2016 Progress Report: Ensemble Analysis of Global Change Projections for US Air Quality Using a Novel Combination of Lagrangian and Gridded Air Quality ModelsEPA Grant Number: R835874
Title: Ensemble Analysis of Global Change Projections for US Air Quality Using a Novel Combination of Lagrangian and Gridded Air Quality Models
Investigators: Lamb, Brian , Avise, Jeremy C. , Fast, Jerome D. , Guenther, Alex , Lee, Yunha , Vaughan, Joseph , Walden, Von P. , Zaveri, Rahul A.
Institution: Washington State University , Pacific Northwest National Laboratory , University of California - Irvine
EPA Project Officer: Keating, Terry
Project Period: January 1, 2016 through December 31, 2018
Project Period Covered by this Report: January 1, 2016 through December 31,2016
Project Amount: $789,547
RFA: Particulate Matter and Related Pollutants in a Changing World (2014) RFA Text | Recipients Lists
Research Category: Air , Climate Change
Our overall goal is to improve our understanding of the effects of global change on future particulate matter (PM) levels in the western United States. Our specific objectives are below:
Develop a novel application of Lagrangian air quality modeling using an ensemble of high-resolution and bias-corrected downscaled climate data based on the Multivariate Adaptive Constructed Analogs (MACA) method to provide comprehensive descriptions of PM changes due to meteorological changes.
Employ this method to examine the effects of the full range of climate projections upon PM levels associated with representative air quality issues in the western United States, including wintertime stagnation events, summertime urban to rural transport cases, and wildfire impacts on rural and urban populations.
Incorporate changes in U.S. anthropogenic emissions, background concentrations and land use changes within the ensemble of Lagrangian modeling cases to assess the sensitivity of PM to these factors in the western United States.
Integrate the results from these simulations for presentation in forms suitable to inform effective air quality management in the western United States and elsewhere.
We have developed a Lagrangian air quality box-scale modeling framework using National Oceanic and Atmospheric Administration HYSPLIT and MOSAIC full gas and aerosols chemistry and dynamics model. Our HYSPLIST-MOSAIC modeling framework has been evaluated against the U.S. Department of Energy CARES field campaign, which was conducted over Sacramento, California, in June 2010. Our preliminary results suggest that the model is able to capture the observed concentrations, particularly in terms of magnitude. Because our model is computationally efficient, ensemble runs would be possible using various future climate realizations for multiple locations and air quality scenarios, at acceptable cost. Using MACA datasets, we have examined future climate changes in key meteorological parameters in 2030s and 2050s. We have installed Community Land Model (CLM) in our Washington State University supercomputer cluster and successfully ran a test simulation.
Application of the modeling framework to selected scenarios for summertime urban-rural transport, wildfires, and wintertime stagnation conditions, and further evaluation of the framework using available observations and AIRPACT forecast simulations for extended periods for comparison.
Perform CLM simulations to provide underlying land surface conditions to the trajectory model and to perform simulations to understand the impact of future land use to biogenic volatile organic compounds and wildfire emissions.
Combine MACA 4 km gridded downscaled climate data with the WRF dynamically downscaled climate data to construct future decade meteorological conditions that can be used for HYSPLIST-MOSAIC.