Effects of rainbow trout farming on water quality around the sea farms in the south of the Caspian Sea

The purpose of this study was to investigate the physical and chemical factors of seawater to determine the water quality index around two marine farms of rainbow trout in the south of the Caspian Sea. Each farm had four floating polyethylene cages with a final fish harvest of 60 tons. The water sampling was performed in January and March 2014 as well as May and August 2015 from around the cages (close: cage shade, 50 m and 100 m; distant: 1000 m) in three geographical directions: east, west, and south. The water quality parameters including pH, temperature, transparency, salinity, electrical conductivity (EC), total dissolved solids, dissolved oxygen, and nutrients (nitrogen and phosphorus compounds) were determined. The results of the analysis of variance of data at both farms showed that changes in physical and chemical parameters of water had only significant differences at the time of sampling (p < 0.05). The highest value of variance in the principal component analysis (PCA; 30.23% from 84.75%) was related to EC, temperature, salinity, total nitrogen, pH, and organic phosphorus. Iran Surface Water Quality Index (IRWQISC) at near and far distances from farms was determined to be moderate (40-55). The main reasons for this result can be attributed to the small-scale and short fish farming period along with the hydrological conditions of the region

Innovative Overview of SWRC Application in Modeling Geotechnical Engineering Problems

The soil water retention curve (SWRC) or soil&ndash;water characteristic curve (SWCC) is a fundamental feature of unsaturated soil that simply shows the relationship between soil suction and water content (in terms of the degree of saturation and volumetric or gravimetric water content). In this study, the applications of the SWRC or SWCC have been extensively reviewed, taking about 403 previously published research studies into consideration. This was achieved on the basis of classification-based problems and application-based problems, which solve the widest array of geotechnical engineering problems relevant to and correlating with SWRC geo-structural behavior. At the end of the exercises, the SWRC geo-structural problem-solving scope, as covered in the theoretical framework, showed that soil type, soil parameter, measuring test, predictive technique, slope stability, bearing capacity, settlement, and seepage-based problems have been efficiently solved by proffering constitutive and artificial intelligence solutions to earthwork infrastructure; and identified matric suction as the most influential parameter. Finally, a summary of these research findings and key challenges and opportunities for future tentative research topics is proposed