Evaluation of traditional and machine learning approaches for modeling volatile fatty acid concentrations in anaerobic digestion of sludge: potential and challenges.

This study evaluates models for predicting volatile fatty acid (VFA) concentrations in sludge processing, ranging from classical statistical methods (Gaussian and Surge) to diverse machine learning algorithms (MLAs) such as Decision Tree, XGBoost, CatBoost, LightGBM, Multiple linear regression (MLR), Support vector regression (SVR), AdaBoost, and GradientBoosting. Anaerobic bio-methane potential tests were carried out using domestic wastewater treatment primary and secondary sludge. The tests were monitored over 40 days for variations in pH and VFA concentrations under different experimental conditions. The data observed was compared to predictions from the Gaussian and Surge models, and the MLAs. Based on correlation analysis using basic statistics and regression, the Gaussian model appears to be a consistent performer, with high R2 values and low RMSE, favoring precision in forecasting VFA concentrations. The Surge model, on the other hand, albeit having a high R2, has high prediction errors, especially in dynamic VFA concentration settings. Among the MLAs, Decision Tree and XGBoost excel at predicting complicated patterns, albeit with overfitting issues. This study provides insights underlining the need for context-specific considerations when selecting models for accurate VFA forecasts. Real-time data monitoring and collaborative data sharing are required to improve the reliability of VFA prediction models in AD processes, opening the way for breakthroughs in environmental sustainability and bioprocessing applications.

Utilization of microbial fuel cells as a dual approach for landfill leachate treatment and power production: a review.

Landfill leachate, which is a complicated organic sewage water, presents substantial dangers to human health and the environment if not properly handled. Electrochemical technology has arisen as a promising strategy for effectively mitigating contaminants in landfill leachate. In this comprehensive review, we explore various theoretical and practical aspects of methods for treating landfill leachate. This exploration includes examining their performance, mechanisms, applications, associated challenges, existing issues, and potential strategies for enhancement, particularly in terms of cost-effectiveness. In addition, this critique provides a comparative investigation between these treatment approaches and the utilization of diverse kinds of microbial fuel cells (MFCs) in terms of their effectiveness in treating landfill leachate and generating power. The examination of these technologies also extends to their use in diverse global contexts, providing insights into operational parameters and regional variations. This extensive assessment serves the primary goal of assisting researchers in understanding the optimal methods for treating landfill leachate and comparing them to different types of MFCs. It offers a valuable resource for the large-scale design and implementation of processes that ensure both the safe treatment of landfill leachate and the generation of electricity. The review not only provides an overview of the current state of landfill leachate treatment but also identifies key challenges and sets the stage for future research directions, ultimately contributing to more sustainable and effective solutions in the management of this critical environmental issue.

Thermoregulation in periparturient rabbit does and their neonatal kits with different litter sizes during West African winter.

The study evaluated the changes in body temperatures and hematological parameters in periparturient rabbit does and the dynamics of body surface temperature in neonatal rabbit kits during West African winter. The variables were measured in 21 rabbits, comprising periparturient (n = 12) and dry (n = 9) does, and after kindling, the kits were grouped based on litter size, into small (4 kits per litter; n = 24) and large (5-7 kits per litter; n = 34) litter. Results revealed that all body temperatures of the does were lower (p < 0.05) during gestation compared with lactation period or dry does in all hours of the day, and the PCV was significantly higher in lactating than dry does. At birth, small litter kits had higher (p < 0.05) body weight and morning body surface temperature than the large litter kits. The large litter kits showed distinct (p < 0.05) diurnal variation in body surface temperature from birth, while diurnal variation in the small litter kits was absent (P > 0.05) in the first three days after birth. It was concluded that pregnant rabbits and large litter kits may have poor thermoregulation during West African winter, and thus, the need for special protective housing to improve productivity.

Economic and efficiency based optimisation of water quality monitoring network for land use impact assessment.

The evaluation of the importance of having accurate and representative stations in a network for river water quality monitoring is always a matter of concern. The minimal budget and time demands of water quality monitoring programme may appear very attractive, especially when dealing with large-scale river watersheds. This article proposes an improved methodology for optimising water quality monitoring network for present and forthcoming monitoring of water quality under a case study of the Selangor River watershed in Malaysia, where different monitoring networks are being used by water management authorities. Knowing that the lack of financial resources in developing countries like Malaysia is one of the reasons for inadequate monitoring network density, to identify an optimised network for cost-efficiency benefits in this study, a geo-statistical technique coupled Kendall's W was first applied to analyse the performance of each monitoring station in the existing networks under the monitored water quality parameters. Second, the present and future changes in non-point pollution sources were simulated using the integrated Cellular Automata and Markov chain model (CA-Markov). Third, Station Potential Pollution Score (SPPS) determined based on Analytic Hierarchy Process (AHP) was used to weight each station under the changes of non-point pollution sources for 2015, 2024, and 2033 prior to prioritisation. Finally, according to the Kendall's W test on kriging results, the weights of non-point sources from the AHP evaluation and fuzzy membership functions, six most efficient sampling stations were identified to build a robust network for the present and future monitoring of water quality status in the Selangor River watershed. This study proposes a useful approach to the pertinent agencies and management authority concerned to establish appropriate methods for developing an efficient water quality monitoring network for tropical rivers.

Dehalogenase producing bacteria from extreme environment: A review

@#Halogenated compounds create the most important class of xenobiotic which commonly lead to pollution. Some of these compounds are very toxic and cause enormous problems to human health and to the environment. Many of these toxic chemicals have been shown to occur in various extreme habitats. Pollutant-degrading microorganisms, adapted to grow in various environments, play an important role in the biological treatment of polluted extreme habitats. The presence of dehalogenase producing microorganisms in extreme habitat in particular is necessary since the enzyme can catalyze the removal of a halogen atom from a substrate. Therefore, it can reduce the toxicity of the halogenated compound and some are of interest for study in industrial application. Thermophiles, psychrophiles, acidophiles, alkaliphiles and halophiles are types of extremophiles. Knowledge of the biodegradation of toxic chemicals in extreme environment is limited. Here, examples of dehalogenase producing bacteria isolated from various extreme conditions and its special characteristics/features will be discussed in this review.

Analysis of 2,2-DCP degrading bacteria isolated from a paddy field at a rural area in Malang, Indonesia

Aims@#The use of herbicide effectively controls weeds in agricultural practice. However, its release to the surrounding surface water bodies may lead to environmental issues. The aim of this study was to isolate the bacteria that were able to remove 2,2-dichloropropionic acid (2,2-DCP) from a paddy field located in Malang. @*Methodology and results@#The 2,2-DCP degrading bacteria were isolated and their ability to grow on higher 2,2-DCP concentrations (50 and 80 mM) was tested. Bacterial degradation of 2,2-DCP was examined through measurement of released chloride ions. The potential isolates were identified according to their 16S rDNA sequences. Two potential isolates, BB9.2 and BC14.3 were observed for their growth on 20, 50, and 80 mM 2,2-DCP. Isolate BC14.3 had the shortest cell doubling time of approximately 4.1 h with 100% 2,2-DCP (20 mM) utilization, whereas BB9.2 was only able to degrade 80% of 2,2-DCP at the same concentration. The 16S rDNA gene sequences suggested that BB9.2 and BC14.3 belong to Acinetobacter calcoaceticus and Pseudomonas plecoglossicida, respectively. @*Conclusion, significance and impact of study@#Bacterial strains with 2,2-DCP degrading potentials were successfully isolated from long-term exposed agricultural soil. They demonstrated notable utilization of the organic halide. This is the first time that strains of A. calcoaceticus and P. plecoglossicida were reported to utilize 2,2-DCP.

In silico molecular analysis of novel L-specific dehalogenase from Rhizobium sp. RC1

Aims: This study presents the first structural model and proposed the identity of four important key amino acid residues, Asp13, Arg51, Ser131 and Asp207 for the stereospecific haloalkanoic acid dehalogenase from Rhizobium sp. RC1. Methodology and results: The enzyme was built using a homology modeling technique; the structure of crystallized LDEX YL from Pseudomonas sp. strain YL as a template. Model validation was performed using PROCHECK to generate the Ramachandran plot. The results showed 80.4% of its residues were located in the most favoured regions suggested that the model is acceptable. Molecular dynamics simulation of the model protein was performed in water for 10 nanoseconds in which Na+ was added to neutralize the negative charge and achieved energy minimization. The energy value and RMSD fluctuation of Cα backbone of the model were computed and confirmed the stability of the model protein. Conclusion, significance and impact of study: In silico or computationally based function prediction is important to complement with future empirical approaches. L-haloacid dehalogenase (DehL), previously isolated from Rhizobium sp. RC1 was known to degrade halogenated environmental pollutants. However, its structure and functions are still unknown. This structural information of DehL provides insights for future work in the rational design of stereospecific haloalkanoic acid dehalogenases.