Water lettuce (Pistia stratiotes L.) increases biogas effluent pollutant removal efficacy and proves a positive substrate for renewable energy production.

Background: Aquatic plants play a crucial role in nature-based wastewater treatment and provide a promising substrate for renewable energy production using anaerobic digestion (AD) technology. This study aimed to examine the contaminant removal from AD effluent by water lettuce (WL) and produce biogas from WL biomass co-digested with pig dung (PD) in a farm-scale biogas digester. Methods: The first experiment used styrofoam boxes containing husbandry AD effluent. WLs were initially arranged in 50%, 25%, 12.5%, and 0% surface coverage. Each treatment was conducted in five replicates under natural conditions. In the second experiment, WL biomass was co-digested with PD into an existing anaerobic digester to examine biogas production on a farm scale. Results: Over 30 days, the treatment efficiency of TSS, BOD5, COD, TKN, and TP in the effluent was 93.75-97.66%, 76.63-82.56%, 76.78-82.89%, 61.75-63.75%, and 89.00-89.57%, respectively. Higher WL coverage increased the pollutant elimination potential. The WL biomass doubled after 12 days for all treatments. In the farm-scale biogas production, the biogas yield varied between 190.6 and 292.9 L kg VSadded-1. The methane content reached over 54%. Conclusions: WL removed AD effluent nutrients effectively through a phytoremediation system and generated significant biomass for renewable energy production in a farm-scale model.

A Comparison of a Conventional Chemical Coagulant and a Natural Coagulant Derived from Cassia fistula Seeds for the Removal of Heavy Metal Ions.

Cassia fistula seed-derived coagulant has been reported to exhibit high coagulating-flocculating activity, environmental friendliness, and cost-effectiveness for the wastewater treatment, especially of textile wastewater. For heavy metal removal, however, research focusing on evaluating the feasibility of this material is still limited. Therefore, this study reports jar-test experiments in which the Zn2+ and Ni2+ removal efficiency of C. fistula coagulant was assessed. Moreover, a comparison of coagulation performance using a conventional chemical coagulant and the natural coagulant was performed. Characterization of the C. fistula seed-derived coagulant revealed the presence of important functional groups and fibrous networks with rough surfaces. A bench-scale study indicated that the coagulation performance of the two coagulants depends strongly on the initial concentration of metal ions, pH level, and coagulant dosage. The C. fistula seed-derived coagulant was found to possess higher removal efficiency than polyaluminum chloride. This natural coagulant removed over 80% of metal ions at the optimal conditions of pH 5.0, a metal ion concentration of 25 ppm, and a dosage of 0.8 and 1.6 g/L for Zn2+ and Ni2+, respectively. This study shows that C. fistula seed-derived coagulant is a potential alternative to chemical coagulants and could be developed to provide an environmentally friendly, economical, and efficient wastewater treatment.

Predicting Heavy Metal Concentrations in Shallow Aquifer Systems Based on Low-Cost Physiochemical Parameters Using Machine Learning Techniques.

Monitoring ex-situ water parameters, namely heavy metals, needs time and laboratory work for water sampling and analytical processes, which can retard the response to ongoing pollution events. Previous studies have successfully applied fast modeling techniques such as artificial intelligence algorithms to predict heavy metals. However, neither low-cost feature predictability nor explainability assessments have been considered in the modeling process. This study proposes a reliable and explainable framework to find an effective model and feature set to predict heavy metals in groundwater. The integrated assessment framework has four steps: model selection uncertainty, feature selection uncertainty, predictive uncertainty, and model interpretability. The results show that Random Forest is the most suitable model, and quick-measure parameters can be used as predictors for arsenic (As), iron (Fe), and manganese (Mn). Although the model performance is auspicious, it likely produces significant uncertainties. The findings also demonstrate that arsenic is related to nutrients and spatial distribution, while Fe and Mn are affected by spatial distribution and salinity. Some limitations and suggestions are also discussed to improve the prediction accuracy and interpretability.