Upload
nathaniel-mckenzie
View
214
Download
0
Embed Size (px)
Citation preview
Use of Satellite Remote Sensing for Public Health Applications: The HELIX-Atlanta Experience
Bill CrossonBill CrossonMohammad Al-Hamdan, Maury Estes, Ashutosh Limaye, Mohammad Al-Hamdan, Maury Estes, Ashutosh Limaye,
Dale Quattrochi, Doug RickmanDale Quattrochi, Doug RickmanNASA/MSFC/NSSTCNASA/MSFC/NSSTC
Partners:Partners:Centers for Disease Control and PreventionCenters for Disease Control and Prevention
Kaiser-Permanente GeorgiaKaiser-Permanente GeorgiaU.S. Environmental Protection AgencyU.S. Environmental Protection Agency
Georgia Environmental Protection DivisionGeorgia Environmental Protection DivisionGeorgia Division of Public HealthGeorgia Division of Public Health
Emory UniversityEmory UniversityGeorgia Institute of TechnologyGeorgia Institute of Technology
HELIX-Atlanta Overview
Health and Environment Linked for Information Exchange in Atlanta (HELIX-Atlanta) was developed to support current and future state and local environmental public health tracking (EPHT) programs to implement data linking demonstration projects which could be part of the EPHT Network.
HELIX-Atlanta is a pilot linking project in Atlanta for CDC to learn about the challenges the states will encounter.
NASA/MSFC and the CDC are partners in linking environmental and health data to enhance public health surveillance.
The use of NASA technology creates value – added geospatial products from existing environmental data sources to facilitate public health linkages.
‘The Process is the Product’ – Proving the feasibility of the approach is the main objective
HELIX-Atlanta Timeline
October 2003: Initiate HELIX-Atlanta
January 2004: Gather information on existing systems of potential in a tracking network
April 2004: Select initial projects
September 2004: Begin implementation
September 2005: Signed Business Associate Agreement between NASA/MSFC and Kaiser-Permanente for exchange of Protected Health Information
February 2006: Evaluate initial projects, recommend next steps
HELIX-Atlanta Challenges
Sharing data between agencies with different missions and mindsets Protecting confidentiality of information Ensuring high quality geocoded data Ensuring appropriate spatial and temporal resolutions of environmental data Developing sound resources and methods for conducting data linkages and data analysis
HELIX-Atlanta Respiratory Health Team
RH Team Pilot Data Linkage Project:Link environmental data (MODIS) related to ground-level
PM2.5 with health data related to asthma
Goals: 1. Produce and share information on methods useful for
integrating and analyzing data on asthma and PM2.5 for
environmental public health surveillance. 2. Generate information and recommendations valuable
to sustaining surveillance of asthma with PM2.5 in the
Metro-Atlanta area.
Time period: 2003Domain: 5-county metropolitan Atlanta
Sources of PM2.5 data: EPA AQS
EPA Air Quality System (AQS) ground measurements National network of air pollution monitors Concentrated in urban areas, fewer monitors in rural areas Time intervals rangefrom 1 hr to 6 days(daily meas. every 6th day) Three monitor types:• Federal ReferenceMethod (FRM)• Continuous• Speciation FRM is EPA-acceptedstandard method;processing time 4-6 weeks
FRM sitesNon-FRM sites
AQS PM2.5 Data ExamplesObserved PM 2.5 - 2002
0
5
10
15
20
25
30
35
40
Date
mic
rog
ram
s/m
3
S. DekalbS. Dekalb (Daily data)DoravilleE. Rivers
Observed PM 2.5 - 2003
05
101520253035404550
Date
mic
rog
ram
s/m
3
S. DekalbGwinnett TechS. Dekalb (Daily data)DoravilleE. Rivers
• Excellent agreement between measurements at multiple sites each day• Small seasonal variations• Large day-to-day variations
Sources of PM2.5 data: MODIS
MODIS Aerosol Optical Depth (AOD) Provided on a 10x10 km grid Available twice per day (Terra ~10:30 AM, Aqua ~1:30 PM) Clear-sky coverage only Available since spring 2000 Used in NOAA/EPA AirQuality Forecast Initiative toproduce air quality forecastsfor northeastern US;forecasts for entire US in 2009
MODIS Aerosol Optical Depth (AOD) Data Examples
Smoothed MODIS AOD - 2002
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Date
MO
DIS
AO
D
S. DekalbDoravilleE. RiversMean
Smoothed MODIS AOD - 2003
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
Date
MO
DIS
AO
DS. DekalbGwinnett TechDoravilleE. RiversMean
• 31-day averages at each site and mean of all sites• 2003 shows higher summer values and more seasonal variation
2002
2003
Estimating PM2.5 from MODIS data
For 2002-2003, obtain MODIS AOD and EPA AQS PM2.5 data
Extract AOD data for AQS site locations
Calculate daily averages from hourly AQS PM2.5 data
Using daily PM2.5 averages from all 5 Atlanta AQS sites, determine statistical regression equations between PM2.5 and MODIS AOD
Apply regression equations to estimate PM2.5 for each 10 km grid cell across region
MODIS AOD - PM2.5 RelationshipPM 2.5 and MODIS AOD - 2002
0
5
10
15
20
25
30
35
40
1/1/
2002
2/1/
2002
3/1/
2002
4/1/
2002
5/1/
2002
6/1/
2002
7/1/
2002
8/1/
2002
9/1/
2002
10/1
/200
2
11/1
/200
2
12/1
/200
2
Date
mic
rog
ram
s/m
3
0.00
0.15
0.30
0.45
0.60
0.75
0.90
1.05
1.20
MO
DIS
AO
D
Mean PM 2.5
Mean MODIS AOD
PM 2.5 and MODIS AOD - 2003
0
5
10
15
20
25
30
35
40
45
Date
mic
rog
ram
s/m
3
0.00
0.20
0.40
0.60
0.80
1.00
1.20
1.40
1.60
1.80
MO
DIS
AO
D
Mean PM 2.5
Mean MODIS AOD
• Daily 5-site means of
observed PM2.5 and MODIS
AOD• MODIS data not available every day due to cloud cover• MODIS AOD follows
seasonal patterns of PM2.5
but not the day-to-day variability in fall and winter
2002
2003
Observed (AQS) and Predicted PM 2.5
Doraville
0
10
20
30
40
4/1/00 10/1/00 4/1/01 10/1/01 4/1/02 10/1/02 4/1/03 10/1/03
Date
PM
2.5
(m
icro
gra
ms/
m3) 31-day mean Observed PM 2.5
31-day mean MODIS-Estimated PM 2.5
South Dekalb
0
10
20
30
40
4/1/00 10/1/00 4/1/01 10/1/01 4/1/02 10/1/02 4/1/03 10/1/03
Date
PM
2.5
(m
icro
gra
ms/
m3)
31-day mean Observed PM 2.5
31-day mean MODIS-Estimated PM 2.5 • MODIS-based predictions
follow seasonal PM2.5 patterns• MODIS AOD is nearly constant in fall and winter,
while observed PM2.5 is not.
Some major events are not captured by MODIS estimates.
PM 2.5 – MODIS AOD Correlations
• Correlations between PM2.5 and MODIS AOD are
generally high (> 0.55) for the warm season.• The lower correlation for MODIS-Aqua in 2002 is for July-September only.
MODIS-Terra MODIS-Aqua2000 --> 0.5792001 --> 0.6432002 --> 0.559 0.4012003 --> 0.661 0.727
April - SeptemberApril - September
0 20 40 60 8010Miles ®
PM2.5 Estimated from MODIS-Terra
Legend
PM2.5
Value
9-11
11-13
13-15
15-17
17-19
19-21
21-23
23-25
25-27
June 24, 2003
Data Linkage
3HealthData
MODISAQS
Linkage
LinkedData
Acute Asthma Visits
Aggregationemailemail
EHTB
NCEH
HELIX - Atlanta Team
EnvironData
EPA NASA
Quality Control Procedure for AQS PM2.5 data
Adapted from CHARM rain gauge network Eliminates anomalous measurements based on ‘Corroborative Neighbor Statistic’ Applied to all daily AQS PM2.5 measurements
before spatial surfaces are built
Suspicious value
Before After
H igh : 50 g/m 3
Low : 0 g/m 3
P M 2 .5 C oncentra tion
E P A s ites
H igh : 50 g/m 3
Low : 0 g/m 3
H igh : 50 g/m 3
Low : 0 g/m 3
P M 2 .5 C oncentra tion
E P A s ites
Spatial surfacing - AQS PM2.5 data
Algorithm adapted from CHARM rain gauge network
1st degree recursive B-spline in x- and y-directions
Daily surfaces created ona 10x10 km grid
Variable number ofmeasurements availableeach day
H igh : 50 g/m 3
Low : 0 g/m 3
P M 2 .5 C oncentra tion
E P A s ites
H igh : 50 g/m 3
Low : 0 g/m 3
H igh : 50 g/m 3
Low : 0 g/m 3
P M 2 .5 C oncentra tion
E P A s ites
Data-rich day
Cross-Validation
a.k.a. ‘bootstrapping’ or ‘omit-one’ analysis Objective: Estimate errors associated with daily spatial surfaces Procedure:
1. Omitting one observation, create surface using N-1 observations2. Compare value of surface at location of omitted observation with
the observed value3. Repeat for allobservations 4. Calculate errorstatistics by day or site
Cross-ValidationDaily Error Statistics
Bootstrap-Observed
0
1
2
3
4
5
6
7
8
9
10
1 26 51 76 101 126 151 176 201 226 251 276 301 326 3512003 Day of Year
RM
SD
(m
icro
gra
ms
/m3 )
RMSD = 2.7 g/m3
Bootstrap-Observed
0
1
2
3
4
5
6
7
8
9
10
235 362 254 250 320 93 282 207 242 337 171 77 63 322 3452003 Day of Year
RM
SD
(m
icro
gra
ms
/m3 )
Rank Order
Time Series
Cross-ValidationError Statistics by Site
Bootstrap-Observed
0
1
2
3
4
5
6
7
1 11 21 31 41 51 61 71 81 91 101 111 121 131 141Site Number
RM
SD
(m
icro
gra
ms
/m3 )
Bootstrap-Observed
0
1
2
3
4
5
6
7
71 72 5 4512513
8 29100 1514
013
7 1 14136 30 64 87 37 33 82 81 5910
1 35 11710
9 17102 89
Site Number
RM
SD
(m
icro
gra
ms/
m3 )
Rank Order
RMSD by Site
MODIS PM2.5 Bias Adjustment
High : 50 g/m 3
Low : 0 g/m 3
PM 2.5 Concentration
EPA sites
H igh : 50 g/m 3
Low : 0 g/m 3
High : 50 g/m 3
Low : 0 g/m 3
PM 2.5 Concentration
EPA sites
65 g/m3
0 g/m3
Assumption: AQS measurements are unbiased relative
to the local mean, but MODIS PM2.5 estimates may have biases. Prefer a ‘regional-scale’ bias adjustment (as opposed to local scale) Procedure:
1. Use a two-step B-spline algorithm to create highly smoothed
versions of the MODIS and AQS PM2.5 daily surface
2. Compute the ‘Bias’ as the difference between the smoothed fields
3. Subtract the bias from the MODIS PM2.5 daily surface to give the
‘bias-corrected’ MODIS daily surfaceSmooth AQSSmooth MODIS
MODIS Bias Legend
Bias
Value
High : 10.59
Low : -22.92
10.6 g/m3
-22.9 g/m3
Merging MODIS and AQS PM2.5 Data
MODIS and AQS data have been merged to produce
final PM2.5 surfaces. MODIS data are weighted lower than AQS. Weights derived through a simplified, time-invariant Kalman filter
approach have been derived and applied to the MODIS PM2.5 estimates.
AQS only
Unadjusted MODIS
Merged(weight = 0.1)
Bias-adjusted MODIS
Linkage of Environmental and Health Data
MembersLON LAT ID AGE GENDER YEAR/MO-84.207 99.200 1 Child M 200301-84.802 99.359 2 Adult M 200301-83.798 99.993 4 Child F 200301
Acute asthma doctor’s office visitsID AGE LON LAT GENDER DATE1811 Child -84.179 99.118 F 1/1/200354767 Adult -84.625 99.802 F 1/1/200384580 Adult -84.679 99.691 F 1/1/2003
Health Data Set
Linkage of Environmental and Health Data
PM2.5 for each visitDate ID Member Lat/Lon Cell Cell Lat/Lon County State Gender Age PM2.51 1 1811 99.572 -84.251 1944 99.552 -84.284 Coweta GA F Child 21.741 2 15299 99.063 -83.860 1608 99.104 -83.806 Upson GA F Child 12.79 1 2 15879 99.727 -84.369 2079 99.731 -84.403 Fulton GA M Child 12.21
Visit counts by grid cell Date Cell Lat Lon County State FC MC FA MA200301 1 99.045 -88.940 Perry MS 1 0 2 0200301 2 99.045 -88.821 Perry MS 0 0 0 0200301 3 99.045 -88.701 Greene MS 0 1 0 1
Data Linkage Outputs
Successes
Negotiated a Business Associate Agreement with a health care provider to enable sharing of Protected Health Information Developed algorithms for QC, bias removal, merging MODIS and AQS PM2.5 data, and others…
Proven the feasibility of linking environmental data (MODIS PM2.5 estimates) with health data (asthma)
Remaining Challenges
Build computer infrastructure to enable public health surveillance Identify and develop environment data sources from NASA or elsewhere that are better suited for public health surveillance Coordinate with state and local agencies to develop public health surveillance networks in their locales