In response to the increasing global water demand and the pressing environmental challenges posed by climate change, the development of advanced wastewater treatment processes has become essential. This study introduces novel electrochemical technologies and examines the scalability of industrial-scale electrooxidation (EO) methods for wastewater treatment, focusing on simplifying processes and reducing operational costs. Focusing on the effective removal of key nutrients, specifically nitrogen and phosphorus, from wastewater, this review highlights recent advancements in electrode materials and innovative designs, such as high-performance metal oxides and carbon-based electrodes, that enhance efficiency and sustainability. Additionally, a comprehensive discussion covers a range of electrochemical methods, including electrocoagulation and electrooxidation, each evaluated for their effectiveness in nutrient removal. Unlike previous studies, this review not only examines nutrient removal efficiency, but also assesses the industrial applicability of these technologies through case studies, demonstrating their potential in municipal and industrial wastewater contexts. By advancing durable and cost-effective electrode materials, this study emphasizes the potential of electrochemical wastewater treatment technologies to address global water quality issues and promote environmental sustainability. Future research directions are identified with a focus on overcoming current limitations, such as high operational costs and electrode degradation, and positioning electrochemical treatment as a promising solution for sustainable water resource management on a larger scale.

Machine learning and computer vision have proven to be valuable tools for farmers to streamline their resource utilization to lead to more sustainable and efficient agricultural production. These techniques have been applied to strawberry cultivation in the past with limited success. To build on this past work, in this study, two separate sets of strawberry images, along with their associated diseases, were collected and subjected to resizing and augmentation. Subsequently, a combined dataset consisting of nine classes was utilized to fine-tune three distinct pretrained models: vision transformer (ViT), MobileNetV2, and ResNet18. To address the imbalanced class distribution in the dataset, each class was assigned weights to ensure nearly equal impact during the training process. To enhance the outcomes, new images were generated by removing backgrounds, reducing noise, and flipping them. The performances of ViT, MobileNetV2, and ResNet18 were compared after being selected. Customization specific to the task was applied to all three algorithms, and their performances were assessed. Throughout this experiment, none of the layers were frozen, ensuring all layers remained active during training. Attention heads were incorporated into the first five and last five layers of MobileNetV2 and ResNet18, while the architecture of ViT was modified. The results indicated accuracy factors of 98.4%, 98.1%, and 97.9% for ViT, MobileNetV2, and ResNet18, respectively. Despite the data being imbalanced, the precision, which indicates the proportion of correctly identified positive instances among all predicted positive instances, approached nearly 99% with the ViT. MobileNetV2 and ResNet18 demonstrated similar results. Overall, the analysis revealed that the vision transformer model exhibited superior performance in strawberry ripeness and disease classification. The inclusion of attention heads in the early layers of ResNet18 and MobileNet18, along with the inherent attention mechanism in ViT, improved the accuracy of image identification. These findings offer the potential for farmers to enhance strawberry cultivation through passive camera monitoring alone, promoting the health and well-being of the population.

Recent advancements have significantly improved turbidity and absorbance measurement techniques, crucial for municipal and industrial wastewater quality monitoring. This experimental system utilizes image analysis and machine learning on monochrome-camera images of real secondary wastewater effluent samples, irradiated with six LEDs, to classify turbidity and predict absorbance in the visible range. It focuses on low turbidity measurements (0–15 nephelometric turbidity units [NTUs]), the hardest challenge for conventional turbidity sensors. Specifically, this camera-based technique was able to classify within a 2 NTU class, 96 turbidity samples collected from a real wastewater treatment plant with precision and accuracy of over 96%. Additionally, it effectively predicted turbidity and absorbance with a neural network, achieving R-squared coefficients of 0.76 and 0.72, respectively. This innovative monitoring system, deployable in several locations of a wastewater treatment plant, not only addresses the limitations of the existing methods for the low turbidity range but also brings the potential for plant-wide process monitoring. Further testing is in progress to validate the proposed approach in other wastewater applications, such as combined sewer overflow monitoring and waste-activated sludge upset detection where more extreme and rapid changes are expected to occur.

Harnessing solar energy offers a sustainable alternative for powering electrolysis for green hydrogen production as well as wastewater treatment. The high costs and logistical challenges of electrolysis have resulted in limited widespread investigation and implementation of electrochemical technologies on an industrial scale. To overcome these challenges, this study designs and tests a new approach to chemical experiments and wastewater treatment research using a portable standalone open-source solar photovoltaic (PV)-powered station that can be located onsite at a wastewater treatment plant with unreliable electrical power. The experimental system is equipped with an energy monitoring data acquisition system. In addition, sensors enable real-time monitoring of gases—CO, CO₂, CH₄, H₂, H₂S, and NH₃—along with temperature, humidity, and volatile organic compounds, enhancing safety during electrochemical experiments on wastewater, which may release hazardous gases. SAMA software was used to evaluate energy-sharing scenarios under different grid-connected conditions, and the system can operate off the power grid for 98% of the year in Ontario, Canada. The complete system was tested utilizing a laboratory-scale electrolyzer (electrodes of SS316L, Duplex 2205, titanium grade II and graphite) with electrolyte solutions of potassium hydroxide, sulfuric acid, and secondary wastewater effluent. The electrolytic cell specifically developed for testing electrode materials and wastewater showed a Faraday efficiency up to 95% and an energy efficiency of 55% at STP, demonstrating the potential for use of this technology in future work.