Abstract
The evolution of low-cost sensors (LCSs) has made the spatio-temporal mapping of indoor air quality (IAQ) possible in real-time but the availability of a diverse set of LCSs make their selection challenging. Converting individual sensors into a sensing network requires the knowledge of diverse research disciplines, which we aim to bring together by making IAQ an advanced feature of smart homes. The aim of this review is to discuss the advanced home automation technologies for the monitoring and control of IAQ through networked air pollution LCSs. The key steps that can allow transforming conventional homes into smart homes are sensor selection, deployment strategies, data processing, and development of predictive models. A detailed synthesis of air pollution LCSs allowed us to summarise their advantages and drawbacks for spatio-temporal mapping of IAQ. We concluded that the performance evaluation of LCSs under controlled laboratory conditions prior to deployment is recommended for quality assurance/control (QA/QC), however, routine calibration or implementing statistical techniques during operational times, especially during long-term monitoring, is required for a network of sensors. The deployment height of sensors could vary purposefully as per location and exposure height of the occupants inside home environments for a spatio-temporal mapping. Appropriate data processing tools are needed to handle a huge amount of multivariate data to automate pre-/post-processing tasks, leading to more scalable, reliable and adaptable solutions. The review also showed the potential of using machine learning technique for predicting spatio-temporal IAQ in LCS networked-systems.
Highlights
Indoor air pollution is placed among the top five environmental public health risks that cause morbidity and mortality globally
We review and consolidate the techniques used for predictive modelling and bring the prevalent best practice and knowledge to develop optimal indoor models, in which previously discussed topics are used as the foundation in the model development
Smart homes equipped with air quality low-cost sensors (LCSs) and integrated processing/predicting tools can offer a healthy environment to occupants
Summary
Indoor air pollution is placed among the top five environmental public health risks that cause morbidity and mortality globally. Of their time in indoor environments [1,2], and health problems and diseases associated with poor indoor air quality (IAQ) can cause a variety of adverse health effects to them [3,4]. The time spent indoors recently increased significantly in year 2020 due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic when people are advised to ‘stay home stay safe’ to protect health workers [5,6]. Air pollutants inside indoor environments can be generated from different sources, including occupants’ exhalation (carbon dioxide; CO2 ), activities such as cooking and smoking, emissions from building materials, etc. CO2 is not counted as an air pollutant, but its level represents the performance of ventilation systems, especially in wintertime, whereas high
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