Environmental science. Advances最新文献

This themed collection in Environmental Science: Advances aims to update recent developments in soil science, soil-dependent ecosystem services, and the impacts on soils and their function in the face of pollution and climate change.

Correction for ‘Deciphering the connection between the informal plastic recycling industry and the microplastic pollution in the Buriganga River’ by Md. Ridwan Mahfuz et al., Environ. Sci.: Adv., 2025, https://doi.org/10.1039/d4va00370e.

Microplastic pollution is an emerging global concern due to its persistent nature, toxic effects, and complex detection techniques. This study analyzed microplastic (MP) abundance in water and sediments of Buriganga and Turag Rivers in Dhaka city, revealing severe contamination in Buriganga (dry season: 26.98–374.84 MPs per L water, 11 360–134,330 MPs per kg sediment; wet season: 5.17–9.07 MPs per L, 2060–10,225 MPs per kg). Turag exhibited lower pollution (dry: 3.02–24.76 MPs per L, 1430–6720 MPs per kg; wet: 1.93–14.57 MPs per L, 1255–6590 MPs per kg). Dominant MPs comprised fragments/films/fibers, with sizes <300 μm (33–75% prevalence) and white particles (26–35%), supplemented by red/black (Buriganga) and brown/black (Turag). Polymers were dominated by polyethylene (75–83% occurrence) and polypropylene (83%), with polystyrene in 4–8% of samples. Moreover, the presence of toxic heavy metals, e.g., Cr, Mn, Pd, Cd, Pb, was observed on the surface of MP samples. Hierarchical Cluster Analysis revealed distinct groupings of sampling stations based on the concentration and weight of MPs, highlighting spatial variations in MP distribution across rivers and seasons, while the Pollution Load Index confirmed moderate risk across both rivers, peaking at 4.11 (water) and 4.02 (sediment) in the dry season of Buriganga. Electrocoagulation (Al–Al electrodes, 15 V) achieved >99% MP removal within 150 minutes, following second-order kinetics with voltage-dependent efficiency. These findings underscore MPs as complex hazards to the environment, urging prioritized source control, scaling of remediation techniques, and standardized monitoring for urban river systems.

Effective removal of trace lead (Pb) from waste matrices is crucial to meet stringent environmental regulations designed to mitigate toxicological risks and protect human health. This study investigates the efficacy of a monolithic solid-phase extraction (m-SPE) column for the selective separation of trace Pb from aqueous matrices, comparing its performance to conventional particle-packed solid-phase extraction (p-SPE) columns. Key operational parameters, including solution pH, flow rate, washing solvent, and eluent, were optimized to maximize Pb retention on both SPE columns. Potential interference from common matrix ions was investigated and found to be minimal. Furthermore, the presence of counter anions enhanced Pb²⁺ retention on the m-SPE column, likely by promoting the formation of ion pairs. Notably, the SPE columns demonstrated reusability over multiple cycles without significant loss of efficiency. The p-SPE and m-SPE columns demonstrated satisfactory Pb²⁺ retention while exhibiting minimal retention of common elements, as confirmed by analysis of certified reference river water with elevated contents of trace elements. The m-SPE column demonstrated enhanced performance compared to the p-SPE column due to its high permeability, low backpressure, and robust porosity. These characteristics resulted in enhanced selectivity, reproducibility, and overall efficiency in the preferential separation of trace Pb from environmental matrices.

The alarming increase in global warming, primarily driven by the rising CO₂ concentration in the atmosphere, has spurred the need for technological solutions to reduce CO₂ concentrations. One widely successful approach is geological sequestration, which involves pressurizing and injecting CO₂ into underground rock formations. Saline aquifers, containing saltwater, are often used for this purpose due to their large storage capacity and broad availability. However, to optimize CO₂ storage and reduce the risk of gas leakage, it is essential to account for capillary forces and the interfacial tension (IFT) between CO₂ and brine within the formation. Traditional methods for characterizing CO₂-brine IFT in saline aquifers, both experimental and theoretical, are well-documented in the literature. Experimental methods, though accurate, are labor-intensive, time-consuming, and require expensive equipment, while theoretical approaches rely on idealized models and computationally demanding simulations. Recently, machine learning (ML) techniques have emerged as a promising alternative for IFT characterization. These techniques allow models of CO₂-brine IFT to be automatically “learned” from data using optimization algorithms. The literature suggests that ML can achieve superior accuracy compared to traditional theoretical methods. However, in its current state, the literature lacks a comprehensive review of these emerging methods. This work addresses that gap by offering an in-depth survey of existing machine learning techniques for IFT characterization in saline aquifers, while also introducing novel, unexplored approaches to inspire future advancements. Our comparative analysis shows that simpler ML models, such as ensemble tree-based models and small multi-layer perceptrons, may be the most accurate and practical for estimating CO₂-brine IFT in saline aquifers.

A comprehensive environmental forensics investigation has been done on a former coal mining site in NE England, and now a country park used for recreation, but which lacks the ability to grow vegetation in certain areas. Initial mapping of the site was done using an unmanned aerial vehicle (UAV) with multispectral imaging (MSI) capability, followed by determination of 7 vegetation indices (VIs). The use of the VIs allowed a direct comparison between the two field sites and provided an indicator of vegetative stress. This was followed by field sampling and laboratory analyses using EX-XRF for metal analyses, soil property determination (pH, CEC and organic matter), metabolomic determination of the main soil metabolites using Hydrophilic Liquid Interaction Chromatography Hi-Resolution Mass Spectrometry, and a comprehensive investigation of soil bacteria and fungi using metagenomics. The results indicate how the soil environment of the top field has recovered to allow an abundance of flora in Spring and Summer, despite the soil having a low pH (4.0) and a high Pb concentration (94.0 mg kg⁻¹) but counterbalanced by the presence of natural plant and soil metabolites, and a high abundance of nutrient producing bacteria. In contrast, the bottom field is characterised by a sparse vegetation coverage on a harsher soil environment reminiscent of marshland, with a soil pH of 6.2, but a lower Pb concentration (58.4 mg kg⁻¹) contrasted with soil with a high sodium content (2050 mg kg⁻¹), the presence of man-made anthropogenic metabolites, and bacteria capable of undertaking soil remediation.

The synthesized nickel ferrite (NiFe₂O₄)/poly(aniline-co-o-toluidine) (PAOT) nanocomposite was successfully characterized using XRD, FTIR, SEM, and EDX, confirming the formation of a stable spinel structure with uniform particle distribution (32–68 nm). The material exhibited a low bandgap energy of 1.24 eV and retained magnetic properties, enabling easy recovery and reuse for up to four cycles. The catalytic activity of the NiFe₂O₄/PAOT nanocomposite was evaluated for the visible-light-assisted reduction of 4-nitrophenol (4-NP) without external reducing agents. The catalyst achieved reduction efficiencies of 85.83% at 2 ppm, 95% at 10 ppm, and 99% at 15 ppm within 60 min, with improved performance at higher catalyst dosages and temperatures (e.g., 50 °C with 20 mg). Kinetic analysis revealed pseudo-first-order behavior. Compared to other reported catalysts, NiFe₂O₄/PAOT offers green synthesis, high efficiency, magnetic recoverability, and operational simplicity, making it a promising material for sustainable wastewater treatment.

Pyrolysis currently emerges as a promising technology capable of treating mixed plastic waste that is otherwise unsuitable for mechanical recycling. However, its large-scale adoption requires a comprehensive understanding of its environmental impacts based on different technology setups. This study uses life cycle assessment (LCA) to compare different pyrolysis configurations, varying in operational parameters such as maximizing or managing process gas. The results reveal a high variability of environmental impacts across configurations. Sensitivity analysis further indicates that a shift towards renewable energy sources has a potential to enhance the overall environmental performance of pyrolysis. Presented findings emphasize the need to carefully select pyrolysis process parameters when considering scale-up and integration into waste management strategies. The study thus provides insights for decision-makers evaluating pyrolysis as an environmentally sound plastic waste treatment solution.

Emerging contaminants (ECs) are widespread in the environment and pose notable health risks, yet their exposure levels among specific groups, such as college students, are underexplored. This study investigated the occurrence of ECs in human urine through suspect screening (537 ECs) and target analysis (50 prioritized ECs), alongside a human health risk assessment. An optimized solid-phase extraction method was compared with liquid–liquid extraction and supported-liquid extraction and was coupled with LC-TQMS analysis. This method demonstrated high reliability (r = 0.997), precision (0.05–14.7%), recoveries (52.6–113%) and sensitivity (LOD: 0.05–5.00 ng mL⁻¹). Urine samples were collected twice from 43 freshmen and once from 33 seniors (students from other grades), with accompanying questionnaires assessing their living environments and lifestyle habits. Eleven ECs were detected, with atrazine exhibiting a 100% detection frequency. Significant variations were observed in the urinary concentrations of 2,4-dinitrophenol, ethylparaben, metformin, and mycophenolic acid between freshmen and seniors, suggesting differences in exposure patterns influenced by living environments and personal habits. Statistical analyses identified correlations between EC exposure and personal care product use, with monobenzyl phthalate being a notable example. Health risk assessments indicated low overall risks but revealed higher hazard quotient (HQ) values for atrazine, 2,4-dinitrophenol, and mycophenolic acid, warranting further investigation. This study successfully developed a high-throughput and sensitive LC/MS method by integrating suspect screening with target analysis. It also provided a preliminary evaluation of EC exposure in a young student population through urine analysis, offering valuable insights for future research on environmental exposure and associated health risks.

Many per- and polyfluoroalkyl substances (PFAS) are known to be persistent in the environment and are associated with adverse health effects including kidney and liver disease and developmental toxicity. While PFAS are also known to have high bioaccumulation potential, whether these compounds can be detected in biological tissue using nuclear magnetic resonance (NMR) has not been established. In this study, we used ¹⁹F solid-state magic angle spinning (MAS) NMR to investigate the accumulation of a legacy PFAS, perfluorooctanoic acid (PFOA), in murine tissue samples including the adult brain, intestine, kidney, liver, uterus, adipose tissue, placenta and fetal brain. Healthy pregnant (n = 4) and non-pregnant (n = 5) female CD-1 mice were exposed to 50 ppm of PFOA through their drinking water for 17 days. PFOA was detected above the limit of detection (10 μg g⁻¹) in all of the liver samples (n = 9/9), 25% (n = 2/8) of the adipose tissue samples, 33.3% (n = 4/12) of the male placenta samples, and 16.7% (n = 2/12) of the female placenta samples. The detection of PFOA in adipose tissue challenges the current understanding about the behaviour of PFAS in the human body. These results demonstrate that ¹⁹F solid-state MAS NMR is a promising tool for detection and quantification of PFAS in tissue samples and motivate further work to evaluate accumulation of unregulated, emerging PFAS that have different chain lengths and head groups.