Spatio-Temporal Analysis of Large Air Pollution Data

Air pollution is one of the most dangerous environmental threats on our planet. Although it is severe in highly populated and industrialized cities of the developing countries, it is also a major concern for developed countries. In the developed world, the data are gathered from a large number of air pollution monitoring stations. Therefore, the volume of data is very high and it is not possible to analyze the data efficiently in real-time using the conventional methods. Large-scale data mining techniques can help in analyzing those data more efficiently and dynamically. In our paper, we propose a method to mine a large amount of air pollution data in order to find air pollution hot spots and time of pollution using clustering methods and time-series analysis and applied to the air pollution data of PM$_{2.5}$, PM$_{10}$ and Ozone in the United Kingdom from 2015-17. The method is able to detect specific pollution zones of those pollutants in the UK. Furthermore, the pollution due to particulate matters was observed to be higher in the winter season whereas Ozone pollution was seen to be downwards trending except some areas