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Chemical status (1) (A. V, 2.4.5)
Interpretation and presentation of groundwater chemical status (WFD - common position) aggregation of results of individual monitoring points for the GW-body as a whole the mean value of the results of monitoring at each point in the groundwater body or group of bodies shall be calculated; and the mean value of these calculations for all monitoring points in the groundwater body or group of bodies shall demonstrate compliance with those standards in the manner prescribed in the relevant Directive.
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Chemical status (1) (A. V, 2.4.5)
Interpretation and presentation of groundwater chemical status (WFD final) the mean values of the results of monitoring at each point in the groundwater body or group of bodies shall be calculated; and in accordance with Art. 17 these mean values shall be used to demonstrate compliance with good groundwater chemical status
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Algorithms for data aggregation
Procedure of aggregation 1 Temporal aggregation (within year and station) 2 Spatial aggregation (over all stations in GW body) by one of the following candidate methods arithmetic mean median (empirical) 70 %ile kriging mean upper confidence limit of the kriging mean maximum likelihood mean maximum likelihood 70 %ile kriging 70 %ile (added)
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Data sheet Results of spatial aggregation over all stations in GW body
for all methods under consideration for all stations where data where available, No examination of the monitoring network (locations and type of stations) was performed, and therefore the results presented cannot be considered validated. Purpose of the results is to illustrate the method, and no conclusions on trends and compliance with limit values can be made.
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Statistical parameters
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Interpretation of percentiles
Represent the concentration in the GW body which is not exceeded at more than 70% (50%, 90%) of the stations (of the area of the gw body) Do not reflect hot spots Do not reflect outliers
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Interpretation of mean values
Represent the average concentration of the stations in the gw body / in the area of the gw body Reflect hot spots Reflect outliers
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Interpretation of confidence limits
Null hypothesis H0: gw body is not in good status To be proven H1: gw body is in good status Test decision at significance level alpha=5% (1%, 0.2% resp.) not in good status if CL95% > limit value (CL99%, CL99.8%) in good status if CL95% < limit value (CL99%, CL99.8%) alpha denotes the probability of making a wrong decision for a good status (although the true, unknown mean exceeds the limit value); alpha could vary for different parameters.
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Classical approach Arithmetic mean _________________
= Mean at station s xts =measurement value, DL = determination limit (if not quantified), ns = number of measurement above DL, ps = number of measurements below DL, w = 0.5
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Classical approach Example: AT154 Ammonium 1999
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Problems of classical approach
No theoretically sound concept for the treatment of measurements below DL. Spatial correlation and inhomogenous distribution of stations are not accounted for.
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Maximum likelihood Method for the treatment of measurements below DL
Iterative calculation of log normal distribution parameters µ and by maximisation of Median = exp{µ} Mean = exp{µ+0.5²} = Median x exp{0.5²} 70-percentile = exp{µ+0.524} = Median x exp{0.524} If =3, then: Mean = x 70% percentile
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Maximum likelihood Example: FR001_FRg 1984 Ammonium
38 stations <0.1, 1 station <0.2 2 stations >DL (1.2 and 4.25)
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Maximum likelihood Example: FR001_FRg 1979 Ammonium
10 stations above DL 37 station below DL
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Maximum likelihood Example: NL002_NL00 1999 Nitrate 8 stations <DL,
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Maximum likelihood Corrects for bias caused by „less than“ measurements under the assumption of log normal distribution Pathological behavior in case of extremely large variability Statistical assumption of log normal distribution is crucial As the variability of data increases to infinity, ML 70 %ile is tending to 0 ML mean is tending to infinity
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Kriging The procedure of kriging
Calculate the exponential variogram (h)=a x exp(-|h|/b) Calculate the kriging matrix Calculate the predicted value at each point of the GW body The average of all predicted values is the kriging mean
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Kriging Example: PTM5_PT00 Nitrate 1995-1999 PT087_021
PT087_024 (1998 cancelled) 1-4.5 mg/l PT590_109 80 mg/l in 1995 40-55 mg/l in
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Kriging Example: PTM5_PT00 Nitrate 1995-1999
Mean 1995: 20.9 mg/l Kriging mean 1995: 28.3 mg/l Mean 1999: 22.3 mg/l Kriging mean 1999: 23.6 mg/l Reduction of arithmetic mean due to cancellation of low level site PT087_024
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Kriging Kriging mean Represents the average concentration in the area of the GW body Close to the arithmetic mean, if the distribution of stations is homogenous
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Kriging Upper confidence limit of the kriging mean 95%CL
Represents the upper confidence limit of the average concentration in the area of the GW body Close to the kriging mean for a very large number of stations
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Kriging Kriging 70 %ile Represents the 70 %ile of kriging surface + prediction error Close to the empirical 70 %ile in case of homogenous station distribution, high density of stations and symmetric distribution of measurements
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Kriging Kriging accounts for heterogenous distribution of stations
downweighting of station clusters upweighting of solitary stations Kriging accounts not for areas without any stations:
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Kriging Kriging without spatial correlation kriging mean = mean
kriging 95% CL = 95% CL of arithmetic mean Example: AT250_ tetrachloroethen Arithmetic Mean: 4.114 Kriging Mean:
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Kriging Data requirements for the software module
EXCEL tables representing the area of the gw body and the coordinates of the stations EXCEL table with the station mean values Output: Word file with tables
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Some minimum requirements
Measurements below DL: Effect of <DL measurements should be neglectable, i.e. the maximum effect should be below of eg. 10% of the mean level. 1. Calculate „mean_min“ with DL=0 2. Calculate „mean_max“ with DL=DL 3. If the difference is lower than 10%, the effect of the DL may be considered neglectable. Otherwise the good status of the gw body cannot be proven.
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Some minimum requirements
Distribution of stations: At least 10 stations (or more) Homogenous monitoring network (to be specified) In cases of changes of stations it is up to the monitoring network manager to proof that the changes do not bias the results (to be specified).
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Further procedure Check spatial correlation with variogram
Apply 95% CL of arithmetic mean or kriging 95% CL if necessary Compare results with 70, 80, 90, 95 %iles
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