spatial associations between health outcomes and air emission sites in nw fl

Spatial associations between health Spatial associations between health outcomes and air emission sites in NW FLoutcomes and air emission sites in NW FL

Johan LiebensZhiyong Hu

Department of Environmental StudiesUniversity of West Florida

K. Ranga RaoCenter for Environmental Diagnostics and Bioremediation

University of West Florida

Partnership for Environmental Research and Community Health(PERCH)

Background: Health outcome studyBackground: 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.

After Studnicki, J. et al. (2004)

Peer ZIP codes

Main ObjectiveMain Objective

Evaluate if spatial and statistical relationships exist between health outcome SMRs and proximity to air emission sites.

DataData

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.

MethodsMethods

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.

– Standardizes emissions based on carcinogenicity. Total emissions for FL DEP database.

3. Calculate average weighted proximity indexes for each ZIP code.

Benzene-equivalent weighted proximity index

Methods Methods (continued)(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.

ResultsResults

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

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800000

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1600000

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worse health outcomes

better health outcomes

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

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500

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3500

wei

ghte

d pr

oxim

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NW FL ZIP codespeer ZIP codes

blacks, >65 all cancers

blacks, > 65lung cancer

Blacks,birth defects

high lowSMR

high lowSMR

high lowSMR

whites, > 65 cardiac

high lowSMR

ConclusionsConclusions

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 Conclusions (continued)(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 YouThank You

Student assistants: Johanna Jenkins, Angela Worley, Kristal Walsh.

EPA cooperative agreement X-97455002. Original health outcome study: Studnicki, J. et al. (2004).

PERCH SymposiumPERCH Symposium

Relationship between Air Pollution and Health Outcomes

Zhiyong Hu, Johan Liebens, K. Ranga Rao

ContentContent 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.

BackgroundBackground 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 MethodsObjective and Methods

ObjectiveInvestigate 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 AreaStudy 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 standardizationStroke 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 DataAir Pollution DataPoint Source

Mobile SourceMobile Source

Air Pollution DensityAir 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 Greenness extracted from Landsat ETM+ imagery

using tasseled cap transformationusing tasseled cap transformation

Dasymetric Mapping of Dasymetric Mapping of Human Activity Area (shown in gray)Human Activity Area (shown in gray)

Average air pollution density for each tract was Average air pollution density for each tract was calculated using grid cells within human activity calculated using grid cells within human activity area onlyarea only

Bayesian hierarchical modelling of relationship between stroke and income and environment exposure

Model FittingModel 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 non-monitored 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 effectsKernel estimates of the posterior densities of the fixed effects

100 * exp( / 10000 / 10000 / 10 )0 1 2 3 4 5RR INC AADT EPNT PPNT GREEN b hi i i i i i i

Findings and ConclusionsFindings 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.

BackgroundBackground 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.

ObjectivesObjectives

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.

MethodsMethods

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 age-race standardized mortality rates (SMRs) of chronic coronary heart disease.

• 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

MODerate-resolution Imaging MODerate-resolution Imaging Spectroradiometer (MODIS)Spectroradiometer (MODIS)

MONITORING AND FORECASTING OF AIR QUALITY: AEROSOLSMONITORING AND FORECASTING OF AIR QUALITY: AEROSOLS

Annual mean PM2.5 concentrations (2002)derived from MODIS AODsAnnual mean PM2.5 concentrations (2002)derived from MODIS AODs

van Donkelaar et al. [JGR 2007]

MODIS DataMODIS 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 two-year 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 Geographically Weighted Regression (PM(PM2.52.5 vs. AOD) vs. AOD) Slope Coefficient SurfaceSlope Coefficient Surface

Geographically Weighted Regression (PM2.5 vs. AOD)

R Square Surface

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.

Summary of PM2.5-AOD Relationship

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.

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).

Background and Objective

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.

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).

LISA Analysis Result

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.