Objectives:To compare water quality parameters in the vicinity of Gas Flaring Area of Ebocha-Obrikom of Rivers State with that of the recommended standards.Methods:The research utilized standard analytical procedures. All sampling, conservation, transportation and analysis followed standard procedures described in APHA (2012). All the samples collected were transported to the laboratory through keeping in an icebox to prevent degradation of the organic substances.Results:Result depicts that Turbidity, DO, BOD, COD, TSS, Magnesium, Iron, Cadmium, Lead, Chromium, and Nickel exceeded the desirable limit meant for drinking purpose as well as could potentially pose threats toward human society. Hence, remain unsuitable for drinking, as the inhabitants were more vulnerable for their total lifetime period of exposure through continuous consumption of unsuitable drinking water.Conclusion:It is recommended that the local government environmental health officers and other regulatory agencies frequently monitor the levels of these pollutants within the area and also ensure strict adherence to guidelines to ensure a healthy environment. As exposure to the above stated parameters can have a remarkable impact on human health living in the vicinity of the gas flaring area by drinking water around the study area; thus, groundwater needs to treated before using for household purpose or drinking. Thus, this study would help in decision making for stakeholders and relevant authorities in the execution of reasonable groundwater management strategies and remediation plans in the area to protect public and environmental health.

Mediterranean areas are characterized by complex hydrogeological systems, where water resources are faced with several issues such as salinity and pollution. Fifty-one water samples were gathered from the Bou-areg coastal and the Gareb aquifers to evaluate the source of water salinity and to reveal the processes of the different sources of pollution using a variety of chemical and isotopic indicators (δ²H–H2O, δ¹⁸O–H2O, δ³⁴S–SO4, and δ¹⁸O–SO4). The results of the hydrochemical analysis of water samples show that the order of dominated elements is Cl⁻ > HCO₃⁻ > SO4₂⁻ > NO₃⁻ and Na⁺ > Ca²⁺ > Mg²⁺ > K⁺ and evidenced extremely high salinity levels (EC up to 22000 μS/cm). All samples exceeded the WHO drinking water guidelines, making them unfit for human consumption. Ion ratio diagrams, isotopic results, and graphical comparing indicate that the mineralization of groundwater in the area, is controlled by carbonate dissolution, evaporite dissolution, ion exchange, and sewage invasion. The return of irrigation water plays a significant role as well in the groundwater recharge and its mineralization by fertilizers mainly. Evaporites (Gypsum), sewage, and fertilizers constitute the main sources of sulfates in the investigated water resources. These scientific results will be an added value for decision-makers to more improve the sustainable management of groundwater in water-stressed regions. The use of chemical and isotopic tracers once again shows their relevance in such zones where systematic monitoring is lacking.

Heavy metals (HMs) in soil and water bodies greatly threaten human health. The wide separation of HMs urges the necessity to develop an expert system for HMs prediction and detection. In the current perspective, several propositions are discussed to design an innovative intelligence system for HMs prediction and detection in soil and water bodies. The intelligence system incorporates the Edge Cloud Server (ECS) data center, an innovative deep learning predictive model and the Federated Learning (FL) technology. The ECS data center is based on satellite sensing sources under human expertise ruling and HMs in-situ measurement. The FL system comprises a machine learning (ML) technique that trains an algorithm across multiple decentralized edge servers holding local data samples without exchanging them or breaching data privacy. The expected outcomes of the intelligence system are to quantify the soil and water bodies' HMs, develop new modified HMs pollution contamination indices and provide decision-makers and environmental experts with an appropriate vision of soil, surface water, and crop health.

Ecological risk assessment of contaminated sediment has become a fundamental component of water quality management programs, supporting decision-making for management actions or prompting additional investigations. In this study, we proposed a machine learning (ML)-based approach to assess the ecological risk of contaminated sediment as an alternative to existing index-based methods and costly toxicity testing. The performance of three widely used index-based methods (the pollution load index, potential ecological risk index, and mean probable effect concentration) and three ML algorithms (random forest, support vector machine, and extreme gradient boosting [XGB]) were compared in their prediction of sediment toxicity using 327 nationwide data sets from Korea consisting of 14 sediment quality parameters and sediment toxicity testing data. We also compared the performances of classifiers and regressors in predicting the toxicity for each of RF, SVM, and XGB algorithms. For all algorithms, the classifiers poorly classified toxic and non-toxic samples due to limited information on the sediment composition and the small training dataset. The regressors with a given classification threshold provided better classification, with the XGB regressor outperforming the other models in the classification. A permutation feature importance analysis revealed that Cr, Cu, Pb, and Zn were major contributors to toxicity prediction. The ML-based approach has the potential to be even more useful in the future with the expected increase in available sediment data.

In this study, an interval two-stage fuzzy fractional programming (TFFP) method is developed to facilitate collaborative governance of economy and water resources. Methods of interval programming, fuzzy programming, two-stage programming, and fractional programming are integrated within a general system optimization framework. The main contribution of TFFP is simultaneously addressing various uncertainties and tackling trade-offs between environmental and economic objectives in the optimized schemes for water resources allocation. A case study of a highly urbanized coastal city (i.e., Shenzhen) in China is provided as an example for demonstrating the proposed approach. According to the results, industrial sectors should receive 34.8% of total water supply, while agricultural sectors should receive 1.5%. For the spatial allocation of water resources, Bao An, Long Gang, and Fu Tian districts should be allocated 21.6%, 20.5%, and 14.8% water to promote the economic development. The discharge analysis indicates that chemical oxygen demand (CODcᵣ) and total phosphorus (TP) would be key pollutants. Moreover, the optimized seawater desalination volume would be negligibly influenced by price, while the upper bounds of desalination would be increased with the raising acceptable credibility levels in the period of 2031–2035. Analysis of desalination prices also reveals that the decision-makers should increase the scale of desalination in the period of 2021–2025. In addition, the effectiveness and applicability of TFFP would be evaluated under economic maximization scenarios. The result showed that the economic maximization scenario could obtain higher economic benefits, but it would be accompanied by a larger number of pollutant discharges. It is expected that this study will provide solid bases for planning water resources management systems in coastal regions.

The plastic debris that washes ashore and litters the shoreline often undergoes weathering under sunlight exposure, such that it fragments to form nanoplastics and microplastics, but the fragmentation rate for many thermoplastics is unknown. In this study, three major thermoplastics were exposed to simulated sunlight in an accelerated weathering chamber to evaluate the speed of photooxidation-induced fragmentation. The initiation of photooxidation-induced fragmentation extrapolated from the accelerated weathering chamber to real sunlight exposure in South Korea followed the order of PS (< 1 year) > PP (< 2 years) > LDPE (> 3 years). The surface cracks created by photooxidation were not directly reflected in the initiation of fragmentation of thermoplastics. The initiation of fragmentation was faster in PS than other polymers, but the total abundance of particles produced, and increasing ratio (exposure/non-exposure) were comparable or lower than those of PP. The increasing ratio pattern between nanoplastics and small microplastics of PP differed noticeably from other polymers. The initiation of nanoplastic and small-microplastic fragmentation determined in this study will be useful for the further estimation of secondary microplastic production by weathering and thus for decision-making regarding methods for the timely removal of plastic litter in the environment.

This paper aims to determine the worldwide research trends on searching queries of “oil spill* and risk assessment*” and “net environmental benefit analysis” and its most productive authors and journals. A bibliometric approach was performed to analyze publications including highly cited papers and only papers that were published in the Dimensions database from 2000 to 2022, April was selected. The necessary data were extracted from the Dimensions database and processed using visualization and mapping software such as VOSviewer 1.6.17 and Tableau Public 2021.1. The findings identified significant study fields, co-cited authors, country contributions, productive journals, as well as the most cited authors' articles. This study contributes significantly to the relevant studies as one of the few that utilizes bibliometric analysis as a network visualization and mapping technique for the analysis of one of the primary oil spill response decision-making tools and risk assessment sciences. The findings of this study can assist the researcher perform their research more effectively by providing insight into journal selection, contributing authors, research trends, countries, and keywords. Further research is recommended in light of longer period data contained in oil spill response strategies, oil spill modeling, or oil spill risk subjects.

Three ²¹⁰Pb-dated sediment cores were used to evaluate the contamination degree and flux ratios of potentially toxic elements (PTEs; As, Cd, Cr, Cu, Ni, Pb, V, and Zn) in seagrass meadows from the northern margin of Términos Lagoon (TL), southern Gulf of Mexico. The sediments displayed minor Cd, Ni, V, and Zn enrichments but moderate to strong enrichment by As. Results from a chemometric analysis revealed that: 1) salinization and grain size, along with 2) the terrigenous inputs are the major factors influencing the PTEs accumulation. The historical trends of PTEs flux ratios nearly follow the large-scale land-use changes around TL, linked to the growth of the Mexican oil industry in the area since the 1970s. Our findings showed the critical role of seagrass meadows as PTEs sinks. This information is useful for decision-makers to develop restoration projects for a vulnerable site within the largest coastal lagoon ecosystem in Mexico.

The worldwide spread of the SARS-CoV-2 caused an unprecedented lockdown measures in most countries with consequences on the world society, economy, and sanitary systems. This situation provided an opportunity to identify the effects of human confinement on natural environments, like touristic sandy beaches, which are stressed due to anthropogenic pressures. Based on previous articles about heavy metals sources and levels in these ecosystems, this paper discusses the dynamic of these pollutants and a regulatory scenario associated with COVID-19 sanitation policies. The main findings suggest that 39% of the studies were on Asian sandy beaches, 16% from Europe, while America and Africa with 23% each. Also Co, Cd, Cu, Cr, Zn, Pb, Ni, Fe and Mn were the most frequently analyzed metals in sediments and in several cases their concentrations exceed international guidelines assessment. Finally, even though beaches are under several metals inputs, tourism plays a key role in these ecosystems quality. After analyzing the potential indirect effect of COVID-19 measures on metals dynamics, we propose some key recommendations and management strategies to mitigate heavy metal pollution on sandy tourist beaches. These proposals are useful for decision-makers and stakeholders to improve sandy beach management, mainly those beaches not addressed from a management perspective; and their implementation should be adapted according to the regulations and legislation of each country.

Proliferation of microplastics in rivers, lakes, estuaries, coastal waters and oceans is a major global challenge and threat to the environment, livelihoods and human health. Reliable predictive tools can play an essential role in developing an improved understanding of microplastics behaviour, exposure and risk in water bodies, and facilitate identification of sources and accumulation hot spots, thereby enabling informed decision-making for targeted prevention and clean-up activities. This study presents a new numerical framework (CaMPSim-3D) for predicting microplastics fate and transport in different aquatic settings, which consists of a Lagrangian, three-dimensional (3D) particle-tracking model (PTM) coupled with an Eulerian-based hydrodynamic modeling system (TELEMAC). The 3D PTM has several innovative features that enable accurate simulation and efficient coupling with TELEMAC, which utilizes an unstructured computational mesh. The PTM is capable of considering spatio-temporally varying diffusivity, and uses an innovative algorithm to locate particles within the Eulerian mesh. Model accuracy associated with different advection schemes was verified by comparing numerical predictions to known analytical solutions for several test cases. The implications of choosing different advection schemes for modeling microplastics transport was then investigated by applying the PTM to simulate particle transport in the lower Saint John River Estuary in eastern Canada. The sensitivity of the PTM predictions to the advection scheme was investigated using six numerical schemes with different levels of complexity. Predicted particle distributions and residence times based on the fourth-order Runge–Kutta (RK4) scheme differed significantly (residence times by up to 100 %) from those computed using the traditional first-order (Euler) method. The Third Order Total Variation Diminishing (TVD3) Runge-Kutta method was found to be optimal, providing the closest results to RK4 with approximately 27 % lower computational cost.