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

Abstract

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 (R2) 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/m3. 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.

Hourly PM2.5 Concentration Prediction Based on Empirical Mode Decomposition and Geographically Weighted Neural Network

“…The proposed model demonstrates promising outcomes and offers a novel perspective on PM2.5 concentration prediction. At the practical application level, the enhanced accuracy in predicting PM2.5 concentration can greatly assist in assessing the exposure to air pollutants for people [44]. Additionally, it can support the formation of environmental policies related to traffic management to mitigate air pollution resulting from vehicles, as vehicles contribute to air pollution to a significant extent [45].…”