Characterizing Spatial Patterns
of Air Pollution
Ryan Allen
Assistant Professor
Faculty of Health Sciences
Simon Fraser University
Workshop on Visualization and
Communication of Climate Change Risk
April 14, 2009
Presentation Overview

Why characterize spatial patterns of
air pollution?

Air pollution epidemiology

Two examples


Traffic-generated air pollution
Residential wood stove emissions

Why Characterize Spatial
Patterns?
Exposure assessment for epidemiology
•
•
•

Risk assessment
•

Explore links with health and determine
concentration-response relationships
Chronic effects studies
Identify individual source effects on health (e.g.
traffic)
Determine (sensitive) populations exposed,
exposure levels, etc.
Risk management
•
Make decisions about how to best eliminate or
minimize health risks
Air Pollution Epidemiology
Chronic Exposure Studies

Harvard 6 Cities
Study:

Prospective cohort
study of ~8,000
adults

14-16 years followup

Assume one
exposure level per
city

Elevated mortality
risks associated with
PM2.5 concentration
Dockery et al.,
1993
Air Pollution Epidemiology
Chronic Exposure Studies
“We observed [PM2.5] effects nearly 3 times
greater than in [studies] relying on
comparisons between communities.”
Air Quality Monitoring Stations
Metro Vancouver, 2007 Lower Fraser Valley Air Quality Report
Traffic-Generated Air Pollution
Zhu et al., 2004
Land Use Regression (LUR)
Slide courtesy of Michael Brauer, UBC
Steps in LUR Model Development
Edmonton, AB and Winnipeg, MB
1. Select monitoring sites
2. Measure “indicator” pollutant
3. Adjust for temporal trends
4. Calculate relevant predictor variables
5. Develop LUR models
6. Estimate exposure for study
participants
-Birth cohort study of asthma and
allergy
Selection of Sampling Locations
Location Allocation Approach


Create “demand” surface

Semivariance of assumed
NO2 concentration

Weighted by population
density
Algorithm to determine
locations of 50 monitors
that attend to greatest
total “demand”

Result: samplers in areas
of concentration gradients
and relatively high
population density, spread
throughout the study area
Kanaroglou, Atmos Environ, 2005
Edmonton, AB
14-Day Sampling
Edmonton Weekly Avg NO
(ppb)
2
Adjusting for Temporal Trends
Annual Avg.
17.8 ppb
35
30
Session E2
15.5 ppb
25
1.
Adjust for temporal
trends: e.g., multiply
all Edmonton round 1
NO2 measurements
by 0.68 (or 26.1 /
17.8)
2.
Average the two “detrended”
measurements from
each of the 50
locations.
20
15
10
5
0
Jul-07
Winnipeg Weekly Avg NO2 (ppb)
Session E1
26.1 ppb
Sep-07
Nov-07
Jan-08
Mar-08
May-08
Jul-08
35
30
25
20
Session W1
10.5 ppb
Annual Avg.
9.8 ppb
Session W2
10.8 ppb
Session W3
6.5 ppb
15
10
5
0
Jul-07
Sep-07
Nov-07
Jan-08
Mar-08
May-08
Jul-08
Calculating Predictor Variables
m
00
10
0m
0
5
Sampling
Location
Slide courtesy of Michael Brauer, UBC
Potential Predictors
75 Variables Screened in LUR Model Development
Category
Units
Buffer Radii (m)
Subcategory
Land Use
Hectares in
circular buffer
300, 400, 500,
750, 1000
Water
Commercial
Residential
Government
Industrial
Open
Road
Length
KM in circular
buffer
50, 100, 200,
300, 500, 750,
1000, 1500
All Roads
Major Roads
Highways
Pop. Density
Persons per
hectare
750, 1000, 1250,
1500, 2000, 2500
Latitude
Longitude
Location
Abbreviation
# of Variables
WTR
COM
RES
GOV
IND
OPN
30
RD
MJR
HWY
24
DENS
6
Y
X
2
Distance to
City Centre
Kilometers
DCC
1
Elevation
Meters
ELEV
1
Proximity to
Major Road
KM to nearest
log (KM to nearest)
MJRdist
logMJRdist
2
Proximity to
Highway
KM to nearest
log (KM to nearest)
HWYdist
logHWYdist
2
Point
Sources
Number in circular
buffer
PS
6
Proximity to
Point Source
M to nearest
PSdist
1
1500, 2000,
2500, 3000,
4000, 5000
City-Specific Models
City
Pollutant
N
Model
Model
R2
LOO
R2
0.72
0.64
0.67
0.56
Edmonton
NO2
49
17.2 – 0.53(DCC) – 0.09(WTR.1000) +
0.04(IND.1000) + 12.7(MJR.50) +
0.31(HWY.500) + 0.81(RD.200)
Winnipeg
NO2
50
-2336 + 0.000424(Y) + 0.13(IND.400)
+ 6.60(MJR.100) – 1.23(logHWYdist)
LOO = leave one out
Predicted NO2 Surfaces
Winnipeg
Edmonton
Residential Wood Burning
http://www.ec.gc.ca/science/sandejan99/article1_e.html
Woodstove Exchange Study

Series of studies
evaluating
environmental & health
impacts of a woodstove
exchange program in
the Bulkley Valley &
Lakes district of BC

One goal to identify air
pollution “hot spots” to
target during
intervention sub-study
Mobile Monitoring

Woodsmoke “tracers” exist, but require
expensive sampling equipment &
analysis

Not feasible to measure at many locations
GPS Receiver
Air Inlet

Mobile measurements of non-specific
pollutant (fine particles) during times
when woodsmoke peaks (evening and
Larson et al., 2007
Smithers, BC

Drive a predetermined route
on cold, clear nights

Adjust for temporal
trends using a fixed
monitor

Repeat over many
nights to identify
spatial patterns
Slide courtesy of Gail Millar, UNBC
Summary

Useful to characterize air pollution
spatial patterns for epidemiology, risk
assessment, risk management

Substantial variability in
concentrations over 10s – 100s of
meters for some pollutants, sources


Routine monitoring networks not
adequately dense
Modeling and mobile sampling
approaches useful for capturing smallscale variations
Funding