Application of a Machine Learning Method for Prediction of Urban Neighborhood-Scale Air Pollution.

ABSTRACT: Urban air pollution has aroused growing attention due to its associated adverse health effects. A model which could promptly predict urban air quality with considerable accuracy is, therefore, important and will benefit the development of smart cities. However, only a computational fluid dynamics (CFD) model could better resolve the dispersion behavior within an urban canyon layer. A machine learning (ML) model using the Artificial Neural Network (ANN) approach was formulated in the current study to investigate vehicle-derived airborne particulate (PM₁₀) dispersion within a compact high-rise-built environment. Various measured meteorological parameters and PM₁₀ concentrations were adopted as the model inputs to train the ANN model. A building-resolved CFD model under

SUBMITTER: Wai KM 

PROVIDER: S-EPMC9915966 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature