Receptor Modeling of Fine Particulate Matter Sources in an Industrialized Coastal Metropolitan Area

Abstract

Fine particulate matter (PM₂.₅) pollution in industrialized coastal metropolitan regions poses significant risks to public health, atmospheric visibility, and environmental sustainability due to the combined influence of industrial emissions, vehicular activities, marine aerosols, and secondary aerosol formation. The present study investigates the source apportionment of PM₂.₅ using receptor modeling techniques to identify and quantify major pollution contributors in a densely populated industrial coastal urban environment. Ambient particulate samples were analyzed for elemental composition, ionic species, carbon fractions, and trace metals to evaluate the chemical characteristics associated with diverse anthropogenic and natural emission sources. Advanced receptor modeling approaches, including Positive Matrix Factorization and chemical mass balance analysis, were employed to estimate the relative contributions of industrial combustion, vehicular exhaust, shipping emissions, construction activities, biomass burning, and sea salt aerosols to atmospheric particulate concentrations. Results indicate that industrial combustion processes and traffic-related emissions represent dominant contributors to PM₂.₅ levels, while coastal meteorological conditions significantly influence pollutant transport and secondary particle formation. Seasonal variations in source contributions reveal increased particulate accumulation during low wind and high humidity conditions, intensifying urban air quality deterioration. The study further demonstrates the effectiveness of receptor modeling in supporting emission control strategies and evidence-based environmental policy development. Comparative assessment with regional air quality standards highlights the urgent need for integrated pollution mitigation measures targeting industrial operations, transportation systems, and coastal emission management.