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 <10µm) and total suspended particles (TSP) samples from multiple monitoring stations in the vicinity of the Horne Smelter—alongside metrological data including wind speed and direction—to reveal the dominant sources of metals and metalloids to ambient air and their emission dynamics. PMF is a widely used receptor modeling technique that resolves diverse multi-species datasets into an optimized number of factors, or chemical sources. Analysis focused on trace metals and metalloids sources and concentrations, including arsenic, lead, and chromium. The PMF analysis resolved seven (7) consistent source factors, five of which are associated with distinct materials and processes related to smelter operations (e.g., Bath Smelting, Feedstock, E-waste, Primary Furnace off gas), while the remaining two factors are crustal & road dust and vehicle emission sources which may come from the town, distance sources, or the smelter. By leveraging long-term datasets, temporal patterns of source contributions are revealed, with road dust and fugitive ore-related sources decreasing during winter months when snow cover is prevalent, while smokestack-related smelting sources show no consistent seasonal patterns. These trends confirm the identification of the sources based on their chemical profiles.

Additionally, we analyze the differences in source profiles between PM10 and TSP datasets and stations with varying time resolution (hourly vs 1-3 day). This secondary analysis explores how representative multi-day average samples are in describing PM in comparison to high resolution measurements, especially for industrial emissions. These findings demonstrate the value of long-term, multi-site PMF analyses for improving source attribution of metal and metalloid-rich PM in industrial regions and provide insights relevant to emission reduction efforts.