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Here we present a new method of estimating global variations in outdoorPM$_{2.5}$ concentrations using satellite images combined with ground-levelmeasurements and deep convolutional neural networks. Specifically, new deeplearning models were trained over the global PM$_{2.5}$ concentration range($<$1-436 $\mu$g/m$^3$) using a large database of satellite images paired withground level PM$_{2.5}$ measurements available from the World HealthOrganization. Final model selection was based on a systematic evaluation ofwell-known architectures for the convolutional base including InceptionV3,Xception, and VGG16. The Xception architecture performed best and the finalglobal model had a root mean square error (RMSE) value of 13.01 $\mu$g/m$^3$(R$^2$=0.75) in the disjoint test set. The predictive performance of our newglobal model (called IMAGE-PM$_{2.5}$) is similar to the currentstate-of-the-art model used in the Global Burden of Disease study but reliesonly on satellite images as input. As a result, the IMAGE-PM$_{2.5}$ modeloffers a fast, cost-effective means of estimating global variations inlong-term average PM$_{2.5}$ concentrations and may be particularly useful forregions without ground monitoring data or detailed emissions inventories. TheIMAGE-PM$_{2.5}$ model can be used as a stand-alone method of global exposureestimation or incorporated into more complex hierarchical model structures.

Predicting Global Variations in Outdoor PM2.5 Concentrations using Satellite Images and Deep Convolutional Neural Networks