Phytoplankton blooms create harmful toxins, scums, and taste and odor compounds and thus pose a major risk to drinking water safety. Climate and land use change are increasing the frequency and severity of blooms, motivating the development of new approaches for preemptive, rather than reactive, water management. While several real-time phytoplankton forecasts have been developed to date, none are both automated and quantify uncertainty in their predictions, which is critical for manager use. In response to this need, we outline a framework for developing the first automated, real-time lake phytoplankton forecasting system that quantifies uncertainty, thereby enabling managers to adapt operations and mitigate blooms. Implementation of this system calls for new, integrated ecosystem and statistical models; automated cyberinfrastructure; effective decision support tools; and training for forecasters and decision makers. We provide a research agenda for the creation of this system, as well as recommendations for developing real-time phytoplankton forecasts to support management.
- NSF-PAR ID:
- 10520024
- Author(s) / Creator(s):
- ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; more »
- Publisher / Repository:
- American Shore and Beach Preservation Association
- Date Published:
- Journal Name:
- Shore & Beach
- ISSN:
- 0037-4237
- Page Range / eLocation ID:
- 64 to 91
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract -
more » « less
Abstract Water pollution has become a major concern in recent years, affecting over 2 billion people worldwide, according to UNESCO. This pollution can occur by either naturally, such as algal blooms, or man‐made when toxic substances are released into water bodies like lakes, rivers, springs, and oceans. To address this issue and monitor surface‐level water pollution in local water bodies, an informative real‐time vision‐based surveillance system has been developed in conjunction with large language models (LLMs). This system has an integrated camera connected to a Raspberry Pi for processing input frames and is further linked to LLMs for generating contextual information regarding the type, causes, and impact of pollutants on both human health and the environment. This multi‐model setup enables local authorities to monitor water pollution and take necessary steps to mitigate it. To train the vision model, seven major types of pollutants found in water bodies like algal bloom, synthetic foams, dead fishes, oil spills, wooden logs, industrial waste run‐offs, and trashes were used for achieving accurate detection. ChatGPT API has been integrated with the model to generate contextual information about pollution detected. Thus, the multi‐model system can conduct surveillance over water bodies and autonomously alert local authorities to take immediate action, eliminating the need for human intervention.
Practitioner Points Combines cameras and LLMs with Raspberry Pi for processing and generating pollutant information.
Uses YOLOv5 to detect algal blooms, synthetic foams, dead fish, oil spills, and industrial waste.
Supports various modules and environments, including drones and mobile apps for broad monitoring.
Educates on environmental healthand alerts authorities about water pollution.
-
Abstract Phytoplankton blooms in coastal oceans can be beneficial to coastal fisheries production and ecosystem function, but can also cause major environmental problems 1,2 —yet detailed characterizations of bloom incidence and distribution are not available worldwide. Here we map daily marine coastal algal blooms between 2003 and 2020 using global satellite observations at 1-km spatial resolution. We found that algal blooms occurred in 126 out of the 153 coastal countries examined. Globally, the spatial extent (+13.2%) and frequency (+59.2%) of blooms increased significantly ( P < 0.05) over the study period, whereas blooms weakened in tropical and subtropical areas of the Northern Hemisphere. We documented the relationship between the bloom trends and ocean circulation, and identified the stimulatory effects of recent increases in sea surface temperature. Our compilation of daily mapped coastal phytoplankton blooms provides the basis for global assessments of bloom risks and benefits, and for the formulation or evaluation of management or policy actions.more » « less
-
more » « less
Abstract Harmful algal and bacterial blooms linked to deforestation, soil loss and global warming are increasingly frequent in lakes and rivers. We demonstrate that climate changes and deforestation can drive recurrent microbial blooms, inhibiting the recovery of freshwater ecosystems for hundreds of millennia. From the stratigraphic successions of the Sydney Basin, Australia, our fossil, sedimentary and geochemical data reveal bloom events following forest ecosystem collapse during the most severe mass extinction in Earth’s history, the end-Permian event (EPE; c. 252.2 Ma). Microbial communities proliferated in lowland fresh and brackish waterbodies, with algal concentrations typical of modern blooms. These initiated before any trace of post-extinction recovery vegetation but recurred episodically for >100 kyrs. During the following 3 Myrs, algae and bacteria thrived within short-lived, poorly-oxygenated, and likely toxic lakes and rivers. Comparisons to global deep-time records indicate that microbial blooms are persistent freshwater ecological stressors during warming-driven extinction events.
-
Aquatic ecosystems are increasingly threatened by multiple human-induced stressors associated with climate and anthropogenic changes, including warming, nutrient pollution, harmful algal blooms, hypoxia, and changes in CO 2 and pH. These stressors may affect systems additively and synergistically but may also counteract each other. The resultant ecosystem changes occur rapidly, affecting both biotic and abiotic components and their interactions. Moreover, the complexity of interactions increases as one ascends the food web due to differing sensitivities and exposures among life stages and associated species interactions, such as competition and predation. There is also a need to further understand nontraditional food web interactions, such as mixotrophy, which is the ability to combine photosynthesis and feeding by a single organism. The complexity of these interactions and nontraditional food webs presents challenges to ecosystem modeling and management. Developing ecological models to understand multistressor effects is further challenged by the lack of sufficient data on the effects of interactive stressors across different trophic levels and the substantial variability in climate changes on regional scales. To obtain data on a broad suite of interactions, a nested set of experiments can be employed. Modular, coupled, multitrophic level models will provide the flexibility to explore the additive, amplified, propagated, antagonistic, and/or reduced effects that can emerge from the interactions of multiple stressors. Here, the stressors associated with eutrophication and climate change are reviewed, and then example systems from around the world are used to illustrate their complexity and how model scenarios can be used to examine potential future changes.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.34237/1009018
