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This study focuses on fully discriminating the origins of particulates by tackling specific secondary organic aerosol (SOA) sources that are difficult to resolve using traditional datasets, especially at a city scale. This is done through the use of additional fit-for-purpose tracers in the Positive Matrix Factorization (PMF) model, which can be obtained using simpler and more targeted techniques, and the comparison of the PMF models from sites in close range but with different urban typologies.
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https://doi.org/10.5194/acp-2020-1144
https://doi.org/10.5194/acp-2020-1144

  10 Dec 2020

10 Dec 2020

Review status: this preprint is currently under review for the journal ACP.

Disparities in particulate matter (PM10) origins and oxidative potential at a city-scale (Grenoble, France) – Part I: Source apportionment at three neighbouring sites

Lucille Joanna S. Borlaza1, Samuël Weber1, Gaëlle Uzu1, Véronique Jacob1, Trishalee Cañete1, Olivier Favez2,3, Steve Micallef4, Cécile Trébuchon4, Rémy Slama5, and Jean-Luc Jaffrezo1 Lucille Joanna S. Borlaza et al.
  • 1University Grenoble Alpes, CNRS, IRD, INP-G, IGE (UMR 5001), 38000 Grenoble, France
  • 2INERIS, Parc Technologique Alata, BP 2, 60550 Verneuil-en-Halatte, France
  • 3Laboratoire Central de Surveillance de la Qualité de l’Air (LCSQA), 60550 Verneuil-en-Halatte, France
  • 4Atmo Auvergne-Rhônes Alpes, 38400 Grenoble, France
  • 5IAB, Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, University of Grenoble Alpes, 38000 Grenoble, France

Abstract. A fine-scale source apportionment of PM10 was conducted in three different urban sites (background, hyper-center, and peri-urban) within 15 km of the city in Grenoble, France using Positive Matrix Factorization (PMF 5.0) on measured chemical species from collected filters (24-hr) from February 2017 to March 2018. To improve the PMF solution, several new organic tracers (3-MBTCA, pinic acid, phthalic acid, MSA, and cellulose) were additionally used in order to identify sources that are commonly unresolved by classic PMF methodologies. An 11-factor solution was obtained in all sites including commonly identified sources from primary traffic, nitrate-rich, sulfate-rich, industrial, biomass burning, aged sea salt, sea/road salt, and mineral dust, and the newly found sources from primary biogenic, secondary biogenic oxidation, and MSA-rich. Generally, the chemical species exhibiting similar temporal trends and strong correlations showed uniformly distributed emission sources in the Grenoble basin. The improved PMF model was able to obtain and differentiate chemical profiles of specific sources even at high proximity of receptor locations confirming its applicability in a fine-scale resolution. In order to test the similarities between the PMF-resolved sources, the Pearson distance and standardized identity distance (PD-SID) of the factors in each site were compared. The PD-SID metric determined homogeneous sources (biomass burning, primary traffic, nitrate-rich, sulfate-rich, primary biogenic, MSA-rich, aged sea salt, and secondary biogenic oxidation) and heterogeneous sources (industrial, mineral dust, and sea/road salt) across different urban sites, thereby allowing to better discriminate localized characteristics of specific sources. Overall, the addition of the new tracers allowed the identification of substantial sources (especially in the SOA fraction) that would not have been identified or possibly mixed with other factors, resulting in an enhanced resolution and sound source profile of urban air quality at a city scale.

Lucille Joanna S. Borlaza et al.

 
Status: open (until 04 Feb 2021)
Status: open (until 04 Feb 2021)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Lucille Joanna S. Borlaza et al.

Lucille Joanna S. Borlaza et al.

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Short summary
This study focuses on fully discriminating the origins of particulates by tackling specific secondary organic aerosol (SOA) sources that are difficult to resolve using traditional datasets, especially at a city scale. This is done through the use of additional fit-for-purpose tracers in the Positive Matrix Factorization (PMF) model, which can be obtained using simpler and more targeted techniques, and the comparison of the PMF models from sites in close range but with different urban typologies.
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