Delhi Air Pollution Modeling Using Remote Sensing Technique

Abstract

The world is urbanizing at an alarming rate. In developing countries like India, urbanization and development usually start and proceed in an unplanned way. This unplanned and uncontrolled urbanization leads to ecological imbalance and, ultimately, ecological collapse. Of all the hazards that our ecology is prone to in today’s environmental scenario, air pollution has become a major concern. The deteriorating air quality has become a high priority with respect to regulation of environment that we live in today. Deterioration of air quality in most of the large cities in India has majorly been a condition driven by industrialization, uncontrolled growth of population, and increased dependence on automobiles. Around three million deaths annually are attributed to particulate matter ambient pollution (PM 2.5 and PM 10). The primary source of information for estimating population exposures to air pollution has been measurements from ground monitoring networks, but, although coverage is increasing, regions remain in which monitoring is limited. Keeping this in view, an attempt has been made to develop a GIS model which will help conveniently obtain air quality information directly from remotely sensed data. Within the multiple linear regression modeling framework, the model allows spatially varying relationships between ground measurements and other factors that estimate air quality. The paper demonstrates the potentiality of Landsat 8 OLI-TIRS for the monitoring of air quality by using GIS as the aiding tool. Study proved that model derived by using different bands of Landsat 8 satellite image accurate estimation can be done for particulate matter (PM2.5 and PM10), while it cannot be applied for gaseous pollutants (NOx, CO, etc.). Furthermore, the model was validated using the ground truth data of 2017.