Analysis and Visualization of Air Quality Using Real Time Pollutant Data

Since industrial revolution, the rate of industrialization and urbanization has increased dramatically. Most of the industry applications create pollution in the air and the vehicle emissions are also dangerous to the health of the people. In the developing countries, air pollution is severe in most of the areas. Air quality is the important factor to measure the quality of air. Most of the air quality measuring systems uses air quality index to tell the people about the air quality of their location. The primary objective of the system is to analyze and visualize air quality from the real time sensor data. The proposed system analyses six critical air pollutants which are, ozone (O 3 ), Particulate Matter (PM 2.5 ), Carbon monoxide (CO), Nitrogen dioxide (NO 2 ) and Sulphur dioxide (SO 2 ) are the most widespread health threats. The Fuzzy c-Means clustering is used to process the polluted air data from the sensors. From the results it is clear that the Fuzzy c-Means algorithm provides better results for the parameter accuracy while evaluating with the other algorithms in the literature.