PM2.5 exceedances and source appointment as inputs for an early warning system

Rincon, Gladys; Morantes, Giobertti; Gonzalez, Ahilymar; Buitrago, Yudeisy; Gonzalez, Jean Carlos; Molina, Constanza; Jones, Benjamin

Abstract

Between June 2018 and April 2019, a sampling campaign was carried out to collect PM2.5, monitoring meteorological parameters and anthropogenic events in the Sartenejas Valley, Venezuela. We develop a logistic model for PM2.5 exceedances (≥ 12.5 µg m−3). Source appointment was done using elemental composition and morphology of PM by scanning electron microscopy coupled with energy dispersive spectroscopy (SEM–EDS). A proposal of an early warning system (EWS) for PM pollution episodes is presented. The logistic model has a holistic success rate of 94%, with forest fires and motor vehicle flows as significant variables. Source appointment analysis by occurrence of events showed that samples with higher concentrations of PM had carbon-rich particles and traces of K associated with biomass burning, as well as aluminosilicates and metallic elements associated with resuspension of soil dust by motor-vehicles. Quantitative source appointment analysis showed that soil dust, garbage burning/marine aerosols and wildfires are three majority sources of PM. An EWS for PM pollution episodes around the Sartenejas Valley is proposed considering the variables and elements mentioned.

Citation

Rincon, G., Morantes, G., Gonzalez, A., Buitrago, Y., Gonzalez, J. C., Molina, C., & Jones, B. (2022). PM2.5 exceedances and source appointment as inputs for an early warning system. Environmental Geochemistry and Health, 44, 4569-4593. https://doi.org/10.1007/s10653-021-01189-2

• Journal Article Type: Article
• Acceptance Date: Dec 20, 2021
• Online Publication Date: Feb 22, 2022
• Publication Date: 2022-12
• Deposit Date: Feb 15, 2022
• Publicly Available Date: Feb 23, 2023
• Journal: Environmental Geochemistry and Health
• Print ISSN: 0269-4042
• Electronic ISSN: 1573-2983
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 44
• Pages: 4569-4593
• DOI: https://doi.org/10.1007/s10653-021-01189-2
• Keywords: Particulate matter, logistic model, SEM-EDS, EWS
• Public URL: https://nottingham-repository.worktribe.com/output/7468082
• Publisher URL: https://link.springer.com/article/10.1007/s10653-021-01189-2