Final Report: Using Advanced Statistical Techniques to Identify the Drivers and Occurrence of Historical and Future Extreme Air Quality Events in the United States from Observations and Models

EPA Grant Number: R835228
Title: Using Advanced Statistical Techniques to Identify the Drivers and Occurrence of Historical and Future Extreme Air Quality Events in the United States from Observations and Models
Investigators: Heald, Colette L. , Cooley, Dan , Hodzic, Alma , Reich, Brian
Institution: Massachusetts Institute of Technology , Colorado State University , National Center for Atmospheric Research , North Carolina State University
EPA Project Officer: Chung, Serena
Project Period: June 1, 2012 through May 31, 2015 (Extended to May 31, 2016)
Project Amount: $749,931
RFA: Extreme Event Impacts on Air Quality and Water Quality with a Changing Global Climate (2011) RFA Text |  Recipients Lists
Research Category: Air Quality and Air Toxics , Global Climate Change , Water and Watersheds , Climate Change , Air , Water

Objective:

Extreme air quality events are largely attributable to meteorological conditions that lead to the formation and collection of pollutants. Events of extreme air quality degradation have costs to human health and society. While the meteorological conditions that lead to high pollution levels are relatively well understood, it is less understood what conditions distinguish extreme events from merely high events. Atmospheric models’ ability to produce or predict behavior at the most extreme levels is largely untested. Further, understanding links between meteorology and extreme air quality events is essential to projection of extreme events under a future changed climate. We quantify and model relationships between extreme air quality events and meteorology based on the observational record and verify with what fidelity models reproduce the relationships between extreme weather and air quality for present day and then project how these might change in the future.

Summary/Accomplishments (Outputs/Outcomes):

We identify which meteorological drivers most strongly control winter and summer particulate matter (PM) and ozone air pollution over the United States. To do so, we employ existing statistical methods like quantile regression, and develop new statistical methods such as spatial modeling with a skew-T distribution and employing extreme value based methods to perform data mining and spatially model meteorological drivers of the most extreme air quality events. We identify that the sensitivity of pollution to meteorology can differ significantly with pollutant levels, for example, 95th percentile O3 concentrations are ~ 50% more sensitive to temperature than 50th percentile ozone. This behavior in O3-T is not uniformly reproduced by global models, and appears to be more strongly influenced by dynamics than by chemistry. At the regional scale, we applied both quantile regression and the tail dependence method to examine the relationship between extreme ozone and meteorological covariate and showed significant differences between observational data and model outputs, which were strongly dependent on the measurement site. Via data mining of the observational record, we are able to discern variables that help lead to extreme behavior such as the wind direction. Spatially modeling the drivers of extreme ozone shows that the strength of the relationship between extreme ozone response and meteorological drivers changes from north to south in our study area of EPA regions 3 and 4. We also developed and tested several new statistical algorithms for extreme value analysis, and made software available for public use. The new methods were applied to estimate a 14.2% (1.6%, 28.9%) increase on the mortality risk of exceeding the current ozone standard due to projected climate change. We also demonstrated at 30% reduction in prediction error for daily ozone by using our new statistical downscaling tools.


Journal Articles on this Report : 10 Displayed | Download in RIS Format

Other project views: All 36 publications 10 publications in selected types All 10 journal articles
Type Citation Project Document Sources
Journal Article Morris SA, Reich BJ, Thibaud E, Cooley D. A space-time skew-t model for threshold exceedances. Biometrics 2017, January 12 [Epub ahead of print]. R835228 (Final)
  • Abstract from PubMed
  • Full-text: Wiley-Full-text PDF
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  • Abstract: Wiley-Abstract & Full-text HTML
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  • Journal Article Parker RJ, Reich BJ, Sain SR. A multiresolution approach to estimating the value added by regional climate models. Journal of Climate 2015;28(22):8873-8887. R835228 (Final)
  • Full-text: AMS-Full Text HTML
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  • Abstract: AMS-Abstract
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  • Other: AMS-Full Text PDF
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  • Journal Article Porter WC, Heald CL, Cooley D, Russell B. Investigating the observed sensitivities of air-quality extremes to meteorological drivers via quantile regression. Atmospheric Chemistry and Physics 2015;15(18):10349-10366. R835228 (2014)
    R835228 (Final)
  • Full-text: ACP-Full Text PDF
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  • Abstract: ACP-Abstract
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  • Other: ResearchGate-Full Text PDF
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  • Journal Article Reich BJ, Shaby BA. A hierarchical max-stable spatial model for extreme precipitation. Annals of Applied Statistics 2012;6(4):1430-1451. R835228 (2013)
    R835228 (2014)
    R835228 (Final)
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  • Abstract from PubMed
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  • Full-text: Project Euclid-Full Text PDF
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  • Abstract: Project Euclid-Abstract
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  • Other: NC State University-Full Text PDF
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  • Journal Article Reich BJ, Chang HH, Foley KM. A spectral method for spatial downscaling. Biometrics 2014;70(4):932-942. R835228 (2013)
    R835228 (2014)
    R835228 (Final)
    R834799 (2014)
    R834799 (2015)
    R834799 (2016)
    R834799 (Final)
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  • Full-text: Europe PMC-Full Text HTML
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  • Abstract: Wiley-Abstract
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  • Other: ResearchGate-Full Text PDF
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  • Journal Article Reich B, Cooley D, Foley K, Napelenok S, Shaby B. Extreme value analysis for evaluating ozone control strategies. Annals of Applied Statistics 2013;7(2):739-762. R835228 (2012)
    R835228 (2013)
    R835228 (2014)
    R835228 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
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  • Full-text: Project Euclid-Full Text-PDF
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  • Abstract: Project Euclid-Abstract
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  • Other: NC State University-Full Text-PDF
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  • Journal Article Russell BT, Cooley DS, Porter WC, Reich BJ, Heald CL. Data mining to investigate the meteorological drivers for extreme ground level ozone events. Annals of Applied Statistics 2016;10(3):1673-1698. R835228 (2013)
    R835228 (Final)
  • Full-text: arXiv.org-Full Text (prepublication)
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  • Abstract: Project Euclid-Abstract
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  • Other: ResearchGate-Abstract
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  • Journal Article Russell BT, Cooley DS, Porter WC, Heald CL. Modeling the spatial behavior of the meteorological drivers' effects on extreme ozone. Environmetrics 2016;27(6):334-344. R835228 (Final)
  • Abstract: Wiley Online-Abstract
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  • Other: ResearchGate-Abstract
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  • Journal Article Sun W, Reich BJ, Cai TT, Guindani M, Schwartzman A. False discovery control in large-scale spatial multiple testing. Journal of the Royal Statistical Society: Series B, Statistical Methodology 2015;77(1):59-83. R835228 (2013)
    R835228 (2014)
    R835228 (Final)
  • Full-text from PubMed
  • Abstract from PubMed
  • Associated PubMed link
  • Full-text: University of Pennsylvania-Full Text PDF
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  • Abstract: Wiley Online-Abstract
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  • Journal Article Wilson A, Reich BJ, Nolte CG, Spero TL, Hubbell B, Rappold AG. Climate change impacts on projections of excess mortality at 2030 using spatially varying ozone-temperature risk surfaces. Journal of Exposure Science and Environmental Epidemiology 2017;27(1):118-124. R835228 (Final)
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  • Abstract: JESEE-Abstract
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  • Other: ResearchGate-Abstract
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  • Progress and Final Reports:

    Original Abstract
    2012 Progress Report
    2013 Progress Report
    2014 Progress Report