Environmental Science and Pollution Research最新文献

The present study describes a set of methodological procedures (seldom applied together), including (i) development of an alternative adsorbent derived from abundant low-cost plant biomass; (ii) use of simple low-cost biomass modification techniques based on physical processing and chemical activation; (iii) design of experiments (DoE) applied to optimize the removal of a pharmaceutical contaminant from water; (iv) at environmentally relevant concentrations, (v) that due to initial low concentrations required determination by ultra-performance liquid phase chromatography coupled to mass spectrometry (UPLC-MS/MS). A central composite rotational design (CCRD) was employed to investigate the performance of vegetable sponge biomass (Luffa cylindrica), physically processed (crushing and sieving) and chemically activated with phosphoric acid, in the adsorption of the antibiotic trimethoprim (TMP) from water. The optimized model identified pH as the most significant variable, with maximum drug removal (91.1 ± 5.7%) achieved at pH 7.5, a temperature of 22.5 °C, and an adsorbent/adsorbate ratio of 18.6 mg µg^-1. The adsorption mechanisms and surface properties of the adsorbent were examined through characterization techniques such as scanning electron microscopy (SEM), point of zero charge (pH_pzc) measurement, thermogravimetric analysis (TGA), specific surface area, and Fourier-transform infrared spectroscopy (FTIR). The best kinetic fit was obtained by the Avrami fractional-order model. The hypothesis of a hybrid behavior of the adsorbent was suggested by the equilibrium results presented by the Langmuir and Freundlich models and reinforced by the Redlich-Peterson model, which achieved the best fit (R² = 0.982). The thermodynamic study indicated an exothermic, spontaneous, and favorable process. The maximum adsorption capacity of the material was 2.32 × 10² µg g^-1 at an equilibrium time of 120 min. Finally, a sustainable and promising adsorbent for the polishing of aqueous matrices contaminated by contaminants of emerging concern (CECs) at environmentally relevant concentrations is available for future investigations.

This study examines the spillover of pollution among the 26 European Union (EU) countries from 1995 to 2020. In order to quantify pollution spillovers among the countries, we estimated the Environmental Kuznets Curve (EKC) using spatial econometric methods. Our research is unique in that it investigates ecological footprint spillovers for EU countries. This study also considers the direct and indirect effects of renewable and fossil energy consumption and globalization on environmental degradation in EU countries. The empirical results favor the validity of the EKC hypothesis. Our results support the presence of positive and significant ecological footprint spillovers among EU countries. Our spatial estimates also reveal the significant spillover impact of explanatory variables on the ecological footprint. The ecological footprint of the local country is declining owing to the consumption of renewable energy in neighboring countries. Furthermore, the fossil energy consumption of the local and neighboring countries has a positive impact on the ecological footprint. Evidence obtained from our spatial estimates provides useful insights to policymakers in developing appropriate environmental policies to combat climate change.

Seafood is abundant in high-quality protein, healthy fats (n-3 and n-6 PUFAs), minerals (calcium, magnesium, copper, selenium, and so on), and vitamin D. Functional compounds in seafood can protect against lifestyle-related diseases. Having had all the merits mentioned, it is also a highly putrefiable food item. To maintain quality and prolong seafood's shelf life, various chemicals have been added, including nitrite, sulfur dioxide, and formaldehyde. In this review, we summarize the uses, the incidence of added formalin contamination, and the approved limit for seafood products. Additionally, worldwide regulations or standards for the use of formalin in seafood products, as well as recent changes relevant to new methods, are highlighted. Although strict limits and regulations have been placed on the utilization of formaldehyde for seafood preservation, there are few incidences reported of formalin/formaldehyde detection in seafood products around Asian countries. In this context, various qualitative and quantitative detection methods for formaldehyde have been developed to ensure the presence of formaldehyde within acceptable limits. Besides this, different rules and regulations have been forced by each country to control formaldehyde incidence. Although it is not an issue of formaldehyde incidence in European countries, strict regulations are implemented and followed.

In recent years, pelagic Sargassum has invaded the Caribbean coasts, and anaerobic digestion has been proposed as a sustainable management option. However, the complex composition of these macroalgae acts as a barrier to microbial degradation, thereby limiting methane production. Microbial adaptation is a promising strategy to improve substrate utilization and stress tolerance. This study aimed to investigate the adaptation of a microbial consortium to enhance methane production from the pelagic Sargassum. Microbial adaptation was performed in a fed-batch mode for 100 days by progressive feeding of Sargassum. The evolution of the microbial community was analyzed by high-throughput sequencing of 16S rRNA amplicons. Additionally, 16S rRNA data were used to predict functional profiles using the iVikodak platform. The results showed that, after adaptation, the consortium was dominated by the bacterial phyla Bacteroidota, Firmicutes, and Atribacterota, as well as methanogens of the families Methanotrichaceae and Methanoregulaceae. The abundance of predicted genes related to different metabolic functions was affected during the adaptation stage when Sargassum concentration was increased. At the end of the adaptation stage, the abundance of the predicted genes increased again. The adapted microbial consortium demonstrated a 60% increase in both biomethane potential and biodegradability index. This work offers valuable insights into the development of treatment technologies and the effective management of pelagic Sargassum in coastal regions, emphasizing the importance of microbial adaptation in this context.

Developing effective strategies to predict areas susceptible to landslides and reducing risk is vital. This involves using ensemble methods to meet the precise prediction and addressing challenges like data limitation. Recent studies have highlighted the potential of using ensemble methods to enhance the prediction of landslide susceptibility maps (LSM). Ensemble methods present a sampling of landslides and non-landslide points from high and low susceptible areas, respectively. Extensive research has explored their application in machine learning processes, particularly in classification-related problems. This study delves into strategies of ensemble as a promising method in future landslide applications. The proposed method was tested considering Kangra district of Himachal Pradesh as study area where three datasets were prepared consisting of presence and absence points. Dataset 1 consisted of initial landslide and randomly generated non-landslide points. In dataset 2, additional landslide points obtained from the very high susceptibility of initial LSM were supplemented with initial landslide data, while the non-landslide points were generated randomly from the study area. Finally, dataset 3 was composed of the landslide points as in dataset 2, and the non-landslide points were obtained from the very low susceptible areas of initial LSM. These datasets are used with random forest (RF) and support vector machine (SVM), thereby preparing six landslide susceptibility maps. To analyze the applicability of the proposed method, we have used metrics such as AUC-ROC, precision, recall, F-score, accuracy and Mathew's correlation coefficient (MCC). The AUC for dataset 1 with SVM and RF is 0.89, which increased to 0.898 and 0.952 for datasets 2 and 3 with SVM and 0.937 and 0.954 with RF. Among all the methods, the precision and recall values were highest for dataset 3 with SVM as well as RF. Hence, based on several accuracy metrics, we conclude that when the landslides and non-landslides samples were sampled from very high and very low susceptible areas respectively, the LSM performed better than all the other methods. Sampling landslides from very high susceptible areas only (dataset 2) does not perform well thereby committing misclassification error. The study demonstrated that the landslide and non-landslide data were obtained from very high and very low susceptibility; the predictive capability of the LSM increased significantly. Thus, the results demonstrate the effectiveness of the proposed ensemble approach in providing precise delineation of landslide zones, facilitating informed decision-making for land and hazard management.

Petrochemical wastewater is a major industrial source of pollution that produces a variety of toxic organic and inorganic pollutants, naturally present or added during the process. These pollutants are a serious threat to the soil, water, environment, and human being due to their complex and hazardous nature. Glycols such as monoethylene glycol (MEG), diethylene glycol (DEG), triethylene glycol (TEG), and aromatics (BTX-benzene, toluene, and xylene) are the most common organic impurities present in petrochemical wastewater. The objective of this paper is to recover aromatics and water from petrochemical industrial wastewater. The reclamation process is used to remove inorganic impurities such as heavy metals Fe, Zn, Pb, Mn, Al, Ni, As, Cr, Cu, Cd, and K and salts. In the present work, 1% sodium bi-carbonate (NaHCO₃) is used to precipitate the inorganic impurities present in the wastewater at 40 °C atmospherically. Aspen Hysys simulation software is used for modeling and simulation for the treatment process using NRTL (non-random-two-liquid) thermodynamic model. The process generated from Aspen Hysys is validated with lab experiments. To support global sustainable development, this study is focused on reducing, reusing, and recycling separation techniques such as centrifuge separation and vacuum distillation have been used. The characterization of regenerated water was performed using ICP-OES (inductively coupled plasma-optical emission spectroscopy) to determine the reduction in heavy metals. It was found that > 99.5% of heavy metals were removed. The regeneration of these aromatics is necessary for economic and environmental reasons so that it can be reused to avoid its disposal in and contamination of natural environments.

The current study evaluated the effects of air pollution on selected street trees in the National Capital Territory during the pre- and post-monsoon seasons to identify the optimally suitable tree for green belt development in Delhi. The identification was performed by measuring the air pollution tolerance index (APTI), anticipated performance index (API), dust-capturing capacity (DCC) and proline content on the trees. The APTI of street trees of Delhi varied significantly among different tree species (F_11,88.91 = 47.18, p < 0.05), experimental sites (F_3,12.52 = 6.65, p < 0.001) and between seasons (F_1,31.12 = 16.51, p < 0.001), emphasizing the relationships between trees and other types of variables such as the climate and level of pollution, among other factors. This variability emphasizes the need to choose trees to use for urban greening in the improvement of air quality in different environments within cities. Ascorbic acid (AA) concentration and relative water content (RWC) had a strong influence on APTI with an extremely significant moderate positive correlation between AA concentration and APTI (r = 0.65, p < 0.001) along with RWC and APTI (r = 0.52, p < 0.001), indicating that higher levels of AA concentration and RWC are linked to increased air pollution tolerance. The PCA bi-plot indicates AA has poor positive loading coefficients with PC1 explaining 29.49% of the total variance in the dataset. The highest APTI was recorded in Azadirachta indica (22.01), Leucaena leucocephala (20.65), Morus alba (20.62), Ficus religiosa (20.61) and Ficus benghalensis (19.61), irrespective of sites and seasons. Similarly, based on API grading, F. religiosa and F. benghalensis were identified as excellent API grade 6 (81-90%), A. indica and Alstonia scholaris as very good API grade 5 (71-80%), M. alba, Pongamia pinnata and Monoon longifolium as good API grade 4 (61-70%) and Plumeria alba as moderate API grade 3 (51-60%) in different streets of Delhi. As these plants are indigenous to the region and hold significant socio-economic and aesthetic significance in Indian societies, they are advisable for avenue plantations as part of various government initiatives to support environmental sustainability.

The technical feasibility of advanced oxidation process, in particular persulfate (PS) oxidation followed by struvite precipitation for landfill leachate treatment and nutrient recovery has been depicted in the current study. Furthermore, the impact of activation of PS with thermal and ultraviolet (UV) irradiation techniques on COD removal efficiency is also investigated. A maximum COD removal efficiency of 96% is accomplished at 65 °C together with supply of UV irradiation. The impact of persulfate dose, pH, and PS/65 °C/UV system on COD and biodegradability is also illustrated in the current study. Additionally, decomposition rate constant values are also ascertained in the present study. Afterwards, nutrient recovery using struvite precipitation is carried out for sustainable utilization of resources. Preliminary treatment of leachate with PS/65 °C/UV system is greatly conducive to recovering high quality struvite crystals. Besides, 94.9%, 83.5%, and 91.3% of PO₄^3- - P, NH₄⁺ - N, and Mg²⁺ recovery efficiency attained respectively at pH 9.5 and 1.2:1:1 molar ratio of Mg²⁺: NH₄⁺ - N: PO₄^3- - P. Additionally, all the nutrient recovery studies are validated using chemical equilibrium model Visual MINTEQ. Later, bioavailable fraction of PO₄^3- - P in the recovered struvite is also investigated for utilization as fertilizer. The presence of Cu and Zn in the recovered struvite precipitate enhanced its economic value as a fertilizer. Since Cu and Zn are vital micronutrients for growth of plants. The low soluble values of recovered struvite precipitate confirmed its utilization as slow releasing fertilizer.

Precipitation extremes have surged in frequency and duration in recent decades, significantly impacting various sectors, including agriculture, water resources, energy, and public health worldwide. Pakistan, being highly susceptible to climate change and extremes, has experienced adverse events in recent times, emphasizing the need for a comprehensive investigation into the relationship between precipitation extremes and crops production. This study focuses on assessing the association between precipitation extremes on crops production, with a particular emphasis on the Punjab province, a crucial region for the country's food production. The initial phase of the study involved exploring the associations between precipitation extremes and crops production for the duration of 1980-2014. Notably, certain precipitation extremes, such as maximum CDDs (consecutive dry days), R99p (extreme precipitation events), PRCPTOT (precipitation total) and SDII (simple daily intensity index) exhibited strong correlations with the production of key crops like wheat, rice, garlic, dates, moong, and masoor. In the subsequent step, four machine learning (ML) algorithms were trained and tested using observed daily climate data (including maximum and minimum temperatures and precipitation) alongside model reference data (1985-2014) as predictors. Gradient boosting machine (GBM) was selected for its superior performance and employed to project precipitation extremes for three distinct future periods (F1: 2025-2049, F2: 2050-2074, F3: 2075-2099) under the SSP2-4.5 and SSP5-8.5 derived from the CMIP6 (Coupled Model Intercomparison Project Phase 6) archive. The projection results indicated an increasing and decreasing trend in CWDs (maximum consecutive wet days) and CDDs, respectively, at various meteorological stations. Furthermore, R10mm (the number of days with precipitation equal to or exceeding 10 mm) and R25mm displayed an overall increasing trend at most of the stations, though some exhibited a decreasing trend. These trends in precipitation extremes have potential consequences, including the risk of flash floods and damage to agriculture and infrastructure. However, the study emphasizes that with proper planning, adaptation measures, and mitigation strategies, the potential losses and damages can be significantly minimized in the future.

Nanoribbons (NRs), leveraging the flexibility of one-dimensional materials and the expansive surface area of two-dimensional materials, offer heightened exposure to edge sites and superior charge transfer rates. Consequently, they present promising prospects within the domain of photocatalysis. Crystalline red phosphorus (cRP), dcharacterized by its layered and fibrous structure, lends itself readily to the production of nanoribbons. Our study demonstrates a robust method for achieving high-yield, high-quality cRP by concurrently introducing mineralizing agent I₂ and Si wafers into the Chemical Vapor Transport (CVT) synthesis process. Through ultrasound assistance, we transformed high-quality cRP into crystalline red phosphorus nanoribbons (cRP NRs) with an average thickness ranging from 7.5 to 17.5 nm and an average width between 75 and 175 nm. cRP NRs (I₂ and Si) showcased impressive degradation capabilities towards Methyl Orange (MO) and Tetracycline (TC), achieving a remarkable 99% degradation of MO within 18 min under the simulated visible-light irradiation. The reactive species capturing experiments confirmed that ·O₂^- was the primary active agent responsible for the photocatalytic degradation of MO.