Black Carbon Concentration Estimation with Mobile-Based Measurements in a Complex Urban Environment

Black carbon (BC) is a significant source of air pollution since it impacts public health and climate change. Understanding its distribution in the complex urban environment is challenging. We integrated a land use model with four machine learning models to estimate traffic-related BC concentrations in Oakland, CA. Random Forest was the best-performing model, with regression coefficient (R-2) values of 0.701 on the train set and 0.695 on the validation set with a root mean square error (RMSE) of 0.210 mg/m(3). Vehicle speed and local road systems were the most sensitive variables in estimating BC concentrations. However, this approach was inefficient at identifying hyperlocal hotspots, especially in a complex urban environment where highways and truck routes are significant emission sources. Using the land use method to estimate BC concentrations may lead to underestimating some localized hotspots. This work can improve air quality exposure assessment for vulnerable populations and help emphasize potential environmental justice issues.

Automated summary

This study integrated a land use model with four machine learning models to estimate traffic-related black carbon (BC) concentrations in Oakland, CA. The best-performing model, Random Forest, showed good accuracy in estimating BC concentrations. However, this approach was inefficient at identifying hyperlocal hotspots, especially in a complex urban environment where significant emission sources like highways and truck routes exist.