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Rapid Inference of Nitrogen Oxide Emissions Based on a Top-Down Method with a Physically Informed Variational

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Area of Science:

  • Atmospheric Chemistry and Physics
  • Environmental Science and Engineering
  • Machine Learning Applications in Environmental Monitoring

Background:

  • Accurate estimation of nitrogen oxides (NOx) emissions is crucial for effective strategies to reduce ozone (O3) and fine particulate matter (PM2.5) pollution.
  • Satellite-based top-down methods offer near-real-time emission constraints but are hindered by the complex emission-concentration response.
  • Existing methods often struggle with accurate NOx emission estimations in diverse geographical areas, leading to biases.

Purpose of the Study:

  • To develop a novel, efficient, and accurate machine learning-based method for inferring NOx emissions from satellite-retrieved surface NO2 concentrations.
  • To reduce the computational burden associated with emission estimation using chemical transport models.
  • To improve the accuracy of NOx emission estimates, particularly correcting biases in rural and urban regions.

Main Methods:

  • Proposed a physically informed variational autoencoder (VAE) emission predictor for inferring NOx emissions.
  • Utilized a neural network trained with a chemical transport model to reduce computational demands.
  • Employed sensitivity analysis to investigate the interpretability of the VAE model, identifying key influencing factors like NO2 concentration and planetary boundary layer (PBL) height.

Main Results:

  • The VAE emission predictor successfully corrected underestimation in rural areas and overestimation in urban areas.
  • Achieved significant improvements in accuracy, with normalized mean biases reduced from -0.8 to -0.4 and R2 values increased from 0.4 to 0.7.
  • Demonstrated that NO2 concentration and PBL height are key features for accurate NOx emission estimation, aligning with scientific understanding.

Conclusions:

  • The proposed VAE emission predictor offers a significant advancement in estimating NOx emissions efficiently and accurately.
  • The method shows great potential for near-real-time emission estimation and evaluating the effectiveness of pollution control strategies.
  • The flexibility and interpretability of the VAE model make it a valuable tool for atmospheric emission monitoring and management.