International Journal of Environmental Research最新文献

Landfilling is the preferred method of solid waste disposal globally, but landfills are sources of several pollutants including heavy metals, phthalates, phenols, and other chemicals to the environment. Phthalates and phenols are widely used in the manufacture of everyday household products and are therefore contained in leachate generated in landfills where these products are disposed. These pollutants have endocrine disruptive capabilities and are considered as priority pollutants. Knowledge of their fate in the environment is of relevance to their management and the reduction of associated human and animal exposure risk. Several studies have profiled these pollutants in wastewater, but little information is available on their presence and fate in landfill environments and surrounding soils. Further to this, their degradation pathways have mostly been reported in laboratory simulated studies where the media may not be as complex and dynamic as landfills and surrounding soil environments. The factors and processes affecting their release from waste materials in landfills and their dispersal into surrounding environments has also not been reported, yet this knowledge is relevant for the design of any remediation measures. This article reviews the sources, occurrence, and degradation of phenols and phthalates in landfilled wastes and landfill leachate, and the factors influencing their fate in the landfill and surrounding soils contaminated with landfill leachates. We conclude with a discussion on the exposure pathways and exposure routes of these compounds from landfills to humans and the associated health risk and possible future research opportunities.

This study evaluated the effectiveness of three plant species (Panicum maximum, Echinochloa pyramidalis, and Typha domingensis), in enhancing the removal of salinity, nitrogen, phosphorus, and chemical oxygen demand during sludge treatment within sludge treatment reed beds (STRBs). The experimental setup consists of 1 m³ tanks composed of sand and gravel, simulating the reed bed configuration. A total of twelve pilot-scale units were used, with each plant species tested in triplicate, while three units were used as non-planted (control) groups. Over a 6-month period, the units received weekly loadings of faecal sludge sourced from a site in southern Benin. Sampling was carried out weekly in order to evaluate the removal efficiency of the assessed parameters. The results showed the superior efficacy of STRBs compared to control beds, attributing this enhancement to the presence of vegetation. At a loading rate of 300 kg TS/m²/year, the system presented good performance. In particular, P. maximum and E. pyramidalis beds demonstrated the highest removal rates (Salinity: 97.95%, PO₄³⁻: 97.75%, TP: 98.08%, NH₄⁺: 98.68, NO₃⁻: 98.71%, TKN: 99.53%, TSS: 99.98%, COD: 99.94%). Moreover, the drying rates of E. pyramidalis and P. maximum beds (49.5% and 48.5%, respectively) surpassed those of T. domingensis beds (34.7%). These findings highlight the potential of P. maximum, a newly tested species, for application in STRBs, emphasizing the need for dedicated research on this species.

Microplastics have received widespread attention in recent years. However, most studies on microplastics have been limited to the aquatic environment. In this review, we categorize and describe the microplastic's degradation pathways and removal methods, including pyrolysis, hydrolysis, biodegradation, and advanced oxidation techniques, such as ultrasonic mechanical degradation. Additionally, we focus on the applicability of photocatalytic degradation as an in-situ removal technique for microplastics in soil, providing a reference for future research. Although soil is an important sink for microplastics and has a much higher abundance than the oceans, there is a lack of comprehensive summary regarding the mechanisms of environmental degradation and removal of microplastics in soil in existing research. In this review, we categorize and describe removal and degradation methods, including pyrolysis, hydrolysis, biodegradation, and advanced oxidation techniques, such as ultrasonic mechanical degradation. Additionally, we focus on the applicability of photocatalytic degradation as an in-situ removal technique for microplastics in soil, providing a reference for future research.

Understanding the spatial and temporal distribution of precipitation globally is advantageous for advancing climate knowledge and improving weather and climate forecasting models. Despite the complexity of determining precipitation distribution, numerous satellite-based precipitation products (SPPs) have been developed in recent decades to estimate precipitation with sufficient coverage and accuracy. This study evaluates the performance of four SPPs, namely Integrated Multi-satellite Retrievals for GPM (IMERG-FRV6), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Tropical Rainfall Measuring Mission (TRMM-3B43V7), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks—Climate Data Record (PERSIANN-CDR) on monthly, seasonal, and annual scales in Iran, and aimed to enhance the accuracy of the evaluation by extending the statistical period and selecting evaluation indicators based on error, efficiency, and correlation. Measured rainfall data from 81 synoptic stations across Iran from 2008 to 2019 were used for this evaluation. To accurately assess the selected SPPs, several statistical indices including Correlation Coefficient (CC), Kling-Gupta Efficiency (KGE), Root Mean Square Error (RMSE), and Bias were calculated and analyzed at all synoptic stations. The results demonstrate that MSWEP has a significant advantage over other products at all time scales. The performance of all four products in areas with high monthly rainfall is associated with more errors. PERSIANN-CDR exhibited the highest monthly RMSE, while TRMM-3B43V7 performed better in drier regions with low to moderate precipitation. MSWEP showed the closest average precipitation to observational data in spring, summer, and winter, while IMERG-FRV6 overestimated precipitation in all seasons.

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Effective hydrogel adsorbents are widely used in toxic dyes removal, but weak toughness and inferior adsorption performance limit their actual application. Herein, a dual-functionalized epoxy and amino groups hyperbranched polysiloxane (HPSi) cross-linker was initially developed, bentonite (BT) was incorporated with sesbania gum (SG), acrylic acid/acrylamide (AA/AAm) was utilized as monomers to synthesize hydrogel adsorbent H-SG/BT(AA/AAm) for the adsorption of methylene blue (MB) and malachite green (MG). Various characterization techniques, adsorption parameters test and mechanism analysis demonstrated that HPSi and BT endowed H-SG/BT(AA/AAm) with enhanced mechanical strength (tentative extensibility of 2645% and elastic recovery was over 90%). The hydroxyl of BT and SG was completely insert into the hydrogel then significantly enhanced the swelling and adsorption capacity. H-SG/BT(AA/AAm) exposed a promising adsorption capacity towards MB and MG with maximum adsorption capacity of 1226 mg/g and 783 mg/g. Kinetic and isotherm studies indicated that the adsorption of MB and MG onto H-SG/BT (AA/AAm) closely followed the pseudo-second-order kinetic model and Langmuir isotherm. The adsorption efficiency of H-SG/BT (AA/AAm) for MB and MG could still reach 80% after five adsorption–desorption cycles. Clearly, H-SG/BT(AA/AAm) composites can be effectively utilized as an eco-friendly adsorbent for dyes adsorption in aqueous environments.

This study focuses on six Eucalyptus species, namely E. badjensis, E. benthamii, E. dunnii, E. grandis, E. globulus, and E. saligna aiming at a sustainable use of their green biomass. These species were grown at the same location and stage of development. Leaf essential oils were extracted by hydrodistillation using a Clevenger apparatus during the four seasons. Gas chromatography–mass spectrometry (GC–MS) and high-performance thin-layer chromatography (HPTLC) techniques were used to analyze the chemical composition. E. badjensis consistently exhibited a dominant composition, with 1,8-cineole being the predominant component. Notably, the proportion of 1,8-cineole in E. badjensis was 77.35% in spring (SP), 69.46% in summer (SU), 95.30% in autumn (AU), and 89.30% in winter (WI). E. globulus also exhibited 1,8-cineole as its primary constituent, with proportions fluctuating slightly across seasons at 84.87%, 79.94%, 81%, and 85.88%, respectively. The proportions and constituents of various species differed significantly. HPTLC was successfully used as a swift technique to monitor the chemical composition of essential oils (EOs) in various Eucalyptus species during the seasonality. GC–MS and HPTLC analysis showed that different Eucalyptus species displayed unique chemical compositions, while both the chemical profile and productivity of all analyzed EOs were affected by seasonality. This finding was demonstrated in the principal components cluster. The analysis of six species has revealed that the EO of E. benthamii is the most potent in thwarting the infestation of Cimex lectularius. Additionally, all six EOs demonstrated antioxidant activity in the ABTS model. EOs of E. benthamii, E. dunnii, and E. grandis showed significant anti-inflammatory activities in experimental models.

Maintaining water quality in aquatic habitats is critical for the health of aquatic species, particularly fish. This study pioneers an innovative method to water quality classification, leveraging IoT-driven data acquisition and meticulous data labelling with the Aqua-Enviro Index (AEI) by considering the fish habitats. Existing mechanisms fail to capture complex temporal dynamics and depend largely on large amounts of labelled data, exposing fundamental limits. In response, we describe the Deep learning based Convolutional Gated Recurrent Unit Tempo Fusion Network (CGTFN) model, which represents a considerable development in the evaluation of water quality. The model addresses these restrictions by seamlessly merging Convolutional Neural Networks (CNNs) for spatial pattern recognition and Gated Recurrent Units (GRUs) for temporal interactions. The Tempo Fusion mechanism combines spatial, temporal, and contextual data harmoniously, allowing for more sophisticated classifications by recognizing subtle interdependencies among environmental elements. The pioneering CGTFN model outperforms previous models, achieving 99.71 and 99.81% accuracy on both public-env and real-time-env datasets, respectively, exceeding established models at 98.2%. These remarkable findings highlight CGTFN’s disruptive potential in water quality evaluation, bridging the gap between technology and environmental management, with ramifications ranging from aquaculture to resource sustainability.

The present study aims to ascertain the causes of environmental vulnerabilities in Pakistan, Bangladesh, and India. The countries are ranked top ten in the Global Climate Risk Index list in 1998–2017, 1999–2018, 2000–2019, 2018, and 2019. Hence, we determine whether the observing nations’ increasing GDP per capita, energy use, and population density intensify greenhouse gas emissions. This study also considers structural breaks in the time-series data from 1990 to 2019. Using the bounds-testing approach, we discover evidence of long-run cointegration among the variables. Furthermore, the long-run autoregressive distributed lag (ARDL) estimations disclose that GDP per capita positively correlates with greenhouse gas emissions, with this effect observed across all sample countries except India in the short run. Additionally, in the long and short run, energy use intensifies greenhouse gas emissions in all sample countries except Bangladesh. Population density also harms the environment in both time horizons in all the observed countries. The study also incorporates fully modified OLS (FMOLS) and dynamic OLS (DOLS) regressions for robust interpretations. Findings of FMOLS and DOLS also support the ARDL model’s outcomes. Moreover, diagnostic tests confirm the stability and reliability of ARDL models. Based on the varying findings on the impacts of energy use on greenhouse gas emissions across countries in the short and long run and the influence of GDP per capita in the short run, policymakers should tailor environmental policies according to nations’ energy and income structures.

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