Drone-based analysis of CO2 and noise emissions using traffic data and low-cost sensors

Traffic-related CO 2 and noise emission assessment remains challenging due to limited availability of high-resolution traffic data and the lack of integrated validation studies. This work presents a data-driven framework for estimating and evaluating traffic CO 2 and noise emissions in urban environments using drone-derived vehicle trajectory data and low-cost environmental sensors. We conduct a two-day case study at an urban highway site in Athens, Greece, where traffic emissions, noise, and CO 2 concentrations are analyzed using independently collected datasets. Results show strong variability in modeled CO 2 emissions reflecting a wide range of naturalistic traffic conditions, including oversaturated flow and high-speed driving. CO 2 concentrations, however, are mainly driven by meteorological conditions rather than instantaneous emissions. Noise emissions are accurately modeled, with small deviations likely due to external sources. Overall, the presented methodology provides evidence of the feasibility of drone-extracted traffic data combined with low-cost environmental sensors for real-world traffic emission analysis.