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Quantifying Transport Sector's Impact on PM2.5 in Delhi Region

Investigating the role of transport in PM2.5 levels in Delhi, highlighting health impacts and complex pollution sources. Utilizing observational data and advanced modeling for insights and future mitigation strategies.

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Quantifying Transport Sector's Impact on PM2.5 in Delhi Region

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  1. European WRF-Chem User Workshop 2019, Munich 7-8 May Quantifying the role of the transport sector on observed variations of PM2.5 over the National Capital Region of Delhi CATERINA MOGNO1, Paul Palmer1, Tim Wallington2 1 The University of Edinburgh, School of Geosciences 2 Research and Advanced Engineering, Ford Motor Company, MI -USA

  2. Outline 01 Context 02 Research Questions 03 Preliminary work 04 Future work 0

  3. Context: PM2.5 and health 01 PM2.5 = particulate matter of 2.5 microns or less in diameter PM2.5 affect human health more than any other pollutant (WHO*) 4.2 million premature deaths worldwide in 2016. 91% occurring in low- and middle-income countries Stroke, heart diseases , lung cancer, respiratory diseases (e.g. asthma) * World Health Organisation, “Ambient (outdoor) air quality and health, factsheet,” 2018. 0

  4. Context: Organic Aerosols (OA) 01 Organic Aerosol evolution in the atmosphere: 0

  5. Context: pollution in Delhi 01 2018 annual mean: 106 μg/m3 WHO guideline value annual mean: 10 μg/m3 Data source: US Embassy in India, Delhi Air Quality Data Information.

  6. Context: pollution in Delhi 01 Reality of Delhi’s pollution is very complex: 0

  7. Context: pollution in Delhi 01 * Reality of Delhi’s pollution is very complex: Multiple local sources Transboundary pollution Influence of meteorology 0 * Sharma, S. K., and T. K. Mandal. "Chemical composition of fine mode particulate matter (PM2. 5) in an urban area of Delhi, India and its source apportionment." Urban Climate 21 (2017): 106-122.

  8. Context: transport in Delhi 01

  9. Context: transport in Delhi 01 * One of major sources: up to 25% of direct emissions of PM2.5 Vehicle emissions are continuous Increase in the number of vehicles air quality policy measures * Data source: Government of india, Ministry of Statistics and Programme Implementation, Statistical Year Book India 2018

  10. DIAGNOSTIC: How much does transport contribute to PM2.5 over Delhi? - primary PM2.5 emitted vs secondary PM2.5 formation from gas precursors (e.g. NOx and VOCs). - chemical factors/ meteorological conditions/ environmental conditions that control this contribution during different seasons. Research questions 02 0

  11. Research questions 02 2)PROGNOSTIC: How transport contribution to PM2.5 over Delhi can be reduced? - sensitivity study on different emissions from transport: seasonal analysis and during extreme events (e.g. Diwali, heatwaves) 0

  12. Preliminary work: experimental design 03 Observation data availability • AOD: AERONET (ground-based) + MODIS (satellite) • Ground PM2.5 observation: CPCB data (38 stations) + US Embassy (1 station) 0

  13. Preliminary work: experimental design 03 WRFchem 4.0 setup: first attempt • Chemistry: MOZART - MOSAIC-VBS (chem_opt =202) (SOA treated with the Volatility Basis Set - POA inert) • Anthropogenic emissions: inventory EDGAR/HTAP • Biogenic emissions: MEGAN • Fires emissions: FINN • BC and IC: MOZART-4 • Meteorology: GFS • Simulation: 1 year (all seasons) 0

  14. Preliminary work: experimental design 03 WRFchem 4.0 setup: first attempt • Outer Domain: 0 • Simulation: 1 year (all seasons)

  15. Preliminary work: experimental design 03 WRFchem 4.0 setup: challenges, other ideas ? Alternative Chemistry: SAPRC99 - MOSAIC-VBS (chem_opt =203) SOA VBS POA considered semivolatile + some emissions to consider gas-to-particle of IVOCs Is it feasible ( preparation of emissions, speciation of inventory etc) ? Alternative inventories ? Nesting domain choice and simulation run(s) ? BC and IC from GEOSchem ? Data assimilation WRF-DA ? WRFchem running on a Web Cloud 0

  16. Future work: next steps 04 • Complete observational data availability and inventory availability • Complete set-up and test runs WRFchem • Baseline simulation and model evaluation 0

  17. THANK YOU! • Presentation template by SlidesCarnival

  18. Preliminary work: experimental design 03 Inventory data availability (anthropogenic emissions) 0

  19. Context: Organic Aerosols (OA) 01 modeling OA Volatility Basis Set (VBS) Key features of OA evolution: VOLATILITY O:C RATIO Donahue, Neil M., et al. "A two-dimensional volatility basis set–Part 2: Diagnostics of organic-aerosol evolution." Atmospheric Chemistry and Physics 12.2 (2012): 615-634. 0

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