This project is comprised of two main components; a predictive cyanobacteria modeling capability and an Android application.
The first component is the predictive cyanobacteria modeling capability (which will be an algorithm) and will provide the ability to forecast the status of cyanobacteria bloom events corresponding to 7, 14 and 28 day intervals. The second component is the development of an Android application. The Android application will display water quality/color data and the predictive modeling outputs for cyanobacteria predictions in a visually appealing manner.
High Level Requirements
Predictive Cyanobacteria Modeling Capability
Algal bloom at Grand Lake St. Mary's, Ohio, 2010. Photo by Russ Gibson, Ohio EPA
Develop a predictive model (algorithm) based on the best available science to forecast the location of Cyano bloom events up to 30-days in advance of the previous available satellite observation. The model would be based on the best and most appropriate scientific information available to predict the growth and movement of Cyano blooms. It’s anticipated that the model would incorporate ancillary data sets in near-real time to provide a robust predictive capability (e.g., Landsat-derived thermal data, meteorological predictions, etc.).
Android Mobile Application
The mobile application will be developed for Android (version 2.3.3 and above). The targeted users in priority of importance for the application are
- Primary “local decision makers”
- Secondary “federal policy makers”
- The “scientific community.”
The Android application must include the following functions:
- Main splash page will have a Google Earth (default) or Google Map (option) image of the U.S. with highlighted regions of the country and local areas populated with data indicated.
- User would be able to zoom in to view most recent cyano categorical image (default product).
- User will be able to switch between cyano, chlorophyll, turbidity, and cyano model predictions (5, 10 and 30 days) using tabs. Fabricated model predictions would be used by the developer to demonstrate application functionality.
- User will be able to switch between data collected on different days within the tab using a drop down menu. The drop down menu will only have the dates for available scenes.
- Ability to select a location either by pointing to a location with the mouse, or typing a location with decimal degree coordinates.
- Once the location is selected the user will have the option to:
- View all derived product values from all previous images on a point and line graph (similar to tracking a stock price).
- The X-axis is the date of the image and y-axis is the value of the product (Cyano=cells/mL, Cyano=mm3/L, Chl-a=mg m-3, and Turbidity=mg L-1).
- New imagery update pushes
- Modeling prediction updates from the Predictive Cyanobacteria Modeling Capability mentioned above.
Algae are natural components of marine and fresh water flora performing many roles that are vital for the health of ecosystems. However, excessive growth of algae becomes a nuisance to users of water bodies for recreation activities and to drinking water providers. Excessively dense algal growth could alter the quantity and quality of light in the water column. Some types of algae may also cause harm through the release of toxins. When conditions like light availability, warm weather, low turbulence and high nutrient levels are favorable, algae can rapidly multiply causing “blooms.” When blooms (or dense surface scums) are formed, the risk of toxin contamination of surface waters increases especially for some species of algae with the ability to produce toxins and other noxious chemicals. These are known as harmful algal blooms (HABs).
To learn more about Cyanobacteria, please go to www.epa.gov, which provides the following resources:
- Health and Ecological Effects
- Causes, Prevention and Mitigation
- Policies and Guidelines
- Links to State Information
- More Information
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