## 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 DivisionThis 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 Inventor y.......................... D-3 C. Air Dispersion Modeling Methodology ............. ..................................... D-6 D. Exposure Assessment Methodology ................. .................................. D-12

III. SUMMARY OF RESULTS ............................ ................................................ D-1 3
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-26E - 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 Ri sk Levels ...................... D-15

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

from On-road Diesel Truck Diesel PM Emissions in 20 03 ......................D-19

Table 5 Modeling Assumptions and Inputs for Agricul tural Truck Health Risk

Assessment ......................................... ................................................ D-2 3

Table 6 Distances from the Facility Boundary wherei n 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 Stud y 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 Tru ck 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 Lynw ood Station in 2002-2005 D-11

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

the Study Area..................................... ................................................ D-1 4

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

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

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

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

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

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

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

the Study Area..................................... ................................................ D-1 8

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

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

I. INTRODUCTION

Diesel engines emit a complex mixture of air pollut ants, composed of gaseous and solid material. In 1998, following a 10-year s cientific 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. Th e visible emissions in diesel exhaust are known as particulate matter or PM, whic h includes carbon particles

or "soot.” The amount of diesel PM in diesel exhau st is used to evaluate the potential health risk from whole diesel exhaust. S ubsequent 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 (NO X ) and oxides of sulfur (SOx) emitted by diesel engi nes. The health impacts of particulate matter (PM10 and PM 2.5) have been studied in epidemiological studies conducted in many differ ent cities. These studies have found an increase of one to two percent in dai ly mortality associated with

each 10 μ g/m 3 increase in PM10 exposure. The most vulnerable su bpopulations are those with preexisting respiratory or cardiovas cular disease, especially the elderly. In addition, increased hospital admission s and illnesses from respiratory disease have been associated with particulate matte r exposure in adults and children. Numerous epidemiological studies have al so found an association between exposures to diesel PM and an increased ris k of lung cancer.

Health risk assessments are a useful tool for compa ring 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 emi ssion 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 ca ncer from exposure to the

model-estimated diesel PM concentrations can be est imated using the Office of Environmental Health Hazard Assessment’s (OEHHA) ca ncer 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 eno ugh and there is a sufficiently large exposed population. Non-cancer impacts inclu de 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 prior itizing risk reduction, rather than as a literal prediction of the incidence of di sease in the exposed population.

To help understand the emissions impacts and the po tential public health risk from exposures to on-road diesel truck PM emissions , ARB staff conducted a health risk assessment for a region in Southern Cal ifornia. The study is designed to enhance our understanding of on-road diesel truc k diesel PM emissions impacts in an urban area and to evaluate how those impacts will change with implementation of the proposed emission control mea sure for on-road diesel trucks. We believe the results from this analysis provide a quantitative assessment of the impacts from on-road diesel truck s in the study area and are generally applicable to other urban areas in Califo rnia, providing a qualitative estimate for those areas. We also conducted a simp lified risk assessment for trucks used in agricultural operations. For this a ssessment we did not evaluate

an exact location. Rather we conducted an assessme nt of a hypothetical operation typical of agricultural truck operation.

Below, in sections II and III, we provide a descrip tion 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 f or on-road diesel trucks operating in the study area;

 air dispersion modeling methodology to predict amb ient diesel PM concentrations; and

 methodology to estimate potential cancer and non-c ancer 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 cen ter at the City of Commerce. This study area was selected due to the large numbe r of freeways and major arterials in the area which historically have had h igh volumes of on-road diesel

truck traffic. This area contains a broad mix of l and 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 commu nities are included in the

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Email: docket-customerservice@epa.govE - 4

Traditionally, on-road mobile emission inventories are generated at the county scale using California’s emission factor model (EMF AC) and then allocated to large grid cells using the Direct Travel Impact Mod el (DTIM). To enhance the spatial resolution for the air dispersion modeling, we estimated emissions based on roadway specific vehicle activity data. As desc ribed 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 spec ific population and average speed and is classified as a freeway, ramp, major a rterial, 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 Coun ty on-road heavy-duty truck activity database which was generated using t he Southern California Association of Governments (SCAG) Heavy Duty Truck Transportation Demand Model. The data was compiled into an activity matr ix (Table 1) composed of a link identification code, hour of the day, speed, a nd 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 i nvolved 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 exampl e in Table 2). These are composite emission factors based on the model y ear distribution for each county and provided in units of grams of emissions per mile traveled. Emission factors are based on test cycles that reflect typic al 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 gr ams per mile specific to each speed. To estimate emissions, the activity wa s matched to the corresponding emission factor (EF). For example, i f 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 provide s 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 fro m diesel trucks for this time period of day the following calculation is made:

VMT EF Length Link Volume EF grams Emissions Total

⋅ = ⋅ ⋅ = ) ( ) (

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

To provide a total daily emission inventory, emissi ons are summed for all links in the study area for all hours of the day. For the 2 003 baseline emissions, we

estimated the total diesel PM emissions from on-roa d diesel trucks (HHDDT + MHDDT) at about 0.64 TPD (or 235 TPY) within the st udy 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 f rom on-road diesel trucks. Evaluating the potential health impacts in future y ears requires the use of emission inventories for future years. Based on th e statewide projection of heavy-duty diesel truck population for future years , we used a scaling approach to forecast the emission inventories for future yea rs 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 adop ted controls such as the new engine standards, clean fuels, and the port drayage truck regulation recently adopted by the Air Resources Board. The lower dott ed line represents projected emissions with both adopted regulations and the pro posed statewide truck and bus regulation. As can be seen, even with substant ial growth, emissions are expected to decrease in the future due to adopted r egulations and the proposedE - 6

statewide truck and bus regulation will provide sig nificant additional emission

reductions.

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

the Study Area

C. Air Dispersion Modeling Methodology

This section provides an overview of the air disper sion modeling performed to estimate the transport and dispersion of diesel PM emissions resulting from the on-road diesel trucks travelling on the freeways an d local streets within the modeling domain. A description of the air dispersi on modeling parameters, including air dispersion model selection, modeling domain and receptor grids, source characterization and parameters, and meteoro logical 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, v olume, or line), emission source and receptor relationship, the meteorologica l and topographic complexities of the area, the complexity of the sou rce distribution, the spatial scale and resolution required for the analysis, the level of detail and accuracy required for the analysis, and averaging times to b e modeled. For this study, 0 50 100 150 200 250

2000 2005 2010 2015 2020 2025

YearProjected DPM (TPY) Adopted

Adopted + Proposed

Truck RuleE - 7

ARB staff selected the U.S. EPA’s approved air disp ersion model AERMOD to estimate the impacts associated with diesel PM emis sions 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 Guidelin e 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 st eady-state conditions that incorporates current concepts about air dispersion based on planetary boundary layer turbulence structure and scaling concepts, in cluding treatment of both surface and elevated sources, and both simple and c omplex terrain. AERMOD is a replacement for its predecessor, the U.S. EPA Ind ustrial Sources Complex (ISC) air dispersion model and it has been successf ully used in many applications including several recent health risk a ssessments of railyards located

in California.

Model Domain and Receptor Grids

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

and covers several communities to the east and sout heast of downtown Los Angeles (Figure 3). This region has a significant amount of diesel truck activities on the freeways and arterial streets. Within the d omain, the majority of the diesel PM emissions (total diesel PM = 0.64 TPD) are gener ated 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 m illion people reside within the boundaries of the modeling domain.

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

AssessmentE - 8

Model receptors are the locations where the model p redicts concentrations. A Cartesian grid receptor network (33 x 33 grids) wit h 500 m x 500 m resolution is used in this study where an array of points is iden tified by their x (east-west) and y (north-south) coordinates (Figure 4). The elevat ion of each receptor within the modeling domain was determined from the United Stat es Geological Service topographic data.

AERMOD was set to run for the period of January 1 t hrough December 31, 2002. A one-year period is necessary to enable estimation of the annual average concentrations which are required in a health impac t assessment.

Figure 4: Modeling Grid Receptor Network

Source Characterization and Parameters

The on-road diesel trucks were simulated as a serie s of polygon area sources. The link widths range from 10 m (city streets) to 3 5 m (freeways, three lanes in each direction + 3 meters wake width on each side). Model parameters for theE - 9

polygon area sources include emission rate/strength , release height, vertice coordinates, and initial vertical ( σ zo ) dimensions of the area source plume. The release height of the emission sources is assumed t o be 5 meters from the ground.

As previously described, the diesel PM emissions we re estimated by the linkspecific activity data for on-road diesel trucks us ing emission factors calculated by the EMFAC model. These emissions were then spat ially allocated into individual roadway links or segments based on the S outhern California Association of Governments (SCAG) Heavy Duty Truck Transportation Demand Model. The emissions were temporally allocated int o 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 emission s within the domain are presented in Figure 5.

Figure 5: Variations of Diesel Truck Emissions Base d on Time of Day

Meteorological Data

In order to run AERMOD, the following hourly surfac e meteorological data are required: wind speed, wind direction, ambient tempe rature, and opaque cloud cover. Daily upper air sounding data are also need ed. These meteorological variables are important to describe the air dispers ion 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

HourEmission %E - 10

atmosphere. The wind speed determines how rapidly the pollutant emissions are diluted and influences the rise of the emission plu me in the air, thus affecting downwind concentrations of pollutants. Wind direct ion determines where pollutants will be transported. The difference bet ween the ambient temperature and the emission plume release temperature determin es the initial plume height of emissions. In general, the greater the temperat ure difference, the higher the initial plume height. The opaque cloud cover and u pper air sounding data are used in calculations to determine dispersion parame ters such as atmospheric stability (a measure of turbulence and the rate at which pollutants disperse laterally and vertically) and mixing height (the ve rtical depth of the atmosphere within which dispersion occurs). The greater the m ixing height is, the larger the volume of atmosphere is available to dilute the pol lutant 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 o ccurring at a location where such data are not available. Typically, the key fa ctors for determining representativeness are proximity of the meteorologi cal station and the presence or absence of nearby terrain features that might al ter 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 variat ions in wind patterns within relatively short distances. For this study, the Ly nwood 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 fro m the Lynwood station, and temperature and cloud cover data from the Los Angel es downtown University of Southern California (USC) station were selected to be used in the AERMET 1

. The USC station is within 10 miles away from the do main. The upper air sounding data were chosen from the San Diego-Mirama r 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 c lass frequency distributions for the meteorological data used in this air disper sion modeling. The annual average wind speed was 1.9 meters per second. As c an 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 AE RMOD. It processes commercially

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

file.E - 11

Figure 6: Wind Rose Plot for Lynwood Station in 200 2-2005

Figure 7: Wind Class Frequency Distribution for Lyn wood Station in

2002-2005E - 12 D. Exposure Assessment Methodology

For this study, we estimated both the cancer and no n-cancer health impacts from the exposures to diesel PM emissions from on-road d iesel trucks in the study area. Below we provide brief descriptions of the r isk assessment methodologies

used.

Potential Cancer Risks

The potential cancer risks were estimated using sta ndard risk assessment procedures based on the annual average concentratio n 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 i n the air

.

The methodology used to estimate the potential canc er risks is consistent with the Tier-1 analysis presented in OEHHA’s Air Toxics Hot Spots Program Guidance Manual for Preparation of Health Risk Asse ssments (OEHHA, 2003). A Tier-1 analysis assumes that an individual is exp osed to an annual average concentration of a pollutant continuously for 70 ye ars. 2 The cancer potency factor was developed by the OEHHA and approved by t he State’s Scientific Review Panel on Toxic Air Contaminants (SRP) as par t of the process of identifying diesel PM emissions as a toxic air cont aminant (TAC). The potential cancer risk is expressed as chances per a million p eople.

The estimated diesel PM concentrations and cancer r isk levels produced by a risk assessment are based on a number of assumption s. 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 l ater 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 con ducted 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 oper ating within the study area. The non-cancer health effects evaluated include pre mature death, hospital admissions, asthma-related and other lower respirat ory symptoms, work loss

2 According to the OEHHA Guidelines, the relatively h ealth-protective assumptions incorporated into the Tier-1 risk assessment make it unlikely th at 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 M atter and Sulfates , (PM Staff Report) (CARB, 2002), we calculated the number of a nnual cases of premature death and other health effects associated with expo sure 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 effe cts associated with air contaminant exposure. Exposures to pollutants that were originally emitted into the air can also occur in different pathways as a r esult of breathing, dermal contact, ingestion of contaminated produce, and ing estion 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 i s evaluated by the inhalation pathway only in this study because the risk contrib utions by other pathways of exposure are insignificant relative to the inhalati on pathway.

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

The potential cancer risks levels associated with t he 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 po tential risk levels are

based on the 80 th percentile breathing rate and 70 year exposure dur ation for residents. In this study, we present the potential cancer risk isopleths focusing on risk levels of 100, 200, and 500 chances in a mi llion, as shown in Figure 8. Note that any risk levels of less than 50 chances i n 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 greate st impact, potential cancer risk over 500 chances in a million, occur in areas surro unding the major freeways I710, I-5, I-10, CA-60, etc. The estimated cancer r isk 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 d iesel PM emissions.

Using the U.S. Census Bureau’s year 2000 census dat a, we estimated the population within the isopleth boundaries. The aff ected population numbers for the risk ranges of 100-200, 200-500, and over 500 c hances in a million have been estimated to be about 260,000, 521,000, and 17 5,000. About 90 percent of 1.1 million people living in the study area are exp osed 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 st udy area. The area in which the risks are predicted to exceed 200 in a million is a lso 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 ov er 500 chances in a million covers about 17 percent of the total area within th e study area. The risk isopleths of 1,000 in a million occur in a narrow a rea 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 ri sk levels associated with all

other sources are not included. For the South Coas t Air Basin, the estimated regional potential risk level was estimated to be a bout 900-1,000 chances in a million from all diesel PM emissions in 2000 (SCAQM D 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 fr om on-road diesel trucks are forecasted to decrease in future years due to the i mplementation of several regulations already adopted by the Air Resources Bo ard and the proposed

statewide truck and bus rule. This decline in emissions will result in the reduct ion of the potential cancer (and non-cancer) risks due to exposures to diesel PM. Figure 9 shows how potential cancer risks will decr ease 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 differ ent 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 po tential cancer risks within the study area for the base year 2003 and future ye ars 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 R egulation within the Study Area

As shown in Figure 12, the predicted potential canc er risks will be reduced by about 82 percent in 2015 but then will begin to inc rease as growth surpasses the reductions required by regulatory programs. The pr edicted 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 Ris k Levels from On-Road Diesel Trucks within the Study Area

Figure 11: Projected Affected Population by Risk L evels from On-Road Diesel Trucks within the Study A rea

0.000 0.200 0.400 0.600 0.800 1.000 1.200Impacted 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 80,000Affected 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 > 10E - 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 ep idemiologic studies have found a strong association between exposure to ambi ent particulate matter (PM) and adverse health effects (ARB, 2002). As part of this study, ARB staff conducted an analysis of the potential non-cancer h ealth 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 ev aluated include premature death, hospital admissions, asthma-related and othe r lower respiratory symptoms, work loss days, and minor restricted acti vity days.

Consistent with U.S. EPA (EPA, 2004c), ARB has been using the PM-mortality relationship from Pope et al. (Pope, 2002) since th e adoption of the Emission Reduction Plan for Ports and Goods Movement (GMERP) (ARB, 2006b). The

374

245

60 72

0 50 100 150 200 250 300 350 400

2003 2010 2015 2020

YearPotential 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 othe r health impacts is described in Appendix A of the GMERP. Ambient leve ls of directly emitted diesel PM from on-road diesel truck activities were predic ted for each 500 meter by 500 meter grid cell within the modeling domain (10 mile s 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 Bure au year 2000 census data. Using U.S. EPA’s BENMAP program, we estimate d the number of annual premature deaths and several other non-cancer healt h 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 e ffect estimates are based on concentration-response functions derived from publi shed epidemiological studies relating changes in ambient concentrations to chang es in health endpoints, the population affected, and the baseline incidence rat es.

Non-cancer Health Impacts in 2003

.

The estimated non-cancer health impacts for directl y emitted diesel PM from onroad diesel trucks in the year 2003 are presented i n Table 4. As shown, we estimate that there would be about 42 premature dea ths (for ages 30 and older), 32 hospital admissions due to respiratory and cardi ovascular 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 Diese l 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 0.0055E - 20

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 exposu res to the model-predicted ambient levels of directly emitted diesel PM. Belo w are the estimated noncancer health impacts from direct diesel PM avoided due to the proposed regulation from year 2010 through 2020 within the s tudy 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 cardiovasc ular (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 9 3,000, 95% CI)

In May 2008 ARB released a draft methodology for es timating premature deaths associated with long-term exposures to fine airborn e particulate matter in California that proposes increasing the relative ri sk factor from 6% to 10% increase in premature death per 10 μg/m 3 increase in PM2.5 exposure (ARB, 2008). The premature deaths listed here were calcu lated 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. T hey involve the selection and applicability of the concentration-response fun ctions to California data, exposure estimation, subpopulation estimation, base line incidence rates, and the threshold. These are briefly described below.

• Premature death calculations were based on the con centration-response function of Pope et al. (Pope, 2002). The ARB staf f assumed that concentration-response function for premature morta lity 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 c onstituents of PM have the same health effects. However, numerous studies have shown that the mortality effects of PM in California are comparabl e 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, t he selection of the concentration-response functions was based on the m ost recent and

relevant scientific literature. Details are in CAR B’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 modeli ng grid. That is, the entire population within each modeling grid of 500 m x 500 m was assumed to be exposed uniformly to modeled concentr ation. This assumption is typical of this type of estimation.

• The ARB staff included only directly emitted PM an d 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 co unty. 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-ca se 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-can cer health impacts for other areas of the State; however, there may be greater u ncertainty 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 dies el 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 pr eviously, the health risk assessment consists of three components: (1) emissi on inventory, (2) air dispersion modeling, and (3) risk assessment. Each component has a certain degree of uncertainty associated with its estimatio n and prediction due to the assumptions made. Therefore, there are uncertainti es and limitations with the

results.

Emission Inventory . We have made several assumptions in developing th e emission inventory. While these assumptions are ap propriate at the county level, they may be less appropriate for the particular are as modeled in this assessment. For example, the county specific defau lt model year distribution within EMFAC was assumed to be applicable for all l inks within the domain modeled. In addition, the emission inventory devel oped for this study only included diesel PM emissions from running exhaust a s 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 ai r dispersion models are based on the state-of-the-art formulations, there are unc ertainties associated with the models. The AERMOD model used in this study is the U.S. EPA’s newly preferred air dispersion model. Evaluating the mod el is beyond the scope of this study. The model inputs may have some uncertaintie s. Among all modeling inputs, emission inventory or rate and meteorologic al conditions have the greatest affect on modeling results. The uncertain ties of the emission inputs have been discussed above. Meteorological conditio ns can play a key role in predicted pollutant concentrations. For this model ing 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 statio n. 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 f rom 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 expose d to diesel exhaust at concentrations approximately ten times typical ambi ent exposures (OEHHA, 2003). This can introduce uncertainty in the poten tial risk estimated for the general public because there is a wide range of res ponses 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 epidem iological studies also suggest

somewhat different levels of risk. When the Scient ific Review Panel (SRP) identified diesel PM as a toxic air contaminant, th ey endorsed a range of inhalation cancer potency factors (1.3 x 10 –4 to 2.4 x 10 –3 (μg/m 3 ) –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 facto r of 1.1 (mg/kg-day) -1 can be calculated, which was used in the study.

This study adopts the standard Tier-1 approach reco mmended by the OEHHA for exposure and risk assessment. A Tier 1 approach is an end-point estimate methodology without the consideration of site-speci fic 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 p er day exposure be used for determining residential cancer risks. Lifetime 70- year exposure is a conservative estimate, but it is a historical benchmark for comp aring facility impacts on receptors and for evaluating the effectiveness of a ir pollution control measures.E - 23

IV. HEALTH RISK ASSESSMENT FOR AGRICULTURAL TRUCKS

To screen the potential cancer risks associated wit h uncontrolled agricultural trucks used to transport agricultural commodities f rom the field to a processing facility, ARB staff developed general assumptions b racketing a range of possible operating scenarios and determined the potential ca ncer risks for a “generic” processing facility. The number of truck trips and the distance from the roadway or processing facility wherein the risk levels reac hed 10 in a million or greater were determined. This can be used to provide a “qu alitative” assessment of the potential risk levels near a roadway used by agricu ltural trucks and the processing facility. Actual risk levels will vary due to site specific parameters, including the number of trucks and associated emiss ion rates, operating schedules, site configuration, site meteorology, an d distance to receptors. As such, this assessment should be viewed as a first s creening of the potential health risks, but more work on an actual facility i s 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 proce ssing location and associated roadway. Rather, we developed a generic source comprised of a 400 m by 200 m commodity processing facility with an at tached 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 th e Bakersfield region and assumed a rural dispersion coefficient. The key as sumptions and parameters are provided in Table 5.

Table 5: Modeling Assumptions and Inputs for the A gricultural 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 creepi ng, 5 min idling per truck Roadway: 1 mile length and 10 m width, Trucks trav el 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 f or two truck volumes (40 and 80 trucks per day). As can be seen, at a truck activi ty 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 (gree n contour line). As the truck volume increases, the risk isopleths wherein the ri sk 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 o f 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 Ag ricultural Truck Volumes

of 40 and 80 Trucks per Day

Table 6 lists the distances from the facility bound ary wherein the risk is 10 in a million for different agricultural truck volumes. For example, as the truck volumeE - 25

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

million could extend to a downwind distance about 1 100 ft from the boundary.

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

Million for Different Agricultural Truck Volumes

Because many of the agricultural processing facilit ies 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 facilit ies would impact residents that live along the roadways. These levels could even b e higher in the event there are multiple processing centers that are accessed f rom common roadways.E - 26

REFERENCES

ARB, 1998. For the "Proposed Identification of Dies el Exhaust as a Toxic Air Contaminant". April, 1998

ARB, 2000. Risk Reduction Plan to Reduce Particulat e Matter Emissions from Diesel-Fueled Engines and Vehicles, Staff Report, O ctober, 2000.

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

Hazard Assessment . Staff Report: Public Hearing to Consider Amendmen ts to the Ambient Air Quality Standards for Particulate M atter 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 Asses sment Study for the Ports of Los Angeles and Long Beach, Final Report. April, 2006.

ARB, 2006b. Goods Movement Emissions Reduction Pla n, Air Resources Board, April 2006.

ARB, 2008. ARB Methodology for Estimating Prematur e Deaths Associated Long-term Exposures to Fine Airborne Particulate Ma tter in California.

OEHHA, 2002. Air Toxics Hot Spot Program Risk Asses sment Guidelines: Part II Technical Support Document for Describing Available Cancer Potency Factors.

Office of Environmental Health Hazard Assessment. D ecember, 2002.

OEHHA, 2003. Air Toxics Hot Spots Program Risk Asse ssment Guidelines: The Air Toxics Hot Spots Program Guidance Manual for Pr eparation 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 Poll ution, 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://w ww.scag.ca.gov/modeling/ [Accessed January 2007].E - 27

U.S. EPA, 2004a. User’s Guide for the AMS/EPA Regul atory Model – AERMOD. Report No. EPA-454/B-03-001. Office of Air Quality Planning and Standards. Emissions Monitoring and Analysis Division, Researc h Triangle Park, NC. September, 2004.

U.S. EPA, 2004b. User’s Guide for the AERMOD Meteor ological Preprocessor. Report No. EPA-454/B-03-002. Office of Air Quality Planning and Standards. Emissions Monitoring and Analysis Division, Researc h Triangle Park, NC. September, 2004.

(USA EPA, 2004c). United States Environmental Prot ection Agency, May, 2004, Final Regulatory Impact Analysis: Control of Emiss ions from Nonroad Diesel Engines, EPA-420-R-04-007, Office of Transportation and Air quality, http://www.epa.gov/otaq/regs/nonroad/equip-hd/2004f r.htm#ria

U.S. EPA, 2005. Federal Register, Part III, 40 CFR Part 51, Vol. 70, No. 216, November 9, 2005.E - 28