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Hidden Costs of Energy: Unpriced Consequences of Energy Production and Use C Description of the Air Pollution Emission Experiments and Policy (APEEP) Model and Its Application The Air Pollution Emission Experiments and Policy (APEEP) analysis model (Muller and Mendelsohn 2006, 2009) is a traditional integrated assessment model (Mendelsohn 1980; Nordhaus 1992; Burtraw et al. 1998; EPA 1999). Like other integrated assessment models, APEEP connects emissions of air pollution through air-quality modeling to exposures, physical effects, and monetary damages. Making these links requires the use of findings reported in the peer-reviewed literature across several scientific disciplines. APEEP is designed to calculate the marginal damage of emissions for nearly 10,000 distinct (individual and aggregated sources of air pollution in the contiguous United States. APEEP computes marginal damages of six pollutants: sulfur dioxide (SO2), volatile organic compounds (VOCs), nitrogen oxides (NOx), fine particulate matter (PM2.5), coarse particulate matter (PM10),1 and ammonia (NH3). The individual and aggregate sources are defined by the U.S. Environmental Protection Agency (EPA 2009). Sources of emissions include both county-aggregated ground-level sources as well as point sources. Ground-level sources include vehicles, residences, and small industrial or commercial facilities without a smokestack. Emissions from individual ground-level sources are aggregated at the county level by EPA. Point sources are dif- 1 The definition of PM10 for the purpose of this modeling analysis is total particles less than 10 microns in size minus PM2.5. This approach ensures that the consequences of PM2.5 are not double counted. PM10 is usually defined as particles that have an aerodynamic diameter less than or equal to 10 microns.
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Hidden Costs of Energy: Unpriced Consequences of Energy Production and Use ferentiated by effective stack height and by location because the height of emissions affects the dispersion patterns from these sources. Emissions from point sources with an effective height of less than 250 m are aggregated to the county level, as are emissions from point sources with an effective height of 250 to 500 m. In contrast, point sources with an effective height of greater than 500 m, such as certain power plants and other large industrial facilities, are modeled individually—that is, APEEP does not aggregate emissions from these sources; they are modeled separately for each facility. The air-quality models in APEEP use the emission data provided by EPA to estimate corresponding ambient concentrations in each county in the coterminous states. The accuracy of the estimated pollution levels produced by the APEEP model has been statistically tested against the Community Multiscale Air Quality (CMAQ) model (Byun and Schere 2006), which is considered the state-of-the-art air-quality model. The results of these statistical comparisons are shown in the accompanying materials to Muller and Mendelsohn (2006). APEEP can be used to compute the marginal damage of emissions on a source-specific basis. This approach isolates the source-specific damage per ton for each of the six pollutants covered by the model. To calculate marginal damages, APEEP uses the following algorithm: First, APEEP estimates total damages due to all sources in the model, producing its baseline (observed) emissions (EPA 2009); next, APEEP adds 1 ton of one pollutant from one source and recomputes total damages. The marginal damage is the damage that occurs after adding 1 ton of pollutant minus the damages due to the baseline emissions. The algorithm isolates the contribution of a single ton of emissions from each source to total national damages. This approach captures the formation of secondary pollutants, such as sulfates and nitrates (constituents in PM2.5) as well as tropospheric ozone (O3) that are formed by the emissions of other substances. APEEP attributes the damage due to such secondary pollutants back to the source of emissions. As shown in Equation 1, the marginal damage is computed by adding the changes in damages across the complete set of receptor counties. (Receptor counties are those counties that receive emissions from a source.) (1) where MDi,p = damage per ton of an emission of pollutant (p) from source (i). Dr = total dollar damage that occurs at receptor county (r). bp = 2002 baseline emissions of p. ep = 2002 baseline emissions plus 1 ton of p from i.
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Hidden Costs of Energy: Unpriced Consequences of Energy Production and Use After computing the marginal damage of emissions for a specific pollutant from source i, this experiment can be repeated for each of the six pollutants covered in APEEP and the approximately 10,000 distinct (individual and grouped) sources in the United States. The total 10,000 sources encompass all anthropogenic emissions of these six pollutants in the lower 48 states. It is important to note that the APEEP model, in its current form, does not test for interactions among emissions of multiple pollutants in terms of the damages that such emissions cause. The model is designed to simulate the emissions of 1 ton of one specific pollutant from a particular source and to estimate its impact rather than the emissions of multiple pollutants from a source and estimating their cumulative impact. The following section briefly highlights the basic structure of the model and some of its most important assumptions. The model uses data on emissions (excluding carbon monoxide and lead and including ammonia) that contribute to the formation of criteria air pollutants. The data were provided by EPA’s 2002 National Emission Inventory (EPA 2009). Concentrations due to the baseline levels of emissions are estimated by the air-quality models in APEEP. The air-quality modeling module makes use of a source-receptor matrix framework. That is, the marginal contribution of emissions in a source county (s) to the ambient concentration in a receptor county (r) is represented as the s,r element in a matrix. Using a linear algebraic approach, APEEP multiplies the matrix times an emission vector to generate a vector of predicted ambient concentrations. When the emission vectors represent changes to existing emissions, the corresponding estimated concentrations reflect changes to the baseline levels, or existing concentrations. When the emission vectors represent the emission rates, then predicted concentrations reflect those rates, not changes to concentrations. The model contains source-receptor matrices for the following pollutants in both summer and winter: NOx → NOx, SO2 → SO2. The matrix governing the relationship between NOx emissions, VOC emissions, and O3 concentrations is calibrated to the summer season. The matrices representing formation and transport of particles (PM2.5 → PM2.5, PM10 → PM10, NOx → PM, SO2 → PM, NH3 → NH4, VOC → PM) produce annual means.2 There is a specific matrix in APEEP for each of the emission-concentration relationships shown above. The particulate matter source-receptor matrices compute the ammonium-sulfate-nitrate equilibrium, which determines the amount of ambient ammonium sulfate (NH4)2, SO4, and ammonium nitrate (NH4NO3) at each receptor county. The equilibrium computations reflect several fundamental 2 PM indicates that the contribution of NOx, VOCs, and SO2 is counted to both PM2.5 and PM10. Parameterization of the relationship between VOC emissions and the formation of PM is based on the work of Grosjean and Seinfeld (1989).
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Hidden Costs of Energy: Unpriced Consequences of Energy Production and Use aspects of this system. First, ambient ammonium (NH4) reacts preferentially with sulfate (H2SO4). Second, ammonium nitrate is only able to form if there is excess NH4 after reacting with sulfate. Finally, particulate nitrate formation is a decreasing function of temperature, so the ambient temperature at each receptor location is incorporated into the equilibrium calculations. To translate VOCs emissions into secondary organic particulates, APEEP uses the fractional aerosol yield coefficients estimated by Grosjean and Seinfeld (1989). These coefficients represent the yield of secondary organic aerosols corresponding to emissions of gaseous VOCs. APEEP simulates O3 concentrations using an empirical model that translates ambient concentrations of VOC, CO, and NOx into ambient O3 concentrations. The model captures many of the factors contributing to ambient concentrations of O3, VOC, CO, and NOx. These factors include forests and agricultural land uses, which produce biogenic hydrocarbons, as well as the ambient air temperature and several geographic variables. For a complete depiction of the O3 modeling in APEEP, see Muller and Mendelsohn (2006). The inclusion of both linear and quadratic forms for NOx, CO, and VOC concentrations in the O3 models allows for the nonlinearity known to exist in O3 production chemistry (Seinfeld and Pandis 1998). Specifically, the quadratic forms capture titration. This approach is critical to accurately predict O3 levels in certain urban areas, where research has shown that additional emissions of NOx can result in reduced O3 concentrations (Tong et al. 2006). The source-receptor matrices in APEEP are derived from the Climatological Regional Dispersion Model (CRDM) (Latimer (1996). The original CRDM matrices have been calibrated to produce estimates of pollution levels that are in good agreement with the predictions produced by CMAQ. The correlations between APEEP’s predicted surfaces and CMAQ’s are especially strong for annual mean PM2.5 levels and summer mean O3 levels; the correlation coefficients are 0.82 and 0.77, respectively.3 The matrices have been expanded in scope to encompass nearly 10,000 sources and source areas. Following the estimation of ambient concentrations, exposures are computed by multiplying county-level populations times county-level pollution concentrations. In APEEP, populations include number of people (differentiated by age),4 crops produced, timber harvested, an inventory of anthropogenic materials, visibility resources, and recreation usage (for each 3 The correlations for PM2.5 are expressed over n = 3,110, reflecting the 3,110 counties in the contiguous 48 states. The correlations for tropospheric O3 are expressed over n = 24,880, reflecting eight hourly observations for the 3,110 counties in the coterminous U.S. 4 Population data are provided by CDC Wonder, which is a database of the Centers for Disease Control and Prevention of the U.S. Department of Health and Human Services.
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Hidden Costs of Energy: Unpriced Consequences of Energy Production and Use county in the contiguous United States). Each type of exposure is computed separately for each pollutant. The sources for each of these inventories are documented in Muller and Mendelsohn (2006). In the next stage of the APEEP model, peer-reviewed concentration-response functions are used to translate exposures into the number of physical effects, including premature mortalities, cases of illness, reduced timber and crops yields, enhanced depreciation of anthropogenic materials, reduced visibility, and recreation usage. The studies that provide the concentration-response functions related to human health impacts are listed in Table C-1. The final stage of the APEEP model attributes a dollar value to each of these physical effects. For effects on goods and services traded in markets (decreased crop yields, for example), APEEP multiplies the change in output due to exposures to air pollution times the market price. For nonmarket goods and services, APEEP uses valuation estimates from the nonmarket valuation literature in economics. APEEP values premature mortality risks using the value of a statistical life (VSL) approach (Viscusi and Aldy 2003). APEEP uses EPA’s preferred VSL, which is equivalent to approximately $6 million (year 2000 real U.S. dollars). APEEP provides the option of using a VSL estimate of approximately $2 million from Mrozek and Taylor (2002) as an alternative to the EPA’s VSL. The values attributed to chronic illnesses, such as bronchitis and asthma, are also derived from the nonmarket valuation literature. Acute illnesses are valued with cost of illness estimates. Each of the values applied to human health effects in APEEP are shown in Table C-3. TABLE C-1 Epidemiology Studies Used in APEEP Health Event Pollutant Study All-cause adult chronic-exposure mortalitya PM2.5 Pope et al. 2002 Infant chronic-exposure mortality PM2.5 Woodruff et al. 2006 Chronic bronchitis PM10 Abbey et al. 1993 Chronic asthma O3 McDonnell et al. 1999 Acute-exposure mortality O3 Bell et al. 2004 Respiratory admissions O3 Schwartz 1995 ER visits for asthma O3 Steib et al. 1996 COPD admissions NO2 Moolgavkar 2000 IHD admissions NO2 Burnett et al. 1999 Asthma admissions SO2 Sheppard et al. 1999 Cardiac admissions SO2 Burnett et al. 1999 aAcute exposure mortality for PM2.5 was not included in this analysis as a separate effect. See Muller and Mendelsohn (2007) for further discussion. SOURCE: Muller and Mendelsohn 2006. Reprinted with permission; copyright 2007, Journal of Environmental Economics and Management.
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Hidden Costs of Energy: Unpriced Consequences of Energy Production and Use The studies that provide the concentration-response functions for the remaining welfare effects are listed in Table C-2. Because PM2.5 is a subset of PM10, APEEP avoids double counting of damages due to PM2.5 and PM10. Specifically, APEEP estimates mortality impacts associated with emissions of PM2.5, and the model measures chronic morbidity impacts of PM10. In reporting the morbidity damages due to emissions of PM10, APEEP nets out the mortality damages due to PM2.5. In effect, the damages for PM10 are expressed as PM10-PM2.5. TABLE C-2 Concentration-Response Studies Used in APEEP Welfare Effect Pollutant Study Crop loss O3 Lesser et al. 1990 Timber loss O3 Reich 1987; Pye 1988 Materials depreciation SO2 Atteras and Haagenrud 1982; ICP 2000 Visibility PM10 Muller and Mendelsohn 2006 Forest recreation SO2, NOx, O3 Muller and Mendelsohn 2006 TABLE C-3 Value of Human Health Effects in APEEPa Health Event Unit U.S. Dollars Chronic Exposure Mortality Case 5,910,000 Chronic Bronchitis Case 320,000 Chronic Asthma Case 30,800 General Respiratory Hospital Admission 8,300 General Cardiac Hospital Admission 17,526 Asthma Hospital Admission 6,700 COPD Hospital Admission 11,276 Ischemic Heart Disease Hospital Admission 18,210 Asthma ER Visit 240 aValues are in 2000 U.S. dollars; see Muller and Mendelsohn 2007. SOURCE: Modified from Muller and Mendelsohn 2006. TABLE C-4 Value of Nonmarket Impacts of Air Pollution Welfare Effect U.S. Dollarsa Location Source Recreation visibility (in-region) 170 Southwest Chestnut and Rowe 1990 Recreation visibility (out-region) 135 Southwest Chestnut and Rowe 1990 Recreation visibility (in-region) 80 Southeast Chestnut and Rowe 1990 Recreation visibility (out-region) 50 Southeast Chestnut and Rowe 1990 Residential visibility (in-region) 174 Eastern McClelland et al. 1993 Forest recreation visit 63 All Kengen 1997 aValues are in 2000 U.S. dollars; see Muller and Mendelsohn 2007. SOURCE: Modified from Muller and Mendelsohn 2006.
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Hidden Costs of Energy: Unpriced Consequences of Energy Production and Use Each of the other nonmarket impacts of air pollution modeled by APEEP (impaired visibility and reduced recreation services) are also expressed in dollar terms. The values used in the APEEP model corresponding to these welfare effects are displayed in Table C-4. REFERENCES Abbey, D.E., F. Peterson, P.K. Mills, and W.L. Beeson. 1993. Long-term ambient concentrations of total suspended particulates, ozone, and sulfur dioxide and respiratory symptoms in a nonsmoking population. Arch. Environ. Health 48(1):33-46. Atteras, L., and S. Haagenrud. 1982. Atmospheric corrosion testing in Norway. Pp. 873-892 in Atmospheric Corrosion, W.H. Ailor, ed. New York: Wiley. Bell, M.L., A. McDermott, S.L. Zeger, J.M. Samet, and F. Domenici. 2004. Ozone and short-term mortality in 95 U.S. urban communities, 1987-2000. JAMA 292(17):2372-2378. Burnett, R.D., M. Smith-Doiron, D. Steib, S. Cakmak, and J. Brook. 1999. Effects of particulate and gaseous air pollution on cardiorespiratory hospitalizations. Arch. Environ. Health 54(2):130-139. Burtraw, D., A. Krupnick, E. Mansur, D. Austin, and D. Farrell. 1998. Costs and benefits of reducing air pollutants related to acid rain. Contemp. Econ. Policy 16 (4):379-400. Byun, D.W., and L.K. Schere. 2006. Review of the governing equations, computational algorithms, and other components of the models-3 Community Multiscale Air Quality (CMAQ) modeling system. Appl. Mech. Rev. 59(2):51-77. Chestnut, L.G., and R.D. Rowe. 1990. Economic valuation of changes in visibility: A state of the science assessment for NAPAP. Pp. 27-153 to 27-175 in Report 27. Methods for Valuing Acidic Deposition and Air Pollution Effects. Acidic Deposition: State of Science and Technology, Vol. 4. Control Technologies, Future Emission, and Effects Valuation, P.M. Irving, ed. Washington, DC: U.S. National Acid Precipitation Assessment Program. EPA (U.S. Environmental Protection Agency). 1999. The Benefits and Costs of the Clean Air Act 1990 to 2010, EPA Report to Congress. EPA-410-R-99-001. Office of Air and Radiation, Office of Policy, U. S. Environmental Protection Agency, Washington, DC. November 1999 [online]. Available: http://www.epa.gov/oar/sect812/1990-2010/chap1130.pdf [accessed Sept. 16, 2009]. EPA (U.S. Environmental Protection Agency). 2009. National Emissions Inventory (NEI) Data & Documentation. Office of Air Quality Planning and Standards, U. S. Environmental Protection Agency, Washington, DC [online]. Available: http://www.epa.gov/ttnchie1/net/2002inventory.html [accessed Sept 16, 2009]. Grosjean, D., and J. Seinfeld. 1989. Parameterization of the formation potential of secondary organic aerosols. Atmos. Environ. 23:1733-1747. ICP (International Co-operative Programme). 2000. International Co-operative Programme on Effects of Air Pollution on Materials, including Historic and Cultural Monuments: Results [online]. Available: http://www.corr-institute.se/ICP-Materials/web/page.aspx?pageid=59263 [accessed Apr. 15, 2010]. Kengen, S. 1997. Forest Valuation for Decision-Making: Lessons of Experience and Proposals for Improvement. Food and Agriculture Organization of the United Nations, Rome, Italy. February 1997 [online]. Available: ftp://ftp.fao.org/docrep/fao/003/W3641E/W3641E00.pdf [accessed Sept. 15, 2009]. Latimer, D.A. 1996. Particulate Matter Source-Receptor Relationships Between all Point and Area Sources in the United States and PSD Class I Area Receptors. Prepared for Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, NC. September 1996.
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