Water quality assessment of Lake Burullus, Egypt, utilizing statistical and GIS modeling as environmental hydrology applications

Evaluation of the water quality and the eutrophication risk in Ramsar site on Moroccan northern Mediterranean (Marchica lagoon): A multivariate statistical approach

This study aimed to assess the extent of eutrophication in the Marchica lagoon, using the trophic index (TRIX), trophic status index (TSI) and Redfield ratio (N/P). Seawater samples were collected monthly from March 2018 to February 2019 from ten locations in the lagoon. The TRIX index ranged from 1.25 to 8.87, indicating oligotrophic to eutrophic conditions. TSI values ranged from 28.4 to 65.1, indicating ultraoligotrophic to supereutrophic seawater. The Redfield ratio consistently showed N/P values below 16, designating phosphorus as the limiting factor for algal growth. Principal component analysis (PCA) and cluster analysis (CA) revealed distinct coastal water masses based on seasons and sampling regions. Given these findings, urgent conservation and management measures are recommended to address the eutrophication issues threatening the delicate balance of the Marchica lagoon.

An applicability test of the conventional and neural network methods to map the overall water quality of the Caspian Sea

This study investigates the water quality of the Caspian Sea by examining the presence of nutrients and heavy metals in the water. Water samples were collected from 22 stations and analyzed for nutrient and heavy metal levels. The study used the fuzzy method to prepare water quality maps and employed ANNs methods to predict microbial contamination for future years. The results revealed that the western and northwestern parts of the region had higher nutrient levels (about 40.2 % of the region), while the eastern and northeastern shores were highly polluted due to increased urbanization (about 70.1 % of the region). The long short-term memory (LSTM) method was found to have the highest accuracy compared to other ANNs methods and indicated a recent increase in pollution (R_{Water quality}²=0.940, R_OECD²=0.950, R_TRIX²=0.840). The study recommends targeted research to identify the causes and means of controlling pollution in light of the predicted increase in pollution in the Caspian Sea.

Investigation and comparative analysis of ecological risk for heavy metals in sediment and surface water in east coast estuaries of India

The sediments and surface water from 8 stations each from Dhamara and Paradeep estuarine areas were sampled for investigation of heavy metals, Cd, Cu, Pb, Mn, Ni, Zn, Fe, and Cr contamination. The objective of the sediment and surface water characterization is to find the existing spatial and temporal intercorrelation. The sediment accumulation index (I_sed), enrichment index (I_En), ecological risk index (I_EcR) and probability heavy metals (p-HMI) reveal the contamination status with Mn, Ni, Zn, Cr, and Cu showing permissible (0 ≤ I_sed ≤ 1, I_En ˂ 2, I_EcR ≤ 150) to moderate (1 ≤ I_sed ≤ 2, 40 ≤ R_f ≤ 80) contamination. The p-HMI reflects the range from excellent (p-HMI = 14.89-14.54) to fair (p-HMI = 22.31-26.56) in off shore stations of the estuary. The spatial patterns of the heavy metals load index (I_HMc) along the coast lines indicate that the pollution hotspots are progressively divulged to trace metals pollution over time. Heavy metal source analysis coupled with correlation analysis and principal component analysis (PCA) was used as a data reduction technique, which reveals that the heavy metal pollution in marine coastline might originate from redox reactions (FeMn coupling) and anthropogenic sources.