Transcript Exposure Model

Air Pollution Exposure and
Health Effects
Halûk Özkaynak
US EPA, Office of Research and Development
National Exposure Research Laboratory, RTP, NC
Presented at the
CMAS Special Symposium on Air Quality
October 13, 2010
Use of Air Quality and Exposure
Information in Health Related Studies
• Epidemiology (acute and chronic health effects at
population or individual level)
• Risk Assessment (health effects due to exposures
to single or multiple pollutants)
• Risk Management (determining optimum local vs.
regional emissions controls by source-specific
modeling)
• Accountability (evaluating impacts of Federal,
State, City and local local-scale pollution
reduction programs)
Role of Exposure Science and Epidemiology in
Developing Air Quality Regulations and Controls
• Exposure science in risk
assessment and risk
management is critical to EPA’s
mission to promote public health
and welfare
• EPA conducts exposure
assessments to determine the
route, magnitude, frequency,
and distribution of exposure
• Epidemiology studies are vital in
estimating the risk and the
impact of air pollution on human
health.
• Latest research in epidemiology
emphasizes the need for more
reliable estimates or surrogates
of human exposures
Role of Exposure Science and
Epidemiology in EPA
Air Quality
Epidemiology
Exposure
• Integrated Science Assessment
• Risk and Exposure Assessment
• Risk Management
Air Quality and Exposure Modeling
Considerations in Air Pollution Epidemiology
• Epidemiologic study designs:




Time-series/case-crossover (e.g., daily or hourly resolution)\
Panel or cohort studies
Cross-sectional (e.g., multi-city, multi-region)
Hybrid (e.g., varied resolution either over space or time, such as
data aggregated over the entire city, by zip code, etc.)
• Health data types:
 Group/population level (e.g., hospital, mortality, insurance data)
 Individual level (e.g., subject data at address level)
• Exposure-related issues:
 Individual pollutants, multiple pollutants, PM species and copollutants, source-specific contributions
Rationale for the NERL Coop Program
• Numerous epidemiologic studies have used measurements
from central-site ambient monitors to estimate exposures to air
pollution
• Central-site monitors may not account for:
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spatial and temporal heterogeneity of urban air ambient pollution
human activity patterns
infiltration of ambient pollutants indoors
contributions of indoor sources that may be effect modifiers
• Central-site are especially problematic for certain PM
components and species (e.g., EC, OC, coarse, ultrafine) that
exhibit significant spatial heterogeneity
• A number of enhanced exposure assessment approaches
have recently been developed and applied in the investigation
of air pollution health effects
Average Time Spent in Each Microenvironment
OUTDOORS (7.6%)
IN VEHICLE (5.5%)
TOTAL
INDOORS:
86.9%
OTHER INDOOR (11%)
IN RESIDENCE
(68.7%)
National Human Activity Pattern Survey
(NHAPS)
Adapted by Klepeis et al., 2001
Exposure Information Relevant to Health Studies
Complexity
Tiers of Exposure Metrics
Ambient Monitoring Data:
Central Site or Interpolated
Input data
Monitoring Data
Land-Use Regression
Modeling
Monitoring Data
Emissions Data
Land-Use/Topography
Air Quality Modeling
(CMAQ, AERMOD, hybrid)
Emissions Data
Meteorological Data
Land-Use/Topography
Statistical modeling
(Data blending)
Monitoring Data
Emissions Data
Meteorological Data
Land-Use/Topography
Exposure Modeling
(SHEDS, APEX)
Monitoring Data
Emissions Data
Meteorological Data
Land-Use/Topography
Personal Behavior/Time Activity
Microenvironmental Characteristics
Health data analysis
Epidemiological statistical models:
log(E(Ykt)) = α + β exposure metrickt + kγkareakt+ …other covariates
Reliability
vs.
Uncertainty
Definition Of Exposure Predictors
Exposure= Outdoor Exposure+ Indoor Exposure


Exposure  Cambient [toutdoor  thome  Finf, home ] / Ttotal

 Cambient [ tother  Finf,
other
 tvehicle  Finf, vehicle] / Ttotal
 tindoor  Cindoor / Ttotal
Assuming no indoor sources and most time spent
indoors (>85%), then:
Exposure ≈ Infiltration Rate x Ambient Concentration
Exposure ≈ Inf x Ambient Concentration
Tiers of Exposure Predictors Used in
Recent Epidemiological Analysis
• Tier 1: Refers to Central Site Monitoring Data (single
monitor or averaged across multiple stations)
• Tier 2: Refers to exposures to ambient pollution that
has infiltrated indoors into residences
• Tier 3: Refers to ambient exposures to Air Quality
Model predicted values
• Tier 4: Refers to exposures based on population
Exposure Model predictions
Estimated Effects of Ambient PM2.5 on
Acute Mortality in the US (Tier 1)*
Community-specific estimates of the percent increase in respiratory mortality with a 10mg/m3
increase in the previous day's PM2.5 concentrations
25
20
10
5
0
-5
-10
-15
Community
▪ represents estimates; lines around ▪ are 95% confidence interval
(*Source of data: Franklin et al. 2007)
Washington DC
Tampa
Seattle
San Diego
Sacramento
Riverside
Pittsburgh
Phoenix
Philadelphia
Palm Beach
Minneapolis
Milwaukee
Memphis
Manhattan
Los Angeles
Las Vegas
Indianapolis
Houston
Fresno
Detroit
Dallas
Columbus
Cleveland
Cincinnati
Chicago
Boston
-20
Birmingham
Percent Increase
15
Influence of an Indoor Infiltration Indicator
(Normalized Leakage) on Estimated Ambient PM2.5
Effect on Respiratory Mortality (Tier 2)
Percent increase in respiratory mortality with a 10mg/m3 increase in previous
day's PM2.5 concentrations at the 25th, 50th, and 75th percentiles of Normalized
Leakage
4.5
4
Percent increase in respiratory mortality
3.5
3
2.5
2
1.5
1
0.5
0
-0.5
-1
25th
50th
Percentile
Source: Baxter et al. 2010 (in preparation)
75th
Findings from Further Analysis of Daily
Ambient PM2.5 and Mortality in the US
• There is considerable heterogeneity in the
predicted PM effects on acute mortality across the
27 cities studied
• The variation in the predicted PM health effects
can partially be explained by the geographic
differences in housing stock which influence how
much outdoor air infiltrates indoors
 Cities with homes that have greater indoor infiltration of
outdoor air (represented by higher normalized leakage)
have larger PM effect estimates potentially due to
greater indoor exposures to ambient PM
Association between 24-hour BC , 24-hour NO2 and HRV for
ambient, outdoor, indoor, and personal exposure measures
BC
NO2
Source: Suh and Zanobetti (2010)
Preliminary Results of Epidemiologic Analysis
of Hospital Admissions in New York
Concentrations
Comparison of ozone concentration vs. exposures,
10 ppb unit change (Source: Shao et al. (2010)
Exposures
• Relative risk is high for exposure, but is
exposure modeling better discerning the
relationships between air quality and
human health outcomes?
Office of Research and Development
15 National Exposure Research Laboratory
• How do we compare risks based on
different exposure metrics (i.e.,
concentration vs. exposure)?
EPA Coop Program Goal and Approach
Goal
• Overall aim is to enhance the results from epidemiologic studies of
ambient PM, PM components and gaseous air pollution through the use of
more reliable approaches for characterizing exposures
Approach
• Research conducted under a two-year, plus 6-12m no-cost extension,
Cooperative (Coop) Agreement program with EPA/NERL and three
academic institutions:
 Emory University
 Rutgers
 University of Washington
• Coops started around the end of 2008 and will run through 2011
• First year activities focus on exposure metric development and second
year research focuses on their applications and evaluations
• EPA is a strong partner in this Coop program and provides air quality
model (e.g., CMAQ, AERMOD) and exposure (e.g., SHEDS, APEX) model
results along with methodologies for assimilating measurement and
modeling information
Research Goals and Objectives
Emory Coop
Develop and evaluate six exposure metrics for ambient traffic-related
(CO, NOx and PM2.5 EC) and regional (O3 and SO42- ) pollutants by
applying them to two of ongoing epidemiologic studies on ambient air
pollution and acute morbidity in Atlanta, GA
Rutgers Coop
Examine associations between PM2.5 mass and species and adverse
health using two established epidemiology studies in New Jersey by
testing four different tiers of exposure specification
University of Washington Coop
Improve air pollution cohort study health effect estimates by (i)
incorporating dispersion model output into an existing spatio-temporal
concentration model and (ii) quantifying the impact of exposure
misspecification and methods for minimizing this source of error
What are Exposure Models?
• Exposure models are structured mathematical
representations used for predicting real-world
exposure events by linking measurements or modeling
information on:
 Emissions
 Concentrations (e.g., Ambient, Indoor, Commuting)
 Behavioral information
 Exposure factors
 Other exposure related data
Stochastic Human Exposure and Dose
Simulation (SHEDS) Model for Air Pollutants
Tools Needed for Advancing
Exposure Science
An integrated modeling system that can be operationally applied in
air quality management practices (e.g., standard setting, standard
implementation, risk mitigation, accountability)
Factors below that Impact Exposures Need to be Incorporated into AQ Model
• Local sources (mobile, area and point) on outdoor residential concentrations
• Local meteorology on outdoor residential concentrations and infiltration rates
• Housing type and house operations on infiltration and indoor concentration
• Locations and Activities on personal exposure
Air Quality
Modeling
Exposure
Modeling
• Emissions
• Human Activity
• Indoor Penetration
• Personal Sources
E
X
P.
or
D
O
S
E
Office of Research and Development
21 National Exposure Research Laboratory
Inhalation
100
Exposure
• Meteorology
• Dispersion
• Chemistry
10
1
0.1
0.01
to
TIME
t1
0
20
40
60
Percentile
80
100
Key Programmatic Issues
• Type of an epidemiologic study design is important for
determining the requirements needed for spatio-temporal
resolution of exposure data or its surrogates needed for health
effects research (one size does not fit all in terms of
sophistication needed in exposure assignment)
• Important to better understand the sources and factors
influencing uncertainties in ambient epidemiology analyses as
well as compounding of errors as exposure metrics are refined
• NERL Coop Program has greatly facilitated exchange and
transfer of relevant information between multiple EPA groups,
scientists and academic institutions
• Establishing causality will depend on building sufficient weight
of evidence regarding the utility/performance of exposure
models and metrics through their application in new or refined
epidemiological analysis
Acknowledgements
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Halûk Özkaynak (EPA)
Vlad Isakov (EPA)
Lisa Baxter (EPA)
Janet Burke (EPA)
Larry Reiter
ST Rao
New York State Dept. of Health
Disclaimer: Although this work was reviewed by EPA and approved for publication, it may
not necessarily reflect official Agency policy