An exposure assessment of [PM.sub.10] from a major highway interchange: are children in nearby schools at risk? (Features).

Introduction

As a subfield of public health, environmental health is concerned with the evaluation of the effects of the environment on human well-being (Wagener, Selevan, & Sexton, 1995). In recent years, to determine the extent to which environmental agents play a role in disease, practitioners of environmental health have rapidly evolved the tools necessary to conduct meaningful risk analysis. Among those is the ability to accurately quantify exposure; these data can subsequently be used to produce estimates of risk from environmental hazards (Gochfeld & Goldstein, 1999), estimates that may be used to aid public-health policy decisions.

One such environmental hazard that has been identified is ambient concentrations of air pollutants. Over the last several years, literature on the consequences of exposure to a variety of air pollutants has grown considerably Studies conducted both in the United States and abroad consistently show that exposure to ambient concentrations of pollutants such as ozone, sulfates, and particulates may cause significant morbidity and mortality numbers. For example, Gauderman et al. (2000) showed that exposures to air pollution in the Southern California area were related to a significant decrease in lung function in a fourth-grade cohort of children studied over a four-year period.

A growing body of evidence points toward particulate matter, small particles less than 10 micrometers in diameter ([PM.sub.10]), as a significant pollutant in terms of producing negative health effects in those exposed (Scarlett, Abott, Peacock, Strachan, & Anderson, 1996). Historically, the majority of studies have focused on acute exposure; however, chronic exposure may in fact be more important to overall public health. Those long-term studies that have been done have generally observed that morbidity and mortality are significantly associated with [PM.sub.10] exposure, even at relatively low doses (Pope, 2000). For instance, Abbey et al. (1999) determined that long-term exposure to ambient concentrations of [PM.sub.10] was associated with an increased risk of premature death--results that generally agree with those of other, similar studies, such as the Six Cities Study (Dockery et al., 1993) and the American Cancer Society Study (Pope, Dockery, Spengler, & Raizenne, 1991).

Background

The Los Angeles Unified School District (LAUSD), located in Southern California, encompasses a large range of geographic, racial, and socioeconomic areas. With over 700,000 students, it is the second largest school district in the United States. More than 70 percent of the student population falls into a variety of ethnicities, although it is predominantly Hispanic (LAUSD, 2000).

In response to community concerns, during April 2000 the Office of Environmental Health and Safety began an environmental exposure study to determine the degree to which [PM.sub.10] load is imposed on schoolchildren from exposure to nearby, heavy-traffic, limited-access roadways. Such roadways are interesting in exposure analysis for several reasons, but primarily because they represent a significant portion of vehicle miles traveled and are therefore a predominant source of exposure in the urban community (Lamoree & Turner, 1999).

Traffic-related air pollution and associated adverse health effects have been well documented. In one broad European study, estimates of public-health impacts from exposure to traffic-related pollution determined that 3 percent of all mortality could be attributed yearly to motor-vehicle exposure (Kunzil et al., 2000). Also, several recent studies have indicated that close proximity to roadways, along with high vehicle density, can lead to significant negative health effects. Ciccone et al. showed that exposure to roadway traffic may have adverse health effects on the respiratory system of children living in areas described as having "high" truck-traffic density (Ciccone et al., 1998). Also Osterlee, Drijver, Lebert, and Brunekreef (1996) have examined the health risk of living along "busy" streets. They conclude that children who live along streets with a higher traffic volume have an increased risk of developing chronic respiratory symptoms compared with a control group (Osterlee et al.). These studies pro vide evidence that not all transportation facilities benefit everyone. Some areas, predominantly low-income communities and communities of color, may be exposed to unacceptable levels of pollution from mobile sources (Forkenbrock & Schweitzer, 1999). Pollution of this type places those exposed at unacceptable levels of risks for respiratory disease.

Several risk factors for these chronic respiratory symptoms have been identified. Children, and primarily minority children, have shown disproportionate rates of such chronic conditions as asthma (Malveaux & Fletcher-Vincent, 1995), the most common of all childhood chronic illnesses (Centers for Disease Control and Prevention, 1996) and a condition shown to be traffic related (Guo et al., 1999). For a variety of reasons, children may be more susceptible to environmental exposures than are adults (Goldman, 1995). Lubin and Lewis (1995) describe several of the factors, which are often the result of developmental changes in children and differences in childhood behaviors, that contribute to increased rates of illness from environmental exposure. Some of these factors are rapid lung growth, increased respiration rates, and high rates of activity (Lubin & Lewis). For example, it has been demonstrated that children, principally younger children, deposit on a breathing surface a localized dose of particulate matter that is approximately three times higher than the doses adults deposit when exposed to the same concentration of the pollutant in the ambient environment (Musante & Martonen, 2000).

Although all children face higher risks of illness from environmental exposure to pollutants, minority children may face even greater dangers, possibly the greatest of any segment of society (Mott, 1995). For instance, in a meta-analysis study conducted by Metzger, Delgadao, and Herrell (1995), it was shown that Hispanics were more than twice as likely as whites to live in areas with elevated particulate matter levels. These data confirm results from other studies that have found race to be the most important variable in determining such factors as distance from residence to a source of detrimental environmental exposure (Szasz, Meuser, Aronson, & Fukurai, 1993).

Purpose

To evaluate the increase in particulate load ([PM.sub.10]) suffered by elementary school students who attend school in proximity to a major highway interchange, the authors conducted a computer-modeled exposure assessment. The purpose of this assessment was to determine if the students in question received a daily exposure of [PM.sub.10] from this local roadway source at levels that would be expected to cause negative health effects.

Four schools were selected for the study: Soto Elementary School, Sunrise Elementary School, Euclid Elementary School, and Lorena Elementary School. Each of these East Los Angeles Schools is located within 500 meters of a major limited-access highway, the East Los Angeles Interchange. Three of the schools, Soto Elementary, Lorena Elementary, and Sunrise Elementary are within 150 meters of the roadway In each school, a majority of students belong to ethnic minorities (more than 90 percent Hispanic).

Methods

In urban communities, highway vehicles contribute significantly to localized concentrations of air contaminants. Typically, emissions generated from these sources are characterized by the rate at which the pollutant is emitted during the course of travel and the number of vehicles traversing the roadway network.

Several processes govern the formation of pollutants generated from the operation of motor vehicles. The U.S. Environmental Protection Agency (U.S. EPA) and the California Environmental Protection Agency (Cal/EPA) maintain large data collection programs to quantify the rate at which pollutants are emitted within a defined vehicle class (e.g., passenger cars, pickup trucks, and large fleet vehicles) and within a technology group (i.e., catalyst, non-catalyst, and diesel). Generally emissions are reported in grams (g) of pollutant per vehicle mile of travel (VMT).

In addition to direct-source emissions, secondary emissions such as the re-entrainment (re-suspension) of paved roadway dust were considered, as they contribute to the generation of airborne particulates. These emissions originate from the deposition and subsequent removal of loose material (e.g., dirt and related debris) deposited on the roadway surface. U.S. EPA reports that the re-entrainment of roadway dust is often a "major" source of atmospheric particulate matter (U.S. EPA Office of Air Quality Planning and Standards, 1995a).

To determine traffic volumes for individual roadway segments, an assessment that identifies the number of vehicles is required. Traffic volumes and activity data can be estimated either through a review of previously documented vehicle counts or through direct observations. Direct observations entail the application of either manual or automated machine surveys. Once collected, these data are incorporated into a dispersion analysis to quantify downwind concentrations, which can be compared to establish exposure thresholds and assess community exposure. In this modeling exercise, the previously documented vehicle count method was used. Following is a discussion of the assessment process for the dispersion analysis.

Assessment Protocol

To determine the contribution of contaminant emissions generated from on-road motor vehicles, the analysis incorporated all relevant assessment methodologies offered under regulatory guidance. Vehicle fleet distributions were based on the mix conversion profile recommended by the Institute of Transportation Studies at the University of California, Davis (Kear, Dougherty, Lee, Eisinger, & Niemeier, 1998). This approach was used to account for increased truck activity traversing the interchange of U.S. Route 5 and State Route 60. Table 1 gives the mobile fleet classifications examined in this assessment.

On-road emission factors reflect the rate at which a pollutant is emitted by a specific operational mode or process. Emission factors are generated from a series of computer-based programs whereby route speed, ambient temperature, vehicle mix, and prediction year are input into the model to produce an emission rate for vehicles traveling along a roadway segment. In California, several programs have been developed to account for the unique emission standards imposed on the California fleet. These programs have resulted in a series of models, the latest of which are EMFAC7F for microscale analysis (i.e., roadways links or segments) and EMFAC7G for regional-source emission inventories (Cal/EPA, California Air Resources Board, 1993a, 1996).

Because of the nature and extent of the roadway configuration used in this assessment, emission factors were generated from the EMFAC7G database.

For particulate re-entrainment, emission estimates were developed from a guidance promulgated by U.S. EPA (U.S. EPA Office of Air Quality Planning and Standards, 1995b). Discrete input variables, including average vehicle weight and road surface silt loading values, were obtained from the California Air Resources Board (Cal/EPA, California Air Resources Board, 1993b) and incorporated into the predictive emission model for vehicles traveling within the South Coast Air Basin.

To determine hourly traffic volumes, the assessment employed traffic count values published by the California Department of Transportation (CalTrans) Traffic and Vehicular Data Systems Unit (State of California, Department of Transportation, Traffic Operations Division, 2000).

Exposure Quantification

Knowledge of the pollutant's airborne concentration is integral to the assessment of ambient exposure. Two methods may be used to obtain these concentration values. One approach is air monitoring, which requires the collection and analysis of ambient air over a defined period of interest. Although air sampling can reveal ambient pollutant levels, it cannot identify the source or origin of the compound collected during the sampling exercise. A second method uses a predictive modeling approach, or mathematical simulation, to calculate the dispersion of pollutants and their relative concentrations on a given population. In addition, a dispersion analysis can be designed to identify individual compounds generated from a specific source and predict their downwind impact on the adjoining community.

To exemplify the viability of a predictive modeling approach, the U.S. EPA Office of Air Quality Planning and Standards (OAQPS) reports that "modeling is the preferred method" for assessing emissions generated from new and existing sources and has the unique capability of predicting impacts from "sources that do not yet exist." Simply, for a determination of potential environmental impairment associated with both existing and future operations, modeling is the "primary analytical tool" (U.S. EPA Office of Air Quality Planning and Standards, 1997).

Although dispersion modeling is the appropriate analytical approach to access pollutants generated from a variety of sources, there are a number of approved "guideline" models available to quantify pollutant concentrations generated from roadways. Notwithstanding, the Industrial Source Complex Short Term (ISCST3) model was selected as the preferred model because of its robust architecture and ability to allow the user to incorporate source and operational profiles to effectively assess the downwind extent of particulate emissions generated from the roadway network (U.S. EPA Office of Air Quality Planning and Standards, 1995b).

ISCST3 is an OAQPS-preferred model for assessing pollutant concentrations from a wide variety of emission sources (i.e., point, area, and volume). It uses Gaussian dispersion algorithms to account for the effects of buoyancy-induced dispersion; treats receptors in flat, intermediate, and complex terrain; allows for one-hour to annual averaging times; and measures continuous air emissions. ISCST3 is capable of quantifying pollutant emissions generated from multiple sources and can accommodate both static emission rates and those that reflect discrete operational periods unique to the source under consideration. The model offers additional flexibility by allowing the user to assign initial vertical and lateral dispersion parameters for a source that is representative of a localized mobile fleet. For this assessment, the volume source algorithm was used to model the emissions generated along the various roadway segments.

To address the spatial distribution of emitted sources and accommodate the detailed configuration of the roadway interchange, a grid spacing of 30 meters was used. This distance was selected to minimize the computational effort associated with a compressed spatial design (i.e., length of the line source by its width) and ensure that the source density achieved was sufficient to preserve the horizontal geometry of the line source configuration. Therefore, 484 sources were identified and programmed into the dispersion model.

Vertical (sigma z) and horizontal (sigma y) dispersion parameters were developed according to several regulatory methodologies. Sigma z values were generated by approximating mixing-zone residence time and quantifying the initial vertical term as performed in the U.S. EPA guideline model Caline3 (Benson, 1979). Sigma y parameters were generated by dividing the source separation distance by a standard deviation of 2.15 (U.S. EPA Office of Air Quality Planning and Standards, 1995b).

To determine contaminant impacts for individuals attending various schools located in proximity to the freeway interchange, the model's scalar option was invoked to predict ground-level concentrations for emissions generated between the hours of 8:00 a.m. and 6:00 p.m. (These hours were selected to account for children at school during class hours and attending after-school programs.)

Digitized terrain data processed by the U.S. Geological Survey (USGS, 1993) were incorporated into the modeling exercise to allow consideration of local terrain variations for all source/receptor combinations. The USGS data set is cast on a Universal Transverse Mercator (UTM) Cartesian coordinate base consisting of 7.5-minute digital elevation arrays with 30-meter grid intervals. Receptor coordinates were proximally located within the center of each school facility.

Dispersion models are sensitive to individual meteorological parameters such as wind speed, stability class, mixing height, and temperature. U.S. EPA recommends that meteorological data used as input into dispersion models be selected on the basis of relative spatial and temporal conditions that exist in the area of concern (e.g., micro, middle, and neighborhood scales) (U.S. EPA Office of Air Quality Planning and Standards, 1997). As a result, hourly surface weather data from the South Coast Air Quality Management Districts downtown Los Angeles monitoring station (the closest station to the study site) were incorporated into the modeling exercise to represent local weather conditions and prevailing winds (South Coast Air Quality Management District, 1981).

Emission Calculations

As previously noted, direct emission estimates were obtained from the EMFAC7G database. Re-entrained emission rates were derived by the following empirical formula, where

Re-entrainment Emission Rate (g/mile) = (Particulate [PM.sub.10] Base Emission Factor) x [(Road Surface Silt Loading/2).sup.0.65] x [(Gross Vehicle Weight/3).sup.1.5].

Total roadway emissions for input into the dispersion model were then calculated as follows: Roadway Mass Emission Rate (g/sec) = [(Direct Emission Rate + Re-entrainment Emission Rate x Traffic Volume)/(1609.3 meters/mile x (3600 sec/hr)] x (Link Length).

Model Variables

The following variables, determined from regulatory guidance documents as previously described, were input into the modeling exercise:

Particulate ([PM.sub.10]) Base Emission Factor: 7.3 g/mile

Road Surface Silt Loading: 0.02 g/[m.sup.2]

Gross Vehicle Weight: 2.7 tons

Particulate ([PM.sub.10]) Mass Emission Rate: 0.06 g/mile

Particulate ([PM.sub.10]) Re-entrainment Mass Emission Rate: 0.31 g/mile

Results

Traffic

Traffic volumes ranged from 24 to 10,666 vehicles per hour for the various links identified in the modeling domain. A total of 46 links were used to characterize the roadway network. A link is identified as a discrete roadway segment with a specified number of vehicles per hour. Link lengths ranged from 30.4 to 759.1 meters.

Source Emission Rates

Direct and re-entrained emission rates were calculated independently for each 46 links and are listed in Table 2. Included in Table 2 are link lengths; a source volume (vehicles per hour) for each link; and emission rates for direct emissions (DE), re-entrained emissions (RE), and total emissions. Links are identified by route number (5 or 60) and direction of vehicle flow: E (east), W (west), N (north), S (south), or combined NS (north and south). Link number (e.g., 60-W1, 5-N2) indicates a specific stretch of roadway with a discrete vehicle volume. The summation of all links gave a total emission rate of 2.42 g/sec (Table 3).

Particulate Concentrations at Receptor Sites

Results of the dispersion analysis revealed that average 24-hour particulate concentrations (assuming a 10-hour school-based exposure duration) were predicted to be 10.45, 14.58, 5.78, and 8.27 micrograms per cubic meter [mu]g/[m.sup.3]) for Soto Street Elementary School, Sunrise Street Elementary School, Euclid Street Elementary School, and Lorena Street Elementary School, respectively Figure 1 represents the roadway configuration and the school locations; the schools, from west to east, are Soto Street Elementary Sunrise Elementary, Euclid Elementary, and Lorena Elementary School. Figure 1 also shows isopleths, indicating areas of same exposure concentrations.

In addition, results of the modeling exercise indicate a trend for increased emissions at school locations in closer proximity to the traffic source. One exception, however, was noted at Sunrise Elementary School which experienced higher emission rates than Soto Elementary School although it is located some 25 meters farther from the source of particulate emissions. Figure 2 shows distance from the roadway source to each school verses exposure concentration.

Discussion

In contrast to other projects, this study focused on assessing exposure to [PM.sub.10] from one specific source (a nearby roadway) on a population of children at various school sites in an East Los Angeles community Other than the home, school is the most important environment for children (Smedje, Norback, & Edling, 1997). School-based exposure to environmental contaminants may be intrinsic, emanating from the school itself, or extrinsic, emanating from an off-site source. A great deal of time, money, and attention are devoted to intrinsic exposures. This focus occurs for a variety of reasons, including ease of identification, perceived degree of hazard (as with asbestos and lead issues), and ease of ability to resolve intrinsic exposure sources. Extrinsic sources are often much more difficult to identify, quantify and remedy and often, fewer resources applied, even though time spent in the school-based environment has been previously described as "high exposure" for many pollutants originating off site (Bea rer, 1995).

Recent studies have provided evidence that schools may in fact be an area of concern. In one study, conducted in the Netherlands, researchers determined that traffic intensity measured near school grounds was found to be significantly associated with chronic respiratory symptoms (Van Vliet et al., 1997). In the Netherlands study, the traffic intensity was 80,000-125,000 vehicles per day including 8,000-17,500 trucks, volumes lower than those seen at the East Los Angeles sites in this study This comparison is particularly troubling, as at least one recent study has indicated that increases of just 25,000 vehicles per day passing by schools on a main road were associated with a significant decrease--0.71 percent (95 percent confidence interval = 0.33 to 1.08)--in the lung function of 10-year-olds (Wjst et al., 1993). The authors have determined approximately 225,000 vehicles per day as an upper estimate on certain links near the schools in this study which would extrapolate to a decrease in lung function of 6.3 9 percent for exposed children.

Although the authors determined the [PM.sub.10] exposure at Soto Elementary School and Sunset Elementary School to be approximately 10 [mu]g/[m.sup.3] and 15 [mu]g/[m.sup.3], respectively it is important to remember that these figures are intended to reflect not total ambient exposure, but the increase over ambient exposure in the specific "neighborhood environment." It is also important to note that although this exposure level is substantially lower than the federal guidelines for particulate matter (150 [mu]g/[m.sup.3]) and the California state guidelines (50 [mu]g/[m.sup.3]) (for a 24-hour period), recent studies have indicated that increases of [PM.sub.10] levels seen at these schools have been shown to cause significant negative health effects. Schwartz (2000) has demonstrated that increases in airborne particles of 10 [mu]g/[m.sup.3] were significantly associated with increases in daily deaths (0.67 percent for a 10 [mu]g/[m.sup.3] increase; 95 percent CI = 0.52-0.81). Daniels, Dominici, Samet, and Zeg ler (2000) analyzed [PM.sub.10] in a daily time-series study of the 20 largest U.S. cities and had similar results: 0.69 percent mortality per increase of 10 [mu]g/[m.sup.3] (95 percent CI = 0.40-0.98). Also, the Committee of the Environmental and Occupational Health Assembly of the American Thoracic Society (1996) reports increases in asthma severity of 3 percent for each increase of 10 [mu]g/[m.sup.3] [PM.sub.10]. Norris et al. (1999) have found an increase in emergency room visits (relative risk = 1.15; 95 percent CI = 1.08-1.23) with a change of 11 [mu]g/[m.sup.3].

Therefore, when schoolchildren are exposed to the levels of [PM.sub.10] seen at Soto and Sunrise Elementary schools, relevant and predictable increased levels of respiratory disease can be expected. Although levels at Euclid and Lorena Elementary Schools were lower, owing to the greater distance of those schools from the roadway studies indicate that there may not be a no-effects level for [PM.sub.10], because the mortality exposure-response relation may be linear (Pope et al., 1991), at least for certain sensitive populations. Accordingly it would be inappropriate to discount potential health effects for children attending these schools; any [PM.sub.10] exposure may play a role in increased illness.

Of additional concern for schoolchildren exposed to environmental pollutants at doses expected to cause respiratory illness is the ability of the children to attend regularly and succeed in academic activities. Several recent studies have concluded that schoolchildren with asthma show decreased success with schoolwork and have increased behavioral problems in the classroom, compared with well children. Also, it is extremely difficult to succeed in school without good attendance. Studies that have compared the absentee rate for asthmatic children with that of healthy children have shown that asthmatics miss three times the amount of school (Fowler, Davenport, & Garg, 1992). In addition, Diette et al. (2000) have found that children with asthma are more likely to be awakened at night with negative respiratory symptoms, the result of which is increased absences from school and reduced academic performance. Although there may be several reasons for reduced academic performance in asthmatic children, increased beh avioral problems is an important factor for schools. A 1995 study found that the severity of asthma in children was related to higher rates of emotional and behavioral problems, which frequently manifested as difficulty performing in the school environment (Bussing, Halfon, Benjamin, & Wells, 1995).

Although [PM.sub.10] from roadway sources is an important health hazard, there are several options for limiting the problem. First is source reduction. Generating a smaller quantity of [PM.sub.10] from roadway source emissions would greatly reduce its impact on the health and safety of those exposed. Recently the California Air Resources Board and U.S. EPA have set into motion new rules governing the amount of [PM.sub.10] that can be emitted from diesel-fueled vehicles, a major roadway contributor of particulates. This new rule will force diesel-fueled vehicles to use less polluting processes, such as combustion of low-sulfur fuels, which can reduce the amount of [PM.sub.10] produced by up to 95 percent. U.S. EPA estimates that with this kind of reduction in diesel-generated particulates, 8,300 premature deaths can be prevented, as can 9,500 hospitalizations yearly and approximately 360,000 cases of childhood asthma (U.S. EPA Air and Radiation Office of Transportation and Air Quality, 2000). It is important, however, to note that direct emissions from mobile sources produce a smaller fraction of the total [PM.sub.10] roadway contribution than does re-entrainment, the leading source. Thus, mobile-source reduction methods of pollution control will have an only partially favorable outcome.

Other methods are available to reduce the incidence of disease produced by [PM.sub.10]. Along with reduction of pollutants at the source, the placement of new school facilities as far from major roadways as possible can reduce risk. Although there are currently no state or federal mandates for the distance schools must be placed from roadways, LAUSD considers distance to polluting sources before any new school can be built. The authors' own studies, as well as those of others (English et al., 1999), suggest a minimum distance of 200 meters from high-volume highways and other heavy-use transportation corridors. This suggestion is not meant to imply that an arbitrary distance makes children safe. Other factors such as meteorological conditions, vehicular volume, and source orientation, may play an equally important role in source-receptor interactions. Taking these factors into consideration, greater distance will inevitably reduce exposure. In addition, it is important to note that receptor elevation appears to play a noteworthy role in emission levels. Although Sunrise Elementary School is some 25 meters further from the traffic source than is Soto Street Elementary School, it is situated at an elevation of approximately 70 feet above the roadway. Sunrise Elementary School experienced a 71 percent increase in emission levels over Soto Street Elementary School, which is situated at grade to the source. Thus, the authors believe that elevation from source to receptor is an area that should be explored in greater detail in later studies.

Often, with limited real estate space available for new school construction in congested urban environments, greater distance from roadway to school is not possible. Thus, locating new schools near highways with lower traffic volumes is an important consideration. Additional studies comparing traffic volumes to health effects are needed to accurately recommend safe volume-receptor interactions. Currently, the South Coast Air Quality Management District (SCAQMD) is conducting a study using monitored field data in the same vicinity this study was conducted in. An evaluation of the SCAQMD study, along with this one, may later aid in understanding which variables are most appropriate to examine, may allow for more specific recommendations, and will give comparison results between monitored and modeled data.

Finally, because of the large amount of time children spend in the classroom, engineering controls may be the most important method to reduce exposure. By appropriately altering ventilation systems, for example through the addition of higher-efficiency filters at schools with unacceptable exposures, the risk can, to some degree, be mitigated.

Conclusion

It is clear that exposure to particulate matter at levels well below limits set by state and federal guidelines can lead to adverse respiratory health effects. These adverse effects are particularly evident in children and even more so in minority children. It is also clear that many of these children spend a great deal of time in environments, such as schools, that have been demonstrated to be sites of significant exposure levels. The authors have documented higher exposure levels at schools that are situated in proximity to major roadway networks. These exposure levels have been shown in other studies to cause serious and predictable levels of negative health effects. Finally, this study supports the argument that the location in which schools are placed is an important public-health consideration.

[FIGURE 1 OMITTED]

[FIGURE 2 OMITTED]

TABLE 1

Adjusted On-road Mobile Fleet Mix

Vehicle Class                  Abbreviation  Percentage

Light-duty auto                     LDA         73.2
Light-duty truck                    LDT         12.8
Medium-duty truck                   MDT          2.0
Light heavy truck (gasoline)       LHTG          2.0
Light heavy truck (diesel)         LHTD          0.6
Medium heavy truck (gasoline)      MHTG          2.7
Medium heavy truck (diesel)        MHTD          3.4
Heavy heavy truck                   HHT          2.4
Motorcycle                          MCY          0.9
TABLE 2

Emissions and Re-entrainment Rates for Sections of the East Los Angeles
Interchange

Link     Length   Volume             Emissions            Re-entrainment
Section    (m)     (VPH)                  Rate  Rate ([micro]g/[m.sup.3]
                          ([micro]g/[m.sup.3])

60-W1     759.1   3970.6             0.0307                     0.1601
60-W2     394.7   1962.3             0.00789                    0.0418
60-W3     668.0   3916.7             0.0266                     0.141
60-W4     151.8   1954.4             0.00302                    0.016
60-W5     485.8    729.4             0.00361                    0.0191
60-W6     182.2   1225.0             0.00227                    0.012
60-W7     425.1    220.8             0.000956                   0.00506
60-W8     334.0   1004.2             0.00342                    0.0181
60-W9     151.8    179.2             0.000277                   0.00147

60-E1     546.6   3529.4             0.0196                     0.104
60-E2     182.2   2000.0             0.00371                    0.0196
60-E3     273.3    295.8             0.000823                   0.00436
60-E4     394.7   2204.2             0.00886                    0.0469
60-E5     182.2   2500.0             0.00464                    0.0264
60-E6     576.9   4500.0             0.0264                     0.14
60-E7     455.5   3850.0             0.0179                     0.0946
60-E8     242.9    316.7             0.000783                   0.00415
60-E9     242.9    170.8             0.000423                   0.00224
60-E10    182.2    141.7             0.000263                   0.00139

5-NS1     364.4  10666.7             0.0396                     0.21
5-NS2     182.2  10583.3             0.0196                     0.104
5-NS3     212.5   6708.3             0.0145                     0.0769
5-N54     364.4     54.2             0.000201                   0.00106
5-NS5    1184.2   6587.5             0.0794                     0.421

5-N1      212.5     91.7             0.000198                   0.00105
5-N1      394.7   3958.3             0.0159                     0.0842
5-N2      303.6   1829.2             0.00566                    0.0299
5-N3      637.6   2129.2             0.0138                     0.0732
5-N4      364.4   2858.5             0.0106                     0.0562
5-N5      455.5    200.0             0.000928                   0.00419
5-N6      516.2     41.7             0.000219                   0.00116

5-S1      455.5   2329.2             0.0108                     0.0572
5-S2      303.6    695.8             0.00215                    0.0114
5-S3      273.3    154.2             0.000429                   0.00227
5-S4      182.2    541.7             0.00101                    0.00532
5-S5      303.6    416.7             0.00129                    0.00682
5-S6      273.3    337.5             0.000939                   0.00497
5-S7      151.8    125.0             0.000193                   0.00102
5-S8      242.9    170.8             0.000423                   0.00224
5-S9       60.7   1333.3             0.000824                   0.00436
5-S10      30.4    458.3             0.000142                   0.00751
5-S11      91.1    958.3             0.000889                   0.00471
5-S12     30.40    833.3             0.000258                   0.00137
5-S13     91.10   1541.7             0.00143                    0.00757
5-S14      91.1     32.5             0.0000302                  0.00016
5-S15      91.1     24.2             0.0000225                  0.000119

Link                    Total
Section  ([micro]g/[m.sup.3])


60-W1               0.1900
60-W2               0.0496
60-W3               0.168
60-W4               0.019
60-W5               0.0227
60-W6               0.0143
60-W7               0.00602
60-W8               0.0215
60-W9               0.00174

60-E1               0.124
60-E2               0.0234
60-E3               0.00518
60-E4               0.0558
60-E5               0.0292
60-E6               0.166
60-E7               0.112
60-E8               0.00493
60-E9               0.00266
60-E10              0.00166

5-NS1               0.249
5-NS2               0.124
5-NS3               0.0914
5-N54               0.00127
5-NS5               0.5

5-N1                0.00125
5-N1                0.1
5-N2                0.0356
5-N3                0.087
5-N4                0.0668
5-N5                0.00584
5-N6                0.00138

5-S1                0.068
5-S2                0.0135
5-S3                0.0027
5-S4                0.00633
5-S5                0.00811
5-S6                0.00591
5-S7                0.00122
5-S8                0.00266
5-S9                0.00519
5-S10               0.000893
5-S11               0.0056
5-S12               0.00162
5-S13               0.009
5-S14               0.00019
5-S15               0.000141

VPH = vehicles per hour.
Table 3

Emission Rates-Totals for All Roadway Links


Re-entrainment rate   2.04 g/sec
Direct emission rate  0.3836 g/sec
Total emissions       2.42 g/sec

Acknowledgements: The authors would like to thank Dr. John Schillinger, Dr. Thomas Hatfield, and Tracy Boobar for their valuable editorial input.

REFERENCES

Abbey D.E., Nishino, N., McDonnell, W.F., Burchette, R.J., Knutsen, S.F., Beeson, W.L., & Yang, J.X. (1999). Long-term inhalable particles and other air pollutants related to mortality in non-smokers. American Journal of Respiratory Care and Critical Care Medicine, 159(2), 373-382.

Bearer, C. (1995). How are children different from adults? Environmental Health Perspectives, 103 (Suppl. 6), 7-12.

Benson, P.E. (1979). A versatile dispersion model for predicting air pollutant levels near highways and arterial streets (Report No. FHWA/CA/TL-79/23). Sacramento, CA: Office of Transportation Laboratory, California Department of Transportation.

Bussing, R., Halfon, N., Benjamin, B., & Wells, K. (1995). Prevalence of behavior problems in U.S. children with asthma. Archives of Pediatric and Adolescent Medicine, 149, 565-572.

California Environmental Protection Agency, California Air Resources Board, Mobile Source Division, Inventory Analysis Branch. (1993a). Methodology for assessing area source emissions: Volume 3 (Revised). Sacramento, CA: Author.

California Environmental Protection Agency, California Air Resources Board, Mobile Source Division, Inventory Analysis Branch. (1993b). Methodology for estimating emissions from on-road motor vehicles (Vol. 3). Sacramento, CA: Author.

California Environmental Protection Agency, California Air Resources Board, Mobile Source Division, Inventory Analysis Branch. (1996). Methodology for estimating emissions from on-road motor vehicles. Sacramento, CA: Author.

Centers for Disease Control and Prevention. (1996). Asthma mortality and hospitalization among children and young adults--United States 1980-1993. Morbidity and Mortality Weekly Review, 45(17), 350-353.

Ciccone, G., Forastiere, F., Agabiti, N., Biggeri, A., Bisanti, L., Chellini, E., Corbo, G., Dell' Orco, V., Dalmasso, P., Volante, T.F., Galassi, C., Piffer, S., Renzoni, E., Rusconi, F., Sestini, P., & Viegi, G. (1998). Road traffic and adverse respiratory effects in children, Occupational and Environmental Medicine, 55, 771-778.

Committee of the Environmental Health Assembly of the American Thoracic Society (1996). Health effects of outdoor air pollution. American Journal of Respiratory Care and Critical Care Medicine, 153(1), 3-50.

Daniels, M., Dominici, F., Samet, J., & Zegler, S. (2000). Estimating particulate matter-mortality dose-response curves and thresholds levels: An analysis of daily time-series for the 20 largest U.S. cities. American Journal of Epidemiology, 152(5), 397-406.

Diette, G., Markson, L., Skinner, E., Nguyen, T., Algatt-Bergstrom, P., & Wu, A. (2000). Nocturnal asthma in children affects school attendance, school performance and parent's work attendance. Archives of Pediatrics and Adolescent Medicine, 154(9), 923-928.

Dockery, D., Pope, A., Xu, X., Spengler, J., Ware, M., Fay, B., Fessis, G., & Speizer, F. (1993). An association between air pollution and mortality in six U.S. cities. New England Journal of Medicine, 329(24), 1753-1759.

English, P, Neutra, R., Scalf, R., Sullivan, M., Waller, L., & Zhu, L. (1999). Examining associations between childhood asthma and traffic flow using a geographic information system. Environmental Health Perspectives, 107(9), 761-767.

Forkenbrock, D., & Schweitzer, L. (1999). Environmental justice in transportation planning. Journal of the American Planning Association, 65(1), 96.

Fowler, M.G., Davenport, M.G., & Garg, R. (1992). School functioning or U.S. schoolchildren with asthma. Pediatrics, 90(6), 939-944.

Gauderman, W.J., McConnell, R., Gilliland, F., London, S., Thomas, D., Avol, E., Vora, H., Berhane, K., Rappaport, E.B., Lurmann, F., Margolis, H.G., & Peters, J. (2000). Association between air pollution and lung function growth in Southern California children. American Journal of Respiratory and Critical Care Medicine, 162, 1383-1390.

Gochfeld, M., & Goldstein, B. (1999). Lessons in environmental health in the twentieth century. Annual Review of Public Health, 20(1), 35-53.

Goldman, L. (1995). Children--Unique and vulnerable. Environmental risks facing children and recommendations for response. Environmental Health Perspectives, 103(Suppl. 6), 13-18.

Guo, Y.L., Lin, Y., Sung, F., Huang, S., Ko, Y., Lai, J., Su, H., Shaw, C., Lin, R., & Dockery, D. (1999). Climate, traffic-related air pollution, and asthma prevalence in middle-school children in Taiwan. Environmental Health Perspectives, 107(12), 1001-1006.

Kear, T., Dougherty K., Lee, K., Eisinger, D., & Niemeier, D. (1998). Transportation project-level carbon monoxide protocol (UCD-ITS-RR-98-9). Davis, CA: California Department of Transportation, Institute of Transportation Studies.

Kunzil, N., Kaiser, R., Medina, S., Studnicka, M., Chanel, O., Filliger, P., Henry M., Horak, F., Jr., Pubonleux-Texier, V., Quenel, P., Schneider, J., Seethaler, R., Vergnard, J.C., & Sommer, H. (2000). Public-health impacts of outdoor and traffic-related air pollution: A European assessment. Lancet, 356, 795-801.

Lamoree, D., & Turner, J. (1999). PM emissions emanating from limited-access highways. Journal of Air and Waste Management, 49, 85-94.

Los Angeles Unified School District. (2000). Fingertip facts 2000-2001. http://lausd.k12.ca.us/lausd/offices/Office_of-Communications/2000-fi ngertip.htm (31 Oct. 2000).

Lubin, B., & Lewis, R. (1995). Biomarkers and pediatric environmental health. Environmental Health Perspectives, 103(Suppl. 6), 99-104.

Malveaux, F., & Fletcher-Vincent, S. (1995). Environmental risk factors of childhood asthma in urban centers. Environmental Health Perspectives, 103(Suppl. 6), 59-62.

Metzger, R., Delgadao, J., & Herrell, R. (1995). Environmental health and Hispanic children. Environmental Health Perspectives, 103(Suppl. 6), 25-32.

Mott, L. (1995). The disproportionate impact of environmental health threats on children of color, Environmental Health Perspectives, 103(Suppl. 6), 33-35.

Musante, C., & Martonen, T. (2000). Computer simulations of particle deposition in the developing human lung. Journal of Air and Waste Management Association, 50, 1426-1432.

Norris, G., Young Pong, S., Koening, J., Larson, T., Sheppard, L., & Stout, J. (1999). An association between fine particles and asthma emergency department visits for children in Seattle. Environmental Health Perspectives, 107(6), 489-493.

Osterlee, A., Drijver, M., Lebert, E., & Brunekreef, B. (1996). Chronic respiratory symptoms in children and adults living along streets with high traffic density Occupational and Environmental Medicine, 53(4), 241-247.

Pope, A. (2000). Invited commentary: Particulate matter-mortality exposure-response relationships and threshold. American Journal of Epidemiology, 152(5), 407-412.

Pope, A., Dockery D., Spengler, J., & Raizenne, M. (1991). Respiratory health and [PM.sub.10] pollution. American Review of Respiratory Disease, 144, 668-674.

Scarlett, J.E, Abbott, K.J., Peacock, J.L., Strachan, D.P., & Anderson, H.R. (1996). Acute effects of summer air pollution on reparatory function in primary school children in Southern England. Thorax, 51, 1109-1114.

Schwartz, J. (2000). Assessing confounding, effects modification, and thresholds in the association between ambient particles and daily death. Environmental Health Perspectives, 108(6), 563-568.

Smedje, G., Norback, D., & Edling, C. (1997). Asthma among secondary schoolchildren in relation to the school environment. Clinical and Experimental Allergy, 27(11), 1270-1278.

South Coast Air Quality Management District. (1981). Meteorological data set for downtown Los Angeles. Diamond Bar, CA: Author.

State of California, Department of Transportation, Traffic Operations Division. (2000). Traffic volumes (annual average daily traffic [AADT]) for all vehicles on California state highways. Sacramento, CA: Author.

Szasz, A., Meuser, M., Aronson, H., & Fukurai, H. (1993). The demographics of toxic releases: The case of Los Angeles County. Paper presented at the Meeting of the American Sociological Association on Class, Race and the Unequal Burdens of Environmental Degradation, Miami, FL.

U.S. EPA Air and Radiation Office of Transportation and Air Quality (2000). Heavy-duty engine and vehicle standards and highway diesel fuel sulfur control requirements (EPA420-F-00-057). Washington, DC: Author.

U.S. EPA Office of Air Quality Planning and Standards. (1995a). Compilation of air pollutant emission factors AP-42, fifth edition, volume 1: Stationary point and area sources. Research Triangle Park, NC: Author.

U.S. EPA Office of Air Quality Planning and Standards, Monitoring and Analysis Division. (1995b). User's guide for the Industrial Source Complex (ISC3) dispersion models, volumes I and II (EPA-454/B-95-003a). Research Triangle Park, NC: Author.

U.S. EPA Office of Air Quality Planning and Standards. (1997). Guideline on air quality models (Revised, EPA-450/2-78-027R). Research Triangle Park, NC: Author.

U.S. Geological Survey. (1993). Digital elevation models: Data users guide 5. Reston, VA: Author.

Van Vliet, P., Knape, M., de Hartog, J., Jannsen, N., Harssema, H., & Brunkreef, B. (1997). Motor vehicle exhaust and chronic respiratory symptoms in children living near freeways. Environmental Research, 74(2), 122-132.

Wagener, D., Selevan, S., & Sexton, K. (1995). The importance of human exposure information: A need for exposure-related databases to protect and promote public health. Annual Review of Public Health, 16, 105-121.

Wjst, M., Reitmeir, P., Sigrid, D., Wulff, A., Nicolai, T., Von Loeffelholz-Colberg, E., & Von Mutius, E. (1993). Road traffic and adverse effects on respiratory health in children. British Medical Journal, 307, 596-600.

RELATED ARTICLE: Practical Stuff!

* Major roadways have been shown to be a significant source of particulate ([PM.sub.10]) air pollution.

* Particulate air pollution is an important factor associated with negative respiratory health effects.

* Children have shown disproportionate rates of chronic respiratory conditions such as asthma.

* For a variety of reasons, children may be more susceptible to environmental exposures than are adults.

* Some of these reasons are rapid lung growth, increased respiration rates, and high rates of activity.

* Minority children may face even greater dangers.

* Race has been found to be the most important variable in determining such factors as distance from residence to a source of detrimental environmental exposure.

* In urban areas, elementary schools also may be in close proximity to major roadways.

* In response to community concerns, the Office of Environmental Health and Safety of the Los Angeles Unified School District conducted an exposure assessment study in the East Los Angeles area.

* The authors conducted a computer-modeled exposure assessment.

* The purpose was to determine if the students received a daily exposure of [PM.sub.10] from a local roadway at levels that would be expected to cause negative health effects.

* A dispersion analysis predicted that average 24-hour particulate concentrations would be 10.45, 14.58, 5.78, and 8.27 micrograms per cubic meter ([micro]g/[m.sup.3]), respectively, for four schools studied.

* These figures reflect not the total ambient exposure, but the increase over ambient exposure.

* This exposure level is substantially lower than the federal guidelines for particulate matter (150 [micro]g/[m.sup.3]) and the California state guidelines (50 [micro]g/[m.sup.3]).

* Nevertheless, recent studies have indicated that the increased [PM.sub.10] levels seen at these schools cause significant negative health effects.

* An analysis of [PM.sub.10] in a daily time-series study of the 20 largest U.S. cities found a 0.69 percent mortality per increase of 10 [micro]g/[m.sup.3].

Corresponding Author: Steven Korenstein, Hazardous Substances Scientist, California Environmental Protection Agency, 3113 Altura Ave., La Crescenta, CA 91214. E-mail: <[e-mail]>.

* Also, the American Thoracic Society reports increases in asthma severity of 3 percent for each increase of 10 [micro]g/[m.sup.3] [PM.sub.10].