This review emphasizes the role of toxic metal remediation approaches due to their broad sustainability and applicability. The rapid developmental processes can incorporate a large quantity of hazardous and unseen heavy metals in all the segments of the environment, including soil, water, air and plants. The released hazardous heavy metals (HHMs) entered into the food chain and biomagnified into living beings via food and vegetable consumption and originate potentially health-threatening effects. The physical and chemical remediation approaches are restricted and localized and, mainly applied to wastewater and soils and not the plant. The nanotechnological, biotechnological and genetical approaches required to more rectification and sustainability. A cellular, molecular and nano-level understanding of the pathways and reactions are responsible for potentially toxic metals (TMs) accumulation. These approaches can enable the development of crop varieties with highly reduced concentrations of TMs in their consumable foods and vegetables. As a critical analysis by authors observed that nanoparticles could provide very high adaptability for both in-situ and ex-situ remediation of hazardous heavy metals (HHMs) in the environment. These methods could be used for the improvement of the inbuilt genetic potential and phytoremediation ability of plants by developing transgenic. These biological processes involve the transfer of gene of interest, which plays a role in hazardous metal uptake, transport, stabilization, inactivation and accumulation to increased host tolerance. This review identified that use of nanoremediation and combined biotechnological and, transgenic could help to enhance phytoremediation efficiency in a sustainable way.
In recent decades, various conventional techniques have been formulated around the world to evaluate the overall water quality (WQ) at particular locations. In the present study, back propagation neural network (BPNN) and adaptive neuro-fuzzy inference system (ANFIS), support vector regression (SVR), and one multilinear regression (MLR) are considered for the prediction of water quality index (WQI) at three stations, namely Nizamuddin, Palla, and Udi (Chambal), across the Yamuna River, India. The nonlinear ensemble technique was proposed using the neural network ensemble (NNE) approach to improve the performance accuracy of the single models. The observed WQ parameters were provided by the Central Pollution Control Board (CPCB) including dissolved oxygen (DO), pH, biological oxygen demand (BOD), ammonia (NH3), temperature (T), and WQI. The performance of the models was evaluated by various statistical indices. The obtained results indicated the feasibility of the developed data intelligence models for predicting the WQI at the three stations with the superior modelling results of the NNE. The results also showed that the minimum values for root mean square (RMS) varied between 0.1213 and 0.4107, 0.003 and 0.0367, and 0.002 and 0.0272 for Nizamuddin, Palla, and Udi (Chambal), respectively. ANFIS-M3, BPNN-M4, and BPNN-M3 improved the performance with regard to an absolute error by 41%, 4%, and 3%, over other models for Nizamuddin, Palla, and Udi (Chambal) stations, respectively. The predictive comparison demonstrated that NNE proved to be effective and can therefore serve as a reliable prediction approach. The inferences of this paper would be of interest to policymakers in terms of WQ for establishing sustainable management strategies of water resources.