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Ground PM2.5 prediction using imputed MAIAC AOD with uncertainty quantification.

Qiang Pu1, Eun-Hye Yoo1

  • 1Department of Geography, The State University of New York at Buffalo, Buffalo, NY, USA.

Environmental Pollution (Barking, Essex : 1987)
|February 2, 2021
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Summary

This study introduces a new model to fill gaps in satellite aerosol optical depth (AOD) data, improving fine particulate matter (PM2.5) predictions. Quantifying imputation uncertainty is crucial for accurate air quality forecasting.

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AOD imputationAerosol optical depth (AOD)Fine particulate matter (PM(2.5))Machine learning methodsUncertainty evaluation

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Area of Science:

  • Environmental Science
  • Atmospheric Science
  • Data Science

Background:

  • Satellite-derived aerosol optical depth (AOD) is vital for predicting ground-level fine particulate matter (PM2.5).
  • Missing satellite AOD values limit prediction accuracy.
  • Uncertainty from AOD imputation and its effect on PM2.5 predictions are understudied.

Purpose of the Study:

  • Develop a missing data imputation model for Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD.
  • Assess the impact of AOD imputation uncertainty on PM2.5 predictions.
  • Compare machine learning algorithms for AOD imputation and PM2.5 modeling.

Main Methods:

  • Developed a novel missing data imputation model for MAIAC AOD.
  • Employed multiple machine learning algorithms for imputation and PM2.5 prediction.
  • Quantified uncertainty propagation from imputed AOD to PM2.5 predictions.

Main Results:

  • The proposed imputation model demonstrated superior performance (R²=0.94, RMSE=0.017) in New York State.
  • Significant uncertainty in PM2.5 predictions was linked to imputed AOD values.
  • Uncertainty from machine learning algorithms in PM2.5 models exceeded AOD imputation uncertainty.

Conclusions:

  • Accurate PM2.5 prediction requires quantifying uncertainties in AOD imputation.
  • Understanding uncertainty propagation is essential for reliable air quality assessments.
  • The developed model offers improved AOD data for PM2.5 forecasting.