Estimating Black Carbon Concentration from Urban Traffic Using Vision-Based Machine Learning

TL;DR

This paper introduces a machine learning system that uses traffic videos and weather data to estimate black carbon levels at street level, providing a cost-effective way to inform urban pollution policies.

Contribution

It presents a novel vision-based machine learning approach to estimate black carbon concentrations from traffic videos, bridging data gaps in urban air quality monitoring.

Findings

Achieved an R-squared of 0.72 in BC estimation.

Model demonstrated RMSE of 129.42 ng/m3.

Leverages existing traffic monitoring infrastructure for environmental insights.

Abstract

Black carbon (BC) emissions in urban areas are primarily driven by traffic, with hotspots near major roads disproportionately affecting marginalized communities. Because BC monitoring is typically performed using costly and specialized instruments. there is little to no available data on BC from local traffic sources that could help inform policy interventions targeting local factors. By contrast, traffic monitoring systems are widely deployed in cities around the world, highlighting the imbalance between what we know about traffic conditions and what do not know about their environmental consequences. To bridge this gap, we propose a machine learning-driven system that extracts visual information from traffic video to capture vehicles behaviors and conditions. Combining these features with weather data, our model estimates BC at street level, achieving an R-squared value of 0.72 and RMSE of 129.42 ng/m3 (nanogram per cubic meter). From a sustainability perspective, this work leverages resources already supported by urban infrastructure and established modeling techniques to generate information relevant to traffic emission. Obtaining BC concentration data provides actionable insights to support pollution reduction, urban planning, public health, and environmental justice at the local municipal level.