints. Therefore, international cooperation among research groups interested in source apportionment studies is highly recommended either for database expansion or validation.