ArticleSensors (Basel, Switzerland)2021
Mapping Urban Air Quality from Mobile Sensors Using Spatio-Temporal Geostatistics.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Citizen Science in Air Pollution Monitoring: Methodological Approaches, Effectiveness, and Integration with Health Research.Current environmental health reports · 2026Review
- Modeling Traffic-Related Air Pollution Burden of Disease using High Spatial Resolution Data.Journal of transport & health · 2025Article
- Smart Dust for Chemical Mapping.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Long-Range Network of Air Quality Index Sensors in an Urban Area.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the advancement of technology and the arrival of miniaturized environmental sensors that offer greater performance, the idea of building mobile network sensing for air quality has quickly emerged to increase our knowledge of air pollution in urban environments. However, with these new techniques, the difficulty of building mathematical models capable of aggregating all these data sources in order to provide precise mapping of air quality arises. In this context, we explore the spatio-temporal geostatistics methods as a solution for such a problem and evaluate three different methods: Simple Kriging (SK) in residuals, Ordinary Kriging (OK), and Kriging with External Drift (KED). On average, geostatistical models showed 26.57% improvement in the Root Mean Squared Error (RMSE) compared to the standard Inverse Distance Weighting (IDW) technique in interpolating scenarios (27.94% for KED, 26.05% for OK, and 25.71% for SK). The results showed less significant scores in extrapolating scenarios (a 12.22% decrease in the RMSE for geostatisical models compared to IDW). We conclude that univariable geostatistics is suitable for interpolating this type of data but is less appropriate for an extrapolation of non-sampled places since it does not create any information.
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Registered trials
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