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Emulating air quality in China

An interactive plot visualising how emission changes impact air pollution exposure and the associated disease burden in China. This work highlights the value of machine learning emulators in air quality research.

Key results

  • We created accurate and fast machine learning emulators to predict long−term air quality and health impacts from emission changes in China.
  • Reducing emissions (especially in the industrial and residential sectors) linearly reduces fine particulate matter (PM2.5) exposure and the associated disease burden (MORT, premature mortality per year), with larger fractional reductions in exposure.
  • Removing emissions from five key sectors in China does not attain the World Health Organization (WHO) Annual Guideline due to remaining pollution from other sources.

For more information, see the papers below or the blog article here.

  • Short−term emulators (code)
  • Long−term emulators (in prep., code)
  • Application to future climate scenarios (in prep.)
  • Application to recent trends over 2010-2020 (in prep.)

Move the sliders to explore the results.

Created by Luke Conibear.

We gratefully acknowledge support from the AIA Group Limited, a European Research Council Consolidator Grant (771492), and the Natural Environment Research Council (NE/S006680/1).