Analyzing Air Quality to Model Human Livability using Machine Learning Techniques

摘要

Pollutants (mostly gases) found in the air that cause damage and negative changes in the environment is termed as air pollution. Air pollutants when released into the environment effects human health directly or in an indirect manner. Along with this it causes multiple environmental changes, such as ozone depletion, global climate change, eutrophication and forest damages. These pollutants also effect on wildlife and crops. Since the science behind air pollution has remained steady, we have made an attempt to develop a model for human livability based on the quantities of four major air pollutants in the air, namely; Sulphur Dioxide (SO2), Nitrogen Dioxide (NO2), Carbon Monoxide (CO), and ground level ozone (O3). The results of this study would help researchers to assess human livability conditions in different areas, using data collected for the aforementioned pollutants in that area. Results are also helpful in drafting policies for improving the quality of living in different places, in a more organized fashion.