# Real-Time Monitoring of Indoor Air Quality with Internet of Things-Based E-Nose
## 5 Conclusions  
Although many people spend most of their lives indoors, they have very limited information about the air quality in their environment. IAQ, which plays an especially important role in the health of children and the elderly, should be measured and necessary ventilation measures should be taken. In recent years, individual air quality measuring devices have been produced owing to the developing information communication technologies, Wi-Fi based microcontrollers and low-cost sensors.  
In this study, an IoT-based personalized air quality measurement and monitoring system was proposed by using air quality sensors and DIY approach. The developed e-nose measurement system made data measurements at one-minute intervals and recorded these data to the cloud server. The measurement data can be monitored instantaneously via the Blynk mobile interface, and if the limit values are exceeded, the application sends a notification to the user to take the necessary measures.  
According to the four-day measurement results, the following inferences have been obtained:  
* IAQ is directly related to the number of people in the household and the activities carried out in the household.
* Activities such as cooking, sleeping, cleaning have a significant effect on CO, NO\({}_{2}\) and CO\({}_{2}\) gas concentrations.
* CO, NO\({}_{2}\), CO\({}_{2}\) gas concentration values of the house reach the highest level during the sleep period (period 1). During this period, PM\({}_{10}\) concentration has the lowest value.
* PM\({}_{10}\) concentration reaches the highest level in period 2, when daily routine tasks such as cleaning and house arrangement are carried out.
* When the daily maximum and minimum values were taken into consideration, there was a 16-fold daily maximum change on day 3 in CO concentration, with minimum values of 0.4 mg m\({}^{-3}\) and maximum 6.51 mg m\({}^{-3}\).
* The largest change in PM\({}_{10}\) concentration was achieved with a minimum of 27.6 ug m\({}^{-3}\) and a maximum of 40.7 ug m m\({}^{-3}\) on day 2, resulting in a 47% change.
* Although CO and CO had the lowest concentration in period 3, NO\({}_{2}\) had the lowest concentration in period 2.
* A rapid decrease in CO, NO\({}_{2}\), CO\({}_{2}\) gas concentrations was observed from the moment of natural ventilation, while a concurrent increase in PM\({}_{10}\) was observed.
* In period 1, the highest positive correlations occurred between time-CO and CO-CO\({}_{2}\); the lowest negative correlations occurred between CO-humidity and time-humidity.
* In period 2, the largest positive correlations occurred between CO-CO\({}_{2}\) and CO-NO\({}_{2}\), whereas the smallest negative correlations occurred between temp-humidity and time-CO.
* The highest positive correlations between time-CO, time-NO\({}_{2}\) and CO-NO\({}_{2}\) occurred in period 3. There was no negative correlation between the weather parameters and gas concentrations during this period.  
IAQ has been observed to change depending on the daily activities of the inhabitants. Low air quality is undoubtedly an important parameter that directly affects our health. Even simple measures such as opening only windows to reduce the concentration of harmful gases in the environment can significantly improve IAQ. The numerical data obtained show that the e-nose system is a feature that can contribute to a healthier living environment. In future studies, it is planned to model the effects of individual activities on IAQ by using the e-nose air quality measurement system used in this study.  
Conceptualization, M.T. and H.G.; methodology, H.G.; software, M.T.; validation, M.T.; formal analysis, M.T.; investigation, M.T. and H.G.; visualization, M.T. and H.G.; resources, H.G.; data curation, M.T.; writing--original draft preparation, M.T.; review and editing, H.G.  
This research received no external funding.  
The authors declare no conflict of interest.