Exploring the triple burden of social disadvantage, mobility poverty, and exposure to traffic-related air pollution.

Understanding the relationships between ultrafine particle (UFP) exposure, socioeconomic status (SES), and sustainable transportation accessibility in Toronto, Canada is crucial for promoting public health, addressing environmental justice, and ensuring transportation equity. We conducted a large-scale mobile measurement campaign and employed a gradient boost model to generate exposure surfaces using land use, built environment, and meteorological conditions. The Ontario Marginalization Index was used to quantify various indicators of social disadvantage for Toronto's neighborhoods. Our findings reveal that people in socioeconomically disadvantaged areas experience elevated UFP exposures. We highlight significant disparities in accessing sustainable transportation, particularly in areas with higher ethnic concentrations. When factoring in daily mobility, UFP exposure disparities in disadvantaged populations are further exacerbated. Furthermore, individuals who do not generate emissions themselves are consistently exposed to higher UFPs, with active transportation users experiencing the highest UFP exposures both at home and at activity locations. Finally, we proposed a novel index, the Community Prioritization Index (CPI), incorporating three indicators, including air quality, social disadvantage, and sustainable transportation. This index identifies neighborhoods experiencing a triple burden, often situated near major infrastructure hubs with high diesel truck activity and lacking greenspace, marking them as high-priority areas for policy action and targeted interventions.

Design, calibration, and testing of a mobile sensor system for air pollution and built environment data collection: The urban scanner platform.

This paper describes a mobile air pollution sampling system, the Urban Scanner, which aims at gathering dense spatiotemporal air quality data to support urban air quality and exposure science. Urban Scanner comprises custom vehicle-mounted sensors for air pollution, meteorology, and built environment data collection (low-cost sensors, wind anemometer, 360 deg camera, LIDAR, GPS) as well as a server to store, process, and map all gathered geo-referenced sensory information. Two levels of sensor calibration were implemented, both in a chamber and in the field, against reference instrumentation. Chamber tests and a set of mathematical tools were developed to correct for sensor noise (wavelet denoising), misalignment (linear and nonlinear), and hysteresis removal. Models based on chamber testing were further refined based on field co-location. While field co-location captures natural changes in air pollution and meteorology, chamber tests allow for simulating fast transitions in these variables, like the transitions experienced by a mobile sensor in an urban environment. The best suite of models achieved an R2 higher than 0.9 between sensor output and reference station observations and an RMSE of 2.88 ppb for nitrogen dioxide and 4.03 ppb for ozone. A mobile sampling campaign was conducted in the city of Toronto, Canada, to further test Urban Scanner. We observe that the platform adequately captures spatial and temporal variability in urban air pollution, leading to the development of land-use regression models with high explanatory power.

Prediction of Short-Term Ultrafine Particle Exposures Using Real-Time Street-Level Images Paired with Air Quality Measurements.

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 R2 = 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.