DeepAir: Deep Learning and Satellite Imagery to Estimate High-resolution at Scale

Air pollution, specifically PM
2.5
, has become a significant global concern owing to its detrimental impacts on public health. Even so, the high-resolution monitoring of air pollution is still a challenge on a global scale. To cope with this, machine learning (ML) techniques have been utilized to infer the concentration of air pollutants at a fine scale. In this study, we propose
DeepAir
, a learning framework for estimating PM
2.5
concentrations at a fine scale with sparsely distributed observations.
DeepAir
integrates a pre-trained convolutional neural network with the LightGBM method. This framework estimates the PM
2.5
concentration of a given patch, utilizing a synergy of geographical information, meteorological conditions, and satellite observations. We select California as the focal region and train the model with data from 2014 to 2017 provided by 130 PM
2.5
observation stations in the state. Upon training, the model can be applied to estimate the daily PM
2.5
concentrations at 1 km resolution across California. Our methodology meticulously incorporates meteorological variables, with a particular emphasis on wildfire propagation, and contemplates the complex interplay of various features. To ascertain the efficacy of our model, we employ the 10-fold cross-validation technique, which confirms that our model surpasses traditional ML and standalone deep learning methods.