The Covid-19 outbreak had a critical impact on a massive amount of human activities as well as the global health system. On the other hand, the lockdown and related suspension of working activities reduced pollution emissions. The use of biomonitoring is an efficient and quite recent tool to assess environmental pollution through the analysis of a proper bioindicator, such as bees. This study set out to ascertain the impact of the Covid-19 pandemic lockdown on the environmental occurrence of eleven heavy metals in the Campania region (Italy) by analyzing bees and bee products. A further aim of this study was the assessment of the Honeybee Contamination Index (HCI) in three different areas of the Campania region and its comparison with other Italian areas to depict the current environmental pollutants levels of heavy metals. The results showed that the levels of heavy metals bioaccumulated by bees during the pandemic lockdown (T1) were statistically lower than the sampling times after Covid-19 restrictions and the resumption of some or all activities (T2 and T3). A comparable trend was observed in wax and pollen. However, bee, pollen, and wax showed higher levels of Cd and Hg in T1 than T2 and T3. The analysis of the HCI showed a low contamination level of the sampling sites for Cd and Pb, and an intermediate-high level as regards Ni and Cr. The biomonitoring study highlighted a decrease of heavy metals in the environmental compartments due to the intense pandemic restrictions. Therefore, Apis mellifera and other bee products remain a reliable and alternative tool for environmental pollution assessment.

The waste-to-energy (WTE) incinerator plant located in the Turin area (Italy) started to recover energy from the combustion of municipal solid waste in 2013. A health surveillance program was implemented to evaluate the potential health effects on the population living near the plant. This program included a longitudinal biomonitoring to evaluate temporal changes of some environmental pollutants, including polycyclic aromatic hydrocarbons (PAHs), in residents living in areas near the Turin incinerator (exposed group, E) compared to those observed in subjects living far from the plant (not exposed group, NE). Ten monohydroxy-PAHs (OH-PAHs), consisting in the principal metabolites of naphthalene, fluorine, phenanthrene, and pyrene, were analyzed in urines collected from the E and NE subjects after one (T₁) and three years (T₂) of plant activity and compared with those determined in the same cohort established before the plant start-up (T₀). Spearman correlation analysis was undertaken to explore possible associations between OH-PAHs and personal characteristics, lifestyle variables, and dietary habits. A linear mixed model (LMM) approach was applied to determine temporal trends of OH-PAHs observed in the E and NE subjects and to evaluate possible differences in trend between the two groups. Temporal trends of OH-PAHs determined by LMM analysis demonstrated that, at all times, the E group had concentrations lower than those assessed in the NE group, all other conditions being equal. Moreover, no increase in OH-PAH concentrations was observed at T₁ and T₂ either in E or in NE group. Significant positive correlations were found between all OH-PAHs and smoking habits. Regarding variables associated to outdoor PAH exposure, residence near high traffic roads and daily time in traffic road was positively correlated with 1-hydroxynaphthalene and 1-hydroxypyrene, respectively. In conclusion, no impact of the WTE plant on exposure to PAHs was observed on the population living near the plant.

A total of 1345 specimens belonging to 58 different species of wild fish and seafood from the western Mediterranean Sea were analyzed to assess total mercury levels and to estimate which species meet the EU recommendations for human consumption (0.5 μg g⁻¹ ww) in all cases. All fish species were caught off the Mediterranean coasts and intended for human consumption. All specimens were collected from local markets located in Spain, Italy and France that sell fish caught by local fishermen (Eivissa, Menorca, Mallorca, Alacant, L'Ampolla, Ametlla de Mar, Marseille, Genoa, Civitavecchia, Alghero) at different time periods. Mercury concentrations were measured by thermal decomposition-gold amalgamator-atomic absorption spectrometry. Only thirteen species were found that did not exceed 0.5 μg g⁻¹ ww in any specimen analyzed. These safe species were sardines (Sardina pilchardus), anchovies (Engraulis encrasicolus), blue whiting (Micromesistius poutassou), picarel (Spicara smaris), blackspot seabream (Pagellus bogaraveo), gilthead seabream (Sparus aurata), pearly razorfish (Xyrichtys novacula), surmullet (Mullus surmuletus), painted comber (Serranus scriba), brown meagre (Sciaena umbra), salema (Sarpa salpa), common dolphinfish (Coryphaena hippurus) and squid (Loligo vulgaris). These species occupy different trophic levels, have different lengths and average weights, but show a low mercury concentration than others living in the same environments. Potential human consumption of these species as sole source of fish would imply estimated weekly intakes representing between 49% and 70% of the recommended provisional tolerable weekly intake of methylmercury in the worst case. Health authorities should pay specific attention to species that do not meet EU thresholds and make appropriate precautionary health recommendations, especially for pregnant women and children.

To evaluate the chemical status of groundwater bodies (GWB) according to the European Groundwater Directive, EU Member States are required to take into account natural background levels (NBLs) where needed. Assessing the NBLs in coastal GWBs is complicated by seawater intrusion which can be amplified by groundwater withdrawals increasing the salinization of such groundwater systems. This paper proposes a new method for the NBLs assessment in coastal areas based on a double pre-selection (PS) with fixed/dynamic limits. A case study in the Apulia region, located in southeastern Italy, is proposed, where we investigated four adjacent GWBs which form the complex karst, fractured Murgia aquifer, hosted in the Jurassic-Cretaceous carbonate platform, bounded by two seas and sustained by saltwater of marine intrusion in the coastal areas. Data related to 139 monitoring stations (MSs) of the regional groundwater monitoring network were used. The first PS, “static”, based on a fixed limit of anthropogenic contamination markers (NO₃ and NH₄), allows for the elimination of MSs impacted by human activities. On these, the second PS, “dynamic”, based on the identification of Cl anomalous values, allows for the identification of additional MSs affected by saline contamination. The residual dataset of MSs was used for the definition of NBLs of Cl, SO₄, F and B. A statistical comparison with historical Cl observations finally allowed us to verify if the salinity of current groundwater is representative of pristine conditions. The calculated NBLs of salinity parameters are higher for the two coastal GWBs, with chloride values between 0.8 and 2 mg/L. Conversely, fluorides always show very low NBLs. The double PS approach seems more effective for NBLs calculation in coastal aquifers affected by saline contamination, where the use of a fixed Cl limit fails. It may respond to the international needs for a standardized procedure for NBL assessment.

While the increasing accumulation of anthropogenic litter in the marine environment has received considerable attention over the last decade, litter occurrence and distribution in rivers, the main source of marine litter, have been comparatively less investigated. Moreover, little information is available about the amount and typology of Riverine Anthropogenic Macro-litter (RAM) entering marine environments from intermittent rivers in low populated areas of the Mediterranean basin. To provide insights on this issue, we investigated density and composition of RAM accumulated over a total of 133 riverbanks, belonging to 37 river basins in the Sardinia Island (Mediterranean Sea). We report here that plastics, especially single-use items, represent the most frequent and abundant RAM category in all investigated basins. Statistical modelling revealed that occurrence of lightweight RAM (especially plastic) is mostly explained by levels of urban (12.3% of the relative contribution) and agricultural (12%) land use of the territory, whereas the proximity of bridges to the sampling point (21%) and the local population density (19.8%) are best predictors of heavy weighted RAM items (i.e., large metal items, appliances) occurrence. Our results confirm that plastics represent an important component of RAM and pinpoint that, beside plastic reduction policies and better waste management, actions aimed at abating and monitoring litter contamination should be localized on the proximity of bridges, whatever the local population density. Finally, to fill existing knowledge gaps in understanding the severity of litter discharge and accumulation in the Mediterranean Sea, land-to-sea systematic monitoring campaigns at appropriate spatial and temporal scales should be put in place.

The consequence of the lockdowns implemented to address the COVID-19 pandemic on human health damage due to air pollution and other environmental issues must be better understood. This paper analyses the effect of reducing energy demand on the evolution of environmental impacts during the occurrence of 2020-lockdown periods in Italy, with a specific focus on life expectancy. An energy metabolism analysis is conducted based on the life cycle assessment (LCA) of all monthly energy consumptions, by sector, category and province area in Italy between January 2015 to December 2020. Results show a general decrease (by ∼5% on average) of the LCA midpoint impact categories (global warming, stratospheric ozone depletion, fine particulate matter formation, etc.) over the entire year 2020 when compared to past years. These avoided impacts, mainly due to reductions in fossil energy consumptions, are meaningful during the first lockdown phase between March and May 2020 (by ∼21% on average). Regarding the LCA endpoint damage on human health, ∼66 Disability Adjusted Life Years (DALYs) per 100,000 inhabitants are estimated to be saved. The analysis shows that the magnitude of the officially recorded casualties is substantially larger than the estimated gains in human lives due to the environmental impact reductions. Future research could therefore investigate the complex cause-effect relationships between the deaths occurred in 2020 imputed to COVID-19 disease and co-factors other than the SARS-CoV-2 virus.

Two areas in central-southern Italy Land of Fires in Campania and Valley of Sacco river in Lazio are known to be contaminated sites, the first due to illegal fly-tipping and toxic fires, and the second due to an intensive industrial exploitation done by no-scruple companies and crooked public administration offices with dramatic consequences for environment and resident people. The work is intended to contribute to Human BioMonitoring (HBM) studies conducted in these areas on healthy young male population by a semiconductor gas sensor array trained by SPME-GC/MS. Human semen, blood and urine were investigated. The fingerprinting of the Volatile Organic Compounds (VOCs) by a gas sensors system allowed to discriminate the different contamination of the two areas and was able to predict the chemical concentration of several VOCs identified by GC/MS.

We investigated the temporal changes from spring to summer of the stranded litter and the composition of plastic encrusting biota along an Italian beach. Our findings highlight a higher quantity of litter (average value 1510.67 ± 581.27 items) in spring, particularly plastic material with a composition driven by currents, winds and waves transported from rivers to sea. During summer the source was caused by anti-social behaviours (e.g. cigarettes). Regarding the plastic size, the most is macroplastic (85.96 %), followed by mesoplastic (13.74 %) and megaplastic (0.30 %) overall, and no seasonal trend was observed. Concerning the encrusting biota, Mollusca was the most frequent phylum found on plastic beach litter, whereas Porifera the most abundant overall. During spring a greater abundance of individuals was recorded compared to summer. The trend of taxa richness was decreasing from spring to summer. Arthropoda, Porifera and Mollusca phyla were significantly more abundant in spring, while Algae in summer.

This study deals with the issue of beach litter pollution in the context of the Descriptor 10 of the Marine Strategy Framework Directive Good Environmental Status of EU waters and Ecological objective 10, Common indicator 22 of IMAP. Analyses of the amount, distribution and categorization of beach litter were conducted on nine beaches during 108 surveys covering the area of 206.620 m² in Albania, Italy and Montenegro. Our findings showed that the level of beach litter pollution on south Adriatic beaches is significantly above the adopted threshold values, with a median item numbers of 327, 258 and 234 per 100 m of beach stretch for Albania, Italy and Montenegro, respectively. It can be concluded that, when it comes to beach litter pollution, GES has not been achieved. Given the defined baseline and threshold values at the EU level, the process of reducing the total amount of marine litter in southern Adriatic Sea will be very challenging and needs urgent and specific actions.

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.