LIFE + Environment Policy and Governance
Project name: Reduction of waste water nitrogen load:
demonstrations and modelling (N-SINK)
Project reference: LIFE12 ENV/FI/597
Duration: August 1st, 2013 – July 31st, 2017
Deliverable D 4.3: Report 5 Final results
ACTION B3. Demonstration of spatially cost-effective allocation of
nutrient abatement measures at watershed level
Beneficiaries: Natural Resource Institute Finland (Luke)
Contributors: Pekka Kinnunen
Due date of deliverable: 30.04.2017
Actual submission date: 30.04.2017
With the contribution of the LIFE financial instrument of the European Community
Deliverable D4.3: Report 5 Final results
Content
1.
Introduction ........................................................................................................................... 3
2.
Lake Vanajavesi and River Porvoonjoki catchments ..................................................... 3
3.
Description of the model ..................................................................................................... 4
4.
Results................................................................................................................................... 9
5.
Model limitations ................................................................................................................ 12
6.
Conclusions ........................................................................................................................ 13
7.
References.......................................................................................................................... 14
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1. Introduction
Excess nutrient loading from human activities causes very severe problems in many fresh water
bodies and sea areas. Agriculture and municipal wastewaters are the most significant nitrogen
sources and in Finland they account for over 70% of the annual nitrogen load (Helcom 2011). In
many previous cost-efficiency studies considering the Baltic Sea, the study catchment area is
divided into roughly 24 subcatchment areas (e.g. Gren et al. 2008, Ahlvik et al. 2014). Large
subcatchment areas lead to broader generalizations and homogenizations which have to be
made about attributes e.g. soil, retention, field composition and wetland capacity. This can cause
inaccuracies if there are major variations in the attributes inside the subcatchments.
In this deliverable, we show the results of applying an economic model to Lake Vanajavesi and
River Porvoonjoki catchment areas. The economic model includes six abatement measures:
reduction of nitrogen fertilization to spring barley, shifting field management from normal tillage
to no tillage, building wetlands, improving wastewater treatment facilities and improving the
coverage of municipal wastewater network. We also demonstrate the cost-efficiency of the
diffusor system installed to the wastewater treatment facilities discharge pipes. The costs and
other attributes for each measure are estimated for each 3rd level watershed division
subcatchment, for which the average size is 50-100 km2. This provides us higher resolution
estimates than in previous nutrient abatement cost-efficiency studies.
Measuring and identifying the actual nutrient runoff from small scale non-point sources (e.g. farmlevel leaching) is difficult and costly. Thus, it impact’s assessments have to be done through
modelling. The scope of this study is not to provide an actual policy on how to achieve the
reduction target but rather providing decision support framework for different policy elements and
to provide cost-efficiency comparison between different nitrogen reduction measures.
Additionally, we show how nutrient abatement could be analysed in high resolution and study the
optimal spatial allocation of nitrogen load reduction measures in two catchment areas with
different characteristics.
2. Lake Vanajavesi and River Porvoonjoki catchments
Figure 1 shows maps of the Lake Vanajavesi and River Porvoonjoki catchment areas. Both areas
are located in Southern Finland. The waters in the River Porvoonjoki discharge straight to the
Gulf of Finland without going through lakes. As there are no major lakes to provide nitrogen
retention, the retention factors are quite similar throughout the whole catchment. The Lake
Vanajavesi catchment area, on the other hand, is part of the larger River Kokemäenjoki
catchment area, which discharges to the Bothnian Sea through several lakes, and thus, the
retention factors inside Lake Vanajavesi catchment area vary significantly between
subcatchment areas. More detailed descriptions of the catchment areas are provided in
deliverables 3.4 and 7.2.
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Figure 1 Maps of Lake Vanajavesi catchment (green color) and River Porvoonjoki catchment (red color). Black
arrows mark the general flow direction in both catchment areas.
3. Description of the model
3.1 Economic optimization
In the model, “social planner” implements a total nitrogen load reduction target as explained in
deliverable 7.2. The model then minimizes the social costs occurring from the abatement
measures which were needed to reach the nitrogen reduction target. The social costs were
formed by comparing the point where private payoffs were maximized to the situation where
social planner has implemented a reduction target. The costs result from the loss of income in
agriculture and other investments and maintenance costs. All the prices were assumed to be
constant, e.g. all the farmers are price takers and even radical changes in fertilization will not
affect the price of the crops or nitrogen fertilizer.
The total nitrogen reduction is compared to the initial loading, i.e. no costs or investments have
yet occurred. As the aim of the model is to diminish nitrogen pollution in the target water body,
the cost minimization problem is constrained by giving the target area a maximum allowed
̅ in terms of agricultural leaching and waste water loading. If we denote total
nitrogen load 𝑁
nutrient load as function g, we get the constraining condition for the minimization problem:
𝒏
𝒂𝒈𝒓𝒊
𝒈(𝑵, 𝒋, 𝝓, 𝒎) = ∑ 𝑳𝒊,𝒋
𝒊
𝒌
𝒓𝒓
̅
(𝑵𝒊,𝒋 ) + ∑ 𝑳𝒘𝒘
𝒇 (𝝓𝒇 ) + 𝑳 (𝒎) ≤ 𝑵
𝒇
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𝑎𝑔𝑟𝑖
where the terms 𝐿𝑖,𝑗 , 𝐿𝑓𝑤𝑤 and 𝐿𝑟𝑟 are the loading from agriculture, waste water plants and rural
areas, respectively. The nitrogen reduction of diffusor system is incorporated to the total
wastewater nitrogen reduction. Indices i and j represent the given subcatchment and
management method, respectively and f represent given wastewater treatment facility.
The model minimizes the social costs and if we denote the total costs as 𝐶𝑡𝑜𝑡 (𝑁, 𝑗, 𝜙, 𝑚), we can
write the minimization problem as follows:
𝒎𝒊𝒏
𝑵, 𝒋, 𝝓, 𝒎
C𝒕𝒐𝒕 (𝑵, 𝒋, 𝝓, 𝒎)
(2)
̅
s.t 𝒈(𝑵, 𝒋, 𝝓, 𝒎) ≤ 𝑵
(3)
This constrained minimization problem can be solved by formulating Lagrangian:
𝓛(𝑵, 𝒋, 𝝓, 𝒎, 𝝀) = 𝑪𝒕𝒐𝒕 (𝑵, 𝒋, 𝝓, 𝒎) − 𝝀(𝒈(𝑵, 𝒋, 𝝓, 𝒎) − 𝑵)
(4)
Here λ can be seen as e.g. a Pigouvian tax: the excessive nutrient pollution can generate
negative externalities and so the polluter has to pay a tax for each nitrogen kilogram that passes
to the target area. The solution for the minimization problem can be reached setting marginal
costs equal to the tax for each measure and thus, the optimal inputs for each measure can be
solved and the solution can be considered cost-effective. In turn, the optimal inputs can be used
to solve the total annual nitrogen load and the corresponding total annual costs for each measure.
Higher the tax levels yields larger nitrogen reductions and setting the tax level right, a certain
nitrogen load reduction target can be reached.
The total nitrogen reduction, 𝑁 𝑟𝑒𝑑 , is then calculated as difference between initial nitrogen loading
𝐿0 and optimal nitrogen loading 𝑔(𝑁𝑜𝑝𝑡 , 𝑗𝑜𝑝𝑡 , 𝜙𝑜𝑝𝑡 , 𝑚𝑜𝑝𝑡 ) :
𝑵𝒓𝒆𝒅 = 𝑳𝟎 − 𝒈(𝑵𝒐𝒑𝒕 , 𝒋𝒐𝒑𝒕 , 𝝓𝒐𝒑𝒕 , 𝒎𝒐𝒑𝒕 )
(5)
To compare annually occurring costs to investment costs, all the investment costs were
annualized using discount factor D from Schou et al (2006):
𝐃=
(𝟏 + 𝐫)𝐧 𝐫
(𝟏 + 𝐫)𝐧 − 𝟏
(6)
where r is the discount rate and n the depreciation period. The depreciation period was assumed
to be 30 years and for the discount rate we used 3.5 % as recommend by Moore et al. (2004).
3.2 Agricultural measures
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The nitrogen reduction measures in agriculture consist of four different management options
shown in table 1. The social planner chooses the optimal management method for each
subcatchment. All the labour, technology, capital and information needed for each management
options are assumed to be available in each subcatchment. The actual locations and upper
catchment areas of the potential wetlands were obtained from modelling done in Finnish
Environment Institute. However, in some subcatchments the modelling yielded zero wetlands
and in those cases the wetland construction was not potential management method. It was
assumed that agricultural land area would remain constant as field areas converted to wetlands
were relatively low.
Table 1 Agricultural management options
Normal tillage Normal tillage + Wetlands
Reduced tillage Reduced tillage + Wetlands
We assume that the farmers in subcatchment i choose the amount of fertilizer and the
management option j to maximize the payoff function:
𝒇𝒊𝒙
𝝅𝒊 = 𝑨𝒊 [𝒑𝑩 𝒚𝒋 (𝑵𝒊,𝒋 ) − 𝒑𝑵 𝑵𝒊,𝒋 − 𝑪𝒋 ] − 𝑪𝒘𝒍
𝒊
(7)
where 𝐴𝑖 is the total crop area in the basin, pB is the barley price (160 Eur / ton) that is assumed
to be independent of crop production levels in Lake Vanajavesi or River Porvoonjoki catchment,
pN is the cost of fertilizer (per kilogram of nitrogen), (667 € / ton) and 𝑦𝑗 (𝑁𝑖,𝑗 )is the yield function
𝑓𝑖𝑥
from INCA-N model. Index j denotes the agricultural management option. Terms 𝐶𝑗
and 𝐶𝑖𝑤𝑙
describe the fixed costs of cultivation per hectare and the costs of constructing wetlands,
respectively. In this study we used fixed costs from Helin et al. (2006) adjusted for inflation. The
cost of fertilizer reduction is the loss of profits to the farmer and the effect follows from leaching
function and the effect of wetlands. Both yield and leaching functions are estimated from INCAN model using linear interpolation (figure 2). Retention parameters for each subcatchment areas
were provided by VEMALA-model (Huttunen et al. 2016).
The costs of the wetlands consisted of the discounted cost of the land together with the
construction and maintenance costs. The costs of the land was formed by combining the
information of the land cover and the size of the wetland with average purchasing prices of
agricultural or forests lands provided by National Land Survey of Finland. The construction and
maintenance costs were estimated from Ahlvik et al. (2014). The discounted cost of the land was
considered to represent the opportunity cost of the land in its current use in either agriculture or
forestry. The land cover data for the constructed wetlands areas were derived from Corine Land
Cover 2012 –dataset. Wetlands, where over 5 % of the land area was located in either
constructed, water or current wetland areas were removed from the data.
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a)
b)
Figure 2 (a) Interpolated yield and (b) leaching functions from INCA-N results
The nitrogen retention provided the wetlands was estimated using work done by Puustinen et al.
(2007):
𝑾𝒊 = 𝟏𝟎. 𝟒𝟕
𝑨𝒘𝒍
𝒊
𝑨𝒘𝒄𝒂
𝒊
(8)
𝑤𝑐𝑎
where 𝑊𝑖 is the nitrogen retention percentage in wetlands, 𝐴𝑤𝑙
are wetland area and
𝑖 and 𝐴𝑖
the size of the wetland catchment area, respectively. The retention factor was aggregated and
averaged for each subcatchment area for the cost-efficiency calculations.
3.3 Wastewater treatment
We used average annual nitrogen load for years 2010-2014 as a reference load for the
wastewater treatment plants (WWTP) and the data was gathered from Finnish environmental
protection database VAHTI. The total accrued costs were estimated to be the difference between
the annual costs at the reference efficiency (i.e. current average efficiency) and the optimal
efficiency level. Nitrogen abatement from improved removal efficiency was estimated similarly.
We categorized WWTPs to four different Person Equivalent-classes (PE)1, similarly as
Hautakangas et al. (2014). The smallest PE-class (10 000 – 80 000 PE) from Hautakangas et al.
(2014) was used also for the minor WWTPs (PE < 10 000), as no other cost information was
available.
1
One person equivalent (PE) is calculated as 70 grams per day of biological oxygen demand over seven days (BOD7)
and it corresponds to 60 grams of oxygen demand per day over five days as it is defined in EU’s directive concerning
urban waste water treatment (91/271/ETY).
WWTP classes: 10 000 – 80 000 PE, 80 000 - 220 000 PE, 220 000 - 500 000 PE, and over 500 000 PE.
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The total cost functions (Eq. 8) of the waste water treatment plants were estimated from
Hautakangas et al. (2014):
𝟐
𝑪𝒘𝒘𝒕
𝒇 (𝒑) = 𝒂𝒇 𝒑 + 𝒃𝒇 𝒑 + 𝒄𝒇
(9)
where 𝐶𝑓𝑤𝑤𝑡 (𝑝) was the total annual cost of a waste water treatment plant with a nitrogen removal
efficiency p. Parameters 𝑎𝑓 , 𝑏𝑓 and 𝑐𝑓 were constants which depend on the size of the treatment
plant. The marginal costs were obtained by deriving the equation 8 from which the optimal
nitrogen removal efficiency can be solved.
We assumed that the diffusor pipeline was possible to install at every WWTP at a uniform cost
of 37 000 € for each plant. The cost data was derived from system demonstration implementation
costs at Keuruu WWTP. In another demonstration location, Petäjävesi, the costs were
significantly lower at only 2 000 €. However, we wanted to use the more conservative estimate
for the costs as the data is still quite limited. As the demonstrations implied that the pipeline would
need little to no annual maintenance, maintenance costs were not included. The additional
denitrification rate provided by the diffusor system was assumed to be 20 %, which we think is
achievable with some modification of the diffusor system on the basis of the results from the
demonstrations. As implementation costs and the nitrogen removal efficiency for the diffusor
system were assumed constant, the marginal cost curve was flat, and depending on the
discharge from plant.
Existing waste water network was described with a 1km x 1km grid using the actual wastewater
pipeline networks provided by the local wastewater treatment plants and centres for Economic
Development, Transport and the Environment for Uusimaa and Häme. The number of inhabitants
were also described with a 1km x 1km grid provided by Statistics Finland. The investment costs
of waste water network expansion was estimated to depend on the distance of a given grid point
as follows,
𝑪𝒓𝒖𝒓𝒂𝒍
= 𝑫𝒅𝒎 𝑪𝒄𝒐𝒏𝒏
𝒎
(10)
where 𝐷 was the waste water discount factor (Eq. 6), 𝑑𝑚 was the distance the existing network
and 𝐶𝑐𝑜𝑛𝑛 was the cost of the network expansion in euros per meter. The average cost of network
expansion (58 €/m) was estimated from Helminen et al. (2013).
The nitrogen load reduction from a given grid point m was estimated as follows,
𝑵𝒓𝒖𝒓𝒂𝒍
= (𝟏 − 𝑹𝒊 )(𝒘𝒘𝒕𝒂𝒗𝒆 − 𝑬)𝒑𝒐𝒑𝒎 𝑴
𝒎
(11)
where 𝑅𝑖 was the retention factor of the catchment area, 𝑤𝑤𝑡𝑎𝑣𝑒 was the average nitrogen
removal efficiency of the treatment plants in the area, E was the nitrogen removal efficiency of
property-specific waste water systems (assumed to be 40 %), 𝑝𝑜𝑝𝑚 was the population in grid
point m and M the average annual nitrogen load per person (5.11 kg N/person).
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4. Results
4.1 Economic modelling
Figures 3 and 4 show the cost of a given nitrogen reduction for the Lake Vanajavesi (figure 3a)
and the River Porvoonjoki (figure 4a) catchments. The distribution between different measures
is similar for both catchments. For reduction targets over 50 tons of nitrogen, the most costefficient measure is to invest in wastewater treatment facilities. Especially, the diffusor system is
very cost-efficient way to handle nitrogen loading, even though the reduction capacity of the
measure is somewhat limited. However, when the target is set to higher nitrogen reductions, i.e.
over 200 tons of nitrogen, major costs occurs also from agriculture. This is true for both catchment
areas and suggests that significant nitrogen load reductions need actions in both agriculture and
wastewater treatment.
Figures 3b and 4b show the relative distribution of nitrogen reduction between each measure.
For both catchments, the pattern for the measures is quite similar as it was with the costs. In
smaller reductions (i.e. less than 50 tons), most of the reduction comes from reducing fertilization
and moving to no tillage. As the reduction target grows, wastewater treatment plants remove
larger share of the total nitrogen. In this modelling, building wetlands was shown to be quite
expensive measure and it is utilized only with largest reduction targets over 200 tons of nitrogen.
a)
b)
Figure 3 (a) Total costs and costs for
each measure with a given nutrient
reduction target. (b) Relative share
of total nitrogen reduction for each
measure’s in Lake Vanajavesi
catchment.
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a)
b)
Figure 4 (a) Total costs and costs for each measure with
a given nutrient reduction target. (b) Relative share of
total nitrogen reduction for each measure’s in River
Porvoonjoki catchment
If we take into account the retention from Lake
Vanajavesi to the Bothnian Sea, the nitrogen
reductions estimated at the sea are significantly
cheaper in the River Porvoonjoki catchment
area. The results show that nitrogen retention has major impact to the spatial allocation of
measures. As the changes in soil characteristics and retention factors in River Porvoonjoki
subcatchments remain relatively low, the proposed nitrogen loading actions are allocated rather
evenly throughout the whole basin. However, in Lake Vanajavesi catchment the measures
correlate more strongly with the nitrogen retention as the wetlands are constructed first to next
to watercourse leading to lake in the upper left corner (figure 5a). For both catchments, no tillage
was the more efficient method compared to normal tillage for all reduction targets. This was due
to lower fixed costs and lower nitrogen leaching even though the yields were slightly lower (figure
2a).
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b)
a)
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Figure 5 Spatial allocation of agricultural management options as the nitrogen reduction
target grows. a) Lake Vanajavesi catchment, b) River Porvoojoki catchment
Figure 6 shows the spatial allocation of nitrogen reductions (fig. 6a) and costs (fig. 6b) between
agriculture and wastewater treatment. Similarly to spatial allocation of abatement measures, in
Lake Vanajavesi (green), retention factors impact the cost-efficiency of the measures. In Lake
Vanajavesi, nitrogen reductions are cost-efficient the closer the subcatchment is located to the
lake. On the other hand, in River Porvoonjoki catchment area, where differences in subcatchment
retention factors are relatively small, the soil characteristics play a more important role in defining
the cost-efficiency of agricultural measures.
Figure 6 (a) Nitrogen reductions between wastewater treatment and agricultural measures. (b) Abatement costs
per kilogram of nitrogen removed for wastewater treatment and agriculture measures. In both figures, Lake
Vanajavesi is depicted with green color and River Porvoonjoki catchment area is depicted with red. Nitrogen load
reduction target for Lake Vanajavesi was 300 tons of nitrogen and for River Porvoonjoki 200 tons of nitrogen.
Using the spatial optimization of fertilization levels and management methods, we provide
different load reduction scenarios to be analysed in INCA-N in more detail. Through this
cooperation between models we can estimate the actual daily concentrations in the catchment
areas. These results are presented in deliverable 3.4.
4.2 Uncertainty analysis
To assess the uncertainty in the initial input values, we performed a Monte Carlo analysis where
the optimization was simulated 10 000 times with mutating input values. All of the variables were
assumed to be normally distributed with discrete model inputs as the mean value. For the
variables were applicable data was available, standard deviations were calculated from historical
time series. For other variables, we assumed a 20 % standard deviation.
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The variation in costs in Lake Vanajavesi (figure 7a) is considerably higher compared to the River
Porvoonjoki (figure 7a). This is probably due to greater heterogeneity associated with the Lake
Vanajavesi catchment area. The results suggest that the modelled abatement costs in Lake
Vanajavesi catchment area can be somewhat underestimated. Also, naturally as reduction
targets rise, the uncertainty about the costs rises similarly. This effect can be seen especially in
the Lake Vanajavesi results.
b)
a)
Figure 7 (a) Monte Carlo analysis for Lake Vanajavesi catchment area and (b) River Porvoonjoki catchment area.
The improvement potential of large wastewater treatment facilities plays a major role in the total
nitrogen reduction potential. If the actual maximum reduction potential is significantly lower, e.g
70 % instead of 90 %, then the overall nitrogen reductions might be lower and costs significantly
higher. To provide a more detailed assessment, each facility should be individually assessed,
which in turn might increase significantly the data gathering costs.
5. Model limitations
This model does not take into account the change in agriculture output. Lower yields caused by
lower fertilization levels in a given area might increase crop imports from other regions, which in
turn can increase nutrient leakage. Thus, the total amount of nitrogen reduction might be less
than calculated by the model. It was also noted in stakeholder meetings, that low nitrogen
fertilization levels that the model estimated, could be unfeasible in many areas.
Annual variation in e.g. weather conditions may cause significant changes in wetland retention
capability, depending on the wetland characteristics (e.g. Koskiaho et al. 2003). Especially in
colder conditions, e.g. Finland, where growing season is short and discharges through wetlands
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might vary substantially, this variation in retention capability poses major uncertainty element. In
the economic model, this variation is taken into account only in the uncertainty analysis.
The feasibility of given wastewater investment was not assessed in this project. We assumed
that every treatment plant was capable of reaching 90 % reduction rate, which was the upper
limit given by Hautakangas et al. (2014). In reality, reaching this reduction level might not be
feasible or even possible. Also, stability of wastewater treatment process inside the facilities
affects the reduction potential. If the nitrogen removal process is unstable or if the plant is unable
to sustain constant rate of denitrification, the variation in cost-efficiency can be significantly
higher.
6. Conclusions
The results show that investing in wastewater treatment facilities and especially diffusor system
is very cost-effective way to reduce nitrogen loading. Though only limited amount of data is
available from the diffusor system, it seems quite promising nitrogen reduction measure in terms
of cost-efficiency. If more data becomes available, the optimization model can be updated easily
to give more representative results. Especially data from larger wastewater treatment facilities
would be interesting to see if the technology can be scaled to larger effect.
The efficient implementation policy for nitrogen load reduction measures depends on the wanted
results. If the target is to reduce sea bound nitrogen load, the most effective measures are in the
areas close to the sea, where nitrogen retention is low. However, if the target is to reduce
nitrogen load also within the catchment area, then the measures have to be more
comprehensive. With agricultural measures, area of impact can be much larger when compared
to wastewater treatment facilities, especially if the facilities are located in the lower parts of the
catchment area.
The results suggest with the modelled set of measures that water protection policies aimed at
reducing significant amounts of nitrogen load should focus on multisectoral approach, i.e. policies
concerning both agriculture and wastewater treatment. As these measures carry different kinds
of uncertainties and impacts, both of these sectors should be utilized in complementary manner
to reduce nitrogen loading.
For future model development, incorporating phosphorus fractions into the model is crucial to
provide more holistic impact assessment of the water protection actions. The dynamic
interactions between nitrogen and phosphorus concerns especially agricultural measures, where
certain nitrogen reducing measures may increase phosphorus loading. These dynamics can be
seen in e.g. constructed wetlands and reduced and no tillage (Dodd & Sharpley 2016).
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7. References
Ahlvik, L., Ekholm P., Hyytiäinen K., Pitkänen H., 2014. An economic-ecological model to
evaluate impacts of nutrient abatement in the Baltic Sea. Environmental Modelling & Software
55: 164-175.
Dodd, R. J., & Sharpley, A. N. 2016. Conservation practice effectiveness and adoption:
unintended consequences and implications for sustainable phosphorus management. Nutrient
Cycling in Agroecosystems, 104(3), 373-392.
Gren, I., Jonzon, Y. & Lindqvist, M. 2008. Costs of nutrient reductions to the Baltic Sea - technical
report. 1. Swedish University of Agricultural Sciences. Uppsala. s. 64.
HELCOM 2011. The Fifth Baltic Sea Pollution Load Compilation (PLC-5). Baltic Sea Environment
Proceedings, 128
Huttunen, I., Huttunen, M., Piirainen, V., Korppoo, M., Lepistö, A., Räike, A., Tattari, S., &
Vehviläinen, B. 2016. A national-scale nutrient loading model for Finnish watersheds—VEMALA.
Environmental Modeling & Assessment, 21(1), 83-109.
Koskiaho, J., Ekholm, P.,Räty, M., Riihimäki, J. & Puustinen, M. 2003. Retaining agricultural
nutrients in constructed wetlands, experiences under boreal conditions. Ecological Engineering.
vol. 20. s. 89-103.
Moore, M.A., Boardman, A., E., Vining, A., R., Weimer, D., L. & Greenberg, D., H. 2004. "Just
give me a number!" Practical Values for the
Social Discount Rate. Journal of Policy Analysis and Management. vol. 23. nro. 4. s. 789-812.
Schou, J., S., Neye, S., T., Lundhede, T., Martinsen, L. & Hasler, B. 2006. Modelling cost-efficient
reductions of nutrient loads to the Baltic Sea - Concept, data and cost functions for the cost
minimisation model. NERI Technical report. 592. National Environmental Research Institute,
Ministry of the Environment - Denmark.
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