HomeMy WebLinkAboutCC AG PKT 2005-12-12 #H
AGENDA REPORT
.
DATE:
December 12,2005
TO:
Honorable Mayor'llJld City Council
.'
THRU:
, ,
John B. Bahorski, City Manager
FROM:
, ,
Lee Whittenberg, Director of Development Services
SUBJECT:
RECEIVE AND FILE - CITY COMMENT LETTER RE:
2005 DRAFT DIESEL PARTICULATE MATTER
EXPOSURE ASSESSMENT STUDY FOR THE PORTS
. ,
OF LOS ANGELES AND LONG BEACH
SUMMARY OF REQUEST:
,
Receive and File Staff Report. Authorize staff to continue to monitor and report as
appropriate regarding this study. Instruct Staff to forward to Environmental Quality
Control Board for information.
e
BACKGROUND:
City staff has read the above referenced document and felt that was imperative for the
City to comment on the document prior to the November 18 deadline date established by
the Califomia Air Resources Board (ARB). A letter was sent on November 15 by the
City Manager to the ARB setting forth the importance of this study to understanding the
emissions being created by the Ports of Los Angeles and Long Beach, and expressing the
concerns of the impacts of that analysis .on the identified health risks to the citizens not
only of Seal Beach but of over 2 million persons within the identified 20 mile square
study area. The letter indicates that:
"Seal Beach is clearly identified as being impacted adversely by the health
risks identified within the study, and is almost totally located within the
identified 100-200 isopleths for all emission sources from the port
facilitiei. In addition to the general exposure to citizens discussed in the
document a large portion of Seal Beach is developed with a 7,700 person
senior living community, Seal Beach Leisure World This senior living
community is completely located within the identified 100-200 isopleths
1 Figure 1, "Estimated Diesel PM CQ1l(;er Riskfrom POLA and POLB", page 8, Drffll Diesel Particulate
Matter Erposure Assessment Study For The Ports Of Los Angeles And Long Beach, October 2005
e Agenda Item /I
Z:\My DocumoDts\AQMPIPOLA It POLB Diosol Particulalc Study Commeot Lotter.CC StaffRoporl.docILWlII-16'{)S
Receive And File - City Comment Letter Re:
2005 Dr'fft Diesel Particulate Matter ErpO,fU1'e Assessment Study
for the Ports of Los Angeles and Long Beach
City Council Staff Report
December 12, 2005
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for all emission sources from the port facilities. Leisure World comprises
approximately 6,000 housing units, with a population of approximately
6,600 persons 65 or older, or approximately 86.5% of the total population
of Leisure World.
The impacts of the port complex diesel particulate emissions upon our
community, and particularly within the Leisure World retirement
community are of extreme concern to our citizens. The report indicates on
page 4 that "The most vulnerable populations are those with preexisting
respiratory or cardiovascular disease especially the elderly". The
identified health effects on the young. elderly, and infirm are of particular
concern to our residents." .
A copy of the comment letter from the City Manager is provided as Attachment I for the
information of the City Council and the complete study is provided as Attachment 2.
Overview of Study Results:
Pumose of Study:
"The purpose of the study was to enhance our understanding of the port-related
diesel particulate matter (PM) emission impacts by evaluating the relative _
contributions of the various diesel PM emission sources at the ports to the potential 'W
cancer risks to people living in communities near the ports. This information will
assist in the efforts underway to reduce diesel PM emissions at the ports by. helping
to identify the sources that have the greatest impact on potential cancer risks to
nearby residents and by providing a tool that will allow evaluation-of the impacts of
measures planned and under development that are designed to reduce diesel PM
emissions.
The study focused on the on-port property emissions from locomotives, on-road
heavy-duty trucks, and cargo handling equipment used to move containerized and
bulk cargo such as yard trucks, side-picks, rubber tire gantry cranes, and forklifts.
The study also evaluated the at-berth and over-water emissions impacts from ocean-
going vessel main and auxiliary engine emissions as well as commercial harbor craft
such as passenger ferries and tugboats. For the ocean-going vessel emissions, the
study evaluated the hotelling emissions, I.e. those emissions from vessel auxiliary
engines while at berth, separately from the maneuvering and transiting emissions.
While there are locomotive and on-road heavy-duty truck emissions associated with
the movement of goods through the ports that occur off the port boundaries, these
were not evaluated in this study. Future analyses will consider the impact of these
off-port emissions.'.2 .
2 Page 1, "Draft Diesel Particulate Matter Erposure Assessment Study for the Ports of Los Angeles and _
Long Beach", California Air Resources Board, October 2005 W
. 2
POLA &; POLB Diose! Particu111ll: Study COllIIDCIlt Letter.CC StalfRcport
Receive And File - City Comment Letter Re:
2005 Dr'fft Diesel Particulate Matter Erposlll'e Assessment Study
for the Ports of Los Angeles and Long Beach
City Council Staff Report
December 12, 2005
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Kev Findinfls ofStudv:
"The key findings from this study are:
CJ Diesel PM emissions from the ports are a major contributor to diesel PM in
the South Coast Air Basin.
The combined diesel PM emissions from the ports are estimated to be about
1,760 tons per year in 2002. This represents a significant component of the
regional diesel PM emissions for the South Coast Air Basin (SCAB) at about
21 percent of the total SCAB diesel PM emissions in 2002. Focusing only on
diesel PM emissions occurring on port property or within California Coastal
Waters (CCW), the emissions from ship activities (transiting, maneuvering,
and hotelling) account for the largest percentage of emissions at about 73
percent, followed by cargo handling equipment (10 %), commercial harbor
craft vessels (14%), in-port heavy duty trucks (2%), and in-port locomotives
(1%).
CJ Diesel PM emissions from the ports impact a large area and the associated
potential health risks are of significant concern.
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Diesel PM emissions from the ports result in elevated cancer risk levels over
the entire 20-mile by 20-mile study area. In areas near "the port boundaries,
potential cancer risk levels exceed 500 in a million. As you move away from
the ports, the potential cancer risk levels decrease but continue to exceed 50
in a million for more than 15 miles.
Primary diesel PM emissions from the ports also result in potential non-
cancer health impacts within the modeling receptor domain. The non-cancer
health ~ffects evaluated include premature death, asthma attacks, work loss
days, and minor restricted activity days. Based on this study, average
numbers of cases per year that would be expected in the modeling area have
been estimated as follows:
CJ 29 premature deaths (for ages 30 and older), 14 to 43 deaths as 95%
confidence interval (CI);
CJ 750 asthma attacks, 180 to 1300 as 95% CI;
CJ 6,600 days of work loss (for ages 18-65), 5,600 to 7,600 as 95% CI;
CJ 35,000 minor restricted activity days (for ages 18-65), 28,000 to 41,000
as 95% CI.
CJ "Hotelling" emissions from ocean-going vessel auxiliary engines and
emissions from cargo handling equipment are the primary contributors to the
higher pollution related health risks near the ports.
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Hotelling emissions from ocean-going vessels account for about 20 percent
of the total diesel PM emissions from the ports. These emissions are
responsible for about 34 percent of the port emissions related risk in the
modeling receptor domain based on the population-weighted average risk.
3
POLA &; POLS Diosel Particu1llll: Study Comment Letter.CC StalfReport
Receive And File - City Comment Letter Re:
2005 Dr'fft Diesel Particulate Matter Exposure Assessment Study
for the Ports of Los Angeles and Long Beach
City Council Staff Report
December 12, 2005
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These emiSSions resulted in the largest area (2,036 acres) where the
potential cancer risk levels were greater than 200 in a million in the nearby
communities. The second highest category contributing to cancer risk levels
above 200 in a million was cargo handling equipment, which impacted a
residential area of 410 acres and is responsible for about 20 percent of the
total risk in the modeling receptor domain based on the population-weighted
average risk. Reducing emissions from these two categories will have the
most dramatic effect on reducing the port emissions related risks in nearby
communities.
CJ Emissions from commercial harbor craft, in-port tNcks, in-port rail, and
ocean-going vessels (transit and maneuvering activities) account for a much
smaller percentage of the near source risk, but are an important contributor to
elevated cancer risk levels over a very large area.
Emissions from commercial harbor craft, on-port trucks, on-port rail, and
oceiin going vessels (maneuvering and transit activities) account for about 70
percent of the total diesel PM emissions for the ports. While emissions from
these source categories do not have a major role in the near port risk levels,
they are significant contributors to the overall elevated risk levels in the study
area. Addressing the emissions from these sources is critical if we are to
significantly reduce the exposure of a large population (over 2 million people)
to cancer risk levels in the 50 in a million range.',3
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What is the ARB's concern about Diesel PM?
"Why is ARB concerned about Diesel PM?
Dies!!' engines emit a complex mixture of air pollutants, composed of gaseous and
solid material. The visible emissions in diesel exhaust are known as particulate
matter or PM, which includes carbon particles or "soot." In 1998, ARB identified
diesel PM as a toxic air contaminant based on its potential to cause cancer,
premature deaths, and other health problems. Health risks from diesel PM are
highest in areas of concentrated emissions, such as near ports, rail yards, freeways,
or warehouse distribution centers. Exposure to diesel PM is a health hazard,
particularly to children whose lungs are still developing and the elderly who may
have other serious health problems.
The most vulnerable subpopulations are those with preexisting respiratory or
cardiovascular disease, especially the elderly. In addition, increased hospital
admissions and morbidity from respiratory disease have been associated with
partiCjJlate matter exposure in adults and children. Particulate matter exposure is
associated with an increased risk of lung cancer in epidemiological stUdies.';.!
> Op. Cit., Page 2 and 3.
· Op. Cit., Page 3 and 4.
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POLA &; POLB Di...1 Partioulllll: Study Comlllonl Letter.CC StalfRqlort
Receive And File - City Comment Letter Re:
2005 Dr'fft Diesel Particulate Matter Erposure Assessment Study
for the Ports of Los Angeles and Long Beach
City Council Staff Report
December 12, 2005
e
What are exposure and risk assessments?
"Risk assessment is a yardstick useful for comparing the potential health impacts of
various sources of air pollution. For this risk assessment, the amount of diesel PM
emitted from each source (e.g. cruise ships) is estimated. An air modeling computer
program uses 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 a
particular level of diesel PM can be estimated using the Office of Environmental
Health Assessment's (OEHHA) cancer potency factor for diesel PM. The noncancer
health impacts of diesel PM exposure are possible to quantify, but the cancer health
impacts have more commonly been used as the yardstick with which to compare the
impacts of various diesel sources.
For cancer health effects, the risk is expressed as the number of chances in
a population of a million people who might be expected to get cancer over a
70-year lifetime. The number may be stated as "10 in a million" or "10
chances per million". Often times scientific notation is used and you may see
it expressed as 1 x 10-5.or 10-5. Therefore, if you have a potential cancer
risk of 10 in a million, that means if one million pimple were exposed to a
certain level of a pollutant or chemical there is a chance that 10 of them may
develop cancer over their 70-year lifetime. This would be 10 new cases 01
cancer above the expected rate of cancer in the population. The expected
rate of cancer for all causes, including smoking, is about 200,000 to 250,000
chances in a million (one in four to five people).
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In a risk assessment, risk is expressed as the number of chances in a population of a
million people who might be expected to get cancer over a 70-year lifetime.
However, for informational purposes only, the risk is sometimes reported for other
exposure times, such as a 3D-year or a 9-year risk. Ttie longer the exposlll'6 to a
given air concentration, the greater the cancer risk will be. In this report, only the 70-
year lifetime risk is presented. The exposure assessment study for the Ports of Los
Angeles and Long Beach focuses on potential cancer cases due to elCposure to
diesel PM emissions. However, there is a growing body of scientific data suggesting
that exposure to fine PM results in "/ll'&mature death and morbidity (illness) due to
respiratory and cardiovascular disease. The sensitive subpopulations include people
with pre-existing cardiovascular disease and respiratory disease, including asthma,
particularly those who are also elderly."s
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, Op. Cit., Page 4 and 5.
5
POLA &; POLB Diose! Particu111ll: S1IIdy COllIIDCIlt Letter.CC StalfRcport
Receive And File - City Comment Letter Re:
2005 Dr'fft Diesel Particulate Matter Erposure Assessment Study
for the Ports of Los Angeles and Long Beach
City Council Staff Report
December 12,2005
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What are the Emission Levels?
"What are the diesel PM emissions from port-related activities at POLA and
POLS?
The emissions of diesel PM from port-related activities were estimated to be
approximately 965 tons per year for the POLA and 795 tons per year for the POlB in
the year 2002, or a total of 1,760 tons per year for both ports. As shown in Table 1,
by source category, ocean-going vessels, ship auxiliary engines' hotelling, harbor
craft, cargo handling equipment, in-port heavy-duty trucks, and in-port locomotives
account for about 53, 20, 14, 10, 2, and 1 percent of the mass emissions,
respectively.
Table1 :
E.stimated 2002 Diesel PM Emissions Inventory for POLA and POLS
OGV HOTEL CHC CHE IPT IPL COMBINED
- .
Diesel PM 942 343 244 172 41 18 1760
Emissions
TN
Percent of 53% 20% 14% 10% 2% 1% 100% e
Total
Note: OGV - Oceangoing vessels; HOTEL - Ship's auxiliary engine hoteJling; CHC -
Commercial harbor crafts; CHE -Cargo handling equipment; IPT -In-Port heavy-
duty trucks; IPL - In- Port locomotive
The diesel PM emissions resulting from port activities have been a significant and
growing contributor to regional air pollution and community exposure to toxic air
pollutants. For example, in the South Coast Air Basin (SCAB), the diesel PM
emissions resulting from the ports activities accounted for about 21 percent of the
total SCAB diesel PM emissions in 2002. Growth forecasts predict that trade at the
POLA and POlB will triple by 2020, resulting in a 60 percent increase in diesel PM
emissions from current levels unless further controls are enacted.,,6
How were the ootential cancer risks from diesel PM estimated?
"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 inhaled. 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
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· Op. Cit, Page 5 and 6.
6
POLA I< POLB Di...1 Particu11lO Study Commont Letter.CC StalfRoport
Receive And File - City Comment Letter Re:
2005 Dr'fft Diesel Porticulate Matter Exposvre Assessment Study
for the Ports of Los Angeles and Long Beach
City COlI1lcil Staff Report
December 12, 2005
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Risk Assessments (September 2003). A Tier-1 analysis assumes that an individual
is exposed to an annual average concentration of a pollutant continuously for 70
years. The cancer potency factor was de'leloped by the OEHHA and approved by
the State's Scientific Review Panel on Toxic Air Contaminants (SRP) as part of the
process 0 f identifying diesel PM emission as a toxic air contaminant (T AC)...7
What is the estimated DOtential cancer risk from all sources at the ports?
"Figure 1 shows the potential cancer risk isQpleths for all emission sources at the
two ports superimposed on a map showing the ports and the nearby communities.
The risk contour of 100 in a million extends beyond the modeling receptor domain to
the north of the ports. The domain boundary is about 10 miles north of the port
boundary. The area with predicted cancer risk levels in excess of 100 in a million is
estimated to be about 93,500 acres, which is 57 percent of the effective land area
(163,400 acres, excluding the port property and the water acreage) within the
modeling receptor domain. The area in which the risks are predicted to exceed 200
in a million is also very large, covering an area of about 29,000 acres (18 percent of
the effective land area within the modeling receptor domain). The areas with the
greatest impact have an estimated potential cancer risk of over 500 in a million and
cover about 2 percent of the effective land area within the domain. The risk isopleths
of 1000 and 1500 in a million occur on the ports' property and the nearby ocean
surfaces, and are not considered in this study as people do not reside in these areas.
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Using the U.S. Census Bureau's year 2000 census data, we estimated the
population within the isopleth boundaries. Nearly 60 percent of the 2 million people
that live in the area around the ports have predicted risks of greater than 100 in a
million. The affected population numbers for the cancer risk ranges of 100-200, 200-
500, and over 500 have been estimated to be about 724,000 people, 360,000
people, al1d 53,000 people, respectively. The affected population numbers account
for about 37, 18 and 3 percent of the total population within the modeling receptor
domain, respectively. Note that the risk isopleth of 10 in a million is not shown in
Figure 1 because it is outside of the modeling receptor domain. Also, note that if the
modeling receptor domain expands, the impacted areas and' affected population
would be increased.
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7 Op. Cit, Page 6 and 7.
7
POLA &; POLB Diose! Porti_ Study c;..."....~.cc SlIfl'llopart
Receive And File - City Comment Letter Re:
2005 Draft Diesel Particulate Matter Exposure Assessment Study
for the Ports of Los Angeles and Long Beach
City Council Staff Report
December 12, 2005
Figure 1
Estimated Diesel PM Cancer Risk from POLA and POLS
3750000
3745000
~ 3740000
E-
O>
c:
€
o
z
3730000
3725000 \\ I
I 0 1 2
miles
380000
385000
390000 395000
Easting (m)
405000
400000
Notes: Wilmington Meteorological Data, Urban Dispersion Coefficients, 80th Percentile Breathing Rate, Emission = 1,760
TPY, Modeling Rec~ptor Domain = 20 mi x 20 mi, Resolution = 200 m x 200 m,nll
· Op. Cit., Page 7 and 8.
8
POLA & POLB Diesel Particulate Study COII1Illen' Letter.CC Staff Report
.
e
.
Receive And File - City Comment Letter Re:
2005 Dr'fft Diesel Particulate Matter Erposure Assessment Study
for the Ports of Los Angeles and Long Beach
City Council Staff Report
December 12, 2005
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What other health risks are of concern?
"What are the non-cancer health endpoints associated with exposures to
Diesel PM from port operations?
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 associated with exposures to the model-predicted
ambient levels of directly emitted diesel PM (primary diesel PM) within the modeling
domain. The non-cancer health effects evaluated include premature death, asthma
attacks, work loss days, and I"l)inor restricted activity days.
e
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) within each 200 meter by 200 !T1eter grid cell within the modeling
domain. The populations within each grid cell were determined from U.S. Census
Bureau year 2000 census data. 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)
(GARB, 2002), we calculated the number of annual cases of death and other health
effects associated with exposure to the PM concentration modeled for each of the
grid cells and then calculated the totals over the entire modeling area. Based on our
analysis, it is estimated that the exposures to the directly emitted diesel PM from on-
port operations within the modeling domain result.in approximately 29 premature
deaths for the 2 million people exposed per year. In addition, these exposures are
predicted to result in 750 asthma attacks, 6,600 work loss days, and approximately
35,000 minor restricted activity days. In each case, the values presented represent
the mean value in cases per year for the health end point listed.
These estimates are based on a well-established methodology for calculating
changes in health endpoints due to changes in air pollution levels. However, since
the estimates apply to a limited modeling domain (20 miles by 20 miles), the affected
population is small, and hence the overall estimated health impacts are smaller than
estimates made on a statewide basis. In addition, to the extent that only a subset of
health outcomes is considered here, the estimates should be considered an
underestimate of the total public health impact.
In this study, we also did not consider the diesel PM emissions of on-road heavy-
duty trucks and locomotives related to port activities that occur off-port boundary
within the SCAB (regional emissions). We estimate the off-port regional diesel PM
emissions to be about 206 TPY for the both ports, or 10 percent of the total port-
related emissions (206 TPY vs 1,970 TPY). These regional emissions are
distributed throughout the SCAB and may result in localized health impacts to people
who are live near freeways and railroad corridors within the SCAB. These health
impacts will be evaluated in future studies.
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9
POLA &; POLB Diosel Particulllll: StudY COllIIDCIlt Letter.CC StalfRcport
Receive And File - City Comment Letter Re:
2005 Dr'fft Diesel Particulate Matter Exposure Assessment Study
for the Ports of Los Angeles and Long Beach
City Council Staff Report
December /2, 2005
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In this study, we did not consider the diesel PM emissions of on-road heavy-duty
trucks and locomotives related to port activities that occur off-port boundary within
the SCAB (regional emissions). We estimated the off-port regional diesel PM
emissions to be about 206 TPY for the both ports, or 10 percent of the total port-
related emissions (206 TPY vs 1,970 TPY). These regional emissions are
distributed throughout the SCAB and may result in localized health impacts to people
who are living near freeways and railroad corridors within the SCAB. These health
impacts will be evaluated in future studies.',9
What is beinf! done about this problem?
"What activities are underway to reduce risks?
There are many efforts currently underway to reduce exposures to diesel PM. POLA
and POLB have instituted voluntary programs to reduce diesel PM emissions from
port operations including installation of diesel oxidation catalysts on yard equipment,
funding the incremental costs of cleaner fuels, cold-ironing of ocean-going ships and
providing monetary support to the Gateway Cities truck fleet modernization program.
In addition, efforts at the State and local level to implement the Diesel Risk
Reduction Plan and to fulfill commitments in the State Implementation Plan will also
reduce emissions. For example, the new off-road engine standards adopted by ARB
and the U.S. EPA will reduce emissions from new off-road engines by over 95%
compared to uncontrolled levels. In the fall of 2005, ARB will consider two measures _
to reduce emissions from sources of diesel emissions at ports. One measure wiil W
require reductions from cargo handling equipment and the other from ship auxiliary
engines. To ensure continued emission declines in .the face of the expected growth,
ARB is leading an effort to develop a Port and Intermodal Goods Movement
Comprehensive Emission Reduction Plan that will build upon current efforts and
define the additional strategies needed to reduce public health impacts from port and
related activities. This effort is part of Governor Schwarzenegger's Goods
Movement Action Plan, a plan that reflects the Governor's desire to improve the
m.ovement of goods in California at the same time we work to improve air quality and
protect pUblic health. ,,10 .
FISCAL IMPACT:
None. Staffwill continue to monitor and report as appropriate on this ongoing study and
related activities by ARB, SCAQMD and the Ports of Los Angeles and Long Beach.
· Op. Cit., Page 11 and 12.
I. Op. Cit., Page 12 and 13.
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POLA &; POLB Diose! Particulllll: Sllldy ComIIIonI Letter.CC StalfReport
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Receive And File - City Comment Letter Re:
2005 Dr'fft Diesel Particulate Matter ExpOSlll'e Assessment Study
for the Ports of Los Angeles and Long Beach
City Council Staff Report
December 12, 2005
RECOMMENDATION:
Receive and File Staff Report. Authorize staff to continue to monitor and report as
appropriate regarding this study. Instruct Staff to forward to Environmental Quality
Control Board for information.
e Whittenberg
Director of Development Service
Attachments: (2)
Attachment 1:
City Comment Letter of November 15, 2005 Re: 2005
Draft Diesel Particulate Matter Exposure Assessment Study
for the Ports of Los Angeles and Long Beach
Attachment 2:
2005 Draft "Diesel Particulate Matter Exposure
Assessment Study for the Ports of Los Angeles and Long
Beach", California Air Resources Board
11
POLA &; POLB Dioscl Particul... Study Comment Lottor.CC StalfRepon
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Receive And File - City Comment Letter Re:
2005 Dr'fft Diesel Particulate Matter Exposure Assessment Study
for the Ports of Los Angeles and Long Beach
City Council Staff Report
December 12, 2005
ATTACHMENT 1
CITY COMMENT LETTER OF NOVEMBER
15, 2005 RE: 2005 DRAFT DIESEL
PARTICULATE MATTER EXPOSURE
ASSESSMENT STUDY FOR THE PORTS OF
LOS ANGELES AND LONG BEACH
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POLA &; POLB Diesol PartioulBto Study Comment Lottor CC StalfReport
. _', . _ . , ,- _ . ~ _ t . . ~ . '. >- ~_ , ,
. . , > 1"' ~, " _ ~
November 15, 2005
California Air Resources Board
Attn: Pingkuan Di, Ph.D., P.E.
P. O. Box 2815
Sacramento, California 95812-2815
Dear Dr. Di:
SUBJECT: 2005 DRAFI' DIESEL PARTICULATE MATTER
EXPOSURE ASSESSMENT STUDY FOR THE PORTS
OF LOS ANGELES AND LONG BEACH
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On behalf of the City of Seal Beach, our Director of Development Services, Mr. Lee
Whittenberg, has read the above referenced document and feels that is imperative that
this important work be provided to all appropriate state and federal agencies that have a
role in regulating diesel particulate emissions from the sources identified within the
document. The potential impacts of the identified diesel particulate matter exposures to
over 2 million persons in the Long Beach, southeast Los Angeles and northern Orange
County areas of California cannot be allowed to remaih unrecognized and unregulated.
I will be suggesting to our City Council that they actively discuss the findings of this
important study with representatives of the South Coast Air Quality Management
District, the California Air Resources Board, Governor Schwarzenegger, our State Senate
and Assembly members, and our Federal Congressional and Senate representatives.
There must be a coordinated and carefully thought out process developed in concert with
all of the appropriate regulatory agencies to address this issue, and it must not be allowed
to continue to flounder in the realm of bureaucratic indifference.
Seal Beach is clearly identified as being impacted adversely by the health risks identified
within the study, and is almost totally located within the identified 100-200 isopleths for
all emission sources from the port facilitiesl. In addition to the general exposure to
citizens discussed in the document a large portion of Seal Beach is developed with a
7,700 person senior living community, Seal Beach Leisure World. This senior living
community is completely located within the identified 100-200 isopleths for all emission
e
I Figure 1, "Estimated Diesel PM Cancer Risk from POLA and POLB", page 8, Draft Diesel Particulate
Matter Exposure Assessment Study For The Ports Of Los Angeles And Long Beach, October 2005
Z:lMy Doc:umODlSIAQMP\200S Diesel Particulate Study-POLA &; POLB.ARB Letter.docILWlll-lS-oS
City of Seal Beoch Letter regarding
"Dr'fft Diesel Particulate Matter ExpOSlll'e Assessment
Study for the Ports of Los Angeles and Long Beach"
November 15,2005
sources from the port facilities. Leisure World comprises approximately 6,000 housing e
units, with a population of approximately 6,600 persons 65 or older, or approximately
86.5% of the total population of Leisure World.
The impacts of the port complex diesel particulate emissions upon our community, and
particularly within the Leisure World retirement community are of extreme concern to
our citizens. The report indicates on page 4 that "The most vulnerable populations are
those with preexisting respiratory or cardiovascular disease especially the elderly'. The
identified health effects on the young, elderly, and infirm are of particular concern to our
residents.
I, therefore, urge your Board. to strengthen CARB staffs proposals to achieve the
maximum feasible reductions from sources under state and federal jurisdiction that are
discussed within this study, and to work in concert with the Federal government to
achieve the necessary regulatory controls to reduce these identified adverse health effects
on 2 million persons to an acceptable level.
Please contact Mr. Lee Whittenberg, Director of Development Services, at (562) 431-
2527, extension 313, or bye-mail atlwhittenberl!@ci.seal-beach.ca.usif you have any
questions regarding this matter or require additional information from Mr. Whittenberg.
o~
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hn B. Bahorski
City Manager
City of Seal Beach
Distribution:
City Council
Planning Commission
Environmental Quality Control Board
Director of Development Services
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2005 Dir:se1 Particu1ate Study-POLA &: POLB.ARB Letter 2
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Receive And File - City Comment Letter Re:
2005 Dr'fft Diesel Particulate Matter Erposure Assessment Study
for the Ports of Los Angeles and Long Beach
City Council Staff Report
December 12, 2005
ATTACHMENT 2
2005 DRAFT "DIESEL PARTICULATE
MATTER EXPOSURE ASSESSMENT STUDY
FOR THE PORTS OF LOS ANGELES AND
LONG BEACH", CALIFORNIA AIR
RESOURCES BOARD
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POLA &; POLB Dios.1 Particulllll: Study Comment Letter cc Staff Report
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Draft
DIESEL PARTICULATE MATTER EXPOSURE ASSESSMENT
STUDY FOR THE PORTS OF Los ANGELES AND LONG BEACH
October 2005
California Environmental Protection Agency
altAir Resources Board
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State of California
AIR RESOURCES BOARD
DIESEL P ARTICULATE MATTER EXPOSURE ASSESSMENT
STUDY FOR THE PORTS OF Los ANGELES AND LONG BEACH
Primary Author
Pingkuan Di, Ph.D., P.E.
Contributina Staff
Anthony Servin, P .E.
Kirk Rosenkranz
Beth Schwehr
Acknowledaements
Air Resources Board staff extends its appreciation to representatives of Starcrest
Consulting Group, LLC and the Ports of Los Angeles and Long Beach for providing
assistance with emissions inventory data and spatial allocation of emissions.
The staff of the Air Resources Board has prepared this report. Publication does not
signify that the contents reflect the views and policies of the Air Resources Board.
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DIESEL PARTICULATE MATTER ExPOSURE AsSESSMENT
STUDY FOR THE PoRTS OF Los ANGELES AND LONG BEACH
TABLE OF CONTENTS
Section .............................................._........................................................................................ Paae
Part I: Summary ...........................................................................................................................1
Part II. Technical Support Document.................................................................................. 14
I. INTRODUCTION ..............................................................................................................14
A. OVERVIEW .............:............................................................................................. 14
B. PURPOSE ............................................................................................................. 15
C. DESCRIPTION OF THE PORTS....................................................................... 16
it. EMISSION INVENTORY DEVELOPMENT .................................................................18
A. PORT OF LOS ANGELES.................................................................................. 18
B. PORT OF LONG BEACH ...................................................................................21
C. IN-PORT AND OUT -OF-PORT EMISSIONS ALLOCATION ........................ 22
D. EMISSION INVENTORY SUMMARy............................................................... 22
III. AIR DISPERSION MODELING .....................................................................................25
A. AIR DISPERSION MODEL SELECTION .........................................................25
B. MODEL DOMAIN AND RECEPTOR NETWORK........................................... 25
C. MODEL PARAMETERS ......................................................................................27
D. SPATIAL AND TEMPORAL ALLOCATION OF EMISSIONS .......................28
E. METEOROLOGICAL DATA ............................................................................... 29
IV. EXPOSURE ASSESSMENT.......................................................................................... 33
A. OEHHA GUIDELINES ......................................................................................... 33
B. EXPOSURE ASSESSMENT ........................:..................................................... 34
C. RISK CHARACTERIZATION .............................................................................34
D. ESTIMATION OF NON-CANCER HEALTH .................................................... 47
V. SUMMARY OF FINDINGS .............................................................................................49
REFERENCES ................................................................................................................. 53
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Appendices
Appendix A:
Appendix B:
List of Tables
TABLE 1
TABLE 2
TABLE 3
TABLE 4
TABLE 5
TABLE 6
TABLE 7
TABLE 8
TABLE 9
Draft
Methodologies for Developing Source Category Emission
Inventories ................................................................................................A-1
Comparison of Estimated Diesel PM Cancer Risks from
Oceangoing Vessel Activity Outside of the Breakwater using
Wilmington and King Harbor Meteorological Data Sets .....................B-1
Estimated 2002 Diesel PM Emissions Inventory for POLA and POLB .........5
Summary of Area Impacted by Risk Levels and
Activity Categories (Acres) ...................................................................................9
Summary of Population Affected by Risk Levels and Activity Categories ....9
Estimated Diesel PM Emissions per Vessel Call and 2002 Port Calls ........ 18
2002 Estimated Diesel PM Emissions for the POLA and POLB... ..............23
Emission Source Model Pararneters ................................................................. 28
Temporal Distribution of Diesel PM Emissions at POLA and POLB............ 29
Summary of Area Impacted by Risk Levels and Activity
Categories (Acres) ...............................................................................................37
Summary of Population Affected by Risk Levels Activity Categories .......... 37
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List of Figures
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Figure 1
Figure 2
Figure 3
Figure 4
Figure 5a
Figure 5b
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
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Figure 14
Figure 15
Figure 16
Figure 17
Figure 18
Figure 19
Figure 20
Figure B-1
Figure B-2
Figure B-3
Figure B-4
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Estimated Diesel PM Cancer Risk from POLA and POLB ..............................8
Aerial Photos of POLA and POLB ..................................................................... 17
Estimated 2002 Diesel PM Emissions for POLA and POLB ......................... 24
In-Port and Out-of-Port Distribution of POLA and POLB Diesel PM
Emissions .............................................................................................................. 24
Model Receptor Domain for the Ports of Los Angeles and
Long Beach ...........................................................................................................26
Depiction of the Emission Source Locations ...................................................27
Locations of Air Quality Measurement Sites around the Ports ..................... 30
Annual Wind Rose at Wilmington ......................................................................31
Wind Speed and Stability Class Frequency Distribution at the
Wilmington Meteorological Site .......................................................................... 32
Estimated Diesel PM Cancer Risk from All Diesel-Fueled
Engines at POLA and POLB .............................................................................. 36
Estimated Diesel PM Cancer Risk from Oceangoing Vessel's
Activity at POLA and POLB ................................................................................ 39
Estimated Diesel PM Cancer Risk from Ship Auxiliary
Engines' Hotelling at POLA and POLB............................................................. 40
Estimated Diesel PM Cancer Risk from Commercial Harbor
Craft Vessel Activity at POLA and POLB ......................................................... 41
Estimated Diesel PM Cancer Risk from Cargo Handling
Equipment Activity at POLA and POLB ............................................................ 42
Estimated Diesel PM Cancer Risk from In-Port
Heavy-Duty Trucks at POLA and POLB........................................................... 43
Estimated Diesel PM Cancer Risk from In-Port Locomotive
Activity at POLA and POLB ................................................................................44
Estimated Diesel PM Cancer Risk from Allin-Port Diesel
Engine Activity at POLA and POLB...................................................................45
Estimated Diesel PM Cancer Risk from All Out-of-Port
Diesel Activity at POLA and POLB .................................................................... 46
Distribution of Diesel PM Emissions by Source Categories
for POLA and POLB in 2002 .............................................................................. 49
Population Affected within the Model Domain by Cancer Risk
Levels and Source Categories ........................................................................... 51
Residential Areas Impacted within the Model Domain by
Cancer Risk Levels and Source Categories .................................................... 51
Locations of Meteorological Monitoring Sites
Around the Ports ...................................................................................................B-2
Wind Rose for King Harbor Meteorological Site ..............................................B-3
Frequency Distributions of Wind Speed and Atmospheric
Stability for King Harbor Meteorological Site ...................................................B-4
Comparison of Estimated Diesel PM Cancer Risks from
OGV's Activity in the Shipping Lanes outside the
Breakwater Using Wilmington and King Harbor
Meteorological Data .............................................................................................B-5
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DIESEL PARTICULATE MATTER EXPOSURE ASSESSMENT
STUDY FOR THE PORTS OF Los ANGELES AND LONG BEACH
PART I: SUMMARY
The California Air Resources Board (ARB or Board) conducted an exposure
assessment (study) to evaluate the impacts from airborne particulate matter emissions
from diesel-fueled engines associated with port activities at the Ports of Los Angeles
and Long Beach (ports) located in Southem California. The purpose of the study was to
enhance our understanding of the port-related diesel particulate matter (PM) emission
impacts by evaluating the relative contributions of the various diesel PM emission
sources at the ports to the potential cancer risks to people living in communities near
the ports. This information will assist in the efforts underway to reduce diesel PM
emissions at the ports by helping to identify the sources that have the greatest impact
on potential cancer risks to nearby residents and by providing a tool that will allow
evaluation of the impacts of measures planned and under development that are
designed to reduce diesel PM emissions.
The study focused on the on-port property ernissions from locomotives, on-road heavy-
duty trucks, and cargo handling equiprnent used to move containerized and bulk cargo
such lis yard trucks, side-picks, rubber tire gantry cranes, and forklifts. The study also
evaluated the at-berth and over-water emissions impacts from ocean-going vessel main
and auxiliary engine ernissions as well as commercial harbor craft such as passenger
ferries and tugboats. For the ocean-going vessel emissions, the study evaluated the
hotelling emissions, I.e. those emissions from vessel auxiliary engines while at berth,
separately from the maneuvering and transiting emissions. While there are locomotive
and on-road heavy-duty truck emissions associated with the mowment of goods
through the ports that occur off the port boundaries, these were not evaluated in this
study. Future analyses will consider the impact of these off-port emissions.
The results from the study are presented in this report which is comprised of two parts.
Part I, "Summary," provides an overview and summary of the study in a less technical
and more easily understood formal. Part II, "Technical Support Document," provides a
description of the supporting technical basis for the study and a more comprehensive
summary of the results. For simplicity, the Summary is presented in question-and-
answer formal. The reader is directed to Part II for more detailed information.
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1. What are the major elements of the study?
The major elements ofthe study were:
· developing a baseline (2002) inventory of diesel PM emissions at the two ports from
ocean going vessels (transit, maneuvering, and hotelling), harbor craft, cargo
handling equipment, in port trucks, and in port trains,
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. estimating the ambiert concentration of diesel PM downwind of the ports, and
. estimating the potential cancer risk levels and other non-cancer health effects
associated with the diesel PM concentrations.
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2. What are the key findings from the study?
The key findings from this study are:
. Diesel PM emissions from the ports are a major contributor to diesel PM in the South
Coast Air Basin.
The combined diesel PM emissions from the ports are estimated to be about
1,760 tons per year in 2002. This represents a significant com pone nt of the
regional diesel PM emissions for the South Coast Air Basin (SCAB) at about 21
percent of the total SCAB diesel PM emissions in 2002. Focusing only on diesel
PM emissions occurring on port property or within California Coastal Waters
(CCW) 1, the emissions from ship activities (transiting, maneuvering, and
hotelling) account for the largest percentage of emissions at about 73 percent,
followed by cargo handling equipment (10 %), commercial harbor craft vessels
(14%), in-port heavy duty trucks (2%), and in-port locomotives (1%).
. Diesel PM emissions from the ports impact a large area and the associated potential
health risks are of significant concern.
Diesel PM emissions from the ports result in elevated cancer risk levels over the e
entire 20-mile by 20-mile study area. In areas near the port boundaries, potential
cancer risk levels exceed 500 in a million. As you move away from the ports, the
potential cancer risk levels decrease but continue to exceed 50 in a million for
more than 15 miles. .
Primary diesel PM emissions from the ports also result in potential non-cancer
health impacts within the modeling receptor domain. The non-cancer health
effects evaluated include premature deat,h, asthma attacks, work loss days, and
minor restricted activity days. Based on this study, average numbers of cases
per year that would be expected in the modeling area have been estimated as
follows:
> 29 premature deaths (for ages 30 and older), 14 to 43 deaths as 95%
confidence interval (CI);
> 750 asthma attacks, 180to 1300 as 95% CI;
)> 6,600 days of work loss (for ages 18-65), 5,600 to 7,600 as 95% CI;
> 35,000 minor restricted activity days (for ages 18-65), 28,000 to 4.1.000 as
95%CI.
I In 1983, the ARB established the California Coastal Waters (CCW) boundmy based on coastal meteOrology within
which pollutants released offshore would be transported onshore. The development of the boundary was based on
over 500,000 island, shipboard, and coastal observations from a variety of!",cotds, including those from the U.S.
Weather Bureau, Coast Guard, Navy, Air Force, Marine Corps, and Anny Air Force (ARB, 1982). The CCW _
boundary ranges from about 2S miles off the coast at the narrowest to just over 100 miles at the widest. ..
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· "Hotelling" emissions from ocean-going vessel auxiliary engines and emissions from
cargo handling equipment are the primary contributors to the higher pollution related
health risks near the ports.
Hotelling emissions from ocean-going vessels account for about 20 percent of
the total diesel PM emissions from the ports. These emissions are responsible
for about 34 percent ofthe port emissions related risk in the mode6ng receptor
domain based on the population-weighted average risk. These emissions
resulted in the largest area (2,036 acres) where the potential cancer risk levels
were greater than 200 in a million in the nearby communities. The second
highest category contributing to cancer risk levels above 200 in a million was
cargo handling equipment, which impacted a residential area of410 acres and is
responsible for about 20 percent of the total risk in the modeling receptor domain
based on the population-weighted average risk. Reducing emissions from these
two categories will have the most dramatic effect on reducing the port emissions
related risks in nearby communities.
· Emissions from commercial harbor craft, in-port trucks, in-port rail, and ocean-going
vessels (transit and maneuvering activities) account for a much smaller percentage
of the near source risk, but are an irnportant contributor to elevated cancer risk
levels over a very large area.
3.
Emissions from commercial harbor craft, on-port trucks, on-port rail, and ocean
going vessels (mane uvering and transit activities) account for about 70 percent of
the total diesel PM emissions for the ports. While emissions from these source
categories do not have a major role in the near port risk levels, they are
significant contributors to the overall elevated risk levels in the study area.
Addressing the emissions from these sources is critical if we are to significantly
reduce the exposure of a large population (over 2 million people) to cancer risk
levels in the 50 in a million range.
Why is ARB concerned about Diesel PM?
Diesel engines emit a complex mixture of air pollutants, composed of gaseous and solid
material. The visible emissions in diesef exhaust are known as particulate matter or
PM, which includes carbon particles or "soot." In 1998, ARB identified diesel PM as a
toxic air contaminant based on its potential to cause cancer, premature deaths, and
other health problems. Health risks from diesel PM are highest in areas of concentrated
emissions, such as near ports, rail yards, freeways, or warehouse distribution centers.
Exposure to diesel PM is a health hazard, particularly to children whose lungs are still
developing and the elderly who may have other serious health problems.
The health impacts of particulate matter (PM 10 and PM 2.5) have been studied in
epidemiological studies conducted in many different cities. Diesel particulate matter is a
major component of particulate matter in many cities. Diesel particulate matter is
composed of carbonaceous particles (soot) and particles that can form from nitrogen
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oxides (NOx) emitted by diesel engines. These studies have found an increase of one
to two percent in daily mortality associated with each 10 Jl9!m3 increase in PM 10 e
exposure. The most vulnerable subpopulations are those with preexisting respiratory or
cardiovascular disease, especially the elderly. In addition, increased hospital
admissions and morbidity from respiratory disease have been associated with
particulate matter exposure in adults and children. Particulate matter exposure is
associated with an increased risk of lung cancer in epidemiological studies.
The ARB staff has estimated that 2,000 premature deaths statewide are linked to direct
diesel PM exposure and 900 premature deaths are associated with indirect diesel PM
exposure in the year 2000 alone. Exposure to fine particulate matter, including diesel
PM 2.5, can also be linked to a number of heart and lung diseases. For example, the
ARB staff has estimated that 5,400 hospital admissions for chronic obstructive
pulmonary disease, pneumonia, cardiovascular disease, and asthma were due to
exposure to direct diesel PM 2.5 in California. An additional 2,400 admissions were
linked to exposure to indirect diesel PM (Lloyd. 2001). There are uncertainties in these
analyses, but the non-cancer public health impacts of diesel PM exposure may
outweigh the considerable public health impacts of diesel PM as a carcinogenic
substance.
4. What are exposure and risk assessments?
Risk assessment is a yardstick useful.for-comparing
the potential health impacts of various sources of
air pollution. For this risk assessment, the amount _
of diesel PM emitted from each source (e.g. cruise W
ships) is estimated. An air modeling computer
program uses 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 a particular level
of diesel PM can be estimated using .
the Office of Environmental Health
Assessment's (OEHHA} cancer
potency factor for diesel PM. The non-
cancer health impacts of diesel PM
exposure are possible to quantify, but
the cancer health impacts have more
commonly been used as the yardstick
with which to compare the impacts of
various diesel sources. Risk
assessment has various uncertainties
in the methodology and is therefore
deliberately designed so that risks are
not under predicted. Risk assessment
is thus best understood as a tool for
comparing risks from various sources,
usually for purposes of prioritizing risk reduction, and not as literal prediction of the
community incidence of disease from exposure. e
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In a risk assessment, risk is expressed as the number of chances in a population of a
million people who might be expected to get cancer over a 70-year lifetime. However,
for informational purposes only, the risk is sometimes reported for other exposure times,
such as a 3D-year or a 9-year risk. The longer the exposure to a given air
concentration, the greater the cancer risk will be. In this report, only the 70-year lifetime
risk is presented. The exposure assessment study for the Ports of Los Angeles and
long Beach focuses on potential cancer cases due to exposure to diesel PM emissions.
However, there is a growing body of scientific data suggesting that exposure to fine PM
results in premature death and morbidity (illness) due to respiratory and cardiovascular
disease. The sensitive subpopulations include people with pre-existing cardiovascular
disease and respiratory disease, including asthma, particularly those who are also
'elderly.
5. Where are the Port of Los Angeles and the Port of Long Beach located and
what port activities occur there?
The Ports of Los Angeles (POlA) and long Beach (POlS) are located adjacent to each
other on San Pedro Say, about 20 miles south of downtown Los Angeles. Together,
they form the third-largest port complex in the world. The primary purpose of the ports
is to move cargo on and off ocean-going ship_li.and onto trucks or railcars. The majority
of goods are transported in containers although the ports also handle non-containerized
goods such as coke and motor vehicles. These activities involve a wide variety of
sources that contribute to diesel PM and oxides of nitrogen (NOx) emissions such as
the ocean-going ships that participate in international trade. Other sources include
trucks, locomotives, cargo handling equipment, and harbor craft such as tug boats, crew
boats, and fishing vessels.
6. What are the diesel PM emissions from port-related activities at POLA and
POLB?
The emissions of diesel PM from port-related activities were estimated to be
approximately 965 tons per year for the POLA and 795 tons per year for the POLS in
the year 2002, or a total of 1,760 tons per year for both ports. As shown in Table 1, by
source category, ocean-going vessel>, ship auxiliary engines' hotelling, harbor craft,
cargo handling equipment, in-port heavy-duty trucks, and in-port locomotives account
for about 53,20,14,10,2, and 1 percent of the mass emissions, respectively.
14%
10%
2%
1%
100%
NoIIl: OGV - Oceangoing VIIssels; HOTEL - Ship'. auxiliary angina hollllDng; CHC - Commercial herbor CI1Ifts; CHE-Cergo
handling aquipmen~ IPT - In-Port haavy-duty IJucks; IPL - h-Port locomotive.
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By source area, about 43 percent of the emissions occur on land -based port property
and over the water within the breakwater2 and the remaining (57 percent) occur outside e
of the breakwater over water. These emissions estimates include only the emissions
that are occurring on port property and the over-water emissions from ocean-going
ships. It does not include the more regional land -based emissions from trucks and
locomotives that occur outside of the port boundaries.
The diesel PM emissions resulting from port activities have been a significant and
growing contributor to regional air poll!Jtion and community exposure to toxic air
pollutants. For example, in the South Coast Air Basin (SCAB), the diesel PM emissions
resulting from the ports activities accounted for about 21 percent of the total SCAB
diesel PM emissions in 2002. Growth forecasts predict that trade at the POLA and
POLB will triple by 2020, resulting in a 60 percent increase in diesel PM emissions from
current levels unless further controls are enacted.
7. How were the diesel PM concentrations near the ports estimated?
ARB staff used the United States Environmental Protection Agency (U.S. EPA)
approved computer model (ISCST3) to estimate the annual average otfsite
concentration of diesel PM resulting fro(ll the activity at the two ports. The key inputs to
the computer model were the diesel PM ernissions inforrnation (magnitude, timing, and
location), the meteorological data (wind speed, direction, etc.), and the dispersion
coefficients (rural or urban). Meteorological data, used as a direct input to the
dispersion model, are obtained from an air quality monitoring study conducted in
Wilmington in 2001. The meteorological observations were located about one. mile from e
the north boundary of the Port of Los Angeles. These data are the most recent and
most representative meteorological data for the dock areas of the Ports of Los Angeles
and Long Beach. Because the area surrounding the ports has urban characteristics,
the modeling was done using the urban dispersion coefficients.
8. How were the potential cancer risks from diesel PM estimated?
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 inhaled.
The methodology used to estimate the potential cancer risks is consistent with the
Tier-1 analysis presented in OEHHA's Air Taxies Hot Spots Program Guidance Manual
for Preparation of Health Risk Assessmenls (September 2003). A Tier-1 analysis
assumes that an individual is exposed to an annual average concentration of a pollutant
2 The breakwater protects POLA and POLB Harbor from rough seas and waves. The breakwater is about nine miles
long (east-west) and was built in a pyramid shape with rocks from Catalina lsland. The bottom on the ocean floor is
200 feet wide and the lop is only 23 feet wide. Construction of the breakwater began in 1899 and look SO years 10
complete. The breakwater is approximately 4.5 miles fromthe ports' north land boundary.
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continuously for 70 years.3 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 emission as a toxic air contaminant
(TAC).
9. What is the estimated potential cancer risk from all sources at the ports?
Figure 1 shows the potential cancer risk isopleths for all emission sources at the two
ports superimposed on a map showing the ports and the nearby communities. The risk
contour of 100 in a million extends beyond the modeling receptor domain to the north of
the ports. The domain boundary is about 10 miles north of the port boundary. The area
with predicted cancer risk levels in excess of 100 in a million is estimated to be about
93,500 acres, which is 57 percent of the effective land area (163,400 acres, excluding
the port property and the water acreage) within the modeling receptor domain. The
area in which the risks are predicted to exceed 200 in a million is also very large,
covering an area of about 29,000 acres (18 percent of the effective land area within the
modeling receptor domain). The areas with the greatest Impact have an estimated
potential cancer risk of over 500 in a million and cover about 2 percent of the effective
land area within the domain. The risk isopleths of 1000 and 1500 in a million occur on
the ports' property and the nearby ocean surfaces, and are not considered in this study
as people do not reside in these areas.
Using the U.S. Census Bureau's year 2000 census data, we estirnated the population
within the isopleth boundaries. Nearly 60 percent ofthe 2 million people that live in the
area around the ports have predicted risks of greater than 100 in a million. The affected
population numbers for the cancer risk ranges of 100-200, 200-500, and over 500 have
been estimated to be about 724,000 people, 360,000 people, and 53,000 people,
respectively. The affected population numbers account for about 37, 18 and 3 percent
of the total popUlation within the modeling receptor domain, respectively. Note that the
risk isopleth of 10 in a million is not shown in Figure 1 because it is outside of the
modeling receptor domain. Also, note that if the modeling receptor domain expands,
the impacted areas and affected population would be increased.
3 According to the OEHHA Guidelines, the relatively health-protective assumptions incorporated into the Tier-l risk
assessml:llt make it unlikely that the risks are underestimated for the gl:lleral population.
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Figure 1
Estimated Diesel PM Cancer Risk from POLA and POLB
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3750
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c
€
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3730000
372500
I
o 1 2
miles
380000
385000
390000 395000
EastIn9 (m)
400000
405000
Notes: Wilmington Meteorological Data, Urban Dispersion Coefficients, 80th Percentile .
Breathing Rate, Emission: 1,760 TPY, Modeling Receptor Domain = 20 mi x 20 mi,
Resolution = 200 m x 200 m. '
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10. What are the relative contributions to the potential cancer risks from the
various diesel PM emission sources at the ports?
The different emission sources are used at various locations on the ports property.
Thus, contributions of these emission sources to exposures in the nearby
neighborhoods are different. As shown in Tables 2 and 3, the emissions from cargo
handling equipment and on-port heavy-duty trucks resultecj in areas within the nearby
communities having risk levels exceeding 500 in a million while the highest risk levels
associated with the other categories were between 200 and 500 in a million. Within the
modeling receptor domain, ship hotelling emissions and cargo handling equipment
impacted the largest areas and affected more people than the other sources of
emissions when considering the risk levels greater than 100 in a million. When
considering'risk levels greater than 10 in a million, all the port sources, other than in-
port heavy-duty trucks and locomotives, had similar impacts, affecting at least 119,000
acres and at least 1.4 million people. By source location, the impacts resulting from the
in-port emissions (within the breakwater) are much larger than those resulting from the
out-of-port emissions (outside the breakwater), although the emission magnitude of the
former is less than the latter (750 TPY vs 1010 TPY). Quantitatively, within the
modeling domain, the population-weighted risk resulting from the in-port emissions is
about 4.5 times greater than the risk resulting from the over water out-of-port
emissions.
Table 2: Summary of Area Impacted by Risk Levels and Activity Categories
(Acres)
'-RlsK-.(;.v.llI( ~.,~OGV.~;;'. :;'HOTEt\;. r",~':C<Hc. \r.;: :L~~CHE.:~~ .:r:','IP,TM(", :~';t~lJ~ i~ t' COMBINED.'
lajq.500~: 0 0 0 50 50 0 2.500
;Rjsk~...'2liO~. 110 2036 20 410 160 40 29.000
RlSl<~.ool!- 227 12700 750 4,100 376 160 94.000
,'RISJt.~-lli'~.. 163 435 160,470 125 250 119000 29 750 11240 163.435
Table 3: Summary of Population Affected by Risk Levels and Activity Categories
(Number of People)
.Risl{;L8viililf t){!PG~I: -!',,:HQTEL'i' "{l:!!Cftct';( f.~'i!.'.tHE~' ..: 1:\! :lI"T_ .,..{ 11"':. ':"IP'L;.u,;~ ;.CQMBINED:\
'Risk.:it.5dDljf, 0 0 0 3200 205 0 53 000
!"RiSI<t:i:'2~i 18 46 020 5,000 11100 1780 680 411 200
. Risk'.>.:'.-lIlO 1 810 221 567 22 960 82 000 8270 4330 1 135,000
~Ris~do 1 9n 760 1 949 850 1 516515 1 444,000 422 910 213,430 1 977,770
Noles:
1. OGv - O"""ngalng vessels; HOTEL- Ship's auxiliary engine hatelllng; CHC - Commercial halllar crafts; CHE -Cargo
handling equipment; 1FT - h-Part heavy-duty trucks; IPl- In-Part lacamaUva
2. The modal receptor dameln of 2D-mlle x 20- mile with urban dlspelllian caetliclants with a receptor resoluUon of 200m x 200m
was used. The etfeclive receptar modeling domain (exclucllng tha part properties and the ocean wate~ Is esUmated to be about
255 lIquere miles; The calculations here ere ONLY besed an tha etfectlve modeling receptor domain.
3. The 8ti" percentile breathing rele for adults lllI8r 701tear IIfeUme was assumed,
4. Metearolaglcel data frarn Wilmington (2001) was used for POLA and POLB.
5. The risks wilt1ln bath ports end lllI8r the ocean waler were ""cluded for calculellans of average risks and affected erees .
6. The estlmatad population in this Table Is ONLY besed an the modeling IBCllptar domain using the U.S. Census Bureau's yeor
2000 census data.
7. W the modeling receptor damaln llXpands, the populellan and area affecled would be increased.
6. The comblnad column provides the populetlOn _ and erea Impacllld for the cumuletive impecls from all the emission
sources. Tho individuBl impecls are nat ackIlllvllsince the combined Impecls are greater than the sum of tha Individual
sources. Far ""emple. corgo handing equipment and commercial holllar craft emissions may inpaclthe same location and
population. While Individually the impacls may l'IIlIullln cancer ~sk _Is b_n 100 and 200 In a million, when you combine
the Impacls, the resulllng risks could be greater then 200 in 0 mllian.
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11. How do the results compare to the SCAQMD MATES-II study?
For comparison purposes, the ARB staff compared the study results to the South Coast
Air Quality Management District (SCAQMD)'s second Multiple Air Toxies Exposure
Study (MATES-II). (SCAQMD, 2000) The MATES-II study indicated the modeled
potential risk in the grid cell containing the Wilmington air quality monitoring station is
1,187 potential cancer cases per million due to diesel PM emissions from port activities,
freeways, and other sources of diesel PM. This Wilmington grid cell is approximately 2
miles north of the ports. Our study shows a risk level of about 400 cases in a million in
the same general vicinity. In the nearby residential areas within one mile from port
boundaries, risk levels (from diesel PM emissions as well as other toxies) ranged from
1000 to 1500 cases in a million based on the MATES-II study. Our study shows a risk
range of 500 to 1000 cases in a million from diesel PM emissions. The differences can
be attributed to different modeling configurations. For example, MATES-II used the
Urban Airshed Model (UAM) model, a grid based model with 2 km grid cells, while our
study used the ISCST3 model, a Gaussian plume model. In addition, MATES-II
simulates diesel PM from all sources (e.g., port activities and freeway emissions) for the
1998 base year while our study is limited to diesel PM from port activities for the year
2002. Also the MATES-II study released ocean-going emissions near ground level
(within the first horizontal layer of the UAM). Our study released ocean going ernissions
at 50 meters above "ground" (sea level) which will result in greater dispersion of
emissions. 4
12. What are the uncertainties associated with risk assessments?
The estimated diesel PM concentrations and 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 risk calculated by a risk assessment is intentionally designed to
avoid underprediction. There are also many uncertainties in the health values used in
the risk assessment. Some of the factors that affect the uncertainty are discussed
below. '
When available, as is the case with diesel PM, scientists will use studies of people
exposed at work to estimate risk from environmental exposures. There can be a wider
range of responses in the general public than in the workers in the epidemiology study
used fo determine the cancer potency factor. Also, the actual worker exposures to
diesel PM were based on limited monitoring data and were mostly derived based on
estimates of emissions and duration of exposure. Different epidemiological studies
suggest somewhat different levels of risk. When the State's Scientific Review Panel
(SRP)5 identified diesel PM as a toxic air contaminant, they endorsed a range of
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· The higher release point was used because the average ship stack height is about 43 m tall. When the emissions
are released from the top ofa ship's exhaust stack, there is a plume rise that occurs which was estimated to average
to be about 7 meters. This results in an average release height of SO meters.
'The Scientific Review Panel (SRP/Panel) is charged with evaluating the risk assessments of substances proposed
for identification as toxic air contaminants by the Air Resources Board (ARB) and the Department of Pesticide _
Regulation (DPR). In ClIIT)'ing out this responsibility. the SRP reviews the exposure and health assessment reports W
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inhalation cancer potency factors (1.3 x 10-4 to 2.4 x 10-3 (1.I9/m3) -1) and a risk factor
of 3x1 0 -4 (l.Ig/m3r1, as a reasonable point estimate of the unit risk. From the unit risk
factor an inhalation cancer potency factor of 1.1 (mg/kg-dayr1 may be calculated.
As mentioned above, there is no direct measurement technique for diesel PM. This
analysis used an air dispersion modeDng to estimate the concentrations to which the
public is exposed. The air dispersion models are based on the state-of-the-science
formulations which have an uncertainty. Additionally, each of the model inputs
(i.e., pollutant emission rates, pollutant release parameters, meteorological conditions,
and dispersion coefficients) has an uncertainty of their own. All of these factors make
up the "uncertainty" in the risk assessment.
13. What are the non-cancer health endpoints associated with exposures to
Diesel PM from port operations?
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 associated with exposures to the model-predicted ambient levels of directly
emitted diesel PM (primary diesel PM) within the modeling domain. The non-cancer
health effects evaluated include prernature death, asthma attacks, work loss days, and
minor restricted activity days.
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)
within each 200 meter by 200 meter grid cell within the modeling domain. The
populations within each grid cell were determined from U.S. Census Bureau year 2000
census data. 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 death and other health effects associated with exposure to
the PM concentration modeled for each of the grid cells and then calculated the totals
over the entire modeling area. Based dn our analysis, it is estimated that the exposures
to the directly emitted diesel PM from on-port operations within the modeling domain
result in approximately 29 premature deaths for the 2 million people exposed per year.
In addition, these exposures are predicted to result in 750 asthma attacks, 6,600 work
loss days, and approximately 35,000 minor restricted activity days. In each case, the
values presented represent the mean value in cases per year for the health end point
listed.
These estimates are based on a well-established methodology for calculating changes
in health endpoints due to changes in air pollution levels. However, since the estimates
apply to a limited modeling domain (20 miles by 20 miles), the affected population is
small, and hence the overall estimated health impacts are smaller than estimates made
and underlying scientific data upon which the reports are based, which are prepared by the ARB, DPR, I\Ild the
Office of Environmental Health Hazard Assessment (OEHHA) pursuant to the sections 39660-39661 of the Health
and safety Code and sections 14022-
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on a statewide basis. In addition, to the extent that only a subset of health outcomes is
considered here, the estil't1ates should be considered an underestimate of the total e
public health impact.
In this study, we also did not consider the diesel PM emissions of on-road heavy-duty
trucks and locomotives related to port activities that occur off-port boundary within the
SCAB (regional emissions). We estimate the off-port regional diesel PM emissions to
be about 206 TPY for the both ports, or 10 percent of the total port-related emissions
(206 TPY vs 1,970 TPY). These regional emissions are distributed throughout the
SCAB and may result in localized health impacts to people who are live near freeways
and railroad corridors within the SCAB. These health impacts will be evaluated in future
studies.
In this study, we did not consider the diesel PM emissions of on-road heavy-duty trucks
and locomotives related to port activities that occur off-port boundary within the SCAB
(regional emissions). We estimated the off-port regional diesel PM emissions to be
about 206 TPY for the both ports, or 10 percent of the total port-related emissions (206
TPY vs 1,970 TPY). These regional emissions are distributed throughout the SCAB
and may result in localized health impacts to people who are living near freeways and
railroad corridors within the SCAB. These health impacts will be evaluated in future
studies.
14. Are there other studies planned that will evaluate the impacts of port-
related diesel PM emissions?
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As mentioned above, during 1998 -1999, the South Coast Air Quality Management
District (SCAQMD) conducted the second Multiple Air Toxics Exposure Study
(MATES-II) to determine the Basin-wide risks associated with major airborne
carcinogens, including diesel PM. Currently, SCAQMD is conducting MATES-III to
assess current air toxics levels within the Air Basin using updated emission inventories,
refined modeling methodologies, and improved assumptions. MATES-III will
incorporate all air toxic emission sources, e.g., stationary, on-road, and off-road mobile
sources, and all air toxics, e.g., diesel PM, 1,3-butadiene, benzene, chromium, etc. In
addition, ARB is conducting a neighborhood assessment study for Wilmington, which is
nearby the ports. This study is a part of ARB's Neighborhood Assessment Program.
The objective is to estimate health risks in Wilmington and surrounding areas. Like
MATES-III, this project will consider all emission sources and all air toxic contaminants.
15. What activities are underway to reduce risks?
There are many efforts currently underway to reduce exposures to diesel PM. POLA
and POLB have instituted voluntary programs tQ !ll.9~~.>diesel PM ernissions.fro.m port
operations including installation of diesel oxidation catalysts on yard equipment, funding
the incremental costs of cleaner fuels, cold-ironing of ocean-going ships and providing
monetary support to the Gateway Cities truck fleet moderniz.ation program. In addition,
efforts at the State and local level to implement the Diesel Risk Reduction Plan and to
fulfill commitments in the State Implementation Plan will also reduce emissions. For _
example, the new off-road engine standards adopted by ARB and the U.S. EPA will W
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reduce emissions from new off-road engines by over 95% compared to uncontrolled
levels. In the fall of 2005, ARB will consider two measures to reduce emissions from
sources of diesel emissions at ports. One measure will require reductions from cargo
handling equipment and the other from ship auxiliary engines. To ensure continued
emission declines in the face of the expected growth, ARB is leading an effort to
develop a Port and Intermodal Goods Movement Comprehensive Emission Reduction
Plan that will build upon current efforts and define the additional strategies needed to
reduce public health impacts from port and related activities. This effort is part of
Governor Schwarzenegger's Goods Movement Action Plan, a plan that reflects the
Gove rnor's desire to improve the movement of goods in California at the same time we
work to improve air quality and protect public health.
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PART II: TECHNICAL SUPPORT DOCUMENT
I. INTRODUCTION
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Emissions from port-related goods movement are a significant and growing contributor
to community air pollution. In communities with significant goods movement activity,
such as communities located adjacent to California maritime ports, a particular concern
is exposure to diesel particulate matter (diesel PM). This pollutant poses a lung cancer
hazard for humans and causes non-cancer respiratory and cardiovascular effects that
increase the risk of premature death (ARB, 1998a). The particles are readily inhaled
because of their small size and can effectively reach the lowest airways of the lung.
Many of the adsorbed compounds are known or suspected mutagens and carcinogens.
(ARB, 2002)
To better understand the impacts from port activities, Air Resources Board (ARB) staff
conducted an exposure assessment study of diesel PM emissions from port-related
activities at the Ports of Los Angeles and Long Beach (ports) located in Southern
California. This part provides the technical details on the exposure assessment. The
reader is directed to Part I, Summary, for a less technical discussion of the study.
A. Overview
Risk assessment is a complex process that requires the analysis of many variables to
model real-world situations. Three steps were taken to perform the exposure
assessment for the ports:
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. developing a diesel PM emissions inventory that reflects the amount of diesel PM
released annually from port-related activities;
. conducting air dispersion modeling to estimate the ambient concentration of diesel
PM that results from these emissions; and
. estimating the potential cancer risk from the modeled exposures.
The following chapters provide a description of each element of the exposure
assessment. Specifically, the following information is provided:
. the methodology used to develop the port-related diesel PM emissions;
. a summary of the estimated diesel PM emissions inventory for the ports;
. a discussion on the air dispersion modeling conducted to estimate ambient
concentrations of diesel PM;
. the results of the air dispersion modeling;
. an estimate ofthe potential impacts (potential cancer risks) to nearby residences
due to exposure to ambient concentrations of diesel PM from port-related activities
at the ports; and
. a comparison between the risk impacts from the various emission sources at the
ports.
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B. Purpose
In the South Coast Air Basin (SCAB), diesel PM emissions from port-related activities
are a significant and growing contributor to regional air pollution and community .
exposures to toxic air pollutants. For example, in the SCAB, the diesel PM emissions
resulting from the movement of goods through the Ports of Los Angeles (POLA) and the
Port of Long Beach (POLB) accounted for about 21 percent of the total SCAB diesel PM
emissions in 2002. Growth forecasts predict that trade at POLA and POLB will triple by
2020, resulting in a 60 percent increase in diesel PM emissions from current levels
unless further controls are enacted. POLA and POLB operate in close proximity to
several communities including San Pedro, Long Beach, and Wilmington. These nearby
communities face potentially higher health risks from the port-generated diesel PM
emissions.
There are many efforts currently underway to reduce exposures to diesel PM. POLA
and POLB have instituted voluntary programs to reduce diesel PM emissions from port
operations including installation of diesel oxidation catalysts on yard equipment, funding
the incremental costs of cleaner fuels, cold-ironing of ocean-going ships, and providing
monetary support to the Gateway Cities truck fleet modernization program. In addition,
efforts at the State and local level to implement the ARB Diesel Risk Reduction Plan
and to fulfill commitments in the State Implementation Plan will also reduce emissions.
New off-road engine standards adopted by ARB and the United States Environmental
Protection Agency (U.S. EPA) will reduce emissions from new off-road engines by over
95% compared to uncontrolled levels. In the fall of 2005, ARB will consider two
measures to reduce emissions from port sources. One measure will require reductions
from cargo handling equipment and the other from ship auxiliary engines. To ensure
continued emission declines in the face of the expected growth, ARB is leading an effort
to develop a Port and Intermodal Goods Movement Comprehensive Emission
Reduction Plan that will build upon current efforts and define the additional strategies
needed to reduce public health impacts from port and related activities.
The purpose of this exposure assessment study is to enhance our understanding of the
port-related diesel PM emission impacts on communities near POLA and POLB .and to
assist in the evaluation of control measures under development or planned. Because
the emission sources are located at various locations on the port property, the
contributions of these emission sources to nearby neighborhoods will be different. Both
the location of the emissions and the magnitude need to be taken into consideration
when determining the degree of health risks to people who are living around the ports.
To summarize, the purpose of the exposure assessment is to:
. investigate the impacts of the various port emission sources on nearby
neighborhoods;
. identify the most significant emission source{s);
. prioritize possible mitigation measures to control diesel PM emissions based on the
relative magnitude of health risks; and
. assist in evaluating the impacts of measures developed to reduce emissions.
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c. Description oftha Ports
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POLA and POLB are located adjacent to each other on the San Pedro Bay, about 20
miles south of downtown Los Angeles. The ports are directly adjacent to the
communities of Long Beach, San Pedro, and Wilmington. The ports are primarily
container ports, moving goods into and out of California in containers. However, they
also handle non-containerized goods such as coke and automobiles. While the majority
of the goods movement occurs during the day, the ports do operate 24 hours a day, 7
days a week, and 365 days a year. The ports are the first and second busiest seaports
in the Western United States. POLA encompasses 7500 acres, 43 miles of waterfront
and features 26 cargo terminals. These terminals handle nearly 150 million metric
revenue tons of cargo annually. In 2004, the POLA moved in 7.4 million TEUs 1, which
was a new national container record. POLB covers about 3000 acres of land. In 2004,
tonnage through POLB was 73.6 million metric tons, and about 5.8 million TEUs moved
through the Port. Combined, POLA and POLB are the world's third-busiest port
complex, after Hong Kong and Singapore.
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I The TEU is the international standard measure used to describe containers. A 20-foot container - 1 TEU.
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2a. Port of Los Angeles (Courtesy of POLA, hllp:llwww.portoflosangeles.org)
2b. Port of Long Beach (Courtesy of POLB, hllp:/Iwww.palb.cam)
Figure 2: Aerial Photos of POLA and POLB
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II.
EMISSION INVENTORY DEVELOPMENT
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Air dispersion' models require emission inputs that properly characterize source-specific
emissions for diesel PM .from various activities in the ports. The port-related activities
are categorized as: ocean-going vessels, auxiliary engine hotelling, commercial harbor
craft, cargo handling equipment, railroad locomotives, and heavy-duty trucks. POLA
and POLB recently hired Starcrest Consulting Group, LLC (Starcrest) to develop
detailed emission inventories for all emission sources for POLA and three sources
(cargo handling equipment, in-port locomotives, and in-port heavy-duty trucks) for the
POLB. At the request of the ports, Starcrest used 2001 as the base year for POLA and
2002 as the base year for POLB. For this exposure assessment study, 2002 was
chosen as the baseline year for both ports. In this chapter, we briefly describe how we
projected the 2001 POLA emission inventory to 2002 and how we developed the 2002
emissions inventory for ocean-going ships, auxiliary engine hotelling and comrnercial
harbor craft for POLB. The basic methodologies used in the emission inventory
development are briefly described in Appendix A.
A. Port of Los Angeles
As stated above, Starcrest prepared an emission inventory for aU emission sources at
the POLA using 2001 as the baseline year. (Starcrest, 2004a) The inventory utilizes an
activity-based approach and focuse~ on emissions of diesel PM for aU significant
sources operating in the Port. In addition to in-port activities, emissions from railroad
locomotives and on-road trucks transporting port cargo were also estimated based on
the activity that occurs outside the Port, but within the South Coast Air Basin
boundaries. Only in-port emissions and over water emissions from ocean-going ships
and harbor craft were evaluated in this exposure assessment. Our methodology for
projecting the 2001 POLA inventory to 2002 is presented below.
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Ocean-aoina Vessels
For 2001, Starcrest estimated emissions from ship cruising (includes transiting and
maneuvering) and hotelling activities. To estimate the 2002 POLA emissions, ARB staff
assumed that the emissions per vessel call would be the same in 2001 and 2002.
Emissions per Ioessel call were calculated from the emissions per vessel call (expressed
in emissions/call number) for each ocean-going vessel (OGV) type (i.e. auto carrier,
bulk, container, cruise, general cargo, reefer, RoRo, tanker) reported in the 2001 POLA
emission inventory data. Emissions per vessel call were estimated for each activity
(transiting, maneuvering, hotelling). ARB staff then estimated the emissions for each
OGV type in 2002 by multiplying the emissions per call in 2001 by the number of vessel
calls for each of the corresponding OGV types in 2002, that is:
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E - EpOLA,200I,i CN
POLA,2002,i CN x POLA,2002,i
POLA,200I,i
(1)
where EpoLA,2002,1 is the estimated emissions ofOGV type i (i = 1, 10) in 2002, EpOLA,2001,i
is the emission of OGV type i at POLA for 2001 (known), CNPOLA.2oo1.1 and CNPOlA,2oo2,J
are the vessel call numbers from POLA in 2001 and 2002 for OGV type i, respectively.
Table 4 provides a summary of the estimated emissions per vessel call and the actual
vessel call numbers for each port in 2002.
Table 4: Estimated Diesel PM Emissions per Vessel Call and 2002 Port Calls
Adjustments to the hotelling emissions were also made based on additional data
obtained subsequent to release of the Starcrest inventories. Specifically, corrections
were made to the emission factor for auxiliary engines running on heavy fuel oil (HFO).
In addition, the assumption on the percentage of engines running on HFO and marine
distillate was modified to reflect new data obtained in an ARB survey conducted in
2004. (ARB, 2004) With respect to the emission factor, for ship auxiliary engines,
Starcrest utilized a single diesel PM emission factor of 0.3 g/kW-hr in calculating
auxiliary engine emissions, regardless of diesel fuel type. Based on a review of
published emissions data, the emission factor for HFO should be much higher. In U.S.
EPA's 2002 .Commercial Marine Emission Inventory Developmenr report prepared by
ENVIRON International Corporation, an emission factor of 1.74 glkW-hr is reported for
engines running on HFO with a 3% sulfur content. (Environ, 2002) ARB staff adjusted
this emission factor to 1.5 glkW-hr based on the average sulfur content of HFO reported
as being used in the 2004 ARB survey and retained the 0.3 g/KW-hr factor for auxiliary
engines operating on marine distillate.2 Starcrest also assumed that 50% of the
2 In July 2002, the European Commission published, "Quantification of Emission from Ships Associated with Ship
Movements between Ports in the European Community" (Entec Report). The Entec report recommended an
emission factor of 0.8 g/kW -hr for auxiliary engines operating on HFO. ARB staffbelieves this emission factor
would result in an underestimation of diesel PM emissions. Applying U.S. EP A's methodology to estimate
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auxiliary engines were operating on HFO and 50% on marine distillate. ARB's survey -
results established that 75 percent of the a uxiliary engines use HFO and 25 percent use W
marine distillate. These two modifications resulted in increasing the hotelling emissions
by a factor of 4..over the estimates that would have resulted from growing the Starcrest
values to 2002 based on the number of ship calls.
Carao Handlino Eouipment
To project the emissions inventory for cargo handling equipment from 2001 to 2002, we
estimated the annual growth factors by interpolating between the 2001 baseline year
and the reported 2005 emissions developed for the No Net Increase (NNI) Task Force
Project. We assumed linear growth between 2001 and 2005. The emissions for cargo
handling equipment developed for the NNI project for 2005 reflect both the impacts from
adopted control measures and any growth that has occurred in activity. This resulted in
a net annual average growth rate of about 4.5%.
In addition, the emissions for cargo handling equiprnent were further modified to reflect
emission inventory adjustments that ARB staff have developed to support a 2005 rule-
rnaking for cargo handling equipment. These adjustments result in about a 34%
decrease in the emissions from cargo handling equipment for the year 2002. The main
inventory changes to the OFFROAD model methodology used to estimate emissions
from cargo handling equipment include: (1) revising zero hour emission factors, and
(2) revising equipment useful life, based on the data provided in a 2004 ARB Cargo
Handling Equipment Survey (ARB, 2004). The zero hour emission factors are revised
by calculating composite emission factors based on the percentages of off-road,
on-road, and retrofitted equipment. Because on-road and retrofitted engines generally
have lower emission factors than off-road engines, these revisions resulted in lower
zero hour emission factors. The useful life of the equipment is used to calculate the rate
that the emissions increase over the life of the equipment. The 2004 ARB CHE Survey
results showed that CHE equipment useful lives are significantly longer than the useful
lives used in the OFFROAD Model. Since the deterioration rate is calculated as a
percentage of the zero hour emissions divided by the useful life, the revised
deterioration rates are lower than the original deterioration rates used in the OFFROAD
Model. Because both the zero hour emission factor and the deterioration rate are lower
than those used in OFFROAD Model, the resultant emissions for cargo handling
equipment are lower than those previously predicted by the OFFROAD Model for use in
the 2001 POLA emission inventory.
emissions of sulfate PM from diesel-fueled engines to an auxiliary engine operating on 2.5% sulfur HFO would
generate 0.8g1kW -br oisulfate PM alone. Because there are many other component& of PM such as ash and semi-
volatile compounds, the 0.8 glkW -hr emission factor appears to only account for the sulfate PM that is generated.
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e Harbor Craft, In-Port Heaw-dutv Trucks, and In-Port Locomotives
To project the emissions inventory for commercial harbor craft, in-port trucks, and in-
port locomotives from 2001 to 2002, we estimated the annual growth factors by
interpolating between the 2001 baseline year and the reported 2005 emissions
developed for the No Net Increase (NNI) Task Force Project. We assumed linear
growth between 2001 and 2005 for each source category. The emissions of each
category developed for the NNI project for 2005 reflect both the impacts from adopted
control measures and any growth that has occurred in activity. The resulted net annual
average growth rates are 0.0, -6.0, and 11.0 percent for commercial harbor craft, in-port
heavy-duty trucks, and i~ortlocomotives, respectively.
B. Port of Long Beach
For POLB, Starcrest developed emission inventories for three categories: cargo
handling equipment, in-port locomotives, and in-port heavy-duty vehicles using 2002 as
the base year. The methodologies used in estimating emissions for these categories
are similar to those used in estimating corresponding emission inventories for the
POLA. To complete the emission inventories for POLB, ARB staff used the
methodologies described below to estimate the emissions for ocean-going vessels
(transiting, maneuvering, and hotelling) and commercial harbor craft vessels.
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Ocean-aoina Vessels
To estimate emissions from ocean-going vessels for POLB, ARB staff assumed that the
emissions per vessel call for each OGV type in POLB in 2002 is the same.as that for the
corresponding OGV type from POLA in 2001 (see Table 3). The emissions for each
OGV type calling on POLB in 2002 are estimated by multiplying the emissions per call
by the number of vessel calls for the corresponding OGV type at POLS in 2002, that Is:
E POLB,2002,i
E POLA.,2001,i CN
CN ,r' 2 . x POLB,2002,i
POlJJ., 001,1
(5)
where EPOLS,2002,1 is the estimated emission of OGV type i at POLS for 2002, EPOLA,2001,1
is the emission of OGV type i at POLA for 2001 (known), CNPOtA,2OO1,I and CNFOLS,2002,i
are the call numbers from POLA in 2001 and from POLB in 2002 for OGV type i,
respectively.
Carao Handlina EauiDment
Consistent with the approach used to adjust the POLA cargo handling equipment
emissions inventory; POLB 2002 cargo handling equipment inventory was decreased by
34 percent to reflect the inventory updates to the methodology used to estimate
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emissions from cargo handling equipment. (See discussion provided under A. Port of -
Los Angeles.) W
Harbor Craft
To estimate emissions from harbor craft vessels operating at POLB, ARB staff used the
estimates of emissions from harbor craft vessels from ARB's 2004 commercial harbor
craft emission inventory. These emission estimates were based on information on
vessels registered (California Department of Fish and Game), permitted (California
Public Utilities Commission), or documented (U.S. Coast Guard) with a "home port"
listed as "Long Beach." These vessels registered as "Long Beach" were then allocated
to the nine categories (commercial fishing, charter fishing, ferries/excursion, crew and
supply, pilot, tugs, tows, work boats, an? others) using the harbor craft vessel
composition developed in ARB's 20OS'eoiTiinercial Harbor Craft Survey (released in
2004). The emissions of each category for POLS in 2004 were estimated using the
emission density (emission/per vehicle per category) multiplied by the corresponding
vessel number in each category, that is:
1 . (E t t '.3 2004(i,j)]
E =LL saeWlue, N C ')
POLB,2004 ,=1,-1 Nstatewide,2004 (i,j) x POLB,2004 I,J
(6)
where EPOLS, 2004 is the estimated emissions for all harbor craft vessels at POLB for
2004, EstatllWlde, 2004(i, j) is the estimated emission for engine type i and harbor craft
vessel type j in the statewide for 2004, N stalew~e, 2004 (i, 1) and NPO\..B, 2004 (i, j) are the
numbers of harbor craft type j for engine type i in the statewide and in POLB for 2004
respectively, i is the index for engine type (propulsion and auxiliary), and j is the index
for ~arbor vessel type 0 = 1 to 9, defined above).
e
Consistent with the growth projections developed for the NNI project, it was assumed no
growth in harbor craft emissions between 2001 and 2005. Based on this assumption.
we assumed that for POLB, th.e total emissions of harbor craft vessels in the 2002
baseline year are equal to that in 2004 as calculated above.
C. In-Port and Out-of-Port Emissions Allocation
The emissions of different source categories are distributed at various locations in the
ports and over the offshore ocean water surfaces. To investigate spatial effects of
emission sources on the nearby neighborhoods, the total emissions of the two ports are
spatially allocated into two broad areas: in-port and out-of-port. In-port refers to the
area inside the breakwater of the ports, which is approximately 5 miles from the
shoreline; the out-of-port refers to the ocean water surface beyond the breakwater,
extending up to 50 miles from the ports. The land-based emissions resulting from
heavy-duty truck and locomotive activities outside of the Port boundaries are not
included in the "out-port" for this modeling analysis.
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POLA-POLB Risk Assessment Drafl1 0/3105 22
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D. Emission Inventory Summary
Emission estimates by source category for POLA and POLS in 2002 are summarized in
Figure 3 and Table 5. As can be seen, for both ports, OGVs (transit and maneuvering)
are the biggest contributor to the combined total emissions. The next highest emission
source is the hotelling of ship's auxiliary engines at berth, followed by commercial
harbor craft. Cargo handling equipment is the fourth largest, irrport trucks fifth, and in-
port locomotives are last. Based on the total combined emissions for the two ports,
OGV accounts for about 53 percent, hotelling accounts for 20 percent, harbor craft
accounts for 14 percent, cargo handling equipment accounts for 10 percent, in-port
truck accounts for 2 percent, and in-port locomotive accounts for 1 percent. The
emissions from POLA comprise about 55 percent of the total emissions from the two
ports. The in-port and out-of-port emissions for both ports are presented in Figure 4.
The in-port emissions comprise about 43 percent of the total emissions in the ports, and
the remaining 57 percent occurs in over water area outside the breakwater. By source
category, only OGVs and commercial harbor craft have emissions generated outside
the breakwater. OGV comprises about 90 percent of the total out-of-port emissions,
while commercial harbor craft accounts for the remaining 10 percent.
Table 5: 2002 Estimated Diesel PM Emissions for the POLA and POLS
.".i"~ );l;i,,~..~~~~t)il:isei'P.M~E:mi~si(jnStraiisrriiirjMe~~&';:;.'!"i!)~~;,
'., ';1 '.
~. ... _.1:;,:-..., . .~. !~ it"':WHh'[' .-..., ""I1C'" a"iiHilfl" ~".r.!."C 'ii~r" h
.W' ";..';~y :'Jr' ?r.'.~!!t~..~~.!~~, l..j~~..,:~....~ )!.t"~SQiJrce -, ate 0: ~~.!;:.~.~~;,~."i:..~i.r "'i1;~:~'~
~" >f~'~
:1. ",q,,~ ;..%'-OGVm' IrF:lbTEc~ 'i'lfCF.lbtftij ~!!!':Cf'lE~~. ~1F?;tlit: r#'1P.1:1~',
.:- k:1r.."
~Rd.lA';j.~ 515 165 178 78 18 11
.,o'<ebl:Bi'f.J 427 178 66 94 23 7
'ilf~. ...
.C' '''''b'' ed' 942 343 244 172 41 18
" om In '
Note: OGV - Oceangoing .......Is; HOTEL - Ship's auxiliary engine holelling; CHC - Commercial harbor crafts; CHE -!:aIgo
handUng equipment; 1FT -In-Port hea"Y-clutytruclal; IPL-In-Port locomotNe.
POLA.POLB Risk Assessment Draft 10/3/05
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2000 e
1800
1800
[ 1400 DPOLA
.POLB
CCorNlirMld
l! 1200
0
J 1000
.D
if 800
I
800 ."
400
200
11211'" 1171'
0
OGV> HOlEl CHI: CHE ,...... In-Part Totol
........ . taco
Figure 3: Estimated 2002 Diesel PM Emissions for POLA and POLB
Notes: OGV = Ocean-going Vessels, Hotel = Ship Auxiliary Engine Holelling; CHC = Commercial Harbor Craft, CHE
= Cargo Handling Equipment; In-Port Loco = In-Port Locomotives
2000
e
18DO
1800
~ 1400
! 1200
! 1000
l 800
I aoo
ClIN-Part
_Out--pcn1
CCarnb ned
'"
400
-
200
. . .. " '10 ,.
0
OGII. HOTEL CHC CHE ,...... ,....... Total
nuck Loco
...
Figure 4: In-Port and Out-of-Port Distribution of POLA and POLB Diesel
PM Emissions
NoleS: OGV = Ocean-going Vessals; Hotel = Ship Auxiliary Engine Holelling; CHC = Commercial Harbor Craft; CHE
= Cargo Handling Equipmen~ In-Port Loco = In-Port Locomotives
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III. AIR DISPERSION MODELING
In this chapter, we describe the air dispersion modeling performed to estimate the
downwind dispersion of diesel PM exhaust emissions resulting from the activities at
POLA and POLB. A description ofthe air quality modeling parameters, including air
dispersion model selection, modeling domain, emission source distribution/allocation,
model parameters, meteorological data selection, and model receptor network, is
provided.
A. Air Dispersion Model Selection
Air quality models are often used to simulate atmospheric processes for applications
where the spatial scale is in the tens of meters to the tens of kilometers. Selection of air
dispersion models depends on many factors, such as, characteristics of emission
sources (point, area, volume, or line), the type of terrain (flat or complex) at the
emission source locations, and source receptor relationships. For this study, ARB staff
selected the U.S: EPA Industrial Source Complex Model Short Term Version 3
(ISCST3, Version 02035) to simulate impacts at nearby receptors due to diesel PM
emissions. The ISCST3 model is a micro-scale, steady-state Gaussian plume
. dispersion model applicable for estimating impacts from a wide variety of emission
release patterns (point, area, line, and volume) such as those found at the ports for
distances up to about 50 kilometers. The model may be used to predict annual average
concentrations. ISCST3 is also able to simulate the dispersion of emissions generated
from multiple sources and accommodate both continuous and intermittent sources in flat
and complex terrain. ARB staff has successfully used ISCST3 model to assess public
heath risk impacts of diesel PM emitted frorn the Roseville Railyard on nearby
residential areas.
B. Model Domain and Receptor Network
The modelng receptor domain (study area) spans a 20 x 20 mile area as shown in
Figure 5a. The domain includes both the ports, the ocean surrounding the ports, and
nearby residential areas which have a population of about 2 million residents. Diesel
PM emissions are released within the modeling receptor domain as well as beyond the
receptor network for ocean-going vessels (see Figure 5b). The land-based portion of
the modeling receptor domain, excluding the property of the ports, comprises about
65 percent ofthe modeling domain. A Cartesian grid receptor network (160 x 160 grids)
with 200 m x 200 m resolution is used in this study. This network is convenient to
identify the emission sources within the ports with respect to the receptors in the nearby
residential areas. Since the exposure assessment was not designed to identify hot
spots, a finer grid receptor network was not used. While receptors within the ports were
included in the network, the risks from these on-site receptors were excluded from the
final risk analyses. The elevation of each receptor within the modeling domain was
determined from the United States Geological Service topographic data.
POLA-POLB Risk Assessment Draft 10/3105
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373
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[
.~ 374000
€
~
Long Beach
Harbor
e
380000
385000
390000
395000 400000
Easting (m)
405000
410000
Figure Sa. Modeling Receptor Domain for the Ports of Los Angeles and
Long Beach
POLA-POLB Risk Assessment Draft 10/3/05
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3760000
3740000
3720000
~
E
~
Dl
C
~ 3700000
..
o
Z
3680000
e
3660000
3640000
e
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OGV+CHC Shipping Lanes
~
.
i
3831l11D 3B55111 388lIIt 3B05lII 3IIIXII II
EIIl"l(ml
28??oo 300000 320000 340000 360000 380000 400000 420000 440000
Easlin9 (m)
Figure 5b. Depiction of the Emission Source Locations (On the electronic
version of the document, the following color codes are used to designate emission sources:
Magenta = OGV+CHC, Dark Brown = CHE. Yellow = IPT, Blue = IPL, Red = Hotelling)
c. Model Parameters
The emission sources in the ports are characterized as area sources except for ship
hotelling, which is modeled as individual point sources. Model parameters for area
sources include emission rate/strength, release height, lengths of X and Y sides of
POLA-POLB Risk Assessment Draft 10/3/05 27
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rectangular areas or vertices for polygons, and initial vertical (oz,) dimensions of the
area source plume. Model parameters for point sources include emission rate, stack
height, stack diameter, stack exhaust temperature, and stack exhaust exit velocity.
e
The OGV emissions are simulated as area sources. Starcrest provided the coordinates
to establish links. The link widths in the ports and in the shipping lanes over the ocean
watersurface are assumed to be 160 m and 800 m, respectively. Commercial harbor
craft emissions are simulated similar to the OGVs. The links are identical to those of
OGVs. Cargo handling equipment ernissions are simulated as area sources with the
polygon features of the dispersion model. Locomotive emissions are also simulated as
area sources. The links were established based on the nodes provided by Starcrest
and/or estimated by ARB staff. Each link width is assumed to be 20 m. The terminal
and off-terminal heavy-duty trucks are simulated similar to the railroad locomotives,
except that the link width is assumed to be 35 m (three lanes in each direction + 3
meters wake width on each side). As mentioned previously, the hotelling emissions
from ship auxiliary engines are simulated as individual point sources at the berths.
Because stack information was not available for individual engines, the average stack
height data (45 meters) provided in the Starcrest inventory report was applied to all
hotelling engines. The modeling parameters for each of the emission source categories
are summarized in Table 6.
Table 6: Emission Source Model Parameters
160
800
23.26
160
800
,.,~I;-I.QJEI,.',#:,
H=43m
T = 618 K
V= 16 m1s
D = 0.5 m
e
2.79
1.1-1.8
2.33
1.86
Note: OGV = Ocean-going vessels, CHC = commercial harbor craft, CHE = cargo handling equipment, H = releese
height, T = exhaust temperature, V = exhaust exit velocity, and D = stack diameter.
D. Spatial and Temporal Allocation of Emissions
Starcrest provided spatial emission allocation for all source categories at POLA and for
three source categories - cargo handling equipment, In-port locomotives, and In-port
trucks at POLB. ARB staff used GIS mapping to allocate the emissions for POLB
OGVs, hotelling, and commercial harbor craft based on the descriptions provided by
Starcrest. ARB staff temporally allocated all the emission sources at both ports based
on discussions with terminal operators and locomotive representatives. The
assumptions for the temporal distribution of the emissions are listed in Table 7. The
ARB staff assumed that the temporal distribution of the emissions is the same for both
ports.
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e Table 7: Temporal Distribution of Diesel PM Emissions at POLA and POLS
,;f."ActiVi - ifJistnbtitioil'f ',M,ffours-'Per. D
80% 16
20% 8
100% 24
80% 12
20% 12
80% g
15% 10
5% 5
80% 12
20% 12
100% 24
E. Meteorological Data
e
Meteorological data are selected on the basis of spatial and temporal
representativeness. There are two available meteorological measurement sites around
the ports: Wilmington and North Long Beach' (see Figure 6). The Wilmington site is
about one mile away from the ports and the measurements were collected in 2001. The
North Long Beach site is about four miles away from the ports where data are archived
for 1981. The South Long Beach site in Figure 6 is an air quality monitoring site where
meteorological data are not archived. Normally five years of the latest consecutive
meteorological data are preferred by U.S. EPA for long term dispersion analyses.
However, one year of data are acceptable if the data are site specific according to
U.S. EPA. Therefore, ARB staff believe the Wilmington data to be the better data with
respect to spatial and temporal representativeness.
The meteorological data from the Wilmington site includes hourly wind direction, wind
speed, and atmospheric temperature. Atmospheric stability, rural mixing height, and
urban mixing height are developed following the U.S. EPA guidance. Figure 7 presents
the wind rose and Figure 8 provides the wind and stability class frequency distributions
for the meteorological conditions at the Wilmington site. Based on the yearly statistics,
the annual average wind speed at Wilmington is 1.8 mls with the predominant wind
directions from the northwest (about 22 percent of the time) and from the south (about
14 percent of the time). For the ISCST3 air quality model urban dispersion coefficients
are used because the area at the impacted receptors is comprised of industrial,
commercial and compact residential land uses.
e
1 The King Harbor meteorological monitoring station is located about 10 miles northwest of the ports on the ocean-
side. To determine if diesel PM emissions transported on the ocean..ide would be better simulated using King
Harbor meteorological data we conducted a sensitivity study (detailed in Appendix B) and found that there is not a
significant difference between using Wilmington and using King Harbor meteorological data sets based on the
population-weighted risks in the modeling domain.
POLA-POLB Risk Assessment Draft 1013105 29
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..,.;
Figure 6: Locations of Surface Meteorological Measurement Sites around the
Ports
POLA-POLB Risk Assessment Drafl1 0/3/05
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-........
..-oPW" .
e
e
pratt
...............-\~...~
//,../ \ .~.......-...
/' ~"'"
. '
" ........-r-..- '"
.. .... I -- ..
/' w.r"" \ ......;, .', ..... ~.
.9 "
/ .....,. ". ...
/ /""""1-' -''''- . ....,. ""
/. ".. .....~.- '-,.... '"
. / ',. ." '\..
/ ;.-' '.
. ., /' . '- .,' \
I! . / \ -", " \ \
I' . ., \' \ \
\1 I I ( '\'"' ~i' ----.-..)
...--~ " \ \
\ \ \ \ / I , J~ i
\ \ '. . I i I
\ __ \ "" . i; I
,\., J"
, . .'_.. I! I
'. . ". . .. I I
\ '. ..',..'
.... .....' .......-
" ' _.,,_' . /" / ,I
-" ' ...--\-......... ,..-
.. ..... \ .,..OM I
". .... ,.,/...
'" -'''-., ,/ /
'. ", -...... 1 ~......,. .'
........ -- -- /'
'. ~/
...- /"
-.. II<. . ..,. ,,,,"
...."""'"-"'....-. -----,..,p,.".~
~
.......
.....
---
,;;iiiod"
--"'"
....
-
....iiiiO'
11.-
~...
-:';:~'"
~""
,\\.,10
--
-.....
:;iioO~
1,Ia-
~
~~
,...,.
..,.1"
f\9U,," T' WI.d _e"" ... ....... "'1M .. ,,,,,'Ill' at'"
Wium\ngton Met.eOfO\OQ\ca\ Site
_---~-;;;--- 7~
PO\..A,f'OLB Rlslt ASsessment Plait '0/3105
3'
50
45
40
35
30
%
25
20
15
10
5
o
35
30
25
20
%
15
10
Wind Class Frequency Distribution
e
03
calms
0.5- 18 '.8- 3.3 33- 5.4
Wind CI... (m/s)
>= SA
Stability Class Frequency Distribution
5
e
o
A
B
E
F
c 0
Slloblllly CI....
MPLOT 10......-....... En\II........uI SllIIvIII~
Figure 8: Wind Speed and Stability Class Frequency Distribution at Wilmington
Meteorological Site.
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POL.A-POLB Risk Assessment Drafl1 013105 32
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e IV. EXPOSURE ASSESSMENT
In this chapter, we briefly describe the OEHHA guidelines on health hazard risk
assessment and how we used the guidelines to characterize potential cancer risks
associated with exposure to diesel exhaust from the ports. We also present preliminary
air dispersion modeling results for the ports.
A. OEHHA Guidelines
The Air Toxies Hot Spots Program Risk Assessment Guidelines: The Air Taxies Hot
Spots Program Guidance Manual for Preparation of Health Risk Assessments (OEHHA
guidelines, 2002a) outlines a tiered approach to risk assessment, providing risk
assessors with flexibility and allowing for consideration of site-specific differences.
Tier- 1 is a standard point-estimate approach that uses a combination of the average
and high-end point-estimates. This approach will be used in this risk assessment.
e
The OEHHA guidelines recommend that all health hazard risk assessments present a
Tier-1 evaluation for the Hot Spots Program, even if other approaches are also
presented. For Tier-1, OEHHA provides two values for breathing rate, one representing
an average and another representing a defined high-end value. The average and high-
end of point-estimates are defined in terms of the probability distribution of values for
that variate. The mean (65th percentile) represents the average values for point-
estimates and the 95th percentile represents the high-end point-estimates from the
distributions identified in the OEHHA guidelines. In 2004, ARB recommended the
interim use of the 80th percentile value (the midpoint value of the 65th and 95th percentile
breathing rate) as the minimum value for risk management decisions at residential
receptors for the breathing pathway. The 80th percentile corresponds to a breathing
rate of 302 Liters/Kilogram-day (302 L1Kg-day). This risk assessment will use the
302 L1Kg-day value and will assume that the receptors will be exposed for 24 hours per
day for 70 years., If a receptor is exposed for a shorter amount of time to the annual
average concentration of diesel PM the cancer risk will be proportionately less.
The relationship between a given level of exposure to diesel PM and the cancer risk is
estimated by using the diesel PM cancer potency factor. A description of how the diesel
cancer potency factor was derived can be found in the Proposed Identification of Diesel
Exhaust as a Toxic Air Contaminant (ARB, 1998) and a shorter description can be
found in the Air Toxics Hot Spot Program Risk Assessment Guidelines, Part II,
Technical Support Document for Describing Available Cancer Potency Factors (OEHHA
2002b). The use of the diesel unit risk factor for assessing cancer risk is described in
the OEHHA Guidelines. The potential cancer risk is estimated by multiplying the
inhalation dose by the cancer potency factor (CPF) of diesel PM (1.1 (mg/kg-dr1).
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B. Exposure Assessment
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A number of variables can have significant impacts on exposure. These include
emission estimates, meteorological conditions, and exposure duration of residents. The
emissions affect the risk levels linearly; as emissions increase, so does the risk.
Meteorological conditions can have a large impact on the resultant ambient
concentration of a toxic air pollutant with higher concentrations found along the
predominant wind direction. Key variables in human exposure are a person's proximity
to the emission plume, how long he or she breathes the emissions (exposure duration),
the person's breathing rate, and body weight. The longer the duration of exposure, the
greater the potential risk.
C. Risk Characterization
Risk characterization is defined as the process of obtaining a quantitative estimate of
risk, including a discussion of its uncertainty. The risk characterization process
integrates the results of air dispersion modeling and relevant toxicity data (e.g., diesel
PM cancer potency factor) to estimate potential cancer or noncancer health effects
associated with contaminant exposure. It is important to note that no background or
arnbient diesel PM concentrations are incorporated into the risk quantification. The risk
assessment only considers the cancer risk by the inhalation pathway because the risk
contributions by other pathways of exposure are known to be negligible relative to the
inhalation pathway and difficult to quantify.
As stated in Chapter III, the modeling receptor domain of 20 mi x 20 mi with a grid
resolution of 200 m x 200 m was used in the modeling exercise. The effective land area
(excluding the Port property and the over water region) is about 255 square miles. The
population within the modeling receptor domain is about 2 million based on the
U.S. Census Bureau's year 2000 census data. The risk numbers, impacted areas, and
affected population presented below are based on the effective land area within the
modeling domain; that is, the risk, the area, and the population within the ports property
and over the ocean surface are excluded from this analysis. Note that if the modeling
domain expands, the risks, impacted areas, and affected population presented in this
analysis would be changed.
e
Risk Characterization for All Emission Sources
Figure 9 shows the risk isopleths for all diesel PM emission sources from POLA and
POLB superimposed on a map that covers the ports and the nearby communities. The
risk contour of 100 in a million exceeds the modeling receptor domain in the north
direction ofthe ports, which is about 10 miles away from the ports boundary. The area
with predicted cancer risk levels in excess of 100 in a million within the modeling
receptor domain is estimated to be about 93,500 acres, which is 57 percent of the
effective land area within the modeling receptor domain (see Table 6). The area in
which the risks are predicted to exceed 200 in a million is also very large, covering an
POLA-POLB Risk Assessment Draft 10/3105
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area of about 29,000 acres (18 percent of the effective land area within the modeling
receptor domain). The areas with the greatest impact have an estimated potential
cancer risk of over 500 in a million, which cover about 2 percent of the effective land
area within the domain. The risk isopleths of 1000 and 1500 in a million occur on port
property and the nearby ocean surfaces, which is not included in this study because
people do not reside in these areas.
Using the U.S. Census Bureau's year 2000 census data, we estimated the population
within the isopleth boundaries. As shown in Table 7, the affected population numbers
for the risk ranges of 100-200, 200-500, and over 500 have been estimated to be about
724,000,360,000, and 53,000, which accountfor 37, 18 and 3 percent of the total
population within the modeling domain, respectively. In other words, nearly 60 percent
of 2 million people live in the area around the ports that has predicted risks of greater
than 100 in a million. Note that the risk isopleth of 10 in a million is not shown in
Figure 9 because it exceeds the modeling receptor domain. Spatially, the emission
sources are located at various locations on port property and the outside of the
breakwater, thus the contributions of these emission SOLl'ces to the nearby
neighborhoods would be different. Below, we discuss the contributions from the various
sources at the ports to the community risks.
POLA-POLB Risk Assessment Draft 10/3105 35
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3725000
e
3750000
3745000
~ 3740000
.s
OJ
.Ii
~
3735000
3730000
I
012
mil..
380000
385000
390000 395000
Easling (m)
400000
405000
Figure 9. Estimated Diesel PM Cancer Risk from All Diesel-Fueled Engines
at POLA and POLS (Wilmington Meteorological Data, Urban
Dispersion Coefflclenlll, BO" Percentile Breathing Rate, Total Emissions = 1,760
TPY. Mo~ling Receptor Domain = 20 mi x 20 ml, Resolution = 200 m x 200 m)
Risk Characterization for Individual Emission Sources
The different emission sources are used at various locations on the ports property in the
harbor and over ocean beyond the breakwater. Thus, the contributions of these
emission sources to exposures in the nearby neighborhoods are different. As shown in
Tables 8 and 9, the emissions from cargo handling equipment and on-port trucks
resulted in areas within the nearby communities having risk levels exceeding 500 in a
POLA-POLB Risk Assessment Draft 10/3105 36
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million while the highest risk levels associated with the other categories were between
200 and 500 in a million. Within the model domain, ship hotelling emissions and cargo
handling equipment impacted the largest areas and affected more people than the other
sources ofemissions when considering the risk levels greater than 100 in a million.
When considering risk levels greater than 10 in a million, all the port sources, other than
in-port trucks and locomotives had similar impacts, affecting at least 119,000 acres and
at least 1.4 million people. By source location, the impacts resulting from the in-port
emissions (within breakwater) are much larger than those re~ulting from the out-port
emissions (outside of breakwater), although the emission rnagnitude of the former is
less than the latter (750 TPY vs 1010 TPY). Quantitatively, within the modeling receptor
domain, the population-weighted risk resulting from the in-port emissions is about
4.5 times of that resulting from the over water out-of-port emissions.
Table 8: Summary of Area Impacted by Risk Levels and Activity Categories
(Acres)
~Rlsk: Le.v.<illf If'::' oGV,1If ~'HOT.E~llf; !!itiOH~' P'IE~HE~~J.:'1) ~1;;,~P.T~~i: If..~ 1P,It"iiJii .J;:CO"MBINEDl
..Risl(,?.i500:fi: 0 0 0 50 50 0 2 500
,;Ris\t'~20'01.t 110 2,036 20 410 160 40 29,000
'ijrsl\!?i(fO.o~ 227 12,700 750 4100 376 160 94,000
~ BisKli'::j 0~;'I;. 163,435 160470 125,250 119,000 29,750 11240 163,435
Table 9: Summary of Population Affected by Risk Levels and Activity Categories
(Number of People)
..RisJf.:r.'iiVii/1l Slt"OGIYlf.!'" ,S'H0TE
\ RiSK'?\"50n.~ 0 0
-Ris1C:;;i:200..' 18 46020
'Riii~.f,rOOI>ll 1,610 221,567
:'Risk-'?~ 0 1 977 760 1 949 850
,'!:kC C :~~: 'l'RI'Cf.lE...i ;.!'f.rIP..T.~~~'i I: II'
o 3200 205 0
5,000 11,100 1780 680
22 960 82,000 8 270 4,330
1 516515 1444000 422910 213430
COMBINED;'.
53,000
411200
1 135000
1 977 770
Notes:
1. OGV - Ocean-90ing vessels; HOTEL - Ship's auxiliary engine hotellng; CHC - Commercial harbor
crafts; CHE -Cargo handling equipment; IPT - In-Port trucks; IPL - In-Port locomotive.
2. The model receptor domain of 20 mile x 20 mile for urban dispersion coefficients with a grid resolution
of 200m x 200m was used. The effective modeling receptor domain (excluding the port properties and
the ocean water) is estimated to be about 255 square miles. The calculations here are ONLY based
on the effective modeling receptor domain.
3. The 80th percentile breathing rate for adults over 70-year lifetime was assumed.
4. Meteorological data from Wilmington (2001) are used for POLA and POLB.
5. The risks within both ports and over the ocean water were excluded for calculations of average risks
and affected areas.
6. The estimated population in this Table is ONLY based on the modeling receptor domain using the
U.S. Census Bureau's year 2000 census data.
7. If the modeling receptor domain expands, the numbers of population and area affected would be
increased.
8. The combined column provides the population affected and area impacted for the cumulative impacts
from all the emission sources. The individual impacts are not additives since the combined impacts
are greater than the sum of the individual sources. For example, cargo handling equipment and
commercial harbor craft emissions may impact the same location and population. While individually
POLA-POLB Risk Assessment Draft 1013105 37
Draft
the impacts may result in cancer risk levels between 100 and 200 in a million, when you combine the _
impacts, the resulting risks could be greater than 200 in a million. W
Below, we provide additional discussion on each of the contributions of each of the
emission source categories and present the predicted risk isopleths for individual
sources.
Ocean-Goina Vessels
Figure 10 presents the predicted risk isopleths for the diesel PM emissions from the
OGVs (transiting and maneuvering emissions only). The area impacted by these
emissions is very large (has a large footprint) and many of the risk isopleths extend
beyond the boundaries of the modeling receptor domain. The area within the modeling
domain in which the cancer risks are predicted to be greater than 100 in a million is
small, covering an area of about 227 acres with a population size of 1 ,800. The
potential cancer risk levels between 50 to 100 in a million are located in nearby areas
north of the ports. All areas within the modeling receptor domain are predicted to have
an estimated potential cancer risk of over 10 in a million. From the point of view of the
emission magnitude, OGVs contributed about half of the total emissions (940 of 1,760
TPV). This disproportional phenomenon can be attributed to the fact that the diesel PM
emissions from OGVs are distributed over a very wide area and most of these
emissions (about 96 percent) are emitted from the offshore shipping lanes which begin _
approximately 5 miles beyond the port breakwater and extend to about 50 miles away W
from the ports. In other words, only a small portion of the transiting and maneuvering
emissions (about 4 percent) are emitted in the ports. In addition, the vessels have an
average physical stack height of 43 meters above the water surface (final plume rise
modeled as 50 m), resulting in diluted plumes over a wide area.
POLA-POLB Risk Assessment Draft 10/3/05
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..
"
€
o
z
3730000
3725000
-=::J
012
mielI
38??oo
385000
390000 395000
Easting (m)
400000
405000
Figure 10. Estimated Diesel PM Cancer Risk from Ocean-Going Vessel's
Activity at POLA and POLB (Wilmington Meteorological Data,
Urban Dispersion Coefficients, 80'" Percentile Breathing Rate, Emission =
942 TPY, Modeling Domain = 20 ml x 20 ml, Reeolution = 200 m x 200 m)
Hotellina
The emissions from ship auxiliary engines' hotelling resulted in a significant risk impact
to the nearby communities. As shown in Figure 11, the potential cancer risk level
ranges from 50 to 200 in a million. The area in which the risks are predicted to exceed
100 in a million has been estimated to be about 12,700 acres with a population of
221,600. Hotelling emissions from auxiliary engines result in cancer risk levels over
10 in a million in about 98 percent of the effective modeling domain. Compared to the
OGVs, the emission from the auxiliary engines hotelling is approximately 36 percent of
POLA-POLB Risk Assessment Draft 1013105 39
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the OGVs (343 TPY vs 942 TPY), but the predicted population-weighted average risk
from the hotelling is about 1.5 times of that from the OGVs. This is not surprising
because the emissions from hotelling activities are located within the ports, which are
close to nearby communities.
e
'"
~
:c
."
o
Z
e
380000
385000
390000 395000
EasIing (m)
400000
405000
Figure 11. Estimated Diesel PM Cancer Risk from Ship Auxiliary Engines'
Hotelling at POLA and POLB (Wilmington Meteorological Data,
Urban Dispersion Coefficients, ao'h Percentile Breathing Rate, Emission = 343
TPY, Modeling Domain = 20 ml x 20 ml, Resolution = 200 m x 200 m)
Commercial Harbor Craft
The emissions from commercial harbor craft resulted in a moderate risk level in the
nearby communities around the ports (Figure 12). The area in which the risks are
predicted to exceed 100 in a million has been estimated to be about 750 acres with a
population of 23,000. Overall, about 77 percent of the effective modeling receptor
POLA-POLB Risk Assessment Draft 1013105
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domain have estimated cancer risk levels of over 10 in a million due to emissions from
commercial harbor craft.
375l!000
3745000
_ 3740000
g
'"
J;;
=
~
Z
3738000
3730000
3725000
-=::J
o 1 2
miles
380000
385000
390000 395000
Eaaling (m)
400000
405000
Figure 12. Estimated Diesel PM Cancer Risk from Commercial Harbor Craft
Vessel Activity at POLA and POLS (Wilmington Meteorological Data,
Urban Dispersion Coefficients, BO~ Percentile Breathing Rate, Emission" 244
TPY, Modeling Domeln " 20 mi x 20 mi, Resolution" 200 m x 200 m)
Carao Handlina EauiDment
The ground-based activities of cargo handling equipment generated an estimated
emission of about 172 TPY, which accounts for about 10 percent of the total emissions
inventory for the ports. The emissions resulted in significant risk impacts on the nearby
residential areas. As shown in Figure 13, the area in which the risks are predicted to
POLA-POLB Risk Assessment Draft 1013105 41
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exceed 100 in a million has been estimated to be about 4,100 acres with a population of
82,000. For the highest risk level of over 500 in a million, the impacted areas have
been estimated to be about 50 acres and about 3,200 people living around the ports are
exposed to the risk level. Overall, about 73 percent of the effective modeling receptor
domain has an estimated risk level of over 10 in a million and about 73 percent of
2 million people who are living in the domain are exposed to the risk level. From
Figure 13, we can see that the finger-like isopleth jutting to the north exists. This is
caused by sources located within the narrow finger-like port property that contribute
about 17 TPY of emissions to the downwind direction area (north). Based on the
population-weighted spatial average risk. the emission sources from cargo handling
equipment are the second biggest contributor to the nearby communities.
e
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38OllOD
385DDD
390000 395000
Eaollng (m)
4000DD
405000
Figure 13. Estimated Diesel PM Cancer Risk from Cargo Handling
Equipment Activity at POLA and POLB (Wiimington Met
Data, Urban Dispersion Coefficients, 80'" Percentile Breathing Rate,
Emission = 172 TPY, Modeling Domain = 20 ml x 20 ml, Resolution =
200 m x 200 m)
In-Port Trucks and Locomotives
Compared with other emission sources, the emissions from in-port heavy-duty trucks
and locomotives are relatively small, accounting for about 3 percent of the emissions
POLA-POLB Risk Assessment Draft 1013105 42
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inventory. These ground-based emissions resulted in localized health risk impacts. As
shown in Figures 14 and 15, the higher risk level of 100 to 200 in a million occurs on
port property. The exposure risk level to the nearby residents is relatively small. For in-
port heavy-duty trucks, about 1 B percent ofthe effective modeling domain has an
estimated risk level of over 10 in a million, affecting about 21 percent of the residents
within the model domain. Similarly, for i~ort locomotives, about 7 percent of the
effective modeling receptor domain has an estimated risk level of over 10 in a million,
affecting about 11 percent of the residents. It is important to note that there are
emissions of heavy-duty trucks and locomotives that are released beyond the
boundaries of the ports and impact residents living along freeways, rail yards and rail
corridors, and distribution centers. The impacts from these emissions (e.g., freeway
diesel PM) are not included in this analysis.
In this study, we did not consider the diesel PM emissions of on-road heavy-duty trucks
and locomotives related to port activities that occur off-port boundary within the SCAB
(regional emissions). We estimated the off-port regional diesel PM emissions to be
about 206 TPY for the both ports, or 10 percent ofthe total port-related emissions (206
TPY vs 1,970 TPY). These regional emissions are distributed throughout the SCAB
and may result in localized health impacts to people who are Ii\oe near freeways and
railroad corridors within the SCAB. These health impacts will be evaluated in future
studies.
e
373
-=:J
o 1 2
m'Iu
3BDOoo 385DD0
3BDDDO 395000
Eastlng(rn)
43
400000
405000
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POLA-POLB Risk Assessment Draft 1013105
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Figure 14. Estimated Diesel PM Cancer Risk from In-Port Heavy Duty
Trucks at POLA and POLB ~Imington, MeteorologIcal Data,
Urban Dispersion Coefficients, 80 Percentile Breathing Rate,
Emission = 41 TPY, Modeling Domain = 20 ml x 20 ml, Resolution
=200mx200m)
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_ !7
,g,
l!'
i
o
z
-==:J
012
ml..
3lIllOIlO
385000
390000 395000
E..ting (m)
4ooODO
405000
Figure 15. 'Estimated Diesel PI!II Cancer Risk from In-Port Locomotive
Activity at POLA and POLB (Wilmington, Meteorological
Date, Urban Dispersion Coefficients, Both Percentile Breathing Rate,
Emission" 41 TPY, Modeling Domain" 20 ml x 20 ml, Resolution" 200
mx200m)
In-Port vs Out-of-Port Emissions
As mentioned previously, a comparison between the impacts from in-port. i.e., those
emissions that occur on port land-based property and within the breakwater zone, and
the out-of-port, i.e., those emissions from oceangoing ships and harbor craft that occur
beyond the breakwater, was made. Although the in-port activities generate fewer
emissions than the out-of-port activities' (750 TPY vs 1010 TPY), the in-port emissions
resulted in much higher health risk level in the nearby communities than the out-of-port
emissions (see Figures 16 and 17). Quantitatively, based on the population-weighted
average cancer risk within the modeling domain, the potential cancer risk level resulting
POLA-POLB Risk Assessment Draft 10/3/05 45
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from the in-port activities is about 4.5 times of that resulting from the out-of-port -
activities. Possible reasons have been explained above. That is, there are greater W
distances between the out-of-port emission sources and the receptors in the nearby
communities. This analysis identifies the emission sources within the ports as the most
significant to health risk to the nearby communities.
'"
c
~
Z
e
,
012
miles
380000
385000
39DOOO 395000
Easting (m)
400000
405000
Figure 16. Estimated Diesel PM Cancer Risk from Allin -Port Diesel Engine
Activity at POLA and POLB 1Wilmington, Meteorological Data,
Urban Dispersion Coefficients, 80 Percentile Breathing Rate, Emission = 750
TPY, Modeling Domain" 20 ml x 20 ml, Resolution" 200 m x 200 m)
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~ 3740000
g
'"
f
"
~
373
373
o , ~
mleS
380000
385000
390000 395000
EasUng 1m)
4??oo0
405000
Figure 17. Estimated Diesel PM Cancer Risk from All Out-of..port Diesel
Activity at POLA and POLS (Wilmington, Meteorological
Data, Urban Dispersion Coefficients, BOu. Percentile Breathing Rate,
Emission = 1010 TPY, Modeling Domain = 20 ml x 20 ml, Resolution =
200 m x 200 m)
POLA.POLB Risk Assessment Draft 1013/05 47
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D. Estimation of Non-cancer Health Endpoints
e
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 ofthis study, ARB staff conducted an analysis of the potential non-cancer health
impacts associated with exposures to the mOdeJ-predicted ambient levels of directly
emitted diesel PM (primary diesel PM) within the modeling domain. The non-cancer
health effects evaluated include premature death, asthma attacks, work loss days, and
minor restricted activity days.
Ambient levels of directly emitted diesel PM were predicted for 200 meter by 200 meter
grid cells within the modeling domain, and the populations within each grid cell were
determined from U.S. Census Bureau year 2000 census data. 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) (CAR8, 2002), we calculated the number of annual cases of death
and other health effects associated with exposure to the PM concentration modeled for
each of the grid cells. The totals over the entire modeling area were then calculated.
For each grid cell, each health effect was estimated 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. The selection of the concentration-response functions was based on
the latest epidemiOlogic literature, as described in the PM Staff Report (CARB, 2002)
and in Lloyd and Cackette (2001).
e
Based on our analysis, we estimate that the average number of cases per year that
would be expected in the modeling area is as follows:
. 29 premature deaths (for ages 30 and older), 14 to 43 deaths as 95% confidence
interval (CI);
. 750 asthma attacks, 180 to 1300 as 95% CI;
. 6,600 days of work loss (for ages 18-65), 5,600 to 7,600 as 95% CI;
. 35,000 minor restricted activity days (for ages 18-65), 28,000 to 41,000 as 95% CI.
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 Krewski et al. (2000) The ARB staff assumed that concentration-response
function for premature mortality in the model domain is comparable to that in the
Krewski study. It is know 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
POLA-POLB Risk Assessment Draft 1013/05 48
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to those found in other locations in the United States, justifying our use of
Krewski et ai's results. Also, the U.S. EPA has been using Krewski'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 CARB's PM Staff Report (CARB. 2002).
. 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 200 m x 200 m was assumed to be
exposed uniformly to modeled concentration. This assumption is typical of this
type of estimation. .
. The ARB staff assumed the grid cell population had similar age distributions as
the county in which it was located. The subpopulation used for each health
endpoint was calculated by multiplying the all-age population for each grid cell by
the county-specific ratio of the subpopulation used for the endpoint over the all-
age population. For example, mortality estimates were based on subpopulations
age 30 or more estimated from ratios of people over 30 over the entire
population, specific for each county. These estimates were needed because
information on the particular subpopulation in each modeling grid was not
available.
. 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.
. Another assumption pertains to the threshold, the lowest level at which health
impacts can be assessed. There is some evidence that the PM effect coefficient
may be larger at lower levels of PM and smaller at higher levels. However, we
assumed no threshold in our calculations. That is, the effects can be estimated
down to zero.
It should be noted that because the estimates apply to a limited modeling domain (20
miles by 20 miles), the affected population is small, and hence the overall estimated
health impacts are smaller than estimates made on a statewide basis. In addition, to
the extent that only a subset of health outcomes is considered here, the estimates
should be considered an under-estimate of the total public health impact.
POLA-POLB Risk Assessment Draft 1013/05
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v.
SUMMARY OF FINDINGS
e
The study evaluated the diesel PM emissions on a mass basis and with respect to what
impacts those emissions have on potential cancer risks in communities near the ports.
With respect to the mass emissions, the combined diesel PM emission from both ports
is estimated to be about 1 ,760 tons per year in 2002. This represents a significant
component of the regional diesel PM emissions for the South Coast Air Basin at about
21% of the total basin wide diesel PM emissions in 2002.
Focusing only on the on-port diesel PM emissions, as shown in Figure 18, the emission
from ship activities (maneuvering, transiting, and hotelling) account for the largest
percentage of emissions at about 73% followed by commercial harbor craft vessels
(14%), cargo handling equipment (10%), in-port heavy-duty trucks (2%), and in-port
locomotives (1%).
IPL
1%
IPT
2%
HOTELlNG
20%
e
HARBOR
14%
OGVs
Maneuverlng+Transil(
)ing
53%
Figure 18: Distribution of Diesel PM Emissions by Source Categories for
POLA and POLS in 2002
The combined diesel PM emissions from the ports result in elevated cancer risk levels
over the entire 20 mile by 20-mile study area. In areas near the Port boundaries,
potential cancer risk levels exceed 500 in a million. As one moves away from the ports,
the potential cancer risk levels decrease but continue to exceed 50 in a million for
POLA-POLB Risk Assessment Draft 1013105 50
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almost the entire modeling domain. Potential cancer risk and the number of acres
impacted for several risk ranges are summarized as follows:
· Risk levels greater than 500 in a million (based on 70 years of exposure) occur
over about 2,500 acres in which about 53,000 people live.
· Risk levels between 200 and 500 in a million occur over about 26,500 acres in
which about 360,000 people live.
· Risk levels between 100 and 200 in a million occur over about 64,500 acres in
which about 724,000 people live.
· Risk levels between 10 and 100 in a mi lIion occur over about 70,000 acres in
which about 843,200 people live.
· The overall, almost all people living within the modeling domain (about 2 million)
are exposed to a risk level of greater than 50 in a million and about 97 percent of
the areas within the domain are impacted at or above this risk level.
The exposure assessment demonstrated that the land-based or near dock diesel PM
emissions were responsible for greater impacts than the emissions that occurred
outside the breakwater. Quantitatively, within the modeling receptor domain, the
population-weighted risk resulting from the in-port or near dock emissions is about
4.5 times of that resulting from the over-water out-of-port emissions. The results from
the exposure assessment also revealed that the contribution of the individual emission
sources to the community exposures does not follow the same relationship as the mass
emissions. Ship hotelling emissions, while responsible for about 20 percent of the mass
emissions, were the emissions that resulted in the largest area and population impacted
where the potential cancer risks were greater than 200 in a million (see Figures 19 and
20). Hotelling emissions are also responsible for 34 percent of the total risk in the
model domain based on the population-weighted average risk. The second highest
category was cargo handling equipment which is responsible for about 22 percent of the
total risk in the model domain based on the population-weighted average risk followed
by commercial harbor craft, and in-port heavy-duty trucks. When considering risks
greater than 10 in a million, all categories except in-port heavy-dUty trucks and in-port
locomotives affected more than 1 .4 million people and impacted more than
119,000 acres where residents live. For the in-port trucks and locomotives, the risk
level of greater than 10 in a million affected about423,000 people and impacted about
30,000 acres.
POLA-POLB Risk Assessment Drafl10/3J05
51
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221,eoo
e
250,000
o 2D1,B1D
o Risk ~ 500
III Risk ~ 200
_Risk ~ 100
200,000
...
..
-
u
~ 150,000
<C
..
o
:;::
~ 100,000
Cl..
o
a.
50,000
o .
o 7lIll",,,o
o
OGV
HOTEL
CHC
CHE
IPT
IPL
Category
Figure 19: Population Affected within the Model Domain by Cancer Risk
Levels and Source Categories
14,000
12.710
12,000
-
III
.. 10,000
..
1.I
<C
-
"C 8,000
J!l
1.I
III
Cl.. 6,000
.5
III
e 4,000
cC
2,000
Q 1'023D 0
0
e
[J Risk ~ 500
m Risk> 200
-Risk ~ 100
50 180380
o 40 18G-
OGV HOTEL CHC CHE IPT IPL
Category
Figure 20: Residential Areas Impacted within the Model Domain by Cancer
POLA-POLB Risk Assessment Draft 10/3105
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Risk Levels and Source Categories
The relationship between the various source categories is summarized below. Based
on the mass emissions, population-weighted awrage risk, the size of areas impacted,
and the number of people affected, the emission sources or categories can be ranked
as the follows:
· By the mass emissions
OGV> HOTEL> HARBOR> CHE > TRUCK> LOCO
· By the risk level (population weighted):
HOTEL> CHE - OGV> HARBOR> TRUCK> LOCO
· By the area impacted (R > 100 in a million):
HOTEL> CHE > HARBOR> TRUCK> OGV> LOCO
· By the population affected (R > 100 in a million):
HOTEL> CHE > HARBOR> TRUCK> LOCO> OGV
· By the area impacted (R > 10 in a million):
OGV> HOTEL> HARBOR> CHE > TRUCK> LOCO
· By the population affected (R > 10 in a million):
OGV> HOTEL> HARBOR> CHE > TRUCK> LOCO
In conclusion, emissions from cargo handling equipment and hotelling emissions from
ocean-going vessel auxiliary engines are the primary contributors to the high potential
cancer risk levels near the ports. Reducing emissions from these two categories will
have a dramatic effect on reducing the cancer risk levels in nearby communities.
Emissions from commercial harbor craft, in-port trucks, in-port rail, and ocean-going
vessel (transit and maneuvering activities) do not contribute greatly to the near source
risk, but are an important contributor to elevated cancer risk levels over a very large
area. While emissions from these source categories do not have a major role in the
near port risk levels, they are significant contributors to the overall elevated risk levels in
the study area. Addressing the emissions from these sources, while not as critical for
reducing near port risk levels, is critical if we are to significantly reduce the exposure of
a large population (over 2 million people) to cancer risk levels in the 50 in a million
range.
POLA.POLB Risk Assessment Draft 10/3105
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REFERENCES
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California Air Resources Board. Report to the Air Resources Board on the Proposed
Identification of Diesel Exhaust as a Toxic Air Contaminant; Part A, Exposure
Assessment, As Approved by the Scientific Review Panel on April 22, 199B.
(ARB,199Ba)
California Air Resources Board. The 2002 California Almanac of Emission and Air
Quality, 2002. (ARB, 2002)
California Air Resources Board. ARB Recommended Interim Risk Management Policy
for Inhalation-Based Residential Cancer Risk. 2004.
California Air Resources Board. Roseville Rail Yard Study, 2004.
California Air Resources Board. Barrio Logan Report- A Compilation of Air Quality
Studies in Barrio Logan, 2004.
California Air Resources Board. 2004 ARB Cargo Handling Equipment Survey, 2004,
http://www.arb.ca.govlmsprog/offroadlcargolpresentationslO51805survey.pdf.
California Air Resources Board. 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 htto:/Iwww.arb.ca.aov/research/aaas/std-rsJom-finallom-final.htm. 2002.
Krewski D, Burnett R, Goldberg MS, Koover K, Siemiatycki J, Jerrett M et a/. Reanalysis
of the Harvard Six Cities Study and the American Cancer Society Study of Particulate
Air Pollution and Mortality. Research Report of the Health Effects Institute, 2000.
OEHHA. The Air Toxics Hot Spot Program Risk Assessment Guidelines: Part IV-
Technical Support Document for Exposure Analysis and Stochastic Analysis. Office of
Environmental Health Hazard As~essment. September, 2000.
OEHHA. 2002a. 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. June, 2002.
OEHHA. 2002b. The Air Toxics Hot Spot Program Risk Assessment Guidelines: Part
/I-Technical Support Document for Describing Available Cancer Potency Factors. Office
of Environmental Health Hazard Assessment. June, 2002.
Pope CA, Thun MJ, Namboodiri MM, Dockery DW, Evans JS, Speizer FE, and Health
CWO Particulate Air Pollution As A Predictor Of Mortality In A Prospective Study Of U. S.
Adults Am. J. Respir. Cril. Car Med 151 :669-674. 1995.
e
POLA-POLB Risk Assessment Draft 1013/05 52
e Port of Los Angeles. 2001 Baseline Emissions Inventory, prepared by Starcrest
Consulting Group, LLC, June, 2004.
Port of Los Angeles. Report to Mayor Hahn and Councilwoman Hahn by No Net
Increase Task Force, June, 2005.
Port of Long Beach. 2001 Baseline Emissions Inventory, prepared by Starcrest
Consulting Group, LLC, February, 2004.
Environ. Commercial Marine Emissions Inventory Development, 2002.
South Coast Air Quality Management District (SCAQMD). The Multiple Air Toxics
Exposure Study (MA TES-II) for the South Coast Air Basin, 2000.
U.S. EPA. User's Guide for the Industrial Source Complex (ISC3) Dispersion Model,
Office of Air Quality Planning and Standards, Research Triangle Park, North Carolina.
EPA-454/B-95-003a, September 1995.
Lloyd, A. C. and Cackette, T. A. Diesel Engines: Environmental Impact and Control, J.
of Air and Waste Management Association, 2001, 51: 809-847.
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POLA-POLB Risk Assessment Draft 10/3/05
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Appendix A
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Methodologies for Developing Source
Category Emission Inventories
A. Port of Los Angeles
Starcrest prepared an emission inventory for all emission sources using 2001 as
the baseline year. The inventory utilizes activity-based approach and focuses on
emissions of diesel PM for all significant sources operating in the Port. In
addition to in-port activities, emissions from railroad locomotives and on-road
trucks transporting port cargo were also estimated based on the activity that
occurs oulside the Port, but within th.e South Coast Air Basin boundaries. Only
on-port emissions were evaluated in this exposure assessment. The basic
methodologies for developing source category diesel PM emission inventory are
briefly described as follows:
Ocean-Goina Vessels
-.
Starcrest staff used the activity-based approach to estimate emissions from
various types of ocean-going vessels (auto carriers, bulk carriers, containerships,
cruise ships, general cargo ships, ocean-going tugboats, refrigerated vessels,
roll-on roll-off ships, bulk liquid tankers). The approach was chosen because it
makes use of actual location-specific information and can account for site-
specific and/or activity-specific emissions levels. For OGVs, emissions were
estimated as a function of vessel power demand (expressed in kW-hrs) multiplied
by an emission factor (expressed in g/kW-hr). The basic equation for calculating
emissions is as follows:
e
E = MCR * LF * A * EF
(A1)
Where E is the emission, MCR is the maximum continuous rated engine power
(kW), LF is the load factor (unitless), A is the activity (hours), EF is the emission
factor (gIkW-hr).
For propulsion engines, the load factor is defined as a ratio of a vessel's power
output at a given speed to the vessel's MCR power. At normal service speed, a'
ship probably has a load factor of close to BO%. For intermediate speeds, the
load factor was calculated as:
LF = (AS / MS)3
(A2)
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Where AS is the actual speed (knots), and MS is the maximum speed (knots).
For propulsion engines, the diesel PM emission factors of 1.92 and 0.72 g/kW-hr
were used for slow speed and medium speed operation modes, respectively.
Note that if an engine load is below 20%, an adjustment factor should be applied.
The following equations can be used to calculate the adjustment factors for
diesel PM when the load is at or lower than 20%:
AF = 9.8238 * (LFro.8117
(A3)
Where AF is the adjustment factor for diesel PM (unitless), and LF is the load
factor (in percent). Note that if the load factor were 20%, the adjustment factor
would be 1.
For auxiliary engines, emissions were estimated following the same logic as for
propulsion engines but differed in estimating load factors, which were based on
data available in technical literature. The emission factors of 0.30 g/kW-hr for
distillate oil and 1.5 g/kw-hr for residual oil were used for both medium-speed and
high-speed diesel engines.
e
For vessel hotelling at berth, emissions were estimated using the same logic as
for auxiliary engines except for activity data. The activity utilized the default
hotelling times (in hours) which were obtained from the Port's vessel call
database and then averaged by terminal and ship type. So, the default hotelling
times (see Table 2.31 in the Report of Los Angeles) represent average hotelling
times for each ship type and can be used when terminal-specific hotelling times
are not available.
Harbor Craft
The harbor craft vessels are categorized as: assist tugboats, towboats and push
boats, ferries and excursion ves~els. crew boats, work boats, government
vessels, dredges and dredging support vessels, commercial fishing vessels, and
recreation vessels. The emissions associated with the harbor vessels are
generated within the port and out at ocean. Based on the survey conducted by
CARB, the percentages of time spent within the port harbor, up to 25 miles, and
from 25 to 50 miles are 54, 35, and 11 percent, respectively.
The basic equation used by Starcrest to estimate harbor vessel emissions is:
E = PW x Act x LF x EF
(M)
Where E is the emission (g/yr), PW is the engine's power (kW), Act is the activity
(hrlyr), LF is the load factor, and EF is the emission factor (g/kW-hr).
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The activity data (engine information and operation time per year) were obtained
from the ARB's survey. The emission factors were obtained from the EPA's
database (EPA, 1999, "Final Regulatory Impact Analysis: Control Emissions from
Compression-Ignition Marine Engines", EPA420-R-99-026). The deterioration
rates were not taken into account for the emission estimates. The engine load
factors were obtained from the EPA NON-ROAD model. For assist tugboats, a
31 % average engine load factor was used, and for the other categories, the 43%
engine load factor was assumed.
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Carao Handline Eauioment
Cargo handling equipment consists of various types of off.road equipment and
vehicles used to move cargo within terminals and other off-road areas. The
emission estimates were estimated using ARB OFFROAD model.
The basic equation for calculating emissions of off-road equipment and vehicles
is as follows:
E :: EF x HP x LF x Act x FCF
(A5)
Where E is the emission (tons), EF is the emission factor (g/hp-hr), HP is the
average rated horsepower for the equipment type and horsepower category
(HP), LF is the load factor (assumed average percentage of full load), Act is the
equipment activity (hrslyr), and FCF is the fuel correction factor.
The activity data were collected by the Port from the terminal operators. The
OFFROAO model was run in "by-model year" mode, meaning that the model took
into account emission factors for specific model year group, and the number of
pieces of equipment in each of the subgroups. The equipment was grouped
based on horsepower range as: up to 25 hp, 26-50 hp, 51-120 hp, 121-175 hp,
176-250 hp, 251-500 hp, 501-750 hp, and 751hp and up. Within the groups, the'
horsepower and annual hours of use were averaged, and the averages were
input into the model. .
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The emission factors can be expressed as a combination of the base emission
factor for the equipment model year (g/hp-hr) plus a deterioration factor, that is:
EF :: EFbas. + OF
(Ae)
Where EFbBse is the base emission factor for a given horsepower range and
model year (glhp-hr), and OF is the deterioration factor (estimate of emission
increase as an engine ages, expressed as g/hp-hr-hr). The OFFROAD model
assumes that the equipment's annual operating hours have been constant over
the life of the equipment. The model also assumes that deterioration continues
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as a constant rate over the life of the equipment. The equation for the
deterioration factor is:
DF = DFbaso X Act x Age
(A7)
Where Act is the equipment activity (hrslyr), and Age is the age of equipment
(yrs).
Railroad Locomotives
Railroad operations are classified into two types of activities: line haul and
switching. Starcrest staff used two methods to estimate emissions. For in-port
switching operations, the emissions were estimated based on the throttllil notch
data and schedule/operational information provided by the switching companies
along with U.S. EPA data on emission rates by throttle notch. Off-port switching
emissions were estimated using throttle notch, U.S. EPA emission factors, and
fuel use data provided by the railroad companies. For the line haul operation
within the Port, emissions were estimated based on schedule and throughput
information provided by terminal operators and on U.S. EPA operational and
emission factors. For off-port line haul operations, the emissions were estirnated
using detailed cargo movement and fuel use information provided by the line haul
companies.
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Heaw-Dutv Vehicles
For this emission inventory, heavy-duty diesel-fueled vehicle (HDV) activity has
been divided into two components: on-road (off-terminal) travel and on-terminal
operations. For estimating on-terminal HDV emissions, Starcrest staff collected
on-terminal traffic information, including gate operation schedules, on-terrninal
speeds, time and distance trave led on terminal while dropping off and/or picking
up loads, and time spent idling at the entry and exit gates, through the interview
with terminal personnel. For estimating on-road (off-terminal) HDV emissions,
the off-terminal truck travel activity was developed by a consultant company
(Meyer Mohaddes Associates, Inc. (MMA)) using a travel demand model. The
on-road truck travel information included the number of trucks traveling on
defined roadway segments, the distance and average speeds on those segments
between defined intersections. Off-terminal and on-terminal emissions were
estimated by multiplying the emission factors derived by EMFAC2002 by the time
and distance parameters established for the terminals. Note that for on-terminal
vehicles, there are two types of activity: engine running with vehicles moving a
given speed, and engine idling with vehicles at rest.
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Port of Long Beach
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For POLB, Statcrest has developed emission inventories for three categories:
cargo handling equipment, i~ort locomotives, and in-port heavy-duty vehicles
using 2002 as the base year. The methodologies used in estimating emissions
for these categories are similar to those used in estimating corresponding
emission inventories for PO LA. To complete the emission inve ntories for POLB,
ARB staff used the scale-up/down approaches to estimate the emissions for
ocean-going vessels (cruises and hotelling) and harbor craft vessels.
OGVs (Cruise and Hotellinc)
To estimate emissions from ocean-going vessels for POLB, ARB staff assumed
that the unit emission for each OGVs type in POLB in 2002 is the same as that
for the corresponding OGVs type from POLA in 2001. The emissions of each
OGVs type for POLB in 2002 are estimated by multiplying the unit emission per
call of POLA in 2001 by the call number of the corresponding OGVs type at
POLB in 2002, that is:
E _ E POLA,200!,i CN A8)
POLB,2002,i CN x POLB,2002,i (
POLA,200!,i
where EPOLB,20D2,1 is the estimated emission of OGV type i at POLB for 2002,
EpOLA,2001,1 is the emission of OGV type i at POLA for 2001 (known), CNPOLA,2001,i
and CNpOLB 2002 i are the call numbers from POLA in 2001 and from POLB in
, .
2002 for OGV type i, respectively.
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Harbor Craft
To estimate emissions from harbor craft vessels operating at POLB, ARB staff
used the estimates of emissions from harbor craft vessels from ARB's 2004
commercial harbor craft emission inventory. These emission estimates were
based on information on vessels registered (California Department of Fish and
Game), permitted (California Public Utilities Commission), or documented (U.S.
Coast Guard) with a 'home port" listed as "Long Beach.' These vessels
registered as 'Long Beach" were then allocated to the nine categories
(commercial fishing, charter fishing, ferries/excursion, crew and supply, pilot,
tugs, tows, work boats, and others) using the harbor craft vessel composition
developed in ARB's 2003 Commercial Harbor Craft Survey (released in 2004).
The emissions of each category for POLB in 2004 were estimated using the
emission density (emission/per vehicle per category) multiplied by the
corresponding vessel number in each category, that is:
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2 .
E POLB 2004 =1:1:
, .=1 J-I
( E t t .., 2004 (i,j) J
s a ewlue, N (' ')
Nstatewide, 2004 (i,n x POLB,2004 I,)
(A9)
where EPOLB, 2004 is the estimated emissions for all harbor craft vessels at POLS
for 2004, E.tatewlde, 2004(i, j) is the estimated emission for engire type i and harbor
craft vessel type j in the statewide for 2004, N statewide, 2004 (i, j) and NPOLB, 2004 (i, j)
are the numbers of harbor craft type j for engine type I in the statewide and in
POLS for 2004 respectively, i is the index for engine type (propulsion and
auxiliary), and j is the index for harbor vessel type a = 1to 9, defined above).
According to the NNI Calculator, the growth of harbor craft vessels from 2001 to
2005 in POLA is almost zero (0.1 percent). We assume that for POLB, the total
emission of harbor craft vessels in the baseline year 2002 is equal to that in 2004
as calculated above.
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Appendix B
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Comparison of Estimated Diesel PM Cancer Risks from Ocean-Going
Vessel Activity Outside ofthe Breakwater using Wilmington and King
Harbor Meteorological Data Sets
Purpose. As discussed in the main report, about 95% of ship's emissions
(maneuvering + transiting) are generated in the outside shipping lanes of the
breakwater. Due to the blocking effect of the Palos Verdes Hills, wide variations in
meteorological conditions could occur within the Ports and in the outside (ocean side) of
the Ports. We conducted a sensitivity study to investigate possible effects of ocean side
meteorological conditions on the cancer risk in the Port's nearby communities using
King Harbor meteorological data set and compare to what we conducted in the main
report using Wilmington meteorological data.
Meteorological Data. Among available meteorological monitoring sites around the
Ports, we have chosen King Harbor site as representative to the ocean side. King
Harbor is about 10 miles the northwest of the Ports (see Figure AP-B-1). The wind rose
for King Harbor site is presented in Figure AP-B-2. The annual average wind speed is
about 2.93 mIs, which is higher than that of Wilmington site (1.83 mls). The winds were
predominantly from the west approximately 15%, west-southwest approximately 22%,
and southwesterly about 18% of the time, with wind speeds ranging from 0.5 to 11 mls. _
As showed in Figure AP-B-3, the data has a higher frequency of atmospherically stable W
conditions (stability E and F) compared with Wilmington met data (37% vs 24%).
Modeling Approach. We used the same air quality model (ISC), modeling receptor
dornain, modeling parameters, emission rate, and receptor spacing as what we utilized
in the main report except for the meteorological data. Note that the emissions resulting
from OGVs within the breakwater are not included in the sensitivity run.
Modeling Results. Estimated diesel PM cancer risks in the nearby communities
around the Ports within the modeling domain using two different meteorobgical data
sets (Wilmington and King Harbor) are presented in Figure AP-B-4. Within the
modeling domain, the contour lines of 100 and 200 in a million lie in the southwest
ocean water surface of the Ports for both meteorological data sets. As with the
population-weighted cancer risks within the domain, Wilmington data set resulted in 4%
higher risk than King Harbor data set. The population-weighted impact difference
~etween using Wilmington and using King Harbor meteorological data sets is not
significant. In other words, using Wilmington or King Harbor meteorological data for
out-of-port diesel PM emissions does not alter our conclusions drawn in the main report.
Conclusions. There does not appear to be a significant difference between using the
Wilmington or King Harbor meteorological data in terms of the population-weighted
cancer risks within the defined modeling receptor domain.
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1l.ilJllIt'41 ~., 1l9'I1.'" I~"
J;.:~J.'~:Jj~~ v
F1_ 0-'. ....,.........---' --...................
1
'""F
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Kin
Harbor
1981
_....fItGT
......1AI12. ,1..'
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-- ~m --
_.....(000) .......
"Uet ~..., .... -
U.I_II" -- ...
u..u. AVG'l'lllDRED --
U4.\!,,4' ... .. '01%
loll_I.'. -.- 1'UJT\'UINlII....... -"""'~
-_.. ....
1.1I1_'" -.- ...,.D.ca, 1981
Mldnlght-1'PY
--.."...--.---
Figure B-2. Wind Rose for King Harbor Meteorological Site
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Wind Class Frequency DlstrIbutlon
'JI
07 D.2
e.8-.11.1 _ 11.1
Stabiflty tiass Frequency Distribution
'JI
1
1
5
__ana
Figure B-3. Frequency Distributions of Wind Speed and Atmospheric
Stability for King Harbor Meteorological Site
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374500
Dl
"
;S
li
z
e
~
I
I
/
(
I
I
miles
380000
385000
390000 395000
Easting (m)
400000
405000
Figure 8-4. Comparison of Estimated Diesel PM Cancer Risks from OGV's Activity in the
Shipping Lanes outside of the Breakwater using Wilmington (solid lines) and
King Harbor (dashed lines) Meteorological Data (Urban Dispersion Coefficients,
80th Percentile Breathing Rate, Emission= 904 TPY, Modeling Receptor Domain = 20 mi x
20 mi, Resolution = 200 m x 200 m)
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