MRI: Acquisition of a Smart Sensor Web for understanding freshwater ecosystems

MRI：获取智能传感器网络以了解淡水生态系统

• 批准号: 1625044
• 负责人: Rick Relyea
• 金额: $ 91.7万
• 依托单位: Rensselaer Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-15 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1625044&HistoricalAwards=false
• 关键词: MRI; Acquisition; Smart; Sensor; Web

An award is made to Rensselaer Polytechnic Institute to acquire a single, highly integrated network of lake sensors that form a "Smart Sensor Web", which will monitor chemical, physical, and biological parameters of Lake George, NY and its major tributaries over more than a decade. Fresh water is a critical global resource for agriculture, electricity generation, industry, human health, and ecosystem health. As a result, there is a tremendous need to understand the functioning of freshwater ecosystems and how human activities affect these functions. The Jefferson Project is a large, interdisciplinary effort to address this need by incorporating research and technology from multiple fields of science. To monitor changes in the lake over space and time, the Smart Sensor Web includes weather stations, tributary stations, vertical profilers, and acoustic Doppler current profilers (ADCPs) equipped with IBM Smart Technology. These sensors can communicate with researchers and each other to make real-time sampling decisions over space and time. These data will be combined with historic data (35 yrs of non-automated weather, tributary, and lake chemistry data), ongoing food-web surveys, and experimental data that examine the underlying causes of how human activities can alter overall water quality. These monitoring and experimental data will then be brought together to develop integrated models of weather, run-off, lake circulation, and food webs. Assembling these components will serve as a global example of how researchers can use advanced technology for environmental monitoring, serve as a basis for real-time and responsible conservation tactics, and to attain a scientifically deep understanding of freshwater ecosystems, which are critical for global security and society. The data and models that are facilitated by the Smart Sensor Web will allow us to address a wide range of transformative questions. These questions include tracking changes in phytoplankton, micro-benthos, and microbial assemblages with respect to spatial and temporal changes in physical and chemical parameters, understanding the effect of species diversity on lake ecosystem processes, and quantifying the effect of spatial and temporal food chain coupling on lake ecosystem processes. We will also be able to monitor and track anthropogenic disturbances (e.g., nutrient inputs, turbidity, and road salt), understand how climate variation is associated with shifts in lake hydrology and temperature profiles, and how water currents affect the dispersal of invasive species. Moreover, the Smart Sensor Web will allow researchers to establish modern proxies for reconstructing the lake?s history via lake coring research. The data produced by the Smart Sensor Web will be combined with many other types of data including non-automated monitoring of other lake parameters, experimental manipulations, and a diverse set of models (i.e. weather, hydrology, lake circulation, and food web models). Together, these efforts will offer the ability to conduct an integrated ecosystem assessment and a lake web observatory that will serve as a global blueprint for a revolutionary approach to understanding freshwater ecosystems. Data provided by the instrument will be used by a diverse group of faculty spanning eight departments and multiple centers at RPI. In addition to the traditional benefits of training students and disseminating results to the public through seminars and web sites, the broader impacts of the Jefferson Project extend much farther. The data, semantic data, and data derivatives will be available to scientists around the world. In addition, the 45 faculty, post-docs, and graduate students involved in the Jefferson Project will collectively conduct a massive amount of K-12 STEM education and communication to the public. Faculty and students are already involved in ongoing outreach, and we are proposing several new activities designed to visualize complex ecosystems, including a 3D online educational game simulation for middle and high school students, a visiting artist program, and a virtual artwork installation, "The World of Plankton". This latter installation is an interactive 3D immersive environment world to be shown in museums, art galleries, and online. These efforts will extend our outreach to diverse communities throughout art, science, education, and cultural venues.

Rensselaer Polytechnic Institute获得了一个单一的，高度集成的湖泊传感器网络，形成了一个“智能传感器网络”，它将在十多年内监测纽约州乔治湖及其主要支流的化学，物理和生物参数。淡水是农业、发电、工业、人类健康和生态系统健康的重要全球资源。因此，非常需要了解淡水生态系统的功能以及人类活动如何影响这些功能。杰斐逊项目是一个大型的，跨学科的努力，以解决这一需要，结合研究和技术，从多个科学领域。为了监测湖泊在空间和时间上的变化，智能传感器网络包括气象站、支流站、垂直剖面仪和配备IBM智能技术的声学多普勒海流剖面仪（ADCP）。这些传感器可以与研究人员和彼此进行通信，以在空间和时间上做出实时采样决策。这些数据将与历史数据（35年的非自动化天气，支流和湖泊化学数据），正在进行的食物网调查以及研究人类活动如何改变整体水质的根本原因的实验数据相结合。然后将这些监测和实验数据汇集在一起，以开发天气、径流、湖泊环流和食物网的综合模型。将这些组件组合在一起，将成为研究人员如何利用先进技术进行环境监测的全球范例，作为实时和负责任的保护策略的基础，并从科学上深入了解淡水生态系统，这对全球安全和社会至关重要。智能传感器网络提供的数据和模型将使我们能够解决广泛的变革性问题。这些问题包括跟踪浮游植物、微型底栖动物和微生物群落的变化与物理和化学参数的时空变化，了解物种多样性对湖泊生态系统过程的影响，以及量化时空食物链耦合对湖泊生态系统过程的影响。我们还将能够监测和跟踪人为干扰（例如，营养物输入，浊度和道路盐），了解气候变化如何与湖泊水文和温度剖面的变化相关联，以及水流如何影响入侵物种的扩散。此外，智能传感器网络将允许研究人员建立现代代理重建湖泊？的历史通过湖泊取芯研究。智能传感器网络产生的数据将与许多其他类型的数据相结合，包括其他湖泊参数的非自动化监测，实验操作和各种模型（即天气，水文，湖泊循环和食物网模型）。这些努力将共同提供进行综合生态系统评估的能力，并建立一个湖网观测站，作为了解淡水生态系统的革命性方法的全球蓝图。该仪器提供的数据将由跨越RPI八个部门和多个中心的多元化教师组使用。除了通过研讨会和网站培训学生和向公众传播成果的传统好处外，杰斐逊项目的更广泛影响延伸得更远。这些数据、语义数据和数据衍生物将提供给世界各地的科学家。此外，参与杰斐逊项目的45名教师，博士后和研究生将集体进行大量的K-12 STEM教育和公众交流。教师和学生已经参与了正在进行的推广活动，我们正在提出几项旨在可视化复杂生态系统的新活动，包括为初中和高中学生提供的3D在线教育游戏模拟，访问艺术家计划和虚拟艺术品装置“浮游生物的世界”。后一个装置是一个交互式3D沉浸式环境世界，可以在博物馆，艺术画廊和网上展示。这些努力将扩大我们的推广到整个艺术，科学，教育和文化场所的不同社区。