Application of nonparametric trend analysis to concentration time series
<p>Groundwater contamination resulted from chemical releases related to anthropogenic activity often proves to be a persistent feature of the affected groundwater regime.&#160; The affected volume (i.e. where the concentration of hazardous substances exceeds a certain threshold) is a complex and dynamic entity commonly called &#8220;contaminant plume&#8221;.&#160; The plume can be described as a spatially dependent concentration pattern with temporal behavior.&#160; Persistent plumes are regularly monitored, concentration data gained by repeated sampling of monitoring points and laboratory analyses of the samples are used to assess the actual state of the plume.&#160; The change of the concentrations at certain points of the plume facilitates the assessment of the temporal behavior of the plume.&#160; Repeated sampling of the monitoring points provides concentration time series.</p><p>Concentration time series are evaluated for trends.&#160; Methods include parametric (regression using least squares) and non-parametric methods.&#160; Mann-Kendall statistic is a commonly used, well known non parametric method.</p><p>When using Mann-Kendall statistics consecutive concentration data are compared to each other, their cumulative relation defines Mann-Kendall statistic &#8216;S&#8217;.&#160; However, when comparing concentration data laboratory uncertainties are usually neglected.&#160; Allowing for laboratory uncertainties, rises the question of what concentrations are considered equal, less or more than other concentrations.&#160; In addition aggravating concentration data will change the previous equal &#8211; more - less status of two concentrations, thus changing the Mann-Kendall statistics value, which sometimes results in differences in trend significance.</p>