  The interpolation, prediction, and feature analysis of fine-gained air
quality are three important topics in the area of urban air computing. The
solutions to these topics can provide extremely useful information to support
air pollution control, and consequently generate great societal and technical
impacts. Most of the existing work solves the three problems separately by
different models. In this paper, we propose a general and effective approach to
solve the three problems in one model called the Deep Air Learning (DAL). The
main idea of DAL lies in embedding feature selection and semi-supervised
learning in different layers of the deep learning network. The proposed
approach utilizes the information pertaining to the unlabeled spatio-temporal
data to improve the performance of the interpolation and the prediction, and
performs feature selection and association analysis to reveal the main relevant
features to the variation of the air quality. We evaluate our approach with
extensive experiments based on real data sources obtained in Beijing, China.
Experiments show that DAL is superior to the peer models from the recent
literature when solving the topics of interpolation, prediction, and feature
analysis of fine-gained air quality.
