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. 2023 Apr 14;23(8):3999.
doi: 10.3390/s23083999.

An Improvement Strategy for Indoor Air Quality Monitoring Systems

Claudio De Capua  1 , Gaetano Fulco  1 , Mariacarla Lugarà  1 , Filippo Ruffa  1
Affiliations

An Improvement Strategy for Indoor Air Quality Monitoring Systems

Claudio De Capua et al. Sensors (Basel). .

Abstract

Air quality has a huge impact on the comfort and healthiness of various environments. According to the World Health Organization, people who are exposed to chemical, biological and/or physical agents in buildings with low air quality and poor ventilation are more prone to be affected by psycho-physical discomfort, respiratory tract and central nervous system diseases. Moreover, in recent years, the time spent indoors has increased by around 90%. If we consider that respiratory diseases are mainly transmitted from human to human through close contact, airborne respiratory droplets and contaminated surfaces, and that there is a strict relationship between air pollution and the spread of the diseases, it becomes even more necessary to monitor and control these environmental conditions. This situation has inevitably led us to consider renovating buildings with the aim of improving both the well-being of the occupants (safety, ventilation, heating) and the energy efficiency, including monitoring the internal comfort using sensors and the IoT. These two objectives often require opposite approaches and strategies. This paper aims to investigate indoor monitoring systems to increase the quality of life of occupants, proposing an innovative approach consisting of the definition of new indices that consider both the concentration of the pollutants and the exposure time. Furthermore, the reliability of the proposed method was enforced using proper decision-making algorithms, which allows one to consider measurement uncertainty during decisions. Such an approach allows for greater control over the potentially harmful conditions and to find a good trade-off between well-being and the energy efficiency objectives.

Keywords: IAQ; IoT; air quality; decision making; monitoring systems; sensing.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Proposed algorithm flowchart.
Figure 2
Figure 2
Distribution of the measurement result around the nominal value m.
Figure 3
Figure 3
Conformity zone and non-conformity zone.
Figure 4
Figure 4
Risk Level.
Figure 5
Figure 5
(a) Legend. (b) Max air quality and healthiness (linear data). (c) Max air quality and healthiness (sinusoidal data). (d) Max energy saving (linear data). (e) Max energy saving (sinusoidal data).
Figure 5
Figure 5
(a) Legend. (b) Max air quality and healthiness (linear data). (c) Max air quality and healthiness (sinusoidal data). (d) Max energy saving (linear data). (e) Max energy saving (sinusoidal data).
Figure 6
Figure 6
CO2, TVOC and PM2.5 in an elderly care facility bedroom over 24 h.
Figure 7
Figure 7
CO2 index comparison for the examined case.
Figure 8
Figure 8
PM2.5 index comparison for the examined case.
Figure 9
Figure 9
Threshold exceeded with the different algorithms on Monte Carlo simulation data.
See this image and copyright information in PMC

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