Development of novel detection and prediction algorithms for Microcystis blooms

开发微囊藻水华的新型检测和预测算法

• 批准号: 8387923
• 负责人: Timothy Moore
• 金额: $ 6.37万
• 依托单位: UNIVERSITY OF NEW HAMPSHIRE
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-24 至 2015-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8387923
• 关键词: Address; Algal Blooms; Algorithms; Animals; Coupled; Cyanobacterium; Data; Data Collection; Data Set; Databases; Detection; Development; Disease Outbreaks; Ecology; Economics; Event; Evolution; Fresh Water; Future; Goals; Great Lakes Region; Health; Health Sciences; Hot Spot; Human; Imagery; In Situ; Lead; Life; Maps; Measurement; Measures; Methods; Metric; Microcystis; Modeling; Monitor; Multivariate Analysis; Nutrient; Optics; Organism; Phytoplankton; Policies; Probability; Property; Public Health; Research; Research Personnel; Risk; Seasons; Shipping; Ships; Spatial Distribution; Stream; Surface; System; Technology; Time; Toxin; Water; Water Supply; Work; base; cost; drinking water; environmental health economics; experience; falls; harmful algal blooms; health economics; improved; indexing; innovation; instrumentation; novel; programs; public health relevance; remote sensing; skills; tool; toxic bloom; water quality

DESCRIPTION (provided by applicant) There are two main objectives to the proposed research: 1) to develop a new quantitative detection algorithm for Harmful Algal Blooms (HABs) in the Great Lakes based on remote sensing imagery by refining and augmenting currently qualitative algorithms; and 2) to develop an ecological niche model to predict the onset of HABs in the Great Lakes with inputs from remotely-sensed data products and state-of-the-art in-situ environmental measurements. Cyanobacteria-dominated HABs currently pose a serious health and economic risk to inhabitants and users of the Great Lakes, often producing toxins in sufficient quantity to cause severe health problems to humans and animals. The investigators' proposed research will benefit the human health and livelihood by providing tools to better manage the human health risks associated with HABs in the future based on a better understanding of the underlying ecology of HABs and improved synoptic detection methods. One component of the investigators' research will consist of implementing a monitoring program consisting of autonomous measurements - combined with ship measurements - of environmental parameters in western Lake Erie and Saginaw Bay critical to HAB initiation and promotion. These measurements will be used in the development of new satellite products that can provide synoptic observation of environmental signatures of these toxic algal blooms. The investigators will combine these new satellite products with other synoptic satellite environmental products and available in-situ environmental measurements to construct an historical data set that characterizes HAB events in the context of associated environmental conditions that cultivate the blooms. This data set will guide the development of an innovative ecological niche model based on multivariate analysis that will predict the spatial and temporal distribution of imminent HAB events from real-time environmental inputs. The investigators' team consists of seasoned oceanographers with many years of experience in instrumentation, bio-optics, remote sensing, phytoplankton and HAB ecology, and algorithm development, bringing a complimentary and comprehensive set of expertise to the problem of HAB detection and prediction. Their ultimate aim is to develop a critical set of tools for effectively managing human health risks associated with cyanobacteria blooms in water bodies around the globe. Public Health Relevance: Cyanobacteria HABs that occur in the Great Lakes can produce a wide range of toxins with severe health impacts on humans and animals, and also cause significant economic disruption. During HAB events, toxin concentration limits in the Great Lakes drinking water supply are often exceeded. The investigators' proposed research will improve current detection and prediction capabilities of HABs in the Great Lakes region, and will be directly beneficial to health and economic aspects of the many U.S. citizens who live in the region. The investigators bring a broad range of experience in remote sensing, in situ observational technology, and phytoplankton ecology that can help address the health and science issues related to toxic blooms in the Great Lakes.

描述（由申请人提供） 拟议的研究有两个主要目标：1）开发一种新的定量检测算法，以根据遥感图像来改进和增强当前定性算法，以根据遥感图像在大湖区中用于有害藻类开花（HAB）； 2）开发一个生态利基模型，以预测大湖区HAB的发作，并通过远程敏感的数据产品和最先进的原位环境测量结果进行投入。 目前以蓝细菌为主的HAB对大湖区的居民和使用者构成了严重的健康和经济风险，通常会产生足够数量的毒素，以便对人类和动物造成严重的健康问题。 调查人员的拟议研究将通过提供工具来更好地管理与HAB相关的人类健康风险，以更好地理解HAB的基本生态和改善的概要检测方法，从而使人类健康和生计受益。研究人员研究的一个组成部分将包括实施一项监测计划，该计划包括由自动尺寸的伊利湖和萨吉诺湾的环境参数组成的自动测量结果，对HAB开始和促进至关重要。这些测量将用于开发新的卫星产品，这些卫星产品可以提供对这些有毒藻类开花的环境特征的概要观察。 研究人员将将这些新的卫星产品与其他概要卫星环境产品和可用的原位环境测量相结合，以构建一个历史数据集，该数据集在培养盛开的相关环境条件下，构建了HAB事件的特征。该数据集将基于多元分析的创新生态利基模型的开发，该模型将预测实时环境输入中迫在眉睫的HAB事件的空间和时间分布。 调查人员的团队由经验丰富的海洋学家组成，具有多年的仪器，生物访问，遥感，浮游植物和HAB生态学以及算法开发的经验，为HAB检测和预测带来了一个免费和全面的专业知识。他们的最终目的是开发一组关键的工具，以有效地管理与全球水体中蓝细菌相关的人类健康风险。 公共卫生相关性：大湖区发生的蓝细菌HAB可能会产生广泛的毒素，对人类和动物产生严重的健康影响，并造成严重的经济破坏。在HAB事件中，大湖中的毒素浓度限制通常会超过饮用水供应。调查人员的拟议研究将改善大湖地区HAB的当前检测和预测能力，并将直接有利于居住在该地区的许多美国公民的健康和经济方面。 调查人员在遥感，原位观察技术和浮游植物生态学方面具有广泛的经验，这些经验可以帮助解决与大湖区有毒花朵有关的健康和科学问题。