Noise is one of the main parameters to be considered to achieve a healthy indoor ambience in ferries. Therefore, the noise standards need to be more specialized and specifically based on real sampled data and case studies. In the present research, the noise levels in a ship, under different working conditions, were sampled and compared with those specified in the new and old standards. An initial study showed two main noise sources- clients and main engine- that influence other indoor environments, reducing the quality of life on board. The real-time data revealed that the maximum noise level limits set by the International Maritime Organization (IMO) in the older Resolution A.468 (XII) was mostly respected, except in areas where maintenance of the noise level was difficult, owing to the continuous influx of people, especially at the time of boarding and disembarking of the passengers and at the food self-service areas. In this sense, under the new Resolution MSC.337 (91), the maximum noise level allowed in the accommodation has been reduced by 5 dB (A), but this environment does not meet the standard. More results show that future standards must not only consider the noise level in a working place and add another variable, such as, the number of working hours, to obtain a representative equivalent energy, and they must also consider that a simple modification of this standard implies a redesign of most of the indoor ambiences onboard.

Climatic condition, geology, and geochemical processes in an area play a major role on groundwater quality. Impact of these on the fluoride content of groundwater was studied in three regions-part of Nalgonda district in Telangana, Pambar River basin, and Vaniyar River basin in Tamil Nadu, southern India, which experience semi-arid climate and are predominantly made of Precambrian rocks. High concentration of fluoride in groundwater above 4 mg/l was recorded. Human exposure dose for fluoride through groundwater was higher in Nalgonda than the other areas. With evaporation and rainfall being one of the major contributors for high fluoride apart from the weathering of fluoride rich minerals from rocks, the effect of increase in groundwater level on fluoride concentration was studied. This study reveals that groundwater in shallow environment of all three regions shows dilution effect due to rainfall recharge. Suitable managed aquifer recharge (MAR) methods can be adopted to dilute the fluoride rich groundwater in such regions which is explained with two case studies. However, in deep groundwater, increase in fluoride concentration with increase in groundwater level due to leaching of fluoride rich salts from the unsaturated zone was observed. Occurrence of fluoride above 1.5 mg/l was more in areas with deeper groundwater environment. Hence, practicing MAR in these regions will increase the fluoride content in groundwater and so physica or chemical treatment has to be adopted. This study brought out the fact that MAR cannot be practiced in all regions for dilution of ions in groundwater and that it is essential to analyze the fluctuation in groundwater level and the fluoride content before suggesting it as a suitable solution. Also, this study emphasizes that long-term monitoring of these factors is an important criterion for choosing the recharge areas.

The sediment transport, involving the movement of the bedload and suspended sediment in the basins, is a critical environmental concern that worsens water scarcity and leads to degradation of land and its ecosystems. Machine learning (ML) algorithms have emerged as powerful tools for predicting sediment yield. However, their use by decision-makers can be attributed to concerns regarding their consistency with the involved physical processes. In light of this issue, this study aims to develop a physics-informed ML approach for predicting sediment yield. To achieve this objective, Gaussian, Center, Regular, and Direct Copulas were employed to generate virtual combinations of physical of the sub-basins and hydrological datasets. These datasets were then utilized to train deep neural network (DNN), conventional neural network (CNN), Extra Tree, and XGBoost (XGB) models. The performance of these models was compared with the modified universal soil loss equation (MUSLE), which serves as a process-based model. The results demonstrated that the ML models outperformed the MUSLE model, exhibiting improvements in Nash–Sutcliffe efficiency (NSE) of approximately 10%, 18%, 32%, and 41% for the DNN, CNN, Extra Tree, and XGB models, respectively. Furthermore, through Sobol sensitivity and Shapley additive explanation–based interpretability analyses, it was revealed that the Extra Tree model displayed greater consistency with the physical processes underlying sediment transport as modeled by MUSLE. The proposed framework provides new insights into enhancing the accuracy and applicability of ML models in forecasting sediment yield while maintaining consistency with natural processes. Consequently, it can prove valuable in simulating process-related strategies aimed at mitigating sediment transport at watershed scales, such as the implementation of best management practices.

Studies on the human body burden of dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in populations around municipal solid waste incinerators (MSWIs) in China are limited. The objective of this study was to assess the potential adverse health effects of an 8-year MSWI on the surrounding population and identify possible exposure pathways. We hypothesized that the MSWI would result in different environmental impacts and population health outcomes between upwind and downwind of its 3 km vicinity. We conducted a 10-year retrospective mortality survey on the population surrounding the MSWI. Then, we selected 50 residents aged 50 years or older on each of the upwind and downwind sides of MSWI to test serum PCDD/Fs. Meanwhile, environmental and food exposures to PCDD/Fs were tested for selected residents. The age-adjusted mortality rates were significantly higher for residents downwind than upwind, but no significant difference was found in the standardized mortality ratio before and after the MSWI operation. The toxic equivalents (TEQ) and major congeners of PCDD/Fs were significantly higher in the sera of the downwind residents than in the upwind. The PCDD/Fs in air, soil, dust, and vegetables on the downwind side were not significantly different from those on the upwind side, but the mean concentrations of PCDD/Fs in downwind hen eggs was significantly higher than those from upwind. In conclusion, downwind residents living within 3 km of the MSWI had higher age-adjusted mortality and serum level of PCDD/Fs than upwind residents. This higher mortality rate among downwind residents was not associated with MSWI. However, the higher levels of PCDD/Fs in downwind hen eggs suggest that the downwind population dioxin exposure was related to their location.

The negative influence of agrochemicals (pesticides: insecticide, fungicide, and herbicide) on biodiversity is a major ecological concern. In recent decades, many insect species are reported to have rapidly declined worldwide, and pesticides, including neonicotinoids and fipronil, are suspected to be partially responsible. In Japan, application of systemic insecticides to nursery boxes in rice paddies is considered to have caused rapid declines in Sympetrum (Odonata: Libellulidae) and other dragonfly and damselfly populations since the 1990s. In addition to the direct lethal effects of pesticides, agrochemicals indirectly affect Odonata populations through reductions in macrophytes, which provide a habitat, and prey organisms. Due to technical restrictions, most previous studies first selected target chemicals and then analyzed their influence on focal organisms at various levels, from the laboratory to the field. However, in natural and agricultural environments, various chemicals co-occur and can act synergistically. Under such circumstances, targeted analyses might lead to spurious correlations between a target chemical and the abundance of organisms. To address such problems, in this study we adopted a novel technique, “Comprehensive Target Analysis with an Automated Identification and Quantification System (CTA-AIQS)” to detect wide range of agrochemicals in water environment. The relationships between a wide range of pesticides and lentic Odonata communities were surveyed in agricultural and non-agricultural areas in Saga Plain, Kyushu, Japan. We detected significant negative relationships between several insecticides, i.e., acephate, clothianidin, dinotefuran, flubendiamide, pymetrozine, and thiametoxam (marginal for benthic odonates) and the abundance of lentic Epiprocta and benthic Odonates. In contrast, the herbicides we detected were not significantly related to the abundance of aquatic macrophytes, suggesting a lower impact of herbicides on aquatic vegetation at the field level. These results highlight the need for further assessments of the influence of non-neonicotinoid insecticides on aquatic organisms.

Air pollution is becoming serious in developing country, and how to quantify the role of local emission and/or meteorological factors is very important for government to implement policy to control pollution. Here, we use a random forest model, a machine learning (ML) approach, combined with a de-weather method to analyze the PM₂.₅ level during the COVID-19 outbreak in Hubei Province. The results show that changes in anthropogenic emissions have reduced PM₂.₅ concentrations in February and March 2020 by about 33.3% compared to the same period in 2019, while changes in meteorological conditions have increased PM₂.₅ concentrations by about 8.8%. Moreover, the impact of meteorological conditions is more significant in the central region, which is likely to be related to regional transport. After excluding the contribution of meteorological conditions, the PM₂.₅ concentration in Hubei Province in February and March 2020 is lower than the secondary standard of China (35 μ g/m³). Our estimates also indicate that under similar meteorological conditions as in February and March 2019, an emission reduction intensity equivalent to about 48% of the emission reduction intensity during the lockdown may bring the annual average PM₂.₅ concentration to the standard (35 μ g/m³). Our study shows that machine learning is a powerful tool to quantify the influencing factors of PM₂.₅, and the results further emphasize the need for scientific emission reduction as well as joint regional control measures in future.

Reservoirs located in middle and high latitudes freeze for months in winter, where the accumulation characteristics of pollutants are changed by superimposed influence of salt exclusion from ice on the surface and pollution release from sediments at the bottom. Taking total nitrogen (TN) of Biliuhe reservoir in Northeast China as an example, we developed a model to simulate TN accumulation characteristics influenced by ice and sediments during the freezing period (NACISF), and quantified contributions of TN from ice and sediments. Model parameters of ice and sediments were determined by laboratory freeze-up simulation experiment and sediment release flux simulation experiment, and water quality data were obtained from field investigations. Results showed that the annual average amount of TN input during the ice-covered period from 2015 to 2020 was 220.77 t, the output was 400.11 t, and the accumulated amount was 589.52 t. TN excluded from ice and released from sediments contributed 8.12% and 7.17% of the total TN inputs in winter, respectively. Analysis showed that the TN excluded from ice was positively correlated with ice thickness and initial TN concentration. The maximum ice thickness of Biliuhe reservoir had a 13 year cyclic feature, and the proportion of TN excluded from ice to the total TN inputs in different periods ranged from 10.68% to 17.30% (mean 13.18%). Meanwhile, TN accumulated seasonally as summer > autumn > winter > spring. The TN exclusion effect in 2050 would be weakened when considering the combined effects of climate change and human activities, with a reduction of about 40.85% compared to the current. It is concluded that the NACISF model took into account the influences of both ice and sediments, which provided a detailed understanding of the accumulation characteristics of TN during freezing period, and had important reference significance for water quality management in winter.

Synthetic microfibers have been identified as the most prevalent type of microplastic in samples from aquatic, atmospheric, and terrestrial environments across the globe. Apparel washing has shown to be a major source of microfiber pollution. We used California as a case study to estimate the magnitude and fate of microfiber emissions, and to evaluate potential mitigation approaches. First, we quantified synthetic microfiber emissions and fate from apparel washing in California by developing a material flow model which connects California-specific data on synthetic fiber consumption, apparel washing, microfiber generation, and wastewater and biosolid management practices. Next, we used the model to assess the effectiveness of different interventions to reduce microfiber emissions to natural environments. We estimate that in 2019 as much as 2.2 kilotons (kt) of synthetic microfibers were generated by apparel washing in California, a 26% increase since 2008. The majority entered terrestrial environments (1.6 kt), followed by landfills (0.4 kt), waterbodies (0.1 kt), and incineration (0.1 kt). California's wastewater treatment network was estimated to divert 95% of microfibers from waterbodies, mainly to terrestrial environments and primarily via land application of biosolids. Our analysis also reveals that application of biosolids on agricultural lands facilitates a directional flow of microfibers from higher-income urban counties to lower-income rural communities. Without interventions, annual synthetic microfiber emissions to California's natural environments are expected to increase by 17% to 2.1 kt by 2026. Further increasing the microfiber retention efficiency at the wastewater treatment plant would increase emissions to terrestrial environments, which suggests that microfibers should be removed before entering the wastewater system. In our model, full adoption of in-line filters in washing machines decreased annual synthetic microfiber emissions to natural environments by 79% to 0.5 kt and offered the largest reduction of all modeled scenarios.

The coastal megacity Shanghai is located in the center of the Yangtze River Delta, a dominant flame retardants (FRs) production region in China, especially for organophosphate esters (OPEs). This prompted us to investigate occurrence and seasonal changes of atmospheric OPEs in Shanghai, as well as to evaluate their sources, environmental behavior and fate as a case study for global coastal regions. Atmospheric gas and particle phase OPEs were weekly collected at two coastal sites - the emerging town Lingang New Area (LGNA), and the chemical-industry zone Jinshan Area (JSA) from July 2016–June 2017. Total atmospheric concentrations of the observed OPEs were significantly higher in JSA (median of 1800 pg m⁻³) than LGNA (median of 580 pg m⁻³). Tris(1-chloro-2-propyl) phosphate (TCPP) was the most abundant compound, and the proportion of three chlorinated OPEs were higher in the particle phase (55%) than in the gas phase (39%). The year-round median contribution of particle phase OPEs was 33%, which changed strongly with seasons, accounting for 10% in summer in contrast to 62% in winter. Gas and particle phase OPEs in JSA exhibited significant correlations with inverse of temperature, respectively, indicating the importance of local/secondary volatilization sources. The estimated fluxes of gaseous absorption were almost 2 orders of magnitude higher than those of particle phase deposition, which could act as sources of organic phosphorus to coastal and open ocean waters.

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.