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Spatio-temporal air pollution modelling using a compositional approach.

Joseph Sánchez-Balseca1, Agustí Pérez-Foguet1

  • 1Research Group on Engineering Sciences and Global Development (EScGD), Civil and Environmental Engineering Department, Universitat Politècnica de Catalunya - BarcelonaTech (UPC), Spain.

Heliyon
|September 28, 2020
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Summary
This summary is machine-generated.

This study introduces a compositional approach for analyzing air pollutant data, addressing statistical issues in traditional models. The new method improves spatio-temporal modeling of atmospheric composition for better air quality insights.

Keywords:
Air qualityAtmospheric scienceCoDaCompositional dataEngineeringEnvironmental analysisEnvironmental chemical engineeringEnvironmental impact assessmentEnvironmental statisticsModellingStatistics

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Area of Science:

  • Environmental Science
  • Statistics
  • Atmospheric Chemistry

Background:

  • Air pollutant data are compositional, representing parts of a whole atmospheric composition.
  • Conventional statistical models often ignore this characteristic, leading to spurious correlations and subcompositional incoherence.
  • Existing air pollution models may not adequately handle the multivariate and spatio-temporal nature of pollutant concentrations.

Purpose of the Study:

  • To propose a novel daily multivariate spatio-temporal model for air pollutant data using a compositional approach.
  • To address the limitations of conventional statistical methods in air quality modeling.
  • To improve the description and modeling of multiple air pollutants simultaneously.

Main Methods:

  • Developed a dynamic linear modelling framework incorporating Bayesian inference.
  • Applied a compositional data analysis approach to multivariate air pollutant concentrations.
  • Utilized a daily spatio-temporal model for urban air quality assessment.

Main Results:

  • The proposed compositional model effectively handles the multivariate nature of air pollutant data.
  • Achieved fast multivariate data description and captured high spatial correlations.
  • Provided adequate modeling for air pollutants exhibiting high variability, including CO, SO2, O3, NO2, and PM2.5.
  • Complemented and improved upon conventional air pollution modeling techniques.

Conclusions:

  • The compositional approach offers a statistically sound and improved method for air pollution modeling.
  • This methodology enhances the understanding of spatio-temporal dynamics and interrelationships between different air pollutants.
  • The model provides a more accurate representation of atmospheric composition and aids in better air quality management.