Grantee Research Project Results
2005 Progress Report: Evaluation of Regional Scale Receptor Modeling
EPA Grant Number: R832156Title: Evaluation of Regional Scale Receptor Modeling
Investigators: Lowenthal, Douglas H. , Chen, Lung-Wen Antony , Watson, John L. , Koracin, Darko
Institution: Desert Research Institute
EPA Project Officer: Chung, Serena
Project Period: January 1, 2005 through December 31, 2007 (Extended to December 31, 2009)
Project Period Covered by this Report: January 1, 2005 through December 31, 2006
Project Amount: $436,687
RFA: Source Apportionment of Particulate Matter (2004) RFA Text | Recipients Lists
Research Category: Air Quality and Air Toxics , Particulate Matter , Air
Objective:
The objectives of this research project are to: (1) evaluate multivariate and trajectory-based receptor models for regional source apportionment relevant to the U.S. Environmental Protection Agency (EPA) Regional Haze Rule; (2) document models currently in use, including classical factor analysis, Positive Matrix Factorization (PMF), Unmix, and Trajectory Mass Balance Regression (TMBR); (3) review previous model applications and critically evaluate the results; (4) apply receptor models to synthetic data generated with an air quality model for two Eastern Interagency Monitoring of Protected Visual Environments (IMPROVE) sites—Brigantine National Wildlife Refuge, New Jersey, and Great Smoky Mountains National Park, Tennessee; (5) document the approach required to reproduce the known regional contributions to sulfate and particulate matter (PM); (6) perform a blind test on a second simulated data set using this guidance; (7) apply models to real-world IMPROVE data at these sites; and (8) finalize guidance for systematic application and validation of these models in future regional-scale applications.
Progress Summary:
Model Development, Emissions Inventory, and Source Profiles
This project is a modeling study and as such requires considerable preanalysis activity. The Electric Power Research Institute (EPRI) is coordinating its model development effort with Ted Russell at Georgia Institute of Technology, who has done similar work with the Sparse Matrix Operator Kernel Emissions (known as SMOKE)/Community Multi-Scale Air Quality (CMAQ) model. EPRI has obtained from VISTAS Fifth Generation Mesoscale Model (MM5) output files (MMOUT files) that cover the period from December 17, 2001, to the end of 2002. The output files are for the second domain (181 x 190 grid points) with a horizontal resolution of 12 km and 34 vertical levels. These data were transferred to Darko Koracin, from the Desert Research Institute (DRI), who will use them in the trajectory-based receptor modeling analysis. EPRI collaborators, Eladio Knipping and Naresh Kumar, will produce synthetic IMPROVE data from the National Emissions Inventory (NEI) for PM2.5, SO2, NH3, NOx, and volatile organic compounds using the CMAQ model and MM5 output from 2002 for a domain encompassing the eastern half of the country. The domain in Figure 1 is divided into seven regions representing Regional Planning Organizations and subsets thereof.
Figure 1. Study Domain for Application of Receptor Models to Synthetic Data
Speciated source emissions profiles are required to generate synthetic IMPROVE data. The emissions inventory produced by EPRI was simplified to account for 95 percent of the total PM2.5, SO2, NOx, and NH3 emitted in the domain. The reduced list of source types was matched to source profiles taken from EPA’s Speciate and DRI’s PM source profile libraries. The ability of receptor models to distinguish regional contributions to PM and SO4 depends on differences in the regional emissions levels and the composite regional source profiles experienced at the receptors. Figure 2 presents annual regional PM2.5 and SO2 emissions derived from the NEI.
Figure 2. Annual PM2.5 and SO2 Emissions (Mt yr-1) in Seven Source Regions
There is very little regional variation in primary PM2.5 emissions, although SO2 emissions are considerably higher in Region 5 (Midwest). Although regional emissions may be similar, regional contributions to the receptors should vary temporally depending on meteorological variation. Regional source profiles were created by averaging individual source types (e.g., coal and oil combustion, mobile, manufacturing) profiles with IMPROVE chemical species on a PM2.5 emission-weighted basis. The seven regional source profiles are compared in Figure 3.
Figure 3. Regional Source Profiles
Figure 3 represents a cursory analysis of what regional source profiles for the Eastern United States might look like. The regional source profiles appear similar, although compositions for some species vary between regions by nearly an order of magnitude. Note that these profiles represent primary emissions. During transport to the receptors, production of secondary SO4, NO3, and organics will age the profiles, which may increase the differences in their chemical compositions.
Application of Receptor Models
One of the main goals is to compare receptor model source apportionment of synthetic and real world data. A problem with the real world IMPROVE data is that many source-specific chemical species, particularly elements determined by X-ray fluorescence spectroscopy, are not determined well in IMPROVE aerosol samples. PMF analysis was done for 340 samples from the Great Smoky National Park during summer (May-September 1996-2003). Figure 4 shows contributions of seven factors (sources) identified by PMF to PM2.5 (MF) and chemical species.
Agreement between the number of PMF sources and the number of regions in Figure 1 is fortuitous. F2, which accounts for most of the PM2.5 and SO4, appears to represent transported coal combustion emissions, whose (SO4/Se) ratio of 12,144 is consistent with long-range transport and highly aged aerosol. F7 is consistent with nearby coal emissions/fly ash with a SO4/Se ratio of only 2623. F6 represents biomass burning (K and organic carbon markers) and contributes about 20 percent of the total PM2.5. Although this apportionment appears qualitatively reasonable, it is not geographically specific. We intend to incorporate trajectory endpoints into this analysis to help locate these sources.
Figure 4. PMF Factor (Source) Contributions During Summer at Great Smoky National Park
Future Activities:
The major objective of the next reporting period is to implement the CMAQ-MM5 models for generating synthetic IMPROVE data and to begin subjecting these data to multivariate and trajectory-based receptor modeling. We will use MM5 outputs to compute forward and back trajectories. The latter will be used in trajectory-based receptor models. The uncertainties in standard hybrid single-particle Lagrangian Integrated Trajectory (known as HYSPLIT) trajectories will be quantified using the Lagrangian random particle dispersion model.
Journal Articles:
No journal articles submitted with this report: View all 10 publications for this projectSupplemental Keywords:
multivariate receptor modeling, air pollution, regional haze, IMPROVE network, air, ecosystem, environmental monitoring, particulate matter mass, atmospheric measurements,, RFA, Scientific Discipline, Air, Ecosystem Protection/Environmental Exposure & Risk, particulate matter, Air Quality, Environmental Chemistry, Monitoring/Modeling, Environmental Monitoring, Atmospheric Sciences, Environmental Engineering, atmospheric dispersion models, atmospheric measurements, model-based analysis, area of influence analysis, source receptor based methods, source apportionment, chemical characteristics, emissions monitoring, environmental measurement, airborne particulate matter, air quality models, air quality model, air sampling, particulate matter mass, analytical chemistry, modeling studies, real-time monitoring, aerosol analyzers, chemical speciation sampling, particle size measurementRelevant Websites:
Progress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.