Viral infection transmission and indoor air quality: A systematic review.

Respiratory disease transmission in indoor environments presents persistent challenges for health authorities, as exemplified by the recent COVID-19 pandemic. This underscores the urgent necessity to investigate the dynamics of viral infection transmission within indoor environments. This systematic review delves into the methodologies of respiratory infection transmission in indoor settings and explores how the quality of indoor air (IAQ) can be controlled to alleviate this risk while considering the imperative of sustainability. Among the 2722 articles reviewed, 178 were retained based on their focus on respiratory viral infection transmission and IAQ. Fifty eight articles delved into SARS-CoV-2 transmission, 21 papers evaluated IAQ in contexts of other pandemics, 53 papers assessed IAQ during the SARS-CoV-2 pandemic, and 46 papers examined control strategies to mitigate infectious transmission. Furthermore, of the 46 papers investigating control strategies, only nine considered energy consumption. These findings highlight clear gaps in current research, such as analyzing indoor air and surface samples for specific indoor environments, oversight of indoor and outdoor parameters (e.g., temperature, relative humidity (RH), and building orientation), neglect of occupancy schedules, and the absence of considerations for energy consumption while enhancing IAQ. This study distinctly identifies the indoor environmental conditions conducive to the thriving of each respiratory virus, offering IAQ trade-offs to mitigate the risk of dominant viruses at any given time. This study argues that future research should involve digital twins in conjunction with machine learning (ML) techniques. This approach aims to enhance IAQ by analyzing the transmission patterns of various respiratory viruses while considering energy consumption.

A European-wide exploratory study to analyse the relationship between training and energy efficiency in the construction sector.

Current evidence that supports the correlation between training and energy efficiency in the construction industry is sparse and lacks an in-depth and sector-wide analysis. Several context-specific (in terms of application, workforce segment, and scope) studies have highlighted several barriers, challenges, and gaps in the training landscape in the European construction sector. However, these do not scale up and translate to robust evidence for the entire industry. The paper aims to address this gap by adopting a quantitative and qualitative Europe-wide consultation that not only seeks to gather evidence about the relationship between training and energy efficiency but also broadens the scope of the investigation beyond this aim to understand the complexity of the training landscape in energy efficiency and to provide context to the resulting evidence, in a way that promotes generalisation of the results. A mixed-method approach is adopted involving secondary (in the form of industry studies and academic publications) and primary sources of evidence. The latter include a questionnaire (n = 52), a series of interviews (n = 28), an expert workshop, and use cases drawn across Europe providing examples of the correlation between training and energy efficiency. Five key themes emerged from the consultation, namely: (a) lack of systematic process to codify best practice into re-usable knowledge, (b) lack of industry-wide shared vision, (c) nature of the training available in the energy efficiency domain, (d) level of reliance on a trained and skilled workforce in energy efficiency, (e) efficiency of legislative frameworks, policies, and government incentives. While the analysis of the results confirms the correlation between training and energy efficiency, further efforts are needed to establish robust quantitative evidence. The research also points to several policy measures, including the need for adapted instruments to promote mutual recognition of energy skills and qualifications in the European construction sector.

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives.

In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings' performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings' management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings' performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings.