  Particulate matter pollution is one of the deadliest types of air pollution
worldwide due to its significant impacts on the global environment and human
health. Particulate Matter (PM2.5) is one of the important particulate
pollutants to measure the Air Quality Index (AQI). The conventional instruments
used by the air quality monitoring stations to monitor PM2.5 are costly,
bulkier, time-consuming, and power-hungry. Furthermore, due to limited data
availability and non-scalability, these stations cannot provide high spatial
and temporal resolution in real-time. To overcome the disadvantages of existing
methodology this article presents analytical equations based prediction
approach for PM2.5 using an Artificial Neural Network (ANN). Since the derived
analytical equations for the prediction can be computed using a Wireless Sensor
Node (WSN) or low-cost processing tool, it demonstrates the usefulness of the
proposed approach. Moreover, the study related to correlation among the PM2.5
and other pollutants is performed to select the appropriate predictors. The
large authenticate data set of Central Pollution Control Board (CPCB) online
station, India is used for the proposed approach. The RMSE and coefficient of
determination (R2) obtained for the proposed prediction approach using eight
predictors are 1.7973 ug/m3 and 0.9986 respectively. While the proposed
approach results show RMSE of 7.5372 ug/m3 and R2 of 0.9708 using three
predictors. Therefore, the results demonstrate that the proposed approach is
one of the promising approaches for monitoring PM2.5 without power-hungry gas
sensors and bulkier analyzers.
