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.

[1]  Richard L. Smith,et al.  The effect of horizontal resolution on simulation of very extreme US precipitation events in a global atmosphere model , 2010 .

[2]  P. Friederichs,et al.  Generating and Calibrating Probabilistic Quantitative Precipitation Forecasts from the High-Resolution NWP Model COSMO-DE , 2012 .

[3]  Bill Ravens,et al.  An Introduction to Copulas , 2000, Technometrics.

[4]  D. Gamerman,et al.  Bayesian analysis of extreme events with threshold estimation , 2004 .

[5]  A. Raftery,et al.  Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .

[6]  Jana Sillmann,et al.  Extreme Cold Winter Temperatures in Europe under the Influence of North Atlantic Atmospheric Blocking , 2011 .

[7]  Stephan R. Sain,et al.  Downscaling extremes: A comparison of extreme value distributions in point-source and gridded precipitation data , 2010, 1010.1604.

[8]  Byeong-Uk Kim,et al.  Likelihood of achieving air quality targets under model uncertainties. , 2011, Environmental science & technology.

[9]  A. O'Hagan,et al.  Bayesian calibration of computer models , 2001 .

[10]  B. Reich.  Spatiotemporal quantile regression for detecting distributional changes in environmental processes , 2012, Journal of the Royal Statistical Society. Series C, Applied statistics.

[11]  G. Hegerl,et al.  Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations , 2007 .

[12]  Jeffrey Young,et al.  Incremental testing of the Community Multiscale Air Quality (CMAQ) modeling system version 4.7 , 2009 .

[13]  Stephan R. Sain,et al.  A comparison study of extreme precipitation from six different regional climate models via spatial hierarchical modeling , 2010 .

[14]  Dave Higdon,et al.  Combining Field Data and Computer Simulations for Calibration and Prediction , 2005, SIAM J. Sci. Comput..

[15]  D. Byun,et al.  Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System , 2006 .

[16]  R. Nelsen.  An Introduction to Copulas , 1998 .

[17]  George Pouliot,et al.  CHANGES TO THE BIOGENIC EMISSIONS INVENTORY SYSTEM VERSION 3 ( BEIS 3 ) , 2005 .

[18]  Jonathan A. Tawn,et al.  Modelling the distribution of the cluster maxima of exceedances of subasymptotic thresholds , 2012 .

[19]  Anthony C. Davison,et al.  Modelling Time Series Extremes , 2012 .

[20]  A. Frigessi,et al.  A Dynamic Mixture Model for Unsupervised Tail Estimation without Threshold Selection , 2002 .

[21]  S. Coles,et al.  An Introduction to Statistical Modeling of Extreme Values , 2001 .

[22]  Jingwen Zhou,et al.  Calibration of Numerical Model Output Using Nonparametric Spatial Density Functions , 2011 .

[23]  M. Fuentes,et al.  Journal of the American Statistical Association Bayesian Spatial Quantile Regression Bayesian Spatial Quantile Regression , 2022 .

[24]  Stephan R. Sain,et al.  Spatial Hierarchical Modeling of Precipitation Extremes From a Regional Climate Model , 2008 .

[25]  G. Grell,et al.  A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5) , 1994 .

[26]  Montserrat Fuentes,et al.  Nonparametric spatial models for extremes: application to extreme temperature data , 2013, Extremes.

[27]  Eric P. Smith,et al.  An Introduction to Statistical Modeling of Extreme Values , 2002, Technometrics.

[28]  D. Maraun,et al.  The influence of synoptic airflow on UK daily precipitation extremes. Part I: Observed spatio-temporal relationships , 2009 .

[29]  Rohit Mathur,et al.  Dynamic evaluation of regional air quality model’s response to emission reductions in the presence of uncertain emission inventories , 2011 .

[30]  Alan E Gelfand,et al.  A Spatio-Temporal Downscaler for Output From Numerical Models , 2010, Journal of agricultural, biological, and environmental statistics.

[31]  Armistead G Russell,et al.  Nonlinear response of ozone to emissions: source apportionment and sensitivity analysis. , 2005, Environmental science & technology.

[32]  Brian J Reich,et al.  Bayesian analysis of a reduced-form air quality model. , 2012, Environmental science & technology.

[33]  S. Solberg,et al.  Atmospheric Chemistry and Physics , 2002 .

[34]  Richard L. Smith,et al.  Models for exceedances over high thresholds , 1990 .