A scalable framework for urban PM source apportionment using biomonitoring and receptor modeling in data-limited environments

Urban air pollution, particularly airborne particulate matter (PM), is a major environmental and public health challenge worldwide, especially in rapidly developing and industrialized regions. This study presents an integrated and scalable framework for PM source apportionment that combines pine-leaf biomonitoring, soil geochemistry, high-resolution elemental analysis (SEM-EDX and ICP-MS), receptor modeling using Positive Matrix Factorization (PMF), and established pollution indices (EF, I_geo, CF, C_deg). By jointly using leaf and soil media, the framework captures both atmospheric deposition and the more cumulative local geochemical signal. A custom-designed air quality monitoring network (AQMN) was deployed across ten sites, where seasonal samples of pine needles and soil were collected over one year. SEM-EDX confirmed particle accumulation and showed seasonal trends in carbonaceous PM, while ICP-MS quantified trace elements associated with both anthropogenic and natural sources. PMF modeling identified four dominant source categories: fossil fuel combustion, industrial metallurgy, vehicular emissions, and natural inputs such as dust and sea salt. Results showed marked seasonal and spatial variation, with industrial emissions peaking in summer and vehicular sources becoming more influential in winter. This combined biomonitoring and receptor-modeling workflow provides a practical, transferable option for source apportionment in under-monitored cities and extends previous single-medium biomonitoring studies by improving source apportionment under data-limited conditions.