Exposure to indoor and outdoor air pollution among children under five years old in urban area

Indoor air pollution associated with cooking and heating biomass fuel burning is estimated to be responsible for 7 million deaths in 2016 and most of these deaths occur in low and middle income countries. In Côte d'Ivoire, 73% of the population is reported using biomass (charcoal or wood) for cooking. The active device 3M EVM-7 was used to measure PM2.5 daily average concentrations inside and outside households in areas close (Andokoi) and far (Lubafrique) to an industrial zone in two popular neighborhoods of Yopougon, the largest and most populated municipality of the city of Abidjan (Côte d’Ivoire). PM2.5 daily average concentrations indoors and outdoors are respectively 121±12 µg/m3 and 117±8 µg/m3 in Andokoi and 32±3 µg/m3 and 41±4 µg/m3 in Lubafrique well above the World Health Organization guideline value (25 µg/m3) for air quality. Using multivariable models, the results were the number of windows in bedrooms and kitchens located outdoor were negatively correlated with the concentration of indoor PM2.5. The outdoor concentrations of PM2.5, were higher according to the cooking fuel type

Railway bridge structural health monitoring and fault detection: state-of-the-art methods and future challenges

Railway importance in the transportation industry is increasing continuously, due to the growing demand of both passenger travel and transportation of goods. However, more than 35% of the 300,000 railway bridges across Europe are over 100-years old, and their reliability directly impacts the reliability of the railway network. This increased demand may lead to higher risk associated with their unexpected failures, resulting safety hazards to passengers and increased whole life cycle cost of the asset. Consequently, one of the most important aspects of evaluation of the reliability of the overall railway transport system is bridge structural health monitoring, which can monitor the health state of the bridge by allowing an early detection of failures. Therefore, a fast, safe and cost-effective recovery of the optimal health state of the bridge, where the levels of element degradation or failure are maintained efficiently, can be achieved. In this article, after an introduction to the desired features of structural health monitoring, a review of the most commonly adopted bridge fault detection methods is presented. Mainly, the analysis focuses on model-based finite element updating strategies, non-model-based (data-driven) fault detection methods, such as artificial neural network, and Bayesian belief network–based structural health monitoring methods. A comparative study, which aims to discuss and compare the performance of the reviewed types of structural health monitoring methods, is then presented by analysing a short-span steel structure of a railway bridge. Opportunities and future challenges of the fault detection methods of railway bridges are highlighted