Water quality assessment in a large plateau lake in China from 2014 to 2021 with machine learning models: Implications for future water quality management.

Amid the global surge of eutrophication in lakes, investigating and analyzing water quality and trends of lakes becomes imperative for formulating effective lake management policies. Water quality index (WQI) is one of the most used tools to assess water quality by integrating data from multiple water quality parameters. In this study, we analyzed the spatio-temporal variations of 11 water quality parameters in one of the largest plateau lakes, Erhai Lake, based on surveys from January 2014 to December 2021. Leveraging machine learning models, we gauged the relative importance of different water quality parameters to the WQI and further utilized stepwise multiple linear regression to derive an optimal minimal water quality index (WQImin) that required the minimal number of water quality parameters without compromising the performance. Our results indicated that the water quality of Erhai Lake typically showed a trend towards improvement, as indicated by the positive Mann-Kendall test for WQI performance (Z = 2.89, p < 0.01). Among the five machine learning models, XGBoost emerged as the best performer (coefficient of determination R2 = 0.822, mean squared error = 3.430, and mean absolute error = 1.460). Among the 11 water quality parameters, only four (i.e., dissolved oxygen, ammonia nitrogen, total phosphorus, and total nitrogen) were needed for the optimal WQImin. The establishment of the WQImin helps reduce cost in future water quality monitoring in Erhai Lake, which may also serve as a valuable framework for efficient water quality monitoring in similar waters. In addition, the elucidation of spatio-temporal patterns and trends of Erhai Lake's water quality serves as a compass for authorities, offering insights to bolster lake management strategies in the future.

Photoelectric properties of the layered raspberry sandwich amorphous ZnCo2S4@MnCo2S4/CP composite counter electrode in semiconductor-sensitized solar cells.

Multistage amorphous materials have promising applications in the catalytic performance of dye-sensitized solar cells (DSSCs). Herein, an amorphous sheet-raspberry sandwich-like ZnCo2S4@MnCo2S4/CP composite material was rationally designed and developed as a counter electrode (CE) for DSSCs by applying a three-step hydrothermal method. The first development of the amorphous composites as CEs resulted in lower charge transfer resistance at the CE/electrolyte interface and improved the fill factor and short-circuit current density. The excellent catalytic performance is mainly attributed to the large number of unsaturated coordination sites generated by the undirected structure of the lamellar-raspberry intercalated amorphous material, the smooth ion transport interface with a self-built corrosion-resistant layer, coupled with the dual catalytic performance of the Zn, Co, and Mn composites, and the good electrical conductivity of the C substrate. When ZnCo2S4@MnCo2S4/CP was used as the CE on a Ti substrate, the photoelectric conversion efficiency was as high as 11.68% (Voc = 0.821, Jsc = 20.14 mA cm-2, and FF = 0.71) under 100 mW cm-2 light illumination. This paper provides a design idea for amorphous materials in terms of catalytic performance and a method for developing alternatives to Pt electrodes.

Habitual feeding patterns impact polystyrene microplastic abundance and potential toxicity in edible benthic mollusks.

That increasing microplastics (MPs, <5 mm) eventually end up in the sediment which may become a growing menace to diverse benthic lives is worthy of attention. In this experiment, three edible mollusks including one deposit-feeding gastropod (Bullacta exarate) and two filter-feeding bivalves (Cyclina sinensis and Mactra veneriformis) were exposed to polystyrene microplastic (PS-MP) for 7 days and depurated for 3 days. PS-MP numbers in the digestive system and non-digestive system, digestive enzymes, oxidative stress indexes, and a neurotoxicity index of three mollusks were determined at day 0, 3, 7, 8 and 10. After seven-day exposure, the PS-MP were found in all three mollusks' digestive and non-digestive systems. And PS-MP in M. veneriformis (9.57 ± 2.19 items/individual) was significantly higher than those in C. sinensis (3.00 ± 2.16 items/individual) and B. exarate (0.83 ± 1.07 items/individual) at day 7. Three-day depuration could remove most of the PS-MP in the mollusks, and higher PS-MP clearance rates were found in filter-feeding C. sinensis (77.78 %) and M. veneriformis (82.59 %) compared to surface deposit-feeding B. exarate (50.00 %). The digestive enzymes of B. exarate significantly reacted to PS-MP exposure, while oxidative responses were found in C. sinensis. After three-day depuration, the changes of digestive enzymes and the oxidative states were fixed, but neurotoxicity induced by PS-MP was not recoverable. Besides, it is noteworthy that changes of digestive enzymes and acetylcholinesterase are related to feeding patterns.