Analysis of the changes in N and P Discharge from Ryaverket Report July 2008

Transcription

1 Gryaab AB Analysis of the changes in N and P Discharge from Ryaverket Report July 2008

2 Analysis of the changes in N and P Discharge from Ryaverket Report July 2008 Agern Allé Hørsholm Tel: Fax: Initials ish/flm/krg/msl/cmu cmu@dhi.dk Web: Client Representative Gryaab AB Jan Mattsson Project Project number Authors Ryaverket Kristine Garde Ian Sehested Hansen Ciarán Murray Date July 2008 Approved by Flemming Møhlenberg 2 Final KRG/CMU CMU FLM 1 Draft KRG/CMU CMU FLM 4 JULY ,DEC 2007/MSL Version Description By Checked by Approved by Date Key words Ryaverket, phosphorus, nitrogen, marine effects, eutrophication, 3D modelling, sewage discharge Classification Open Internal Proprietary Distribution Gryaab : Jan Mattsson ISH, CMU, Bibl. Number of copies 1+pdf 3

3 LIST OF CONTENTS 1 INTRODUCTION SET UP OF 3D MODEL MODEL SOFTWARE SIMULATION PERIOD BOUNDARIES AND BATHYMETRY Meteorological Data Loads from Land EUTROPHICATION MODEL Phytoplankton Growth MEASUREMENTS FOR CALIBRATION OF THE MODEL PROCEDURE FOR SIMULATIONS MODEL RESULTS HYDRODYNAMIC RESULTS EUTROPHICATION MODEL RESULTS Comparison of Model Results with Measurements Nutrient Mass Balance SCENARIO RUNS RESULTS DISCUSSION AND CONCLUSION LARGE MODEL AREA SMALL MODEL AREA FURTHER REDUCTION IN NITROGEN DIFFERENCES BETWEEN THE 2005 AND 2007 SCENARIO RUNS CONCLUSIONS REFERENCES...25 ANNEXES A B C D E Scenario Plots Discharge and Nutrient Loads from Sources Time series of Measured and Simulated Salinity and Temperature Time series of measured and simulated Eutrophication Model Variable Tables of Scenario Difference Statistics 1

4 1 INTRODUCTION Gryaab has in June 2007 commissioned to undertake a modelling study of the far-field effects of the treated sewage discharge from Ryaverket, the treatment plant in Gothenburg. The scope of the modelling study is described in s proposal of 10 May undertook a modelling study for Gryaab and Bohuskustens Vattenvårdforbund back in 2005 on the near-effect effects of discharge from Ryaverket /1,2/. This study focused on the area within a distance of approximately 30 km from the discharge position at Ryaverket. Impacts of various loads were assessed; including load conditions from the 1980 s, the present treatment level being implemented at Gryaab and potential loads if further treatment stages were implemented. The former modelling study revealed that there could be some impacts outside the applied model domain. The focus of the present modelling study has been to quantify these more remote effects to the water quality parameters in the Kattegat. Therefore, the present study is based on an extended model that includes the entire Kattegat and also The Sound and parts of Skagerrak. 2

5 2 SET UP OF 3D MODEL 2.1 Model Software The model set up was carried out in s model system MIKE 3 (version 2005). The non-hydrostatic (full 3D) version of the hydrodynamic module and a eutrophication module established in the ECO Lab equation solver are used. For a more detailed description of MIKE 3 and ECO Lab refer to / 2005/. The output from a standard 3D hydrodynamic/eutrophication model includes: water level salinity, temperature and current (in profile over the water column) nutrients, inorganic and total N and P (IN, TN, IP, TP) and oxygen (DO) conditions (in profile), including oxygen depletion dead particulate matter (detritus) C, N and P (in profile) primary production of plankton algae (in profile) biomass of plankton algae stated in terms of chlorophyll and C, N and P (in profile) biomass of zooplankton stated in terms of C The eutrophication module includes exchange of nutrients between water and sediment phases. In the previous study, sufficient information was available from samples in the model area to estimate quantities of organic and inorganic sediment nutrient pools. These nutrient pools could thereby be modelled explicitly. In contrast, there is insufficient information on sediment composition over the larger area covered by this present model to justify modelling the sediment nutrient pools explicitly. Nutrient exchange with sediment is still included, but in a more simplified manner: Instead of maintaining an inventory of sediment content, a proportion of all organic nitrogen and phosphorous which settle out of the water column are recycled instantaneously as inorganic nutrients. The proportions recycled are dependent on temperature and oxygen conditions at the seabed. 2.2 Simulation Period As for the earlier Ryaverket study, the model was set up for the period from January 1 to December 31, For this period, boundary data for the model could be generated from s operational service, the Water Forecast ( Both hydrodynamic and bio-chemical measurement data for testing the model can be 3

6 found. In 2003 there were major dredging activities in the model area under the project Säkrare farleder which could have had influence on the bio-chemical measurements, and therefore 2002 was chosen as the preferred model year. 2.3 Boundaries and bathymetry The model was set up to cover the Kattegat and the Eastern region of the Skagerrak. This extended model region intended to cover the extent of any significant influence of the discharge from Ryaverket. The MIKE 3 uses a rectangular calculation grid to represent the bathymetry of the model area. Selecting the spatial resolution of the model grid is a balance between the need for representing details and for reducing the calculation time for the model. Figure 2.1 Model bathymetry with 1 nautical mile (1852 m) grid spacing (The area shown in red is not modelled) 4

7 The implemented model has a horizontal resolution of 1 nautical mile (1852m). Figure 2.1 shows the model bathymetry which was generated on the basis of depth information from C-MAP. The model has 110 vertical layers and a vertical resolution of 2m. The surface layer is 3m thick and varies in time with the water level. Additionally, the bottom layer thickness varies to accommodate bathymetry in deeper areas. For example, the bathymetry at a given point in the model is -230 m. The uppermost 109 layers extend down to -219m. The thickness of the bottom cell at this point is then 11m. The model has 4 open boundaries (here with their approximate locations): Skagerrak, from Hirtshals (DK) to Larvik (N) Limfjorden, at Aalborg Southern Kattegat, from Ebeltoft to Gilleleje Øresund, from Strøby (DK) to Falsterbo (SE) s Waterforecast operates a regional model covering the North Sea and the Baltic sea. Variation in water level along the boundaries, and variation in salinity, temperature and eutrophication module variables in profile along the boundaries of our local model were obtained from the Waterforecast Meteorological Data Meteorological data, including wind speed and direction, air temperature and precipitation were used in forcing the model. These data were also obtained from the Waterforecast service Loads from Land Data on discharge and nutrient loading for the model came from a number of sources. A comprehensive list over flow volumes and nutrient loads from land which were applied to the model is available in Annex B, along with figures showing the location of each discharge point. Figure 2.2 below shows the location of a number of sources within the Bohus coast area. For example source no. 38 represents Göta Älv and no. 39 represents Nordre Älv. Where possible, load data from the previous Ryaverket study was reused.. For Swedish rivers and runoff from land, estimates of flow volumes were obtained as monthly averaged data from the Swedish Meteorological and Hydrological Institute, SMHI. Annual estimates of nutrient loads are available from the Swedish institute for Environmental Assessment, In some cases both flow volumes and nutrient loads were directly attributable to a single specific river. In these cases, it was relatively straightforward to estimate the loading over the year. Where nutrient loads were attributed to a coastal region or to a number of smaller rivers, the loading was distributed proportionately with flow volumes. 5

8 Figure 2.2 Location of sources in the Bohus Coast area Information on loading from industrial point sources was obtained via Gryaab from the Administrative Boards for the three Swedish counties within the model area. For the county of Skåne, it was not possible to locate each individual point source. Here the nutrient loads were summarized by municipality and the resulting load applied at a single location. For the non-swedish discharges, data was obtained from the Waterforecast regional model. 2.4 Eutrophication Model The eutrophication model used in this model study comprises s standard ECO Lab module with a few pelagic extensions. This module is described in detail in / 2005/ and describes processes related to 11 pelagic components: Phytoplankton C, N, and P Chlorophyll-a Zooplankton C 6

9 Detritus C, N and P Inorganic N and P Dissolved oxygen In this model inorganic N is divided into ammonium (NH 4 ) and nitrate (NO 3, including nitrite). For zooplankton a fixed C:N:P relationship is assumed and therefore only the C pool is described explicitly. As with the model for the earlier Ryaverket study, the present model describes how organic material accumulates at the seabed via a settling process. In contrast with the earlier model, nutrients in the sediment are not described explicitly. In the present model varying proportions of the N and P in this organic material are recycled directly to the water phase. The proportion of nutrients recycled in this way is dependent on temperature at the base of the water column Phytoplankton Growth Description of phytoplankton growth in the EcoLab model, and relation to empirical data for phytoplankton groups dominating along the Bohus coast. Only one bulk phytoplankton state variable is included in the model, but forced changes in temperature optima will enable a reasonable temporal variation in growth and biomass development. The growth of phytoplankton in the applied model is described mechanistic being dependent on light availability, nutrient availability (DIN and PO4), and temperature. Sub-optimal conditions in any of these factor groups invariable will result in growth rates below the maximum. Effect of light on primary production and growth rate is described by a saturation function, where the light saturation parameter (Ek) can be used for calibration. Effect of nutrients on growth rate is described by a Droop equation, where the cellular nutrient contents (quota) determine growth rather than the dissolved nutrients in the water surrounding the algae. The temperature-dependence of growth rate is described by an Arrhenius equation (Teta = 1.08), but with changing temperature-optimum through seasons. Prior to 24 March the optimum temperature is set to 5 C, later to 20 C and after mid October again to 5 C. The exact dates where optimal temperatures are changed can be regulated during calibration so that a cold water spring bloom of diatoms can be simulated and summer blooms of warm-water adapted algae also be reproduced using only one state variable. 7

10 The max growth rates in the current simulations were set to 1.4/d for spring algae and 1.5/d for summer algae. The phytoplankton along the Bohus coastline has been monitored regularly in the past 17 years ( växtplanktonrapporter) and a brief examination of the yearly reports show significant spatial, seasonal and year-toyear variation in species dominance. Overall however, diatoms (especially Skeletonema costatum, Chaetoceros, Ceratulina, Cylindrotheca, Proboscia alata) dominate in spring, and blooms during summer and autumn. Dinoflagellates such as Ceratium (several species), Heterocapsa and the potential toxic Dinophysis and Prorocentrum minimum are regular found during late summer and autumn. Based on literature reviews (Banse 1982, Sarthou et al. 2005) and extensive experimental studies (Dixon & Syrett 1988; Skovgaard & Menden-Dauer 2003), the growth rate of nutrient-saturated plankton algae under saturating light conditions is found to vary between 0.3 to 2.5 per day, where high growth rates are characteristic for diatoms and much lower rates are characteristic for dinoflagellates. Within the taxonomic groups the maximum growth rate generally increases with diminishing cell size, e.g. with a doubling of growth rate with a 3-4 fold decrease in cell volume. The max growth rate of Skeletonema costatum with a typical volume of 500 µm 3 is around 2.2/d while a large Rhizosolenia with a cell volume > 10 5 µm 3 has a max growth rate of /d. Using the species lists from semi-quantitative phytoplankton monitoring along the Bohus coast typically taxons are represented in each sample, each with their characteristic maximum growth rate and nutrient dependency on growth. Therefore using a single set of parameter values to represent a wide variety of algae always will be a compromise, but years of experience have demonstrated that use of max growth rates of /d works in most situations. The simulated growth rate in the model is however, much lower than the maximal rates due to light limitation (spring and autumn) and nutrient limitation during summer. A plot of the modelled growth rates at the stations Danafjord and Fladen is shown in Figure 2.3. Figure 2.3 Modelled phytoplankton specific growth rates (day -1 ) at Danfjord (Blue) and Fladen (Green) 8

11 2.5 Measurements for Calibration of the Model Figure 2.4 Location of marine monitoring stations and water level stations Observation data was obtained from the Swedish marine monitoring data bank held by SMHI. These observations are made approximately once per month and include measurements through the water column of a number of physical and chemical parameters, such as salinity, temperature, inorganic and total nitrogen and phosphorous, amongst others. Figure 2.4 shows the location of the monitoring stations from which data was used. Water level observations from Hornbæk and Gothenburg were available with a much higher temporal resolution (10 minutes and 60 minutes, respectively). Along with salinity and temperature, these water level measurements used to calibrate the hydrodynamic model. The Eutrophication module was calibrated by comparing model results with observation data for concentrations of chlorophyll-a, inorganic nitrogen, total nitrogen, inorganic and total phosphorus, and dissolved oxygen. 9

12 2.6 Procedure for Simulations The model was calibrated first by adjusting parameters in the hydrodynamic module and subsequently parameters in the processes described by the eutrophication module. The calibration of the model was undertaken using actual loads for Following the calibration a reference scenario was run, based on the present loads from Ryaverket. Following this, five runs were made, each with a different nutrient load scenario for Ryaverket. The specific nutrient load from Ryaverket for each of the scenarios is described in detail in Chapter 4. 10

13 3 MODEL RESULTS 3.1 Hydrodynamic Results Simulated results for the water levels at Gothenburg and Hornbæk are compared with the observed water levels in, respectively, figure 3.1 and figure 3.2. Figure 3.1 Comparison of measured and simulated water level at Gothenburg Figure 3.2 Comparison of measured and simulated water level at Hornbæk The simulated water level follows well the measured water level. The typical level and the longer term variations are reproduced well, though with occasional periods of deviation. The daily tidal variation in Gothenburg is around 0.2 m. There is a tendency for the model to overestimate this, with typical model variation around 0.3 m. The model daily tidal variation at Hornbæk is very close to the observed range. Figure 3.3 Comparison of measured and simulated salinity at Fladen at surface (0-5 m) and at 50 m depth 11

14 Figure 3.4 Comparison of measured and simulated temperature at Fladen at surface (0-5 m) and at 50 m depth Model salinity and temperature are compared with measured values for the station Fladen in figures 3.3 and 3.4. Both these and the salinity and temperature comparison plots for other stations can be found in Annex C. The results from Fladen are reasonably typical. They illustrate the general good agreement between model salinity both at the surface and at depth, in this case 50m. And as well as demonstrating the good agreement between measurements and simulated temperature at the surface, they also show a problem with the model not capturing the observed increase in temperature in deeper water in the southern Kattegat during late summer. Fladen is perhaps the station where this problem is most obvious but can also be observed at Anholt. This could possibly have an influence on the recycling of nutrients after settling to the seabed, as described in

15 3.2 Eutrophication Model Results Comparison of Model Results with Measurements Figure 3.5 Comparison of measured and simulated chlorophyll-a at Fladen at surface (0-5 m) and at 20 m Figure 3.5 shows the time series for modelled and measured chlorophyll a concentrations at Fladen. Plots of measured and modelled variables for the monitoring stations shown in Figure 2.4 are collected in Annex D. Examining the plots of measured versus modelled variables, it can be seen that the calibration of the model certainly leaves room for improvement. However, the purpose of the model is to compare the differences between a number of scenarios for nutrient discharge from Ryaverket. As such it is the relative differences in simulated concentrations which are of interest, rather than the absolute values. The level of calibration which has been achieved is judged to be sufficient and the model therefore well-suited to the task of comparing the relatively small variations in resulting concentrations between the scenarios. 13

16 3.2.2 Nutrient Mass Balance Figure 3.6 Control area for difference statistics (Area 1), showing also the smaller control area for the 2005 Ryaverket study (Area 2). As in the 2005 study, the model was used to estimate the total transport of nutrients through a control volume for the year. Figure 3.6 shows the location of the control box. The control box covers an area of approximately km 2 and contains a volume of roughly 1100 km 3. The results of the mass balance calculation (Area 1) are summarized in Table 3.1. Table 3.1 Transport of nutrients during 2002 Inorganic P Total P Inorganic N Total N (tons) (tons) (tons) (tons) From land Transport across open boundaries Processes (including exchange with sediment) Accumulation Net accumulation in sediment The pool of Total Nitrogen within the control area of the model was tons at the beginning of The reduction in mass, by tons, represents approximately 20% of the Total Nitrogen pool, not including biounavailable nitrogen. The phosphorus contained within the control is reduced by around 13%. If a comparison is made with the 2005 Ryaverket study, then we can see that in the earlier study the net change in storage was almost zero. This appears to have been 14

17 conservative. The reduction in inorganic nitrogen of tons in the present model corresponds to an average reduction in concentration over the control volume of 28 µg/l. If we compare the measured values of inorganic nitrogen at a number of monitoring stations at the beginning and end of 2002, we obtain the change in measured inorganic nitrogen. A visualization of the changes in measured inorganic nitrogen during 2002 at each measurement depth at the stations Å13, Å15, Anholt E and Fladen are shown in Figure 3.7. The change in measured values shows a wide range of variation from -90 to +90 µg/l. Though it could be argued that the reduction in nitrogen storage was too great, the average concentration change lies well within the limits observed at the measurement stations. There is also some uncertainty attached to the calculation of nitrogen storage due to the failure to calibrate more precisely for ammonium and nitrate/nitrite. It is still maintained that this uncertainty does not prevent the use of the model for scenario comparisons. 15

18 Figure 3.7 Change in measured concentration of inorganic nitrogen [µg/l] by depth from January to December 2002, at Å13, Å15, Anholt E and Fladen 16

19 4 SCENARIO RUNS To be able to quantify the effect of a range of N and P loads in the sewage discharge from Ryaverket, the model was set up to run five scenarios where the discharge of N and P from Ryaverket were the only variable parameters. Table 5.1 shows the discharge conditions of nitrogen and phosphorus from treated sewage and from the overflow from Ryaverket. The reference scenario represents the situation of discharge from Ryaverket in 2010 when new treatment facilities are in use. These facilities will increase the removal of both phosphorus and nitrogen. Scenario 1 represents the situation in 1980, and Scenario 2 represents the P loads from 1980, while the N load corresponds to future theoretical maximum N treatment. Scenario 3 represents the situation with the theoretical lowest N and P loads, as a result of maximum N and P treatment. Scenarios 4 and 5 represent the situation of maximum P treatment, and N load corresponding to extensive N-treatment and to no special N-removal (1980 situation), respectively. For all other loads and discharges in the five scenario runs, the conditions are identical with those used for the calibration of the model for Table 4.1 Discharge of N and P from Ryaverket in the treated waste water and from overflow in the five scenario runs. The distribution between the different N and P fractions is also given. DN: Detritus N, IP: inorganic P, DP: Detritus P Scenario Treated mg/l Overflow mg/l Distribution N NH 4 /NO 3 /DN Distribution P IP/DP N P N P Treated Overflow Treated Overflow Reference /6/11 49/0/51 30/70 25/ /7/15 55/3/42 40/60 40/ /33/33 49/0/51 40/60 40/ /33/33 49/0/51 30/70 25/ /6/11 49/0/51 30/70 25/ /7/15 55/3/42 30/70 25/ Results The results from the five scenario runs are shown as area plots for different parameters in Annex A, Figures A1.1 A5.17. The area plots show the distribution of the mean values (mv) at different periods of the year. 17

20 For each model parameter the absolute distribution is shown for the scenario and the difference between the values from the reference and the scenario. Thus, negative differences correspond to reduced values in the scenario and positive differences correspond to increased values. The differences between the scenarios and the reference in model areas corresponding to the two control boxes shown in Figure 3.6 are also presented as statistics in Annex E (Tables E.1 E.10). The mean difference between the scenarios and the reference is given as absolute values and as percentage with 5% and 95% percentiles. Tables E.1 to E.5 show statistics calculated for the larger area, Area 1, and Tables E.6 to E.10 show the statistics for Area 2, the smaller control area corresponding to the region considered in the previous Ryaverket study. The following parameters were chosen for presentation: Mean value of chlorophyll-a concentration at the surface for the periods winter (January-February), spring (March-May) and summer (June-September) Mean value of DIN (inorganic nitrogen) concentration at the surface for the periods winter (January-February), spring (March-May) and summer (June- September) Mean value of TN (total nitrogen) concentration at the surface for the periods winter (January-February), spring (March-May) and summer (June-September) Mean value of IP (inorganic phosphorus) concentration at the surface for the periods winter (January-February), spring (March-May) and summer (June- September) Mean value of TP (total phosphorus) concentration at the surface for the periods winter (January-February), spring (March-May) and summer (June- September) Mean value of Secchi depth for the period from January to September Frequency of the concentration below 4 mg/l for oxygen in the bottom waters. It should be noted that in more closed bays the scenario results show examples of local deviations from the more regional concentration conditions. These deviations are not confirmed through measurements and are possibly incorrect. However, it is estimated that the general changes of the scenario analysis are not significantly influenced by these local conditions. 18

21 Scenario 1 - N and P loads corresponding to the 1980 situation For the large model area (see Fig. 3.6) the changes in nutrients and chlorophyll a were modest compared to the reference scenario with an increase in chlorophyll a concentration at 1.5 and 1.8% for spring and summer, respectively (Table 4.2). In contrast, for the small model area (Area 2, Fig. 3.6) larger differences are estimated, i.e. approx. 17% and 41% increase in chlorophyll a for spring and summer, respectively (Table 4.7). For the large model area the most notable changes was an increase in duration of oxygen depletion, i.e. almost 10% increase in % time of less than 4 mg/l oxygen in the bottom water (Table 4.2). Again local effects calculated for the small model area were more prominent, with an increase in time of oxygen depletion (approx. 45% higher % time of less than 4 mg/l oxygen in the bottom water). For the large model area changes in nutrients did not exceed 3% (increase in TP during summer, 2.5% decrease in inorganic nitrogen during summer), while nutrient changes in the small model area were larger with averages ranging between 3 and 41% depending on season and nutrient species. Scenario 2 - P load = 3 mg/l (1980 situation), N-load = 1.5 mg/l (theoretical maximum N reduction) The high phosphorus and low nitrogen loads from Ryaverket gave only rise to small changes in nutrients and chlorophyll a in the large model area, i.e. reductions (inorganic nitrogen in summer) and increase (total phosphorus in summer, chlorophyll a during spring) of up to 3% (Table 4.3). In the smaller model area phosphorus increased between 15-44%, chlorophyll a increased between 17 and 22%, and nitrogen decreased between 3 and 36% compared to the reference (Table 4.8). Scenario 3 - P-load = 0.22 mg/l (2010 situation), N-load = 1.5 mg/l (theoretical maximum N reduction) This scenario represents the condition with the lowest nutrient load from Ryaverket. In the large model area only concentration of inorganic nitrogen deviate by more than 1% form the reference values (Table 4.4). In the small model area inorganic nitrogen had approx. 4, 8 and 16% lower concentrations during winter, spring and summer (Table 4.9). The reductions in nitrogen is partly reflected in the chlorophyll a concentrations that is 0.05 to 0.8% lower than the reference value in the large model area, but up to 3.5% lower in the small model area. For inorganic phosphorus the concentrations were very similar to the reference conditions. A slightly improvement in Secchi depth ( m) in the small model area was estimated, while no considerable difference was noticed for oxygen concentration in either modelled areas. Scenario 4 - P-load = 0.22 mg/l (2010 situation), N-load = 6.0 mg/l (extensive N reduction 2010 situation) This scenario represents the optimal effluent situation from Ryaverket achievable when the new treatment facilities are in operation in The higher N load in scenario 4 compared to scenario 3, gave rise to chlorophyll a concentrations in spring and summer still lower than the reference conditions, but the difference was smaller as for scenario 3 (large model area: 0.1 and 0.3% in spring and summer, respectively (Table 4.5); small model area: 0.5 and 1.3% in spring and summer, 19

22 respectively (Table 4.10)). The highest reduction in respect to the reference conditions was estimated for inorganic nitrogen, that were approx. 2, 3 and 7% lower in the small model area, in summer, winter and spring, respectively, but % lower in the large model area. No considerable difference was noticed for inorganic phosphorus, Secchi depth or oxygen concentration. Scenario 5 - P-load = 0.22 mg/l (2010 situation), N-load = 20 mg/l (1980 situation) High N load from Ryaverket corresponding to the 1980 conditions and a P load achieved in 2010 resulted in slightly elevated chlorophyll a concentrations in respect to the reference conditions in summer; i.e. 0.9% in the large model area and 2.4% in the small model area (Tables 4.6 and 4.11). Inorganic nitrogen was 21% higher than the reference conditions during summer in the small model area, while deviations from reference in the large model area were much less. In the small model area Secchi depth was reduced by 0.12 m and duration of oxygen depletion increased (approx. 9% higher % time of less than 4 mg/l oxygen in the bottom water). Except for nitrogen, deviations from reference values never exceeded 1% in the large model area. 20

23 5 DISCUSSION AND CONCLUSION 5.1 Large Model Area The results of the scenario runs show that the reduction of P- and N-concentrations in the effluent from Ryaverket has a very limited effect on nutrients and phytoplankton in the large model area (Kattegat and Skagerrak). IP [µg/l] IN [µg/l] IP Spring IP Summer IN Spring IN Summer Chl-a [µg/l] CH Spring CH Summer Figure 5.1 Mean values of inorganic phosphorus, inorganic nitrogen and chlorophyll a in surface waters during spring and summer in the large model area (Kattegat and Skagerrak) as result of different load scenarios from Ryaverket. The model calculations show that inorganic phosphorus will be reduced with 0.1 µg/l in 2010 compared to 1980 conditions, and that chlorophyll a will be reduced 21

24 on average by 0.1 µg/l during summer. All other load scenarios had comparable minor effects on nutrient and chlorophyll a (Fig. 5.1). The insignificant influence of various load scenarios for Ryaverket on the environmental conditions in greater Kattegat and Skagerrak is to be expected considering the magnitude of other nutrient sources and the advection of nutrients through the area. 5.2 Small Model Area In contrast to the minor effects on large scale, the various nutrient loads scenarios were clearly reflected locally in the small model area. For the scenarios with phosphorus loads from Ryaverket corresponding to the 1980 conditions (i.e. 3 mg/l, Sc 1 and Sc 2), the concentration of inorganic phosphorus was between µg/l higher than the reference condition and chlorophyll a between 0.8 and 1.6 µg/l higher than the reference (Fig. 5.2). Nitrogen was both higher and lower than the reference condition in these two scenarios. The most pronounced effect was estimated for the highest N-load (1980 condition), especially during summer where the chlorophyll a concentration was more than 40% higher than reference. The scenarios (3, 4, 5) with variation in inorganic nitrogen load and constant P- load at 0.22 mg/l had much less influence on the spring and summer concentration of inorganic phosphorus and chlorophyll a than the scenarios with high P load (Sc 1 & 2). For chlorophyll a, the highest nitrogen concentration (20 mg/l) stimulated summer chlorophyll a with 0.1 µg/l, while chlorophyll a concentrations were lower than the reference for the other scenarios (Figure 5.2). The highest chlorophyll a reduction (0.1 µg/l) was estimated for Scenario 3 with the lowest inorganic nitrogen concentration (1.5 mg/l). 5.3 Further Reduction in Nitrogen For the scenarios with the low phosphorus load, inorganic phosphorus was always depleted in the small model area independent of the inorganic nitrogen concentration. This is in accordance with the near coastal areas being phosphorus limited, and the high P-loads lead to higher chlorophyll a production in this area. At larger distance from the coast, nitrogen becomes the controlling nutrient, and chlorophyll a concentrations respond to variation in N-load. 22

25 IP Spring IP Summer IP [µg/l] IN Spring IN Summer IN [µg/l] Chl-a [µg/l] CH Spring CH Summer Figure 5.2 Mean values of inorganic phosphorus, inorganic nitrogen and chlorophyll a in surface waters during spring and summer in the small model area as result of different load scenarios from Ryaverket. The present model estimations indicate that reducing the load of inorganic nitrogen from Ryaverket from 2010 level to 1.5 mg/l, will lower the chlorophyll a concentrations in the small model area especially along the west coast with up to 0.1 µg/l in spring and up to 1 µg/l during summer (see Annex A, Figure A3.3). Inorganic nitrogen will be reduced by 1 to 10 µg/l in an area reaching from the southern part of the Gothenburg Archipelago to Lysekil in winter and spring. (Annex A, Figures A3.4 and A3.5). In summer, inorganic nitrogen will be reduced by 1 to 10 µg/l in an area reaching from the southern part of the Gothenburg Archipelago to Tjörn (Annex A, Figure A3.6) However, a more realistic scenario is to reduce the concentration of inorganic nitrogen to 6 mg/l, which corresponds to optimal treatment with today s 23

26 technology. During this scenario, reduction in chlorophyll a is estimated for a smaller area along the west coast, with up to 0.1 µg/l as the highest estimated reduction in chlorophyll a. For inorganic nitrogen the reduction is estimated at 1 to 10 µg/l in an area reaching from the southern part of the Gothenburg Archipelago up to Skärhamn in winter, up to Gullholmen in spring and to Åstol in summer. In general, the above effects are positive to the environmental quality but they are very modest, and will be difficult to document through an ordinary monitoring programme. 5.4 Differences between the 2005 and 2007 scenario runs Despite being modest in absolute terms, the simulated effects of P-reduction in Ryaverket s effluent from the 1980 condition was about 2-3 times larger for phosphorus and chlorophyll-a than previously estimated for the same model area ( 2005). We attribute this discrepancy to the different treatment of sediments in the two model exercises: in the 2005 modelling sediment nutrients were modelled explicitly implying an uncoupling of inputs and efflux and a delay between nutrient inputs by sedimentation and efflux driven by temperature and oxygen concentration: In contrast, in the present model sediment in- and efflux are tightly coupled with the result that a part of sedimented nutrients become readily available for production. 5.5 Conclusions The scenario runs reveal that the reduction in phosphorous loads from Ryaverket during the period had an obvious result on the environmental quality of the waters in the Gothenburg Archipelago. Both chlorophyll a and inorganic phosphorous reduced significantly. The scenario runs also reveal an extended advection of the effluent, reaching large areas of the northeast Kattegat and southeast Skagerrak. However, the advection also means that the effluent concentration in this large area is very low. Reduction of nitrogen loads from Ryaverket is of no significance for phytoplankton growth in the near coastal areas of the Gothenburg Archipelago and further north. This is due to a surplus of nitrogen, originating mainly from the river Göta and Nordre Älv. This nitrogen is not used by phytoplankton and other plants because their growth is limited by lack of light and phosphorous. At greater distances from the coast, nitrogen becomes the controlling nutrient and reduction in nitrogen loads from Ryaverket reduces phytoplankton growth. The reduction in chlorophyll a per litre water is very small but it affects a large area (about the size of lake Vänern) 24

27 6 REFERENCES /1/ Analysis of the Effects of Phosphorus Discharge from Ryaverket, Report, March 2005 /2/ Udredning af effekterne af fosforudledning fra Ryaverket, Note, September /3/ MIKE 21/3 Ecological Modelling Mike 21/3 Eco Lab FM Short Desciption, /4/ Banse K. (1982). Cell volumes, maximal growth rates of unicellular algae and ciliates, and the role of ciliates in the marine pelagial. Limnol Oceanogr 27: /5/ Dixon GK & Syrett PJ (1988) The Growth of Dinoflagellates in Laboratory Cultures New Phytologist, Vol. 109, No. 3 pp /6/ Sarthou G, Timmenmans KR, Blain S & P Tréguer. (2005) Growth physiology and fate of diatoms in the ocean: a review. J Sea Res. 53: /7/ Skovgaard A & Menden-Dauer S (2003) Long-term exposure of dinoflagellates to 14 carbon: effects on growth rate and measurements of carbon content. Journal of Plankton Research Vol.25 no.8 pp