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|Created:||Dec 11, 2017 at 10:58 p.m.|
|Last updated:|| Feb 20, 2018 at 1:09 a.m.
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Recovery efforts from natural disasters can be more efficient with data-driven information on current needs and future risks. We aim to advance open-source software infrastructure to support scientific investigation and data-driven decision making with a prototype system using a water quality assessment developed to investigate post-Hurricane Maria drinking water contamination in Puerto Rico. The widespread disruption of water treatment processes and uncertain drinking water quality within distribution systems in Puerto Rico poses risk to human health. However, there is no existing digital infrastructure to scientifically determine the impacts of the hurricane. After every natural disaster, it is difficult to answer elementary questions on how to provide high quality water supplies and health services. This project will archive and make accessible data on environmental variables unique to Puerto Rico, damage caused by Hurricane Maria, and will begin to address time sensitive needs of citizens. The initial focus is to work directly with public utilities to collect and archive samples of biological and inorganic drinking water quality. Our goal is to advance understanding of how the severity of a hazard to human health (e.g., no access to safe culinary water) is related to the sophistication, connectivity, and operations of the physical and related digital infrastructure systems. By rapidly collecting data in the early stages of recovery, we will test the design of an integrated cyberinfrastructure system to for usability of environmental and health data to understand the impacts from natural disasters. We will test and stress the CUAHSI HydroShare data publication mechanisms and capabilities to (1) assess the spatial and temporal presence of waterborne pathogens in public water systems impacted by a natural disaster, (2) demonstrate usability of HydroShare as a clearinghouse to centralize selected datasets related to Hurricane Maria, and (3) develop a prototype cyberinfrastructure to assess environmental conditions and public health impacted by natural disasters. The project thus serves to not only document post-disaster conditions, but develops a process to track the impact of recovery over time, as monitored through health, power availability and water quality.
PLAIN LANGUAGE SUMMARY
There is an urgent need to understand the impacts of infrastructure damage on public health after natural disasters. One limitation to effective disaster response is easy and rapid access to diverse information about available resources and maps of community resource needs and risks. We aim to expand access to diverse datasets useful for understanding disaster related environmental conditions, with a focus on drinking water quality information. The research products will be made publicly available using a collaborative, online sharing platform – HydroShare. Curating a central repository of assembled data has the potential to greatly facilitate coordinated disaster responses of all types, with opportunities to improve the monitoring of the recovery process. We will prototype this system with an assessment of drinking water, environmental, and public health concerns unique to Puerto Rico in the aftermath of Hurricane Maria. By working directly with public water utilities, we intend to characterize and map the severity of impaired water resources and distribution systems in Puerto Rico. Developing cyber and social infrastructure to understand the dynamics of drinking water contamination after natural disasters will improve disaster preparedness and response, and contribute to efforts across the nation and the world to build for a resilient future.
Poster presented at AGU Fall Meeting New Orleans Ernest N. Morial Convention Center
Session: NH23E Late-Breaking Research Related to the 2017 Hurricane Season in the Americas (Harvey, Irma, Jose, Maria): Poster Contributions
Program: Natural Hazards
Day: Tuesday, 12 December 2017
This resource was created using funding from the following sources:
|Agency Name||Award Title||Award Number|
|National Science Foundation||OAC 1810886|
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This resource is shared under the Creative Commons Attribution CC BY.http://creativecommons.org/licenses/by/4.0/