Fine-Grained Urban Air Quality Mapping from Sparse Mobile Air Pollution Measurements and Dense Traffic density

Xuening Qin, Tien Do Huu, Jelle Hofman, Esther Rodrigo Bonet, Valerio Panzica La Manna, Nikos Deligiannis, Wilfried Philips

Onderzoeksoutput: Articlepeer review

2 Citaten (Scopus)


Urban air quality mapping has been widely applied in urban planning, air pollution control and personal air pollution exposure assessment. Urban air quality maps are traditionally derived using measurements from fixed monitoring stations. Due to high cost, these stations are generally sparsely deployed in a few representative locations, leading to a highly generalized air quality map. In addition, urban air quality varies rapidly over short distances (<1 km) and is influenced by meteorological conditions, road network and traffic flow. These variations are not well represented in coarse-grained air quality maps generated by conventional fixed-site monitoring methods but have important implications for characterizing heterogeneous personal air pollution exposures and identifying localized air pollution hotspots. Therefore, fine-grained urban air quality mapping is indispensable. In this context, supplementary low-cost mobile sensors make mobile air quality monitoring a promising alternative. Using sparse air quality measurements collected by mobile sensors and various contextual factors, especially traffic flow, we propose a context-aware locally adapted deep forest (CLADF) model to infer the distribution of NO2 by 100 m and 1 h resolution for fine-grained air quality mapping. The CLADF model exploits deep forest to construct a local model for each cluster consisting of nearest neighbor measurements in contextual feature space, and considers traffic flow as an important contextual feature. Extensive validation experiments were conducted using mobile NO2 measurements collected by 17 postal vans equipped with low-cost sensors operating in Antwerp, Belgium. The experimental results demonstrate that the CLADF model achieves the lowest RMSE as well as advances in accuracy and correlation, compared with various benchmark models, including random forest, deep forest, extreme gradient boosting and support vector regression.
Originele taal-2English
Aantal pagina's21
TijdschriftRemote Sensing
Nummer van het tijdschrift11
StatusPublished - 1 jun 2022

Bibliografische nota

Funding Information:
Funding: This research was funded by imec Belgium through AAA funding, by the Internet of Things (IoT) team of imec-Netherlands under the project EI2 and by the Flemish Government (AI Research Program).

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Copyright 2022 Elsevier B.V., All rights reserved.


Duik in de onderzoeksthema's van 'Fine-Grained Urban Air Quality Mapping from Sparse Mobile Air Pollution Measurements and Dense Traffic density'. Samen vormen ze een unieke vingerafdruk.

Citeer dit