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Particulate matter estimation using satellite datasets: a machine learning approach.

Sunita Verma1, Ajay Sharma1,2, Swagata Payra3

  • 1Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, 221105, Uttar Pradesh, India.

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Summary
This summary is machine-generated.

This study developed an interpretable machine learning model to estimate Particulate Matter 10 (PM10) concentrations in India using satellite Aerosol Optical Depth (AOD). The model achieved a strong R-squared value of 0.78, showing potential for accurate air quality monitoring.

Keywords:
Aerosol optical depth (AOD)INSAT-3DMODISParticulate matterRandom forest (RF)

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Area of Science:

  • Environmental Science
  • Atmospheric Science
  • Machine Learning Applications

Background:

  • Accurate estimation of ground-level Particulate Matter 10 (PM10) concentrations is crucial for air quality assessment in India.
  • Satellite-derived Aerosol Optical Depth (AOD) offers a potential data source for PM10 estimation, but requires careful validation and calibration.
  • Existing methods may lack interpretability or struggle with regional variations in aerosol properties.

Purpose of the Study:

  • To develop and validate an interpretable machine learning model for estimating PM10 concentrations across India.
  • To utilize AOD data from INSAT-3D and Moderate Resolution Imaging Spectroradiometer (MODIS) satellites, alongside ground-based measurements.
  • To assess the performance of different satellite AOD products and their correlation with ground-level data.

Main Methods:

  • Development of an interpretable Random Forest machine learning model for PM10 estimation.
  • Utilized 7 years (2014-2020) of satellite AOD data (INSAT-3D, MODIS) and ground PM10 data from Central Pollution Control Board (CPCB).
  • Validated satellite AOD against Aerosol Robotic Network (AERONET) data and applied filtering techniques to INSAT-3D AOD for improved correlation.

Main Results:

  • MODIS AOD showed good correlation with AERONET AOD; INSAT-3D AOD improved significantly after filtering, achieving correlations of 0.66 (Jaipur) and 0.57 (Kanpur).
  • The trained Random Forest model achieved a high R-squared (R²) of 0.78 for PM10 estimation against observed concentrations in 2020.
  • The model demonstrated effective training, minimizing overestimation and underestimation, though some instances persisted, suggesting a need for larger datasets.

Conclusions:

  • An interpretable machine learning model using calibrated satellite AOD is optimal for PM10 estimation over India.
  • The study highlights the importance of AOD data quality control and calibration for accurate air quality modeling.
  • Further refinement of the model with expanded datasets can enhance the accuracy of PM10 concentration estimates.