Likelihood of achieving air quality targets under model uncertainties

Chemical Sensitivity Analysis and Uncertainty Analysis of Ozone Production in the Comprehensive Air Quality Model with Extensions Applied to Eastern Texas

Chemical sensitivity analysis (CSA) is a new probing tool for sampling sensitivities to chemistry parameters during a three-dimensional (3-D) simulation. CSA was applied to rank all of the parameters in the Carbon Bond 6 revision 4 (CB6r4) mechanism and to create an ensemble of six chemical mechanisms representing higher and lower O₃ formations than CB6r4. This ensemble of mechanisms was used to estimate the uncertainty from the chemistry in a 3-D simulation and combined with uncertainties from other model inputs obtained from calculations of their sensitivities. The overall uncertainty (1σ) in O₃ predictions for eastern Texas was 10-11 ppb in the Gulf of Mexico near Galveston and 7-8 ppb in much of the rest of the domain on the higher O₃ days of June 2012. As a percent of the O₃ concentration, the uncertainty was more uniform over the domain, 11-14%. Chemistry and emissions make the largest contributions to the O₃ uncertainty. Uncertainty in the dry deposition velocities is less important in urban areas and the Gulf, but it is similar in importance to the uncertainty in chemistry and emissions at most other locations. Uncertainty in O₃ boundary concentrations is the least important.

Extreme value analysis for evaluating ozone control strategies

Tropospheric ozone is one of six criteria pollutants regulated by the US EPA, and has been linked to respiratory and cardiovascular endpoints and adverse effects on vegetation and ecosystems. Regional photochemical models have been developed to study the impacts of emission reductions on ozone levels. The standard approach is to run the deterministic model under new emission levels and attribute the change in ozone concentration to the emission control strategy. However, running the deterministic model requires substantial computing time, and this approach does not provide a measure of uncertainty for the change in ozone levels. Recently, a reduced form model (RFM) has been proposed to approximate the complex model as a simple function of a few relevant inputs. In this paper, we develop a new statistical approach to make full use of the RFM to study the effects of various control strategies on the probability and magnitude of extreme ozone events. We fuse the model output with monitoring data to calibrate the RFM by modeling the conditional distribution of monitoring data given the RFM using a combination of flexible semiparametric quantile regression for the center of the distribution where data are abundant and a parametric extreme value distribution for the tail where data are sparse. Selected parameters in the conditional distribution are allowed to vary by the RFM value and the spatial location. Also, due to the simplicity of the RFM, we are able to embed the RFM in our Bayesian hierarchical framework to obtain a full posterior for the model input parameters, and propagate this uncertainty to the estimation of the effects of the control strategies. We use the new framework to evaluate three potential control strategies, and find that reducing mobile-source emissions has a larger impact than reducing point-source emissions or a combination of several emission sources.

Bayesian analysis of a reduced-form air quality model

Numerical air quality models are being used for assessing emission control strategies for improving ambient pollution levels across the globe. This paper applies probabilistic modeling to evaluate the effectiveness of emission reduction scenarios aimed at lowering ground-level ozone concentrations. A Bayesian hierarchical model is used to combine air quality model output and monitoring data in order to characterize the impact of emissions reductions while accounting for different degrees of uncertainty in the modeled emissions inputs. The probabilistic model predictions are weighted based on population density in order to better quantify the societal benefits/disbenefits of four hypothetical emission reduction scenarios in which domain-wide NO(x) emissions from various sectors are reduced individually and then simultaneously. Cross validation analysis shows the statistical model performs well compared to observed ozone levels. Accounting for the variability and uncertainty in the emissions and atmospheric systems being modeled is shown to impact how emission reduction scenarios would be ranked, compared to standard methodology.

Development of risk-based air quality management strategies under impacts of climate change

Climate change is forecast to adversely affect air quality through perturbations in meteorological conditions, photochemical reactions, and precursor emissions. To protect the environment and human health from air pollution, there is an increasing recognition of the necessity of developing effective air quality management strategies under the impacts of climate change. This paper presents a framework for developing risk-based air quality management strategies that can help policy makers improve their decision-making processes in response to current and future climate change about 30-50 years from now. Development of air quality management strategies under the impacts of climate change is fundamentally a risk assessment and risk management process involving four steps: (1) assessment of the impacts of climate change and associated uncertainties; (2) determination of air quality targets; (3) selections of potential air quality management options; and (4) identification of preferred air quality management strategies that minimize control costs, maximize benefits, or limit the adverse effects of climate change on air quality when considering the scarcity of resources. The main challenge relates to the level of uncertainties associated with climate change forecasts and advancements in future control measures, since they will significantly affect the risk assessment results and development of effective air quality management plans. The concept presented in this paper can help decision makers make appropriate responses to climate change, since it provides an integrated approach for climate risk assessment and management when developing air quality management strategies.

IMPLICATIONS

Development of climate-responsive air quality management strategies is fundamentally a risk assessment and risk management process. The risk assessment process includes quantification of climate change impacts on air quality and associated uncertainties. Risk management for air quality under the impacts of climate change includes determination of air quality targets, selections of potential management options, and identification of effective air quality management strategies through decision-making models. The risk-based decision-making framework can also be applied to develop climate-responsive management strategies for the other environmental dimensions and assess costs and benefits of future environmental management policies.

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.

Photochemical modeling of the Ozark isoprene volcano: MEGAN, BEIS, and their impacts on air quality predictions

Biogenic volatile organic compounds (BVOCs) contribute substantially to atmospheric carbon, exerting influence on air quality and climate. Two widely used models, the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and the Biogenic Emission Inventory System (BEIS) are employed to generate emissions for application in the CMAQ air quality model. Predictions of isoprene, monoterpenes, ozone, formaldehyde, and secondary organic carbon (SOC) are compared to surface and aloft measurements made during an intensive study in the Ozarks, a large isoprene emitting region. MEGAN and BEIS predict spatially similar emissions but magnitudes differ. The total VOC reactivity of the emissions, as developed for the CB05 gas-phase chemical mechanism, is a factor of 2 different between the models. Isoprene estimates by CMAQ-MEGAN are higher and more variable than surface and aloft measurements, whereas CMAQ-BEIS predictions are lower. CMAQ ozone predictions are similar and compare well with measurements using either MEGAN or BEIS. However, CMAQ-MEGAN overpredicts formaldehyde. CMAQ-BEIS SOC predictions are lower than observational estimates for every sample. CMAQ-MEGAN underpredicts SOC ∼ 80% of the time, despite overprediction of precursor VOCs. CMAQ-MEGAN isoprene predictions improve when prognostically predicted solar radiation is replaced with the GEWEX satellite product. CMAQ-BEIS does not exhibit similar photosensitivity.