Intake-to-delivered-energy ratios for central station and distributed electricity generation in California

ARTICLE IN PRESS

Atmospheric Environment 41 (2007) 9159–9172 www.elsevier.com/locate/atmosenv

Intake-to-delivered-energy ratios for central station and distributed electricity generation in California Garvin A. Heatha,, William W. Nazaroffb a

Energy and Resources Group, University of California, Berkeley, CA 94720-3050, USA Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720-1710, USA

b

Received 28 February 2007; received in revised form 23 July 2007; accepted 30 July 2007

Abstract In previous work, we showed that the intake fraction (iF) for nonreactive primary air pollutants was 20 times higher in central tendency for small-scale, urban-sited distributed electricity generation (DG) sources than for large-scale, central station (CS) power plants in California [Heath, G.A., Granvold, P.W., Hoats, A.S., Nazaroff, W.W., 2006. Intake fraction assessment of the air pollutant exposure implications of a shift toward distributed electricity generation. Atmospheric Environment 40, 7164–7177]. The present paper builds on that study, exploring pollutant- and technology-specific aspects of population inhalation exposure from electricity generation. We compare California’s existing CS-based system to one that is more reliant on DG units sited in urban areas. We use Gaussian plume modeling and a GIS-based exposure analysis to assess 25 existing CSs and 11 DG sources hypothetically located in the downtowns of California’s most populous cities. We consider population intake of three pollutants—PM2.5, NOx and formaldehyde—directly emitted by five DG technologies—natural gas (NG)-fired turbines, NG internal combustion engines (ICE), NG microturbines, diesel ICEs, and fuel cells with on-site NG reformers. We also consider intake of these pollutants from existing CS facilities, most of which use large NG turbines, as well as from hypothetical facilities located at these same sites but meeting California’s best-available control technology standards. After systematically exploring the sensitivity of iF to pollutant decay rate, the iFs for each of the three pollutants for all DG and CS cases are estimated. To efficiently compare the pollutant- and technology-specific exposure potential on an appropriate common basis, a new metric is introduced and evaluated: the intake-to-delivered-energy ratio (IDER). The IDER expresses the mass of pollutant inhaled by an exposed population owing to emissions from an electricity generation unit per quantity of electric energy delivered to the place of use. We find that the central tendency of IDER is much greater for almost every DG technology evaluated than for existing CS facilities in California. r 2007 Elsevier Ltd. All rights reserved. Keywords: Distributed generation; Electricity generation; Exposure assessment; Intake fraction; Intake-to-delivered-energy ratio

1. Introduction

Corresponding author. Present address: Integral Consulting,

12303 Airport Way, Suite 370, Broomfield, CO 80021, USA. Tel.: +1 303 404 2944. E-mail address: [email protected] (G.A. Heath). 1352-2310/$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.atmosenv.2007.07.055

Electricity generation is an important source of air pollution owing to the large quantities of fuels burned. Conventionally, fuel-powered electricity generation occurs at large central stations (CS) and the combustion byproducts are emitted from

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tall stacks, usually after passing through emission control devices. Electricity produced at these stations is conveyed over a transmission and distribution grid to the points of use, often over long distances and with some erosion of electric energy (‘‘line loss’’). An alternative model known as ‘‘distributed generation’’ (DG) is emerging. With DG, small units generate electricity close to the site of use. As with CS facilities, most DG facilities emit air pollutants. Because electricity loads are concentrated in urban areas, DG units are likely to have higher nearby population densities than CS facilities, which are typically deliberately located away from densely populated areas. In addition, for economic and safety reasons and for ease of connection to the natural gas distribution network, most DG units are expected to emit their airborne effluents near the ground. Taken in combination, these factors increase the potential for air pollutants emitted from DG units to be inhaled by nearby populations. In an earlier paper, we investigated the impact that a shift from CS to DG might have on air pollutant exposures associated with electricity generation in California (Heath et al., 2006). In that study, we evaluated intake fractions (iFs) for 25 existing CS facilities and for hypothetical DG units, the latter sited at the city halls of the 11 most populous cities in California. The assessment focused exclusively on nonreactive primary pollutants and used the iF within 100 km of each source as the primary evaluation metric. The median annual-average iF for the 11 hypothetical city hall DG units—16 per million—was found to be about a factor of 20 higher than the corresponding value for the 25 existing CS units. In the present paper, we extend the previous analysis by considering the relationship between electricity generation and inhalation intake for specific primary pollutants from selected CS units and DG technologies in California. While some primary pollutants can be considered as conserved species, others are transformed during transport from source to receptor, reducing the concentration of those pollutants experienced by the exposed population. This paper systematically evaluates the dependence of the iF for primary pollutants on the first-order transformation rate constant, k, for DG and CS emissions. We also introduce and evaluate a new metric for assessing the pollutant-specific exposure potential of electricity generating units: the intake-to-delivered-energy ratio (IDER). This

measure expresses the attributable pollutant mass inhaled by an exposed population normalized by the amount of electric energy delivered to the place of use. The IDER facilitates direct comparison of pollutant exposure potential amongst different electricity generating technologies. We determine annual-average IDERs for three important primary pollutants—PM2.5, NOx, and formaldehyde (HCHO)—for the same 25 existing CS and 11 city hall DG locations assessed in Heath et al. (2006). For the CS facilities, we consider emissions from the sites as reported in 1999 (the latest year available for all three pollutants) as well as if those same facilities were retrofitted to meet California’s best-available control technology (BACT) standards. For the hypothetical DG units, we consider 5 technologies: low-temperature fuel cells with onsite natural gas (NG) reformers, NG-fired microturbines, NG turbines, NG internal combustion engines (ICE), and diesel ICEs. 2. Methods This section summarizes the approach used to calculate the intake-to-delivered energy ratio for the DG and central station cases considered. The methods and site selection overlap with the iF analysis we have previously reported (Heath et al., 2006) and the reader is referred to that paper for additional information. Full details have been reported by Heath (2006). 2.1. Intake-to-delivered-energy ratio The IDER quantifies the ratio of attributable pollutant mass inhaled by an exposed population to the electric energy delivered to the point of use. By ‘‘attributable mass,’’ we mean that portion of the inhaled pollutant mass whose presence is caused by emissions from the source under investigation. Values of the IDER are evaluated and reported in this paper using IDER ¼

EF  iF mg inhaled ½¼ Ztrans MWh delivered

(1)

where, EF is an emission factor (g of pollutant emitted per MWh of electricity generated), iF is the intake fraction (mg of pollutant inhaled per g emitted), and Ztrans is the transmission efficiency for electricity (MWh of electricity delivered to the place of use per MWh generated). An important feature of the IDER is that it expresses pollutant

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inhalation intake per unit of delivered energy. Normalizing by delivered energy accounts for an important benefit of DG in reducing line loss. The IDER has several virtues as a measure of an adverse environmental consequence of electricity generation. It permits comparison across divergent scales of sources; it is site- and pollutant-specific; and it is a proxy for population health impact, especially for those pollutants that exhibit a linear, no-threshold dose-response relationship. 2.2. Intake fraction As indicated by Eq. (1), the IDER is proportional to the iF. For the analysis presented in this paper, the annual-average iF within 100 km is determined by a variation of the method of Heath et al. (2006). In that work, a Gaussian plume model was used to compute the ground-level, attributable pollutant concentrations, hour-by-hour across an annual cycle of typical meteorological conditions, for nonreactive pollutants emitted from each of the studied sources. In the present paper, we extend that approach to account for the decay of primary pollutants between source and receptor. We represent such decay as a first-order process with a rate constant, k (s1). iFs for a decaying pollutant can be calculated by multiplying the conserved pollutant concentrations (Heath, 2006) by an exponential loss term: ( pffiffiffi  ) Z n ð2iM  H E Þ2 2QB xmax P X iF ¼ pffiffiffi exp  sz i¼n 2s2z pU E 0   kx  exp  dx. ð2Þ UE In this equation, QB represents the average volumetric breathing rate of the exposed population (m3 s1 person1), UE is the wind speed (m s1) at the effective stack height, HE (m), P is the population density (persons per km2), sz is the dispersion parameter in the vertical direction (m), M is the mixing height (m), and x is the distance (m) from the source in the wind direction. This equation is evaluated for each hour of an annual meteorological cycle typical of that region of California, allowing variation in the parameters (UE, sz, M, k, and the wind direction, which affects P(x)). Table 1 of Heath et al. (2006) reports the annual average HE values for each CS facility. HE is treated as constant for all DG technologies at 5 m. Analysis indicates that population intake is insensitive to effective

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stack height for HEo20 m (Heath, 2006; Gulli, 2006). For some hours, the wind speed is rated ‘‘calm’’ (0 m/s) because it is below the detection limit. iF in Eq. (2) is indeterminate for a wind speed of zero. Consequently, these hours are excluded from the analysis and the annual-average iF is the average of values obtained for each noncalm hour. (Tables 1 and 3 of Heath et al. (2006) report the proportion of annual hours with calm winds for each case.) For hours in which HE4M, the iF is assumed to be zero. The population-average QB is taken to be 1.4  104 m3 s1 person1 ( ¼ 12 m3 d1 person1) (Layton, 1993). The annual-average iF is the average of values obtained for each noncalm hour. Eq. (2) implicitly assumes that people’s timeaveraged exposure concentration equals the Gaussian-modeled ambient concentration. This assumption is reasonable for outdoor exposures to ambient releases and for indoor exposures to ambientreleased conserved pollutants. However, for many pollutants, being indoors offers some protection against pollutants of outdoor origin (e.g., Riley et al., 2002; Sarnat et al., 2000). To the extent that buildings are protective, our approach will overestimate the true iF and, correspondingly, the true IDER values. Heath (2006) estimated that, for the pollutants evaluated here, the extent of overestimation is likely to be 0–50%. It is expected that this bias would be approximately consistent amongst the cases evaluated, supporting comparative analysis. Furthermore, results reported in this paper are appropriate for assessing health impacts because most epidemiology-based dose–response functions implicitly incorporate the impact of the building barrier by virtue of determining relative risks in relation to ambient concentrations. Because each DG technology is hypothetically located at the same 11 city halls and is assumed to have a constant 5 m HE, all DG technologies at the same location have the same annual-average iF. Likewise, BACT-equipped CSs are assumed to be located at the same sites and have the same stack configurations as the existing units, and thus have the same iF as the existing unit (Heath, 2006). 2.3. Emission factors and transmission efficiencies Evaluation of IDER requires emission factors for each electricity generation unit (EGU), in units of mass of pollutant emitted per unit of electricity generated. When EFs are expressed as the mass of

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Table 1 Thermal efficiency (Z), total electricity generated (G, for year 1999), pollutant emission factors, intake fractions and intake-to-delivered energy ratios (IDER) for selected central electricity generation facilities in Californiaa Facility

Alamitos Coachella Contra Costa El Segundo Encina Etiwanda Humboldt Bay Hunters Point Huntington Beach Kearny Magnolia Mandalay Morro Bay Moss Landing Mountainview North Island Oakland Ormond Beach Pittsburg Puente Hills Redding Power Redondo Beach Riverside Canal South Bay Valley Median

Z (%)

32 25 45 47 41 31 43 27 33 43 32 35 34 35 29 40 28 32 45 30 23 33 25 32 26

G (106 MWh yr1)

3.4 0.01 3.1 3.1 4.0 1.1 0.43 0.66 0.88 0.03 0.001 1.3 3.1 6.1 0.046 0.005 0.005 1.5 5.0 0.38 0.029 1.3 0.02 1.8 0.13

Emission factor (g MWh1)

Intake fraction (per million)

IDER (mg MWh1 del)

PM2.5

NOx

HCHO

PM2.5

NOx

HCHO

PM2.5

NOx

HCHO

29 74 20 18 22 31 25 36 28 390 2400 27 26 26 32 330 260 28 20 9.1 31 27 35 28 0.07 29

240 2000 230 78 240 340 930 440 380 890 1600 51 220 350 720 1500 1300 35 210 32 180 220 1200 380 580 350

1.1 0.65 0.11 0.43 3.3 0.16 0.14 0.25 0.77 18 6.9 0.81 0.032 1.7 0.33 5.8 4.4 0.48 0.49 0.015 0.062 1.6 0.0008 4.4 1.6 0.65

1.9 0.66 0.15 3.1 0.14 1.1 0.057 0.88 1.4 0.78 2.5 0.45 0.054 0.12 1.7 1.0 0.63 0.52 0.25 1.5 0.12 0.45 2.0 1.5 1.9 0.78

1.5 0.48 0.090 2.5 0.11 0.82 0.048 0.74 1.1 0.66 2.0 0.33 0.040 0.082 1.1 0.85 0.57 0.27 0.18 1.2 0.099 0.39 1.4 1.3 1.5 0.66

1.5 0.56 0.12 2.5 0.11 0.85 0.051 0.74 1.1 0.66 2.0 0.35 0.046 0.095 1.2 0.85 0.57 0.38 0.19 1.2 0.11 0.39 1.5 1.3 1.5 0.66

60 54 3.4 62 3.4 38 1.6 35 43 330 6600 13 1.6 3.3 59 360 180 16 5.7 15 4.2 14 79 47 0.2 35

410 1100 23 220 30 310 50 360 450 660 3500 19 10 32 900 1400 840 11 40 44 20 96 1800 530 960 310

1.9 0.40 0.014 1.2 0.42 0.16 0.008 0.20 0.94 13 16 0.31 0.002 0.19 0.45 5.5 2.7 0.20 0.10 0.021 0.008 0.69 0.001 6.1 2.6 0.40

a Efficiency and generation data from EPA (2003a); emission factor data sources: EPA (2003b) (HCHO) and EPA (2004) (PM2.5 and NOx), except Magnolia and Coachella PM2.5 (CARB, 2004) and Magnolia, Coachella and Valley NOx (EPA, 2003a).

pollutant emitted per unit of heat input, knowing the thermal energy-to-electricity conversion efficiency allows determination of the EF in the units required here. For each central station, we calculated this efficiency from heat input and electric output data provided by eGRID (EPA, 2003a). We assume that these emission factors are constant over all hours of the year. The transmission efficiency accounts for the loss of electric power between where it is generated and where it is used. We applied line losses of 10% to electricity generated by CS plants (Ztrans ¼ 0.90) and 0% for the DG technologies (Ztrans ¼ 1.0) (EIA, 1999; CARB, 2001).

each. Table 1 of the present paper reports thermal efficiency, total electricity generation, and emission factors of selected pollutants from these units. New DG could displace production from either existing or new units. Emission factors for new CS units are necessary to estimate their IDERs. Almost all of California’s EGUs that have gone on-line since 1999 burn natural gas, mainly in combinedcycle gas turbines (CCGT) (CEC, 2005). CCGTs are highly efficient, with electrical conversion efficiencies over 40%. They also have very low emission factors, typically better than the best-controlled existing CS. Thus, they provide a ‘‘best-case’’ central station for this study.

2.4. Central station cases

2.5. Distributed generation technologies and characteristics

Fig. 1 and Table 1 of Heath et al. (2006) display the location of the 25 CS units and report information relevant to the estimation of iF for

While DG is not limited to any particular scale, the demand of 475% of commercial electricity

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decay rate (per h) Intake fraction (per million)

30

Intake fraction (per million)

0

2

0.2

0.4

0.6

0.8

1 Santa Ana Anaheim Oakland Sacramento Riverside

25 20 15 10 5 0

Valley Puente Kearny Pittsburg Redding

1.6 1.2 0.8 0.4 0 0

0.2

0.4

0.6

0.8

1

decay rate (per h) Fig. 1. Dependence of the annual-average intake fraction on the first-order decay rate constant, k, for (a) 5 cases of DG units at city halls and (b) 5 existing central station cases. The selected sites correspond to the 10th percentile (DG: Sacramento city hall; CS: Redding power plant), 25th (Riverside; Pittsburg), median (Oakland; Kearny), 75th (Anaheim; Puente Hills), and 90th (Santa Ana; Valley), based on cumulative conserved-pollutant intake fraction to 100 km.

users is o1 MW (Lovins et al., 2002). The DG technologies modeled here are typical of those at or below 1 MW. We consider technologies most likely to have been installed before the California Air Resources Board’s (CARB) 2003 DG emission standard as well as ones certified under CARB’s 2003 and 2007 standards (CARB, 2002). ICEs and turbines are both mature technologies that represent the largest fraction of installed DG in the US and California (EPA, 2003a; CEC, 2005). Representing units installed before 2003, we consider these two technologies burning popular fuels: diesel ICEs, NG ICEs, and NG turbines. CARB has certified 10 DG products for meeting the 2003 or 2007 standard, all of them small-scale technologies (o1 MW) (CARB, 2007). Four microturbines met the 2003 standard (now expired); 1 microturbine and 5 fuel cells meet the 2007 standard. Thus, microturbines and fuel cells are modeled as post-2003 and post-2007 classes of cases, respectively. While the approved fuel cells can operate at both high and low temperatures, we model low-temperature fuel cells because these are

more mature (Energy Nexus Group, 2002). Table 2 summarizes efficiencies and emission factors for the 5 DG technologies considered. While DG can be operated in several different modes (e.g., peaking, load-following or baseload), we model all DG units in continuous (baseload) mode for consistency with the treatment of the existing CS units. Heath et al. (2006) explore the implications of the assumption about baseload operation on iFs. Fig. 1 of Heath et al. (2006) displays the locations of the 11 selected city halls, and Table 2 of that paper summarizes relevant attributes of the sites. 2.6. Pollutant selection We selected pollutants for which to model population exposure based on a hazard ranking of emissions. The figure of merit was an emission factor divided by a health-based concentration guideline. This ratio represents a source-oriented, technology-specific hazard potential associated with the specified pollutant. Based on the outcome of this

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Table 2 Generation efficiency (Z), emission factors, and IDER multipliers for air-pollutant emitting, distributed-generation technologiesa Technology

Diesel ICE Natural gas ICE Natural gas turbine Microturbine Low-T fuel cell

Z (%)

44 35 28 25 36

IDER multiplierb

Emission factor (g MWh1) PM2.5

NOx

HCHO

PM2.5

NOx

HCHO

1400 220 41 38 28

7700 1500 560 320 32

4.2 91 3.9 4.4

36 5.7 1.1 1 0.75

24 4.6 1.8 1 0.10

0.94 21 0.89 1

c

c

a

Abbreviations: ICE ¼ internal combustion engine, T ¼ temperature. Data sources for emission factors: for PM2.5 and NOx, Tables 2, 7, and 8 of Samuelsen et al. (2005); for HCHO, AP-42 (EPA, 2000), with efficiency from Samuelsen et al. (2005); for California BACT, CARB (1999) for PM2.5 and NOx and EPA (2000) for HCHO. b To calculate IDER for small-scale DG technologies, multiply the city-specific results in Table 3 by the appropriate IDER multiplier, which is determined as the emission-factor ratio of the respective technology as compared with microturbines. c No emission factor for formaldehyde from low-temperature fuel cells has been reported in the literature.

assessment (Heath, 2006), we selected three pollutants to model. Primary PM2.5 and nitrogen oxides, which represents a mixture of primary and secondary pollutants (NOx ¼ NO+NO2), displayed the highest hazards. (This assessment assumes that all NOx at the point of exposure is NO2.) Primary formaldehyde had the highest hazard ranking of any hazardous air pollutant (HAP) assessed. 2.7. Dependence of intake fraction on pollutant decay rate The dependence of iF on pollutant decay rate was investigated by systematically exercising the iF calculation across a range of k values, up to 1 h1. The calculations were performed for 10 cases representing the 10th, 25th, 50th, 75th and 90th percentiles of the distributions of conserved pollutant iF for both CS and city hall DG (see Fig. 1). The slopes of all traces decrease with higher values of k, indicating diminishing effects of the decay rate in reducing iF. For a rate constant of k ¼ 0.25 h1, the average reduction in iF relative to that for a conserved pollutant for the 5 DG cases is 36% (range ¼ 27–52%); the analogous result for the five CS cases is 21% (range ¼ 16–32%). 2.8. Input data Meteorological and demographic data needed to evaluate Eq. (2) are the same as reported in Heath et al. (2006). This section summarizes the pollutantspecific input data: emission factors and decay rates. Additional details are reported in Heath (2006).

2.8.1. Emission factors Emission factors for existing CS are estimated from 1999 data. We only consider emissions resulting from electricity generation (i.e., cogeneration units were not considered). Emissions of formaldehyde are from the 1999 National Emission Inventory (NEI) for HAPs (EPA, 2003b). For most facilities, emissions data for primary PM2.5 and NOx are taken from the 1999 NEI for criteria pollutants (EPA, 2004). Two CS (Magnolia and Coachella) could not be unambiguously identified in the NEI for criteria pollutants. For these sites, PM2.5 and NOx emissions were obtained from CARB’s Facility Search Engine (CARB, 2004) and eGRID (EPA, 2003a), respectively. eGRID-reported NOx emissions were used instead of the NEI-reported NOx emissions for Valley owing to the inconsistency of the NEI value with data for other facilities. Table 1 reports the emission factors utilized for each CS case. Emission factors for NOx and HCHO conform reasonably well to lognormal distributions (NOx GM ¼ 380 g/MWhdel, GSD ¼ 3.2; HCHO GM ¼ 0.57 g/MWhdel, GSD ¼ 8.9); the fit is not as good for primary PM2.5 (GM ¼ 38 g/MWhdel, GSD ¼ 5.9) (see Fig. 4-2 of Heath, 2006). Emission factors for a few facilities differ considerably from others. Valley and Riverside Canal exhibit EFs for PM2.5 and HCHO, respectively, that are well below expectations given the distributions of values for the other plants; Magnolia’s PM2.5 EF is considerably higher. It is unclear whether the emission factors from these plants represent actual conditions for 1999 or if erroneous reporting might contribute to these uncharacteristic values.

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California BACT guidance mandates compliance with output-based emission factors for criteria pollutant emissions from new CS (CARB, 1999). Emission factors appropriate for determining IDERs for primary PM2.5 and NOx are calculated for new CCGTs using these standards. California BACT does not include a standard for formaldehyde. We estimated a formaldehyde emission factor for a well-controlled, new central station from data in EPA’s AP-42 for a CCGT using catalytic reduction control technology (EPA, 2000). The estimate of a ‘‘CA BACT-compliant’’ formaldehyde emission factor assumes that the efficiency of a new CCGT in California is similar to that of a plant meeting BACT for PM2.5 (42%) (CARB, 1999). The emission factors assumed to apply to new CS in California are thus 8.0 g/MWhdel for PM2.5, 32 g/MWhdel for NOx, and 0.067 g/MWhdel for HCHO. For the hypothetical DG technologies that are compared with the CS, we utilize output-based EFs for criteria pollutants reported in a recent review (Samuelsen et al., 2005). The only source for formaldehyde EFs reports mass emissions per heat input (EPA, 2000) and thus requires efficiency adjustment. We assume HCHO emissions are uncontrolled for all DG technologies. A formaldehyde EF for fuel cells is not available. Table 2 reports typical efficiencies and emission factors for all three pollutants evaluated for the five DG technologies. 2.8.2. Pollutant decay The Gaussian plume model can be easily modified to account for first-order decay with a constant value of k, as illustrated in Eq. (2). The primary pollutants considered in the present paper are transformed within or removed from air parcels by multiple pathways at rates that vary with environmental conditions. Representing those processes as first-order with a consistent rate constant is clearly a simplified approximation of a more complex reality. We believe that—at this stage of our understanding of the exposure consequences of CS and DG pollutant emissions—such an approximation is warranted given the alternatives of either (a) only considering nonreactive primary pollutants or (b) needing to use a model that may better represent atmospheric transformations at the expense of poorly representing near-source transport. Recognizing the limitations of our approach, the following subsections describe our treatment of the

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atmospheric transformations of primary PM2.5, HCHO, and NOx. Particulate matter (PM2.5): Using dry deposition velocity data from Seinfeld and Pandis (1998) for PM of diameter 0.2–2 mm, losses over a transport distance of 100 km are estimated to be 1–8%. These small loss rates justify treating PM2.5 as a conserved pollutant. iF values from Heath et al. (2006) are applied to this species without adjustment. Formaldehyde: Given an absence of measured HCHO decay rates in CS or DG plumes, our approach was to identify important reactions leading to HCHO decay and to estimate decay rates based on published rate data relevant to California conditions. Two reactions contribute to the decay of HCHO on time scales of interest: photolysis and reaction with the hydroxyl radical (OH) (Atkinson, 2000). Using data from Demerjian et al. (1980) and FinlaysonPitts and Pitts (1986), we estimated the average photolysis rate for ‘‘typical’’ conditions during the 6 h surrounding noon for Los Angeles and applied these results for all cases during daytime hours. Formaldehyde reaction with a constant OH concentration can be treated as a first-order process. With data from Finlayson-Pitts and Pitts (1986) on typical atmospheric OH concentrations and with the IUPAC (2001) recommended rate constant, the daytime reaction rate constants for 2 general pollution conditions, moderate and low, were estimated. We apply these to urban and rural cases, respectively. (Table 1 from Heath et al. (2006) reports the urban/rural designation for each CS case; all DG cases are located in urban areas.) The sum of the rate constants for photolysis and for reaction with OH is the rate constant used to model daytime decay of primary HCHO emissions: 0.30 h1 for rural cases and 0.54 h1 for urban cases. Neither photolysis nor reaction with OH is a significant HCHO loss mechanism at night (Atkinson, 2000) and negligible nocturnal decay has been confirmed in Tennessee (Lee et al., 1998). Therefore, HCHO emitted during the night is considered to be a conserved pollutant. Since emissions are assumed to be time invariant for all cases, a plume encounters an approximately equal proportion of daytime and night-time conditions over the course of a year. Thus, the arithmetic average of HCHO iF for daytime and night-time conditions yields the estimated annual average. Nitrogen oxides (NOx): Decay of NOx has been reliably measured in both power plant and urban plumes. Some measurements have been made in

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California, although most are from the Southern Oxidants Studies in Tennessee. We rely on these studies, because it is unlikely that producing independent estimates of NOx decay rates based on elementary reactions and assumed plume conditions would be any more accurate. A literature review of NOx decay in power plant and urban plumes is presented in Appendix E of Heath (2006). This section provides a summary. We define loss of NOx as both direct loss of the primary species (NO or NO2) and transformation to NOz. Despite the nonlinearity of NOx decay, researchers commonly use pseudo first-order rates to express the average atmospheric decay behavior (e.g., CEC/EPRI, 2003). Decay rates measured in California power plants are directly applicable to the CS cases. The summer, daytime rates of decay of NOx emissions from the Moss Landing, CA (rural) and Pittsburg, CA (urban) power plant plumes were recently estimated (CEC/EPRI, 2003). Despite differences in the locations of the 2 power plants, the average decay rates based on multiple samplings of each power plant’s plume were nearly identical: 0.23 (70.17) h1. The average value from all flights is applied as the NOx decay rate for daytime conditions for all of the CS sites. In spite of limited contrary evidence (Russell et al., 1985; Munger et al., 1998; Liang et al., 1998; Brown et al., 2004), we make the simplifying assumption that NOx decay is invariant with time of day and season. With more conclusive information on the variability of plume NOx chemistry with atmospheric conditions, future investigations could incorporate refined loss rates. Emissions from small sources in urban areas quickly mix with the larger urban plume. Consequently, emissions from DG units located in urban areas are treated as following the transformation pathway of the urban plume into which it is emitted. NOx decay has not been studied in California urban plumes with modern, accurate measurement technologies. Recent investigations of the Nashville, TN, urban plume yielded estimates of NOx decay rates in the range 0.47–0.50 h1, nearly 50% higher than that measured in the plumes of nearby power plants (Nunnermacker et al., 1998, 2000). The faster decay of NOx in urban plumes is expected owing to the higher ratio of VOC to NOx compared with power plant plumes and owing to enhanced dispersion in urban areas. In translating the evidence from the Southeastern US to California, the relative magnitude of NOx decay in urban compared with

power plant plumes is probably more applicable than the absolute values. Thus, we upwardly adjust the decay rate observed in California power plant plumes by 50%, yielding a point estimate of k ¼ 0.34 h1 for NOx from DG sources sited in California urban areas. As above, we make the simplifying assumption that the NOx decay rate is invariant by time of day and season.

3. Results 3.1. Pollutant-specific intake fractions Pollutant-specific iFs for the 25 existing CS and DG units located at the city halls of the 11 most populous cities in California are reported in Tables 1 and 3, respectively. Median iFs for CS units are approximately 0.78 per million for primary PM2.5, 0.66 per million for HCHO, and 0.66 per million for NOx. For city hall DG, median iFs are 16 per million for primary PM2.5, 13 per million for HCHO, and 11 per million for NOx. We emphasize that these values are not technology-specific and thus apply to any source emitting from the same height at the same location. Given the uncertainties in modeling transport and fate, the iF values reported here should be considered as indicative rather than definitive determinations. Table 3 Intake fractions and intake-to-delivered energy ratios (IDER) for microturbines located at the city hall of the eleven most populous cities in Californiaa City

Anaheim Fresno Los Angeles Long Beach Oakland Riverside Sacramento San Diego San Jose Santa Ana San Francisco Median a

Intake fraction (per million)

Microturbine IDER (mg MWh1 del)

PM2.5 NOx

HCHO PM2.5

NOx

HCHO

28 7.8 33 19 16 12 10 12 12 29 19 16

20 6.5 26 14 13 7.8 7.2 9.7 9.8 23 16 13

4800 1900 7100 3500 3400 1500 1800 2700 2700 6000 4400 3400

89 29 115 61 56 34 32 43 43 100 69 56

15 5.8 22 11 11 4.9 5.7 8.4 8.4 19 14 11

1100 290 1300 710 590 440 360 460 470 1100 710 590

Intake fractions are constant across all technologies. IDER values are technology specific. To calculate IDER for other smallscale DG technologies use the multiplicative factors presented in Table 2.

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location, IDERs for different DG technologies differ only by the ratio of their characteristic emission factors. Table 2 contains multiplicative factors to obtain IDERs for the other DG technologies. To highlight the exposure impact of shifting from a centralized electricity generation system to one with substantial DG in urban areas, Figs. 2–4 compare primary PM2.5, NOx, and formaldehyde IDERs, respectively, for selected DG technologies located at 11 California city halls to both the existing CS and to hypothetical, new electricity generation units at the same sites that meet CA BACT standards. The primary result illustrated by these figures is that—for each pollutant considered—the central tendency of pollutant intake per unit of electricity delivered is much greater for almost every DG technology assessed than for existing CS in California. The only exception is for NOx from fuel cells, for which the central tendency is only 10% greater than for existing CS. The results also demonstrate that for all pollutants evaluated, IDERs for the DG technologies representing the existing stock in California (ICEs and turbines) generally present significantly greater exposure burden per unit of electricity delivered

For the 25 CS evaluated in this paper, the decaying pollutant’s iF is, on average, 82% and 77% of the conserved pollutant result for HCHO and NOx, respectively. For the 11 city hall DG cases considered, the analogous values are 77% and 63%. Thus, in this assessment, decay has greater relative impact in reducing population exposure for sources sited at ground level in the urban core than for elevated releases at CS sites. It is important to note that the variability of pollutant-specific iF results among facilities is far greater than variability in characteristic iFs among pollutants. A central result of our previous investigation was a 20  increase in conserved pollutant iF for city hall DG compared with that for existing CS (Heath et al., 2006). The result for decaying pollutants is similar: 19  for HCHO and 16  for NOx. Thus, decaying pollutants dampen to only a small degree the divergence in iF between the two modes of electricity generation. 3.2. Intake-to-delivered-energy ratios Tables 1 and 3 report IDERs for the existing CS and for DG microturbines hypothetically sited at 11 California city halls, respectively. Given the same

DG: diesel ICE 104

IDER (µg/MWhdel)

DG: NG ICE 1000

DG: NG microturbine

100

10

CS: existing CS: CA BACT-controlled

1 5

10

25

40

50

60

75

90

95

cumulative percentile (%) Fig. 2. Distribution of annual-average intake-to-delivered-energy ratios (IDERs) for primary PM2.5 for cases of existing and hypothetically BACT-controlled CS (n ¼ 25) and for 3 DG technologies hypothetically located at city halls of the 11 most populous cities in California (n ¼ 11). The lines represent lognormal distributions with the following parameters. (GM ¼ geometric mean (mg/MWhdel); GSD ¼ geometric standard deviation.) CS: CA BACT-controlled (GM ¼ 4.8, GSD ¼ 3.3), CS: existing (GM ¼ 22, GSD ¼ 8.8), DG: NG microturbine (GM ¼ 610, GSD ¼ 1.6), DG: NG ICE (GM ¼ 3500, GSD ¼ 1.6), and DG: diesel ICE (GM ¼ 22,000, GSD ¼ 1.6). DG technologies not plotted are low-temperature fuel cell (GM ¼ 460, GSD ¼ 1.6) and NG turbine (GM ¼ 670, GSD ¼ 1.6).

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105

DG: diesel ICE

DG: NG ICE

IDER (µg/MWhdel)

104

1000

DG: NG microturbine

100 CS: existing 10 CS: CA BACT-controlled 1 5

10

25

40

50

60

75

90

95

cumulative percentile (%) Fig. 3. Distribution of annual-average intake-to-delivered-energy ratios (IDERs) for NOx for cases of existing and hypothetically BACTcontrolled CS (n ¼ 25) and for 3 DG technologies hypothetically located at city halls of the 11 most populous cities in California (n ¼ 11). The lines represent lognormal distributions with the following parameters. (GM ¼ geometric mean (mg/MWhdel); GSD ¼ geometric standard deviation.) CS: CA BACT-controlled (GM ¼ 14, GSD ¼ 3.4), CS: existing (GM ¼ 170, GSD ¼ 6.0), DG: NG microturbine (GM ¼ 3200, GSD ¼ 1.7), DG: NG ICE (GM ¼ 1.5  104, GSD ¼ 1.7), and DG: diesel ICE (GM ¼ 7.8  104, GSD ¼ 1.7). DG technologies not plotted are low-temperature fuel cell (GM ¼ 320, GSD ¼ 1.7) and NG turbine (GM ¼ 5800, GSD ¼ 1.7).

1000 DG: NG ICE

IDER (µg/MWhdel)

100

DG: NG microturbine

10

1

0.1

CS: existing CS: CA BACT-controlled

0.01

0.001 5

10

25

40

50

60

75

90

95

cumulative percentile (%)

Fig. 4. Distribution of annual-average intake-to-delivered-energy ratios (IDERs) for primary HCHO for cases of existing and hypothetically BACT-controlled CS (n ¼ 25) and for 2 DG technologies hypothetically located at the city halls of the 11 most populous cities in California (n ¼ 11). The lines represent lognormal distributions with the following parameters. (GM ¼ geometric mean (mg/ MWhdel); GSD ¼ geometric standard deviation.) CS: CA BACT-controlled (GM ¼ 0.03, GSD ¼ 3.3), CS: existing (GM ¼ 0.27, GSD ¼ 14), DG: NG microturbine (GM ¼ 55, GSD ¼ 1.6) and DG: NG ICE (GM ¼ 1100, GSD ¼ 1.6). DG technologies not plotted are NG turbine (GM ¼ 49, GSD ¼ 1.6) and diesel ICE (GM ¼ 52, GSD ¼ 1.6).

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than for the newer DG technologies (microturbines and fuel cells). Also noteworthy: if CS are required to reduce emission factors to levels equivalent to CA BACT standards, the gap in exposure potential between CS units and DG units sited in the urban core could substantially widen. Since emission factors used here are point estimates, and thus do not contribute to the distributional characteristics of the IDER, the spread in the distribution of predicted IDERs for each small-scale DG technology is identical. The hypothetically BACT-controlled CS also use point estimates for emission factors, but their distributions include variability in the iFs of the existing units owing to differences in effective stack height. The IDERs for the existing CS units reflect both iF and emission factor variability. 4. Discussion One of the important potential benefits of DG is the potential to utilize waste heat in combined heat and power (CHP) systems (Strachan and Farrell, 2006). If waste heat utilization displaces natural gas boilers, then the exposure differences between DG with heat recovery and the central station plus local boiler are much smaller than those between DG and CS, considering electricity generation alone (Heath et al., 2006; Heath, 2006). The extent to which the exposure consequences of DG can be ameliorated by this means depends critically on a high level of utilization of combined heat and power. Alternative means for mitigating the increased exposure potential of DG are explored by Heath (2006) and Heath et al. (2006). An IDER is proportional to a source’s iF times its emission factor. Among the species considered here, the former factor is weakly dependent on pollutant characteristics whereas the latter factor varies strongly among pollutants. The relative influence of iF and EF in accounting for the disparity between characteristic IDERs of California CS and urban-sited DG technologies depends on the technology and pollutant in question. For primary PM2.5, the median emission factor for existing CS units is similar to those for fuel cells, microturbines and NG turbines (Tables 1 and 2). Thus, the difference between the existing CSs’ primary PM2.5 IDERs and those for the fuel cell, microturbine and NG turbine is primarily attributable to differences in intake fractions. In the case of HCHO, for all DG technologies except NG ICEs, the divergence in IDER from existing CS partially results from

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differences in EFs (5  ) and partially from differences in iFs (19  ). Comparing NG ICEs with existing CS units, most of the difference in HCHO IDER originates from the difference in emission factors (42 orders of magnitude). For NOx, microturbines have a similar emission factor to the central tendency for existing CS, leaving the 16  difference in iF to account for DG’s greater IDER. For the other DG technologies, both EF and iF contribute to the difference in NOx IDERs compared with the existing CS. As for comparisons amongst the 5 DG technologies, because iFs are not a function of technology, it is the differences in emission factors alone that cause the IDERs for small-scale DG technologies to vary. The most significant limitations in our estimation of IDERs relate to the uncertainty and assumptions associated with emission factors. However, to keep the discussion on emission factors in context, remember that because IDER is linear in EF, any change in emission factor translates to a proportional change in IDER. While the present assessment uses the best available emission factor data for existing units and for new DG technologies, these values are both imprecise and potentially inaccurate. Because DG technologies are newer than CS, their emissions have not been as extensively monitored and we expect their emission factors to be less certain. Samuelsen et al. (2005) report the range of point estimates of EFs for DG technologies available in the literature. For microturbines, all reviewed sources agree on the EF for PM within a narrow range; however, for NG ICEs, the estimates vary by an order of magnitude. A similar result is reported for NOx EFs. There is little evidence from which to infer whether the existing EFs for the natural gasfired DG technologies that are the focus of this paper are under- or overestimates of the true EFs. An issue related to uncertainty in DG emission factors is variability. Owing to the dearth of emission tests performed on DG technologies, there is little quantitative evidence of the extent of variability. However, variability could be high for these small-scale technologies owing to variable operation, combustion conditions, and maintenance practices. Any variability in EFs from the point estimates used here will lead to different local IDERs, with concomitant implications for environmental health risk assessments. Emissions are uncertain for CS, too. Only the largest plants are required to use continuous

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emission monitors (CEMs), which aim to accurately measure emissions of selected pollutants (e.g., SO2, NOx, and CO2). To develop the EPA and CARB emission inventories, smaller plants are allowed to estimate their emissions using regulator-approved, pollutant-specific emission factors. The EPA Inspector General recently audited EPA’s emission factor program, concluding that the reliability of the majority of published EFs is either ‘‘below average’’ or ‘‘poor’’ and that use of these EFs likely resulted in underreporting of over one million tons of pollutant emissions in just three industry sectors (EPA, 2006). Furthermore, even for plants required to use CEMs, emissions of HAPs are typically unmonitored. Thus, we expect the published formaldehyde emissions to be more uncertain than those for criteria pollutant emissions. Given the EPA Inspector General’s findings, CS likely underreport emissions when using EFs, leading to a likely underestimation of IDER from these facilities. Limitations in this assessment also arise with regard to an important assumption of this research: that all cases operate continuously at the specified emission factor. Consequently, our results may be biased if periods of non-operation are correlated with meteorological conditions that affect exposure. The treatment in this paper likely overestimates emissions during periods of poor dispersion (e.g., low system electricity demand occurs during the night when mixing heights and wind speeds are low). This could lead to an overestimate of population intake, the magnitude of which is inversely related to the plant’s capacity factor. Because reported emission factors are based on typical operating conditions, the assumption of constant emission factors could underestimate true annual average population intake per unit of electric energy delivered. Every period of nonoperation has associated start-up and shut-down emissions, which can be considerably higher per unit of fuel burned than those under steady-state operation (CARB, 1999; EPA, 2000). It is unlikely that even baseload plants operate at full-load for all operating hours. The effect of load on emissions varies by technology, pollutant and the presence of control technologies; part-load conditions can increase or (more rarely) decrease emission factors, often considerably (CARB, 1999; EPA, 2000). The emission factors used for DG technologies assume consistent performance and adequate maintenance. In truth, especially as units remain in service for long periods, their emission and efficiency

performance is likely to become degraded compared with their condition when new or when tested for regulatory certification in a laboratory. Owing to performance degradation, units in service for long periods would be expected to emit more pollutants per unit of electric power produced than newer units. It is even feasible that some units could become ‘‘superemitters,’’ analogous to an important motor vehicle issue (Sawyer et al., 2000). If so, then our estimates of exposure from DG technologies may be biased low compared with a future scenario in which substantial electricity is provided by numerous DG units. We emphasize that the analysis here has focused on conditions in California, and we encourage caution in extrapolating our findings to other locales. California has two important features that are unusual. First, its highly populous urban regions tend to be far from other downwind urban centers. This means that the 100 km range employed in our California analysis is less sensitive to truncation errors than it would be, say, for the eastern half of the United States. Second, while coal is a major fuel for electricity generation throughout much of the US and in many other industrialized and industrializing countries, it is an unimportant fuel for electricity generation in California. For other regions, it might be more reasonable to compare iFs and IDERs for coal-fired CS against natural-gas fired DG units, leading to less divergent results. Nevertheless, we do believe that the way we have framed the key questions can be usefully generalized, and that the methods employed here are useful for other settings, even if some modification is required. 5. Conclusions This paper introduces pollutant- and technologyspecific aspects to the comparison of population intake resulting from California natural gas-fired CS and air pollutant-emitting DG technologies. Because DG units could displace either existing or new CS generation, we contrast results for DG technologies both to existing CS and to new ones that would meet CA BACT standards. Compared with the case of conserved pollutants, pollutant decay reduces population iF. DG iF is reduced somewhat more than CS iF. This outcome dampens, but only slightly, the 20  divergence in iF determined for the case of conserved pollutants. Also, pollutant decay increases the emphasis on

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near-source population exposures, more so for the ground-based DG units than for CS (Heath, 2006). Pollutant-specific iFs multiplied by pollutant- and source-specific emission factors yields a new metric, the IDER. The IDER compares the exposure potential of air pollutant emissions from the 2 modes of electricity generation per amount of electric energy delivered to the place of use, in units that would be familiar to an energy planner. On this basis, even low-emitting DG technologies, such as fuel cells and microturbines, sited in downtowns of large cities have an exposure potential that is, in central tendency, 10–100  greater than the stock of existing CS in California. (An exception is for NOx emissions from fuel cells, which exhibit IDERs only 10% greater than for existing CSs.) For higheremitting DG technologies more characteristic of the existing stock in California, IDERs range up to 1000  those for existing CS. For all DG technologies, this difference increases by another order of magnitude when a comparison is made with IDERs for new combined-cycle gas turbines. Given the uncertainties in emission factors, especially for new DG technologies, these results may shift as more emission-testing results become available. However, it seems unlikely that the pattern and extent of increase in population exposure potential per unit electric energy delivered for DG compared with CS facilities will be substantially altered. Acknowledgments This work was supported by a University of California (UC) Energy Institute’s California Studies Grant (#07427) and a UC Superfund Basic Research Program Fellowship. Additional support was provided by a UC Toxic Substances Research and Training Program Fellowship and the California Air Resources Board (Contract #01-341). We acknowledge the contributions of P. Granvold and A. Hoats to the foundation on which this research rests.

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