Innovative Sensors and Models for City-Level Air Pollution Exposure Monitoring

Innovative Sensors and Models for City-Level Air Pollution Exposure Monitoring

Otjes, R.P.
Hoek, G.
van der Sterren, S.
Hamm, N.A.S.
Vasquez Gomez, E.A.
Dash, I.
van Zoest, V.
van der Wiel, A.G.
Stein, A.

2016

Results: The exploratory analysis showed that the variability in the ILM PM data increased from 10% to 16% (coefficient of variation) between 2013 and 2015 The ILM data tended to record lower values than the LML data, although the peaks and troughs were still observed. The 10-minute data were typically too noisy to allow the identification of spatial correlation, although this was clear when averaged to 1-hour averages and for longer time periods. Using BME we were able to integrate the dispersion model and ILM data, yielding an RMSE of 1 µg m-3 for daily PM2.5. The SOA was effective to combine different datasets (e.g., ILM and LML) and to implement simple geostatistical models in an automated fashion. Conclusions: Using low-cost sensors allowed us to identify spatial and temporal patterns that are valuable for spatial prediction at sub-daily time resolutions. These patterns can be identified and modelled using space-time geostatistics e.g., BME. Standards-based tools should be used for organizing, archiving and disseminating data and are essential for future automated analysis.

http://resolver.tudelft.nl/uuid:f5873e91-59ea-4149-a316-33ee2ddc33fd

849728

ECN-M--16-033

ECN, Petten

conference paper