As the size of the air quality data increases, it is difficult toforecastthe air quality metrics due to the non-stationary and randomization form of data distribution. Air quality prediction refers to the problem of finding the air quality by using statistical inference measures. However, traditional air prediction models are based on static fixed parameters for quality prediction. Also, it is difficult to classify and predict the air quality index for both rural and urban areas due to change

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... data drift and distribution. PM 2.5 is one of the major factor to predict the air quality index (AQI) and its severity level. Due to high noisy and outliers in the PM 2.5 data, it is difficult to classify and predict the air quality by using the traditional quality prediction models. In order to overcome these issues, an optimized Bayesian networks based probabilistic inference model is designed and implemented on the air quality data. An IOT enabled Air pollution monitoring system includes a DSM501A Dust sensor which detects PM 2.5 , PM 1.0 , MQ series sensor interfaced to a Node MCU equipped with ESP32 WLAN adaptor to send the sensor reading to Thing Speak cloud. In the proposed model, the data is initially gathered from the ICAO records of Safdarjung weather station and pre-processed.An improved discrete and continuous parameter estimation and bayes score optimization are implemented on the air quality prediction process. Experimental results show that the present optimized Bayesian network classify and predicts the air quality data with high less computational error rate and high accuracy. Further the proposed optimized model is applied on the real data which is gathered using IOT enabled gas sensors and the model is giving best results in predicting the air quality Index.