Particulate Matter Prediction and Source Attribution for U.S. Air Quality Management in a Changing WorldEPA 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 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 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 3 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; (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.
The expected key results will include: the advanced state of the prediction system that 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 that will provide actionable information for broad stakeholders to design effective strategies to meet the air quality standards and achieve sustainability in a changing world.