Source apportionment of urban road dust using four multivariate receptor models

Road dust is one of the biggest contributors to airborne particulate matter (PM) in many urban regions. Due inherent heterogeneity road dust, it important that its sources are identified and mitigated. Multivariate receptor models used for source apportionment PM cities. In recent years, these finding more applications outside scope apportionment. this study, four multivariate (Unmix, Positive Matrix Factorization, Principal Component Analysis, Multiple Curve Regression) at Vellore City, India. The elemental composition samples from 18 locations three seasons (summer, winter, monsoon) measured using acid digestion followed by Inductively Coupled Plasma–Optical Emission Spectroscopy. Irrespective models, results showed crustal material (100–68%) resuspended (82–15%) study region. Brake wear, tire biomass combustion, vehicular emission, industrial some other models. Receptor modeling performance MCR PCA unsatisfactory. PMF Unmix gave acceptable results. From comparing characteristics, found be ideal model dataset. This research clarifies constraints different information obtained critical development future policy regulation.