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TiO2 is the most effective photocatalyst but it has low adsorption in the visible region. Many efforts have been done to improve its photocatalytic activity in the solar region. In this study we use Mo-oxy species supported on titania surface since it is well known that titania surface can dispersed Mo oxo species through the development of intense interactions. The binary system was further modified with another metal ion M2+ (M: Co, Cu, Zn). These ions have different chemical properties and they alter the physicochemical properties of the ternary system. The photocatalytic activity of the prepared catalysts were determined for the degradation of 0.5 mg L-1 sulfamethoxazole under solar irradiation. It was found that the deposition of Mo has a detrimental effect on the activity of TiO2 while the deposition of Zn or Cu can improve the activity of the ternary system. The sulfamethoxazole degradation follows first order kinetics and the activity of the catalysts is well correlated with the interactions developed between support and supported phases.

Water being a precious commodity for every person around the world needs to be quality monitored continuously for ensuring safety whilst usage. The water data collected from sensors in water plants are used for water quality assessment. The anomaly present in the water data seriously affects the performance of water quality assessment. Hence it needs to be addressed. In this regard, water data collected from sensors have been subjected to various anomaly detection approaches guided by Machine Learning (ML) and Deep Learning framework. Standard machine learning algorithms have been used extensively in water quality analysis and these algorithms in general converge quickly. Considering the fact that manual feature selection has to be done for ML algorithms, Deep Learning (DL) algorithm is proposed which involve implicit feature learning. A hybrid model is formulated that takes advantage of both and presented it is data invariant too. This novel Hybrid Convolutional Neural Network (CNN) and Extreme Learning Machine (ELM) approach is used to detect presence of anomalies in sensor collected water data. The experiment of the proposed CNN-ELM model is carried out using the publicly available dataset GECCO 2019. The findings proved that the model has improved the water quality assessment of the sensor water data collected by detecting the anomalies efficiently and achieves F1 score of 0.92. This model can be implemented in water quality assessment.

The Amvrakikos Bay is one of the most important ecosystems in the Mediterranean with great environmental, ecological and biological value. However, over the years, water quality has suffered severe degradation and is now one of the most polluted environmental areas in Greece. In the present study, using the analytical data of concentrations of precipitated metal deposits at the bottom of Amvrakikos Gulf, at its three largest lagoons: Logaros, Tsoukalio and Rodia, and by applying the indicators PLI, Igeo and EF, an assessment of the level of pollution of the area was made. The use of these indicators may show the degree of pollution of the area with heavy metals, and reveal the source of pollution i,e. whether the heavy metals are of anthropogenic or of natural processes, assessing simultaneously the degree of anthropogenic effect. Based on the PLI indicator, the marine zone in Menidi and Paliovarka as well as the Rodia and Tsoukalio lagoons are considered to be polluted. According to the Igeo Index, the sampling areas are unchanged to slightly burden by heavy metals. The largest charge is caused by Ni, followed by Mn and Pb. Based on EF, the concentrations of Cr, Mn, Co, Pb and Ni are significantly influenced by the various anthropogenic activities.

This study was conducted to investigate the effect of different types of organic wastes (grass and newspaper wastes, cow dung and their combination) on physicochemical properties during vermicomposting using Eisenia fetida. Three set-ups were conducted in which three experiments were for composting (controls) indicated as C1 for newspaper waste, C2 for grass waste and C3 for cattle dung and the corresponding replicates for vermicomposting processes for grass and cattle dung were T1 to T7 and mixed of newspaper, grass and cattle dung were T8-T13. A significant difference was detected among weight and number of Eisenia fetida in treatments, which the highest weight and population growth of worms occurred in T7 (60% of newspaper, 30% of cattle dung and 10% of grass waste). Compared to control treatment (without earthworms), vermicomposting treatment resulted in a decrease of organic carbon and C/N ratio after 30 days. Vermicomposting caused significant reduction in organic carbon (3.9-32.8% ) and C/N ratio (7.9–33.6%). The heavy metals degradation was evaluated after 90 days and at the end of the experiment the concentration of As, and Cd significantly decreased. This study clearly indicates that vermicomposting is a suitable technology for bioconversion of newspaper waste, grass clipping and cow dung to valuable material.