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Particulate matter pollution is one of the deadliest types of air pollutionworldwide due to its significant impacts on the global environment and humanhealth. Particulate Matter (PM2.5) is one of the important particulatepollutants to measure the Air Quality Index (AQI). The conventional instrumentsused by the air quality monitoring stations to monitor PM2.5 are costly,bulkier, time-consuming, and power-hungry. Furthermore, due to limited dataavailability and non-scalability, these stations cannot provide high spatialand temporal resolution in real-time. To overcome the disadvantages of existingmethodology this article presents analytical equations based predictionapproach for PM2.5 using an Artificial Neural Network (ANN). Since the derivedanalytical equations for the prediction can be computed using a Wireless SensorNode (WSN) or low-cost processing tool, it demonstrates the usefulness of theproposed approach. Moreover, the study related to correlation among the PM2.5and other pollutants is performed to select the appropriate predictors. Thelarge authenticate data set of Central Pollution Control Board (CPCB) onlinestation, India is used for the proposed approach. The RMSE and coefficient ofdetermination (R2) obtained for the proposed prediction approach using eightpredictors are 1.7973 ug/m3 and 0.9986 respectively. While the proposedapproach results show RMSE of 7.5372 ug/m3 and R2 of 0.9708 using threepredictors. Therefore, the results demonstrate that the proposed approach isone of the promising approaches for monitoring PM2.5 without power-hungry gassensors and bulkier analyzers.

Analytical Equations based Prediction Approach for PM2.5 using Artificial Neural Network