A PM2.5 concentration prediction framework with vehicle tracking system: From cause to effect

TL;DR

This paper presents a framework that combines vehicle tracking and traffic density estimation with a mathematical model to predict PM2.5 air pollution levels in urban areas, demonstrating a strong correlation with actual measurements.

Contribution

It introduces a novel traffic surveillance system and a mathematical model linking vehicle count to PM2.5 levels, addressing air pollution prediction from traffic data.

Findings

Traffic density correlates with PM2.5 levels with a time lag

The proposed model accurately estimates PM2.5 based on vehicle count

Vehicle tracking enhances air pollution monitoring in urban environments

Abstract

Air pollution is an emerging problem that needs to be solved especially in developed and developing countries. In Vietnam, air pollution is also a concerning issue in big cities such as Hanoi and Ho Chi Minh cities where air pollution comes mostly from vehicles such as cars and motorbikes. In order to tackle the problem, the paper focuses on developing a solution that can estimate the emitted PM2.5 pollutants by counting the number of vehicles in the traffic. We first investigated among the recent object detection models and developed our own traffic surveillance system. The observed traffic density showed a similar trend to the measured PM2.5 with a certain lagging in time, suggesting a relation between traffic density and PM2.5. We further express this relationship with a mathematical model which can estimate the PM2.5 value based on the observed traffic density. The estimated result showed a great correlation with the measured PM2.5 plots in the urban area context.