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.

Land-use types may affect soil aggregates' stability and organic carbon (OC) distribution characteristics, but little is known about the effects on the distribution characteristics of microplastics (MPs) in the aggregates. Hence, the MPs abundance of soil aggregates and analyzed aggregates’ stability, composition, and OC content from two soil layers of four land-use types in Gansu Province were investigated in this study. The total MPs abundances in woodland, farmland (wheat, maize, and potato), orchard, and intercropping (potato + apple orchard) of top and deep soils were 1383.3 and 1477.9, 1324.6 and 931.1, 1757.1 and 1930.9, 2127.2 and 1998.0, 1335.9 and 886.7, and 1777.5 and 1683.3 items kg⁻¹, respectively. The largest MPs abundance was detected in the >5 mm fractions of topsoil in potato (3077.3 items kg⁻¹), followed by maize (3044.7 items kg⁻¹) and intercropping (2718.4 items kg⁻¹). In the topsoil, the total MPs abundance increased significantly with decreasing aggregate stability, and also was positively correlated with bulk density, microbial biomass, and total nitrogen contents of bulk soil. Summarizing, the abundance distribution of MPs correlates with the soil aggregate characteristics of the different land-use types.

The 7th Military World Games held in Wuhan (WH) in Oct 2019 provided an opportunity to clarify the impact of short-term control measures on air quality. Fine particulate matters (PM₂.₅) were collected in WH, Huangshi (HS), and Huanggang (HG) during the control (Oct 13–28, 2019) and non-control periods (Oct 29- Nov 5, 2019). The results showed that air quality was good during the control period, with the concentrations of PM₂.₅ and gaseous pollutants being below the Grade Ⅱ of China Ambient Air Quality Standard. Concentrations of PM₂.₅ and its major chemical components in the control period were significantly lower than those in the non-control period, with reductions ranging from 17% (trace elements) to 46% (elemental carbon). However, higher contributions of secondary components such as SO₄²⁻, NO₃⁻, NH₄⁺ and secondary organic carbon (SOC) to PM₂.₅ were observed during the control period, suggesting the important role of secondary transformation. Potential source contribution function (PSCF) of PM₂.₅ showed that the main source regions were potentially located in surrounding cities Hubei Province, but regional transport can't be ignored. Six sources were identified by positive matrix factorization (PMF) for both control and non-control period. The contributions of combustion emissions and vehicle emissions were amplified in the control period, while the contribution of construction dust increased significantly when the control measures ended. Emission reductions contributed more to PM₂.₅ concentration decrease in WH (55%) than that in HS (51%) and HG (49%), which was consistent with the stricter control measures implemented in WH. These results indicated that short-term controls were effective at lowering PM₂.₅ concentration. However, the elevated contributions of secondary aerosols and the influence of regional transport on the study areas also need to be paid attention for air quality improvement in the future.

Human health risks brought by fine atmospheric particles raise scholarly and policy awareness about the role of urban trees as bio-filters of air pollution. While a large number of empirical studies have focused on the characteristics of vegetation leaves and their effects on atmospheric particle retention, the dry deposition of particles on branches, which plays a significant role in capturing and retaining particles during the defoliation period and contributes substantially to total removal of atmospheric particles, is under-investigated. To fill in this knowledge gap, this case study examined the dry deposition velocities (Vd) of submicron particulate matters (PM₁) on the branches of six common deciduous species in Shanghai (China) using laboratory experiments. And the association between Vd and key branch anatomical traits (including surface roughness, perimeter, rind width proportion, lenticel density, peeling, and groove/ridge characteristics) was explored. It was found that surface roughness would increase Vd, as a rougher surface significantly increases turbulence, which is conducive to particle diffusion. By contrast, peeling, branch perimeter, and lenticel density would decrease Vd. Peeling represents the exfoliated remains on the branch surfaces which may flutter considerably with airflow, leading to particle resuspension and low Vd. When branch perimeter increases, the boundary layer of branches thickens and a wake area appears, increasing the difficulty of particles to reach branch surface, and reducing Vd. While lenticels can increase the roughness of branch surface, their pointy shape would uplift airflow and cause a leeward wake area, lowering Vd. This finely wrought study contributes to a better understanding of branch dry deposition during leaf-off seasons and potential of deciduous trees serving as nature-based air filters all year round in urban environments.

Bibliometric network analysis has revealed that the widespread distribution of microplastics (MPs) has detrimental effects on marine organisms; however, the combined effects of MPs and climate change (e.g., warming) is not well understood. In this study, Prorocentrum donghaiense, a typical red tide species in the East China Sea, was exposed to different MP concentrations (0, 1, 5, and 10 mg L⁻¹) and temperatures (16, 22, and 28 °C) for 7 days to investigate the combined effects of MPs and simulated ocean warming by measuring different physiological parameters, such as cell growth, pigment contents (chlorophyll a and carotenoid), relative electron transfer rate (rETR), reactive oxygen species (ROS), superoxide dismutase (SOD), malondialdehyde (MDA), and adenosine triphosphate (ATP). The results demonstrated that MPs significantly decreased cell growth, pigment contents, and rETRₘₐₓ, but increased the MDA, ROS, and SOD levels for all MP treatments at low temperature (16 °C). However, high temperatures (22 and 28 °C) increased the pigment contents and rETRₘₐₓ, but decreased the SOD and MDA levels. Positive and negative effects of high temperatures (22 or 28 °C) were observed at low (1 and 5 mg L⁻¹) and high MP (10 mg L⁻¹) concentrations, respectively, indicating the antagonistic and synergistic effects of combined warming and MP pollution. These results imply that the effects of MPs on microalgae will likely not be substantial in future warming scenarios if MP concentrations are controlled at a certain level. These findings expand the current knowledge of microalgae in response to increasing MP pollution in future warming scenarios.

Environmental quality data sets are typically imbalanced, because environmental pollution events are rarely observed in daily life. Prediction of imbalanced data sets is a major challenge in machine learning. Our recent work has shown deep cascade forest (DCF), as a base learning model, is promising to be recommended for environmental quality prediction. Although some traditional models were improved by introducing the cost matrix, little is known about whether cost matrix could enhance the prediction performance of DCF. Additionally, feature extraction is also an important way to potentially improve the model's ability to predict the imbalanced data. Here, we developed two novelty learning models based on DCF: cost-sensitive DCF (CS-DCF) and DCF that combines unsupervised learning models and greedy methods (USM-DCF-G). Subsequently, CS-DCF and USM-DCF-G were successfully verified by an imbalanced drinking water quality data set. Our data presented both CS-DCF and USM-DCF-G show better prediction performance than that of DCF alone did. In particular, USM-DCF-G shows the best performance with the highest F1-score (95.12 ± 2.56%), after feature extraction and selection by using unsupervised learning models and greedy methods. Thus, the two learning models, especially USM-DCF-G, were promising learning models to address environmental imbalanced issues and accurately predict environmental quality.

Water pollution by microplastics (MPs) is a contemporary issue which has recently gained lots of attentions. Despite this, very limited studies were conducted on the degradation of MPs. In this paper, we reported the treatment of synthetic mono-dispersed suspension of MPs by using electrooxidation (EO) process. MPs synthetic solution was prepared with distilled water and a commercial polystyrene solution containing a surfactant. In addition to anode material, different operating parameters were investigated such as current intensity, anode surface, electrolyte type, electrolyte concentration, and reaction time. The obtained results revealed that the EO process can degrade 58 ± 21% of MPs in 1 h. Analysis of the operating parameters showed that the current intensity, anode material, electrolyte type, and electrolyte concentration substantially affected the MPs removal efficiency, whereas anode surface area had a negligible effect. In addition, dynamic light scattering analysis was performed to evaluate the size distribution of MPs during the degradation. The combination of dynamic light scattering, scanning electron microscopy, total organic carbon, and Fourier-transform infrared spectroscopy results suggested that the MPs did not break into smaller particles and they degrade directly into gaseous products. This work demonstrated that EO is a promising process for degradation of MPs in water without production of any wastes or by-products.

As a driving factor of global changes, microplastics have gradually attracted widespread attention. Although MPs are extensively studied in aquatic systems, their presence and fate in terrestrial systems and soil are not fully understood. In China, construction-land must be mulched by dust-proof nets to prevent and control fine particulate pollution, which may cause MPs pollution and increase ecological risks. In order to understand the pollution characteristics and sources of MP in the soil covered by dust nets, we conducted a case study in Beijing. Our results revealed that the abundance of MPs in soil mulched by dust-proof nets ranged from 272 to 13,752 items/kg. Large-sized particles (>1000 μm) made up a significant proportion (49.83%) of MPs in the study area. The dominant MP polymer types were polyethylene (50.12%) and polypropylene (41.25%). The accumulation of MPs in construction-site soil mulched by dust-proof nets (average, 4910.2 items/kg) was significantly higher (P < 0.05) than that in unmulched soil (average, 840.8 items/kg), which indicates a dust-proof nets as an essential source of microplastics in the soil of construction land. We applied a remote-sensing data analysis technique based on remote imagery acquired from a high-resolution remote-sensing satellite combined with deep-learning convolutional neural networks to automatically detect and segment dust-proof nets. Based on high-resolution remote sensing images and using a U-net convolutional neural network, we extract the coverage area of Beijing’s dust-proof nets (18.6 km²). Combined the abundance of MPs and the dust-proof nets’ coverage area, we roughly estimate that 7.616 × 10⁹ to 3.581 × 10¹¹ MPs accumulated in the soil mulched by the dust-proof nets in Beijing. Such a large amount of MPs may cause a series of environmental problems. This study will highlight the understanding of soil MPs pollution and its potential environmental impacts for scientists and policymakers. It provides suggestions for decision-makers to formulate effective legislation and policies, so as to protect human health and protect the soil and the wider environment.