Within-city ultrafine particle (UFP) concentrations vary
sharply
since they are influenced by various factors. We developed prediction
models for short-term UFP exposures using street-level images collected
by a camera installed on a vehicle rooftop, paired with air quality
measurements conducted during a large-scale mobile monitoring campaign
in Toronto, Canada. Convolutional neural network models were trained
to extract traffic and built environment features from images. These
features, along with regional air quality and meteorology data were
used to predict short-term UFP concentration as a continuous and categorical
variable. A gradient boost model for UFP as a continuous variable
achieved R
2 = 0.66 and RMSE = 9391.8#/cm3 (mean values for 10-fold cross-validation). The model predicting
categorical UFP achieved accuracies for “Low” and “High”
UFP of 77 and 70%, respectively. The presence of trucks and other
traffic parameters were associated with higher UFPs, and the spatial
distribution of elevated short-term UFP followed the distribution
of single-unit trucks. This study demonstrates that pictures captured
on urban streets, associated with regional air quality and meteorology,
can adequately predict short-term UFP exposure. Capturing the spatial
distribution of high-frequency short-term UFP spikes in urban areas
provides crucial information for the management of near-road air pollution
hot spots.
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