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PM(2.5) exceedances and source appointment as inputs for an early warning system

Between June 2018 and April 2019, a sampling campaign was carried out to collect PM(2.5), monitoring meteorological parameters and anthropogenic events in the Sartenejas Valley, Venezuela. We develop a logistic model for PM(2.5) exceedances (≥ 12.5 µg m(−3)). Source appointment was done using elemen...

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Autores principales: Rincon, Gladys, Morantes Quintana, Giobertti, Gonzalez, Ahilymar, Buitrago, Yudeisy, Gonzalez, Jean Carlos, Molina, Constanza, Jones, Benjamin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9675665/
https://www.ncbi.nlm.nih.gov/pubmed/35192100
http://dx.doi.org/10.1007/s10653-021-01189-2
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author Rincon, Gladys
Morantes Quintana, Giobertti
Gonzalez, Ahilymar
Buitrago, Yudeisy
Gonzalez, Jean Carlos
Molina, Constanza
Jones, Benjamin
author_facet Rincon, Gladys
Morantes Quintana, Giobertti
Gonzalez, Ahilymar
Buitrago, Yudeisy
Gonzalez, Jean Carlos
Molina, Constanza
Jones, Benjamin
author_sort Rincon, Gladys
collection PubMed
description Between June 2018 and April 2019, a sampling campaign was carried out to collect PM(2.5), monitoring meteorological parameters and anthropogenic events in the Sartenejas Valley, Venezuela. We develop a logistic model for PM(2.5) 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10653-021-01189-2.
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spelling pubmed-96756652022-11-21 PM(2.5) exceedances and source appointment as inputs for an early warning system Rincon, Gladys Morantes Quintana, Giobertti Gonzalez, Ahilymar Buitrago, Yudeisy Gonzalez, Jean Carlos Molina, Constanza Jones, Benjamin Environ Geochem Health Original Paper Between June 2018 and April 2019, a sampling campaign was carried out to collect PM(2.5), monitoring meteorological parameters and anthropogenic events in the Sartenejas Valley, Venezuela. We develop a logistic model for PM(2.5) 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10653-021-01189-2. Springer Netherlands 2022-02-22 2022 /pmc/articles/PMC9675665/ /pubmed/35192100 http://dx.doi.org/10.1007/s10653-021-01189-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Original Paper
Rincon, Gladys
Morantes Quintana, Giobertti
Gonzalez, Ahilymar
Buitrago, Yudeisy
Gonzalez, Jean Carlos
Molina, Constanza
Jones, Benjamin
PM(2.5) exceedances and source appointment as inputs for an early warning system
title PM(2.5) exceedances and source appointment as inputs for an early warning system
title_full PM(2.5) exceedances and source appointment as inputs for an early warning system
title_fullStr PM(2.5) exceedances and source appointment as inputs for an early warning system
title_full_unstemmed PM(2.5) exceedances and source appointment as inputs for an early warning system
title_short PM(2.5) exceedances and source appointment as inputs for an early warning system
title_sort pm(2.5) exceedances and source appointment as inputs for an early warning system
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9675665/
https://www.ncbi.nlm.nih.gov/pubmed/35192100
http://dx.doi.org/10.1007/s10653-021-01189-2
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