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Copyright © 2006 Air & Waste Management Association
TECHNICAL PAPER
ISSN:1047-3289 J. Air & Waste Manage. Assoc. 57:893–900
DOI:10.3155/1047-3289.57.8.893
Copyright 2007 Air & Waste Management Association
Spatial Modeling for Air Pollution Monitoring Network Design:
Example of Residential Woodsmoke
Jason G. Su
Department of Geography, University of British Columbia, Vancouver, British Columbia, Canada
Timothy Larson
School of Occupational and Environmental Hygiene, University of British Columbia, Vancouver,
British Columbia, Canada; and Department of Civil and Environmental Engineering, University of
Washington, Seattle, WA
Anne-Marie Baribeau and Michael Brauer
School of Occupational and Environmental Hygiene, University of British Columbia, Vancouver,
British Columbia, Canada
Michael Rensing
Environmental Quality Branch, British Columbia Ministry of Environment, Victoria, British
Columbia, Canada
Michael Buzzelli
Department of Geography, University of British Columbia, Vancouver, British Columbia, Canada;
and Department of Geography, University of Western Ontario, London, Ontario, Canada
ABSTRACT
The purpose of this paper is to demonstrate how to develop an air pollution monitoring network to characterize
small-area spatial contrasts in ambient air pollution concentrations. Using residential woodburning emissions as
our case study, this paper reports on the first three stages
of a four-stage protocol to measure, estimate, and validate
ambient residential woodsmoke emissions in Vancouver,
British Columbia. The first step is to develop an initial
winter nighttime woodsmoke emissions surface using
inverse-distance weighting of emissions information from
consumer woodburning surveys and property assessment
data. Second, fireplace density and a compound topographic index based on hydrological flow regimes are
used to enhance the emissions surface. Third, the spatial
variation of the surface is used in a location-allocation
algorithm to design a network of samplers for the
woodsmoke tracer compound levoglucosan and fine particulate matter. Measurements at these network sites are
IMPLICATIONS
Recent research highlights the importance of small-area
contrasts in air pollution emissions and concentrations as
predictors of health impacts. Using the example of residential woodburning emissions, this paper shows that relevant
spatial covariates can be used to identify the microgeography intraurban air pollution gradients before deployment
of more costly monitoring resources.
Volume 57 August 2007
then used in the fourth stage of the protocol (not presented here): a mobile sampling campaign aimed at developing a high-resolution surface of woodsmoke concentrations for exposure assignment in health effects studies.
Overall the results show that relatively simple data inputs
and spatial analysis can be effective in capturing the spatial variability of ambient air pollution emissions and
concentrations.
INTRODUCTION
Recent studies have shown that the spatial variability of
selected air pollutants within urban areas is greater than
typically recognized and is associated with previously unaccounted for variability in health impacts.1–5 This attention toward characterizing the intraurban variation of
long-term average air pollution levels has led to growing
interest in the development of high spatial resolution
exposure assessment methods. These include proximitybased assessments, statistical interpolation, land use regression models, new uses of line dispersion models, integrated emission-meteorological models, and hybrid
models.6 For regulatory purposes, an effective monitoring
network is necessary for measuring and managing air
quality. However, many regulatory monitoring networks
are based on qualitative criteria and personal experience.7
In addition, monitoring networks may not reflect emergent priorities and conditions, such as new types or
sources of emissions. Recent intensive monitoring programs and land use regression models have demonstrated
that regulatory monitoring networks may not accurately
Journal of the Air & Waste Management Association 893
Su et al.
represent the spatial variability of ambient pollution patterns.8 To assist in assessing spatial variability in air pollution, we describe here the design of a monitoring network for residential wood combustion. The approach
discussed here, based on residential woodburning, could
be used to capture the impacts of other emission sources
of interest.
In many urban and semiurban areas of Canada and the
Northern United States, households have increasingly
turned to woodburning as an alternate method for domestic
heating because of rising energy costs and the uncertain
availability of petroleum and natural gas.9 In Canada, it is
estimated that approximately 400,000 homes use wood as
the primary heating fuel, and many others use fireplaces and
wood stoves as supplementary sources of heat or for esthetics.9 Increased usage of woodburning appliances has raised
concerns about woodsmoke exposures and health effects,
including in British Columbia.10
As part of a large interdisciplinary research program
in the Georgia Basin-Puget Sound transboundary airshed,
the Border Air Quality Study (BAQS) is aimed at improved
understanding of ambient air pollution and health effects. The BAQS woodsmoke exposure project included
monitoring network design, sampling and model development, and application of models in epidemiological
studies and risk assessment. This paper focuses on network design. Our research question is as follows: can
readily available geographic data be used to optimally
design a network to characterize spatial variation in ambient air pollutant emissions and concentrations?
EXPERIMENTAL WORK
The research followed a series of steps from estimating
an initial woodsmoke emission surface to the final step,
reported in Larson et al.,11 of designing and running a
mobile network. Figure 1 outlines the steps taken in
this protocol. This paper reports on the estimation of
woodsmoke emissions in the first three stages as a
means of demonstrating how an air pollution monitoring network can be developed. First, an initial residential woodsmoke emission surface is estimated using
data from a consumer residential woodburning survey.
Second, the emissions surface is enhanced with residential fireplace density data derived from the property
assessment records and with use of topographic information to reflect a prevailing drainage phenomenon
when residential woodburning is widespread. Third,
the estimated emissions surface is used to select locations for seven fixed-site samplers for woodsmoke fine
particulate matter (PM2.5), specifically the woodsmoke
tracer levoglucosan, in winter 2004 –2005 to evaluate
the effectiveness of the enhanced emissions surface. As
noted above, the final stage uses these data for a mobile
monitoring campaign and development of a high-resolution woodsmoke concentration surface. These steps
are described in detail below.
Stage 1: Developing an Initial Emissions Surface
The British Columbia Ministry of Environment and the
Greater Vancouver Regional District (GVRD) undertook
consumer woodburning surveys in 2002 as part of the
region’s spatially resolved emissions inventory (see Figure 2).12 In total, 500 households were asked whether
they had a woodburning appliance, its type and age,
quantity/weight of wood burned, and related woodburning practices. PM2.5 emission factors for each category of appliance (Table 1) were estimated based on
the National Emissions Inventory and Projections Task
Figure 1. Flowchart of spatial modeling for air monitoring network design.
894 Journal of the Air & Waste Management Association
Volume 57 August 2007
Su et al.
values before transformation. Accordingly, the initial interpolated surface was normalized as follows:
Emi norm ⫽ 共IniEmii ⫺ Min 共IniEmii兲兲 /
i
i ⫽ 1,n
共Mean共IniEmii兲 ⫺ Min 共IniEmii兲兲
i ⫽ 1,n
Figure 2. FSAs and their municipalities in the GVRD.
Group13 Guidebook and U.S. Environmental Protection
Agency publication AP-42. Each survey respondent’s
location was represented by a three-digit postal code or
Forward Sortation Area (FSA), of which the average size
is 1800 hectares (ha) for metropolitan regions (e.g., the
GVRD) and 400 ha for dense urban areas (e.g., the city
of Vancouver). Once the survey responses were geocoded/located in the region, calculated emissions were
aggregated to 87 unique FSAs (Figure 2) to compute
mean PM2.5 emission values.
The estimated survey emissions were then combined
with property inventory data from the 2003 British Columbia Assessment. A total of 592,568 street addresses,
76% of which had at least one woodburning appliance,
were identified.14 The property assessment data were used
to assign emissions at the individual property level based
on the woodburning survey responses in proximate locations. Housing units without fireplaces or wood stoves
were assigned a value of zero. For those with at least one
appliance, the housing units located inside the 87 FSAs
were assigned the mean emissions estimated for that FSA.
Where property data/housing units fell within the GVRD
but not within the survey-related 87 FSAs, emissions were
assigned using an inverse distance weighted interpolator
(IDW) of mean emissions from the nearest six FSAs that
had survey respondents.
The next step was to estimate a regional woodsmokederived PM2.5 emissions surface. The dense network of housing units from the 2003 British Columbia Assessment data
suggests that interpolation with IDW and splining would be
sufficient15,16 and cost effective. Between these two methods, splines produced slightly higher peak values, so an
initial PM2.5 emission surface was created based on more
conservative IDW at a 25-m resolution. To make surfaces
comparable and amenable to later enhancements, a onemean normalization (eq 1) was applied to all of the surfaces.
The one-mean normalization sets the mean of the transformed dataset to 1, the minimum value to 0, and the
maximum value to ⬀ (infinity).
V ⫽
X ⫺ Min
Mean ⫺ Min
(1)
where X and V are the values before and after normalization, and Mean and Min are the average and minimum
Volume 57 August 2007
(2)
i ⫽ 1,n
where IniEmii is the interpolated emission value at location i; Min and Mean are the minimum and average emission values within the study area; and Emiinorm is a normalized emission at location i with output values ranging
from zero to ⫹⬀. We assumed that the minimum
woodsmoke emission was zero, and that the random
burning of wood created uncertainty in identifying the
possible highest emissions. So the normalization process
created a woodsmoke emission surface with values ranging from 0 to ⫹⬀.
Stage 2: Emission Surface Enhancements
We enhanced the initial emission surface by taking into
account the distribution of fireplaces and local topography.
The fireplace data were resolved at the level of a dissemination area (DA; this is a small geographic unit composed of
one or more blocks with a population of 400 –700 persons),
the smallest standard geographic area for which all census
data in Canada are collected (roughly corresponds with the
census block in the United States). A FSA typically contains
30 DAs. We assumed that areas with a higher density of
fireplaces and woodstoves generate more woodsmoke emissions. A net residential fireplace/emission density, Den, was
calculated by dividing the DA area into the total number of
fireplaces and wood stoves within that DA and then normalizing this value over all DAs, as follows:
Den inorm ⫽
冉
冉 冊冊
冉 冉 冊 冉 冊冊
Nifireplaces
Nifireplaces
⫺
Min
AreaiDA
AreaiDA
/
i ⫽ 1,n
Mean
i ⫽ 1,n
Nifireplaces
AreaiDA
⫺ Min
j ⫽ 1,n
Nifireplaces
AreaiDA
(3)
Table 1. GVRD woodburning survey PM2.5 emission factor.
Appliance
1 Fireplace; conventional with glass doors
2 Woodstove; conventional
3 Woodstove; advanced technology
4 Woodstove; catalytic
5 Pellet stove
6 Other equipment
1 and 2
1 and 3
1 and 5
1 and 6
2 and 3
1, 2, and 3
1, 3, and 4
No. of
Users
PM2.5 (kg/t)
367
72
14
10
1
6
20
3
1
3
1
1
1
12.90
23.20
4.80
4.80
1.10
13.60
18.05
8.85
7.00
13.25
14.00
13.60
7.50
Journal of the Air & Waste Management Association 895
Su et al.
where Nifireplaces represents the number of fireplaces within
the ith DA region; AreaiDA is the area of the ith DA region;
Min and Mean are the minimum and average fireplace
density values within the study area; Deninorm is a normalized fireplace density of the ith DA region with output
values ranging from 0 to ⫹⬀. The adjusted emission surface was computed as follows:
Emi i ⫽ IniEmiinorm * Deninorm
(4)
Because emissions of woodsmoke are related to the
heating degree days (HDDs), we used the HDDs of the
2002–2003 winter period. Based upon daily average
temperatures from December 1, 2002, to February 28,
2003, all 90 days in this period were HDDs (⬍18 °C).
During this same period, hourly wind speed, wind direction, and cloud cover measurements from 19 GVRD
monitoring stations were used to estimate atmospheric
stability classes and prevailing dispersion directions for
woodsmoke.17 When considering both daytime and
nighttime measurements, 71% of the period was classified as nonneutral, whereas 79% of the nighttime periods were in stability classes conducive to atmospheric
inversion and drainage flow along slope surfaces.17
Most woodburning occurs during nighttimes on HDDs.
Under these conditions, the surface wind was influenced by drainage flow, and a given location was systematically downwind of uphill sources of woodsmoke.
Meteorological dispersion modeling is complex in this
case because of the prevailing drainage flow phenomenon that occurs during the highest HDDs. We, therefore, further adjusted Emi for the influence of topography. Browning et al.18 found that the distribution of
ambient fine particles under stagnant conditions could
be better described using watershed and hydrological
concepts than simply using elevation. We, therefore,
explored a “flow accumulation” model to mimic the
drainage flow during stable nighttime conditions.19,20
To implement this model, a compound topographic
index (CTI) was used to further adjust the emission
surface. The CTI is a steady-state wetness index (sometimes called topographic wetness index), and it is a
function of both the slope and the upstream contributing area per unit of width orthogonal to the flow direction. CTI gets larger when accumulation increases. The
CTI was defined by Gessler et al.21 as follows:
CTI ⫽ ln(Contributing Area/Slope Gradient)
Contributing Area is a field representing at each point
the magnitude of the drainage area upslope of that
point. Flow Accumulation is an indirect way of measuring upslope source areas. It is an integer number and
represents the number of upstream digital elevation
model (DEM) cells of which the flow paths “pass
through” the given DEM cell (in units of grid cells). In
ArcGIS (Environmental Systems Research Institute,
Inc.) software, each flow direction cell contains an integer indicating one of the eight possible Flow Directions. A DEM from DMTI Spatial Inc. was used to create
the CTI with a cell resolution of 25 m. By combining
topographic drainage, a further enhancement to the
emission surface was made by the following:
Emi i * CTIiNorm
(8)
The superscript indicates that these variables were
normalized to values between 0 and ⫹⬀. A final adjustment to the enhanced surface from eq 8 was done by
rescaling to a total of 610 t of PM2.5 22 emitted from the
overall surface as estimated from the GVRD inventory.
This final predicted fine-particle woodsmoke emission
surface is shown in Figure 3. However, although we
made these adjustments, we were interested in the relative spatial distribution, not the absolute levels. A
monitoring network was, therefore, designed rather
than relying on model predictions. An estimate of the
relative emission densities of woodsmoke and its subsequent enhancements should provide a first approximation of the spatial distribution of neighborhood
concentrations, especially during calm nighttime
conditions.
Stage 3: Allocation of Fixed-Site Woodsmoke
Samplers
The emissions surface estimated from eq 8 was used as
the primary criterion (a demand surface) for spatially
allocating woodsmoke samplers. Following the general
steps laid out by Kanaroglou et al.,23 13 locations were
initially chosen based on the emissions surface and
(5)
where:
Contributing Area ⫽ 共Flow Accumulation ⫹ 1兲 * Cell Size
(6)
and
Flow Accumulation ⫽ f 共Flow Direction兲
896 Journal of the Air & Waste Management Association
(7)
Figure 3. Estimated residential woodsmoke surface and location of
fixed-site woodsmoke and regulatory samplers.
Volume 57 August 2007
Su et al.
included areas of high, intermediate, and low estimated
woodsmoke emissions. These 13 locations were treated
as candidate locations for a location-allocation algorithm to select fixed-site samplers.
First, a surface of variability was created using the
estimated woodsmoke emissions surface as expressed in
eq 9:
ˆ 共x៮ 兲 ⫽
␥
1
2
冘
(z(x៮ ) ⫺ z(x៮ ⫹ h៮ )) 2
(9)
ⱍh៮ ⱍⱕ5000m
where x៮ is represented by one of the 13 locations identified. z(x៮ ) and z(x៮ ⫹ h៮ ) are the estimated woodsmoke emissions at location x៮ and x៮ ⫹ h៮ (h៮ is the distance to x៮ ),
respectively. Variability, ␥ˆ (x៮ ), at location x៮ was calculated
by applying the summation function over the pairs that
were formed between x៮ and all the cells within 5 km from
x៮ . Because we were interested in placement of samplers in
areas where the density of the population of interest was
high, a weighting scheme23 was applied to the variability
surface:
WR ⫽
PR/PT
␥ˆ R/␥ˆ T
(10)
where PR and PT are the population of interest in region
R and for the entire study area, T, respectively. Similarly, ␥ˆ R and ␥ˆ T are the emissions variability of interest
in region R and for the entire area, respectively.
The weighting function in eq 10 was used to locate
seven stationary samplers taking into account the demand surface for monitoring. An ARC/INFO (Environmental Systems Research Institute, Inc.) environment
was used through its attendance maximizing algorithm
such that the sum of weighted distances for all of
the demand locations from their nearest station was
maximized.
Detailed site placement of samplers included additional criteria relating to topography (elevation) and
existing monitoring infrastructure. Elevations were
classified into low (⬍10 m above sea level), middle low
(10 –50 m), middle high (50 –100 m), and high (⬎100
m) categories. To identify the possible independent
influence of elevation on woodsmoke emissions, the
allocation of woodsmoke samplers included the requirement that each elevation class have at least one
sampler. In addition, attention was paid to the areas
with high population density (population ⬎30 persons
per ha at DA level), and we placed no more than one
sampler in any of the 22 municipalities of the GVRD.
Allocation was also constrained by the relative location
of samplers and by the location of the existing PM2.5
regulatory monitoring stations in the GVRD. We colocated one sampler at one of these stations to facilitate
temporal adjustments and measurement comparisons.
Because the existing GVRD sampling network is not
Volume 57 August 2007
optimized a priori to capture woodsmoke, we required
that the woodsmoke samplers be located as far from
each other as possible once the basic inputs above were
considered. The exact location (microplacement) of
each sampler also accounted for issues of access and
security based on operator judgment.
Sampler Filter Analysis
At the sites identified by the location-allocation procedure, PM2.5 Harvard impactor (HI) samplers were
operated at 10 L/min with a 10/60 min on/off pump
duty cycle such that the overall filter sample time was
48 hr during each 2-week sampling period. The pumps
(Leland Legacy, SKC Inc.) were powered by both internal batteries and a supplementary solar array (4.75 W,
15 V, 320 mA, Solar Module SFR05, Edmonds Batteries).
Total pump sample volume was recorded at the end of
each 2-week sample period. Pump flows were checked
after each sample period with a DryCal DC Lite Flow
calibrator (BIOS Corp.). The impactor/pump assembly
was housed in a waterproof case and attached to a
utility pole at an elevation of 3– 4 m. The Teflon filters
were equilibrated 48 hr before and after sampling in a
temperature- and humidity-controlled room (22 °C,
standard deviation [SD] ⫽ 0.67 °C and 53% relative
humidity, SD ⫽ 6%) and weighed on a microbalance
(Sartorius M3P; 1-␮g resolution, 2-␮g sensitivity) before
and after sampling to compute the PM2.5 mass concentration. Filters were weighed until three consecutive
weighings were within 10 ␮g of each other. Quality
control filters were also weighed before each weighing
session and checked against their historical quality
control charts. Average precision of mass change was 3
␮g. Ebelt et al.24 describes the weighing procedure in
detail.
Filters were subsequently analyzed for levoglucosan,
a stable product of cellulose combustion found in the
particle phase.25 Levoglucosan has been used to trace the
impacts of woodsmoke on urban PM2.5.25,26 Because
highly varying patterns exist in the emission profiles of
molecular markers, the emission factors of levoglucosan
are a function of fuel type and combustion phase.27,28
Levoglucosan was used to evaluate the effectiveness of the
emission surface and to identify whether emission factors
differed at locations around the seven samplers. Further
details of both the sampling and analysis are given in
Larson et al.11
RESULTS
Figure 3 shows the location of the seven woodsmoke
sampling sites selected with the location-allocation procedure, as well as population density, elevation, and the
existing regulatory monitoring sites. The woodsmoke
sites were operated continuously over the entire sampling
period (October 2004 to April 2005). One woodsmoke
sampling site at Surrey was moved approximately 1 km to
the southeast in late January because of vandalism. The
downtown site was terminated in mid-January and
moved to Richmond in early February after mobile
monitoring, and our initial analysis indicated high PM
Journal of the Air & Waste Management Association 897
Su et al.
regulatory sites, which are designed to reflect urban
background levels.
Figure 4. A scatterplot of the 2-week average levoglucosan concentrations plotted against the estimated woodsmoke-derived PM2.5
concentration.
levels in the latter area,11 consistent with our estimated
surface.
Figure 4 shows the 2-week average levoglucosan
measurements versus the estimated woodsmoke PM2.5
from stage 2. Levoglucosan at the Pitt Meadows site
was predicted to be lower than observed, the only significant outlier from stages 2 and 3 in this protocol.
Nonetheless the correlation is reasonably strong (0.45)
and in a sensitivity analysis with the Pitt Meadows data
removed (discussed further in the next section), we see
a much stronger fit (0.83) between PM2.5 emission estimates and measured levoglucosan.
Table 2 compares the average PM2.5 during the
fixed-site sampling period at both the fixed sites and
the existing regulatory sites. Elevation and DA level
population density are also listed. PM2.5 concentrations
were usually higher at lower elevations. The highest
fixed-site average exceeded the highest regulatory site
average by 74% during this period. The fixed-site average population density also exceeds the regulatory site
average by 55%. Even taking downtown Vancouver out
of the comparison, higher population density still exists for the fixed-site average. The sampling sites have
higher spatial variation and better represent the variability of residential woodsmoke as compared with the
DISCUSSION AND CONCLUSIONS
This research is the first example in which spatial analytic techniques are used to design and validate a network to measure the intraurban variability of residential woodsmoke concentration. Accordingly this
research serves as an example of how to design a sampling network to capture the small-area variability of
ambient air pollution. The data and materials required
to design this network are relatively simple, centering
on a small consumer woodburning survey and enhancements with a geographic information system and
spatial analysis. Although we applied the location-allocation methodology to air pollution arising from residential wood combustion, it could also be used to design networks that capture the intraurban variability
from traffic,8,27–31 home heating,32,33 and industrial
sources.34 For example, if the aim is to site passive
samplers for traffic emissions, the initial surface could
use the length of major roads and traffic counts in
place of the consumer woodburning survey used
here.
As noted earlier, the protocol reported here is part
of a larger four-stage analysis, the fourth a mobile sampling campaign11 aimed at modeling woodsmoke to
validate the results produced here and translate emissions into concentrations by producing high spatial
resolution estimates of woodsmoke PM2.5 with further
spatial analytic enhancements and mobile sampling.
Comparison of the average mobile sampling values
(i.e., adjusted light scattering) near the fixed sites with
levoglucosan concentrations measured showed a strong
correlation (R2 ⫽ 0.70). High concentration variability
(from Table 2) and strong correlation with light-scattering measurements at the seven fixed sites showed the
usefulness of using this protocol to analyze and validate
simple data inputs for air pollution exposure analysis.
Regional contrasts in air pollution levels, if not absolute
values, can be reasonably estimated to then deploy
pilot surveillance monitoring, both for validation and
measurements, before the initiation of more costly
Table 2. Fixed-site PM2.5 mass and regulatory site PM2.5 readings (␮g m⫺3) over a series of 2-week periods averaged from October 2004 to
April 2005.
Regulatory Site PM2.5 Reading
Fixed-Site PM2.5 Reading
Name
Average (range)
Elevation
(m)
Population
Densitya
Burnaby Southb
Downtown
North Vancouver
Pitt Meadows
Richmond
Southwest Surrey
White Rock
All sites
7.77 (2.96–11.71)
8.22 (5.53–9.61)
6.40 (3.64–12.72)
9.24 (2.78–18.14)
12.34 (9.27–16.68)
8.72 (4.60–13.57)
7.74 (4.26–12.73)
8.19 (2.78–18.14)
118
15.18
155.8
19.06
5.82
86.41
14.39
59.24
33
221
33
32
35
58
37
64
Name
Average (range)
Elevation
(m)
Population
Density
Burnaby South
Port Moody
Kitsilano
Langley
Pitt Meadows
Vancouver International Airport
5.21 (2.37–7.91)
5.58 (2.93–8.08)
6.27 (3.15–8.66)
5.39 (2.15–9.11)
5.38 (1.97–9.84)
6.26 (3.21–10.4)
118
0.57
35
90
2.2
2
33
11
31
3
1
1
All sites
5.68 (1.97–10.4)
41.30
13
Notes: aUnit of population density is persons per hectare. bColocated with the GVRD regulatory site.
898 Journal of the Air & Waste Management Association
Volume 57 August 2007
Su et al.
monitoring and assessment programs. The correlation
between estimated PM2.5 and levoglucosan was reasonably strong and improved considerably with removal of
the apparent outlier of Pitt Meadows. Notably, when
estimated (stage 2) and measured (stage 3) data are
compared, the correlation including Pitt Meadows is
0.84, suggesting that the weaker correlation with levoglucosan at Pitt Meadows may be influenced by localized alternative fuel types and/or woodburning appliances,27 variation in its fraction of PM2.5,35,36 or simply
other sources of nighttime ambient fine particles in Pitt
Meadows.
The higher PM2.5 readings at the fixed sites were
because of the higher population density around the
fixed site neighborhoods (Table 2), which had higher
fireplace densities compared with the regulatory sites. A
typical example is Pitt Meadows, where the fixed site
had an average population density of 32 persons per ha,
whereas at the regulatory site it was 1 person per ha.
More woodburning was, therefore, expected from the
neighborhood surrounding the fixed-site sampler than
from the regulatory site. Table 2 also reflects the
influence of elevation such that when it was higher,
ambient woodsmoke concentrations were lower and
vice versa. This is consistent with the work by Browning
et al.18 in Seattle and also underlines our use of a
compound topographic index for enhancing the initial
emissions surface estimate. Overall, this approach estimated and validated higher fine particle mass than is
typically captured by the existing regional monitoring
network and reflected its usefulness in sampling network design.
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900 Journal of the Air & Waste Management Association
About the Authors
Jason G. Su is a postdoctoral research fellow in the Department of Geography, University of British Columbia.
Timothy Larson is a professor in the Department of Civil and
Environmental Engineering, University of Washington. Michael Brauer is professor and director of the School of
Occupational and Environmental Hygiene, University of
British Columbia. Anne-Marie Baribeau was a research assistant in the School of Occupational and Environmental
Hygiene, University of British Columbia. Michael Rensing is
an air quality program analyst at the Environmental Quality
Branch in the British Columbia Ministry of Environment.
Michael Buzzelli is an assistant professor in the Department
of Geography at the University of Western Ontario and
adjunct assistant professor in the Department of Geography, University of British Columbia. Please address correspondence to: Michael Brauer, School of Occupational and
Environmental Hygiene, The University of British Columbia,
2206 East Mall, Vancouver, British Columbia, Canada V6T
1Z3; phone ⫹1-604-822-9585; fax ⫹1-604-822-9588; email: [email protected].
Volume 57 August 2007