Integrating in-situ detection technologies and developing data assimilation strategies to improve forecast accuracy and assess climate change impacts for Microcystis blooms in Lake Erie

整合原位检测技术并制定数据同化策略，以提高预测准确性并评估气候变化对伊利湖微囊藻水华的影响

• 批准号: 9976544
• 负责人: Thomas Bridgeman
• 金额: $ 3.46万
• 依托单位: BOWLING GREEN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-30 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9976544
• 关键词: Address; Adsorption; Anabolism; Anaerobic Bacteria; Assimilations; Biological; Biomass; Climate; Collaborations; Communities; Consumption; Data; Databases; Detection; Development; Development Plans; Devices; Event; Frequencies; Fresh Water; Gene Expression; Genes; Genetic; Goals; Great Lakes Region; Growth; Health; Human; In Situ; Lead; Location; Measurement; Methods; Microcystis; Modeling; Molecular; Monitor; Municipalities; Natural Resources; Nutrient; Oceans; Output; Oxygen; Particulate; Phase; Phosphorus; Physiologic pulse; Property; Public Health; Research Personnel; Research Priority; Resolution; Sampling; Scientist; Severities; Solid; Structure; System; Technology; Temperature; Time; Toxic effect; Toxin; Water; Work; anthropogenesis; base; biophysical model; climate change; climate impact; data streams; drinking water; experimental study; harmful algal blooms; improved; improved outcome; land use; local economy; microcystin; novel; portability; predictive modeling; programs; remote sensing; response; sensor; spatiotemporal; toxic bloom; trend; water quality; water testing; web page

Abstract/Project Summary Cyanobacterial harmful algal blooms (cHABs) have become more frequent and intense over the past few decades and are projected to continue to increase in severity and toxicity due to a warming climate and anthropogenically-enhanced nutrient loading. As such, detecting and monitoring cHAB development and toxicity are of growing importance, especially for freshwater systems such as the Laurentian Great Lakes that supply drinking water to many municipalities. Traditional sampling and analysis methods are time-consuming, labor intensive, and generally implemented on only a weekly or bi-weekly basis, which may fail to detect ephemeral yet highly toxic bloom events. Fortunately, novel, fit-for-purpose detection technologies are becoming available to address previous constraints by providing near-real time data. This project directly addresses four research priorities listed in the COHH3 RFA: (1) compare and correlate current observing systems for monitoring ocean and Great Lakes properties including Harmful Algal Blooms, (2) evaluate long-term field application potential of newly developing in situ sensors for monitoring ocean and Great Lakes properties, (3) evaluate real-time, in-water observations of physicochemical properties, as well as the detection of HAB species and toxins, to provide data streams for assimilation by predictive models, (4) develop appropriate and efficient monitoring strategies for algal toxins (particularly in drinking water) that are protective of public health. The specific aims of the proposed project are to integrate in-situ sensing and sampling technologies with data assimilation strategies to improve forecast accuracy, provide regional stakeholders with advanced warning of cHAB development and toxic events, and evaluate the impacts of climate change on cHABs and internal phosphorus loading in Lake Erie. We will accomplish these aims by integrating an autonomous, in-situ Environmental Sample Processor, Solid Phase Adsorption Toxin Tracking devices, water quality probes, and field-portable sampling methods, along with satellite remote sensing with the broader outcome of improving bloom forecasting models and to develop a more timely and complete spatio-temporal picture of developing cHAB toxicity and biomass as well as internal phosphorus loading in Lake Erie. Collectively, GLERL's long-term water quality monitoring and NOAA's advanced cHAB forecasting model (HAB tracker), which integrates satellite data, physicochemical, biological, molecular, and toxicity (this project) data to forecast bloom location, size and toxicity with a 5-day lead time, will facilitate informed, timely decisions to reduce the impacts of toxic cHABs on public health, natural resources, and local economies. Project outputs will also contribute to the Center Program's goal of better understanding the influence of climate change on the frequency and severity of cHABs in Lake Erie and other Great Lakes' regions, and thereby inform long-term planning for development of land use as well as management and mitigation strategies.

摘要/项目摘要 在过去的几个 数十年，预计由于气候变暖和 人为增强的营养负荷。因此，检测和监视CHAB的开发以及 毒性越来越重要，尤其是对于诸如laurentian大湖泊等淡水系统 向许多市政当局提供饮用水。传统的抽样和分析方法是耗时的， 劳动密集型，通常仅每周或每两周实施，这可能无法检测到 短暂而有毒的花朵事件。幸运的是，新颖的，适合用途的检测技术是 通过提供近乎真实的时间数据来解决以前的约束。 该项目直接解决了COHH3 RFA中列出的四个研究优先级：（1）比较和比较和 将目前的观察系统与监测海洋和大湖泊特性（包括有害藻类）相关联 Blooms，（2）评估新开发的原位传感器以监测的长期现场应用潜力 海洋和大湖区的特性，（3）评估物理化学的实时，水中观察 特性以及HAB物种和毒素的检测，以提供数据流以通过 预测模型，（4）为藻类毒素制定适当有效的监测策略（尤其是在 饮用水）保护公共卫生。拟议项目的具体目的是 将原位感测和采样技术与数据同化策略相结合 预测准确性，向区域利益相关者提供有关CHAB开发的高级警告和 有毒事件，并评估气候变化对CHAB和内部磷的影响 在伊利湖。我们将通过整合一个自主，原位环境样本来实现这些目标 处理器，固相吸附毒素跟踪设备，水质探针和现场携带的采样 方法，以及卫星遥感以及改进开花预测模型的更广泛结果 并开发出开发CHAB毒性和生物量的更及时，更完整的时空图片 以及伊利湖的内部磷载荷。总体而言，格莱尔的长期水质监测 以及NOAA的高级CHAB预测模型（HAB Tracker），该模型集成了卫星数据， 物理化学，生物学，分子和毒性（此项目）数据，以预测布鲁姆的位置，大小和 有5天的交货时间的毒性将有助于及时，及时的决定，以减少有毒的CHAB的影响 公共卫生，自然资源和地方经济。项目输出也将为中心做出贡献 计划的目标是更好地理解气候变化对频率和严重性的影响 伊利湖和其他大湖地区的CHAB，从而为长期计划提供了开发 土地使用以及管理和缓解策略。