Grantee Research Project Results
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: R835228Title: 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. , Reich, Brian , Cooley, Dan
Current Investigators: Heald, Colette L. , Hodzic, Alma , Reich, Brian , Cooley, Dan
Institution: Massachusetts Institute of Technology , North Carolina State University , Colorado State University
Current 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 , Climate Change , Watersheds , Air , Water
Objective:
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.
Approach:
We will analyze the more than decade-long observational air quality record of ozone and particulate matter (PM2.5) in the United States in concert with observed meteorological drivers using quantile regression and extreme value theory to quantify the joint and conditional probability of extreme air quality events and air quality exceedences. This same analysis will be applied to a suite of models, both regional and global, to examine whether models reproduce the observed dependencies for present-day. Model performance will be analyzed in light of differences in chemical mechanism, spatial resolution and use of assimilated or free-running dynamics. We will then project future extreme air quality events based on simulated climate and the observed statistical relationships and compare this with simulated future air quality. We bring together investigators from two diverse communities: statistics and atmospheric chemistry to address this problem.
Expected Results:
This proposed project will result in fundamentally new insights into the connections between extreme weather and air quality. This will include probabilistic relationships between pollutants (PM2.5 and O3) and important meteorological drivers regionally within the United States based on the observational record. Furthermore we will comprehensively evaluate model skill in reproducing and projecting these relationships for current and future climate, providing much needed insight as to the fidelity of extreme-event hazard prediction with climate models. This work directly addresses several of the goals of the solicitation to apply techniques to look at the historical data on extreme events and their impact on air quality and identify how air quality models can be enhanced to represent these events.
Publications and Presentations:
Publications have been submitted on this project: View all 38 publications for this projectJournal Articles:
Journal Articles have been submitted on this project: View all 12 journal articles for this projectSupplemental Keywords:
ozone, particulate matter, extreme value analysis, quantile regression, multi-variate extreme value analysis, spatial forecast verification, probability, CESM, WRF-Chem,Progress and Final Reports:
The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.