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In coastal lagoons, eutrophication is a significant ecological and environmental problem. The majority of the pollution and deterioration problems that coastal zones and their ecosystems face are due to human influence through urbanization and industrialization. In this research, evaluations of water quality were made throughout a few lagoons in India during both the wet and dry seasons to establish their suitability for harboring aquatic life and the impact of seasonality on their quality. In order to reduce the effect of environmental influences on sensor based on nanoparticle sensitive film, an enhanced deep Back Propagation (BP) neural system was developed. The incompatibility between high performance and low calculation time was typically found in today’s modern sensor compensation techniques. Moreover, higher recognition errors were always the outcome of weak self-learning abilities. To solve these problems, a 16-layer deep BP neural network model was developed after hyper parameter searching. In Addition, the stochastic gradient descent (SGD) method and the mini-batch method were employed to achieve a proper balance between model performance and training time difficulties. The enhanced deep BP neural network was a great compensation solution for the sensor used in complicated environments due to its self-learning and self-adaptability. The proposed method is named as Imp_BPNN_SGD is analyzed in terms of various parameters and found that it achieves 35.6% of RMSE, 21.6% of MAE, 19.6% of RAE and 97.2% of D2

BTEX, also known as benzene, toluene, ethyl benzene, and xylene, is a common gasoline oxygenate. The contamination of groundwater and surface water with BTEX and its primary breakdown product has drawn a lot of attention. However, surface-modified zeolite's sorption mechanisms and affinity for BTEX can be employed to potentially remove these toxins from water. Tert-butyl alcohol (TBA) was utilized to alter the morphology of the adsorbent. Based on the findings, it can be concluded that zeolites have a greater capability for removing emerging chemicals. Some of the emerging compounds' relatively large dimensions limit their natural ability to lower values. Gas chromatography was used to determine the BTEX concentration decrease range. The Yoon-Nelson and Adams-Bohart model's use of mathematical modelling to determine the efficacy of the column's adsorption. The model with the highest R2 values for characterizing equilibrium isotherm data describes equilibrium adsorption data. To gain an understanding of the shape, pore structure, and active sites, the characterisation is concentrated on quantitative analysis using XRD, qualitative analysis using FTIR, and optical study using SEM.

For the past few decades, owing to human activities, urbanization, and industrialization, air pollution is becoming severe across several countries. Deep Learning (DL) and Machine Learning (ML) techniques had great contribution to the development of methods in various aspects of prediction, planning, and uncertainty analysis of smart cities and urban advancement in the current scenario. Many of the cities which are developed suffered from severe air quality (AQ) because of the rapid growth in industrialization and population. In this paper, we introduce a deep learning based air pollution prediction model for smart environment design planning (DLAPP-SEDP). The presented DLAPP-SEDP technique majorly intends to predict the level of air pollution in the smart environment. It follows a three-stage process namely data pre-processing, air pollution prediction, and hyperparameter tuning. At the initial stage, the presented DLAPP-SEDP technique performs various levels of data pre-processing such as missing value replacement, categorical value encoding, normalization, and feature selection. In the next stage, the DLAPP-SEDP technique employs graph convolutional network (GCN) model. Finally, the DLAPP-SEDP technique utilizes atomic orbital search optimization (AOSO) algorithm for optimal hyperparameter tuning process, showing the novelty of the work. To demonstrate the enhanced predictive efficiency of the DLAPP-SEDP method, a wide-ranging experimental analysis can be carried out. The experimental values assured the enhancements of the DLAPP-SEDP method over other recent techniques.

ABSTRACT Plastic pollution is increasing and plastic waste usually ends in environment. Microplastics (MP) as insoluble and anthropogenic micropollutant is ubiquitous and its concentration is expected to increase in the environment. One of important sources are wastewater treatment plants, where most of MP end in waste sludge. Promising technology for stabilization of waste sludge and reducing the volume and odor is anaerobic digestion, where anaerobic microorganisms digest organic matter and form biogas. The aim of this work was to evaluate inhibition on formed biogas and methane yield with and without ozonation of contaminated waste sludge with Polyvinylchloride (PVC). The inhibition of anaerobic microorganisms was determined by OxiTop® method, where biogas production was measured in terms of increased pressure (hPa). PVC increased biogas production up to 27% followed by increased CH4 production (up to 18%). Ozonation as a sludge pretreatment method, could inhibit or increase CH4 yield, depending on ozone dose (3.54 g h-1; lower dose or 3.99 g h-1; higher dose) and time of exposure to ozone (from 10 to 20 min). Higher dose of ozone with 10 minutes of ozonation, showed as the most efficient and improved biogas production up to 15% and CH4 production up to 14%.