Skip to main content
NCBI home page
Environmental Health Perspectives logoLink to Environmental Health Perspectives
. 2011 Nov 2;120(1):68–76. doi: 10.1289/ehp.1103440

Air Quality and Exercise-Related Health Benefits from Reduced Car Travel in the Midwestern United States

Maggie L Grabow 1,2,✉, Scott N Spak 1,3,4, Tracey Holloway 1,4, Brian Stone Jr 5, Adam C Mednick 1,6,7, Jonathan A Patz 1,2,8
PMCID: PMC3261937  PMID: 22049372

Abstract

Background: Automobile exhaust contains precursors to ozone and fine particulate matter (PM ≤ 2.5 µm in aerodynamic diameter; PM2.5), posing health risks. Dependency on car commuting also reduces physical fitness opportunities.

Objective: In this study we sought to quantify benefits from reducing automobile usage for short urban and suburban trips.

Methods: We simulated census-tract level changes in hourly pollutant concentrations from the elimination of automobile round trips ≤ 8 km in 11 metropolitan areas in the upper midwestern United States using the Community Multiscale Air Quality (CMAQ) model. Next, we estimated annual changes in health outcomes and monetary costs expected from pollution changes using the U.S. Environmental Protection Agency Benefits Mapping Analysis Program (BenMAP). In addition, we used the World Health Organization Health Economic Assessment Tool (HEAT) to calculate benefits of increased physical activity if 50% of short trips were made by bicycle.

Results: We estimate that, by eliminating these short automobile trips, annual average urban PM2.5 would decline by 0.1 µg/m3 and that summer ozone (O3) would increase slightly in cities but decline regionally, resulting in net health bene-fits of $4.94 billion/year [95% confidence interval (CI): $0.2 billion, $13.5 billion), with 25% of PM2.5 and most O3 bene-fits to populations outside metropolitan areas. Across the study region of approximately 31.3 million people and 37,000 total square miles, mortality would decline by approximately 1,295 deaths/year (95% CI: 912, 1,636) because of improved air quality and increased exercise. Making 50% of short trips by bicycle would yield savings of approximately $3.8 billion/year from avoided mortality and reduced health care costs (95% CI: $2.7 billion, $5.0 billion]. We estimate that the combined benefits of improved air quality and physical fitness would exceed $8 billion/year.

Conclusion: Our findings suggest that significant health and economic benefits are possible if bicycling replaces short car trips. Less dependence on automobiles in urban areas would also improve health in downwind rural settings.

Keywords: air pollution, BenMAP, bicycling, built environment, climate change, ozone, particulate matter, physical activity, urban design, vehicle emissions

The current fossil fuel–based transportation system of the United States negatively impacts human health by increasing air pollution and automobile accidents and by decreasing physical activity. Here, we consider how replacing short automobile trips with bicycle transport might yield health benefits through improved air quality and physical fitness, with a focus on the upper midwestern United States as our study region.

Both ozone (O3) and fine particular matter ≤ 2.5 µm in aerodynamic diameter (PM2.5) in the ambient air exacerbate bronchitis and asthma and may contribute to cardio-vascular mortality (Brunekreef and Holgate 2002). Asthma affects 8.2% of U.S. citizens, and an estimated 10 million adults have diagnosed chronic obstructive pulmonary disease (COPD) (Centers for Disease Control and Prevention 2009). In addition, recent estimates attribute 63,000–88,000 pre-mature deaths per year due to PM2.5 [U.S. Environmental Protection Agency (EPA) 2010c]. In the United States, on-road vehicles are responsible for about 26% of volatile organic compounds (VOCs) and 35% of nitrogen oxide (NOx) emissions (U.S. EPA 2005c, 2005d). NOx and VOCs combine to form O3 and contribute to nitrate and secondary organic aerosols, important components of PM2.5. Nearly 240 U.S. counties, with > 118 million total residents, exceeded U.S. EPA O3 standards in 2011, and > 200 counties (> 88 million total residents) failed to meet PM2.5 standards, in part because of pollution from short car trips (U.S. EPA 2011a, 2011b).

Transport-related inactivity, that is, the use of motorized transport rather than walking and bicycling, has been linked to increased mortality and decreases in healthy life years, with the greatest impacts on chronic diseases including heart disease, stroke, colon cancer, diabetes mellitus type 2, obesity, breast cancer, and osteoporosis [World Health Organization (WHO) 2002]. Carlson et al. (2009) estimated that 32.4% of the U.S. population is fully inactive (no moderate-intensity or vigorous-intensity physical activity lasting at least 10 min at a time), while only 33.5% is physically active, defined as 30 min/day with moderate-intensity activity, ≥ 5 days/week. In a recent Dutch study, Johan de Hartog et al. (2010) concluded that shifting from short car trips to bicycle trips would reduce all-cause mortality, with estimated reductions in mortality due to increased physical activity that were nine times greater than estimated increases in mortality due to increased pollution inhalation and traffic-related fatality estimates in the Netherlands.

In the United States, 28% of all car trips are ≤ 1.6 km (1 mi), which is the distance that a typical European would walk (European Commission 2001; Pucher and Dijkstra 2003). Another 41% of all trips are ≤ 3.2 km (2 mi), a distance that many Europeans would be as likely to bicycle as to walk (European Commission 2001; Pucher and Dijkstra 2003). If we use European travel behavior as a point of comparison for walking and bicycling activity for the United States, these data suggest that many car trips in the United States could be avoided.

Amplifying the potential benefits of increased bicycle use is the nonlinear relation-ship of vehicle emissions to travel time. A large fraction of emissions (25% of VOC and 19% of primary PM2.5) are emitted in just the first few minutes of automobile operation, often known as “cold start,” before pollution-control devices operate [Federal Highway Administration (FHWA) 2006]. Because emissions control systems reach operating temperature only after several miles of travel and typically cool below operating range in < 1 hr (Singer et al. 1999), reducing the number of short trips could disproportionately curtail pollutant emissions from on-road vehicles.

In the present study, we quantified the potential health and monetary bene-fits of replacing short (≤ 4 km one way) car trips with travel by bicycle (50% of trips) in the 11 largest mid-western metropolitan statistical areas (MSAs): Chicago, Illinois; Cincinnati, Cleveland, Columbus, and Dayton, Ohio; Detroit and Grand Rapids, Michigan; Indianapolis, Indiana; Madison and Milwaukee, Wisconsin; and Minneapolis/St. Paul, Minnesota. This study builds on the Projected Land Use and Transportation (PLUTO) modeling framework developed by Stone et al. (2007). We estimated changes in regional emissions and air quality, as well as resulting health benefits, across the upper mid-western states [see Supplemental Material, Figure 1 (http://dx.doi.org/10.1289/ehp.1103440) for a map of the area]. In addition, we estimated the benefits of increased physical activity using the Health Economic Assessment Tool (HEAT) for cycling developed by the WHO (Rutter et al. 2007).

Figure 1.

Figure 1

Open in a new tab

Results of air quality analysis for PM2.5 (A,B) and O3 (C,D) by location. (A) 2002 annual average PM2.5 concentration (µg/m3). (B) Estimated reduction in 2002 annual average PM2.5 concentration (µg/m3) due to changes in urban and suburban mobile emissions. (C) 2002 average daily 8-hr maximum O3 concentration (ppb) for the O3 season (1 May–30 September). (D) Estimated change in 2002 average daily 8-hr maximum O3 concentration (ppb) for the O3 season due to changes in urban and suburban mobile emissions. Data were generated in BenMAP 4.0 and mapped in ArcGIS 10 (ESRI, St. Paul, MN).

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.

City/data Mean annual PM2.5 reductiona Mortality Asthma Chronic bronchitis Respiratory problemsb Cardiovascular problemsc Work-loss daysd Total savings
Chicago
Incidence 0.05 (0.02, 0.15) 162 (63, 260) 802 (91, 2,301) 29 (5, 53) 36,690 (30,233, 43,145) 253 (99, 407) 5,923 (5,161, 6,685)
Savings 1,273 (176, 3,361) 0.04 (0.01, 0.13) 13 (1, 44) 2.47 (1.44, 3.61) 16.7 (5, 40.9) 1 (0.96, 1.2) 1,305 (184, 3,449)
Cincinnati
Incidence 0.03 (0.01, 0.10) 26 (10, 42) 100 (11, 288) 4 (0.7, 7) 4,751 (3,918, 5,583) 34 (13, 54) 763 (665, 861)
Savings 212 (29, 549) 0.005 (0.001, 0.02) 1.6 (0.14, 6) 0.32 (0.18, 0.46) 2.27 (0.68, 5.6) 0.13 (0.11, 0.15) 212 (30, 561)
Cleveland
Incidence 0.05 (0.02, 0.16) 53 (21, 85) 184 (21, 527) 7 (1, 13) 8,804 (7,264, 10,345) 74 (28.5, 119) 1,405 (1,224, 1,586)
Savings 418 (58, 1,105) 0.01 (0.001, 0.03) 3 (0.3, 11) 0.60 (0.35, 0.88) 4.86 (1.4, 11.9) 0.23 (0.2, 0.27) 427 (60, 1,129)
Columbus
Incidence 0.04 (0.02, 0.14) 27 (11, 43) 124 (14, 355) 4 (1, 8) 5,854 (4,829, 6,879) 35 (13.5, 55.9) 951 (828, 1,073)
Savings 212 (29, 561) 0.007 (0.001, 0.02) 1.9 (0.2, 6.7) 0.39 (0.23, 0.57) 2.37 (0.71, 5.8) 0.16 (0.14, 0.18) 217 (31, 574)
Dayton
Incidence 0.04 (0.03, 0.10) 14 (6, 23) 47 (5, 136) 2 (0.4, 3.4) 2,278 (1,880, 2,676) 18 (7, 29) 365 (318, 412)
Savings 112 (15, 296) 0.003 (0, 0.008) 0.8 (0.07, 2.8) 0.15 (0.09, 0.22) 1.22 (0.36, 3) 0.06 (0.05, 0.07) 114 (16, 302)
Detroit
Incidence 0.05 (0.02, 0.16) 106 (41, 171) 462 (52, 1,325) 17 (3, 31) 21,181 (17,462, 24,899) 158 (61.5, 254) 3,395 (2,958, 3,832)
Savings 836 (115, 2,208) 0.025 (0.003, 0.08) 7.3 (0.6, 26) 1.4 (0.83, 2.09) 10.46 (0.26, 2.2) 0.66 (0.58, 0.75) 856 (120, 2,262)
Grand Rapids
Incidence 0.03 (0.02, 0.06) 7 (3, 11) 45 (5, 130) 2 (0.3, 2.9) 2,023 (1,667, 2,379) 13 (4.75, 20.3) 327 (285, 369)
Savings 54 (7, 143) 0.002 (0, 0.007) 0.66 (0.06, 2.3) 0.13 (0.08, 0.19) 0.88 (0.26, 2.2) 0.054 (0.047, 0.061) 56 (8, 148)
Indianapolis
Incidence 0.03 (0.01, 0.09) 19 (7, 30) 85 (10, 243) 3 (0.5, 5.2) 3,676 (3,024, 4,328) 24 (9.3, 38.8) 592 (516, 669)
Savings 146 (20, 386) 0.005 (0.001, 0.01) 1.2 (0.11, 4.3) 0.25 (0.14, 0.36) 1.59 (0.48, 3.9) 0.1 (0.09, 0.12) 149 (21, 394)
Madison
Incidence 0.02 (0.02, 0.04) 1 (0.44, 1.8) 10 (1, 28) 0.42 (0.08, 0.8) 565 (469, 661) 3 (1.2, 5) 93 (81, 105)
Savings 8.8 (1.2, 23.3) 0.001 (0, 0.002) 0.18 (0.02, 0.6) 0.037 (0.022, 0.053) 0.23 (0.067, 0.55) 0.016 (0.014, 0.018) 9 (1, 24)
Milwaukee
Incidence 0.04 (0.02, 0.08) 12 (5, 19) 73 (8, 210) 3 (0.5, 5) 3,407 (2,809, 4,005) 21 (7.7, 34.3) 545 (475, 616)
Savings 93 (13, 246) 0.004 (0, 0.012) 1.18 (0.10, 4.2) 0.22 (0.13, 0.33) 1.72 (0.51, 4.23) 0.095 (0.08, 0.1) 96 (14, 254)
Twin Cities
Incidence 0.01 (0.00, 0.06) 7 (2.7, 11) 87 (10, 248) 3 (0.65, 6) 4,379 (3,619, 5,139) 27 (10, 44) 709 (618, 800)
Savings 54 (7, 142) 0.005 (0.001, 0.014) 1.46 (0.13, 5.2) 0.28 (0.17, 0.42) 1.95 (0.58, 4.8) 0.13 (0.11, 0.15) 57 (8, 152)
Total MSAs
Incidence 0.01 (0.00, 0.16) 433 (169, 698) 2,018 (228, 5,790) 75 (14, 137) 93,607 (77,175, 110,037) 659 (255, 1,062) 15,067 (13,128, 17,006)
Savings 3,484 (480, 9,199) 0.109 (0.012, 333) 32 (2.8, 112) 6.16 (3.59, 9.01) 43.4 (13, 106.3) 2.6 (2.38, 3.1) 3,570 (500.7, 9,875)
Outside MSAs total
Incidence 92 (35, 149) 541 (60.9, 1,552) 21.64 (3, 40.2) 579 (278, 878) 200.6 (71.8, 332) 4,280 (3,729, 4,830)
Savings 726.7 (100, 1,919) 0.0227 (0.0026, 0.069) 7.35 (0.640, 26) 1.36 (0.792, 1.99) 11.1 (3.27, 27.4) 0.489 (0.426, 0.552) 747.02 (105.1, 1,975)
Region total
Incidence 525 (204, 806) 2,559 (289, 7,342) 97 (17, 177) 94,186 (77,453, 110,915) 860 (327, 1,394) 19,347 (16,857, 21,837)
Savings 4,143 (571, 10,937) 0.132 (0.015, 0.402) 39.1 (3.41, 138) 7.52 (4.38, 10.99) 54.5 (16.2, 132) 3.24 (2.82, 3.65) 4,247.5 (598, 11,222)
Values for incidence represent estimated incidence per adverse health effect avoided due to a change in air pollution in the given city per year; savings are presented in millions of dollars. Values in parentheses are 95% confidence intervals, and all changes are annualized. aChange in PM2.5 (µg/m3) was calculated as area averaged and reported with a range of minimum and maximum grid cell values; data for PM2.5-related health effects estimated in this analysis (and the source of the PM concentration–response functions used to estimate the change in incidence) are from Abbey et al. (1995), Dockery et al. (1996), Ito (2003), Laden et al. (2006), Moolgavkar (2000a, 2003), Norris et al (1999), Ostro (1987), Ostro and Rothschild (1989), Ostro et al. (2001), Peters et al. (2001), Pope et al. (1991, 2002), Schwartz and Neas (2000), Sheppard (2003), and Vedal et al. (1998). bRespiratory problems include upper and lower respiratory symptoms, hospital admissions (respiratory), emergency room visits (respiratory), and cases of acute bronchitis. cCardiovascular problems include nonfatal acute myocardial infarctions and cardiovascular hospitalizations. dYearly work-loss-day incidence based on estimates from the 1996 National Health Interview Survey (Adams et al. 1999).
Open in a new tab

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 NOx, 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 NOx 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 PM2.5 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 PM2.5 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 PM2.5 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.

City/data Change in O3 (ppm)a Acute respiratory symptoms ER visits (respiratory) HA (respiratory) Mortality School-loss days Worker productivity Total savings
Chicago
Incidence –0.09 (–0.23, 0.39) –5,780 (–10,131, –1,431) –3 (–12, 2.4) –8 (–18. 0.87) –14 (–22, –6) –1,762 (–3,174, –351) –13,564
Savings –0.365 (–0.718, 0.047) –0.001 (–0.004, 0.001) –0.19 (–0.41, 0.03) –108 (–305, 38) –0.17 (–0.3, –0.03) –0.017 –109 (–306, 37)
Cincinnati
Incidence –0.13 (–0.25, 0.21) 483 (–229, 1,194) 0.08 (–1.1, 1.3) 0.53 (–0.72, 1.76) 0.78 151 (–75, 377) 1,279
Savings 0.03 (–0.02, 0.09) 0 (0, 0) 0.011 (–0.02, 0.04) 6.2 (–20.2, 37.3) 0.014 (–0.007, 0.036) 0.002 6.2 (–20.2, 37.5)
Cleveland
Incidence –0.17 (–0.33, 0.70) 353 (–871, 1577) –0.15 (–2.6, 2.2) 0.35 (–2.2, 2.8) 0.54 (–2.3, 3.4) 106 (–271, 483) 2,490
Savings 0.022 (–0.07, 0.12) 0 (–0.001, 0.001) 0.008 (–0.05, 0.06) 4.4 (–47.4, 61) 0.01 (–0.03, 0.05) 0.003 4.412 (–47.5, 61.3)
Columbus
Incidence –0.2 (–0.27, 0.23) 199 (–555, 953) –0.07 (–1.3, 1.09) 0.19 (–0.9, 1.3) 0.45 (–0.85, 1.8) 58 (–182, 297) 1,697
Savings 0.013 (–0.04, 0.07) 0 (0, 0) 0.005 (–0.02, 0.03) 3.64 (–20, 29.7) 0.005 (–0.017, 0.028) 0.002 3.669 (–20, 29.9)
Dayton
Incidence –0.21 (–0.26, 0.01) 713 (362, 1,064) 0.48 (0, 1.4) 0.99 (0.26, 1.8) 2 (1.3, 2.9) 209 (100, 318 2,568
Savings 0.045 (0.02, 0.08) 0 (0, 0.001) 0.02 (0.004, 0.038) 16 (2.5, 40.6) 0.02 (0.01, 0.03) 0.003 16.462 (2.6, 40.7)
Detroit
Incidence –0.17 (–0.29, 0.18) 830 (–976, 2,634) 0.46 (–1.7, 3) 0.58 (–2.3, 3.3) 1 (–2.5, 4.7) 257 (–326, 840) 1,389
Savings 0.052 (–0.07, 0.19) 0 (–0.001, 0.001) 0.011 (–0.05, 0.07) 8.5 (–48, 68) 0.024 (–0.031, 0.08) 0.002 8.631 (–48.5, 68.7)
Grand Rapids
Incidence –0.23 (–0.29, 0.07) 1,124 (571, 1,677) 0.65 (0, 1.9) 1 (0.34, 1.8) 2 (1.5, 3.4) 363 (174, 552) 12,235
Savings 0.071 (0.033, 0.12) 0 (0, 0) 0.02 (0.005, 0.035) 19.3 (3, 48) 0.035 (0.02, 0.05) 0.015 19 (3, 48)
Indianapolis
Incidence –0.15 (–0.22, 0.12) 532 (13, 1,051) 0.32 (–0.39, 1.3) 0.72 (–0.38, 1.8) 1 (0.23, 2.1) 169 (–9.2, 347) 2,036
Savings 0.034 (–0.002, 0.076) 0 (0, 0) 0.017 (–0.009, 0.04) 9.4 (–8, 33) 0.016 (–0.001, 0.033) 0.003 9.476 (–8, 33)
Madison
Incidence –0.12 (–0.16, 0.04) 135 (33, 237) 0.03 (–0.01, 0.11) 0.11 (0.02, 0.21) 0.25 (0.12, 0.39) 41 (12, 70) 436
Savings 0.009 (0.002, 0.017) 0 (0, 0) 0.002 (0, 0.004) 1.99 (0.28, 5.2) 0.004 (0.001, 0.007) 0.001 2.002 (0.28, 5.2)
Milwaukee
Incidence –0.12 (–0.20, 0.14) 134 (–292, 559) –0.09 (–0.74, 0.45) 0 (–0.61, 0.57) 0.2 (–0.6, 1.1) 21 (–116, 158) –60
Savings 0.008 (–0.02, 0.04) 0 (0, 0) 0.001 (–0.01, 0.01) 1.65 (–11.5, 16.9) 0.002 (–0.01, 0.02) 0 1.66 (–11.6, 16.9)
Twin Cities
Incidence –0.05 (–0.11, 0.38) –2,190 (–3,590, –790) –0.76 (–2.5, 0.24) –2 (–3.7, –0.3) –4 (–6.5, –2.3) –588 (–994, –181) –7,881
Savings –0.138 (–0.25, –0.039) 0 (–0.001, 0) –0.039 (–0.07, –0.003) –34.7 (–88.9, 2.57) –0.056 (–0.095, –0.0017) –0.01 –34.9 (–89.3, 2.5)
Total MSAs
Incidence –0.07 (–0.33, 0.70) –3,467 (–15,663, 8,723) –963 (–8,281, 5,764) –5.97 (–28, 16) –9 (–32, 14) –976 (–4,860, 2,909) 2,627
Savings –0.22 (–1.15, 0.72) –0.00096 (–0.0083, 0.0058) –0.134 (–0.63, 0.36) –71.7 (–543, 380) –0.093 (–0.46, 0.28) 0.0033 –72.14 (–545.38, 381.47)
Outside MSAs total
Incidence 33,628 (17,087, 50,167) 17 (0, 51.5) 47 (10.9, 83) 91 (53.3, 129.6) 9,608 (4,610, 14,606) 282,669
Savings 212 (0.995, 3.6) 0.0073 (0, 0.019) 1.01 (0.222, 1.81) 763.2 (118, 1,1893) 0.914 (0.439, 1.39) 0.353 767.6 (120, 1,900)
Region total
Incidence 30,161 (1,423, 58,891) 14.63 (–22.4, 66.8) 41.13 (–17.2, 98.8) 82.6 (21.3, 143.3) 8,632 (–250.2, 17,515) 285,296
Savings 1.91 (–0.14, 4.33) 0.0062 (–0.0083, 0.025) 0.876 (–0.42, 2.17) 691.5 (–425, 2,273) 0.822 (–0.0239, 1.67) 0.357 695.5 (–425, 2,281)
Values for incidence represent estimated incidence per adverse health effect avoided due to a change in air pollution in the given city per year; costs are expressed as negative and benefits as positive (millions of dollars). Values in parentheses for incidence and savings are 95% confidence intervals (in most cases rounded to nearest decimal), and all changes are annualized. aChange in O3 season average daily maximum 8-hr are calculated as mean area (range of grid cell values). Data for O3-related health effects (and the source of the O3 concentration–response functions used to estimate the change in incidence) estimated in this analysis are from Bell et al. (2004, 2005), Burnett et al. (2001), Chen et al. (2000), Crocker et al. (1981), Gilliland et al. (2001), Huang et al. (2005), Ito et al. (2005), Jaffe et al. (2003), Levy et al. (2005), Moolgavkar et al. (1997), Ostro and Rothschild (1989), Peel et al. (2005), Schwartz (1994a, 1994b,1995, 2005), and Wilson et al. (2005).
Open in a new tab

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 ($7.4 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 PM2.5 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 PM2.5 [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 PM2.5 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.

MSA No. of trips by bicycle (millions) No. of trips/day (millions) Average distance (km) Maximum annual benefit (millions of $)a Savings/individual cyclist/year ($) Savings per trip ($) Mean annual benefit (millions of $) No. of people to benefit Lives saved
Suburban
Chicago 280.77 0.77 3.65 972 2,298 10.19 724 211,566 131
Cincinnati 53.15 0.15 3.55 179 2,235 9.91 133 40,047 24
Cleveland 93.02 0.26 3.55 313 2,235 9.91 233 70,083 42
Columbus 51.19 0.14 3.55 172 2,235 9.91 128 38,567 23
Dayton 33.09 0.09 3.55 111 2,235 9.91 83 24,935 15
Detroit 197.11 0.54 3.55 664 2,235 9.91 495 148,506 90
Grand Rapids 35.14 0.10 3.98 133 2,506 11.11 99 26,474 18
Indianapolis 57.46 0.16 3.98 217 2,506 11.11 162 43,294 29
Madison 25.23 0.07 3.64 87 2,291 10.16 65 19,010 12
Milwaukee 50.85 0.14 3.64 176 2,291 10.16 131 38,314 24
Twin Cities 109.30 0.30 3.58 371 2,253 10 277 82,346 50
Total suburban 986.31 2.70 3.66 3,396 2,302 10.21 2,530 743,142 459
Urban
Chicago 197.16 0.54 3.43 652 2,160 9.58 4,859 151,022 88
Cincinnati 29.03 0.08 3.34 92 2,103 9.33 685 21,870 12
Cleveland 49.56 0.14 3.34 157 2,103 9.33 1,170 37,338 21
Columbus 33.45 0.09 3.34 106 2,103 9.33 790 25,205 14
Dayton 10.08 0.03 3.34 34 2,103 9.33 252 7,597 5
Detroit 62.70 0.17 3.54 211 2,228 9.89 1,569 47,238 28
Grand Rapids 10.64 0.03 3.54 36 2,228 9.89 266 8,013 5
Indianapolis 21.21 0.06 4.47 90 2,815 12.48 670 15,982 12
Madison NA NA 3.79 NA NA NA NA NA NA
Milwaukee 30.80 0.08 3.79 111 2,386 10.58 83 23,208 15
Twin Cities 58.97 0.16 3.5 196 2,203 9.77 146 44,430 26
Total urban 503.60 1.38 3.58 1,684 2,039 9.05 1,255 381,902 228
Grand total 1489.92 4.08 3.62 5,080 2,171 9.63 3,784 112,5045 687
NA, not applicable. aThe maximum annual benefit is the total value of reduced mortality based on the level of cycling entered by the user.
Open in a new tab

We estimated that substitution of non-emitting modes for short trips would achieve average annual reductions in the 24-hr average PM2.5 concentrations considered in U.S. PM2.5 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 NOx and/or VOC emissions (Sillman 1995). In our emissions inventory, motor vehicles were responsible for most of the NOx (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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5 reductions in other cities, we estimated that PM2.5 would be reduced by 0.05 µg/m3 (Figure 1B). Nearly all of the estimated reduction in PM2.5 would be due to decreases in secondary aerosols, especially nitrate formed from NOx 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 PM2.5 mass. Reductions in PM2.5 in urban areas and downwind would be greatest during high-pollution episodes exceeding the 24-hr average PM2.5 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 PM2.5 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 PM2.5 would be reduced in all MSAs (Table 1). Reductions in PM2.5-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 $4.14 billion. Savings of > $7.5 million would result from fewer respiratory cases, hospital admissions, and ER visits, whereas a reduction in COPD would save > $39 million per year; reductions in non-fatal acute myo-cardial infarctions and cardio-vascular hospitalizations would save > $54 million. We estimate that total savings from reducing adverse health effects due to PM2.5 would be about $4.25 billion/year (95% CI: $598 million–$11.2 billion). Projections suggest that PM2.5 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.

Figure 2

Open in a new tab

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 NOx emissions by 5–12% and total VOC emissions by 10–25%.

Although we estimate that NOx and VOCs would both be reduced, the response to NOx 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 PM2.5 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 $1.9 million) and 8,632 fewer school-loss days (savings of almost $822,000). 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 PM2.5, 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:NOx 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 > $2.5 billion for short suburban bicycle trips and nearly $1.25 billion for short urban trips (Table 3), for a total of approximately $3.8 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 PM2.5 and O3 would result in net health bene-fits of $4.94 billion/year (95% CI: $0.2 billion, $13.5 billion). Completing 50% of short trips by bicycle would yield $3.8 billion/year in savings (95% CI: $2.7, $5.0 billion), about $1.5 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 $8.7 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 $2,500 to $50,000/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 $10,000/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 $140,000 (City of Chicago 2011). Increasing this cost estimate to $100,000/block, double the U.S. average cost per mile for bike lane conversion and addition, the $2 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 PM2.5. 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 PM2.5 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 PM2.5 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 PM2.5 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 PM2.5. 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 PM2.5 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 PM2.5 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

Acknowledgments

We thank G. Allez and the two anony-mous reviewers for their useful comments and suggestions and J. Sledge and S. Ventura (University of Wisconsin–Madison) for advice on urban geospatial approaches.

Footnotes

Research by M.G. and J.P. was supported by the U.S. Environmental Protection Agency (EPA) STAR program (grant R832752010), Health Risks from Climate Variability and Change in the Upper Midwest: a Place-based Assessment of Climate-related Morbidity. M.G. was also supported by NSF grant DGE-0549407, an Integrative Graduate Education and Research Traineeship (IGERT) titled “Vulnerability and Sustainability in Coupled Human-Natural Systems.” S.S., T.H., B.S., and A.M. were supported by U.S. EPA STAR grant R831840.

Contents of this article are solely the responsibility of the grantee and do not necessarily represent the views of the U.S. EPA.

The authors declare they have no actual or potential competing financial interests.

References

  1. Abbey DE, Ostro BE, Petersen F, Burchette RJ. Chronic respiratory symptoms associated with estimated long-term ambient concentrations of fine particulates less than 2.5 microns in aerodynamic diameter (PM2.5) and other air pollutants. J Expo Anal Environ Epidemiol. 1995;5:137–159. [PubMed] [Google Scholar]
  2. Abt Associates Inc. BenMAP User’s Manual. 2010. Available: http://www.epa.gov/oaqps001/benmap/models/BenMAPManualAugust2010.pdf [accessed 1 November 2010]
  3. Adams PF, Hendershot GE, Marano MA.1999Current estimates. from the National Health Interview Survey, 1996. Vital Health Stat 10(2001–212. [PubMed] [Google Scholar]
  4. Anderson LB, Schnohr P, Schroll M, Hein HO. All-cause mortality associated with physical activity during leisure time, work, sports, and cycling to work. Arch Intern Med. 2000;160(11):1621–1628. doi: 10.1001/archinte.160.11.1621. [DOI] [PubMed] [Google Scholar]
  5. Bell ML, Dominici F, Samet JM. A meta-analysis of time-series studies of ozone and mortality with comparison to the national morbidity, mortality, and air pollution study. Epidemiology. 2005;16(4):436–445. doi: 10.1097/01.ede.0000165817.40152.85. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Bell ML, Ebisu K, Peng JD, Samet JM, Dominici F. Hospital admissions and chemical composition of fine particle air pollution. Am J Respir Crit Care Med. 2009;179:1115–1120. doi: 10.1164/rccm.200808-1240OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Bell ML, McDermott A, Zeger SL, Samet JM, Dominici F. Ozone and short-term mortality in 95 US urban communities, 1987–2000. JAMA. 2004;292(19):2372–2378. doi: 10.1001/jama.292.19.2372. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Besser LM, Dannenberg AL. Walking to public transit: steps to help meet physical activity recommendations. Am J Prev Med. 2005;29(4):273–280. doi: 10.1016/j.amepre.2005.06.010. [DOI] [PubMed] [Google Scholar]
  9. Bonnel P, Lee-Gosselin M, Zmud J, Madre J, eds. Bingley, UK: Emerald Group Publishing Limited; 2009. Transport Survey Methods: Keeping up with a Changing World. [Google Scholar]
  10. Bowman JL, Ben-Akiva ME. Activity-based disaggregate travel demand model system with activity schedules. Transp Res A. 2001;35:1–18. [Google Scholar]
  11. Brunekreef B, Holgate ST. Air pollution and health. Lancet. 2002;360(9341):1233–1242. doi: 10.1016/S0140-6736(02)11274-8. [DOI] [PubMed] [Google Scholar]
  12. Burnett RT, Smith-Doiron M, Stieb D, Raizenne ME, Brook JR, Dales RE, et al. Association between ozone and hospitalization for acute respiratory diseases in children less than 2 years of age. Am J Epidemiol. 2001;153(5):444–452. doi: 10.1093/aje/153.5.444. [DOI] [PubMed] [Google Scholar]
  13. Byun DW, Schere KL. Review of the governing equations, computational algorithms, and other components of the models-3 community multiscale air quality modeling system. Appl Mech Rev. 2006;59:51–77. [Google Scholar]
  14. Carlson SA, Densmore D, Fulton JE, Yore MM, Kohl HW., III Differences in physical activity prevalence and trends from 3 U.S. surveillance systems: NHIS, NHANES, and BRFSS. J Phys Act Health. 2009;6(suppl 1):S18–S27. doi: 10.1123/jpah.6.s1.s18. [DOI] [PubMed] [Google Scholar]
  15. Centers for Disease Control and Prevention. Summary Health Statistics for U.S. Adults: National Health Interview Survey, 2008 Series 10, No. 242. 2009. Available: http://www.cdc.gov/nchs/data/series/sr_10/sr10_242.pdf [accessed 19 October 2010]
  16. Chen J, Zhao R, Li Z. Voronoi-based k-order neighbour relations for spatial analysis. ISPRS J Photogramm Remote Sens. 2004;59(1–2):60–72. [Google Scholar]
  17. Chen L, Jennison BL, Yang W, Omaye ST. Elementary school absenteeism and air pollution. Inhal Toxicol. 2000;12(11):997–1016. doi: 10.1080/08958370050164626. [DOI] [PubMed] [Google Scholar]
  18. City of Chicago. Kinzie Protected Bike Lane Completed. 2011. Available: http://www.cityofchicago.org/city/en/depts/cdot/provdrs/bike/news/2011/jul/kinzie_protectedbikelanecompleted.html [accessed 1 September 2011]
  19. Community Modeling and Analysis System. Sparse Matrix Operators Kernel Emissions Model (SMOKE). 2007. Available: http://www.smoke-model.org [accessed 1 November 2007]
  20. Crocker TD, Horst RL., Jr Hours of work, labor productivity, and environmental conditions: a case study. Rev Econ Stat. 1981;63:361–368. [Google Scholar]
  21. Davidson K, Hallberg A, McCubbin D, Hubbell B. Analysis of PM2.5 using the environmental benefits mapping and analysis program. J Toxicol Environ Health A. 2007;70:332–346. doi: 10.1080/15287390600884982. [DOI] [PubMed] [Google Scholar]
  22. de Hartog JJ, Boogard H, Nijland H, Hoek G. Do the health benefits of cycling outweigh the risks? Environ Health Perspect. 2010;118:1109–1116. doi: 10.1289/ehp.0901747. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Dockery DW, Cunningham J, Damokosh AI, Neas LM, Spengler JD, Koutrakis P, et al. Health effects of acid aerosols on North American children: respiratory symptoms. Environ Health Perspect. 1996;104:500–505. doi: 10.1289/ehp.96104500. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. European Commission. Thematic Synthesis of Transport Research Results. Paper 5 of 10: Urban Transport. 2001. Available: http://www.transport-research.info/Upload/Documents/200408/20040809_152835_59539_urban_transport.pdf [accessed 1 October 2009]
  25. FHWA (Federal Highway Administration) 1997. User’s Guide for the Public Use Data Files: 1995 Nationwide Personal Transportation Survey. Publication No. FHWA-PL-98-002. Research Triangle Park, NC:Research Triangle Institute.Available: http://nhts.ornl.gov/1995/Doc/UserGuide.pdf [accessed 22 November 2011].
  26. FHWA (Federal Highway Administration) 2006. Transportation Air Quality Facts and Figures January 2006. Available http://www.fhwa.dot.gov/environment/air_quality/publications/fact_book/page15.cfm [accessed 8 March 2011].
  27. Frank LD, Engelke P. Multiple impacts of the built environ-ment on public health: walkable places and the exposure to air pollution. Int Reg Sci Rev. 2005;28(2):193–216. [Google Scholar]
  28. Frank LD, Stone B, Jr, Bachman W. Linking land use with household vehicle emissions in the central Puget Sound: methodological framework and findings. Transp Res Part D: Transport Environ. 2000;5(3):173–196. [Google Scholar]
  29. Gent JF, Koutrakis P, Belanger K, Triche E, Holford TR, Bracken MB, et al. Symptoms and medication use in children with asthma and traffic-related sources of fine particle pollution. Environ Health Perspect. 2009;117:1168–1174. doi: 10.1289/ehp.0800335. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Gilliland FD, Berhane K, Rappaport EB, Thomas DC, Avol E, Gauderman WJ, et al. The effects of ambient air pollution on school absenteeism due to respiratory illnesses. Epidemiology. 2001;12:43–54. doi: 10.1097/00001648-200101000-00009. [DOI] [PubMed] [Google Scholar]
  31. Huang Y, Dominici F, Bell ML. Bayesian hierarchical distributed lag models for summer ozone exposure and cardio-respiratory mortality. Environmetrics. 2005;16:547–562. doi: 10.1002/env.721. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Hubbell BJ, Fann N, Levy JI. Methodological considera-tions in developing local-scale health impact assessments: balancing national, regional, and local data. Air Qual Atmos Health. 2009;2:99–110. [Google Scholar]
  33. Illinois Department of Transportation. Illinois Highway and Street Mileage Statistics. 2009. Available: http://www.dot.state.il.us/travelstats/2009_ILHS.pdf [accessed 7 July 2011]
  34. Ito K. In: Revised Analyses of Time-Series Studies of Air Pollution and Health. Boston:Health Effects Institute, 143–156; 2003. Associations of particulate matter components with daily mortality and morbidity in Detroit, Michigan. [Google Scholar]
  35. Ito K, De Leon SF, Lippmann M. Associations between ozone and daily mortality: analysis and meta-analy-sis. Epidemiology. 2005;16(4):446–457. doi: 10.1097/01.ede.0000165821.90114.7f. [DOI] [PubMed] [Google Scholar]
  36. Jaffe DH, Singer ME, Rimm AA. Air pollution and emergency department visits for asthma among Ohio Medicaid recipients, 1991–1996. Environ Res. 2003;91(1):21–28. doi: 10.1016/s0013-9351(02)00004-x. [DOI] [PubMed] [Google Scholar]
  37. Kaiser Family Foundation. Individual State Profiles [Illinois, Indiana, Michigan, Ohio, Wisconsin]. 2004. Available: http://www.statehealthfacts.org/profile.jsp [accessed 6 July 2011]
  38. Laden F, Schwartz J, Speizer FE, Dockery DW. Reduction in fine particulate air pollution and mortality: extended follow-up of the Harvard Six Cities Study. Am J Respir Crit Care Med. 2006;173:667–672. doi: 10.1164/rccm.200503-443OC. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. League of American Bicyclists. Bicycle Friendly Community. 2010. Available: http://www.bikeleague.org/programs/bicyclefriendlyamerica/communities/index.php [accessed 3 December 2010]
  40. Levy JI, Chemerynski SM, Sarnat JA. Ozone exposure and mortality: an empiric Bayes metaregression analysis. Epidemiology. 2005;16(4):458–468. doi: 10.1097/01.ede.0000165820.08301.b3. [DOI] [PubMed] [Google Scholar]
  41. Moolgavkar SH. Air pollution and hospital admissions for chronic obstructive pulmonary disease in three metropolitan areas in the United States. Inhal Toxicol. 2000a;12(suppl 4):75–90. doi: 10.1080/089583700750019512. [DOI] [PubMed] [Google Scholar]
  42. Moolgavkar SH. Air pollution and hospital admissions for diseases of the circulatory system in three U.S. metropolitan areas. J Air Waste Manag Assoc. 2000b;50(7):1199–1206. doi: 10.1080/10473289.2000.10464162. [DOI] [PubMed] [Google Scholar]
  43. Moolgavkar SH. In: Revised Analyses of Time-Series Studies of Air Pollution and Health. Boston:Health Effects Institute, 83–198; 2003. Air pollution and daily deaths and hospital admissions in Los Angeles and Cook counties. [Google Scholar]
  44. Moolgavkar SH, Luebeck EG, Anderson EL. Air pollution and hospital admissions for respiratory causes in Minneapolis, St. Paul and Birmingham. Epidemiology. 1997;8(4):364–370. doi: 10.1097/00001648-199707000-00003. [DOI] [PubMed] [Google Scholar]
  45. Norris G, YoungPong SN, Koenig JQ, Larson TV, Sheppard L, Stout JW. 1999An association between fine particles and asthma emergency department visits for children in Seattle. Environ Health Perspect 107489–493. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Ostro BD. Air pollution and morbidity revisited: a specification test. J Environ Econ Manage. 1987;14:87–98. [Google Scholar]
  47. Ostro B, Lipsett M, Mann J, Braxton-Owens H, White M. Air pollution and exacerbation of asthma in African-American children in Los Angeles. Epidemiology. 2001;12(2):200–208. doi: 10.1097/00001648-200103000-00012. [DOI] [PubMed] [Google Scholar]
  48. Ostro BD, Rothschild S. Air pollution and acute respiratory morbidity: an observational study of multiple pollutants. Environ Res. 1989;50(2):238–247. doi: 10.1016/s0013-9351(89)80004-0. [DOI] [PubMed] [Google Scholar]
  49. Peel JL, Tolbert PE, Klein M. Ambient air pollution and respiratory emergency department visits. Epidemiology. 2005;16(2):164–174. doi: 10.1097/01.ede.0000152905.42113.db. [DOI] [PubMed] [Google Scholar]
  50. Peters A, Dockery DW, Muller JE, Mittleman MA. Increased particulate air pollution and the triggering of myocardial infarction. Circulation. 2001;103(23):2810–2815. doi: 10.1161/01.cir.103.23.2810. [DOI] [PubMed] [Google Scholar]
  51. Pope CA, III, Burnett RT, Thun MJ, Calle EE, Krewski D, Ito K, et al. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. JAMA. 2002;287:1132–1141. doi: 10.1001/jama.287.9.1132. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Pope CA, III, Dockery DW, Spengler JD, Raizenne ME. Respiratory health and PM10 pollution: a daily time series analysis. Am Rev Respir Dis. 1991;144:668–674. doi: 10.1164/ajrccm/144.3_Pt_1.668. [DOI] [PubMed] [Google Scholar]
  53. Pucher J, Buehler R, Bassett DR, Dannenberg AL. Walking and cycling to health: a comparative analysis of city, state, and international data. Am J Public Health. 2010;100(10):1986–1992. doi: 10.2105/AJPH.2009.189324. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Pucher J, Dijkstra L. Public health matters. promoting safe walking and cycling to improve public health: Lessons from the Netherlands and Germany. Am J Public Health. 2003;93(9):1509–1516. doi: 10.2105/ajph.93.9.1509. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Rutter H, Cavill N, Dinsdale H, Kahlmeier S, Racioppi F, Oja P. Health Economic Assessment Tool for Cycling. Copenhagen:World Health Organization Regional Office for Europe. 2007. Available: http://www.thepep.org/en/workplan/candw/documents/Cycling_HEAT_v1.0.xls [accessed 10 August 2010]
  56. Schwartz J. Air pollution and hospital admissions for the elderly in Detroit, Michigan. Am J Respir Crit Care Med. 1994a;150(3):648–655. doi: 10.1164/ajrccm.150.3.8087333. [DOI] [PubMed] [Google Scholar]
  57. Schwartz J. PM10, ozone, and hospital admissions for the elderly in Minneapolis-St Paul, Minnesota. Arch Environ Health. 1994b;49(5):366–374. doi: 10.1080/00039896.1994.9954989. [DOI] [PubMed] [Google Scholar]
  58. Schwartz J. Short term fluctuations in air pollution and hospital admissions of the elderly for respiratory disease. Thorax. 1995;50(5):531–538. doi: 10.1136/thx.50.5.531. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Schwartz J. How sensitive is the association between ozone and daily deaths to control for temperature? Am J Respir Crit Care Med. 2005;171(6):627–631. doi: 10.1164/rccm.200407-933OC. [DOI] [PubMed] [Google Scholar]
  60. Schwartz J, Neas LM. Fine particles are more strongly associated than coarse particles with acute respiratory health effects in schoolchildren. Epidemiology. 2000;11(1):6–10. doi: 10.1097/00001648-200001000-00004. [DOI] [PubMed] [Google Scholar]
  61. Sheppard L. In: Revised Analyses of Time-Series Studies of Air Pollution and Health. Boston:Health Effects Institute, 227–230; 2003. Ambient air pollution and nonelderly asthma hospital admissions in Seattle, Washington, 1987–1994. [Google Scholar]
  62. Sillman S. The use of NOy, H2O2, and HNO3 as indicators for ozone-NOx-hydrocarbon sensitivity in urban locations. J Geophys Res. 1995;100(D7):14175–14188. [Google Scholar]
  63. Singer BC, Kirchstetter TW, Harley RA, Hesson JM, Kendall GR. A fuel-based approach to estimating motor vehicle cold start emissions. J Air Waste Manag Assoc. 1999;49(2):125–135. doi: 10.1080/10473289.1999.10463785. [DOI] [PubMed] [Google Scholar]
  64. Skamarock WC, Klemp JB. A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. J Comput Phys. 2008;227(7):3465–3485. [Google Scholar]
  65. Skamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Duda MG, et al. Boulder, CO: National Center for Atmospheric Research; 2008. NCAR Technical Note: A Description of the Advanced Research WRF. Version 3. NCAR/TN–475+STR. [Google Scholar]
  66. Spak SN, Holloway T.2009Seasonality of speciated aerosol transport over the Great Lakes region. J Geophys Res 114D08302; doi: 10.1029/2008JD010598[Online 22 April 2009] [DOI] [Google Scholar]
  67. Stone B, Jr, Mednick AC, Holloway TA, Spak SN. Is compact growth good for air quality? J Am Plann Assoc. 2007;73(4):404–418. [Google Scholar]
  68. Tran H, Alvarado A, Garcia C, Motallebi N, Miyasato L, Vance W. Sacramento, CA: California Environmental Protection Agency, Air Resources Board; 2009. Methodology for Estimating Premature Deaths Associated with Long-Term Exposures to Fine Airborne Particulate Matter in California. [Google Scholar]
  69. University of North Carolina Institute for the Environment, Community Modeling and Analysis System Center, Environmental Protection Agency. Class Training. 2008. Available: http://www.epa.gov/air/benmap/docs.html [accessed 30 May 2009]
  70. U.S. Census Bureau. American Community Survey 2008, Table S0801: Commuting Characteristics by Sex. 2009. Available: http://factfinder.census.gov/servlet/STTable?_bm=y&-state=st&-context=st&-qr_name=ACS_2008_1YR_G00_S0801&-ds_name=ACS_2008_1YR_G00_&-tree_id=53&-redoLog=false&-geo_id=01000US&-_lang=en [accessed 1 October 2009]
  71. U.S. EPA (U.S. Environmental Protection Agency) Washington, DC: U.S. EPA; 2004a. Air Quality Criteria for Particulate Matter. EPA/600/P-99/002aF. [Google Scholar]
  72. U.S. EPA (U.S. Environmental Protection Agency) Washington, DC: U.S. EPA; 2004b. Technical Guidance on the Use of MOBILE6.2 for Emission Inventory Preparation. EPA/420/R-04/013. [Google Scholar]
  73. U.S. EPA (U.S. Environmental Protection Agency) Clean Air Interstate Rule Emissions Inventory Technical Support Document, Docket OAR-2003-0053-2047. 2005a. Available: http://www.epa.gov/cair/pdfs/finaltech01.pdf [accessed 1 December 2010]
  74. U.S. EPA (U.S. Environmental Protection Agency) Emission Facts: Calculating Emissions of Greenhouse Gases: Key Facts and Figures. EPA420-F-05-003. 2005b. Available: http://nepis.epa.gov/Adobe/PDF/P1001YTV.PDF [accessed 12 October 2011]
  75. U.S. EPA (U.S. Environmental Protection Agency) National Summary of Nitrogen Oxides Emissions. 2005c. Available: http://www.epa.gov/air/emissions/nox.htm [accessed 5 July 2011]
  76. U.S. EPA (U.S. Environmental Protection Agency) National Summary of VOC Emissions. 2005d. Available: http://www.epa.gov/air/emissions/voc.htm [accessed 5 July 2011]
  77. U.S. EPA (U.S. Environmental Protection Agency) Final Ozone NAAQS Regulatory Impact Analysis. EPA-452/R-08-003. Washington, DC:U.S. Environmental Protection Agency, Office of Air and Radiation. 2008. Available: http://www.epa.gov/ttn/ecas/regdata/RIAs/452_R_08_003.pdf [accessed 1 October 2009]
  78. U.S. EPA (U.S. Environmental Protection Agency) Environmental Benefits Mapping and Analysis Program (BenMAP) 4.0. 2010a. Available: http://www.epa.gov/air/benmap [accessed 10 September 2010]
  79. U.S. EPA (U.S. Environmental Protection Agency) Frequently Asked Questions on Mortality Risk Valuation. 2010b. Available: http://yosemite.epa.gov/ee/epa/eed.nsf/pages/MortalityRiskValuation.html#currentvsl [accessed 19 November 2010]
  80. U.S. EPA (U.S. Environmental Protection Agency) Quantitative Health Risk Assessment for Particulate Matter. EPA-452/R-10-005. Research Triangle Park, NC:U.S. EPA. 2010c. Available: http://www.epa.gov/ttn/naaqs/standards/pm/data/PM_RA_FINAL_June_2010.pdf [accessed 6 July 2011]
  81. U.S. EPA (U.S. Environmental Protection Agency) 8-Hour Ozone Nonattainment Area Study. 2011a. Available: http://www.epa.gov/air/oaqps/greenbk/gnsum.html [accessed 19 June 2011]
  82. U.S. EPA (U.S. Environmental Protection Agency) Particulate Matter (PM-2.5) 1997 Nonattainment Area Summary. 2011b. Available: http://www.epa.gov/air/oaqps/greenbk/qnsum.html [accessed 19 June 2011]
  83. Vedal S, Petkau J, White R, Blair J. Acute effects of ambient inhalable particles in asthmatic and nonasthmatic children. Am J Respir Crit Care Med. 1998;157(4):1034–1043. doi: 10.1164/ajrccm.157.4.9609008. [DOI] [PubMed] [Google Scholar]
  84. Wilkinson RG, Picket KE. Income inequality and socio-economic gradients in mortality. Am J Public Health. 2008;98(4):699–704. doi: 10.2105/AJPH.2007.109637. [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Wilson AM, Wake CP, Kelly T. Air pollution and weather, and respiratory emergency room visits in two northern New England cities: an ecological time-series study. Environ Res. 2005;97(3):312–321. doi: 10.1016/j.envres.2004.07.010. [DOI] [PubMed] [Google Scholar]
  86. WHO (World Health Organization) Geneva: WHO; 2002. World Health Report: Reducing Risks Promoting Healthy Life. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

(217 KB) PDF
Click here for additional data file. (211.4KB, pdf)

Articles from Environmental Health Perspectives are provided here courtesy of National Institute of Environmental Health Sciences

RESOURCES