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Air quality data-base implementation by using time series statistic filling

Author(s): G. Latini, G. Passerini & S. Tascini Abstract: In this work we present a set of procedures to achieve an extended air quality Data Base starting from sets of raw data which can be affected by wide gaps due to malfunction of sensors and/or weaknesses in collection software. The focus is on the statistic filling of incomplete time series, after evaluating a short range of methods. In the first step we analyse statistics and substituted undoubtedly abnormal isolated values by means of specific algorithms such as Wilcoxon rank sum test, Box-Jenkins model, trend analysis, regression etc. [3]. Then we proceed with missing data filling along time series choosing among linear interpolation, nearest neighbor and spatial averaging algorithms. Standard filling protocols such as those proposed by EPA (Environmental Protection Agency) and by NWS (National Weather S... Pages: 10 Size: 1,236 kb Paper DOI: 10.2495/AIR010581

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