Cyclist exposure to UFP and BC on urban routes in Antwerp, Belgium

Peters, Jan; Bossche, Joris Van den; Reggente, Matteo; Poppel, Martine Van; Baets, Bernard De; Theunis, Jan

doi:10.1016/j.atmosenv.2014.03.039

Cited by 85 publications

(57 citation statements)

References 38 publications

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“…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. Van Poppel et al (2013) applied background correction to reduce the number of repetitions required to obtain representative results of the spatial variability of pollutants at different micro-environments in a city.…”

Section: Introductionmentioning

confidence: 99%

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

Bossche

Peters

Verwaeren

et al. 2015

Atmospheric Environment

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195

132

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Mobile monitoring is increasingly used as an additional tool to acquire air quality data at a high spatial resolution. However, given the high temporal variability of urban air quality, a limited number of mobile measurements may only represent a snapshot and not be representative. In this study, the impact of this temporal variability on the representativeness is investigated and a methodology to map urban air quality using mobile monitoring is developed and evaluated.A large set of black carbon (BC) measurements was collected in Antwerp, Belgium, using a bicycle equipped with a portable BC monitor (micro-aethalometer). The campaign consisted of 256 and 96 runs along two fixed routes (2 and 5 km long). Large gradients over short distances and differences up to a factor of 10 in mean BC concentrations aggregated at a resolution of 20 m are observed. Mapping at such a high resolution is possible, but a lot of repeated measurements are required. After computing a trimmed mean and applying background normalisation, depending on the location 24 to 94 repeated measurement runs (median of 41) are required to map the BC concentrations at a 50 m resolution with an uncertainty of 25 %. When relaxing the uncertainty to 50 %, these numbers reduce to 5 to 11 (median of 8) runs. We conclude that mobile monitoring is a suitable approach for mapping the urban air quality at a high spatial resolution, and can provide insight into the spatial variability that would not be possible with stationary monitors. A careful set-up is needed with a sufficient number of repetitions in relation to the desired reliability and spatial resolution. Specific data processing methods such as background normalisation and event detection have to be applied.

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Section: Introductionmentioning

confidence: 99%

“…Other studies address the potential of using mobile measurements to construct air pollution maps at a high spatial resolution (e.g. Hagler et al, 2010;Choi et al, 2012;Hudda et al, 2014;Peters et al, 2014). Mobile monitoring techniques also receive an increasing attention for participatory sensing and crowd-sourcing methods.…”

Section: Introductionmentioning

confidence: 99%

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

Bossche

Peters

Verwaeren

et al. 2015

Atmospheric Environment

Self Cite

195

132

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“…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.…”

Section: Opportunistic Mobile Monitoringmentioning

confidence: 99%

“…In contrast, mobile platforms are able to acquire air quality data at a high spatial resolution. As such, mobile data are increasingly used to assess the variability within the urban environment and to map the concentration levels people are effectively exposed to (Peters et al, 2014). Given the high tem-poral variability of urban air quality, a limited number of mobile measurements may only represent a snapshot and may thus not be representative.…”

Section: Introductionmentioning

confidence: 99%

“…In the urban environment, the exposure to air pollution is largely influenced by the activity patterns of the population and the high spatial and temporal variability in pollutant concentrations (Setton et al, 2011;Dons et al, 2012). Especially traffic-related pollutants such as NOx, ultrafine particles (UFP) and black carbon (BC) show large differences in concentration levels in space and time on a small scale (Vardoulakis et al, 2011;Peters et al, 2014). It is important to take this within-city variability into account for accurate exposure estimation (Fruin et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

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

Bossche

Theunis

Elen

et al. 2016

Atmospheric Environment

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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.

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Spatiotemporal Air Quality Inference of Low-Cost Sensor Data; Application on a Cycling Monitoring Network

Hofman

Huu

Qin

et al. 2021

Pattern Recognition. ICPR International Workshops and Challenges

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Cyclist exposure to UFP and BC on urban routes in Antwerp, Belgium

Cited by 85 publications

References 38 publications

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

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

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

Spatiotemporal Air Quality Inference of Low-Cost Sensor Data; Application on a Cycling Monitoring Network

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