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Air Quality, Atmosphere & Health

, Volume 10, Issue 6, pp 695–711 | Cite as

Accountability assessment of regulatory impacts on ozone and PM2.5 concentrations using statistical and deterministic pollutant sensitivities

  • Lucas RF HennemanEmail author
  • Howard H Chang
  • Kuo-Jen Liao
  • David Lavoué
  • James A Mulholland
  • Armistead G Russell
Article

Abstract

Since the 1990 Clean Air Act Amendments, the USA has seen dramatic decreases in air pollutant emissions from a wide variety of source sectors, which have led to changes in pollutant concentrations: both up and down. Multiple stakeholders, including policy-makers, industry, and public health professionals, seek to quantify the benefits of regulations on air pollution and public health, a major focus of air pollution accountability research. Two methods, one empirical, the other based on a chemical transport model (CTM), are used to calculate the sensitivities of ozone (O3) and particulate matter with diameters less than 2.5 μ m (PM2.5) to electricity-generating unit (EGU) and mobile source emissions. Both methods are applied to determine impacts of controls on daily concentrations (which are important in assessing acute health responses to air pollution), accounting for nonlinear, meteorologically, and emission-dependent responses of pollutant concentrations. The statistical method separates contributions of nearby EGU, regional EGU, and mobile source emissions on ambient city-center concentrations. Counterfactual emissions, an estimate of emissions under a scenario where no new controls were implemented on local EGU sources after 1995, regional EGUs after 1997, and mobile sources after 1993, are combined with these sensitivities to estimate counterfactual concentrations that represent what daily air quality in Atlanta, GA would have been had controls not been implemented and other emissions-reducing actions not been taken. Regulatory programs are linked with reduced peak summertime O3, but have had little effect on annual median concentrations at the city-center monitoring site, and led to increases in pollutant levels under less photochemically-active conditions. The empirical method and the CTM method found similar relationships between ozone concentrations and ozone sensitivity to anthropogenic emissions. Compared to the counterfactual between 2010 and 2013, the number of days on which O3 (PM2.5) concentrations exceeded 60 p p b (12.0 μ g m −3) was reduced from 396 to 200 (1391 to 222). In 2013, average daily ambient O3 and PM2.5 concentrations were reduced by 1.0 p p b (2 %) and 9.9 μ g m −3 (48 %), respectively, and fourth highest maximum daily average 8-h O3 was reduced by 14 p p b. Comparison of model-derived sensitivities to those derived using empirical methods show coherence, but some important differences, such as the O3 concentration where the sensitivity to NOx emissions changes sign.

Keywords

Air pollution modeling Sensitivity Accountability Statistical modeling 

Notes

Acknowledgments

This material is based upon work supported by Health Effects Institute and the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1148903. Detailed data and guidance was provided for MOVES modeling by Gil Grodzinsky and Jon Morton of the Georgia Environmental Protection Division Air Protection Branch. We thank Charles Huling, formerly of Southern Company, for his input.

Supplementary material

11869_2017_463_MOESM1_ESM.pdf (7.9 mb)
(PDF 7.85 MB)

References

  1. NAP (2004) Air Quality Management in the United States. The National Academies Press, Washington, DC, http://www.nap.edu/catalog/10728/air-quality-management-in-the-united-states
  2. Anderson DC, Loughner CP, Diskin G, Weinheimer A, Canty TP, Salawitch RJ, Worden HM, Fried A, Mikoviny T, Wisthaler A, Dickerson RR (2014) Measured and modeled CO and NOy in DISCOVER-AQ: An evaluation of emissions and chemistry over the eastern US. Atmos Environ 96:78–87. doi: 10.1016/j.atmosenv.2014.07.004, http://linkinghub.elsevier.com/retrieve/pii/S1352231014005251 CrossRefGoogle Scholar
  3. Appel KW, Gilliland AB, Sarwar G, Gilliam RC (2007) Evaluation of the Community Multiscale Air Quality (CMAQ) model version 4.5: Sensitivities impacting model performance. Atmos Environ 41(40):9603–9615. doi: 10.1016/j.atmosenv.2007.08.044, http://linkinghub.elsevier.com/retrieve/pii/S1352231007007534 CrossRefGoogle Scholar
  4. Blanchard C, Hidy G, Tanenbaum S (2010) NMOC, ozone, and organic aerosol in the southeastern United States, 19992007: 2. Ozone trends and sensitivity to NMOC emissions in Atlanta, Georgia. Atmos Environ 44 (38):4840–4849. doi: 10.1016/j.atmosenv.2010.07.030, http://linkinghub.elsevier.com/retrieve/pii/S1352231010005996 CrossRefGoogle Scholar
  5. Blanchard CL, Hidy GM (2005) Effects of SO2 and NOx Emission Reductions on PM2.5 Mass Concentrations in the Southeastern United States. J Air Waste Manag Assoc 55 (3):265–272. doi: 10.1080/10473289.2005.10464624 CrossRefGoogle Scholar
  6. Bloomfield P, Royle JA, Steinberg LJ, Yang Q (1996) Accounting for meteorological effects in measuring urban ozone levels and trends. Atmos Environ 30(17):3067–3077. doi: 10.1016/1352-2310(95)00347-9, http://linkinghub.elsevier.com/retrieve/pii/1352231095003479 CrossRefGoogle Scholar
  7. Brock CA, Washenfelder RA, Trainer M, Ryerson TB, Wilson JC, Reeves JM, Huey LG, Holloway JS, Parrish DD, Huebler G, Fehsenfeld FC (2002) Particle growth in the plumes of coal-fired power plants. J Geophys Res: Atmos 107(D12):AAC 9–1–AAC 9–14. doi: 10.1029/2001JD001062 CrossRefGoogle Scholar
  8. Byun DW, Schere K (2006) Review of the governing equations, computational algorithms, and other components of the Models-3 Community Multscale Air Quality (CMAQ) modeling system. Appl Mech Rev pp 51–77Google Scholar
  9. Byun DW, Kim ST, Kim SB (2007) Evaluation of air quality models for the simulation of a high ozone episode in the Houston metropolitan area. Atmos Environ 41(4):837–853. doi: 10.1016/j.atmosenv.2006.08.038, http://linkinghub.elsevier.com/retrieve/pii/S1352231006008752 CrossRefGoogle Scholar
  10. Camalier L, Cox W, Dolwick P (2007) The effects of meteorology on ozone in urban areas and their use in assessing ozone trends. Atmos Environ 41(33):7127–7137. doi: 10.1016/j.atmosenv.2007.04.061, http://linkinghub.elsevier.com/retrieve/pii/S1352231007004165 CrossRefGoogle Scholar
  11. Chang HH, Hao H, Sarnat SE (2014) A statistical modeling framework for projecting future ambient ozone and its health impact due to climate change. Atmos Environ 89:290–297. doi: 10.1016/j.atmosenv.2014.02.037, http://linkinghub.elsevier.com/retrieve/pii/S1352231014001332 CrossRefGoogle Scholar
  12. Cohan DS, Hakami A, Hu Y, Russell AG (2005) Nonlinear response of ozone to emissions: source apportionment and sensitivity analysis. Environ Sci Technol 39(17):6739–48. http://www.ncbi.nlm.nih.gov/pubmed/16190234 CrossRefGoogle Scholar
  13. Davies T, Kelly P (1992) Surface Ozone Concentrations in Europe’ Links With the Regional-Scale Atmospheric Circulation. J Geophys Res 97:9819–9832. http://onlinelibrary.wiley.com/doi/10.1029/92JD00419/full CrossRefGoogle Scholar
  14. Digar A, Cohan DS (2010) Efficient characterization of pollutant-emission response under parametric uncertainty. Environ Sci Technol 44(17):6724–30. doi: 10.1021/es903743t, http://www.ncbi.nlm.nih.gov/pubmed/20701284 CrossRefGoogle Scholar
  15. Dunker AM (1981) Efficient calculation of sensitivity coefficients for complex atmospheric models. Atmospheric Environment (1967) (1), http://www.sciencedirect.com/science/article/pii/000469818190305X
  16. Dunker AM (1984) The decoupled direct method for calculating sensitivity coefficients in chemical kinetics. J Chem Phys 81(5):2385. doi: 10.1063/1.447938 CrossRefGoogle Scholar
  17. EPD (2009) Georgia’s State Implementation Plan for the Atlanta 8-Hour Ozone Nonattainment AreaGoogle Scholar
  18. EPD (2012) Georgia’s Redesignation Request and Maintenance Plan for the Atlanta Nonattainment Area for the 1997 PM2.5 NAAQSGoogle Scholar
  19. Foley KM, Dolwick P, Hogrefe C, Simon H, Timin B, Possiel N (2015a) Dynamic evaluation of CMAQ part II: Evaluation of relative response factor metrics for ozone attainment demonstrations. Atmos Environ 103:188–195. doi: 10.1016/j.atmosenv.2014.12.039, http://linkinghub.elsevier.com/retrieve/pii/S135223101400987X
  20. Foley KM, Hogrefe C, Pouliot G, Possiel N, Roselle SJ, Simon H, Timin B (2015b) Dynamic evaluation of CMAQ part I: Separating the effects of changing emissions and changing meteorology on ozone levels between 2002 and 2005 in the eastern US. Atmos Environ 103(x):247–255. doi: 10.1016/j.atmosenv.2014.12.038, http://linkinghub.elsevier.com/retrieve/pii/S1352231014009868
  21. Garcia VC, Gego E, Lin S, Pantea C, Rappazzo K, Wootten A, Rao ST (2011) An evaluation of transported pollution and respiratory-related hospital admissions in the state of New York. Atmos Pollut Res 2 (1):9–15. doi: 10.5094/apr.2011.002 CrossRefGoogle Scholar
  22. Gégo E, Porter PS, Gilliland A, Rao ST (2007) Observation-Based Assessment of the Impact of Nitrogen Oxides Emissions Reductions on Ozone Air Quality over the Eastern United States. J Appl Meteorol Climatol 46 (7):994–1008. doi: 10.1175/JAM2523.1 CrossRefGoogle Scholar
  23. Georgia EPD (2013) Rules for Air Quality Control. Tech. rep., Georgia EPD, http://www.georgiaair.org/airpermit/html/planningsupport/naa.htm
  24. Georgia Power (2007) 2007 Georgia Power Integrated Resources PlanGoogle Scholar
  25. Gilliland AB, Hogrefe C, Pinder RW, Godowitch JM, Foley KL, Rao S (2008) Dynamic evaluation of regional air quality models: Assessing changes in O3 stemming from changes in emissions and meteorology. Atmos Environ 42(20):5110–5123. doi: 10.1016/j.atmosenv.2008.02.018, http://linkinghub.elsevier.com/retrieve/pii/S1352231008001374 CrossRefGoogle Scholar
  26. Goldberg DL, Vinciguerra TP, Anderson DC, Hembeck L, Canty TP, Ehrman SH, Martins DK, Stauffer RM, Thompson AM, Salawitch RJ, Dickerson RR (2016) CAMx Ozone Source Attribution in the Eastern United States using Guidance from Observations during DISCOVER-AQ Maryland. Geophys Res Lett (43):2249–2258. doi: 10.1002/2015GL067332
  27. Grell G, Dudhia J, Stauffer D (1994) A description of the fifth generation Penn State/NCAR mesoscale model (MM5). NCAR Technical Note, NCAR/TN-398+STR. National Center for Atmospheric Research, Boulder, ColoradoGoogle Scholar
  28. Hakami A, Odman M, Russell A (2003) Environmental science and technology. Atmos Environ 37:2442–2452Google Scholar
  29. Hakami A, Odman T, Russell AG (2004) Nonlinearity in atmospheric response: A direct sensitivity analysis approach. J Geophys Res 109(D15):D15,303. doi: 10.1029/2003JD004502 CrossRefGoogle Scholar
  30. Hanna SR, Lu Z, Frey HC, Wheeler N, Vukovich J, Arunachalam S, Fernau M, Hansen DA (2001) Uncertainties in predicted ozone concentrations due to input uncertainties for the UAM-V photochemical grid model applied to the July 1995 OTAG domain. Atmos Environ 35(5):891–903. doi: 10.1016/S1352-2310(00)00367-8 CrossRefGoogle Scholar
  31. Hansen DA, Edgerton ES, Hartsell BE, Jansen JJ, Kandasamy N, Hidy GM, Blanchard CL (2003) The southeastern aerosol research and characterization study: Part 1Overview. J Air Waste Manag Assoc 53(12):1460–1471. doi: 10.1080/10473289.2003.10466318 CrossRefGoogle Scholar
  32. Harrington W, Morgenstern R, Shih JS, Bell ML (2012) Did the Clean Air Act Amendments Of 1990 really improve air quality? Air Qual, Atmos Health 5(4):353–367. doi: 10.1007/s11869-012-0176-5 CrossRefGoogle Scholar
  33. HEI (2003) Assessing the health impact of air quality regulations: Concepts and methods for accountability research. Technical report, The Health Effects InstituteGoogle Scholar
  34. Henneman L, Holmes H, Russell A, Mullholand J (2015) Meteorological Detrending of Primary and Secondary Pollutant Concentrations: Method Application and Evaluation Using Long-Term (2000-2012) Detailed Data. Atmospheric EnvironmentGoogle Scholar
  35. Houyoux M, Vukovich J, Coats C, Wheeler N, Kasibhatla P (2000) Emission inventory development and processing for the Seasonal Model for Regional Air Quality (SMRAQ) project. J Geophys Res: Atmos 105:9079–9090CrossRefGoogle Scholar
  36. Hu Y, Balachandran S, Pachon JE, Baek J, Ivey C, Holmes H, Odman MT, Mulholland JA, Russell AG (2014) Fine particulate matter source apportionment using a hybrid chemical transport and receptor model approach. Atmos Chem Phys 14(11):5415–5431. doi: 10.5194/acp-14-5415-2014 CrossRefGoogle Scholar
  37. Kuebler J, Van den Bergh H, Russell AG (2001) Long-term trends of primary and secondary pollutant concentrations in Switzerland and their response to emission controls and economic changes. Atmos Environ 35 (8):1351–1363. doi: 10.1016/S1352-2310(00)00401-5
  38. Liao KJ, Tagaris E, Napelenok SL, Manomaiphiboon K, Woo JH, Amar P, He S, Russell AG (2008) Current and future linked responses of ozone and PM2.5 to emission controls. Environ Sci Technol 42 (13):4670–5. http://www.ncbi.nlm.nih.gov/pubmed/18677989 http://www.ncbi.nlm.nih.gov/pubmed/18677989 CrossRefGoogle Scholar
  39. Marais EA, Jacob DJ, Jimenez JL, Campuzano-Jost P, Day DA, Hu W, Krechmer J, Zhu L, Kim PS, Miller CC, Fisher JA, Travis K, Yu K, Hanisco TF, Wolfe GM, Arkinson HL, Pye HOT, Froyd KD, Liao J, McNeill VF (2016) Aqueous-phase mechanism for secondary organic aerosol formation from isoprene: Application to the southeast United States and co-benefit of SO2 emission controls. Atmos Chem Phys 16(3):1603–1618. doi: 10.5194/acp-16-1603-2016 CrossRefGoogle Scholar
  40. Mozurkewich M (1993) The dissociation constant of ammonium nitrate and its dependence on temperature, relative humidity and particle size. Atmos Environ Part A Gen Top 27(2):261–270. doi: 10.1016/0960-1686(93)90356-4 CrossRefGoogle Scholar
  41. Muller N, Tong D, Mendelsohn RO (2009) Regulating NOx and SO2 Emissions in Atlanta. BE J Econ Anal Policy: Contrib Econ Anal Policy 9(2): . http://ideas.repec.org/a/bpj/bejeap/v9y2009i2n3.html
  42. Napelenok S, Cohan D, Hu Y, Russell A (2006) Decoupled direct 3D sensitivity analysis for particulate matter (DDM-3D/PM). Atmos Environ 40:6112–6121CrossRefGoogle Scholar
  43. Pope CA, Ezzati M, Dockery DW (2009) Fine-particulate air pollution and life expectancy in the United States. Engl J Med 360(4):376–86. doi: 10.1056/NEJMsa0805646, http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3382057&tool=pmcentrez&rendertype=abstract CrossRefGoogle Scholar
  44. Porter PS, Rao ST, Hogrefe C, Gego E, Mathur R (2015) Methods for reducing biases and errors in regional photochemical model outputs for use in emission reduction and exposure assessments. Atmos Environ 112:178–188. doi: 10.1016/j.atmosenv.2015.04.039, http://linkinghub.elsevier.com/retrieve/pii/S1352231015300455 CrossRefGoogle Scholar
  45. R Core Team (2015) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org/
  46. Rich DQ, Kipen HM, Huang W, Wang G, Wang Y, Zhu P, Ohman-Strickland P, Hu M, Philipp C, Diehl SR, Lu SE, Tong J, Gong J, Thomas D, Zhu T, Zhang JJ (2012) Association between changes in air pollution levels during the Beijing Olympics and biomarkers of inflammation and thrombosis in healthy young adult. J Am Med Assoc 307(19):2068–2078. doi: 10.1001/jama.2012.3488, NIHMS150003CrossRefGoogle Scholar
  47. Seaman N (2000) Fast, direct sensitivity analysis of multidimensional photochemical models. Atmos Environ 34:2231–2259CrossRefGoogle Scholar
  48. Seinfeld J, Pandis S (2006) Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, 2nd edn. Wiley, New YorkGoogle Scholar
  49. Simon H, Baker KR, Phillips S (2012) Compilation and interpretation of photochemical model performance statistics published between 2006 and 2012. Atmos Environ 61:124–139. doi: 10.1016/j.atmosenv.2012.07.012 CrossRefGoogle Scholar
  50. Souri AH, Choi Y, Jeon W, Li X, Pan S, Diao L, Westenbarger DA (2016) Constraining NOx emissions using satellite NO2 measurements during 2013 DISCOVER-AQ Texas campaign. Atmos Environ 131 (2):371–381. doi: 10.1016/j.atmosenv.2016.02.020 CrossRefGoogle Scholar
  51. Tong DQ, Muller NZ, Mauzerall DL, Mendelsohn RO (2006) Policy Analysis Integrated Assessment of the Spatial Variability of Ozone Impacts from Emissions of Nitrogen Oxides. Environ Sci Technol 40(5):1395–1400CrossRefGoogle Scholar
  52. Travis KR, Jacob DJ, Fisher JA, Kim PS, Marais EA, Zhu L, Yu K, Miller CC, Yantosca RM, Sulprizio MP, Thompson AM, Wennberg PO, Crounse JD, St. Clair JM, Cohen RC, Laughner JL, Dibb JE, Hall SR, Ullmann K, Wolfe GM, Pollack IB, Peischl J, Neuman JA, Zhou X (2016) Why do models overestimate surface ozone in the Southeast United States?. Atmos Chem Phys 16(21):13,561–13,577. doi: 10.5194/acp-16-13561-2016 CrossRefGoogle Scholar
  53. U.S. EPA (2014) Health Risk and Exposure Assessment for Ozone First External Review Draft. Technical report, Washington, DC, https://www3.epa.gov/ttn/naaqs/standards/ozone/data/20140829healthrea.pdf
  54. U.S. EPA (2015a) National Ambient Air Quality Standards for Ozone Final RuleGoogle Scholar
  55. U.S. EPA (2015b) Overview of EPA’s Updates to the Air Quality Standards for Ground-Level Ozone. Technical report, https://www.epa.gov/sites/production/files/2015-10/documents/overview_of_2015_rule.pdf
  56. USEPA (1999a) Regulatory Impact Analysis - Control of Air Pollution from New Motor Vehicles : Tier 2 Motor Vehicle Emissions Standards and Gasoline Sulfur Control Requirements. Tech. rep., United States Environmental Protection AgencyGoogle Scholar
  57. USEPA (1999b) Technical Support Document for the Tier 2 / Gasoline Sulfur Ozone Modeling Analyses. Technical Report December, http://www.epa.gov/scram001/reports/tsd_for_tier_2_gasoline_sulfur_rule.pdf
  58. USEPA (2000a) Regulatory Impact Analysis: Heavy-Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements, Chapter 3. Technical report, United States EPAGoogle Scholar
  59. USEPA (2000b) Technical Support Document for the Heavy-Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements: Air Quality Modeling Analyses. Technical report, United States EPAGoogle Scholar
  60. USEPA (2005) Technical Support Document for the Final Clean Air Interstate Rule Air Quality Modeling. Tech. Rep. March, United States Environmental Protection AgencyGoogle Scholar
  61. USEPA (2009) NOx Budget Trading Program - Basic Information. Tech. rep., http://www.epa.gov/airmarkets/progsregs/nox/docs/NBPbasicinfo.pdf
  62. USEPA (2012a) Acid Rain Program. 40 Code of Federal Regulations Parts 72-78Google Scholar
  63. USEPA (2012b) Motor vehicle emissions simulator (moves) user guide for moves2010b. http://www.epa.gov/otaq/models/moves/
  64. USEPA (2012c) Motor vehicle emissions simulator (moves) v. 2010b. http://www.epa.gov/otaq/models/moves/
  65. USEPA (2013) United states epa air markets program data. http://ampd.epa.gov/ampd/
  66. van Erp AM, O’Keefe R, Cohen AJ, Warren J (2008) Evaluating the effectiveness of air quality interventions. J Toxicol Environ Health Part A 71(9-10):583–7. doi: 10.1080/15287390801997708, http://www.ncbi.nlm.nih.gov/pubmed/18569630
  67. Vijayaraghavan K, DenBleyker A, Ma L, Lindhjem C, Yarwood G (2014) Trends in on-road vehicle emissions and ambient air quality in Atlanta, Georgia, USA, from the late 1990s through 2009. J Air Waste Manag Assoc 64(7):808–816. doi: 10.1080/10962247.2014.892039 CrossRefGoogle Scholar
  68. Weber RJ, Guo H, Russell AG, Nenes A (2016) High aerosol acidity despite declining atmospheric sulfate concentrations over the past 15 years. Nat Geosci 9(April):1–5. doi: 10.1038/NGEO2665 Google Scholar
  69. Xie Y, Elleman R, Jobson T, Lamb B (2011) Evaluation of O 3 -NO x -VOC sensitivities predicted with the CMAQ photochemical model using Pacific Northwest 2001 field observations. J Geophys Res 116(D20):D20,303. doi: 10.1029/2011JD015801 CrossRefGoogle Scholar
  70. Xing J, Zhang Y, Wang S, Liu X, Cheng S, Zhang Q, Chen Y, Streets DG, Jang C, Hao J, Wang W (2011) Modeling study on the air quality impacts from emission reductions and atypical meteorological conditions during the 2008 Beijing Olympics. Atmos Environ 45(10):1786–1798.  10.1016/j.atmosenv.2011.01.025, http://search.ebscohost.com/login.aspx?direct=true&db=a9h&AN=59334233&site=ehost-live&scope=cite$\delimiter”026E30F$nhttp://www.sciencedirect.com/science/article/pii/S135223101100032X
  71. Yang Y, Wilkinson J, Russell A (1997) Fast, direct sensitivity analysis of multidimensional photochemical models. Environ Sci Technol 31:2965–2976Google Scholar
  72. Zhang W, Trail MA, Hu Y, Nenes A, Russell AG (2015) Use of high-order sensitivity analysis and reduced-form modeling to quantify uncertainty in particulate matter simulations in the presence of uncertain emissions rates: A case study in Houston. Atmos Environ 122:103–113. doi: 10.1016/j.atmosenv.2015.08.091 CrossRefGoogle Scholar
  73. Zhou W, Cohan DS, Napelenok SL (2013) Reconciling NOx emissions reductions and ozone trends in the U.S., 20022006. Atmos Environ 70(x):236–244. doi: 10.1016/j.atmosenv.2012.12.038, http://linkinghub.elsevier.com/retrieve/pii/S1352231013000058 http://linkinghub.elsevier.com/retrieve/pii/S1352231013000058 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Lucas RF Henneman
    • 1
    Email author
  • Howard H Chang
    • 2
  • Kuo-Jen Liao
    • 3
  • David Lavoué
    • 1
  • James A Mulholland
    • 1
  • Armistead G Russell
    • 1
  1. 1.Georgia Institute of TechnologyAtlantaUSA
  2. 2.Emory UniversityAtlantaUSA
  3. 3.Department of Environmental EngineeringTexas A&M University-KingsvilleKingsvilleUSA

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