Environmental Health Perspectives • VOLUME 110 | NUMBER 10 | October 2002 1009

Elemental Carbon and PM2.5 Levels in an Urban Community Heavily Impacted

by Truck Traffic

T. Suvendrini Lena,1 Victor Ochieng,2 Majora Carter,3 José Holguín-Veras,2 and Patrick L. Kinney1

1Mailman School of Public Health, Columbia University; 2Civil Engineering Department, City College of New York; 3The Point

Community Development Corporation, New York, New York, USA

In recent years, people living in inner cities

have become increasingly concerned about

motor vehicle emissions and associated health

effects (1). These concerns are compounded

by high rates of asthma morbidity and mortality among minority children living in

underprivileged urban communities (2,3).

Community organizations have presented

evidence that diesel emission sources are often

disproportionately concentrated in underprivileged urban neighborhoods. For example, in

New York City (NYC), seven of the eight bus

depots serving Manhattan are located in

Harlem, a traditionally minority community.

A recent survey in Boston, Massachusetts,

found that there were 15 bus and truck

depots concentrated within the low-income

community of Roxbury, together housing

more than 1,150 diesel vehicles (4). The

Hunts Point section of the South Bronx,

NYC, is home to a low-income population of

African Americans and Latin Americans. It is

also the principal food-processing terminal

for NYC, serviced by 10,000 trucks/day (5).

A recent NYC Department of Health survey (2) found that 

children from low-income areas of NYC were over

four times more likely to be hospitalized for asthma

than children from high income areas during 1997. 

Among the five boroughs of NYC, the Bronx

has ranked highest in both asthma hospitalizations and deaths in recent years. Furthermore,

the 1997 asthma hospitalization rate among

children 0–14 years of age in the Hunts

Point–Mott Haven neighborhood of the

South Bronx ranked highest among the

Bronx’s seven neighborhoods, at 23.2/1,000

population.

The present study was initiated at the

request of The Point Community Development

Corporation (The Point CDC) and was

designed to characterize the relationship

between airborne particle concentrations and

heavy-duty truck traffic within Hunts Point.

Our specific aims were to identify the spatial

distribution and travel patterns of truck traffic through the community, to monitor airborne concentrations of particles < 2.5 μm in

aerodynamic diameter (PM2.5) and elemental

carbon (EC) at the sidewalk level adjacent to

several intersections that vary in truck density, and to identify potential routing alternatives to minimize residential exposure to

emissions.

The human health effects of airborne particulate matter have been examined in

numerous recent epidemiologic studies

(6–11), several of which highlight the special

health significance of PM2.5, which is a heterogeneous mixture of particles that vary in

both composition and sources. In the northeastern United States, a major part of PM2.5

is composed of sulfate compounds, which are

formed as secondary particles in region-wide

air masses as a result of primary sulfur dioxide

emissions. Relatively small spatial variations

in PM2.5 concentrations exist within or

between urban areas in this region, reflecting

the dominant influence of region-wide sulfate

aerosols as major drivers of local PM2.5 concentrations (12–14). In contrast, PM2.5 components associated with local fossil-fuel

combustion, such as diesel exhaust particulate

(DEP), exhibit greater spatial variations, and

these variations have been associated with

local traffic sources (15–18). In a recent community-driven pilot study, Kinney et al. (18)

measured concentrations of PM2.5 and EC on

sidewalks in Harlem, NYC, and tested

whether spatial variations in concentrations

were related to local diesel traffic density.

Mean concentrations of PM2.5 exhibited only

modest site-to-site variation, reflecting the

importance of broader regional sources of

PM2.5. In contrast, EC concentrations varied

4-fold across sites (from 1.5 to 6 μg/m3) and

were associated with bus and truck densities

on adjacent streets. These pilot results

demonstrate that local diesel sources in

Harlem create spatial variations in sidewalk

concentrations of DEP (18).

DEP has diameters in the submicrometer

range and is composed largely of EC. DEP

has relatively large surface areas onto which a

wide range of organic compounds are

adsorbed. This organic fraction is a complex

mixture containing toxicologically important

compounds such as benzene, toluene, ethylbenzene, xylene, and polycyclic aromatic

hydrocarbons (PAHs). Common PAHs

include phenanthrenes, fluorenes, naphthalenes, fluoranthrenes, and pyrenes, many

of which are known mutagens and carcinogens (19–22). DEP surfaces also act as a site

for the concentration of airborne allergens.

Thus, because of their small particle size and

Address correspondence to P. Kinney, Columbia School

of Public Health, Department of Environmental

Health Sciences, 60 Haven Avenue, B-1, New York,

NY 10032 USA. Telephone: 212-305-3663. Fax:

212-305-4012. E-mail: plk3@columbia.edu

We thank the volunteers and student interns who

assisted in the collection of traffic and air quality data

for this study. We also recognize the contribution of

staff at The Point Community Development

Corporation and at We Stay/Nos Quedamos. 

This research was conducted with partial support

from the New York City Department of Health,

Columbia University, and the City College of New

York and from grant ES09089 from the National

Institute of Environmental Health Sciences.

Received 19 June 2001; accepted 12 March 2002.

Articles

Hunts Point, a 690-acre peninsula in the South Bronx, New York City, is a hub in the tristate

(New York, New Jersey, and Connecticut) freight transportation system. This study was carried

out in response to community concerns about potential health effects of exposure to diesel exhaust

particulate (DEP). We measured particulate matter < 2.5 μm in aerodynamic diameter (PM2.5)

and elemental carbon (EC) on sidewalks and tested whether spatial variations in concentrations

were related to local truck traffic density. Ten-hour integrated air samples for EC and PM2.5 were

collected for 9 days over a 3-week period in the summer of 1999 at seven geographically distinct

intersections. Simultaneous traffic counts were carried out for each sampling event. Traffic was

classified into three classes: passenger cars, small trucks, and large trucks (diesel vehicles). Mean

diesel vehicle volumes ranged from 9.3 to 276.5 vehicles/hr across sites. Mean EC concentrations

by site ranged from 2.6 μg/m3 at the control site to 7.3 μg/m3 along a designated truck route.

Linear regression of site-specific mean EC concentration on mean large truck counts predicted an

increase of 1.69 μg/m3 EC per 100 large trucks/hr (SE = 0.37; p = 0.01; R2 = 0.84). Average

PM2.5 concentrations by site ranged 1.6-fold (19.0–29.9 μg/m3) and were more weakly associated

with local traffic. Variations over time for PM2.5 were more pronounced, ranging almost 4-fold

(8.9–34.4 μg/m3). These results show that airborne EC concentrations, an important component

of DEP, are elevated in Hunts Point and that the impact varies across the community as a function of large truck traffic. Key words: diesel exhaust, Hunts Point, inner cities, outdoor air pollution, PM2.5, urban. Environ Health Perspect 110:1009–1015 (2002). [Online 26 August 2002]

http://ehpnet1.niehs.nih.gov/docs/2002/110p1009-1015lena/abstract.html
ability to act as a carrier for toxic organic

chemicals and allergens, DEP may play a role

in transporting allergens and toxic compounds deep into the respiratory tree (23,24).

Recent epidemiologic studies have demonstrated associations between residential proximity to traffic sources and adverse respiratory

outcomes, including asthma hospitalizations

among children (25), increased respiratory

symptoms (26,27), diminished lung function

(15,16), and increased prevalence of atopy and

allergic disease (28). Exposure assessment

methodology varies among studies. Few

include specific measures of truck and/or bus

density or ambient sampling of DEP.

Significantly, studies of health outcomes

as a function of truck density or specific DEP

exposure have reported stronger associations.

Studies in two different German cities using

almost identical methodology found that

adolescents who reported constant truck traffic outside their homes were two times more

likely to also report wheeze than were those

who reported no truck traffic (29,30). In one

recent study, chronic respiratory symptoms

and lung function decrements in children

were associated with local truck traffic density

and with black smoke concentrations in

schools, whereas no such associations were

observed for car traffic, suggesting a specific

effect of diesel exhaust (16). The results of

these studies support the observations and

concerns of inner-city residents with respect

to a possible relationship between diesel emission exposure and respiratory disease.

Outdoor exposure to DEP may be of special importance for inner-city residents

because sidewalks function both as pathways

for pedestrian movement and as venues for

play and congregation for children and adults,

including the elderly. These uses are especially

prevalent in the urban core areas of NYC,

where many people live in small apartments

without balconies and air conditioning and

where public green space is scarce. From a scientific perspective, exposure characterization is

a necessary first step in investigating a possible

relationship between diesel exhaust exposures

and asthma. There are few data on levels and

patterns of human exposures to DEP in urban

areas. Data relating spatial variations in source

density to variations in ambient DEP concentrations in congested urban core neighborhoods are especially lacking.

Materials and Methods

Community background. Hunts Point is a

690-acre peninsula on the southeastern

shore of the borough of the Bronx, NYC. It

is a hub for freight transportation in the tristate area (New York, New Jersey, and

Connecticut). A sprawling food terminal

located at the tip of the peninsula handles

80% of the New York metropolitan region’s

fresh produce. Approximately 10,000 trucks

service the food terminal every day. In addition, solid-waste-transfer and construction

and demolition stations in the peninsula

process 40% of Manhattan’s commercial

waste. 

Hunts Point is also home to 10,000 residents who live and work in the area (including 3,000 children). According to the 1990

U.S. Census (31), 73% of residents are

Hispanic Americans, and 25% are African

Americans, with 2% belonging to other ethnicities. Half of community residents live at

or below the poverty line. One out of three

Hunts Point children suffers from asthma,

and the risk of hospitalization for asthma

among children is 12 times the national average (2). Figure 1 shows the location of the

market and designated legal truck routes

through the community.

Selection of monitoring sites. We identified seven potential monitoring sites on the

basis of known traffic patterns and extensive

consultation with The Point CDC and area

residents. The stations were chosen to meet

two criteria: first, the need to measure a range

of traffic densities and associated emissions,

and second, the need to monitor air pollution

at sites of importance to the community.

Community members were particularly interested in assessing exposures near popular

gathering spaces, in high-density residential

areas, and near schools and playgrounds. Pilot

traffic counts were made at the potential sites

to ensure that the selected sites would indeed

provide a spectrum of traffic densities.

Figure 2 shows the locations at which traffic

and air monitoring were conducted.

Site 1 was located at the intersection of

Tiffany Street and Spofford Avenue, a busy

intersection marking the boundary between

residential and commercial areas. Tiffany

Street is a designated truck route that links

the Bruckner Boulevard expressway on the

northwest border of Hunts Point with the

food-processing terminal and over 30 wastetransfer and construction and demolition

facilities located in the commercial area. An

apartment building also stands at this intersection. Site 2 was located at the intersection

of Longfellow and Lafayette Avenues.

Buildings surrounding this intersection

include a school, a number of automotive

used parts depots, and apartment buildings.

Neither Longfellow Avenue nor Lafayette

Avenue is a designated truck route, so this site

was expected to have little truck traffic. Site 3

was located at the intersection of Spofford

Avenue and Coster Street, in the heart of the

residential area. A school and a daycare center

border on this intersection. After a fatal accident in which a young girl was killed by a

truck, residents have succeeded in having traffic-calming devices and medians constructed

around this intersection. Site 4 was located at

the intersection of Tiffany Street and Randall

Avenue, both of which serve as truck routes

to the warehouses, demolition facilities,

sewage-processing facilities, and food terminal. Thus, this intersection was highly congested. Site 5 was located at Hunts Point and

Randall Avenues; here again, both streets were

designated truck routes. This intersection, in

the commercial area, was unusually spacious

and open. Site 6, located in a no-truck-traffic

zone in the sheltered garden of a home on a

residential street, served as a control site for air

monitoring.

Sampling design. Air monitoring (all

sites) and traffic counting (sites 1–5) were

conducted by teams of two student interns

and/or community residents who sat on sidewalks adjacent to the intersections. These

14–18-year-old interns were participants in a

Articles • Lena et al.

1010 VOLUME 110 | NUMBER 10 | October 2002 • Environmental Health Perspectives

Figure 1. Major truck routes in Hunts Point.
environmental leadership development program run jointly by Hunter College and community organizations in Hunts Point, Nos

Quedamos, and Sunset Point, also in Bronx,

NYC. Staff from Columbia University trained

the students and community partners in the

operation, calibration, and proper placement

of air sampling equipment. A scientific staff

member from Columbia University rotated

from site to site throughout all sampling periods and was responsible for oversight of the

sampling operations at each site.

Air sampling and traffic counting were

carried out on Monday, Tuesday, and

Wednesday for 3 consecutive weeks beginning 25 July 1999 and ending 12 August

1999. Because of staff and equipment constraints, all sites could not be monitored on all

days. At sites 1–3, measurements were conducted on Mondays and Wednesdays. Sites 4

and 5 were monitored on Tuesdays. To provide an estimate of temporal changes in background concentrations, air monitoring at site

6, the control site, was carried out on all days

(i.e., Monday through Wednesday each week).

No traffic counts were made at site 6 because

the air sampling equipment was placed within

an enclosed garden. Measurements at each site

covered a 10–12-hr period starting at 600 hr

each day. The sampling schedule reflected the

best compromise between the need for maximum duration of counting and sampling periods, the need to capture traffic volumes during

morning peaks (600–800 hr) and evening

peaks (1600–1800 hr), and availability of field

staff. We chose July and August because of the

availability of summer interns and to avoid the

heating season when coal and oil furnaces emit

EC. The study was limited to weekdays to

focus on typical commercial traffic volumes.

Particle concentration measurements.

Integrated 10–12-hr PM2.5 samples were 

collected at each site using 4-L/min batteryoperated personal sampling pumps (Gil-Air 5;

Gillian Instrument Corp., W. Caldwell, NJ)

attached by flexible tubing to polyethylene filter sampling cartridges (University Research

Glassware, Carrboro, NC). The cartridge had

an inlet nozzle and a greased impactor plate

that eliminated particles > 2.5 μm in aerodynamic diameter from the air stream before collection on the filter. The cartridge contained a

preweighed Teflon filter for gravimetric PM2.5

analysis and reflectance analysis. For a subset

of sampling events, colocated particle samples

were collected onto quartz fiber filters. These

were subsequently analyzed for EC. The

quartz filter samples were collected at 3 L/min

with no impactor because virtually all EC is

contained in particles smaller than 1 μm. 

Air sampling at sites 1–5 occurred on

sidewalks adjacent to the intersection. Pumps

were placed on chairs approximately 1m from

the curbside, and the sample cartridges were

taped to the chair backs.

Flow rates were checked by scientific staff

with precalibrated rotameters before, during,

and after each sampling event. After sampling, cartridges were separated from the tubing, capped, and then placed in resealable

bags for hand transport to a laboratory at

Hunter College. At the laboratory, scientific

staff disassembled the cartridges and removed

the filters under a positive-pressure, particlefree hood. Teflon filters were placed in individual sterile petri dishes and shipped to

external laboratories for gravimetric PM2.5

analysis. Cartridges with quartz filters were

shipped intact to external laboratories for EC

analysis.

Gravimetric PM2.5, EC, and reflectance

analysis procedures. Gravimetric PM2.5 analysis was performed in the laboratories of P.

Koutrakis at the Harvard School of Public

Health (Boston, MA). Quartz filters were

analyzed for EC by Sunset Laboratories, Inc.

(Forest Grove, OR). The methods have been

described in detail previously (18).

Reflectance analysis was performed in the

laboratories of S. Chillrud (Lamont Doherty

Earth Observatory, Columbia University) as

described previously (18). The blackness of

the filter deposit was measured using an EEL

(Evans Electroselenium Ltd.) smoke stain

reflectometer (model 43D; Diffusion Systems

Ltd., London, UK). An absorption coefficient

was determined for each filter.

Traffic counting. Vehicle counts per unit

time were determined visually at each intersection for each day of sampling. Two community interns were stationed at each

intersection, with each intern counting traffic

on a different street so that all traffic at a

given intersection was counted. Data entry

sheets for recording traffic counts were provided by traffic engineers at City University

of New York (CUNY). The data entry sheets

allowed traffic counters to record type, volume, and direction of traffic flow (including

right and left turns). One data entry sheet was

used to count traffic in both directions on

one street for a 15-min period. On each data

entry sheet, observers recorded the station reference, time of day, interval of observation,

weather, traffic volumes exiting intersections

in different directions, and the traffic control

devices (e.g., traffic lights, stop signs). Traffic

was counted in three classes: passenger cars,

including vans and sport utility vehicles (P);

pickup trucks and double-axle trucks (small

trucks, T1); and trucks with two axles that

have two tires on the front axle and four tires

on the rear axle, or trucks with more than

two axles (large trucks, T2).

U.S. Department of Commerce data (32)

on characteristics of vehicles registered in the

tristate area indicates that < 4.0% of trucks in

our T1 category have diesel engines. In contrast, 95% of trucks with more than three

axles have diesel engines. Approximately 45%

of trucks with two axles (front axle has two

tires, rear has four tires) have diesel engines.

Both types of vehicles were classified as T2 in

our study; thus, this category is likely to be

relatively sensitive for DEP exposure (32).

A traffic engineer from CUNY trained

interns and staff from Columbia University

and The Point CDC in traffic counting techniques. During two training sessions at a

busy intersection in Hunts Point, observers

were instructed in the use of data entry

sheets, the proper traffic lanes and directions

to be counted, and delineation between the

three classes of traffic under study. Traffic

data were entered onto a spreadsheet and

expressed in counts per hour for each vehicle

category, for all traffic (P + T1 + T2) and for

all trucks (T1 + T2).

Articles • Urban diesel particle exposures

Environmental Health Perspectives • VOLUME 110 | NUMBER 10 | October 2002 1011

Figure 2. Sampling sites for the Hunts Point air pollution study.
Statistical analysis. Simple descriptive statistics were tabulated to display spatial and

temporal variations in traffic density and EC

and PM2.5 concentrations across the six monitoring sites. Also, correlations among the

counts of passenger cars, small truck, and

large trucks were examined. Likewise, correlations were calculated between traffic counts

and both EC and PM2.5 concentrations.

Analytical EC measurements on quartz

fiber filters were available for a subset of 13 of

33 sampling events. However, the Teflon filters used to measure PM2.5 concentrations

were subsequently analyzed by reflectance to

determine the adsorption coefficient of the

particle deposit, a surrogate for EC content of

the sample. To estimate EC concentrations

for sampling events lacking quartz filter

analyses, we used a regression equation of EC

(analyzed on quartz filters) on absorption

coefficient (analyzed on colocated Teflon filters) developed within the data subset. The

scatter plot of EC versus absorption coefficient for the 12 colocated samples indicated a

close correspondence between the two measures, with a correlation of 0.90. A linear

regression through the origin was fit, yielding

a slope of 0.5236 μg/m3 EC per unit absorption coefficient. Using this slope, estimated

EC concentrations (in micrograms per cubic

meter) were calculated as a linear function of

absorbance for all sampling events that lacked

an EC determination.

To assess the relative magnitude of spatial

and temporal variations in large truck counts,

EC, and PM2.5 concentrations, data were

analyzed by two-way analysis of variance

(ANOVA) with site and date as the two main

random effects. To quantify the relationship

between large truck counts and airborne particle concentrations, site-mean EC and PM2.5

concentrations were regressed on site-mean

truck counts.

As noted above, although the control site

(site 6) was sampled on all 9 study days, logistical constraints required that sampling at the

remaining five sites be staggered, with sites

1–3 sampled on Mondays and Wednesdays,

and sites 4 and 5 sampled on Tuesdays over

the 3-week study period. This somewhat

unbalanced design could have resulted in

confounding between temporal and spatial

variations in air quality if, by chance, air quality differed on average between the two sampling frames. To address this concern, we

compared the Monday and Wednesday data

with the Tuesday data for EC and PM2.5 at

the control site, which was monitored on all

days. This comparison was made using the

unpaired t-test.

Results

Average hourly counts of large trucks, small

trucks, and passenger cars for each site and day

are summarized in Table 1. Each value represents the average hourly count observed over

the course of a 10–12-hr counting period on

each day. Table 1 also presents the means and

standard errors of traffic counts by site. These

data show that average vehicle counts varied

markedly from site to site and that these intersite differences were especially great for large

trucks. Site-specific mean passenger car counts

ranged just 2.2-fold across sites, from 155.4 to

348.3 vehicles/hr. Small truck counts showed a

slightly higher range across sites of 4.9-fold,

from 33.3 to 161.7 vehicles/hr. However, sitespecific means for large trucks ranged from 9.3

to 276.5 vehicles/hr, almost a 30-fold range. In

a two-way random-effects ANOVA, the siteto-site differences were highly significant for all

three vehicle types, whereas the day-to-day differences were consistently nonsignificant.

Passenger car counts exceeded small and

large truck counts at all sites; however, the

ratio of passenger cars to trucks varied by site.

At site 3, this ratio was 19:1, but at site 4, at

the intersection of two truck routes, the ratio

was 1.25:1. All traffic classes were correlated

with one another, with large trucks and small

trucks (r = 0.90) being most correlated and

Articles • Lena et al.

1012 VOLUME 110 | NUMBER 10 | October 2002 • Environmental Health Perspectives

Table 1. Average hourly counts of passenger cars, small trucks, and large trucks at five sites in Hunts Point.

Week 1 Week 2 Week 3

Monday Tuesday Wednesday Monday Tuesday Wednesday Monday Tuesday Wednesday

Vehicle type/site 7/26/99 7/27/99 7/28/99 8/2/99 8/3/99 8/4/99 8/9/99 8/10/99 8/11/99 Mean SE

Passenger cars/hr

1 295.2 252.5 227.7 339.5 277.6 375.5 294.7 22.4

2 237.8 178.2 96.4 183.4 65.7 171.1 155.4 25.7

3 171.6 169.3 188.2 170.9 194.7 172.7 177.9 4.4

4 331.0 319.4 385.3 345.2 20.3

5 314.8 381.7 348.3 33.4

Small trucks/hr

1 91.4 93.8 107.2 97.8 85.5 98.0 95.6 3.0

2 70.2 57.5 46.5 61.0 12.8 73.6 53.6 9.1

3 52.1 37.9 25.9 29.2 28.4 26.2 33.3 4.2

4 171.4 169.7 144.2 161.7 8.8

5 134.6 177.4 156.0 21.4

Large trucks/hr

1 109.9 130.6 119.1 124.2 134.5 126.6 124.2 3.6

2 17.4 14.3 11.4 13.8 7.2 13.1 12.9 1.4

3 7.8 12.1 10.1 9.6 9.1 7.1 9.3 0.7

4 231.4 304.3 293.7 276.5 22.7

5 149.8 155.3 152.5 2.7

Table 2. Airborne elemental carbon concentrations (μg/m3) at six sites in Hunts Point.

Week 1 Week 2 Week 3

Monday Tuesday Wednesday Monday Tuesday Wednesday Monday Tuesday Wednesday

Site 7/26/99 7/27/99 7/28/99 8/2/99 8/3/99 8/4/99 8/9/99 8/10/99 8/11/99 Mean SE

1 6.20 5.44 5.13 6.00 4.71 7.70 5.86 0.43

2 2.94 3.25 2.54 1.99 1.17 3.86 2.62 0.39

3 2.70 3.29 2.29 1.43 3.30 2.60 0.35

4 7.24 6.34 8.45 7.34 0.61

5 4.03 3.56 3.80 0.24

6 1.90 2.46 2.82 2.06 1.94 2.96 0.72 4.75 3.56 2.57 0.38

Mean 3.44 4.58 3.70 3.24 3.95 3.31 2.01 6.60 4.60

SE 0.95 1.41 0.59 0.96 1.29 0.92 0.91 1.85 1.04
passenger cars and large trucks being least

correlated (r = 0.82).

Mean EC concentrations by site ranged

almost 3-fold, from approximately 2.6 μg/m3

at the control site (site 6) and the two residential street sites (sites 2 and 3) to 3.8 (site 5),

5.9 (site 1), and 7.3 μg/m3 (site 4) along the

truck routes (Table 2). Day-to-day variations

in EC concentrations were less pronounced

than were site-to-site variations. In a two-way

random-effects ANOVA, intersite differences

accounted for 80% of the total variance in EC

concentrations, whereas interday differences

accounted for just 16%. This reflects the relative importance of local motor vehicle emissions, as opposed to long-range transport or

alternating weather fronts, as the key driver of

ambient EC concentrations in NYC.

To further investigate the influence of

local vehicle traffic on intersite variations in

EC concentrations, we correlated site-specific

mean EC concentrations with site-specific

mean vehicle counts per hour. The largest correlation (r = 0.92) was observed between EC

and large truck counts; for passenger cars and

small trucks, the correlations with EC were

0.72 and 0.75, respectively. Figure 3 displays

the scatter plot and linear regression of sitemean EC versus large truck counts per hour,

indicating a strong positive relationship. The

linear regression predicted an increase of 1.69

μg/m3 EC/100 large trucks/hour (SE = 0.37;

p = 0.01), with an R2 of 0.84.

Average PM2.5 concentrations by site

ranged 1.6-fold, from 19.0 μg/m3 at the control site to 29.9 μg/m3 at site 1 (Table 3).

Variations over time were more pronounced,

ranging almost 4-fold, from 8.9 to 34.4 μg/m3.

A two-way ANOVA showed that interday differences accounted for 55% of the total variance in PM2.5 whereas intersite differences

accounted for 24% of total variance. This

reflects the relative importance of long-range

transport and alternating weather fronts, as

opposed to local emissions, as drivers of ambient PM2.5 concentrations in NYC. Correlations

between site-specific mean PM2.5 concentrations and mean vehicle counts per hour were

somewhat lower than was observed for EC:

0.62, 0.58, and 0.72 for passenger cars, small

trucks, and large trucks, respectively. Figure 4

displays the scatter plot and regression of sitemean PM2.5 versus large truck counts per

hour. A weak positive relationship was

observed, with PM2.5 concentrations increasing

by 3.26 μg/m3/100 large trucks/hr (SE = 1.56;

p = 0.11), with an R2 of 0.52.

The ratios of EC to PM2.5 for individual

samples ranged from 0.09 to 0.38, with a

mean of 0.17. Average EC/PM2.5 ratios at the

control and nontruck route sites (sites 2, 3,

and 6) were lower, ranging from 0.13 to

0.16. In contrast, average EC/PM2.5 ratios

were higher at the truck route sites (sites 1, 4,

and 5), ranging from 0.20 to 0.28.

Using the regression slopes noted above for

both EC and PM2.5 in relation to large truck

counts, we estimated the fraction of total

truck-related PM2.5 that was represented by

EC in the present study. The ratio of the

regression slopes (expressed in units of micrograms per cubic meter per 100 trucks),

1.69/3.26 or 0.52, indicates that EC represented 52% of the impact that large trucks had

on PM2.5 concentrations. Conversely, we can

estimate the total PM2.5 impact of large trucks

by multiplying the observed EC concentrations by the ratio of the PM2.5 slope on trucks

to the EC slope on trucks (i.e., 3.26/1.69, or

1.93). Applying this factor to the observed sitespecific EC concentrations yields estimates of

truck-related PM2.5 concentrations ranging

from 5.0 μg/m3 at site 6 to 14.2 μg/m3 at site

4. These estimates of truck-related PM2.5 represent 26% and 50%, respectively, of the

observed mean PM2.5 concentrations at these

two sites.

Sites 1–3 were sampled on Mondays and

Wednesdays, whereas sites 3 and 4 were sampled only on Tuesdays; site 6 (control) was

sampled all days. To address whether this

somewhat unbalanced sampling design might

have resulted in confounding of intersite differences by day-of-week differences, we tested

whether mean concentrations of EC or PM2.5

at the control site were different on Mondays

and Wednesdays compared with Tuesdays

using a simple unpaired t-test. There was no

evidence for differences across the two sampling frames for either EC (p = 0.41) or

PM2.5 (p = 0.49), alleviating concerns about

potential confounding by day of week.

Discussion

In this study we demonstrated that local variations in traffic density were strongly associated

with spatial variations in EC concentrations

measured on sidewalks in the Hunts Point

neighborhood of the South Bronx. Because

diesel trucks were the principal source of traffic-related EC emissions in this community,

this finding highlights the important role

played by truck traffic as a determinant of spatial variations in exposures to the carbonaceous components of fine particles in this

community. Sidewalk levels of total PM2.5

were more weakly associated with large truck

traffic, being more strongly influenced by

long-range transport of well-mixed sulfate

and nitrate aerosols and only partially influenced by local traffic emissions. The mean

EC concentration observed in this study,

3.77 μg/m3, represented 17% of the mean

PM2.5 concentration, 22.3 μg/m3.

EC represents only a portion of the particle emissions from diesel vehicles; other components of DEP include a variety of organic

compounds and metals. In addition, particles

can be resuspended from roadways by the

movement of tires. Although not directly

measured in this study, the total contribution

of large truck traffic to PM2.5 concentrations

can be estimated. One method for deriving

this estimate is based on the ratio of the

regression slope of EC on large trucks to the

slope of PM2.5 on large trucks, or 0.52 in the

present study. This implies that EC represents

52% of the total PM2.5 generated by large

Articles • Urban diesel particle exposures

Environmental Health Perspectives • VOLUME 110 | NUMBER 10 | October 2002 1013

Figure 3. Plot of site-specific mean EC (± SE) versus

mean large truck counts, with linear regression

line. EC = 0.0169 × trucks + 2.51 (R2 = 0.84).

9

8

7

6

5

4

3

2

1

0

EC concentration (μg/m3)

0 50 100 150 200 250 300

Large trucks/hr

Table 3. Airborne PM2.5 concentrations (μg/m3) at six sites in Hunts Point.

Week 1 Week 2 Week 3

Monday Tuesday Wednesday Monday Tuesday Wednesday Monday Tuesday Wednesday

Site 7/26/99 7/27/99 7/28/99 8/2/99 8/3/99 8/4/99 8/9/99 8/10/99 8/11/99 Mean SE

1 26.5 24.4 22.5 43.0 14.7 48.1 29.9 5.25

2 24.8 22.9 19.8 9.5 28.6 21.1 3.22

3 13.6 18.7 17.3 6.9 31.9 18.2 3.39

4 36.3 26.3 22.0 28.2 4.25

5 27.0 14.4 20.7 6.28

6 17.8 27.5 20.1 17.6 14.1 17.0 4.5 23.3 29.2 19.0 2.46

Mean 22.5 30.3 20.2 19.6 18.3 25.8 8.9 22.7 34.4

SE 1.95 3.03 2.39 1.22 4.00 7.46 2.20 0.69 4.61
trucks. Furthermore, if we assume that diesel

vehicles were the dominant source of EC in

this study (which is reasonable given the lack

of oil or coal combustion for space heating in

the summer months), it is possible to estimate

a total PM2.5 contribution from diesel traffic

emissions of 7.25 μg/m3 (i.e., 1/0.52 × 3.77

μg/m3). This represents 32.5% of the mean

PM2.5 concentration measured in this study

(ranging from 26% to 50% for individual

sites). In a previous study in Los Angeles,

California, Cass and Gray estimated that EC

represented 59.5% of the mass of DEP

observed in the Los Angeles atmosphere, a

value similar to our estimate of 52% (33,34).

Applying the Cass and Gray estimate to the

mean EC concentration observed in this

study (1/0.595 × 3.77 μg/m3) yields an estimated total DEP concentration of 6.34

μg/m3 (33,34).

Correlations among traffic classes (passenger vehicles, small trucks, and large trucks)

were high in this study, ranging from 0.82 to

0.90, making it difficult to separate the influences of different vehicle classes on air quality. However, a stronger correlation was seen

between large trucks and EC than between

small trucks and EC (r = 0.92 vs. r = 0.75).

In this study, trucks with two axles and

six wheels (two in front, four behind) and

vehicles with three axles or more were

counted together as a single traffic class (T2).

In pilot counts with student interns, we

found that this classification scheme achieved

the greatest precision in measurement.

However, grouping vehicles in this way

diminished our ability to distinguish the specific pollutant contribution of diesel trucks.

In the local area, approximately 45% of twoaxle, six-wheel trucks have diesel engines. The

proportion with diesel engines is closer to

65% for trucks of this kind that are used in

wholesale businesses, which are common in

Hunts Point. Thus, in our study area, the

proportion of trucks in this class with diesel

engines is likely to have been between 45%

and 65%. In contrast, > 90% of trucks with

three axles (100% for wholesale freight transport) have diesel engines (32). Thus, our

measurement of the relationship between

diesel trucks and sidewalk-level DEP exposures would have been more accurate if we

had counted three-axle vehicles separately.

Indeed, the relationship between diesel trucks

and sidewalk-level EC and PM2.5 is likely to

be stronger than reported in this study.

In our study, local variations in traffic

density were measured using simple visual

traffic counts at the same intersection where

air monitoring occurred. A more complete

analysis of local traffic impacts on air quality

would account for traffic not only on the adjacent street but also on other nearby streets,

especially those upwind of the monitoring

site. In the Hunts Point area, the contribution of trucks idling at warehouses and construction and demolition sites may be

particularly important. Modeling the cumulative impact of sources throughout the community would have required sophisticated

dispersion models, which were beyond the

scope of this small study.

The significance of the DEP concentrations

observed in this study for asthmatic persons living in the Hunts Point community is not

known. In a study of lung function and air pollution from truck traffic conducted in the

Netherlands, Brunekreef et al. (16) measured

black smoke concentrations ranging from 5.15

μg/m3 to 20.78 μg/m3 in schools located near

motorways. Black smoke was determined using

reflectance analysis of PM10 samples, in contrast to PM2.5 samples used here. Lung function was associated with the concentration of

black smoke, and the association was stronger

in girls than in boys (16). Recent experimental

studies have demonstrated that a one-time,

intranasal dose of 0.30 mg DEP in saline can

synergize with coadministered allergen to

enhance allergen-specific IgE production, histamine release, and proinflammatory cytokine

levels in the upper respiratory tract (35–37). 

We did not make direct measurements of

respiratory health indicators among community residents. As a step toward understanding

the health significance of DEP exposures

measured in Hunts Point, we have estimated

both a 24-hr and a 2-week dose for a child

living in this community. In Hunts Point,

scarcity of air conditioning may increase

indoor concentrations of ambient pollutants

during the summer. The lack of air conditioning may also drive residents outdoors,

thereby increasing exposure to ambient pollutants. The small size of apartments, high

population density, and lack of sheltered

green spaces may also increase reliance on

streets as places for children to play and

neighbors to meet and talk. Furthermore, in

a recent study, Chillrud et al. (38) showed a

1-to-1 relationship between indoor and outdoor black carbon measurements at the

homes of 38 high school students in NYC.

For these reasons we assume that outdoor

and indoor DEP concentrations in Hunts

Point are roughly equivalent.

An 8-year-old child who spends 12 hr/day

resting, 10 hr engaged in light activity, and 2

hr engaged in heavy activity would have a 24-

hr cumulative dose of 28.4 μg DEP. This calculation is based on our measured average

DEP concentration of 6.34 μg/m3, a deposition fraction in the tracheobronchial tree of

0.4 for PM2.5, and exertion-dependent minute

ventilation for an 8-year-old child with height

and weight of 127 cm and 27 kg, respectively

(39). This amounts to a cumulative dose of

396 μg DEP over 2 weeks. Based on our data,

a maximum exposure for a child from the

community over a 24-hr period would be

67.8 μg. A 2-week dose at maximum exposure

levels would be 949 μg. Significantly, the average 2-week dose of 396 μg exceeds the dose

used by Diaz-Sanchez et al. (35) to elicit

enhanced IgE production and cytokine

release. Minute ventilation can increase dramatically with exercise; thus, active children

are likely to receive much higher doses.

To estimate individual exposure and internal dose more precisely, studies of ambient air

pollutants must be combined with indoor air

Articles • Lena et al.

1014 VOLUME 110 | NUMBER 10 | October 2002 • Environmental Health Perspectives

Figure 4. Plot of site-specific mean PM2.5 (± SE)

versus mean large truck counts, with linear regression line. PM2.5 = 0.0326 × trucks + 19.73 (R2 = 0.52).

40

35

30

25

20

15

10

5

0

PM2.5 concentration (μg/m3)

0 50 100 150 200 250 300

Large trucks/hr

Table 4. Elemental carbon concentrations in ambient air.

Concentration 

Location and year (μg/m3) Reference

Annual average outdoor concentrations across 10 sites in the Los Angeles, 3–5 (33)

California, Basin 1982

Maximum daily concentration

Los Angeles, 1987 5.4 (40)

Welby, Colorado 1.7 (41)

Brighton, Colorado 1.2 (41)

Harlem, NYC, daily averages (4 sites, sidewalk level) 1.5–6.0 (18)

Dusseldorf urban street level, 1991–1992 

Annual workday average 16 (42)

Annual weekend average 10 (42)

Duisberg, residential area 

Annual average, 1991–1992 3 (42)

Hunts Point, 1999

Average over six sites 3.8 This study

Range of site averages 2.6–7.3 This study
measurements and analysis of activity patterns

of residents who differ by age and socioeconomic status. Furthermore, experimental and

epidemiologic studies that differentiate

between the respiratory effects of chronic and

acute exposures to DEP will contribute to our

understanding of how DEP exposure may

affect respiratory health in this community.

EC levels observed in the present study

were somewhat higher than those reported in

previous studies. In a recent study in Harlem,

daytime EC concentrations measured on sidewalks averaged between 1.5 and 6.2 μg/m3

across four sites that varied markedly in diesel

vehicle traffic (18). Average concentrations of

EC at the sidewalk level in the Hunts Point

area ranged from 2.57 to 7.34 μg/m3. Table 4

presents results from recent studies of ambient EC levels measured in North America

and Europe. Recent source apportionment

studies by the U.S. Environmental Protection

Agency (EPA) reported that long-term average DEP concentrations, of which EC represents the major part, range between 1.2

μg/m3 and 3.6 μg/m3 in urban/suburban

areas (40). Concentrations in rural or remote

areas are generally lower than 1.0 μg/m3 (41).

Thus, DEP levels in Hunts Point were relatively high, perhaps because of the high concentration of both local diesel traffic and the

upwind proximity of two major highways

with heavy truck traffic.

Studies in Berlin and NYC indicate a high

degree of correlation between EC concentrations measured analytically on quartz fiber filters and absorption coefficient measurements

of PM2.5 collected on Teflon filters (42). In

this study, we report a similarly high correlation (r = 0.90). Our results provide further

validation of the use of absorption coefficient

as an economical surrogate for measurement

of fine-particle EC concentrations.

This study provides new data on the relationship between DEP, PM2.5, and local traffic

density. We have also demonstrated the feasibility of community–university partnerships

working to address environmental health problems in marginalized communities. The study

design represented a true synthesis of community and university objectives, reflected in the

choice of monitoring sites, designation of traffic categories, methods of measurement, and

outcomes measured. Although we have not

reported the results here, an important additional community objective was to monitor

illegal use of nondesignated truck routes, for

which some evidence was obtained. The study

also provided young people from the community with exposure to scientific methods

applied to environmental health problems.

This model of community-driven research is

particularly appropriate in underprivileged

communities that have traditionally been subject to studies conducted by and for the benefit

of outside parties.

REFERENCES AND NOTES

1. Northridge ME, Yankura J, Kinney PL, Santella RM,

Shepherd P, Riojas Y, Aggarwal M, Strickland P. Diesel

exhaust exposure among adolescents in Harlem: a community driven study. Am J Public Health 89(7):998–1002 (1999).

2. New York City Department of Health. Asthma Facts, New

York City Childhood Asthma Initiative. New York:New York

City Department of Health, 1999. Available: http://

www.ci.nyc.ny.us/html/doh/pdf/asthma/facts.pdf [cited 10

July 2002].

3. Eggleston PA, Buckley TJ, Breysee PN, Wills-Karp M,

Kleeberger SR, Joakkola JK. The environment and asthma

in US inner cities. Environ Health Perspect 107(suppl

3):439–450 (1999).

4. Loh P. Personal communication.

5. Carter M. Personal communication. 

6. Ostro BD. Air pollution and morbidity revisited: a specification test. J Environ Econ Manag 14:87–98 (1987).

7. Thurston GD, Ito K, Hayes CG, Bates DV, Lippman M.

Respiratory hospital admissions and summertime haze

and air pollution in Toronto Ontario: consideration for the

role of acid aerosols. Environ Res 65:271–290 (1994).

8. Dockery DW, Pope CA, Xu X, Spengler JD, Ware JH, Fay

ME, Ferris BG, Speizer FE. An association between air pollution and mortality in six U.S. cities. N Engl J Med

329:1753–1759 (1993).

9. Dockery DW, Pope CA. Acute respiratory effects of particulate air pollution. Annu Rev Public Health 15:107–132 (1994).

10. Pope CA, Thun MJ, Namboodiri MM, Dockery DW, Evans

JS, Speizer FE, Heath CW. Particulate air pollution as a

predictor of mortality in a prospective study of US adults.

Am J Respir Crit Care Med 151:669–674 (1995).

11. Schwartz J, Slater D, Larson TV, Pierson WE, Koenig JQ.

Particulate air pollution and hospital emergency visits for

asthma in Seattle. Am Rev Respir Dis 147:826–831 (1993).

12. Suh HH, Nishioka Y, Allen GA, Koutrakis P, Burton RM.

The Metropolitan acid aerosol characterization study:

results from the summer 1993 Washington D.C. field study.

Environ Health Perspect 105:826–834 (1997).

13. Thurston GD, Ito K, Kinney PL, Lippmann P. A multi-year

study of air pollution and respiratory hospital admissions

in three New York State metropolitan areas: results for

1988 and 1989 summers. J Expos Anal Environ Epidemiol

2:429–450 (1992).

14. Burton RM, Suh HH, Koutrakis P. Spatial variation in particulate concentrations within metropolitan Philadelphia.

Environ Sci Technol 30:400–407 (1996).

15. Nitta H, Sato T, Nakai S, Maeda K, Aoki S, Ono M.

Respiratory health associated with exposure to automobile exhaust: 1. Results of cross-sectional studies in 1979,

1982, and 1983. Arch Environ Health 48:53–58 (1993).

16. Brunekreef B, Janssen NAH, de Hartog J, Hassema H,

Knape M, van Vliet P. Air pollution from truck traffic and lung

function in children living near motorways. Epidemiology

8:298–303 (1997).

17. Nielsen T. Traffic contribution of polycyclic aromatic

hydrocarbons in the center of a large city. Atmos Environ

30:3481–3490 (1996).

18. Kinney PL, Aggarwal M, Northridge ME, Janssen NAH,

Shepard P. Airborne concentrations of PM2.5 and diesel

exhaust particles on Harlem sidewalks: a community based

pilot study. Environ Health Perspect 108:213–218 (2000).

19. International Program on Chemical Safety. Diesel Fuel and

Exhaust Emissions. Environmental Health Criteria 171.

Geneva:WHO, 1996.

20. Sawyer RF, Johnson J. Diesel emissions and control technology. In: Diesel Exhaust: A Critical Analysis of Emissions,

Exposure and Health Effects: A Special Report of the

Institute’s Diesel Working Group. Cambridge, MA:Health

Effects Institute, 1995;65–81. 

21. Sjogren M, Li H, Banner C, Rafter J, Westerholm R, Rannug

U. Influence of physical and chemical characteristics of

diesel fuels and exhaust emissions on biological effects of

particle extracts: a multivariate statistical analysis of ten

diesel fuels. Chem Res Toxicol 9:197–207 (1996).

22. Miguel AH, Kirchstetter TW, Harley RA, Herring SV. Onroad emissions of particulate polycyclic aromatic hydrocarbons, and black carbon from gasoline and diesel vehicles.

Environ Sci Technol 32:450–455 (1998). 

23. Knox RB, Sulphioglu C, Taylor P, Desai R, Watson HC,

Peng JL, Bursill LA. Major grass pollen allergen Lol p 1

binds to diesel exhaust: implications for asthma and air

pollution. Clin Exp Allergy 27:246–251 (1997). 

24. Ormstad H. Suspended particulate matter in indoor air:

adjuvants and allergen carriers. Toxicology 152(1–3):53–68

(2000).

25. Edwards J, Walters S, Griffiths RK. Hospital admissions

for asthma in pre-school children: relationship to major

roads in Birmingham, United Kingdom. Arch Environ

Health 49:223–227 (1994).

26. Van Vliet P, Knape M, de Hartog J, Janssen N, Harssema

H, Brunekreef B. Motor vehicle exhaust and chronic respiratory symptoms in children living near freeways.

Environ Res 74:122–132 (1997). 

27. Wjst M, Reitmeir O, Dold S, Wulffe A, Nicolai T, LoeffelholzColberg EFV, Mutius EV. Road traffic and adverse effects on

respiratory health in children. Br Med J 307:596–600 (1993).

28. Kramer U, Koch T, Ranft U, Ring J, Behrendt H. Trafficrelated air pollution is associated with atopy in children living in urban areas. Epidemiology 11:64–70 (2000).

29. Weiland SK, Mundt KA, Ruckmann A, Keil U. Self reported

wheezing and allergic rhinitis in children and traffic density on street of residence. Ann Epidemiol 4:243–247 (1994). 

30. Duhme H, Weiland SK, Keil U, Kraemer B, Schmid M,

Stender M, Chambless L. The association between selfreported symptoms of asthma and allergic rhinitis and self

reported traffic density on streets of residence in adolescents. Epidemiology 7:578–582 (1996).

31. U.S. Bureau of the Census. 1990 U.S. Census. Washington,

DC: Bureau of the Census, 1998.

32. U.S. Bureau of the Census. Vehicle Inventory and Use

Survey. 1997 Economic Census. EC97TV-NY, EC97TV-US.

Washington, DC:Bureau of the Census, 1998.

33. Cass GR, Gray HA. Regional emissions and atmospheric

concentrations of diesel engine particulate matter: Los

Angeles as case study. In: Diesel Exhaust: A Critical

Analysis of Emissions, Exposure, and Health Effects: A

Special Report of the Institute’s Diesel Working Group.

Cambridge, MA:Health Effects Institute, 1995;125–137. 

34. Gray HA, Cass GR. Characteristics of atmospheric organic

and elemental carbon particle concentrations in Los

Angeles. Environ Sci Technol 20:580–589 (1986).

35. Diaz-Sanchez D, Penichet-Garcia M, Saxon A. Diesel

exhaust particles directly induce activated mast cells to

degranulate and increase histamine levels and symptom

severity. J Allergy Clin Immunol 106(6):1140–1146 (2000).

36. Salvi S, Frew A, Holgate S. Is diesel exhaust a cause for

increasing allergies? [Editorial]. Clin Exp Allergy 29:4–8

(1999).

37. Diaz-Sanchez D. The role of diesel exhaust particles and

their associated polyaromatic hydrocarbons in the induction

of allergic airway disease. Allergy 52(suppl 38):52–56 (1997).

38. Chillrud SN, Ross JM, Morris A, Pederson D, Aggarwal M,

Ramstrom S, Spengler JD, Kinney PL. Filter reflectance as

a tracer for the outdoor contribution to indoor fine particle

concentration: results from the New York City TEACH

Study. In: Engineering Solutions to Indoor Air Quality

Problems: Proceedings of a Symposium, 17–19 July 2000,

Raleigh, NC. Pittsburgh:Air and Waste Management

Association, 2000;130–139.

39. Phalen RF, Oldham MJ, Kleinman MT, Crocker TT.

Tracheobronchial deposition predictions for infants, children and adolescents. In: Inhaled Particles VI: Proceedings

of an International Symposium and Workshop on Lung

Dosimetry (Dodgson J, McCallum RI, Bailey MR, Fisher DR,

eds). Oxford:Pergamon Press, 1988.

40. Chow JC, Watson JG, Fujita EM. Temporal and spatial variations of PM2.5 and PM10 aerosol in the Southern California

air quality study. Atmos Environ 28:2061–2080 (1994).

41. National Research Council. Research Priorities for

Airborne Particulate Matter: Immediate Priorities and a

Long-Range Research Portfolio. North Front Range Air

Quality Study, Colorado, Vol 1. Washington, DC:National

Academy Press, 1998. 

42. Elbers G, Richter J. Measurement of traffic induced soot

emissions [in German]. Staub Reinhalt Luft 54:19–24 (1994). 

Articles • Urban diesel particle exposures

Environmental Health Perspectives • VOLUME 110 | NUMBER 10 | October 2002 1015