Cartography for air quality monitoring: the geostatistical approach

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Courtesy of Courtesy of GEOVARIANCES

Being able to provide quick and accurate pollutant maps from readings at isolated measurement stations is becoming more important today in light of the European norms on air quality and the public’s demand to be informed. Commonly used algorithms for cartography are quick but their accuracy remains to be determined.

Firstly, the choice of method is arbitrary and based on user’s subjective perception. Secondly, none of these methods can take relevant auxiliary information (like meteorological data, traffic density or industrial emissions) into account, thus limiting the realism of the final map. The geostatistical mapping algorithm, called kriging, is based on the specific spatial behaviour of the mapped pollutant via a spatial correlation function calculated from the sample measurements. Because the form of this correlation function determines the shape of the final map, the geostatistical algorithm adapts itself directly to the spatial characteristics of the data, thus eliminating the need for a subjective evaluation.

Auxiliary information can also be incorporated in the final map by integrating any relationship (linear or non-linear) with the mapped pollutant into the kriging algorithm. The paper begins with a presentation of the concepts behind the geostatistical mapping algorithm. Two practical applications, mapping benzene (C6H6) over Rouen and nitrogen dioxide (NO2) over Paris, are used to highlight the capacity of the geostatistical approach to produce realistic maps that take traffic information and emissions into account when monitoring air quality in real-time. All calculations are made with the ISATIS geostatistical software package.

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