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Updated: Oct 25, 2026

Measuring Sub-23 Nanometer Real Driving Particle Number Emissions Using the Portable DownToTen Sampling System
Published on: May 22, 2020
Characterization of PM10 sources in the Riotinto copper mining district (SW Spain) using multi-time-resolution
Pablo Pérez-Vizcaíno1, Marta Via2, Griša Močnik2
1Associate Unit CSIC-University of Huelva "Atmospheric Pollution", Center for Research in Sustainable Chemistry - CIQSO, University of Huelva, Huelva E21007, Spain; Department of Earth Sciences, Faculty of Experimental Sciences, University of Huelva, Campus El Carmen s/n, Huelva E21007, Spain.
Abstract:
Mining activities significantly contribute to the emission of heavy metals and metalloids to the atmosphere. Identifying and quantifying the different emission sources is a key aspect in characterizing the air quality of a region. This year-long study (July 2024-July 2025) aimed to comprehensively characterize PM10 sources in the Riotinto copper mining district (southwestern Spain) using a highly time-resolved X-ray fluorescence monitor and an Aethalometer, together with a high-volume sampler operating on a daily basis with quartz fiber filters. Filters were chemically analyzed for multiple pollutants. High-time-resolution (1-h) and low-time-resolution (24-h) data subsets were integrated within a multi-time-resolution positive matrix factorization approach. The source apportionment analysis identified seven major PM10 sources: Waste Rock (4.9 µg·m-3), Regional + Traffic (3.6 µg·m-3), Dust (1.1 µg·m-3), Open Pit (1.6 µg·m-3), Aged Marine (1.2 µg·m-3), Copper Concentrate (0.7 µg·m-3), and Combustion (1.2 µg·m-3). The incorporation of species measured at daily resolution enabled the identification of traffic tracers and secondary organic aerosol in several factors, which was not possible using only the hourly-resolution subset. During the study period, As represented the main geochemical anomaly because of its elevated concentration. This work highlights the need to conduct high-time-resolution studies to improve the understanding of the levels and temporal variability of chemical pollutants with harmful effects on human health. It also emphasizes the advantages of combining data with different temporal resolutions in source apportionment analyses for a better characterization of emission sources in environmentally vulnerable areas.
