2013 Progress Report: Air Quality Impacts of Extreme Weather Events: Historical Analysis and Future Projection

EPA Grant Number: R835204
Title: Air Quality Impacts of Extreme Weather Events: Historical Analysis and Future Projection
Investigators: Wang, Yuhang , Deng, Yi , Loadholt, Jay , Park, Taewon , Song, Yongjia , Zeng, Tao , Zhang, Henian , Zhang, Yuzhong
Current Investigators: Wang, Yuhang , Deng, Yi , Loadholt, Jay , Park, Taewon , Song, Yongjia , Zeng, Tao , Zhang, Henian , Zhang, Yuzhong , Zou, Yufei
Institution: Georgia Institute of Technology
Current Institution: Georgia Institute of Technology , Georgia Environmental Protection Division
EPA Project Officer: Hunt, Sherri
Project Period: June 1, 2012 through May 31, 2015 (Extended to May 31, 2016)
Project Period Covered by this Report: June 1, 2013 through May 31,2014
Project Amount: $749,859
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 , Water Quality , Climate Change , Air , Water

Objective:

Atmospheric systems are strongly affected by extreme weather events (EWEs). Previous studies have demonstrated large sensitivities of air quality to the variability in atmospheric systems. The primary objective of this project is to characterize and understand the links between air quality extremes and regional weather/climate systems such that EWE-based air quality projections with explicit uncertainty estimates can be developed.

Progress Summary:

In this project, we focused our effort on analyzing historical air quality (ozone and PM2.5) and weather/climate data. An emphasis was placed on understanding the characteristics of extreme ozone and PM2.5 events and how they relate to extreme meteorological events such as heat wave and drought. Indices for heat wave and drought previously developed in climate research were adopted and modified to suit the needs of air quality research. Statistical distribution and correlation, cluster analysis, empirical orthogonal function (EOF), linear inverse modeling (LIM), and historical modeling using regional air quality and global chemistry-climate models, to analyze surface ozone (since 1980) and PM2.5 (since 2000) measurements from EPA observation networks. An ensemble analysis of all events from single day to multi-day episodes in the past three decades places all episodes into a continuum of time and geospatial coordinates. Inter-annual patterns linked to source concentrations and seasonal transport are evident, but anomalies such as unseasonable and persistent anticyclones to winter events over snow cover can also be identified. Overlapping events between ozone and temperature extremes are identified. They tend to occur in eastern and western coast regions with significant local variability. The occurrence frequency of overlapping events decreased from 1980s to 2000s. PM2.5 extreme showed more sensitivity to extreme temperature than drought index. When being divided by two periods (2000-2004 and 2005-2009), the second period had more extreme PM events at lower temperature in winter time. An EOF analysis was conducted to examine how regional and hemispheric climate variability affects the ozone extreme events. The seasonal change in controlling weather systems plays a key role in how regional climate affects air quality. We also investigated the feasibility of long-range (1 month) statistical forecasting of ozone and PM2.5. Lastly, regional and global (CESM-CAM5) model simulations were investigated to understand the mechanisms and identify required model improvements for simulating air quality extremes and trends.

Future Activities:

We plan to continue the ongoing statistical data analysis to characterize and understand the links between air quality extremes and regional weather/climate systems. After consolidating research results and further analysis, we expect to write journal papers to publish these results. Completing the journal papers will be a main focus of our work effort. Analysis of regional and global model simulations will continue. New simulation experiments will be considered in light of the statistical analysis results in order to use the model simulations to examine the relationships we found in the statistical analysis. By evaluating model simulations with the observations, we will also try to understand potential limitations in current regional and global modeling capabilities. We will explore Bayesian statistical approach in seasonal/yearly air quality extreme projection, through which we investigate how climate projections can be applied in air quality projection.

Journal Articles:

No journal articles submitted with this report: View all 9 publications for this project

Supplemental Keywords:

Air quality projection, climate change, uncertainty

Relevant Websites:

http://apollo.eas.gatech.edu Exit

Progress and Final Reports:

Original Abstract
2012 Progress Report
2014 Progress Report
Final Report