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Atmospheric Environment 43 (2009) 1962–1971

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Atmospheric Environment

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Exposure of PM2.5 and EC from diesel and gasoline vehicles in communitiesnear the Ports of Los Angeles and Long Beach, California

Jun Wu a,*, Douglas Houston b,d, Fred Lurmann c, Paul Ong b, Arthur Winer d

a Program in Public Health and Department of Epidemiology, College of Health Sciences, University of California, Irvine, CA, USAb Department of Urban Planning, School of Public Affairs, University of California, Los Angeles, CA, USAc Sonoma Technology, Inc., Petaluma, California, USAd Department of Environmental Health Sciences, School of Public Health, University of California, Los Angeles, CA, USA

a r t i c l e i n f o

Article history:Received 14 May 2008Received in revised form10 January 2009Accepted 12 January 2009

Keywords:Particulate matterElemental carbonTrafficIntake fractionDispersion modeling

* Corresponding author. Program in Public Health, CTheory Dr., Suite 100, University of California, Irvine,824 0548; fax: þ1 949 824 1343.

E-mail address: junwu@uci.edu (J. Wu).

1352-2310/$ – see front matter � 2009 Elsevier Ltd.doi:10.1016/j.atmosenv.2009.01.009

a b s t r a c t

The Ports of Los Angeles and Long Beach are the entry point for almost half of all cargo containersentering the United States. The use of diesel trucks to move Port-related goods has raised significantpublic health concerns associated with black carbon and other air pollutants. It is difficult to reliablyestimate people’s exposure to vehicle-related pollutants due to the narrow impact zone of traffic, usuallywithin 200–300 m downwind of major roadways. Previous studies suffer from the lack of traffic countdata on surface streets and the lack of neighborhood-level population data. We examined seasonal andannual average exposures of particulate matter less than 2.5 mm (PM2.5) and elemental carbon (EC) ata neighborhood scale for communities heavily impacted by diesel trucks near these ports. We assembleda traffic-activity database that distinguishes gasoline and diesel vehicles on both freeways and surfacestreets, by consolidating information from several sources, including our own field measurements. TheCALINE4 model was used to estimate residential exposure of the study population to PM2.5 and EC. Parcelproperty data were used to allocate Census block group (BG) population to increase spatial resolution.The annual average PM2.5 and EC exposure due to local traffic was 3.8 and 0.4 mg m�3, respectively. Onaverage, surface streets contributed a little more than freeways (55% vs. 45% for EC and 57% and 43% forPM2.5). Light-duty vehicles contributed significantly more than heavy-duty trucks for PM2.5 (61% vs. 39%),but slightly less than heavy-duty trucks for EC (49% vs. 51%). Community mean population exposure wassimilar using parcel, census block, and BG population data, but extreme values and standard deviationsvaried significantly at different spatial resolutions. The intake fraction for the study population was in therange of 1.0–2.2 � 10�5 by vehicle type, roadway type, and season.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

The Port of Los Angeles (POLA) and the Port of Long Beach(POLB) are the entry point for almost half of all cargo containersentering the United States annually. The communities adjacent tothe ports are transected by major commuter freeways, includingthe main truck route leading out of the ports (I-710) on which 15%of all containers arriving in the US travel (Beverly, 2005). Theenvironmental consequences of air pollution resulting from goodsmovement for port-adjacent communities are substantial, and haveraised significant public health concerns.

ollege of Health Sciences, 100CA 92697-7555. Tel.: þ1 949

All rights reserved.

Multiple field studies indicate concentrations of primarypollutants emitted from motor vehicles are high near heavily-traveled roadways, and concentrations of ultrafine particles andblack carbon decline from high levels to background levels within150–500 m of major roadways (Hitchins et al., 2000; Zhu et al.,2002). Subjects who live near busy roadways are more likely tosuffer from respiratory and allergic conditions than people who live300 m or more away from such roadways (Delfino, 2002; Gauder-man et al., 2007; Heinrich and Wichmann, 2004; McConnell et al.,2006; Sarnat and Holguin, 2007). Heavy-duty diesel trucks (HDT)are of particular concern since they emit high levels of particulatematter (PM) and a complex mixture of gas pollutants with highhealth risks (Adar and Kaufman, 2007; Heinrich and Wichmann,2004; Nel et al., 2001; Seagrave et al., 2006). Technical ‘‘fixes’’ to theproblem of diesel truck exhaust, including the federal 2007 emis-sion standards and newly-available ultra-low sulfur diesel, offerpotential for reducing vehicle-related PM near roadways. However,

J. Wu et al. / Atmospheric Environment 43 (2009) 1962–1971 1963

given the 2–3 decades diesel fleet turnover times, near-roadwayexposures to fine and ultrafine particles and their associated toxiccompounds will likely remain a persistent problem for many years.

There are two major limitations in previous studies exam-ining neighborhood-level exposure from local traffic emissions,including the lack of traffic-activity data (especially diesel trucks)on surface streets, and the lack of population data at localized areasof aggregation. Most previous studies focused on freeways andhighways (Martuzevicius et al., 2004; Phuleria et al., 2007; vanVlietet al., 1997; Zhu et al., 2002), although many major arterials inurban cities experience high traffic volumes and they bring trafficinto much closer proximity to residences and human activities inurban environments. Furthermore, most previous exposure studiesused Census block (BLK), block group (BG), and tract data to esti-mate population exposure (Burke et al., 2001; Fruin et al., 2001;Georgopoulos et al., 2005). Property assessment parcel data can beused to improve the spatial resolution in air pollution exposureassessments (Setton et al., 2005), although the lack of residentialpopulation data limited their applications in population exposureassessment studies.

Previous studies have used intake fraction (iF) to quantify theemissions-to-inhalation relationship for health risk assessment andeconomic and policy analyses. The iF is defined as the fraction ofa pollutant emitted from a source that is inhaled by a definedpopulation (Bennett et al., 2002; Evans et al., 2002; Marshall et al.,2003). It summarizes the extent to which emissions from varioussources might impact the population. No study has examined theintake fractions of local-traffic generated air pollution in the studyregion.

The main objectives of this study were to 1) examine thecontributions of HDT traffic on surface streets to vehicle-relatedpollutant exposures for residents living adjacent to Southern Cal-ifornia ports; 2) assess the influence of different spatial resolutions(Census BG, BLK, and parcel boundaries) on estimated populationexposures to vehicle-related pollutants; and 3) estimate the intakefractions for local-traffic generated air pollutants in the studyregion. Specifically, we investigated the exposures of fine particu-late matter less than 2.5 mm (PM2.5) and elemental carbon (EC) atparcel, BLK and BG resolutions for communities heavily impactedby diesel trucks, using the CALINE4 dispersion model with detailedtruck count information. EC and PM2.5 were chosen because of theirpotential health effects and because EC has been widely used asa marker of diesel exhaust particles from traffic (Birch and Cary,1996; Kleeman et al., 2000; Shi et al., 2000).

2. Methods

2.1. Study region

Our study region was located in the immediate vicinity of theports of Los Angeles and Long Beach in southern Los Angeles County,California. The region was defined by extending 4 km outside theboundary of the I-110 freeway on the west, I-405 freeway on thenorth, I-710 freeway on the east, and the port boundary on the south(Fig. 1). This region was chosen because of the relatively highdensities of total traffic and port-related diesel trucks on freewaysand surface streets. Port-related trucks often reached 400–600 perhour for several hours, immediately upwind of sensitive land usessuch as schools and residences (Houston et al., 2008).

2.2. Heavy-duty and light-duty traffic counts on freeways andsurface streets

We constructed a traffic database that accounted for gasolineand diesel vehicle splits on freeways and surface streets, by

consolidating information from several sources, including theCalifornia Department of Transportation (Caltrans) total traffic(segment-level) and truck traffic counts at selected locations,episodic truck counts on surface streets from intersection studiesconducted by the Los Angeles Department of Transportation(LADOT), the Port of Los Angeles, and the UCLA Traffic Count Study,and a map of truck-prohibited streets and truck routes. Table 1describes the five types of data, while Fig. 1 shows the locations oftraffic count sites. In the CALINE4 model, Caltrans annual averagedaily traffic (ADT) counts were used as the basis for total trafficcounts, while fractions of HDT integrated from different sourceswere used to obtain total HDT volumes.

Fig. 2 illustrates the method to assign HDT fractions to roadwaysegments. Among the five sources with HDT information, Caltransdata were given the highest rank because they were long-termannual daily averages that considered diurnal, weekday/weekend,and seasonal fluctuations, while the others were for only a limitednumber of days and daytime hours. The UCLA Traffic Count Studymeasured four-direction (S, N, E, W) traffic, compared to twodirections (S–N and E–W) in the LADOT data, and intersection-level counts in the POLA data. Road segments within 5 km ofmeasurement sites were given measured HDT fractions. Theremaining segments with no measurements nearby were assignedHDT fractions based on roadway types and the average profiles ofmeasured data. We validated the assignment of HDT fractionsby calculating the root mean squared (RMS) error using a leave-one-out cross-validation approach (Sharaf et al., 1986). Eachobservation from the original sampling sites was used once as avalidation site, and the remaining observations were employed topredict the value at the validation site based on the same modelwe used to generate final HDT fractions. The HDT fractions rangedfrom 1% to 59% in both the measurements (mean ¼ 7.5%) and thepredictions (mean ¼ 6.1%). The Pearson’s correlation coefficient ofpredicted and measured data was 0.66. The predictions had anRMS error of 5.5% and a normalized mean bias of �18.1%. Theseindicated that our algorithm can predict HDT fractions reasonablywell although it may underestimate HDT fractions on certain truckroutes.

Light-duty gasoline vehicle (LDV) and HDT diurnal profiles onfreeways in 2001 and 2002 were obtained from two CaltransWeigh-in-Motion (WIM) stations (on I-710 and I-91). Traffic diurnalprofiles on the majority of surface streets were obtained froma previous study conducted by the California Air Resources Board(ARB) in 2002 (Sullivan et al., 2004), which examined LDV and HDTdiurnal distributions at 10 neighborhoods in the Los Angeles AirBasin. UCLA Traffic Count Study data were used to develop HDTdiurnal profiles at locations close to the ports. Weekday andweekend traffic profiles were also developed from the WIM dataand from the ARB study (Sullivan et al., 2004). Table 2 lists relativetraffic distribution used in this study by day-of-week and by vehicletype (adapted from the ARB study results).

2.3. CALINE4 model modification

The CALINE4 model, developed by Caltrans and the U.S.Federal Highways Agency (Benson, 1989), is a Gaussian diffusionline source model that predicts pollutant concentrations givensource strength, meteorology, and site geometry. In this studytwo improvements were conducted for the original CALINE4model, including removing the restriction of up to 20 receptorsand 20 road links, and incorporating a user-specified distancecriterion in the model. Due to flat tails in Gaussian distributions,the CALINE4 model may overestimate concentrations far awayfrom a source. The original model attempted to incorporate a 10-km distance threshold to avoid estimating contributions from

Fig. 1. Study region with the locations of traffic sampling sites and segment HDT fractions.

Table 1Description and sources of traffic data used in this study.

Data Sources Type of Traffic Data Location Year Time Reference

California Departmentof Transportation

Annual average daily traffic Freeways and majorsurface streets

2005 Daily http://traffic-counts.dot.ca.gov/

Annual average daily truck traffic 20 street intersections 2005 DailyPort of Los Angeles Total and truck traffic 91 street intersections 2001 8–9 AM and 4–5 PM Los Angeles Department of

Transportation (2006)Los Angeles Department

of TransportationTotal and truck traffic 77 street intersections 2003 7–10 AM and 3–6 PM Meyer Mohaddes Associates (2004)

UCLA Traffic Count Study Total and truck traffic 11 street intersections 2006 7 AM–6 PM Houston et al. (2008)City of Los Angeles Map of truck-prohibited

streets and truck routesAll near-port streets in City ofLos Angeles

2005 City of Los Angeles (2005)

J. Wu et al. / Atmospheric Environment 43 (2009) 1962–19711964

Truck-prohibitedroadways

Non truck-prohibitedroadways

Assigned0.5% HDTfraction

1.HDT fractions at samplingsites were calculated andassigned to these roadwaysegments. 2.If there were >1 samplingsites at one location, wefollow a priority list:Caltrans > UCLA >LADOT > POLA

Roadways withthe same name asthe sampling sites(within 5 km)

Otherroadways

Truck-routes

Non-truckroutes

Assigned a5% HDTfraction

Assigned a2% HDTfraction

Freewaysand majorarterials

Minorarterials andcollectors

Assigned a1% HDTfraction

All freeways andmajor arterials inthe study region

Fig. 2. Flow chart of assigning fraction of heavy-duty trucks.

J. Wu et al. / Atmospheric Environment 43 (2009) 1962–1971 1965

distant roads more than 10-km away. However, due to a problemin programming, the original model established a 10-kmthreshold for ‘‘upwind’’ links only, but not for ‘‘downwind’’ links.The ‘‘upwind’’ and ‘‘downwind’’ links were used by the CALINE4to represent different wind, road, and receptor combinations.Therefore, the CALINE4 was found to significantly overestimatecontributions from distant links at communities where busytraffic activities were present more than 10-km away from thecenter of the communities (unpublished data). We modified themodel and incorporated a user-specified distance restriction forall the road segments in the study region (the CALINE4 codemodifications are available from the corresponding author).Sensitivity tests were conducted for the user-defined distancecriteria using a series of distances (3, 10, 25, and 35 km) at threeSouthern California communities with different traffic and land-use settings.

2.4. CALINE4 model simulations

Vehicle emission factors were obtained from the ARB’sEMFAC2007 vehicle emissions model (http://www.arb.ca.gov/msei/on-road/latest_version.htm). Paved road-dust emissions for PM2.5

were from Southern California in-roadway measurements (Fitz andBufalino, 2002). The EC fractions of exhaust PM2.5 emissions werebased on composite profiles from U.S. EPA 2006 SPECIATE profiles(U.S. EPA, 2006). The contributions of LDV and HDT emissions onfreeways and surface streets were estimated separately.

Meteorological data (temperature, and wind speed and direc-tion) in 2005 were obtained from the National Weather Service atthe Long Beach Air Port located at the eastern edge of the studyregion. Summarized mixing heights by season and hour wereobtained at the Los Angeles International Airport (20 km northwest

Table 2Relative contribution of traffic volumes on an average weekday and an averageweekend day to total weekly traffic volumes segregated by roadway type andvehicle type.

Vehicle Type Day-of-Week Freeways Surface streets

LDV Weekday 15.3% 15.0%LDV Weekend 11.9% 12.6%HDT Weekday 18.4% 17.6%HDT Weekend 4.0% 6.0%

of the study region) from the 1997 Southern California Ozone Study.We modeled hourly PM2.5 and EC concentrations at centroids ofeach residential parcel, BLK, and BG in the study region for onemonth in a cool season (January) and one month in a warm season(August) in 2005. Intra-seasonal meteorological patterns inSouthern California are not highly varied, making it reasonable touse a summer and a winter month to estimate seasonal exposures.

2.5. Population exposure

Population exposures were estimated by weighting modeledconcentrations by population data at three spatial scales (parcel,BLK, and BG) to examine if estimated exposures were significantlydifferent by spatial resolutions. Residential parcel boundaries andattribute data were obtained from the Los Angeles County Asses-sor’s Office and contained information on the type of residentialbuildings (single-family house, condominium, 2–4 units, 5 or moreunits). BLK and BG population data were obtained from 2000 USCensus Summary File 1 (SF1) and Summary File 3 (SF3), respec-tively. BG data contain housing structure information, includingowner-occupied, renter-occupied, detached/attached, and thenumber of units in each structure.

Parcel population was estimated by disaggregating a parcel’s BGcounts. Both BG and parcel data were assigned into three residentialcategories: residents in single-family residential (SFR), multi-familyresidential (MFR), and mobile homes (MH). Condominiums inparcel data were classified as MFR to correspond with BG data sincecensus respondents in condominiums would most likely identifytheir structure as attached or in a multi-unit building. BG popu-lation for each structure category was disaggregated into parcels ofthe same category using an area-based allocation method.

Population exposures are estimated for residents at their homeand, due to data limitations, do not take into consideration thatmost residents likely leave their residence over the course ofa typical day, and that they could spend time in multiplemicroenvironments.

2.6. Intake fraction

We estimated intake fraction to quantify the impact ofemissions from different types of vehicles and roadways on totalpopulation exposure. For inhalation of a primary pollutant, theiF can be expressed as

Intake FractionðiFÞ ¼ Population IntakeTotal Emissions

¼

RNTi

�PPi¼1ðCiðtÞQiðtÞÞ

�dt

R T2T1

EðtÞdt

T1 and T2 are the starting and ending times of the emission (s); P isthe number of people in the exposed population; Qi(t) is thebreathing rate for individual i at time t (m3 s�1); Ci(t) is the incre-mental concentration, attributable to a specific source at time t inthe breathing zone of individual i (mg m�3); and E(t) is that source’semissions at time t (g s�1). A nominal breathing rate of 20 m3 perday was employed, and the iFs were estimated based on estimatedexposures at parcel resolution.

3. Results

3.1. Traffic diurnal profiles

Fig. 3 shows traffic diurnal profiles used in this study, whichwere developed from the ARB study (Sullivan et al., 2004) and from

0.0

0.5

1.0

1.5

2.0

2.5

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23Hour

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23Hour

Ho

urly relative d

istrib

utio

n

Weekday LDV

Weekend LDV

Weekday HDT

Weekend HDT

Relative Diurnal Patterns of Traffic Counts on Surface Streets

Relative Diurnal Patterns of Traffic Counts on Freeways

0.0

0.5

1.0

1.5

2.0

2.5

Ho

urly relative d

istrib

utio

n

Weekday total

Weekend total

Weekday HDT T1

Weekday HDT T2

Weekday HDT T3

Weekday HDT T4

a

b

Note: all the results were adapted from the ARB study (Sullivan et al., 2004) except theHDT distributions on figure 3b, which came from the UCLA Traffic Count Study (Houston et al., 2008).

Fig. 3. Diurnal traffic count distribution on freeways and surface streets.

J. Wu et al. / Atmospheric Environment 43 (2009) 1962–19711966

the UCLA Traffic Count Study (Houston et al., 2008). LDVs peakedaround 7 AM and 5 PM on weekdays on both freeways and surfacestreets. No major peaks were observed for LDVs on weekends.Instead, LDV traffic gradually increased and reached the maximumcapacity from noon to early or mid-afternoon on freeways and atnoon time on surface streets. HDT activity peaked around latemorning to noon time and dropped sharply after mid-afternoon onfreeways on both weekdays and weekends. HDT traffic on localstreets near the ports was more variable than on nearby freeways.We grouped and summarized the data into four street types.Variations across these HDT diurnal profiles generally reflectsegment-level differences including commuter passenger traffic inthe morning and evening periods, its proximity to commercial,employment, or other activity centers, and its proximity to a terminal.

3.2. Sensitivity analysis of the distance criteria in the CALINE4model

Sensitivity tests of the distance criteria in the CALINE4 wereconducted for an urban area (Long Beach around our study region),a suburban area (Alpine in San Diego), and a rural area (Atascaderoin San Luis Obispo). Incorporating traffic within 10-, 25-, and 35-km

distances resulted in carbon monoxide concentrations 2, 3.4, and3.8 times of those using the 3-km distance at Long Beach.Compared to the 3-km distance results, concentrations estimatedusing the 35-km distance criteria almost quadrupled at Long Beach,doubled at Alpine, and increased 30% at Atascadero. The impact ofdistant roadways varied among the three communities due tovaried roadway and land-use settings. Long Beach was surroundedby high-density busy roads extending more than 35 km away;therefore, incorporating distant roads significantly increased esti-mated concentrations. In contrast, estimated concentrations inAtascadero did not change significantly extending the distancefrom 3 km to 35 km since there were sparse roadways and littletraffic around the community.

3.3. CALINE4 estimated pollutant concentrations at residentialparcels

In this study, we used a 3-km distance criterion to account forthe contribution of local traffic emissions only. Fig. 4 shows esti-mated CALINE4 modeled EC concentrations for residential parcelsand the relationship of parcel, BLK and BG boundaries in the studyregion. The estimated concentrations for parcels were right-

Fig. 4. Maps of EC concentrations (annual average) and spatial distribution of parcels, Census blocks and block groups.

J. Wu et al. / Atmospheric Environment 43 (2009) 1962–1971 1967

skewed. The PM2.5 concentrations ranged from 0.2 to 24.2 mg m�3,with a median of 3.3 mg m�3, a mean of 3.7 mg m�3, and a standarddeviation (SD) of 2.0 mg m�3. The EC concentrations ranged from0.02 to 3.8 mg m�3, with a median of 0.34 mg m�3, a mean of0.40 mg m�3, and a SD of 0.25 mg m�3. Annual average EC and PM2.5

concentrations were highly correlated, with Pearson’s correlationcoefficient approximately 0.98 for each of the three spatial scales.

Percentage contributions were calculated by roadway type,vehicle type, and season (Table 3). There was little variation byseason. On average, surface streets contributed a little more thanfreeways (52% vs. 48% for EC and 57% vs. 43% for PM2.5). LDVscontributed significantly more than HDTs for PM2.5 (70% vs. 30%),while HDTs contributed slightly more than LDVs for EC (51% vs.49%). The results indicated that HDTs are a major source of EC fromlocal traffic, even for the residential parcels located in areas withlow HDT fractions. This is mainly due to the much higher ECemission factor of HDTs, 26 times that of LDVs. Although vehicle

Table 3Percentage contributions of roadways and vehicles types to pollutantconcentrations.

Vehicle Roadway Pollutant Summer Winter

HDT Freeway EC 32% 31%LDV Freeway EC 17% 17%HDT Surface Streets EC 20% 20%LDV Surface Streets EC 31% 32%HDT Freeway PM2.5 19% 18%LDV Freeway PM2.5 25% 25%HDT Surface Streets PM2.5 12% 12%LDV Surface Streets PM2.5 45% 46%

kilometers traveled (VKT) by LDVs was 19 times that of HDTs, thehigh emission rate from HDTs resulted in 38% higher total ECemissions from HDTs than from LDVs.

Although freeways usually have higher traffic density thansurface streets, the traffic on arterials can contribute significantly tovehicle-related pollution and can be a major source of risk becauseof a denser coverage from arterials. Vehicle kilometers traveled perday was 14.9 � 106 on freeways and 18.9 � 106 on surface streets.Note that our traffic database only included relatively large surfacestreets with traffic count data, thus minor streets with no trafficcount information were not included in the above calculations. Theactual impact of local traffic could be even larger.

3.4. Concentrations and population exposures by parcel, block andblock group

Fig. 5 compares the spatial distribution of population density atdifferent spatial resolutions. There were 424 BGs, 4604 BLKs, and122,136 residential parcels with greater-than-zero population,respectively. Because residential properties are unevenly distrib-uted within BGs, parcel population estimates could provide moreprecise exposure estimates.

Table 4 shows a statistical summary of estimated PM2.5

concentrations and population-weighted exposures at differentresolutions. The results were grouped by the percentage of resi-dential areas contained by each BLK and BG to examine the impactof non-residential areas on estimated concentrations and expo-sures. EC data had a similar pattern as PM2.5, thus were not shown.The average concentrations were similar at the three spatial reso-lutions, but considerable differences existed in the upper tails of the

Fig. 5. 2000 population density by block group and parcels.

J. Wu et al. / Atmospheric Environment 43 (2009) 1962–19711968

estimated concentration distributions. Extreme concentrationswere observed at parcel resolution. The highest PM2.5 concentra-tion was 24.2, 24.0, and 17.8 mg m�3 at parcel, BLK, and BG reso-lutions, respectively. The standard deviation (SD) was 54%, 53%, and61% of the average concentrations at parcel, BLK, and BG resolu-tions, respectively. The non-residential sub-group of BLKs and BGshad higher mean concentrations and exposures than the residentialsub-group (4.4 mg m�3 vs. 3.2 mg m�3 at parcel resolution) perhaps

Table 4Comparison of PM2.5 concentrations estimated using parcel, BLK, and BG methodsby the type of BLK and BG regarding to (unit: mg m�3).

Concentration Exposure

Parcel BLK BG Parcel BLK BG

AllN 122,136 4604 424 The sameMin 0.2 0.3 0.5Max 24.2 24.0 17.8Std 2.0 2.0 2.3 N/AMean 3.7 3.8 3.8 3.8 3.8 3.750th percentile 3.3 3.5 3.3 3.5 3.4 3.490th percentile 5.9 5.8 5.9 5.8 5.7 5.695th percentile 7.2 7.1 8.0 6.9 6.8 7.598th percentile 9.2 9.6 11.2 9.0 8.8 9.5BLK and BG contained >50% residential area (residential)N 64,553 2040 210 The sameMin 0.2 0.3 0.5Max 11.2 10.4 9.8Std 1.6 1.5 1.4 N/AMean 3.2 3.3 3.2 3.3 3.3 3.350th percentile 3.0 3.1 3.0 3.1 3.1 3.090th percentile 5.4 5.3 5.1 5.1 5.1 5.095th percentile 6.0 5.9 5.7 5.8 5.8 5.798th percentile 7.1 6.9 6.4 6.7 6.6 6.2BLK and BG contained �50% residential area (non-residential)N 57,583 2564 214 The sameMin 0.2 0.3 0.5Max 24.2 24.0 17.8Std 2.2 2.3 2.4 N/AMean 4.1 4.2 3.9 4.2 4.2 3.850th percentile 3.7 3.7 3.4 3.8 3.7 3.490th percentile 6.7 6.5 6.1 6.4 6.3 5.795th percentile 8.7 8.4 8.5 8.3 8.1 7.898th percentile 10.6 11.1 12.3 10.5 10.2 9.7

because the former tended to be closer to freeways and majorarterials. All BGs in the non-residential sub-group with �98thpercentile concentrations were crossed by a freeway in the center,with clustered residential parcels at one side relatively away fromthe freeway. Using BG resolution will significantly overestimate theresidential population exposure in these types of BGs.

In addition to statistics over all BLKs and BGs, we also examinedvariations of estimated parcel-level concentrations within each BLKand BG. EC had a slightly higher spatial variation within a BLK anda BG than PM2.5, with the average SD (based on parcel estimates) 5%(ranged 0–250%) and 15% (ranged 0–230%) of the community meanEC concentration within a BLK and a BG, respectively. A residentialBLK (approximately 100–200 m wide) is typically bounded byroadways on all sides, and contains uniformly distributed parcelsalong the roads; thus the centroid of all parcels within a BLK is closeto the centroid of the BLK and the distances of the parcel centroidsto roadways do not differ much within a BLK. In comparison, a BG islarger than a BLK (typically 300–800 m wide), and BLKs are lessuniformly distributed in a BG than residential parcels in a BLK(Fig. 4b). Therefore, higher variations were observed in a BG than ina BLK.

Average population-weighted exposures at parcel resolutionwere 3.8 and 0.4 mg m�3 for PM2.5 and EC, respectively. Populatedexposures were uniformly higher in the upper percentiles at parcelresolution in both the residential and non-residential sub-groups.Exposures were similar to estimated concentrations at the 50thpercentile, but lower than the estimated concentrations at theupper percentiles (>90%), indicating that high pollution areas wereless populated. This is supported by further analysis showing that25% of parcels were located within 200 m of a major roadway(ADT> 50,000), but they contained only 13% of the total pop-ulation. Our results showed similar population exposure distribu-tions at the three spatial resolutions, indicating that BLKs and BGscan be used to estimate community mean residential exposure tolocal traffic emissions in the study region. There are three mainreasons for this finding. First, we excluded BLKs and BGs with noresidential parcels, which made the remaining entities moreuniform with regards to the spatial relationship to roadways.Second, we considered emissions from only freeways and majorsurface streets that form the boundaries of a majority of BGs.

J. Wu et al. / Atmospheric Environment 43 (2009) 1962–1971 1969

Although the centroids of BGs are usually farther away from majorroadways than the centroids of some parcels or BLKs within the BG,the average distance from the centroids of parcels and BLKs tomajor roadways is similar for that of the centroids of the BG. Third,areas adjacent to heavily-traveled roadways were less populatedthan those farther away. However, the variations within a BLK ora BG cannot be neglected. In general, a BG had 3 times higher SDthan a BLK, indicating that BG spatial resolution underestimatesexposure variability and the maximum individual exposureconcentration (compared to BLK or parcel spatial resolution).

3.5. Intake fraction

The iFs for PM2.5 were almost the same as those for EC; there-fore, we only show the iFs for PM2.5 in Fig. 6. The average iF forPM2.5 was 14 per million. Intake fraction in the winter time was 1.4times that in the summer time for these directly-emitted pollutantsfrom traffic. This is expected since the Los Angeles Basin experi-ences substantial surface inversions in the winter, during whichtraffic emissions will be trapped in the inversion layer resulting inhigh surface concentrations. Traffic on surface streets had an iF 1.4times of that on freeways, mainly because residences are generallycloser to surface streets than to freeways. Although there werea number of high-density apartments close to freeways, on averagepopulation density was higher near surface streets than that nearfreeways in this study region. The iF of LDVs was 1.1 times that ofHDTs on freeways, and 1.4 times that of HDTs on surface streets.This indicated that HDTs were concentrated in less populated areathan LDVs, and suggested the effectiveness of using truck prohibi-tions and routing to ensure heavy-duty vehicles travel as far awayfrom residential areas as possible.

4. Discussion

This study investigated the impact of local traffic on air pollutionexposures of PM2.5 and EC in communities adjacent to the ports ofLos Angeles and Long Beach. Contributions were separately esti-mated for traffic on freeways and on surface streets and for LDVsand HDTs. To our knowledge, this is the first study that examinedthe impact of local traffic on population air pollution exposure bytypes of vehicle and roadway. It is also the first study that evaluatedintake fractions of traffic emissions by types of vehicle androadway.

This study estimated population exposure solely from localtraffic emissions and did not account for other sources (e.g. shipsand port-related facilities, industrial sources) and regional back-ground contributions. The North Long Beach station operated bythe southern California Children’s Health Study showed an average

0.0E+00

5.0E-06

1.0E-05

1.5E-05

2.0E-05

2.5E-05

Freeway HDV Freeway LDV Surface HDV Surface LDV

In

take F

ractio

n o

f P

M2.5

SummerWinter

Fig. 6. Intake fraction of PM2.5 by types of vehicles and roadway and season.

PM2.5 and EC mass of 17.4 and 1.33 mg m�3 from 1994 to 2001(Peters et al., 2004). ARB measured an average of less than1.0 mg m�3 EC in particulate matter less than 10 mm (PM10) in theWilmington area from 2001 to 2002. The annual average concen-tration of daily 24-h PM2.5 measurements at the North Long Beachstation was 15.9 mg m�3 in 2005. Our results showed that in thestudy area, local traffic contributed approximately 22–24% to thetotal PM2.5 concentrations, and 30–40% to the total EC concentra-tions. Emissions from ships, port facilities, and regional backgroundsources (including traffic emission outside the study area)contributed over 75% to the total PM2.5 and 60–70% to the total ECconcentrations. EC have been measured as an indicator of on-roadvehicles, particular diesel trucks, in urban areas (Cyrys et al., 2003;Holguin et al., 2007; Lena et al., 2002). However, we need to becautious in using EC as a single marker for on-road diesel traffic ina complex environment with other sources.

We acknowledge the uncertainties of our results, including theemission factors used in this study, particularly for EC. ModeledPM2.5 and EC concentrations were highly correlated mainlybecause 1) we only modeled a single source – local traffic; and 2)EC emission rates were estimated based on PM2.5 emission factorsand composite profiles from the U.S. EPA 2006 SPECIATE profiles.We applied diurnal and day-of-week profiles from measurementsat two WIM stations to all freeway traffic, and from measure-ments at 30 surface street sites to most surface street traffic inour study region. The measurements (conducted in 2001 and2002) may be outdated. Furthermore, in the EMFAC model weused a general profile of speed fractions by vehicle type in the LosAngeles County, which may be inapplicable to specific roads andtimes in the study region. In addition, traffic emissions can bemuch higher than estimated values under conditions of acceler-ation from stop lights and intersections, and idling on congestedfreeways and surface streets. Future improvement in emissioninventories of traffic-related pollutants will refine our estimatedexposures.

Despite the advantage of high spatial and temporal resolutions,there are some unrealistic assumptions in the Gaussian dispersionmodel. For example, the CALINE4 model does not handle low windspeed situations well. The model also tends to overpredict concen-trations for parallel-to-road wind directions. We assumed all theroad segments were at grade in the CALINE4 simulation. Althoughthe majority of the road segments fit this typical configuration, wedid not consider tunnels and elevated freeways due to input datalimitations. We also did not account for the impact from builtenvironment (e.g. skyscrapers, smaller buildings, and sound walls),vegetation, and micro-level meteorology (e.g. wind). Although thestudy region did not generally have deep street canyons (buildingheight/road width >2.0), ideally we would consider building andland-use effect and use more resolved meteorological data.

We only considered residential populations and assumed thatpopulation exposures occur at residential locations due to the lackof population time-activity data. Accounting for people’s exposurein other microenvironments (e.g. workplace and in-transit) mightyield different and more heterogeneous exposure levels. Forinstance, residences are usually uniformly distributed within a BLK;however, other places (e.g. restaurants, stores, and commercial andoffice buildings) may have different spatial patterns and proximityto freeways and major arterials.

Our estimated iFs were consistent with limited prior research,which showed a range of 1–77 per million for iFs of mobile sourceemissions. Greco et al. (2007) estimated an iF of 12 per million forprimary PM2.5 from roadway emissions in the Boston Metro Corearea using the CAL3QHCR line source model. They assumed a fixedartificial traffic count and a uniform street configuration over all thesegments. Since no traffic composition data were applied, they did

J. Wu et al. / Atmospheric Environment 43 (2009) 1962–19711970

not examine iFs by roadway type and vehicle type. In this study, wederived refined iF estimates by incorporating actual traffic fleetcomposition and roadway configurations using available data, aswell as parcel-level population estimates. Marshall et al. (2003)estimated an iF of 77 per million (after normalizing the breathingrate to 20 m3 d�1) for primary pollutants from motor vehicleemissions in the South Coast Air Basin, using regional air qualitymeasurement data and population-level time-activity patterns.Their estimated iF was significantly higher than ours (77 vs. 14 permillion), likely due to two reasons: 1) they examined total pollutantconcentrations from both local and distant traffic emissions, whilewe only investigated contributions from local traffic; and 2)time-activity patterns, which included time in vehicles with highexposure, were considered.

5. Conclusions

We found the average PM2.5 and EC exposures due to localtraffic were 3.8 and 0.4 mg m�3 in the study region, which wereapproximately 22–24% and 30–40% of the total PM2.5 and ECconcentrations in the area, respectively. On average, surface streetscontributed a little more than freeways (52% vs. 48% for EC and 57%and 43% for PM2.5). LDVs contributed significantly more than HDTsfor PM2.5 (70% vs. 30%), but slightly less than HDTs for EC (49% vs.51%). The minimum and maximum concentrations were observedat parcel resolution, followed closely by the BLK resolution. Theparcel resolution also estimated higher upper tails (�90%) of theconcentration distributions in the residential sub-group. Pop-ulation mean exposure varied little at parcel, BLK, and BG spatialresolutions, indicating that BLK and BG can be used to estimatecommunity mean home residential exposure to local traffic emis-sions in our study region. The standard deviation of pollutantconcentrations was relatively small within a BLK but much largerwithin a BG. The intake fraction was in the range of 1.0–2.2 � 10�5

by vehicle type, roadway type, and season. Traffic on surface streetshad an iF 1.4 times of that on freeways since residences aregenerally closer to surface streets than to freeways. Our modeledresults showed the effectiveness of restricting heavy-duty vehiclesfrom traveling in residential areas, and that LDV emissions makea significant contribution to residential exposure to vehicle-relatedemissions from local traffic.

Acknowledgements

We acknowledge the University of California TransportationCenter for funding the UCLA Traffic Count Study. We acknowledgesupport from the National Institute of Environmental HealthScience (2P01ES011627-06 and 5P30ES007048). We also thankMark Solem at the California Department of Transportation andNathan Neumann of the City of Los Angeles Bureau of Engineeringfor providing traffic count data. Sue Lai of the Port of Los Angelesand Leah Brooks of McGill University provided assistance in usingport traffic count and parcel data, respectively.

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