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Combining Empirical Orthogonal Function and Extreme Value Theory Methods to Characterize Observed and Future Changes in Extreme U.S. Air Pollution EventsEPA Grant Number: R835206
Title: Combining Empirical Orthogonal Function and Extreme Value Theory Methods to Characterize Observed and Future Changes in Extreme U.S. Air Pollution Events
Investigators: Fiore, Arlene M , Polvani, Lorenzo M
Institution: Columbia University in the City of New York
EPA Project Officer: Leinbach, Alan
Project Period: June 1, 2012 through May 31, 2015 (Extended to May 31, 2016)
Project Amount: $749,951
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
The occurrence of meteorological conditions associated with poor air quality (i.e. elevated levels of ozone and particulate matter) that are classified as extreme events at present are expected to increase in a warmer climate. Using state-of-the-art statistical techniques, we will construct (for several U.S. regions) probability tables and simple statistical models which will translate future climate model projections under different pollutant emission scenarios into statistical forecasts of extreme events.
We will identify and characterize changes in extreme ozone and particulate matter events and their underlying meteorological drivers (including associated feedbacks) leading to extreme air pollution events over the past several decades (with observations and models), as well as for a variety of future climate and emission scenarios. Since particulate matter depends on precipitation (the major sink), we will also examine changes in extreme precipitation events (intensity and duration) and extended periods with minimal precipitation (extreme drought), to provide information on two of the key hazards affecting water quality.
We will first apply empirical orthogonal function (EOF) analysis to observed and simulated ozone and particulate matter fields, in order to identify U.S. regions that covary in time, as well as the regional meteorological conditions conducive to extreme air pollution events. We will then use extreme value theory (EVT) to characterize distributions of both the air pollutants and the meteorological drivers. For regions where dominant meteorological drivers cannot be identified, we propose to use synthetic tracers, which can be easily implemented into physical climate simulations at minimal computational cost, to quantify changes in extreme events. The combination of EOF and EVT methods provides us with the necessary tools to translate massive datasets generated from climate model projections into digestible, probabilistic information needed by air quality planners e.g., the likelihood of an air pollution event above some threshold in some region occurring for some number of days over some time).
We will deliver probabilistic tables with estimates of the frequency, duration, and intensity of extreme air pollution and precipitation events for several climate and air pollution emission scenarios (including feedbacks with the biosphere) for the 21 st century for several U.S. regions. We will provide the statistical models used to generate the tables from meteorological drivers, and in some cases synthetic tracers of air pollutants, to enable rapid translation of new climate model projections into statistical forecasts for use in policy planning.
Publications and Presentations:Publications have been submitted on this project: View all 33 publications for this project
Journal Articles:Journal Articles have been submitted on this project: View all 7 journal articles for this project
Supplemental Keywords:sustainable air quality management, sustainable water management, global change, regional climate change, air pollution episodes, ozone, aerosol,
Progress and Final Reports:2012 Progress Report
2013 Progress Report
2014 Progress Report