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

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.

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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

    CAS  Article  Google 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

    CAS  Article  Google 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

    CAS  Article  Google 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

    CAS  Article  Google 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

    CAS  Article  Google 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

    Article  Google 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 51–77

  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

    CAS  Article  Google 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

    CAS  Article  Google 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

    CAS  Article  Google 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

    CAS  Article  Google 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

    CAS  Article  Google 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

    CAS  Article  Google 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

    CAS  Article  Google Scholar 

  17. EPD (2009) Georgia’s State Implementation Plan for the Atlanta 8-Hour Ozone Nonattainment Area

  18. EPD (2012) Georgia’s Redesignation Request and Maintenance Plan for the Atlanta Nonattainment Area for the 1997 PM2.5 NAAQS

  19. EPD (2014) Council regulation (EU) no 269/2014. http://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1416170084502&uri=CELEX:32014R0269

  20. 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

  21. 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

  22. 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

    CAS  Article  Google Scholar 

  23. 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

    Article  Google Scholar 

  24. Georgia EPD (2013) Rules for Air Quality Control. Tech. rep., Georgia EPD, http://www.georgiaair.org/airpermit/html/planningsupport/naa.htm

  25. Georgia Power (2007) 2007 Georgia Power Integrated Resources Plan

  26. 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

    CAS  Article  Google Scholar 

  27. 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

  28. 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, Colorado

    Google Scholar 

  29. Hakami A, Odman M, Russell A (2003) Environmental science and technology. Atmos Environ 37:2442–2452

    CAS  Google Scholar 

  30. 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

    Article  Google Scholar 

  31. 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

    CAS  Article  Google Scholar 

  32. 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

    CAS  Article  Google Scholar 

  33. 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

    CAS  Article  Google Scholar 

  34. HEI (2003) Assessing the health impact of air quality regulations: Concepts and methods for accountability research. Technical report, The Health Effects Institute

  35. 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 Environment

  36. 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 105:9079–9090

    CAS  Article  Google Scholar 

  37. 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

    Article  Google Scholar 

  38. 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

  39. 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

    CAS  Article  Google Scholar 

  40. 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

    CAS  Article  Google Scholar 

  41. 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

    Article  Google Scholar 

  42. 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

  43. Napelenok S, Cohan D, Hu Y, Russell A (2006) Decoupled direct 3D sensitivity analysis for particulate matter (DDM-3D/PM). Atmos Environ 40:6112–6121

    CAS  Article  Google Scholar 

  44. 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

    CAS  Article  Google Scholar 

  45. 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

    CAS  Article  Google Scholar 

  46. R Core Team (2015) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org/

  47. 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, NIHMS150003

    CAS  Article  Google Scholar 

  48. Seaman N (2000) Fast, direct sensitivity analysis of multidimensional photochemical models. Atmos Environ 34:2231–2259

    CAS  Article  Google Scholar 

  49. Seinfeld J, Pandis S (2006) Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, 2nd edn. Wiley, New York

    Google Scholar 

  50. 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

    CAS  Article  Google Scholar 

  51. 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

    CAS  Article  Google Scholar 

  52. 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):1395–1400

    CAS  Article  Google Scholar 

  53. 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

    CAS  Article  Google Scholar 

  54. 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

  55. U.S. EPA (2015a) National Ambient Air Quality Standards for Ozone Final Rule

  56. 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

  57. 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 Agency

  58. 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

  59. USEPA (2000a) Regulatory Impact Analysis: Heavy-Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control Requirements, Chapter 3. Technical report, United States EPA

  60. 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 EPA

  61. USEPA (2005) Technical Support Document for the Final Clean Air Interstate Rule Air Quality Modeling. Tech. Rep. March, United States Environmental Protection Agency

  62. USEPA (2009) NOx Budget Trading Program - Basic Information. Tech. rep., http://www.epa.gov/airmarkets/progsregs/nox/docs/NBPbasicinfo.pdf

  63. USEPA (2012a) Acid Rain Program. 40 Code of Federal Regulations Parts 72-78

  64. USEPA (2012b) Motor vehicle emissions simulator (moves) user guide for moves2010b. http://www.epa.gov/otaq/models/moves/

  65. USEPA (2012c) Motor vehicle emissions simulator (moves) v. 2010b. http://www.epa.gov/otaq/models/moves/

  66. USEPA (2013) United states epa air markets program data. http://ampd.epa.gov/ampd/

  67. 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

  68. 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

    CAS  Article  Google Scholar 

  69. 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 

  70. 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

    Article  Google Scholar 

  71. 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

  72. Yang Y, Wilkinson J, Russell A (1997) Fast, direct sensitivity analysis of multidimensional photochemical models. Environ Sci Technol 31:2965–2976

    Google Scholar 

  73. 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

    CAS  Article  Google Scholar 

  74. 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

    CAS  Article  Google Scholar 

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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.

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Correspondence to Lucas RF Henneman.

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Henneman, L.R., Chang, H.H., Liao, K. et al. Accountability assessment of regulatory impacts on ozone and PM2.5 concentrations using statistical and deterministic pollutant sensitivities. Air Qual Atmos Health 10, 695–711 (2017). https://doi.org/10.1007/s11869-017-0463-2

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Keywords

  • Air pollution modeling
  • Sensitivity
  • Accountability
  • Statistical modeling