Proximity of schools in Detroit, Michigan to automobile and ... - Nature

Journal of Exposure Science and Environmental Epidemiology (2006) 16, 457–470 r 2006 Nature Publishing Group All rights reserved 1559-0631/05/$30.00

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Proximity of schools in Detroit, Michigan to automobile and truck traffic YI-CHEN WU AND STUART A. BATTERMAN Environmental Health Sciences, University of Michigan, 1420 Washington Heights, Room 6075, Ann Arbor, MI 48109-2029 USA

Exposure to traffic-related air pollutants, which has been associated with a range of adverse health effects, often is represented using indirect proxies or surrogate exposure measures, most commonly, the proximity to busy roads. This study examines the proximity of grade K-12 schools to high traffic roads in Wayne County, Michigan, an area including the industrialized city of Detroit as well as outlying urban and suburban communities. Unlike earlier studies, commercial and non-commercial traffic is distinguished, and effects of school type (public, charter, private), socio-economic variables, demographic factors, and mapping errors are evaluated. We find that total traffic flow, as measured by annual average daily traffic (AADT), does not reflect the substantial differences between trucking and commuting routes. Thus, AADT alone may inadequately capture traffic-related exposures, especially given the large differences between diesel and gasoline emissions. Based on close proximity (school–road distance r150 m) to heavy traffic (AADTZ50,000), 4.9% of the 845 Wayne County schools are traffic exposed at school. In the urban core area, 7.2% of schools and 7.6% of students are traffic exposed at school. A larger proportion of grade 7–12 students in public schools are exposed than K-6 students. Considering truck emissions, 2.8% of the schools are within 150 m of roads with 5000 or more trucks per day. In Wayne County, students attending schools near high traffic roads are more likely to be Black or Hispanic, to be enrolled in a meal program, and to reside in a poor area. Many of these results are driven by the large minority population in the densely populated core area of Detroit. The findings show that a large fraction of children have high exposures to traffic-related pollutants, especially in Detroit, and the need for exposure measures that account for both the composition and volume of traffic. Journal of Exposure Science and Environmental Epidemiology (2006) 16, 457–470. doi:10.1038/sj.jes.7500484; published online 19 April 2006

Keywords: air pollution, children, diesel, environmental justice, exposure, geographic information systems (GIS), traffic.

Introduction Noise, congestion, and pollutant emissions from vehicles constitute important environmental stressors on urban populations that may affect health, safety, quality of life, and property values. Transportation is the largest emission source of many pollutants in urban areas, accounting for most of the carbon monoxide (CO) and air toxics in cities, half of the nitrogen oxides (NOx), and a third of particulate matter (PM) in the US (US EPA, 1998; Rosenbaum et al., 1999). Traffic has been linked to pollutant exposures for commuters and individuals living or working near busy roads (Briggs et al., 1997; Nakai et al., 1999; Kinney et al., 2000; Levy et al., 2001; Batterman et al., 2002; Lena et al., 2002; Zhu et al., 2002; Janssen et al., 2003; Gulliver and Briggs, 2004). In turn, traffic-related exposures have been associated with many adverse health outcomes, including cardiopulmonary mortality (Hoek et al., 2002), all-cause mortality rate advancement (Finkelstein et al., 2004), respiratory disease and especially asthma (Braun-Fahrlander et al., 1992; Nitta

Address all correspondence to: Dr. Stuart A. Batterman, Environmental Health Sciences, University of Michigan, 1420 Washington Heights, Room 6075, Ann Arbor, MI 48109-2029 USA. Tel.: þ 734/763 2417. Fax: þ 734/936 7283. E-mail: [email protected] Received 16 June 2005; accepted 23 February 2006; published online 19 April 2006

et al., 1993; Roemer et al., 1993; Edwards et al., 1994; Oosterlee et al., 1996; Brunekreef et al., 1997; van Vliet et al., 1997; English et al., 1999; Hirsch et al., 1999; Kunzli et al., 2000; Friedman et al., 2001; Delfino 2002; Gehring et al., 2002; Lwebuga-Mukasa et al., 2003; Kim et al., 2004a), and adverse birth outcomes (Wilhelm and Ritz, 2003). Several studies have reported stronger associations between adverse health outcomes and truck traffic than total vehicular traffic (Weiland et al., 1994; Duhme et al., 1996; Brunekreef et al., 1997; van Vliet et al., 1997; Ciccone et al., 1998; Janssen et al., 2003). However, not all studies have found significant associations between traffic-related air pollutants, asthma and lung function (e.g., Dockery et al., 1989; Wjst et al., 1993; Wilkinson et al., 1999; Gehring et al., 2002), and few studies have used direct measures of traffic exposures given their cost and logistics, instead relying on surrogate measures of exposure, for example, proximity to highways. Environmental justice studies examining exposure to traffic pollutants also have depended on surrogate measures. Green et al. (2004) investigated the proximity of K-12 public schools in California to major roads, determining the number of schools and students within 150 m of high traffic roads, defined as having Z50,000 vehicles/day. Traffic-exposed schools had higher enrollment percentages of Black and Hispanic students, and over 60% of students were enrolled in free or reduced-price meal programs. Gunier et al. (2003) found that low income and children of color were far more

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likely to live in high traffic areas, defined as census block groups with 4500,000 vehicle miles traveled (VMT) mile2. Other studies examining traffic and environmental justice are reviewed by Schweitzer and Valenzuela (2004).

Traffic-related exposures Concentrations of traffic-related emissions generally decrease with distance from the road due to dispersion, reaction (for chemically reactive gases and vapors), and coagulation (for ultrafine particles), and also depend on vehicle emissions, terrain, built features, and the prevailing meteorology. Dispersion models show rapid drops in concentrations as distances from roads increase, for example, under worstcase conditions (stable conditions, light winds), concentrations at 50 m are a few percent of the curbside level (Jones et al., 2000; Korenstein and Piazza 2002). Such results apply to primary pollutants like CO under idealized and averaged conditions, for example, flat terrain. Traffic impacts are more easily discerned with low levels of background pollutants, thus gradients are more commonly seen for black carbon (BC), elemental carbon (EC), ultrafine particles and NOx than for PM2.5 (Kingham et al., 2000; Monn, 2001; Gehring et al., 2002). Traffic-related pollutant impacts have been observed within a few hundred meters of roads. Roorda-Knape et al. (1998) found that concentrations of NO2 and black smoke (but not PM10, PM2.5 or benzene) decreased with distances up to 305 m from major roads in the Netherlands, with most of the change occurring over the first 165 m. Nakai et al. (1999) found higher levels of NO2 in homes within 150 m of roads with heavy traffic in Tokyo, Japan. Hitchins et al. (2000) found that PM1 levels at 15 m from two busy roads in Brisbane, Australia halved at distances of 100–150 m. Zhu et al. (2002) saw exponential decreases in CO, BC, and PM number concentrations from 20 to 100 m from a Los Angeles, California freeway. Briggs et al. (1997, 2000) associated NO2 levels with traffic intensity at distances from 50 to 350 m from roads in Britain. Gehring et al. (2002) associated PM2.5, PM2.5 reflectance and NO2 with traffic intensity in Munich, Germany at distances from 50 to 300 m. Kim et al. (2004a) found that BC and NO levels were elevated at schools within 300 m of major roads in the San Francisco, California area. Epidemiological studies have used similar distances (50–500 m) to investigate the association of traffic with respiratory symptoms (English et al., 1999; Gehring et al., 2002; Janssen et al., 2003; Kim et al., 2004a). While commercial vehicles represent a minority of vehicles and only 8% of the total vehicle-kilometers traveled (VKT, US DOT, 2000), truck emissions far exceed those from cars on a per vehicle basis and, for some pollutants in urban areas, total emissions from trucks exceed those from cars. This is shown in four ways. First, using US 1998–2003 heavy-duty diesel truck standards (expressed as g pollutant/bhp/h) and 458

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assuming 300 bhp (224 kW) power and 88 km/h (55 mph) speed, tailpipe emission limits for hydrocarbons, CO, NOx, and PM are 4.4, 52.8, 13.6, and 0.34 g/km, respectively, or 17, 24, 54, and 7 times higher than the Tier 1 standards that apply to 1994–2001 cars. Second, tests of in-use vehicles, which may better represent actual emissions than standards, show yet larger differences between cars and trucks. As examples, tunnel tests show diesel truck PM emission rates of 0.9–1.9 g/km (Pierson and Brachaczek, 1983; Pierson et al., 1996; Kirchstetter et al., 1999; Allen et al., 2001), much higher than standards, and Mazzoleni et al. (2004) found PM emission rates of 4.6 and 215 mg/km, respectively, for gasoline and diesel vehicles in Las Vegas, Nevada, a ratio of 47. Third, apportionments of ambient concentrations due to diesel and gasoline sources based on chemical signatures and ‘‘receptor models’’ typically show ratios of the diesel:gasoline contribution of PM2.5 of 2.3 (Atlanta, Georgia; Kim et al., 2004b), 3.0 (west Los Angeles), and 3.2 (Pasadena, California; Schauer et al., 1996). A ratio of 0.1 was found for Washington, DC, an outlier, but this is considered a low diesel environment (Kim and Hopke, 2004). Emission inventories provide a fourth method to rank sources. In the three county area including and surrounding Detroit, Michigan, for example, the current (year 2003) mobile source estimates for PM2.5 are 1616 and 2146 Tyear1 for on- and non-road diesel sources, respectively, and 411 and 457 Tyear1 for on- and non-road gasoline sources. These approaches show the dominance of diesel sources for PM2.5 emissions, and they highlight the importance of tracking commercial/diesel vehicles.

Objectives This study uses traffic flows and proximity to major roads as surrogate measures to determine the numbers of schools and children in Detroit, Michigan and surrounding Wayne County that potentially have exposure to traffic-related emissions at school. Exposure to traffic-related pollutants from commercial (truck/diesel) and non-commercial (passenger vehicle/gasoline) traffic is distinguished. We also evaluate whether school type (public, charter, private), socio-economic variables (income), and demographic factors (race/ethnicity) are associated with systematic differences in exposure potential. This study has several motivations. First, given the current research and regulatory focus on diesel exhaust, we contrast the effect of using total vehicle traffic as compared to truck traffic to identify traffic-exposed schools. Second, as gradients of traffic-related (and other) pollutants are continuous and there is no specific distance beyond which traffic exposure ceases, we consider a range of distances in identifying exposed schools. Third, while geographic information system (GIS) software easily allows determination of proximity indicators, we want to quantify the errors involved. Lastly, we want to document potential environmental justice issues, specifically, disproportionate exposures Journal of Exposure Science and Environmental Epidemiology (2006) 16(5)

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occurring to minority and low-income children attending schools near major roads.

Methods Study area Traffic, school, demographic, and other data were assembled for Wayne Country in southeast Michigan. Encompassing the city of Detroit, Wayne County is a mixed urban/ commercial/industrial region of 991 km2 containing 2.06 million people. In 2000, Detroit had 1.0 million residents in an area of 351 km2, and the seven-county metropolitan area had 4.8 million people in 7427 km2. The area has numerous industries, for example, refining, plastics production, specialty steel production, casting, coatings, manufacturing, waste disposal, trucking, meatpacking, and the large (4.45 km2) Ford Rouge industrial complex. Detroit has the busiest US– Canadian border crossing for truck traffic, the Ambassador Bridge (3.5 million trucks per year), and several very high traffic roads. Wayne County has 296 km (9% of total) of congested roads, defined as volume exceeding capacity. In year 2000, there were 823,000 jobs in Wayne County and 2.21 million in the metropolitan area (SEMCOG, 2003). Public transportation is limited. Most people drive to work (98% in Wayne County) and generally alone (80%) with an average commuting time of 25 min, similar to the national average. The rate of inter-county commuting is 31% in the metropolitan area, most of which occurs between Wayne, Oakland, and Macomb counties immediately to the north. Data sources and preliminary processing Annual average daily traffic (AADT), which includes both trucks and cars, and commercial average daily traffic (CADT) data, which includes buses and trucks, were obtained from the Michigan Department of Transportation (MDOT) for the year 2000, corresponding to the dates of the census and school data sets. Michigan follows federal guidelines for traffic monitoring (FHWA, 2001), and traffic counts are updated annually using permanent traffic recorders and 48-h manual classifications. Here, commercial traffic includes FHWA classes 4–13, namely, buses, two axle/ six tire trucks, and larger vehicles. The freeway shapefile and detailed road map for Wayne County were obtained from the Michigan Geographic Data Library Website (http:// www.mcgi.state.mi.us/mgdl), a digital GIS base map series (Michigan Geographic Framework Base v3b) built by the Center for Geographic Information, Michigan Department of Information Technology. Roads in the detailed shapefile were split into segments that matched those in the traffic data set, then both files were linked. This analysis includes 285 segments representing 491 km of roads: five interstate highways; two US routes; and 12 Michigan highways. These segments are referred to as ‘‘major’’ roads in this paper. Journal of Exposure Science and Environmental Epidemiology (2006) 16(5)

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Information describing K-12 schools in Wayne County was obtained from the School Code Master Website (http:// cepi.state.mi.us/scm) maintained by Michigan’s Center for Educational Performance and Information (CEPI). Special education, adult education, and early day care were excluded. The remaining 845 schools were categorized as local education agency (LEA) schools (564 schools), public school academy (PSA) schools (68 schools), and private schools (213 schools). LEA and PSA schools are both public schools, however, PSA schools are charter schools that can recruit students outside their local area. Schools were separated into elementary (grades K-6) and middle/high schools (grades 7– 12). For the few schools that used other divisions, we classified K-7 (and sometimes higher) schools as K-6 schools, and schools for grades 4 (or higher)-12 as 7–12 schools. School locations were geocoded using the detailed road map (the same as for the road shapefile). For the 54 schools where addresses did not match, locations were checked using Mapquest, YahooMap, Map Viewer of ArcWeb Showcase, and/or country maps. Schools with low (o80) match scores (33 schools) or tied match scores (8 schools) also were manually checked and verified. Public school headcount data for 2000–2001 were downloaded from CEPI, which collects data from school districts via the Single Record Student Database (SRSD) three times annually. This data set, which included school district code and name, school building code and name, and enrollment by grade, gender, and race, was linked to the school point shapefile using district and building codes. Private school headcount data were obtained from CEPI staff, however, only total enrollment was available, thus the racial/ethnic distribution could not be analyzed for these schools. Headcount data were not obtained for three public and 27 private schools. The participation of students enrolled in free or reducedprice meal programs in 2000–2001 was used as an economic/ poverty indicator. Meal program data were obtained for LEA and PSA schools from the Michigan Department of Education. Percentages of students participating in free, reduced-price, and either free or reduced-price programs were calculated for each school. Data were unavailable for 47 public schools and all of the private schools. Population and housing data, including education, employment status, and income, were taken from Summary Files 1 and 3 (SF1, SF3) from the Census Bureau American FactFinder Website (http://factfinder.census.gov). Population, race, journey to work, household income, and poverty status statistics were summarized to the block group level. The Census 2000 TIGERs shapefiles, providing geographic information at the block group level, were downloaded from the Environmental Systems Research Institute (ESRI, http:// www.esri.com/data/download/census2000_tigerline/index.html) and linked with the SF1/SF3 data to develop district-level socioeconomic indicators. 459

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GIS and statistical analyses Traffic and school shapefiles were overlaid using the ‘‘North American Datum for 1983 Hotine Oblique Mercator Azimuth Natural Origin’’ as the projected coordinate system using ArcMap in ArcGIS 8.2. (ESRI, USA). Circular buffers with radii from 25 to 2000 m were created for each school. The highest AADT and CADT among road segments that intersected in each buffer defined exposure indicators for that school. Traffic counts were then grouped into high, medium, and low exposure groups, respectively, using AADTZ50,000, 25,000–49,999, and o25,000 vehicles per day on any road segment within the buffer, and CADT Z10,000, 5000–9999, and o5000 trucks per day. Following Green et al. (2004), we emphasized ‘‘traffic-exposed’’ schools as those within 150 m of highways with AADT Z50,000. The relationship between AADT and CADT was examined using plots, distributions, maps, and correlation coefficients. Most of the statistical analyses compared low, medium, and high traffic exposure categories to the no attributed traffic case. w2 single and multiple comparisons used P-values of 0.05 for statistical significance, as did two sample proportion tests with unequal sample sizes. Analyses used Excel (Microsoft, Redmond, WA, USA), @Risk (Palisade Corp., Newfield, NY, USA), and SAS 9.1 (SAS Institute, Cary, NC, USA). Geocoding errors may be important for proximity measures, especially at short distances. Most often, shapefiles represent roads as line segments approximating the road centerline. Errors can result from many factors, for example, segments representing centerlines only approximately match

the ‘‘true’’ centerlines on the detailed reference maps, and centerlines do not account for the roadway width or distances to the curbside. These errors were estimated by determining the distance between the single line in the road shapefile and the center and edges of the road on the reference detailed road map at 33 road intersections approximately 1 km apart in the downtown Detroit area. Other errors, such as misalignment of the road, school and census maps, were not considered.

Results AADT and CADT Traffic on Wayne County roads totals 3.7  107 vehicle-km per day with the highest flows in downtown Detroit and on three interstate highways (Figure 1). The route-average AADT exceeds 100,000 on I-75, I-94, I-96, M-10, and M-39 (Table 1). I-96 has the highest average AADT and maximum segment flows (152,000 and 193,000 vehicles per day, respectively). In contrast, the highest commercial traffic flows are found on I-75, I-94, I-96, and I-275, with one segment of I-75 reaching 17,900 trucks per day. Overall, commercial traffic averages 7.3% of total VMT in Wayne Country, similar to the US CADT/AADT average of 7.5% (US DOT, 2000). Based on road averages, I-75, I-94, I-275, and M-97 have over 10% commercial traffic. At the level of road segments, CADT and AADT had different distributions: CADT fit a skewed Pearson-type or lognormal distribution, compared to AADT that fit beta,

Table 1. AADT and CADT statistics for roadways in Wayne County, year 2000 Road name Length (km) Segments

I-75 I-94 I-96 I-275 I-375 US-12 US-24 M-1 M-3 M-5 M-8 M-10 M-14 M-39 M-53 M-85 M-97 M-102 M-153

51.5 61.3 39.6 35.9 1.7 45.1 39.1 13.6 19.2 13.5 4.5 20.6 7.3 24.3 7.8 24.5 4.8 34.3 32.4

32 42 27 9 3 29 24 6 11 8 3 21 2 19 3 10 3 13 20

AADT (1000 vehicles/day)

CADT (1000 vehicles/day)

%CADT (CADT/AADT)

Min

Max

Average

Min

Max

Average

Min

Max

Average

46.0 83.1 36.0 32.7 18.3 6.3 15.7 19.1 8.8 15.4 30.1 49.1 63.9 41.8 17.1 9.0 10.3 24.7 11.2

181.5 165.1 193.1 130.7 72.4 53.9 78.4 27.8 38.7 29.5 87.4 157.7 87.5 169.7 21.5 51.7 12.8 82.6 70.7

109.7 125.7 151.8 74.4 45.7 28.3 50.6 21.6 21.9 21.8 65.5 120.5 75.7 114.2 19.4 29.5 11.7 49.5 40.9

6.0 7.0 2.9 6.7 1.0 0.6 0.3 0.7 1.0 0.8 1.8 1.0 5.2 2.2 1.4 0.4 1.2 0.9 0.4

17.9 16.6 14.8 8.4 1.0 1.1 2.0 0.7 1.0 0.8 1.8 2.5 5.2 5.0 1.4 1.2 1.2 4.0 1.6

12.6 12.0 9.1 7.5 1.0 1.1 0.9 0.7 1.0 0.8 1.8 2.1 5.2 4.2 1.4 0.9 1.2 2.3 1.2

6.6 4.3 3.7 6.4 1.4 2.1 1.2 2.6 2.5 2.7 2.1 1.0 5.9 2.8 6.7 1.4 9.3 1.1 1.8

34.8 17.3 8.1 20.5 5.5 17.9 5.4 3.8 11.2 5.2 6.0 2.9 8.1 5.6 8.4 5.5 11.6 16.2 7.7

13.2 10.1 6.0 12.1 3.0 5.2 2.1 3.4 5.6 3.8 3.4 1.8 7.0 4.0 7.5 3.4 10.3 6.0 3.3

Correl-Coef.

0.12 0.27 0.72 0.96 F 0.21 0.63 F F F F 0.76 F 0.84 F 0.64 F 0.46 0.62

%CADT shows percentage of commercial traffic. Correlation coefficient is for AADT and CADT among segments on each road.

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Figure 1. AADT (top) and CADT (bottom) and school locations in Wayne County.

triangular and even rectangular distributions (Figure 2a and b). The CADT/AADT percentages vary greatly (from 1% to 35%), and distributions fit a shifted exponential Journal of Exposure Science and Environmental Epidemiology (2006) 16(5)

(l ¼ 5.35, shift ¼ 0.974, K-S ¼ 0.047, P2w ¼ 0.129) and shifted lognormal (m ¼ 5.95, s ¼ 6.79, shift ¼ 0.628; K-S ¼ 0.061, P2w ¼ 0.014; Figure 2c, Table 1). Considering segments 461

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20,000

AADT

80 60 40

15,000

20 0

0

20

40

b

60 80 100 120 140 160 180 200 AADT (1000 vehicles / day) CADT

Frequency

80

40 20

c

0 0

2

4

6 8 10 12 14 CADT (1000 vehicles/day)

16

0

18

Percentage CADT 80

50,000

100,000

150,000

200,000

AADT-CADT (cars/day)

Figure 3. Commercial versus non-commercial flows on 285 road segments in Wayne County. Best fit line shown: CADT ¼ 0.0615 (AADT–CADT) þ 624 (R2 ¼ 0.345).

60 40

0.7 Detroit Schools Only

20

0.6 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 Percentage CADT (%)

Figure 2. (a) Distribution of AADT on 285 highway segments in Wayne County. (b) Distribution of CADT on 285 highway segments. (c) Distribution of percentage CADT of total traffic on same segments.

among individual roads, the correlation between AADT and CADT varies widely (0.46–0.96, Table 1), indicating that the percentage of cars and trucks varied significantly on different sections of the same road. Pooling segments across all roads, the correlation coefficient between AADT and CADT is 0.66, and the correlation between commercial traffic and non-commercial traffic (taken as AADT F CADT) is 0.59. Many road segments have high flows of commercial traffic but low flows of non-commercial traffic, and vice versa (Figures 3 and 4). This analysis shows that schools identified as trafficexposed based on proximity to high AADT roads might not have high exposure to commercial traffic, and vice versa. Table 2 examines the 80 schools that have major roads within 150 m, giving the joint frequency when classified by quartiles of commercial and non-commercial traffic. (Figure 5 shows the locations of these schools.) The quartiles have only low to moderate concordance, for example, most of the schools in the top quartile of non-commercial traffic are in the third quartile of commercial traffic, while schools in the third quartile of non-commercial traffic fall into all quartiles of commercial traffic. Given the much higher PM2.5 emission

Cumulative Probability

0

462

10,000

5,000

60

0

Frequency

CADT (trucks/day)

Frequency

a

Wayne Country excluding Detroit All Wayne Country Schools

0.5 0.4 0.3 0.2 0.1 0.0

25

50

100 150 250 Distance (m)

500

1000

Figure 4. Cumulative fraction of schools within selected distances of roads by school location.

Table 2. Distribution of 80 schools within 150 m of attributable traffic by quartiles of non-commercial and commercial traffic Non-commercial quartile

Q1 Q2 Q3 Q4

Commercial traffic-quartile Q1

Q2

Q3

Q4

12 8 3 0

7 7 4 0

2 3 4 11

0 1 10 8

rates of diesel vehicles, AADT by itself is insufficient to characterize exposure. Exposure proxies should consider both commercial (diesel) and non-commercial traffic. Journal of Exposure Science and Environmental Epidemiology (2006) 16(5)

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Figure 5. (a) Wayne County map showing population density, school district boundaries, and most traffic-exposed schools (o150 m of roads with AADT Z50,000). (b) Wayne County map showing fraction of Blacks, major roads with AADT indicated, and most traffic-exposed schools. (c) Map showing fraction of Hispanics, otherwise as (b). (d) Map showing fraction of Asians, otherwise as (b). Several school locations overlap.

Distance errors Based on the Wayne county data, errors between estimated and actual road centerlines average 2279 m and range up to 44 m (Table 3). Distances to the curb involve similar errors (22715 m). Errors are typically are about half of the road width (average of 44717 m). The literature using proximity or distance measures appears to calculate distances from the curb or roadside, not the road centerline. The use of distances from the road centerline will bias these estimates by about 22 m, or about half the road width. This analysis, which is based on a limited number of intersections in Detroit that may not fully reflect other Wayne County locations or school sites, provides only a preliminary assessment of geocoding errors. It does not consider other and potentially larger errors such as: (1) misalignment of road layers and the base or other Journal of Exposure Science and Environmental Epidemiology (2006) 16(5)

Table 3. Distance errors and road widths (m) in Detroit Type

Mean

SD

Minimum

Maximum

Errors Center Near curb Far curb Both curbs

21.7 11.0 33.1 22.1

9.2 7.7 11.3 14.7

6.4 0.5 17.1 0.5

44.2 28.5 66.1 66.1

Road width

44.2

17.4

21.6

86.9

Sample size is n ¼ 33 except for both curbs where n ¼ 66.

maps (not an issue here since roads were geocoded using the base map); (2) location errors associated with address ranges; (3) representation of schools as points, not polygons; and (4) 463

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misalignment of the census/TIGER map files and polygons. Collectively, these errors are likely to be much larger than those reported here.

Schools near high traffic roads Wayne County contains a total of 845 K-12 schools, of which 564, 68, and 213 are LEA, PSA and private schools, respectively. K-12 enrollment totals 385,397, and 320,967, 23,838, and 40,592 students attend LEA, PSA, and private schools, respectively. Private schools tend to be much smaller than public schools. (LEA, PSA, and private school enrollments average 569, 351, and 191 students, respectively.) Of the county’s 34 school districts, Detroit City School District is the largest by far, with 390 schools (46.2% of the Wayne County’s total) and 175,790 students (45.6%). This district enrolls 15,894 students in 47 PSA schools, a disproportionately high fraction (69.1%) of this school type. It also has 89 private schools enrolling 11,299 students, slightly less than proportional in terms of schools (41.8%) and students (27.8%). School district boundaries, which match geopolitical boundaries, are shown in Figure 5a. Of the Wayne County schools, 9.5% (54 LEA and PSA and 26 private schools) are within 150 m of major roads, and the percentage climbs sharply at distances beyond 250 m (Figure 4). A much higher percentage of schools in the Detroit School District are within 150 m of major roads (14.4%, 55 schools), compared to the remainder of the county (5.5%, 25 schools; Figure 5b). This large discrepancy, which holds for all buffer sizes (25–1000 m, Po0.01), is not surprising as the Detroit City District constitutes the more densely populated urban core and schools are never far from major roads. Indeed, most of the schools near high traffic roads (r150 m, AADT Z50,000) are in this district (Figure 5a). However, other areas in the country also have a substantial number of schools near major roads, for example, 5.5% within 150 m and 8.3% within 250 m. At the county level, a higher fraction of private schools (12.2%) are within 150 m of major roads than public schools (8.5%), but this difference is not significant (P ¼ 0.07) and also surprising as private schools are more uniformly distributed throughout the county. This trend did not apply to enrollments. The proportion of students enrolled in public schools within 150 m of major roads (9.7%) is greater than that in private schools (7.8%, pr0.001). The 80 schools within 150 m of major roads collectively enroll 36,692 students (9.5% of the total number of students in the county), of which just over half (41 schools, 18,290 students) are within 150 m of high traffic roads defined as AADT Z50,000. Most of these schools (25) and children (13,277) are in the Detroit City District. In this potentially more exposed group, there are no public–private differences in the proportions of schools (P ¼ 0.06) or enrollments (P ¼ 0.30). A total of 23 schools and 10,328 students are within 150 m of 464

Proximity of schools to traffic

high commercial traffic, defined as CADT Z5000. Again, the fraction of private schools near high traffic truck roads (4.2%) is slightly, but not significantly, higher than public schools (2.2%, P ¼ 0.09), and enrollment proportions are identical (2.7%). Most of the public/private differences appear to result from sample and school size effects. Some of the differences between school number and school enrollment with respect to proximity may result from relatively recent trends of closing older smaller schools and opening larger schools. While several of these differences are statistically significant, percentage differences are not large. Comparing by grade level, Wayne County has 35 K-6 public schools and 19 grade 7–12 public schools within 150 m of major roads (Table 4). The proportions of the K-6 (8.0%) and 7–12 (9.7%) schools are not statistically different (P ¼ 0.25), although the proportion of K-6 students attending these schools (7.6%) is significantly lower than for grade 7–12 students (12.5%, Po0.001). In part, this reflects the many primary schools sited in the midst of residential communities away from major roads, compared to the larger middle and secondary schools that may be sited closer to major roads for the ease of busing. Private schools within 150 m of major roads include 20 (11.2%) K-6 schools and six (17.1%) grade 7–12 schools, and no grade-level differences for the proportions of schools (P ¼ 0.19) or enrollments (P ¼ 0.34) are indicated. In summary, a larger proportion of children attend public middle and high schools near major roads than children in public K-6 schools.

Race/ethnicity At the county level, Whites, Blacks, Hispanics, and Asians comprise 51.7%, 42.2%, 3.7%, and 1.7%, respectively, of the year 2000 population, similar to the breakdown in the K12 public schools (50.7%, 44.3%, 3.1%, and 1.5%, respectively). Blacks comprise over 80% of the population in three school districts (Detroit City, Highland Park City, and Inkster-Edison). The demographics at schools largely reflects the segregation found in the county: portions of Detroit are overwhelmingly Black (Figure 5b); Hispanics constitute a large proportion in the SW area of the Detroit City School District where I-75 crosses US-12 (Figure 5c); and high Asian fractions are found in the Plymouth-Canton Community School District in the northwest part of the county (Figure 5d). As an indicator of whether the race/ ethnicity of children attending a particular school might differ from the composition of the immediate neighborhood, we calculated correlation coefficients between the percentages of a specified ethnic/racial category among schools and the census block groups containing the schools. For LEA schools, correlation coefficients are 0.95, 0.93, 0.94, 0.55, and 0.22 for Black, White, Hispanic, Asian, and Indian categories, respectively. The lower correlations for Asian and Indian categories may reflect low numbers of students. For the other classifications, however, census block variables Journal of Exposure Science and Environmental Epidemiology (2006) 16(5)

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Table 4. Enrollment and number of schools within 150 m of major roads with specified traffic flows Variable

Public/ Grade Private level

Enrollment Public

Private

Both

Schools

Public

Private

Both

No attributable traffic within 150 m

o25,000 vehicles/day

25,000–49,999 vehicles/day

Z50,000 vehicles/day

o5000 trucks/day

5000–9999 trucks/day

Z10,000 trucks/day

(n)

(%)

(n)

(%)

(n)

(%)

(n)

(%)

(n)

(%)

(n)

(%)

(n)

(%)

K-6 7-12 K-12 K-6 7-12 K-12 K-6 7-12 K-12

180,850 130,411 311,261 29,606 7838 37,444 210,456 138,249 348,705

(92.4) (87.5) (90.3) (92.3) (92.1) (92.2) (92.4) (87.7) (90.5)

3308 4330 7638 565 30 595 3873 4360 8233

(1.7) (2.9) (2.2) (1.8) (0.4) (1.5) (1.7) (2.8) (2.1)

4006 5517 9523 534 112 646 4540 5629 10,169

(2.0) (3.7) (2.8) (1.7) (1.3) (1.6) (2.0) (3.6) (2.6)

7575 8808 16,383 1380 527 1907 8955 9335 18,290

(3.9) (5.9) (4.8) (4.3) (6.2) (4.7) (3.9) (5.9) (4.7)

11,055 13,268 24,323 1892 149 2041 12,947 13,417 26,364

(5.6) (8.9) (7.1) (5.9) (1.8) (5.0) (5.7) (8.5) (6.8)

2470 1470 3940 587 520 1107 3057 1990 5047

(1.3) (1.0) (1.1) (1.8) (6.1) (2.7) (1.3) (1.3) (1.3)

1364 3917 5281 0 0 0 1364 3917 5281

(0.7) (2.6) (1.5) (0.0) (0.0) (0.0) (0.6) (2.5) (1.4)

K-6 7-12 K-12 K-6 7-12 K-12 K-6 7-12 K-12

401 177 578 158 29 187 559 206 765

(92.0) (90.3) (91.5) (88.8) (82.9) (87.8) (91.0) (89.2) (90.5)

9 4 13 4 2 6 13 6 19

(2.1) (2.0) (2.1) (2.2) (5.7) (2.8) (2.1) (2.6) (2.2)

9 6 15 4 1 5 13 7 20

(2.1) (3.1) (2.4) (2.2) (2.9) (2.3) (2.1) (3.0) (2.4)

17 9 26 12 3 15 29 12 41

(3.9) (4.6) (4.1) (6.7) (8.6) (7.0) (4.7) (5.2) (4.9)

26 (6.0) 14 (7.1) 40 (6.3) 13 (7.3) 4 (11.4) 17 (8.0) 39 (6.4) 18 (7.8) 57 (6.7)

6 1 7 5 2 7 11 3 14

(1.4) (0.5) (1.1) (2.8) (5.7) (3.3) (1.8) (1.3) (1.7)

3 4 7 2 0 2 5 4 9

(0.7) (2.0) (1.1) (1.1) (0.0) (0.9) (0.8) (1.7) (1.1)

No attributable traffic means no major roads within 150 m. Percentage of total in parentheses.

reflect the race/ethnicity of the LEA schools. Among PSA schools, correlation coefficients are lower, 0.77, 0.76, 0.72, 0.54, and 0.11 for the five classifications, showing that charter schools enrollment draws children from a larger and more diverse area. The following focuses on differences between Whites and Blacks because the numbers of students in the other categories are small. Also, we primarily draw on demographic data collected from school rather than census sources. At the county level, the non-white fraction significantly increases at traffic-exposed schools, for example, enrollment is 48.5% Black and 46.5% White for schools with no major roads within 150 m, and 71.1% Black and 24.0% White for major roads within 150 m (Table 5; Po0.001). As AADT increases, the Black percentage decreases somewhat, for example, schools near high traffic roads (r150 m, Z50,000 ADT) are 61.4% Black and 33.5% White. Schools near high traffic roads also have larger Hispanic fractions (4.3% compared to 3.1% overall; Po0.001) and smaller Asian fractions (0.5% compared to 1.4% overall, Po0.001). Similar trends are seen for schools near commercial traffic. While the Black percentage in Wayne County tends to decrease as AADT (or CADT) increases, which occurs as the more highly traffic-exposed schools (r150 m, AADT Z50,000) contain a slightly lower proportion of schools in the largely Black Detroit City District, minorities represent a much larger fraction of children attending schools within 150 m of major highways than children in schools at greater distances from major roads. Journal of Exposure Science and Environmental Epidemiology (2006) 16(5)

To examine whether these trends are primarily due to differences between the urban core (Detroit City District) and outlying suburban areas, a geographically stratified analysis was performed. Again, only public schools (where race/ethnicity information exists) are considered. The Detroit district has 39 schools within 150 m of major roads that enroll 24,059 students with 91.6% Black, 2.8% Hispanic, and 2.9% White. The race fractions in other (unexposed) Detroit District schools show small, but statistically significant differences, for example, 91.0% Black, 4.5% Hispanic, and 3.7% White (Po0.001). Thus, there are small increases in the minority fraction (of Blacks) at schools near major roads in Detroit. Elsewhere in Wayne County, there are 25 schools within 150 m of major roads that enroll 11,025 students with 16.3% Black, 2.1% Hispanic, and 66.5% White, compared to 12.0% Black, 1.8% Hispanic, and 69.9% White at schools that are more distant from roads. In this case, changes for Blacks and Whites are statistically significant (Po0.001), but in the opposite direction from that seen at the county level, that is, the minority fraction slightly decreases at traffic-exposed schools. This analysis shows that countywide results are driven largely by the race/ethnicity differences between Detroit City School District and the other school districts. Simply said, of the 33,544 children attending public schools near (o150 m) high traffic roads in Wayne County, most (71%) are in the Detroit School District which is predominantly (91%) Black. 465

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Wu and Batterman

Table 5. Number and percentage of students by race/ethnicity for LEA (public) and PSA (public-charter) schools School type o25,000 vehicle/day 25,000–49,999 vehicle/day Z50,000 vehicle/day o5,000 trucks/day 5,000–9,999 trucks/day Z10,000 trucks/day (n)

(%)

(n)

LEA schools Total Indian Asian Black Hispanic White

6714 8 219 6273 36 178

(2.09) (0.52) (4.32) (4.03) (0.37) (0.12)

PSA schools Total Indian Asian Black Hispanic White

924 0 24 598 59 243 PSA 9523 25 359 6907 95 2137

Total LEA & Total Indian Asian Black Hispanic White

(%)

(n)

(%)

(n)

(%)

(n)

(%)

(n)

(%)

8489 25 359 5876 95 2134

(2.64) (1.62) (7.09) (3.78) (0.96) (1.43)

14,068 50 66 8182 698 5072

(4.38) (3.24) (1.30) (5.26) (7.08) (3.40)

20,469 44 594 15,106 197 4528

(6.38) (2.85) (11.73) (9.71) (2.00) (3.04)

3521 3 7 3471 2 38

(1.10) (0.19) (0.14) (2.23) (0.02) (0.03)

5281 36 43 1754 630 2818

(1.65) (2.33) (0.85) (1.13) (6.39) (1.89)

(3.88) (0.00) (26.09) (3.11) (7.07) (6.66)

1034 0 0 1031 0 3

(4.34) (0.00) (0.00) (5.36) (0.00) (0.08)

2315 0 15 1878 11 411

(9.71) (0.00) (16.30) (9.76) (1.32) (11.27)

3854 0 39 3088 70 657

(16.17) (0.00) (42.39) (16.06) (8.38) (18.01)

419 0 0 419 0 0

(1.76) (0.00) (0.00) (2.18) (0.00) (0.00)

0 0 0 0 0 0

(0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

(2.76) (1.59) (6.96) (3.95) (0.89) (1.40)

16,383 50 81 10,060 709 5483

(4.75) (3.18) (1.57) (5.76) (6.63) (3.59)

20,469 44 594 15,106 197 4528

(5.94) (2.80) (11.52) (8.65) (1.84) (2.97)

24,323 44 633 18,194 267 5185

(7.05) (2.80) (12.28) (10.41) (2.50) (3.40)

3940 3 7 3890 2 38

(1.14) (0.19) (0.14) (2.23) (0.02) (0.02)

5281 36 43 1754 630 2818

(1.53) (2.29) (0.83) (1.00) (5.90) (1.85)

Percentage of total in parentheses.

Table 6. Median percentages of students in free and reduced meal programs, and sample size School type and meal plans

Total

Vehicles per day

Trucks per day

o25,000

25,000–49,999

Z50,000

o5,000

5,000–9,999

Z10,000

LEA schools Free meal (%) Reduced meal (%) Free or reduced price meal (%) No. of schools in sample No. of schools excluded

36.5 3.8 42.6 564 18

53.1 2.5 56.4 10 0

59.5 5.2 62.6 12 1

49.8 3.8 59.1 21 1

63.6 3.2 67.4 30 2

49.3 3.2 55.4 6 0

34.2 5.9 40.2 7 0

PSA schools Free meal (%) Reduced meal (%) Free or reduced price meal (%) No. of schools in sample No. of schools excluded

20.3 3.0 31.2 68 29

54.3 8.2 69.9 3 1

26.8 9.9 36.6 3 1

34.0 4.3 46.3 5 2

44.2 9.0 58.1 10 3

0.0 0.0 0.0 1 1

0.0 0.0 0.0 0 0

Enrollment in meal programs Table 6 shows percentages of students enrolled in free and reduced-price meal programs at the 632 schools where data were available. Significant differences exist for schools near major roads. For the LEA schools, 41% of students are enrolled in meal programs at schools with no major roads within 150 m, and 59% at schools with major roads. The 466

corresponding figures for PSA schools are 22% and 46%. There is no clear trend in the fraction of students enrolled in these programs with AADT volumes, and the percentage enrolled in meal programs at LEA schools declines from 67% to 40% as CADT increased, indicating fewer impoverished families of children attending public schools near the busiest commercial routes. Still, enrollment in meal Journal of Exposure Science and Environmental Epidemiology (2006) 16(5)

Proximity of schools to traffic

programs remained well above levels at schools without traffic exposure. Although the ability to discern trends when the sample is stratified by AADT or CADT is weakened due to sample size issues, and enrollment in meal plans must be regarded as an indirect indicator, these results show higher rates of poverty for children attending public schools near major roads. As seen for proximity, much of the poverty effect is driven by the difference between the Detroit City School District, which has much lower family incomes than the other school districts, and the more affluent outlying school districts. There is a strong relationship between race/ethnicity and poverty, for example, the correlation between the percentage of Blacks enrolled at schools and the percentage of students enrolled in free or reduced-price meal programs is 0.73 at LEA schools (although only 0.08 at PSA schools). Examining the census block surrounding each school, the percentage of families with incomes below the poverty level is correlated with the percentage of children enrolled in free or reducedprice meals (r ¼ 0.68 for LEA schools, r ¼ 0.27 for PSA schools). Again, these trends are linked to the low family incomes found in the Detroit School District. Thus, bivariate analyses cannot attribute school siting to either poverty or racial/ethnic distribution. It is clear, however, that children attending public schools near major roads are disproportionately poor and minority, and that the majority attend schools in the Detroit City District.

Discussion and conclusion This study’s findings in many ways parallel and amplify conclusions reached in recent California studies examining schools (Green et al., 2004) and residence location (Gunier et al., 2003). Green et al. (2004) found 2.3% of California schools endure heavy traffic exposure, defined as AADT Z50,000 with a school–road distance r150 m. We observed much higher rates: 4.9% in Wayne County schools and 7.2% in the Detroit School District. We also found that 2.8% of the schools were near roads with heavy truck traffic, defined as CADT Z5000, again using a school–road distance of 150 m. When enrollment is considered, 7.6% of Detroit District children endure heavy traffic exposure. At the county level, California has similar statistics: Los Angeles and San Francisco counties both have 5.4% of schools within 150 m of roads with AADT Z50,000, slightly above the rate for Wayne County, while Alameda and San Diego counties have 3.3% and 3.1%, respectively (unpublished data; Green, 2005). These differences may be explained by population density and the growth and development of communities and the transportation infrastructure. While many schools in large urban communities are sited near high traffic roads, older and more densely populated areas in Detroit are crisscrossed with busy highways built well after the commuJournal of Exposure Science and Environmental Epidemiology (2006) 16(5)

Wu and Batterman

nities and schools were established. A historical analysis of development patterns, including transportation routes, might shed light on the origin of these differences. The road layout vis-a`-vis community features like schools would now be considered to be compromised with respect to our knowledge of social, health and environmental impacts. For example, California’s recent revisions to the education code prohibit siting of new schools within 500 ft (168 m) of roads with AADT exceeding 50,000 or 100,000 in rural and urban areas, respectively (California Senate, 2003). There is unequivocal evidence that students attending schools near high traffic roads in Wayne County are more likely to be minority, to be enrolled in a meal program, and to reside in a poor area. Contrary to the California trends (Green et al., 2004), the Black percentage in Wayne County schools within 150 m of highways tends to decrease as AADT (or CADT) increases, although significant disparities remain compared to the racial/ethnic mix in the unexposed schools. Most of these students attend schools in the Detroit City District, and the trends are largely explained by the pattern of housing segregation in Wayne County. As stated by Green et al. (2004), environmental justice concerns are raised in that economically-disadvantaged and/or minority children are much more likely to attend schools in areas with high traffic exposure. Of course, roads running through or adjacent to densely populated areas may affect many people other than school-age children. In Wayne County, both younger and older groups are also likely to be disproportionately poor and minority. As the layout of highways and schools in Wayne County will differ from other regions, we are cautious about generalizations. Still, our results likely will apply to the older and larger cities in the eastern US. We saw few significant differences regarding sites of public and private schools. However, higher proportions of students attend public middle and secondary schools near major roads than those in elementary schools, likely reflecting a deliberate attempt to decrease travel time and increase convenience for students traveling by car or bus. These differences are dwarfed, however, by the very large differences in proximity to major roads, race/ethnicity, and income between schools and students in the Detroit City District compared to the 33 other districts in the county. The spatial distribution of commercial (truck) and noncommercial traffic shows strong differences, reflecting that certain roads and/or road segments mainly serve local and/or long distance commercial traffic, while others mainly transport commuters from outlying urban, suburban, and ‘‘exurban’’ communities. The geographic separation between industrial, commercial, and residential areas, and possibly the tendency of trucks to avoid congested commuting routes (and vice versa), adds to these differences. As gasolinepowered cars and diesel-powered trucks have substantially different emissions, both commercial and non-commercial traffic should be considered in classifying exposures. For 467

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Wu and Batterman

example, a traffic-exposed site might be defined as being within 150 m of a road carrying AADT Z50,000 or CADT Z2,500. CADT might be weighted to account for trucks’ higher emission rates, although this is simplistic given the many factors affecting emissions. The distinction between gasoline and diesel vehicles has important implications for exposure assessment in studies examining health outcomes where areas or individuals are classified as traffic-exposed using traffic flow proxies and the traffic mix (car/truck) varies over space or space.

Methodological issues Errors in determining distances from roads to schools (and other sites) using GIS tools arise due to geocoding errors, mapping errors, and inaccurate representations, for example, schools as points rather than polygons, and roads as lines rather than ribbons (Brauer et al., 2003). Mapping errors for the large roads in Wayne County averaged about 22 m, both for centerline and curbside distances. This suggests the potential for misclassification with very small buffers (o50 m), and GIS-based determinations at this scale may not be appropriate unless very precise maps are available. On the other hand, errors may be inconsequential for the larger (4150 m) buffers employed in many of the exposure studies. These errors are small in comparison to those resulting from the use of (large) census tracks or zip code areas that cannot provide the spatial resolution needed for traffic exposure studies. While we found a much higher fraction of schools sited near major roads than in California, we also found a much lower fraction (9.5%) of schools within 150 m of any roads with attributable traffic, compared to the 70% in California (Green et al., 2004). The Michigan data set included only interstate highways, US routes and state roads. In comparison, the California data also included the larger arterial and collector roads. Applications Proximity-based measures of exposure have been widely used in air pollution epidemiology, hazard evaluation, risk assessment, and environmental justice applications. Proximity can be a useful indicator: it may serve as a proxy for exposure to traffic-related pollutants and noise; proximity has been shown to correlate with health problems such as asthma; it relates to risks residents perceive; and proximity generally reflects the visual intrusion of roads (Schweitzer and Valenzuela, 2004). However, many factors affect the relevance of this proxy. We and others have criticized proximity-based exposure surrogates for deficiencies that include lack of accuracy, assumptions of homogeneity within the zones considered, and the potential for exposure misclassification (Huang and Batterman, 2000; Kingham et al., 2000; Perlin et al., 2001; Brauer et al., 2003). The exposure surrogates used in epidemiological studies to classify 468

populations have mostly used either proximity to traffic within 20–500 m, a large range, or traffic intensity measures (e.g., highest AADT within a specified distance, VKT integrated over the nearby area, self-reported traffic frequencies). Without exposure measurements, neither absolute nor relative concentrations can be established and exposure misclassification is likely. Here, we show another issue: differences between total traffic (AADT) versus commercial/ truck traffic (CADT). Some information regarding concentrations may be gleaned from ‘‘traffic-impacted’’ sites often included in the larger urban ambient air quality monitoring networks, however, the density of sites in most networks is too low to capture the spatial gradients due to traffic, fixed sites may not represent traffic-related impacts (e.g., Gulliver and Briggs, 2004), and it can be difficult to distinguish diesel and gasoline pollutants. While possibly useful to explore hypotheses related to exposure and environmental inequities, proximity measures alone cannot quantify exposures. Finally, the choice of a cutoff distance from schools (or other sites of interest) to high traffic roads strongly affects the number of schools designated as traffic exposed. It is more realistic to consider pollutant exposure as a continuum with distance and dependent on many factors, including but not limited to both car and truck traffic.

Limitations and recommendations Several study and data limitations restrict our analysis. Traffic flows were unavailable for smaller roads. Race/ ethnicity and subsidized meal information was unavailable for the private schools. While we investigated spatial/ geocoding errors, data accuracy remains an important concern. The uncertainties in traffic counts, and especially the estimated commercial fraction, require further investigation. Comparisons with other counties in Michigan or elsewhere would help to generalize results. Future studies might investigate potentially more realistic, but also more complex, exposure measures, for example, inverse distanceweighted measures or differential weightings of car and truck traffic that account for emission rates. This analysis examined only the potential for exposures at schools, and exposure misclassification will result if children live or play in a low traffic area but attend schools near a busy highway. GISbased exposure measures should be confirmed and quantified by ambient monitoring at schools, homes, and other important locations. Finally, while justice issues at schools sited near major roads were noted, aspects that might help interpret these issues, such as the chronology and basis of siting decisions, were not investigated.

Acknowledgements We appreciate the suggestions and inputs of Hien Le at the University of Michigan, Lawrence Whiteside at MDOT, Journal of Exposure Science and Environmental Epidemiology (2006) 16(5)

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Shelley Green of the California Office of Environmental Health Hazard Assessment, and the reviewers.

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Journal of Exposure Science and Environmental Epidemiology (2006) 16(5)