Application of diffusive gradient in thin-film passive samplers to assess mercury availability and mobility in a fresh water river system

The accurate measurement of mercury in sediment porewater is a challenge using conventional sampling techniques which commonly require removal of sediment, transportation, and processing. Passive sampling is an alternative technique that measures sediment porewater concentrations in-situ and without significant sample disturbance. One passive sampling technique for mercury in sediment porewater is Diffusive Gradient in Thin-Films (DGT) samplers; a technique that has been employed since the 1990’s but is relatively new for mercury and has been primarily utilized in the laboratory. The approach estimates porewater concentrations of mercury species in-situ based upon the rate at which the species diffuses through a thin film of controlled thickness. The modification of this technique for field applications could significantly improve measurement of mercury porewater concentrations; however the technique lacks examples established quality assurance and control protocols, commercial availability, and examples of its successful implementation in a field setting. Sediment systems are important to mercury fate in aquatic systems due to their role as both a sink for inorganic mercury and source for methylmercury. Within the sediment, porewater chemistry is important to understanding mercury speciation and reactivity. The interaction between the solid and dissolved mercury species ranges greatly between systems and controls availability of mercury for methylation, direct exposure, and transport. This research uses DGT samplers in field applications to assess mercury speciation and mobility in sediment porewater. A representative site, the South River (Virginia, USA) was selected for evaluation of DGT sampling, development of sampling protocols and utilization of the technique for improving our ability to identify sources of mercury flux and evaluate of the biogeochemistry of a site. Through the use of DGT samplers, the river banks were identified as a potential source of mercury into the channel during flood events and the subsequent bank drainage. This behavior had not been identified using traditional sampling techniques and was not taken into account in the site conceptual model for mercury sources into the river. Using the DGT sampler data, a mercury flux budget was performed for a bank drainage event and it was determined that the river bank contributes significantly more mercury during large flood events than during baseline flow conditions. Laboratory studies were performed using South River bank sediment to better understand the biogeochemical behavior observed in the field.Civil, Architectural, and Environmental Engineerin

Application of Transfer Learning and Convolutional Neural Networks for Autonomous Oil Sheen Monitoring

Oil sheen on the water surface can indicate a source of hydrocarbon in underlying subaquatic sediments. Here, we develop and test the accuracy of an algorithm for automated real-time visual monitoring of the water surface for detecting oil sheen. This detection system is part of an automated oil sheen screening system (OS-SS) that disturbs subaquatic sediments and monitors for the formation of sheen. We first created a new near-surface oil sheen image dataset. We then used this dataset to develop an image-based Oil Sheen Prediction Neural Network (OS-Net), a classification machine learning model based on a convolutional neural network (CNN), to predict the existence of oil sheen on the water surface from images. We explored the effectiveness of different strategies of transfer learning to improve the model accuracy. The performance of OS-Net and the oil detection accuracy reached up to 99% on a test dataset. Because the OS-SS uses video to monitor for sheen, we also created a real-time video-based oil sheen prediction algorithm (VOS-Net) to deploy in the OS-SS to autonomously map the spatial distribution of sheening potential of hydrocarbon-impacted subaquatic sediments

Application of Transfer Learning and Convolutional Neural Networks for Autonomous Oil Sheen Monitoring

Oil sheen on the water surface can indicate a source of hydrocarbon in underlying subaquatic sediments. Here, we develop and test the accuracy of an algorithm for automated real-time visual monitoring of the water surface for detecting oil sheen. This detection system is part of an automated oil sheen screening system (OS-SS) that disturbs subaquatic sediments and monitors for the formation of sheen. We first created a new near-surface oil sheen image dataset. We then used this dataset to develop an image-based Oil Sheen Prediction Neural Network (OS-Net), a classification machine learning model based on a convolutional neural network (CNN), to predict the existence of oil sheen on the water surface from images. We explored the effectiveness of different strategies of transfer learning to improve the model accuracy. The performance of OS-Net and the oil detection accuracy reached up to 99% on a test dataset. Because the OS-SS uses video to monitor for sheen, we also created a real-time video-based oil sheen prediction algorithm (VOS-Net) to deploy in the OS-SS to autonomously map the spatial distribution of sheening potential of hydrocarbon-impacted subaquatic sediments