2012 Progress Report: Creating Building Blocks for a More Dynamic Air Quality Management FrameworkEPA Grant Number: R835215
Title: Creating Building Blocks for a More Dynamic Air Quality Management Framework
Investigators: Demerjian, Kenneth L. , Mao, Huiting
Current Investigators: Demerjian, Kenneth L. , Beauharnois, Mark , Bielawa, Robert , Civerolo, Kevin , Hogrefe, Christian , Ku, Michael , Mao, Huiting , Yun, Jeongran
Institution: SUNY College of Environmental Science and Forestry
Current Institution: The State University of New York at Albany , New York State Department of Environmental Conservation , SUNY College of Environmental Science and Forestry , U.S. EPA
EPA Project Officer: Chung, Serena
Project Period: June 1, 2012 through May 31, 2015 (Extended to May 31, 2016)
Project Period Covered by this Report: June 1, 2012 through May 31,2013
Project Amount: $499,945
RFA: Dynamic Air Quality Management (2011) RFA Text | Recipients Lists
Research Category: Air Quality and Air Toxics , Air
The overall objectives of the proposed work are to: (1) develop a prototype system for providing real-time information on the contribution of short-term emission sources to air quality in relation to other source categories and the potential air quality benefits from episodic control measures; and (2) perform a comprehensive multi-pollutant air quality assessment that would examine trends in pollutant concentrations versus emission controls, co-pollutant effects, and develop possible indicators that may aid in improved tracking of the effect of emission controls.
Over the past year, we have been studying the relationship between power load demand and EGU emissions utilizing load data from the New York Independent Service Operators and US EPA Continuous Emission Monitoring (CEM) program. Combining these data with concurrent temperature observations, we have developed a statistical algorithm that estimates power load demand and subsequently EGU emissions based on temperature. The algorithm will be integrated into the emission module used in an existing air quality forecast modeling system to assess the impact of temperature adjusted EGU emissions on forecasted air quality. Preliminary findings indicate that temperature adjusted daily EGU NOx emissions can be significant, with daily peak load adjusted emissions as much as 25% higher than average EGU emission estimates.
In addition, we have started to evaluate the temperature dependent variables in the MOVES mobile source emissions model and will assess if incorporating day specific forecasted temperatures improves mobile source emissions estimates and subsequently air quality model predictions.
Preparations of the SMOKE emission components in support of the application of the Direct Decoupled Method (DDM) with the Community Multiscale Air Quality (CMAQ) model are near completion. The DDM CMAQ production runs for calculating sensitivities of predicted pollutant concentrations to emission perturbations reflecting a variety of hypothetical emission controls will start this month. Some delays have occurred in getting the DDM CMAQ operationally optimized for our Intel complier based multi-processor compute cluster, but with help from our EPA collaborators we have now demonstrated reproducible, highly efficient benchmarks for the DDM sensitivity test run. The DDM template for the sensitivity variables (i.e., 68 factors) to be considered has been prepared with the expectation that it will take several months of computation time to complete the matrix of sensitivity runs.
Progress in the second objective of this project is proceeding on schedule. We have compiled the emissions and air quality concentration data resources for selected monitoring sites in the Northeast to track the comparability of emissions and concentration trends. We have begun analyses of emission tracers and multi-pollutant relationships, including CO, NOx, NOy, SO2, CO vs. O3, NOy vs. O3, CO vs. NOx, Hg0 vs. CO, Hg0 vs. SO2 and SO2 vs. NOx as well as analyses of their annual trends and factors impacting inter-annual variability. The comparison of observed trends in multi-pollutant relationships with DDM sensitivities will be considered in the coming year, when DDM results become available. The results will then be analyzed by air quality planners collaborating on the project to identify avenues for making progress towards a more adaptive, dynamic air quality planning framework.
Integrate temperature/power load/ emissions algorithm into an emission module used in an existing air quality forecast modeling system. Carry out DDM sensitivity computations for the period May – September of 2007. Continue analyses of air quality measurements in the northeast.