Environmental science. Advances最新文献

Per- and poly-fluoroalkyl substances (PFAS) molecules have long been used in a variety of applications as they are chemically robust and resistant to chemical transformations. However, it has recently come to light that these compounds are toxic, and remediation efforts are required to remove them from our society. In a recent study (Jenness et al., Env. Sci. Proc. Impacts, 2022, 24, 2085) we explored the use of silylium-carborane for the degradation of perfluorobutanoic acid (PFBA) and three derivatives. In the course of our study, we found the degradation of the C–F bond was facilitated by a low-lying unoccupied anti-bonding orbital. Based on this finding, we propose the usage of metal catalysts for the degradation of the C–F bond as metals have been shown to take advantage of such low-lying anti-bonding orbitals. Utilizing density functional theory (DFT) calculations, we explored how the C–F bond in PFBA can be split by the entirety of the d-block metals. Deriving a series of linear scaling relationships, we demonstrate that metals conforming to the bcc point-group perform the best for this chemistry. In particular, iron (Fe) has a good balance of fluorine and PFBA binding and reaction energies and would be a worthy candidate for further studies.

A one-step thermal polymerization approach was adopted to combine C₃N₅ with montmorillonite nanosheets (MMT Ns) to form xCN-MMT for the degradation of pollutants in water. Benefitting from the abundant hydroxyl groups on the MMT surfaces, double defects (–CN and N defects) were introduced in xCN-MMT catalysts to promote the adsorption of tetracycline (TC), peroxymonosulfate (PMS), and oxygen. 10CN-MMT exhibited superior adsorption performance toward TC, with the adsorption capacity being 5.65-fold that of MMT Ns and 2.64-fold that of C₃N₅. Further, 10CN-MMT exhibited better PMS activation performance than MMT Ns and C₃N₅, which could degrade 95% of TC within 120 min. Moreover, the total organic carbon (TOC) removal efficiency of the present system reached 81.1%, and the chemical oxygen demand (COD) decreased from 50.7 to 12.2 mg L⁻¹. The degradation process of TC was characterized using liquid chromatography-tandem mass spectrometry (LC-MS), and a reasonable degradation pathway and catalytic mechanism were given by combining with active species analysis. The toxicological analysis of the degradation products also showed a significant decrease in toxicity. The degradation experiments in different water environments were also simulated, and it was found that 10CN-MMT showed good adsorption effects. This study provides a green metal-free clay-based catalyst and shows good applicability in removing antibiotics.

Life cycle assessment (LCA) has been recognised as an important environmental systems analysis tool due to its potential for providing systematic results about the environmental impacts of alternative production and consumption systems that can lead to decisions towards greater sustainability in both private and public-policy contexts. However, LCA has been under increased scrutiny due to the wide range of published results on similar systems, such as biofuels, which can be contrasting. This variability is, in part, due to the proliferation of guidelines that have emerged over the last 20 years, which may undermine the perceived robustness of LCA as a decision-support tool. Following some interesting discussions on this topic in different fora, we took the pulse of the LCA community via a survey. We received 124 responses from respondents who varied in their background and experience in LCA (most were academics and/or had more than 10 years' experience), as well as in their opinions on whether they saw the inconsistency of published results problematic, or not, for decision making. Results suggest that respondents are of the opinion that (i) there is no single right way of performing LCA; (ii) the ISO 14040-44 standards were failing in their guiding of LCA practice, and that (iii) further efforts in harmonizing LCA practice would be beneficial, despite mixed opinions shown by respondents, which indicates the divisive nature of this topic in the LCA community. For example, there was no clear agreement on whether the significant flexibility with which practitioners perform LCA undermines its validity as a robust tool for decision making, though practitioners concerned with greenwashing were unified in the need for improved guidelines and harmonisation. Further harmonisation would help to ensure consistency in the application of the tool by practitioners which, in turn, would ensure results would be less variable, arguably more meaningful, and less prone to greenwashing. It is likely that methodological issues will remain unresolved in the near future, as some practitioners value the flexibility with which the ISO standards can be applied, even if that leads to inconsistent results. We recommended tighter standardization.

The increasing need for food and agricultural resources necessitates using pesticides to protect plants, but this approach also poses pesticide poisoning and environmental hazards. Although designing an effective pesticide detection method is challenging, various technologies collaborate to develop an effective electrochemical sensor for detection of various pesticides. This review article examines the various metal oxides, their synthesis techniques, and their applications in electrochemical sensors, particularly for environmental applications, to detect pesticides in a variety of contaminated environmental samples. Metal oxides have unique properties that make them useful for pesticide detection because of their more active sites and electrical, optical, and semiconducting properties. Samarium molybdate-based electrode materials are considered the most promising direction for the development of electrode materials for pesticide sensors due to their economy, chemical stability, multiple valences, low detection limit, high sensitivity, and high electrocatalytic activity performance. In addition, this study investigates the current research trend in the detection of pesticides using metal oxide-based sensors in environmental samples, and researchers should expect new research perspectives and ideas. Overall, the metal oxide-based pesticide detection sensors will surely aid in meeting the growing demands for food and environmental monitoring and protection.

Silicon alloys are produced by carbothermic reduction of quartz in a submerged arc furnace. This high-temperature pyrolytic process is a source of polycyclic aromatic hydrocarbons (PAHs), which are a group of aromatic organic molecules with known mutagenic and carcinogenic properties. In this study, the emission of oxy- and nitro-PAHs from a pilot-scale Si furnace, with varying process conditions such as oxygen level, flue gas recirculation (FGR), and off-gas flow, was investigated. Analysis shows the presence of both oxy- and nitro-PAH species in all experiments, believed to be formed from radical-induced substitution reactions initiated by SiO combustion and NO_x formation. During Si production without FGR, the levels of oxy- and nitro-PAHs range between 1.1 and 4.4 μg Nm⁻³, independent of the flue gas flow rate. With increasing FGR (0–82.5%) and decreasing oxygen level (20.7–13.3%), the concentrations of both oxy- and nitro-PAHs increase to 36.6 and 65.9 μg Nm⁻³, respectively. When the levels of substituted PAHs increase, species such as 4-nitropyrene and 1,2-benzanthraquinone are in abundance compared to their parent PAHs. Experiments at lower flue gas flow (500 Nm³ h⁻¹versus 1000 Nm³ h⁻¹) generally produce less substituted PAHs, as well as SiO₂ particulate matter and NO_x, where the latter two parameters have a 99% correlation in this study.

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The investigation focused on a methodology for concentrating and analyzing Cr(VI) in aqueous samples. This objective was accomplished through the creation of a cellulose triacetate (CTA) matrix-based membrane optode. This optode was constructed by physically integrating a specific chromophore, 1,3-benzenediamine,N,N′-bis(2-furanylmethylene) (BDBFM), known for its selectivity towards Cr(VI), alongside the plasticizer dioctylphthalate (DOP). The effectiveness of integrating Aliquat 336, an anion exchanger, was evaluated in the process of immobilizing both BDBFM and the Cr(VI)–BDBFM complex within the optode matrix. The progressive intensification of the violet color observed on the optodes, directly correlating with the amount of loaded Cr(VI), highlights the potential of this method for colorimetric screening of Cr(VI) in aqueous samples. The developed optode was also employed for the determination of the total chromium content by converting Cr(III) to Cr(VI) via oxidation using 0.1 M hydrogen peroxide. The concentration of Cr(III) can be quantified by subtracting the amount of Cr(VI) from the total chromium content. This optode enabled the quantitative detection of Cr(VI), even at levels as low as 2.85 ng mL⁻¹. The suggested sensor displayed a low detection limit, fast response time, cost effectiveness, ease of preparation and also remarkable selectivity regarding some anions and cations. Regeneration of the optode can be easily accomplished by employing 0.05 M HNO₃, while demonstrating remarkable reproducibility and reversibility in its response, with a relative standard deviation (RSD) below 1.9. The suggested method was effectively utilized to measure chromium levels in a diverse range of samples, such as food, water, and environmental, and biological samples.

Small microplastics (SMPs, 1–20 μm) and nanoplastics (NPs, 1–1000 nm) are contaminants of high concern, but they were only documented in a few studies due to challenges during pre-treatment and characterization in environmental samples. In this study, weathered plastic pieces and surrounding sediments were collected from 3 areas of Yangtze Estuary, China. A top-down method was used to generate SMPs/NPs from plastic pieces using an ultrasonic cleaner in the lab, and the abundance and size distribution of SMPs/NPs generated, as well as those found from the surrounding sediments of the pieces in the field were measured and subsequently compared after verifying polymer types using Raman spectroscopy. The results revealed that each plastic piece generated an average of 3 × 10⁴ particles of MPs, and NPs with size down to 620 nm in lab samples. However, the number of SMPs found in surrounding sediments was almost 3 times lower than that generated from one plastic piece. Furthermore, the particle size ranges do not align with those generated in the lab. It indicated that smaller and more abundant SMPs/NPs could be generated from the weathered plastic pieces, but few SMPs were found in surrounding environments. We assume that the current sampling and identification methods limit the representativeness of samples and the accuracy of SMP/NP detection.

Machine learning is increasingly popular and promising in environmental science due to its potential in solving various environmental problems. One such worldwide issue is the pollution caused by the persistent chemicals – per- and polyfluoroalkyl substances (PFAS), threatening the environment and human beings. Here, we introduce a no-code machine learning approach for modelling the quantitative structure–retention relationship (QSRR) of liquid chromatographic retention time (LC-RT) for PFAS. This approach aims to streamline the modelling process, particularly for environmental professionals who may find intensive coding cumbersome. The QSRR models were developed using the no-code machine learning tool, Orange, employing simple 2D molecular descriptors as input features. Through a systematic analysis, 12 descriptors were identified as pivotal properties essential for developing optimal models (including multiple linear regression – MLR and support vector machine – SVM). These selected models demonstrate great internal validation metrics (R² > 0.98, MAE < 6.5 s) and reasonable external robustness (R² > 0.80, MAE ∼ 40 s). Furthermore, a concise model interpretation was conducted to elucidate the molecular factors influencing LC-RT. It is anticipated that our models, capable of predicting the LC-RT for over 2000 PFAS within the Norman Network, will be instrumental in addressing this environmental challenge. This study not only contributes valuable insights into PFAS LC behaviour but also serves as a catalyst for future endeavours in the development and applications of no-code machine learning models.

Alaska has the lowest rate of access to in-home water services in the United States. At the same time, the state also has the world's oldest Universal Basic Income (UBI) program, and every Alaska resident receives an annual payment through the Alaska Permanent Fund Dividend (PFD) program. In this study, we use a panel dataset of rural Alaska water and sewer utilities in 18 Alaska villages from 2012 to 2016 to explore the impact of the PFD on residential payments. We estimate fixed effects for eight models. Models are developed by grouping villages by low and high variability in payments, enrollment in Alaska Native Claims Settlement Act (ANCSA) regional corporations and Community Development Quota (CDQ) organizations. We find that on average, each utility is missing $14 710 in customer payments yearly, and have a median residential delinquency rate of 14%. The model with all the villages (p < 0.01), ANCSA models (p < 0.05), and CDQ models (p < 0.05) all show a significant increase in residential payments when the PFD is paid in October. Average residential payments in October are $3671 to $10 058 higher than in other months. The increased payments represent 2% to 6% of the total revenue of utilities. We estimate that across rural Alaska the PFD generates between $734 200 to $2 011 600 in additional payments for water utilities. These findings suggest that the PFD and other unrestricted cash transfers can play an important role in increasing household water security in rural Alaska and other places with similar problems.