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Cancer risk assessment of selected hazardous air pollutants in Seattle

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The risk estimates calculated from the conventional risk assessment method usually are compound specific and provide limited information for source-specific air quality control. We used a risk apportionment approach, which is a combination of receptor modeling and risk assessment, to estimate source-specific lifetime excess cancer risks of selected hazardous air pollutants. We analyzed the speciated PM(2.5) and VOCs data collected at the Beacon Hill in Seattle, WA between 2000 and 2004 with the Multilinear Engine to first quantify source contributions to the mixture of hazardous air pollutants (HAPs) in terms of mass concentrations. The cancer risk from exposure to each source was then calculated as the sum of all available species' cancer risks in the source feature. We also adopted the bootstrapping technique for the uncertainty analysis. The results showed that the overall cancer risk was 6.09 x 10(-5), with the background (1.61 x 10(-5)), diesel (9.82 x 10(-6)) and wood burning (9.45 x 10(-6)) sources being the primary risk sources. The PM(2.5) mass concentration contributed 20% of the total risk. The 5th percentile of the risk estimates of all sources other than marine and soil were higher than 110(-6). It was also found that the diesel and wood burning sources presented similar cancer risks although the diesel exhaust contributed less to the PM(2.5) mass concentration than the wood burning. This highlights the additional value from such a risk apportionment approach that could be utilized for prioritizing control strategies to reduce the highest population health risks from exposure to HAPs.
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Cancer risk assessment of selected hazardous air pollutants in Seattle
Chang-fu Wu
a,b,
, Szu-ying Wu
b
, Yi-Hua Wu
b
, Alison C. Cullen
c
, Timothy V. Larson
d
,
John Williamson
e
, L.-J. Sally Liu
f,g
a
Department of Public Health, National Taiwan University, Taipei, Taiwan
b
Institute of Environmental Health, Institute of Occupational Medicine and Industrial Hygiene, National Taiwan University, Taipei, Taiwan
c
Evans School of Public Affairs, University of Washington, Seattle, WA, USA
d
Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA
e
Air Quality Program, Washington State Department of Ecology, Bellevue, WA, USA
f
Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
g
Institute of Social & Preventive Medicine, University of Basel, Switzerland
abstractarticle info
Article history:
Received 19 June 2008
Accepted 24 September 2008
Available online 8 November 2008
Keywords:
Risk assessment
Source apportionment
Bootstrapping
Diesel exhaust
Air toxics
The risk estimates calculated from the conventional risk assessment method usually are compound specic
and provide limited information for source-specic air quality control. We used a risk apportionment
approach, which is a combination of receptor modeling and risk assessment, to estimate source-specic
lifetime excess cancer risks of selected hazardous air pollutants. We analyzed the speciated PM
2.5
and VOCs
data collected at the Beacon Hill in Seattle, WA between 2000 and 2004 with the Multilinear Engine to rst
quantify source contributions to the mixture of hazardous air pollutants (HAPs) in terms of mass
concentrations. The cancer risk from exposure to each source was then calculated as the sum of all available
species' cancer risks in the source feature. We also adopted the bootstrapping technique for the uncertainty
analysis. The results showed that the overall cancer risk was 6.0910
5
, with the background (1.6110
5
),
diesel (9.8210
6
) and wood burning (9.45 10
6
) sources being the primary risk sources. The PM
2.5
mass
concentration contributed 20% of the total risk. The 5th percentile of the risk estimates of all sources other
than marine and soil were higher than 1 10
6
. It was also found that the diesel and wood burning sources
presented similar cancer risks although the diesel exhaust contributed less to the PM
2.5
mass concentration
than the wood burning. This highlights the additional value from such a risk apportionment approach that
could be utilized for prioritizing control strategies to reduce the highest population health risks from
exposure to HAPs.
© 2008 Elsevier Ltd. All rights reserved.
1. Introduction
In conventional risk assessment studies of air pollutants, the
estimated cancer risk, i.e. the additional risk of developing cancer
due to continuous lifetime exposures to carcinogenic compounds,
is calculated as the product of unit risk and the exposed concen-
tration (USEPA, 2005a). The concentration estimates are usually
based on measurements of individual hazardous air pollutants
(HAPs) and the risk estimates are compound specic. For example,
using measurements at 25 air monitoring sites in Minnesota, Pratt
et al. (2000) estimated that the cancer risks of 16 pollutants ranged
between 4.710
5
and 11.010
5
. Similarly, Tam and Neumann,
(2004), utilizing measurements at ve air monitoring sites in Port-
land, OR, calculated the cancer risks for 43 HAPs. They showed
that 17 HAPs exceeded the cancer risk level of 1 10
6
at all sites,
with carbon tetrachloride, 1,3-butadiene, formaldehyde, and 1,1,2,2-
tetrachloroethane contributing 50% of the total lifetime cancer risks
(2.4710
4
).
From the risk reduction viewpoint, compound-specicrisk esti-
mates provide limited information for source-specic air quality
management, due to contributions of multiple sources to each
compound. For example, formaldehyde was identied as one of the
main risk contributors in many regions (e.g. Sax et al., 2006; Woodruff
et al., 2000). However, in an urban environment it comes from a
multitude of sources such as diesel exhaust, wood burning smoke,
industrial activities, photochemical products, etc. For developing
effective control strategies for sources imposing the highest health
risks, source-specicrisk estimates would provide more indicative
information. The US National Scale Air Toxics Assessment (NATA)
study adapted this approach with modeled ambient concentrations as
the main inputs for generating source-specic population exposure
and risk estimates with regard to major, area, and mobile sources. It
was estimated that the national-wide total risk was 4.1510
5
with
the greatest contribution from on-road mobile sources (USEPA, 2006).
The NATA approach depends largely on emission inventory data and
various models without actual measurements. Errors propagating
Environment International 35 (2009) 516522
Corresponding author. National Taiwan University, Room 717, No.17, Xu-Zhou Rd.,
Taipei 100, Taiwan. Tel./fax: +886 2 3322 8096.
E-mail address: changfu@ntu.edu.tw (C.-f. Wu).
0160-4120/$ see front matter © 2008 Elsevier Ltd. All rights reserved.
doi:10.1016/j.envint.2008.09.009
Contents lists available at ScienceDirect
Environment International
journal homepage: www.elsevier.com/locate/envint
along the modeling processes could heavily inuence the risk
estimates. In addition, these risk estimates were limited to those
pre-specied source categories available from the emission inventory
(Cook et al., 2007; USEPA, 2006).
An alternative method less constrained by the aforementioned
limitations is the risk apportionment approach (Mukerjee and Biswas,
1992), which is a combination of risk assessment and receptor modeling
using actual measurements. Receptor models estimate contributions
from individual sources in terms of mass concentrations. Mukerjee and
Biswas (1992) made the initial demonstration by using the Chemical
Mass Balance model to apportion six samples of total suspended
particulate matters collected at a receptor site near several industrial
sources. The source-specic risks were calculated by summing the
species-specic risks for each source prole including paved road dust
(7.510
5
), blast furnace (6.810
6
) and sinter plants (3.410
5
).
Our previous source apportionment study in Seattle, WA (Wu et al.,
2007) estimated that wood burning smoke and secondary sulfate
contributed equally to the ne particulate matters (PM
2.5
, aerody-
namic particle diameter b2.5 µm) mass concentrations (24%), higher
than that from diesel exhaust (10%). However, the source apportion-
ment results alone do not provide information on health hazards of
these sources. In this study, we expanded and improved the risk
apportionment approach to estimate source-specic lifetime exces-
sive cancer risks of selected hazardous air pollutants in Seattle, WA.
We calculated source-specic concentrations of PM
2.5
and volatile
organic compounds (VOCs) using the Multilinear Engine (ME) model
without a priori source proles (Paatero, 1999). Cancer risks and the
associated uncertainties were further calculated using bootstrapping,
with an emphasis on health risks of the diesel particulate matters
(DPM).
2. Materials and methods
2.1. Sample collection and source apportionment
Speciated PM
2.5
and VOCs samples were collected at the Beacon
Hill monitoring site in Seattle from 2000 to 20 04 (N=268). Beacon Hill
is an urban-scale semi-residential site, located within 2 km of two
major interstate freeways and arterial roads, as well as within 4 km of
a warehousing area and a major seaport. It represents average PM
2.5
concentrations in a typical Seattle residential neighborhood (Goswami
et al., 2002). The PM
2.5
samples were collected using the IMPROVE
method and analyzed for mass, trace elements, sulfate (SO
4
2
), nitrate
(NO
3
), organic carbon (OC), and elemental carbon (EC). The VOCs,
collected using the DNPH cartridge and the SUMA canister (Wu et al.,
2007), included formaldehyde, acetaldehyde, Benzene, 1,3-Butadiene,
Carbon Tetrachloride, Chloroform, Tetrachloroethylene, and Trichlor-
oethylene. These VOCs were on the list of the top 33 urban HAPs
(USEPA,1999) and were chosen for monitoring due to the expected high
concentrations and health risks in the Seattle area.
The source-specic concentrations of each species were calculated
in our previous study using the ME model (Wu et al., 2007). In the ME
Table 1
Modeled and measured mass concentrations (ng/m
3
) of each species
Element Base data Bootstrap Unit risk
(risk/ng/m
3
)
Abbr Measured Modeled Diff (%)
a
p25
b
Median p75
b
(PM
2.5
)
Organic carbon OC 2561.9 2475.3 3.4 1443.4 2312.8 3250.0
Elemental carbon EC 612.9 585.6 4.5 407.7 551.9 720.8
Ammonium NH
4
456.1 450.5 1.2 310.6 400.2 548.9
Arsenic As 1.0 1.3 33.0 0.7 1.1 1.8 4.3E06
c
Bromine Br 2.0 2.4 19.9 1.8 2.4 3.1
Calcium Ca 26.7 25.1 6.1 19.6 24.8 31.2
Chlorine Cl 60.2 59.3 1.5 51.1 58.9 65.6
Chromium Cr 1.6 2.3 42.5 1.3 1.9 2.7 2.4E06
e
Copper Cu 4.3 4.4 1.9 3.1 4.1 5.4
Iron Fe 51.6 49.9 3.3 30.8 45.1 63.7
Lead Pb 3.7 5.2 38.6 2.7 4.7 6.9 1.2E08
d
Manganese Mn 3.0 3.9 30.4 2.4 3.3 4.7
Nickel Ni 2.3 2.4 4.4 1.3 2.0 2.8 4.8E07
f
Nitrate nonvolatile NO
3
459.3 460.1 0.2 357.3 425.6 544.6
Potassium ion K 29.5 38.5 30.6 25.1 36.5 49.1
Silicon Si 42.1 40.6 3.4 28.0 39.3 55.2
Sodium ion Na 151.8 147.6 2.8 117.7 136.9 169.4
Sulfate SO
4
1191.2 1188.9 0.2 831.5 1090.8 1446.7
Tin Sn 5.1 7.7 51.8 4.7 7.5 10.2
Titanium Ti 2.6 3.6 36.1 2.5 3.5 4.5
Vanadium V 3.5 3.9 12.2 2.3 3.2 4.6
Zinc Zn 8.8 8.3 6.1 5.5 8.0 10.8
Sum 5681.2 5566.5 2.0 3651.0 5164.5 7002.6
(VOCs)
Acetaldehyde Ace 1448.4 1297.0 10.5 711.4 1171.1 1652.1 2.2E09
c
Formaldehyde For 1505.8 1257.6 16.5 595.8 1116.3 1698.1 1.3E08
c
Benzene Ben 1329.7 1123.6 15.5 757.4 1071.7 1443.7 7.8E09
c
1,3-Butadiene But 119.1 147.0 23.4 101.1 139.4 183.5 3.0E08
c
Chloroform Chl 237.8 234.6 1.4 149.5 215.9 287.1 2.3E08
c
Carbon tetrachloride Car 641.2 634.9 1.0 462.4 602.4 752.8 1.5E08
c
Tetrachloroethylene Tet 182.4 179.2 1.8 124.3 172.0 227.6 5.6E09
d
Trichloroethylene Tri 169.2 208.0 23.0 146.8 197.2 248.2 2.0E09
d
Sum 5633.6 5081.7 9.8 3048.7 4685.9 64 93.1
a
Diff%=(modeled concentrationmeasured concentration) /measured concentration 100%.
b
p25 and p75 represent the 25th and 75th percentiles calculated from the bootstrapping process.
c
Reference from IRIS, website: http://www.epa.gov/iris/index.html.
d
Reference from Cal EPA, website: http://www.oehha.ca.gov/air/hot_spots/pdf/May2005Hotspots.pdf.
e
Reference from IRIS (assuming that 20% of all atmospheric chromium is hexavalent).
f
Reference from IRIS (unit risk for nickel subsulde).
517C.-f. Wu et al. / Environment International 35 (2009) 516522
Fig. 1. Source feat ures from the ME mod el with the error estimates. The y-axis is the mas s fraction (%) o f each species in the feature p role. The solid bar represents the point
estimates from the base factor. The upper and lower li ne and circle sy mbol represen ts the 75th percentile, 25th percentiles and median calculated fromthebootstrapping
factors.
518 C.-f. Wu et al. / Environment International 35 (2009) 516522
model, individual species measured in each air sample is expressed as
the sum of contributions from individual sources as below:
xij =
p
k=1
fjkgki +eij ð1Þ
where x
ij
is the jth species concentration measured in the ith sample,
g
ki
is the source contribution dened as the mass concentration from
the kth source contributing to the ith sample, f
jk
is the source feature
dened as the jth PM species or VOCs mass fraction from the kth
source, e
ij
is the error term associated with the jth species con-
centration measured in the ith sample, and pis the total number of
independent sources. The main model outputs contain the species
composition of each source (i.e. f
jk
or source features) and the overall
contributions of each source (i.e. g
ki
). We used the missing mass, i.e.,
the concentration of the measured total particle mass minus the sum
of all analyzed species, as an additional variable and implemented an
auxiliary equation to constrain the sum of all species mass fractions
to be 100% (Larson et al., 2006; Wu et al., 2007). This 100% constraint
prevents any serious under- or overestimation in the subsequent risk
estimates.
Using both PM
2.5
and VOCs measurements in the ME model, we
identied ten features, including wood burning, secondary sulfate,
nitrate, gasoline, diesel, fuel oil, aged sea salt, soil, marine, and the
urban background concentration of VOCs (i.e., other)(Wu et al.,
2007). We found a better model t for the PM
2.5
(R
2
= 0.88) than for the
VOCs (R
2
=0.72) portion. The major PM
2.5
emission sources included
wood burning (24%), secondary sulfate (24%) and nitrate (20%). The
majority of the measured vapor phase HAPs came from the general
urban background (26%), wood burning (14%), and diesel exhaust
(14%).
In this study, we further calculated the mean source specic
concentrations of individual HAPs (x
jk
) as follows:
xjk
=fjk ×gkð2Þ
where x
jk
is the concentration of the jth species from the kth source,
f
jk
is the source feature in Eq. (1), and gkis the mean contribution from
the kth source.
2.2. Risk assessment
With x
jk
, the cancer risk from exposure to the kth source (R
k
) can
be calculated as the sum of cancer risks of all available species in its
source feature:
Rk=x
jk ×Unit Riskj
 ð3Þ
Unit risk values for each species were taken rst from the
Integrated Risk Information System (IRIS) (USEPA, 2005b). When the
IRIS unit risk of a specic species is not available, the Unit Risk
Estimate (URE) provided by the California Environmental Protection
Agency (CalEPA) was used (CalEPA, 2005). Species without any unit
risk values were not included in the risk assessment process. The unit
risk of chromium was adjusted by multiplying a factor of 0.2, assuming
that approximately 20% of the ambient airborne chromium was in the
toxic hexavalent form (Bell and Hipfner, 1997). The unit risk for nickel
was adopted from the unit risk for nickel subsulde in IRIS, assuming
that all the monitored nickel was in the insoluble and carcinogenic
form.
2.3. Uncertainty analysis
As the uncertainty estimates of source features and source
contributions were not provided by the standard ME model, we
used the bootstrapping technique (Eforn and Tibshirani, 1993)to
obtain the uncertainties. The bootstrapping involves creating multiple
sets of subsamples and requires no prior statistical assumptions about
the underlying distribution of the dataset. Each set of the subsamples
is generated from re-sampling the data with replacement (i.e. any data
point could be sampled multiple times or not at all). The boot-
strapping process was executed with the SAS statistical software
(Version 8.02, SAS Institute Inc., Cary, NC, USA) in this study. To obtain
reliable results, 300 sets of different input data were generated with
299 sets of resampled data and one basedata without any replace-
ment (Eberly, 2005). Each dataset with 268 samples was analyzed
with the ME model, resulting in 299 sets of bootstrapping solutions as
well as one set of basesolutions. While the physical meaning of each
source feature in the base solutions was interpreted individually in
Wu et al. (2007), it is not practical to do so for all 299 bootstrapping
solutions in this study. Thus, these bootstrapping features were
matched or mappedto the base features automatically through cor-
relating the time series of the source contributions in the boot-
strapping results to those in the base results. The pair with the highest
correlation coefcient was retained, requiring that coefcient to be
larger than 0.6 (Eberly, 2005).
3. Results and discussion
The measured and modeled concentrations of each species were shown in Table 1.
Results shown in the Modeledcolumn represent the reconstructed concentrations
estimated from the base data while results in the Bootstrapcolumns represent the
statistics from the 299 sets of reconstructed concentrations through the bootstrap
resampling processes. The sums of the PM
2.5
and VOCs mass concentrations were
underestimated by only 2% and 10%, respectively. For individual species, the absolute
percent difference between the measured and modeled values ranged between 0 and
52%, with the larger differences (i.e. arsenic, chromium, lead, manganese, potassium, tin,
and titanium) due mostly to the higher percentages of samples below detection limit
(N20%) for these species. These biases would propagate into the following risk
assessment process. However, the bootstrapping results indicated that the measured
concentrations were within the 25th to 75th percentile of the bootstrapping range
(Table 1), suggesting that the variation from the ME modeling process wasmostly taken
into account in this uncertainty analysis.
The source features from the base data and the associated uncertainties calculated
from the bootstrapping process are shown in Fig. 1. During the mapping process, most
Table 2
Point estimates of the cancer risk (per million) for each source by the risk apportionment approach
Sources As Cr Ni Pb Ace Ben But Car Chl For Tet Tri Sum PM risk Sum VOCs risk Sum risk
Other 0.74 1.60 0 0.01 0.76 1.76 1.43 4.19 2.49 2.63 0.25 0.17 2.36 13.7 16.1
Diesel 0.33 2.17 0.32 0.01 0.2 2.18 0.96 0.31 0 3.17 0.13 0.05 2.83 6.99 9.82
Wood 3.17 0 0 0.02 0.37 2.08 1.07 0.6 0.36 1.64 0.12 0.03 3.18 6.27 9.45
Aged 0.32 0.44 0.02 0.01 0.26 0.08 0.44 1.69 0.7 1.79 0.07 0.08 0.79 5.11 5.90
Fuel 0.4 0.45 0.61 0.004 0.68 0.27 0.15 0.24 0.27 2.64 0.006 0.02 1.47 4.26 5.73
Sulfate 0.31 0 0.14 0.0 04 0.18 0.26 0.2 1.24 0.78 2.10 0.11 0.04 0.45 4.91 5.36
Nitrate 0.24 0.01 0 0.002 0.09 1.08 0.17 0.09 0.18 1.01 0.006 0 0.25 2.63 2.87
Gasoline 0 0.1 0 0.001 0.27 0.65 0 0.69 0.1 0.73 0.26 0 0.1 2.70 2.80
Soil 0 0.44 0 0 0.04 0.26 0 0.06 0.45 0.64 0.017 0.009 0.44 1.47 1.91
Marine 0.03 0.19 0.04 0 0.004 0.15 0 0.41 0.07 0 0.03 0.02 0.27 0.68 0.94
Sum 5.55 5.40 1.13 0.062 2.85 8.77 4.42 9.52 5.4 16.35 1 0.42 12.14 48.72 60.88
519C.-f. Wu et al. / Environment International 35 (2009) 516522
sources were mapped successfully to one of the base features at least more than 280
times with an rN0.6, while soil and othersources were mapped only 220 and 267
times, respectively, implying higher uncertainties for these two source features. Ideally,
the range (25th to 75th percentile) of the error estimates should cover the results from
the base data and the median from bootstrapping factor should be close to that from the
base data. In this study, although the source features from the base dataset may not
always agree well with those from the bootstrapping dataset, good agreement was
found for most important marker species that had high contributions in each source
Fig. 2. Source contributions to a) PM
2.5
and b) VOCs mass concentrations (data from Wu et al., 2007). Risk estimates by source contributions for c) PM
2.5
, d) VOCs, and e) the sum of
PM
2.5
and VOCs.
520 C.-f. Wu et al. / Environment International 35 (2009) 516522
feature (Fig. 1, e.g. the VOCs fraction in othersource; SO
4
and NH
4
in secondary sulfate
source; V and Ni in fuel source; Fe and Mn in diesel source; and the Si and Ca in soil
source).
The unit risks were available for As, Cr, Pb andNi in the PM
2.5
fraction and for all the
monitored VOCs (Table 1). The point estimates of excessive cancer risks from the base
data were summarized in Table 2 by source and species. The source specic risk values
ranged from 1.610
5
to 9.410
7
. All sources except for marinegave a sum of cancer
risks higher than 1 10
6
.Other, diesel and wood burning sources were the primary risk
sources in both PM
2.5
and VOCs fractions (Fig. 2). The sum of cancer risks were 6.110
5
,
with the PM
2.5
portion contributing 20% (1.210
5
). Formaldehyde, carbon tetrachloride
and benzene posed the highest cancer risks as estimated in other studies (Morello-
Frosch et al., 2000; Tam and Neumann, 2004). The overall risk value (11.810
5
)
estimated in the 1999 NATA study at the census tract of the Beacon Hill site was 2 times
higher than the overall risk in this study. This is mainly because more species (85 species
vs. 12 in this study) were simulated in the NATA study. Compared to the risk estimates
based on the measured ambient concentrations of 19 species in other cities (Los Angeles,
CA: 1.010
4
; New York City, NY: 1.3 10
4
), the overall risk in this study was still lower
than theirs (Sax et al., 2006).
The main risk contributors to the othersource were t he VOCs fraction (13.7/16.110 0% =
85%) with carbon tetrachloride, formaldehydeandchloroformasthetopcontributors.The
othersource from the ME model was considered as the background concentrations of
VOCs since the contribution for eachVOC was high in the source feature and the reconstructed
time series did not show obvious temporal variation (Wu et al., 2007). In the NATA study, the
background concentration was dened as components attributable to long-range transport,
unidentied emission sources, resuspensionof historical emissions, and other natural sources.
NATA assigned background concentrations to 29 species (i.e. 27 gaseous HAPs including
those 8 VOCs in our study and mercury and diesel PM)on top of the modeled concentrations
(USEPA, 2006). The 1999 NATA study estimated a cancer risk of 2.3 10
5
for the back-
ground concentrations of HAPs in the Beacon Hill area, which is comparable to our estimate
(1.610
5
).
The diesel and wood burning sources presented similar cancer risks (Fig. 2cd),
even though the diesel exhaust contributed less to the PM
2.5
mass concentration than
the wood burning (Fig. 2a). On the other hand, secondary sulfate, which was mainly
formed in southern Washington, along the Canadian border, and in southwestern
British Colombia, Canada (Kim et al., 2004), contributed equally to the PM
2.5
mass
concentration as wood burning (Fig. 2a) but posed less health risks (Fig. 2c). The above
observations highlight the additional information from such a risk apportionment
approach that could be utilized for prioritizing control strategies to reduce the highest
population health risks from exposure to HAPs.
The uncertainties in risk assessment of each source were shown in Fig. 3, with the
point estimates displayed in lled circles. For other, sulfate, gasoline and soil sources,
the point estimates using the base dataset were close to the median of the distribution,
suggesting small uncertainties. Point estimates for fuel, aged sea salt, wood burning and
diesel sources were near the higher end of the bootstrapping results, while the point
estimates of marine and nitrate were near the lower end. Nevertheless, the 5th per-
centile of the risk estimates still showed that most sources gave the sum of risks higher
than 110
6
, except for marine and soil.
For risks of DPM, we also utilized the simulated concentrations of DPM from NATA's
study and the unit risk (310
4
/µg/m
3
)fromCalEPA (2008) to calculate the cancer risk of
DPM. The modeled exposure concentrations of DPM from the 1999 NATA study at the
BeaconHill census tract,and within the bufferzones of 1 km, 3 km and5 km were 2.17,1.83,
1.63 and 1.55 µg/m
3
, respectively. The apportioned DPM mass concentration in our study
was 0.7 µg/m
3
. Multiplyingthese modeled concentrations by the unitrisk of DPM resulted
in estimated cancer risks of 6.5210
4
, 5.4910
4
,4.910
4
and 4.6610
4
, respectively.
The DPM risk (2.8310
6
,Table 2) from our risk apportionment approach was 2 orders of
magnitude lower. This is probably due to our inclusion of only four metal species and no
polycyclic aromatic hydrocarbons (PAHs) for DPM in risk calculations. Even after adding
risks fromthe DPM associated VOCs,risk from diesel exhaustwas still low (9.8210
6
). On
the other hand, the cancer risk estimate (2.0910
4
)calculatedusingtheMEmodeled
DPM sourcecontributions (0.7µg/m
3
) and the CalEPA's DPM unit risk wasclose to those in
the NATA study.
The discrepancy for the DPM risk estimates can be attributed to the following
reasons. First, the risk apportionment approach tends to underestimate because the
results depend on the number of species taken into account. Although 30 species have
been analyzed by the ME model in this study, the unit risks are available only for twelve
of them. Further, species that are not analyzed cannot be included in the risk
calculations. Second, apportioning the diesel exhaust involves considerable uncertain-
ties, as evidenced by the fact that the point estimate of the DPM cancer risks fell outside
the 25th and 75th percentile range (Fig. 3). Third, many toxicological studies have
showed that the overall chemical reactivity and toxicity of PM or DPM may be greater
than the sum of individual components (e.g. Pan et al., 2004). Thus it is likely that the
DPM risk estimate based on the sum of risks from individual species is lower than that
from using a single totalunit risk for DPM. Fourth, the validity of the CalEPA's DPM unit
risk itself is still being debated, even though that value is derived from epidemiological
data (CalEPA, 2008). The US EPA in its health assessment document concluded that “…
human exposure-response data [for diesel exhaust] are considered too uncertain to
derive a condent quantitative estimate of cancer unit risk(USEPA, 2002). Further-
more, many studies did not have adequate quantitative exposure data, did not control
for potential confounders, and might not be suitable for newly developed diesel engine
technology (Hesterberg et al., 2006).
One major limitation of the risk apportionment approach is that the estimated risk
values could be underestimated by the limited set of species considered and the
incomplete data of toxicity. The 12 species considered in our study were among the 33
most important urban HAPs based on emissions and toxicities in a 1995 ranking
analysis (USEPA, 1999). Other urban HAPs were not monitored because they are
considered less stable or lacked approved sampling and analytical methods (PSCAA,
2003). For example, a group of 7 PAHs, which are probable human carcinogens and are
used as a surrogate for the much more complex mixture of polycyclic organic matter in
the US EPA National Air Toxics Program (USEPA, 1999 ), are not monitored routinely at
the Beacon Hill site. In the 1999 NATA study, naphthalene was estimated to contribute
13.9% to the total risks, after subtracting the risks from the background concentrations
(USEPA, 2005b). Yu et al. (2008) also estimated that the mean cancer risk calculated
from 17 species of PAHs in an urban environment was 2.9 10
4
. It is possible to include
PAHs in the source apportionment models and then calculate their contributions to the
estimated cancer risks. One previous source apportionment study identied six sources
(i.e. oil, coal, gasoline, diesel, wood, and other) of PAHs in the urban atmosphere (Larsen
and Baker, 2003). Another limitation of this study is that the risk estimates pertained to
outdoor air at a stationary location. Indoor exposure sources might also have important
contributions (Payne-Sturges et al., 2004; Sax et al., 2006).
4. Conclusions
The cancer risks associated with the major sources at Beacon Hill
were calculated by the risk apportionment approach. These estimated
risks were calculated based on the assumption of lifetime exposures to
ambient pollutants. They are not actual risk values and are generally
regarded as for screening purposes and for preliminary estimation. We
further adopted the bootstrapping technique for the uncertainty
analysis. The results showed that most of the sources, other than marine
and soil, had cancer risks exceeding the acceptable level of 110
6
.
The sourcewith the greatest health concernwas the background source
which gave a cancer risk of 1.6110
5
(5th95th percentile: 1.2 10
5
2.010
5
), which requires concerted regional efforts to address. The
source-specic risks estimated in this study were likely to beunderesti-
mated mainly because only a limited number of species was monitored.
This is more apparent for the DPM, for which usinga single unit risk that
is scientically derived and well examined might be more appropriate
for the risk calculation. For the other sources, the risk apportionment
approach might be the most feasible method since no source-specic
unit risks were developed. Despite the potential underestimation, the
estimated risks still exceeded the acceptable level, suggesting that the
HAPs in this region are worth further investigation. Our risk apportion-
ment results can provide the guidance for future healthrisk managers to
design the risk reduction strategy more effectively.
Acknowledgements
This study was conducted as a collaborative effort among the U.S.
EPA Region X, the Washington State Department of Ecology (the
Fig. 3. The risk estimates for each source. The box plot represents the 25th percentile,
median, and 75th percentile of the risk estimates. The left and right line represents the
10th and 90th percentiles, and the square symbols represent the 5th and 95th
percentiles. The circle symbols represent the risk estimates from the base factors.
521C.-f. Wu et al. / Environment International 35 (2009) 516522
Ecology), the Puget Sound Clean Air Agency, the Washington State
University, and the University of Washington. This study was funded
by the Ecology under a cooperative agreement with the Washington
Cooperative Fish & Wildlife Research Unit. This study was also
partially funded by the U.S. Environmental Protection Agency through
its Ofce of Research and Development under EPA Grant R827355.
References
Bell RW, Hipfner JC. Airborne hexavalent chromium in southwestern Ontario. J Air
Waste Manage Assoc 1997;47:90510.
CalEPA. Air toxics hot spots program risk assessment guidelines: part II technical
support document for describing available cancer potency factors. Ofce of
Environmental Health Hazard Assessment; 2005.
CalEPA. Findings of the scientic review panel on the report on diesel exhaust, http://
www.arb.ca.gov/toxics/dieseltac/de-fnds.htm; accessed June 18, 2008.
Cook R, Strum M, Touma JS, Palma T, Thurman J, Ensley D, et al. Inhalation exposure
and risk from mobile source air toxics in future years. J Expo Sci Environ Epidemiol
2007;17:95-105.
Eberly S. EPA PMF 1.1 user's guide, 27711. Research Triangle Park, NC: US Environmental
Protection Agency; 2005.
Eforn B, Tibshirani RJ. An introductionto the bootstrap. London: Chapmanand Hall; 1993.
Goswami E, Larson T, Lumley T, Liu LJS. Spatial characteristics of ne particulate matter:
identifying representative monitoring locations in Seattle, Washington. J Air Waste
Manage Assoc 2002;52:32433.
Hesterberg T, Bunn WB, Chase GR, Valberg PA, Slavin TJ, Lapin CA, et al. A critical
assessment of studies on the carcinogenic potential of diesel exhaust. Crit Rev
Toxicol 2006;36:72776.
Kim E, Hopke PK, Larson T V, Maykut NN, Lewtas J. Factor analysis of Seattle ne
particles. Aerosol Sci Technol 2004;38:72438.
Larsen RK, Baker JE. Source apportionment of polycyclic aromatic hydrocarbons in the urban
atmosphere: a comparison of three methods. Environ Sci Technol 2003;37:187381.
Larson TV, Covert DS, Kim E, Elleman R, Schreuder AB, Lumley T. Combining size
distribution and chemical species measurements into a multivariate receptor model
of PM
2.5
. J Geophys Res Atmos 2006:111.
Morello-Frosch RA, Woodruff TJ, Axelrad DA, Caldwell JC. Air toxics and health risks in
California: the public health implications of outdoor concentrations. Risk Anal
2000;20:27391.
Mukerjee S, Biswas P. A concept of risk apportionment of air emission sources for risk
reduction considerations. Environ Technol 1992;13:63546.
Paatero P. The multilinear engine a table-driven, least squares program for solving
multilinear problems, including the n-way parallel factor analysis model. J Comput
Graph Stat 1999;8:85488.
Pan CJ, Schmitz DA, Cho AK, Froines J, Fukuto JM. Inherent redox properties of diesel
exhaust particles: catalysis of the generation of reactive oxygen species by
biological reductants. Toxicol Sci 2004;81:22532.
Payne-Sturges DC, Burke TA, Breysse P, Diener-West M, Buckley TJ. Personal exposure
meets risk assessment: a comparison of measured and modeled exposures and
risks in an urban community. Environ Health Perspect 2004;112:58998.
Pratt GC, Palmer K, Wu CY, Oliaei F, Hollerbach C, Fenske MJ. An assessment of air toxics
in Minnesota. Environ Health Perspect 2000;108:81525.
PSCAA.Final report: Puget soundair toxicsevaluation. Pudget SoundClear Air Agency; 2003.
Sax SN, Bennett DH, Chillrud SN, Ross J, Kinney PL, Spengler JD. A cancer risk assessment
of inner-city teenagers living in New York City and Los Angeles. Environ Health
Perspect 2006;114:155866.
Tam BN, Neumann CM. A human health assessment of hazardous air pollutants in
Portland, OR. J Environ Manag 2004;73:13145.
USEPA. National air toxics program: the integrated urban strategy. Fed Regist 1999;64
(137):FRL-63766377.
USEPA.Health assessment document for dieselengine exhaust. Washington,DC: National
Center for Environmental Assessment; 2002.
USEPA. Guidelines for carcinogen risk assessment EPA/630/P-03/001B. Risk assessment
forum,. Washington, DC: National Center for Environmental Assessment; 2005a.
USEPA. Integrated risk information system. Available from: bhttp://www.epa.gov/irisN.
2005b.
USEPA. National-scale air toxics assessment for 1999 Available from: bhttp://www.epa.
gov/ttn/atw/nata1999/N.2006.
Woodruff TJ, Caldwell J, Cogliano VJ, Axelrad DA. Estimating cancer risk from outdoor
concentrations of hazardous air pollutants in 1990. Environ Res 2000;82:194206.
Wu CF, Larson TV, Wu SY, Williamson J, Westberg HH, Liu LJS. Source apportionment of PM
2.5
and selected hazardous air pollutants in Seattle. Sci Total Environ 2007;386:4252.
Yu YJ, Guo HC, Liu Y, Huang K, WangZ, Zhan XY. Mixed uncertainty analysis of polycyclic
aromatic hydrocarbon inhalation and risk assessment in ambient air of Beijing.
J Environ Sci (China) 2008;20:50512.
522 C.-f. Wu et al. / Environment International 35 (2009) 516522
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A technique for fitting multilinear and quasi-multilinear mathematical expressions or models to two-, three-, and many-dimensional data arrays is described. Principal component analysis and three-way PARAFAC factor analysis are examples of bilinear and trilinear least squares fit. This work presents a technique for specifying the problem in a structured way so that one program (the Multilinear Engine) may be used for solving widely different multilinear problems. The multilinear equations to be solved are specified as a large table of integer code values. The end user creates this table by using a small preprocessing program. For each different case, an individual structure table is needed. The solution is computed by using the conjugate gradient algorithm. Non-negativity constraints are implemented by using the well-known technique of preconditioning in opposite way for slowing down changes of variables that are about to become negative. The iteration converges to a minimum that may be local or global. Local uniqueness of the solution may be determined by inspecting the singular values of the Jacobian matrix. A global solution may be searched for by starting the iteration from different pseudorandom starting points. Application examples are discussed—for example, n-way PARAFAC, PARAFAC2, Linked mode PARAFAC, blind deconvolution, and nonstandard variants of these.
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Ambient particulate matter less than or equal to2.5 mum in aerodynamic diameter (PM2.5) samples were collected at a centrally located urban monitoring site in Seattle, WA on Wednesdays and Saturdays using Interagency Monitoring of Protected Visual Environments ( IMPROVE) samplers. Particulate carbon was analyzed using the thermal optical reflectance method that divides carbon into four organic carbon (OC), pyrolyzed organic carbon (OP), and three elemental carbon (EC) fractions. A total of 384 samples that were analyzed for 36 species were collected between March 1996 and February 2000. These data were analyzed with the standard factor analysis model using the Multilinear Engine ( ME). Eleven sources were identified: sulfate-rich secondary aerosol (26%), diesel emissions (22%), wood smoke (16%), gasoline vehicle (10%), aged sea salt (8%), airborne soil (7%), nitrate-rich secondary aerosol (5%), sea salt (4%), oil combustion (3%), paper mill (2%), and ferrous metal processing (1%). The use of ME provided enhanced source separations, including the nitrate-rich aerosol source and two industrial sources that were not deduced in a previous PMF2 solution. Conditional probability functions using surface wind data and resolved source contributions aid in the identifications of local sources. Potential source contribution function analysis tentatively shows southern Washington State, along the Canadian border, and southwestern British Colombia, Canada as the possible source areas and pathways that give rise to the high contribution of the sulfate-rich secondary aerosol.
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Receptor and dispersion modeling techniques are extended to develop a health risk apportionment concept in which inhalation exposure to emission sources of ambient element pollutants are estimated. A preliminary demonstration of the concept is performed using ambient and emission inventory data from an industrial air shed located in a residential area. It is shown that risks from identified emission sources can be quantified and that a total, additive risk can be estimated for the sources in the air shed. Potential risk reduction measures can then be considered on the main risk sources without arbitrarily reducing risk for all existing sources in the air shed. Dispersion modeling is utilized from emission inventory data so that risk estimates for the primary sources can be modeled and compared with both ambient and receptor model risk estimates.
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Sumario: Introduction -- Methods -- Modeled estimates of outdoor air toxics concentrations -- Application of toxicity information to assess health risks -- assessment of cancer risks -- Assessing noncancer risks -- Results -- Geographic Distribution of air toxics concentrations and cumulative health risks by county -- Cumulative cancer and noncancer hazard estimates by Census Tract -- Pollutant-specific health risk estimates -- Discussion -- Limitations of toxicity information and uncertainties in science-policy assumptions -- Performance of dispersion model -- Emission source allocations -- Conclusions
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A public health concern regarding hazardous air pollutants (HAPs) is their potential to cause cancer. It has been difficult to assess potential cancer risks from HAPs, due primarily to lack of ambient concentration data for the general population. The Environmental Protection Agency's Cumulative Exposure Project modeled 1990 outdoor concentrations of HAPs across the United States, which were combined with inhalation unit risk estimates to estimate the potential increase in excess cancer risk for individual carcinogenic HAPs. These were summed to provide an estimate of cancer risk from multiple HAPs. The analysis estimates a median excess cancer risk of 18 lifetime cancer cases per 100,000 people for all HAP concentrations. About 75% of estimated cancer risk was attributable to exposure to polycyclic organic matter, 1,3-butadiene, formaldehyde, benzene, and chromium. Consideration of some specific uncertainties, including underestimation of ambient concentrations, combining upper 95% confidence bound potency estimates, and changes to potency estimates, found that cancer risk may be underestimated by 15% or overestimated by 40-50%. Other unanalyzed uncertainties could make these under- or overestimates larger. This analysis used 1990 estimates of concentrations and can be used to track progress toward reducing cancer risk to the general population.
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Of the 188 hazardous air pollutants (HAPs) listed in the Clean Air Act, only a handful have information on human health effects, derived primarily from animal and occupational studies. Lack of consistent monitoring data on ambient air toxics makes it difficult to assess the extent of low-level, chronic, ambient exposures to HAPs that could affect human health, and limits attempts to prioritize and evaluate policy initiatives for emissions reduction. Modeled outdoor HAP concentration estimates from the U.S. Environmental Protection Agency's Cumulative Exposure Project were used to characterize the extent of the air toxics problem in California for the base year of 1990. These air toxics concentration estimates were used with chronic toxicity data to estimate cancer and noncancer hazards for individual HAPs and the risks posed by multiple pollutants. Although hazardous air pollutants are ubiquitous in the environment, potential cancer and noncancer health hazards posed by ambient exposures are geographically concentrated in three urbanized areas and in a few rural counties. This analysis estimated a median excess individual cancer risk of 2.7E-4 for all air toxics concentrations and 8600 excess lifetime cancer cases, 70% of which were attributable to four pollutants: polycyclic organic matter, 1,3 butadiene, formaldehyde, and benzene. For noncancer effects, the analysis estimated a total hazard index representing the combined effect of all HAPs considered. Each pollutant contributes to the index a ratio of estimated concentration to reference concentration. The median value of the index across census tracts was 17, due primarily to acrolein and chromium concentration estimates. On average, HAP concentrations and cancer and noncancer health risks originate mostly from area and mobile source emissions, although there are several locations in the state where point sources account for a large portion of estimated concentrations and health risks. Risk estimates from this study can provide guidance for prioritizing research, monitoring, and regulatory intervention activities to reduce potential hazards to the general population. Improved ambient monitoring efforts can help clarify uncertainties inherent in this analysis.