Modeling Air Pollution and Temperature Components to Identify Their Effects on India’s Capital using Python

Abstract

Various past research works have shown that temperature can alter the effect of ambient fine particles category PM 2.5 which causes the high mortality risk. In surveying air contamination impacts, temperature is generally considered as a confounder. In any case, encompassing temperature can change individuals' physiological reaction to air contamination and might alter the effect of air contamination on wellbeing results. This study investigates the interaction between monthly values of PM2.5 and monthly average temperature values in Delhi, India using data for the period 2010–2018. The computer language Python is used to analysis facts and produce the outcomes which can shape the future research work and policies to overcome both global issues- pollution and Global warming.