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

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.