Review of the 43 mg/l threshold for
groundwater NVZ assessment
Report Reference: UC11460.01
February 2016
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Review of the 43 mg/l threshold for
groundwater NVZ assessment
Authors:
Robert Davison
Date:
February 2016
Report Reference:
UC11460.01
Project Manager:
Rob Stapleton
Project No.:
16426-2
Client:
Environment Agency
Client Manager:
Alwyn Hart
Graduate Scientist
Catchment Management
Andrew Davey
Senior Statistician
Catchment Management
Document History
Version
Purpose
Issued by
number
Quality Checks
Date
Approved by
V1
Draft report issued to EA for comment.
Andrew Davey
Rob Stapleton
26/02/16
V2
Final report issued to EA
Andrew Davey
Rob Stapleton
29/02/16
© Environment Agency 2016
The contents of this document are subject to copyright and all rights are reserved. No part of this document may be
reproduced, stored in a retrieval system or transmitted, in any form or by any means electronic, mechanical,
photocopying, recording or otherwise, without the prior written consent of Environment Agency.
This document has been produced by WRc plc.
Contents
Summary .................................................................................................................................. 1
1.
Introduction .................................................................................................................. 4
1.1
Background ................................................................................................................. 4
1.2
Aim and Objectives ..................................................................................................... 6
2.
Methodology and Results ............................................................................................ 7
2.1
What is an appropriate universal adjustment factor for the 2017
Review? ....................................................................................................................... 7
2.2
How are the 2017 risk assessment results influenced by using an
adjustment factor of 50/43 instead of 50/45? ............................................................ 10
2.3
At which monitoring points might a universal adjustment factor not be
appropriate? .............................................................................................................. 12
2.4
How would the 2017 risk assessment results be altered by the use of
site-specific adjustment factors? ............................................................................... 17
3.
Conclusions ............................................................................................................... 22
3.1
Summary of findings ................................................................................................. 22
3.2
Conclusions ............................................................................................................... 23
References ............................................................................................................................. 24
List of Tables
Table 2.1
th
Estimate of the 95 percentile and 90% lower confidence
limit (LCL) with 12 samples and an RSD of 0.17 ...................................... 9
List of Figures
Figure 1.1
Illustration of the adjustment factor used to convert an
estimate of the mean concentration into an estimate of the
th
lower 90% confidence limit on the 95
percentile
concentration ............................................................................................. 5
Figure 2.1
Relationship between mean and standard deviation of TIN
concentrations at 5,423 groundwater monitoring points ........................... 7
Figure 2.2
Influence of number of samples on RSD .................................................. 8
Figure 2.3
Location of groundwater monitoring points that pass the 50
mg NO3/l test using an adjustment factor of 50/45 but fail
using a factor of 50/43 ............................................................................. 11
Figure 2.4
Size of adjustment factor required to estimate the 95
percentile LCL at monitoring sites with low (a) and high (b)
variability in nitrate concentrations over time .......................................... 13
Figure 2.5
Size of adjustment factor required to estimate the 95
percentile LCL at monitoring sites with low (a) and high (b)
number of nitrate concentrations measurements over time .................... 14
Figure 2.6
Histogram of RSD values at 4,182 monitoring points used in
adjustment factor analysis. 288 monitoring points have RSD
> 0.960..................................................................................................... 15
Figure 2.7
Histogram of number of samples at 4,209 monitoring points
used in adjustment factor analysis. 261 monitoing points
have > 150 samples ................................................................................ 15
Figure 2.8
The effect of different sample numbers and RSD values,
which represent variability in the data, on the ratio between
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the lower confidence limit on the 95 percentile and the
mean ........................................................................................................ 16
Figure 2.9
Histogram of adjustment factors calculated for 4,458
monitoring points. 12 monitoring points have adjustment
factors >2.000 .......................................................................................... 17
Figure 2.10
Location of monitoring points that pass using a universal
50/43 adjustment factor but fail using a site-specific
adjustment factor ..................................................................................... 19
Figure 2.11
Location of monitoring points that fail when using a universal
50/43 adjustment factor but pass using a site-specific
adjustment factor ..................................................................................... 20
Figure 2.12
Comparison of 95 percentile LCL concentration estimates
at monitoring points using a universal 50/43 or site-specific
adjustment factor ..................................................................................... 21
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Environment Agency
Summary
i
Background
In reviewing its implementation of the Nitrate Directive in England, the Environment Agency
th
uses the 95 percentile as the primary statistic to measure nitrate concentrations in
groundwater. However, most groundwater sampling points lack sufficient number of samples
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to be able to reliably estimate the 95 percentile using the Environment Agency‟s usual
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statistical tools. Since the mean is always lower than the 95 percentile, an adjustment factor
is used to convert the central estimate of the mean concentration into an estimate of the 95
th
percentile lower confidence limit concentration.
The aim of this study was to understand the implications of choosing to use a universal
(nationwide) adjustment factor of 50/43 for the 2017 NVZ Review.
ii
Objectives
The specific objectives of this study were:
1.
to use up-to-date monitoring data to determine an appropriate universal adjustment
factor;
2.
to quantify the influence on the risk assessment results of using an adjustment factor of
50/43 instead of 50/45;
3.
to identify monitoring points where a universal adjustment factor may not be
appropriate; and
4.
to examine how the 2017 risk assessment results would be altered by the use of sitespecific adjustment factors.
iii
Results
This re-analysis of the 43 mg/l rule using monitoring data up to 2014 produced results very
similar to those from the 2013 NVZ review. At a typical monitoring point, the ratio of the 95
th
th
percentile to the mean was 1.30 and the ratio of the 95 percentile lower confidence limit
(LCL) to the mean was 1.18. When the LCL exactly equals the 50mg NO3/l threshold, the
mean concentration is expected to be 42.4 mg/l. Despite the inclusion of a significant number
of new monitoring points since the 2013 review, patterns of variability in nitrate concentrations
are similar to those reported previously, so the adjustment factor required for a typical
monitoring point is only marginally lower than that recommended at the time of the 2013
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Review. This updated analysis therefore validates the choice of 50/43 as the adjustment
factor used in the 2017 NVZ Review
Defra‟s choice to opt to apply a 50/43 adjustment factor in the 2017 Review, instead of a
50/45 adjustment factor as in the 2013 Review, was found to have relatively little influence on
the groundwater risk assessment at a national level. At 80 monitoring points, where nitrate
concentrations were close to the 50 mg NO3/l threshold, the use of a larger adjustment factor
led to a higher risk score being applied to the monitoring lines of evidence. At eight monitoring
locations this could have strengthened the case for designating new NVZs, but only if the
monitoring evidence was critical to the overall risk score reaching the level required for
designation.
A universal adjustment factor of 50/43 was found to provide a reasonable basis for converting
a mean concentration to an estimate of the 95th percentile LCL at most groundwater
monitoring points. However, the use of a lower adjustment factor could be justified at a small
proportion of points with unusually stable water quality, and a higher adjustment factor might
be justified for points with unusually high variability.
The use of a site-specific adjustment factor was found to alter the outcome of the nitrate
pollution test for 2.9% of groundwater monitoring points that had unusually variable or stable
water quality, of which 0.3% were located outside existing NVZ designations. Most of the
points where the results were sensitive to the choice of adjustment factor were borderline
points where nitrate concentrations were close to the 50 mg NO 3/l threshold. Overall, it
appears that the use of a universal adjustment factor produces a reasonable assessment of
nitrate pollution risk in the vast majority of cases, and that the use of a site-specific adjustment
factor would have only a limited, localised impact on groundwater risk scores.
iv
Conclusions
At a national level, it appears that the risk scores from the monitoring lines of evidence used
in the 2017 NVZ Review are relatively insensitive to the choice of adjustment factor. There are
some local situations where the application of a different adjustment factor would have altered
the outcome of the pollution test and hence the risk score, but further investigation would be
required to determine whether or not these changes would have had a material impact on the
decision to designate.
Whilst the use of site-specific adjustment factors would account for local patterns in water
quality variability, there remain some practical difficulties in implementing such an approach.
To put the findings from this study in context, it is useful to note that the uncertainty regarding
the most appropriate adjustment factor to use is small relative to the uncertainty that arises
from extrapolating historic trends to estimate present day and future nitrate concentrations.
Furthermore, the subsequent use of kriging has the effect of smoothing out some local
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anomalies in the groundwater monitoring data, and the reduction of this information to a
simple 0, 1 or 2 risk score further mitigates the effect of decisions about what adjustment
factor to use.
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1.
Introduction
1.1
Background
The Nitrates Directive (91/676/EEC) is intended to protect waters against nitrate pollution from
agricultural sources. Member States are required to identify waters which are or could
become polluted by nitrates and to designate these waters and all land draining to them as
Nitrate Vulnerable Zones (NVZs). Farmers in designated areas must follow an Action
Programme to reduce pollution from agricultural sources of nitrate. The criteria for identifying
waters as polluted are established in the Directive, which also sets out monitoring
requirements. NVZ designations must be reviewed at least every four years.
The Directive sets the following criteria for identifying polluted waters:

Surface freshwaters which contain or could contain, if preventative action is not taken
1
(i.e. Action Programme measures), more than 50 mg NO3/l nitrate.

Groundwater which contains or could contain, if preventative action is not taken, more
than 50 mg NO3/l nitrate.
th
The 95 percentile is the primary statistic used to measure nitrate concentrations in both
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surface waters and groundwater. The degree of certainty in the 95 percentile is quantified by
constructing a 90% confidence interval around the central estimate. If the lower confidence
limit (LCL) exceeds 50 mg NO3/l then one can be at least 95% confident that the water at that
monitoring point is polluted. This provides a comparable level of protection for groundwater to
that achieved by the statistical test for surface waters.
Unlike surface waters, however, most groundwater sampling points lack sufficient number of
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samples to be able to reliably estimate the 95 percentile using the Environment Agency‟s
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usual statistical tools. Since the mean is always lower than the 95 percentile, an adjustment
factor is used to convert the central estimate of the mean concentration into an estimate of the
th
95 percentile LCL concentration (Figure 1.1). In this way, results for all monitoring points can
be compared to a consistent threshold concentration of 50 mg NO3/l, and analysed together
when kriging.
1
50 mg/l nitrate as NO3 is equivalent to 11.29 mg/l nitrate as N
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Figure 1.1
Illustration of the adjustment factor used to convert an estimate of the
th
mean concentration into an estimate of the lower 90% confidence limit on the 95
Nitrate concentration
percentile concentration
Adjustment
factor
Upper90%CL
95th percentile
lower90%CL
Mean
Time
data
During the 2008 Review, mean concentrations were adjusted by a factor of 50/45 or 1.111 to
th
estimate the 95 percentile LCL (Defra 2008). The choice of 45 mg NO3/l was informed by an
analysis which showed that the mean concentration was on average 5 mg NO3/l lower than
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the 95 percentile LCL for monitoring points with large numbers of samples. Using this
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method, testing whether the 95 percentile LCL exceeds 50 mg NO3/l is equivalent to testing
whether the mean concentration exceeds 45 mg NO 3/l. For this reason, this adjustment
became known as the “45 mg/l rule”.
During the 2013 Review, a re-analysis of the 45 mg/l rule using up-to-date monitoring data
recommended that a conversion factor of 50/43 (or 1.163) would be appropriate (Environment
Agency 2012). However, Defra‟s Methodology Review Group opted to stick with a conversion
factor of 50/45 (or 1.111) for consistency with the 2008 methodology. Sensitivity analysis
indicated that the proportion of monitoring points failing the pollution test was similar using
1.111 or 1.163.
For the 2017 Review, Defra opted to accept the recommendation of the 2013 Review and
adopt a conversion factor of 50/43 (the so-called “43 mg/l rule”). The central estimate of the
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mean concentration was therefore multiplied by 1.163 to convert it into an estimate of the 95
percentile LCL concentration (WRc 2015). If this adjusted value exceeded 50 mg NO3/l the
monitoring point was deemed to have failed the pollution test with high (95%) confidence. This
was equivalent to testing whether or not the mean concentration exceeded 43 mg NO3/l (or
9.71 mg N/l).
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1.2
Aim and Objectives
The aim of this study was to understand the implications of choosing to use a universal
(nationwide) adjustment factor of 50/43 for the 2017 NVZ Review.
The specific objectives were:
1.
to use up-to-date monitoring data to determine an appropriate universal adjustment
factor;
2.
to quantify the influence on the risk assessment results of using an adjustment factor of
50/43 instead of 50/45;
3.
to identify monitoring points where a universal adjustment factor may not be
appropriate; and
4.
to examine how the 2017 risk assessment results would be altered by the use of sitespecific adjustment factors.
These four objectives are addressed in turn in Sections 2.1 to 0, and Chapter 3 draws some
general conclusions.
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2.
Methodology and Results
2.1
What is an appropriate universal adjustment factor for the 2017 Review?
As illustrated in Figure 2.4, the size of the adjustment factor required to convert a mean
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concentration into a 95 percentile LCL depends upon how variable nitrate concentrations are
over time. To determine an appropriate universal adjustment factor, we therefore need to
understand the level of variability at a typical groundwater monitoring point.
Nitrate monitoring data from 5,426 monitoring points across England was collated from the
Environment Agency and water companies (see WRc 2015 for details). The monitoring period
of the data was 1980-2014. Three monitoring points were excluded from this analysis owing
to anomalous mean and standard deviation values (Site IDs 25828100MI, 25828110MI and
60912250MI).
Following the approach used at the time of the 2013 Review, the degree of temporal
variability in TIN concentration at each the remaining 5,423 monitoring points was quantified
by dividing the standard deviation of the measured concentrations by the mean to calculate a
relative standard deviation (RSD). Plotting the standard deviations against the means showed
that points with higher mean TIN concentrations also tended to have more variable water
quality (Figure 2.1). The slope of a linear regression model fitted through the origin was then
used as an estimate of the RSD at a typical groundwater monitoring point.
Figure 2.1
Relationship between mean and standard deviation of TIN concentrations
at 5,423 groundwater monitoring points
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Exploratory analysis showed that standard deviation estimates were less reliable for
monitoring points with fewer water quality samples. We therefore explored how the „typical
RSD‟ value (i.e. the slope of the regression line) changed as the regression analysis was
progressively restricted to a subset of monitoring points with a greater minimum number of
samples. Figure 2.2 shows how the typical RSD value decreases from ca. 0.24 when all 5,423
points are included in the analysis, to a more stable value of ca. 0.17 when only points with
150+ samples are included. Based on this analysis, we took 0.17 to be a robust estimate of
the RSD at a typical groundwater monitoring point. Coincidently, this was the same as the
RSD value derived from an analysis of 1980-2009 monitoring data for the 2013 NVZ Review.
Figure 2.2
Influence of number of samples on RSD
Assuming that the SD is directly proportional to the mean with a RSD of 0.17, we calculated
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by how much the mean will under-estimate the 95 percentile LCL over a range of nitrate
concentrations. Further assuming that nitrate concentrations at each monitoring point follow a
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log-normal distribution, we estimated the 95 percentile parametrically and computed its 90%
LCL (Ellis 1989). Setting the number of samples at 12, for consistency with previous reviews,
gave the results in Table 2.1. Almost identical results were obtained assuming a Normal
distribution.
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Table 2.1
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Estimate of the 95 percentile and 90% lower confidence limit (LCL) for a
‘typical’ monitoring point with 12 samples and an RSD of 0.17
Ratio of
Ratio of
n
Mean
SD
RSD
95%ile
95%ile
to mean
LCL
LCL to
mean
12
2.00
0.34
0.17
2.60
1.30
2.35
1.18
12
4.00
0.68
0.17
5.21
1.30
4.70
1.18
12
6.00
1.02
0.17
7.81
1.30
7.06
1.18
12
8.00
1.36
0.17
10.41
1.30
9.41
1.18
12
10.00
1.70
0.17
13.01
1.30
11.76
1.18
12
12.00
2.04
0.17
15.62
1.30
14.11
1.18
12
14.00
2.38
0.17
18.22
1.30
16.46
1.18
12
16.00
2.72
0.17
20.82
1.30
18.82
1.18
12
18.00
3.06
0.17
23.42
1.30
21.17
1.18
12
20.00
3.40
0.17
26.03
1.30
23.52
1.18
12
22.00
3.74
0.17
28.63
1.30
25.87
1.18
12
24.00
4.08
0.17
31.23
1.30
28.22
1.18
12
26.00
4.42
0.17
33.83
1.30
30.58
1.18
12
28.00
4.76
0.17
36.44
1.30
32.93
1.18
12
30.00
5.10
0.17
39.04
1.30
35.28
1.18
12
32.00
5.44
0.17
41.64
1.30
37.63
1.18
12
34.00
5.78
0.17
44.25
1.30
39.99
1.18
12
36.00
6.12
0.17
46.85
1.30
42.34
1.18
12
38.00
6.46
0.17
49.45
1.30
44.69
1.18
12
40.00
6.80
0.17
52.05
1.30
47.04
1.18
12
42.00
7.14
0.17
54.66
1.30
49.39
1.18
12
44.00
7.48
0.17
57.26
1.30
51.75
1.18
12
46.00
7.82
0.17
59.86
1.30
54.10
1.18
12
48.00
8.16
0.17
62.46
1.30
56.45
1.18
12
50.00
8.50
0.17
65.07
1.30
58.80
1.18
The key points to note from Table 2.1 are:
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The ratio of the 95 percentile to the mean is a constant (1.30); likewise, the ratio of the LCL
to the mean is always 1.18.
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
When the LCL exactly equals the 50 mg/l threshold, the mean concentration is
expected to be 42.4 mg/l.
These findings are influenced by, but not unduly sensitive to, the value chosen for n. For
example, if n=27 (the median number of samples in the current dataset) instead of n=12:

The ratio of the 95 percentile to the mean is still 1.30, but the ratio of the LCL to the
mean is 1.21.

When the LCL exactly equals the 50 mg/l threshold, the mean concentration is
th
expected to be 41.3 mg/l.
In conclusion, this re-analysis of the 43 mg/l rule using groundwater monitoring data up to
2014 has produced results very similar to those from the 2013 Review. At a typical monitoring
th
th
point, the ratio of the 95 percentile to the mean was 1.30 and the ratio of the 95 percentile
lower confidence limit (LCL) to the mean was 1.18. When the LCL exactly equals the 50mg
NO3/l threshold, the mean concentration is expected to be 42.4 mg/l. Despite the inclusion of
a significant number of new monitoring points since the 2013 Review, patterns of variability in
nitrate concentrations are similar to those reported previously, so the adjustment factor
required for a typical monitoring point is only marginally lower than that recommended at the
time of the 2013 Review. This updated analysis therefore validates the choice of 50/43 as the
adjustment factor used in the 2017 NVZ Review.
2.2
How are the 2017 risk assessment results influenced by using an
adjustment factor of 50/43 instead of 50/45?
Of the 5,426 monitoring points across England analysed for the 2017 Review, 953 had
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sufficient data to estimate directly the 95 percentile concentration. Of the remaining 4,473
monitoring points that required the use of an adjustment factor, we excluded three because
they had anomalous mean and standard deviation values, leaving 4,470 for further analysis.
For each monitoring point, we multiplied the present day mean concentration by a factor of
th
50/45 to estimate the 95 percentile LCL and compared this to the LCL used in the 2017
Review based upon a factor of 50/43. Monitoring points at which the LCL exceeded the 50 mg
NO3/l threshold were classed as having failed the nitrate pollution test.
Of the 4,470 monitoring points, 3,550 (79.42%) passed using both the 50/45 and 50/43
adjustment factors and 840 sites (18.79%) failed in both cases. At only 80 points (1.79%) did
the choice of adjustment factor affect the pass/fail result, and all of these were borderline sites
where present day nitrate concentrations were close to the 50 mg NO3/l threshold. Since
50/43 is a larger adjustment factor, the use of the ‟43 mg/l rule‟ causes some points to fail the
pollution test that would have passed had a factor of 50/45 been used instead.
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Figure 2.3 shows the location of these 80 points in relation to current (2013) NVZ boundaries.
A total of 72 are located within a surface water, groundwater or eutrophic water NVZ, and
eight are located outside (5GWQ0173SO, 6022GW02SW, 6024GW02SW, 8048GW03SW,
88006840NW, 88021973NW, F0011634SO and PGWU0657TH).
In conclusion, Defra‟s choice to apply a 50/43 adjustment factor in the 2017 Review, instead
of a 50/45 adjustment factor as in the 2013 Review, had relatively little influence on the
groundwater risk assessment at a national level. At 80 monitoring points, where nitrate
concentrations were close to the 50 mg NO3/l threshold, the use of a larger adjustment factor
led to a higher risk score being applied to the monitoring lines of evidence. At eight monitoring
locations, this could have strengthened the case for designating new NVZs, but only if the
monitoring evidence was critical to the overall risk score reaching the level required for
designation.
Figure 2.3
Location of groundwater monitoring points that pass the 50 mg NO3/l test
using an adjustment factor of 50/45 but fail using a factor of 50/43
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2.3
At which monitoring points might a universal adjustment factor not be
appropriate?
In the 2008, 2013 and 2017 NVZ Reviews, a universal adjustment factor was applied to all
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groundwater monitoring points where there was insufficient data to estimate directly the 95
percentile nitrate concentration.
One criticism of this approach is that it provides a variable level of protection to groundwaters
depending upon the variability in nitrate concentrations observed at a monitoring point over
time and the number of water quality samples taken. The use of a universal adjustment factor
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assumes that the mean concentration will always under-estimate the 95 percentile LCL by a
constant amount. In practice, the mean will under-estimate the LCL by a greater amount, and
therefore a larger adjustment factor will be needed, at:

monitoring points where nitrate concentrations show greater variability through time
(Figure 2.4); and

monitoring points that have a greater number of water quality samples (Figure 2.5).
Thus, the use of a universal adjustment factor is appropriate for a „typical‟ monitoring point
that has an average level of variability in measured nitrate concentrations and a moderate
number of samples, but may be less appropriate for others.
Figure 2.6 shows the distribution of RSD values at groundwater monitoring points that used
an adjustment factor in the 2017 Review. The majority of points had RSDs in the range 0.10 –
0.30, but with a third showing unusually low or high variability. Closer inspection of the data
reveals that very high RSD values are usually due to one or more outliers in the dataset,
which causes the level of variability to be over-estimated.
Similarly, Figure 2.7 shows the distribution of sample numbers values. All monitoring points
analysed in the 2017 review had a minimum of 6 samples, the median number was 27, and a
quarter had more than 40 samples in total.
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Figure 2.4
Size of adjustment factor required to estimate the 95 percentile LCL at
monitoring sites with low (a) and high (b) variability in nitrate concentrations over time
Nitrate concentration
(a)
Adjustment
factor
Upper90%CL
95th percentile
lower90%CL
Mean
Time
data
Nitrate concentration
(b)
Adjustment
factor
Upper90%CL
95th percentile
lower90%CL
Mean
Time
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Figure 2.5
Size of adjustment factor required to estimate the 95 percentile LCL at
monitoring sites with low (a) and high (b) number of nitrate concentrations
measurements over time
Nitrate concentration
(a)
Adjustment
factor
Upper90%CL
95th percentile
lower90%CL
Mean
Time
data
Nitrate concentration
(b)
Adjustment
factor
Upper90%CL
95th percentile
lower90%CL
Mean
Time
© Environment Agency 2016
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data
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Figure 2.6
Histogram of RSD values at 4,182 monitoring points used in adjustment
factor analysis. 288 monitoring points have RSD > 0.960
Figure 2.7
Histogram of number of samples at 4,209 monitoring points used in
adjustment factor analysis. 261 monitoing points have > 150 samples
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To explore how water quality variability and sample size interact to affect the size of the
th
adjustment factor needed, we computed the ratio of the 95 percentile LCL to the mean for a
range of RSD values and three sample sizes (12, 27 and 100). Figure 2.8 shows that the
LCL:mean ratio, and therefore the size of the adjustment factor required, increases as water
quality becomes more variable. Sample size has relatively little effect, except at high RSD
values.
Figure 2.8
The effect of different sample numbers and RSD values, which represent
variability in the data, on the ratio between the lower confidence limit on the 95
th
percentile and the mean
Compared to a „typical monitoring point with a RSD of 0.17 and 12 water quality samples,
which requires an adjustment factor of ca. 1.18, 80% of monitoring points are calculated to
require an adjustment factor between 1.06 and 1.59 (Figure 2.9).
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Figure 2.9
Histogram of adjustment factors calculated for 4,458 monitoring points.
12 monitoring points have adjustment factors >2.000
In conclusion, a universal adjustment factor of 50/43 provides a reasonable basis for
th
converting a mean concentration to an estimate of the 95 percentile LCL at most
groundwater monitoring points. However, the use of a lower adjustment factor could be
justified at a small proportion of points with unusually stable water quality, and a higher
adjustment factor might be better for points with unusually high variability.
2.4
How would the 2017 risk assessment results be altered by the use of
site-specific adjustment factors?
From Section 2.3, it is evident that the level of variability in nitrate concentrations, and to a
lesser extent the number of water quality samples, influences the extent to which the mean
th
concentration under-estimates the 95 percentile LCL. Using this information, it is therefore
possible to derive a site-specific adjustment factor that overcomes the limitations of a
universal adjustment factor.
For each of the 4,470 monitoring points where the 95
th
percentile could not be estimated
directly, we calculated a site-specific adjustment factor based upon the RSD of the measured
TIN concentrations. We opted not to take into account the total number of samples at each
monitoring point because, in practice, the concentration that we are interested in is not the
th
mean over the entire 1980-2014 period, but the „present-day‟ 95 percentile concentration in
2015. Recent samples have greater influence on the present day estimate than older
samples, so the effective sample size is somewhat less than the total sample size. Since the
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number of samples has a limited effect on the LCL:mean ratio, except at sites with very high
variability (Figure 2.8), we chose to fix the sample size at 12 for every monitoring point.
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The site-specific adjustment factors were then applied to estimate the 95 percentile LCL at
each monitoring point. Points at which the LCL exceeded the 50 mg NO 3/l threshold were
classed as having failed the nitrate pollution test. These pass/fail results were then compared
with those from the 2017 Review, which used a universal 50/43 adjustment factor, to identify
where the use of a site-specific adjustment factor might make a material difference to the risk
assessment score.
Of the 4,470 monitoring points, 3,492 (78.1%) passed using both the site-specific and 50/43
adjustment factors, 847 points (19.0%) failed in both cases, and only 101 points (2.9%) give
conflicting results from the two tests.
Of these 101 points, 58 (1.3%) passed when the 50/43 adjustment factor was applied but
failed when a site-specific adjustment factor was used. 12 of these points (455F0158NE,
49400683NE, 49400830NE, 49701260MI, 5GWQ0641SO, 5GWQ0648SO, 6022GW07SW,
8048GW01SW, 88021531NW, C3205300SW, PGWU0817TH and PGWU1557TH) were
outside of current NVZs (Figure 2.10). At these locations, nitrate concentrations were
unusually variable, and consequently it is possible that the 2017 Review slightly underth
estimated the 95 percentile LCL concentration and under-estimated the nitrate pollution risk
in some areas not currently covered by NVZ designations (see Zone A in Figure 2.12). It is
also possible, however, that the site-specific adjustment factors were unduly influenced by
outliers in the monitoring data in some cases.
The other 73 sites (1.6%) with conflicting results failed when 50/43 adjustment factor was
used but passed when the site-specific factor was used. Only five of these sites
(49400686NE, 5GWQ0173SO, 6024GW02SW, 88006840NW and 88006840NW) were
currently outside of NVZs (Figure 2.11) .At these locations, nitrate concentrations were
unusually stable, and consequently it is possible that the 2017 Review slightly over-estimated
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the 95 percentile LCL concentration and over-estimated the nitrate pollution risk in some
areas not currently covered by NVZ designations (see Zone B in Figure 2.12).
In conclusion, the use of a site-specific adjustment factor in place of the universal 50/43
adjustment factor was found to alter the outcome of the nitrate pollution test for 2.9% of
groundwater monitoring points that had unusually variable or stable water quality, of which
0.3% were located outside existing NVZ designations. Most of the points where the results
were sensitive to the choice of adjustment factor were borderline points where nitrate
concentrations were close to the 50 mg NO 3/l threshold (Figure 2.12). Overall, it appears that
the use of a universal adjustment factor produces a reasonable assessment of nitrate
pollution risk in the vast majority of cases, and that the use of a site-specific adjustment factor
would have only a limited, localised impact on groundwater risk scores.
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Figure 2.10 Location of monitoring points that pass using a universal 50/43
adjustment factor but fail using a site-specific adjustment factor
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Figure 2.11
Location of monitoring points that fail when using a universal
50/43 adjustment factor but pass using a site-specific adjustment factor
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Figure 2.12
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Comparison of 95 percentile LCL concentration estimates at monitoring
points using a universal 50/43 or site-specific adjustment factor
A
B
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3.
Conclusions
3.1
Summary of findings
The first objective of this study was to use up-to-date monitoring data to determine an
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appropriate universal adjustment factor. At a typical monitoring point, the ratio of the 95
th
percentile to the mean was 1.30 and the ratio of the 95 percentile lower confidence limit
(LCL) to the mean was 1.18. When the LCL exactly equals the 50mg NO 3/l threshold, the
mean concentration is expected to be 42.4 mg/l. This re-analysis of the 43 mg/l rule using
monitoring data up to 2014 has produced results very similar to those from the 2013 NVZ
review. Despite the inclusion of a significant number of new monitoring points since the 2013
review, patterns of variability in nitrate concentrations are similar to those reported previously,
so the adjustment factor required for a typical monitoring point is only marginally lower than
that recommended at the time of the 2013 Review. This updated analysis therefore validates
the choice of 50/43 as the adjustment factor used in the 2017 NVZ Review
The second objective was to quantify the influence on the risk assessment results of using an
adjustment factor of 50/43 instead of 50/45. Defra‟s choice to opt to apply a 50/43 adjustment
factor in the 2017 Review, instead of a 50/45 adjustment factor as in the 2013 Review, was
found to have relatively little influence on the groundwater risk assessment at a national level.
At 80 monitoring points, where nitrate concentrations were close to the 50 mg NO 3/l threshold,
the use of a larger adjustment factor led to a higher risk score being applied to the monitoring
lines of evidence. At eight monitoring locations this could have strengthened the case for
designating new NVZs, but only if the monitoring evidence was critical to the overall risk score
reaching the level required for designation.
The third objective was to identify monitoring points where a universal adjustment factor may
not be appropriate. A universal adjustment factor of 50/43 was found to provide a reasonable
basis for converting a mean concentration to an estimate of the 95th percentile LCL at most
groundwater monitoring points. However, the use of a lower adjustment factor could be
justified at a small proportion of points with unusually stable water quality, and a higher
adjustment factor might be justified for points with unusually high variability.
The final objective was to examine how the 2017 risk assessment results would be altered by
the use of site-specific adjustment factors. The use of a site-specific adjustment factor was
found to alter the outcome of the nitrate pollution test for 2.9% of groundwater monitoring
points that had unusually variable or stable water quality, of which 0.3% were located outside
existing NVZ designations. Most of the points where the results were sensitive to the choice of
adjustment factor were borderline points where nitrate concentrations were close to the 50 mg
NO3/l threshold. Overall, it appears that the use of a universal adjustment factor produces a
reasonable assessment of nitrate pollution risk in the vast majority of cases, and that the use
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of a site-specific adjustment factor would have only a limited, localised impact on groundwater
risk scores.
3.2
Conclusions
At a national level, it appears that the risk scores from the monitoring lines of evidence used
in the 2017 NVZ Review are relatively insensitive to the choice of adjustment factor. There are
some local situations where the application of a different adjustment factor would have altered
the outcome of the pollution test and hence the risk score, but further investigation would be
required to determine whether or not these changes would have had a material impact on the
decision to designate.
Whilst the use of site-specific adjustment factors would account for local patterns in water
quality variability, there remain some practical difficulties in implementing such an approach.
First, the presence of outliers can cause the relative standard deviation to be over-estimated,
and lead to an excessively large adjustment factor being used. Second, there is no clear
theoretical basis for determining the effective sample size used to estimate present day nitrate
concentrations at each monitoring point.
To put the findings from this study in context, it is useful to note that the uncertainty regarding
the most appropriate adjustment factor to use is small relative to the uncertainty that arises
from extrapolating historic trends to estimate present day and future nitrate concentrations.
Furthermore, the subsequent use of kriging has the effect of smoothing out some local
anomalies in the groundwater monitoring data, and the reduction of this information to a
simple 0, 1 or 2 risk score further mitigates the effect of decisions about what adjustment
factor to use.
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References
Defra (2008) Description of the methodology applied in identifying waters and designating Nitrate
Vulnerable Zones in England.
Ellis (1989) Handbook on the design and interpretation of monitoring programmes. WRc Report NS29.
Environment Agency (2012) Method Statement for Nitrate Vulnerable Zone review – Groundwaters
Environment Agency report to Defra and Welsh Government – supporting paper for the Implementation
of the Nitrates Directive 2013 – 2016 April 2012.
WRc (2015) Statistical Methods for Nitrate Vulnerable Zone Review 2017. WRc Report UC10943.07 to
the Environment Agency, November 2015.
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