In Light of Challenging Problems and Solutions for Air Pollution Monitoring

The rapid increase in urban activities and human population led to an effect on global ambient air quality. Polluted air in the surrounding environment treated to be as one of the vital factor affecting the human life and is considered as one of the greatest environmental risks. Effectively collecting air quality data and visualizing those collected data can help monitor environmental activities and address related issues, which will benefit society. Approaches that provide the pathway for monitoring air pollution generally use expensive, complex equipment, limiting data collection and access to data. Therefore, this research article gives a pilot analysis in getting collective knowledge regarding the usage of low-cost sensing devices for air quality data collection, data visualization, and data standardization through pre-processing activities, challenges in the raw data collection, performance analysis of the designed model with some basic pre-processing activities and among the specified machine learning algorithms RF has shown better prediction result with 72% accuracy while predicting the air quality index for the air pollution.