Rising magnitude of disasters and vulnerability on a global scale The continued function and survival of any human society is dependent, to a significant degree, on its adaptability, resilience, and vulnerability to environmental events (
Young et al. 2006). In spite of our increased awareness of environmental hazards and risks, the human, ecologic, and economic costs of disasters continue to rise sharply worldwide. Over the last two decades, disasters including floods, drought, hurricanes, earthquakes, and tsunamis have affected vast regions and claimed nearly a million human lives, indirectly impacted a billion people, and generated damages in the trillions of dollars [
Nates and Moyer 2005;
U.K. Department for International Development (DFID) 2006]. The effects of these disasters on populations can be explained in part by a growing human footprint in the environment, particularly in vulnerable areas (e.g., low-lying coastal areas, flood plains, steep hillsides), which makes encountering risk and the possibility of exposure to environmental hazards more likely (
Nates and Moyer 2005). At the same time, global climate change is expected to increase the frequency and intensity of floods, hurricanes, and wildfires, and lead to sea level change (
Barnett et al. 2001;
Running 2006;
Westerling et al. 2006).
As globalization accelerates the rate and spatial scale of human–environment interactions, the distinction between natural and man-made disasters becomes blurred. So-called natural disasters are often exacerbated by anthropogenic factors. In the case of Hurricane Katrina, for instance, the development of and loss of low-lying wetlands () greatly diminished a natural ecosystem buffer that would have otherwise provided a greater degree of protection from the storm surge that killed many people and did severe damage to industrial facilities. The later led to a number of chemical spills that exacerbated the damage already caused by the hurricane.
The negative impact of environmental events can be reduced by good planning. For example, this is evident in New Orleans when we compare the impact of flooding in newer neighborhoods built on risk-prone, low-lying wetlands (i.e., New Orleans East) with older sections of the city that the Spanish and French built at safer higher elevations. Similarly, the operation of industrial facilities near densely populated areas can lead to increased hazards. This is exemplified by the Murphy Oil spill in the St. Bernard Parish of New Orleans, where a storage tank at the Meraux Murphy Oil Refinery, adjacent to a heavily populated area, was damaged by flood waters and spilled an estimated 1.05 million gallons of crude oil affecting some 1,800 homes (
Pezzoli et al. 2006).
Poverty-stricken communities experience the most severe social and economic impacts of disasters, often with long-lasting and devastating effects (
Daniels et al. 2006;
Dilley et al. 2005;
U.K. DFID 2006). The Lower Ninth Ward, a poverty-stricken part of New Orleans, was utterly devastated (). Disasters highlight the interdependence of communities at local, regional, and global scales as well as the importance of building disaster response systems that are flexible, adaptable, and resilient (
Daniels et al. 2006).
New modes of knowledge production and networking In recent years, information and communications technology experts, in partnership with researchers, have built sophisticated virtual environments that are enabling multidisciplinary and geographically distributed teams to work together more effectively. This includes sharing resources and knowledge and responding to grand challenges, such as those consequent to the hurricanes and floods that devastated the Gulf Coast in 2005. New modes of knowledge production and networking are exemplified by e-science and cyberinfrastructure that involve collaborative use of distributed high-speed networks, high-performance supercomputing, and massive storage to tackle the processing and storage-intensive questions of modern scientific research (
Buetow 2005;
Hey and Trefethen 2005;
Lin et al. 2005a;
Mattes et al. 2004). In the same way that early bioinformatics played a crucial role in the completion of the Human Genome Project, the next phase, the Human Proteonome Folding Project, is now using grid-based technologies to elucidate the structure and function of all the proteins encoded by the Human Genome. This grid-based approach promises to provide valuable knowledge for understanding and curing disease. Computing that takes advantage of grid technology can shorten computer time by many orders of magnitude.
The European Council for Nuclear Research (CERN) is one of the world’s leaders in grid development.
CERN (2006) defined the grid in these terms:
Whereas the Web is a service for sharing information over the Internet, the Grid is a service for sharing computer power and data storage capacity over the Internet. The Grid goes well beyond simple communication between computers and aims ultimately to turn the global network of computers into one vast computational resource.
One of the best developed examples of grid-based cyberinfrastructure is the Telescience model utilized by the NIEHS Portal. Through collaborative arrangements agreed upon by those developing and using the resource, the Telescience model integrates distributed ultra–high speed networks, supercomputing capacity, and access to massive storage and databases with applications, virtual collaboration spaces, advanced visualization, and remote control of laboratory instruments and field sensors with advanced authentication and authorization features (
Lathers et al. 2006;
Lin et al. 2005b). This kind of collaborative cyberinfrastructure is essential to build capacity for data mining and data sharing in the environmental health sciences.
Another significant development involves “ontologies”—a kind of knowledge map to facilitate data organization and mining for certain predefined purposes (
Noy and Musen 2003;
Rubin et al. 2006;
Stoeckert et al. 2006). Ontologies or knowledge maps that include formal definitions of terms and their relationship to one another can help to distinguish between irrelevant information and information of interest, while also addressing important issues of communication among members of multidisciplinary science teams (
Martone et al. 2004). The NIEHS Portal aims to facilitate, among other things, knowledge networking within communities by creating task- and domain-specific ontologies that can be used for applied purposes. The development of such ontologies must be participatory in so far as it requires a common language that cuts across disciplines and professional boundaries (
Spivey 2004).
Assembling and implementing the portal technology The portal’s infrastructure was assembled using components developed within the University of California, San Diego Telescience project (
Lin et al. 2005b). The portal-based GIS application is built over a collection of geo-referenced data layers using Microsoft-based ESRI ArcIMS (ESRI, Redlands, CA), ERMapper Image Web Server (ERMapper, San Diego, CA), and open-source solutions for managing spatial databases and large data grid storage space as the core software (). In addition, street data are served via a dedicated web mapping service, whereas navigation support is provided via Inxight’s Vizserver (Inxight Software, Inc, Sunnyvale, CA). The user–interface components, including an online map viewer and the StarTree hyperbolic tree visualization application (Inxight Sotware, Inc.), are integrated in a GridSphere-based portal (gridsphere.org), which also supports a variety of browse, query, and collaboration tools (Inxight Software, Inc.). A detailed discussion of the system architecture, specifications, and function is presented by
Zaslavsky et al. (2006).
Within the main portal environment, a GIS application manages the large collection of geo-referenced data. Standard components of the NIEHS Portal also enable local decision makers and communities to contribute data, participate in discussions, and maintain research and collaboration workspaces. The online GIS, a core component of the NIEHS Portal, assembles spatially registered data for Texas, Louisiana, and Mississippi, with higher resolution layers available for areas affected by the two most severe 2005 hurricanes, Katrina and Rita (). The information is extracted from publicly available sources and organized in the following data categories:
- Post-hurricane project-related data
- Potential contaminant sources: U.S. EPA TRI facilities (hazardous air pollutants, sources of carcinogens, metals, persistent bioaccumulative toxic substances, and other organic compounds); multiple types of industrial and agricultural facilities, oil and gas facilities (including gas stations, oil and gas wells and platforms, refineries, storage facilities, lube plants, and pipelines) and wastewater treatment plants, among others
- Electric facilities and drinking water intakes
- Hurricane damage: Katrina and Rita damage for all available dates (including impassable bridges and roads, and different degrees of damage), extent of oil spills and water contamination, extent and duration of flooding, debris, and estimated replacement costs
- Demographic data: racial composition and income stratification by census tracts and block groups
- Base map layers: states, counties, and cities; urban areas, streets and landmarks, hydrologic layers, and elevation
- Imagery layers: 1-m aerial photography for New Orleans taken in September 2005, 15-m Terracolor imagery, 1-m Terraserver aerial photographs.
Each of the vector layers—that is, data in the form of points, lines, and polygons—can be downloaded from the portal along with metadata (i.e., details about the data itself, such as who collected it, over what time period, etc.). Although the NIEHS Portal has the potential to bring forward crucial time-sensitive information that might not otherwise be readily available (e.g., types and quantities of hazardous materials generated or stored at an industrial facility), decision makers will still have to rely on trained and experienced professionals to analyze and interpret those data and make intelligent and informed management decisions. There are dangers associated with the misuse of data—a reality facing many information systems. The NIEHS Portal takes this fact into account by including security measures and authentication protocols. Other features of the portal, including clearly documented metadata, are designed to help reduce and manage the risk of data misuse.
Users can navigate spatial layers and explore layer metadata using StarTree hyperbolic tree software. In addition to metadata management, the current version of the GridSphere-based online mapping environment provides a range of map navigation, address geocoding, and query and spatial selection tools, including Web Portal, which provides a means for local decision makers and communities to contribute data, participate in discussions, and maintain research and collaboration workspaces. As an example of online-mapping functionality available through the public section of the portal, shows a screen capture of a workspace in which a user selected TRI facilities within 1 mile of drinking water intakes. Password-protected sections of the portal may provide access to additional resources and custom functions. These secure areas of the portal help users collaborate in specific projects.
The NIEHS Portal is a work in progress; it aims to engage users and communities that need to share important information pertinent to an environmental disaster. Customized research environments are identified and developed according to two models: a) project-based (the portal team is enlisted by prospective end users to provide information technology support for a particular project); and b) field of research [through conversations with multiple teams of investigators, the portal team provides data, knowledge, and tools for a select field of research (e.g., basic and applied studies concerning contaminated sediments)].
In the first model, the portal team is expected to a) create a separate space on the portal for the selected project; b) sculpt the particular project’s user interface to include only the portal’s most useful existing data layers (i.e., avoid clutter by eliminating layers deemed nonessential); c) do some original data preparation/packaging to meet the needs of the particular project; and d) provide tools for secure data archiving, sharing, integration, visualization, and analysis. In this model, the portal team becomes (among other things) a service provider helping users meet particular needs (e.g., the need to select a study site).
As an example of the first model of portal applications, the portal team has developed resources to support and is communicating with the Head-Off Environmental Asthma in Louisiana (HEAL) research project. The floods caused by Hurricanes Katrina and Rita left many homes in the Gulf Region inundated, leading to prolific mold growth [according to the Centers for Disease Control and Prevention (CDC), almost 46% of homes inspected had mold growth]. The degradation of indoor/outdoor air quality and disruption of the health care delivery system had significant effects on children living with asthma, which is already the most common chronic disease among children. The goal of the HEAL section of the portal is to support an epidemiologic study and the examination of genetic and environmental factors related to asthma, focusing on the implementation of a case management program for children with asthma in the impacted region. GIS support for the HEAL initiative can help investigators and other stakeholders gauge levels of environmental risk associated with schools that re-opened after the hurricane. GIS helps in this type of field evaluation, given the need to integrate multiple sources of information including data about the populations and households served by those schools, flooding depth and duration, potential environmental exposures, proximity to industrial facilities, proximity to roadways, and sociodemographic data ().
illustrates a printable report for a given point location that specifies the location of each school vis-à-vis objects in several other selected layers (e.g., potential contaminant sources, debris sites, duration of flooding). Available metadata at the “feature level” include many more parameters beyond those shown in . It is an extensive collection that also includes facility identification information that is available in the original database. Metadata at the “layer level” contains a link to the original data source. In the example shown in , there are nine feature-level metadata characteristics for debris sites around a particular school. The debris site shapefile, downloadable from the portal, contains 35 fields, including the purpose and operations at the debris site, its size, land use and ownership, and detailed information on what specific activities or items are permitted on the site. The generation of metadata reports of this sort is not limited to schools. Metadata reports can be created on an as-needed basis by the user for a wide range of particular locations.
In the second model, the portal team is expected to: a) create a separate space on the portal for a strategically selected field or domain of research—with a focus, initially, on one geographic area/hotspot of concern; b) make the data accessible (while respecting confidentiality, security, intellectual property rights, etc.) to those who want to use it by putting it on the portal’s own servers and/or by enabling access to it via a participant’s external server through federated (collaborative grid-enabled) arrangements; and c) provide tools for secure data archiving, sharing, integration, visualization, and analysis. The emphasis in this second model is to enable a network of scientists to collectively develop a data grid in a mutually defined area of concentration (i.e., field or domain, as opposed to particular project) that would be beyond the capacity of any one group to build and maintain on their own. This data grid would then become a collective resource for multiple and diverse projects.
As an example of the second model of portal applications, the portal team is developing a customized environment to support basic and applied studies of contaminated sediments. Since early September 2005, the U.S. EPA has collected approximately 1,800 sediment and soil samples from over 430 sites in Jefferson, Orleans, Plaquemines, and St. Bernard Parishes (parishes that were flooded with > 3 m of water from Lake Pontchartrain and the Mississippi River/Gulf of Mexico outlet). Most of the samples, as noted in the U.S. EPA’s most recent findings on this matter (
U.S. EPA 2006), were analyzed for > 200 metals and organic chemicals. The U.S. EPA collaborated with many agencies in this process, including the Louisiana Department of Environmental Quality, the CDC, the Agency for Toxic Substances and Disease Registry, the Louisiana Department of Health and Hospitals, and FEMA. According to the U.S. EPA’s final report (
U.S. EPA 2006),
The sample results indicate that, in general, the sediments left behind by the flooding from the hurricanes are not expected to cause adverse health impacts to individuals returning to New Orleans.
However, the same report indicates that hot spots of concern do exist. Some of the sites sampled showed elevated levels of arsenic, lead, benzo[a]pyrene, and diesel and oil range organic petroleum chemicals. Besides the U.S. EPA, other groups including environmental non-governmental organizations, have also collected contamination data and have expressed strong environmental health concerns. To support integration of sediment sampling data from different sources with hurricane damage and potential exposure routes, the portal hosts the “sediments” research space and an online GIS. The system is being designed to enable analysis of spatial patterns in contaminated sediments and help prioritize selection of additional sampling sites, as well as to provide relevant information for risk assessment, risk communication, and remediation. Both of the examples cited above exemplify the flexibility of the NIEHS Portal.