Methods

We estimated that eliminating short car trips (≤ 8 km round trip) in urban areas of Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin would reduce residential vehicle use by 20%. This estimate is based on a census-tract level travel and mobile emissions inventory by Stone et al. (2007), who combined 1995 Nationwide Personal Transportation Survey (NPTS) responses (FHWA 1997), demo-graphic modeling of household vehicle travel, and the U.S. EPA MOBILE6 emissions factor model (U.S. EPA 2004b). From that contemporary emissions inventory, we estimated current emissions levels if all round trips of ≤ 8 km in urban and suburban census tracts were made using alternate modes of transportation. To inform the potential impact of a range of realistic policies and choices, we used these estimated reductions to quantify the maxi-mum potential response to a change in travel behavior. Although arbitrary, this assumed reduction in short auto trips would be consistent with the use of active (cycling or walking) transportation in European cities similar in density and population to the MSAs considered here. These values represent theoretical upper bounds on short-trip transportation choices under current travel patterns and population density. We assume that no change occurs in rural travel because distances between residential and commercial areas are typically too great for bicycling or walking and because rural populations are too low to support mass transportation.

Specifically, we compared transportation modes used in the study-area cities, with populations ranging from 837 persons/km² in Grand Rapids to 4,884 persons/km² in Chicago (average 2,051 persons/km²), to five European cities with similar population densities (range 901–5,971, average 2,910 persons/km²) [see Supplemental Material, Table 1 (http://dx.doi.org/10.1289/ehp.1103440)]. Although public transportation use was similar, only 39% of trips were made by automobile in the European cities, compared with 80% of trips in the Great Lakes region. Although the configurations and historical growth patterns of the European cities differ from their American counter-parts, the fact that half of all trips used active transportation suggests that active transport for 50% of short trips is feasible for similar travel distances in mid-sized American cities of similar density and that greater active transportation need not be limited to areas of highest density.

Table 1. Estimated PM2.5 reductions, reductions in health impacts, and valuation of reduced PM2.5 exposure.

We estimated changes in emissions only for on-road light-duty passenger vehicles with internal combustion engines and only for round trips ≤ 8 km. We modeled changes in primary emissions (including NO_x, carbon monoxide, sulfur dioxide, ammonia, VOCs, elemental carbon, organic carbon, and primary fine and coarse particulate matter) from all stages of vehicle operation, as well as emissions from evaporation, brake dust, resuspended road dust, and refueling. Reducing the number of short trips further lessens the frequency of cold starts from 59.9% to 21.9% of trips in urban tracts and from 55.6% to 20.3% in suburban tracts, with corresponding reductions in VOC and NO_x emissions. We mapped emissions from the census-tract level to the 12 × 12 km² model grid by area-weighted averaging using the U.S. EPA Sparse Matrix Operator Kernel Emissions (SMOKE) model, version 2.4 (Community Modeling and Analysis System Center 2007). Emissions from sources other than motor vehicles were from the 2001 National Emissions Inventory (U.S. EPA 2005a) and were held constant in both scenarios.

We estimated changes in ambient air PM_2.5 and O₃ concentrations using hourly regional chemical transport simulations with the Community Multiscale Air Quality Model (CMAQ), version 4.6 (Byun and Schere 2006), driven by meteorology from the weather research and forecasting model for the full year of 2002 (Skamarock and Klemp 2008; Skamarock et al. 2008). Simulations with CMAQ were conducted on a 12 × 12 km² grid and included gas phase, aqueous, and heterogeneous chemical reactions and equilibrium aerosol thermo-dynamics. We followed the model configuration used by Spak and Holloway (2009), with boundary conditions from a 36 × 36 km² simulation over continental North America.

We used the Environmental Benefits Mapping and Analysis Program (BenMAP) version 4.0.35 (U.S. EPA 2010a) to estimate health impacts due to CMAQ-simulated changes in ambient air pollution resulting from reduced car travel. Because BenMAP addresses both mobile and stationary sources (U.S. EPA 2004a, 2008), it has been used to support the creation of environ-mental regulations in several countries.

After air quality data is loaded into BenMAP, the program determines the change in ambient air pollution. BenMAP then uses concentration–response functions (CR) to calculate the relationship between the pollution and certain health effects, applying the relationship to the exposed population (Abt Associates 2010). Finally, BenMAP uses a “damage function” to estimate health costs and benefits from changes in air quality. A damage function quantifies the health benefits and economic value of reduced exposure to pollutants (Davidson et al. 2007).

BenMAP 4.0 (i.e., version 4.0.35) incorporates hourly air pollution data and county-level baseline incidence rates for the following health outcomes: overall mortality, asthma exacerbations, chronic bronchitis, hospital admissions, acute myo-cardial infarctions, acute and chronic respiratory infections, upper and lower respiratory infections, work-loss days, and school-loss days. Spatial speci-ficity in baseline incidence data varies by health outcome and location; where county-level data are not available, BenMAP distributes state estimates to the county level using age-specific rates for each health outcome within each county. For mortality estimates, BenMAP combines national-level census mortality rate projections and county-level age-specific incidence rates from 2006 with projected changes in study area populations to derive county-level mortality rate projections for 2010. For the present study, BenMAP used state-level hospitali-za-tion data to estimate county-level incidence for Minneapolis/St. Paul, Chicago, and Indianapolis; county-level incidence data for all cities in Ohio; and city hospital discharge data for Milwaukee, Madison, Detroit, and Grand Rapids. For emergency room (ER) admissions, we used midwest regional incidence data for Detroit, Grand Rapids, Chicago, and Indianapolis; state-level data for Minneapolis/St. Paul; county-level data for Ohio cities; and hospital discharge-level data for Milwaukee and Madison. For all cities, non-fatal acute myo-cardial infarction incidence rates were based on regional hospitalization data. All other health end point data were based on national figures.

BenMAP assigns monetary values to the reduction of adverse health effects based on national averages that do not reflect intra-city or inter-city variability in costs. The BenMAP analysis was conducted on the 12 × 12 km² grid, using 2010 census projection allocation to the grid by the U.S. EPA. Valuation is in 2010 dollars.

We combined air quality estimates for 2002 from CMAQ with 2002 U.S. EPA monitoring using spatial scaling by Voronoi nearest neighbor averaging (e.g., Chen et al. 2004). This pairing yields air quality inputs to BenMAP including complete spatial and temporal coverage by high-resolution hourly modeling, constrained to match concentrations observed near monitors. We then used the expert-derived PM_2.5 CR functions, valua-tion estimates, and pooling methods used for the U.S. EPA 2006 Regulatory Impact Analysis, plus O₃ exposure–response functions for 2008 National Ambient Air Quality Standard (NAAQS) evaluations (U.S. EPA 2004a, 2008; University of North Carolina Institute for the Environment, Community Modeling and Analysis System Center 2008). Because multiple studies exist for each given health incidence, pooling techniques are often used to statistically combine the results. Using BenMAP, we ran each CR function and pooling of incidence and valuation for each health end point in a 5,000-member Monte Carlo ensemble. Sources of CR functions used in this analysis are presented in Tables 1 and 2. As standard practice, the U.S. EPA does not pool mortality studies. Thus, we used the Harvard Six Cities study (Pope et al. 2002) as BenMAP input for PM_2.5 mortality; that study included the most representative sites. We selected the 2010 population database to use in BenMAP because the sensitivity studies we conducted indicated that choice of year has no substantial impact (1–2% difference) on incidence of health threats.

Table 2. Estimated O3 changes, changes in health impacts, and valuation of changes in O3 exposure.

To address the potential health and economic co-benefits that would result if half of all short trips were made by bicycle, we used HEAT. This model uses relative risk data (Anderson et al. 2000) to estimate cost savings from reduced all-cause mortality. Controlling for socio-economic variables (e.g., age, sex, smoking) and leisure time activity, HEAT calculates risk reduction for days spent cycling based on estimates of total number of days cycled, distance, and average speed (Rutter et al. 2007).

We used HEAT analysis to estimate the monetized health benefits associated with the conversion of one-half of short trips (< 8 km round trip) by car to be made by bicycle. This represents 10% of vehicle miles traveled (VMT) for the region. We used the U.S. EPA value of a statistical life (.4 million) (U.S. EPA 2010b) and the annual percentage of all-cause working-age mortality [0.00390; 95% confidence interval (CI): 0.00277, 0.00503] (Wilkinson and Pickett 2008). We assumed an average of 124 days of cycling per year, HEAT’s default value (Rutter et al. 2007), which is representative of the climate of the upper midwest, where bicycle commuting is most common from April through October. We also assumed that only 50% of these trips would be under-taken by people who do not currently cycle, thus excluding the small percentage of the population already benefitting from cycling, as well as elderly individuals or those physically unable to bicycle. We used the NPTS average commute distance for each MSA (from 3.34 to 3.98 km) with an average speed for commuter cyclists of 14 kph. Finally, we used the HEAT-recommended default percentage (90%) of cyclists completing a round trip each day.

Results

Simulations yielded unique hourly estimates of surface-level PM_2.5 throughout the year (Figure 1A) and O₃ during the warm season (1 May 30–September) (Figure 1C) on a 12 × 12 km² grid for 2002. The CMAQ simulations described here captured spatial and temporal variability in PM_2.5 [see Supplemental Material, Table 2 (http://dx.doi.org/10.1289/ehp.1103440)] and O₃ (see Supplemental Material, Table 3) when compared with U.S. EPA monitoring data throughout the region, with performance for PM_2.5 and O₃ both exceeding community and U.S. EPA expectations for chemical transport modeling in policy and research applications.

Table 3. Results of HEAT analysis assuming that 50% of short trips are completed by bicycle.

We estimated that substitution of non-emitting modes for short trips would achieve average annual reductions in the 24-hr average PM_2.5 concentrations considered in U.S. PM_2.5 regulations (Figure 1B). Regional O₃ would also be reduced throughout the May–September summer season (calculated based on daily maximum 8-hr and 1-hr averages, consistent with U.S. O₃ regulations) but daytime O₃ would increase in the largest cities because of VOC-limited O₃ production conditions in urban environments (Figure 1D). Effects of transportation on O₃ concentrations within the MSAs are complex because of the non-linear inter-play of emissions and meteorology in atmospheric chemistry and transport, whereby local ambient O₃ concentrations often increase in response to reductions in NO_x and/or VOC emissions (Sillman 1995). In our emissions inventory, motor vehicles were responsible for most of the NO_x (70–98%) and VOC (40–95%) emissions in the MSAs, with the highest percentages of emissions from motor vehicles in the most urbanized areas. Although Figure 1B and D show long-term averages (annual for PM_2.5 and summer for O₃), we used hourly values from CMAQ to estimate the potential health benefits of increased active transport.

Fine particulates (PM2.5). We observed changes in PM_2.5 and O₃, associated health outcomes, and monetary savings for each MSA and for the combined total of all grid cells outside the 11 MSAs (Tables 1 and 2). We estimated that eliminating short car trips would reduce annual average PM_2.5 across the study region by 0.08–0.15 µg/m³ (1.0–2.0%) in most MSA urban centers. In the upwind MSAs of Madison and Minneapolis/St. Paul, which would see little bene-fit from PM_2.5 reductions in other cities, we estimated that PM_2.5 would be reduced by 0.05 µg/m³ (Figure 1B). Nearly all of the estimated reduction in PM_2.5 would be due to decreases in secondary aerosols, especially nitrate formed from NO_x and secondary organic aerosols from VOCs. Primary particle emissions from motor vehicles are negligible, so the reduced VMT scenario would not significantly affect this smaller fraction of PM_2.5 mass. Reductions in PM_2.5 in urban areas and downwind would be greatest during high-pollution episodes exceeding the 24-hr average PM_2.5 NAAQS. In urban grid cells, the average estimated reduction during NAAQS exceedances was 0.20 µg/m³, equivalent to the maximum change in annual average PM_2.5 in Chicago [see Supplemental Table 4 (http://dx.doi.org/10.1289/ehp.1103440)]. In addition, we estimated that the reduction in short auto trips would result in one fewer exceedance per year in a typical urban grid cell and a 5–25% reduction in the number of annual exceedances.

Our results indicate that adverse health outcomes related to PM_2.5 would be reduced in all MSAs (Table 1). Reductions in PM_2.5-related mortality across the midwest are shown in Figure 2A, with the total impact across the 37,000-mi² region being 525 fewer deaths. We estimated that asthma exacerba-tions would decrease annually by > 2,500 cases. In addition, there would be approximately 100 fewer COPD cases, whereas net respiratory symptoms, hospital admissions, and ER visits would decrease by 94,186 cases annually. Regarding cardio-vascular disease, there would be approxi-mately 860 fewer cases of non-fatal acute myocardial infarction and hospital admissions. Savings from reduced annual mortality would reach almost .14 billion. Savings of > .5 million would result from fewer respiratory cases, hospital admissions, and ER visits, whereas a reduction in COPD would save > million per year; reductions in non-fatal acute myo-cardial infarctions and cardio-vascular hospitalizations would save > million. We estimate that total savings from reducing adverse health effects due to PM_2.5 would be about .25 billion/year (95% CI: 8 million–.2 billion). Projections suggest that PM_2.5 exposure would also be reduced in populations outside MSAs and that resulting reductions in adverse health effects would account for roughly 25% of the total benefit.

Figure 2. Examples of altered incidence of negative health outcomes by county. (A) Annual reduction in premature mortalities due to reduced PM2.5; units reflect the reduction in number of deaths per year. (B) Annual reduction in cases of acute respiratory symptoms due to changes in O3; units reflect the number of cases of acute respiratory symptoms per year. Data were generated in BenMAP 4.0 and mapped in ArcGIS 10.

Ozone. Estimated effects of eliminating short car trips on O₃ pollution vary in relation to the size and density of urban areas. For large urban areas, estimated daily 8-hr maximum, 1-hr maximum, and daily average O₃ concentrations during the May–September O₃ season generally increased in city centers, whereas concentrations decreased in suburbs, some smaller urban areas, and in areas downwind of the MSAs (Figure 1D). Simulated changes in transportation and reductions in cold-start frequency would decrease total NO_x emissions by 5–12% and total VOC emissions by 10–25%.

Although we estimate that NO_x and VOCs would both be reduced, the response to NO_x reductions would be more pronounced, resulting in increased O₃ in urban cores, consistent with previous studies in the region (Sillman 1995). Changes in estimated O₃ concentrations were greater during the warmest months (July–August) when concentrations are highest, with increases and decreases of up to 2 ppb. We estimate that daily 8-hr maximum O₃ would increase on a population-weighted basis (Table 2) but that area-averaged O₃ levels would decrease in every MSA. BenMAP analysis indicated net regional savings from declines in mortality, school-loss days, hospitalizations, ER visits, and acute respiratory symptoms, but some increases in costs in cities such as Chicago, Cleveland, Columbus, Milwaukee, and Minneapolis/St. Paul due to changes in O₃ levels. Costs resulting from O₃ increases due to reduced VMT were statistically significant for only Chicago and Minneapolis/St. Paul, but estimated savings from PM_2.5 reductions were greater than increased costs due to O₃ in all cities.

We estimated that areas outside the MSAs would experience net benefits for all O₃-related health outcomes. For nine of the cities (excluding Chicago and Minneapolis/St. Paul), we estimated a potential reduction of approximately 30,000 cases in acute respiratory symptoms associated with the potential changes in O₃ (resulting in savings of almost .9 million) and 8,632 fewer school-loss days (savings of almost 2,000). This distinct reduction in acute respiratory symptoms to areas outside the MSAs is shown in Figure 2B.

Estimated changes in health outcomes due to changes in O₃ are less correlated with MSA density or size than estimated changes due to reduced PM_2.5, particularly for outcomes related to daily peak values, such as acute respiratory symptoms. Instead, estimated changes in O₃-related health impacts were often more pronounced in smaller MSAs such as Dayton and Grand Rapids, reflecting differences in total VOC:NO_x ratios and the degree to which reductions in local motor vehicle emissions would alter them. Thus, estimated effects of eliminating short car trips on population O₃ exposures are highly sensitive to urban size, density, and travel patterns.

Benefit from physical activity. Based on WHO HEAT analysis, we estimated that completing 50% of short trips by bicycle would result in average annual savings of > http://www.eoearth.org/rss/features.5 billion for short suburban bicycle trips and nearly .25 billion for short urban trips (Table 3), for a total of approximately .8 billion in bene-fits across an estimated population of 2 million people and a reduction in pre-mature mortality of almost 700 deaths/year.

Discussion

In the study region with a population of 31.3 million, we estimated that eliminating short car trips and completing 50% of them by bicycle would result in mortality declines of approximately 1,295 deaths/year (95% CI: 912, 1,636), including 608 fewer deaths due to improved air quality and 687 fewer deaths due to increased physical activity. Changes in PM_2.5 and O₃ would result in net health bene-fits of .94 billion/year (95% CI: {rss}http://www.eoearth.org/rss/features{/rss}.2 billion, .5 billion). Completing 50% of short trips by bicycle would yield .8 billion/year in savings (95% CI: http://www.eoearth.org/rss/features.7, .0 billion), about .5 billion less in savings than from reductions in air pollution. We estimate that the combined benefit from improved air quality and physical fitness for the region would exceed .7 billion/year, which is equivalent to about 2.5% of the total cost of health care for the five midwestern states in the present study in 2004 (Kaiser Family Foundation 2004).

Of course, an added benefit of removing 20% of VMT from the region is also reduced emissions of greenhouse gases that cause global climate change. The annual reduction would be > 1.8 teragrams carbon dioxide (CO₂) (3.9 billion pounds), using the fleet average passenger car fuel economy of 22.1 mi/gal, with 1 gal gasoline producing 0.882 lb CO₂ (U.S. EPA 2005b).

Few studies have addressed how changes in behavior can affect air quality (Frank and Engelke 2005; Frank et al. 2000), and none have quantified the potential benefits of travel behavior change for pollution control. Comparison with prior BenMAP cost–benefit regulatory analyses suggests that health bene-fits from reduced air pollution through behavioral changes in personal transportation would be comparable with effects of such top-down meas-ures as the Clean Air Interstate Rule and the Nonroad Diesel Rule, both air quality regulations having potential for substantial impacts on human health (Hubbell et al. 2009). The magnitude of regional impacts from urban travel mode substitution would be comparable with the annualized benefit of reducing O₃ nationwide to full compliance with the current 75 ppb NAAQS (U.S. EPA 2008).

Compliance with federal air quality standards through conventional measures such as emissions controls entails direct costs to govern-ments and private industry. In contrast, changing personal travel behavior distributes costs and bene-fits—both financial and otherwise—in a more complex manner, including potentially large personal savings for individuals given the high cost of vehicle owner-ship and operation. However, in addition to public outreach, education, and incentive programs, drastic decreases in residential VMT would require infrastructure investments to support pedestrian and bicycle traffic, as well as increased public transit. For example, cities would need to designate bicycle lanes on streets, add bicycle lanes or mixed-use nonmotorized paths, and provide additional signage, physical barriers, bicycle traffic signals, and bicycle parking. Infrastructure costs for converting existing roadways to bicycle lanes in the United States range from http://www.eoearth.org/rss/features,500 to ,000/block, depending on the infrastructure needs. In 2010 Portland, Oregon, converted 10 blocks of high-traffic streets to include two-way bike lanes at a cost of ,000/block, reducing motor vehicle traffic by one lane. In 2011, Chicago added protected bicycle lanes with flexible marker posts and a parking lane for automobiles along four blocks, including a bridge, at a cost of 0,000 (City of Chicago 2011). Increasing this cost estimate to 0,000/block, double the U.S. average cost per mile for bike lane conversion and addition, the http://www.eoearth.org/rss/features billion in health cost savings in the MSA of Chicago alone could retrofit 20,000 blocks (2,500 mi or 4,020 km) with bike lanes. The greater Chicago metropolitan area has > 23,500 mi of urban roads, not including interstate or freeways (Illinois Department of Transportation 2009), so the health care savings could cover the costs of adding bike lanes to every road in 1–10 years.

Although U.S. pedestrians and cyclists may be at higher risk of mortality than their Dutch counterparts (Pucher and Dijkstra 2003), the Dutch results provide a model for safer walking and cycling. Seven of the cities studied here—Chicago, Columbus, Dayton, Indianapolis, Madison, Milwaukee, and Minneapolis/St. Paul—have earned bicycle-friendly rankings from the League of American Bicyclists because they actively support bicycling by providing safe accommodation for cycling and encouraging people to bike (League of American Bicyclists 2010). Thus, some U.S. communities may be more likely than others to exhibit charac-teristics of Dutch cities that make bicycling feasible. There is already an observed trend of increasing bicycle share across all of the 11 midwestern MSAs, one that is consistent and very large (U.S. Census Bureau 2009). Moreover, there is evidence that U.S. cities with enhanced levels of active transport experience health benefits. Pucher et al. (2010) found that cities with the highest rates of commuting by bicycle or on foot have obesity and diabetes rates 20% and 23% lower, respectively, than cities with the lowest rates of active commuting.

Strengths, limitations, and uncertainties. Our research, for the first time, has joined models of health effects (BenMAP), census-based vehicle use and emissions (PLUTO), and regional air pollution (CMAQ) to link highly localized changes in travel behavior to regional health outcomes. We also used the newest version of U.S. EPA BenMAP (4.0), which includes baseline incidence rates at the county (versus the regional) level, thus providing greater local specificity than previously possible.

Our results may be a conservative estimate of pollution reductions. We did not evaluate changes in exposure for people who live or work near highways, nor did we assess health effects from decreases in other pollutants (e.g., carbon monoxide, sulfur dioxide) or the synergistic effects of combined changes in O₃ and PM_2.5. We would expect the reduction in the number of automobiles on the road at any given time to change average speeds and resultant emissions, with variable effects on arterial and local roadways. Comprehensive analysis would require travel-demand modeling (e.g., Bowman and Ben-Akiva 2001) incorporating traveler decision making, spatially specific changes in roadway and transit networks, demographic information, and employment data to calculate those differences in vehicle activity. Finally, health impacts from changes in long-range transport of O₃ and PM_2.5 to states downwind of the modeling domain and to neighboring Canadian regions were not analyzed.

Our health benefits analysis also may be conservative because, following current U.S. EPA practice, we used total PM_2.5 mass and did not differentiate between aerosol species. Recent epidemiological studies suggest that traffic-related emissions may contain more hazardous particulate chemical components. Gent et al. (2009) found more frequent asthma symptoms and inhaler use in children after exposure to PM_2.5 emissions attributable to motor vehicles compared with emissions from other sources. Bell et al. (2009) found differing associations between cardio-vascular and respiratory hospitalization across various chemical species of PM_2.5. Particles comprising vanadium, nickel, and elemental carbon showed the strongest associations (vanadium and nickel come primarily from transportation emissions). However, because these epidemiological studies included high diesel truck traffic and its specific emissions profile, these results have slightly less bearing on our analysis of decreases in light-duty automobile emissions.

Our estimates for physical fitness bene-fits stemming from bicycling 50% of short car trips (≤ 8 km) may under-estimate the full benefits of removing these car trips. Not included are the remaining trips that presumably would be achieved by some form of mass transportation or direct walking for very short trips. According to the 2001 National Household Travel Survey, Americans who use mass transit spend a median of 19 min daily walking to and from transit (Besser and Dannenberg 2005). Accounting for fitness benefits from this mode of active transport would involve complex geo-spatial modeling. Future analyses should consider geographic information system (GIS) technologies in conjunction with energy expenditure measure-ment tools, such as accelerometers or biometric monitors, to more accurately assess the speed, distance, intensity, and terrain of the cyclist (Bonnel et al. 2009). Finally, for urban planning purposes, assumptions for determining levels of benefits for new bicyclists will stem from city-specific estimates of current bicycling levels and city-wide demographics. We used current European bicycling levels to guide our maxi-mum benefit level potentially achievable.

In our study we used chemical transport modeling simulations and empirical CR functions, an experimental framework that adds incremental uncertainty at each step: in the emissions inventory, modeled meteorology, and processes included in the chemical transport model. In addition, the ability of the model to reproduce observed ambient surface-level O₃ and PM_2.5 and their respective sensitivities to emissions changes adds uncertainty. We used the same suite of response functions and pooling chosen by the U.S. EPA for air pollution rule making; however, the empirical epidemiological CR functions of BenMAP and the choice of valuation estimates are an additional source of uncertainty. The valuation estimates are a function of BenMAP, based on the configuration used by the U.S. EPA. Sensitivity analysis by the California Air Resources Board confirmed that the mean and distribution of premature mortalities from long-term exposure to PM_2.5 are not sensitive to the random-effects pooling of CR functions (Tran et al. 2009). We found few outliers among the individual CR calculations that contribute to the reported pooled values. Although we chose to simulate a year (2002) that is representative of the regional climate of the past decade, the magnitude of bene-fits achieved in any given year depends on inter-annual variability in meteorology and the resultant ambient air quality.

Conclusion

Our study demonstrates that reduced car travel and enhanced bicycle commuting in urban areas can improve health outcomes within urban, suburban, and even in downwind rural areas. Our results demonstrate that reduced car travel can benefit air quality, human health, and the economy.

Correction

In the manuscript originally published online, the weight of the annual reduction of CO₂ was noted in the “Discussion” as “3.9 trillion pounds” instead of “3.9 billion pounds,” and information on health bene-fits accruing outside the MSA regions was inadvertently omitted. Information for outside the MSA regions and for subsequent savings for the entire region is now included in Tables 1 and 2, and all values have been corrected here.

Supplemental Material

(217 KB) PDF.

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The idea that chemicals in the environment could be contributing to the obesity epidemic is often credited to an article by Paula Baillie-Hamilton, published in the Journal of Alternative and Complementary Medicine in 2002.¹⁰ Her article presented evidence from earlier toxicologic studies published as far back as the 1970s in which low-dose chemical exposures were associated with weight gain in experimental animals. At the time, however, the original researchers did not focus on the implications of the observed weight gains.

Multiple Modes of Action

The main role of fat cells is to store energy and release it when needed. Scientists also now know that fat tissue acts as an endocrine organ, releasing hormones related to appetite and metabolism. Research to date suggests different obesogenic compounds may have different mechanisms of action, some affecting the number of fat cells, others the size of fat cells, and still others the hormones that affect appetite, satiety, food preferences, and energy metabolism.¹⁵ Some obesogenic effects may pass on to later generations through epigenetic changes, heritable modifications to DNA and histone proteins that affect when and how genes are expressed in cells, without altering the actual genetic code.^15,16,17

Bruce Blumberg, a biology professor at the University of California, Irvine, coined the term “obesogen” in 2006 when he discovered that tin-based compounds known as organotins predisposed laboratory mice to gain weight.¹⁸ “If you give tributyltin [TBT] to pregnant mice, their offspring are heavier than those not exposed,” he says. “We’ve altered the physiology of these offspring, so even if they eat normal food, they get slightly fatter.”

Human exposure and health-effect data are relatively rare for organotins, but studies have documented the presence of these compounds in human blood,¹⁹ milk,²⁰ and liver²¹ samples. Although phased out as a biocide and marine antifouling agent, TBT is still used as a wood preservative and, along with dibutyltin, as a stabilizer in polyvinyl chloride; it pollutes many waterways and contaminates seafood.²²

Blumberg was studying endocrine disruptors in the early 2000s when he heard at a meeting in Japan that TBT causes sex reversal in multiple fish species. “I decided to test whether TBT activated known nuclear receptors, expecting it to activate a sex steroid receptor,” Blumberg says. Instead, it activated peroxisome proliferator–activated receptor gamma (PPARγ), the master regulator of adipogenesis, the process of creating adipocytes, or fat cells.²³ PPARγ is evolutionarily conserved between mice and humans, and it may be particularly susceptible to chemical “imposters” because it has a large ligand-binding pocket that can accommodate many chemical structures. When a molecule capable of activating the receptor enters the pocket, it turns on the adipogenic program.

“If you activate PPARγ in a preadipocyte, it becomes a fat cell. If it already is a fat cell, it puts more fat in the cell,” Blumberg says. “TBT is changing the metabolism of exposed animals, predisposing them to make more and bigger fat cells.” PPARγ selectively causes multipotent stromal cells to differentiate into bone or fat, and Blumberg found TBT exposure caused these stem cells to show an increased commitment to becoming adipocytes at the expense of the bone lineage. “The insidious thing is that our animals are exposed in utero to TBT, then never again, yet TBT caused a permanent effect.”

A Growing List of Potential Obesogens

Obesity is strongly linked with exposure to risk factors during fetal and infant development.¹⁵ “There are between fifteen and twenty chemicals that have been shown to cause weight gain, mostly from developmental exposure,” says Jerry Heindel, who leads the extramural research program in obesity at the National Institute of Environmental Health Sciences (NIEHS). However, some obesogens have been hypothesized to affect adults, with epidemiologic studies linking levels of chemicals in human blood with obesity²⁴ and studies showing that certain pharmaceuticals activate PPARγ receptors.^15,25

Chemical pesticides in food and water, particularly atrazine and DDE (dichlorodiphenyldichloroethylene—a DDT breakdown product), have been linked to increased BMI in children and insulin resistance in rodents.^26,27 Certain pharmaceuticals, such as the diabetes drug Avandia® (rosiglitazone), have been linked to weight gain in humans and animals,^9,17 as have a handful of dietary obesogens, including the soy phytoestrogen genistein²⁸ and monosodium glutamate.¹⁵

Most known or suspected obesogens are endocrine disruptors. Many are widespread,²⁹ and exposures are suspected or confirmed to be quite common. In one 2010 study, Kurunthachalam Kannan, a professor of environmental sciences at the University at Albany, State University of New York, documented organotins in a designer handbag, wallpaper, vinyl blinds, tile, and vacuum cleaner dust collected from 24 houses.³⁰ Phthalates, plasticizers that also have been related to obesity in humans,³¹ occur in many PVC items as well as in scented items such as air fresheners, laundry products, and personal care products.

One of the earliest links between human fetal development and obesity arose from studies of exposure to cigarette smoke in utero.^32,33 Although secondhand-smoke exposure has decreased by more than half over the past 20 years, an estimated 40% of nonsmoking Americans still have nicotine by-products in their blood, suggesting exposure remains widespread.³⁴ Babies born to smoking mothers are frequently underweight, but these same infants tend to make up for it by putting on more weight during infancy and childhood.³⁵ “If a baby is born relatively small for its gestational age, it tries to ‘play catch-up’ as it develops and grows,” explains Retha Newbold, a developmental biologist now retired from the NTP.

This pattern of catch-up growth is often observed with developmental exposure to chemicals now thought to be obesogens, including diethylstilbestrol (DES), which Newbold spent the last 30 years studying, using mice as an experimental model. Doctors prescribed DES, a synthetic estrogen, to millions of pregnant women from the late 1930s through the 1970s to prevent miscarriage. The drug caused adverse effects in these women’s children, who often experienced reproductive tract abnormalities; “DES daughters” also had a higher risk of reproductive problems, vaginal cancer in adolescence, and breast cancer in adulthood.³⁶ Newbold discovered that low doses of DES administered to mice pre- or neonatally also were associated with weight gain,³⁷ altered expression of obesity-related genes,^38,39 and modified hormone levels.^38,39

“What we’re seeing is there’s not a difference in the number of fat cells, but the cell itself is larger after exposure to DES,” Newbold says. “There was also a difference in how [fat cells] were distributed—where they went, how they lined up, and their orientation with each other. The mechanism for fat distribution and making fat cells are set up during fetal and neonatal life.”

High-Profile Exposures

Animal studies have also implicated another suspected obesogen: bisphenol A (BPA), which is found in medical devices, in the lining of some canned foods, and in cash register receipts.⁴⁰ “BPA reduces the number of fat cells but programs them to incorporate more fat, so there are fewer but very large fat cells,” explains University of Missouri biology professor Frederick vom Saal, who has studied BPA for the past 15 years. “In animals, BPA exposure is producing in animals the kind of outcomes that we see in humans born light at birth: an increase in abdominal fat and glucose intolerance.”

Many endocrine disruptors exhibit an inverted U-shaped dose–response curve, where the most toxic response occurs at intermediate doses.⁴¹ However, in a recent unpublished study, vom Saal found that BPA affected rodent fat cells at very low doses, 1,000 times below the dose that regulatory agencies presume causes no effect in humans, whereas at higher doses he saw no effect. Receptors typically respond to very low levels of hormone, so it makes sense that they may be activated by low levels of an endocrine mimic, whereas high levels of a chemical may actually cause receptors to shut down altogether, preventing any further response.⁴¹ This is known as “receptor downregulation.” As a result, some endocrine disruptors have greater effects at low than at high doses; different mechanisms may be operating.¹⁵

Still another widespread obesogen is perfluorooctanoic acid (PFOA), a potential endocrine disruptor and known PPARγ agonist.⁴² “Pretty much everyone in the U.S. has it in their blood, kids having higher levels than adults, probably because of their habits. They crawl on carpets, on furniture, and put things in their mouth more often,” explains NIEHS biologist Suzanne Fenton. PFOA is a surfactant used for reduction of friction, and it is also used in nonstick cookware, Gore-Tex™ waterproof clothing, Scotchgard™ stain repellent on carpeting, mattresses, and microwavable food items. In 2005 DuPont settled a class-action lawsuit for 7.6 million after its factory outside Parkersburg, West Virginia, tainted nearby drinking water supplies with PFOA.⁴³ In December 2011 an independent science panel found the first “probable link” between PFOA and a human health outcome, pregnancy-induced hypertension⁴⁴ (for more information, see “Pregnancy-Induced Hypertension ‘Probably Linked’ to PFOA Contamination,” p. A59 this issue⁴⁵).

Fenton studied how PFOA levels similar to those in the tainted drinking water affected the hormone levels and weight of rodent offspring exposed in utero.⁴⁶ “We gave pregnant mice PFOA only during pregnancy. It has a long half-life, so it hangs around during lactation and gets delivered in milk to babies,” Fenton says. “Once the offspring reached adulthood, they became obese, reaching significantly higher weight levels than controls.”

Exposed offspring also had elevated levels of leptin, a hormone secreted by adipose tissue that affects appetite and metabolism. Leptin normally suppresses appetite, but obese people and animals have elevated leptin levels, leading researchers to suspect the brain can become resistant to its effects.⁴⁷ Fenton did not observe weight gain when mice were exposed to PFOA as adults, although her team did find abnormalities in the uterus and mammary gland in exposed adults.

Eye on Prevention

If exposure during pregnancy predisposes people to gain weight, can diet and exercise ultimately make any difference? Blumberg does not consider the situation hopeless. “I would not want to say that obesogen exposure takes away free will or dooms you to be fat,” he says. “However, it will change your metabolic set points for gaining weight. If you have more fat cells and propensity to make more fat cells, and if you eat the typical high-carbohydrate, high-fat diet we eat [in the United States], you probably will get fat.”

Blumberg postulates that the effects of early-life exposure are irreversible, and those people will fight a life-long battle of the bulge. However, if such people reduce their exposure to obesogens, they will also reduce health effects that may arise from ongoing adulthood exposures. Blumberg believes it’s good to reduce exposure to all kinds of endocrine-disrupting chemicals. “Eat organic, filter water, minimize plastic in your life,” he says. “If there’s no benefit and some degree of risk, why expose yourself and your family?”

Heindel hopes the NIH’s new grant-making effort will yield important discoveries. “It’s a very new field, and people are always skeptical of new fields,” he says. “It’s up to us to get more data to show that chemicals are actually interfering with the endocrine system that controls weight gain and metabolism. And there’s still the question of how important is this to humans. We’re never going to know until we get more data.”

Transmission electron micrograph of human fat cells. Research to date suggests that different obesogens may have different mechanisms of action, affecting either the number or size of fat cells or the hormones that affect appetite, satiety, food preferences, and metabolism. © David M. Phillips/Photo Researchers, Inc.

In one study by NIEHS biologist Suzanne Fenton, mice exposed prenatally to PFOA were more likely than controls to become obese when they reached adulthood.46 Christopher G. Reuther/EHP

“What if this was really true and chemicals are having a significant effect on obesity?” muses Heindel. “If we could show environmental chemicals play a major role, then we could work on reducing exposure during sensitive windows, and that could have a huge effect [on obesity prevalence].” It would change the focus from treating adults who are already obese to preventing obesity before it starts—a fundamental shift in thinking about obesity.

The NIEHS is crafting priorities for research on potential obesogens. Thayer was the primary force behind the workshop “The Role of Environmental Chemicals in the Development of Diabetes and Obesity,”⁴⁸ held in January 2011 and cosponsored by the NTP, the Environmental Protection Agency, and the Food and Drug Administration National Center for Toxicology Research. “The idea was to have the experts look through the literature and see which might be the most compelling signals, and which areas were emerging but warranted more research,” Thayer explains. These findings will help identify priorities for future research, and a ser ies of papers from the workshop are being submitted for publication.

“We were surprised at the number of chemicals that seem to be interacting with signaling pathways involved in weight regulation,” Thayer says. She adds that evidence also suggests these same compounds are linked with diabetes and metabolic syndrome, “an understudied but natural research direction that brings together the obesity and diabetes issues.”

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