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Related Experiment Video

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An explainable integrated optimization methodology for source apportionment of ambient particulate matter components.

Juanyong Shen1, Qianbiao Zhao2, Qi Ying3

  • 1Shanghai Environmental Protection Key Lab of Environmental Big Data and Intelligent Decision-making, School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.

Journal of Environmental Management
|February 27, 2022
PubMed
Summary

This study introduces a new method to improve air pollution predictions by adjusting chemical transport models. The approach quantifies and corrects errors in emission data and chemical processes, leading to more accurate source apportionment of fine particulate matter (PM2.5).

Keywords:
Chemical mechanismChemical transport modelEmission inventoryModeling bias

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Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Air Pollution Modeling

Background:

  • Accurate source apportionment of fine particulate matter (PM2.5) is essential for effective air pollution control strategies.
  • Chemical transport models (CTMs) are widely used for source apportionment, but their prediction accuracy is a significant source of uncertainty.
  • Existing efforts to improve CTM-based source apportionment primarily focus on mathematical algorithms, often overlooking the root causes of model uncertainties.

Purpose of the Study:

  • To develop an integrated optimization methodology to quantify deviations in emission inventories and chemical mechanisms within CTMs.
  • To improve the prediction and source apportionment accuracies of CTMs by addressing identified model deviations.
  • To provide a framework for adjusting CTM simulations based on observational data and receptor model constraints.

Main Methods:

  • An optimization algorithm was employed to calculate emission deviations for primary aerosols and gaseous pollutants, incorporating observational and receptor model constraints.
  • The emission inventory was subsequently adjusted, followed by a new CTM simulation.
  • Deviations in chemical mechanisms governing secondary pollutant formation were assessed by comparing model predictions with observational data.

Main Results:

  • The integrated optimization methodology successfully adjusted emissions, aligning CTM predictions more closely with observational data.
  • Significant emission deviations were quantified for key pollutants, including elemental carbon (+59.6%), organic carbon (+95.9%), and sulfur dioxide (-6.4%).
  • Major discrepancies in chemical mechanisms were identified, with notable deviations in SO2 to secondary sulfate (-77.3%) and NH3 to secondary ammonium (-38.8%) conversions.

Conclusions:

  • The developed methodology effectively quantifies deviations in both emissions and chemical mechanisms, leading to substantial improvements in source apportionment accuracy.
  • This approach offers an efficient means to enhance the reliability of CTMs for air pollution management and control.
  • The case study demonstrated the practical applicability and success of the integrated optimization methodology in a real-world scenario.