Partnership for Environmental Research and Community Health
(PERCH)
Spatial associations between health
outcomes and air emission sites in NW FL
Johan Liebens
Zhiyong Hu
Department of Environmental Studies
University of West Florida
K. Ranga Rao
Center for Environmental Diagnostics and Bioremediation
University of West Florida
Background: Health outcome study

Studnicki, J. et al., 2004 (PERCH).

Health outcomes responsive to air pollution.

Mortality and morbidity, by age and race.

Standardized mortality and morbidity ratios (SMRs)
– State average = 1


ZIP code as spatial unit.
Comparison with "peer" ZIP codes.
Peer ZIP codes
After Studnicki, J.
et al. (2004)
Main Objective

Evaluate if spatial and statistical relationships exist
between health outcome SMRs and proximity to air
emission sites.
Data

Spatially referenced air emission data from:
1. Toxic Release Inventory - TRI (EPA website).
 Major industrial emitters.
–
16 TRI sites in NW Florida, 107 in peer ZIP codes.
2. FL DEP (Florida Department of Environmental
Protection) headquarters.
 Major and minor emitters statewide.
–
34 sites in NW Florida, 1111 in peer ZIP codes.
Methods

Calculate proximity indexes for ZIP codes:
1.
Determine indexes for average distance from census block
centroid to air emission sites within 10 km.
2.
Weight proximity indexes for block centroids with emissions:

Benzene-equivalent pounds for TRI sites.
–

3.
Standardizes emissions based on carcinogenicity.
Total emissions for FL DEP database.
Calculate average weighted proximity indexes for each ZIP
code.
Benzene-equivalent weighted proximity index
Methods (continued)

Statistically compare weighted proximity indexes:
– For ZIP codes with contrasting SMRs within NW Florida.
– For NW Florida ZIP codes and their respective peer ZIP codes.

Make comparison for:
– Cumulative health outcomes.
– Specific health outcomes.
Results
Cumulative health outcomes:

No statistically significant difference between weighted
proximity indexes.
– For ZIP codes with contrasting SMRs within NW Florida.
– For NW Florida ZIP codes with high/low SMR and peer ZIP
codes.
Benzene-equivalent weighted proximity index (TRI sites)
Comparison within NW Florida
benzene weighted prox. index
1600000
worse health outcomes
better health outcomes
1200000
800000
400000
0
325
325
325
325
325
325
325
325
325
325
325
325
325
325
325
325
325
325
68
07
06
08
26
05
14
35
66
01
77
70
83
03
33
71
04
34
Results
Specific health outcomes:

Qualitative differences in weighted proximity indexes for
3 (4?) causes of mortality and 3 causes of morbidity.
– Mortality: white, >65, cardiac
black, >65, lung cancer, (all cancers ?)
black, birth defects
– Morbidity: black, all ages, asthma
black, >65, cardiac
white, >65, pneumonia
– NW FL ZIPs with a high SMR for these health outcomes are:
 closer to emission sites than NW FL ZIPs with a low SMR.
 closer to emission sites than their peer ZIP codes.
FL DEP database, emission weighted proximity index
mortality
3500
weighted proximity index
3000
blacks, >65
all cancers
blacks, > 65
lung cancer
whites, > 65
cardiac
Blacks,
birth defects
2500
2000
NW FL ZIP codes
peer ZIP codes
1500
1000
500
0
high low
SMR
high low
SMR
high low
SMR
high low
SMR
Conclusions

No statistical difference between weighted proximity
indexes when ZIP codes are classified based on
cumulative health outcomes.

Qualitative differences in weighted proximity indexes
when ZIP codes are classified using specific health
outcomes.
Conclusions (continued)

Issues:
–
–
–
–

ZIP code as geographic unit of analysis in original health study.
Spatial quality of databases.
Aggregating emission data from various sources.
Mobility, lifestyle.
Solution:
– Raster-based spatial statistical modeling.
– Remote sensing-based exposure assessment.
Thank You

Student assistants: Johanna Jenkins, Angela Worley,
Kristal Walsh.

EPA cooperative agreement X-97455002.

Original health outcome study: Studnicki, J. et al. (2004).
PERCH Symposium
Relationship between Air Pollution and
Health Outcomes
Zhiyong Hu, Johan Liebens, K. Ranga Rao
Content



Stroke mortality and air pollution.
Chronic heart disease and PM2.5.
Chronic heart disease and aerosol particulate
pollution as indicated by satellite derived aerosol
optical depth (AOD) data.
Background

Stroke: A type of cardiovascular disease that affects the arteries
leading to and within the brain. It occurs when a blood vessel that
carries oxygen and nutrients to the brain is either blocked by a clot
(ischemic) or bursts (hemorrhagic).

Air pollution is known to be associated with cardiovascular disease,
but relatively few studies have examined the association between air
pollution and stroke mortality.

Results from existing studies on air pollution and stroke are
inconsistent and inclusive.

Studies have documented positive effects of green areas on human
health. No studies have been done to investigate the association
between stroke and greenness.

Considerable evidence of income inequality affecting health. Does
income affect stroke mortality?
Objective and Methods

Objective
Investigate if there are associations between stroke and air
pollution, income and greenness in northwest Florida.

Methodology:
- Ecological geographical approach: disease & income data
based on 77 census tracts, spatial support for environmental
variables transformed to match census units.
- GIS analysis, satellite remote sensing, and dasymetric
mapping.
- Bayesian hierarchical modelling
Study Area

Escambia and Santa Rosa counties in northwest Florida.

Fact: In 2003, stroke age-adjusted death rate (per 100,000) is:
53.5
U.S.
42.4
Florida
74.1
Escambia County
50.0
Santa Rosa County.
Stroke mortality rate standardization




Stroke death count data at the census tract level
compiled from Florida Vital Statistics in a 5-year
(1998-1992) aggregate.
To adjust for age effect, expected number of
stroke deaths for each census tract was
calculated using indirect standardization.
Use the US South population as standard
population.
Standardized mortality rates (SMRs) were
calculated by dividing the observed count by the
expected value.
Air Pollution Data
Point Source
Mobile Source
Air Pollution Density
We used polluter density surfaces
to derive surrogate variables
representing air pollution.
Point source
- Based on emission amount
- Based on abundance of points
Traffic data
- Based on kernel surface
The density surfaces were further
used to calculate aggregate zonal
statistics (average density) based
on dasymetric mapping.
Greenness extracted from Landsat ETM+ imagery
using tasseled cap transformation
Dasymetric Mapping of
Human Activity Area (shown in gray)
Average air pollution density for each tract was
calculated using grid cells within human activity
area only
Bayesian hierarchical modelling of relationship between
stroke and income and environment exposure
Model Fitting




Markov chain Monte Carlo (MCMC) simulation and Gibbs sampling
algorithm.
WinBUGS software - an interactive Windows version of the BUGS
(Bayesian inference Using Gibbs Sampling).
Spatial weights as input to CAR generated using GIS.
A total of 10,000 iterations with 5,000 burn-in was run. Inference was
based on iterations 5,001 to 10,000.
Trace plots of the 10,000 Markov
Chain Monte Carlo (MCMC) updates.
Simulation trace plots for the intercept,
income effect, traffic air pollution effect,
effect of EPA and Florida DEP monitored
point source air emission, effect of nonmonitored point source air pollution, and
greenness effect for the Bayesian
hierarchical model with a convolution
prior.
Kernel estimates of the posterior densities of the fixed effects
RR  100 * exp(    INC / 10000   AADT / 10000   EPNT   PPNT   GREEN / 10  b  h )
i
0
1
i
2
i
3
i
4
i
5
i
i
Findings and Conclusions




An excess risk of stroke mortality in areas
with high air pollution levels.
Higher risk of stroke mortality occurs in
areas with lower income.
Exposure to more green space could reduce
the risk of stroke mortality.
The findings point to the issues of
environmental injustice, socioeconomic
injustice and health inequality.
Background




Numerous studies have found adverse health effects of acute and
chronic exposure to fine particulate matter (particles smaller than 2.5
micrometers, PM2.5).
Air pollution epidemiological studies relying on ground measurements
provided by monitoring networks are often limited by sparse and
unbalanced spatial distribution of the monitors.
The repetitive and broad-area coverage of satellites may allow
atmospheric remote sensing to offer a unique opportunity to monitor
air quality at continental, national and regional scales.
Studies have found correlations between satellite aerosol optical
depth (AOD, which describes the mass of aerosols in an atmospheric
column) and PM2.5 in some land regions. Satellite aerosol data may
be used to extend the spatial coverage of PM2.5 exposure
assessment.
Objectives



Investigate correlation between PM2.5 and AOD
in the conterminous USA.
Derive a spatially complete PM2.5 surface by
merging satellite AOD data and ground
measurements based on the potential
correlation.
Examine if there is an association of chronic
coronary heart disease (CCHD) with PM2.5.
Methods




Years 2003 and 2004 daily MODIS (Moderate Resolution
Imaging Spectrometer) Level 2 AOD images were collated
with US EPA PM2.5 data covering the conterminous USA.
Pearson’s correlation and geographically weighted regression
(GWR) analyses of the relationship between PM2.5 and AOD.
The GWR model was used to derive a PM2.5 grid surface
using the mean AOD raster.
Fitting of a Bayesian hierarchical model linking PM2.5 with agerace standardized mortality rates (SMRs) of chronic coronary
heart disease.
MODerate-resolution Imaging
Spectroradiometer (MODIS)
•
•
Onboard NASA Satellites Terra & Aqua
– Launched 1999, 2002
– 705 km polar orbits, descending (10:30
a.m.) & ascending (1:30 p.m.)
Sensor Characteristics
– 36 spectral bands ranging from 0.41 to
14.385 µm
– Cross-track scan mirror with 2330 km
swath width
– Spatial resolutions:
• 250 m (bands 1 - 2)
• 500 m (bands 3 - 7)
• 1000 m (bands 8 - 36)
– 2% reflectance calibration accuracy
MONITORING AND FORECASTING OF AIR QUALITY: AEROSOLS
Annual mean PM2.5 concentrations (2002)derived from MODIS AODs
van Donkelaar et al. [JGR 2007]
MODIS Data




Daily level 2 MODIS data (2003-2004) were obtained from the
NASA Level 1 and Atmosphere Archive and Distribution System
(LAADS Web) [35].
A two-year average AOD raster data layer (10 km by 10 km grid)
was calculated.
Data from both Terra and Aqua satellites were used.
MODIS AOD data are not available every day due to cloud cover.
Data for cold seasons (October to March) were not used in the twoyear average calculation and correlation analysis. Cloud cover, snow
reflectivity, and diminished vertical mixing all reduce the accuracy of ground-level
pollutant levels measured in winter. During warm seasons, vertical columns in the
atmosphere are more integrated. AOD measures correlate best with ground-based
monitoring in warm months, likely because of stronger boundary layer mixing during
the warmer months.
Correlation Analysis Result
Correlation ( r ) Surface
Geographically Weighted Regression
(PM2.5 vs. AOD)
Slope Coefficient Surface
Geographically Weighted Regression
(PM2.5 vs. AOD)
R Square Surface
Summary of PM2.5-AOD
Relationship
 Pearson’s correlation analysis and geographically weighted regression
(GWR) found that the relationship between PM2.5 and AOD is not spatially
consistent across the conterminous states.
 The average correlation is 0.67 in the east and 0.22 in the west of the 100° longitude line

GWR predicts well in the east and poorly in the west.

Therefore, GWR was used to derive a PM2.5 surface for the east.
PM2.5 surface calculated by merging MODIS AOD and
EPA PM2.5 ground measurements (RMSE = 1.67 µg/m3).
Association of Chronic Coronary Heart
Disease (CCHD) with PM2.5:
CCHD mortality rate increases with
exposure to PM2.5.
Background and Objective
Recent attention has focused on the chronic effect of particulate matter
on heart disease.
In the previous study (Hu, 2009), satellite-derived aerosol optical depth
(AOD) was found to be correlated with PM2.5 in the eastern US.
 By directly linking pixels with people, this study uses satellite AOD data
as an air pollution indicator to assess the effect of fine aerosol particles
on chronic ischemic heart disease (CIHD).
Methods

An ecological geographic study method was employed.

Race and age standardized mortality rate (SMR) of CIHD was computed for
each of the 2306 counties for the time period 2003-2004.

A mean AOD raster grid for the same period was derived from MODIS aerosol
data and the average AOD was calculated for each county.

Analyses of the relationship between AOD and CIHD.
- Bivariate Moran’s I scatter plot
- Local indicator of spatial association (LISA)
- Regression models (OLS, spatial lag, and spatial error)
Results
The global Moran's I value is 0.2673 (p =0.001), indicating an overall
positive spatial correlation of CIHD SMR and AOD.
LISA Analysis
Result
The entire study area is dominated by spatial clusters of AOD against
CIHD SMR (high AOD and high SMR in the east, and low AOD and low
SMR in the west) (permutations = 999, p=0.05).
Of the three regression models, the spatial error model achieved the
best fit. The effect of AOD is positive and significant (beta = 0.7774, p=
0.01).
Conclusions
Aerosol particle pollution has adverse effect on CIHD mortality risk.
High risk of CIHD mortality was found in areas with elevated levels of outdoor
aerosol air pollution as indicated by satellite derived AOD.
Remote sensing AOD data could be used as an alternative health-related
indictor of air quality.