No abstract

A comparative study of the two northeastern ports of the Adriatic Sea indicated that the port of Rijeka is microbiologically more loaded than the port of Pula and posing a greater threat to other ports through a potential transfer of pathogens by ballast water. Fecal indicator bacteria, Escherichia coli and intestinal enterococci, were investigated seasonally in 2014–2015 in the ports and during the bathing season monitoring in the two bays where ports are located in 2009–2020. In addition, the indicators and pathogens related to human health were determined in the ports’ seawater and sediment. The determined factors contributing to microbiological pollution were higher number of tourists and locals, potential wastewater and ballast water discharge and enclosed port configuration, with high solar radiation and low precipitation reducing the negative effects. Our research points to the necessity of including Clostridium perfringens in monitoring beach sand during the bathing seasons and a wider list of pathogens in port monitoring due to a potential transfer by shipping ballast water.

Numerical analyses of environmental discharges are commonly conducted on pre-generated numerical grids with refinements implemented in regions of interest or influence on the flow field. This approach to problem formulation relies on insights into the flow specifics so that appropriate attention is given to relevant segments of the domain. In this paper we investigated the applicability of adaptive mesh refinement (AMR) on a commonly considered environmental problem—a jet in crossflow. The assessment was made using the OpenFOAM toolbox. Several RANS turbulence models and grid generation approaches were compared in terms of accuracy to previous studies and experimental results. Main emphasis is given to the computational efficiency of the methodology with a focus on load distribution. Our findings indicate that the results are acceptable in terms of accuracy with load balancing providing significant computational savings thus enabling AMR methodology to outperform the conventional approach.

Coastal water quality management is a public health concern, as poor coastal water quality can potentially harbor pathogens that are dangerous to human health. Tourism-oriented countries need to actively monitor the condition of coastal water at tourist popular sites during the summer season. In this study, routine monitoring data of Escherichia Coli and enterococci across 15 public beaches in the city of Rijeka, Croatia, were used to build machine learning models for predicting their levels based on environmental parameters as well as to investigate their dynamics and relationships with environmental stressors. Gradient Boosting algorithms (Catboost, Xgboost), Random Forests, Support Vector Regression and Artificial Neural Networks were trained with routine monitoring measurements from all sampling sites and used to predict E. Coli and enterococci values based on environmental features. The evaluation of stability and generalizability with 10-fold cross validation analysis of the machine learning models, showed that the Catboost algorithm performed best with R 2 values of 0.71 and 0.68 for predicting E. Coli and enterococci, respectively, compared to other evaluated ML algorithms including Xgboost, Random Forests, Support Vector Regression and Artificial Neural Networks. We also use the SHapley Additive exPlanations technique to identify and interpret which features have the most predictive power. The results show that site salinity measured is the most important feature for forecasting both E. Coli and enterococci levels. Finally, the spatial and temporal predictive accuracy of both ML models were examined at sites with the historically lowest coastal water quality. The spatial E.Coli and enterococci models achieved strong R 2 values of 0.85 and 0.83, while the temporal models achieved R 2 values of 0.74 and 0.67. The temporal model also achieved moderate R 2 values of 0.44 and 0.46 at a site with historically high coastal water quality.

In recent years, microplastic pollution has been given increasing attention in marine environments due to the hazard it poses for aquatic organisms. Plastic pipes are now being widely used in shipbuilding, and due to easy processing, they are often installed directly on ships. This includes the cutting and preparation of pipes for welding, which produces plastic debris in the immediate vicinity of the marine environment. Such plastic debris can easily become airborne, and when it is ultimately deposited into the water, it can be a contributor to marine microplastic pollution. This could be reduced if, during the design stage and outfitting stage, engineers would take into consideration ecological aspect of their design, which is currently not the case. Therefore, in this paper, suggestions for green shipbuilding practices, focused on the piping design and production phases, are presented for the possible reduction in operations with plastic pipes, with the main aim of reducing microplastic pollution. Based on these recommendations, additional economic and feasibility investigations are needed to obtain optimal results, which would be beneficial both from a manufacturing and ecological perspective.