Global NEST Journal

The microbial consortia incorporated in bio concrete should have traits such as high alkali resistance and the ability to form endospores to overcome the mechanical and chemical stresses produced during concrete preparation. The primary intent of our work is to segregate and identify the calcium carbonate precipitating microorganisms with a strong urease activity, compare their potential for the preparation of bio concrete, and evaluate its performance, durability, and effects on heavy metal toxicity. Bacteria, algae, protozoans, yeasts, and fungi are predominantly reported in the consortia of microbes in a construction sample, making it an excellent source of wild microbial species and strains which have adapted to the environment of the construction place which consists of a consortium of limestone, fine sand, and curing water. Consortia of organisms were isolated from fermented lime mortar and curing construction water, which was recognized using the 16S rRNA gene sequencing technique. Atomic Absorption Spectrometry[AAS] and Inductively Coupled Plasma Mass Spectrometry[ICP-MS] were carried out to analyze the deduction of heavy metal toxicity present in the produced bio concrete. The results of strength and toxicity tests were compared with bio-concrete produced from standard cultures of Bacillus megaterium (NDRI-067) and Bacillus licheniformis(NDRI-598) obtained from the National Dairy Research Institute. (NDRI), and also compared with conventional concrete.

Water pollution has been acknowledged as an issue of concern due to recalcitrant pollutants like textile dyes. Therefore, the eco-friendly synthesis of nano-particles (NPs) through green chemistry approach from sources and materials of organic origin has not only enhanced the properties of substance at nanoscale but has also reduced the toxicological impacts on human health and the environment. In this study a successful attempt was made to synthesize TiO2 NPs by Mangifera indica leaves extract as organic source. The structural morphology was determined by carrying out FTIR, SEM, EDX and XRD techniques. The removal of Methylene Blue (MB) dye was assessed by the application of the photocatalysis technique. The experiments were designed using the RSM (CCD) approach for selected parameters such as pH, TiO2 NPs, Dye concentration and Time. The maximal dye removal efficiency of 98% was achieved at optimum conditions of 4.7 pH, 0.04g of nano photocatalyst in 250ml of dye solution having 8ppm concentration, allowed to react for 45 min. The regression coefficient value of 79.37% was determined by ANOVA for MB dye. The results clearly indicate that the application of green nanoparticles for the removal of MB dye is a feasible option to overcome with the ever-increasing issue of water pollution and environmental degradation.

People are prompted to complain about air pollution by offensive odours. They can produce psychological consequences, such as nausea, headaches, lack of appetite, breathing difficulties, and various adverse reactions in some circumstances. Among various industries, the dairy industry emits the most noxious odours. The human nose is the only trustworthy sensor, and numerous laboratory and field methods for quantifying human observations have been devised. Most odours are difficult to quantify, as evidenced by the fact that contemporary technology has yet to provide a fully precise method for quantifying them. As a result, the current study examines the possibility of employing fuzzy logic to predict odour intensity from dairy effluent characterisation. In this work, an Adaptive Neuro Fuzzy Inference System (ANFIS) based prediction model using Sugeno controller is developed for finding the Odour Index of the dairy effluent. COD and BOD are considered as input parameters for the model with triangular membership function for five linguistic variables. The standard odour index estimated from the literatures is used for modelling. The predicted odour index obtained from the ANFIS model for synthetic dairy wastewater is validated against the standard odour index and the average error is found to be ± 0.16 %.

Identifying and classifying anomalies in on-line monitoring systems of drainage systems is important to reduce urban water pollution. In the context of big data, the mini-batch K-means combined with the XGBoost drainage network abnormal flow identification and classification model is proposed to precisely identify and classify abnormalities that occur in real-time updates of online drainage network data while resolving problems with subjectivity and the lack of uniform standards for classification. First, using Mini Batch K-means, the unclassified drainage network data were sorted into four categories: normal drainage, sneaky drainage, rainwater and sewage mixing, and inflow and infiltration. Next, XGBoost performs data modeling to create a model for classification and identification of drainage flow anomalies. To increase the accuracy of the model, the features were ultimately chosen based on the ranking of the importance of the features, and the model parameters were established using grid search and cross-validation. The results showed that the XGBoost Drainage Network Anomaly Classification and Identification Model can accurately identify four drainage network situations with high classification accuracy and good performance. It was also validated through the application of data from the online system monitoring points in Changsha, China, in 2020.