During the last few years, the induction of toxicological mechanisms by atmospheric ultrafine particles (UFP) has become one of the most studied topics in toxicology and a subject of huge debates. Fine particles (FP) and UFP collected at urban and rural sites in Lebanon were studied for their chemical composition and toxicological effects. UFP were found more enriched in trace elements, secondary inorganic ions, total carbon and organic compounds than FP. For toxicological analysis, BEAS-2B cells were exposed for 24, 48 and 72 h to increasing concentrations of FP, water-UFP suspension (UFPw) and UFP organic extract (UFPorg). Our findings showed that UFP caused earlier alterations of mitochondrial metabolism and membrane integrity from the lowest concentrations. Moreover, a significant induction of CYP1A1, CYP1B1 and AhRR genes expression was showed after cells exposure to UFPorg and to a lesser extent to UFPw and FP samples.

Heavy metal contamination has been and continues to be a worldwide phenomenon that has attracted a great deal of attention from governments and regulatory bodies. In this context, our study proposes a regression-tree model to predict the concentration level of zinc in the soils of northern Lebanon (as a case study of Mediterranean landscapes) under a GIS environment. The developed tree-model explained 88% of variance in zinc concentration using pH (100% in relative importance), surroundings of waste areas (90%), proximity to roads (80%), nearness to cities (50%), distance to drainage line (25%), lithology (24%), land cover/use (14%), slope gradient (10%), conductivity (7%), soil type (7%), organic matter (5%), and soil depth (5%). The overall accuracy of the quantitative zinc map produced (at 1:50.000 scale) was estimated to be 78%. The proposed tree model is relatively simple and may also be applied to other areas. GIS regression-tree analysis explained 88% of the variability in field/laboratory Zinc concentrations.

In Lebanon, previous studies have indicated an onset of cardiovascular diseases 12 years earlier than in other parts of the world, suggesting the presence of additional risk factors specific to Lebanon. Measurements of airborne particles in Lebanon surpass the recommendations of the World Health Organization by over 150%. This study examined the association between obstructive coronary artery disease (CAD), assessed by a novel marker calculated from coronary catheterization, and markers of air pollution, specifically polycyclic aromatic hydrocarbons (PAHs), in a cohort of 258 patients seen at the American University of Beirut Medical Center since 2014. The concentrations of four types of hydroxylated polycyclic aromatic hydrocarbons (OHPAHs), 2-OHNAP, 2-OHFLU, 3-OHPHE, and 1-OHPYR, were measured in the urine samples of these patients using high performance liquid chromatography coupled with fluorescence detector. Results showed that the OHPAH concentrations were higher than what was reported in high-income countries and, most notably, the levels for non-smokers in this study were higher than those of smokers and some occupational workers in other countries. This implies that patients were exposed to high levels of PAHs, which originate from combustion sources. In particular, 1-OHPYR showed a significant association with presence of obstructive CAD, even after adjusting for covariates like age, sex, and diabetes. Smokers or not, this association has implications for public health and calls for urgent need to pass regulations to reduce the emissions of PAH sources, such as cars, diesel generators, and incinerators.

The mussel, Brachidontes variabilis, and the limpet, Patella sp., were used as indicators to monitor cadmium, lead and mercury concentrations along the Lebanese coast. Studies were carried out in order to define the best strategy for assessing and minimizing the effects of size and physiological condition on the metal contents of the molluscs, and corrective models were constructed. Metal concentrations in surface water were measured to estimate bioconcentration factors (BCFs). The BCFs varied from 8.3 x 10(3) to 3.4 x 10(4), from 7.5 x 10(3) to 8.0 X 10(3) and from 3.0 x 10(4) to 3.2 x 10(4), for Cd, Pb and Hg, respectively. For limpets, BCFs varied from 1.7 x 10(4) to 7.4 x 10(4) for Cd, from 2.5 x 10(3) to 6 x 103 for Pb and remained fairly constant at around 104 for Hg. The highest BCFs were associated with lowest contamination levels. The results of the geographical survey exhibited a similar large-scale spatial pattern for the two species and followed the metal concentration distributions measured in the waters.

Tripoli harbour is among the most important ports on the Mediterranean Sea eastern basin. The persistent organic pollutants (POPs) were monitored (28 PCBs, 16 PAHs and 18 Me-PAHs) in 15 stations of Tripoli harbour basins, which are influenced by anthropogenic activities. Total PAHs concentrations ranged from 243 to 2965μgkg−1dw, total Me-PAH concentrations ranged from 54 to 1638μgkg−1dw, while total PCB levels ranged from 18 to 302μgkg−1dw. PCBs profiles were dominated by four and six-chlorinated congeners while the PAHs were dominated by four and five rings. For identifying pollution emission sources of PAHs, different ratios were used. The results show that the pollution origin was predominated by pyrogenic process related to the deposition of coal dust and the combustion of biomass and coal. Based on Sediments Quality Guidelines the biological adverse effects on aquatic ecosystems were expected rarely to occasionally for PAHs and PCBs contamination.

This paper tests the adequacy of using the lattice Boltzmann method in large-scale oil spill modelling, such as the Lebanon oil spill. Several numerical experiments were performed in order to select the most appropriate lattice and to decide between the single- and two-relaxation time models. Large-scale oil spills require simulations with short computational times. In order to speed up the computation and preserve adequate accuracy of the model, five different flux limiting interpolation techniques were compared and evaluated. The model was validated on the Lebanon oil spill with regard to the oil-slick position and concentrations in the sea, and the beaching area on the coast. Good agreement with satellite images of the slick and field data on beaching was achieved. The main advantages of the applied method are the capability of simulating very low oil concentrations and computational times that are by an order of magnitude shorter compared to similar models.

MOON (Mediterranean Operational Oceanography Network http://www.moon-oceanforecasting.eu) provides near-real-time information on oil-spill detection (ocean color and SAR) and predictions [ocean forecasts (MFS and CYCOFOS) and oil-spill predictions (MEDSLIK)]. We employ this system to study the Lebanese oil-pollution crisis in summer 2006 and thus to assist regional and local decision makers in Europe, regionally and locally. The MEDSLIK oil-spill predictions obtained using CYCOFOS high-resolution ocean fields are compared with those obtained using lower-resolution MFS hydrodynamics, and both are validated against satellite observations. The predicted beached oil distributions along the Lebanese and Syrian coasts are compared with in situ observations. The oil-spill predictions are able to simulate the northward movement of the oil spill, with the CYCOFOS predictions being in better agreement with satellite observations. Among the free MEDSLIK parameters tested in the sensitivity experiments, the drift factor appears to be the most relevant to improve the quality of the results.

The levels of some persistent organic pollutants (POPs) along the Lebanese coastal zone (LCZ) were assessed by collecting15 surface sediments from five hotspot stations. Such stations were influenced by various industrial units, riverine input, and touristic activities. The levels of polycyclic aromatic hydrocarbons (∑16PAHs), their methylated derivatives (∑18Me-PAHs), and polychlorinated biphenyls (∑28PCBs) were in the range of 537–3773 μg∙kg⁻¹ dw, 187–1541 μg∙kg⁻¹ dw, and 143–303 μg∙kg⁻¹ dw respectively. Significant contamination was found at Beirut Port that is surrounded by a densely populated area and is subjected to multidisciplinary activities. Source identification of PAHs was confirmed by using the diagnostic ratio of PAHs with low molecular weight and high molecular weight (LMW/HMW). For PCBs, LCZ is polluted by higher chlorinated congeners with 4 to 9 chlorinated atoms generated from volatilization and combustion processes. Toxicity and biological risks were assessed using toxic equivalent quantity (TEQcarc) and sediment quality guideline quotient (SQGq).

This paper aims to identify the concentrations of PAHs in the sediments of four coastal zones in Lebanon and determine their possible sources and effects. For each region (Tripoli, Jounieh, Dora, and Tyre), sampling, lyophilization, Soxhlet extraction, rotary evaporation, and gas chromatography were performed on 11, 10, 7, and 11 samples, respectively. The total PAHs concentrations ranged from 1.22 to 731.93μg/kg dry weight. The lowest concentrations were found in Tyre and the highest in Dora and Jounieh. The level of PAHs was classified as low to moderate and their source was mainly pyrogenic.

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.