  Air pollution is the origination of particulate matter, chemicals, or
biological substances that brings pain to either humans or other living
creatures or instigates discomfort to the natural habitat and the airspace.
Hence, air pollution remains one of the paramount environmental issues as far
as metropolitan cities are concerned. Several air pollution benchmarks are even
said to have a negative influence on human health. Also, improper detection of
air pollution benchmarks results in severe complications for humans and living
creatures. To address this aspect, a novel technique called, Discretized
Regression and Least Square Support Vector (DR-LSSV) based air pollution
forecasting is proposed. The results indicate that the proposed DR-LSSV
Technique can efficiently enhance air pollution forecasting performance and
outperforms the conventional machine learning methods in terms of air pollution
forecasting accuracy, air pollution forecasting time, and false positive rate.
