Resolving Metal-Rich Industrial Fingerprints: Multi-Site PMF Insights from PM10 and TSP at a Canadian Copper Smelter

Ambient particulate matter (PM) in industrially influenced environments often contains a complex chemical composition, reflecting interactions among local emissions, regional transport, and natural background sources. When chemically-speciated datasets are available, receptor modeling provides a powerful framework for attributing observed concentrations of emissions—such as metals and metalloids—to their contributing sources, particularly in settings where industrial signatures are chemically distinct from urban PM and locally resuspended dust.The Horne Smelter in Rouyn-Noranda, Quebec, Canada, is the only remaining copper smelter in the country and the largest processor of metals from electronic scrap in North America. In recent years, emissions from the facility have been subject to heightened scrutiny due to elevated concentrations of metals and metalloids measured at surrounding monitoring stations. Robust PM source attribution is therefore critical for interpreting long-term monitoring data and informing emission reduction strategies. In particular, there is a need to quantify the relative contributions of different smelter-related activities to elemental concentrations measured at locations throughout the town of Rouyn-Noranda, which directly borders the facility.This study applies Positive Matrix Factorization (PMF) to multi-year datasets of chemically speciated PM10 (PM