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.

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.

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.

Identification of pollution point source in rivers is strenuous due to accidental chemical spills or unmanaged wastewater discharges. It is crucial to take physical characteristics into account in the estimation of pollution sources. In this study, an integrated inverse modeling framework is developed to identify a point source of accidental water pollution based on the contaminant concentrations observed at monitoring sites in time series. The modeling approach includes a Markov chain Monte Carlo method based on Bayesian inference (Bayesian-MCMC) inverse model and a genetic algorithm (GA) inverse model. Both inverse models can estimate the pollution sources, including the emission mass quantity, release time, and release position in an accidental river pollution event. The developed model is first tested for a hypothetical case with field river conditions. The results show that the source parameters identified by the Bayesian-MCMC inverse model are very close to the true values with relative errors of 0.02% or less; the GA inverse model also works with relative errors in the range of 2%–7%. Additionally, the uncertainties associated with model parameters are analyzed based on global sensitive analysis (GSA) in this study. It is also found that the emission mass of pollution source positively correlates with the dispersion coefficient and the river cross-sectional area, whereas the flow velocity significantly affects release position and release time. A real case study in the Fen River is further conducted to test the applicability of the developed inverse modeling approach. Results confirm that the Bayesian-MCMC model performs better than the GA model in terms of accuracy and stability for the field application. The findings of this study would support decision-making during emergency responses to river pollution incidents.

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.

Decision-making related to nitrogen (N) fertilization is a crucial step in agronomic practices because of its direct interactions with agronomic productivity and environmental risk. Here, we hypothesized that soil apparent N balance could be used as an indicator to determine the thresholds of N input through analyzing the responses of the yield and N loss to N balance. Based on the observations from 951 field experiments conducted in rice (Oryza sativa L.) cropping systems of China, we established the relationships between N balance and ammonia (NH₃) volatilization, yield increase ratio, and N application rate, respectively. Dramatical increase of NH₃ volatilizations and stagnant increase of the rice yields were observed when the N surplus exceeded certain levels. Using a piecewise regression method, the seasonal upper limits of N surplus were determined as 44.3 and 90.9 kg N ha⁻¹ under straw-return and straw-removal scenarios, respectively, derived from the responses of NH₃ volatilization, and were determined as 53.0–74.9 and 97.9–112.0 kg N ha⁻¹ under straw-return and straw-removal scenarios, respectively, derived from the maximum-yield consideration. Based on the upper limits of N surplus, the thresholds of N application rate suggested to be applied in single, middle-MLYR, middle-SW, early, and late rice types ranged 179.0–214.9 kg N ha⁻¹ in order to restrict the NH₃ volatilization, and ranged 193.3–249.8 kg N ha⁻¹ in order to achieve the maximum yields. If rice straw was returned to fields, on average, the thresholds of N application rate could be theoretically decreased by 17.5 kg N ha⁻¹. This study provides a robust reference for restricting the N surplus and the synthetic fertilizer N input in rice fields, which will guide yield goals and environmental protection.

In the last decade many studies have described the ingestion of plastic in marine animals. While most studies were dedicated to understanding the pre-ingestion processes involving decision-making foraging choices based on visual and olfactory cues of animals, our knowledge in the post-ingestion consequences remains limited. Here we proposed a theoretical complementary view of post-ingestion consequences, attempting to connect plastic ingestion with plastic-induced satiety. We analyzed data of plastic ingestion and dietary information of 223 immature green turtles (Chelonia mydas) from tropical Brazilian reefs in order to understand the impacts of plastic ingestion on foraging behavior. Generalized linear mixing models and permutational analysis of variance suggested that plastic accumulations in esophagus, stomach and intestine differed in their impact on green turtle’s food intake. At the initial stages of plastic ingestion, where the plastic still in the stomach, an increase in food intake was observed. The accumulation of plastic in the gastrointestinal tract can reduce food intake likely leading to plastic-induced satiety. Our results also suggest that higher amounts of plastics in the gastrointestinal tract may led to underweight and emaciated turtles. We hope that adopting and refining our proposed framework will help to clarify the post-ingestion consequences of plastic ingestion in wildlife.

Human factors/errors (such as inappropriate actions by operators and unsafe supervision by organizations) are a primary cause of oil spill incidents. To investigate the influences of active operational failures and unsafe latent factors in offshore oil spill accidents, an integrated human factor analysis and decision support process has been developed. The system is comprised of a Human Factors Analysis and Classification System (HFACS) framework to qualitatively evaluate the influence of various factors and errors associated with the multiple operational stages considered for oil spill preparedness and response (e.g., oil spill occurrence, spill monitoring, decision making/contingency planning, and spill response); coupled with quantitative data analysis by Fuzzy Set Theory and the Technique for Order Preference by Similarity to Ideal Solution (Fuzzy-TOPSIS) to enhance decision making during response operations. The efficiency of the integrated human factor analysis and decision support system is tested with data from a case study to generate a comprehensive priority rank, a robust sensitivity analysis, and other theoretical/practical insights. The proposed approach improves our knowledge on the significance of human factors/errors on oil spill accidents and response operations; and provides an improved support tool for decision making.

Environmental complexity leads to differences in the spatial distribution of heavy metal pollution in soil and rice. Such spatial differences will seriously affect the safety of planted rice and can impact regional management and control. How to scientifically reveal these spatial differences is an urgent problem. In this study, the spatial mismatch relationship between Cd pollution in soil and rice grains (brown rice) was first explored by the interpolation method. To further reveal the causes of these, the specific recognition rules of the spatial relationship of Cd pollution were extracted based on a decision tree model, and the results were mapped. The results revealed a spatial mismatch in Cd pollution between the soil and rice grains in the study area, and the main results are as follows: (i) slight soil pollution and safe rice accounted for 68.88% of the area; (ii) slight soil pollution and serious rice pollution accounted for 13.39% of the area and (iii) safe soil and serious rice pollution accounted for 11.63% of the area. In addition, 11 recognition rules of Cd spatial pollution relationship between soil and rice were proposed, and the main environmental factors were determined: SOM (soil organic matter), Dis-residence (distance from residential area), soil pH and LAI (leaf area index). The average accuracy of rule recognition was 75.90%. The study reveals the spatial mismatch of heavy metal pollution in soil and crops, providing decision-making references for the spatial accurate identification and targeted prevention of heavy metal pollution spaces.

Natural and synthetic steroid hormones and many persistent organic pollutants are of concern for their endocrine-disrupting activities observed in receiving surface waters. Apart from the demonstrated presence of estrogen- and estrogen-mimicking compounds in surface waters, antagonistic (anti-estrogenic) responses originating from wastewater effluent have been reported but are less known. Estrogenicity and anti-estrogenicity were assessed using recombinant yeast estrogen receptor binding assays (YES/YAES) at ten South African wastewater treatment works (WWTWs) and receiving rivers in two separate sampling campaigns during the summer- and winter periods in the area. Four WWTWs were then further investigated to show daily variation in estrogenic endocrine-disrupting activities during the treatment process. Although estrogenicity was notably reduced at most of the WWTWs, some treated effluent and river water samples were shown to be above effect-based trigger values posing an endocrine-disrupting risk for aquatic life and potential health risks for humans. Furthermore, estrogenicity recorded in samples collected upstream from some WWTW discharge points also exceeded some calculated risk trigger values, which highlights the impact of alternative pollution sources contributing towards endocrine disrupting contaminants (EDCs) in the environment. The YAES further showed variable anti-estrogenic activities in treated wastewater. The current study highlights a variety of factors that may affect bioassay outcomes and conclusions drawn from the results for risk decision-making. For example, mismatches were found between estrogenic and anti-estrogenic activity, which suggests a potential masking effect in WWTW effluents and highlights the complexity of environmental samples containing chemical mixtures having variable endocrine-disrupting modes of action. Although the recombinant yeast assay is not without its limitations to show endocrine-disrupting modulation in test water systems, it serves as a cost-effective tier-1 scoping assay for further risk characterisation and intervention.