Fuels for Urban Transit Buses: A Cost-Effectiveness Analysis

Mar 1, 2003 - Thus, our analysis indicates that the cost of the health benefits afforded by ECD and CNG is high, although not out of line when compare...
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Environ. Sci. Technol. 2003, 37, 1477-1484

Fuels for Urban Transit Buses: A Cost-Effectiveness Analysis J O S H U A T . C O H E N , * ,† J A M E S K . H A M M I T T , †,‡ A N D J O N A T H A N I . L E V Y †,‡,§ Harvard Center for Risk Analysis, Department of Health Policy and Management, Harvard School of Public Health, and Department of Environmental Health, Harvard School of Public Health, Harvard University, Boston, Massachusetts 02115

Public transit agencies have begun to adopt alternative propulsion technologies to reduce urban transit bus emissions associated with conventional diesel (CD) engines. Among the most popular alternatives are emission controlled diesel buses (ECD), defined here to be buses with continuously regenerating diesel particle filters burning low-sulfur diesel fuel, and buses burning compressed natural gas (CNG). This study uses a series of simplifying assumptions to arrive at first-order estimates for the incremental costeffectiveness (CE) of ECD and CNG relative to CD. The CE ratio numerator reflects acquisition and operating costs. The denominator reflects health losses (mortality and morbidity) due to primary particulate matter (PM), secondary PM, and ozone exposure, measured as quality adjusted life years (QALYs). We find that CNG provides larger health benefits than does ECD (nine vs six QALYs annually per 1000 buses) but that ECD is more cost-effective than CNG ($270 000 per QALY for ECD vs $1.7 million to $2.4 million for CNG). These estimates are subject to much uncertainty. We identify assumptions that contribute most to this uncertainty and propose potential research directions to refine our estimates.

1. Introduction Outdoor air quality in general, and the impact of heavy duty vehicles on air quality in particular, have received increasing attention in recent years. In an effort to mitigate that impact, emission standards and fuel standards for heavy duty vehicles in the United States have been tightened (Table 2 in ref 1). Public transportation agencies have been among the first organizations to pilot the use of alternative propulsion technologies, in urban transit buses. This study evaluates the incremental health benefits, changes in greenhouse gas emissions, and resource costs for two of the most prominent near-term technologiessemission controlled diesel (ECD) and compressed natural gas (CNG)scomparing them to the conventional diesel (CD) technology that propels most urban transit buses in the United States. * Corresponding author phone: (617)432-0394; fax: (617)432-0190; e-mail: [email protected]. Corresponding author address: Harvard Center for Risk Analysis, 718 Huntington Avenue, Boston, MA 02115. † Harvard Center for Risk Analysis. ‡ Department of Health Policy and Management, Harvard School of Public Health. § Department of Environmental Health, Harvard School of Public Health. 10.1021/es0205030 CCC: $25.00 Published on Web 03/01/2003

 2003 American Chemical Society

Reducing emissions generated by urban transit buses has been motivated largely by concern over the health effects associated with exposure to particulate matter (PM), secondary byproducts of nitrogen oxides (NOx) and sulfur dioxide (SO2), and potentially carcinogenic compounds in diesel exhaust. Fine particles, or selected components of the particles, have been implicated in respiratory and cardiovascular disease and death (e.g., refs 2 and 3). Diesel PM may also cause cancer or may be a proxy for carcinogenic compounds in diesel exhaust. NOx and volatile organic compounds (VOCs) form ground level ozone (smog) as the result of chemical reactions triggered by heat and the ultraviolet radiation in sunlight. U.S. EPA (p 3 in ref 4) has reported that short-term exposure to ozone is associated with respiratory-related hospital admissions and that repeated exposure increases susceptibility to lung tissue damage. Ozone may also be involved in the development of asthma and death (5). Finally, emissions reductions have also been motivated by concerns over global warming that may be due in part to a buildup of CO2, methane, and other gases that trap heat in the earth’s atmosphere. There are many alternative propulsion technologies being developed to address these emissions. ECD and CNG are among the most frequently adopted by transit agencies. For example, CNG-powered buses represented 60% of all “diesel alternative” buses in 1997 (Table 1 in ref 1), and New York City Transit (NYCT) planned to equip all 3500 of its diesel powered buses with ECD technology by 2003 (6). ECD and CNG represent two approaches to reducing emissions. Broadly speaking, ECD (as defined here) reduces emissions by removing certain fuel contaminants (sulfur in particular) and by enhancing engine exhaust treatment by adding a diesel particulate filter. Proponents of this approach say that ECD makes use of the existing fuel distribution, vehicle maintenance, and vehicle storage infrastructure, a factor that may reduce financial costs. On the other hand, this approach adds complexity to fuel production and to the engineering of diesel exhaust treatment systems. Proponents of CNG claim that because it is an “inherently cleaner” fuel than diesel (p 6 in ref 7), use of CNG promises to lower emissions to a greater extent than technologies aimed at cleaning up diesel. On the other hand, the fact that CNG is a gas that must be stored on buses at very high pressure (several thousand pounds per square inch) complicates fuel handling, bus storage, and the on-board vehicle fuel distribution system. This analysis characterizes the incremental resource costs and health benefits associated with a contemporary purchase (i.e., in 2001) of a fleet of either ECD or CNG buses, compared with a fleet of new CD buses. We use cost-effectiveness analysis (CEA) to quantitatively compare these alternatives. The assessment is meant to be a first-order comparison in order to provide a sense of what the answer is likely to be, what key sources of uncertainty must be addressed in order to improve the level of confidence in such a calculation, and to provide a framework for a more detailed analysis.

2. Methodology The cost-effectiveness (CE) ratio for each alternative technology (CEalt) is defined as

CEalt )

Costalt - CostCD QALYsCD - QALYsalt

(1)

where QALYs (Quality Adjusted Life Years) are a measure of health damages (reductions in longevity or health status) VOL. 37, NO. 8, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 1. Technologies Evaluated in This Study conventional diesel

ECD

CNG

engine 1995 Detroit Diesel Series 50 (8.5 L) 1998 Detroit Diesel Series 50 (8.5 L) 1999 Detroit Diesel Series 50G (8.5 L) exhaust Nelson oxidizing catalytic converter Nelson oxidizing catalytic converter closed loop emission control treatment (UCP part #G107348) (UCP part #G107348); Johnson technology Matthey CRT Particulate Filter fuel “low sulfur diesel”, i.e., e500 ppm “ultralow sulfur diesel”, i.e., natural gas sulfur, with an actual level e30 ppm sulfur, with an of 350 ppm sulfur actual level of 30 ppm

TABLE 2. Emissions Generated by Buses emission component PM NOx SO2 CO2 equivalenta

estimate low central high low central high low central high low central high

vehicle operation emissions (g/mile) CD ECD CNG 0.17 0.32 0.51 23.0 28.7 37.3 0.15 0.29 0.58 2500 2800 3100

0.01 0.03 0.09 23.0 28.7 37.3 0.0036 0.0072 0.014 2500 2800 3100

0.02 0.05 0.09 9.7 16.2 25.0 0 0 0 2900 2900 2900

CD

upstream emissions (g/mile) ECD CNG

0.093 0.093 0.093 0.99 0.99 0.99 0.55 0.55 0.55 570 570 570

0.096 0.126 0.156 1.15 1.21 1.26 0.55 0.55 0.55 660 660 660

0.080 0.095 0.110 2.3 2.7 3.0 0.71 0.83 0.95 960 1130 1300

a The total CO equivalent is computed as CO + 21 × CH + 310 × N O. This conversion takes into account each gas’s global warming potential, 2 2 4 2 as described by the Intergovernmental Panel on Climate Change (p 9 in ref 10).

(8). The smaller this ratio is, the more favorable the alternative technology, as a smaller value indicates that each incremental QALY is saved at a smaller cost. Negative CE values reflect a condition in which the alternative technology both costs more and decreases health (positive numerator and negative denominator) or one in which the alternative technology both improves health and saves resources (negative numerator and positive denominator). Although we did not do so, it is also possible to express health benefits in terms of their monetary value (e.g., willingness to pay to reduce risk) and to compare them directly to resource costs (i.e., benefitcost analysis). The analysis has been conducted for a hypothetical transit district. We quantify health impacts using estimated relationships between exposure to PM and ozone and QALYs lost. Exposures to PM and ozone, in turn, are estimated using the “intake fraction” methodology (9), which expresses atmospheric modeling results as the fraction of pollutants or their precursors emitted from a source eventually inhaled by some member of the population, allowing us to apply the findings from past modeling studies to our analysis. Note that past publications have referred to the “intake fraction” by other names, most prominently “exposure efficiency”. We incorporate nonhealth-related effects into the numerator (the cost term) of the CE ratio. Differences in GHG emissions are evaluated by converting them to CO2 equivalent emissions using the Global Warming Potential (GWP) coefficients specified by the Intergovernmental Panel on Climate Change (IPCC) (10) and monetizing the resulting damage impacts. Resource costs include vehicle procurement, infrastructure development, and operations (vehicle maintenance, facility maintenance, and fuel). Because the analysis includes costs and health impacts regardless of who incurs them, our results reflect a societal perspective (8). The analysis includes an evaluation of each parameter’s contribution to uncertainty in the CE ratios. We identify central, low-end, and high-end estimates for each parameter and compute the extent to which each of these parameters individually influences the CE ratio, while all other parameters 1478

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are set to their central values (i.e., a one-way sensitivity analysis). The time horizon is 12 years, corresponding to the assumed life of an urban transit bus. We also evaluate how our results are affected by geographic differences that influence pollutant formation and exposure. The CD, ECD, and CNG technologies are defined in terms of engine characteristics, exhaust treatment, and fuel characteristics, as detailed in Table 1. The specific definitions correspond to the characteristics of vehicles for which comparable emissions data are available. 2.1. Health Effects. The QALYs lost due to a single year of exposure to pollutant j that is formed from emission component i is

∆QALYij ) Emiti × IFij × βj

(2)

where Emiti ) emission rate for emission component i (µg/ year) (Section 2.1.1); iFij ) intake fraction for pollutant j relative to emission component i, defined to be the ratio of µg of j inhaled summed over all members of the population to µg of i emitted (Section 2.1.2); and βj ) QALYs lost per µg of pollutant j inhaled (Section 2.1.3). The term βj is computed by dividing γj (QALYs lost per year per million people exposed per µg/m3 of pollutant j) by the annual inhalation rate for 1 000 000 members of the population (20 million m3 inhaled per 1 000 000 person-days × 365 days per year). We evaluate emissions of PM, NOx, and SO2 considering mortality risks from primary and secondary PM exposure (including lung cancer) and mortality and chronic asthma risks from ozone exposure. The analysis takes into account both vehicle operating emissions and upstream fuel-cycle emissions (feedstock extraction and fuel production activities). The following sections briefly describe our approach for each component of the analysis. The Supporting Information provides an in-depth discussion of our methods. 2.1.1. Emissions. We divide emissions into two componentssemissions from vehicle operation and emissions from upstream activities (i.e., feedstock extraction and fuel production operations). Table 2 summarizes our esti-

TABLE 3. Intake Fraction Estimates intake fraction parameter primary PMs near-source component primary PMs far-source contribution NOx to PM (nitrate)

central estimate value

uncertainty

10-6

10-6

1×

9 × 10-6 5 × 10-8

NOx to ozone

2 × 10-6

SO2 to PM (sulfate)

3 × 10-7

spatial variability 10-6,

parameter ranges from 1 × to 5 × reflecting the use of air dispersion modeled values in place of values from a regression fit to these values parameter ranges from 4 × 10-6 to 2 × 10-5, reflecting uncertainty associated with transport modeling parameter ranges from 1 × 10-8 to 3 × 10-7, reflecting uncertainty in atmospheric chemistry assumptions and assumptions regarding background conditions uncertainty judged to be similar in magnitude to the uncertainty associated with the NOx to PM (nitrate) iF parameter; value therefore ranges from 4 × 10-7 to 1 × 10-5 parameter ranges from 1 × 10-7 to 9 × 10-7, reflecting uncertainty in atmospheric chemistry assumptions and assumptions regarding background conditions

mates (Section 2.2 discusses the GHG emissions, quantified in Table 2 in terms of their CO2 equivalent). We estimate vehicle operation emissions using published studies that reported dynamometer measurements of emissions generated by transit buses evaluated using the central business district (CBD) test cycle (see Tables S-1-3, Supporting Information). For CD, we omit measurements from buses using ultralow sulfur diesel, buses with two stroke engines, and buses not equipped with catalytic converters. We also limit ECD measurements to vehicles with four-stroke engines. CNG measurements are limited to vehicles with closed loop technology, which ensures an optimal air-tofuel ratio more consistently and hence a reduction in both PM and NOx emissions. We estimate upstream emissions for CD and CNG using the GREET model, described by Wang (11). We assume that ultralow sulfur diesel fuel production increases upstream emissions reported by Wang for CD fuel by 35% for PM and 22% for NOx (the midpoint values estimated from Beer et al. (12) and CSIRO (13)) and that ECD upstream SO2 emissions are the same as the corresponding emissions for CD. Issues that may complicate interpretation of the data, including the impact of vehicle age on emissions and differences between total PM and fine fraction PM (particles with an aerodynamic diameter of less than 2.5 microns, denoted “PM2.5”), are discussed in Section S-1.1 (Supporting Information). Aging does not substantially influence our assumptions because newer engines with electronic controls exhibit stable emission characteristics over time when properly maintained. Although most emissions studies have not specifically quantified PM2.5, which may be most responsible for adverse health effects associated with PM, differences between total PM and PM2.5 emission measurements are likely to be small because PM2.5 comprises the bulk of total PM in vehicle emissions. 2.1.2. Atmospheric Dispersion and Population Exposure. Section S-1.2 (Supporting Information) details development of intake fraction (iF) parameter values for primary PM, secondary PM, and ozone. In addition to estimating central estimate values, we identify bounds to characterize uncertainty. Because these iF values depend on local atmospheric chemistry characteristics and population patterns, we also describe how the central estimate values may vary geographically. For analytical reasons described in the Supporting Information, we divide computation of the iF for primary PM into a near-source and far-source contribution. Table 3 summarizes our estimates. To quantify the contribution of

assumed to be minimalssee text

parameter central estimate ranges from 4 × 10-6 (Western U.S.) to 2 × 10-5 (Northeastern U.S.) central estimate ranges from 4 × 10-9 (Northeast U.S.) to 2 × 10-7 (Western U.S.) central estimate ranges from -1 × 10-6 (states of NJ, PA) to 4 × 10-6 (states of TN, AL, GA) central estimate ranges from 1 × 10-7 (Northeast U.S.) to 7 × 10-7 (Industrial Midwest U.S.)

primary PM to distant population exposures (i.e., the “far source contribution”), we use results reported by Wolff (14), who estimated exposure associated with 20 nationally representative urban highway segments. To quantify the contribution of primary PM to exposure among individuals within 15 km of an emissions source (i.e., the “near source contribution”), we fit an exponential decay function to concentrations calculated using CAL3QHCD (15) for a 30 km × 30 km region bisected by a hypothetical bus route. Within this region, we assumed a population density of 5000/km2, representative of a large urban area. We estimate the NOx and SO2 contributions to secondary PM from the source-receptor matrix created by Abt Associates (16) and corroborate the magnitude of the estimates with the Wolff (14) application of the CALPUFF model (17). We estimate the amount of ozone inhaled per unit NOx emitted using a source-receptor matrix generated by Krupnick et al. (18) for typical ozone episodes in the eastern half of the U.S. Developing a representative value is complicated by the dependence of ozone levels on VOC concentrations and ultraviolet radiation levels and the nonlinearity of these relationships. We use an average value across six geographic regions as our central estimate and also describe how this iF value varies geographically, noting that in some locations, it is negative (implying net ozone scavenging). 2.1.3. Health Effects. Section S-1.3 (Supporting Information) details the computation of γj (see discussion following eq 2), which is the number of QALYs lost per year per 1 000 000 people exposed per µg/m3 of pollutant j. Table 4 summarizes our estimates. Our estimate of the impact of PM on all-cause mortality is based on both the cohort mortality and time series literature. The time series data imply a smaller impact on mortality incidence and fewer QALYs lost per death compared to the cohort evidence. Based on the cohort data, for which we relied on the Pope et al. (19) follow-up to the original American Cancer Society (ACS) study (2), we estimate 500 life years lost per 1 000 000 people exposed per µg/m3 PM2.5 per year. This serves as our central estimate. As a lower bound, we use the revised time series mortality estimate (20) from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) (3), which we calculate to be 8 life years per 1 000 000 people exposed per µg/m3 of PM2.5 per year. To convert life years to QALYs, we assume that victims suffered from preexisting coronary or respiratory disease, and that for individuals with these conditions, each life year is worth 0.8 QALYs (21). VOL. 37, NO. 8, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 4. QALY Loss Estimates QALYs lost per million people exposed per µg/m3 of pollutant pollutant

health effect

low

central

high

uncertainty addressed quantitatively

PM

all-cause mortality

6

400

400

diesel primary PMa CNG primary PM

cancer cancer

0 0

30 0

80 80

ozone

all-cause mortality asthma

0 0

6 1

6 3

It is uncertain if the association observed in the cohort mortality studies is causal. It is uncertain if the observed association is causal. Central estimate of zero, upper bound equal to diesel’s carcinogenicity reflects lack of evidence regarding CNG carcinogenicity It is uncertain if the observed association is causal. It is uncertain if the observed association is causal, and if so, whether it applies to men and women, or just men.

a The potential association between cancer and exposure to diesel exhaust or CNG exhaust may be due to toxins in the exhaust (e.g., polycyclic aromatic hydrocarbons (41)). The PM component of the exhaust serves as an exposure proxy. However, because of the numerous other constituents of ambient PM in the all-cause mortality studies, we assume that the carcinogenic impact is above and beyond the QALY loss estimate listed in the first row of this table.

Evidence for a relationship between ozone exposure and mortality is limited to the time-series literature. A recent review (5) found that these data imply a 0.5% increase in mortality per 10 µg/m3 increase in 24-h average ozone concentrations. We assume ozone mortality is associated with the loss of as many as 2 life years and that each life year lost is worth 0.8 QALYs. We use a lower bound of zero to reflect the possibility that the relationship between ozone and mortality is not causal. Ozone may also contribute to morbidity by increasing the incidence of asthma. Because many studies have failed to find such an association, we assume that ozone’s contribution to asthma development may be zero. However, because the Adventist Health Study of Smog (AHSMOG) (22) did find such an association, we assume this loss may be as great as three QALYs per 1 000 000 people exposed per µg/ m3 of ozone per year. Finally, we consider the potential for either diesel or CNG exhaust to increase the incidence of lung cancer. As described in the Supporting Information, the evidence linking diesel exposure to lung cancer is controversial, while there is little evidence supporting or negating CNG’s carcinogenic potential. Assigning potency values to either pollutant is therefore difficult. We derive our central and upper bound estimates for diesel’s contribution to lung cancer incidence from the Dawson and Alexeeff (23) reanalysis of the Garshick et al. (24) railroad worker study. Others have suggested that use of these data to estimate risk is inappropriate (25, 26) and that the Dawson and Axeleeff finding reflects confounding (26). Our lower bound estimate for this potency is therefore zero. Although no direct evidence indicates CNG exhaust is carcinogenic, it does contain agents thought to be responsible for diesel’s putative carcinogenicity. Studies have disagreed as to which technology emits more particle-bound PAHs (27, 28), but these studies did not investigate the specific technologies evaluated here. Given the available information, our central estimate for CNG’s carcinogenicity is zero with an upper bound equal to diesel’s. In any case, our sensitivity analysis indicates that the carcinogenicity assumptions have only a small impact on our results. 2.2. Greenhouse Gas Emissions. Section S-2 (Supporting Information) details development of our assumptions for greenhouse gas (GHG) emissions. GHGs from vehicle operation and upstream activities include CO2, CH4, and N2O. To quantify vehicle operation emissions, we use measurements from the same studies used to quantify PM and NOx emissions. To quantify upstream activity emissions, we use the Wang (11) lifecycle analysis and estimates reported by 1480

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Beer et al. (12). Table 2 summarizes our GHG emission assumptions. We quantify GHG damages using estimates of the incremental damage or optimal carbon tax calculated using integrated assessment models. Based on a summary of optimal carbon tax studies (29), we estimate damages for one ton of CO2 ranges from $2 to $22, with the geometric mean of these bounds ($7) serving as our central estimate for this parameter. For a bus traveling 40 000 miles per year, incremental annual GHG damages amount to between $8 and $88 (central estimate of $28) for ECD and between $64 and $704 (central estimate of $224) for CNG. 2.3. Transit Agency Costs. Resource costs, which are incurred by transit agencies, include vehicle procurement (Section 2.3.1), infrastructure (Section 2.3.2), and operations (Section 2.3.3). The analysis time horizon is 12 years, the expected life of a transit bus (p 30 in ref 30). The assumed real discount rate is 3%. Table 5 summarizes our estimates. Section S-3 (Supporting Information) details development of these estimates. 2.3.1. Vehicle Procurement. Incremental ECD costs consist of the particle filter’s purchase and its installation, estimated to total $7500 (31), or $750 per year. Incremental CNG costs may be as high as $36 000 per bus ($3600 per year) (30), although some observers claim this difference will decrease as CNG production volumes increase (32). Nonetheless, some CNG vehicle components (e.g., fuel tanks) appear to be inherently more expensive than the corresponding diesel bus components. We therefore assume that the incremental cost for CNG buses is at least $20 000 ($2000 per year). 2.3.2. Infrastructure Costs. We assume ECD facility requirements are identical to CD requirements and hence that there are no incremental infrastructure costs for this technology. Because natural gas is delivered from municipal distribution systems at relatively low pressure, compressors must be used so that large quantities of gas can be loaded into each vehicle’s fuel tanks. Moreover, because natural gas is a vapor at room temperature, special steps must be taken to reduce the risk of an explosion in facilities for these vehicles (32). Available data indicate CNG facility costs (Section S-3.2, Supporting Information) vary substantially due to climate (which determines if the facility is outdoors or indoors) and land availability (which determines if facilities can be built on a single level, substantially simplifying ventilation). In low cost areas, we estimate that annual infrastructure costs amount to $950 per bus. In high cost areas, these costs amount to between $1900 and $11 000, with a central estimate of $6200. Annualized costs reflect amortization of the facilities over a period of 50 years.

TABLE 5. Summary of Incremental Transit Agency Costs Compared to CD (Annualized Costs Per Bus Per Year) ECD

CNG

cost category

low ($)

central ($)

high ($)

low ($)

central ($)

high ($)

bus procurement infrastructure low cost region high cost region operational costs fuel (40 000 miles/year) vehicle maintenance infrastructure maintenance total low cost region high cost region

750

750

750

2000

2800

3600

0 0

0 0

0 0

950 1900

950 6200

950 11000

400 130 0

800 130 0

1200 130 0

1200 0 600

3200 6000 2300

5200 12000 4000

1300 1300

1700 1700

2100 2100

4800 5800

15000 21000

26000 36000

2.3.3. Operating Costs. Operating costs reflect fuel, bus maintenance, and facility maintenance. Section S-3.3 (Supporting Information) details development of estimates for these costs. These costs are based on the assumption that a bus travels 40 000 miles per year. Reducing fuel sulfur levels slightly increases fuel costs for ECD compared to CD ($400 to $1200 per year, with a central estimate of $800). CNG buses have higher fuel costs than CD because CNG engines are less efficient. That penalty increases annual fuel costs between $1200 and $5200 per year, with a central estimate of $3200. Maintenance of the particle filters on ECD vehicles involves removal of ash generated by lubricating oils every 60 000 miles (33), a task costing approximately $200. Comparing diesel and CNG maintenance costs is complicated by a variety of factors (e.g., the vehicles studied are not the same age, the engines differ in performance, or the cost estimates fail to reflect repairs made under warranty). We judged data from NYCT to provide the best comparison because the CNG and CD vehicles evaluated were the same age. The NYCT findings imply that CNG maintenance costs exceed CD maintenance costs by $0.30/mile (30). Because it is plausible that CNG and CD maintenance costs are the same, we estimate that incremental CNG vehicle maintenance costs range from $0.00 to $0.30/mile ($12,000/year), with a central estimate of $0.15/mile ($6000/year). Finally, based on data from Los Angeles, New York, and estimates made by the Transportation Research Board (34), we assume that incremental annual facility maintenance costs for CNG amount to between $600 and $4000 per vehicle, with a central estimate of $2300.

FIGURE 1. Fraction of total conventional diesel emissions by emitted pollutant.

3. Results 3.1. Health Impacts - Central Estimate Results. Figures 1-3 illustrate the central results. Both ECD and CNG substantially decrease total PM emissions (Figure 1), although both technologies slightly increase the upstream PM component. Only CNG decreases NOx emissions. Both ECD and CNG virtually eliminate vehicle operation SO2 emissions, but substantially increase the upstream SO2 component. As illustrated in Figure 2, ECD and CNG emissions produce virtually the same decrease in PM exposure. However, CNG decreases ozone exposure, whereas ECD does not. As illustrated in Figure 3, both ECD and CNG substantially reduce risks associated with vehicle operation PM emissions. Health impacts associated with NOx are the same for ECD and CD but lower for CNG. Purchasing a fleet of 1000 ECD vehicles saves six QALYs annually relative to a fleet of CD buses. The corresponding value for CNG buses is almost nine QALYs. 3.2. Cost-Effectiveness of ECD and CNG - Central Estimate Results, Uncertainty, and Variability. Dividing the central estimate values for incremental QALYs saved into

FIGURE 2. Fraction of total conventional diesel exposure by inhaled pollutant. the central estimate financial cost yields CE ratio estimates for ECD and CNG relative to CD. Omitting the monetized impact of GHG emissions yields central estimate CE ratios of $270 000 for ECD and either $1.7 million (low cost region) or $2.4 million (high cost region) for CNG. Relative to ECD, CNG costs $5 million to $8 million per QALY saved. Inclusion of even high end estimates for GHG impacts increases these CE ratios by only 5%. Because their monetized impact is relatively negligible in this analysis, we omit further discussion of GHG emissions. Uncertainty: The CE ratios have a large plausible range. Transit agency incremental costs (the CE ratio’s numerator) span a factor of approximately two for ECD and a factor of approximately six for CNG (see Table 5). The range for QALYs VOL. 37, NO. 8, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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TABLE 6. Parameter Contribution to Cost-Effectiveness Ratiosa,b univariate impact of parameter on CE value CE value CE value for ECD for CNG Emissions from Vehicle Operations (g/mile)

FIGURE 3. Annual QALYs lost due to emissions from a fleet of 1000 buses. saved annually by these technologies is even greater. For a fleet of 1000 buses, this value ranges from 0 to 41 QALYs for ECD and from 0.01 to 65 QALYs for CNG. Hence, for ECD buses, the CE ratio can be as small as nearly $30 000 per QALY or infinite (no health improvement per dollar spent). For low cost areas, the CNG CE ratio ranges from $70 000 to more than $2 billion. In high cost areas, the corresponding range is $90 000 to more than $3 billion. To identify factors contributing the most substantially to this uncertainty, we calculated the impact on the CE ratio of replacing each parameter with its two bounding values while holding all other parameters equal to their central values (Table 6). Table 6 omits the upstream emission rate parameters because their influence was in all cases no more than 1.2 (ECD upstream PM emissions) and in most cases less than 1.1 (all others). Spatial variability: Because we did not have sufficient information to calculate the full set of iF values for different locations, we were unable to calculate specifically how health impacts vary geographically. In lieu of this information, we report the range of CE ratios corresponding to the use of all lower bound iF values and then all upper bound values listed in the right column of Table 3 (even though the locations with low iF values for some pollutants often had high iF values for other pollutants). For ECD, the values range from $130 000 to $560 000. For CNG, they range from either $730 000 to $4.7 million (low cost area assumptions in Table 5) or $1.0 million to $7.0 million (high cost area assumptions in Table 5).

4. Discussion Incremental QALYs saved by CNG relative to CD are approximately 50% greater than the corresponding ECD benefit (based on parameter central value estimates), but the cost per QALY saved for CNG is from six to nine times that of ECD. The CE ratios can be put into perspective by comparing them to other interventions that reduce morbidity and mortality. Direct comparisons reveal numerous more favorable interventions in the following domains: medical prevention of cancer and coronary heart disease, reduction of motor-vehicle injury trauma and fatalities, and prevention of infectious disease (35). Moreover, although there has been no explicit calculation of the value of a saved QALY in the context of public health CEA, Hammitt (36) noted that “interventions that cost less than $50,000-100,000 per qualityadjusted life year are often considered desirable in the U.S.”. However, because interventions in different domains have different goals, it is not surprising that CE ratios also vary widely across regulatory domains. For example, Tengs et al. (37) found that among interventions considered by govern1482

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CD PM CD NOx CD SO2 ECD PM ECD NOx ECD SO2 CNG PM CNG NOx CNG SO2

3.9 1.5 1.0 1.4 1.4 1.0

2.6 1.4 1.0

1.2 1.5 1.0

QALYs Lost per Year per Million People Exposed per µg/m3 of Pollutant PM (all-cause mortality) 12.4 diesel PM (cancer) 1.2 CNG PM (cancer) ozone (mortality) 1.0 ozone (asthma) 1.0

5.3 1.3 1.0 1.1 1.0

Intake Fraction Values inhaled PM from near-source PM emissions inhaled PM from far-source PM emissions inhaled PM from NOx emissions inhaled PM from SO2 emissions inhaled ozone from NOx emissions

1.3 3.6 1.9 1.0 1.5

1.5 4.0 1.0 1.1 1.0

a The value reported is the ratio (or its reciprocal) of the CE value computed using the upper bound value of the specified parameter to the CE value computed using the lower bound value for the specified parameter. For this computation, all other parameters are set equal to their central value. b Upstream emission parameters are omitted because they had a very small impact on the results: 1.2 for ECD upstream PM emissions and less than 1.1 for all other upstream emission parameters.

ment agencies, costs per unit health benefit accrued vary substantially by agency. Median costs per life-year saved by regulations considered by different agencies were as follows: EPA - $7.6 million, FAA - $23 000, CPSC - $68 000, NHTSA - $78 000, and OSHA - $88 000. Because of differences in goals and at-risk populations, comparisons of economic efficiency may be more appropriately made with other interventions in the same domain. To put our CE ratio estimates into the most relevant context, we compare them with other interventions aiming to reduce human exposure to airborne pollutants. Wolff (14) computed for nine interventions the cost per ton reduction in PM2.5 inhaled, which we converted to a monetary cost per QALY saved. Assuming that exposure of 1 000 000 individuals to an incremental µg/m3 of PM2.5 results in the loss of 400 QALYs annually, stationary source control CE ratios range from tens of thousands to more than $1 000 000 per QALY. For mobile source measures, the corresponding range is from tens of thousands to nearly $4 000 000. The CE ratio for ECD falls within the middle of these ranges, whereas the CE ratio for CNG buses exceeds the upper end of the stationary source range and is toward the high end of the mobile source range. Thus, our analysis indicates that the cost of the health benefits afforded by ECD and CNG is high, although not out of line when compared to other air pollution control measures. However, we note that we have used a series of simplifying assumptions. First, our emissions data were limited to the relatively small set of dynamometer measurements made on buses equipped with the technologies studied here. It is also unclear if the commonly used CBD test cycle accurately characterizes how buses are typically driven in urban transit

settings. We assume engines and emission control equipment will be well maintained. We have not analyzed how maintenance failures would affect the technologies considered. Second, our exposure estimates are based on simplified models that do not account for atmospheric complexities or characteristics of specific roads or cities. It is likely that these types of models are reasonable in many settings, but for urban vehicles, they could introduce substantial errors due to the relative importance of road and source configuration. In addition, our exposure metric implicitly assumes that annual average exposure is the proper dose metric and hence does not allow for the possibility of a dose-rate dependence or a threshold. Third, our characterization of the impact of PM2.5 on health outcomes does not address the possibility that this impact is due to the ultrafine fraction. If the number of ultrafine particles emitted by candidate technologies is not proportional to PM2.5, our results may be invalid. Accounting for ultrafine particles would require emission measurements and further data on their specific contribution to health risk. In addition, while this analysis focuses on monetary costs and aggregate disease reduction benefits, there are other factors that may be important in deciding among alternative propulsion technologies. First, diesel buses are often described as noisy, and diesel exhaust can have a strong, unpleasant odor. Whether the diesel particle filter, lower sulfur content, or other new engine characteristics mitigate these impacts has not been well-studied. In addition, in colder climates, diesel buses are often left running in bus depots throughout the night during the winter to avoid problems that can occur when starting these engines on cold mornings. This practice can compound quality of life issues (and health risks) for individuals who live near bus depots. Second, different fuels have different safety risks. According to the Transportation Research Board (p 22 in ref 34), CNG will spontaneously ignite if its concentration in air reaches 5% to 15%. CNG’s high pressure can also pose a hazard. For example, a loose fitting can become a high velocity projectile (p 22 in (34)). CNG’s high pressure makes regular inspection of fuel tanks critical. The Transportation Research Board noted one example of a new composite tank rupturing and exploding without warning on a bus parked in a service area (34). NRC also noted reports of CNG tank pressure relief valve failures (34), although recent design improvements have reduced the failure rate for these valves. Because diesel fuel is not stored under pressure and does not spontaneously ignite, it does not pose the same safety risks. However, its toxicity can pose a hazard if inadvertently ingested. Moreover, a spill can contaminate soil and groundwater. Third, although the aggregate impact of these alternative technologies on population risk is relatively small, the impacts on individual risk may be substantially greater. For example, for the near-source component of primary PM exposure, the fitted relationship between distance from road and average pollutant concentration indicates that PM concentrations within 1 km of the road average 12 times the value within 15 km of the road. Individuals spending substantial time even closer to bus routes may be exposed to an even greater extent. This calculation highlights the importance of better characterizing near-range exposures associated with bus emissions. Finally, we did not address energy security issues related to reliance on foreign sources of energy (38). With these caveats in mind, we compare our findings to other analyses of alternative propulsion systems for urban transit buses. At least two such analyses have reached more enthusiastic conclusions regarding CNG (7, 39), while another analysis conducted for the Massachusetts Bay Transportation Authority concluded that “CNG buses are clean and reasonably reliable, but significantly more expensive to purchase, store, and operate than diesel buses” (p ES-10 in ref 40). However,

the analysis described in our paper is, as far as we know, the first to compute and compare aggregate incremental costs and health benefits for bus propulsion technologies. In addition, we are unaware of any analysis that has discussed both the costs and health benefits of ECD technology. Our analysis provides some perspective on which scientific questions must be addressed to reduce the substantial uncertainty in our results. Of the parameters listed in Table 6, three appear to be dominant contributors to uncertainty: the CD vehicle operation PM emission rate, the impact of PM on all-cause mortality, and the far-source primary PM iF value. The value of the emission rate parameter could be refined by testing additional vehicles and using alternative operating cycles. The iF parameter estimate could be improved by use of more complex atmospheric modeling, while the magnitude of PM’s impact on health would require new epidemiological evidence and/or the use of expert judgment to evaluate the strength of the existing evidence. CNG’s aggregate cost also contributes substantially to uncertainty, with low-end cost values consistent with a CNG CE ratio that is a factor of three to four times smaller (more favorable) than the central estimate. This parameter’s uncertainty could be reduced by refining maintenance cost estimates. Experience gained by transit agencies now making large-scale use of CNG vehicles should help in this regard. Among factors that vary geographically, differences in atmospheric chemistry and population density have a moderate (factor of 2) impact on the CE ratio for CNG. For ECD, the impact is small in part because the ECD technology evaluated here does not reduce NOx emissions. Although ECD technology does reduce SO2 emissions compared to CD, the relatively lower emission rate of SO2 compared with NOx makes the SO2 contribution to health impacts in our analysis relatively small. It is worth noting that some issues that have been contentious contribute little to uncertainty in the context of the evaluation described here. Neither diesel exhaust’s carcinogenicity nor the climatic effects of GHG emissions are quantitatively important in determining the costs and health benefits of these alternative propulsion systems.

Acknowledgments This study was supported by a grant from International Truck and Engine Corporation. We benefited from an advisory panel consisting of Frank Bucalo (Metropolitan Transit Authority of Harris County), Frank Cihak (American Public Transportation Association), Louis Anthony Cox, Jr. (Cox Associates), John Evans (Harvard Center for Risk Analysis), Harry Harris (Connecticut Department of Transportation), Anne Herzenberg (Massachusetts Bay Transportation Authority), Marty Lassen (Johnson Matthey), Dana Lowell (New York City Transit Authority), Timothy Lynch (Motor Freight Carriers Association), Heather MacLean (University of Toronto), Roger McClellan (Chemical Industry Institute of Toxicology), Allissa Oppenheimer (Gas Technology Institute), Jack Requa (Washington Metropolitan Area Transit Authority), Michael Riley (Motor Transportation Association of Connecticut), Julie Ross (Massachusetts Department of Environmental Protection), Allen Schaeffer (Diesel Technology Forum), Paul Skoutelas (Pittsburgh Port Authority), and George Sverdrup (National Renewable Energy Laboratory).

Supporting Information Available A more complete description of our methodology for quantifying health effects (vehicle PM, NOx, and SO2 emissions, population exposure, and the relationship between exposure and health), greenhouse gas emissions, and transit agency costs. This material is available free of charge via the Internet at http://pubs.acs.org. VOL. 37, NO. 8, 2003 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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Received for review January 7, 2002. Revised manuscript received January 14, 2003. Accepted January 15, 2003. ES0205030