Found 10000 publications. Showing page 104 of 400:
Abstract. Airborne microplastics are a recently identified atmospheric aerosol species with potential air quality and climate impacts, yet they are not currently represented in global climate models. Here, we describe the addition of microplastics to the aerosol scheme of the UK Earth System Model (UKESM1.1): the Global Model of Aerosol Processes (GLOMAP). Microplastics are included as both fragments and fibres across a range of aerosol size modes, enabling interaction with existing aerosol processes such as ageing and wet and dry deposition. Simulated microplastics have higher concentrations over land, but can be transported into remote regions including Antarctica despite no assumed emissions from these regions. Lifetimes range between ∼17 d to ∼1 h, with smaller, hydrophilic microplastics having longer lifetimes. Microplastics are present throughout the troposphere, and the smallest particles are simulated to reach the lower stratosphere in small numbers. Dry deposition is the dominant microplastic removal pathway, but greater wet deposition occurs for smaller hydrophilic microplastic, due to interactions with clouds. Although microplastics currently contribute a minor fraction of the total aerosol burden, their concentration is expected to increase in future if plastic production continues to increase, and as existing plastic waste in the environment degrades to form new microplastic. Incorporating microplastics into UKESM1.1 is a key step toward quantifying their current atmospheric impact and offers a framework for simulating future emission scenarios for an assessment of their long term impacts on air quality and climate.
2025
This document describes a measurement campaign intended to provide data for the further development and assessment of non-exhaust emissions and the models used to describe them.
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Design of multi-luminescent silica-based nanoparticles for the detection of liquid organic compounds
Tracer testing in reservoir formations is utilised to determine residual oil saturation as part of optimum hydrocarbon production. Here, we present a novel detection method of liquid organic compounds by monodisperse SiO2 nanoparticles (NPs) containing two luminophores, a EuIII:EDTA complex and a newly synthesised fluorophore based on the organic boron-dipyrromethene (BODIPY)-moiety. The particles exhibited stable EuIII PL emission intensity with a long lifetime in aqueous dispersion. The fluorescence of the BODIPY was also preserved in the aqueous environment. The ratiometric PL detection technique was demonstrated by using toluene and 1-octanol as model compounds of crude oil. The optimal synthesis conditions were found to give NPs with a diameter of ~100 nm, which is suitable for transport through porous oil reservoir structures. The cytotoxicity of the NPs was confirmed to be very low for human lung cell and fish cell lines. These findings demonstrate the potential of the NPs to replace the hazardous chemicals used to estimate the residual oil saturation. Moreover, the ratiometric PL detection technique is anticipated to be of benefit in other fields, such as biotechnology, medical diagnostics, and environmental monitoring, where a reliable and safe detection of a liquid organic phase is needed.
2024
2016
Abstract This study introduces a community-focused eCoach recommendation system aimed at enhancing physical activity by leveraging demographic data, wearable sensor inputs, and machine learning algorithms to generate both individual and community-based activity recommendations using advice-based collaborative filtering. Existing eCoaching systems largely focus on personalized feedback without incorporating social reinforcement or group-level motivation, creating a gap in leveraging community influence for sustained health behaviors. Our system combines real-time activity tracking through wearable sensors and advice-based collaborative filtering to deliver adaptive recommendations. We collected data from 31 participants (16 using MOX2-5 sensors and 15 from a public Fitbit-based dataset), targeting daily activity levels to generate actionable guidance. Through decision tree classification and SHAP-based interpretability, we achieved a model accuracy of 99.8%, with F1, precision, recall, and MCC metrics confirming robustness across both balanced and imbalanced datasets. Ethical considerations, including privacy, bias mitigation, and informed consent, were integral to our design and implementation. Limitations include potential biases due to sample size and data imbalances, suggesting the need for future validation on independent datasets. This system demonstrates the potential to integrate with real-world healthcare initiatives, offering trust, transparency, and user engagement opportunities to meet public health objectives.
2025
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Detection of Body Shaming in Social Media: Traditional Machine Learning vs. Transformer-based Models
2025