Opportunistic mobile air pollution monitoring: A case study with city wardens in Antwerp

Abstract

The goal of this paper is to explore the potential of opportunistic mobile monitoring to map the exposure to air pollution in the urban environment at a high spatial resolution. Opportunistic mobile monitoring makes use of existing mobile infrastructure or people's common daily routines to move measurement devices around. Opportunistic mobile monitoring can also play a crucial role in participatory monitoring campaigns as a typical way to gather data.A case study to measure black carbon was set up in Antwerp, Belgium, with the collaboration of city employees (city wardens). The Antwerp city wardens are outdoors for a large part of the day on surveillance tours by bicycle or on foot, and gathered a total of 393 hours of measurements. The data collection is unstructured both in space and time, leading to sampling bias. A temporal adjustment can only partly counteract this bias. Although a high spatial coverage was obtained, there is still a rather large uncertainty on the average concentration levels at a spatial resolution of 50 m due to a limited number of measurements and sampling bias. Despite of this uncertainty, large spatial patterns within the city are clearly captured.This study illustrates the potential of campaigns with unstructured opportunistic mobile monitoring, including participatory monitoring campaigns. The results demonstrate that such an approach can indeed be used to identify broad spatial trends over a wider area, enabling applications including hotspot identification, personal exposure studies, regression mapping, etc. But, they also emphasize the need for repeated measurements and careful processing and interpretation of the data.

Development and evaluation of land use regression models for black carbon based on bicycle and pedestrian measurements in the urban environment

“…The micro-aethalometers have been compared several times during the campaign to a reference monitoring station. More details on data collection, processing and quality control can be found in Van den Bossche et al (2016).…”

“…The temporal adjustment was performed through a combination of the additive and multiplicative method. More details can be found in Van den Bossche et al (2016). The trimmed mean used in this study was calculated as the arithmetic mean after removing the 0.5 % largest and 0.5 % smallest values ( Van den Bossche et al, 2015).…”

“…As described in Van den Bossche et al (2016), the data at 1 s resolution were aggregated over segments of approximately 50 m resolution along the roads (assigned to the midpoint of the corresponding segment). This resulted in different passages for each segment, where one passage is a continuous period of time during which measurements are performed in that segment.…”

“…This case study is based on measurements gathered by city wardens during their surveillance tasks, which were presented in Van den Bossche et al (2016). The measurement campaign resulted in a higher spatial density of measurement locations compared to most LUR studies (sampling points at an approximate resolution of 50 m along the roads).…”

“…Mobile measurements can also be collected in participatory and community-based campaigns. Volunteers can systematically collect targeted data sets, or data are collected opportunistically during (repeated) daily activities or trips, to provide improved estimates of spatial variability (Snyder et al, 2013;Van den Bossche et al, 2016).…”