Mapping Urban Air Quality from Mobile Sensors Using Spatio-Temporal Geostatistics
| Autor: | Vincent Judalet, Yacine Mohamed Idir, Olivier Orfila, Patrice Chatellier, Benoit Sagot |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
010504 meteorology & atmospheric sciences
Mean squared error Computer science Extrapolation Context (language use) Geostatistics spatio-temporal geostatistics TP1-1185 010501 environmental sciences 01 natural sciences Biochemistry Article mobile sensors Analytical Chemistry ozone concentration Kriging Air Pollution Inverse distance weighting Statistics Electrical and Electronic Engineering Instrumentation Air quality index 0105 earth and related environmental sciences Air Pollutants Spatial Analysis Mathematical model Chemical technology Models Theoretical air quality Atomic and Molecular Physics and Optics Environmental Monitoring |
| Zdroj: | Sensors, Vol 21, Iss 4717, p 4717 (2021) Sensors Volume 21 Issue 14 Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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