Grantee Research Project Results
2006 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, 2006 through December 31, 2007
Project Amount: $436,687
RFA: Source Apportionment of Particulate Matter (2004) RFA Text | Recipients Lists
Research Category: Particulate Matter , Air Quality and Air Toxics , Air
Objective:
Evaluate multivariate and trajectory-based receptor models for regional source apportionment relevant to the USEPA Regional Haze Rule. Document models currently in use, including classical factor analysis, Positive Matrix Factorization (PMF), UNMIX, and Trajectory Mass Balance Regression (TMBR). Review previous model applications and critically evaluate the results. Apply receptor models to synthetic data generated with an air quality model for two eastern IMPROVE sites: Brigantine National Wildlife Refuge (BRIG), NJ and Great Smoky Mountains National Park (GRSM), TN. Document the approach required to reproduce the known regional contributions to sulfate and particulate matter (PM). Perform a “blind” test on a second simulated data set using this guidance. Apply models to real-word IMPROVE data at these sites. Finalize guidance for systematic application and validation of these models in future regional-scale applications.
Progress Summary:
Generation of Synthetic IMPROVE Data Sets
This project is a cooperative effort with Drs. Naresh Kumar and Eladio Knipping of EPRI. EPRI’s role is to provide synthetic IMPROVE data sets using the SMOKE/ CMAQ/MM5 modeling system. EPRI has subcontracted this task Sonoma Technology, Inc. (STI), which has sufficient computing resources to accomplish it in a timely manner. Figure 1 shows the modeling domain divided into 7 source regions representing Regional Planning Organizations (RPOs) and subsets thereof. There were 895 sources of criteria pollutants in the 2002 NEI inventory at source classification category level III. The source list was reduced to 205 sources that accounted for 95% of the total PM2.5, SO2, NOx, CO, VOC, and NH3 emitted in the domain. One hundred and six of these sources emit primary PM2.5. Eighty-four percent of the SO2 emissions come from coal, oil, and natural gas combustion. Sixty-eight percent of the primary PM2.5 emissions come from road dust, agricultural production, coal combustion, mobile emissions, and residential wood burning. The reduced list of PM2.5 source categories was matched to 43 chemically-speciated source profiles taken from the EPA’s Speciate and DRI’s PM source profile libraries. The profiles were used in the CMAQ model to produce multi-species IMPROVE-style concentration data. Forty-three additional variables (T1-43) were added to each profile, with unique values equal to the primary PM2.5 emitted by that source. This allows us to follow each source’s primary PM2.5 contribution to the receptor.
Figure 1. Study Domain for Application of Receptor Models to Synthetic Data. The BRIG and GRSM sites are indicated by the heavy black dots in southern NJ and eastern TN.
Individual regional contributions to sulfate and other PM species concentrations at the receptors were determined with a partial emissions in/emissions out method. The model was first run with all source regions in. It was then run seven times with 30% of the emissions of each region removed in sequence. This provides the “true” contributions of each region to the receptor sites. Model-predicted and actual IMPROVE sulfate concentrations for the summer of 2002 are compared in Figure 2, which shows good correlations at both sites.
Figure 2. Predicted Versus Measured 24-Hour Sulfate Contributions at BRIG and GRSM.
The average “true” regional contributions to sulfate at both sites are shown in Figure 3. At BRIG, the major sulfate source is its own northeastern region (MANE-VU). VISTAS (east) and the upper Midwest are also significant contributors. At GRSM, the principal contributor is its own region (VISTAS west). VISTAS east and VISTAS south are the next most significant contributors, followed by the upper Midwest. Note that CENRAP (north and south) is the smallest contributor (<3%) at both sites. This unique modeling activity and the resulting data sets meet the requirements of this project and will continue to provide valuable insights into large-scale atmospheric chemistry and transport processes in the eastern U.S.
Figure 3. “True” Regional Contributions to Sulfate at BRIG and GRSM.
Source Apportionment by PMF/UNMIX
Multivariate source apportionment was applied to 6-hour average concentrations (N=368) at each site. Preliminary results were obtained with all primary species and sulfate (24 species). UNMIX identified 6-7 factors based on R2 ≥0.98 and signal/noise ≥1.9 but no feasible solutions were found for either site except for a 6-factor solution at BRIG. PMF was run with 7 factors that accounted well for the ambient species concentrations (R2 generally > 0.95) at both sites. Tables 1 and 2 identify the associations between true regional source contributions to primary PM2.5 and the PMF factors.
The factors were related mainly to primary emissions from various individual source types and mixtures thereof. One factor at both sites was characterized solely as “sulfate”. This is the “secondary sulfate” factor commonly seen in previous PMF analyses. Figure 4 shows the PMF factor associations with the true regional contributions to sulfate at BRIG and GRSM. In both cases, the factors are not uniquely related to the regional contributions to sulfate. Rather, every region’s contribution to sulfate is associated with more than one PMF factor. However, the largest associations of the sulfate factors (F4 at BRIG and F3 at GRSM) are with the largest regional contributors to sulfate, i.e., R1 at BRIG and R3 at GRSM. These results illustrate the weakness of the PMF model for identifying unique contributions from large-scale source regions.
Trajectory Mass Balance Regression (TMBR)
HYSPLIT trajectories were calculated every 3 hours using EDAS wind fields starting at 100, 200, 500, 1000, 1500 and 3000 m above each site. The TMBR model was applied to daily sulfate concentrations regressed on the number of 1-hour trajectory endpoints over each region. The best and worst fit results for BRIG and GRSM are shown in Figure 5.
BRIG |
GRSM |
Figure 4. PMF Factor Associations with Regional Contributions to Ambient Sulfate (ASO4).
The TMBR results agree qualitatively with the true contributions and demonstrate the
Figure 5. Comparison of TMBR and “True” Regional Source Contributions to Sulfate at BRIG and GRSM for Different Trajectory Starting Elevations.
sensitivity of the results to the starting elevation of the trajectories, as is seen for the unrealistically large contribution from region 5 at BRIG for the 3000 m starting elevation.
Future Activities:
Future PMF and UNMIX analysis will focus on the effects of the choice of chemical species and their uncertainties. HYSPLIT trajectories will be calculated using MM5 wind fields and the TMBR results compared with those derived from the EDAS wind fields. The representativeness of the HYSPLIT trajectories will be evaluated with DRI’s Lagrangian random particle dispersion model. Generation of the winter data sets is under way will be completed shortly. The winter data will be used in a “blind” test, where the true source contributions will be unknown to the PMF/TMBR modellers. The results will be summarized in the context of previous developments and applications of these models.
Journal Articles:
No journal articles submitted with this report: View all 10 publications for this projectSupplemental Keywords:
RFA, Ecosystem Protection/Environmental Exposure & Risk, Scientific Discipline, Air, particulate matter, Air Quality, Environmental Chemistry, Monitoring/Modeling, Atmospheric Sciences, Environmental Engineering, Environmental Monitoring, atmospheric dispersion models, source receptor based methods, air sampling, chemical characteristics, airborne particulate matter, emissions monitoring, atmospheric chemistry, real-time monitoring, modeling studies, environmental measurement, area of influence analysis, particulate matter mass, air quality model, aerosol analyzers, source apportionment, chemical speciation sampling, particle size measurement, air quality models, analytical chemistry, atmospheric measurementsRelevant 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.