Effective and sustainable adsorbent materials for oil spill cleanup based on a multistage desalination process.

Oil spills, which are often caused by crude oil transportation accidents, contaminate coastal waters and land and can harm aquatic life, seabirds, humans, and the entire ecosystem. Ocean currents and wind complicate oil spill cleanup and extend the oil spill area. This study proposes a new approach to control oil spills using solids recovered from the treatment of reject brine through a novel multistage desalination process. The aim is to produce applicable adsorbent for oil spill cleanup especially in the final cleaning stages. The multistage desalination process is based on the electrochemical treatment of high-salinity reject brine and Solvay and modified Solvay liquid effluents in a closed Plexiglas electrocoagulation cell. After the electrochemical treatment, the collected solids were dried and ground for utilization as adsorbents in oil spill cleanup. Results were promising for the adsorbent produced from the electrochemical treatment of the modified Solvay effluent. A maximum adsorption capacity of 2.8 g oil/g adsorbent was achieved, with an oil recovery of 98%. In addition, the regenerated solids after toluene extraction process were recycled and achieved an adsorption capacity of 2.1 g oil/g adsorbent in the second oil spill clean-up cycle. The structural and chemical characteristics of the adsorbents produced from the multistage desalination process were investigated using X-ray powder diffraction, Fourier transform infrared spectroscopy, and scanning electron microscopy. Results support the adoption of the collected solids as effective oil-adsorbent materials.

Capturing children food exposure using wearable cameras and deep learning.

Children's dietary habits are influenced by complex factors within their home, school and neighborhood environments. Identifying such influencers and assessing their effects is traditionally based on self-reported data which can be prone to recall bias. We developed a culturally acceptable machine-learning-based data-collection system to objectively capture school-children's exposure to food (including food items, food advertisements, and food outlets) in two urban Arab centers: Greater Beirut, in Lebanon, and Greater Tunis, in Tunisia. Our machine-learning-based system consists of 1) a wearable camera that captures continuous footage of children's environment during a typical school day, 2) a machine learning model that automatically identifies images related to food from the collected data and discards any other footage, 3) a second machine learning model that classifies food-related images into images that contain actual food items, images that contain food advertisements, and images that contain food outlets, and 4) a third machine learning model that classifies images that contain food items into two classes, corresponding to whether the food items are being consumed by the child wearing the camera or whether they are consumed by others. This manuscript reports on a user-centered design study to assess the acceptability of using wearable cameras to capture food exposure among school children in Greater Beirut and Greater Tunis. We then describe how we trained our first machine learning model to detect food exposure images using data collected from the Web and utilizing the latest trends in deep learning for computer vision. Next, we describe how we trained our other machine learning models to classify food-related images into their respective categories using a combination of public data and data acquired via crowdsourcing. Finally, we describe how the different components of our system were packed together and deployed in a real-world case study and we report on its performance.