This study investigates heavy metal contamination in seawater and sediments in three coastal regions of Oman – Darsait, Azaibah and Seeb. In 2022, sample collections were made in subtidal ecosystems in these three areas, and the samples were analysed to assess the potential environmental impacts of treated wastewater. The study employed a comprehensive sampling strategy adhering to the Oman Environmental Impact Assessment (EIA) Guidelines to evaluate key parameters such as temperature, salinity, dissolved oxygen, and heavy metal concentrations see Appendix 1. Results indicate that the levels of most heavy metals contamination had not changed significantly since the baseline surveys in 2009 and 2012, but that some levels were still above EIA guidelines. Recommendations are made to reduce these levels, ensuring the protection of marine environments and humans while supporting industrial growth. This research contributes to our understanding of anthropogenic impacts on marine ecosystems in Oman and highlights the importance of regular monitoring and adaptive management practices.

The paper introduces a machine learning method of detecting multiple sources of water contamination caused by wildfire. The method includes changing the water flow regime, monitoring the time series of the contaminant concentration caused by regime changes, and associating the signature of the contaminant changes over time with sources locations. The contaminant signature from multiple sources starting at the moment of changing water velocity are defined by extending the approach for one contamination source. The intensity, location of each source, and diffusion coefficient are defined to satisfy the minimum square between monitoring and theoretical concentrations. The equations derived from the criteria of the best fit between experimental and modeling data are solved using the theory of hypernumbers. The initial values for hypernumber solutions are computed using the transient process of contaminant transport curve analysis. The defined in this paper algorithm can by used for detecting location of the arbitrary impurity in water network system.