# Presentation: 263320

## Use of Satellite Remote Sensing for Public Health Applications: The HELIX-Atlanta Experience

**Bill Crosson****Mohammad Al-Hamdan, Maury Estes, Ashutosh Limaye, Dale Quattrochi, Doug Rickman**
**NASA/MSFC/NSSTC**
**Partners:**
**Centers for Disease Control and Prevention**
**Kaiser-Permanente Georgia**
**U.S. Environmental Protection Agency**
**Georgia Environmental Protection Division**
**Georgia Division of Public Health**
**Emory University**
**Georgia Institute of Technology**

## HELIX-Atlanta Overview

** ****Health and Environment Linked for Information Exchange in Atlanta (HELIX-Atlanta) was developed to s****upport 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 ****PM****2.5**** with health data related to asthma**
**Goals:**** **
**Produce and share information on methods useful for integrating and analyzing data on asthma and PM****2.5**** for environmental public health surveillance.**** **
**Generate information and recommendations valuable to sustaining surveillance of asthma with PM****2.5**** in the Metro-Atlanta area.**
**Time period: 2003**
**Domain: 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 range**
**from 1 hr to 6 days**
**(daily meas. every 6****th**** day)**
** ****Three monitor types:**
**     ****Federal Reference**
**Method (FRM)**
**     ****Continuous**
**     ****Speciation**** **
** ****FRM is EPA-accepted**
**standard method;**
**processing time 4-6 weeks**

## AQS PM2.5 Data Examples

** ****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 Air**
**Quality Forecast Initiative to**
**produce air quality forecasts**
**for northeastern US;**
**forecasts for entire US in 2009**

## MODIS Aerosol Optical Depth (AOD) Data Examples

** ****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 PM****2.5**** data**
** ****Extract AOD data for AQS site locations**
** ****Calculate daily averages from hourly AQS PM****2.5**** data**
** ****Using daily PM****2.5**** averages from all 5 Atlanta AQS sites, determine statistical regression equations between PM****2.5**** and MODIS AOD**
** ****Apply regression equations to estimate PM****2.5**** for each 10 km grid cell across region**

## MODIS AOD - PM2.5 Relationship

** ****Daily 5-site means of observed ****PM****2.5**** and MODIS AOD**
** ****MODIS data not available every day due to cloud cover**
** ****MODIS AOD follows seasonal patterns of ****PM****2.5**** but not the day-to-day variability in fall and winter**

**2002**

**2003**

## Observed (AQS) and Predicted PM 2.5

** ****MODIS-based predictions follow seasonal PM****2.5**** patterns**
** ****MODIS AOD is nearly constant in fall and winter, while observed PM****2.5**** is not.  Some major events are not captured by MODIS estimates.**

## PM 2.5 – MODIS AOD Correlations

** ****Correlations between PM****2.5**** 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.**

**April - September**

## PM2.5 Estimated from MODIS-Terra

**PM****2.5**** Estimated from MODIS-Terra**

**June 24, 2003**

## Data Linkage

## 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 PM****2.5**** measurements before spatial surfaces are built**

## Spatial surfacing - AQS PM2.5 data

** ****Algorithm adapted from CHARM rain gauge network**
** ****1****st**** degree recursive B-**
**spline in x- and y-directions**
** ****Daily surfaces created on**
**a 10x10 km grid**
** ****Variable number of**
**measurements available**
**each day**

- Data-rich day

## Cross-Validation

** ****a.k.a. ‘bootstrapping’ or ‘omit-one’ analysis**
** ****Objective: Estimate errors associated with daily spatial surfaces**
** ****Procedure:**
** ****Omitting one observation, create surface using N-1 observations**
** ****Compare value of surface at location of omitted observation with the observed value**
** ****Repeat for all**
**observations **
** ****Calculate error**
**statistics by day or site****  **

## Cross-ValidationDaily Error Statistics

**RMSD = 2.7 ********g/m****3**

**Rank Order**

**Time Series**

## Cross-ValidationError Statistics by Site

**Rank Order**

**RMSD by Site**

## MODIS PM2.5 Bias Adjustment

**Assumption: AQS measurements are unbiased relative **
**to the local mean, but MODIS PM****2.5**** estimates may have biases.**
**Prefer a ‘regional-scale’ bias adjustment (as opposed to local scale) **
**Procedure:**
** ****Use a two-step B-spline algorithm to create highly smoothed versions of the MODIS and AQS PM****2.5**** daily surface**
** ****Compute the ‘Bias’ as the difference between the smoothed fields**
** ****Subtract the bias from the MODIS PM****2.5**** daily surface to give the ‘bias-corrected’ MODIS daily surface**

**Smooth AQS**

**Smooth MODIS**

**MODIS Bias**

## Merging MODIS and AQS PM2.5 Data

**MODIS and AQS data have been merged to produce**
**	****final PM****2.5**** 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 PM****2.5**** estimates.   **

**AQS only**

**Merged**
**(weight = 0.1)**

## Linkage of Environmental and Health Data

**Members**
**LON****	****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 visits**
**ID****	****AGE****	****LON****	****LAT****	****GENDER   DATE****	**
**1811****	****Child****	****-84.179****	****99.118****	****      F****	****  1/1/2003**
**54767****	****Adult****	****-84.625****	****99.802****	****      F****	****  1/1/2003**
**84580****	****Adult****	****-84.679****	****99.691****	****      F****	****  1/1/2003**

**Health Data Set**

## Linkage of Environmental and Health Data

**PM****2.5**** for each visit**
**Date    ID    Member Lat/Lon  Cell    Cell Lat/Lon    County State Gender Age   PM2.5**
**1  1    1811   99.572  -84.251   1944  99.552 -84.284  Coweta  GA      F       Child  21.74**
**1  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   MA**
**200301    1   99.045  -88.940  Perry       MS       1      0      2       0**
**200301    2   99.045  -88.821  Perry       MS       0      0      0       0**
**200301    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 PM****2.5**** data, and others...**
**  ****Proven the feasibility of linking environmental data (MODIS PM****2.5**** 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**