Efficacy of recent state implementation plans for 8-hour ozone.

The development of state implementation plans (SIPs) for attainment of criteria pollutant standards is an integral component of air quality management in the United States. However, the content and efficacy of SIPs have rarely been examined systematically. Here, 20 SIPs developed in response to the 1997 8-hr ozone standard are reviewed as case studies of attainment efforts at the state level. Comparison of observed and model predicted ozone concentrations shows the US Environmental Protection Agency (EPA) recommended modeled attainment test to be a somewhat conservative predictor of attainment. Among 12 SIPs for regions that sought attainment by 2009, the test correctly predicted attainment and nonattainment in four and five regions, respectively; in the other three regions, attainment was observed despite predictions of nonattainment. However weight-of-evidence determinations and deviations from the recommended modeled attainment test methodology led five of these SIPs to predict attainment that was not in fact observed by 2009; three of those regions achieved attainment in 2010. Ozone and NO2 concentrations declined across much of the United States during the period covered by the SIPs, with rates of improvement strongly correlated with the initial pollution levels and hence greatest in nonattainment regions. However at monitors with mid-range levels of ozone initially, rates of reduction were largely independent of the initial attainment status of the region. This is consistent with thefact that apart from California, the majority of ozone precursor reductions documented by SIPs resulted from federal measures rather than from state or local controls specific to the nonattainment regions.

Uncertainties influencing health-based prioritization of ozone abatement options.

The primary goal of air quality management is protection of human health. Therefore, formulation of ground-level ozone mitigation policies could be informed by considering not just attainment of regulatory standards but also how control measures benefit public health. However, evaluation of health impacts is complicated by uncertainties associated with photochemical modeling and epidemiological studies. This study demonstrates methods to characterize uncertainties influencing health-benefits estimation of ozone reduction (averted premature mortalities due to short-term exposure) in the Dallas-Fort Worth (DFW) region. Uncertainty in photochemical modeling and the selection of temporal metric (duration of ozone exposure) for concentration-response relationships can each affect the health-based prioritization of ozone control options. For example, deterministic results (neglecting uncertainties) based on 8-h daily maximum ozone reduction shows DFW anthropogenic NO(x) controls to yield 9.23 times as much benefit per ton as VOC controls. However, the rankings reverse under 5.7% of the cases (including 2.8% cases that exhibit incremental mortalities due to NO(X) control) when uncertainties in the photochemical model are considered. Evaluated ozone exposure on a 24-h rather than an 8-h basis also reverses the rankings.

Likelihood of achieving air quality targets under model uncertainties.

Regulatory attainment demonstrations in the United States typically apply a bright-line test to predict whether a control strategy is sufficient to attain an air quality standard. Photochemical models are the best tools available to project future pollutant levels and are a critical part of regulatory attainment demonstrations. However, because photochemical models are uncertain and future meteorology is unknowable, future pollutant levels cannot be predicted perfectly and attainment cannot be guaranteed. This paper introduces a computationally efficient methodology for estimating the likelihood that an emission control strategy will achieve an air quality objective in light of uncertainties in photochemical model input parameters (e.g., uncertain emission and reaction rates, deposition velocities, and boundary conditions). The method incorporates Monte Carlo simulations of a reduced form model representing pollutant-precursor response under parametric uncertainty to probabilistically predict the improvement in air quality due to emission control. The method is applied to recent 8-h ozone attainment modeling for Atlanta, Georgia, to assess the likelihood that additional controls would achieve fixed (well-defined) or flexible (due to meteorological variability and uncertain emission trends) targets of air pollution reduction. The results show that in certain instances ranking of the predicted effectiveness of control strategies may differ between probabilistic and deterministic analyses.

Efficient characterization of pollutant-emission response under parametric uncertainty.

An essential requirement of modeling for air quality management is to accurately simulate the responses of pollutant concentrations to changes in emissions. Uncertain model input parameters such as emission rates and reaction rate constants lead to uncertainty in model responses. However, traditional methods for characterizing parametric uncertainty are exceedingly computationally intensive. This paper presents methods for using high-order sensitivity coefficients in analytical equations to efficiently represent how the responsiveness of pollutants to emission reductions in the underlying photochemical model varies with simultaneous perturbations in multiple model input parameters. Separate approaches are introduced for characterizing the parametric uncertainty of pollutant response to a fixed or a variable amount of emission reduction. The approaches are demonstrated for an air pollution episode used in recent attainment planning in Georgia. For hypothetical scenarios in which domain-wide emission rates and photolysis rates are perturbed simultaneously by 50%, the reduced form models yield highly accurate predictions of the ozone impacts due to 50% reductions in nitrogen-oxide emissions in Atlanta (normalized mean bias 6.0%, normalized mean error 9.7%, R2=0.992) and inorganic particulate responses to Atlanta sulfur-dioxide emissions (-2.9% bias, 3.7% error, R2=1.000). Similar accuracy is achieved for pollutant responses to power plant emission controls.