Outdoor ultrafine particles (UFP,
<0.1 μm) and black carbon
(BC) vary greatly within cities and may have adverse impacts on human
health. In this study, we used a hybrid approach to develop new models
to estimate within-city spatial variations in outdoor UFP and BC concentrations
across Bucaramanga, Colombia. We conducted a mobile monitoring campaign
over 20 days in 2019. Regression models were trained on land use data
and combined with predictions from convolutional neural networks (CNN)
trained to predict UFP and BC concentrations using satellite and street-level
images. The combined UFP model (R
2 = 0.54)
outperformed the CNN (R
2 = 0.47) and land
use regression (LUR) models (R
2 = 0.47)
on their own. Similarly, the combined BC model also outperformed the
CNN and LUR BC models (R
2 = 0.51 vs 0.43
and 0.45, respectively). Spatial variations in model performance were
more stable for the CNN and combined models compared to the LUR models,
suggesting that the combined approach may be less likely to contribute
to differential exposure measurement error in epidemiological studies.
In general, our findings demonstrated that satellite and street-level
images can be combined with a traditional LUR modeling approach to
improve predictions of within-city spatial variations in outdoor UFP
and BC concentrations.
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