1 / 15

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)

arnold
Download Presentation

AIR QUALITY MODELING CONFESSIONS OF A MODELER TURNED POLICY MAKER

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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

More Related