article · Journal of Marine Science and Engineering
Managing coastal zones requires robust techniques to evaluate environmental quality and support long-term decision-making. Over two years, surface sediment samples were gathered from the Gamasa estuary and littoral shelf along the Mediterranean coast of Egypt. The concentrations of seventeen elements were measured using mass spectrometry, and multiple pollution and ecological risk indices were computed. The evaluation showed that all analysed sediment samples were considerably contaminated, with elements including chromium, cobalt, nickel, and copper originating from anthropogenic sources. Despite this, the overall ecological risk remained low. Furthermore, seventy percent of estuary samples were classified as polluted under the pollution load index, whereas most littoral shelf samples were unpolluted. Machine learning models, specifically random forest and back-propagation neural networks, were deployed and effectively predicted the contamination indices from the elemental data.
Coastal and estuarine ecosystems are vulnerable to human activities that release toxic metals into the environment. Assessing sediment quality helps identify the extent and origin of pollutants. By demonstrating that data-driven neural networks can accurately calculate complex pollution indices, this work illustrates how computational tools can streamline environmental monitoring and support sustainable management of marine and coastal zones.
The machine learning methodology could enable environmental consultants, coastal management authorities, and monitoring agencies to automate and accelerate pollution index calculations from chemical data. As an applied and tested data-driven approach on real-world field samples, it represents an analytical tool that could be integrated into environmental assessment software, though commercial deployment would require packaging into user-friendly monitoring platforms.
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Coastal environmental assessment techniques have evolved into one of the most important fields for the long-term development and management of coastal zones. So, the overall aim of the present investigation was to provide effective approaches for making informed decisions about the Gamasa coast sediment quality. Over a two-year investigation, sediment samples were meticulously collected from the Gamasa estuary and littoral shelf. The inductively coupled plasma mass spectra (ICP-MS) was used to the total concentrations of Al, Fe, Ti, Mg, Mn, Cu, P, V, Ba, Cr, Sr, Co, Ni, Zn, Pb, Zr, and Ce. Single elements environmental pollution indices including the geoaccumulation index (Igeo), contamination factor (CF), and enrichment factor (EF), as well as multi-elements pollution indices comprising the potential ecological risk index (RI), degree of contamination (Dc), and pollution load index (PLI) were used to assess the sediment and the various geo-environmental variables affecting the Mediterranean coastal system. Furthermore, the Dc, PLI, and RI were estimated using the random forest (RF) and Back-Propagation Neural Network (BPNN) depending on the selected elements. According to the Dc results, all the investigated sediment samples categories were considerably contaminated. Cr, Co, Ni, Cu, Zr, V, Zn, P, and Mn showed remarkable enrichment in sediment samples and were originated from anthropogenic sources based on the CF, EF, and Igeo data. Moreover, the RI findings revealed that all the samples tested pose a low ecologically risk. Meanwhile, based on PLI, 70% of the Gamasa estuary samples were polluted, while 93.75% of littoral shelf sediment was unpolluted. The BPNNs -PCs-CD-17 model performed the best and demonstrated a better association between exceptional qualities and CD. With R2 values of 1.00 for calibration (Cal.) and 1.00 for validation (Val.). The BPNNs -PCs-PLI-17 models performed the best in terms of measuring PLI with respective R2 values of 1.00 and 0.98 for the Cal. and Val. datasets. The findings showed that the RF and BPNN models may be used to precisely quantify the pollution indices (Dc, PLI, and RI) in calibration (Cal.) and validation (Val.) datasets utilizing potentially toxic elements of surface sediment.
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DOI: 10.3390/jmse10060816
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