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Using Air Quality Models for Emissions Management Decisions

Using Air Quality Models for Emissions Management Decisions. S. Trivikrama Rao U.S. Environmental Protection Agency, Research Triangle Park, NC and Christian Hogrefe University at Albany, Albany, NY. Air Quality Standards.

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Using Air Quality Models for Emissions Management Decisions

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  1. Using Air Quality Models for Emissions Management Decisions S. Trivikrama Rao U.S. Environmental Protection Agency, Research Triangle Park, NC and Christian Hogrefe University at Albany, Albany, NY

  2. Air Quality Standards • The NAAQS focus on the extreme values of the distribution of observed concentrations: • Ozone • 1-hr standard: 4th highest 1-hr ozone concentration over a consecutive 3-year period must not exceed 0.12 ppm (124 ppb). • 8-hr standard: 4th highest 8-hr ozone concentration in each year, averaged over a consecutive 3-year period, must not exceed 0.08 ppm (84 ppb). • PM2.5 mass • 24-hr standard: The 98th percentile of 24-hr concentrations in each year, averaged over a consecutive 3-year period, must not exceed 65 mg/m3 • PM10 mass • 24-hr standard: The 99th percentile of 24-hr concentrations in each year, averaged over a consecutive 3-year period, must not exceed 150 mg/m3.

  3. Disparity between the extreme-value focused NAAQS and the performance of regional-scale photochemical modeling systems in reproducing extreme values: • Poor performance in predicting individual extremes (uncertainty is on the order of 20% - 40%) • This disparity warrants against the direct use of model-predicted extreme concentration values in the attainment demonstrations • Promulgation of “relative reduction factors” (RRF) in attainment demonstrations by the US EPA (EPA, 1999) • RRF is site-specific and is defined as the relative change in model-predicted average daily maximum ozone concentrations due to emission reduction. • RRF is used to scale the observations to determine if the scaled design value is in compliance with the NAAQS • Examine model-to-model differences in the RRFs • Integrate observations and model predictions into a probabilistic framework using appropriate statistical techniques (e.g. extreme values statistics, Bootstrapping)

  4. Uncertainties in the Attainment Demonstration (Modeling) Process • Inherent uncertainty: caused by processes that operate on space-time scales not resolved by the grid-based models • Reducible uncertainty: caused by our incomplete scientific understanding of the various dynamical processes (manifested in the different predictions from different modeling systems, different meteorological models or parameterizations, different chemical mechanisms, or uncertainties in the emissions inventories) • Interannual variability: US EPA’s attainment demonstration process focuses on a fixed 3-year base window (e.g. ENSO affects pollutant levels)

  5. RRF Differences Between Models • Relative ozone response to two hypothetical emission reduction scenarios is very similar between two different modeling systems

  6. Estimating Exceedance Probabilities • The CDF of the k-th highest value of a distribution with CDF F(x) is given by equation: • Extreme value theory is applicable to the U.S. 1-hr ozone design value • For the U.S. 8-hr ozone design value: Apply the combination of exponential tail fit and bootstrap (resampling method) to estimate the design value CDF

  7. Predicted NAAQS Exceedance Probabilities • Predicted 8-hr ozone NAAQS exceedance probabilities for 1994 – 1996 base period (top) and 25%/50% NOx/VOC emission reduction scenarios simulated by two different modeling systems (middle and bottom) • Combining the extreme value approach with model-predicted relative responses to emission reductions yields very similar spatial patterns of NAAQS exceedance probabilities for different modeling systems

  8. Real World Emission Control Strategies • The probability of exceeding the 8-hr ozone NAAQS for (a) base period, b) 2007 SIP scenario, c) 2007 SIP + Tier 2 scenario, d) 2030 SIP scenario, and e) 2030 SIP + Tier 2 scenario

  9. Summary • The predicted relative changes in the daily maximum ozone concentrations and the estimated probabilities of violating the NAAQS stemming from emission controls are more similar between different modeling simulations than the predictions of absolute daily maximum ozone concentrations • The presented approach enables policy-makers to examine the probability of achieving compliance with the NAAQS under varying emission control options • The results reveal that emission reductions proposed in the US EPA’s SIP call can substantially reduce the probability of violating both the 1-hr and 8-hr ozone NAAQS over a large portion of the eastern United States

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