The air pollution monitoring data records are not entirely complete, making it difficult to estimate the required statistical parameters with an acceptable error and level of confidence. An intercomparison of various strategies for interpolating daily values over the Madrid urban air pollution monitoring (UAPM) network has been undertaken for the following set of air pollutants: SO2, NO2, NOx, CO, and suspended particulate matter (SPM). The methodology accounts for either the algorithms [distance, spatial correlation (or Alexandersson's) and multiple regression] or the data input (original, log-transformed and log-standardized). The well-known Alexandersson's test has been implemented as a spatial correlation method in order to interpolate missing daily concentration data. In general, the distance method and the spatial correlation method are greatly superior to multiple regression regardless of the pollutant considered; they provide good estimates when applied to log-standarized and original data, respectively. Given that the distance method does not lead to a parsimonious model and is difficult to apply (it is not clear and is not intuitive), the spatial correlation method is proposed as the best interpolating algorithm.
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