Complexity of Factor Analysis for Particulate Matter (PM) Data: A Measurement Based Case Study in Delhi-NCR

Abstract

Developing countries are home to the most polluted cities in the world. Particulate Matter (PM), one of the most serious air pollutants, needs to be measured at scale across urban areas in such countries. Factors potentially affecting PM like road traffic, green cover, industrial emissions etc., also need to be quantified, to enable fine-grained correlation analyses among PM and its causes. This paper presents an IoT platform with multiple sensors, latest deep neural network based edge-computing, local storage and communication support - to measure PM and its associated factors. Through real world deployments, the first in depth empirical analysis of a government enforced traffic control policy for pollution control, is presented as a use case of our IoT platform. We demonstrate the potential of IoT and edge computing in urban sustainability questions in this paper, especially in a developing region context. At the same time, we show how complex a real system like Particulate Matter's factor analyses can be, and urge environmentalists to use sensors networks and fine-grained empirical datasets as ours in future, for more nuanced and data-driven policy discussions.