Examensarbete i Hållbar Utveckling 80
Relation between Contemporary Water
Chemistry and Historical pH from
Paleolimnology to Estimate Reference
Conditions in Swedish Lakes
Relation between Contemporary Water
Chemistry and Historical pH from
Paleolimnology to Estimate Reference
Conditions in Swedish Lakes
- Development of a simple tool for acidification assessment
Ivonne González
- Development of a simple tool for acidification assessment
Ivonne González
Uppsala University, Department of Earth Sciences
Master Thesis E, in Sustainable Development, 30 credits
Printed at Department of Earth Sciences,
Geotryckeriet, Uppsala University, Uppsala, 2012.
Master’s Thesis
E, 30 credits
Examensarbete i Hållbar Utveckling 80
Relation between Contemporary Water
Chemistry and Historical pH from
Paleolimnology to Estimate Reference
Conditions in Swedish Lakes
- Development of a simple tool for acidification assessment
Ivonne González
Acknowledgements
First of all, I would like to thank my parents Jairo and Xiomara, and my sister Julie for
encouraging me with my academic studies and supporting me to study abroad. My dog
Tomas had to spend the past two years without someone to take care of him like I do.
Without them I just wouldn’t be here.
Second, I would like to thank my theacher and supervisor Jens Fölster for always being
supportive and taking time for explanations, suggestions and corrections. He is a truly patient
man.
Third, I would like to thank Salar Valina for always being welcoming and taking time to
explain and discuss.
Fourth, the Swedish inhabitants for subsidizing this education.
Last but not least, my friends in Sweden who make my live more interesting, and supported
me, especially Alex Campbell for language corrections, and my friends back home for always
encourage me by saying YOU CAN DO IT Voncito !!!
¡GRACIAS TOTALES!
ii
Relation between contemporary water chemistry and
historical pH from paleolimnology to estimate reference
conditions in Swedish lakes - Development of a simple tool
for acidification assessment
IVONNE GONZÁLEZ
González, I., 2012: Relation between contemporary water chemistry and historical pH from
Paleolimnology to estimate reference conditions in Swedish lakes – Development of a simple tool for
acidification assessment. Master thesis in Sustainable Development at Uppsala University, 49 pp, 30
ECTS/hp
Abstract: Acidification was categorized as the main environmental problem in Scandinavia during the
1960’s. Fortunately for Sweden, as with other countries in Scandinavia, has shown a process of
recovery from acidification, by emission control. Further Sweden has had an extended liming program
to mitigate the effects from acidification. Regarding the acidification assessment of the EU Water
Framework Directive, it requires that EU members attain a ‘good ecological status’ in their surface
waters. The status is defined as a deviation from a reference value and this is achieved using a
reference reflecting a preindustrial state. As the waters are recovering from acidification the liming
program can be reduced. Hence, there is a need to develop tools that can be used for decisions to
stop liming in single waters. This study states three approaches: the well known hydrochemical model
MAGIC, a meta-MAGIC model which calibrates the reference value computed by MAGIC model and
paleolimnology studies. The latter is the key because it is the one that measures the reference value
by immediate samples insitu. However, all methods have advantages and disadvantages, which
allowed the development of an additional tool called meta-paleo for the same purpose. This metapaleo model is designed based on an 11 years mean of contemporary water chemistry and
paleolimnology data of 71 lakes. This tool for acidification assessment enables to work with few
parameters of water chemistry. However it is concluded that the model has uncertainties, which
should be evaluated so it can be used as a tool for decision making.
Keywords: Water Framework Directive, Sustainable development, reference conditions, good
ecological status, paleolimnology, acidification assessment.
Ivonne González, Department of Earth Sciences, Uppsala University, Villavägen 16, SE- 752 36
Uppsala, Sweden
Supervisor: Jens Fölster
Swedish University of Agricultural Science Department of water and environmental
assessment
Evaluator: Salar Valina
Swedish University of Agricultural Science Department of water and environmental
assessment
Examiner: Malgorzata Moczydlowska-Vidal
Uppsala University
Department of Earth Sciences
iii
Relation between contemporary water chemistry and
historical pH from paleolimnology to estimate reference
conditions in Swedish lakes - Development of a simple tool
for acidification assessment
IVONNE GONZÁLEZ
González, I., 2012: Relation between contemporary water chemistry and historical pH from
Paleolimnology to estimate reference conditions in Swedish lakes – Development of a simple tool for
acidification assessment. Master thesis in Sustainable Development at Uppsala University, 49 pp, 30
ECTS/hp
Summary: Acidification of surface waters caused by air pollution from human activities, most notably
in the form of sulphur and nitrogen oxides was considered as the major environmental impact in
Scandinavia during the 1960’s. Consequently, two solutions to counteract this problem were
established. First, the Convention on Long Range Transboundary Air pollution (CLRTAP) was signed
by European countries, which aimed to decrease emissions and resulted in a process of recovery in
surface waters. The other solution was to apply lime in surface waters, in order to increase the water
pH. This caused controversy in the past because of the existence of natural acidity in northern
Sweden.
For the past decade Sweden implemented the Water Framework directive, and one of its
requirements is to reach good ecological status (GES) in surface waters by 2015. For this purpose the
application of lime continues. This GES requires surface waters to be close to an undisturbed state,
with minor deviations accepted. The undisturbed state is equivalent to a reference condition, which for
acidification is regularly defined as 1860. To fulfil this requirement and to calculate the change of pH or
acid neutralizing capacity (ANC), Sweden uses the hydrogeochemical model called MAGIC. This
model calculates the ANC, and from this parameter it is possible to calculate the pH. Also there is a
MAGIClibrary which matches similar lake parameters. Paleolimnological studies are also used to
reconstruct time series of pH. This study describes the advantages and disadvantages of the methods
as well as the need of a second opinion for expert judgement.
Also, the study developes a meta-model based on paleolimnological studies and the contemporary
physiochemical parameters called meta-paleo to predict the change of pH since pre-industrial time
during time. This reference dataset includes 71 reference lakes of the national monitoring programe.
The meta-paleo model uses partial least squares (PLS) and multiple linear regression (MLR) to
identify the most important variables to be included in the model. Consequently two possible models
were created. One based on statistical analysis (ST-model) and the other one based on the basic
knowledge of acidification (BK-model). Both models were tested in two datasets called MAGIC and
Omdrev, both datasets include the calculation of the change of pH by MAGIC model, which allows a
comparison of results of ST-model and BK models. As a result BK model seems to be better than STmodel; therefore it was chosen as the meta-paleo model. Furthermore the ecological status
classification was used to observe the classification of lakes as acidified or not. By comparing these
results it was decided that meta-paleo model should not be used as a second opinion for expert
judgement due to the bias of the model.
Finally the study also shows some ethical implications related to the reference condition and the goal
of reaching good ecological status.
Keywords: Water Framework Directive, Sustainable development, reference conditions, good
ecological status, paleolimnology, acidification assessment .
Ivonne González, Department of Earth Sciences, Uppsala University, Villavägen 16, SE- 752 36
Uppsala, Sweden
iv
Abbreviatons
ANC Acid neutralizing capacity
BDM Boreal dissolve model
BK-model Basic knowledge of acidification model
CLRTAP Convention on Long Range Transboundary Air pollution
DOC Dissolve organic carbon
EQR Ecological quality ratio
ES Ecological status
EU European Union
GES Good ecological status
MAGIC Model of acidification of groundwater in catchments
MLR Multivariate linear regression
PLS Partial least squares
PP Public participation
PRP Precautionary principle
RF Reference condition
ST-model Statistical analysis model
TOC Total organic carbon
WFD Water Framework Directive
v
Table of Contents
1 INTRODUCTION ..................................................................................................................... 1
1.1 THEORETICAL BACKGROUND – THE EUROPEAN UNION W ATER FRAMEWORK DIRECTIVE (WFD)
.............................................................................................................................................. 3
1.2 ...... PROBLEM BACKGROUND – REFERENCE CONDITION TO REACH ‘GOOD ECOLOGICAL STATUS’
.............................................................................................................................................. 4
1.3 PROBLEM – COMPLEXITY AND RELIABILITY OF DIFFERENT METHODS TO CALCULATE PH FOR
REFERENCE CONDITIONS ......................................................................................................... 6
1.4 AIM AND DELIMITATIONS ..................................................................................................... 8
1.4 OUTLINE ........................................................................................................................... 8
2 BACKGROUND ................................................................................................................10
2.1 DEFINITION AND HISTORY OF ACIDIFICATION, AND THE SWEDISH LIMING PROGRAM ...............10
2.2 SWEDISH IMPLEMENTATION OF THE WFD APPLIED FOR ECOLOGICAL STATUS .......................11
2.3 DEFINITON OF METHODS TO CALCULATE PRE-INDUSTRIAL ACIDIFICATION .............................12
3.1 ETHICAL IMPLICATIONS TO REACH A GOOD ECOLOGICAL STATUS FOR ACIDIFICATION .............13
4 METHOD...........................................................................................................................15
4.1 EMPIRICAL MONITORY DATA ..............................................................................................15
Lakes .............................................................................................................................15
Water chemistry .............................................................................................................15
Paleolimnological data ...................................................................................................16
MAGIC data ...................................................................................................................16
Geografical data ............................................................................................................16
4.1.1 LAKES DATA SELECTION .................................................................................................17
4.2 STATISTICAL ANALYSIS .....................................................................................................17
4.2.1 PLS .......................................................................................................................17
4.2.2 MLR ......................................................................................................................18
a)
Stepwise linear regression ...................................................................................19
b)
Leverage ..............................................................................................................19
4.3 MODEL SELECTION ...........................................................................................................19
4.4 TESTING THE META-PALEO MODEL .....................................................................................20
4.5 CLASSIFICATION OF ECOLOGICAL STATUS (ES) ..................................................................20
5 RESULTS .........................................................................................................................21
5.1 SCANNING OF FACTORS CORRELATED TO ACIDIFICATION – PLS RESULTS ............................21
5.2 SELECTING PARAMETERS FOR THE MLR ............................................................................22
5.2.1 Stepwise regression to select the final candidates for the models .........................23
5.2.2 Two final models ...................................................................................................24
5.3 APPLYING ST-MODEL AND BK-MODEL TO MAGIC AND OMDREV DATABASES. ....................26
5.3.1 Testing the ST-model and BK-model on the lakes in the MAGIC database ...........26
5.3.2 Testing of the ST-model and BK-model in OMDREV database .............................31
5.5 APPLYING THE CLASSIFICATION OF ES TO PALEOLIMNOLOGY AND META-PALEO MODEL ........37
6 ANALYSIS AND DISCUSSION ........................................................................................38
6.1 META-PALEO MODEL AS A TOOL FOR ACIDIFICATION ASSESSMENT .......................................38
6.2 ACIDIFICATION, REFERENCE CONDITION AND ETHICAL IMPLICATIONS ....................................40
7 CONCLUSIONS ................................................................................................................43
BIBLIOGRAPHY ..................................................................................................................45
vi
List of tables
Table 1. Water chemistry data ..............................................................................................15
Table 2. Land use data .........................................................................................................16
Table 3. Classification of dpH to categorize acidification (SEPA, 2007). ...............................20
Table 4. Values of the components. .....................................................................................21
Table 5. Multivariate correlation Analysis..............................................................................23
Table 6. Stepwise fit for dpH. RMSE = root mean square error ............................................23
Table 7. Lis of variables for ST-model and BK-model. ..........................................................24
Table 8. Summary of fit of ST model ....................................................................................25
Table 9. Summary of fit for BK- model ..................................................................................26
Table 10. Sum(dpHst) = sum of negatives ∆pH for ST model. Sum(dpHbk) = sum of
negatives ∆pH for BK-model. Applied to the MAGIC dataset..........................................27
Table 11. Sum(dpHmp) = sum of negatives ∆pH for BK-model, includes new condition (sum
of variables equals to 5). Applied to MAGIC the dataset. ...............................................28
Table 12. Sum(dpHmp) = negatives ∆pH value, includes new condition (sum of variables
equals to 4). Applied to the MAGIC dataset. ..................................................................28
Table 13. ∆pH's means difference for MAGIC dataset. .........................................................31
Table 14. Comparison between MAGIC and meta-paleo ES for MAGIC dataset ..................31
Table 15. Sum(dpHst) = sum of negatives ∆pH for ST model. Sum(dpHmp) = sum of
negatives ∆pH for BK-model. Applied to the Omdrev dataset.........................................32
Table 16. Sum(dpHmp) = sum of negatives ∆pH for BK-model, includes new condition (sum
of variables equals to 5). Applied to the Omdrev dataset. ..............................................32
Table 17. Sum(dpHmp) = negatives ∆pH value, includes new condition (sum of variables
equals to 4). Applied to the Omdrev dataset. .................................................................33
Table 18. ∆pH's means difference for OMDREV dataset. .....................................................36
Table 19. Comparison between MAGIC and meta-paleo ES for OMDREV dataset. .............36
Table 20. Comparison between paleo and meta-paleo ecological status..............................37
List of equations
Equation 1. Ecological quality ratio ........................................................................................ 4
Equation 2. ANC concentration ............................................................................................16
Equation 3. ANC concentration with BC and anions .............................................................16
Equation 4. ∆pH ...................................................................................................................20
Equation 5. ∆pH paleo - water chemistry……………………..………………….……...............37
Equation 6. ∆pH meta-paleo - water chemistry .....................................................................37
vii
1 Introduction
The anthropocene is a concept that denotes the “period of anthropogenic global
environmental change”, which started with the Industrial revolution (Zalasiewicza, et al.,
2008). Despite the short geological time that humans have existed on Earth, their actions
have begun to influence entire ecosystems from last century (Zalasiewicz, et al., 2011).
Acidification of freshwater from long range transboundary pollution could be interpreted as a
contributor to the anthropocene. It was considered one of the significant environmental
impacts in surface waters in Scandinavia since 1960 (Norberg, et al., 2010). Acidification is
caused by sulphur (SOX) and nitrogen oxides (NOX) from emissions of various industrial
activities, fossil fuels combustion, and mining of metals (Warfvinge & Bertills, 2000). The
emission sources of this air pollution were traced indicating that they were coming mostly
from Eastern Europe, Germany and Great Britain (Henrikson & Brodin, 1995, Lundqvist,
2003, Norberg, et al., 2010). In Sweden, acidification began to be perceived in the aquatic
ecosystem during the mid-1960s with visible fish mortality in lakes and streams (Henriksen,
et al., 1992).
Acidification is also an issue of importance for sustainable development; it affects humans
and the economy because of the environmental management performance regarding
ecosystem services such as recreation, productive ecosystems and availability of food
(Henrikson & Brodin, 1995). Environmental impacts have considerable biological
consequences such as the reduction of biological taxa and ecosystem modification, which
results in an extensive magnitude and long time-scale impacts. Furthermore, secondary
effects can impact the aquatic ecosystem due to acid deposition, such as the mobilization of
inorganic aluminum leading to increased toxicity in the water bodies (Lawrence, et al., 2006).
In the future, climate change is likely to impact water chemistry, as there are projected
increases in the mobilization of organic matter (Institute of Environment and Sustainability ,
2006). This can be seen as an external influence on the internal systems of acidification,
both natural and by deposition and therefore climate change will cause a regime shift in
surface waters.
One viable solution to mitigate the impact of acidification in Sweden was to decrease sulphur
and nitrogen emissions. Thus, in 1979 the international negotiation so-called Convention on
Long Range Transboundary Air pollution (CLRTAP) was signed by 34 countries of Europe,
which aimed to decrease emissions. Since its inception, a series of protocols have been
created/published regarding this subject (Warfvinge & Bertills, 2000). Between 1980 and
1993 sulphur emissions decreased by 80%, but its effort was limited due to the effects of
other acidifying compounds such as NO3. Nowadays CLRTAP is ongoing with new emission
reductions commitment by 2020 for 27 countries of Europe (UNECE, 2012). Consequently,
emissions of sulphur decreased considerably in Europe and waters have started to recover
from acid deposition; this is noticed by trends of improved pH as well as Acid Neutralizing
Capacity (ANC) and decreased on sulfate concentrations (Jenkins, et al., 2003).
Other solutions implemented to remediate this problem include the application of lime in
water bodies with the purpose to increase lake water pH. The increase of pH aims to recover
ecosystems to a pre-acidification state. This measure was done at a cost of millions of
Swedish kronor (SEK) (Warfvinge & Bertills, 2000), and was initiated in 1970 with some
lakes located in southwestern Sweden. This region is important because the considerable
history of liming, and high investment costs. These lakes are naturally sensitive to
acidification, but also it is the region with high acid deposition concentration (Renberg, et al.,
1993). The liming moved from areas in southern Sweden that were exposed to high
acidifiying deposition to the northern parts where the deposition of acidiying compunds was
1
lower. However, it was found that northern Swedish lakes were acidic and this was assumed
to be due to acid deposition. Consequently the Swedish government started to finance
liming in the northern part of the country since 1991 (Bishop, et al., 2001). This solution
resulted in a debate in the scientific community since natural acidity in lakes exists especially
in the northern part of Sweden (Guhrén, et al., 2007).
Either the solution is a reduction of emissions or liming Keller, et al., 1998 showed that
restocking fish might help water body ecosystem restoration, since natural fish
recolonization is difficult and can take long time.
The problem of acidification became a controversy because it engages different
stakeholders/interests such as politicians, scientists, the public and nature itself. Politicians
bankroll public procurement services to apply lime in waters, civilians defend productive
ecosystems regarding fish stock as a public good and scientists disagreed about the natural
acidity in the north and the ecological implication of liming. Moreover, authorities like SEPA
base their decisions on scientific knowledge, but there is no consensus between scientists
on the appropriate measures to take to remedy acidification (Lundqvist, 2003). Around 208
million SEK are spent annually in liming (Naturvårdsverket , 2011).
For the past decade, Sweden has applied lime to accomplish certain environmental
requirements, such as reaching a ‘good ecological status’ (GES) of water bodies in line with
the Water Framework Directive (WFD, EC, 2011). Lakes are a component of the
environment and acidification is part of long-scale environmental impacts. The overall
management goal of the WFD is to reach good ecological status for water bodies by 2015.
This includes both biological and chemical considerations (EC, 2000).
To reach the GES, the WFD requires the water bodies to be close to the undisturbed state
(with minor deviations accepted). The undisturbed state equals a reference condition (RF)
wich for acidification is often defined as 1860 (SEPA, 2007). This RF is not only important for
the WFD but also for the CLRTAP concerning critical loads of acid deposition (UNECE,
2012). Nonetheless in the past decade a polemic has arisen between scientists in terms of
applying a RF in light of sustainability. Firstly, that there has been changes in land use
before, during and after industrialization. Secondly, that there has always been a dynamic
human-nature interaction; and thirdly, also, the WFD does not require public participation
(PP) for goal setting, such that local knowledge may not be fully included in the decision
making process (Valina, et al., 2012).
Despite the controversy over RF, Sweden has agreed to fulfill the requirements of the WFD.
To calculate a change in ANC Sweden uses the hydrogeochemical model called Model of
Acidification of Groundwater in Catchments (MAGIC) (Cosby, et al., 1985a,b,c; Cosby, et al.,
2001; Moldan, et al., 2004) and afterwards calculates the pH from the ANC. In addition
paleolimnological studies (Renberg, et al., 1993) are used to reconstruct time series of pH
(Erlandsson, et al., 2007). However, the two models have some disadvantages because it is
difficult and complecated to use them.
To simplify the procedure of determining pre-industrial ANC, a meta-model based on MAGIC
that relates MAGIC reconstructions of pre-industrial lake buffering capacity and current
parameters was developed (Erlandsson, et al., 2008). Since there are no measurements
from preindustrial time, the model results cannot be truelly validated and the results can be
questioned by stakeholders. Consequently, there is a need for alternative models to give a
“second opinion” for the stakeholders. The aim of this thesis is to make an additional model
that relates present data on physiochemical parmameters to paleolimnological data. To
accomplish this, statistical methods are applied, to data from 71 time series lakes within the
national monitoring program. This model is named meta-paleo.
2
1.1 Theoretical background – The European Union Water
Framework Directive (WFD)
The EU community policy on the environment bases its objectives for environmental
damage on the precaurionary principle and preventing action at source. Concerning the use
of natural resources, the objectives are based on preserving protecting and ameliorating the
environmental quality (EC, 2000). Relating this to water management, the WFD was
adopted in 2000 and establishes objectives for the future water protection regarding the
decrease and control pollution, while improving ecological quality in surface waters.
The European Commission, the European parliament, Non Governmental Organizations
(NGO’s), the regional authorities and water users can participate in an open consultation
process (EC, 2011). The WFD is a legislation that has its base on the doctrine of sustainable
water management policy concerning the EU member states. The main aim of the WFD
regarding surface waters is to reach ‘good ecological status’ at least 15 years after the
establishment of the Directive (EC, 2000).
As lakes are designated as surface waters, they are classified as one of the quality elements
for the classification of ecological status (ES). In general this classification is categorized in 5
different status: high, good, moderate and bad. Surface water status is “…determined by the
poorer of the ecological status and chemical status”. The ES “ is an expression of the quality
of the structure and functioning of aquatic ecosystems associeted with surface water” (EC,
2003, p.24)
The general definiton of ‘GES’ is: “The values of the biological quality elements for the
surface water body type show low levels of distortion resulting from human activity, but
deviate only slighly from those normally associated with the surface water body type under
undisturbed conditions” (EC, 2000; Annex V, p.45).
The RF for hydromorphological and physicochemical conditions is determined as the ‘high
ecological status’ and is stated as “There are no, or only minor, anthropogenic alterations to
the values of the physico-chemical and hydromophological quality elements for the surface
water body type from those normally associated with that type under undisturbed conditions.
The values of the biological quality elements for the surface water body reflect those
normally associated with that type under undisturbed conditions, and show no, or only very
minor, evidence of distortion. These are type-specific conditions and communities” (EC,
2000; Annex V, p.45).
The definition of RF in the WFD is quite vague and no direct indication of what a RF is. The
actual defienition of when an undisturbed state could occur is written in The Guidance
Document No.10 (EC, 2003) is: “High status or reference condition is a state in the present
or in the past corresponding to very low pressure, without the effect of major industralisation,
urbanisation and intensification of agriculture, and with only very minor modification of
physico-chemistry, hydromorphology and biology” (EC, 2003;p.29).
To determine the ES the assessment of biological elements, hydromorpholohical elements
and physico-chemical elements are required (EC, 2003). The pH or ANC value is the unit of
measure of acidification status which is considered under the category “chemical and
physicochemical elements supporting the biological elements” (EC, 2000; Annex V,1.1.2).
3
A more specific definition for ‘high ES’ (reference condition) for pH and/or ANC is “do not
show signs of anthropogenic disturbance and remain within the range normally associated
with undisturbed conditions” and for ‘GES’ for pH and/or ANC is “do not reach levels outside
the range established so as to ensure the functioning of the ecosystem and the achivement
of the values specified for the biological quality elements” (EC, 2000; Annex V, p.52).
This ES classification depend on the calculation of ecological quality ratios:
Equation 1. Ecological quality ratio
The RF does not represent an entirely natural state untouched by humans such as postglacial state, because it is unrealistic. Instead it should correspond completely, or almost
completely to an undisturbed condition (EC, 2003). The methods used to determine the RC
by the use of a reference value are based on (EC, 2003, p.31-32):





Reference conditions that use data from monitoring sites and are spatially based
Predictive modelling
Using either historical data or paleorecontruction
Finding sites without any anthropogenic disturbance
A combination of these methods
If it is not possible to apply these methods, then expert judgment must be exercised.
1.2 Problem background – reference condition to reach ‘good
ecological status’
The availability of reference values allows one to have an approximation of the magnitude
and the importance of the environmental impacts as a result of anthropogenic activities. The
pre-industrial pH is the RC to assess the impact caused by anthropogenic acidification in
waters bodies (Battarbee, et al., 2011), which is indispensable to calculate the requirements
established under the WFD.
This reference value is established based on the conditions before the industrial revolution,
as since then there has been an increase in social and economic capital at the expense of
environmental capital (Roseland, 2007). It has also created long-term environmental
impacts, which are difficult to perceive immediately. For instance, most of the acidification
discovered in water bodies in the 1960s in the Nordic countries is a negative consequence
of decades of industrial activity (Renberg, et al., 2009). Notably, the earliest impacts were in
the mid-to-late nineteenth century; supporting the idea that the mid-ninteenth century is a
good reference to accomplish the ‘GES’ in surface waters according to the WFD. A study
based on paleolimnology in Europe shows that there are few cases of acidification before
the mid-ninteenth century (Battarbee, et al., 2011).
4
Nevertheless the RC for acidification or the undisturbed state has been called into question
(Bishop, et al., 2009; Renberg, et al., 1993; Guhrén, et al., 2007; Renberg, et al., 2009;
Norberg et al., 2008). First, some studies explain the historical perspective of acidification in
Sweden and why some lakes are naturally acidified because of changes in land use after the
deglaciation period. Likewise an anthropogenic alkalization period was observed in some
lakes earlier than human influenced acidification (Renberg, et al., 1993; Renberg, et al.,
2009).
Second, a series of three published studies done by Guhrén, et al (2007); Norberg et al
(2008); Norberg, et al (2010) using paleolimnology in twelve limed lakes that were acidified,
resulted in only five of them being acidified by deposition. Additionally, between lakes they
show variability regarding the diatom assemblage, their fallout in minor shifts or the diatom
assemblage did not correspond to the time before anthropogenic acidification, or what was
found dating back a few thousand years. The authors assure that other studies based on
phytoplankton and diatom responses to liming are in the same position. Concerning fish,
benthic invertebrates and zooplankton population, these improved mostly in all lakes. They
also claim that the RC is not sustainable due to the fact that liming is done to achieve a good
status as established by the WFD. According to its definition it implies little or very few
alterations by human impacts in the biological and physicochemical characteristics of the
water bodies, which is unrealistic.
Third Valina, et al., (2012) showed the importance of PP, which can be used to help
assessing the RC and actions to improve the water quality. PP is not only important for the
description of GES and RC, but also useful for lake history, such as visible changes in the
water chemistry, fish population and landscape. In particular when scientific knowledge,
modeling and expert judgment have uncertainties.
In addition parameters as DOC (Dissolve Organic Carbon) affect acidification in the lake
environment. A study conducted in the UK using paleolimnology showed that the
relationship between the reductions in acid deposition is not linear concerning water acidity.
This is because deposition decreases the solubility and therefore increases the organic acid
concentrations. Also it claims that the RC changes from site to site (i.e. natural acidity), thus
the recovery has to be evaluated by explicit place characteristics (Battarbee, et al., 2008).
Moldan, et al., 2004 mentions the dependence of lakes characteristics based on their
catchments and soil characteristics; and found that reversal acidification in lakes only occurs
if it happens in soils first.
Renberg, et al., 2009 states that to establish right environmental goals it is important to know
about the atmospheric history pollution, and take into account that in Europe even in the
most isolated place doesn’t count with natural conditions.
On the other hand, the use of liming as a means to reach the WFD reference condition has
also been questioned. Some assert that it may create an unnatural pH that damages lake
ecosystems or produce negative consequences because of its natural acidity, mostly in
Northern Sweden (Bishop, et al., 2001).
In relation with the above, the application of Precautionary Principle (PRP) in the case of
increase the pH in Swedish lakes, Bishop (1997, p.57) stated that “…it is important to make
a difference between the application of the PRP to pollution control measures intended to
treat a cause of pollution and the application of PRP to remedial measures intended to treat
the symptom of pollution”. In addition he pointed out that the PRP in this case is a reason for
not using liming as an action due to uncertainties such as, if an ecosystem is acidified by air
pollution. Not only because liming involves environmental risks such as the destruction of
5
the acid-tolerant biota proper of their environment and geographical variation. Hence the
measure of fish population cannot be an indicator of environment restoration. But also the
destruction of the wetlands vegetation are consequences of environmental changes or
damages of an ecosystem through lime (Bishop, 1997).
However, acidity can be caused naturally by other mechanisms such as organic acids,
natural sulfide oxidation in coastal areas, and high CO2 pressure. Although these may be
natural, millions of SEK have been spent for this cause in northern Sweden (Warfvinge, et
al., 1995). For the period of 1983 until 2007 more than 300 million Euros were spent on the
liming program that covered 8000 lakes and 12’000 km of watercourses (Renberg, et al.,
2009). An additional study points out that modifications in fish population in Nordic countries
is mostly due to manipulation of fish stocks rather than environmental alterations like
acidification (Tammi, et al., 2003).
As shown, anthropogenic influences have been prevalent in countries with long history
record of agriculture and industrialization. This means humans have been dominating nature
for millennia because they are a part of the whole ecological system including all fauna and
flora, thus all interactions between organisms mold the environment (Renberg, et al., 2009).
Therefore long-term ecological records are essential for managing plans for conservation
purposes for example. It is not sustainable or wise to use short range of time of ecological
records because they can result in ineffective management plans (Willis & Birks, 2006).
However, from a philosophical basis some argue that these management plans are nothing
more than man re-dominating nature: ¨The ecologist is forced to treat nature as essentially
non-living, as a machine to be dissected, interpreted, and manipulated…” (Evernden, 1999,
p.20). Viewing the environment in this way leads one to believe that nature conservation can
be reduced to a management problem, with a focus on human needs through time and not
keeping the environment safe for its own sake (Sachs, 1999).
As a result a question that arises from all of these studies is: What is the purpose of reaching
a ‘good ecological status’? On one hand it implies that Sweden is complying with the WFD,
but to what end? On the other hand, Sweden needs to question whether it is doing it for the
sake of returning it to an undisturbed state, a state that is easier to manage, or a state that
benefits society or the economy most. These questions can be seen in the light of
environmental ethics, which helps to place human intervention in nature into the context of
sustainable development.
1.3 Problem – Complexity and reliability of different methods to
calculate pH for reference conditions
Despite the controversy over acidification; Sweden and other European countries have
agreed to fulfill the requirements of the WFD. Consequently, scientists used different
methods to calculate the reference value as accurately as possible. The different methods
used allows management practices such as critical loads and helps policy makers to take
decisions for emission reductions regarding the process of recovery (Cosby, et al., 2001;
Moldan, et al., 2004).
An array of methods exists to assess acidification, but the problem is that these methods
can be difficult and complicated to use. The methods used are: the MAGIC model, and
paleolimnology (SEPA, 2007). Simpler assessment tools like MAGIClibrary and metaMAGIC are also available for classification of acidification. Each one has advantages and
disadvantages that have prompted research according to the assessment of the reference
value for acidification.
6
For instance paleolimnology shows directly historical perspectives of pH (Guhrén, et al.,
2007) and it is the unique method capable of studying and supplying information about the
chemistry and biology of lakes and their catchments in long term-scale (Battarbee, et al.,
2008; Norberg, et al., 2008). In addition it provides information about the aquatic systems’
earlier natural conditions as well as the degree of human impacts (changes in habitats)
showing the relevance of long-term studies (Norberg, et al., 2008). It is applied to study
invasions, conservation assessment and biodiversity maintenance (Willis & Birks, 2006); and
it helps to guide comparison with other models to improve pH reconstructions (Battarbee, et
al., 2005). The issue of spatial scale from individual lake to regional lake calls into questions
using individual lakes as the unit of analysis is appropriate (Battarbee, et al., 2011).
There are uncertainties with the use of paleolimnology such as samples are affected by low
sedimentation accumulation rate. Sediments can be disturbed and/or mixing caused by
bioturbation or repeated sampling (Guhrén, et al., 2007) as well as fossil record and the
precision of chronological dating of the sediment core (Battarbee, et al., 2005). These
uncertaintes can make the data insufficient to be able to do a complete assessment if the
lakes were acidified and if liming has returned the pH to RC (Norberg, et al., 2008). Moreover
lakes only have one deep basin and benthic invertebrates, zooplankton and fish do not give
an unambiguous image of acidification (Norberg, et al., 2008); and diatoms are the average
populations that describe the whole lake and all seasons (Erlandsson, et al., 2007). Hence, it
might represent the annual mean of pH but doesn’t display the episodic acidification (Bishop
& Laudon, 2000).
In Sweden the MAGIC model is the model applied to predict RC with respect to pH and the
basis for the assessment tool MAGIClibrary (SEPA, 2007). Usually the model is applied for
both lakes and streams to simulate short-term periodic responses (Cosby, et al., 2001) and it
can be applied to many lakes with multiple scenarios (Moldan, et al., 2004). The model is
used to predict long-term effects in soils and water of acidification (Cosby, et al., 2001) and it
is able to simulate annual and monthly average concentrations of the main ions of the water
chemistry and soil chemistry because the model is based on mass balance, exchanges
between hydrogen ion-base cation (Moldan, et al., 2004; Cosby, et al., 1985c). Additionally it
takes into account the nitrogen and sulphur dynamics (Cosby, et al., 2001) and
improvements in the model have been made concerning the organic acid buffering and the
aluminum solubility (Cosby, et al., 2001). The complexity of the model is considered
intermediate (Cosby, et al., 2001). In the MAGIClibrary, lakes and streams without MAGIC
modeling can be assessed by finding the most alike lake or stream in a database with
MAGIC models (IVL Svenska Miljöinstitutet, 2011).
The MAGIC model has some disadvantages and uncertainties. First, small-scale variability in
data is hard to perceive (Moldan, et al., 2004), and it needs well-described processes and
accurate input data as it works at catchment scale (Cosby, et al., 2001; Erlandsson, et al.,
2007). Hence it is a complex dynamic model which requires modeling skills as well as large
set of input parameter which limits the number of lakes of study (Erlandsson, et al., 2008).
Second, as the model is based on mass balance, so it is better for predicting ANC that is
calculated from base cations and strong acid anions, rather than pH which is also affected by
organic carbon and carbon dioxide (Erlandsson, et al., 2007). Further the Aluminium
concentration affects pH (Erlandsson, et al., 2007; Moldan, et al., 2004). Thus, the model
tends to predict higher values for alkaline lakes and lower values for acid lakes (Moldan, et
al., 2004). Third it also requires necessary estimations like DOC and pCO2 which influence
model predictions. Finally for the Swedish Ecocological Quality Criteria (EQC), the model
was calibrated for the year 1997 and the hydrological conditions were assumed to be from
1961 to 1990 so the reference value corresponds only to an average of 30 years
(Erlandsson, et al., 2007). As a response to the complexity of MAGIC, besides MAGIClibrary,
7
another model called meta-MAGIC was developed in 2008 and is based on MAGIC. It does
not require too many input parameters and modeling skills as MAGIC does (Erlandsson, et
al., 2008), and short-term hydroclimatological change influences the model, enabling the
reference value to fluctuate. Therefore when there is no data and resources enough for the
MAGIC model it can give stable assessments of acidification. (Erlandsson, et al., 2008). But
obviusly it is dependant on the precision of MAGIC model. As well long period of present
water chemistry averaged results on a slow response considering the significant trends in the
water chemistry (Erlandsson, et al., 2008).
1.4 Aim and delimitations
The aim of this study is two-fold. The foremost aim is to develop a tool that calculates the
acidification of lakes change between present water chemistry and change in pH according
to paleolimnological studies. The tool should serve as a complement to the official tool based
on geochemical modeling (MAGIC) for expert judgment on single objects, especially when
deciding if liming can be stopped. The final aim is to relate the concepts behind the goal of
the WFD to reach ‘GES’ to sustainable development.
The study aims to address the following research questions:
 Why is it important to develop an additional model as a second opinion to calculate the
change of pH between present physiochemical parameters and change in pH since
preindustrial conditions according to Paleolimnology?
 Should the new meta-paleo model be use as an additional tool? This will be done by
evaluating the model on monitoring data and comparing it to the MAGIClibrary.
The model is based on 71 reference lakes with available data of water chemistry from 2000
to 2010, paleolimnological studies and land use in the catchment area in Sweden for the
construction of the predicted model (see figure 2). This model is applied to two databases.
One is the MAGIC-modelled lakes within the MAGIClibrary, called MAGIC dataset. The other
is the OMDREV, which covers limed and non-limed lakes within the National Monitoring
Program for a survey of random selected lakes. These databases don’t include any
paleolimmnology data, but the results are compared to results from the MAGIC model for the
MAGIC-dataset and from MAGIClibrary for the OMDREV dataset.
1.4 Outline
8
1 Intrduction
2 Background
3 Theoretical perspectives
4 Method
5 Results
6 Analysis and discussion
7 Conclusions
• This chapter presents the theoretical background, problem
background and the problem of the study. Also the aim, the
research questions and the delimitations of the study
• This chapter enclose the history of acidification, the Swedish
liming program, the Swedish implementation of the WFD and
the definition of the methods used for acidification assessment
• This chapter includes the theory of sustainable development
and some ethics conceps that influence the therms of
reference condition and the 'good ecological status'.
• This chapter briefly covers the description of the variables,
the statistical analysis, the model testing and the classification
of the ecological status
• In this chapter graphs and results of the model developed and
tested are found.
• This chapter helds the analysis and discussion based on the
theory, research questions and results.
• This chapter covers the conclusions and suggestions for
future studies regarding the analysis.
Figure 1. Thesis outline
9
2 Background
2.1 Definition and history of acidification, and the Swedish
liming program
Acidification in surface waters is defined as a change in pH caused by sulphur and nitrogen
deposition as a result of anthropogenic activities. The impact of airborne pollution and
forestry activities caused by the uptake of base citations (SEPA, 2007). Warfvinge & Bertills
(2000, p.9) define acidification as “acidifying pollutants disperse via the atmosphere and
return to the earth’s by deposition, in form of gases or airborne particles or dissolved in rain
or snow, far away from the emission sources.” The environment can be naturally acidic and
is characterized by low pH and oligotrophic conditions. The cause of this dates back to the
deglaciation at the end of the last ice age, 10’000 years ago (Imbrie & Palmer, 1979). When
the glaciers retreated, land was uncovered and had a high concentration of base cations.
The base cations were distributed locally depending on geological factors. Since then
surface soils were leached by hydrological cycle causing the formation of peats and
podsoils. A consequence of this was high rate of organic acids flowing into the lake from the
soil, whilst also decreasing the rate of bases (Renberg, et al., 1993). Also, natural acidity is
attributed to spring floods and dissolved organic acids as a result of chemical, physical and
biological processes thereby negatively influenced by acid deposition (Warfvinge & Bertills,
2000)
Renberg, et al. (1993) briefly and comprehensively describe the historical perspective of
acidification in lakes in Sweden, divided in four periods: (I)The natural long term acidification,
(II) The anthropogenic alkalization period caused by agriculture, forest burning and other
natural perturbations of the forest, (III) The recent acidification period discovered in the
1950’s and 1960’s, (IV) The liming period.
The last period is based on the Swedish liming program, and has caused controversy.
Liming was regarded as a large-scale solution to mitigate acidified surface water; it also
reduces concentrations of inorganic aluminum (which is toxic), as well as other metals such
as Hg, Pb, Zn and Cd. Usually fossil fuels combustion and mining release these metals that
comes with the atmospheric acid deposition (Lydersen & Löfgren, 2002).
Liming started in 1976, financed by government subsidies and is predicted to carry on for at
least 50 years. The compound that is used to neutralize acidify waters is limestone (CaCO3)
and the Swedish Environmental Protection Agency (SEPA) are responsible to conduct the
evaluation of programs for research and monitoring the effects of liming (Henrikson &
Brodin, 1995). Liming program covers 8000 lakes and 12’000 km of watercourses (Renberg,
et al., 2009).
According to Henrikson & Brodin (1995) liming improved water quality, biodiversity and is
considered good for recreation and fisheries. However it doesn’t rehabilitate the ecosystem
completely and, when the lime is spread on wetlands, they are deteriorated. Consequently
liming can only be seen as a preliminary measure until reductions have decreased.
To pursue long-term chemical and biological effects of liming, and to assess if the Swedish
liming program restores lake ecosystems to the pre-industrial period, SEPA initiated a
program called ISELAW (Integrated Studies of the Effects of Liming in Acidified Waters)
(Appelberg, et al., 1995). The beginning of the controversy of the Swedish liming program
was based on natural acidity, mostly in northern Sweden. Consequently some scientist
expressed their disagreement with it. For instance Bishop, et al., (2001) stated that liming
10
can creat unnatural pH levels, producing negative impacts in the natural acid lake
ecosystem. However, process of recovery caused by efforts in the emission reductions has
been succesful (Laudon & Bishop, 2002). Nowadays some experts are skeptical of liming not
only because it is an unnutural process of recovery, but also because the large and
unnecesary inversment that it requires in some parts of the country.
2.2 Swedish implementation of the WFD applied for ecological
status
The Swedish Environmental Protection Agency (SEPA) is the authority in charge for
applying the environmental quality standards. Therefore it has to consider the demands of
the WFD and employs the method of ES classification to illustrate the environmental data
collected (SEPA, 2007) (see table 6). For acidification assessment it is important to make
distinction between natural acidity and anthropogenic acidification. This is possible to do it
through MAGIC model, MAGIC library and episodic model Boreal Dilution Model (BDM).
“The acidification impact is classified as the deviation from a reference status calculated
using the dynamic geochemical model MAGIC” (SEPA, 2007, Annex A, p.120).
For classification status, the reference value for ANC is defined around 1860 corresponding
to pre-industrial conditions. For the calculation for the EQR a present observed value or a
value in time is compared with the reference value. This EQR or ANC change usually is
transformed to pH because the pH is more strongly connected to organisms sensitive to
acidification in Sweden rather than ANC (Fölster, et al., 2007). The data required for the
assessment with MAGIClibrary is power of hydrogen (pH), Total Organic Carbon (TOC) or
Dissolve Organic Carbon (DOC), Calcium (Ca), Chlorine (Cl), Magnesium (Mg) and Sulfate
(SO4) (SEPA, 2007).
The calculation of the reference condition is not only important to determine the ES, but it is
also used for the calculation of critical loads of acid deposition stated in the Convention of
Long range transboundary pollution (CLRTAP) (Erlandsson, et al., 2007). The MAGIC-model
primary calculates a reference value for ANC, but a pH-value can be calculated from ANC,
DOC and the pressure of CO2. Since the change in DOC and CO2 pressure since the
preindustrial conditions is not known, this will cause uncertainty in the assessment. The
importance of calculating the pH is because as it was mention above it is the best indicator
that is related to biota (Fölster, et al., 2007).
The impact of acidification on lakes results from a deviation from the reference value. Fölster
et al. (2007) and SEPA (2007) define ∆pH > 0.4 as the “threshold for acidification or the limit
for acidification” (Fölster, et al., 2007), meaning that the GES is yet to be attained.
The MAGIC model calculates the reference value for ANC and needs data of atmospheric
deposition, hydrology, mineralogy, land use, contemporary water chemistry including
Dissolved Organic Carbon (DOC) and pCO2. The model normally assumes steady state for
these last two parameters, but it was calibrated with 1997 contemporary data for 133 lakes
belonging to the national monitory program (Moldan, et al., 2004; Erlandsson, et al., 2007).
However, Erlandsson et al. (2007) compare results from MAGIC and paleolimnology
claiming that results can be similar on avergare, but errors can be large for single lakes
because of the natural variability of DOC and pCO2 over time; and more precisly
Erlandsson, et al. (2011) have shown the importance of the variability for TOC/DOC for the
reference pH and acidification assessment vis-à-vis the classification of lakes as acidified.
11
2.3 Definiton of methods to calculate pre-industrial acidification
There are diverse methods to measure the pre-industrial pH. In this paper only three of them
are explained. Following a short definition of them is stated.
 Paleolimnology:
It studies lakes history through their sediments. Sediments are made of airborne material
and diatom assemblage. It is important for acidification because they contain historical
information and it describes the ∆pH over time (Renberg, et al., 1993).
 MAGIC (model of acidification of groundwater in catchments):
Dynamic model based on mass balance (IVL Svenska Miljöinstitutet, 2011) usually
applied in Europe and North America during 15 years (Cosby, et al., 2001). MAGIC
requires as input data information based on hydrology, land use, catchments
characteristics, historic atmospheric deposition, mineralogy, runoff, volume of precipitation
and current water chemistry (Erlandsson, et al., 2007). It takes into account TOC and
pCO2 but as constant variables corresponding to 1990, which creates uncertainty
because these two variables change over time.
 MAGIClibrary:
Is a tool for acidification assessment and is based on MAGIC calculations for 2631 lakes
and 130 streams to predict the state of acidification of unlimed lakes and streams. The
tool selects the most alike water within the library according to some physiochemical
parameters and assumes that the change in pH is similar to that lake (IVL Svenska
Miljöinstitutet, 2011).
12
3 Theoretical perspectives
3.1 Ethical implications to reach a good ecological status for
acidification
Established policies and remediation measures frequently work in conjunction. For instance,
the RC to achieve a ‘GES’ and applying lime into the lakes imply that ethics must take into
account and involve sustainable development and precautionary principle (PRP)
approaches. Controversies arise when questions about what is natural, what is not, what is
good and what is bad are mentioned. On the other hand human interests and the focuses of
intrinsic values are questioned. Thus for reference values, one may ask for whom or for what
does this policy serve?
To analyze this, it is important to define some concepts that usually are implicated when it
comes to policies.
 Sustainable development: "is development that meets the needs of the present without
compromising the ability of future generations to meet their own needs" (WCED, 1987,
p.308).
 Inter-generational anthropocentrism: “only humans have moral standing, but that our
moral obligation are not limited to humans beings that are now alive; they include future
generations as well” (Stenmark, 2007, p.45).
“Resource is valuable only as it promotes humans good” (Koggel, 2006, p.264)
 Ecocentrism:”interaction and interdependence between human beings and all other
organisms in the system of nature; humans beings form an integrated part of nature”
(Stenmark, 2007, p.42).
 Naturalistic fallacy is derived from the ‘Is-ought Problem’ and involves an appeal to nature
as well as the definition of what good means in relation to natural properties.
o Is-ought problem: it is not always apparent how to derive a normative statement (what
ought to be) from a descriptive statement (what is). “[Hume] has been almost
universally read as asserting that there are two classes of assertion, factual and moral,
whose relationship is such that no set of factual promises can entail a moral
conclusion” (Maclntyre, 2002, p.166)
o Appeal to nature: “The naturalistic fallacy is a fallacy; the political and social questions
about what technologies to build and what transformations of the landscape to
countenance are political and social questions, it seems to us, and we want to reject
the naturalism that thinks it can find the answer to such questions by an appeal to an
asocial, pre-historical, apolitical nature—by appeal, that is, to an origin from which we
have strayed and to which we are called to return… because each appeal to nature as
independent of the social turns out upon analysis to possess its own social meaning
and its own historical pedigree, and hence cannot in truth achieve the origin it claims to
know” (Vogel, 1998, p.172).
o Definition of good: “ [G.E.] Moore takes it that the things which ought to exist for their
own sake are those which we call intrisically good. How do we know what is
intrinsically good? The answer is that we can not fail recognize the property of intrinsic
goodness when confronted with it…This is because good is the name of the simple
unanalyzable property, which Moore calls ‘non-natural’ because it cannot be identified
13
with any natural property. Moore holds that good is indifinable” (Maclntyre, 2002, p.
241-242).
 Precautionary principle (PRP): The PRP has been applied by the countries surrounding
the North Sea in order to promote sustainable development (Nordgren, 1997). PRP
“…asserts that parties should take measures to protect the public health and the
environment, even in the absence of clear, scientific evidence of harm. It provides for two
conditions. First, in the face of scienific uncertainties, parties should refrain from actions
that might harm the environment, and second, that the burden of proof for assuring the
safety of an action fall on those who propose it” (Raffensperger & Tickner, 1999, p.
preface xxiii). This principle is applied to take decisions regarding environmental studies.
Another way of describing it is: “…one should take action to avoid potentially damaging
impacts on nature even when there is no scientific evidence to prove a causal link
between emissions and effects” (Nordgren, 1997).
The difficulty of use the PRP for acidification can be exemplified; in the past Scandinavia
stated that the acidification of its lakes was caused by sulphur emissions from the British
power plants, and the British didn’t take any cost action because of the lack of knowledge
of cause-effect. Consequently it is not clear who should take remedial actions. Had Britain
agreed that they were the ones causing harm, they would have been responsible under
the PRP to take action (Deville & Harding, 1997).
14
4 Method
4.1 Empirical monitory data
The study included three sets of data: Reference lakes which were used for developing the
model, MAGIC-modelled lakes and OMDREV-lakes that were used to test the model.
Lakes
1) 71 eference lakes from the national monitoring of reference lakes. Since 1987 the water
chemistry has regularly been measured by the certified laboratory of the Department of
Aquatic Sciences and Assessment at the Swedish University of Agricultural Science
(SLU). The lakes were sampled 4 times a year. In this study, means of water chemistry
from 2000 to 2012 were used. This dataset also included data from MAGICmodelling,
Paleolimnological
data
and
geographical
data.
2) MAGIC-lakes
Lakes from the MAGIC library including 2324 lakes. The dataset includes present water
chemistry and results from MAGIC-modelling. (Set 1 is asubset of set 2).
3) OMDREV-lakes
Random selected lakes within the national lake survey program. The dataset included
present water chemistry and acidification assessments from MAGIC-library. (11% of the
lakes were affected by liming, and then the water chemistry was corrected).
Most of the lakes in sets 2 and 3 were only sampled once a year during autumn.
Water chemistry
Contemporary water chemistry dataset: The variables that are taking into account are:
Table 1. Water chemistry data
Name
Transparency
Temperture
Absorbance
pH
Alkalinity
Acid neutralizing capacity
Total organic carbon
Calcium
Chloride
Sulfate
Nitrate
Ammonium
Fluoride
Phosphate
Total phosphorus
Silicium
Iron
Manganese
Copper
Zinc
Aluminum
Cadmium
15
Abbreviation in dataset
Sik
T°
Abs_F
pH
Alk
ANC
TOC
Ca
Cl
SO4
NO3
NH4
Fl
PO4
Tot-P
Si
Fe
Mn
Cu
Zn
Al
Cd
Lead
Chromium
Nickel
Cobalt
Arsenic
Vanadium
Pb
Cr
Ni
Co
As
V
To calculate the ANC the following formula was used:
Acid neutralizing capacity (ANC):
Equation 2. ANC concentration
Equation 3. ANC concentration with BC and anions
BC = base cations
AN = strong acid anions
Paleolimnological data
Paleolimnological reconstruction of lakes pH through diatom assemblage composition
collected from lakes sediments at 30 cm depth. This corresponds to a sample age of 100 to
400 years. The study was done by the Department of Ecology and Environmental Science at
the University of Umea for 96 lakes and has an uncertainty of ± 0.3 pH units (Renberg, et al.,
1993). For the Southern data points, +0.3 was added to the pH to calibrate the values. The
best analogue (with the highest pH) was chosen between the northern value and the
calibrated southern value. Acidification as change in pH (∆pH) was used in this study.
MAGIC data
Acidification assessments from MAGIC-simulations as dpH were used in this study for set 2.
For set 3, assessments from MAGIC laibrary were used.
Geografical data
Land use for the catchments of the 71 reference lakes according to Corine Land Cover data
was used (Hagner, et al., 2005).
Table 2. Land use data
Name
Urban Area
Explored land
Green area
Agricultural land
Other farmed land
Grazing land
Deciduous forest
Coniferous forest
Mixed forest
Other vegetation
Open wetland
Deciduous on wetland
Coniferous wetland
Mixed forest wetland
Water
Abbreviation in dataset
UA
EL
GA
AL
OFL
GL
DF
CF
MF
OV
OW
DW
CW
MFW
W
16
4.1.1 Lakes data selection
For the present water chemistry eleven years mean (2000-2010) was used. This original
dataset had 112 lakes. The dataset was selected to assure that data of present water
chemistry, paleolimnology and land use data was present for each lake. This means that
lakes lacking one variable or more were excluded.
Furthermore the lakes that did not provide the compulsory information and those lakes that
had an increase in the pH larger than 0.3 units regarding the paleolimnological error
(Renberg, et al., 1993) were discriminated. Increases in pH more since preindustrial times
are assumed unrealistic and due to disturbance of the sediments or unknown changes in the
lake ecosystem not related to acidification. Further, lakes with a pH > 7 were excluded since
they are not likely to be acidified and not relevant for assessments of acidification. The
selection resulted in 71 lakes selected for the dataset.
Lakes within dataset 2 and 3 with a pH > 7 were excluded.
Figure 2. 71 Swedish reference lakes
4.2 Statistical Analysis
4.2.1 PLS
The aim of the use of this technique in this study was to identify the most important variables
that affect the ability of the model to predict the change in pH since the pre-industrial period.
17
This is done by Variable Importance in the Projection (VIP) analysis, which illustrates the
order of importance of the variables included in the analysis. Consequently it is considered
that variables with a VIP more than or equal to one are the significant variables due to the
fact that the sum of squares of all VIP’s is equal to the number of terms in the model thus the
VIP is equal to 1. Assuming that variables with large VIP values are the most relevant to
explain the modeled variable (Y) (Umetrics, 2008)
The PLS analysis is made by the software SIMCA-P+ 12.0.1. The analysis included
contemporary water chemistry, paleolimnological and land used data. No interaction terms
were analysed.
Partial least squares regression is a statistical technique that came to prominence in the late1960s. It is usually used to analyze a large range of associated predictor variables, with a
small sample size compared to the number of independent variables (Carrascal, et al.,
2009). Additionally it transforms the predictor variables into orthogonal components, which
solves to the problem of multi-collinearity as well as allowing a reduction of the
dimensionality of the space of predictor variables (Vega-Vilca & Guzmán, 2011).
This technique is normally used when the predictor variables are highly correlated (i.e
problems of multi-collinearity), when the number of observations falls behind or is
comparable to the number of predictor variables (i.e problems of overfitting) (Carrascal, et
al., 2009).
PLS consists of two main steps. First, it transforms the matrix of predictors X of order
with the help of the Y vector of order
into a latent variables not correlated or a matrix of
components,
of order
so-called PLS components. This contrasts to the
PCA (principal components analysis) where the components are obtained by using only the
X predictor matrix. Second, it computed the regression model estimated by using the original
responses vector and as predictors the components PLS (Vega-Vilca & Guzmán, 2011).
Based on the PLS-analysis, canditates for the final MLR-model were selected by regarding
that MLR does not allow strong intercorrelations between the independent variables (xvariables). This was done by correlation analysis (Pearsons) in the softwater JMP 8.
4.2.2 MLR
Multiple linear regression analysis is the prolongation of simple linear regression (SLR) but
with multiple explanatory variables (Helsel & Hirsch, 1995). The aim of this statistical
technique is to explain the variation observed between the response and the predictor
(explanatory) variables, dependent and independent respectively. They are measured with
standardized regression coefficients and represent the partial effects, which affect the
variability of the response variable. Due to these repeated forms of covariation, a cause
effect relationship can be derived. (Carrascal, et al., 2009).
There exists a virtually constant issue with MRL attributed to sensitive autocorrelation. This
happens when the data is assembled over time or assembled on a spatial basis. In the first
case, regarding the nature of residuals, there is often a stronger connection between errors
in time periods that are nearer than further apart, called serial correlation. The second case
of spatial autocorrelation occurs when no measure characteristics are added to the error
(Hoffmann, 2005).
18
a) Stepwise linear regression
Stepwise is a procedure that uses a series of t-test or partial F to assess the significance of a
variable. It also includes forward and backward directions/swaps between adding and
removing variables and testing the significance of individual variables within and outside the
model. After one step the most significant variables are entered into the model, but it is
possible that they will be removed later if they are determined to be insignificant (Helsel &
Hirsch, 1995; SAS Institute Inc., 2002). In this study only forward direction was used.
Two possibilities to develop the model resulted after applying this approach (figure 10). First
by the evaluation done by the statistical analysis. Second by taking into account the base
knowledge inherent to acidification (figures 11 & 12).
b) Leverage
Leverage statistic is the distance
between a given point X0 and the
center of the sample observations. It has two main uses in MRL; first it uses Simple linear
regression (SLR) to recognize non-conformities of the predictor variables, which indicate
possible errors or a poor model. Second, it use for developing predictive capacities. The
leverage value should not be greater than the biggest
of the original dataset. If
exceeds
the leverage value, then an extrapolation is required to go beyond the original dataset (Helsel
& Hirsch, 1995)
To understand whether an effect can be considered significant, it is important to view it from
the perspective of the hypothesis for the effect. The effect in a model is tested for
significance by comparing the sum of squared residuals to the sum of the squared residuals
of the model without the effect. In the case of residual errors, they are much smaller when
the effect is included in the model, showing that the effect significantly contributes to the
overall fit. Leverage plots demonstrate the residual for each point when the effect is taken
into account in the model, and when it isn’t (figures 11 & 12). The residual with the effect can
be determined by the distance from the point to the line of fit. The residual error without the
effect can be determined by the distance from the point to the horizontal line. Therefore,
comparing the residual errors from the line of fit to the horizontal line allows one to
graphically represent how the model behaves when the effect is hypothesized (constrained)
to be trivial (SAS Institute Inc., 2002).
The term leverage is employed to describe these plots, as the points which are further from
the center in the horizontal direction have more pull. At either extreme, the effect hypothesis
being tested is more influenced as the difference in the aforementioned residuals contributes
to a greater portion of the sum of squares (SAS Institute Inc., 2002).
The MLR, effect leverage and stepwise are computed through the software JMP Pro.
4.3 Model selection
The principle statistical methods used were PLS and MRL. PLS was used for scanning all
variables, but it doesn’t provide a functional model based on the principles of acidification. In
addition it doesn’t make the difference between cause and effect and secondary effects.
Consequently MRL was used, even though it is sensitive to intercorrelation between the Xvariables. For this reason only selected variables were used. The variables selection is
explain in the results part but it is indispensable to mention that the major variable selection
19
is based on two principles. On one hand was the scanning from the PLS were the VIP was
applied and on the other hand was taking into account the important variables inherent to the
base knowledge of acidification and to avoid variables that are not often measured. Further
when two variables were strongly intercorrelated, only one was chosen.
4.4 Testing the meta-paleo model
For the construction of the model the effect leverage was considered and the equation 4 was
applied in MAGIC and OMDREV datasets to find the ∆pH between present time and around
1860.
Equation 4. ∆pH
For each dataset, an extrapolation analysis of each variable is tested to prove if it is possible
for the model to work with higher values than the maximum value of that variable in the
reference dataset.
To investigate if the model works for acidification assessment, absolute differences mean
values between the ∆pHs units of MAGIC and the meta-model were done. In addition it is
important to mention that MAGIC calculates the ANC value, but it is possible to calculate the
pH from it; whilst the meta-model calculates the ∆pH directly. Furthermore an EQC for ∆pH
regarding table 6 is applied to indicate how many lakes are considered acidify based on the
model.
4.5 Classification of Ecological Status (ES)
The classification of status for ∆pH is applied in the original data set to compare the results
from the classification of acidification status using paleolimnology and meta-paleo ∆pH data.
As well it is done to compare meta-paleo and MAGIC from the testing datasets (MAGIC and
OMDREV). The ES displays that if the natural pH declines more than 0.4 units then the
acidification is considered as significant (Erlandsson, et al., 2007)
Table 3. Classification of dpH to categorize acidification (SEPA, 2007).
Class
1
2
3
4
5
∆pH
<0.2
0.2 – 0.4
0.4 – 0.6
0.6 – 0.8
>0.8
Status
High status
Good status
Moderate status
Poor status
Bad status
20
5 Results
5.1 Scanning of factors correlated to acidification – PLS results
The PLS model had two significant components, where the first one explained 61% of the
variation and together with the second component 72% of the variation was explained for the
Y-matrix. The cross validation gave a Q2 of 64% which means that the model was rather
stable. Table 4 shows the values of R2 and Q2 for the two components.
Table 4. Values of the components.
Comp No. M1.R2Y(cum) M1.Q2(cum)
Comp[1]
0,609
0,548
Comp[2]
0,716
0,641
R2 = the goodness of fit
Q2 = fraction of the total variation that can be predicted by a component, as estimated by cross validation
R2Y (cum) = the total quantity of explained by Y variation
Q2Y (cum) = the total quantity predicted Y variation
To see the behavior of the lakes (observations) proyected in the T(X) and U(Y) space of the
first component, a plot was done (figure 3). It should exhibit the good fit of U space in the T
space. The plot should ideally be linear, but the model shows a slightly curved relationship.
This suggests that a transformation might improve the model, as the Q2-value is high; the
linear model was kept, making the results easier to interpret.
Figure 3. PLS - t[component 1] / u[component1] – Reference lakes
∆pH was negatively correlated to Alk, ANC and pH which is expected. High values on these
parameters mean that the water is well buffered and resistant to acidification. Cl, NO 3 and
SO4 are positively correlated to ∆pH. NO3 and SO4 are both reflecting acid deposition and Cl
is high along the Swedish west coast where the deposition is high. Other parameters that
were strongly correlated to ∆pH were the metals as Cobalt (Co), Zinc (Zn), Arsenic (As) and
Aluminum (Al), but in acidification they are seen as secondary effects (figure 4).
21
Figure 4. Loading plot of all variables. W*C[component 1]/W*C[component 2]. ▲ = X variables. ■ = Y
variable. W* = weights that combine the original X variables to form the scores T. C = present the
correlation between the Y variable and the X variables scores T(x). W*C combination of W* and C.
The most importan factors in the PLS model regarding the basic knowledge of acidification in
order, and not taking into account the metals are: Alk, ANC, NO3, Cl, Na, SO4, and Ca which
had a VIP higher than 1 that is regarded as a threshold for significant variables. However
TOC had value less than 1 as well as other base cations (Mg and K) or just the BC in total as
an individual variable (figure 5).
Figure 5. PLS-VIP [Last component]. The error bars explain the confidence intervals.
5.2 Selecting parameters for the MLR
The selection of variables begins with the preceding plot (figure 5). For instance from this plot
as well as figure 2, it can be observed that ANC and Alkalinity are very important variables;
but one must be chosen. It was decided to choose alkalinity because in both PLS and MLR
alkalinity describes a slightly higher correlation with pH than ANC. As well variables such as
Na and Ca show strong correlation to ∆pH, but not K and Mg, which all of them are part of
22
the BC. BC as a variable and TOC weren’t perceived as VIP, and were integrated regarding
the basic knowledge of acidification, which can’t be overlooked. Second all the metals were
discriminated because of its secondary effects that have considering acidification and liming.
The chosen variables were used to apply a multivariate correlation analysis by restricted
maximum likelihood. This statistical analysis was done to observe the correlation of the
variables; evidently the predictor variable (∆pH) was included. From comparing the R2 values
for each correlation it can be noticed that in general BC shows a stronger correlation with the
other variables than alkalinity, but less so with ∆pH. SO4 vs BC, SO4 vs NO3, and Cl vs NO3
are well correlated as well (table 6).
Table 5. Multivariate correlation Analysis.
∆pH
BC
Alk
SO4
Cl
NO3
TOC
∆pH
1,000
0,172
-0,640
0,447
0,570
0,637
0,101
BC
0,172
1,000
0,312
0,741
0,700
0,464
0,227
Multivariate correlations
Alk
SO4
-0,640
0,448
0,312
0,741
1,000
-0,142
-0,142
1,000
-0,352
0,640
-0,272
0,465
-0,058
0,047
Cl
0,570
0,700
-0,352
0,640
1,000
0,667
0,031
NO3
0,638
0,464
-0,272
0,465
0,668
1,000
0,0003
TOC
0,101
0,227
-0,058
0,047
0,031
0,0003
1,000
5.2.1 Stepwise regression to select the final candidates for the models
The final selection of variables of the model was made by a stepwise regression.
Additionally, to attempt if a combination of them enhanced the possibility of acquiring better
results. This resulted in six significant variable explaining 71% of the variation (Table 6). The
most importan variable in order was Alk, NO3, SO4, TOC, BC, and Cl.
Table 6. Stepwise fit for dpH. RMSE = root mean square error
Step
1
2
3
4
5
6
Total
Total R2 adj
RMSE
Parameter
Alk
NO3
SO4
TOC
BC
Cl
R2
0,4099
0,6421
0,6681
0,6721
0,6733
0,7105
0,7105
0,6833
0,2339
The earlier step history leads for 2 alternatives models. The first model was based on the
stepwise regression (ST-model) including the 3 valiables that contributed more than 0,10 to
the r2-value. The second model (BK-model) was made based on the basic knowledge of the
processes of acidification for a better fit model (equations 2 & 3) (table 7). BC reflects the
weathering of the soil giving buffering capacity, SO4, and NO3 represents the anthropogenic
acidification, TOC the natural acidification and Cl improved the model when it was included.
23
Table 7. Lis of variables for ST-model and BK-model.
Variables ST-model
Alk
NO3
SO4
Variables BK-model
BC
SO4
NO3
TOC
Cl
5.2.2 Two final models
The two final alternative models were calculated by MLR including the chosen variables
(table 7). As it was expeted the BK-model make better predictions for ∆pH than ST-model,
because the R2 for BK-model was higher (69,16%) than for ST-model (66,81%), in addition
the RMSE is smaller for BKmodel (0,2395) than for ST-model (0,2447) (tables 8 & 9).
Also, for both models and specifically for the leverage plots, it can be observed that the
models describe a lineal correlation instead of a curve correlation (ST-molde figures 6, 7, 8, 9
and BK-model 10, 11, 12, 13, 14, 15). Both models show the significance of the variables by
effect leverage and for the model as a whole (ST-model figure 6 and BK-model figure 10).
The pointed red lines on the graphs are confidence curves at 95%, which describe whether
an effect is significant, hence if these curves cross the horizontal line the effect is considered
significant. Finally the prediction expression is the formula predicted by each model to
calculate the ∆pH around the pre-industrial period (1860) (tables 8 & 9).
Figure 6. Actual by predicted plot. ST
model made with Standard least square
and emphasis effect leverage
Figure 7. Alk - leverage plot
Figure 8. SO4 - leverage plot
Figure 9. NO3 - leverage plot
24
Table 8. Summary of fit of ST model
Rsquare
Rsquare adjust
RMSE
Observations
Prediction expression
0,6681
0,6532
0,2447
71
0,2085 + (-2,6323)Alk
+ 1,3996 SO4 +
0,0046 NO3 (µeqv/L)
The following plots embody the whole BK-model (mta-paleo model):
Figure 10. Actual by predicted plot BK- model
made with standard least square and
emphasis effect leverage
Figure 11. BC - leverage plot
Figure 12. SO4 - leverage plot
Figure 13. Cl - leverage plot
25
Figure 14. NO3 - leverage plot
Figure 15. TOC - leverage plot
Table 9. Summary of fit for BK- model
Rsquare
Rsquare adjust
RMSE
Observations
Prediction expression
0,6916
0,6679
0,2395
71
0,1337 + 3,9801 SO4 +
0,0047 NO3 + (-2,3675 BC) +
2,2787 Cl (all units µeqv/L
except TOC mg/L)
Regarding the two previous models (figures 6 to 15) in accordance with acidification
principles, it can be inferred that the BK-model provides more reliable values then it is
chosen as the meta-paleo model. It gives a better R2, the RMSE is smaller and it includes
more variables that are relevant for the basic knowledge of acidification. Also, this conclusion
was drawn after testing both models by applying each prediction expression to find the ∆pH
between present time and around 1860 to MAGIC and OMDREV datasets in following
sections 5.3.1 and 5.3.2. Nonetheless as it was mentioned in the methodology, it was proved
that by applying the equation of the models to pH higher than 7 in the contemprary water
chemistry, neither the ST model nor the BK-model are able to predict consistent values. That
however is not a problem, because a pH larger than 7 indicates a well buffered lake.
5.3 Applying ST-model and BK-model to MAGIC and OMDREV
databases.
5.3.1 Testing the ST-model and BK-model on the lakes in the MAGIC database
For this analysis 2324 lakes in the MAGIC database with pH < 7 were used. When the ∆pH
from MAGIC is plot against the ∆pH from the ST and BK models, the results are quite similar
but it is possible to observe that the BK-model shows less dispersion between the data
(figure 16 & 17) compared to the ST model.
The red points represent negative ∆pH for both models, the green dots a negative ∆pH just
for the ST model and the blue points which are barely seen a -∆pH for the meta-paleo model.
Despite the fact that both plots are nearly identical, it can be pointed out that the BK-model
makes a better prediction of the ∆pH rather than the ST model based on the results.
26
Figure 16. Relationship between dpH (X) and
∆pH (Y) for ST model in MAGIC dataset
Figure 17. Relationship between dpH (X) and
∆pH (y) fore BK- model in MAGIC dataset
The errors found are 0.245 and 0.240 for ST model and BK model respectively (table 8 & 9).
When these errors are applied for -∆pH, the number of lakes increase again (red dots are
replaced by black dots) (figures 18 & 19).
Figure 18. Relationship between dpH (X) and
∆pH (Y) for ST model in MAGIC dataset,
including errors
Figure 19. Relationship between dpH (X) and
∆pH (Y) for meta-paleo model in MAGIC
dataset, including errors.
Therefore the amount of red and green points decreased and the blue points disappeared.
The final sum of the total negative ∆pH for ST-model is 23 and for meta-paleo is 6 (table10).
Table 10. Sum(dpHst) = sum of negatives ∆pH for ST model. Sum(dpHbk) = sum of negatives ∆pH for BKmodel. Applied to the MAGIC dataset.
N Lakes
Sum(dpHst)
Sum(dpHbk)
2324
23
6
For the reasons above (table 10), and the fact that it was determined to use the best-fit
model concerning basic knowledge of acidification; the BK-model is best qualified to pursue
the analysis as meta-paleo model. Thus, the selection process continued with the analysis of
the negative ∆pH by applying minimums and maximum values from the original data for each
variable for the respective tables of MAGIC. It was evident that minimum values for NO 3, SO4
and TOC are not important because what matters is the high concentrations as they acidify
the most; especially SO4 because it is mostly produced by anthropogenic activities.
Consequently it was assumed to work with maximums only, to observe whether it is possible
or not to extrapolate these variables, particularly TOC. This was because TOC affects
acidification, but it is inherent to biological processes that change over time. It is redundant to
27
say that as a result, the sum of the five variables should be five. After this analysis for the
sum of negative ∆pH, it can be noticed that is the same number of negative ∆pH but the
number of lakes is reduced to 1981 because of the new condition (table11).
Table 11. Sum(dpHmp) = sum of negatives ∆pH for BK-model, includes new condition (sum of variables
equals to 5). Applied to MAGIC the dataset.
N lakes
Sum(dpHBK)
1981
6
It was proved that it is possible to work with values going beyond the maximum, such as
TOC. Thus the number of the lakes analyzed increases again. However, it was determined
that a combination of SO4, NO3, Cl and TOC with greater values than the maximum is
possible to work with. Whilst for BC it is not possible to extrapolate, because when the same
analysis was applied, the results for pH were higher than 7, which mean the model doesn’t
work, and almost all ∆pH values are negatives. Likewise the combination of all the variables
with high BC and lower or below the maximum for the rest of them, the probability to obtain
negative ∆pH as an outcome is quite high.
Table 12. Sum(dpHmp) = negatives ∆pH value, includes new condition (sum of variables equals to 4).
Applied to the MAGIC dataset.
Variables
TOC
SO4
NO3
Cl
BC
N lakes
262
5
39
5
-
Sum(dpHBK)
0
0
0
0
-
The six lakes with negative values of dpH were all located in Northern Sweden where the
deposition is low (figure20).
28
Figure 20. Lakes location. The ▲ = negatives ∆pH lower than the error. Applied to MAGIC dataset.
From figure 21 it can be analyzed that dpH calculated with MAGIC and ∆pH with BK-model
(meta-paleo model), had both skewed distributions with high maximum values compared to
the median, but ∆pH is less skewed. For the other variables, which the prior ones were
computed and are influenced by, it can be seen that the DOC distribution has a few very high
maximum values. NO3 has a skew due to the elevated maximum values and with many lakes
close to zero. In contrast BC is the less skewed variable in comparison but not perfectly
normal distributed. SO4 is also skewed but less in relation to the dpH, NO3 and Cl,
additionally it is a bit more even distributed, and Cl is skewed aswell.
29
Figure 21. Distribution of variables. dpH = change of pH calculated by MAGIC. ∆pH = change of pH
calculated by BK-model. DOC = dissolve organic compounds. NO3 = Nitrate. BC = base cations. SO4 =
sulfate. Cl = chloride. Applied to MAGIC dataset.
Figures 22 to 26 show the correlation between each variable by the ∆pH computed by BKmodel. From first sight it can be seen that if all variables increase, then the difference of pH
does too. NO3 is the variable that best exposes a proportional relationship with ∆pH.
If BC is observed carefully, it can be noticed that its values are more dispersed.
Correlations between ∆pH BK-model with each variable (DOC, NO3, BC, SO4, Cl):
Figure 22. ∆pH meta-paleo by DOC for
MAGIC dataset.
Figure 23. ∆pH meta-paleo by NO3 for
MAGIC dataset.
Figure 24. ∆ph meta-paleo by BC for
MAGIC dataset
Figure 25. ∆pH meta-paleo by SO4 for
MAGIC dataset
30
Figure 26. ∆pH meta-paleo by Cl for
MAGIC dataset
5.3.1.1 Analysis of the mean difference of the ∆pH’s of MAGIC database
The difference between ∆pH’s calculated with MAGIC and meta-paleo model (BK-model) is
larger than the BK-model error 0.240 as well as the individuals’ ∆pH means (table 13).
Table 13. ∆pH's means difference for MAGIC dataset.
N lakes
Mean dpH Magic
2324
Mean dpH
paleo model
meta- Mean of Abs |dpH
Magic – dpH metapaleo|
0,388
0,241
0,329
5.3.1.2 Ecological status from meta-paleo model for the MAGIC database
The ES estimated for ∆pH of meta-paleo and dpH of MAGIC are quite different, particularly
for the high, moderate and poor classification, indicating tha meta-paleo classify more lakes
as acidified (table 14).
Table 14. Comparison between MAGIC and meta-paleo ES for MAGIC dataset
EQC status classification Number of lakes ( MAGIC) Number of lakes (meta-paleo)
High
1221
837
Good
524
572
Moderate
209
406
Poor
121
210
Bad
249
299
5.3.2 Testing of the ST-model and BK-model in OMDREV database
All previous analyses were done as well for the OMDREV table, which has ∆pH estimated by
MAGIClibrary. The graphs beneath (figures 27 to 30) are the same as figures 16 to 19 and
the colors of the dots have the same significance. The errors from the MAGIC data are not
taken into consideration, but figures 30 and 31 include them.
31
Figure 27. Relationship between dpH (X) and
∆pH (Y) for ST model in OMDREV dataset
Figure 28. Relationship between dpH (X) and
∆pH (Y) for BK-model in OMDREV
dataset
Figure 29. Relationship between dpH (X) and
∆pH (Y) for ST model in OMDREV dataset,
including errors
Figure 30. Relationship between dpH (X) and
∆pH (Y) for BK-model in OMDREV dataset,
including errors
The sum of the negative ∆pH for ST-model is 95 and 68 for BK-model (meta-paleo model)
(table 15).
Table 15. Sum(dpHst) = sum of negatives ∆pH for ST model. Sum(dpHmp) = sum of negatives ∆pH for BKmodel. Applied to the Omdrev dataset.
N Lakes
Sum(dpHst)
Sum(dpHBK)
1741
95
68
As with section 5.3.1, table 15 shows that BK-model should be used. The sum of -∆pH taking
into account the parameters that are below the maximum with a total of 5 is 29 and the
number of lakes is reduced to 1316 (table 16).
Table 16. Sum(dpHmp) = sum of negatives ∆pH for BK-model, includes new condition (sum of variables
equals to 5). Applied to the Omdrev dataset.
N Rows
Sum(dpHBK)
1316
29
The analysis of minimums and maximum, and only maximums done with the MAGIC dataset
is repeated in this case as well. Likewise Omdrev table is related with the original data table
regarding the maximum values of each parameter (table 17).
32
Table 17. Sum(dpHmp) = negatives ∆pH value, includes new condition (sum of variables equals to 4).
Applied to the Omdrev dataset.
Variables
TOC
SO4
NO3
Cl
BC
N Lakes
226
6
42
6
34
Sum(dpHBK)
1
0
0
0
14
Similar observations can be remarked concerning what was described beforehand by the
results in MAGIC application about base cations. Although in this case there are a high
number of negative ∆pH outcomes with the 5 variables involved below the maximum.
The coordinates of position of the lakes that correspond to the Omdrev table are plotted
(figure 31).
Figure 31. Lakes location. ▲ = ∆pH < -0.3. ▲ = lakes where not ∆pH matches with MAGIC library were
found. ▲ = mixture between the prior cases. Applied to Omdrev dataset.
33
It was established that the values that weren’t calculated by MAGIC (blue and green dots)
and then computed by meta-paleo model gave unintuitive results, and were therefore
discriminated (figure 32). Otherwise, it would have affected the analysis of figures 33 until 38.
The only blue dots that are displayed on the subsequent plot are those that show “rational”
values concerning the ∆pH.
Figure 32. Lakes location. ▲ = ∆pH < -0.3 ▲ = lakes where not ∆pH matches with MAGIC library were
found. Applied to Omdrev dataset.
From figure 33 it can be seen that SO4 is a little bit skewed, Cl is also fairly skewed and not
really good distributed, NO3 is not uniformly distributed and it presents a drastic skew with
many lakes close to zero. So far TOC is the best-distributed variable, but still a bit skewed.
BC is slightly skewed and has two points with extreme values. Finally from the predicted
variables it can be observed that dpH is not evenly distributed, though ∆pH is better
distributed but it has two points with extreme values.
34
Figure 33. Distribution of variables. dpH = change of pH calculated by MAGIC. ∆pH = change of pH
calculated by BK-model. DOC = dissolve organic compounds. NO3 = Nitrate. BC = base cations. SO4 =
sulfate. Cl = chloride. Applied to the Omdrev dataset
The last group of graphs (figure 34 to 38) exhibits the correlation of the variables against the
∆pH calculated by meta-paleo (BK) model. The first one is an essential part for the study and
it can be interpreted by the same basis from figure 22 to 26. Nonetheless, it can be remarked
that the ∆pH is progressively increasing as the concentration of NO3 raises.
Figure 34. ∆pH meta-paleo by SO4 for
OMDREV dataset
Figure 35. ∆pH meta-paleo by Cl for
OMDREV dataset
Figure 36. ∆pH meta-paleo by NO3 for
OMDREV dataset
Figure 37. ∆pH meta-paleo by TOC for
OMDREV dataset
35
Figure 38. ∆pH meta-paleo by BC for
OMDREV dataset
5.3.2.1 Analysis of the mean difference of the ∆pH’s of OMDREV database
The difference between ∆pH’s calculated with MAGIC and meta-paleo model is bigger than
the error 0.240 as well as the individuals’ ∆pH means (table 18).
Table 18. ∆pH's means difference for OMDREV dataset.
N Lakes
1729
Mean dpH Magic
0,3380
Mean dpH meta- Mean of Abs |dpH
paleo model
Magic – dpH metapaleo|
0,3970
0,2899
5.3.2.2 Ecological status from meta-paleo model for the OMDREV database
The ES for ∆pH calculated by meta-paleo and MAGIC are quite different for high, moderate
and bad classification. The meta-paleo classifys more lakes as acidified (table 19).
Table 19. Comparison between MAGIC and meta-paleo ES for OMDREV dataset.
ES classification Number of lakes (MAGIC) Number of lakes (meta-paleo)
High
887
656
Good
344
325
Moderate
165
302
Poor
131
180
Bad
189
266
As it was decided to work with meta-paleo (BK-model), it was worthy to compare it with its
basis of Paleolimnology, to understand the inconsistence results.
5.4 Analysis of the RMSE of ∆pH’s of paleolimnoloy and metapaleo model
The RMSE of meta-paleo modelis 0.240; absolute mean difference between both ∆pH by
applying equations 5 and 6 is 0.180 units, which indicates that is below the error of 0,3 units
in the paleolimnological method. From the 71 reference lakes it was found that 18 results
were higher than the error with a mean value of 0.382. The red points in the models (figures
6 to 15) indicate those 18 lakes.
36
Equation 5. ∆pH paleo - water chemistry
Equation 6. ∆pH meta-paleo - water chemistry
5.5 Applying the classification of ES to Paleolimnology and
meta-paleo model
The ES estimated for the reference lakes is based on table 3, for the ∆pH calculated by
Paleolimnology, differ from the ES of meta-paleo, this difference is higher for the
classification of moderate and bad status (table 20).
Table 20. Comparison between paleo and meta-paleo ecological status
ES
Number of lakes paleo Number of lakes meta-paleo
High
29
23|
Good
15
15
Moderate
7
16
Poor
8
10
Bad
12
7
Equation 5 and 6 are applied in the reference lakes to compare the ∆pH calculated by
Paleolimnology and meta-paleo model. As meta-paleo is based on the data of
Paleolimnology it is not surprising that there is a proportional correlation between them
(figure 39).
Figure 39. dpH paleo Vs ∆pH meta-paleo
The meta-plaeo model (BK-model) apparently was the best option. Before developing the
ST-model and BK-model, when the PLS was applied, two options were found; based the
analysis either on pHpaleo or on the change of the pH (∆pH).The pHpaleo gave a higher R2 =
0,6713 and Q2 = 0,5827 than the ∆pH, but worse results regarding the very important (VIP)
graph, as it didn’t show SO4, NO3, TOC, Cl, as important variables because their values were
less than 1.
Likewise two other PLS models were developed for ∆pH, one with the normal data and other
with log transformation suggested by the program. The VIP graphs were established and
compared, such that the application of the log transformation in some variables showed a
better picture considering the basic knowledge of acidification. However, due to the fact that
PLS didn’t show good results, then MLR was applied for both scenarios, displaying a better
R2 and model for the data without log transformation, R2 = 69,16% (meta-paleo model) and
for log transformation R2 = 58,79%.
37
6 Analysis and discussion
6.1 Meta-paleo model as a tool for acidification assessment
The goal of this study was to develop a tool for acidification assessment based on
paleolimnology as a complement to the official ES that is based on dynamic modeling
(MAGIC) to give a second opinion for expert judgements of single sites. The model should
use parameters that are often available for acidified waters, be simple to use and in
accordance to basic knowledge of acidification processes.
In the process of the tool development, PLS was the first statistical technique to be applied in
order to scan all variables and analyze its influence in the ∆pH. Water chemistry, land use
and paleolimnological data are the predictor variables. A two components PLS model could
explain 72 % of the variation in ∆pH and a cross validation showed on a rather stable model.
The final simplified meta-paleo model explained as much as 69% of the variation which
shows than not much is gained by including more variables.
Some of the most important variables of the model were also factors known to be important
for acidification sensitivity and pressure of acidification. Other factors correlated to ∆pH in the
PLS-analysis were assumed to be more of a secondary effect of acidification or correlated to
deposition of pollutants in general like metals. Their mobility can change with acidification;
therefore this is seen as a secondary effect.
In general statistical models don’t deal with cause and effect, therefore the PLS result didn’t
give a good approach. Linear regression was applied leading to the possibilities of two
models. One based on purely statistical analysis (ST-model) and the other one based on the
knowledge of acidification processes, using it for the selection of the parameters (BK-model
or meta-paleo). Both models were applied to different datasets and the BK-model was
chosen because it gave a better simulation and results, when the model is tested and
compared with values computed by MAGIC. Nonetheless they are different most of the time.
When comparing the predicted values of pH by meta-paleo with the values of ∆pH from
paleolimnoloy, it was observed that the original ∆pH was higher for 37 lakes out of 71 (table
20). Furthermore the meta-paleo seemed to even out the extremes with more lakes in the
intermediate classes moderate and bad, but fewer lakes in the high and very bad classes.
When ∆pH’s are classified regarding the ES classification, paleolimnology classifies 27 out of
71 lakes as acidified and meta-paleo 33 lakes. Analyzing and comparing these results of
individual lakes, it was observed that this difference frequently happens in the most acidic
values and those that have a water chemistry pH close to 7. For the values in the middle
range of the results they are quite similar with smaller errors if individual lakes are observed.
There was also a tendency of bias of the model by overpredicting the degree of acidification
(section 5.4 & 5.5). This can be seen as a problem of model overfitting, there might be
variables that weren’t necessary to include or a problem of underfitting in the case that
important variables were missing (Hoffmann, 2005). It was difficult to find the causes to this
bias. But also, this can be attributed to the fact that meta-paleo model doesn’t use all the
variables and doesn’t take into account physicochemical processes, climatilogical and
environmental conditions or natural variations that influence the pH. It can also be due to the
fact that meta-paleo is a simple statistic model that tries to predict rather complex dynamic
processes.
38
When the model is tested in the MAGIC and OMDREV datasets similar observation as with
plaeolimnology are observed, for most of the acidic values of the pH water chemistry, the
absolute ∆pH calculated separately by MAGIC and meta-paleo still predicting acidic
conditions, but they differ about from one unit, with meta-paleo being less acidic for preindustrial pH. Consequently it was worthy to compare the meta-paleo model with MAGIC, as
it is a dynamic model that also includes more variables and processes.
The absolute average difference between the ∆pH calculated by MAGIC and meta-paleo
model were 0.241 and 0.290 for the MAGIC library and OMDREV datasets respectively. This
means that both values are higher than the meta-paleo RMSE (0,240). In addition it is
important to remark that the meta-paleo frequently gives higher values of ∆pH than MAGIC;
because meta-paleo for the most acidified lakes tends to predict that the lakes have higher
pre-industrial pH than the one calculated by MAGIC. For instance, for the MAGIC dataset
1245 out of 2324 data have higher ∆pH for meta-paleo and for OMDREV dataset 844 out of
1729 higher ∆pH for meta-paleo as well, which indicates not a good approximation, because
in both cases almost the half of the total gave different results. Moreover, it is important to
consider as it was shown in the results, that there are some lakes that have negative ∆pH,
which means that the meta-model predicts that in those few cases the pre-industrial pH is a
little bit lower than the contemporary pH. This is however not realistic since all water have
been subject to acid deposition more or less. From a management point of view, however it
is not so important with poor predictions of well buffered or very acidified lakes as long as the
tool only is used for classification of lakes into acidified and non- acidified lakes by the criteria
of 0,4 pH units (SEPA, 2007).
The model gave reasonable result when extrapolated above the range of the training set of
the model except for BC. This was expected since waters high in BC are well buffered and
are not likely become a change in pH from acidification. Although it was observed in both
data sets that the pre-industrial pHs calculated by MAGIC are higher than 7, this means that
these lakes should not be part of the acidification assessment. However, paleolimnological
studies have shown that there was an alkalinization period before the pre-industrial time
(Renberg, et al., 1993). Consequently it would be worthy to conduct paleolimnological
studies in those lakes in the case they don’t have history records, to know if these pHs were
influenced by anthropogenic impact or natural variations. A lot of these lakes are located in
the northern part of Sweden, which is supposed to have natural acidity (Bishop, et al., 2001)
(figures 20, 31 & 32). For the OMDREV dataset some of the lakes have been subjected to
liming, although the water chemsistry was adjusted for the lime effect.
MAGIC assessed more lakes with high status, quite similar for good status, but highly
different for moderate, bad and poor status compared to meta-paleo. In contrast MAGIC
classifies 579 lakes as acidifed and meta-paleo 915 for MAGIC dataset. Likewise the ES was
applied for the OMDREV dataset and it gives quite similar results, MAGIClibrary classifies
485 lakes as acidify acidified and meta-paleo 748. This strong overprediction of acidification
by meta-paleo compared to the official MAGIC-based Ecological Quality Criteria shows that
the metapaleo cannot be used as a complemtary tool for an expert judgement of ecologicl
status until this discrepancy has been explained.
Furthermore other studies have shown (Guhrén, et al., 2007; Norberg, et al., 2008) that in
the past some lakes were classified as acidified when they weren’t. As meta-paleo model is
based on paleolimnology, this can be considered as a reason why the model gives higher
values than MAGIC. Nevertheless, it is important to keep in mind that the ∆pH of meta-paleo
tends to be unestable for the most acidic acid waters and and those close to 7 values,
increasing the number of lakes as acidify.
39
Accordingly, it can be inferred that it is quite difficult to assume whether meta-paleo is wrong
or not for the moderate acidified lakes values when individual lakes are compared because it
gives quite similar results like MAGIC. Obviously the model present uncertainties and the
model is biased because the resulting ∆pH’s calculated by meta-paleo and MAGIC are very
different when they are calculated for the more acidic values, the meta-paleo tends to
calculate the pre-industrial pH with higher values in contrast to MAGIC. Thus the ∆pH is
bigger and the lakes ended up classified as acidifed. This same error is repeated and
perceived when comparing with Paleolimnology, MAGIC and OMDREV datasets.
Finally it can be state that for both, MAGIC and meta-paleo models contain errors, because
already the calculation of the pH is a large source of error. In addition the TOC and pCO2
change over time in an unidentified but influence way. And the number of the representative
reference lakes of Paleolimnology is small and normally they are medium in size and depth.
6.2 Acidification, reference condition and ethical implications
The three elements of naturalistic fallacy as described in the theoretical background: is-ought
dichotomy, appeal to nature and the definition of the word ‘good’ and PRP.
Applying is-ought dichotomy to this paper, it can be understood as the interplay between the
role of science and the decision-making processes (such as defining a RC and liming.)
Science is matter of fact and predictions, with scientific knowledge it is possible to estimate
and describe how the environment was in the past, its present state and future. It can also be
used to run simulations, such as predicting the state of the environment without or with
anthropocentric intervention. Science is instrumental in terms of studying human impacts,
environmental degradation and establishing realistic solutions to evade massive destruction.
However, as nature is a resilient dynamic system, it is not possible to limit it to an
‘undisturbed state’ (RC, which means very low distortion) this concept is difficult to define
and model. Nature is governed by dynamic and chaotic laws that go beyond current human
understanding, thereby predicting the past and future of the environment with a model is a
difficult task.
To reach ‘GES’ according to the WFD with additional of limestone is a potential solution for
the problem of anthropocentric acidification. This solution can be seen as a moral decision
because it is an action that is taking place for those who have moral standing. The
government decision policy can be seen as normative (i.e.) how things ought to be. The
problem is that it is not possible to derive ought from is, so science is limited in its use and
must give way to moral theory at some point. Liming and the ecological status as a normative
means to return to an ‘undisturbed state’ call into question who/what has moral standing. The
answer can be based on two perspectives. First, from an ecocentric perspective, the
environment has the same moral standing as humans. Second, from an inter-generational
anthropocentric perspective, only humans have moral standing to decide how the
environment should be to guarantee the survival of present and future generations. This is
reflected in the conversational article “Nature as the ‘natural’ goal for water management”
(Bishop, et al., 2009). The latter standing is the one that usually is applied under sustainable
development and it is reflected in the basis of the WFD. However, there is appears to be a
fallacy in assuming that there should be a return to an undisturbed state if humans are the
only ones who have moral standing.
The second element of the naturalistic fallacy is the appeal to nature. First, the WFD seeks to
define a RC with very ‘low distortion’, and thus uses pre-industrial times (i.e. mid 19th century)
as a RC. However, times were different in 1860 as the Swedish population was growing and
40
changing from a rural society to an industrialized society. Humans were still embedded in the
natural ecosystem, which calls into question how the rate of distortion changed with time.
Also, it is complicated to set a RC to determine spatial and temporal changes in lake
ecosystems, as there are a lot of uncertainties involved. Therefore it is fundamental to take
into account that each historical period of time is different for each lake. Thus one can argue
that the pre-industrial period, considered as the RC, is not realistic because at that time there
was less population and lifestyle/agriculture were different (Renberg, et al., 2009). Climatic
influences have caused changes in the ecosystems over time as well. This means that
humans affect the ecosystem in different ways and at different scales and will continue doing
so. This is reflected in the uncertainties of the models and difficulties of modeling; for
instance the flora and fauna can adapt to changes in TOC and pCO2. If these variables are
considered to be in a static range of values that allow for small changes for the GES, it
implies that human are dominating nature once more. In the future values of TOC could be
reduced to a management issue by setting a dynamic RC that would be increasingly affected
by anthropogenic climate change. Some would argue that this type of management system
would be ‘restoring’ nature back into balance. Others could argue that due to nature’s
resilient and adaptive capacities, that the natural balance would be best restored by itself,
without human intervention. This natural balance would involve adaptation and evolution of
flora and fauna, and perhaps it is the humans who should be adapting to the natural
restoration of balance, not the other way around. Hence under the WFD the establishment of
a RC in order to achieve GES could be seen as a perverted appeal to nature. The ones
seeking to return nature to an ‘undisturbed state’ use appeal to nature as an argument to
gain stakeholder support. This is fallacious, as the means of restoring nature into balance
involve artificial management techniques, such as liming. Philosophically, this is complicated
territory as it is questionable whether an artificial means to a natural end is morally
acceptable.
Second the idea of biodiversity, which is based on nature can be misunderstood and lead to
erroneous policies. In the case of liming, politicians and the public in general can use the RC
and the term of biodiversity and ecosystem productivity as an argument to lime. For instance,
in a set of studies based on 12 limed lakes, it was concluded that it is not possible to assure
if liming has restored the ecosystem or if it has reached a GES (Guhrén, et al.,2007; Norberg
et al.,2008; Norberg, et al.,2010). An increase in the number and diversity of fish doesn’t
necessarily imply an amelioration of the environment. Consequently this can be considered
as a human impact as well, because the ecosystem conditions are transformed. Therefore, it
is misleading to argue that liming brings the ecosystem back to an ‘undisturbed state’ as the
means of doing so is itself ‘unnatural’.
Regaring the definition of the RC and establishing a ‘GES’ there are questions as to what
exactly the goal is referring to. The naturalistic fallacy point out that just because something
is natural doesn’t mean that it is good. Then the ‘GES’ can be described but doesn’t define
what the word ‘good’ means by itself. The WFD has developed a set of criteria that defines
what it believes to be a ‘GES’, but by using the word ‘good’ it simply imbues the definition of
the word with a secondary set of definitions/criteria. Therefore the word ‘good’ is subjective,
anthropocentric and ‘unnatural’.
Despite the controversy surrounding the definition of reference values, the decisions of liming
or critical loads are important to analyze the behavior of the lakes’ ecosystems through time.
It is also important to understand how environmental impacts cause regime shifts in the
catchment areas. PRP can be used in the implications of acidification, such as for liming,
which is used as a remedial measure to the impact already done by air pollution, but not as a
precautionary measure to avoid the pollution. Then the interpretation of the PRP for the
decision of liming is ambiguous, vague and confusing. Also, liming causes environmental
risk for acid-tolerant biota and wetlands, despite those consequences liming was done
41
(Bishop, et al., 2001). Liming has been conducted for decades and some lakes were restored
to the environmental RC (Renberg, et al., 1993; Guhrén, et al., 2007; Renberg, et al., 2009;
Norberg et al., 2008). However, it is questionable whether this is the correct course of action
or not, as one cannot expect that the conditions of the lakes would be the same today as
they were in pre-industrial times, assuming that acid deposition never occurred. Then this
can be interpreted as going against the natural variability of the lake itself. On the other hand
the PRP is well interpreted considering the emissions control because the impact which in
this case is acidification, despite whether it is known or not; as it is a precautionary measure
taken before the pollution is widespread without any control measure.
There are numerous philosophical issues with establishing RC, politicians and experts may
take a moral stance on the issue and decide that despite the shortcomings, it is the best way
to proceed. The question is whether the mechanisms of the WFD are done for the sake of
nature itself or for society and the economy. It appears that due to the uncertainties in
modeling nature, as shown in this thesis, there is inherent risk with any option that is chosen.
No choice is easily made, and all appear to be flawed from a logical perspective. This is
paradoxical; it would seem that we are damned if we do and damned if we don’t.
42
7 Conclusions
This section presents the conclusions and recommendations. Recalling back the aims of this
paper which was to develop a complementary model to MAGIC model for expert judgemnt
for acidification assessment and to relate the concepts behind the goal of the WFD to reach
‘GES’ to sustainable development. Furthermore the main questions that this paper tries to
answer are: Why is it important to develop an additional model as a second opinion to
calculate the change of pH between present physiochemical parameters and change in pH
since preindustrial conditions according to Paleolimnology? Should the new meta-paleo
model be use as an additional tool?
During recovery from acidification, there is a need for assessments of acidification to make
decisions if liming can be finished. Present tools however have sometimes shown to give
unexpected results that have been questioned by perfomers of liming. Hence, the importance
of accuracy of the methods used to measure or predict past pH is important to avoid
mishaps. Furthermore, to circumvent the previous problem and for environmental
management in general, it would be worthy not to have a unique RC for each lake. Perhaps
at least one RC that describes each historical period of time could be useful. Otherwise
historical data for each lake could analyze the behavior through time and show how they are
affected either by pollution or natural variability or both. These could be used in accordance
establishing goals to the lake history and not a single event of history.
For acidification assessment it would be worthy to conduct more studies based on
paleolimnology to observe the accuracy and the error of the meta-paleo. It could also be
useful to compare it to other models and define if it is worth working with MAGIC or other
less complex models that can provide a good approximation.
A multivariate analysis (PLS) including a large number of physiochemical and geographical
data could explain 72% of the variation in pH change since preindustrial time according to
paleolimnology for 71 lakes. A simpler linear regression model (MLR) only including 5
parameters manages to explain almost as much (69%) as the more complex PLS-model.
This MLR model (meta-paleo) was selected for further evaluation.
The meta-paleo model seemed to over estimate acidification when compared to
assessments by the official Ecological Quality Criteria based on the MAGIC-model. This
discrepancy makes it difficult to recommend the meta-paleo model as a complementary tool
for assessments of acidification before it has been further investigated.
Finally, due to the uncertainties of all the models, it can be concluded that the meta-paleo
model might not be used, because it has more uncertainties if compared to MAGIC. It works
for pH smaller than 7 but it is important to take into account that for very low pH the model is
sensitive. It would be worthy for future studies to analyse the possibility of including more
variables or other statistical techniques that relate better to the process of acidification, and
which do not have problems with time series data (autocorrelation). Also, the possibility of
including variables that can describe dynamic processes could be used to increase the
accuracy of the model. This is the main reason for deciding that the model needs to be
investigated further; what is missing or what makes the model so sensitive to the most acid
values.
Considering the second part of the aim and relating it to the results it can be concluded that
the reference condition established by the WFD to achieve a ‘GES’ doesn’t describe an
‘undisturbed’ state itself, because nature is dynamic and chaotic. For instance, it has natural
variability that humans cannot fully control in all likelihood; nature is vulnerable and reacts to
43
pollution. Nature has a resilience that balances it when undesirable impacts occur, and this
takes time. In the case of lake acidification, liming is used as a shortcut to the process of
recovery, but studies have shown that responses to lime fluctuate from lake to lake from a
long-term perspective.
Environmental monitoring and assessment are important to comprenhend how nature works
and how humans can lead sustainable lives without altering the natural system. But the
reference condition and the efforts to achieve a good ecological status can be seen or
missinterpreted as man trying to dominate nature through management for their own benefit.
The reference condition is a way to claim that humans are concerned about nature. One
must recognize that humans’ activies and their life styles should be controlled, not nature,
because it has always existed, and always will, with or without us.
The erroneous decision of liming lakes that were considered acidified when they weren’t is
an example of the failure of the use of the precautionary principle. Not only because liming is
a remedial measure, but also because of the uncertainties if they were acidified or not, plus
the environmental risk that it implies if they were naturally acidic lakes. Consequently liming
should be considered as an anthropogenic impact done to accelerate lakes recovery for the
benefit of the public good of present and future generations instead of the recovery of the
ecosystem itself. It is fundamental to mention that the problem of acidification is influenced
by other sources of humans’ impacts such as land use.
To sum up as the general conclusion of the study regarding the good ecological status, the
WFD should have a flexible range of values for classification depending on the current state
of each lake. It is not realistic to close nature to a past state or a state that is beneficial for
humans. For instance climate change will change physico-chemical and biological
parameters, such as TOC. Therefore it is important to study the influence of those changes
through time to understand them with a holistic approach, instead of trying to go back in day
or limit them to a fix rang of value.
44
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