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 在过去的几年里，蓝藻有害藻华（cHABs）变得更加频繁和激烈 几十年来，由于气候变暖， 增强营养负荷。因此，检测和监测cHAB的发展， 毒性越来越重要，特别是对淡水系统，如劳伦特五大湖， 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，通常仅每周或每两周执行一次，可能无法检测到 短暂但毒性很强的水华幸运的是，新的，适合目的的检测技术， 通过提供接近实时的数据，变得可用于解决先前的限制。 该项目直接涉及COHH 3 RFA中列出的四个研究重点：（1）比较和 关联当前的观测系统，以监测海洋和五大湖的特性，包括有害藻类 Blooms，（2）评估新开发的原位监测传感器的长期现场应用潜力 海洋和五大湖的性质，（3）评估实时，在水中观测的物理化学 特性，以及检测有害藻华物种和毒素，以提供数据流， 预测模型，（4）制定适当和有效的藻类毒素监测战略（特别是在 饮用水，保护公众健康。拟议项目的具体目标是 将现场传感和采样技术与数据同化战略相结合， 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cHABs和内部磷负荷的影响 在伊利湖。我们将通过整合一个自主的、原位的环境样本来实现这些目标。 处理器、固相吸附毒素跟踪设备、水质探头和现场便携式采样 方法，沿着卫星遥感，更广泛的成果是改进水华预测模型 并制定一个更及时和完整的时空发展的cHAB毒性和生物量的图片 以及伊利湖内部磷负荷。总的来说，GLERL的长期水质监测 和NOAA的先进的cHAB预测模型（HAB跟踪器），它集成了卫星数据， 物理化学，生物，分子和毒性（本项目）数据，以预测水华的位置，大小和 毒性，提前5天，将有助于知情，及时的决定，以减少有毒的cHABs的影响， 公共卫生、自然资源和地方经济。项目产出也将有助于中心 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，从而为长期发展规划提供信息， 土地使用以及管理和缓解战略。