  Air pollution has long been a serious environmental health challenge,
especially in metropolitan cities, where air pollutant concentrations are
exacerbated by the street canyon effect and high building density. Whilst
accurately monitoring and forecasting air pollution are highly crucial,
existing data-driven models fail to fully address the complex interaction
between air pollution and urban dynamics. Our Deep-AIR, a novel hybrid deep
learning framework that combines a convolutional neural network with a long
short-term memory network, aims to address this gap to provide fine-grained
city-wide air pollution estimation and station-wide forecast. Our proposed
framework creates 1x1 convolution layers to strengthen the learning of
cross-feature spatial interaction between air pollution and important urban
dynamic features, particularly road density, building density/height, and
street canyon effect. Using Hong Kong and Beijing as case studies, Deep-AIR
achieves a higher accuracy than our baseline models. Our model attains an
accuracy of 67.6%, 77.2%, and 66.1% in fine-grained hourly estimation, 1-hr,
and 24-hr air pollution forecast for Hong Kong, and an accuracy of 65.0%,
75.3%, and 63.5% for Beijing. Our saliency analysis has revealed that for Hong
Kong, street canyon and road density are the best estimators for NO2, while
meteorology is the best estimator for PM2.5.
