ARTICLE IN PRESS
Atmospheric Environment 40 (2006) 1971–1990
www.elsevier.com/locate/atmosenv
Winter measurements of children’s personal exposure and
ambient fine particle mass, sulphate and light absorbing
components in a northern community
Melanie Noulletta,, Peter L. Jacksona, Michael Brauerb
a
Environmental Science and Environmental Engineering, University of Northern British Columbia, 3333 University Way, Prince George, BC,
Canada V2N 4Z9
b
School of Occupational and Environmental Hygiene, University of British Columbia, 2206 East Mall, Vancouver, BC, Canada V6T1Z3
Received 17 June 2005; received in revised form 18 November 2005; accepted 18 November 2005
Abstract
The relationship between ambient fine particle (PM2.5) concentration and children’s personal exposure was investigated
in Prince George, British Columbia. Repeated personal exposure measures (10 per subject) of 15 children and ambient
concentrations at their neighbourhood schools were collected for a 6-week winter period in 2001. PM2.5 mass, sulphate
(SO2
4 ) and light absorbing carbon (ABS) were determined for all samples and the relationship between ambient
concentration and personal exposure was investigated. Overall, lower particle exposures and a lower personal–ambient
regression slope were found for Prince George children compared to results from other longitudinal studies of children.
This suggests that in this setting indoor environments may have less influence from ambient sources and greater influence
from non-ambient sources. Comparison of personal exposures and ambient concentrations for each individual indicated
higher Spearman correlations for SO2
(median ¼ 0.95) and ABS (median ¼ 0.73) compared to total PM2.5 mass
4
(median ¼ 0.55). A large degree of individual variability in the personal–ambient correlation was found for PM2.5 mass,
while SO2
4 showed very consistent results, supporting its use as an indicator of exposure to particulate matter of ambient
origin. ABS was slightly more variable than SO2
4 due to the influence of non-ambient or very local sources in a low
number of samples. The impact of local meteorology was also investigated and inversion conditions were connected to all
high ambient levels (430 mg m3). In addition, associations were found between inversion strength and personal exposure.
This finding suggests that reduction of ambient concentrations during stagnant periods would result in lower personal
exposure levels. This study highlights the importance of both ambient and non-ambient sources, supports the use of both
SO2
and ABS as tracers of background ambient particle exposure and demonstrates the significant effect of winter
4
meteorology on both outdoor levels and personal exposure in a valley community.
r 2005 Elsevier Ltd. All rights reserved.
Keywords: Air pollution; Personal monitoring; PM2.5; Spatial distribution; Winter meteorology; Absorption coefficient/reflectance;
Sulphate (SO2
4 )
Corresponding author. Tel.: +1 250 960 5762; fax: +1 250 960 5539.
E-mail address: [email protected] (M. Noullett).
1352-2310/$ - see front matter r 2005 Elsevier Ltd. All rights reserved.
doi:10.1016/j.atmosenv.2005.11.038
ARTICLE IN PRESS
1972
M. Noullett et al. / Atmospheric Environment 40 (2006) 1971–1990
1. Introduction
Epidemiological studies have demonstrated associations between ambient concentrations of fine
particulate matter and a range of health impacts
(e.g. CEPA/FPAC, 1999; EPA, 2003; NRC, 1998;
Pope, 2000; Vedal, 1997). To support the use of
ambient measurements as a surrogate for exposure,
a number of studies have evaluated the relationship
between personal exposure to particulate matter
and ambient concentrations. Earlier results from
cross-sectional comparisons indicated substantial
between-person variability in exposures relative to
a central monitoring site and overall non-significant
correlations between actual exposure and ambient
concentrations (Clayton et al., 1993; Dockery and
Spengler, 1981; and Ozkaynak et al., 1996). More
recent studies evaluating the relationship on a
longitudinal basis (i.e. the relationship over time
within individuals) have shown relatively higher
correlations between personal measurements and
outdoor concentrations. The degree of association,
however, varies widely by individual (Ebelt et al.,
2000; Janssen et al., 1999, 2000; Liu et al., 2003;
Rojas-Bracho et al., 2000; Sarnat et al., 2000;
Williams et al., 2003). These studies demonstrate
that correlations between personal exposures and
ambient concentrations are higher for the sulphate
(SO2
4 ) component of fine particles, relative to the
mass concentration, since indoor sources affect PM
mass exposures whereas indoor sulphate sources are
negligible. This suggests that sulphate is an appropriate marker of exposure to particles of ambient
origin (Brauer et al., 1989; Ebelt et al., 2000, 2005;
Sarnat et al., 2000; Stieb et al., 1998; Suh et al.,
1992). Particle light absorbing carbon (ABS), a
measure highly correlated with the elemental carbon
(EC) content of PM, has also shown a strong
association between personal exposure and ambient
concentrations (Janssen et al., 2000).
In this study, we measure children’s personal
exposure in part due to local concern in the
community. Most personal exposure studies have
investigated the exposure of healthy or susceptible
adults and only a small number of studies have
measured children’s actual personal exposure
(Gauvin et al., 2002; Janssen et al., 1997, 1999;
Liu et al., 2003; Rojas-Bracho et al., 2002; Wu et al.,
2005). Although there is also a large body of
research that shows significant health effects from
air pollution on children using ambient data from a
central monitoring site, there is a need for more
research that characterizes the relationship between
measured children’s exposure and ambient concentration in areas with high ambient concentrations
and associated health impacts. For example, children may exhibit different exposure relationships
than adults as they tend to remain in their
neighbourhoods whereas adults may commute over
larger distances.
The majority of PM exposure studies have been
conducted in warm/temperate climates and/or in the
summer. To assess the personal–ambient relationship
in a cold winter climate and to evaluate the impact of
spatial variation and meteorology in a complex
airshed, we conducted a field study in Prince George,
British Columbia, Canada during the winter of 2001.
Prince George is located at a latitude of 531530 north
and longitude of 1221410 west on the interior plateau
of central British Columbia. The Prince George
airshed has many local sources of air pollution
including several major industrial sources (pulp mills,
sawmills and an oil refinery), vehicle emissions,
locomotives, unpaved and paved road surfaces,
vegetative burning, residential and commercial heating, etc. (Prince George Airshed Technical Management Committee, 1996). During the study period the
temperature ranged from 20 to +13 1C with an
average temperature of 4 1C for the 6-week period.
The normal minimum and maximum temperatures
for the months of February and March, respectively,
are 10.3 to 0.7 1C and 6.0 to 4.6 1C with mean
temperatures of 5.4 1C and 0.7 1C (Environment
Canada, 2003). The city is a unique location
compared to those in other exposure studies due to
its high latitude, cold winter temperatures and its
location within a river valley along with several
industrial sources of fine particulate matter. The
combination of topography, meteorological conditions and location of local industry results in
frequent episodes of poor air quality (Fudge et al.,
2003, 2004). A more detailed description of this
research can be found in Noullett (2004).
2. Methods
2.1. Study design
The general design of the study was to collect 10
personal exposure measurements from each of three
10 to 12 year old children attending one of 5
elementary schools located in different areas of the
city. Ambient PM2.5 measurements were collected on
the roof of the five elementary schools to complement
ARTICLE IN PRESS
M. Noullett et al. / Atmospheric Environment 40 (2006) 1971–1990
the existing air quality and meteorological network
(Fig. 1). Both ambient and personal exposure samples
were collected at each school on week days only for
the 6-week duration of the study (5 February to
16 March 2001). All ambient and personal samples
were collected for 24 h beginning and ending between
1973
8:00 and 10:30 a.m. on each day. Personal sampling
equipment was rotated between 3 students at each
school until each student completed 10 monitoring
sessions within the 6-week period. Sampling intermittently rather than successively for each individual
was done to reduce bias in measurements for an
Fig. 1. Map of the City of Prince George with each ambient monitoring site labelled and contour lines to depict the valley topography of
the area.
ARTICLE IN PRESS
1974
M. Noullett et al. / Atmospheric Environment 40 (2006) 1971–1990
individual and provide samples that were more
representative of their range of exposure.
2.2. Sampling methods
PM2.5 Harvard Personal Environment Monitors
(HPEM2.5, Demokritou et al., 2001) with a PTFE
Teflon filter (Pall Gelman R2PJ037) were used for
both the ambient and personal sampling. All
cleaning and loading of samplers followed the
standard operating procedure provided by the
Harvard School of Public Health (Ward Brown,
2000). A BIOS frictionless piston meter (DryCal
DC-1) was the primary instrument used to measure
flow rates at the beginning and end of sampling and
for all evening checks during each personal sampling session. The target flow rate for all samples
was 4 litres per minute. Samples with pre- or postsampling flow rates with more than 10% deviation
were excluded from further analysis.
At ambient sampling sites, the HPEM2.5 was
suspended approximately 4 ft above the school
rooftop (20 ft from the ground at all schools),
connected to a large flow controlled pump and
situated in an open area on the roof free of air vents,
exhausts or intakes. BGI air sampling pumps and
battery packs (BGI-400S and BGI-401) were used
for the personal monitoring and were contained in a
child-size backpack. The sampler was attached to
the strap of the backpack in the breathing zone of
the child with the inlet facing downwards and
protected by a 4-in piece of plastic tubing. Subjects
were required to wear the pack whenever possible
and otherwise to keep the pack close to them and as
close to their breathing zone as possible (at least in
the same room).
Each child completed a time activity diary every
30 min on the days that they carried the monitor. A
motion sensor (HOBO, Onset Computer Corporation) was also placed in each pack and data from the
sensor was downloaded each morning and then
compared to each child’s time activity diary as a
quality assurance measure. Visual comparisons were
made each day between the motion sensor and the
time activity diary to verify that the sampler was
moving or stationary at the appropriate times;
ensuring reliability of the diaries.
2.3. Pilot study and winter sampling
A pilot study was conducted from 26 November
2000 to 19 January 2001 to test sampling equipment
and procedures, provide a comparison of ambient
concentrations obtained from HPEM2.5 samplers to
those obtained from a tapered element oscillating
microbalance (TEOM) and to determine the relationship between light absorbing carbon (ABS), and
EC in this airshed. Light absorbing carbon has been
suggested as a reliable indicator of traffic-related
particulate matter (Cyrys et al., 2003; Fischer et al.,
2000; Janssen et al., 2000, 2001; Kingham et al.,
2000) but can also be produced from other
combustion sources.
Two HPEM2.5 samplers were collocated with a
government network TEOM PM2.5 monitor
(1400AB, Rupprecht & Patashnick). Each sampler
collected a 24-h integrated sample; one on the
Teflon filter to determine PM2.5 mass and the other
on a quartz fibre filter (Pall 2500QAT-UP #7201),
for EC analysis. The TEOM was operated continuously at a flow rate of 16.7 litres per minute by
the BC Ministry of Water, Land and Air Protection,
and heated to 401C to remove water vapour; no
corrections were made to the data to account for the
possible loss of semi-volatile particulate matter.
The pilot study identified several problems
associated with cold weather conditions, including
formation of frost over the sampling inlet, inconsistent operation of the flow measuring devices, and
unstable pump flows. This necessitated several
design modifications including heating the HPEM2.5
inlet with plumbers heat tape, keeping the flow
meter inside a heated enclosure and having subjects
activate hand warmers to place inside the packs
beside the sampling pump if they were going to be
outdoors for an extended period.
2.4. Filter analysis
Following sample collection Teflon filters were
equilibrated for 48 h in a temperature (21.970.3 1C)
and humidity (4173%) controlled weighing room
and triplicate measurements of filter weight were
made (Sartorious M3P; 1 mg resolution). Agreement
was required to be within 5 mg for each of the
triplicate weights. An external balance calibration
was performed daily using 5, 10 and 20 mg NISTtraceable weights. Frequent internal calibration and
triplicate weighing of a test blank filter every 25 filters
ensured that accuracy of the instrument was maintained during weighing sessions. After gravimetric
analysis and a quality assurance check of the data,
a reflectometer (M43D, Diffusion Systems Ltd.,
London, UK) was used to measure the ‘‘blackness’’
ARTICLE IN PRESS
M. Noullett et al. / Atmospheric Environment 40 (2006) 1971–1990
of the PM2.5 filter or the reflection of the incident
light. The reflectance analysis followed the standard
operating procedure from the ULTRA (1998) study
to determine the absorption coefficient reported in
other studies that represents the light absorbing
component of the sample (Cyrys et al., 2003; Fischer
et al., 2000; Gotshci et al., 2002; Janssen et al., 2000;
Kingham et al., 2000). Light absorbing carbon is
used in this research as an indicator of combustion
source PM. Sulphate analysis was also performed on
the samples after gravimetric and reflectance measurements were completed. Filters were extracted by
wetting with 100 ml of ethanol and sonicating in 5 ml
of distilled/deionized water for 15 min in polyethylene containers (Ebelt et al., 2000; Koutrakis et al.,
1988). The extract was then analyzed using an ion
chromatograph (Dionex, DX-300) with suppressed
conductivity detection.
For EC measurements, quartz fibre filters were prefired at 500 1C for 3 h to remove any possible
contamination and then wrapped in aluminum foil
and stored in a sealed glass jar kept in a refrigerator.
After sampling, the quartz filters were stored in an
Analyslide holder (Pall 7231) and stored in a freezer.
A mask was used with the quartz filters when
sampling to concentrate the sample on a smaller area
of the filter (3/4 in) and a stainless steel punch was
used to cut and remove the concentrated area of the
filter for analysis. EC analysis was performed by the
Air Quality Research Branch of the Meteorological
Service of Canada (Toronto, Ontario) by thermal
optical transmittance (TOT) as described by Sharma
et al. (2002). This hybrid method provides two
separate sets of results that are comparable to the
two accepted methods of organic and EC determination that yield slightly different results. Comparison
between the TOT method and both the NIOSH 5040
method and the Desert Research Institute (DRI)
IMPROVE Thermal Optical Reflectance (TOR)
approach is also shown by Sharma et al. (2002).
2.5. Data analysis
2.5.1. Meteorological data
The relationship between wind and particle
concentration was characterized using ambient
PM2.5 hourly concentrations from the Ministry of
Water, Land and Air Protection TEOM and wind
speed and direction data from the Plaza monitoring
station (Fig. 1). The relationship between inversion
conditions and hourly PM2.5 concentration was
investigated by calculating an index of inversion
1975
strength from the temperatures recorded at two
meteorological sites. Hourly inversion strength was
calculated by subtracting hourly temperature data at
a valley meteorological site (Plaza) from a higher
elevation site (UNBC). The temperature difference
was normalized for 100 m by dividing by the
difference in elevations (601.5 ASL and 790 m
ASL, respectively) and than multiplying by 100.
Positive values were associated with inversion
conditions and all negative values were set to zero.
A 24-h inversion strength was calculated by summing the positive temperature differences for a given
day and dividing by 24 to normalize, which enabled
a comparison to the 24-h averaged study data.
2.5.2. Statistical analysis
All statistics were performed using Statistica 5.1
(StatSoft, Inc., 1997). Graphical output was generated using Microsoft Excel 2002. Personal and
ambient measurements for PM2.5, sulphate and light
absorbing carbon were not normally distributed and
were all positively skewed. Log transformation did
not improve normality but both the geometric mean
and geometric standard deviation for the pooled data
are reported for descriptive purposes. Distribution of
the personal and ambient data at each school is
shown using box plot diagrams depicting median
values and the inter-quartile range. Spearman
correlations were used to assess the relationship
between each measure on both the personal and
ambient samples. The impact of wind speed and
wind direction on hourly PM2.5 concentrations
during the study period was assessed using wind
rose and pollution rose diagrams. Time-series plots
of concentration and inversion strength and Spearman correlations were used to assess the relationship
between these variables. Spatial variation of ambient
levels between neighbourhoods was assessed using
the non-parametric Friedman 2-way ANOVA to
account for differences over time. Finally, individual
longitudinal Spearman correlations describing the
personal–ambient association for total PM2.5, sulphate and light absorbing carbon were determined to
more accurately assess the personal–ambient relationship and quantify the variability across subjects.
3. Results
3.1. Data quality
Field blanks collected were 27% (n ¼ 13) and
10% (n ¼ 31) of the total sample size for the pilot
ARTICLE IN PRESS
M. Noullett et al. / Atmospheric Environment 40 (2006) 1971–1990
1976
and main field study, respectively, with an average
percent change in mass of 0.009% and 0.001%.
Limits of detection (LOD) are summarized in
Table 1 (calculation shown in caption) and were
comparable or lower than those reported in similar
studies (Cyrys et al., 2003; Janssen et al., 2000; Liu
et al., 2003; Sarnat et al., 2000). Almost all samples
were above the LOD for each measure except 8% of
the light absorbing carbon samples; no exclusion or
alteration was made to the data. Collocated samples
were 8–10% of total sample size for personal and
ambient samples, respectively. There were no
significant differences between paired samples and
a high Pearson correlation (r ¼ 0:99) was found for
the field study pairs. The precision, calculated as the
standard deviation of the duplicate differences
divided by the square root of 2, of the field study
personal and ambient PM2.5 concentrations was 1.4
and 2.4 mg m3, respectively. This precision compares well to the 2.2 mg m3 obtained by Liu et al.
(2003) using the same HPEM2.5 samplers. Only 6
personal samples and 1 ambient sample were
excluded from analysis due to flow problems and
one additional personal sample was lost due to a
compliance issue. In total, 97% of the total samples
collected for the field study were successful and were
included in the analysis.
Collocated HPEM2.5 and TEOM measurements
from the pilot study indicated that the HPEM2.5
measurements were significantly higher than those
obtained with the TEOM but the data were strongly
correlated (slope of the linear regression of
HPEM2.5 versus TEOM ¼ 1.3870.04; intercept ¼ 0.6270.67; R2 ¼ 0:93). This strong correlation confirms that ambient sampling with the
HPEM2.5 monitors in Prince George’s cold climate
was successful and appropriate. The mean difference between the HPEM2.5 and TEOM was
4.3 mg m3 with a standard deviation of 3.2 mg m3.
This is likely due to operation of the TEOM at 40 1C
and the consequent loss of volatile material (Allen
et al., 1997; Chow, 1995; Oh et al., 1997; Williams
et al., 2000).
EC concentrations from the pilot study were
calculated by two different methods to enable
comparison with light absorbing carbon. Agreement
was high in both cases (rs ¼ 0:88, 0.92 for NIOSH
and DRI/IMPROVE comparable results, respectively), although the NIOSH comparable results
had blank levels closer to zero, a lower LOD
and better agreement between co-located samples
(Fig. 2). This supports the use of the reflectance
method and light absorbing carbon as a surrogate
measure for EC concentration.
3.2. Time activity patterns
On average the study participants spent 9572%
of their time indoors and 9772% in their own
neighbourhood and therefore relatively close to the
ambient monitor located on their school roof. For
the majority of their day the children were exposed
to similar microenvironments with their average
amounts of time spent at home and at school being
6675% and 2173%, respectively. The largest
difference in activity pattern between subjects was
for time spent in other indoor location (2–14%) and
time spent outdoors (1–7%). The small sample of
subjects were not selected using probability-based
sampling; therefore the results are only indicative of
Table 1
3
Summary of limits of detection (LOD) and co-located (COLO) mean differences 7s.d. for PM2.5, SO2
4 and EC concentrations (mg m )
and ABS level (105 m1)
Pilot study
Field study
LOD
COLO (Ambient)
LOD
COLO (Ambient)
COLO (Personal)
PM2.5
SO2
4
4 (3)a
0.08
171
0.0670.06
3
0.06
272
0.0470.02
ABS
ECNIOSH
ECDRI/IMPROVE
0.1
0.07
0.64
0
0.1470.10
0.5370.39
0.2
n/a
n/a
273
0.4971.01
(0.0870.11)b
0.170.1
n/a
n/a
0.170.2
n/a
n/a
LOD calculated from field blanks collected during the pilot and field studies. LOD ¼ 3s.d. of the field blanks divided by the mean sample
volume. For ABS LOD ¼ 3s.d.
a
One extreme blank was removed due to obvious contamination.
b
Three extreme differences removed: 2 were 25s.d. from the mean and 1 was 4s.d. from the mean.
ARTICLE IN PRESS
M. Noullett et al. / Atmospheric Environment 40 (2006) 1971–1990
Elemental Carbon Concentration (NIOSH) Versus
Light Absorbing Carbon
Elemental Carbon Concentration (DRI/IMPROVE) Versus
Light Absorbing Carbon
1.6
3.5
y = 0.34(±0.02) x + 0.03(±0.04)
1.4
y = 0.57(±0.05) x + 0.82(±0.09)
3.0
R2 = 0.80(±0.12)
[EC] (µg m-3)
1.2
[EC] (µg m-3)
1977
1.0
0.8
0.6
R2 = 0.73(0.13)
2.5
2.0
1.5
1.0
0.4
0.5
0.2
0.0
0.0
0.0
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
ABS (m-1x10-5)
(a)
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
ABS (m-1x10-5)
(b)
Fig. 2. Linear least-squares regression of EC concentrations determined via thermal optical transmittance and light absorbing carbon
determined from Teflon filters. The elemental carbon concentrations were calculated to enable comparison to 2 methods of EC
determination. One extreme outlier was removed (N ¼ 48).
Table 2
3
5 1
m )
Summary statistics for PM2.5 and SO2
4 concentrations (mg m ) and ABS level (10
Measurement
N
Mean
s.d.
Median
Range
GM
GSD
Ambient PM2.5
Ambient SO2
4
Ambient ABS
Personal PM2.5
Personal SO2
4
Personal ABS
149
149
149
142 (140)a
142
142
18
2.72
1.4
21 (18)a
1.33
1.0
15
3.11
1.0
22 (13)a
1.47
1.7
14
1.23
1.2
16 (16)a
0.61
0.7
1–61
0.13–12.76
0.1–4.7
3–179 (3–87)a
0.11–8.44
0.0–15.3
13
1.39
1.0
16 (16)a
0.76
0.6
3
3.39
2.6
2 (2)a
2.92
2.6
Ambient and personal refer, respectively, to all outdoor and personal samples collected during the study period. Mean ¼ arithmetic mean;
s.d. ¼ standard deviation; GM ¼ geometric mean; GSD ¼ geometric standard deviation.
a
Two extreme outliers were removed from the data.
average children’s activity patterns and exposure in
the city of Prince George during the winter and
should not be extrapolated to a larger population.
3.3. Personal exposure and ambient data summary
Descriptive statistics are summarized in Table 2
for both the ambient and personal measures of total
PM2.5, sulphate and light absorbing carbon. For
total PM2.5 concentration, the mean ambient and
personal levels were essentially equal when two
extreme personal values were removed. These two
extreme values of 149 and 179 mg m3 were from the
same individual, had a distinct faint yellow colour
not seen on any other samples and were both
collected on occasions where fried chicken with
curry was prepared in the home, suggesting a high
particle load due to cooking. The exclusion of these
samples is justified as the presence of a rare
occurrence of high PM exposure from an unusual
situation can seriously bias any regression analysis
and does not represent the day-to-day variation of
the usual indoor and personal sources of PM that
the general public would encounter (Mage et al.,
1999). However, these samples do not appear to be
rare for this individual in the study and demonstrate
the range of possible exposures that may exist,
therefore, results are reported with and without
these two outliers included.
The distributions of both the personal and ambient
measures at each school are shown in Fig. 3. For
PM2.5 mass concentration the range was usually
greater for personal exposure due to occasional peaks
of high exposure, but the general variability for all
measures as indicated by the inter-quartile range was
greater for ambient concentration. The higher personal
exposure median and upper quartile at Carney Hill in
comparison with the other schools may be the result of
higher ambient concentrations in that area. The lower
ambient median value compared to personal exposure
ARTICLE IN PRESS
M. Noullett et al. / Atmospheric Environment 40 (2006) 1971–1990
1978
Ambient (left)
Personal (right)
100
PM2.5
(µg m-3)
75
50
25
0
1
2
3
4
5
SCHOOL
* 2 personal extreme values at school 1 are outside of the scale.
1
2
1
2
15
SO42(µg m-3)
10
5
0
3
4
SCHOOL
5
6.0
ABS
(m-1 x 10-5)
4.5
3.0
1.5
0.0
3
4
5
SCHOOL
* 1 personal extreme value at schools 3 and 5 are outside of the scale.
Fig. 3. Box plots for ambient concentration and personal
exposure of each measure with data pooled by school. Whiskers
show minimum and maximum values excluding outliers () and
extreme values (J); box represents inter-quartile range and
median. 1 ¼ Carney Hill, 2 ¼ Gladstone, 3 ¼ Lakewood,
4 ¼ Westwood and 5 ¼ Glenview.
at 3 of the 4 remaining schools was likely the result of a
higher baseline personal exposure for those subjects,
which could be due to higher indoor exposures at
either the home or school or possibly to a personal
cloud that can result from proximity to sources and
personal activities (Wallace, 2000). These findings
suggest that the ambient levels have an important
influence on personal exposures but non-ambient
sources are also playing a significant role.
For sulphate there is no evidence of indoor or
personal sources and personal exposures were a
fraction of the ambient levels. This was expected
given the limited amount of time that was spent
outdoors and the general absence of indoor sulphate
sources (Sarnat et al., 2002). Comparing personal
and ambient light absorbing carbon values showed
the same trend as for sulphate with personal
exposure levels a fraction of ambient levels except
at Glenview. There were several samples at Glenview where the personal level was higher than
ambient suggesting the possibility of an indoor
source at the school or homes or a local ambient
source that was not well-represented by the ambient
monitor on the school roof. One possible explanation could be residential wood-smoke. There were a
few similar occurrences of this at Lakewood and
Westwood. The corresponding time activity data for
these samples did not reveal any relevant information except that the subject from Glenview with the
greatest number of high personal exposures had a
pellet stove in his home that was used occasionally
throughout the study period.
For data pooled across all ambient sites, high
Spearman correlations existed between ambient
PM2.5 and both the sulphate (rs ¼ 0:88) and light
absorbing components (rs ¼ 0:95) suggesting that
each of these markers may be a good surrogate
measure for background ambient PM2.5 but possibly
not all local ambient PM2.5. There was also a high
overall correlation between ambient sulphate and
ambient light absorbing carbon (rs ¼ 0:78), suggesting that a proportion of each of these components
may have come from a common source. On the
personal samples the relationship between total PM2.5
and its components was not as strong with overall
Spearman correlations of 0.43 and 0.56 for sulphate
and light absorbing carbon, respectively. The relationship between sulphate and light absorbing carbon
for the personal samples was slightly lower than
found for the ambient samples (rs ¼ 0:66).
3.4. Individual personal and ambient associations
The longitudinal associations between personal
exposure and ambient data were investigated for
ARTICLE IN PRESS
M. Noullett et al. / Atmospheric Environment 40 (2006) 1971–1990
each individual and are reported in Table 4. Only
five of the 15 subjects showed a significant correlation between personal and ambient PM2.5 mass
concentration with a median (range) of 0.55
(0.18–0.83), which was not as consistent as that
found between personal and ambient sulphate or
light absorbing carbon. The individual Spearman
correlations between ambient and personal sulphate
were very high across all individuals with a median
of 0.95 (0.83–1.00). This confirms that there were
limited non-ambient sources of sulphate exposure
and supports the use of sulphate as an indicator of
ambient exposure. Spearman correlations for individual light absorbing carbon were also high with
a median of 0.73 (0.23–0.94) but there was an
insignificant correlation for 3 subjects from 3
different schools that likely resulted from high
personal exposures due to an indoor combustion
source or a local ambient source such as traffic or
residential wood-smoke. Although the personal–
ambient associations for both PM2.5 mass concentration and light absorbing carbon showed similar
variability, the higher median correlation for light
absorbing carbon suggests that a smaller number of
samples were influenced by personal, indoor or local
ambient sources. Use of light absorbing carbon as
an indicator of exposure to ambient PM2.5 sources
may still be possible if samples with high personal
exposures can be linked to specific indoor sources.
However, if this link cannot be made the high
exposure may have resulted from a very local
ambient source and light absorbing carbon can
only be used as an indicator for background
ambient PM2.5 exposure and not total ambient
exposure.
3.5. Meteorological influence
3.5.1. Wind speed and direction
Wind and pollution roses for the study period
using hourly data from the network TEOM and
meteorological station are shown in Fig. 4. During
the study period the highest percentage of winds
came from the south and from the east. There was a
low proportion of wind speeds over 6 m s1 for the
period (8%) and higher winds generally came
from the south and occasionally from the north and
south-west. When higher pollution levels were
observed the wind was generally coming from an
easterly direction, carrying emissions from the main
industrial sources (3 pulp mills and an oil refinery)
towards the TEOM monitor when valley topogra-
1979
phy is also considered. Wind speeds from the east
were always less than 5 m s1 with a greater
proportion of winds below 2 m s1. Analysis of data
when the PM2.5 concentration was greater than
15 mg m3 showed that approximately 60% of winds
were from an easterly direction and 20% were from
a south-westerly direction and when winds below
1 m s1 were removed to account for the lower limit
of sensitivity of the wind monitor, winds only came
from the east. These results suggest that generally,
air quality on days with high concentration of PM2.5
was influenced mainly by industrial emissions from
the three pulp mills and oil refinery to the east.
Lower concentrations at the Glenview outdoor
monitoring site are expected not only due to
increased distance from local industry but also due
to the prevailing wind direction for the period.
Analysis of the sulphate and EC content of the
samples also suggested that industrial sources made
a significant contribution to total PM2.5 levels
during periods of high concentrations. During the
study period the mean sulphate content of the
ambient PM2.5 samples was 13.076.9% while the
estimate of EC content was 3.371.9%. An analysis
of higher pollution days showed that as PM2.5 mass
concentration increased, the mean and median
concentrations of sulphate increased by an average
amount of 1.0 mg m3 for every 5 mg m3 increase in
ambient PM2.5 concentration while the EC concentration only changed slightly by an average of
0.06 mg m3 for every 5 mg m3. On days where
ambient concentration exceeded 15 and 30 mg m3,
the median sulphate content increased to 16.1% and
19.5% and the EC content decreased to 2.6% and
2.3%, respectively. These findings suggest that
sources of sulphate made a greater contribution to
higher pollution levels at the school rooftops than
sources of EC such as wood burning or road and
rail traffic.
3.5.2. Inversion strength
Levels of PM2.5 during the 6-week period of this
study were strongly influenced by inversion conditions. Inversion strength, calculated as the temperature difference between UNBC and Plaza per 100 m
elevation, was used to assess the relationship of both
ambient concentration and personal exposures with
inversion conditions. During the study period, 62%
of the days experienced inversion conditions for a
portion of the day. Inversion periods typically
started between the early to late evening hours
and then dissipated by noon the following day. All
ARTICLE IN PRESS
M. Noullett et al. / Atmospheric Environment 40 (2006) 1971–1990
1980
Wind Rose: Hourly Average Wind Speed and Direction
N
NW
%
18
NE
>10.0
%
12
6.0 - 10.0
6%
3.5 - 6.0
W
E
2.0 - 3.5
1.0 - 2.0
6%
Calm Periods:
Wind Speed < 1 m s-1 = 33.8%
%
12
SW
* Diagram % does not include
calm periods.
SE
%
18
S
Pollution Rose: Hourly Average Wind Direction and PM2.5 Concentrations
N
NW
%
18
NE
>60.0
%
12
45.0 - 60.0
30.0 - 45.0
6%
15.0 - 30.0
W
E
6%
0.0 - 15.0
Calm Periods:
Mean Concentration = 25 ug m-3
%
12
SW
SE
* Diagram % does not include
calm periods.
%
18
S
Fig. 4. Wind (top) and pollution (bottom) rose diagrams for 6-week study period. Diagrams show the percentage of each speed or
concentration class associated with winds from a given direction. Data from the Ministry of Water, Land and Air Protection OminecaPeace Region Plaza air quality and meteorological monitoring site.
of the days where hourly ambient PM2.5 concentrations exceeded 30 mg m3 were associated with the
existence of an inversion on that day. Five main
episodes were identified during the study as periods
where there were hourly concentrations greater than
30 mg m3 for more than 6 consecutive hours on 2 or
ARTICLE IN PRESS
M. Noullett et al. / Atmospheric Environment 40 (2006) 1971–1990
more consecutive days. All five episodes of high
PM2.5 concentration were associated with inversion
conditions. The hourly ambient PM2.5 concentrations from the TEOM site and inversion strength for
one of the episodes are shown in Fig. 5. Concentrations greater than 30 mg m3 appeared to have a
diurnal variation related to the existence of an
inversion. During several of the episodes, peak
PM2.5 levels occurred in between daily inversions
instead of during the actual inversion period. This is
clearly illustrated on the last 3 days of the episode in
Fig. 5.
Time series graphs showing 24-h inversion
strength and both ambient and personal PM2.5 at
each school are shown in Fig. 6. The ambient timeseries clearly shows the five episodes during the
study period. Ambient sulphate and light absorbing
carbon showed an identical pattern as seen for total
PM2.5 mass concentration (figures not shown) and it
is clear that inversions are the cause of increased
levels for all three measures at the five schools
including Glenview and Gladstone, which are
located at a higher elevation outside of the city
‘‘bowl’’. The time-series of inversion strength and
1981
personal exposure to PM2.5 mass concentration
does not show the relationship as clearly (Fig. 6).
This was also the case for personal exposure to light
absorbing carbon (Fig. 7). In contrast, the relationship between inversion strength and personal
exposure to sulphate was almost identical to the
ambient plots showing a clear pattern with inversion
strength (Fig. 7). Table 3 summarizes the statistical
associations between inversion strength and both
ambient concentrations and personal exposures at
each school, clearly showing statistically significant
Spearman correlations with both ambient levels and
exposures for all three measures. Although the time
series do not show as clear a relationship between
inversion strength and personal light absorbing
carbon as is present for personal sulphate, the
correlations show that this is likely due to a small
number of high personal exposures impacted by
non-ambient or very local ambient sources.
3.6. Spatial variation
Carney Hill, the site closest to the main industrial
area in the city, experienced the highest ambient
[PM2.5] and Inversion Strength for an 11-Day Episode
During the Study Period
100
3.5
PM2.5
90
Inversion Strength
3.0
2.5
[PM2.5] (µg m-3)
70
60
2.0
50
1.5
40
30
1.0
Inversion Strength (ºC per 100m)
80
20
0.5
10
0.0
2/23 0
2/23 12
2/22 0
2/22 12
2/21 12
2/21 0
2/20 12
2/20 0
2/19 0
2/19 12
2/18 0
2/18 12
2/17 12
2/17 0
2/16 12
2/16 0
2/15 0
2/15 12
2/14 0
2/14 12
2/13 12
2/13 0
2/12 12
2/12 0
2/11 0
2/11 12
0
Date and Time (Sun Feb 11 - Friday Feb 23, 2001)
Fig. 5. Episode during the study period that demonstrates the build-up of PM2.5 when inversion conditions occur on consecutive days.
Peak concentrations often occur after the inversion dissipates. Hourly PM2.5 data is from the Ministry of Water, Land and Air Protection
permanent TEOM monitor.
ARTICLE IN PRESS
M. Noullett et al. / Atmospheric Environment 40 (2006) 1971–1990
1982
Amblent PM2.5 Concentration and Inversion Strength
70
1.2
Carney Hill
Gladstone
Lakewood
Westwood
Glenview
Inversion Strength
50
1
[PM2.5] (µg/m-3)
0.8
40
0.6
30
0.4
20
Inversion Strength (°C per 100m)
60
0.2
10
0
3/16/2004
3/14/2004
3/10/2004
3/12/2004
3/8/2004
3/6/2004
3/4/2004
3/2/2004
2/29/2004
2/27/2004
2/25/2004
2/23/2004
2/21/2004
2/19/2004
2/17/2004
2/15/2004
2/13/2004
2/11/2004
2/9/2004
2/7/2004
2/5/2004
0
Personal PM2.5 Exposure and Inversion Strength
1.2
80
Carney Hill
Gladstone
Lakewood
Westwood
Glenview
Inversion Strength
60
1
[PM2.5] (µg/m-3)
0.8
50
0.6
40
30
0.4
20
Inversion Strength (°C per 100m)
70
0.2
10
0
3/16/2004
3/14/2004
3/10/2004
3/12/2004
3/8/2004
3/6/2004
3/4/2004
3/2/2004
2/29/2004
2/27/2004
2/25/2004
2/23/2004
2/21/2004
2/19/2004
2/17/2004
2/15/2004
2/13/2004
2/11/2004
2/9/2004
2/7/2004
2/5/2004
0
Fig. 6. Time series of 24-h inversion strength and both ambient PM2.5 concentrations (top) and personal exposures (bottom) from the five
schools. Personal exposure data is pooled across the 3 individuals at each school. Two extreme personal exposures at Carney Hill are out
of the range of the graph.
concentrations. Assessment of the number of days
with concentrations above 15 and 30 mg m3 (the
current annual Canada-wide standard (CWS)) at
each school suggested spatial variation of ambient
PM2.5 in the Prince George Airshed. During the
6-week study period, the rooftop monitor at Carney
ARTICLE IN PRESS
M. Noullett et al. / Atmospheric Environment 40 (2006) 1971–1990
1983
Personal Sulphate Exposure and Inversion Strength
2.5
14
Carney Hill
Gladstone
12
Lakewood
2
10
Glenview
[SO42-] (µg/m-3)
Inversion Strength
1.5
8
6
1
4
Inversion Strength (°C)
Westwood
0.5
2
0
3/16/2004
3/14/2004
3/10/2004
3/12/2004
3/8/2004
3/6/2004
3/4/2004
3/2/2004
2/29/2004
2/27/2004
2/25/2004
2/23/2004
2/21/2004
2/19/2004
2/17/2004
2/15/2004
2/13/2004
2/11/2004
2/9/2004
2/7/2004
2/5/2004
0
Personal Light Absorbing Carbon Exposure and Inversion Strength
4
2.5
Carney Hill
Gladstone
Lakewood
2
Westwood
3
ABS (m-1X10-5)
Glenview
Inversion Strength
2.5
1.5
2
0.5
1.5
Inversion Strength (°C)
3.5
1
1
0.5
0
3/16/2004
3/14/2004
3/10/2004
3/12/2004
3/8/2004
3/6/2004
3/4/2004
3/2/2004
2/29/2004
2/27/2004
2/25/2004
2/23/2004
2/21/2004
2/19/2004
2/17/2004
2/15/2004
2/13/2004
2/11/2004
2/9/2004
2/7/2004
2/5/2004
0
Fig. 7. Time series of 24-h inversion strength and personal SO2
4 exposure (top) and personal ABS exposure (bottom) with data pooled
across subjects at each school. Breaks in the concentration data are due to weekends where no sampling occurred. For light absorbing
carbon, four extreme points from Glenview students and one from Lakewood are out of the range of the graph.
Hill experienced 9 days greater than 30 mg m3,
which resulted in the CWS being exceeded for that
year. The other four schools had 5–7 days during
the study period where the 30 mg m3 level was
exceeded. A non-parametric 2-factor Friedman’s
ANOVA indicated significant spatial differences
ARTICLE IN PRESS
1984
M. Noullett et al. / Atmospheric Environment 40 (2006) 1971–1990
Table 3
Spearman correlations between inversion strength and both ambient concentration and personal exposure for PM2.5, sulphate (SO2
4 ) and
light absorbing carbon (ABS)
Ambient concentration and inversion strength
Personal exposure and inversion strength
PM2.5
SO2
4
ABS
PM2.5
SO2
4
ABS
ALL
N ¼ 149
CH
N ¼ 29
GS
N ¼ 30
LW
N ¼ 30
WW
N ¼ 30
GV
N ¼ 30
0.67
0.55
0.65
0.68
0.57
0.73
0.63
0.54
0.65
0.65
0.55
0.71
0.65
0.58
0.61
0.76
0.60
0.65
N ¼ 141
N ¼ 29
N ¼ 29
N ¼ 27
N ¼ 27
N ¼ 29
0.40 (0.41)a
0.58
0.53
0.41 (0.48)a
0.63
0.66
0.44
0.59
0.71
0.62
0.60
0.63
0.21
0.64
0.32
0.42
0.51
0.43
Correlations are shown for the pooled data and at each school. CH ¼ Carney Hill; GS ¼ Gladstone; LW ¼ Lakewood; WW ¼ Westwood; and GV ¼ Glenview.
Significant at a ¼ 0.05 level.
a
Two extreme outliers were removed from the data.
between schools for all three measures (po0:001).
For PM2.5 this significant difference between
schools no longer existed if Carney Hill was
removed from the analysis (p ¼ 0:125). But the
spatial difference for both sulphate and light
absorbing carbon persisted when both Carney Hill,
the school closest to the industrial sources and had
the highest ambient concentrations, and Glenview,
the site furthest out of the valley and had the lowest
concentrations, were removed from the analysis
concurrently (pp0:023).
Significant spatial variation in light absorbing
carbon was expected as traffic-related pollutants
have been shown to be more spatially variable in an
area compared to total PM2.5 measurements
(Oglesby et al., 2000; Fischer et al., 2000). Another
possible explanation for the observed light absorbing carbon difference between schools could be the
presence of wood-smoke in some neighbourhoods,
which is another significant source of EC (Chow,
1995). The finding that sulphate was significantly
different between schools was unexpected as sulphate is generally considered to be homogenous
throughout an airshed due to its atmospheric
formation time. Because Prince George has local
industrial sources of sulphur dioxide and complex
valley topography, spatial variation in sulphate is
possible. The prevailing light wind direction (winds
o2 m s1 were predominantly from the east) during
the study period carried the main industrial plumes
in the direction of four of the study monitors and
away from the fifth. Complex steering of valley
winds by the local topography and limited disper-
sion during periods of calm winds could be
responsible for the spatial difference.
Although these results show that the central
permanent monitoring site at Plaza may not
adequately represent absolute ambient levels over
time for all three measures, the correlation between
schools was very high with median (range) Spearman correlations of 0.95 (0.71–0.96) for PM2.5, 0.97
(0.86–0.98) for sulphate and 0.85 (0.67–0.91) for
light absorbing carbon. There were also strongly
significant Spearman correlations between the
central site TEOM PM2.5 data and the HPEM2.5
data at each school with Glenview showing the
lowest correlation (0.83) and very high correlations
for each of the other schools (0.95–0.97). Due to the
low number of individuals sampled at each school
and the high amount of variation between individuals, analysis of spatial differences in personal
exposures throughout the city yielded inconclusive
results (Table 4).
4. Discussion
The two main purposes of the work described in
this paper were to characterize the relationship
between personal exposure and ambient concentrations for children in a cold winter climate and to
investigate the impact of meteorological conditions
on exposure. Only a limited number of studies have
examined children’s longitudinal exposure to fine
particulate matter (Janssen et al., 1999; Liu et al.,
2003; Rojas-Bracho et al., 2002; Wu et al., 2005),
although even in these studies a variety of sampling
ARTICLE IN PRESS
M. Noullett et al. / Atmospheric Environment 40 (2006) 1971–1990
Table 4
Spearman correlation (rs) between personal exposure and
ambient concentration for each pollutant measure by subject
School
Subject ID #
N
PM2.5
SO2
4
ABS
Carney Hill
1001
1002
1003
10
9 (7)
10
0.66*
0.00 (0.64)
0.55**
0.99*
0.98*
0.98*
0.71*
0.87*
0.94*
Gladstone
2001
2002
2003
9
10
10
0.83*
0.53
0.72*
0.93*
0.99*
0.98*
0.80*
0.89*
0.92*
Lakewood
3001
3002
3003
10
8
9
0.50
0.71*
0.70*
0.95*
1.00*
0.93*
0.62**
0.60
0.63**
Westwood
4001
4002
4003
10
7
10
0.18
0.39
0.35
0.85*
1.00*
0.92*
0.73*
0.71**
0.27
Glenview
5001
5002
5003
10
9
10
0.49
0.40
0.60**
0.93*
0.83*
0.90*
0.79*
0.23
0.81*
0.51
0.22
0.53
0.28
0.94
0.05
0.95
0.06
0.70
0.21
0.73
0.22
Mean
s.d.
Median
IQR
(0.55)
(0.17)
(0.55)
(0.24)
Note: *Significant at a ¼ 0.05 level. **Significant at a ¼ 0:10
level. (2 extreme outliers removed for subject 1002). Arithmetic
mean, median, standard deviation (s.d.) and inter-quartile range
(IQR) of the individual results are also reported.
techniques have been used, limiting the ability to
make direct comparisons with our study. Comparisons between these studies do show that activity
patterns between the panels were similar but overall
PM exposures were lower in the current study
(Table 5).
The summary in Table 5 also shows that the
longitudinal relationship between personal exposure
and ambient concentration for healthy children in
Prince George was comparable but slightly higher
than observed for asthmatic children in Seattle,
Washington (Liu et al., 2003) and Alpine, California
(Delfino et al., 2004) and significantly lower than
measured for healthy children in both Wageningen,
The Netherlands (Janssen et al., 1999) and Santiago,
Chile (Rojas-Bracho et al., 2002). The high correlation reported for Wageningen subjects may be due
to more time spent outdoors (activity patterns were
not reported). In Santiago, the high correlation was
likely due to the much higher ambient concentrations, and therefore a greater contribution of
ambient PM to exposure, compared to the other
1985
studies. The lower correlations in Seattle and Alpine
may be due to data from multiple seasons or a result
of sampling a wider age range of subjects than the
other studies. Seattle and Alpine also had the lowest
ambient concentrations resulting in outdoor levels
potentially having less influence on total PM
exposure.
Studies of healthy and susceptible adults have
also investigated the personal–ambient PM2.5 relationship. The median (range) correlation of 0.55
(0.18–0.83) found in Prince George for healthy
children was higher than the winter correlations
found by Sarnat et al. (2000) who reported Spearman correlations of 0.25 (0.38–0.81) and 0.76
(0.21–0.95) for winter and summer, respectively,
in Baltimore. This difference in the winter correlations between Prince George and Baltimore could
be due to differences in activity patterns between
children and largely immobile senior citizens and
possible differences in ambient concentrations.
Other studies have reported even lower median
(range) individual correlations between personal
exposures and ambient concentrations for PM2.5,
for example 0.34 (0.57–0.98) for the mixed panel
of asthmatic children and susceptible adult groups
in Seattle (Liu et al., 2003), 0.37 (0.01–0.87) for
susceptible adults in Boston, MA (Rojas-Bracho et
al., 2000) and 0.39 (0.00–0.65) for healthy and
susceptible adults in Research Triangle Park, NC
(Williams et al., 2003). All three of these studies
took place over 1 or 2 years and included data from
both winter and summer seasons.
Although seasonal differences, activity patterns,
differences in ambient concentrations and housing
characteristics may explain these overall lower
correlations found in adult studies compared to
the studies of children, these comparisons suggest
that children may have increased exposure to PM of
ambient origin and therefore their personal exposures are more closely related to ambient concentrations. An alternate interpretation is that children’s
exposure may be less influenced by non-ambient
sources and therefore, the personal–ambient relationship is stronger. The only study to measure
exposure relationships for both children and adults,
however, found no significant difference in the
longitudinal correlation between groups (Liu et al.,
2003).
Interestingly, two of the children studies also
reported a slope for the linear relationship between
personal exposure and ambient concentrations. In
Santiago and Wageningen, almost identical slopes
Healthy 10–12
years
Healthy 10–12
years
Prince George, BC
CAN
Wageningen,
Netherlands (Janssen
et al., 1999)
Santiago, Chili (RojasBracho et al., 2002)
Seattle, WA USA
(Liu et al., 2003)
Alpine, CA USA
(Wu et al., 2005;
Delfino et al., 2004)
1999/00 (Fall
and spring)
202 (170)
263 (272)
19
20
87
142 (149)
140a
55
# of
samples
personal
(ambient)
18
9
1995 (Spring)
1998/99
(Winter)
1999/01 (All)
15
# of
subjects
2001 (Winter)
Study period
(season)
24
13 (8)
70 (25)
21 (22)
18 (13)a
24 (5)
—
—
58
16
16a
—
20 (2)
11 (2)
—
16 (2)
16 (2)a
—
2–72
1–49
20–202
3–179
3–87a
19–33
Range
13
11 (6)
68 (28)
17 (3)
18 (15)
—
—
61
—
14
12 (2)
10 (2)
—
—
13 (3)
Median GM
(GSD)
Avg (SD)
GM
(GSD)
Avg (SD)
Median
Ambient concentrations
Personal exposures
All units are mg m3.
a
Results are reported with two extreme outliers (149 and 179 mg m3) removed from the analysis.
b
Article does not report whether this longitudinal correlation is a median or mean value.
Healthy 10–12
years
Asthmatic 6–13
years
Asthmatic 9–17
years
Subject health
and age
Location
4–32
3–40
17–190
15–22
1–61
Range
0.44
0.41 (0.34)
(0.64)b
0.53 (0.49)
0.55 (0.49)
(0.86)
Median
longitudinal
personal–
ambient
spearman
(Pearson)
correlation
Table 5
Comparison of children’s personal exposure, ambient concentration and the personal–ambient correlation from various locations where multiple samples were collected per subject
1986
M. Noullett et al. / Atmospheric Environment 40 (2006) 1971–1990
ARTICLE IN PRESS
ARTICLE IN PRESS
M. Noullett et al. / Atmospheric Environment 40 (2006) 1971–1990
of 0.71 (R2 ¼ 0:64) and 0.70 (R2 ¼ 0:74) were
reported using mixed model analysis and a median
slope from individual longitudinal regressions,
respectively (Rojas-Bracho et al., 2000; Janssen
et al., 1999). For the current study a much lower
median slope of 0.28 (R2 ¼ 0:24) was reported using
individual longitudinal regression analysis with two
extreme outliers removed (Noullett, 2004). The low
slope for Prince George children could be a result of
housing characteristics. House and building construction may be more tightly sealed resulting in less
influence of ambient levels on total personal
exposure and an increased influence of non-ambient
sources due to reduced ventilation. The low R2
value for this relationship also suggests a large
contribution from a non-ambient source.
These comparisons demonstrate that the contribution of non-ambient sources can make it difficult
to assess the actual relationship between ambient
concentrations and personal exposure due to
ambient sources (Ebelt et al., 2005). Because the
majority of research that shows health impacts
associated with fine particles is based upon ambient
concentration measurements from central monitoring sites as surrogates for exposure, it is the
relationship between personal exposure to particles
originating outdoors and outdoor concentrations
that is of interest to those managing outdoor levels
of air pollution (Mage et al., 1999; Wallace, 2000).
In this study, the high correlation found between
PM2.5 mass and both the sulphate and light
absorbing carbon components of the ambient
samples suggests that these parameters could be
used as tracers of PM2.5 of ambient origin. The
strong correlation between personal and ambient
sulphate across all subjects in this study shows there
are virtually no indoor sources of sulphate and
suggests that personal–ambient sulphate ratios
could be used to determine exposure to particles
of ambient origin. For light absorbing carbon the
personal–ambient correlations were more variable
than for sulphate but higher overall than those for
PM2.5 mass, suggesting that light absorbing carbon
ratios could also be used to determine exposure to
ambient generated particles if the impact of indoor
sources is excluded. Exclusion of data that may be
impacted by indoor sources may also result in
removal of high exposures due to very local ambient
sources; therefore the estimate of exposure may only
represent exposure to neighbourhood-scale background ambient levels. The sulphate based estimate
may also be an estimate of exposure to background
1987
ambient particles as there are likely limited local
ambient sources of sulphate.
Understanding exposure patterns for different
groups of the population is important in order to
better characterize the health impacts from fine
particles and other air pollutants, but research must
also focus on the cause of high exposure levels and
how they can be reduced. In Prince George and
other valley communities, meteorology plays an
important role in dispersing pollutants or allowing
them to accumulate to unhealthy levels. This study
is unique in its attempt to link meteorological
conditions to measurements of personal exposure,
as well as the more common linkage to ambient
concentrations. During the study period, low wind
speeds and an easterly flow resulted in stagnant
conditions, trapping of pollutants by topography
and thermal inversions, and transport of industrial
pollutants into residential neighbourhoods. During
an inversion, emissions likely linger close to their
source with high point source emissions collecting
within the inversion layer and smaller local sources,
such as residential wood-smoke, remaining within a
neighbourhood. This could explain the peaks in
concentration after an inversion began to break
apart (Fig. 5) and the spatial variation observed for
each of the pollutant measures. For this study
group, these peaks in high PM2.5 concentration
often occurred during recess and lunch hour when
the students were out in the playground. Concentrations measured at the rooftop monitors during
the inversion period were likely not experiencing the
highest concentrations in the airshed. As dispersion
started to increase within the valley, accumulated
emissions from higher elevation industrial sources
as well as lower sources began to mix upward and
downward resulting in fumigation and higher
concentrations at the rooftop monitors (Lu and
Turco, 1994).
The presence of a thermal inversion was significantly correlated with all ambient pollutant
levels monitored in this study and, importantly,
with personal exposures. A number of studies have
found that low wind speeds and the formation of an
inversion can lead to the accumulation of pollutants
(Connell et al., 2005; Hien et al., 2002; Marcazzan
et al., 2002; Roosli et al., 2000; Sheppard et al.,
2001), but to our knowledge there has been no
published work relating inversions to personal
exposure. The findings of this study demonstrate
the importance of the ambient contribution to
personal exposure in Prince George and suggest
ARTICLE IN PRESS
1988
M. Noullett et al. / Atmospheric Environment 40 (2006) 1971–1990
that prediction of episodes and reduction of sources
for short periods could reduce personal exposures in
addition to reducing the number of days where
ambient standards are exceeded.
5. Conclusion
There has been little research characterizing the
relationship between personal exposure and ambient concentrations of PM2.5 in colder winter
climates. This work shows that with minor adaptations, HPEM2.5 personal samplers can be successfully used for both personal and ambient sampling
at cold temperatures. In Prince George, a combination of topography, meteorological conditions and
location of ambient sources resulted in episodic
levels of fine particulate matter during the short
study period in the winter of 2001. The study period
was characterized by calm periods and low wind
speeds from the east, the location of main industrial
sources of PM2.5 in the city. Thermal inversions
were moderately associated with both high ambient
levels and personal exposures and were likely
responsible for the spatial variation and, in combination with wind, the temporal variation in ambient
concentrations throughout the city. The clear link
between thermal inversions and both high ambient
levels and measured personal exposures during
PM2.5 episodes supports management strategies to
reduce ambient sources during periods of limited
dispersion in an effort to reduce exposure levels.
Despite the significant spatial variation found in
ambient levels throughout the city for all three
measures, there was a high correlation between the
outdoor sites suggesting that a single monitor would
represent temporal trends.
Although this research was conducted over a
short period, for a small panel of non-randomly
selected subjects from non-smoking homes who
lived in close proximity to their school, the results
still provide a useful guide for airshed managers
regarding the range of exposures that children
experience in the city of Prince George. The results
indicate that Prince George children have lower
exposures than those found in the limited number of
other personal exposure studies of children, perhaps
due to reduced indoor infiltration during the winter
season. In support of this explanation, we measured
a low slope for the linear relationship between
personal exposure and ambient concentrations,
suggesting more tightly sealed homes and schools
in Prince George. This could mean that building
characteristics in Prince George may offer protection from outdoor levels but may also result in
increased exposure to indoor sources. Elevated
ambient levels in Prince George do exceed health
based air quality guidelines and suggest that efforts
should be made to reduce exposure and investigate
health impacts.
Similar to the findings in other studies, both
sulphate and light absorbing carbon showed a
stronger personal–ambient association than PM2.5
mass supporting their use as tracers of ambient
PM2.5. Although there was more variability for light
absorbing carbon compared to sulphate, there were
still much stronger personal–ambient correlations
for light absorbing carbon overall compared to
PM2.5 mass concentrations. These findings indicate
that personal–ambient ratios of sulphate and light
absorbing carbon could be used to estimate ambient
generated exposure to background levels of ambient
PM2.5.
Acknowledgements
This research was made possible through funding
from the Natural Sciences and Engineering
Research Council of Canada through Discovery
Grants to Jackson, and a postgraduate scholarship
to Noullett, Science Council of BC, the Canadian
Petroleum Products Institute Clean Air Fund,
UNBC Northern Land Use Institute, BC Ministry
of Water, Land and Air Protection and Canadian
Forest Products Ltd. In-Kind contributions were
made by the Harvard School of Public Health, UBC
School of Occupational and Environmental Hygiene and the Air Quality Research Branch of the
Meteorological Service of Canada. A special thanks
to the teachers, parents and children of School
District No. 57 and the many individuals that
helped with various logistical and technical aspects
of this project including Victor Leung, Julie Hsieh,
Jeff Brook, Dave Sutherland, Steve Lamble, Dennis
Fudge, Stefanie Ebelt, Amanda Wheeler, Kathleen
Brown, Melissa Darney, Sara Reiffer, Julianne
Trelenberg, Ron Bealey, Jason Bealey and Mike
Noullett.
References
Allen, G., Sioutas, C., Koutrakis, P., Reiss, R., Lurmann, F.,
Roberts, P., 1997. Evaluation of the TEOM method for
measurement of ambient particulate matter in urban areas.
ARTICLE IN PRESS
M. Noullett et al. / Atmospheric Environment 40 (2006) 1971–1990
Journal of Air & Waste Management Association 47,
682–689.
Brauer, M., Koutrakis, P., Spengler, J.D., 1989. Personal
exposures to acidic aerosols and gases. Environmental Science
& Technology 23, 1408–1412.
CEPA/FPAC Working Group on Air Quality Objectives and
Guidelines, 1999. National Ambient Air Quality Objectives
for Particulate Matter—Part 1: Science Assessment Document. Minister, Public Works and Government Services,
Canada [On-line]. Available http://www.hc-sc.gc.ca/hecs-sesc/
air_quality/publications/particule_matter.htm [14 March
2004].
Chow, J.C., 1995. Measurement methods to determine compliance with ambient air quality standards for suspended
particles. Journal of Air & Waste Management Association
45, 320–382.
Clayton, C.A., Perritt, R.L., Pellizzari, E.D., Thomas, K.W.,
Whitmore, R.W., Ozkaynak, H., Spengler, J.D., Wallace,
L.A., 1993. Particle Total Exposure Assessment Methodology
(PTEAM) study: distributions of aerosol and elemental
concentrations in personal, indoor, and outdoor air samples
in a Southern California community. Journal of Exposure
Analysis of Environmental Epidemiology 3, 227–250.
Connell, D.P., Withum, J.A., Winter, S.E., Statnick, R.M.,
Bilonick, R.A., 2005. The Stuebenville comprehensive air
monitoring program (SCAMP): analysis of short-term and
episodic variations in PM2.5 concentrations using hourly air
monitoring data. Journal of Air & Waste Management
Association 55, 559–573.
Cyrys, J., Heinrich, J., Hoek, G., Meliefste, K., Lewne, M.,
Gehring, U., Bellander, T., Fischer, P., Van Vliet, P., Brauer,
M., Wichmann, H.E., Brunekreef, B., 2003. Comparison
between different traffic-related particle indicators: elemental
carbon (EC), PM2.5 mass, and absorbance. Journal of
Exposure Analysis and Environmental Epidemiology 13,
134–143.
Delfino, R.J., Quintant, P.J.E., Floro, J., Gastanaga, V.M.,
Samimi, B.S., Kleinman, M.T., Liu, L.-J.S., Bufalino, C., Wu,
C.-F., McLaren, C.E., 2004. Association of FEV1 in
asthmatic children with personal and microenvironmental
exposure to airborne particulate matter. Environmental
Health Perspectives 112, 932–941.
Demokritou, P., Kavouras, I.G., Ferguson, S.T., Koutrakis, P.,
2001. Development and laboratory performance evaluation of
a personal multipollutant sampler for simultaneous measurement of particulate and gaseous pollutants. Aerosol Science &
Technology 35, 741–752.
Dockery, D.W., Spengler, J.D., 1981. Personal exposure to
respirable particulates and sulphates. Journal of Air Pollution
Control Association 31, 153–159.
Ebelt, S.T., Petkau, A.J., Vedal, S., Fisher, T.V., Brauer, M.,
2000. Exposure of chronic obstructive pulmonary disease
patients to particulate matter: relationships between personal
and ambient air concentrations. Journal of Air & Waste
Management Association 50, 1081–1094.
Ebelt, S.T., Wilson, W.E., Brauer, M., 2005. Exposure to ambient
and non-ambient components of particulate matter: a
comparison of health effects. Epidemiology 16, 396–405.
Environment Canada, 2003. Canadian Climate and Water
Information, Prince George. Available: http://www.smcmsc.ec.gc.ca/climate/climate_normals_1990/show_normals_e.cfm?station_id=195&prov=BC [22 October 2004].
1989
Environmental Protection Agency (EPA), 2003. Air Quality
Criteria for Particulate Matter (Fourth External Review
Draft). Office of Research and Development, National
Center for Environmental Assessment, Research Triangle
Park, NC, EPA/600/P-99/002aD and bD [On-line]. Available:
http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=2832
[20 February 2004].
Fischer, P.H., Hoek, G., van Reeuwijk, H., Briggs, D.J., Lebret,
E., van Wijnen, J.H., Kingham, S., Elliott, P.E., 2000. Trafficrelated differences in outdoor and indoor concentrations of
particles and volatile organic compounds in Amsterdam.
Atmospheric Environment 34, 3713–3722.
Fudge, D.C., Lamble, S.J., Sutherland, D.J., 2003. 2001 Annual
Air Quality Report for Prince George. Environmental
Protection Division Ministry of Water, Land and Air
Protection, Omineca-Peace Regions, British Columbia [Online]. Available: http://cirrus.unbc.ca/pgarwg/ [24 February
2004].
Fudge, D.C., Lamble, S.J., Sutherland, D.J., 2004. 2002 Annual
Air Quality Report for Prince George. Environmental
Protection Division Ministry of Water, Land and Air
Protection, Omineca-Peace Regions, British Columbia [Online]. Available: http://cirrus.unbc.ca/pgarwg/ [22 October
2004].
Gauvin, S., Reungoat, P., Cassadou, S., Dechenaux, J., Momas,
I., Just, J., Zmirou, D., 2002. Contribution of indoor and
outdoor environments to PM2.5 personal exposure of
children—VESTA study. The Science of the Total Environment 297, 175–181.
Gotshci, T., Oglesby, L., Mathys, P., Monn, C., Manalis, N.,
Koistinen, K., Jantunen, M., Hanninen, O., Polanska, L.,
Kunzli, N., 2002. Comparison of black smoke and
PM2.5 levels in indoor and outdoor environments of four
European cities. Environmental Science & Technology 36,
1191–1197.
Hien, P.D., Bac, V.T., Tham, H.C., Nhan, D.D., Vinh, L.D.,
2002. Influence of meteorological conditions on PM2.5 and
PM2.5–10 concentrations during the monsoon season in Hanoi,
Vietnam. Atmospheric Environment 36, 3473–3484.
Janssen, N., Hoek, G., Harssema, H., Brunekreef, B., 1997.
Childhood exposure to PM10: relation between personal,
classroom and outdoor concentrations. Occupational &
Environmental Medicine 54, 888–894.
Janssen, N.A.H., Harssema, H., Brunekreef, B., 1999. Personal
exposure to fine particles in children correlates closely with
ambient fine particles. Archives of Environmental Health 45,
95–101.
Janssen, N.A.H., de Hartog, J.J., Hoek, G., Brunekreef, B., 2000.
Personal exposure to fine particulate matter in elderly
subjects: relation between personal, indoor, and outdoor
concentrations. Journal of Air & Waste Management
Association 50, 1133–1143.
Janssen, N.A.H., van Vliet, P.H.N., Aarts, F., Harssema, H.,
Brunekreef, B., 2001. Assessment of exposure to traffic related
air pollution of children attending schools near motorways.
Atmospheric Environment 35, 3875–3884.
Kingham, S., Briggs, D., Elliott, P., Fischer, P., Lebret, E., 2000.
Spatial variations in the concentration of traffic-related
pollutants indoor and outdoor air in Huddersfield, England.
Atmospheric Environment 34, 905–916.
Koutrakis, P., Wolfson, J., Spengler, J., 1988. An improved
method for measuring aerosol strong acidity: results from a
ARTICLE IN PRESS
1990
M. Noullett et al. / Atmospheric Environment 40 (2006) 1971–1990
nine-month study in St. Louis, Missouri and Kingston,
Tennessee. Atmospheric Environment 22, 157–162.
Lu, R., Turco, R.P., 1994. Air pollutant transport in a coastal
environment. Part I: two-dimensional simulations of seabreeze and mountain effects. American Meteorological
Society 51, 2285–2308.
Liu, L.-J.S., Box, M., Kalman, D., Kaufman, J., Koenig, J.,
Larson, T., Lumley, T., Sheppard, L., Williams, L., 2003.
Exposure assessment of particulate matter for susceptible
populations in seattle. Environmental Health Perspectives
111, 909–918.
Mage, D., Wilson, W.E., Hasselblad, V., Grant, L., 1999.
Assessment of human exposure to ambient particulate matter.
Journal of Air & Waste Management Association 49, 1280–1291.
Marcazzan, G.M., Valli, G., Vecchi, R., 2002. Factors influencing
mass concentration and chemical composition of fine aerosols
during a PM high pollution episode. The Science of the Total
Environment 298, 65–79.
National Research Council, 1998. Research Priorities for
Airborne Particulate Matter. National Academy Press,
Washington, DC.
Noullett, M., 2004. Exposure to fine particulate matter air
pollution in Prince George, British Columbia. M.Sc. Thesis,
University of Northern British Columbia, Prince George, BC.
Oglesby, L., Kunzli, N., Roosli, M., Braun-Fahrlander, C.,
Mathys, P., Stern, W., Jantunen, M., Kousa, A., 2000.
Validity of ambient levels of fine particles as surrogate for
personal exposure to outdoor air pollution—results of the
European EXPOLIS-EAS study (Swiss Center Basel). Journal
of Air & Waste Management Association 50, 1251–1261.
Oh, J., Suh, H., Lawrence, J., Allen, G., Koutrakis, P., 1997.
Characterization of particulate mass concentrations in South
Boston, MA. In: Proceedings of the AIR and Waste
Management Association’s Special Conference on Measurement of Toxic and Related Air Pollutants, vol. 1, pp. 397–404.
Ozkaynak, H., Xue., J., Spengler, J., Wallace, L., Pellizzari, E.,
Jenkins, P., 1996. Personal exposure to airborne particles and
metals: results from the Particle TEAM study in Riverside,
California. Journal of Exposure Analysis and Environmental
Epidemiology 6, 57–78.
Pope III, C.A., 2000. Review: epidemiological basis for particulate air pollution health standards. Aerosol Science and
Technology 32, 4–14.
Prince George Airshed Technical Management Committee, 1996.
Prince George Air Quality Management Background Report.
Ministry of Environment, Lands and Parks: British Columbia
[On-line]. Available: http://cirrus.unbc.ca/pgarwg/ [24 February 2004].
Rojas-Bracho, L., Suh, H.H., Koutrakis, P., 2000. Relationships
among personal, indoor, and outdoor fine and coarse particle
concentrations for individuals with COPD. Journal of
Exposure Analysis and Environmental Epidemiology 10,
294–306.
Rojas-Bracho, L., Suh, H.H., Oyola, P., Koutrakis, P., 2002.
Measurements of children’s exposures to particles and
nitrogen dioxide in Santiago, Chili. The Science of the Total
Environment 287, 249–264.
Roosli, M., Braun-Fahrlander, C., Kunzli, N., Oglesby, L., Theis,
G., Camenzind, M., Mathys, P., Staehelin, J., 2000. Spatial
variability of different fractions of particulate matter within
an urban environment and between urban and rural sites.
Journal of the Air & Waste Management Association 50,
1115–1124.
Sarnat, J.A., Koutrakis, P., Suh, H.H., 2000. Assessing the
relationship between personal particulate and gaseous exposures of senior citizens living in Baltimore, MD. Journal of
Air & Waste Management 50, 1184–1198.
Sarnat, J.A., Long, C.M., Koutrakis, P., Coull, B.A., Schwartz,
J., Suh, H., 2002. Using sulfur as a tracer of outdoor fine
particulate matter. Environmental Science & Technology 36,
5305–5314.
Sharma, S., Brook, J.R., Cachier, H., Chow, J., Gaudenzi, A.,
Lu, G., 2002. Light absorption and thermal measurements of
black carbon in different regions of Canada. Journal of
Geophysical Research 107, AAC11-1–AAC11-11.
Sheppard, L., Levy, D., Checkoway, H., 2001. Correcting for the
effects of location and atmospheric conditions on air
pollution exposures in a case-cross-over study. Journal of
Exposure Analysis and Environmental Epidemiology 11,
86–96.
StatSoft Inc., 1997. Statistica 5.1 Software, Tulsa, OK
Stieb, D.M., Brook, J.R., Broder, I., Judek, S., Burnett, R.T.,
Beveridge, R.C., 1998. Personal exposure of adults with
cardiorespiratory disease to particulate acid and sulfate in
Saint John, New Brunswick, Canada. Applied Occupational
& Environmental Hygiene 13, 461–468.
Suh, H.H., Spengler, J.D., Koutrakis, P., 1992. Personal
exposures to acid aerosols and ammonia. Environmental
Science & Technology 26, 2507–2517.
ULTRA, 1998. Determination of absorption coefficient using
reflectometric method—SOP ULTRA/KTL-L-1.0 [On-line].
Available:
http://www.ktl.fi/ultra/adobe/out/sop-abs.pdf
[7 April 2004].
Vedal, S., 1997. Ambient particles and health: lines that divide.
Journal of Air & Waste Management Associations 47,
551–581.
Wallace, L., 2000. Correlations of personal exposure to particles
with outdoor air measurements: a review of recent studies.
Aerosol Science and Technology 32, 15–25.
Ward Brown, K., 2000. SOP for Personal Environmental
Monitors (PEMs) Cleaning, Assembly and Disassembly.
Environmental Science and Engineering Program, Harvard
School of Public Health.
Williams, R., Suggs, J., Rodes, C., Lawless, P., Zweidinger, R.,
Kwok, R., Creason, J., Sheldon, L., 2000. Comparison of
PM2.5 and PM10 monitors. Journal of Exposure Analysis and
Environmental Epidemiology 10, 497–505.
Williams, R., Suggs, J., Rea, A., Leovic, K., Vette, A.,
Croghan, C., Sheldon, L., Rodes, C., Thornburg, J., Ejire,
A., Herbst, M., Sanders Jr., W., 2003. The Research
Triangle Park particulate matter panel study: PM mass
concentration relationships. Atmospheric Environment 37,
5349–5363.
Wu, C., Delfino, R.J., Floro, J.N., Quintana, P.J.E., Samimi,
B.S., Kleinman, M.T., Allen, R.W., Liu, L.-J.S., 2005.
Exposure assessment and modeling of particulate matter for
asthmatic children using personal nephelometers. Atmospheric Environment 39, 3457–3469.