Search

Heavy metals (HMs) enter waterbodies through various means, which, when exceeding a threshold limit, cause toxic effects both on the environment and in humans upon entering their systems. Recent times have seen an increase in such HM influx incident rates. This requires an instant response in this regard to review the challenges in the available classical methods for HM detection and removal. As well as provide an opportunity to explore the applications of artificial intelligence (AI) and machine learning (ML) for the identification and further redemption of water and wastewater from the HMs. This review of research focuses on such applications in conjunction with the available in-silico models producing worldwide data for HM levels. Furthermore, the effect of HMs on various disease progressions has been provided, along with a brief account of prediction models analysing the health impact of HM intoxication. Also discussing the ethical and other challenges associated with the use of AI and ML in this field is the futuristic approach intended to follow, opening a wide scope of possibilities for improvement in wastewater treatment methodologies. [Display omitted] • Heavy metals are detrimental contaminants which are toxic in nature to human disorder. • There are various heavy metal removal techniques where AI and ML tends to be the most promising one. • Prediction of heavy metals using Artificial Intelligence (AI) and Machine learning shows better results. • ANN based adsorption separation of heavy metals in wastewater. [ABSTRACT FROM AUTHOR]

The clear and reliable detection of effluent plumes using satellite data is especially challenging. The surface signature of such events is of a small scale; it shows a complex interaction with the local environment and depends greatly on the effluent and marine water constitution. In the context of remote sensing techniques for detecting treated wastewater discharges, we study the surface signature of small river plumes, as they share specific characteristics, such as higher turbidity levels and increased nutrient concentration, and are fresh compared to the salty marine water. The Bulgarian Black Sea zone proves to be a challenging study area, with its optically complex waters and positive freshwater balance. Additionally, the Bulgarian Black Sea coast is a known tourist destination with an increased seasonal load; thus, the problem of the identification of wastewater discharges is a topical issue. In this study, we analyze the absorption components of the Inherent Optical Properties (IOPs) for 84 study points that are located at outfall discharging areas, river estuaries and at different distances from the shoreline, reaching the open sea area at a bottom depth of more than 2000 m. The calculations of IOPs take into account all available Sentinel 2 cloudless acquisitions for three years from 2017 until 2019 and are performed using the Case-2 Regional CoastColour (C2RCC) processor, implemented in the Sentinel Application Platform (SNAP). The predominant absorber for each study area and its temporal variation is determined, deriving the specific characteristics of the different areas and tracking their seasonal and annual course. Optical data from the Galata AERONET-OC site are used for validating the absorption coefficient of phytoplankton pigment. A conclusion regarding the possibility of distinguishing riverine, marine and coastal water is derived. The study provides a sound basis for estimating the advantages and drawbacks of optical satellite data for tracking the extent of effluent and fluvial plumes with unknown concentrations of optically significant seawater constituents. [ABSTRACT FROM AUTHOR]