Appendix E 

Health Risk Assessment for On-Road Diesel Trucks 

Health Risk Assessment for On-Road Diesel Trucks 

Principal Author

Pingkuan Di, Ph.D, P.E. 

Technical Analysis Section 

Emission Assessment Branch 

Stationary Source Division 

Contributing Author

Nicole Dolney 

Regulatory Support Section 

Mobile Source Analysis Branch 

Planning and Technical Support Division 
This page is intentionally blank. 
E - i 

Health Risk Assessment for On-Road Diesel Trucks 

Table of Contents 

Section Page

I. INTRODUCTION............................................................................................. D-1 

II. STUDY OVERVIEW........................................................................................ D-2 

A. Study Area................................................................................................ D-2 

B. On-Road Diesel Truck Diesel PM Emission Inventory.......................... D-3 

C. Air Dispersion Modeling Methodology .................................................. D-6 

D. Exposure Assessment Methodology ................................................... D-12 

III. SUMMARY OF RESULTS ............................................................................ D-13 

A. Potential Cancer Risk ............................................................................ D-13 

B. Non-Cancer Health Impacts .................................................................. D-18 

C. Uncertainty and Limitations.................................................................. D-21 

IV. HEALTH RISK ASSESSMENT FOR AGRICULTURAL TRUCKS............... D-23 

References................................................................................................................... D-26 
E - ii 

List of Tables Page

Table 1 Activity Matrix Example.......................................................................... D-4 

Table 2 Emission Factor Matrix Example for On-Road Diesel Trucks ................ D-5 

Table 3 Impacted Area and Affected Population by Risk Levels ...................... D-15 

Table 4 Estimated Non-cancer Health Impacts in the Study Area Resulting 

from On-road Diesel Truck Diesel PM Emissions in 2003 ......................D-19 

Table 5 Modeling Assumptions and Inputs for Agricultural Truck Health Risk 

Assessment ......................................................................................... D-23 

Table 6 Distances from the Facility Boundary wherein the Risk is 10 in a Million 

for Different Agricultural Truck Volumes .............................................. D-25 

List of Figures Page

Figure 1 Aerial Map of the Southern California Study Area for On-Road Diesel 

Truck Health Risk Assessment.............................................................. D-3 

Figure 2 Estimated Future Year On-Road Diesel Truck Emissions within the 

Study Area............................................................................................. D-6 

Figure 3 Modeling Domain for the On-Road Diesel Truck Health Risk AssessmentD-7 

Figure 4 Modeling Grid Receptor Network........................................................... D-8 

Figure 5 Variations of Diesel Truck Emissions Based on Time of Day ................ D-9 

Figure 6 Wind Rose Plot for Lynwood Station in 2002-2005.............................. D-11 

Figure 7 Wind Class Frequency Distribution for Lynwood Station in 2002-2005 D-11 

Figure 8 Estimated Cancer Risk Isopleths from On-Road Diesel Trucks within 

the Study Area..................................................................................... D-14 

Figure 9 Projected Cancer Risk Isopleths from On-Road Diesel Trucks for 2015 

with the Proposed Regulation within the Study Area ........................... D-16 

Figure 10 Projected Impacted Area (acres) by Risk Levels from On-Road Diesel 

Trucks within the Study Area ............................................................... D-17 

Figure 11 Projected Affected Population by Risk Levels from On-Road Diesel 

Trucks within the Study Area ............................................................... D-17 

Figure 12 Projected Average Cancer Risks from On-Road Diesel Trucks within 

the Study Area..................................................................................... D-18 

Figure 13 Risk Isopleths of 10 in a Million for Agricultural Truck Volumes of 40 

and 80 trucks per Day.......................................................................... D-24 
E - 1 

I. INTRODUCTION 

Diesel engines emit a complex mixture of air pollutants, composed of gaseous 

and solid material. In 1998, following a 10-year scientific assessment process, 

ARB identified whole diesel exhaust as a toxic air contaminant based on its 

potential to cause cancer and other health problems, including respiratory 

illnesses, and increased risk of heart disease. The visible emissions in diesel 

exhaust are known as particulate matter or PM, which includes carbon particles 

or "soot.” The amount of diesel PM in diesel exhaust is used to evaluate the 

potential health risk from whole diesel exhaust. Subsequent to this action, 

research has shown that diesel PM also contributes to premature deaths (ARB, 

2002). Health risks from diesel PM are highest in areas of concentrated 

emissions, such as near freeways, ports, railyards, or warehouse distribution 

centers. 

Diesel PM is a significant component of particulate matter in many cities. Diesel 

PM is composed of carbonaceous particles (soot) and particles that can form 

from nitrogen oxides (NOX) and oxides of sulfur (SOx) emitted by diesel engines. 

The health impacts of particulate matter (PM10 and PM 2.5) have been studied 

in epidemiological studies conducted in many different cities. These studies 

have found an increase of one to two percent in daily mortality associated with 

each 10 μg/m3 increase in PM10 exposure. The most vulnerable subpopulations 

are those with preexisting respiratory or cardiovascular disease, especially the 

elderly. In addition, increased hospital admissions and illnesses from respiratory 

disease have been associated with particulate matter exposure in adults and 

children. Numerous epidemiological studies have also found an association 

between exposures to diesel PM and an increased risk of lung cancer. 

Health risk assessments are a useful tool for comparing the potential health 

impacts of various sources of air pollution. In a risk assessment, the amount of 

diesel PM emitted from each source (e.g., truck or ship) is estimated. An air 

dispersion modeling computer program uses these emission estimates and local 

meteorological data (e. g. wind speed and direction) to estimate the annual 

average ground level concentrations of diesel PM in the communities around the 

facility. The increased risk of developing lung cancer from exposure to the 

model-estimated diesel PM concentrations can be estimated using the Office of 

Environmental Health Hazard Assessment’s (OEHHA) cancer potency factor for 

diesel PM. 

Non-cancer health impacts of diesel PM exposure can also be quantified if the 

expected concentrations of a pollutant are high enough and there is a sufficiently 

large exposed population. Non-cancer impacts include premature death, hospital 

admissions, respiratory illnesses/asthma, and lost school/work days. 
E - 2 

Risk assessments have various uncertainties in the methodology and risk 

assessments are therefore deliberately designed so that risks are not under 

predicted. Risk assessments are best understood as a tool for comparing risks 

from various sources, usually for purposes of prioritizing risk reduction, rather 

than as a literal prediction of the incidence of disease in the exposed population. 

To help understand the emissions impacts and the potential public health risk 

from exposures to on-road diesel truck PM emissions, ARB staff conducted a 

health risk assessment for a region in Southern California. The study is designed 

to enhance our understanding of on-road diesel truck diesel PM emissions 

impacts in an urban area and to evaluate how those impacts will change with 

implementation of the proposed emission control measure for on-road diesel 

trucks. We believe the results from this analysis provide a quantitative 

assessment of the impacts from on-road diesel trucks in the study area and are 

generally applicable to other urban areas in California, providing a qualitative 

estimate for those areas. We also conducted a simplified risk assessment for 

trucks used in agricultural operations. For this assessment we did not evaluate 

an exact location. Rather we conducted an assessment of a hypothetical 

operation typical of agricultural truck operation. 

Below, in sections II and III, we provide a description of the health risk 

assessment study and the results from the analysis for the region in Southern 

California. In section IV, we provide a discussion on the health risk assessment 

for agricultural trucks. 

II. STUDY OVERVIEW 

In the following sections, we provide a description of the key elements of the 

health risk assessment: 

 identification of the study area; 

 development of the diesel PM emissions inventory for on-road diesel 

trucks operating in the study area; 

 air dispersion modeling methodology to predict ambient diesel PM 

concentrations; and 

 methodology to estimate potential cancer and non-cancer health impacts. 

A. Study Area 

The study area selected for analysis was located in the South Coast Air Basin. A 

10 mile by 10 mile region was selected with the center at the City of Commerce. 

This study area was selected due to the large number of freeways and major 

arterials in the area which historically have had high volumes of on-road diesel 

truck traffic. This area contains a broad mix of land uses including industrial, light 

industrial, commercial and residential. An aerial photo of the study area is shown 

in Figure 1. Several communities or parts of communities are included in the 
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Contact the EPA Docket Center’s Public Reading Room to view 

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docket-customerservice@epa.gov 
E - 4 

Traditionally, on-road mobile emission inventories are generated at the county 

scale using California’s emission factor model (EMFAC) and then allocated to 

large grid cells using the Direct Travel Impact Model (DTIM). To enhance the 

spatial resolution for the air dispersion modeling, we estimated emissions based 

on roadway specific vehicle activity data. As described below, this entailed 

obtaining link-specific activity data that was then used to estimate the emissions 

for each roadway link individually. A roadway link is defined as a discrete section 

of roadway with unique estimates for the fleet specific population and average 

speed and is classified as a freeway, ramp, major arterial, minor arterial, 

collector, or centroid connector. 

Link-Specific Activity Data

The link-specific activity data needed to estimate emissions from on-road diesel 

trucks are vehicle speed and vehicle miles traveled (VMT). VMT is a product of 

vehicle volume (population) and link length. Link-specific activity data within the 

study area were extracted from the Los Angeles County on-road heavy-duty 

truck activity database which was generated using the Southern California 

Association of Governments (SCAG) Heavy Duty Truck Transportation Demand 

Model. The data was compiled into an activity matrix (Table 1) composed of a 

link identification code, hour of the day, speed, and VMT. 

Table 1: Activity Matrix Example 

LINK ID Hour Speed 

(mph) 

HHDDT 

VMT (miles) 

49761 12 45 5.51 

49761 9 45 2.00 

49761 3 35 2.38 

50234 4 55 2.92 

Emissions Estimation

The second step of the emission inventory process involved developing emission 

factors for all vehicle categories for a specified time period, emission type, and 

pollutant. Running exhaust emission factors based on vehicle type, fuel type and 

speed were developed from the EMFAC2007 (see example in Table 2). These 

are composite emission factors based on the model year distribution for each 

county and provided in units of grams of emissions per mile traveled. Emission 

factors are based on test cycles that reflect typical driving patterns, and vehicle 

non-extended idling. 
E - 5 

Table 2: Emission Factor Matrix Example for On-Road Diesel Trucks 

Speed 

(mph)

Diesel PM Emission Factors 

(g/mile) 

10 3.03 

25 1.17 

45 0.78 

60 1.00 

Diesel PM emission (DPM) factors are provided as grams per mile specific to 

each speed. To estimate emissions, the activity was matched to the 

corresponding emission factor (EF). For example, if a 0.25 mile long link at 9 am 

in the morning has 8 diesel trucks traveling at 45 miles per hour, the VMT would 

be 2.00 miles (8 trucks*0.25 miles). EMFAC provides a gram per mile emission 

factor for diesel trucks traveling at 45 mph in Los Angeles County as 0.78 grams 

DPM/mile. In order to estimate total emissions from diesel trucks for this time 

period of day the following calculation is made: 

Total Emissions(grams) = EF ⋅(Volume ⋅ Link Length) = EF ⋅VMT

miles grams

mile

grams Total Emissions(grams) = EF ⋅VMT = 78.0 ⋅ 00.2 = 56.1

To provide a total daily emission inventory, emissions are summed for all links in 

the study area for all hours of the day. For the 2003 baseline emissions, we 

estimated the total diesel PM emissions from on-road diesel trucks (HHDDT + 

MHDDT) at about 0.64 TPD (or 235 TPY) within the study area. 

Future Emissions Inventory

One of the goals of this health risk assessment was to estimate both baseline 

and future health risks associated with emissions from on-road diesel trucks. 

Evaluating the potential health impacts in future years requires the use of 

emission inventories for future years. Based on the statewide projection of 

heavy-duty diesel truck population for future years, we used a scaling approach 

to forecast the emission inventories for future years 2010, 2015, 2020 and 2025 

within the modeling domain. Figure 2 provides the future year emission 

estimates with and without the proposed regulation in the domain. The upper 

solid line represents projected emissions with adopted controls such as the new 

engine standards, clean fuels, and the port drayage truck regulation recently 

adopted by the Air Resources Board. The lower dotted line represents projected 

emissions with both adopted regulations and the proposed statewide truck and 

bus regulation. As can be seen, even with substantial growth, emissions are 

expected to decrease in the future due to adopted regulations and the proposed 
E - 6 

statewide truck and bus regulation will provide significant additional emission 

reductions. 

Figure 2: Estimated Future Year On-Road Diesel Truck Emissions within 

the Study Area 

C. Air Dispersion Modeling Methodology 

This section provides an overview of the air dispersion modeling performed to 

estimate the transport and dispersion of diesel PM emissions resulting from the 

on-road diesel trucks travelling on the freeways and local streets within the 

modeling domain. A description of the air dispersion modeling parameters, 

including air dispersion model selection, modeling domain and receptor grids, 

source characterization and parameters, and meteorological data, is provided. 

Air Dispersion Model Selection

The selection of an air dispersion model depends on many factors, such as, the 

nature of the pollutant (e.g., gaseous, particulate, reactive, inert), the 

characteristics of emission sources (point, area, volume, or line), emission 

source and receptor relationship, the meteorological and topographic 

complexities of the area, the complexity of the source distribution, the spatial 

scale and resolution required for the analysis, the level of detail and accuracy 

required for the analysis, and averaging times to be modeled. For this study, 

0

50

100

150

200

250

2000 2005 2010 2015 2020 2025

Year

Projected DPM (TPY)

Adopted

Adopted + Proposed

Truck Rule
E - 7 

ARB staff selected the U.S. EPA’s approved air dispersion model AERMOD to 

estimate the impacts associated with diesel PM emissions on major freeways, as 

well as major and minor arterials within the domain. AERMOD is a regulatory 

dispersion model specified by the U.S. EPA Guideline for Air Quality Methods (40 

CFR Part 51, Appendix W) (U.S. EPA, 2005). AERMOD is designed for nearfield (tens of meters to tens of kilometers) and steady-state conditions that 

incorporates current concepts about air dispersion based on planetary boundary 

layer turbulence structure and scaling concepts, including treatment of both 

surface and elevated sources, and both simple and complex terrain. AERMOD is 

a replacement for its predecessor, the U.S. EPA Industrial Sources Complex 

(ISC) air dispersion model and it has been successfully used in many 

applications including several recent health risk assessments of railyards located 

in California. 

Model Domain and Receptor Grids

The model domain or study area spans a 10 mi x 10 mi (or 16 km x 16 km) area 

and covers several communities to the east and southeast of downtown Los 

Angeles (Figure 3). This region has a significant amount of diesel truck activities 

on the freeways and arterial streets. Within the domain, the majority of the diesel 

PM emissions (total diesel PM = 0.64 TPD) are generated from heavy-duty diesel 

trucks travelling on freeways I-5, I-710, I-10, CA-60 and major local streets. 

There are about 3,300 roadway links and about 1.1 million people reside within 

the boundaries of the modeling domain. 

Figure 3: Model Domain for On-Road Diesel Truck Health Risk 

Assessment 
E - 8 

Model receptors are the locations where the model predicts concentrations. A 

Cartesian grid receptor network (33 x 33 grids) with 500 m x 500 m resolution is 

used in this study where an array of points is identified by their x (east-west) and 

y (north-south) coordinates (Figure 4). The elevation of each receptor within the 

modeling domain was determined from the United States Geological Service 

topographic data. 

AERMOD was set to run for the period of January 1 through December 31, 2002. 

A one-year period is necessary to enable estimation of the annual average 

concentrations which are required in a health impact assessment. 

Figure 4: Modeling Grid Receptor Network 

Source Characterization and Parameters

The on-road diesel trucks were simulated as a series of polygon area sources. 

The link widths range from 10 m (city streets) to 35 m (freeways, three lanes in 

each direction + 3 meters wake width on each side). Model parameters for the 
E - 9 

polygon area sources include emission rate/strength, release height, vertice 

coordinates, and initial vertical (σzo) dimensions of the area source plume. The 

release height of the emission sources is assumed to be 5 meters from the 

ground. 

As previously described, the diesel PM emissions were estimated by the linkspecific activity data for on-road diesel trucks using emission factors calculated 

by the EMFAC model. These emissions were then spatially allocated into 

individual roadway links or segments based on the Southern California 

Association of Governments (SCAG) Heavy Duty Truck Transportation Demand 

Model. The emissions were temporally allocated into individual roadway links or 

segments based on the same database or model of the SCAG. The average 

time of day variations of the diesel truck emissions within the domain are 

presented in Figure 5. 

Figure 5: Variations of Diesel Truck Emissions Based on Time of Day 

Meteorological Data

In order to run AERMOD, the following hourly surface meteorological data are 

required: wind speed, wind direction, ambient temperature, and opaque cloud 

cover. Daily upper air sounding data are also needed. These meteorological 

variables are important to describe the air dispersion of pollutants in the 

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

9.00%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Hour

Emission %
E - 10 

atmosphere. The wind speed determines how rapidly the pollutant emissions are 

diluted and influences the rise of the emission plume in the air, thus affecting 

downwind concentrations of pollutants. Wind direction determines where 

pollutants will be transported. The difference between the ambient temperature 

and the emission plume release temperature determines the initial plume height 

of emissions. In general, the greater the temperature difference, the higher the 

initial plume height. The opaque cloud cover and upper air sounding data are 

used in calculations to determine dispersion parameters such as atmospheric 

stability (a measure of turbulence and the rate at which pollutants disperse 

laterally and vertically) and mixing height (the vertical depth of the atmosphere 

within which dispersion occurs). The greater the mixing height is, the larger the 

volume of atmosphere is available to dilute the pollutant concentration. 

The meteorological data used for the air dispersion modeling was selected on the 

basis of “representativeness.” Representativeness is determined primarily on 

whether the wind speed/direction distributions and atmospheric stability 

estimates generated through the use of a particular meteorological station (or set 

of stations) are expected to mimic those actually occurring at a location where 

such data are not available. Typically, the key factors for determining 

representativeness are proximity of the meteorological station and the presence 

or absence of nearby terrain features that might alter airflow patterns. Because 

the area within the study area for this health risk assessment is generally flat, it 

would not be expected to exhibit significant variations in wind patterns within 

relatively short distances. For this study, the Lynwood station was chosen as the 

most representative meteorological station for this area. However, the Lynwood 

station did not record temperature and cloud cover data from 2000 to 2005. 

Therefore, hourly wind speed and direction data from the Lynwood station, and 

temperature and cloud cover data from the Los Angeles downtown University of 

Southern California (USC) station were selected to be used in the AERMET1

. 

The USC station is within 10 miles away from the domain. The upper air 

sounding data were chosen from the San Diego-Miramar NAS stations. For this 

study, one year of 2002 of meteorological data from Lynwood and USC stations 

were processed and used in the AERMOD runs. 

Figure 6 and 7 present the wind rose and the wind class frequency distributions 

for the meteorological data used in this air dispersion modeling. The annual 

average wind speed was 1.9 meters per second. As can be seen in Figure 6, the 

prevailing wind over the modeling domain blows from southwest to northeast. 

Figure 7 shows that over 85% of the time the winds were les than 2.1 m/s. 

 

1

AERMET is a meteorological data preprocessor for AERMOD. It processes commercially 

available or custom on-site met data and creates two files: a surface data file and a profile data 

file. 
E - 11 

Figure 6: Wind Rose Plot for Lynwood Station in 2002-2005 

Figure 7: Wind Class Frequency Distribution for Lynwood Station in 

2002-2005 
E - 12 

D. Exposure Assessment Methodology 

For this study, we estimated both the cancer and non-cancer health impacts from 

the exposures to diesel PM emissions from on-road diesel trucks in the study 

area. Below we provide brief descriptions of the risk assessment methodologies 

used. 

Potential Cancer Risks

The potential cancer risks were estimated using standard risk assessment 

procedures based on the annual average concentration of diesel PM predicted 

by the model and a health risk factor (referred to as a cancer potency factor) that 

correlates cancer risk to the amount of diesel PM in the air. 

The methodology used to estimate the potential cancer risks is consistent with 

the Tier-1 analysis presented in OEHHA’s Air Toxics Hot Spots Program 

Guidance Manual for Preparation of Health Risk Assessments (OEHHA, 2003). 

A Tier-1 analysis assumes that an individual is exposed to an annual average 

concentration of a pollutant continuously for 70 years.2 The cancer potency 

factor was developed by the OEHHA and approved by the State’s Scientific 

Review Panel on Toxic Air Contaminants (SRP) as part of the process of 

identifying diesel PM emissions as a toxic air contaminant (TAC). The potential 

cancer risk is expressed as chances per a million people. 

The estimated diesel PM concentrations and cancer risk levels produced by a 

risk assessment are based on a number of assumptions. Many of the 

assumptions are designed to be health protective so that potential risks to 

individuals are not underestimated. Therefore, the actual cancer risk calculated 

is intentionally designed to avoid under-prediction. There are also many 

uncertainties in the health values used in the risk assessment. Some of the 

factors that affect the uncertainty are discussed later in Section III. 

Non-Cancer Health Impacts

A substantial number of epidemiologic studies have found a strong association 

between exposure to ambient particulate matter (PM) and adverse health effects 

(CARB, 2002). As part of this study, ARB staff conducted an analysis of the 

potential non-cancer health impacts over the study domain associated with 

exposures to the model-predicted ambient levels of directly emitted diesel PM 

(primary diesel PM) from on-road diesel trucks operating within the study area. 

The non-cancer health effects evaluated include premature death, hospital 

admissions, asthma-related and other lower respiratory symptoms, work loss 

 

2

According to the OEHHA Guidelines, the relatively health-protective assumptions incorporated 

into the Tier-1 risk assessment make it unlikely that the risks are underestimated for the general 

population. 
E - 13 

days, and minor restricted activity days. The non-cancer health risks are 

expressed as the number of cases per year. 

ARB staff assessed the potential non-cancer health impacts associated with 

exposures to the model-predicted ambient levels of directly emitted diesel PM 

(primary diesel PM) over the entire modeling domain. The population in the 

domain was determined from U.S. Census Bureau year 2000 census data and 

then was projected to the year of 2003. Using the methodology peer-reviewed 

and published in the Staff Report: Public Hearing to Consider Amendments to 

the Ambient Air Quality Standards for Particulate Matter and Sulfates, (PM Staff 

Report) (CARB, 2002), we calculated the number of annual cases of premature 

death and other health effects associated with exposure to the PM concentration 

modeled over the entire study area. 

III. SUMMARY OF RESULTS 

A. Potential Cancer Risk 

Risk characterization is defined as the process of obtaining a quantitative 

estimate of risk. The risk characterization process integrates the results of air 

dispersion modeling and relevant toxicity data (e.g., cancer potency factor) to 

estimate potential cancer or non-cancer health effects associated with air 

contaminant exposure. Exposures to pollutants that were originally emitted into 

the air can also occur in different pathways as a result of breathing, dermal 

contact, ingestion of contaminated produce, and ingestion of fish that have taken 

up contaminants from water bodies. These exposures can all contribute to an 

individual’s health risk. However, diesel PM risk is evaluated by the inhalation 

pathway only in this study because the risk contributions by other pathways of 

exposure are insignificant relative to the inhalation pathway. 

Potential Cancer Risks from On-Road Diesel Trucks in the Study Area (2003)

The potential cancer risks levels associated with the estimated 2003 diesel PM 

emissions from the on-road diesel trucks within the domain are displayed by 

using isopleths superimposed on a base map. The potential risk levels are 

based on the 80th percentile breathing rate and 70 year exposure duration for 

residents. In this study, we present the potential cancer risk isopleths focusing 

on risk levels of 100, 200, and 500 chances in a million, as shown in Figure 8. 

Note that any risk levels of less than 50 chances in a million are not displayed in 

Figure 8 because they are outside of the study area. 

As indicated in Figure 8, the areas with the greatest impact, potential cancer risk 

over 500 chances in a million, occur in areas surrounding the major freeways I710, I-5, I-10, CA-60, etc. The estimated cancer risk of 500 chances per million 

occurs at approximately 500 to 5,000 feet from the edges of the major freeways. 
E - 14 

Figure 8: Estimated Cancer Risk Isopleths from On-Road Diesel 

 Trucks within the Study Area 

(2003 On-Road Diesel Truck Diesel PM Emissions) 

At about a quarter to several miles from the edges of the major freeways, the 

estimated cancer risks drop to about 200 chances in a million. The overall 

average potential cancer risk within the study area is about 375 chances in a 

million. 

Table 3 presents the exposed population and area of the various impacted zones 

of cancer risks associated with the on-road truck diesel PM emissions. 

Using the U.S. Census Bureau’s year 2000 census data, we estimated the 

population within the isopleth boundaries. The affected population numbers for 

the risk ranges of 100-200, 200-500, and over 500 chances in a million have 

been estimated to be about 260,000, 521,000, and 175,000. About 90 percent of 

1.1 million people living in the study area are exposed to predicted risks of equal 

to or greater than 100 chances in a million. 
E - 15 

From the point view of impacted area, the area with predicted cancer risk levels 

in excess of 100 chances in a million is estimated to be about 63,000 acres, 

which is 93 percent of the total area within the study area. The area in which the 

risks are predicted to exceed 200 in a million is also very large, covering an area 

of about 51,000 acres (75 percent of the total area within the study area). The 

areas with an estimated potential cancer risk of over 500 chances in a million 

covers about 17 percent of the total area within the study area. The risk 

isopleths of 1,000 in a million occur in a narrow area along the major freeways, 

predominately at major freeway intersections. 

It should be noted that these risk levels represent the predicted risks only due to 

the on-road diesel trucks. Any potential cancer risk levels associated with all 

other sources are not included. For the South Coast Air Basin, the estimated 

regional potential risk level was estimated to be about 900-1,000 chances in a 

million from all diesel PM emissions in 2000 (SCAQMD MATES-II, 2000). 

Table 3: Impacted Area and Affected Population by Risk Levels 

(2003 On-Road Diesel Truck Diesel PM Emissions) 

Future Projections of Potential Cancer Risks in the Study Area due to On-Road 

Diesel Trucks

As mentioned previously, the diesel PM emissions from on-road diesel trucks are 

forecasted to decrease in future years due to the implementation of several 

regulations already adopted by the Air Resources Board and the proposed 

statewide truck and bus rule. This decline in emissions will result in the reduction 

of the potential cancer (and non-cancer) risks due to exposures to diesel PM. 

Figure 9 shows how potential cancer risks will decrease as emissions from onroad diesel trucks are reduced. As is shown, it is anticipated that the risk levels 

greater than 100 in a million will be significantly reduced in future years. 

The impacted area and affected population by different risk levels within the 

study area due to the emission reductions in future years are presented in 

Figures 10 and 11. Figure 12 shows the averaged potential cancer risks within 

the study area for the base year 2003 and future years 2010, 2015 and 2020 and 

the corresponding risk reductions.
E - 16 

Figure 9: Projected Cancer Risk Isopleths from On-Road Diesel 

 Trucks in 2015 with the Proposed Regulation within the 

 Study Area 

As shown in Figure 12, the predicted potential cancer risks will be reduced by 

about 82 percent in 2015 but then will begin to increase as growth surpasses the 

reductions required by regulatory programs. The predicted remaining cancer risk 

in the 2020 timeframe will be about 75 chances in a million within the study area. 

In future years after 2020, the averaged potential cancer risk will be greater than 

75 chances in a million due to the growth. 
E - 17 

Figure 10: Projected Impacted Areas (acres) by Risk Levels from On-Road 

 Diesel Trucks within the Study Area 

Figure 11: Projected Affected Population by Risk Levels from On-Road 

 Diesel Trucks within the Study Area 

0.000

0.200

0.400

0.600

0.800

1.000

1.200

Impacted Population (million)

2003

2010

2015

2020

2003 0.041 0.175 0.700 0.955 1.100

2010 0.017 0.066 0.440 0.800 1.100

2015 0.000 0.000 0.026 0.110 1.050

2020 0.000 0.000 0.038 0.160 1.060

R > 1000 R > 500 R > 200 R > 100 R > 10

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

Affected Area (acres)

2003

2010

2015

2020

2003 3,300 12,000 50,500 63,000 67,000

2010 1,300 5,000 31,000 57,000 67,000

2015 0 0 2,000 8,000 66,000

2020 0 100 3,000 11,000 67,000

R > 1000 R > 500 R > 200 R > 100 R > 10
E - 18 

Figure 12: Projected Average Cancer Risks from On-Road Diesel 

 Trucks within the Study Area 

B. Non-Cancer Health Impacts 

As discussed previously, a substantial number of epidemiologic studies have 

found a strong association between exposure to ambient particulate matter (PM) 

and adverse health effects (ARB, 2002). As part of this study, ARB staff 

conducted an analysis of the potential non-cancer health impacts associated with 

exposures to the model-predicted ambient levels of directly emitted diesel PM 

(primary diesel PM) in the study area for diesel PM resulting from on-road diesel 

truck activities. The non-cancer health effects evaluated include premature 

death, hospital admissions, asthma-related and other lower respiratory 

symptoms, work loss days, and minor restricted activity days. 

Consistent with U.S. EPA (EPA, 2004c), ARB has been using the PM-mortality 

relationship from Pope et al. (Pope, 2002) since the adoption of the Emission 

Reduction Plan for Ports and Goods Movement (GMERP) (ARB, 2006b). The 

374

245

60

72

0

50

100

150

200

250

300

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400

2003 2010 2015 2020

Year

Potential Cancer Risk (in one million)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Risk Reduction (%)

Risk

Reduction %
E - 19 

methodology for estimating premature death and other health impacts is 

described in Appendix A of the GMERP. Ambient levels of directly emitted diesel 

PM from on-road diesel truck activities were predicted for each 500 meter by 500 

meter grid cell within the modeling domain (10 miles by 10 miles) using the 

AERMOD model. The population within each grid cell (about 1.1 million people 

in the domain) was determined from U.S. Census Bureau year 2000 census 

data. Using U.S. EPA’s BENMAP program, we estimated the number of annual 

premature deaths and several other non-cancer health effects that are likely to 

occur within the study area due to exposure to the directly emitted diesel PM 

emissions from on-road diesel trucks. The health effect estimates are based on 

concentration-response functions derived from published epidemiological studies 

relating changes in ambient concentrations to changes in health endpoints, the 

population affected, and the baseline incidence rates. 

Non-cancer Health Impacts in 2003. 

The estimated non-cancer health impacts for directly emitted diesel PM from onroad diesel trucks in the year 2003 are presented in Table 4. As shown, we 

estimate that there would be about 42 premature deaths (for ages 30 and older), 

32 hospital admissions due to respiratory and cardiovascular causes, 1,400 

asthma-related and other lower respiratory symptoms, 7,400 days of work loss; 

and 43,000 minor restricted activity days. 

Table 4: Estimated Non-cancer Health Impacts in the Study Area 

Resulting from On-Road Diesel Truck Year 2003 Diesel PM 

Emissions 

Endpoint # of Cases per 

Year 

(Mean) 

# of Cases per Year 

95 % Confidence 

Interval 

Tons-per-Case 

Factor 

(TPY/case, 

based on 

mean) 

Premature Death 42 12 - 72 5.6 

Hospital Admission 

(Respiratory & 

Cardiovascular) 

32 13 - 50 7.3 

Asthma - Related & Other 

Lower Respiratory 

Symptoms 

1,400 540 – 2,200 0.17 

Acute Bronchitis 120 0 - 260 1.95 

Work Loss Day 7,400 6,200 – 8,900 0.032 

Minor Restricted Activity 

Days 

43,000 36,000 – 50,000 0.0055 
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Non-cancer Health Impacts in Future Years

ARB staff also conducted a quantitative analysis of six potential non-cancer 

health impacts associated with the change in exposures to the model-predicted 

ambient levels of directly emitted diesel PM. Below are the estimated noncancer health impacts from direct diesel PM avoided due to the proposed 

regulation from year 2010 through 2020 within the study area. These benefits 

are due only to the reduction in emissions expected from implementation of the 

proposed statewide truck and bus regulation within the study area. Statewide, 

much greater benefits are expected to be realized. 

• 78 premature deaths ( 20 to 130, 95% CI) 

• 60 hospital admissions –respiratory and cardiovascular (24 to 90, 95% CI) 

• 2,600 asthma attacks (1,000 to 4,000, 95% CI) 

• 220 acute bronchitis ( 0 to 48, 95% CI) 

• 14,700 work loss days (11,500 to 16,500, 95% CI) 

• 80,000 minor restricted activity days (70,000 to 93,000, 95% CI) 

In May 2008 ARB released a draft methodology for estimating premature deaths 

associated with long-term exposures to fine airborne particulate matter in 

California that proposes increasing the relative risk factor from 6% to 10% 

increase in premature death per 10 μg/m3

 increase in PM2.5 exposure (ARB, 

2008). The premature deaths listed here were calculated using the 6% value. If 

the 10% value were used the estimates of premature deaths would increase by 

about 70%. 

Several assumptions were used in our estimation. They involve the selection 

and applicability of the concentration-response functions to California data, 

exposure estimation, subpopulation estimation, baseline incidence rates, and the 

threshold. These are briefly described below. 

• Premature death calculations were based on the concentration-response 

function of Pope et al. (Pope, 2002). The ARB staff assumed that 

concentration-response function for premature mortality in the model 

domain is comparable to that in the Pope’s study. It is known that the 

composition of PM can vary by region, and not all constituents of PM have 

the same health effects. However, numerous studies have shown that the 

mortality effects of PM in California are comparable to those found in other 

locations in the United States, justifying our use of Pope et al’s results. 

Also, the U.S. EPA has been using Pope’s study for its regulatory impact 

analyses since 2000. For other health endpoints, the selection of the 

concentration-response functions was based on the most recent and 

relevant scientific literature. Details are in CARB’s PM Staff Report 

(CARB, 2002). 
E - 21 

• The ARB staff assumed the model-predicted exposure estimates could be 

applied to the entire population within each modeling grid. That is, the 

entire population within each modeling grid of 500 m x 500 m was 

assumed to be exposed uniformly to modeled concentration. This 

assumption is typical of this type of estimation. 

• The ARB staff included only directly emitted PM and did not account for 

secondary PM formed from NOx and SOx emissions. 

• The ARB staff assumed the baseline incidence rates were uniform across 

each modeling grid and in many cases across each county. This 

assumption is consistent with methods used by the U.S. EPA for its 

regulatory impact assessment. The incidence rates match those used by 

U.S. EPA. 

It should be noted again that the estimates in this study apply only to the limited 

study area (10 miles by 10 miles). The tons-per-case factors (TPY DPM per 

case) provided in Table 4 could be used to provide an estimate of the non-cancer 

health impacts for Los Angeles County, or the South Coast Air Basin. It is also 

possible to use this factor to estimate the non-cancer health impacts for other 

areas of the State; however, there may be greater uncertainty in the estimates. 

C. Uncertainty and Limitations 

Health risk assessment is a complex procedure which requires the integration of 

many variables and assumptions. The estimated diesel PM concentrations and 

risk levels produced by a risk assessment are based on several assumptions, 

many of which are designed to be health protective so that potential risks to 

individual are not underestimated. As described previously, the health risk 

assessment consists of three components: (1) emission inventory, (2) air 

dispersion modeling, and (3) risk assessment. Each component has a certain 

degree of uncertainty associated with its estimation and prediction due to the 

assumptions made. Therefore, there are uncertainties and limitations with the 

results. 

Emission Inventory. We have made several assumptions in developing the 

emission inventory. While these assumptions are appropriate at the county level, 

they may be less appropriate for the particular areas modeled in this 

assessment. For example, the county specific default model year distribution 

within EMFAC was assumed to be applicable for all links within the domain 

modeled. In addition, the emission inventory developed for this study only 

included diesel PM emissions from running exhaust as it is the primary diesel 

source from on-road diesel trucks. Emissions from other modes such as idling 

and starts, were not included. 
E - 22 

Air Dispersion Modeling. There is no direct measurement technique for diesel

PM. This analysis used air dispersion modeling to estimate the concentrations of 

diesel PM to which the public is exposed. While air dispersion models are based 

on the state-of-the-art formulations, there are uncertainties associated with the 

models. The AERMOD model used in this study is the U.S. EPA’s newly 

preferred air dispersion model. Evaluating the model is beyond the scope of this 

study. The model inputs may have some uncertainties. Among all modeling 

inputs, emission inventory or rate and meteorological conditions have the 

greatest affect on modeling results. The uncertainties of the emission inputs 

have been discussed above. Meteorological conditions can play a key role in 

predicted pollutant concentrations. For this modeling study, we used the wind 

speed and direction data from the Lynwood station, and temperature and cloud 

cover data from the Los Angeles downtown USC station. We assumed that this 

meteorological data was applicable over the entire study area (100 square 

miles). 

Risk Assessment. The toxicity of toxic air contaminants is often established by 

available epidemiological studies, or, where data from humans are not available, 

the use of data from animal studies. The diesel PM cancer potency factor is 

based on long-term study of railyard workers exposed to diesel exhaust at 

concentrations approximately ten times typical ambient exposures (OEHHA, 

2003). This can introduce uncertainty in the potential risk estimated for the 

general public because there is a wide range of responses among all individuals, 

and there can be a wider range of responses in the general public than in the 

workers in an epidemiology study. Different epidemiological studies also suggest 

somewhat different levels of risk. When the Scientific Review Panel (SRP) 

identified diesel PM as a toxic air contaminant, they endorsed a range of 

inhalation cancer potency factors (1.3 x 10 –4 to 2.4 x 10 –3 (μg/m3

) –1) and a risk 

factor of 3x10 -4 (μg/m3)-1, as a reasonable estimate of the unit risk. From the 

unit risk factor an inhalation cancer potency factor of 1.1 (mg/kg-day)-1 can be 

calculated, which was used in the study. 

This study adopts the standard Tier-1 approach recommended by the OEHHA for 

exposure and risk assessment. A Tier 1 approach is an end-point estimate 

methodology without the consideration of site-specific data distributions. It also 

assumes that an individual is exposed to an annual average concentration of a 

pollutant continuously for a specific time period. The OEHHA recommends the 

lifetime 70-year exposure duration with a 24-hour per day exposure be used for 

determining residential cancer risks. Lifetime 70-year exposure is a conservative 

estimate, but it is a historical benchmark for comparing facility impacts on 

receptors and for evaluating the effectiveness of air pollution control measures. 
E - 23 

IV. HEALTH RISK ASSESSMENT FOR AGRICULTURAL TRUCKS 

To screen the potential cancer risks associated with uncontrolled agricultural 

trucks used to transport agricultural commodities from the field to a processing 

facility, ARB staff developed general assumptions bracketing a range of possible 

operating scenarios and determined the potential cancer risks for a “generic” 

processing facility. The number of truck trips and the distance from the roadway 

or processing facility wherein the risk levels reached 10 in a million or greater 

were determined. This can be used to provide a “qualitative” assessment of the 

potential risk levels near a roadway used by agricultural trucks and the 

processing facility. Actual risk levels will vary due to site specific parameters, 

including the number of trucks and associated emission rates, operating 

schedules, site configuration, site meteorology, and distance to receptors. As 

such, this assessment should be viewed as a first screening of the potential 

health risks, but more work on an actual facility is needed to more fully 

understand the potential impact. 

Below we describe the key inputs for the assessment. The methodology used to 

determine the potential cancer risk is the same as that used for on-road trucks 

discussed in previously in this Appendix. 

A. Emission Inputs and Modeling Parameters 

As mentioned, we did not model an actual food processing location and 

associated roadway. Rather, we developed a generic source comprised of a 400 

m by 200 m commodity processing facility with an attached 1 mile segment of a 

two lane roadway. Due to the high concentration of agricultural activities in the 

San Joaquin Valley, we selected meteorology from the Bakersfield region and 

assumed a rural dispersion coefficient. The key assumptions and parameters 

are provided in Table 5. 

Table 5: Modeling Assumptions and Inputs for the Agricultural Truck 

Health Risk Assessment 

Diesel PM Emission Factors and 

Rates for Agricultural Trucks 

Idle: 0.05528 g/idle-minute 

25 mph: 1.52957 g/mile 

Truck Volumes 20, 40, 60, 80 and100 truck trips per day 

Temporal Emission Distribution 288 days/year, 24 hrs/day for weekdays, 12 hrs/day on 

Saturday, no operation on Sunday 

Model AERMOD 

Dispersion Coefficients Rural 

Site Scenario Processor Facility Size: 200m x 400m 

Truck Activity at Processor: Assume 400 m of creeping, 5 

min idling per truck 

Roadway: 1 mile length and 10 m width, Trucks travel at 

25 mph, varying number of truck trips per day 

Source Characterization Polygon area emission sources 

Caner Risk Characterization 70 year lifetime 

Meteorological Data Set Central Valley (Bakersfield, 2002) 
E - 24 

B. Results 

Figure 13 shows the risk level of 10 in a million for two truck volumes (40 and 80 

trucks per day). As can be seen, at a truck activity level of about 40 trucks per 

day, the impacted area by the risk level of 10 in a million extends outside the 

processing facility and all along the roadway (green contour line). As the truck 

volume increases, the risk isopleths wherein the risk levels exceed 10 in a million 

extend further beyond the facility and roadway into areas where residents could 

reside. As shown in Figure 13, at a truck volume of 80 trucks per day, the 10 in a 

million risk isopleths extends over 250 m from the roadway leading into the 

facility (red contour line). 

Figure 13: Risk Isopleths of 10 in a Million for Agricultural Truck Volumes 

of 40 and 80 Trucks per Day 

Table 6 lists the distances from the facility boundary wherein the risk is 10 in a 

million for different agricultural truck volumes. For example, as the truck volume 
E - 25 

increases to 100 trucks per day or 28,800 trucks per year, the risk level of 10 in a 

million could extend to a downwind distance about 1100 ft from the boundary. 

Table 6: Distances from the Facility Boundary wherein the Risk is 10 in a 

Million for Different Agricultural Truck Volumes 

Because many of the agricultural processing facilities are located in communities, 

it would be expected that for some facilities, the diesel PM emissions from the 

trucks traveling to and from the processing facilities would impact residents that 

live along the roadways. These levels could even be higher in the event there 

are multiple processing centers that are accessed from common roadways. 
E - 26 

REFERENCES 

ARB, 1998. For the "Proposed Identification of Diesel Exhaust as a Toxic Air 

Contaminant". April, 1998 

ARB, 2000. Risk Reduction Plan to Reduce Particulate Matter Emissions from 

Diesel-Fueled Engines and Vehicles, Staff Report, October, 2000. 

(ARB, 2002). California Air Resources Board and Office of Environmental Health 

Hazard Assessment. Staff Report: Public Hearing to Consider Amendments to 

the Ambient Air Quality Standards for Particulate Matter and Sulfates, available 

at http://www.arb.ca.gov/research/aaqs/std-rs/pm-final/pm-final.htm. California 

Air Resources Board. 2002. 

ARB, 2004. ARB Recommended Interim Risk Management Policy for InhalationBased Residential Cancer Risk, March, 2004. 

http://www.arb.ca.gov/toxics/harp/rmpolicyfaq.htm

ARB, 2006. Diesel Particulate Matter Exposure Assessment Study for the Ports 

of Los Angeles and Long Beach, Final Report. April, 2006. 

ARB, 2006b. Goods Movement Emissions Reduction Plan, Air Resources 

Board, April 2006. 

ARB, 2008. ARB Methodology for Estimating Premature Deaths Associated 

Long-term Exposures to Fine Airborne Particulate Matter in California. 

OEHHA, 2002. Air Toxics Hot Spot Program Risk Assessment Guidelines: Part II 

Technical Support Document for Describing Available Cancer Potency Factors. 

Office of Environmental Health Hazard Assessment. December, 2002. 

OEHHA, 2003. Air Toxics Hot Spots Program Risk Assessment Guidelines: The 

Air Toxics Hot Spots Program Guidance Manual for Preparation of Health Risk 

Assessments. Office of Environmental Health Hazard Assessment. August, 

2003. 

(Pope, 2002). Pope, C.A., III et.al, Lung Cancer, Cardiopulmonary Mortality, 

and Long-Term Exposure to Fine Particulate Air Pollution, J. Am. Med. Assoc., 

287, pp. 1132-1141. 

SCAQMD, 2000. Multiple Air Toxics Exposure Study in the South Coast Air Basin 

(MATES-II), Final Report, March, 2000. 

SCAG, 2007. SCAG Transportation Modeling, http://www.scag.ca.gov/modeling/ 

[Accessed January 2007]. 
E - 27 

U.S. EPA, 2004a. User’s Guide for the AMS/EPA Regulatory Model – AERMOD. 

Report No. EPA-454/B-03-001. Office of Air Quality Planning and Standards. 

Emissions Monitoring and Analysis Division, Research Triangle Park, NC. 

September, 2004. 

U.S. EPA, 2004b. User’s Guide for the AERMOD Meteorological Preprocessor. 

Report No. EPA-454/B-03-002. Office of Air Quality Planning and Standards. 

Emissions Monitoring and Analysis Division, Research Triangle Park, NC. 

September, 2004. 

(USA EPA, 2004c). United States Environmental Protection Agency, May, 2004, 

Final Regulatory Impact Analysis: Control of Emissions from Nonroad Diesel 

Engines, EPA-420-R-04-007, Office of Transportation and Air quality, 

http://www.epa.gov/otaq/regs/nonroad/equip-hd/2004fr.htm#ria

U.S. EPA, 2005. Federal Register, Part III, 40 CFR Part 51, Vol. 70, No. 216, 

November 9, 2005. 
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