Monitoring PM10 and Ultrafine Particles in Urban Environments Using Mobile Measurements

Abstract

Mobile platforms are increasingly used to acquire air quality data at a high spatial and temporal resolution in complex urban environments. As such, mobile measurements provide a solution for short-term studies to acquire a spatially spread data set that would not be feasible if using stationary measurements. Mobile monitoring campaigns were carried out with a bicycle platform at two different urban locations, consisting of 20 and 24 repeated runs along a fixed route over a threeweek period. The measurement runs were carried out on different days and at different times of the day, without systematical temporal coverage. Significant differences in UFP concentration were found within the day and between days, and also between several streets along the measurement route. These differences were related to traffic intensity and street characteristics. In contrast, PM 10 concentrations differed between measurement days, but the within-day variability of PM 10 was mostly non-significant. Additionally, the spatial variability was limited and the PM 10 concentrations were only significantly different between busy streets, with high concentrations, and quiet background streets, with low ones. The results indicate that for most streets the number of runs was sufficient to give a good approximation of median daytime UFP concentration levels for the measurement period, and for some streets this number could even be reduced to less than 10. However, for PM 10 a higher number of runs is needed, and this may be attributed to the significant background contribution to the roadside PM 10 concentration, and the high variability of this. We conclude that a limited set of mobile measurements makes it possible to map locations with systematically higher or lower UFP and PM 10 concentrations in urban environments.

Mobile monitoring for mapping spatial variation in urban air quality: Development and validation of a methodology based on an extensive dataset

“…It can provide insight into the spatial variability that would not be possible with stationary monitors. Previous case studies already showed the potential of repeated mobile measurements for the assessment of spatial variability of pollutants at different micro-environments in a city at street level Peters et al, 2013) and within street-level (Zwack et al, 2011b;Peters et al, 2014).…”

“…For one experiment, the procedure explained above is repeated a high number of times (1000 iterations, where each iteration is one random combination of the runs) to minimize the influence of random effects. This data experiment is based on the work of Peters et al (2013), but differs from it in the application of a random selection with replacement instead of without replacement. When using random selection without replacement (each run can only be selected once), the average would converge exactly to the overall average when more runs are added and convergence would always be obtained.…”

“…Peters et al (2013) indicate the need for repeated measurements to map local pollution patterns. In the study of Peters et al (2014) it is demonstrated that the variability in urban BC and ultra-fine particles (UFP) concentrations can be mapped at a spatial resolution of 10 m. However, they used up to 256 repeated measurement runs, which is not always practically feasible.…”

“…Mobile measurements are performed with different platforms, e.g. pedestrians (Zwack et al, 2011a), bicycles (Berghmans et al, 2009;Boogaard et al, 2009;Peters et al, 2013;Sullivan and Pryor, 2014), trams (Hagemann et al, 2014;Hasenfratz et al, 2014) and cars (Westerdahl et al, 2005;Hu et al, 2012;Hudda et al, 2014). Mobile measurements are used for a range of different purposes, e.g.…”

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

“…This contrasts with targeted mobile monitoring, which is a coordinated, goal-driven approach in which the mobile measurements are deliberately planned and carried out with a specific purpose in mind (see e.g. case studies described in Peters et al (2013), Peters et al (2014) and Van den Bossche et al (2015)). Opportunistic mobile monitoring is a promising approach to collect large data sets that give useful additional information at a reasonable cost compared to classical data collection methods.…”