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

TL;DR

This paper evaluates spatio-temporal geostatistics methods for mapping urban air quality using mobile sensors, demonstrating significant improvements over traditional interpolation techniques in data interpolation accuracy.

Contribution

It compares three geostatistical methods for air quality mapping and assesses their effectiveness in urban environments, highlighting their strengths and limitations.

Findings

Geostatistical models improved RMSE by 26.57% over IDW in interpolation.

Kriging with External Drift achieved the highest accuracy among tested methods.

Models were less effective for extrapolating data to unsampled locations.

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.