Study of AQI Monitoring System of Indoor Environment Using Machine Learning Model and IoT Device

Rocznik Ochrona Środowiska 2025, vol. 27, pp. 152-163

Isha Talati¹ Ten adres pocztowy jest chroniony przed spamowaniem. Aby go zobaczyć, konieczne jest włączenie w przeglądarce obsługi JavaScript., Kunjan Shah², Om Patel², Jaymin Tanna², Akshat Jain², Ankit D. Oza³, Amruta Arun Yadav⁴, Mohammed J. Alshayeb⁵, Mohammad Amir Khan⁶ Ten adres pocztowy jest chroniony przed spamowaniem. Aby go zobaczyć, konieczne jest włączenie w przeglądarce obsługi JavaScript., Saiful Islam⁵

	1. Pandit Deendayal Energy University, India 2. Karnavati University, India 3. Chandigarh University, India 4‏. Yeshwantrao Chavan College of Engineering, India 5. King Khalid University, Saudi Arabia 6. Galgotias College of Engineering and Technology, India
	Ten adres pocztowy jest chroniony przed spamowaniem. Aby go zobaczyć, konieczne jest włączenie w przeglądarce obsługi JavaScript. Ten adres pocztowy jest chroniony przed spamowaniem. Aby go zobaczyć, konieczne jest włączenie w przeglądarce obsługi JavaScript.
	https://doi.org/10.54740/ros.2025.013

Indoor air quality has a direct impact on human health. Thus, it's essential to comprehend the various aspects of indoor air quality. It supports both the implementation of preventative measures and the monitoring of indoor air pollution. Monitoring and forecasting air pollution is extremely essential, especially in developing countries like India. This study proposes a system that employs ESP8266 (NodeMCU) data sent to the cloud to monitor the levels of air pollutants such as ozone, particle matter, carbon monoxide, carbon dioxide, temperature, and total volatile organic compounds. Our sensors include the ozone sensor MQ-131, the dust sensor GP2Y1010-AU0F, the TVOC sensor AGS02MA, the carbon monoxide sensor MQ-9, the carbon dioxide sensor MQ-135, and the humidity sensor DHT11. The IoT device continuously shows the indoor air quality level (IAQL). The next step was to accurately anticipate the Internal Air Quality Level (IAQL) and pollution levels from dangerous gases for the next seven days using the LSTM, Seasonal ARIMA, and Linear Regression models. The Authors could accurately predict the observations of the following seven days after using data from the previous ninety days to create our best model. This implies that our model can accurately predict the values for each parameter with an accuracy of at least 95%. Therefore, we believe such a solution would be advantageous if a large-scale installation were implemented. If consumers can remotely verify the air quality in their homes, the pollution in the interior atmosphere will decrease. This has the potential to make civilization healthier.

Internet of Things, Indoor Air Quality, Linear Regression, LSTM, SARIMAX

AMA Style
Talati I, Shah K, Patel O, Tanna J, Jain A, Oza A, Yadav A, Alshayeb M, Khan M, Islam S. Study of AQI Monitoring System of Indoor Environment Using Machine Learning Model and IoT Device. Rocznik Ochrona Środowiska. 2025; 27. https://doi.org/10.54740/ros.2025.013

ACM Style
Talati, I., Shah, K., Patel, O., Tanna, J., Jain, A., Oza, A., Yadav, A., Alshayeb, M., Khan, M., Islam, S. 2025. Study of AQI Monitoring System of Indoor Environment Using Machine Learning Model and IoT Device. Rocznik Ochrona Środowiska. 27. DOI:https://doi.org/10.54740/ros.2025.013

ACS Style
Talati, I.; Shah, K.; Patel, O.; Tanna, J.; Jain, A.; Oza, A.; Yadav, A.; Alshayeb, M.; Khan, M.; Islam, S. Study of AQI Monitoring System of Indoor Environment Using Machine Learning Model and IoT Device Rocznik Ochrona Środowiska 2025, 27, 152-163. https://doi.org/10.54740/ros.2025.013

APA Style
Talati, I., Shah, K., Patel, O., Tanna, J., Jain, A., Oza, A., Yadav, A., Alshayeb, M., Khan, M., Islam, S. (2025). Study of AQI Monitoring System of Indoor Environment Using Machine Learning Model and IoT Device. Rocznik Ochrona Środowiska, 27, 152-163. https://doi.org/10.54740/ros.2025.013

ABNT Style
TALATI, I.; SHAH, K.; PATEL, O.; TANNA, J.; JAIN, A.; OZA, A.; YADAV, A.; ALSHAYEB, M.; KHAN, M.; ISLAM, S. Study of AQI Monitoring System of Indoor Environment Using Machine Learning Model and IoT Device. Rocznik Ochrona Środowiska, v. 27, p. 152-163, 2025. https://doi.org/10.54740/ros.2025.013

Chicago Style
Talati, Isha, Shah, Kunjan, Patel, Om, Tanna, Jaymin, Jain, Akshat, Oza, Ankit_D., Yadav, Amruta_Arun, Alshayeb, Mohammed_J., Khan, Mohammad_Amir, Islam, Saiful. 2025. "Study of AQI Monitoring System of Indoor Environment Using Machine Learning Model and IoT Device". Rocznik Ochrona Środowiska 27, 152-163. https://doi.org/10.54740/ros.2025.013

Harvard Style
Talati, I., Shah, K., Patel, O., Tanna, J., Jain, A., Oza, A., Yadav, A., Alshayeb, M., Khan, M., Islam, S. (2025) "Study of AQI Monitoring System of Indoor Environment Using Machine Learning Model and IoT Device", Rocznik Ochrona Środowiska, 27, pp. 152-163. doi:https://doi.org/10.54740/ros.2025.013

IEEE Style
I. Talati, K. Shah, O. Patel, J. Tanna, A. Jain, A. Oza, A. Yadav, M. Alshayeb, M. Khan, S. Islam, "Study of AQI Monitoring System of Indoor Environment Using Machine Learning Model and IoT Device", RoczOchrSrod, vol 27, pp. 152-163. https://doi.org/10.54740/ros.2025.013