• Nanowire-assisted LEEFT is applied for water disinfection with low voltages.• LEEFT inactivates bacteria by disrupting cell membrane through electroporation.• Multiple electrodes and device configurations have been developed for LEEFT.• The LEEFT is low-cost, highly efficient, and produces no DBPs.• The LEEFT can potentially be applicable for water disinfection at all scales. Water disinfection is a critical step in water and wastewater treatment. The most widely used chlorination suffers from the formation of carcinogenic disinfection by-products (DBPs) while alternative methods (e.g., UV, O3, and membrane filtration) are limited by microbial regrowth, no residual disinfectant, and high operation cost. Here, a nanowire-enabled disinfection method, locally enhanced electric field treatment (LEEFT), is introduced with advantages of no chemical addition, no DBP formation, low energy consumption, and efficient microbial inactivation. Attributed to the lightning rod effect, the electric field near the tip area of the nanowires on the electrode is significantly enhanced to inactivate microbes, even though a small external voltage (usually<5 V) is applied. In this review, after emphasizing the significance of water disinfection, the theory of the LEEFT is explained. Subsequently, the recent development of the LEEFT technology on electrode materials and device configurations are summarized. The disinfection performance is analyzed, with respect to the operating parameters, universality against different microorganisms, electrode durability, and energy consumption. The studies on the inactivation mechanisms during the LEEFT are also reviewed. Lastly, the challenges and future research of LEEFT disinfection are discussed. 相似文献
The sigma (SIG) coordinate system in ocean circulation simulation models results inevitably in horizontal pressure gradient error. This problem also emerges in models of deep lakes or reservoirs with the same characteristics of underwater terrain mutation. SIG coordinates reflect vertical relative stratification but cannot be used to calculate horizontal pressure gradient force in places with drastic topographic changes; this results in vertical water temperature and circulation errors. In deep lakes or reservoirs, differences in water density caused by the temperature difference between upper and lower water bodies is the primary cause of thermal stratification phenomena. Lake Mead was used as a case study on steep topography based on Environmental Fluid Dynamics Code (EFDC) model in this study. SIG coordinates result in close agreement between the calibrated temperature time series at the top and middle water layers, but disparity in the bottom water layer. The error emerges in the horizontal pressure gradient error due to the SIG coordinate transformation. Neither increasing the vertical resolution nor adjusting the horizontal viscosity coefficient resolve this error. We test the sigma-zed (SGZ) coordinate which combines Z coordinate and SIG coordinate as a replacement for the SIG coordinate to find that they effectively reduce the model’s runtime and simulation efficiency. The vertical temperature distribution in SGZ coordinate mode is more accurate than the distribution in SIG coordinate mode. The Navier-Stokes horizontal gradient and advection diffusion equation results under SIG coordinates are very sensitive to the pressure gradient. The replacement also enhances resolution near the thermocline, facilitates reclosing of the water bottom and the equal sigma surface, lends significant advantages in terms of vertical temperature in the simulation for local deep water with steep terrain, and shortens runtime for 0.14 h. SGZ mixed coordinates are recommended in the simulation of deep lakes or reservoirs wherein the underwater topography is large (with abundant continuous deep trenches or reefs).
We introduce robust procedures for analyzing water quality data collected over time. One challenging task in analyzing such data is how to achieve robustness in presence of outliers while maintaining high estimation efficiency so that we can draw valid conclusions and provide useful advices in water management. The robust approach requires specification of a loss function such as the Huber, Tukey’s bisquare and the exponential loss function, and an associated tuning parameter determining the extent of robustness needed. High robustness is at the cost of efficiency loss in parameter loss. To this end, we propose a data-driven method which leads to more efficient parameter estimation. This data-dependent approach allows us to choose a regularization (tuning) parameter that depends on the proportion of “outliers” in the data so that estimation efficiency is maximized. We illustrate the proposed methods using a study on ammonium nitrogen concentrations from two sites in the Huaihe River in China, where the interest is in quantifying the trend in the most recent years while accounting for possible temporal correlations and “irregular” observations in earlier years. 相似文献