<i>Habits – implicit associations between psychologically satisfying outcomes and the sequence of actions that repetitively produced these outcomes in a relatively stable environment – are crucial for human health, self-regulation, and day-to-day functioning. The present research claims that the primary function of habit mechanism is stress-reduction, associated with decision-making and environmental assessment. Next, addressing the theoretical contradictions of the traditional model of habit development, we propose a new two-phase model, differentiating the old-habit extinction phase and the new-habit formation phase and choosing appropriate cognitive strategies for each. The model was tested via 6-week longitudinal qualitative study, assessing its effectiveness and alignment with participants’ experience. Ten participants who expressed the wish to develop a habit of practicing mindfulness meditation, and who had never meditated regularly before, participated. The study protocol was designed to strategically increase their awareness of routine during the first two weeks of the study and decrease it during the remaining four weeks of the study, thus minimizing the stress entailed by the routine restructuring. All participants reported developing a meditation habit, and thematic analysis has shown that the experiences of 8/10 participants fit the two-phase model. Finally, participants’ accounts support the synthesis of outcome-insensitivity and cue-dependence issues in habit formation and initiate broader discussion about personal differences in routine following.</i>

Environmental risk assessment following Brazilian mining dam collapse: A diffusion approach.

A novel framework integrating a coupled mixing rule with deep learning for toxicity prediction and environmental risk assessment of antibiotic mixtures.

Understanding the environmental impact of products such as bioethanol requires comprehensive tools, and life cycle assessment (LCA) plays a central role in this. However, LCAs often rely on numerous assumptions and data sources, which introduce uncertainties that complicate decision making. This study presents a practical way to break down and better understand these uncertainties using a combination of Monte Carlo simulation (MCS) and the logarithmic mean Divisia index (LMDI) method. Focusing on bioethanol production from broken rice in India, the research applied a cradle-to-gate LCA using the ReCiPe 2016 method across seven key environmental impact categories. By running 1,000 MCS iterations, the study simulated the range of possible outcomes based on data variability. The LMDI method was then used to trace how much each input—such as electricity, steam, or additives—contributed to the overall uncertainty in the results. The findings show that electricity and polydimethylsiloxane are the biggest sources of uncertainty, although the latter contributes little to the actual environmental impact. This highlights a critical insight: while insignificant, some minor ingredients can introduce large uncertainty levels due to poor data quality or lack of reliable emission factors. Overall, this approach makes it easier to see where improvements in data or methods can significantly boost the confidence in LCA results. The method supports smarter environmental choices, more reliable assessments, and better-informed sustainability planning by making the uncertainty more transparent and easier to interpret.

As the biggest greenhouse gas (GHG) emitter globally, China has pledged to achieve carbon neutrality by 2060. However, the environmental sustainability of this goal has not been assessed comprehensively on a life cycle basis. Focusing on the electricity sector, which contributes >40% to China's GHG emissions, this study evaluates the role and the potential of renewables for achieving net-zero by estimating their life cycle impacts across 31 provinces in China. A future (2050) renewable electricity grid is designed considering daily demand and generation curves, as well as resource potential and future technological advancements. Most of the electricity is generated by solar and wind (70%), followed by hydro (9%), biomass with and without carbon capture and storage (6%), and energy storage (14%). This mix achieves a net-negative climate change impact of −12 kg CO 2 eq./MWh electricity generated (compared to the current 877 kg CO 2 eq./MWh). The net negative impact is found in 18 provinces (−2.1 to −166.1 kg CO 2 eq. per MWh) owing to the biomass energy with carbon capture and storage (BECCS). The rest of the provinces have a net-positive but still relatively low impact (0–42 kg CO 2 eq./MWh) because of the high share of renewables. The majority of the remaining 17 impacts are also significantly lower (5.5–96%) than the impacts of the current grid, except for metal depletion, water consumption and freshwater and marine ecotoxicity. The minimum requirements for achieving the net-zero target for the electricity sector are either the utilisation of 55% of the total estimated biomass energy potential of 22 EJ, or BECCS share of 46% in the total capacity of biomass plants, equivalent to 2.25% of electricity generation. These results help to identify the environmental trade-offs in meeting the decarbonisation targets and to guide a future deployment of net-zero electricity in China.

Concentrated solar-powered sulfur-iodine thermochemical cycle hydrogen production system: Energy, economic, and environmental assessment

Energy, exergy, economic and environmental assessments on a simple hydrogen liquefaction process under variable working conditions

Hydrogen production from wastewater: technology readiness, economic and environmental assessment, and scale-up challenges with mitigation strategies

Clean hydrogen production via waste plastic-biomass co-gasification with carbon capture and sequestration: Techno-economic and environmental assessment under policy frameworks

Pyrolytic recycling of glass-removed photovoltaic laminates: Kinetics, product evolution, fluorine migration, and economic and environmental assessment

Prospects of power-to-sustainable aviation fuel: A multi-criteria decision analysis integrated with economic and environmental assessment

Selective low-temperature depolymerization of retired wind turbine blades via a Ru-Ni/CeO2 catalyst: Process performance, fiber preservation and environmental assessment

Using effect biomarker thresholds in regulatory risk assessment: an OECD-based framework for assessing cumulative exposures to chemical mixtures.

Subacute dimethylated monothioarsenate (DMMTA) exposure induces hepatotoxicity and disrupts the gut microbiota-bile acid-liver axis: A multi-omics study in mice.

Human serum is a clinically relevant yet analytically challenging matrix. Its high abundance of proteins interferes with the detection of low-concentration biomarkers and environmental contaminants, including bisphenol A (BPA), an endocrine disruptor monitored as a marker of human exposure. High-abundance proteins introduce matrix effects that can enhance, distort or suppress analytical signals, ultimately compromising assay performance. Aqueous biphasic systems (ABS) provide a promising pretreatment strategy to address these limitations by selectively depleting high-abundance proteins and generating a cleaner matrix that supports effective analyte recovery and improved detection sensitivity. In this work, a dual-strategy ABS platform was introduced and shown to deliberately direct protein depletion via two complementary mechanisms: (i) interphase precipitation, where serum proteins aggregate at a solid interphase, and (ii) bottom phase retention, where proteins accumulate in the salt-rich bottom phase. By combining polypropylene glycol (PPG) with either cholinium or sodium salts, the salt composition tunes phase behavior, selectively depletes high-abundance proteins, and enhances BPA recovery. Sodium salts generally outperformed cholinium salts due to stronger hydration and salting-out effects. Between the two implemented strategies, systems promoting interphase precipitation achieved up to 94% protein depletion, while simultaneously driving BPA into the largely protein-depleted PPG-rich top phase, minimizing matrix bias and enabling more efficient detection. This work demonstrates, for the first time, that rational control of ABS composition can selectively switch between protein precipitation and liquid-liquid partition, establishing a versatile and tunable serum pretreatment platform. This approach enables efficient depletion of high-abundance proteins and sensitive recovery of BPA and potentially other low-concentration analytes. Supported by green and blue analytical chemistry metrics, it represents a significant advance in bioanalytical workflows and environmental exposure assessment, while offering a more sustainable alternative to conventional methods.

Hospital wastewater as a hotspot for environmental dissemination of a carbapenem representative and enzyme inhibitors: insights from a multi-hospital study.

"Bentogypsum", a phosphoric acid purification residue: Characterization and implications for environmental and human health risk in Gabes (SE Tunisia).

Effects of a formulation of the veterinary drug moxidectin on the performance of a plant-insect food chain.

Shipboard carbon capture and storage (SCCS) is a viable retrofit for maritime decarbonisation, yet its environmental and techno-economic performance remains under-quantified. This study links life-cycle assessment with life-cycle costing to evaluate SCCS across four fuel configurations comprising very low sulphur fuel oil (VLSFO), marine gas oil (MGO), liquefied natural gas (LNG), and methanol, analysed with/without capture. Fuel system boundaries follow International Maritime Organization guidance. The SCCS boundary covers post-combustion monoethanolamine (MEA) absorption, compression/liquefaction and onboard liquid CO 2 storage; one-off manufacture/transport/installation and periodic maintenance are included, whereas port offloading and downstream transport, storage or utilisation are excluded. To enhance generalisability, conservative settings are adopted, assuming post-combustion monoethanolamine at 1.5 kg/t CO 2 , 58% capture efficiency, 3.7 GJ/t CO 2 energy use, and a 30-year service life with 5-year maintenance. Under these settings, installing SCCS lowers well-to-propeller greenhouse-gas emissions by 48.8–49.5% across all fuels after including 8.5–9.2% SCCS self-emissions. These net reductions support technical feasibility through policy alignment, with attained Energy Efficiency Existing Ship Index decreasing and Carbon Intensity Indicator improving by up to two grades for representative container, bulk-carrier, and tanker vessels. MGO with SCCS attains decarbonisation comparable to methanol, hence the economic comparison focuses on these two pathways. Over 30 years, MGO with SCCS is 7.1% less costly, with fuel prices the primary driver. Probabilistic analysis indicates SCCS on MGO is the lower-cost option in 69.8% of cases. Overall, within the stated boundary, the findings demonstrate the significance and effectiveness of SCCS for ship decarbonisation. • Study links life cycle assessment and costing for ship carbon capture and storage. • Shipboard carbon capture cuts well-to-propeller emissions by 48.7% to 49.5%. • Carbon capture on ships helps meet 2030 International Maritime Organization rules. • Marine gas oil with carbon capture costs 7.4% less than that of methanol conversion. • Shipboard carbon capture is the lower-cost option in 81.7% of cases studied.

The construction industry produces a large amount of carbon emissions, with a significant portion attributed to embodied carbon (EC). Prefabrication has the potential to reduce EC through streamlined workflows and controlled production environments. However, inefficiencies such as rework, material waste, excessive labor input, and quality issues can offset these benefits, necessitating a flexible digital technology approach. This study proposes the integration of mixed reality (MR) into prefabrication workflows to mitigate these issues and reduce EC emissions. A hybrid input-output (I-O) life cycle assessment (LCA) model combined with Monte Carlo (MC) simulation was employed to evaluate EC emission under uncertainty. Four prefabrication scenarios representing increasing levels of MR integration, ranging from conventional methods to high-level digitalization, were analyzed across several life cycle stages. Results emphasize that EC emissions were reduced by 8.5%, 14.9%, and 21.2% in the Lower, Medium, and Higher MR scenarios, respectively, due to significant reductions in rework, rejections, labor hours, and energy use. MC simulation confirmed the robustness of the results, showing decreased variability and tighter confidence intervals with higher levels of MR integration. While the experimental context focuses on precast panel production, the proposed approach is generalizable to broader prefabrication systems with appropriate adaptations to the MR model. This study contributes to the body of knowledge by introducing a novel integration of MR with hybrid I-O LCA for EC accounting in prefabrication and by demonstrating how MC simulation enhances the reliability of environmental impact assessments under uncertainty. The proposed hybrid I-O LCA approach offers a replicable and data-driven method for sustainable decision-making in construction engineering and management.