  Here we present a new method of estimating global variations in outdoor
PM$_{2.5}$ concentrations using satellite images combined with ground-level
measurements and deep convolutional neural networks. Specifically, new deep
learning 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 with
ground level PM$_{2.5}$ measurements available from the World Health
Organization. Final model selection was based on a systematic evaluation of
well-known architectures for the convolutional base including InceptionV3,
Xception, and VGG16. The Xception architecture performed best and the final
global 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 new
global model (called IMAGE-PM$_{2.5}$) is similar to the current
state-of-the-art model used in the Global Burden of Disease study but relies
only on satellite images as input. As a result, the IMAGE-PM$_{2.5}$ model
offers a fast, cost-effective means of estimating global variations in
long-term average PM$_{2.5}$ concentrations and may be particularly useful for
regions without ground monitoring data or detailed emissions inventories. The
IMAGE-PM$_{2.5}$ model can be used as a stand-alone method of global exposure
estimation or incorporated into more complex hierarchical model structures.
