2012 Progress 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 ModelsEPA 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 Period Covered by this Report: June 1, 2012 through May 31,2013
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
Extreme weather events can be accompanied by extreme air quality degradation with associated costs to human health and society. The relationship between extreme weather and air quality is poorly understood, and relatively untested in models. Given expected changes to climate, we will quantify this hazard 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.
This first year of the project has focused on the development of statistical methods for investigating the drivers of air quality extremes and quantifying their influence. This methodology (based in extreme value theory) has been applied to a simulation study and to surface ozone observations over the last two decades from Atlanta and Charlotte. A manuscript is in preparation on these results. In addition, we have been investigating the application of reduced form models to study the impacts of emission reductions on surface ozone (Reich et al., 2013). Work is also underway to develop new statistical methods to spatially interpolate extreme ozone.
Our extreme value analysis will be scaled up to all air quality (both O3 and PM2.5) measurements in the United States over the past decades. In parallel, we will begin to investigate how models represent present-day predictors for air quality extremes and quartile regression statistics in comparison with observed relationships. The development of methods for the spatial interpolation of air quality extremes will also be finalized and submitted for publication.
Journal Articles on this Report : 1 Displayed | Download in RIS Format
|Other project views:||All 36 publications||10 publications in selected types||All 10 journal articles|
||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.||
Supplemental Keywords:Ozone, particulate matter, extreme value analysis, quantile regression, CESM, WRF-Chem
Progress and Final Reports:Original Abstract
2013 Progress Report
2014 Progress Report