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Mapping Urban Air Quality from Mobile Sensors Using Spatio-Temporal Geostatistics.

Yacine Mohamed Idir^1,2,3, Olivier Orfila¹, Vincent Judalet³

¹COSYS-PICS-L, Gustave Eiffel University, IFSTTAR, F-78000 Versailles, France.

Sensors (Basel, Switzerland)

|July 24, 2021

Summary

Spatio-temporal geostatistics methods improve urban air quality mapping by reducing errors in interpolation. These methods are effective for interpolating air pollution data but less so for extrapolation into unsampled areas.

Keywords:

air quality mobile sensors ozone concentration spatio-temporal geostatistics

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Published on: December 20, 2016

Area of Science:

Environmental Science
Geospatial Analysis
Data Science

Background:

Miniaturized environmental sensors enable mobile network sensing for urban air quality monitoring.
Accurate air quality mapping requires sophisticated mathematical models to aggregate diverse data sources.

Purpose of the Study:

To evaluate spatio-temporal geostatistics methods for urban air quality mapping.
To compare the performance of Simple Kriging (SK), Ordinary Kriging (OK), and Kriging with External Drift (KED) against Inverse Distance Weighting (IDW).

Main Methods:

Exploration of spatio-temporal geostatistics, specifically SK, OK, and KED.
Comparative analysis using Root Mean Squared Error (RMSE) for interpolation and extrapolation scenarios.
Evaluation against the standard Inverse Distance Weighting (IDW) technique.

Main Results:

Geostatistical models demonstrated an average 26.57% improvement in RMSE over IDW for interpolation.
KED, OK, and SK showed RMSE improvements of 27.94%, 26.05%, and 25.71%, respectively.
Geostatistical models showed a less significant 12.22% RMSE decrease compared to IDW in extrapolation scenarios.

Conclusions:

Univariable geostatistics is well-suited for interpolating urban air quality data.
Geostatistics is less appropriate for extrapolating air quality to non-sampled locations due to a lack of new information generation.