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For this purpose, thirty samples were selected from a set of 128 samples collected over one year (Dec 2019-Nov 2020), based on the highest load for each source (both mass and %) previously assessed by source apportionment studies (using Positive Matrix Factorisation, a total of 7 different sources were identified: soil, secondary sulphate, fuel-oil combustion, sea, vehicle non-exhaust, vehicle exhaust and industry). The OP associated with the water-soluble components of PM<jats:sub>2.5<\/jats:sub> was assessed using the dithiothreitol (DTT) method. The samples had a mean DTT activity (normalised to the mass) of 12.9\u2009\u00b1\u20096.6 pmol min<jats:sup>\u2212\u20091<\/jats:sup> \u00b5g<jats:sup>\u2212\u20091<\/jats:sup>, ranging from 3.5 to 31.8 pmol min<jats:sup>\u2212\u20091<\/jats:sup> \u00b5g<jats:sup>\u2212\u20091<\/jats:sup>. The DTT activity (normalised to the volume, <jats:inline-formula><jats:tex-math>$${\\text{O}\\text{P}}_{\\text{V}}^{\\text{D}\\text{T}\\text{T}}$$<\/jats:tex-math><\/jats:inline-formula>) showed to have a significant positive association with PM<jats:sub>2.5<\/jats:sub> levels (R<jats:sup>2<\/jats:sup>\u2009=\u20090.714). Considering that the mass contributions of the different sources to the PM<jats:sub>2.5<\/jats:sub> levels were known, Spearman correlations were assessed and significant correlations were found between <jats:inline-formula><jats:tex-math>$${\\text{O}\\text{P}}_{\\text{V}}^{\\text{D}\\text{T}\\text{T}}$$<\/jats:tex-math><\/jats:inline-formula> and three different sources: vehicle exhaust (\u03c1\u2009=\u20090.647, <jats:italic>p<\/jats:italic>-value\u2009=\u20090.001), fuel-oil combustion (\u03c1\u2009=\u20090.523, <jats:italic>p<\/jats:italic>-value\u2009=\u20090.012) and industry (\u03c1\u2009=\u20090.463, <jats:italic>p<\/jats:italic>-value\u2009=\u20090.018). Using a multiple linear regression analysis, these three sources were found to explain 82% of the variability in <jats:inline-formula><jats:tex-math>$${\\text{O}\\text{P}}_{\\text{V}}^{\\text{D}\\text{T}\\text{T}}$$<\/jats:tex-math><\/jats:inline-formula>, with vehicle exhaust being the most influential source.<\/jats:p>","DOI":"10.1007\/s11869-024-01556-9","type":"journal-article","created":{"date-parts":[[2024,4,5]],"date-time":"2024-04-05T09:01:52Z","timestamp":1712307712000},"page":"2005-2015","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Pollution sources affecting the oxidative potential of fine aerosols in a Portuguese urban-industrial area - an exploratory study"],"prefix":"10.1007","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0856-8448","authenticated-orcid":false,"given":"Nuno","family":"Canha","sequence":"first","affiliation":[]},{"given":"Sara","family":"Gon\u00e7alves","sequence":"additional","affiliation":[]},{"given":"Diogo","family":"Sousa","sequence":"additional","affiliation":[]},{"given":"Carla","family":"Gamelas","sequence":"additional","affiliation":[]},{"given":"Sergio","family":"Mendez","sequence":"additional","affiliation":[]},{"given":"Sandra","family":"Cabo Verde","sequence":"additional","affiliation":[]},{"given":"Susana Marta","family":"Almeida","sequence":"additional","affiliation":[]},{"given":"Anna Rita","family":"de Bartolomeo","sequence":"additional","affiliation":[]},{"given":"Maria Rachele","family":"Guascito","sequence":"additional","affiliation":[]},{"given":"Eva","family":"Merico","sequence":"additional","affiliation":[]},{"given":"Daniele","family":"Contini","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,5]]},"reference":[{"key":"1556_CR1","doi-asserted-by":"publisher","first-page":"1364","DOI":"10.3390\/ijerph19031364","volume":"19","author":"L Abecasis","year":"2022","unstructured":"Abecasis L, Gamelas CA, Justino AR et al (2022) Spatial distribution of Air Pollution, hotspots and sources in an urban-industrial Area in the Lisbon Metropolitan Area, Portugal\u2014A Biomonitoring Approach. 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