IoT-Based Approaches for Monitoring the Particulate Matter and Its Impact on Health

Abstract

Scenario: The particulate matter (PM) is associated with all particles (solid and liquid) suspended in the air. Depending on the kind and size of the particle, each one represents different kinds of risks for human health. The emerging of tiny, available, and accessible devices related to the Internet of Things (IoT) has allowed the implementation of different monitoring strategies. Objective: To identify and characterize the IoT-based real-time monitoring strategies that have implemented a measurement process to study the effect of the PM on human health. Methodology: A wide analysis based on the systematic mapping study was performed on September 4, 2020. The Association for Computing Machinery (ACM), IEEE, ScienceDirect, SpringerLink, Scopus, and Wiley databases were considered in the exploration. Results: 48 articles addressing the IoT-based PM measurement were obtained, falling them between 2010 and 2020 with growing interest. The main use of this technology is related to increase the coverage and density of environmental monitoring stations due to the impact of PM on human health. Also, approaches to monitoring air quality and their potential effects on people's affections are described. Conclusions: Collaborative, people-aware, global proposals tend to get increasing interest. Only six (12.5%) articles incorporated some recommendation system based on PM measures. The accuracy and precision are the main concern around low-cost sensors for measuring PM. Thus, the calibration process is highlighted in 64.44% of articles. The main challenges reside in a combination of uncertainties in PM measurement, health impacts, data quality, and the influence of environmental variables on all of them.