Collaborative Research: MSA: Uncovering local and regional controls on organic matter processing in freshwaters using in situ optical sensors

合作研究：MSA：利用原位光学传感器揭示淡水中有机物处理的地方和区域控制

• 批准号: 2106111
• 负责人: Jacob Hosen
• 金额: $ 26.23万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106111&HistoricalAwards=false
• 关键词: Collaborative; Research; MSA; Uncovering; local

Inland waters, including streams, rivers, wetlands, and lakes, are critically important to the global carbon cycle. A portion of the organic carbon produced on land by plants is eventually transported into inland waters. Organic carbon that enters waterways is processed by aquatic organisms and sunlight into carbon dioxide. Aquatic plants and algae convert some of this carbon dioxide back to organic carbon via photosynthesis. These processes are well understood, but how environmental conditions such as temperature, salinity, and nutrient levels influence the relative importance of each process is not. Furthermore, the molecular composition of the organic carbon created and consumed in waterways remains unclear. To fill this knowledge gap, the investigators will use a national network of water quality sensors to estimate organic carbon processing in streams, rivers, and lakes at a continental scale. The research team will build interpretive websites for interested individuals to view how organic carbon processing varies in streams and rivers across the United States. This website will be used to communicate how such models can be used to predict the presence of harmful algae blooms or organic carbon compounds that cause harmful disinfection byproducts in water purification systems. Alterations to carbon cycling by inland waters can negatively impact water quality. Therefore, up-to-date information is needed to prevent and manage emerging water quality problems. Models based on data from optical sensors provide a solution for rapid and automated analysis of aquatic carbon cycling. The National Environmental Observatory Network (NEON) maintains a network of sensors in streams, rivers, and lakes that measure ultraviolet and visible light absorption of water, which can be used to estimate the amount and composition of organic carbon in water. To improve the reliability and accessibility of these datasets for the scientific community, the investigators will develop correction factors to convert raw sensor data to chemically relevant absorbance units. To estimate how environmental conditions in streams, lakes, and rivers influence organic carbon production by photosynthesis and consumption by heterotrophic metabolism and photo-oxidative degradation, the investigators will analyze the calibrated sensor data entailing sunlight levels, water chemistry, temperature, and hydrology data from NEON. The research team will perform laboratory incubation experiments to measure actual rates of organic carbon consumption and production under controlled conditions, thereby disentangling the different processes influencing organic carbon production and consumption in freshwater ecosystems across North America. All code produced for this project will be annotated and made publicly available via a GitHub repository. Resulting data products will be published and models will be integrated with existing data streams where possible. This project will support early-career investigators including one post-doctoral scientist and one research experience for undergraduates (REU) trainee.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

内陆水域，包括溪流，河流，湿地和湖泊，对全球碳循环至关重要。植物在土地上生产的有机碳的一部分最终被运输到内陆水域。进入水道的有机碳由水生生物和阳光加工成二氧化碳。水生植物和藻类通过光合作用将其中一些二氧化碳转换为有机碳。这些过程是充分理解的，但是温度，盐度和营养水平等环境条件如何影响每个过程的相对重要性。此外，在水道中产生和消耗的有机碳的分子组成尚不清楚。为了填补这一知识差距，调查人员将使用全国性的水质传感器网络来估算溪流，河流和湖泊中的有机碳加工。研究团队将建立解释性网站，供有兴趣的人查看有机碳加工在美国各地的流和河流中如何变化。该网站将用于传达如何使用此类模型来预测有害藻类开花或有机碳化合物的存在，从而在水净化系统中引起有害的消毒副产品。内陆水域对碳循环的改变可能会对水质产生负面影响。因此，需要最新信息来预防和管理新兴的水质问题。基于光学传感器数据的模型为快速自动分析水生碳循环提供了解决方案。国家环境天文台网络（NEON）维持了溪流，河流和湖泊中的传感器网络，该网络测量了紫外线和可见光的水吸收水，可用于估计水中有机碳的数量和组成。为了提高这些数据集对科学界的可靠性和可访问性，研究人员将开发校正因素，以将原始传感器数据转换为化学相关的吸光度单元。为了估算溪流，湖泊和河流中环境状况如何通过光合作用和消耗来影响有机碳的产生，而异养代谢和光氧化降解，研究人员将分析校准的传感器数据，包括阳光水平，水化学，温度和水文数据，从氖。研究小组将进行实验室孵化实验，以衡量受控条件下有机碳消耗和生产的实际速率，从而解散影响北美淡水生态系统的有机碳生产和消费的不同过程。该项目生产的所有代码将被注释并通过GitHub存储库公开提供。结果数据产品将在可能的情况下发布，并将模型与现有数据流集成。该项目将支持早期职业研究人员，包括一名博士后科学家和一项针对本科生的研究经验（REU）培训生。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估评估标准的评估来支持的。 。