Indoor air quality prediction systems for smart environments: A systematic review

Abstract

Air quality is a critical matter of concern in terms of the impact on public health and well-being. Although the consequences of poor air quality are more severe in developing countries, they also have a critical impact in developed countries. Healthcare costs due to air pollution reach $150 billion in the USA, whereas particulate matter causes 412,000 premature deaths in Europe, every year. According to the Environmental Protection Agency (EPA), indoor air pollutant levels can be up to 100 times higher in comparison to outdoor air quality. Indoor air quality (IAQ) is in the top five environmental risks to global health and well-being. The research community explored the scope of artificial intelligence (AI) in the past years to deal with this problem. The IAQ prediction systems contribute to smart environments where advanced sensing technologies can create healthy living conditions for building occupants. This paper reviews the applications and potential of AI for the prediction of IAQ to enhance building environment and public health. The results show that most of the studies analyzed incorporate neural networks-based models and the preferred evaluation metrics are RMSE, R 2 score and error rate. Furthermore, 66.6% of the studies include CO2 sensors for IAQ assessment. Temperature and humidity parameters are also included in 90.47% and 85.71% of the proposed methods, respectively. This study also presents some limitations of the current research activities associated with the evaluation of the impact of different pollutants based on different geographical conditions and living environments. Moreover, the use of reliable and calibrated sensor networks for real-time data collection is also a significant challenge.

Internet of Things (IoT)-based indoor plant care system

“…Potted ornamental plants can be used to decontaminate indoor environments, since they can help to reduce indoor pollution levels, though their care may be a challenge for their owners. In some cases, indoor pollution levels can be up to 100 times higher than outdoor pollution levels [24]. Indoor pollution can be caused by the entry of vehicles either to load or unload goods in factories, workshops, or warehouses, or simply to park them in garages when these are inside homes [21].…”

A low-cost air quality monitoring system based on Internet of Things for smart homes

“…Interest in air quality measurement systems has recently increased with the production of low-cost sensors. The development of IoT-based and low-cost devices to monitor IAQ increases our awareness of indoor air pollutants, which are invisible to the eye but have adverse health effects [46].…”

ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment

“…Other than this, several studies have also been published on the development of prediction systems based on neural networks, machine learning, and deep learning approaches to assist building occupants with a prior indication of harmful pollutant concentrations. Furthermore, the authors have published a systematic review to highlight the contribution of existing researchers in the field along with the gaps in literature [ 25 ].…”