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

伦斯勒理工学院获得了一个单一的、高度集成的湖泊传感器网络，形成了一个“智能传感器网络”，它将在十多年的时间里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关键全球资源。因此，迫切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。杰斐逊项目是一项大型的跨学科努力，旨在通过整合来自多个科学领域的研究和技术来满足这一需求。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、垂直剖面仪和配备了IBM Smart Technology的声学多普勒水流剖面仪(ADCP)。这些传感器可以与研究人员和彼此通信，以在空间和时间上做出实时采样决定。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)、正在进行的食物网络调查以及检验人类活动如何改变总体水质的根本原因的实验数据结合在一起。然后，这些监测和实验数据将被结合在一起，以开发关于天气、径流、湖泊循环和食物网的综合模型。组装这些组件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、微型底栖生物和微生物群落在物理和化学参数的时空变化中的变化，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，营养物质输入、浊度和道路盐分)，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，通过湖泊取心研究重建S湖的历史。智能传感器网络产生的数据将与许多其他类型的数据结合在一起，包括对其他湖泊参数的非自动化监测、实验操作和一套不同的模型(即天气、水文、湖泊循环和食物网络模型)。总而言之，这些努力将提供进行综合生态系统评估的能力，并建立一个湖泊网络观测站，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被RPI的八个系和多个中心的不同教职员工使用。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还延伸得更远。数据、语义数据和数据衍生品将向世界各地的科学家开放。此外，参与杰斐逊项目的45名教职员工、博士后和研究生将共同开展大规模的K-12 STEM教育和面向公众的交流。教师和学生已经参与了正在进行的推广活动，我们正在提议几项新的活动，旨在可视化复杂的生态系统，包括为初中生和高中生设计的3D在线教育游戏模拟，访问艺术家计划，以及虚拟艺术品装置，《浮游生物的世界》。后一个装置是一个交互式的3D沉浸式环境世界，将在博物馆、美术馆和在线展示。这些努力将把我们的服务扩展到艺术、科学、教育和文化场所的不同社区。

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.