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Regional Air Quality Modeling: Insights and Recommendations for Southern California and Delhi

This document presents a comprehensive overview of regional air quality modeling, focusing on methodologies applicable to the South Coast Air Quality Management District and Delhi. It aims to establish a “carrying capacity” for assessing air quality while facilitating planning tools for long-term strategies. Key considerations include data requirements, model selection, and domain definition, highlighting the performance limitations due to variable data quality. Insights into meteorological and emissions data are discussed, complemented by recommendations for enhancing monitoring networks aimed at improving air quality assessments.

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Regional Air Quality Modeling: Insights and Recommendations for Southern California and Delhi

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  1. Regional Modeling Joseph Cassmassi South Coast Air Quality Management District USA

  2. Regional Modeling Applications • Determine a “carrying capacity” for a region • Provides a view of current, past and future air quality • Planning tool to develop a long term control strategy

  3. Expectations vs. Limitations • Performance of regional models is as good as the worst component • In data rich environments modeling errors range between 15-50% • Requires a significant investment -- data -- man hours -- computation resources

  4. Modeling Overview • Goal or purpose • Conceptual model • Model selection • Define modeling domain • Emissions inventory • Air quality and meteorological data • Define an episode for model application

  5. Conceptual Model • Understanding of the overall picture -- extent of data availability -- confidence in the data -- processes that are contributing to the observed air quality • Meteorological and air quality description of the episode • Understanding of why the episode stands apart from normal or average conditions

  6. Model Selection • Air quality -- platform (dedicated or family) -- chemistry module -- dispersion • Meteorological -- objective analysis -- diagnostic analysis -- prognostic models

  7. Modeling Domain • Regional approach -- focus on Delhi? -- up and down wind areas (from the conceptual model) • Meteorological modeling domain may be greater than the air quality domain • Grid scales may be different • Need accurate terrain file

  8. General Data Requirements • Data required is domain dependent -- size -- complexity of terrain -- pollutant sensitive • Levels of model applications -- limited surface and upper air -- moderately dense surface and multiple upper air -- intensive field study

  9. Air Quality Data Requirements • Surface -- routine air quality monitoring -- chemical speciation ` -- define boundaries/initialization • Upper air -- boundaries/initialization -- transport

  10. Meteorological Data Requirements • Surface -- winds, temperature, humidity -- insolation and cloud cover • Upper Air -- winds, temperature, moisture -- lapse rates and stability -- mixing heights

  11. Southern California Monitoring • Air Quality Monitoring -- 75+ ambient monitoring sites -- 10+ sites speciated data -- upper air characterization - as needed • Meteorology -- 125+ monitoring or reporting sites -- 5 sounding/aircraft sites -- 10 radar profilers/sounders

  12. Delhi Monitoring • ~ 6-10 air monitoring sites -- fixed & mobile vans -- representative of high traffic areas -- limited speciation • Surface meteorological at air monitoring locations • Twice daily soundings: Delhi, Gwalior, Jodhpur

  13. Emissions • Gridded emissions inventory -- point -- mobile -- area • Day specific • Allocation factor/surrogates • Temporal profile • Weekdays vs. weekends

  14. Applications: Episode Selection • Data Availability -- meteorology -- air quality -- boundary conditions • Episode severity • Representative of problem • Field study data available

  15. Delhi Profile • Reasonably flat terrain • Upper air -- limited upper air meteorology may characterize domain • Surface meteorology -- river valleys will channel winds -- urban heat island -- Great Indian Desert -- need additional monitoring

  16. Delhi Profile (continued) • Ambient air quality monitoring -- may not characterize regional issue -- need to characterize background profile -- identify locations of maximum pollution impact

  17. Recommendations • Meteorology -- enhance surface network -- consider upper air profiling • Air quality -- enhance network -- speciation profiles -- characterize boundaries • Investigate potential for field study -- PM - fall, Ozone - late spring

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