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

内陆沃茨，包括溪流、河流、湿地和湖泊，对全球碳循环至关重要。植物在陆地上产生的一部分有机碳最终被输送到内陆沃茨。进入水道的有机碳被水生生物和阳光处理成二氧化碳。水生植物和藻类通过光合作用将部分二氧化碳转化为有机碳。这些过程是很好的理解，但如何环境条件，如温度，盐度和营养水平影响每个过程的相对重要性是不。此外，水道中产生和消耗的有机碳的分子组成仍然不清楚。为了填补这一知识空白，研究人员将使用一个全国性的水质传感器网络来估计大陆范围内溪流、河流和湖泊中的有机碳处理。该研究小组将为感兴趣的个人建立解释性网站，以查看美国各地溪流和河流中有机碳处理的变化。该网站将用于交流如何使用这些模型来预测有害藻类水华或有机碳化合物的存在，这些化合物会导致水净化系统中有害的消毒副产品。内陆沃茨碳循环的改变会对水质产生负面影响。因此，需要最新的信息来预防和管理新出现的水质问题。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。国家环境观测网络（氖）在溪流、河流和湖泊中建立了一个传感器网络，测量水的紫外线和可见光吸收，可用于估计水中有机碳的数量和组成。为了提高科学界对这些数据集的可靠性和可访问性，研究人员将开发校正因子，将原始传感器数据转换为化学相关的吸光度单位。为了估计溪流、湖泊和河流中的环境条件如何影响光合作用产生的有机碳以及异养代谢和光氧化降解的消耗，研究人员将分析校准的传感器数据，这些数据包括来自氖的阳光水平、水化学、温度和水文数据。该研究小组将进行实验室孵化实验，以测量在受控条件下有机碳消耗和生产的实际速率，从而解开影响北美淡水生态系统中有机碳生产和消耗的不同过程。为这个项目生成的所有代码都将通过GitHub存储库进行注释和公开。由此产生的数据产品将予以公布，模型将尽可能与现有数据流相结合。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名本科生（REU）实习生的研究经验。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.