Please describe the objectives to be pursued by the proposed activities and specific impacts they are looking to achieve, and the details of the methodology it will use to achieve them. (This should relate to, but not repeat, the information given in the project summary and activity plan below)

Please describe the objectives to be pursued by the proposed activities and specific impacts they are looking to achieve, and the details of the methodology it will use to achieve them. (This should relate to, but not repeat, the information given in the project summary and activity plan below)

This project will be executed in three overlapping phases:
1. Mapping of heavy metals (Hg, Cd, Al, Co, Ni, Zn, Cu) and organic (TOC, COD) concentrations in the groundwater of the Al- Batinah region.
Analysis of samples from groundwater sources will be combined with existing digital terrain and hydrology modelling to generate a GIS highlighting those areas exhibiting highest contamination levels according to water quality index. Techniques: inverse distance weightage interpolation; Matlab.
2. Development of multisensory arrays (MSAs) for the real-time detection of heavy metal and organic contaminants and optimisation of low-cost, environmentally sustainable materials for the water treatment.
3D Graphene and its composite materials will be used for the construction/design of high-performance MSA using screen-printed electrodes for target pollutant detection using electrochemical methods. Date palm leaflets will be used to prepare and functionalise both dehydrated and activated carbons to successfully produce surface tailored carbon materials for targeting the different pollutants for green filtration
3. Development of an integrated system for real-time water resource management combining monitoring and remediation. The real-time data observations will be captured by developing a decision support system using machine learning techniques and contamination event detection algorithm. Techniques: Decision capable software element utilizing partial least squares regression