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Unveiling air pollution patterns in Yemen: a spatial-temporal functional data analysis.

Mohanned Abduljabbar Hael1,2

  • 1School of Statistics, Jiangxi University of Finance and Economics, Nanchang, 330013, China. 2014mohanned@gmail.com.

Environmental Science and Pollution Research International
|February 15, 2023
PubMed
Summary

This study introduces novel spatiotemporal functional analysis tools to reveal dynamic patterns in air pollution, specifically tracking carbon monoxide (CO), particulate matter (PM2.5), ozone (O3), and sulfur dioxide (SO2) in Yemen.

Keywords:
Air pollutantsClusteringFunctional data analysisMulti-sitesVisualizationYemen

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Area of Science:

  • Environmental Science
  • Data Science
  • Atmospheric Chemistry

Background:

  • Spatiotemporal functional analysis techniques are underutilized in environmental pollution research.
  • Understanding temporal dynamics and spatial dependencies of air pollutants is crucial for effective environmental management.

Purpose of the Study:

  • To develop and apply spatiotemporal functional data clustering and visualization tools for analyzing multiple air pollutants.
  • To identify temporal dynamic patterns and spatial dependence of particulate matter (PM2.5), ozone (O3), carbon monoxide (CO), and sulfur oxides (SO2) in Yemen.

Main Methods:

  • Utilized Fourier transformation, B-spline functions, and generalized-cross validation for data smoothing.
  • Employed static and dynamic visualization methods for data representation.
  • Applied a functional mixture model to capture hidden spatiotemporal patterns in pollutant concentrations.

Main Results:

  • Carbon monoxide (CO) levels peaked between 1990-1996, while particulate matter (PM2.5) peaked in 2018.
  • Ozone (O3) showed fluctuations with a peak in 2014-2015, and sulfur dioxide (SO2) rose significantly from 1997-2010 before stabilizing.
  • Identified specific cities with severe pollution levels for each pollutant, providing localized insights.

Conclusions:

  • The developed tools offer valuable insights into the spatiotemporal cycles of air pollutants.
  • Findings can assist policymakers in identifying pollution sources and implementing targeted reduction strategies.
  • The study provides a foundation for future research on predicting multivariate air pollution in the region.