Environmental science. Advances最新文献

Mineral carbonation is a promising strategy for mitigating carbon emissions and combating climate change. This study investigates the efficacy and sustainability of MgO-based stabilization techniques for soft marine soils, incorporating supplementary cementitious materials (SCMs) such as biochar and slag. A combination of laboratory experiments and rigorous analyses was utilized to elucidate the complex interplay between the additives and their impacts on soil hydraulic characteristics, carbon sequestration potential, embodied energy, and economic viability. Mercury intrusion porosimetry (MIP) was employed to characterize pore structure changes induced by carbonation, while X-ray diffraction (XRD) and scanning electron microscopy (SEM) were used to correlate mineral formations. The results indicate that MgO–biochar-treated soils exhibit enhanced soil air content, pore connectivity, and carbon sequestration efficiency compared to MgO–slag-treated soils, exhibiting reduced pore volumes and limited CO₂ diffusion. Integrating biochar with MgO enhanced brucite and nesquehonite precipitation due to biochar's porous structure and functionalized surface area, facilitating gas diffusion and nucleation for mineral formation. Sustainability assessments highlight the environmental and economic trade-offs, positioning MgO–biochar and MgO–slag combinations as cost-effective and environmentally friendly alternatives. This research provides theoretical guidance for sustainable soil stabilization and efficient CO₂ mineralization, offering valuable insights for researchers, practitioners, and policymakers addressing climate change challenges.

Considering the significant toxicity of arsenite (AsO₂⁻), arsenate (AsO₄³⁻), and hydrogen sulphide (H₂S), the early detection of these ions and gas using simple methods like naked-eye chemosensing could have substantial implications for environmental and industrial applications. With these factors in mind, we have developed a novel and straightforward colorimetric chemosensor called NADNP (2-hydroxy naphthaldehyde conjugated 2,4-dinitrophenyl hydrazine) for swift paper-based colorimetric detection of arsenite, arsenate, and H₂S, based on a deprotonation mechanism. NADNP exhibits strong binding affinity towards sulfide, arsenite, and arsenate, with very lower detection limits (LOD) of 0.17 μM, 0.15 μM and 0.15 μM respectively, and the binding stoichiometry between these detected ions and NADNP is determined to be 1 : 1 through Job's plot analysis. Structural elucidation and electronic properties calculation have been conducted via DFT (Density Functional Theory) studies for correlation with the spectroscopic analyses. The ‘three-in-one’ paper strip-based chemosensor could be considered a promising colorimetric tool for rapid, cost-effective, selective, and sensitive “on-spot” sensing and monitoring of arsenite, arsenate, and sulfide in environmental samples.

The high volume of plastic waste generated and its potential harm to wildlife and ecosystems are negative consequences of poor end-of-life food packaging management. An essential part of designing food packaging is minimizing its environmental impact, which is a significant challenge for the industry. The aim of this study was to examine existing life cycle assessment (LCA) approaches for investigating the environmental advantages of novel food packaging systems in the field of ready-to-eat fish and meat products. The scope of studies differed, with some including food products and others focusing on the direct and/or indirect environmental impacts of packaging. The reviewed LCA performances showed how different focuses could be used as sequential steps in obtaining a comprehensive understanding of the environmental impact of a food-packaging system. By considering a holistic LCA approach and evaluating the environmental performance of different packagings, industry stakeholders can make informed decisions. Therefore, playing an active role that balances necessity and wastefulness and creates efficient and sustainable packaging solutions.

Green bonds are becoming a popular investment option as a result of growing investor awareness of social and environmental issues. Green bonds are financial securities used to fund initiatives aimed at mitigating the effects of global industrialization on the environment and climate change, as well as initiatives that make use of cutting-edge technology. For the SDGs to be achieved, this kind of financial product must be successfully promoted. Therefore, the objective of this research work is to statistically analyze the characteristics of green and brown bond yields. In addition, to ascertain how the two yields relate to one another and how they change over time.

With a unique geographical location and a fragile ecological environment, the Arctic has been a major concern of contamination by persistent organic pollutants (POPs), such as dioxins, also termed polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) due to their high toxicity. Under the influence of global warming, increasing wildfires have occurred in northern territories of the Northern Hemisphere (NH) in the recent decade. Given the proximity of these natural sources, the Arctic is likely subject to growing risks of local and nearby wildfire emissions of POPs. By implementing an updated global PCDD/Fs atmospheric emission inventory from 2011 to 2020 into an atmospheric transport model, we quantitatively assessed the PCDD/Fs pollution in the Arctic atmosphere. We explored the impact of wildfire combustion on PCDD/Fs pollution in the Arctic atmosphere and evaluated the relative significance of local and remote emissions from wildfire and anthropogenic sources. The results revealed that PCDD/Fs emissions from wildfire sources played an increasingly important role in PCDD/Fs pollution in the Arctic, contributing to about 70% of PCDD/Fs concentrations in Arctic air in 2020. Within the Arctic circle, wildfire emissions have also exceeded anthropogenic emissions since the late 2010s. This study provides data support for further assessment of wildfires' impact on the Arctic region's ecological environment and valuable information for assessing the effectiveness of PCDD/Fs (and other POPs) emission control.

Pesticides are biologically active compounds and their application may alter soil microbial communities and thus could possibly impact greenhouse gas (GHG) emissions. However, this aspect of agricultural production is rarely studied at the field scale. To address this knowledge gap, we conducted a 2 year field study growing maize (corn) under three pesticide application levels (none, medium, and high) in two agricultural practices: bare soil (conventional) and using cereal rye as a cover crop. In plots with no pesticide inputs, weeds were managed through hand removal weekly. We quantified GHG emissions, changes in soil labile carbon (C), nitrogen (N), and other typical growth parameters in the Iron Horse Farm, Georgia. Corn grain yields were within 93% of the estimated site yield potential, with yield significantly higher in 2021 than in 2022. Using a linear mixed model, including the data in both 2021 and 2022 for soil nutrients, soil temperature, soil moisture, agricultural practice, and pesticide levels as fixed effects and date and plot as random effects, soil surface carbon dioxide (CO₂) fluxes were statistically significantly associated with soil temperature and soil moisture. Soil nitrous oxide (N₂O) emissions were only associated with soil moisture. Soils in general served as a sink for methane (CH₄) in all the agricultural practices and soil CH₄ fluxes were also only associated with soil moisture. Three plots with a high soil C/N ratio with a visible presence of biochar resulted in several high CH₄ flux events during the growing season. Soils from all plots were net sources of GHG and there were no significant differences in the amount of soil C sequestered between the plots. Our study shows that none of the variables we analyzed – yield, individual/net GHG emissions or the amount of C sequestered – in the two years of our experiment were impacted by the magnitude of pesticide application. However, this may change in a long-term experiment. Further research is also warranted to understand the underlying mechanism for high CH₄ pulses, whether reactive oxygen species from the application of biochar might be the cause of large negative consequences on climate, depending on conditions.

In an effort to mitigate the impact of climate change, e.g., by reducing the emission of greenhouse gases, hydrogen is becoming an increasingly attractive alternative energy source, replacing conventional long-chain hydrocarbon fuels in the energy and transport sector. While there is a shift in individual transport towards battery-electric applications, the maritime and energy production sectors rely on a high energy density and time- and location-independent availability of the energy carrier. Therefore, large-bore engines powered by renewable fuels have the potential to shift the industry towards a climate-neutral operation. Besides the emission of greenhouse gases, internal combustion engines are known for emitting pollutant emissions, harming human health and the environment. Research on particle emissions of natural gas and hydrogen engines has mainly focused on automotive and heavy-duty applications. Hence, this study investigates particle emissions of a large-bore single-cylinder research engine powered by hydrogen, compared to natural gas, for the first time. Investigations on particles with a diameter as low as 10 nm showed particle numbers of 10⁴ to 10⁵ # cm⁻³, unexpectedly achieving slightly higher particle numbers in hydrogen than in natural gas operations. This is due to particles from lubricant oil and a stronger fuel interaction with the liner oil film in hydrogen operation, demonstrated within a 3D-CFD simulation. The concentrations are still lower by several orders of magnitude than in long-chain hydrocarbon fuel operations of identical engines. An extended emissions analysis based on the gaseous components THC, CO, and CO₂ shows the negligible carbonaceous emissions induced by these oil-based particles.

In healthcare and human life, and with the growing need for environmentally friendly materials to replace synthetic ones, biomaterials are essential. Desirable biomaterials may now be created using a wide range of extracted natural polymers. Mycelium-based biomaterials are being developed into more adaptable, inexpensive, and self-replicating products. Some fungal species, like Pleurotus ostreatus and Ganoderma lucidum, have been recognised as excellent sources of biomaterials with unique morphological, mechanical, and hydrodynamical characteristics. Thermomyces lanuginosus and Purpureocillium lilacinum are two fungal strains that may be used to create biomaterials. This article seeks to introduce these strains and use experimentation to identify their distinctive characteristics. The fungus was cultivated in a lab, and the growth kinetics of the fungus were estimated. The strains of P. lilacinum and T. lanuginosus had maximum specific growth rates (μ_max) of 1.34 ± 0.024 and 3.09 ± 0.019 L⁻¹ d⁻¹, respectively. Decellularization of the fungal biomass was performed using 0.1% SDS solution, after which the scaffolds were created by drying the biomass in plastic moulds. Following that, analysis using scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), and Fourier transform infrared spectroscopy (FT-IR) was carried out. The porosity and swelling ratio were also determined and hydrodynamic characterization was performed for the samples. The results show that mycelia have the potential to serve as inexpensive, all-natural bio-scaffolds and T. lanuginosus-prepared materials have a larger swelling ratio and increased porosity, which makes them better myco-materials than those formed from P. lilacinum.

Coagulation/flocculation is a widely used water and wastewater treatment process due to its low cost, simplicity, and effectiveness. However, the process is not effective in the treatment of per- and polyfluoroalkyl substances (PFAS), the presence and treatment of which is an ongoing challenge for water providers. Here, we explore cationic surfactant-enhanced coagulation as a process modification to target the removal of PFAS in existing coagulation/flocculation systems. Batch experiments, in jar testing apparatus, were performed to assess the removal of two short-chain and two long-chain PFAS at an initial concentration of 10 μg L⁻¹ with the addition of cetyltrimethylammonium chloride (CTAC) as the coagulant-aid. Our findings suggest that elevated coagulant dose (60 mg L⁻¹ of alum or 100 mg L⁻¹ of FeCl₃) coupled with the addition of a cationic surfactant (1 mg L⁻¹ of CTAC) significantly enhanced the removal of both short-chain (perfluorobutane sulfonate: PFBS removal to >40%) and long-chain PFAS (perfluorooctanoic acid: PFOA and perfluorooctane sulfonate: PFOS removal to >80%), with FeCl₃ showing better performance than alum. Sulfonates (PFBS, PFOS) were shown to be removed more efficiently compared to carboxylates (PFBA, PFOA), presumably due to their higher hydrophobicity leading to better interactions with the flocs. Furthermore, CTAC in combination with traditionally used additives such as powdered activated carbon (PAC), served as a better aid for PFAS treatment and improved the removal of PFBS, PFOA, and PFOS to >98%. This study highlights that introducing a cost-effective pre-treatment with a cationic surfactant to existing conventional treatment systems can improve the performance efficiency in treating PFAS-contaminated waters.

The computational cost of accurate quantum chemistry (QC) calculations of large molecular systems can often be unbearably high. Machine learning offers a lower computational cost compared to QC methods while maintaining their accuracy. In this study, we employ the polarizable atom interaction neural network (PaiNN) architecture to train and model the potential energy surface of molecular clusters relevant to atmospheric new particle formation, such as sulfuric acid–ammonia clusters. We compare the differences between PaiNN and previous kernel ridge regression modeling for the Clusteromics I–V data sets. We showcase three models capable of predicting electronic binding energies and interatomic forces with mean absolute errors of <0.3 kcal mol⁻¹ and <0.2 kcal mol⁻¹ Å⁻¹, respectively. Furthermore, we demonstrate that the error of the modeled properties remains below the chemical accuracy of 1 kcal mol⁻¹ even for clusters vastly larger than those in the training database (up to (H₂SO₄)₁₅(NH₃)₁₅ clusters, containing 30 molecules). Consequently, we emphasize the potential applications of these models for faster and more thorough configurational sampling and for boosting molecular dynamics studies of large atmospheric molecular clusters.