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 在过去的几年中，蓝藻有害藻华（cHAB）变得更加频繁和严重 几十年来，由于气候变暖，预计其严重性和毒性将继续增加 人为增强的养分负荷。因此，检测和监测 cHAB 的发展和 毒性变得越来越重要，特别是对于淡水系统，例如劳伦森五大湖 为许多城市提供饮用水。传统的采样和分析方法耗时、 劳动密集型，一般仅每周或每两周实施一次，可能无法检测到 短暂但剧毒的水华事件。幸运的是，新颖的、适合用途的检测技术正在出现。 通过提供近乎实时的数据来解决以前的限制。 该项目直接解决 COHH3 RFA 中列出的四个研究重点：(1) 比较和 关联当前监测海洋和五大湖特性（包括有害藻类）的观测系统 Blooms，（2）评估新开发的原位监测传感器的长期现场应用潜力 海洋和五大湖的特性，(3) 评估物理化学的实时水下观测 特性，以及 HAB 种类和毒素的检测，为同化提供数据流 预测模型，(4) 制定适当且有效的藻类毒素监测策略（特别是在 饮用水），保护公众健康。拟议项目的具体目标是 将原位传感和采样技术与数据同化策略相结合，以改进 预测准确性，为区域利益相关者提供 CHAB 发展的预警 有毒事件，并评估气候变化对 CHAB 和内部磷负荷的影响 在伊利湖。我们将通过整合自主的原位环境样本来实现这些目标 处理器、固相吸附毒素跟踪装置、水质探头和现场便携式采样 方法，以及卫星遥感，以及改进水华预测模型的更广泛成果 并绘制出更及时、更完整的 cHAB 毒性和生物量发展的时空图 以及伊利湖的内部磷负荷。总的来说，GLERL 的长期水质监测 以及 NOAA 先进的 cHAB 预报模型（HAB 跟踪器），该模型集成了卫星数据， 物理化学、生物、分子和毒性（本项目）数据来预测水华位置、大小和 毒性的提前期为 5 天，将有助于做出明智、及时的决策，以减少有毒 cHAB 对 公共卫生、自然资源和地方经济。项目成果也将为该中心做出贡献 该计划的目标是更好地了解气候变化对气候变化频率和严重程度的影响 伊利湖和其他五大湖地区的 cHAB，从而为伊利湖和其他五大湖地区的长期发展规划提供信息 土地利用以及管理和缓解战略。