2016 Progress Report: Particulate Matter Prediction and Source Attribution for U.S. Air Quality Management in a Changing World

EPA Grant Number: R835876
Title: Particulate Matter Prediction and Source Attribution for U.S. Air Quality Management in a Changing World
Investigators: Liang, Xin-Zhong , Dickerson, Russell R. , He, Hao , Tao, Zhining , Wuebbles, Donald J.
Current Investigators: Liang, Xin-Zhong , Dickerson, Russell R. , He, Hao , Sanyal, Swarnali , Sun, Chao , Tao, Zhining , Wuebbles, Donald J.
Institution: University of Maryland - College Park , Goddard Earth Sciences Technology & Research , University of Illinois at Urbana-Champaign
EPA Project Officer: Chung, Serena
Project Period: April 1, 2016 through March 31, 2019
Project Period Covered by this Report: April 1, 2016 through March 31,2017
Project Amount: $790,000
RFA: Particulate Matter and Related Pollutants in a Changing World (2014) RFA Text |  Recipients Lists
Research Category: Air , Climate Change


The objectives of this study are to better understand how global changes in climate and emissions will affect the U.S. pollution, focusing on particulate matter and ozone, project their future trends, quantify key source attributions, and thus provide actionable information for U.S. environmental planners and decision makers to design effective dynamic management strategies, including local controls, domestic regulations and international policies, to sustain air quality improvements in a changing world. We will apply a state-of-the-science dynamic prediction system that couples global climate-chemical transport models with regional climate-air quality models over North America to determine the individual and combined impacts of global climate and emissions changes on U.S. air quality from the present to 2050 under multiple scenarios. We will quantify pollution sources and assign their attribution – natural vs. anthropogenic emissions, national vs. international agents, natural variations vs. climate changes – with associated probability and uncertainty. We will develop a time line for the global change factors to become significant such that effective actions can be taken. The level of significance will be defined following the cross-state air pollution rule as 1% of nonattainment areas with the goal of bringing all areas into attainment for the National Ambient Air Quality Standards (NAAQS). Our hypothesis is that the integration of the most advanced modeling system, most updated emissions treatment, multi-scale processes representation, and multi climate-emission scenarios assessment will improve the predictive capability and result in more reliable projection of future changes in particular matter, ozone, and related pollutants as well as their global and regional sources.

We will conduct three primary experiments using the dynamic prediction system: (1) historical simulations for period 1994-2013 to establish the credibility of the system and refine process-level understanding of U.S. regional air quality; (2) projections for period 2041-2060 to quantify individual and combined impacts of global climate and emissions changes under multiple scenarios; and (3) sensitivity analyses to determine future changes in pollution sources and their relative contributions from anthropogenic and natural emissions, long-range pollutant transport, and climate change effects. We will make a major contribution to a key goal of the EPA Strategic Plan to address climate change. The advanced state of the prediction system will produce more complete scientific understanding of the challenges from global climate and emissions changes imposed on U.S. air quality management and a more reliable projection of future pollution sources and attribution changes. The outcome will provide actionable information for U.S. federal, regional, and state agencies to design effective strategies to meet the air quality standards and achieve sustainability in a changing world.

Progress Summary:

In the 2016-2017 project year, we completed the following activities:

  • Continued analyzing future PM2.5 simulations and source attributions from the previous EPA STAR project (RD-8337302), determining that under Intergovernmental Panel on Climate Change (IPCC) scenarios A1B and A1Fi, PM2.5 is significantly reduced in the eastern United States and slightly increased in the western United States. Our analysis of changes in PM2.5 components and source attribution suggests that future U.S. PM2.5 pollution is sensitive to SO2 pollution and is moderately impacted by changes in NOx and NH3 emissions.
  • Completed CWRF integration driven by the (European Center for Medium-Range Weather Forecasts (ECMWF) ERA-Interim (ERI) reanalysis for 1979-2015. We improved CWRF simulation by incorporating more advanced physical schemes which help CWRF produce precipitation and temperature fields more realistically than do the assimilation products.
  • Completed global CAM-Chem simulations driven by NASA Modern-Era Retrospective analysis for Research and Applications (MERRA) reanalysis data. We tested and ran the global model CESM in the NCSA supercomputers Bluewaters at a high resolution (0.9o x 1.25o) with fully coupled chemistry using the latest chemistry module of CAM-Chem 5.
  • Created the project emissions modeling system. We compiled and processed anthropogenic emissions based on the National Emission Inventory 2011 (NEI2011) and Global Fire Emissions Database version 3 (GFEDv3) emissions based on satellite observations, and biogenic emissions based on BELD3 landuse/landcover data, through various programs including SMOKEv3.7.
  • Developed a program to merge all aforementioned emissions into a single dataset ready for CMAQ to use, in which the chemical species suitable for the selected chemical/aerosol mechanisms are put into the right time and CMAQ grids.
  • Investigated the long-term record of air pollution (focusing on ozone and PM2.5) in the United States from 1990 to 2015, calculated violation days, and identified years with statistically high pollution events. These will be used to identify extreme years and related climate factors.
  • Modified the MCIP model and developed the coupled CWRF-CMAQ modeling system. We simulated the year 2005 as a test case to evaluate model performance, and are now analyzing the regional simulations in order to optimize configuration of the dynamic modeling system.

Future Activities:

In the 2017-2018 project year, we will focus on the following objectives:

  • Update emissions over the North American region for the 25-yr historical runs
  • Use ERI reanalysis data to do the CAM5-Chem historical runs for model validation
  • Couple the CAM5-Chem simulation with CMAQ-CWRF model simulation to establish a dynamic prediction system
  • Prepare consistent regional and global emissions projections for 2050
  • Conduct sensitivity experiments to study uncertainty due to emissions/climate projection
  • Conduct experiments to study impacts of projected global climate and emissions changes
  • Diagnose outputs to quantify relative roles of global climate and emission changes.

Journal Articles on this Report : 1 Displayed | Download in RIS Format

Other project views: All 2 publications 2 publications in selected types All 2 journal articles
Type Citation Project Document Sources
Journal Article He H, Liang X-Z, Lei H, Wuebbles DJ. Future U.S. ozone projections dependence on regional emissions, climate change, long-range transport and differences in modeling design. Atmospheric Environment 2016;128:124-133. R835876 (2016)
R835876 (2017)
R833373 (Final)
  • Full-text: ScienceDirect-Full Text HTML
  • Abstract: ScienceDirect-Abstract
  • Other: ScienceDirect-Full Text PDF
  • Supplemental Keywords:

    Air quality, modeling, PM2.5, ozone, climate change, emissions change, chemical transport

    Progress and Final Reports:

    Original Abstract
  • 2017 Progress Report