AIR QUALITY MODELING CONFESSIONS OF A MODELER TURNED POLICY MAKER

AIR QUALITY MODELINGCONFESSIONS OF A MODELER TURNED POLICY MAKER

2012 Community Modeling and Analysis System ConferenceChapel Hill, NCOctober 15, 2012

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

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Historical Perspectives

WW1 to 1960s – Single plume dispersion

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1960s – superposition of single plume models

Single Station Regional Average

Gibson and Peters (1977)

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1970s – Eulerian model development for urban air pollution1980s – 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 0.416DD 32 97 -0.504 3.031 47 -0.190 1.469DSA 98 130 -0.140 1.327 100 -0.010 1.020

KL 203 99 +0.344 0.488 113 +0.285 0.557LX 132 87 +0.205 0.659 84 +0.222 0.636

LCW 16 122 -0.768 7.625 29 -0.289 1.812PM 26 101 -0.591 3.885 30 -0.071 1.154RR 133 84 +0.226 0.632 63 +0.357 0.474SAL 124 122 +0.008 0.984 91 +0.153 0.734SIU 161 143 +0.059 0.888 92 +0.273 0.571SWP 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)

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1990s – Extension to hemispheric and global situations

Peters and Jouvanis (1979)

Saylor and Peters (1991)

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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

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The Atmosphere Scrambles Information

Peters et al. (1995)

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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)

cn/tn = gn(vi, Kij, kp, Sm, T, RH, …)

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Model Types

Lagrangian Statistical

Eulerian Source apportionment

Mixed

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Meaningful Applications

Understanding the science in a complicated environment where controlled experiments are not possible

Interpretation of data

Uncertainty analysis (particularly of policy decisions)

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Inappropriate Applications

Not a Substitute for Real Data

Epidemiological studies

Detailed policy implementation

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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?

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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.

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A model is a compass …

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… not a GPS

AIR QUALITY MODELING CONFESSIONS OF A MODELER TURNED POLICY MAKER

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