Quantifying the contribution of environmental variables to cyclists' exposure to PM_2.5 using machine learning techniques.

ABSTRACT: Cyclists are particularly vulnerable to travel-related exposure to air pollution. Understanding the factors that increase exposure is crucial for promoting healthier urban environments. Machine learning models have successfully predicted air pollutant concentrations, but they tend to be less interpretable than classical statistical ones, such as linear models. This study aimed to develop a predictive model to assess cyclists' exposure to fine particulate matter (PM_2.5) in urban environments. The model was generated using geo-temporally referenced data and machine learning techniques. We explored several models and found that the gradient boosting machine learning model best fitted the PM_2.5 predictions, with a minimum root mean square error value of 5.62 μg m^-3</

SUBMITTER: Rodriguez Nunez M 

PROVIDER: S-EPMC10828810 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature