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Forecasting and Disseminating AQI in Delhi

Forecasting and Disseminating AQI in Delhi. Joseph Cassmassi South Coast Air Quality Management District U.S.A. Program Objectives. Communicate AQI to public -- real-time cautionary measures -- forecast health statements

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Forecasting and Disseminating AQI in Delhi

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  1. Forecasting and Disseminating AQI in Delhi Joseph Cassmassi South Coast Air Quality Management District U.S.A

  2. Program Objectives • Communicate AQI to public -- real-time cautionary measures -- forecast health statements • Short-term control program -- actions based on attained air quality -- forecast initiated emissions reductions

  3. Delhi Profile • Growing! • Approximately 10 million residents • 1,500 squared kilometers • Traffic congestion • Expanding urbanization • Expanding industrial output

  4. Terrain • Generally flat - Yamuna river valley • Great Indian Desert to west • Alwar Hills to the southwest • Mewat Plain to the south • Ganges River Valley to the east

  5. Pollutant Background • Pollutants monitored -- particulate matter -- ozone -- sulfur oxides (sulfates) -- carbon monoxide -- nitrogen dioxide • Toxics Pollutants • Particulate levels significant

  6. Expectations • Success of forecasting and reporting AQI hinges upon -- reliable telemetry -- effective communications system -- relaying the message -- timeliness • Public/School/Industry outreach programs are necessary

  7. Forecasting Expectations • Targets: -- preventive health warnings -- mandatory emissions reductions • Realistic goals: -- what level of pollution reduction is achievable? -- what level of pollution reduction is acceptable?

  8. Forecast Accuracy • Set realistic goals • Prediction accuracy for categories of AQI -- minimum acceptable accuracy 50% -- target accuracy 65% or higher • Prediction accuracy is dependent upon the number of AQI categories forecast • Concentration forecast error ~ 10% of maximum observed concentration

  9. Likely Pollutants to Be Forecast • Highest Priority: -- Particulate Matter PM10/PM2.5 (current levels > 300 ug/m3) -- Carbon Monoxide -- Ozone • Lesser Priority -- Nitrogen dioxide -- Sulfur Dioxide -- Sulfates

  10. Components of Multi-pollutant Forecasting • Understanding the Problem • Developing the forecasting tools • Communicating the right message

  11. Understanding the Problem • Seasonality of the different pollutants • Pollutant specific impact zones • Overlapping impacts -- time -- space

  12. Developing Tools • Availability of historical data • Blending forecast requirements • Adjusting for trends Developing Tools

  13. AQI Prediction Algorithm • Empirical Analysis/Pattern Recognition -- fewer data requirements -- site specific prediction -- no requirement for emissions -- flexible - requires limited time • Numerical Simulation -- data intensive including emissions -- not flexible

  14. Modeling Techniques • Persistence • Multivariate Regression • Updated Stratified Multivariate Regression • MOS Linked Multivariate Regression • Nearest Neighbor Analog Prediction Algorithm

  15. Air Quality Data Availability • Minimum of 2-3 years of data • Gases -- hourly data • Particulates -- Hi Vol PM10 (6th – day) -- Real-time PM10 sampling (BAM or TEOM) • Special studies

  16. Meteorological Data Availability • Vertical temperature structure (soundings and pressure surface analyses) • Winds or surrogate (pressure gradients) • Humidity: surface and aloft • Numerical Forecast Model Output

  17. Daily Operational Requirements

  18. Data Routinely Used By Forecast

  19. Man Power/Liaisons • Continuous communication between monitoring and forecast groups • Liaison and communication between agencies (air quality - meteorology) to provide data • Forecasting is a daily job requiring sufficient staff • Liaison to media dissemination

  20. Forecast/AQI Coverage • Global forecast or AQI message -- covers total area -- single air quality description • Forecast zones -- source areas (notification for emissions curtailment actions) -- receptor areas (area specific air quality notification)

  21. Source/Receptor Areas (SRA)

  22. Forecast Zones

  23. Knowledge of Emissions Sources • Which sources contribute -- inventory -- speciation profiles • To what reasonable level can a source be asked to curtail emissions? -- process -- technology • Monitoring & enforcement • Exemptions

  24. Burning Issues • Control agricultural burning -- criteria in forecast -- smoke management program • Banning open burning of refuse -- stagnant meteorological conditions -- seasonally

  25. Getting the Message Out

  26. Typical Media Forecast

  27. First Steps • Evaluate ability to develop reliable data liaisons: forecasting and reporting • Expand the monitoring network to provide enhanced characterization of problem • Decide on the scope of the AQI/forecast message: global or site specific

  28. Second Steps • Develop basic conceptual model • Evaluate forecaster’s ability to identify the general profile • Start with a categorical AQI prediction • Develop confidence in forecast • Data analysis to develop a simple model • Develop confidence in model predictions

  29. Acceptance • AQI acceptance is tied to its simplicity in conveying a message • AQI forecast needs to reasonably accurate -- avoid “cry wolf” -- capture events if not peak concentrations • Industry will usually accept a program that is designed to be unbiased

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