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. [Display omitted] •Selection of sampling routes in mobile monitoring campaigns is a complex task.•LUR models are sensitive to the visits per road segment and to their location.•Mobile measurement protocols can lead to different exposure surfaces in the same city.•We found a large sensitivity of the LUR model to the segment selection. The selection of sampling routes in mobile monitoring campaigns is a complex task and mobile measurement protocols can lead to different exposure surfaces in the same city.