Mobile Air Quality monitoring with daily commuters in Rotterdam
- 1Flemish Institute for Technological Research (VITO), 2400 Mol, Belgium (martine.vanpoppel@vito.be)
- 2Institute for Risk Assessment Sciences (IRAS), Division of Environmental Epidemiology, Utrecht University, 3584 CK Utrecht, The Netherlands
- 3DCMR - Environmental Protection Agency Rijnmond (DCMR), 3112 NA Schiedam, The Netherlands
Air pollution is the fourth cause of premature mortality (HEI, 2020) and in Europe, more than 0.3 million premature deaths are due to air pollution (EEA, 2021). In urban environments, people are exposed to a complex mixture of air pollutants with a large spatial variability. However, highly spatially resolved measurement data on air pollutants is lacking. These fine-grained data is needed to correctly assess personal exposure to air pollution for epidemiolocal studies and to support air quality management scenarios.
Within RI-URBANS different innovative approaches to get insights into novel air quality parameters, source contributions, exposure to air pollution and associated health effects will be developed and tested. One of the approaches relies on mobile measurements with citizens to derive spatial air pollution maps. Mobile measurements can contribute to understand spatial variability of short-living constituents of air pollution from a diversity of pollution sources.
The monitoring campaign is performed with volunteers, who are all employees of DCMR or the city of Rotterdam. They are asked to measure during their daily bicycle commutes. Before the measurement campaign, a training session was organized for the volunteers. Measurements were performed in winter (November 2022 – February 2023) and will be repeated in spring 2023.
Measurements are based on the airQmap approach; more information on the approach and previous studies can be found on https://www.airQmap.com. Measurements of Black Carbon (BC) are performed using a microaethalometer (microAeth®, AE51, AethLabs) and a GPS. BC is measured at 1s temporal resolution and a flow rate of 150 mL min-1. To reduce the noise in BC measurements, the ONA (Optimized Noise-reduction Averaging, Hagler et al., 2011) algorithm was used with an attenuation threshold of 0.05. The geo-tagged measurements were aggregated (trimmed mean) and attributed to fixed points 20 m apart from each other along the cycling route.
The dataset will be used to test different data processing techniques (a.o. temporal aggregation, background correction approaches) to construct representative BC maps. The collected spatiotemporal BC measurements will be analysed to identify main sources of BC in the area. The pilot study will result in guidance on best practices for mobile air quality monitoring involving citizens.
This paper will present the results of the winter campaign.
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EEA, 2021. HI of Air Pollution in EU
Hagler, G.S., Yelverton, T.L., Vedantham, R., Hansen, A.D., Turner, J.R., 2011. Postprocessing method to reduce noise while preserving high time resolution in Aethalometer real-time black carbon data. Aerosol Air Qual. Res. 11, 539-546.
HEI, 2020. State of Global Air
How to cite: Van Poppel, M., Hofman, J., Peters, J., Hoek, G., Kerckhoffs, J., Willers, S., and Özdemir, E.: Mobile Air Quality monitoring with daily commuters in Rotterdam, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-7684, https://doi.org/10.5194/egusphere-egu23-7684, 2023.
