Bicycle-based aerosol measurements in the inner city of Budapest

Atmospheric aerosol pollution in densely populated urban areas is a pressing concern. Complex building patterns, local circulation systems and widely varying sources of pollution make traditional aerosol measurements insufficient. However, using portable OPCs and low-cost, fixed and mobile sensors enables researchers to obtain high spatial and temporal resolution air quality data in urban areas.

In recent years, the popularity of cycling and other forms of public transport has increased in Budapest, the capital city of Hungary, raising concerns about individual aerosol exposure and its health effects. In our study, calibrated instruments mounted on bicycles are used to assess aerosol pollution along some of the most important bicycle routes in Budapest. The objectives of this project are to create a quality-assured reference database for further research (e.g. human health-related calculations), to identify trends and hotspots of air pollution, to develop a measurement methodology for mobile measurements by low-cost instruments, and to develop a relevant metadata structure.

Two DustTrak™ II Aerosol Monitor 8532 instruments equipped with physical impactors were used to measure PM₁₀ and PM_2.5 in parallel. Air temperature and relative humidity were recorded using a Testo 635-2 datalogger with additional sensor shielding; both were sampled at 2-second resolution and averaged over 10 seconds. Detailed GPS data was recorded using a mobile phone application at a 1-second resolution. About 150 measurement datasets were recorded on pre-selected routes. The data is processed using a primarily automated algorithm written in Matlab. In order to allow comparison of individual routes and further statistical calculations, the data is projected onto a domain of 0.2° × 0.2° covered by a regular geographic grid with 200 grid cells in each direction (one grid cell measuring approximately 110 × 110 meters).

This study outlines the measurement setup, the gridded dataset and demonstrates the applicability of our database through case studies. The ratio of PM_2.5/PM₁₀ and its spatial and temporal patterns are assessed using the entire dataset and in selected situations. Additionally, the percentage deviation of each PM fraction from the median for an entire measurement route and the spatial distribution of the deviations are presented.

The research was funded by the National Multidisciplinary Laboratory for Climate Change, RRF-2.3.1-21-2022-00014 project.