Spatio- Temporal Analysis of Large Air Pollution Data

Air pollution is one of the most dangerous environmental threats in our planet. Although it is severe in highly populated and industrialized cities of the developing countries, it is a major concern for developed countries as well. In the developed world, air quality data is gathered from a large number of air pollution monitoring stations. However, the volume of data is very high and it is not possible to analyze the data efficiently in real-time using the conventional methods. Hence, large scale data mining techniques can help in analyzing those data more efficiently and dynamically. In this paper, a method for mining large amount of air pollution data is proposed for finding air pollution hot spots and time of pollution using clustering methods and time-series analysis. The results, after using the method to the air pollution data of PM₂.₅, PM₁₀and ozone in the United Kingdom from 2015-17, has shown that the pollution due to particulate matters was higher in winter season and ozone pollution had downward trend except some areas.