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Emerging Sensors and their Implications for the Future of Environmental Observatories

机译:新兴传感器及其对环境观测站未来的启示

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Considering the range of space and timescales involved and the diverse nature of watershed-scale processes, environmental measurements can only be meaningful when they are performed long-term at high spatial-temporal density. The establishment of coastal observatories will depend a lot on our ability to obtain such high density datasets requiring long-term in situ unattended data acquisition. Also, the interplay of environmental and forces and bio-geochemical cycling necessitates long-term studies and different variables need to be measured to develop adequate forecast and response systems. Since nutrients partition to particulate matter, embayment productivity can be directly correlated with particulate matter mass flux at flux points or across system boundaries. Without the prerequisite sensors our ability to quantify those fluxes will be limited.Particulate biogenic matter (e.g., phytoplankton) in surface waters is directly related to water quality. Phytoplankton abundance can be an indicator of aquatic health in lakes, bays and estuaries which can be linked to exceedingly high productivity most often due to high nutrient (N, P) loading into the receiving water body. According to the 2000 report from the Committee on Environment and Natural Resources, the annual cost of HABs in the US is $300-$700 million. However there has been relatively small progress in developing dynamic sensor systems for bio-chemical characterization of aquatic environments to date, seriously hampering the development of effective monitoring programs for critical water resources. Against this backdrop, bio-chemical sensors are required to enable better understanding of human-dominated water-environments, their stressors, and the links between them. There is a need for novel instrumentation to measure and monitor two critical nutrients nitrogen and phosphorous (total and available N, P) in surface and ground waters concurrent with determination of the impact of nutrient loading on important biota in real time.Phytoplankton growth kinetics is also critical to water quality models but the representation of biomass based on chlorophyll-'a' measurements can lead to erroneous parameterization of such models. Proper quantification of phytoplankton abundance should be a key thrust of instrumentation development which will combine both the quantification of biomass growth (specifically plankton) with information obtained on the underlying biochemical cycling (nutrients, CO_2). With advancements in photonics, the enabling technology exists in image processing, pattern recognition, digital imagery, high-resolution optical detectors, and we can match this challenge. Efforts are now geared towards developing sensor array designed purposely to be a near real-time nutrient sensor directly coupled with a trainable optical recognition particle identification and characterization unit to detect and identify biogenic particles. The ability to track N and P loading and biomass in real-time will afford the scientific community the opportunity to develop improved water quality models and better resource management programs for the nation's water resources.
机译:考虑到所涉及的空间和时间尺度的范围以及流域尺度过程的多样性,环境测量仅在以高时空密度长期执行时才有意义。沿海天文台的建立将在很大程度上取决于我们获得需要长期就地无人值守数据采集的高密度数据集的能力。同样,环境和力量以及生物地球化学循环的相互作用需要长期研究,并且需要测量不同的变量以建立适当的预测和响应系统。由于养分分配到颗粒物上,因此可以将捕捞生产率与通量点处或系统边界处的颗粒物质量通量直接相关。如果没有必备的传感器,我们量化这些通量的能力将受到限制。 地表水中的颗粒生物源物质(例如浮游植物)与水质直接相关。浮游植物的丰度可以指示湖泊,海湾和河口的水生健康状况,这通常是由于向接收水体中装载大量营养(N,P)而导致的极高生产力。根据环境与自然资源委员会2000年的报告,在美国,HAB的年度成本为300到7亿美元。然而,迄今为止,在开发用于水生环境生物化学表征的动态传感器系统方面进展相对较小,严重阻碍了对关键水资源的有效监控程序的开发。在这种背景下,需要生物化学传感器来更好地了解人类主导的水环境,其压力源以及它们之间的联系。需要新颖的仪器来实时测量和监测地表水和地下水中的两种关键营养素氮和磷(总氮和可用氮,磷),同时实时确定营养物负荷对重要生物群系的影响。 浮游植物的生长动力学对水质模型也很关键,但是基于叶绿素-a'测量值表示的生物量可能会导致这种模型的参数错误。浮游植物丰度的正确定量应该是仪器开发的重点,它将结合生物量增长的定量(特别是浮游生物)和有关基础生化循环的信息(营养素,CO_2)。随着光子学的进步,使能技术存在于图像处理,模式识别,数字图像,高分辨率光学探测器中,我们可以应对这一挑战。现在,人们正致力于开发专门设计为接近实时营养传感器的传感器阵列,该传感器传感器直接与可训练的光学识别颗粒识别和表征单元相结合,以检测和识别生物颗粒。实时跟踪氮,磷负荷和生物量的能力将为科学界提供机会,为该国的水资源开发改进的水质模型和更好的资源管理程序。

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