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People generally spend most of their time indoors; thus, household air pollution (HAP), especially indoor air pollution, is of paramount importance. In this study, real‐time particulate matter (PM2.5) monitors were used to collect high temporal resolution concentrations of fine PM2.5 in different microenvironments from 104 urban and rural households in Guizhou Province to investigate the urban–rural difference in HAP and the contribution of cooking activity to HAP. Results showed that PM2.5 concentrations among different rural microenvironments varied largely, ranging from 26.10 ± 33.27 (mean ± standard deviation) in the bedroom to 69.20 ± 72.50 μg/m3 in the kitchen while varying slightly across various urban microenvironments, ranging from 32.30 ± 17.5 in the living room to 34.65 ± 16.37 μg/m3 in the kitchen. Cooking activity can rapidly increase PM2.5 concentrations in the kitchen, whose peaks were at least five times higher than baseline levels. The contribution of cooking to the kitchen PM2.5 was 18.43 ± 12.17% and 5.39 ± 4.15% in rural and urban homes, respectively. Biomass burning affected indoor PM2.5 concentrations significantly, where rural households using biomass had the highest PM2.5 levels than households burning other fuels. This study identified the urban–rural disparities in household PM2.5 pollution and the contributions of cooking activities to HAP, illustrating the importance of cooking activities to indoor air pollution, which can assist in controlling indoor air pollution. [ABSTRACT FROM AUTHOR]

PM2.5 pollution variations in different microenvironments would result in PM2.5 exposure inequity between rural and urban residents. In this study, the real-time PM2.5 exposure of urban and rural residents in China was examined based on portable PM2.5 sensors together with activity patterns derived from questionnaire surveys, with a focus on students and senior citizens who are sensitive to air pollution. The results showed that PM2.5 exposure varied significantly among different resident groups, with higher PM2.5 exposure of rural residents than those of urban residents. PM2.5 exposure peaks mostly occurred during (Accompanied) cooking activities owing to strong emissions. Sleeping and resting were the main activities that affected PM2.5 exposures of different resident groups, accounting for 60.7%–94.5% of total daily exposures. Furthermore, the long duration of sleeping makes it the predominant activity contributing to PM2.5 exposure inequity. It is necessary to obtain point-to-point respiratory volume (respiratory rate) data when measuring real-time PM2.5 exposure data and incorporate respiratory volume (respiratory rate) into the analysis of PM2.5 exposure. For the first time, this study quantified the PM2.5 exposure inequality based on a novel method and can provide useful information for further studies on the exposure inequity. [ABSTRACT FROM AUTHOR]

Composite materials are increasingly important in making high-performance products. However, contemporary composites manufacturing processes still encounter significant challenges that range from inherent material stochasticity to manufacturing process variabilities. This paper proposes a novel smart Industrial Internet of Things framework, which is also referred to as an Artificial Intelligence of Things (AIoT) framework for composites manufacturing. This framework improves production performance through real-time process monitoring and AI-based forecasting. It comprises three main components: (i) an array of temperature, heat flux, dielectric, and flow sensors for data acquisition from production machines and products being made, (ii) an IoT-based platform for instantaneous sensor data integration and visualisation, and (iii) an AI-based model for production process forecasting. Via these components, the framework performs real-time production process monitoring, visualisation, and prediction of future process states. This paper also presents a proof-of-concept implementation of the framework and a real-world composites manufacturing case study that showcases its benefits. [ABSTRACT FROM AUTHOR]

The Resin Transfer Moulding process receives great attention from both academia and industry, owing to its superior manufacturing rate and product quality. Particularly, the progression of its mould filling stage is crucial to ensure a complete reinforcement saturation. Contemporary process simulation methods focus primarily on physics-based approaches to model the complex resin permeation phenomenon, which are computationally expensive to solve. Thus, the application of machine learning and data-driven modelling approaches is of great interest to minimise the cost of process simulation. In this study, a comprehensive dataset consisting of mould filling patterns of the Resin Transfer Moulding process at different injection locations for a composite dashboard panel case study is presented. The problem description and significance of the dataset are outlined. The distribution of this comprehensive dataset aims to lower the barriers to entry for researching machine learning approaches in composite moulding applications, while concurrently providing a standardised baseline for evaluating newly developed algorithms and models in future research works. [ABSTRACT FROM AUTHOR]