Using NASA Data and Models to Improve Heat Watch Warning Systems for Decision Support
Daniel Johnson/Wright State University Heat related death is currently the number one weather-related killer in the United States. Mortality from these events is expected to increase as a function of climate change. The proposed activity seeks to augment the current Heat Watch/Warning System (HWWS) with NASA instruments and models used in conjunction with socioeconomic and heat-related mortality data. This activity will enable the production of a more spatially specific warning for areas of risk within the cities, a current limitation of the HWWS. We plan to use the MODerate resolution Imaging Spectroradiometer (MODIS), Landsat Enhanced Thematic Mapper (ETM+), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and once available the Visible/Infrared Imager/Radiometer (VIIRS) system to model surface temperatures during extreme heat events in three U.S. cities. Modeling the thermal properties of each city, incorporating established socioeconomic factors of risk, and heat-related mortality data will enable us to reach this realization. The modeling approach will use logistic regression as well as artificial neural networks to enable us to fine tune a risk model and make it spatially and temporally extensible. We see the models developed as able to be implemented in other U.S. cities once the project is completed. The primary end users of this system will be the health departments of each city. These departments are primarily responsible for intervention during heat alerts. The evaluation of this proposed system will use phone surveys before and after implementation to determine effectiveness of the extension. We will additionally measure a baseline of performance of the current HWWS in each city and evaluate against this near the time of project completion. We anticipate developing a model of vulnerability, which will lead to a reduction of mortality during such extreme heat events, thus providing a direct societal need using NASA data and other geospatial assets.
Enhancing Environmental Public Health Tracking with Satellite-Driven Particle Exposure Modeling and Epidemiology
Yang Liu/Harvard School of Public Health Environmental epidemiological studies have linked exposure to ambient PM2.5 (airborne particles less than 2.5 micrometers in size) with adverse health effects such as reduced lung function, increased asthma incidence and heart attacks. Characterizing population exposures to PM2.5, therefore, has emerged as a major environmental health initiative. Satellite aerosol remote sensing may help expand the coverage of PM2.5 monitoring to rural and suburban areas not currently located near ground-monitoring networks. The proposed study will examine the use of satellite aerosol remote sensing as a potential means to extend the coverage of the National Environmental Public Health Tracking Network (Tracking Network) - a CDC decision-support system - and the utility of satellite-derived air quality estimates for use by the Air Pollution and Respiratory Health Branch (APRHB) at CDC. Specifically, using data from multiple NASA Earth sciences missions together with meteorology and land use information, this study aims at providing accurate, timely information on the temporal and spatial characteristics of PM2.5 concentrations through an advanced spatial modeling framework that can be used by CDC and its federal, state and local partners to support, and evaluate public health policy and practice related to health impacts of air pollution.
This study will consist of three research components. Component A will integrate aerosol retrievals from MODIS, GOES, MISR, and OMI, meteorology, land use information, and EPA PM2.5 measurements over the 20-county metropolitan Atlanta area between 2000 and 2007. Component B will develop a spatial statistical model using this database to estimate daily PM2.5 exposures, and generate daily concentration estimates for the model grid. These estimates will be compared to Tracking Network's two current methods of estimating PM2.5. Model predictions will also be validated prospectively. Component C will incorporate the validated estimates in the largest single-city, U.S. time-series epidemiologic analyses examining the association between PM2.5 and cardiorespiratory emergency department visits and comparing the results to those generated using CDC’s current PM2.5 exposure estimation methods. Together, this study comprises the most comprehensive effort to utilize satellite remote sensing information in a public health tracking context, while leveraging an existing extensive network of air quality researchers and data.
The anticipated results will include detailed analyses of the spatial/temporal patterns of PM2.5 pollution in the domain, a statistical evaluation of the advantages of estimating PM2.5 concentrations with this model as compared to methods already available, and an assessment of the potential benefit of including satellite observations to the Tracking Network and the APRHB. This project will help NASA to achieve its objectives of understanding and improving predictive capability for changes in air quality associated with changes in atmospheric composition, and expanding and accelerating the realization of societal benefits from Earth system science.
Linking NASA Environmental Data with a National Public Health Cohort Study to Enhance Public Health Decision Making
Leslie McClure/University of Alabama at Birmingham The study proposed herein has goals that are threefold: (1) characterize PM2.5, solar insolation and land surface temperature using NASA satellite observations, EPA ground level monitor data and North American Regional Reanalysis (NARR) data products on a national scale; (2) link these data with public health data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study and determine whether these environmental risk factors are related to cognitive decline; and (3) disseminate the environmental datasets and public health linkage analyses to end-users for decision making through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) system. CDC WONDER furthers CDC's mission of health promotion and disease prevention by speeding and simplifying access to public health information for state and local health departments, the Public Health Service, and the academic public health community. CDC WONDER is used in decision making, priority setting, program evaluation, public health research, and resource allocation.
This study directly addresses the public health focus of NASA’s Applied Sciences Program, utilizing NASA Earth Sciences products, by addressing issues of environmental health to enhance public health decision making. This study will address concerns expressed in the NRC Decadal Survey regarding the need for continued work to firmly establish the predictive relationships between remotely-sensed environmental data and patterns of environment-related health effects. In addition, the linkage of these data with the CDC WONDER system substantially expands public access to NASA data, making their use by a wide range of decision makers feasible. By successful completion of this research, decision-making activities, including policy making and clinical decision making, can be affected through utilization of the data products and analyses provided on the CDC WONDER system.
Incorporating Space-Borne Measurements to Improve Air Quality Decision Support Systems
Arastoo Pour Biazar/University of Alabama in Huntsville This project addresses the Air Quality Application Area, and its purpose is to improve the performance of the air quality management Decision Support Tools (DSTs) used in the State Implementation Plan (SIP) process for development and evaluation of emission controls under the provisions of the Clean Air Act. The specific DST targeted is the U.S. EPA’s developed CMAQ modeling system used by many State and Local air quality management agencies to determine future compliance with the National Ambient Air Quality Standards (NAAQS). Satellite data will be used to improve the quality and accuracy of retrospective baseline simulation in which proposed emission reductions are tested. Nationally, billions of dollars in emission reduction scenarios are tested using these DSTs. Thus, accuracy in the DST is critical to determining efficient cost effective strategies for attaining the NAAQS. Two critical areas in the DSS will be targeted for improvement. The first is in improving model location and timing of clouds. Clouds have a profound role in photolysis activity, boundary-layer development and deep vertical mixing of pollutants and precursors. Satellite products to be incorporated or used for evaluation are GOES Vis and IR, CLOUDSAT liquid water, and Aqua/Terra thermodynamic profiles. Also, a new NASA Lightning NO-production Model (LNOM) that accounts for lightning NO production in convective clouds will be tested. The second area targeted is in improving chemical transboundary and initial conditions in the air quality model. The satellite products include MODIS aerosol and newly available OMI ozone profiles that can significantly impact the realization of the chemical state of the atmosphere. The project will facilitate routine utilization of NASA satellite products in the DST. The applied partner in this project is EPA’s Atmospheric Modeling Division (AMD) at the National Environmental Research Laboratory (NERL). Additionally, Georgia and Texas will serve as test-beds for end users.
Universities Space Research Association
Celebrating 40 years of service to the space research community
Heat related death is currently the number one weather-related killer in the United States. Mortality from these events is expected to increase as a function of climate change. The proposed activity seeks to augment the current Heat Watch/Warning System (HWWS) with NASA instruments and models used in conjunction with socioeconomic and heat-related mortality data. This activity will enable the production of a more spatially specific warning for areas of risk within the cities, a current limitation of the HWWS. We plan to use the MODerate resolution Imaging Spectroradiometer (MODIS), Landsat Enhanced Thematic Mapper (ETM+), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and once available the Visible/Infrared Imager/Radiometer (VIIRS) system to model surface temperatures during extreme heat events in three U.S. cities. Modeling the thermal properties of each city, incorporating established socioeconomic factors of risk, and heat-related mortality data will enable us to reach this realization. The modeling approach will use logistic regression as well as artificial neural networks to enable us to fine tune a risk model and make it spatially and temporally extensible. We see the models developed as able to be implemented in other U.S. cities once the project is completed. The primary end users of this system will be the health departments of each city. These departments are primarily responsible for intervention during heat alerts. The evaluation of this proposed system will use phone surveys before and after implementation to determine effectiveness of the extension. We will additionally measure a baseline of performance of the current HWWS in each city and evaluate against this near the time of project completion. We anticipate developing a model of vulnerability, which will lead to a reduction of mortality during such extreme heat events, thus providing a direct societal need using NASA data and other geospatial assets.
Environmental epidemiological studies have linked exposure to ambient PM2.5 (airborne particles less than 2.5 micrometers in size) with adverse health effects such as reduced lung function, increased asthma incidence and heart attacks. Characterizing population exposures to PM2.5, therefore, has emerged as a major environmental health initiative. Satellite aerosol remote sensing may help expand the coverage of PM2.5 monitoring to rural and suburban areas not currently located near ground-monitoring networks. The proposed study will examine the use of satellite aerosol remote sensing as a potential means to extend the coverage of the National Environmental Public Health Tracking Network (Tracking Network) - a CDC decision-support system - and the utility of satellite-derived air quality estimates for use by the Air Pollution and Respiratory Health Branch (APRHB) at CDC. Specifically, using data from multiple NASA Earth sciences missions together with meteorology and land use information, this study aims at providing accurate, timely information on the temporal and spatial characteristics of PM2.5 concentrations through an advanced spatial modeling framework that can be used by CDC and its federal, state and local partners to support, and evaluate public health policy and practice related to health impacts of air pollution.
The study proposed herein has goals that are threefold: (1) characterize PM2.5, solar insolation and land surface temperature using NASA satellite observations, EPA ground level monitor data and North American Regional Reanalysis (NARR) data products on a national scale; (2) link these data with public health data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study and determine whether these environmental risk factors are related to cognitive decline; and (3) disseminate the environmental datasets and public health linkage analyses to end-users for decision making through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) system. CDC WONDER furthers CDC's mission of health promotion and disease prevention by speeding and simplifying access to public health information for state and local health departments, the Public Health Service, and the academic public health community. CDC WONDER is used in decision making, priority setting, program evaluation, public health research, and resource allocation.
This project addresses the Air Quality Application Area, and its purpose is to improve the performance of the air quality management Decision Support Tools (DSTs) used in the State Implementation Plan (SIP) process for development and evaluation of emission controls under the provisions of the Clean Air Act. The specific DST targeted is the U.S. EPA’s developed CMAQ modeling system used by many State and Local air quality management agencies to determine future compliance with the National Ambient Air Quality Standards (NAAQS). Satellite data will be used to improve the quality and accuracy of retrospective baseline simulation in which proposed emission reductions are tested. Nationally, billions of dollars in emission reduction scenarios are tested using these DSTs. Thus, accuracy in the DST is critical to determining efficient cost effective strategies for attaining the NAAQS. Two critical areas in the DSS will be targeted for improvement. The first is in improving model location and timing of clouds. Clouds have a profound role in photolysis activity, boundary-layer development and deep vertical mixing of pollutants and precursors. Satellite products to be incorporated or used for evaluation are GOES Vis and IR, CLOUDSAT liquid water, and Aqua/Terra thermodynamic profiles. Also, a new NASA Lightning NO-production Model (LNOM) that accounts for lightning NO production in convective clouds will be tested. The second area targeted is in improving chemical transboundary and initial conditions in the air quality model. The satellite products include MODIS aerosol and newly available OMI ozone profiles that can significantly impact the realization of the chemical state of the atmosphere. The project will facilitate routine utilization of NASA satellite products in the DST. The applied partner in this project is EPA’s Atmospheric Modeling Division (AMD) at the National Environmental Research Laboratory (NERL). Additionally, Georgia and Texas will serve as test-beds for end users.
