Estimating Air Pollution Infiltration Efficiencies for Exposure
Assessment and Epidemiology
Ryan Allen, PhD Faculty of Health Sciences
Simon Fraser University Burnaby, BC
Overview• Why estimate residential infiltration efficiencies?• How?
–Tracer–Recursive model
• Description, validation, examples
• Application to epi–Panel studies–Model building for other study designs
• Windsor results–And possible future directions
AmbientPM2.5
“Leaky”
AmbientPM2.5
Infiltration Efficiency (Finf )
AmbientPM2.5
“Tightly Sealed”
–Total exposure = ambient + nonambient
• The fraction of the ambient concentration that penetrates indoors and remains suspended
• Function of AER, penetration, deposition
• Exposure to ambient pollution depends on Finf & time spent outdoors
Why?• To better interpret epi. results from different
locations and/or from different seasons
• Reduce exposure misclassification in epi studies
• To tease apart health impacts of ambient vs. nonambient pollution
• To better interpret epi. results from different locations and/or from different seasons
• Reduce exposure misclassification in epi studies
• To tease apart health impacts of ambient vs. nonambient pollution
Hystad et al., JESEE, in press
Why?
72%
8%
18%2%
Vegetative BurningMobile
SecondaryCrustal
58%
4%
14%
24%
Why?• To better interpret epi. results from different locations and/or from different
seasons
• Reduce exposure misclassification in epi studies
• To tease apart health impacts of ambient vs. nonambient pollution
Larson et al., JAWMA, 2004
Outdoors (12.2 ug/m3) Indoors (9.4 ug/m3)
How?• Tracer (most commonly sulfur or sulfate)
–Requires that there be no (or few) indoor or personal sources
–Indoor/outdoor ratio or slope gives Finf
• Recursive model–Requires continuous indoor/outdoor
measurements (e.g. nephelometer, DustTrak)–Does not require absence of indoor sources–Shows promise for estimating Finf of pollutants
without good tracers (e.g. ultrafines)
Average indoor concentration during the hour
=
Some fraction of the average outdoor concentrationduring the hour
+
Some fraction of the indoor concentrationthat remains from the PREVIOUS hour
+Contribution from indoor sources during the hour
int
int
outt
int SCCC 121 ++= −ββ
~0
2
1inf 1 β
β−
=+
=ka
PaF
Recursive Model Technique
Allen et al., ES&T, 2003
0
1
2
3
4
5
6
7
8
22-Feb 24-Feb 26-Feb 28-Feb 1-Mar 3-Mar
Ligh
t Sca
tterin
g C
oeffi
cien
t (x1
0 -5 m-1
)
OutdoorsIndoorsIndoor Source Hour
Recursive Model Technique
Allen et al., ES&T, 2003
Recursive Model Technique Validation
Allen et al., ES&T, 2003
Recursive Model Technique Validation
Allen et al., JESEE, 2007
Recursive Model Technique Examples
Barn et al., JESEE, 2008
• Air cleaner effectiveness in woodsmoke and forest fire impacted community
Recursive Model Technique Examples
Polidori et al., JAWMA, 2007
• Contributions of ambient and nonambient sources at retirement facilities in southern CA
Application of Finf to Epidemiology Panel Studies
Allen et al., Inhal. Tox., 2008
Application of Finf to Epidemiology Model Development for Large Studies
Koenig et al., EHP., 2005
R2 = 0.60
R2 = 0.66
Application of Finf to Epidemiology Model Development for Large Studies
Hystad et al., JESEE., In Press
• Victoria, BC
• Spatial property assessment data (SPAD)
• Model including season predicts 54% of total variance
• Potentially allows Finf to be estimated in many homes without I/O monitoring
Windsor PM2.5 Finf
Mean: 0.36IQR: 0.26 – 0.44
Mean: 0.33IQR: 0.25 – 0.40
52 homes monitored in both seasons
Windsor PM2.5 Finf Models
Season Predictor(s) R2
Summer I-O Temp Diff, Window Opening 0.36
Winter Building age, air cleaner use 0.20
Windsor UFP Finf
Mean: 0.34IQR: 0.19 – 0.50
Mean: 0.21IQR: 0.13 – 0.27
27 homes monitored in both seasons
Windsor UFP Finf Models
Season Predictor(s) R2
Summer Outdoor Temp, Window Opening 0.56
Winter ----- -----
PM2.5 Finf vs. UFP Finf
r = 0.44
r = 0.68
Other Possibilities for Windsor Data• Continue development of Finf models• Comparison of Finf for different PM species
–PM2.5–UFP–LAC
• Health effects–Ambient / nonambient PM vs. lung function
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