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Outlining A New Method To Quantify Uncertainty In Nitrogen Critical Loads

Outlining A New Method To Quantify
Uncertainty In Nitrogen Critical Loads
William M. Briggs
New York City, NY
Jaap Hanekamp;
University College Roosevelt, Middelburg, the Netherlands
Environmental Health Sciences
University of Massachusetts, Amherst, MA, USA
March 22, 2021
We highlight deficiencies and improvements of a nitrogen critical
load model. An original model using logistic regression augmented
observations with fictitious data. We replace that with actual data,
and show how to incorporate uncertainty in nitrogen measurement
into the modeling process. In the end, however, we show a basic
logistic regression model has irremovable deficiencies, giving positive
probability of harmful effects of nitrogen even when no nitrogen is
Keywords: critical loads, logistic regression, nitrogen, uncertainty
1 Introduction
Nitrogen loads play a decisive role in environmental policies. Critical loads
(CL) for nitrogen are usually defined as follows: “A quantitative estimate of
an exposure to one or more pollutants below which significant harmful effects
on specified sensitive elements of the environment do not occur according to
present knowledge” [3].
As a means to fathom the actual modelling of CLs for nitrogen, we will
explore the methods of [1]. We will subsequently present an improvement of
their methods, and suggest new directions modeling should take.
Their method was as follows:
Collect data from a series of planned nitrogen experiments, in which
known amounts of nitrogen were added to background atmospheric amounts
placed on small plots. Various measures of growth of plant matter on these
plots were then measured. If the growth was higher on experimental than
control (no added nitrogen) plots, in a statistical sense, an adverse or harmful
effect was noted. The amounts added were then scaled from the small plots
up to the hectare.
They produced the following table:
Figure 1: Table 2 from [1]. The explanation of the columns is in the text.
The “Reference” points to the papers from which the experimental data
was extracted, and where the experiments were performed at the “Location.”
The “Effect” was whether the nitrogen-added plots had greater statistical
plant growth (1) than the control plots, or not (0), as noted in the references.
The “First level of N deposition where effect was observed (kg ha1yr1)”
is the amount of nitrogen scaled up from the small-plot experiments, and
“Lowest estimated background deposition (kg ha1yr1)” was also gleaned
from the references.
After reviewing the papers referenced, there is room for different interpre-
tations of the statistical results, leading to values different to those presented
in the Table. For example, Banin [1] used the lowest background nitrogen
levels, but a good case can be made to pick the average value observed. How-
ever, for the purposes of our simple demonstration, we take all values here
as they are presented in the Table.
The next step was to estimate a function at which a known amount of
nitrogen, in (kg ha1yr1), corresponded to a probability of an harmful
effect. The level of 20% was picked as a threshold requiring action. A
standard logistic regression was picked for this function.
Banin used only the nitrogen-added data in the model data and not the
background levels per se. This turns out to be a crucial point. Since there was
only one instance of an effect = 0 in the added nitrogen column, the logistic
regression’s parameters could not be estimated (this is a standard statistical
limitation). To overcome this difficulty, the authors added 90 zeros to both
the effect and levels of added nitrogen. This represents a sort of pseudo data.
In other words, the authors padded the 19 data points with 90 fictitious
observations of nitrogen = 0, and 90 fictitious observations of effect = 0.
The authors gave no justification for the number of fictitious data points
used (why not 80? why not 100?). As for adding the fictitious data itself, they
surmised that no nitrogen would incur no defined harmful effect of nitrogen,
which is surely true.
2 Suggested Modeling Approach
The data need not be padded with zeros. In place of the pseudo data, the
observed background levels could be instead, which are actual measures and
associated with effect = 0 (no harmful effects due to nitrogen).
The substitution of the fictitious zeros with the actual background rates
represents the first point of departure from our new proposed method with
The second is to account for the uncertainty in the measures themselves.
This arises in two ways.
First, in the references themselves, the nitrogen values are given not as
certain values, but values with a plus-and-minus attached, or with standard
deviations or other statistical measures of variability (usually because of vari-
ability in the background measurements). We intend to use this variability,
though since we do not yet have a complete survey of all references (those
from the Table plus quite a few others), we do not know what the variability
is for all entries in the Table.
Merely for demonstration purposes, we take the square root of the Table
nitrogen entries as representing the standad deviation (square root of the
variance). This is because in the data we have collected so far, this is a
reasonable if imperfect approximation. For example, a mean of 20.6 kg ha1
yr1(the first entry) is assigned a standard deviation of 4.54 kg ha1yr1.
Again, we stress this approximation is only for the purposes of illustration.
Second, to derive the correct variances from the reported data in the
references, we need to account for the scaling of the plot-sized nitrogen values
to the hectare. This scaling induces variability that must be accounted for.
This is simply because we can’t be certain the amounts added to a square-
meter plot exactly scales up a hectare, which are 10,000 times larger. Here
we use the approximation that the amounts of nitrogen can be represented
with normal distributions.
The amounts of nitrogen added were in g m2, but with times of experi-
ments not yet noted. Converting to kg ha1yr1amounts to a factor of 10,
as long as we assume the time of the experiments is the same, which it likely
was not for each. We have yet to explore this, but will in future efforts. In
any case, given the plot-sized variance is vp, it is easy to figure the variance of
the hectare-scaled data, which is vh= 102vp, a standard calculation. Using
that on a few entries of the table gives rise to the square-root approximation
mentioned earlier.
Here in Fig. 1 are the methods of [1] using added zeros (0-padding) com-
pared to a second logistic regression using the background low rates instead
(all with effect = 0). The variability in measurement is not yet accounted
for here. The 0-padded data is in black, and the background-added-data is
in green. This plot represents the central estimate of the logistic regression
in a thick line, and the uncertainty due to the parameter estimation in thin
lines; i.e. the 95% confidence intervals. A horizontal line at 20% is overlaid.
0 10 20 30 40 50 60 70
0.0 0.2 0.4 0.6 0.8 1.0
Parametric Uncertainty of Nitrogen Critical Loads
Nitrogen kg ha1yr1
Pr (Effect = 1 | data)
Original; 0−padded
With lows no padding
Figure 2: Parametric uncertainty of nitrogen critical load uncertainty, using
0-padded (black) and background low levels (green), with central estimates
(thick lines) and 95% confidence intervals (thin lines).
The 0-padded estimate crosses the 20% threshold at about 7 kg ha1yr1
of N, with a range of about 2 to 11 kg ha1yr1N. The background-level
data central estimate begins above 20%, with a range of 0 to about 9 kg ha1
yr1N. This means that, even with background levels, and with no nitrogen
whatsoever, there is an estimated greater than 20% chance of an harmful
effect due to nitrogen.
This is, of course, not possible. Obviously, the answer is not nearly enough
data is available, or that different interpretations can be given to the presently
measured data, or in inadequacies of the model form itself. We think all
explanations are partly true. However, none of these ideas are explored in
this paper.
In any case, it is clear something has gone wrong with a model that gives
positive probability of nitrogen having an ill effect at 0 levels of nitrogen. This
result is also found (but not noted) in [1], as there was a definite positive
probability of an ill effect with 0 nitrogen in the 0-padded data. I.e., the
black line at 0 kg ha1yr1N is about 5% in their Fig. 2 (not shown here).
0 10 20 30 40 50 60 70
0.0 0.2 0.4 0.6 0.8 1.0
Predictive Uncertainty of Nitrogen Critical Loads
Nitrogen kg ha1yr1
Pr (Effect = 1 | data)
Original; 0−padded
With lows no padding
Figure 3: Predictive uncertainty of nitrogen critical load uncertainty, using 0-
padded (black) and background low levels (green). This shows the probability
of an effect with a given level of N.
Passing over these impossibilities, the next step is to account for the
uncertainty in the parameter estimates, presenting the curves in a predictive
way instead. This is pictured in Fig. 3.
This plot gives direct statements of Pr(Effect|N level, model, data) (the
condition is shown as just “data” in the plots). This represents a Bayesian
approach to the model, showing the predictive posterior distributions of the
model, see [2]. This is a more actionable form than the standarad parametric
uncertainty displays, because it’s never clear what to with the confidence
interval. Here, once a level of nitrogen is specified, direct probabilities are
given, with no ambiguity in interpretation.
In any case, the story is the same. The level at which the threshold is
crossed for the 0-padded data is about 5 kg ha1yr1N. And again, even
with 0 nitrogen in the atmosphere, there is a greater than 20% of nitrogen
causing an effect with the background-data model.
Even though it is by now clear the data, or the model or both, have diffi-
culties, we present a picture of how to incorporate measurement uncertainty
to the model, using the variance approximation mentioned above. In other
words, the regression model now makes use of the plus-or-minus attached to
each observation. This is pictured in Fig. 4
0 10 20 30 40 50 60 70
0.0 0.2 0.4 0.6 0.8 1.0
Parametric Mean Uncertainty of Nitrogen Critical Load With Error
Nitrogen kg ha1yr1
Pr (Effect = 1 | data)
Ordinary logistic
Uncertainty logistic
Figure 4: Measurement uncertainty added model.
Here we ignore the 0-padded data (which has no uncertainties). Because
of the difficulties mentioned, and because our variance estimates are only
approximations, it is the shape that is important here, and not the exact
values, which are only an approximation. The green lines are the ordinary
logistic regression with confidence interval, using the background low data.
The red line is the model expanded to allow uncertainty in the measurements.
This model is choppier than the green because there is a great increase
in the number of parameters due to the measurement uncertainties, which
makes estimates a bit more difficult to make. In any case, it is clear there are
many changes in the final model, compared against the ordinary, uncertainty-
free model. These changes will very likely be present in the actual data, once
it is compiled.
[1] L. Banin, B. Bealey, R. Smith, M. Sutton, C. Campbell, and N. Dise.
Quantifying uncertainty in critical loads. Technical report, CEH Report
to SEPA, 2014.
[2] W. M. Briggs. Uncertainty: The Soul of Probability, Modeling & Statis-
tics. Springer, New York, 2016.
[3] J. Nilsson and P. Grennfelt. Critical loads for sulphur and nitrogen. report
from a workshop held at Skokloster, Sweden, 19-24 mar 1988. Technical
report, Nordisk Ministerraad, 1988.
... An approach to modeling nitrogen critical loads was given in, 5 which we reviewed in. 6 Briefly, in 5 data was gathered either from papers detailing planned small-scale (time and space) nitrogen-added experiments or in papers detailing large-scale observations. The data from each paper was comprised of a mix of plant growth, plant chemistry and nitrogen-uptake measurements. ...
Full-text available
Nitrogen Critical Loads (NCL), as purported ecological dose-response outcomes for nitrogen deposition from anthropogenic sources, play a central role in environmental policies around the world. In the Netherlands, these NCL are used to assess, via calculations using the model AERIUS, to what extent NCL are exceeded for different habitats as a result of different sources such as industry, agriculture, traffic. NCL are, however, not well defined, and are subject to hitherto unrecognized forms of uncertainty. We will address this with reference to a number of key studies that forms the basis for several NCL. We will subsequently propose amendments that could be applicable to future nitrogen studies and their enhanced relevancy in decision making.
A group of Nordic experts has tried to draw conclusions on critical loads for sulphur and nitrogen. The critical load is defined as “The highest load that will not cause chemical changes leading to long-term harmful effects on most sensitive ecological systems”. Most soils, shallow groundwaters and surface waters would probably not be significantly changed by a load of 10–20 keq H+·km2·yr−1 in areas with a low content of base cations in the deposition. The total deposition of hydrogen ions in southwestern Scandinavia is in the order of 100 keq ·km−2·yr−1. The long-term critical load for nitrogen is in the range of 10–20 kg N·ha·1-yr−1 in most forest ecosystems. In high productive sites it might be as high as 20–45 kg N·ha− yr−1 in southern Sweden, and amounts to 30–40 kg·ha−1·yr−1 and even more over large areas in central Europe. The current deposition of sulphur and nitrogen must be substantially reduced to keep the long-term changes in sensitive ecosystems within acceptable limits.
Quantifying uncertainty in critical loads
  • L Banin
  • B Bealey
  • R Smith
  • M Sutton
  • C Campbell
  • N Dise
L. Banin, B. Bealey, R. Smith, M. Sutton, C. Campbell, and N. Dise. Quantifying uncertainty in critical loads. Technical report, CEH Report to SEPA, 2014.
Uncertainty: The Soul of Probability, Modeling & Statistics
  • W M Briggs
W. M. Briggs. Uncertainty: The Soul of Probability, Modeling & Statistics. Springer, New York, 2016.