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AIR QUALITY MODELING CONFESSIONS OF A MODELER TURNED POLICY MAKER

AIR QUALITY MODELING CONFESSIONS OF A MODELER TURNED POLICY MAKER. 2012 Community Modeling and Analysis System Conference Chapel Hill, NC October 15, 2012. Why Model?. Understanding the underlying physico -chemical processes Guidance in policy development (beginning with SIP’s 35 years ago)

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AIR QUALITY MODELING CONFESSIONS OF A MODELER TURNED POLICY MAKER

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  1. AIR QUALITY MODELINGCONFESSIONS OF A MODELER TURNED POLICY MAKER 2012 Community Modeling and Analysis System Conference Chapel Hill, NC October 15, 2012

  2. Why Model? Understanding the underlying physico-chemical processes Guidance in policy development (beginning with SIP’s 35 years ago) Guidance in policy implementation It’s fun and challenging

  3. Historical Perspectives WW1 to 1960s – Single plume dispersion

  4. Historical Perspectives 1960s – superposition of single plume models Single Station Regional Average Gibson and Peters (1977)

  5. Historical Perspectives 1970s – Eulerian model development for urban air pollution 1980s – Regional air quality models – extension of Eulerian urban model methodology Table 2. Comparison of modeled and observed sulfate wet deposition for simulations KYSIMP and KYMET (observed and modeled depositions given as mgm-2) Simulation KYSIMP Simulation KYMET Model Fractional M/O Model Fractional M/O Site Observation result difference* ratio† result difference* ratio† BR 89 109 -0.101 1.225 39 +0.391 0.438 CFH 77 89 -0.072 1.156 32 +0.413 0.416 DD 32 97 -0.504 3.031 47 -0.190 1.469 DSA 98 130 -0.140 1.327 100 -0.010 1.020 KL 203 99 +0.344 0.488 113 +0.285 0.557 LX 132 87 +0.205 0.659 84 +0.222 0.636 LCW 16 122 -0.768 7.625 29 -0.289 1.812 PM 26 101 -0.591 3.885 30 -0.071 1.154 RR 133 84 +0.226 0.632 63 +0.357 0.474 SAL 124 122 +0.008 0.984 91 +0.153 0.734 SIU 161 143 +0.059 0.888 92 +0.273 0.571 SWP 193 133 +0.184 0.689 154 +0.112 0.798 *Fractional difference= (observation – model result)/(observation + model result). †M/O ration = model result/observation. Saylor, Peters, and Mathur (1991)

  6. Historical Perspectives 1990s – Extension to hemispheric and global situations Saylor and Peters (1991) Peters and Jouvanis (1979)

  7. Historical Perspectives • WW1 to 1960s – Single plume dispersion • 1960s – superposition of single plume models • 1970s – Eulerian model development for urban air pollution • 1980s – Regional air quality models – extension of Eulerian urban model methodology • 1990s – Extension to hemispheric and global situations

  8. The Atmosphere Scrambles Information Peters et al. (1995)

  9. The Atmosphere Scrambles Information c = f(x, t) is the main goal – from this we can get exposures, deposition, etc. c = f(advection, convection, turbulence, chemical reactions, sources, cloud formation/presence, surface removal) dcn/dtn = gn(vi, Kij, kp, Sm, T, RH, …)

  10. Model Types Lagrangian Statistical Eulerian Source apportionment Mixed

  11. Meaningful Applications • Understanding the science in a complicated environment where controlled experiments are not possible • Interpretation of data • Uncertainty analysis (particularly of policy decisions)

  12. Inappropriate Applications • Not a Substitute for Real Data • Epidemiological studies • Detailed policy implementation

  13. Too Complicated?(Keep it as simple as possible … but no simpler!) • Are models too complicated for the non-expert? • Are models helpful for good, reliable interpretation of data?

  14. Concluding Thoughts • Do we know when good is good enough – I don’t think I do. • We are being challenged to use models as a substitute for real data with models that have questionable fidelity. • The costs of implementing CSAPR have been estimated to be $2-3 billion annually compared to very questionable estimates of benefits.

  15. A model is a compass … … not a GPS

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