Capturing the sensitivity of land-use regression models to short-term mobile monitoring campaigns using air pollution micro-sensors.

The development of reliable measures of exposure to traffic-related air pollution is crucial for the evaluation of the health effects of transportation. Land-use regression (LUR) techniques have been widely used for the development of exposure surfaces, however these surfaces are often highly sensitive to the data collected. With the rise of inexpensive air pollution sensors paired with GPS devices, we witness the emergence of mobile data collection protocols. For the same urban area, can we achieve a 'universal' model irrespective of the number of locations and sampling visits? Can we trade the temporal representation of fixed-point sampling for a larger spatial extent afforded by mobile monitoring? This study highlights the challenges of short-term mobile sampling campaigns in terms of the resulting exposure surfaces. A mobile monitoring campaign was conducted in 2015 in Montreal; nitrogen dioxide (NO2) levels at 1395 road segments were measured under repeated visits. We developed LUR models based on sub-segments, categorized in terms of the number of visits per road segment. We observe that LUR models were highly sensitive to the number of road segments and to the number of visits per road segment. The associated exposure surfaces were also highly dissimilar.

Operational and environmental determinants of in-vehicle CO and PM2.5 exposure.

This study presents a modeling framework to quantify the complex roles that traffic, seasonality, vehicle characteristics, ventilation, meteorology, and ambient air quality play in dictating in-vehicle commuter exposure to CO and PM2.5. For this purpose, a comprehensive one-year monitoring program of 25 different variables was coupled with a multivariate regression analysis to develop models to predict in-vehicle CO and PM2.5 exposure using a database of 119 mobile tests and 120 fume leakage tests. The study aims to improve the understanding of in-cabin exposure, as well as interior-exterior pollutant exchange. Model results highlighted the strong correlation between out-vehicle and in-vehicle concentrations, with the effect of ventilation type only discerned for PM2.5 levels. Car type, road conditions, as well as meteorological conditions all played a significant role in modulating in-vehicle exposure. The CO and PM2.5 exposure models were able to explain 72 and 92% of the variability in measured concentrations, respectively. Both models exhibited robustness and no-evidence of over-fitting.