Prospective Life Cycle Assessment for the Electrochemical Oxidation Wastewater Treatment Process: From Laboratory to Industrial Scale

Unveiling the existence and ecological hazards of trace organic pollutants in wastewater treatment plant effluents across China

The presence of trace organic pollutants in the effluent of wastewater treatment plants (WWTPs) poses considerable risks to aquatic organisms and human health. A large-scale survey of 302 trace organic pollutants in the effluent of 46 Chinese WWTPs was conducted to gain an improved understanding of their occurrence and ecological risks. The survey data showed that 216 compounds in 11 chemical classes had been detected in effluents. The sum concentrations of the trace contaminants in effluent ranged from 1,392 ng/L to 35,453 ng/L, with the maximum concentration of perfluoroalkyl substances (PFASs) recorded as the highest (30,573 ng/L), which was markedly less than the reported 185,000 ng/L for the 38 American WWTPs. The concentration of bisphenol analogs (BPs) was up to 4,422 ng/L, significantly higher than those reported in France, Germany, Japan, Korea, and the U.S. PFASs and BPs were the major pollutants, accounting for 59% of the total pollution. Additionally, a total of 119 contaminants were found to have ecological risks (RQ > 0.01). Among these, 23 contaminants (RQ > 1.0) warrant higher attention and should be prioritized for removal. This study lists valuable information for controlling contaminants with higher priority in WWTP effluent in China.

Treatment and optimization of high-strength egg-wash wastewater effluent using electrocoagulation and electrooxidation methods

Egg-washing wastewater contains a high concentration of nutrition and organic matter since eggs are broken during the washing and cleaning processes. Moreover, the wastewater contains small amounts of detergents or sanitizing agents. These contaminants may pose environmental challenges when they are not properly managed or treated. The study scrutinizes the efficiency of electrocoagulation (EO) and electrooxidation (EO) approaches for egg-wash wastewater treatment. The response surface methodology was employed to optimize the operational parameters. The removal efficiencies of soluble chemical oxygen demand (sCOD 90%), ammonia (NH3-N 91%), nitrate (NO3--N 97%), nitrite (NO2--N 89.3%), total dissolved nitrogen (TDN 91%), and phosphate (90%) were measured under various treatment conditions. The optimum treatment conditions achieved in the combined EC + EO process were pH 6.0, current density 20 mA cm-2, and electrolysis time of 60 min, respectively. Degradation kinetics of the egg-wash pollutants showed a significant reduction in half-life (t1/2) with EO (after EC-Aluminum) at 15 min, 12 min, 17 min, and 15 min for sCOD, NO2--N. NO3--N, and TDN, respectively. Whereas the half-life of NH3-N (18 min) and phosphate (17 min) reduced significantly with the EO (after EC-iron). Al and Fe electrodes coupled with boron-doped diamond were found efficient for pollutant removal. Environmental implication. Egg-wash wastewater has a high protein content and contains nutrients that are essential for living organisms. While these compounds can be valuable for agricultural use by increasing soil phosphate concentration, they can also become an issue if the excess nutrients are not properly managed. The soil has a threshold limit for holding phosphate, and any excess amount may be transported through surface runoff or contaminate groundwater through leachate, potentially affecting aquatic ecosystems and water quality. This study explores the efficiency of electrocoagulation and electrooxidation methods in treating egg-wash wastewater. These methods aim to remove pollutants and reduce their environmental impact.

Machine Learning Models for Inverse Design of the Electrochemical Oxidation Process for Water Purification

In this study, a machine learning (ML) framework is developed toward target-oriented inverse design of the electrochemical oxidation (EO) process for water purification. The XGBoost model exhibited the best performances for prediction of reaction rate (k) based on training the data set relevant to pollutant characteristics and reaction conditions, indicated by Rext2 of 0.84 and RMSEext of 0.79. Based on 315 data points collected from the literature, the current density, pollutant concentration, and gap energy (Egap) were identified to be the most impactful parameters available for the inverse design of the EO process. In particular, adding reaction conditions as model input features allowed provision of more available information and an increase in the sample size of the data set to improve the model accuracy. The feature importance analysis was performed for revealing the data pattern and feature interpretation by using Shapley additive explanations (SHAP). The ML-based inverse design for the EO process was generalized to a random case for tailoring the optimum conditions with phenol and 2,4-dichlorophenol (2,4-DCP) serving as model pollutants. The resulting predicted k values were close to the experimental k values by experimental verification, accounting for the relative error lower than 5%. This study provides a paradigm shift from conventional trial-and-error mode to data-driven mode for advancing research and development of the EO process by a time-saving, labor-effective, and environmentally friendly target-oriented strategy, which makes electrochemical water purification more efficient, more economic, and more sustainable in the context of global carbon peaking and carbon neutrality.