Big Data Visualization by MapReduce for Discovering the Relationship Between Pollutant Gases

Abstract

Big data mining and pollution are extremely important issues in todays. An innovative method in this study was used for visually discovering the relationship between pollutant gases by MapReduce. One dimensional, two-dimensional, and three-dimensional visualization used to visualize the data, that was processed as an hourly reading for one year from an air quality monitoring station to study the behaviors of pollutant gases distribution, and to show graphically the distribution of one, two, or three gases. The number of readings used in this paper are 8760 hourly readings for each of the five pollutant gases under this study. Pearson correlation used to explore numerically the correlation between the pollutant gases, and eta factor used to evaluate the effect of one gas on the other pollutant gases. We found out by both methods, visually and numerically the same facts that related between the pollutant gasses. The ozone has a moderate negative correlation of value -0.622 with nitrogen dioxide, and weak negative correlation of value -0.248 with carbon monoxide, and -0.155 with carbon dioxide. Ozone has approximately no correlation of value .060 with silver dioxide. The carbon monoxide has moderate positive correlation of value 0.364 with carbon dioxide. The eta factor between ozone and nitrogen dioxide is very weak of values 0.292, and 0.009 with Sulphur dioxide, this proved an important fact that the ozone, nitrogen dioxide, and Sulphur dioxide sources are different. The study recommends that each country must analysis visually and numerical the big data that was collected yearly from the monitoring stations to control the pollution gases especially near the large industrial factories.