Trends and characteristics of employing cavitation technology for water and wastewater treatment with a focus on hydrodynamic and ultrasonic cavitation over the past two decades: A Scientometric analysis.

Cavitation-based technologies have emerged as a sustainable and effective way to treat natural waters and wastewater, considering their increasing scarcity due to pollution and climate change. For this reason, this work aimed to conduct a scientometric analysis on the topic of cavitation for water and wastewater treatment during the last 20 years, from 2001 to August 2022. We focused on hydrodynamic and ultrasonic cavitation as the prevalent methods of inducing cavitation. Furthermore, an in-depth study on the main trends regarding the number of publications and citations, keywords co-occurrence and evolution, and countries' publication trends was carried out to investigate the future direction of this research topic. The data was gathered from the Web of Science database and analyzed by the Visualization Of Similarities software. This work focused on: i) publication and citation trends, ii) scientific categories, iii) countries' contribution to the topic of cavitation, iv) prominent journals, v) keyword co-occurrence and cluster analysis, and vi) keyword evolution analysis. Results showed a significant increase in publications during the past 5 years. The scientific categories with the highest number of publications were "environmental sciences" and "environmental engineering," with a combined share of 19.4 % of publications. Keywords evolution analysis showed that limited focus was given to topics related to "energy" and "energy efficiency" in the field of cavitation, but with the rising importance of each process's sustainability, the attention given to these concepts will increase in the future. Future directions for the topic of cavitation-related water and wastewater treatments will shift towards more environmentally friendly applications of hydrodynamic and ultrasonic cavitation as well as towards more green and sustainable approaches to address the increasing water pollution problems and shortage. Moreover, it will include other uses besides water treatment such as manufacturing nanomaterials food production and medicine.

Combined landfill leachate treatment methods: an overview.

Landfill leachate is commonly heavily contaminated and consists of high amount of organic compounds, inorganic salts, toxic gases, halogenated hydrocarbons, and heavy metals that exerts a serious threat to public health and the environment. Thus, it requires treatments before direct release into receiving waters. Selecting the efficient method for leachate treatment is still a major challenge. While physicochemical treatment methods such as coagulation-flocculation, adsorption, membrane filtration, ozonation, air stripping, and advanced oxidation processes (AOP) are appropriate for mature leachate, young leachate requires biological treatments including membrane bioreactor (MBR), activated sludge (AS), upflow anaerobic sludge blanket (UASB), and rotational biological contactor (RBC). Recently, the integration of biological processes and physicochemical methods has been demonstrated to be very efficient. It is found that combined coagulation-flocculation/nanofiltration and activated sludge/reverse osmosis are more efficacious than other integrated physicochemical methods and combined physicochemical/biological methods, respectively.

Application of radial basis function neural network to predict soil sorption partition coefficient using topological descriptors.

The soil sorption partition coefficient logKoc is an indispensable parameter that can be used in assessing the environmental risk of organic chemicals. In order to predict soil sorption partition coefficient for different and even unknown compounds in a fast and accurate manner, a radial basis function neural network (RBFNN) model was developed. Eight topological descriptors of 800 organic compounds were used as inputs of the model. These 800 organic compounds were chosen from a large and very diverse data set. Generalized Regression Neural Network (GRNN) was utilized as the function in this neural network model due to its capability to adapt very quickly. Hence, it can be used to predict logKoc for new chemicals, as well. Out of total data set, 560 organic compounds were used for training and 240 to test efficiency of the model. The obtained results indicate that the model performance is very well. The correlation coefficients (R2) for training and test sets were 0.995 and 0.933, respectively. The root-mean square errors (RMSE) were 0.2321 for training set and 0.413 for test set. As the results for both training and test set are extremely satisfactory, the proposed neural network model can be employed not only to predict logKoc of known compounds, but also to be adaptive for prediction of this value precisely for new products that enter the market each year.