Field assessment of engineered bioretention as microplastics sink through site characterization and hydrologic modeling.

Green stormwater infrastructure (GSI) practices like bioretention are considered a sink for microplastics washed in from urbanized land uses-land covers, and thereby regulating the environmental dispersion of microplastics. However, the capacity of GSI in microplastic sequestration remain unclear. This work investigated the spatial distributions of microplastics within bioretention cells and their soils, concentration in the GSI groundwater monitoring well, and the overall potential of GSI as a sink for microplastics.

Co-cultivation of microalgae and bacteria for optimal bioenergy feedstock production in wastewater by using response surface methodology.

This work uses response surface methodology (RSM) to study the co-cultivation of symbiotic indigenous wastewater microalgae and bacteria under different conditions (inoculum ratio of bacteria to microalgae, CO, light intensity, and harvest time) for optimal bioenergy feedstock production. The findings of this study demonstrate that the symbiotic microalgae-bacteria culture not only increases total microalgal biomass and lipid productivity, but also enlarges microalgal cell size and stimulates lipid accumulation. Meanwhile, inoculum ratio of bacteria to microalgae, light intensity, CO and harvest time significantly affect biomass and lipid productivity.

IoT-based Edge Computing (IoTEC) for Improved Environmental Monitoring.

This research considered several applications of a coupled Internet of Things sensor network with Edge Computing (IoTEC) for improved environmental monitoring. Two pilot applications, covering environmental monitoring of vapor intrusion and system performance of wastewater-based algae cultivation, were designed to compare data latency, energy consumption, and economic cost between the IoTEC approach and the conventional sensor monitoring method. The results show that the IoTEC monitoring approach, compared with conventional IoT sensor networks, could significantly reduce data latency by 13%, and the amount of data transmission decreased by an average of 50%.