To evaluate BPA's potential risk to health, it is important to know human daily intake. This study describes a simple but effective method to estimate the levels of human BPA intake among four different populations based on urinary concentration data. Nationally, of the 30 countries examined, the top ten countries for adult intake were Italy, Sweden, Denmark, France, Cyprus, Australia, Israel, Ghana, Jamaica, and Belgium. When the urinary excretion sample size was large enough and over 1000, it was found that the national estimated BPA daily intakes in the child group among countries, showed a good linear relationship with those of their corresponding adult group. Except the infant group with limited data, the global estimated BPA daily intakes for children and pregnant women were 2 and 1.4 times that of the adult group. Although the national and global estimated BPA daily intakes were generally below the temporary tolerable daily intake (tTDI) recommended by the European Food Safety Authority (EFSA), but some normal individuals' daily intakes exceeded the tTDI.

Oil spill models are combined with bathymetric, meteorological, oceanographic, and geomorphological data to model a series of oil spill accidents in the Eastern Mediterranean Sea. A total of 104 oil spill simulations, computed for 11 different locations in the Levantine Basin, show that oil slicks will reach the coast of Cyprus in four (4) to seven (7) days in summer conditions. Oil slick trajectories are controlled by prevailing winds and current eddies. Based on these results, we support the use of chemical dispersants in the very few hours after large accidental oil spills. As a corollary, we show shoreline susceptibility to vary depending on: a) differences in coastline morphology and exposure to wave action, b) the existence of uplifted wave-cut platforms, coastal lagoons and pools, and c) the presence of tourist and protected environmental areas. Mitigation work should take into account the relatively high susceptibility of parts of the Eastern Mediterranean.

The intelligent method proposed herein is formulated on a deep learning technique which can identify, localise and map the shape of plastic debris in the marine environment. Utilising images depicting plastic litter from six beaches in Cyprus, the developed tool pointed to a plastic litter density of 0.035 items/m². Extrapolated to the entire shorelines of the island, the intelligent approach estimated about 66,000 plastic articles weighting a total of ≈1000 kg. Besides deducing the plastic litter density, the dimensions of all documented plastic litter were determined with the aid of the OpenCV Contours image processing tool. Results revealed that the dominant object length ranged between 10 and 30 cm which is in agreement with the length of common plastic litter often spoiling these coastlines. Concluding, only in-situ visual scan sample surveys and no manual collection means were used to predict the density and the dimensions of the plastic litter.

The eastern Mediterranean is a region that has been relatively understudied with regards to anthropogenic marine litter despite potential for environmental and social costs. Here, coastal marine litter accumulation was assessed at eight beach locations along the coast of Northern Cyprus. Monthly surveys were performed between January 2017 and January 2019. All items ≥ 2.5 cm in diameter within the same 250 m² plot were collected and processed. A total of 59,556 separate items were collected with a total mass of 697 kg. At the worst affected site (Ronnas Bay) litter accumulation averaged 1114 items and 11.9 kg per month. Plastic and polystyrene litter accounted for 82% (622.71 kg) of all litter types by mass and 96% (57,231) by frequency. Plastic bags, plastic pieces, drinks containers, caps/lids were the four most abundant forms of plastic by mass and by frequency. This suggests dumping/poor domestic waste governance as the main driver of marine litter in the region.

A field experiment in the waters off the south-eastern coast of Cyprus was carried out to study near-field formation of sediment plumes from dumping. Different loads of sediment were poured into calm and limpid waters one at the time from just above the sea surface. The associated plumes, gravitating towards the seafloor, were filmed simultaneously by four divers situated at different depths in the water column, and facing the plume at different angles. The processes were captured using GoPro-Hero-series cameras. The high-quality underwater footage from near-surface, mid-depth and near-bed positions gives unique insight into the dynamics of the descending plume and near-field dispersion processes, and enables good understanding of flow and sediment transport processes involved from-release-to-deposition of the load in a non-scaled environment. The high resolution images and footages are available through the link provided herein. Observations support the development of a detailed multi-fractional sediment plume model.

The increasing level of marine plastic pollution poses severe threats to the marine ecosystem and biodiversity. Open remote sensing data and advanced machine learning (ML) algorithms could be a cost-effective solution for identifying large plastic patches across the scale. The potential application of such resources in detecting and discriminating marine floating plastics (MFP) are not fully explored. Therefore, the present study attempted to explore the full functionality of open Sentinel satellite data and ML models for detecting and classifying the MFP in Mytilene (Greece), Limassol (Cyprus), Skala Loutron, Greece, Calabria (Italy), and Beirut (Lebanon). Two ML models, i.e. Support Vector Machine (SVM) and Random Forest (RF), were utilized to perform the classification analysis. In-situ plastic location data was collected from the control experiments conducted in Mytilene, Greece (in 2018 and 2019), Skala Loutron, Greece (2021), and Limassol, Cyprus (2018), and the same was considered for training the models. The accuracy and performances of the trained models were further tested on unseen new data collected from Calabria, Italy and Beirut, Lebanon. Both remote sensing bands and spectral indices were used for developing the ML models. A spectral signature profile for marine plastic was created for discriminating the floating plastic from other marine debris. A newly developed index, kernel Normalized Difference Vegetation Index (kNDVI), was incorporated into the modelling to examine its contribution to model performances. Both SVM and RF were performed well in five models and test case combinations. Among the two ML models, the highest performance was measured for the RF. The inclusion of kNDVI was found effective and increased the model performances, reflected by high balanced accuracy measured for model 2 (~89% to ~100% for SVM and ~92% to ~98% for RF). An automated floating plastic detection system was developed and tested in Calabria and Beirut using the best-performed model. The trained model had detected the floating plastic for both sites with ~80%–90%% accuracy. Among the six predictors, the Floating Debris Index (FDI) was the most important variable for detecting marine floating plastic. These findings collectively suggest that high-resolution remote sensing imagery and the automated ML models can be an effective alternative for the cost-effective detection of MFP. Future research will be directed toward collecting quality training data to develop robust automated models and prepare a spectral library for different plastic objects for discriminating plastic from other marine floating debris and advancing the marine plastic pollution research by taking full advantage of open-source data and technologies.

The heavy metals (HMs) concentration range, the metal/Al and metal/Fe value were determined in the sediment in order to evaluate the correlation coefficient of HMs at the regions of South East Mediterranean Sea, Cyprus. The results showed that the mean concentrations of the HMs in the sediment samples followed the order Al > Fe > Mn > Cr > Ni > Zn > Cu > Pb > As>Hg, respectively. The pollution indices of contamination factor (CF), geoaccumulation index (Igeo), enrichment factor (EF), and soil pollution index (SPI) were calculated. The Igeo, CF and EF values for Cu were obtained moderately to strongly polluted, moderate contamination and moderately severe enrichment, respectively. The remaining elements indicated unpolluted or low contamination results. Also, SPI indicated a low level of contamination for the all of the metals. The cluster analysis of data set indicates that the HMs are separated with 50% similarity in two categories of Al category (As, Pb, Cr, Ni) and Fe category (Mn, Zn, Cu, Hg).

Mediterranean Sea is among the world's leading tourist destinations; however, the sharp increase in tourists during the high season may affect coastal seawater. The main aim of this study was to evaluate the occurrence and temporal variation of anthropogenic nutrients in coastal seawater in relation to tourist flows in three Mediterranean islands (Cyprus, Sicily and Rhodes), through short-term macroalgae deployments, coupled with δ¹⁵N analysis and GIS mapping. In all islands, an overall increase in macroalgae δ¹⁵N occurred over the deployment carried out in August in the tourist sites, suggesting the presence of anthropogenic nutrients. Decreasing δ¹⁵N values occurred at increasing distance from the coastline in two out of the three islands (Cyprus and Sicily). This study revealed the usefulness of the approach used in the assessment of tourism impact in terms of trophic enrichment and its potential to support competent authorities for the development of sustainable coastal management plans.

In this study the online meteorological and chemical transport model “Weather Research and Forecasting coupled with Chemistry” (WRF/Chem) is implemented over Cyprus and evaluated against ground-based air quality and meteorological observations. Hourly O3 concentrations are strongly overestimated and a reduction in the lateral boundaries of ozone by 30% improved model agreement with observations. Utilization of grid spacing closer to the resolution of the emission data leads to an improvement in the simulation of air pollutants, while further reduction of the grid spacing mostly impacts the model performance related to meteorological parameters. The method of speciation of volatile organic compounds can also affect model results. Reduction of NOx emissions can reduce fine particulate (PM2.5) levels relatively effectively due to the important role of nitrate over Cyprus. The present case study indicates that the performance of the WRF/Chem model is reasonable for air quality and meteorological variables over Cyprus when boundary conditions are cautiously adjusted or improved, while there is a need to pursue a high-resolution local emission inventory.

The objective of the present study was to evaluate the distribution, sources, origins, and environmental risk assessment of polycyclic aromatic hydrocarbons (16 US EPA priority pollutants) pollution in 23 surface sediments from Cyprus coast. The mean total polycyclic aromatic hydrocarbons (PAHs) concentrations in the sediments from Gemi Konagi, Girne and Gazi Magusa areas were found 47, 52 and 50ng/g, respectively. Molecular ratios and principle component analysis indicated that PAH pollution originated mainly from fossil sources, with higher pyrolytic contributions. The 2–3 ring PAHs were dominant in Cyprus sediments. Concentrations of PAHs observed in this study were compared with available soil quality guidelines and the concentrations were lower than the guideline values. The guideline values suggested that the Cyprus sediments were likely to be not contaminated by toxic PAH compounds.