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

Gladys Rincon1,2, Giobertti Morantes Quintana3,4, Ahilymar Gonzalez5

  • 1Escuela Superior Politécnica del Litoral, ESPOL, Facultad de Ingeniería Marítima y Ciencias del Mar (FIMCM), Guayaquil, Ecuador. grincon@espol.edu.ec.

Environmental Geochemistry and Health
|February 22, 2022
PubMed
Summary

This study developed a 94% accurate logistic model to predict particulate matter (PM2.5) pollution in Sartenejas Valley, identifying forest fires and vehicle traffic as key factors. An early warning system is proposed to mitigate air quality issues.

Keywords:
EWSLogistic modelParticulate matterSEM–EDS

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Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Air Quality Monitoring

Background:

  • Particulate matter (PM2.5) poses significant health risks.
  • Understanding PM2.5 sources is crucial for effective air quality management.
  • Previous studies in Sartenejas Valley lacked comprehensive source apportionment and predictive modeling.

Purpose of the Study:

  • To develop a predictive logistic model for PM2.5 exceedances in Sartenejas Valley.
  • To identify significant meteorological and anthropogenic variables influencing PM2.5 levels.
  • To propose an early warning system (EWS) for PM pollution episodes.

Main Methods:

  • Collected PM2.5 samples and monitored meteorological data from June 2018 to April 2019.
  • Utilized logistic regression for predictive modeling of PM2.5 exceedances (≥12.5 µg m⁻³).
  • Employed Scanning Electron Microscopy with Energy Dispersive Spectroscopy (SEM-EDS) for PM source apportionment.

Main Results:

  • The logistic model achieved a 94% success rate, with forest fires and motor vehicle flow as significant predictors.
  • SEM-EDS analysis revealed carbon-rich particles (biomass burning) and soil dust components (motor vehicles).
  • Quantitative analysis identified soil dust, garbage burning/marine aerosols, and wildfires as major PM sources.

Conclusions:

  • A robust logistic model can effectively predict PM2.5 pollution events.
  • Biomass burning and motor vehicle emissions are primary contributors to PM2.5 in the study area.
  • The proposed EWS, based on identified variables and sources, can enhance air quality management in Sartenejas Valley.