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Human-model hybrid Korean air quality forecasting system.

Lim-Seok Chang1, Ara Cho1, Hyunju Park1

  • 1a National Institute of Environmental Research , Incheon , Korea.

Journal of the Air & Waste Management Association (1995)
|July 25, 2016
PubMed
Summary

The Korean national air quality forecasting system struggles with particulate matter (PM) underpredictions, especially for PM10. Optimizing meteorological inputs and emissions data significantly improves forecast accuracy for better public health protection.

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Area of Science:

  • Atmospheric Science
  • Environmental Science
  • Computational Science

Background:

  • The Korean national air quality forecasting system, operational since 2013, targets particulate matter (PM) and ozone.
  • Factors influencing PM forecasting accuracy include meteorological data, emissions, forecaster expertise, and model limitations.

Purpose of the Study:

  • To investigate issues in the current air quality forecasting system and explore methods for improving accuracy.
  • To enhance the reliability of national air quality forecasts for public health benefits.

Main Methods:

  • Conducted numerical experiments using different meteorological inputs (GFS, UM) and emissions inventories (MICS-Asia, INTEX-B, CAPSS).
  • Evaluated the impact of data assimilation (MACC) on Community Modeling and Analysis (CMAQ) performance.
  • Assessed forecaster error patterns, particularly around high PM events.

Main Results:

  • Significant underpredictions of PM mass and components (sulfate, organic carbon) were observed with both emissions datasets.
  • CMAQ demonstrated better performance for PM2.5 (NMB: -20~-25%) compared to PM10 (NMB: -43~-47%).
  • The optimal configuration combined UM meteorological data with MICS-Asia and CAPSS 2010 emissions, yielding an NMB of -12.3%, RMSE of 16.6 μ/m³, and R² of 0.68.

Conclusions:

  • Improving CMAQ inputs, particularly meteorological fields and emissions, is crucial for accurate PM forecasting.
  • Data assimilation with MACC enhances CMAQ performance, especially for coarse particulate matter.
  • Ensemble methods and data assimilation are recommended for further accuracy improvements, particularly for high PM events.