Final Report: Emissions Inventory and Process Reconciliation Using Molecular Markers and Hybrid/Inverse Photochemical Modeling with Direct Sensitivity Analysis

EPA Grant Number: R831076
Title: Emissions Inventory and Process Reconciliation Using Molecular Markers and Hybrid/Inverse Photochemical Modeling with Direct Sensitivity Analysis
Investigators: Russell, Armistead G. , Odman, Mehmet Talat , Zheng, M.
Institution: Georgia Institute of Technology
EPA Project Officer: Chung, Serena
Project Period: November 1, 2003 through October 30, 2006 (Extended to September 30, 2008)
Project Amount: $449,899
RFA: Measurement, Modeling, and Analysis Methods for Airborne Carbonaceous Fine Particulate Matter (PM2.5) (2003) RFA Text |  Recipients Lists
Research Category: Air , Air Quality and Air Toxics , Particulate Matter

Objective:

The main objective of this research is to identify errors in the estimate of primary particulate organic and inorganic carbon emissions and provide improved information on the formation of secondary organic fine particulate matter (or PM2.5, particulate matter less than 2.5 μm in diameter). Building on the source-oriented modeling of PM conducted in the eastern United States, as well as the source apportionment using molecular markers, inverse air quality modeling is used to estimate biases in emissions estimates using three periods: July 2001, January 2002, August 1999, all periods for which EPA Supersites were conducting intensive measurements in the eastern United States. Our primary focus is on the eastern United States, utilizing the eastern Supersites ESP01 and ESP02 data, and speciated organic analysis of filters from the EPA speciation network. An updated, detailed PM emissions inventory is also being developed for Georgia. As for air quality models, CMAQ, with SAPRC99 mechanism, an advanced treatment of SOA formation and gas-particle partitioning, and direct sensitivity analysis is used. Chemical mass balance (CMB) models are used as receptor-based models. Inverse modeling is accomplished using ridge regression along with the sensitivities to individual source emissions. The primary objectives of this research are to:

Field measurements:

  • Identify and quantify primary and secondary organic molecular markers in airborne PM 2.5 using gas chromatography/mass spectrometry (GC/MS)
  • Investigate detailed composition of ambient PM2.5 and fine organic matter collected from roadside, urban and rural sites in the summer and the winter
  • Characterize chemical composition of PM2.5 in an aged prescribed burning plume or wildfire plumes
  • Measure organic tracers of SOA in the atmosphere and quantitatively determine SOA contribution in PM2.5 and fine OC

Performing and improving source apportionment of PM2.5:

  • Apportion source contributions of PM2.5 and fine OC using an air quality model and receptor models
  • Elucidate discrepancies of PM2.5 sources apportioned from the air quality model with those from the receptor model
  • Assess and improve the emissions inventory for primary PM2.5 in the Southeastern United States
  • Estimate the response of ambient PM2.5 to emissions changes by source and quantify the impact of controls (e.g., burning restrictions)
  • Further develop application of a method for assessing emissions estimates of pollutant precursors and their impact on air quality by reconciling bottom-up and top-down emissions estimates using inverse modeling.
  • Quantify the fraction of primary vs. secondary organic aerosol (SOA) and the fractions of SOA that are biogenic and anthropogenic.
  • Assess the information added by using molecular markers vs. only speciated PM2.5 in the inverse modeling
  • Update SOA treatment in CMAQ

Summary/Accomplishments (Outputs/Outcomes):

All Source apportionment of PM2.5 results calculated using three CMB models and CMAQ suggest that biomass burning and mobile vehicles are the largest contributor to the ambient PM2.5 levels in the Southeastern United States. Biomass burning contributes more than 50% primary emissions especially in winter Prescribed burning dominates biomass burning impacts, contributing about 55% and 80% of PM2.5 in January and March, respectively, followed by land clearing and agriculture field burning through out seasons, as well as increased contribution from residential wood combustion in fireplaces and woodstoves in January. Source apportionment of PM2.5 from CMB and an emission-based model (CMAQ) agree on major sources of PM2.5 with differences in detail numbers. The receptor model captured more of the temporal variation in source impacts at a specific receptor site but less spatial representativeness than the emission based model and vice versa. To compensate these model-specific pros and cons, an ensemble trained CMB approach is developed. Results show that ensemble-trained CMB approaches, using both summer profiles and winter profiles, effectively reduce days of zero impact from sources known to be present, suggesting improved results.

Effort to reduce biases in source impact analysis significantly improved model results.CMB source apportionment is strongly related to the source profiles, and application of different profiles could result in large discrepancies in source contributions. To better apportion local source contribution, profiles of two important primary PM2.5 sources, prescribed burning and onroad mobile emissions, were derived from the field measurements in the Southeast. Firstly, season-specific on-road mobile source primary PM2.5 and OC profiles were developed by using differences of PM2.5 and OC component concentrations between the highway and the nearby sites. Secondly, source profiles for aged prescribed fire and wildfire were also developed by using differences between non-smoke days and smoke days. These field measurements also suggest alteration of organic compounds during transport from burning sites to monitors. Then, impacts from aging process of smoke plume should be considered in source apportionment methodology using CMB model. To improve precision of observations, another source of biases in CMB results, the derivatization method of silylation was used for sample analysis, significantly increasing the capability to identify and quantify polar organic compounds from ambient samples and reduces their detection limits in GC/MS analysis.

Emission estimates are one of the major sources of biases in the emission-based models, thus potential biases in emission inventories were estimated by using inverse modeling with DDM-3D/PM (for EC and OC in 2004) and a regression analysis (for PM2.5 in July 2001 and January 2002). Results from regression analysis using trace metals suggest that the biases appear season and location (urban versus rural) dependent. Wood burning emission estimates in rural areas during the July are a factor of three low while are a factor of two low in urban and suburban areas. Current emission estimates of mobile source exhaust appear low by a factor of one to four, while soil dust emission estimates appear biased high by a factor of 2 to 30. Results from regression analysis using Organic molecular markers give similar direction of biases, with different magnitudes.

OC is often simulated low in CMAQ due to missing mechanism and/or missing precursors of SOA. Here, SOA module in CMAQ version 4.5 with SAPRC99 is updated by 1) modifying SOA formation from monoterpenes using species-specific parameters and emissions, 2) adding additional SOA from isoprene and sesquiterpene, and 3) simulating aged aerosol by photochemical oxidation. Aged aerosol contributes more than half of SOA, both in July 2001 and January 2002. Adding aged aerosol and isoprene SOA decreases hourly changes, but diurnal changes in simulated OC are still larger than measured values.

Tracers for biogenic SOA were measured from ambient samples to assess SOA formation in the summer and the winter. Significant seasonal differences are observed for 2-methyltetrols, cispinonic acid and pinic acid, organic tracers of biogenic secondary organic aerosols (SOA). 2-methyltetrols are measured much higher in the summer while the levels in the winter are near or below the detection limit. However, cis-pinonic acid and pinic acid are highest in the winter. Moreover, little correlation is found between 2-methyltetrols with cis-pinonic or pinic acid, implying different origins and formation paths for the two kinds of biogenic SOA tracers. Cispinonic acid and pinic acid exhibit very similar patterns in both seasons, suggesting the same sources, formation pathways, and properties in the atmosphere. Large quantities of biogenic volatile organic compounds (VOCs) and semi-VOCs were measured both as products of combustion and unburned vegetation heated by the fire. Higher leaf temperature during the fires can stimulate biogenic VOC and SVOC emissions, which enhanced formation of secondary organic aerosols (SOA) in the atmosphere, which is supported by elevated ambient concentrations of secondary organic tracers.

Conclusions:

In this project, two types of air quality models, receptor based and emission based models were utilized for source apportionment of PM2.5. Each model has biases or errors in the source impact results, thus various efforts to reduce the uncertainties were made: for receptor models, uncertainty analysis in source profiles, optimization of source profiles, and updating source profiles with measurements. For CMAQ, biases in emission estimates were reduced by sensitivity analysis using DDM-3D/PM, a brute force method, or a ridge regression analysis. Comparing source impact of PM2.5 from CMAQ and different types of CMB models quantifies biases in emission estimates and source profiles. Forest fire, mobile sources and secondary organic aerosol are addressed as the major sources of PM2.5 in the Southeastern United States, thus extensive studies on these sources were performed using both receptor models and CMAQ. Secondary organic aerosol module in CMAQ was updated with new precursors and parameters, significantly improving OC simulations in the United States. All these efforts successfully improved source apportionment of PM2.5 using air quality models, which, in turn, will benefit health studies.


Journal Articles on this Report : 40 Displayed | Download in RIS Format

Other project views: All 75 publications 40 publications in selected types All 40 journal articles
Type Citation Project Document Sources
Journal Article Ding X, Zheng M, Yu L, Zhang X, Weber RJ, Yan B, Russell AG, Edgerton ES, Wang X. Spatial and seasonal trends in biogenic secondary organic aerosol tracers and water-soluble organic carbon in the southeastern United States. Environmental Science & Technology 2008;42(14):5171-5176. R831076 (Final)
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  • Journal Article Habermacher FD, Napelenok SL, Akhtar F, Hu Y, Russell AG. Area of influence (AOI) development: fast generation of receptor-oriented sensitivity fields for use in regional air quality modeling. Environmental Science & Technology 2007;41(11):3997-4003. R831076 (Final)
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  • Journal Article Hu Y, Odman MT, Russell AG. Mass conservation in the Community Multiscale Air Quality model. Atmospheric Environment 2006;40(7):1199-1204. R831076 (Final)
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  • Journal Article Hu Y, Odman MT, Chang ME, Jackson W, Lee S, Edgerton ES, Baumann K, Russell AG. Simulation of air quality impacts from prescribed fires on an urban area. Environmental Science & Technology 2008;42(10):3676-3682. R831076 (Final)
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  • Journal Article Hu Y, Napelenok SL, Odman MT, Russell AG. Sensitivity of inverse estimation of 2004 elemental carbon emissions inventory in the United States to the choice of observational networks. Geophysical Research Letters 2009;36(15):L15806 (5 pp.) R831076 (Final)
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  • Journal Article Hu Y, Odman MT, Russell AG. Top-down analysis of the elemental carbon emissions inventory in the United States by inverse modeling using Community Multiscale Air Quality model with decoupled direct method (CMAQ-DDM). Journal of Geophysical Research 2009;114(D24):D24302 (12 pp.). R831076 (Final)
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  • Journal Article Kaynak B, Hu Y, Martin RV, Russell AG, Choi Y, Wang Y. The effect of lightning NOx production on surface ozone in the continental United States. Atmospheric Chemistry and Physics 2008;8(17):5151-5159. R831076 (Final)
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  • Journal Article Lee D, Balachandran S, Pachon J, Shankaran R, Lee S, Mulholland JA, Russell AG. Ensemble-trained PM2.5 source apportionment approach for health studies. Environmental Science & Technology 2009;43(18):7023-7031. R831076 (Final)
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    R833866 (2009)
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  • Journal Article Lee S, Baumann K, Schauer JJ, Sheesley RJ, Naeher LP, Meinardi S, Blake DR, Edgerton ES, Russell AG, Clements M. Gaseous and particulate emissions from prescribed burning in Georgia. Environmental Science & Technology 2005;39(23):9049-9056. R831076 (Final)
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  • Journal Article Lee S, Russell AG. Estimating uncertainties and uncertainty contributors of CMB PM2.5 source apportionment results. Atmospheric Environment 2007;41(40):9616-9624. R831076 (Final)
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  • Journal Article Lee S, Russell AG, Baumann K. Source apportionment of fine particulate matter in the southeastern United States. Journal of the Air & Waste Management Association 2007;57(9):1123-1135. R831076 (Final)
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  • Journal Article Lee S, Kim HK, Yan B, Cobb CE, Hennigan C, Nichols S, Chamber M, Edgerton ES, Jansen JJ, Hu Y, Zheng M, Weber RJ, Russell AG. Diagnosis of aged prescribed burning plumes impacting an urban area. Environmental Science & Technology 2008;42(5):1438-1444. R831076 (Final)
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  • Journal Article Liao K-J, Tagaris E, Manomaiphiboon K, Napelenok SL, Woo J-H, He S, Amar P, Russell AG. Sensitivities of ozone and fine particulate matter formation to emissions under the impact of potential future climate change. Environmental Science & Technology 2007;41(24):8355-8361. R831076 (Final)
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  • Journal Article Liao K-J, Tagaris E, Napelenok SL, Manomaiphiboon K, Woo J-H, Amar P, He S, Russell AG. Current and future linked responses of ozone and PM2.5 to emission controls. Environmental Science & Technology 2008;42(13):4670-4675. R831076 (Final)
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  • Journal Article Marmur A, Unal A, Mulholland JA, Russell AG. Optimization-based source apportionment of PM2.5 incorporating gas-to-particle ratios. Environmental Science & Technology 2005;39(9):3245-3254. R831076 (2004)
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  • Journal Article Marmur A, Park S-K, Mulholland JA, Tolbert PE, Russell AG. Source apportionment of PM2.5 in the southeastern United States using receptor and emissions-based models:conceptual differences and implications for time-series health studies. Atmospheric Environment 2006;40(14):2533-2551. R831076 (2007)
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  • Journal Article Marmur A, Mulholland JA, Russell AG. Optimized variable source-profile approach for source apportionment. Atmospheric Environment 2007;41(3):493-505. R831076 (Final)
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  • Journal Article Napelenok SL, Cohan DS, Hu Y, Russell AG. Decoupled direct 3D sensitivity analysis for particulate matter (DDM-3D/PM). Atmospheric Environment 2006;40(32):6112-6121. R831076 (2006)
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  • Journal Article Napelenok SL, Habermacher FD, Akhtar F, Hu Y, Russell AG. Area of influence (AOI) sensitivity analysis: application to Atlanta, Georgia. Atmospheric Environment 2007;41(27):5605-5617. R831076 (Final)
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  • Journal Article Park SK, O'Neill MS, Vokonas PS, Sparrow D, Schwartz J. Effects of air pollution on heart rate variability: the VA Normative Aging Study. Environmental Health Perspectives 2005;113(3):304-309. R831076 (Final)
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  • Journal Article Park SK, O'Neill MS, Wright RO, Hu H, Vokonas PS, Sparrow D, Suh H, Schwartz J. HFE genotype, particulate air pollution, and heart rate variability: a gene-environment interaction. Circulation 2006;114(25):2798-2805. R831076 (Final)
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  • Journal Article Park SK, O'Neill MS, Stunder BJ, Vokonas PS, Sparrow D, Koutrakis P, Schwartz J. Source location of air pollution and cardiac autonomic function: trajectory cluster analysis for exposure assessment. Journal of Exposure Science & Environmental Epidemiology 2007;17(5):488-497. R831076 (Final)
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  • Journal Article Park S-K, Russell AG. Regional adjustment of emission strengths via four dimensional data assimilation. Asia-Pacific Journal of Atmospheric Sciences 2013;49(3):361-374. R831076 (Final)
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  • Journal Article Park S-K, Marmur A, Kim SB, Tian D, Hu Y, McMurry PH, Russell AG. Evaluation of fine particle number concentrations in CMAQ. Aerosol Science and Technology 2006;40(11):985-996. R831076 (2005)
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  • Journal Article Park S-K, Cobb CE, Wade K, Mulholland J, Hu Y, Russell AG. Uncertainty in air quality model evaluation for particulate matter due to spatial variations in pollutant concentrations. Atmospheric Environment 2006;40(Suppl 2):563-573. R831076 (2006)
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  • Journal Article Park S-K, Marmur A, Russell AG. Environmental risk assessment: comparison of receptor and air quality models for source apportionment. Human and Ecological Risk Assessment 2013;19(5):1385-1403. R831076 (Final)
    R834799 (2015)
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  • Journal Article Sarnat JA, Marmur A, Klein M, Kim E, Russell AG, Sarnat SE, Mulholland JA, Hopke PK, Tolbert PE. Fine particle sources and cardiorespiratory morbidity: an application of chemical mass balance and factor analytical source-apportionment methods. Environmental Health Perspectives 2008;116(4):459-466. R831076 (Final)
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  • Journal Article Sarnat SE, Klein M, Sarnat JA, Flanders WD, Waller LA, Mulholland JA, Russell AG, Tolbert PE. An examination of exposure measurement error from air pollutant spatial variability in time-series studies. Journal of Exposure Science and Environmental Epidemiology 2010;20(2):135-146. R831076 (Final)
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  • Journal Article Tagaris E, Manomaiphiboon K, Liao K-J, Leung LR, Woo J-H, He S, Amar P, Russell AG.  Impacts of global climate change and emissions on regional ozone and fine particulate matter concentrations over the United States.  Journal of Geophysical Research 2007;112(D14):D14312 (11 pp.). R831076 (Final)
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  • Journal Article Tagaris E, Liao K-J, Manomaiphiboon K, Woo J-H, He S, Amar P, Russell AG. Impacts of future climate change and emissions reductions on nitrogen and sulfur deposition over the United States. Geophysical Research Letters 2008;35(8):L08811 (6 pp.). R831076 (Final)
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  • Journal Article Tagaris E, Liao K-J, Manomaiphiboon K, He S, Woo J-H, Amar P, Russell AG. The role of climate and emission changes in future air quality over southern Canada and northern Mexico. Atmospheric Chemistry and Physics 2008;8(14):3973-3983. R831076 (Final)
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  • Journal Article Tagaris E, Liao K-J, DeLucia AJ, Deck L, Amar P, Russell AG. Potential impact of climate change on air pollution-related human health effects. Environmental Science & Technology 2009;43(13):4979-4988. R831076 (Final)
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  • Journal Article Tagaris E, Liao K-J, DeLucia AJ, Deck L, Amar P, Russell AG. Sensitivity of air pollution-induced premature mortality to precursor emissions under the influence of climate change. International Journal of Environmental Research and Public Health 2010;7(5):2222-2237. R831076 (Final)
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  • Journal Article Tian D, Wang Y, Bergin M, Hu Y, Liu Y, Russell AG. Air quality impacts from prescribed forest fires under different management practices. Environmental Science & Technology 2008;42(8):2767-2772. R831076 (Final)
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  • Journal Article Tian D, Hu Y, Wang Y, Boylan JW, Zheng M, Russell AG. Assessment of biomass burning emissions and their impacts on urban and regional PM2.5:a Georgia case study. Environmental Science & Technology 2009;43(2):299-305. R831076 (Final)
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  • Journal Article Weber RJ, Sullivan AP, Peltier RE, Russell A, Yan B, Zheng M, de Gouw J, Warneke C, Brock C, Holloway JS, Atlas EL, Edgerton E. A study of secondary organic aerosol formation in the anthropogenic-influenced southeastern United States. Journal of Geophysical Research 2007;112(D13):D13302 (13 pp.). R831076 (2007)
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  • Journal Article Woo JH, He S, Tagaris E, Liao K-J, Manomaiphiboon K, Amar P, Russell AG. Development of North American emission inventories for air quality modeling under climate change. Journal of the Air & Waste Management Association 2008;58(11):1483-1494. R831076 (Final)
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  • Journal Article Yan B, Zheng M, Hu YT, Lee S, Kim HK, Russell AG. Organic composition of carbonaceous aerosols in an aged prescribed fire plume. Atmospheric Chemistry and Physics 2008;8(21):6381-6394. R831076 (2007)
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  • Journal Article Yan B, Zheng M, Hu Y, Ding X, Sullivan AP, Weber RJ, Baek J, Edgerton ES, Russell AG. Roadside, urban, and rural comparison of primary and secondary organic molecular markers in ambient PM2.5. Environmental Science & Technology 2009;43(12):4287-4293. R831076 (Final)
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  • Journal Article Zeng T, Wang Y, Yoshida Y, Tian D, Russell AG, Barnard WR. Impacts of prescribed fires on air quality over the Southeastern United States in spring based on modeling and ground/satellite measurements. Environmental Science & Technology 2008;42(22):8401-8406. R831076 (Final)
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  • Supplemental Keywords:

    Fine particulate matter, organic molecular marker, secondary organic aerosol, mobile source emission, aged prescribed fire, wildfire, source profiles, source apportionment, uncertainty analysis, chemical mass balance model, CMAQ, RFA, Scientific Discipline, Health, Air, Ecosystem Protection/Environmental Exposure & Risk, Air Quality, particulate matter, air toxics, Environmental Chemistry, Air Pollution Effects, climate change, Monitoring/Modeling, Risk Assessments, Environmental Monitoring, Engineering, Chemistry, & Physics, Environmental Engineering, Atmosphere, carbon aerosols, air quality modeling, particle size, atmospheric particulate matter, health effects, aerosol particles, atmospheric particles, mass spectrometry, human health effects, ambient air monitoring, air modeling, air quality models, air sampling, gas chromatography, thermal desorption, carbon particles, air quality model, emissions, molecular markers, direct sensitivity analysis, particulate matter mass, human exposure, ambient particle health effects, particle phase molecular markers, photchemical modeling, aersol particles, particle dispersion, aerosol analyzers

    Relevant Websites:

    http://people.ce.gatech.edu/~trussell/ Exit

    Progress and Final Reports:

    Original Abstract
  • 2004 Progress Report
  • 2005 Progress Report
  • 2006 Progress Report
  • 2007 Progress Report