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The world’s water supplies have been contaminated due to large effluents containing toxic pollutants such as dyes, heavy metals, surfactants, personal care products, pesticides, and pharmaceuticals from agricultural, industrial, and municipal resources into water streams. Water contamination and its treatment have emerged out as an escalating challenge globally. Extraordinary efforts have been made to overcome the challenges of wastewater treatment in recent years. Various techniques such as chemical methods like Fenton oxidation and electrochemical oxidation, physical procedures like adsorption and membrane filtration, and several biological techniques have been recognized for the treatment of wastewater. This review communicates insights into recent research developments in different treatment techniques and their applications to eradicate various water contaminants. Research gaps have also been identified regarding multiple strategies for understanding key aspects that are important to pilot-scale or large-scale systems. Based on this review, it can be determined that adsorption is a simple, sustainable, cost-effective, and environmental-friendly technique for wastewater treatment, among all other existing technologies. However, there is a need for further research and development, optimization, and practical implementation of the integrated process for a wide range of applications. Graphical abstract

In recent years, rapid development in the industrial sector has offered console to the people but at the same time, generates numerous amounts of effluent composed of toxic elements like nitrogen, phosphorus, hydrocarbons, and heavy metals that influences the environment and mankind hazardously. While the technological advancements are made in industrial effluent treatment, there arising stretch in the techniques directing on hybrid system that are effective in resource recovery from effluent in an economical, less time consuming and viable manner. The key objective of this article is to study, propose and deliberate the process and products obtained from different industries and the quantity of effluents produced, and the most advanced and ultra-modern theoretical and scientific improvements in treatment methods to remove those dissolved matter and toxic substances and also the challenges and perspectives in these developments. The findings of this review appraise new eco-friendly technologies, provide intuition into the efficiency in contaminants removal and aids in interpreting degradation mechanism of toxic elements by various treatment assemblages.

Nowadays, the presence of microplastics in drinking water is of concern worldwide due to potential impacts on human health. This paper has examined the presence of microplastics along the Llobregat river basin (Catalonia, Spain) and studied their behaviour and elimination along the drinking water treatment plant (DWTP). Due to different water composition, different sampling and sample preparation protocols were used to determine microplastics from river water and in the DWTP. Identification of microplastics of size range from 20 μm to 5 mm was performed by fourier-transform infrared spectroscopy (FTIR). Microplastics were detected in 5 out of 7 points along the Llobregat basin, with concentrations ranging between non-detected and 3.60 microplastics/L. In the intake of the DWTP, the mean concentration was 0.96 ± 0.46 microplastics/L ( n =5), with a predominance of polyester (PES) and polypropylene (PP) and at the outlet the mean concentration was of 0.06 ± 0.04 microplastics/L with an overall removal efficiency of 93 ± 5%. Sand filtration was identified as the key stage in microplastic removal (78 ± 9%). Furthermore, the results showed that ultrafiltration/reverse osmosis (advanced treatment) is more effective for microplastic removal than ozonation/carbon filtration stage (upgraded conventional treatment). In addition, a preliminary migration test of the different materials used in the DWTP has been performed to identify potential sources of microplastics in each treatment step.

The extensive release of heavy metals into the natural water bodies has become globally prevalent from past few decades. Heavy metal toxicity is becoming a serious threat to human and the environment. Due to their prolonged half-life, potential accumulation in different parts of body, and non-biodegradability, metal ions are being obvious entities that can cause several hazardous health risks. A number of methods have been developed for the detection of heavy/toxic metals based on sensors. Among the various new technologies, chemical and optical nano sensors are emerging technology to detect toxic heavy metals. Several nano sensors have been developed using nano materials, synthesized from green or chemical methods. The nano sensors are convenient to prepare and provide enhanced limit of detection, limit of quantification, and onsite detection. This review covers the recent work reported from 2013 to 2019 for the detection of heavy metals using sensors based on nano materials synthesized by different routes. Graphical abstract

Pyrazoles are five-membered heterocyclic compounds that contain nitrogen. They are an important class of compounds for drug development; thus, they have attracted much attention. In the meantime, pyrazole derivatives have been synthesized as target structures and have demonstrated numerous biological activities such as antituberculosis, antimicrobial, antifungal, and anti-inflammatory. This review summarizes the results of published research on pyrazole derivatives synthesis and biological activities. The published research works on pyrazole derivatives synthesis and biological activities between January 2018 and December 2021 were retrieved from the Scopus database and reviewed accordingly.

We aim to understand the dynamics of crypto asset prices and, specifically, how price information is transmitted among different bitcoin market exchanges, and between bitcoin markets and traditional ones. To this aim, we hierarchically cluster bitcoin prices from different exchanges, as well as classic assets, by enriching the correlation based minimum spanning tree method with a preliminary filtering method based on the random matrix approach. Our main empirical findings are that: (i) bitcoin exchange prices are positively related with each other and, among them, the largest exchanges, such as Bitstamp, drive the prices; (ii) bitcoin exchange prices are not affected by classic asset prices, but their volatilities are, with a negative and lagged effect.

Conditional value-at-risk (CVaR) and value-at-risk, also called the superquantile and quantile, are frequently used to characterize the tails of probability distributions and are popular measures of risk in applications where the distribution represents the magnitude of a potential loss. buffered probability of exceedance (bPOE) is a recently introduced characterization of the tail which is the inverse of CVaR, much like the CDF is the inverse of the quantile. These quantities can prove very useful as the basis for a variety of risk-averse parametric engineering approaches. Their use, however, is often made difficult by the lack of well-known closed-form equations for calculating these quantities for commonly used probability distributions. In this paper, we derive formulas for the superquantile and bPOE for a variety of common univariate probability distributions. Besides providing a useful collection within a single reference, we use these formulas to incorporate the superquantile and bPOE into parametric procedures. In particular, we consider two: portfolio optimization and density estimation. First, when portfolio returns are assumed to follow particular distribution families, we show that finding the optimal portfolio via minimization of bPOE has advantages over superquantile minimization. We show that, given a fixed threshold, a single portfolio is the minimal bPOE portfolio for an entire class of distributions simultaneously. Second, we apply our formulas to parametric density estimation and propose the method of superquantiles (MOS), a simple variation of the method of moments where moments are replaced by superquantiles at different confidence levels. With the freedom to select various combinations of confidence levels, MOS allows the user to focus the fitting procedure on different portions of the distribution, such as the tail when fitting heavy-tailed asymmetric data.

Microplastics (MP) as emerging persistent pollutants were found in raw and drinking water worldwide. Since different methods were used, there is an urgent need for harmonized protocols for sampling, sample preparation, and analysis. In this study, a holistic and validated analytical workflow for MP analysis in aqueous matrices down to 5 μm is presented. For sampling of several cubic meters of water, an easily portable filter cascade unit with different pore sizes (100–20–5 μm) was developed and successfully applied for the sampling of three processed drinking waters, two tap waters and one groundwater. The size distribution and polymer types of MP were determined using a two-step semi-automated Raman microspectroscopy analysis. For quality control, comprehensive process blanks were considered at all times and a recovery test yielded an overall recovery of 81%. The average concentration of identified MP was 66 ± 76 MP/m 3 ranging from 1 MP/m 3 to 197 MP/m 3 . All found concentrations were below the limit of quantitation (LOQ) of 1880 MP/m 3 . The majority consisted of PE (86% ± 111%) while comparatively low numbers of PET (10% ± 25%), PP (3% ± 6%), and PA (1% ± 4%) were found. 79% of all particles were smaller than 20 μm. In summary, this study presents the application of a workflow for sampling and analysis of MP down to 5 μm with first results of no significant contamination in drinking water and groundwater.

In polypharmacology drugs are required to bind to multiple specific targets, for example to enhance efficacy or to reduce resistance formation. Although deep learning has achieved a breakthrough in de novo design in drug discovery, most of its applications only focus on a single drug target to generate drug-like active molecules. However, in reality drug molecules often interact with more than one target which can have desired (polypharmacology) or undesired (toxicity) effects. In a previous study we proposed a new method named DrugEx that integrates an exploration strategy into RNN-based reinforcement learning to improve the diversity of the generated molecules. Here, we extended our DrugEx algorithm with multi-objective optimization to generate drug-like molecules towards multiple targets or one specific target while avoiding off-targets (the two adenosine receptors, A 1 AR and A 2A AR, and the potassium ion channel hERG in this study). In our model, we applied an RNN as the agent and machine learning predictors as the environment . Both the agent and the environment were pre-trained in advance and then interplayed under a reinforcement learning framework. The concept of evolutionary algorithms was merged into our method such that crossover and mutation operations were implemented by the same deep learning model as the agent . During the training loop, the agent generates a batch of SMILES-based molecules. Subsequently scores for all objectives provided by the environment are used to construct Pareto ranks of the generated molecules. For this ranking a non-dominated sorting algorithm and a Tanimoto-based crowding distance algorithm using chemical fingerprints are applied. Here, we adopted GPU acceleration to speed up the process of Pareto optimization. The final reward of each molecule is calculated based on the Pareto ranking with the ranking selection algorithm. The agent is trained under the guidance of the reward to make sure it can generate desired molecules after convergence of the training process. All in all we demonstrate generation of compounds with a diverse predicted selectivity profile towards multiple targets, offering the potential of high efficacy and low toxicity.