Results of Monitoring at Olkiluoto in 2009

Transkriptio

1 Working Report Results of Monitoring at Olkiluoto in 2009 Environment Editor: Anne Haapanen October 2010 POSIVA OY Olkiluoto FI EURAJOKI, FINLAND Tel Fax

2 Working Report Results of Monitoring at Olkiluoto in 2009 Environment Editor: Anne Haapanen Haapanen Forest Consulting October 2010 Base maps: National Land Survey, permission 41/MML/10 Working Reports contain information on work in progress or pending completion. The conclusions and viewpoints presented in the report are those of author(s) and do not necessarily coincide with those of Posiva.

3 This Working Report presents the main results of Posiva Oy's environmental monitoring programme on Olkiluoto Island in These summary reports have been published since The environmental monitoring system supervised by Posiva Oy produces input for biosphere modelling for long-term safety purposes as well as for monitoring the state of the environment during the construction (and later operation) of ONKALO underground characterization facility. Part of the monitoring is performed by the company running the nuclear power plants on the island, Teollisuuden Voima Oy (TVO). Monitoring has been carried out for varying periods of time depending on the sector: some monitoring activities performed by TVO originate from the 1970s and the repository-related environmental monitoring of Olkiluoto from the early 2000s. The monitoring programme evolves according to the experiences gained from the modelling work and an increased understanding of the site. Augmentations in 2009 include e.g. establishment of a new forest intensive monitoring plot (FIP14), continuation of studies on fine roots and on the species composition and abundances of small mammals. Line transect samplings of ants, terrestrial snails and earthworms were carried out and a systematic monitoring of island birds was started. In addition, a project was started where the sediment load and factors affecting the sediment transportation into Eurajoensalmi bay is examined. Dust produced during construction of the third nuclear power unit (OL3), ONKALO and related infrastructure can be seen in the soil solution and deposition results. Furthermore, the construction works and road traffic have a raising effect on the noise levels of the immediate surroundings. The land-use continues to change,. The young age of the soils and the closeness of the sea are reflected in the soil properties. Mammalian fauna on the island is typical of coastal areas in Southwestern Finland. Game catches vary according to hunting pressure and natural variation in populations. The condition of the nearby marine environment is affected by the continuous land uplift, the shallowness of the area, the weather conditions, the general condition of the Bothnian Sea, the nutrient and sediment loads carried by rivers Eurajoki and Lapinjoki, and the cooling water from the nuclear power plant. Keywords: environmental monitoring, ecosystem, baseline condition, change.

4 YMPÄRISTÖN MONITOROINTIOHJELMA OLKILUODOSSA VUONNA 2009 Tässä työraportissa esitetään päätulokset Posiva Oy:n toimintaan liittyvästä Olkiluodon saaren ympäristön monitorointiohjelmasta vuodelta Yhteenvetoraportteja on julkaistu vuodesta 2005 lähtien. Posivan ympäristön monitoroinnin ohjelma tuottaa tietoa pitkän ajan turvallisuusanalyysien vaatimaan mallinnukseen sekä ympäristön tilan seurantaan ONKALOn rakennus- ja käyttöaikana. Osa tuloksista saadaan Teollisuuden Voima Oy:n (TVO) ympäristönseurannoista. Seuranta on ollut osa-alueesta riippuen käynnissä eri pituisia aikoja: jotkin TVO:n ohjelmaan kuuluvat jo 1970-luvulta ja pääosa Posivan omista tutkimuksista 2000-luvun alkupuolelta lähtien. Posivan seurantaohjelmaa kehitetään kertyneiden kokemusten ja lisääntyneen tiedon myötä. Vuonna 2009 ohjelman lisäyksiin kuuluivat esim. uusi metsien intensiiviseurannan koeala (FIP14), hienojuurien ja piennisäkästutkimusten jatko edellisvuodesta, muurahaisten, etanoiden ja matojen linja-otannat sekä saaristolinnuston systemaattisen tarkkailun aloittaminen. Lisäksi aloitettiin projekti, jossa tutkitaan Eurajoensalmeen tulevaa sedimenttikuormaa ja siihen vaikuttavia tekijöitä. Rakennustoiminnan (OL3, ONKALO ja niihin liittyvä infrastruktuuri) tuottama pöly näkyvät maavesi- ja laskeumatuloksissa. Rakentaminen ja liikenne nostavat myös melutasoja toimintojen välittömässä läheisyydessä. Saaren maankäyttö muuttuu edelleen, mutta luonnonympäristö vastaa muita rannikon alueita. Saaren nuori ikä ja meren läheisyys näkyvät maaperän ominaisuuksissa. Nisäkäslajisto on tyypillinen Lounais-Suomen rannikkoseuduille. Riistasaaliit vaihtelevat metsästyksen intensiteetin ja populaatioiden luonnonvaihtelun mukana. Saarta ympäröivän merialueen tilaan vaikuttavat maankohoaminen, alueen mataluus, sääolot, Selkämeren yleistila, Eura- ja Lapinjokien tuomat ravinne- ja sedimenttimäärät sekä voimalaitoksen jäähdytysvedet. Avainsanat: ympäristön seuranta, ekosysteemi, perustilanne, muutos.

5 TABLE OF CONTENTS ABSTRACT TIIVISTELMÄ 1 INTRODUCTION MONITORING SYSTEM AND SCHEDULE RESULTS I: INPUT TO BIOSPHERE MODELLING Landscape properties Meteorology Weather conditions Surface runoff Radionuclides Terrestrial systems Description of the forest monitoring network Bulk deposition and stand throughfall Soil solution Tree stand transpiration Litterfall production and element return to forest floor Defoliation Fine root elongation and longevity Changes in tree characteristics Terrestrial animals Anthropogenic and social effects Limnic systems Marine/brackish ecosystems Of the monitoring Physical and chemical properties Marine vegetation Marine fauna Seafloor mapping Anthropogenic and social effects Historical and future properties RESULTS II: INPUT TO ENVIRONMENTAL IMPACT ASSESSMENTS Air quality Noise Water quality Drainage water from rock heaps Private drilled wells Overburden Flora and fauna Landscape, land-use and traffic Supplementary environmental information SUMMARY REFERENCES Appendix A. List Of Monitoring Locations... 77

6 Appendix B. Remote sensing data acquired in 2009 and earlier Appendix C. Silvicultural Actions Carried Out In The Felling Year 2009/ APPENDIX D. WEATHER MONITORING RESULTS in APPENDIX E. CHEMICAL ANALYSES OF WATER ON automatic measuring weirs in Appendix F. Results of radionuclide monitoring in APPENDIX G. Results of SMALL MAMMAL monitoring in 2008 and APPENDIX H. RESULTS OF SNAIL SURVEY IN APPENDIX I. RESULTS OF THE MONITORING OF LIMNIC SYSTEMS IN APPENDIX J. RESULTS OF THE MONITORING OF SEA ENVIRONMENT IN APPENDIX K. RESULTS FROM THE AQUATIC MACROPHYTE STUDIES IN APPENDIX L. RESULTS OF Water quality ANALYSES IN

7 1 INTRODUCTION In July 2004, Posiva began to construct an underground rock characterization facility called ONKALO on Olkiluoto Island (Fig. 1). The facility is planned for use in the early 2010s. The construction of ONKALO and subsequently the construction of the repository, will affect the surrounding rock mass and the groundwater flow system as well as the environment. In December 2003, a programme for monitoring at Olkiluoto during construction and operation of ONKALO was presented (Posiva 2003b). A summary of the observations and measurements is reported annually for each discipline: Rock Mechanics, Hydrogeology, Hydrogeochemistry, Environment and Foreign materials. The aim of this report is to give an overview of the progress of monitoring the environment. The environmental measurements and observations in 2009 are presented here. The results are divided into two parts: first, the data collected as input for biosphere modelling for long-term safety purposes are presented, followed by the data needed for monitoring the state of the environment during the construction work. Naturally, these data partly overlap. The earlier results and progress of the environmental monitoring have been presented by Haapanen ( ). Figure 1. Olkiluoto site. Map layout by Jani Helin/Posiva Oy.

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9 2 MONITORING SYSTEM AND SCHEDULE The environmental monitoring system is described in Posiva Report (Posiva 2003b). Refinements to the system have been done based on experiences, and reported in the research-specific Working Reports, as well as in previous summary reports on environmental monitoring (Haapanen ). The current environmental monitoring schedule is presented in Table 1. Further changes will be applied according to experiences from modelling work and increasing understanding of the site. For example, in 2009 a new forest intensive monitoring plot (FIP14) was established with corresponding weather station (WOM5) and studies on fine root elongation and their longevity were continued on the other FIP plots. Inventory of species composition and abundances of small mammals was continued. Line transect samplings of ants, terrestrial snails and earthworms were carried out and a systematic monitoring of island birds was started. In addition, a project was started with Luode Oy, with the aim to examine the sediment load carried by rivers Eurajoki and Lapinjoki to Eurajoensalmi bay, as well as the external factors affecting the sediment transportation in the bay area. In this context, an extra weather monitoring station (WOM6) was located on the dockyard. Annual registration of realised silvicultural actions was also started. Part of the monitoring is performed by the company running the nuclear power plants on the island, Teollisuuden Voima Oy (TVO). TVO's radionuclide sampling system is comprehensively described by Ikonen (2003) and Roivainen (2005) and TVO's marine environment monitoring system, for example, by Ikonen et al. (2003). The monitoring has been carried out for varying periods of time depending on the sector: some monitoring activities (performed by TVO) originate from the 1970s and the repositoryrelated environmental monitoring of the Olkiluoto Island from early 2000s. The Baseline Condition Report (Posiva 2003a) summarises the results of studies carried out mainly during the environmental impact assessment process toward the end of the 1990s. Major points of the monitoring design as well as maps of monitoring locations are mainly presented at the beginning of each result sector. A list of monitoring locations is presented in Appendix A. Earlier, details of the comprehensive forest and mire monitoring system were presented in a separate appendix, as well as the result tables originally presented in memos by the Finnish Forest Research Institute (FFRI). However, from this year on, the FFRI experiment results are published in a Posiva Working Report and thus only short summary and references to that report are given here. Surface runoff monitoring results were transferred here from hydrology reporting in 2008, and, since 2009, the meteorological data have been presented in this report only both years referring to the reporting time, i.e. the data are from 2007 and 2009, respectively.

10 Table 1. Environmental monitoring schedule: X = annually, O = several times per year, grey cells = continuous. The double line separates studies producing input to biosphere modelling from those producing input to environmental impact assessments. Studies carried out by TVO have been marked Aerial imagery X X X X X X X Satellite imagery X X Maintenance of observation plots (FET) X X Tree measurements (FET) X Soil sampling (FET sampling plots) X Vegetation inventory and sampling (FET sampling plots) X Needle a and leaf b sampling (FET sampling plots) X a X b X Light soil and vegetation investigations on land-to-sea transects X Registration of realised clear-cuttings X X X Forest intensive monitoring (FIP) 1) Soil water Wet deposition and precipitation 2) Litterfall Stand micrometeorology, diam. growth Evapotranspiration Vegetation coverage X X X X Biomass and chemical composition of the vegetation and humus layers X Crown condition X X X X X X Tree measurements 2) FIP4 a, FIP10 b, FIP11 c, FIP14 d X a X b X c X d Tree growth FIP4 a, FIP10 b, FIP11 c X a X b X c Soil profile and soil properties Soil microbes 2) Deposition on needles 2) X X X X X Root growth X X X X X Game statistics (game animals and birds) X X X X X X X X X Small mammals a, ground beetles b, bats c, reptiles and frogs d X abc X abd X a Ants a, snails b and earthworms c X a X abc Field inventory of birds X X Monitoring of island birds X X X X Hydrochemical characterisation of seawater X X X Sea water quality (TVO) O O O O O O O O O Zooplankton O O O O O Phytoplankton (TVO) O O O O O O O O O Aquatic macrophytes (TVO a or Posiva b ) X a X a X ab Sea bottom fauna (TVO) X X X X X X X X X Test fishing (TVO) X X Account fishing (TVO) Fishing interviews (TVO) X X X X X Noise (TVO) X X X X X X X X X Drainage waters from rock heaps O O O O O O O Water table of the private drilled wells O O O O O O O O O Water quality of the private drilled wells X X X X X X X X X Scenery (from aerial photographs) X X X X X X X Meteorology (TVO and Posiva) 1) FIP measurements serve the monitoring of conservation area, as well. 2) Performed on MRK-network, as well X X

11 3 RESULTS I: INPUT TO BIOSPHERE MODELLING 3.1 Landscape properties The landscape of Olkiluoto Island has been under rapid changes during the time of Posiva's environmental monitoring programme. Part of the changes are related to power production and part to nuclear waste disposal: in addition to Posiva's activities, construction of the third nuclear power plant on Olkiluoto (OL3) started in 2003, with corresponding infrastructure (Table 2). Colour-infrared aerial photographs have formed the basis for mapping the baseline situation and serve as a benchmark for the monitoring of changes. Some other remotely sensed material have occasionally been acquired, as well, such as low oblique aerial photographs, visible band aerial photographs, a series of older aerial photographs, and an IRS P6-LISS3 satellite image. A hyperspectral imaging campaign was carried out in See Appendix B for remote sensing data acquired by Posiva. Table 2. Recent construction activities on Olkiluoto Island. Infrastructure Construction time ONKALO area 2003 OL Rock piling and crushing area (OL3+ONKALO) 2004 Main road (reparation, paving) Wind generator 2004 Gas turbine reserve power plant Main power lines 2005 Roads, pipelines, parking areas etc New gatehouse and extension to main office New visitor centre Accommodation village 2005 Concrete station Laboratory extension New boat landing stage (by parking area) New dumping place 2007 Gas turbine safety pool by the main gate 2007 Training simulator 2007 Dockyard extension 2008 An area of approx. 2 ha cleared for storage (north of the concrete station, incl one FET sampling plot) Extension of ONKALO area and building of new warehouses 2009 Alignments for the road and pedestrian/bicycle way to ONKALO started 2009

12 The vegetation and forest inventories by homogeneous polygons (VCP) in 2002 and 2003 describe the vegetated landscape at those time points (Miettinen & Haapanen 2002, Rautio et al. 2004). Starting from this report on, a list of silvicultural activities carried out in these polygons is presented (Appendix C). The monitoring of forests and mires on the island is based on a systematic grid with a density of 1 plot/ha, called FET. The first rounds of measurements on FET grid in 2004 and its subset in 2005 provide a statistical basis for the monitoring of forested parts of the landscape (Saramäki & Korhonen 2005, Huhta & Korpela 2006, Tamminen et al. 2007). To estimate the extent of all current land-use types, the FET network was extended to cover all land-use/land-cover classes and intermediate plots were added between the plots to create a 50 x 50 m grid. The land-use/land-cover of each plot was visually interpreted from the aerial photographs taken in 1946 and 2007 (statistics presented in Haapanen 2009). 3.2 Meteorology Weather conditions For the biosphere analysis in the safety assessments, especially typical conditions of temperature, precipitation and humidity are important to be known. The information on weather and climate conditions gives boundary conditions also to the hydrogeological and hydrogeochemical modelling of the site. The measurements, analyses and results of meteorological data have been reported by Ikonen (2002, 2005, 2007a). A summary of the previous reports and the latest data is given here, and detailed weather monitoring results are presented in Appendix D. Meteorological observations are mandatory for a nuclear power plant, thus a comprehensive database of major meteorological parameters is available from a weather mast WOM1 (Fig. 2). Within the forest intensive monitoring plots FIP4, FIP10, FIP11 and, starting from November 2009, FIP14, meteorological measurements are recorded once an hour from corresponding weather masts WOM2 5. The parameters are air temperature, minimum and maximum temperature inside the crown layer and above the canopy (latter only on mast WOM2, which reaches above the three canopies), relative humidity, precipitation (1 m above ground level), soil moisture content, and soil temperature. Photosynthetically active radiation (PAR), solar radiation, air pressure, wind speed and its direction are measured only on WOM2. See Figs. 3 6 for examples of recorded data. In addition, a weather monitoring station related to the studies of Luode Oy (WOM6) is located on the dockyard and will operate for two years starting from summer Depth of ground frost and the thickness of the snow cover are measured manually on FIP4 (2 ground frost measuring points) and a snow monitoring transect LL2 (20 snow and 7 ground frost measuring points) and on lake Olkiluodonjärvi (1 ground frost point) and on Liiklansuo (1 ground frost point).

13 Bulk precipitation and stand throughfall are measured as a part of the forest monitoring system, on a network called MRK, and the results are presented with other forest monitoring results (Section 3.4.2). Annual means of central meteorological parameters are given in Table 3, along with reference data from nearby locations Kuuskajaskari and Pori airport. Monthly mean and extreme temperatures and monthly total precipitation at Olkiluoto for the period of are shown in Fig. 7. Long-term monthly temperature and precipitation statistics at Olkiluoto station WOM1 are presented in Table 4. Annual growth conditions at Olkiluoto in based on the data of stations WOM1 WOM4 are presented in Tables 5 8. Figure 2. Locations of Olkiluoto weather stations WOM1 WOM6. Map layout by Jani Helin/Posiva Oy.

14 1/2007 7/2007 1/2008 7/2008 1/2009 7/2009 Relative humidity, % Temperature, C PAR, μmol/s/m² 25 Temperature at 9 m PAR at 24 m Figure 3. Examples of data from meteorological measurements on WOM2: temperature at 9 m and PAR at 24 m. Daily averages between September 1, 2004 and December 31, WOM1 WOM2 WOM3 WOM4 WOM5 Figure 4. Examples of data from meteorological measurements on WOM1 WOM5: relative humidity at 2 m, %. Monthly averages between January 2007 and December 2009 (WOM4 from July 2007 on and WOM5 from November 2009 on).

15 9/2004 1/2005 5/2005 9/2005 1/2006 5/2006 9/2006 1/2007 5/2007 9/2007 1/2008 5/2008 9/2008 1/2009 5/2009 9/2009 Temperature, C 1/2007 7/2007 1/2008 7/2008 1/2009 7/2009 Temperature, C WOM1 WOM2 WOM3 WOM4 WOM5-10 Figure 5. Examples of data from meteorological measurements on WOM1 WOM5: temperature at 2 m, C. Monthly averages between January 2007 and December 2009 (WOM4 from July 2007 on and WOM5 from November 2009 on) cm 50 cm 90 cm 0-2 Figure 6. Examples of data from meteorological measurements on WOM2: ground temperature at depths of 10, 50 and 90 cm, C. Monthly averages between September 1, 2004 and December 31, 2009.

16 Figure 7. Monthly mean and extreme temperatures (left) and monthly total precipitation (right) at Olkiluoto for the period of The black lines represent the monthly mean temperature and total precipitation in 2009, dashed black the month's lowest and highest temperature in 2009, gray line the long-term monthly mean, purple lines the long-term mean low/high, the blue lines long-term low/high, the bars lowest and highest monthly precipitation recorded and the white cuts in the bars the long-term monthly mean precipitation.

17 Table 3. Long-term average temperature, annual precipitation and average wind speed at Olkiluoto station WOM1 ( ), at Kuuskajaskari Island 13 km SSW ( ) and at Pori Airport 30 km NE ( ). Data for the two latter stations from Drebs et al. (2002). Olkiluoto, WOM Kuuskajaskari Pori Airport Climate class Dsc(lk)/DC(lk) Dfb(lo)/DC(lo) 1 Dfc(lo)/DC(lo) 1 humid continental with mild summer and cool winter* humid continental with mild summer and cold winter* humid continental with mild summer and cold winter* Average temperature, C coldest month -4.2 (Feb) -5.0 (Feb) -5.6 (Feb) - warmest month 17.1 (Jul) 15.9 (Jul) 16.3 (Jul) Extreme temperature, C - lowest observed (Jan 1999) (Dec 1978) (Dec 1978) - highest observed 31.6 (Jul 1999) 31.3 (Jul 1999) 31.8 (Jul 1994) Average number of - heat-wave days ice days frost days cold days Annual precipitation, mm Max. monthly precip (Aug 2005) (Aug 1987) (Jul 1979) Min. monthly precip. 0.0 (Feb, Dec) 0.1 (Jul 1994) 1.1 (Jul 1994) Max. daily precipitation 51.8 ( ) Precipitation days (avg.) 0.1 mm mm mm Avg. relative humidity, % Prevailing wind direction S SE S SW SE Average wind speed, m/s * Humid continental: humid with severe winter, no dry season, warm summer. 1 Based on statistics given in (Drebs et al. 2002) omitting some detailed values; instead more averaged input values is used in classification and the climate classes for the reference sites are to be considered only illustrative. 2 Daily maximum temperature > 25 C 3 Daily maximum temperature < 0 C 4 Daily minimum temperature < 0 C 5 Daily minimum temperature < -10 C

18 Table 4. Long-term monthly temperature and precipitation statistics at Olkiluoto station WOM1 ( ). Mean minimum/maximum is the typical variation as the mean of the monthly minimum/maximum values. Extreme minimum/maximum is the largest variation within each month during the period. WOM 1 Monthly temperature ( C) Monthly precipit. sum (mm) Mean Mean Mean Extr. Extr. min. max. min. max. Mean Min. Max. January February March April May June July August September October November December Table 5. Annual growth conditions at Olkiluoto in based on the data of station WOM1 (exposed to marine influence). WOM 1 Year Beginning date Ending date Duration (days) Sum of effective temperature (ºCd) Precipitation sum of season (mm) a , , , , , , , b , c , , , , , d , , e , , f Mean , Minimum , Maximum , FMI avg g , a for 1992 and 1993, daily precipitation values of 8 and 2 days are missing, respectively, and not accounted for in here b growth period started first time already but ended 10 days later; without this the length would be 209 d, temperature sum 1551 Cd and precipitation sum 255 mm c ending to just the 5 days period of <5 C in the criteria; if that is ignored the end date would be , length 241 d, temperature sum 1458 Cd, and precipitation sum 465 mm d ending to just 6 days period of <5 C; if that is ignored the end date would be , length 202 d, temperature sum 1521 Cd, and precipitation sum 472 mm e there was a 5-day period meeting the criteria for thermal growth season in April, one week before the on-set of the longer period starting with 3 and 2 days of regression, separated by one warmer day, during the first two weeks f 2009 sum of effective temperature was not used in calculated mean, min and max values as data from 28 days in September was missing g for , estimated regional value ( accessed 16 October 2007)

19 Table 6. Annual growth conditions at Olkiluoto in based on the data of station WOM2 (Scots pine forest). WOM 2 Year Beginning date Ending date Duration (days) Sum of effective temperature (ºCd) Precipitation sum of season (mm) a , , , , , Mean , Minimum , Maximum , FMI avg b ,200 a precipitation under the forest canopy b for , estimated regional value ( accessed 16 October 2007) Table 7. Annual growth conditions at Olkiluoto in based on the data of station WOM3 (old Norway spruce forest). WOM 3 Year Beginning date Ending date Duration (days) Sum of effective temperature (ºCd) Precipitation sum of season (mm) a , , , , Mean , Minimum , Maximum , FMI avg b ,200 a precipitation under the forest canopy b for , estimated regional value ( accessed 16 October 2007) Table 8. Annual growth conditions at Olkiluoto in based on the data of station WOM4 (young mixed forest). NA=no data available. WOM 4 Year Beginning date Ending date Duration (days) Sum of effective temperature (ºCd) Precipitation sum of season (mm) a 2007 NA NA NA NA , , Mean , Minimum , Maximum , FMI avg b ,200 a precipitation under the forest canopy b for , estimated regional value ( accessed 16 October 2007)

20 3.2.2 Surface runoff Monitoring of surface runoff produces data for surface hydrology modelling work. The locations of the automatic measuring weirs are shown in Fig. 8. The old V-shaped measuring weirs were replaced by new weirs in early These new measuring weirs have produced data as of the following dates: MP1 MP and MP The weirs are maintained and data provided by EHP-tekniikka Oy. Measured parameters include water level, conductivity, redox, temperature and ph. The current ph sensors will be replaced by new ones in In 2009, chemical analyses of the water were carried out, as well. The results are presented in Appendix E. In 2009, the measuring weirs MP1, MP3 and MP4 worked relatively reliably, with some exceptions. However, measuring weir MP2 has encountered several problems, and much of the data it has produced is scattered and unreliable. The dates of missing data are presented on the Table 9 below. Monitoring results from 2009 are shown in Figs Since the sensors are located approximately cm beneath the water table (0-level), in cases of no flow the parameters are measured in still water, and no useful results are produced. The monitored parameters of the periods of no flow are removed from the graphs, as well as the unreliable data presented on Table 9. Table 9. Problems in the data produced by measuring weirs MP1 MP4 in Information collected from a report by the data provider. Data gaps not explained in the report are given in Italic. Discharge Temperature ph Conductivity Redox Water level MP MP , , , , a , MP b, , , MP , b a the system had a maximum ph value of 7, and therefore no higher values are shown regardless of the actual water ph. b values deviate from normal, but may still be correct.

21 Discharge, L/s Figure 8. Locations of measuring weirs MP1 MP4. Map layout by Jani Helin/Posiva Oy MP1 MP2 MP3 MP4 Figure 9. Measured flows (runoffs) in the measuring weirs MP1 MP4 in Data are missing from MP2 on May 7 20 and June 24 December 31, see Table 9 above.

22 Conductivity, ms/m ph MP1 MP2 MP3 MP Figure 10. Measured ph in the measuring weirs MP1 MP4 in Data are missing on several occasions from MP2 and on July 25 December 8 from MP4, see Table 9 above MP1 MP2 MP3 MP Figure 11. Measured conductivity in the measuring weirs MP1 MP4 in Data are missing from MP3 on several occasions, see Table 9 above.

23 Bq/kgDW 3.3 Radionuclides Radioactivity in the environment is monitored by the nuclear power plant operated by TVO. Samples from air, terrestrial environment, terrestrial foodstuffs and marine environment are relevant for Posiva as well. TVO's sampling activities have focused on the marine ecosystem. Before the operation of the encapsulation plant and the repository starts, the terrestrial baseline regarding radionuclides must also be more comprehensively studied. The major anthropogenic sources of radionuclides in the area originate from the Chernobyl fallout in April 1986 and from authorized effluents and air emissions from the nuclear power plant. The most common nuclides clearly originating from the power plant include, for example, Mn-54, Co-60 and Ag-110m (not exclusively). Naturally abundant nuclides, such as Be-7 and K-40 are found in every sample if determined. Their concentrations represent natural variations in the ecosystem and also reflect the quality of the measurement. In addition, the behaviour of these elements and nuclides is relatively well known enabling, for example, model adjustments against the measurements. The radioactivity monitoring locations are shown in Fig. 13. The results of radionuclide analyses in 2009 are shown in Appendix F. Some examples of radionuclides in terrestrial environment are given in Fig. 12. Radionuclides in soil and non-wood forest products are measured at four-year intervals, 2009 being a sampling year. Radionuclides in sea sediments are measured at four-year intervals, as well, the latest results being from 2007 (reported in Haapanen 2008). National scale comparison values can be obtained from publications of the Finnish Radiation and Nuclear Safety Authority (STUK) pine needles lichen pine needles lichen pine needles lichen Be-7 K-40 Cs-137 Figure 12. Radionuclides in terrestrial environment in the vicinity of the weather mast WOM1 (see location in Fig. 2) in , Bq/kgDW (Haapanen ).

24 Figure 13. The radioactivity monitoring locations in Map layout by Jani Helin/Posiva Oy.

25 3.4 Terrestrial systems Description of the forest monitoring network The forest monitoring system consists of several overlapping levels (Fig. 14). As the intensity of the sampling efforts increases towards the sixth monitoring level, the number of the monitored plots and the spatial coverage of these observations decreases. The first level is used for following changes in land-use by interpreting aerial images. The second level is vegetation-type mapping for the purpose of classifying the vegetation and its distribution (VCP; Miettinen & Haapanen 2002). Forest resources have also been mapped from the same vegetation polygons (Rautio et al. 2004). The third monitoring level (FET measurement plots, Fig. 15) is a grid of systematically located plots with the purpose of describing the biomass distribution of forests and monitoring changes in the tree stands (Saramäki & Korhonen 2005). A part of the FET measurement plots has been selected for further studies (FET sampling plots). In these plots, the vegetation is inventoried and the soil, needles and vegetation are sampled at intervals of 5 10 years in order to describe soil properties, vegetation composition and nutrient concentrations of plants and trees (Huhta & Korpela 2006, Tamminen et al. 2007). The grid has been modified (plots added/removed) according to increased knowledge of data needs and land-use changes on the island. The MRK network with rainwater and snow collectors monitors the dust effects of the ongoing construction activities on the forests. From these plots, conifer needles are also analysed biannually. The most intensive level, the FIP network, aims at continuous follow-up of changes taking place in the nutrient budgets and fluxes in the soil, tree stands and vegetation at both the stand and the catchment level, to cover the seasonal, annual and long-term variation. Monitoring activities and their frequency on the FIP plots are shown in Table 10. (Aro et al. 2010). Land-use grid VCP FET measurement plots FET sampling plots MRK FIP Intensity Figure 14. Forest monitoring levels (Aro et al. 2010).

26 Figure 15. Forest monitoring locations in Map layout by Jani Helin/Posiva Oy. Table 10. The performed monitoring activities and their frequency on the FIP plots (Aro et al. 2010). Performed activities Establishment, start of equipment installations Location and measurement of trees Vegetation inventory (OA3) 2003, 2004, 2005, 2008 FIP4 FIP10 FIP11 FIP , 2004, 2005, 2008 Normal frequency 2008 Every 3 yrs Soil condition Every 10 yrs Stand throughfall and precipitation measurements (MRK, OA2) Continuous Sap flow measurements no no Continuous Soil water sampling (OA2) Continuous Litterfall sampling (OA2) Continuous Foliage sampling (OA2) 2003, 2004, 2005, 2006, 2007, , 2005, 2006, 2007, 2009 no 2009 Every 2 yrs Micrometeorology (OA2) Continuous Stem diameter growth (OA2) no no Continuous Tree growth (OA1) Every 5 yrs Crown condition survey no no Annual

27 Table 11. The basic stand characteristics of the alder-dominated plot FIP14 (sub-plot OA2) in November Dominant height is the average height of 100 thickest (measured at 1.3 m height) trees per ha. (Aro et al. 2010). Tree species Stem number Basal area, m 2 /ha Mean diameter weighted with basal area, cm Mean height Dominant (arithmetical), m height, m Lower limit of crown, m Black alder Norway spruce Other deciduous Total Stem volume with bark, m 3 /ha According to Aro et al. (2010), in 2008, five new FET sampling plots were established, the trees were measured and soil profile description and soil sampling were carried out. In addition, one plot was established in Kaunissaari Island, but not yet measured. The results will be reported after the data collection has been completed. In May 2009, one new MRK plot (MRK13) was established in an open area. In 2009, one new FIP area was established, as well, in a black alder stand (FIP14). Deviating from other FIP areas, it consists of a single square sub-plot (OA2, total area 900 m 2 ) where litter sampling, deposition, soil water collection and micro-meteorological measurements are carried out. Plot FET (total area 300 m 2 ) is located next to the OA2 sub-plot and used for tree growth measurements and vegetation studies. The trees growing on the black alder-dominated plot (FIP14) were measured in November The stand characteristics are presented in Table 11. In the sub-sections below, results are presented for those monitoring modules that have produced data in The foliar samples of MRK plots were collected as well, but the analyses and reporting will be completed later. Details on the monitoring system are mentioned only in cases there have been some problems or changes. For those activities not carried out in 2009, results can be found in earlier reports (Haapanen and their references) Bulk deposition and stand throughfall Deposition loads on the forest and forest floor are monitored using a deposition monitoring network (MRK plots) established on Olkiluoto Island in The monitoring continued on MRK2 (open area), MRK4 (a Scots pine stand), MRK10 (a Norway spruce stand) and MRK11 (a young mixed stand) as in previous years. In addition, bulk deposition was monitored in a new open area (MRK13) from June 2009 onwards and stand throughfall in an alder-dominated stand (MRK14) from July 2009 onwards. (Aro et al. 2010). The locations of the MRK plots are shown in Fig. 16.

28 Figure 16. Location of MRK plots. Plots MRK2, MRK4, MRK10, MRK11 and MRK13 are used in wet deposition monitoring, but all forested locations (MRK11 excluded) are subject to needle sampling and analyses. The results for bulk deposition and stand throughfall during the period January 7, 2009 January 11, 2010 are presented by Aro et al. (2010) and summarised here. The deposition for the period is denoted in the following as deposition for the year The results for 2009 are compared with the deposition load of on Olkiluoto, as well as with the deposition load on two intensively monitored plots in Juupajoki and two plots in Tammela, Southern Finland (EU Forest Focus, UN/ECE ICP Forests monitoring plots). (Aro et al. 2010). The amount of precipitation in open areas (bulk deposition, BD) and stand throughfall (TF) was lower than in earlier years (Fig. 17; interception of precipitation by the tree crowns in is shown in Fig. 18). There have been no clear increasing or decreasing trends in ph of BD or TF during the period , although the mean ph was rather high (>5.5) in BD during On Olkiluoto, the ph values have been at a level slightly above the values measured at the reference plots. (Aro et al. 2010). There has been variation in the deposition of total nitrogen, NH4-N and NO 3 -N in BD and TF during , but the values have been comparable with those measured at the reference plots. The NO 3 -N deposition was lower in 2009 than in earlier years. The deposition of nitrogen compounds in TF was generally lower than that in BD due to nitrogen uptake by the tree canopies. This is a well-documented phenomenon in coniferous stands in Finland. (Aro et al. 2010).

29 Precipitation, mm In 2009, the sulphur (SO 4 -S) deposition in BD on MRK2 was the highest of the whole monitoring period ( ). On plot MRK13, the sulphur deposition in BD was somewhat lower and comparable to the reference plot at Tammela. Regarding TF, the situation was opposite, since the lowest sulphur deposition values for the whole monitoring period were determined in The sulphur deposition at the Tammela Norway spruce plot has been clearly higher than at Olkiluoto or Juupajoki during the monitoring period. (Aro et al. 2010). On MRK2, the deposition of base cations (Ca, Mg, K) in BD was somewhat higher or at a similar level compared with the reference plots, and the deposition of Ca was the highest of the monitoring period. The relatively high deposition of Cl (with associated Na) at Olkiluoto is due to the proximity of the sea. The dissolved organic carbon (DOC) amounts in BD and TF have been comparable to the values in the reference plots. The deposition of Al, Fe, Mn, Si, Cu, Zn and PO 4 -P in BD and TF were relatively similar in 2009 compared with the values in earlier years. (Aro et al. 2010) MRK2 MRK13 Mean (MRK 2, 7, 9) REF MRK4 (pine) REF (pine) MRK10 (spruce) REF (spruce) MRK11 (mixed) Mean (MRK1, 3-6, 8, 10) Open area, bulk deposition Stand throughfall Figure 17. Amounts of precipitation in bulk deposition (open) and stand throughfall (Scots pine, Norway spruce or mixed) on MRK plots in and the mean values for the earlier monitoring network for the years The precipitation and deposition values of 2009 for MRK 13 have been measured , earlier dates copied from MRK2 in order to obtain a figure for a full year. REF = reference values: average of two corresponding stands (one in Tammela and one in Juupajoki).

30 Interception of precipitation by tree crowns, % MRK4 MRK10 Mean (MRK1, 3-6, 8, 10) Figure 18. Interception of precipitation by the tree crowns as percentage of precipitation in the open on MRK4 and MRK10 in and the mean values for the earlier monitoring network for the years Soil solution The proportion of percolation water passing down to a depth of 5 cm on plot FIP4 varied between 16 to 23 % of the input to the forest floor (stand throughfall) during the period (Fig. 19). Corresponding values on the plots FIP10 (during ) and FIP11 (during ) were 1 28% and 1 17%, respectively. The lowest values on FIP10 in were caused by problems with the lysimeters, which are now functioning correctly. The amount of water passing down through the organic layer on FIP11 in 2009 was lower than on the other plots, presumably due to the highly effective interception of rainwater and strong rate of evapo-transpiration by the abundant ground vegetation and dense sapling stand. (Aro et al. 2010). The ph of the soil solution on FIP4 clearly increased with increasing depth. The values remained relatively constant and fully comparable to a site of similar fertility at Tammela throughout the 6-year monitoring period, with the exception of the depth of 10 cm, which showed some large variation in 2006 and On FIP10, there were some changes in the ph of soil solution during On FIP11, the ph of the soil solution is relatively high at all sampling depths. (Aro et al. 2010). The DOC concentration of the soil solution on FIP4 clearly decreased with increasing depth. The concentrations at 5 cm on FIP4 and all three depths on FIP10 were considerably higher than at the reference site in all six years, but not excessively high for organic matter-rich forest soils under a coniferous tree stand. At other depths on FIP4, the DOC concentrations have decreased relatively strongly since Installation of the suction-cup lysimeters in 2003 undoubtedly caused a short-term flush of DOC. In FIP10, there are no signs of the DOC concentrations decreasing now that

31 the lysimeters have already been 5 years in the soil. In FIP11, the DOC concentrations are high, as well. (Aro et al. 2010). Total nitrogen, which includes dissolved organic nitrogen (DON) as well as ammonium and nitrate, obviously closely followed the pattern of the DOC concentrations on FIP4 and FIP10. At all depths, DON accounts for most of the total nitrogen dissolved in the soil solution, and ammonium and nitrate for only about 10%. The NH 4 -N, and especially the NO 3 -N concentrations, were extremely low at all depths in the mineral soil of the FIP plots throughout the monitoring period. The low concentrations are primarily due to the fact that nitrogen is the main factor limiting tree growth in coniferous stands in Finland; the available nitrogen (NH 4 and NO 3 ) mineralized from the organic layer is rapidly taken up by the roots of the trees and ground vegetation. (Aro et al. 2010). Sulphate concentrations at 5 cm depth on FIP4 were considerably lower in all six years than those at the reference site, but relatively similar at other depths. There was a clear overall increase in sulphate concentrations with increasing depth on FIP4. (Aro et al. 2010). Similar trends in the sulphate concentration have been reported at all the ICP Forests Level II plots in Finland (Derome et al. 2007). There appears also to be a gradual decrease in sulphate concentrations in the mineral soil. (Aro et al. 2010). Chloride concentrations were extremely high at all depths on all three FIP plots throughout the monitoring period. It is clear that there is a considerable input of NaCl in deposition derived from the sea. Phosphate concentrations were in general very low, which is typical of most forested sites in Finland. (Aro et al. 2010, Derome et al. 2007). The concentrations of the three important plant nutrients (Ca, Mg, K) were relatively elevated, especially in the mineral soil (10, 20 and 30 cm) on FIP4 at the start of the monitoring period in Since then, the concentrations have fallen and are now approaching the levels of the reference site. This indicates a short-term flush of the nutrients following the installation of the lysimeters. On FIP10 and FIP11, however, the concentrations of Ca, Mg and K were strongly elevated at all depths in the soil during the monitoring period. Weathering processes are relatively strong on these plots with very young mineral soil, causing abundant release of these nutrients. The high concentrations of Na at all depths are due to both the input from the sea and the weathering of minerals. (Aro et al. 2010). On FIP4, the concentrations of total Al remained rather constant at all depths during the monitoring period. The concentrations of Al at depths of cm and Al 3+ at all depths were higher than the values of the reference site. The concentrations are still much lower than the widely accepted toxicity level of 2 mg/l. The Fe concentrations showed considerable year-to-year variation, and were higher at depths of cm than at the reference site. The Mn concentrations at all depths were very similar in all six years, and similar to those of the reference site. The Si concentrations at depth of 5 cm were considerably higher in than in 2004, and higher than the concentrations at the reference site. (Aro et al. 2010).

32 mm On FIP10, the total Al concentrations were lower than the reference values at 5 cm depth, but the Al 3+ (at 5 cm) and Mn concentrations (all depths) were relatively similar to the reference values. The concentrations of Fe and Si were strongly elevated at depths of 20 and 30 cm. The high silicon values are undoubtedly due to the young soil: silicon plays an important role in soil-forming processes (podzolisation) under coniferous tree species stands. (Aro et al. 2010). On FIP11, elements associated with soil-forming processes (e.g. Al, Fe, Si) are present in relatively high concentrations, but this is to be expected, because the intensive uptake of nutrients (and corresponding release of protons) by the roots of the young stand and dense ground vegetation result in an increase in the dissolution of these elements through the weathering of soil minerals. Manganese concentrations are extremely low. This may be related to the fact that the ph is somewhat elevated. (Aro et al. 2010). The concentrations of heavy metals (Cd, Cr, Ni, Pb) at all depths during continued in many cases to be close or below the limit of quantification (LOQ for Cd = mg/l, for Cr = mg/l, for Ni = mg/l and for Pb = mg/l) (Aro et al. 2010) Stand throughfall Figure 19. The amount of percolation water passing down to a depth of 5 cm during the snow-free period in in FIP4. The amount of stand throughfall is also shown.

33 Stand transpiration, mm Tree stand transpiration Some problems occurred in the sap flow measurements in 2009, especially during the winter season. The quality of data was poorer on the Scots pine plot FIP4 in 2009 than in previous years. Some measuring observations were missing resulting in artificial peaks in the calculated transpiration. The quality of transpiration data from the Norway spruce plot FIP10 was better, but several errors were nevertheless detected in the sap flow signals during the winter season. The tree transpiration values can be considered reliable for the period April November. During the growing season in 2009, the stand transpiration was higher in the Scots pine stand than in the Norway spruce stand (Fig. 20). The monthly transpiration on plot FIP4 was comparable to previous years ( ), but that of FIP10 was clearly lower than in (Aro et al. 2010) FIP4 FIP Jan 09 Feb 09 Mar 09 Apr 09 May 09 Jun 09 Jul 09 Aug 09 Sep 09 Oct 09 Nov 09 Dec 09 Figure 20. Monthly stand level transpiration on FIP4 and FIP10 in The results are reliable only for the period April November. (Aro et al. 2010) Litterfall production and element return to forest floor Litterfall collection results are presented for the year Collection started on the plots FIP4, FIP10, FIP11 on April 1. Since the last collection date in 2007 was in mid- November, the mass of the first collection in 2008 represents the litterfall of the whole previous winter. In 2008, the collected litter was divided into eight different fractions. (Aro et al. 2010): 1= Scots pine brown needles 2= Scots pine green needles 3= Norway spruce needles 4= leaves 5= remaining litter 6= small branches 7= branches 12= remaining litter in branch traps

34 In order to collect the branch litter missed by the funnel type litterfall traps used in the ICP Forests programme, a new type of traps developed in the Finnish Forest Research Institute were used. These nylon fabric traps are positioned on the ground (Fig. 21). The branch traps were used on plots FIP4 and FIP10, and the litter collection started in autumn 2008 (sampling dates July 23, August 20, September 18 and October 15). (Aro et al. 2010). Despite the new branch traps, the annual total litterfall production was somewhat smaller than in This is natural annual variation. In 2008, the total litterfall production on FIP4 was 409 g/m², on FIP g/m² and on FIP11 88 g/m 2. (Aro et al. 2010). Concerning the elemental concentrations, the most notable differences between the monitoring plots were those of Al and N concentrations. Al is commonly higher in living Scots pine needles than in Norway spruce needles. High Al and Fe concentrations in fraction 5 (remaining litter) are most likely due to soil dust. The highest N concentrations were detected in fraction 4 (leaves). The highest N concentrations in leaves occurred during summer (i.e. non-senescent leaves), but the senescent leaves contained approximately as much N than green Scots pine needles, as well. (Aro et al. 2010). Figure 21. Litterfall traps on a FIP plot (photo by Lasse Aro/Finnish Forest Research Institute).

35 3.4.6 Defoliation The degree of defoliation was determined on FIP4 and FIP10 during August 31 September 1, The average defoliation level of Scots pine was 4.6±1.0 % and of Norway spruce 24.1±2.5 %, indicating good crown condition: the Scots pines were classified as non-defoliated and the Norway spruces as slightly defoliated. The defoliation degree levels were in good agreement with the results for the ICP Level II plots in Tammela (Lindgren et al. 2007). The increase in defoliation of Scots pine in 2007 was due to a severe infection by Peridermium stem rust on one Scots pine on FIP4-OA2. In 2008, this tree was already dead and replaced with a new one. The results for are presented in Table 12. (Aro et al. 2010). Table 12. Defoliation degree of trees on plots FIP4 (Scots pine) and FIP10 (Norway spruce) by sub-plot during Number of assessed trees on each sub-plot is 20. (Aro et al. 2010). Plot Sub-plot Defoliation, % FIP Mean SD FIP Mean SD Fine root elongation and longevity A fine root study was started in 2008, with the aim of determining the biomass of tree fine roots and understorey fine roots and rhizomes, the ectomycorrhizal (EcM) status of tree fine roots, and the elongation and longevity (turnover time) of fine roots and ectomycorrhizas. The results are reported in Helmisaari et al. (2009) and summarised in Haapanen (2009). The investigation continued in Fine root elongation and longevity were monitored using the minirhizotrone method on the intensive monitoring plots FIP4, FIP10 and FIP11. Images were taken five times during the 2009 growing season (May 26, July 1, August 4, September 3 and October 6). None of the roots died in 2008, only minor share died during the growing season 2009, and substantially more are expected to die during Thus the imaging and analysis will have to continue for one more growing season. (Aro et al. 2010).

36 The SRL (specific root length, length/weight) and SRA (specific root area, surface area/weight) for the finest roots (diameter <1 mm) were determined from biomass samples taken in SRL was highest for birch trees: 36.8±10.0 metres per gram of fine roots active in nutrient uptake. The respective values for Scots pine and Norway spruce were 20.0±4.5 and 22.0±5.1 m/g. Birch also had the highest SRA (specific root area) for nutrient uptake, 47.1±10.7 m 2 per kg of fine roots. For Scots pine and Norway spruce the SRAs were 37.3±7.3 and 36.0±6.3 m 2 /kg, respectively. These differences between tree species are in accordance with the trends reported in Ostonen et al. (2007). Increases in SRA and SRL permit a tree to increase the volume of soil explored per unit biomass invested in fine roots (Ostonen et al. 2007, Aro et al. 2010). The fine root diameter differed by tree species, being highest for Scots pine (0.61±0.07 mm), followed by Norway spruce (0.53±0.06 mm) and lowest for birch (0.41±0.03 mm). The root diameters measured from minirhizotrone images of the first monitoring year were ca. half of these (0.23 to 0.35 mm; Helmisaari et al. 2009), but the differences between species were comparable in these two studies. The new roots analysed from 2008 minirhizotrone images were less than a year old, whereas the larger diameter of soil core fine roots indicates that the bulk of fine roots may be older than one year. This was confirmed in the minirhizotrone images filmed in 2009, as said earlier (within the two first growing seasons most of the roots observed as new stayed alive). Therefore, for determining the root turnover rate, the minirhizotrone images will be taken also in the growing season (Aro et al. 2010). An additional root sampling was carried out in August 2009 in the FIP11 stand for analyses on fine root ectomycorrhizal (EcM) root tip morphology and dominating EcM colonising fungi by morphotyping and DNA sequencing. A DGGE analysis was performed for the samples as well to determine the diversity of the microbial communities in the rhizosphere. This study completes the fine root biomass study (Helmisaari et al. 2009) and the results will be reported later in the final report of the root studies. (Aro et al. 2010).

37 3.4.8 Changes in tree characteristics The tree stand characteristics of sub-plot OA1 of FIP4 and FIP10 were re-measured after five growing seasons. The Scots pine-dominated FIP4 was measured on March 26, 2009 and the Norway spruce-dominated FIP10 on September 29, Tree species, canopy layer, diameter at a height of 1.3 m in two directions, tree height and the lower limit of living crown, as well as the state of health (damage symptoms, their cause and degree) were recorded or measured for each tree. The results are presented in Table 13. There were signs of infection by Peridermium stem rust in several Scots pines. Birch trees on FIP10 have reached maturity. Self-thinning caused by competition is a normal age-related phenomenon in natural stands and will probably continue during the following monitoring period. (Aro et al. 2010). In addition, the diameter growth of two trees on plots FIP4 and 10 is being measured continuously with girth bands, and recorded once an hour (see example of data in Figs. 22 and 23). Table 13. The basic stand characteristics of Scots pine (FIP4) and Norway sprucedominated plots (FIP10) during The measurements were made on sub-plot 1 of each plot. Dominant height is the average height of 100 thickest (measured at 1.3 m height) trees per ha. (Aro et al. 2010). Plot Year Tree species Stem number Basal area, m 2 /ha Mean Mean diameter height, weighted with (arithm.), m basal area, cm Lower limit of crown, m Dom. height, m Stem volume with bark, m 3 /ha FIP Scots pine Scots pine FIP Norway spruce Norway spruce Birch Birch

38 mm mm Figure 22. Data from girth band of tree 395 on FIP4 in Reading at the starting day (September 1, 2004) has been subtracted from daily mean Figure 23. Data from girth band of tree 29 on FIP10 in Reading at the starting day (May 23, 2005) has been subtracted from daily mean.

39 3.4.9 Terrestrial animals Animal life on Olkiluoto Island is inventoried in the field (e.g., track counts, line transects, traps for small animals) at varying intervals. The estimates of game populations in Olkiluoto are based on annual interviews of local hunters (Olkiluodon Metsästysseura), and other available statistical material. Inventory results are reported in Posiva Working Reports and interviews in memos or Working Reports (most recent by Jussila & Nieminen 2009) and these have been summarised by Haapanen ( ). Concerning hunting, so far only estimates of the population sizes of cervids after hunting season were available: 3 individuals of moose, 12 of white-tailed deer and ca. 10 of roe deer. A small mammal study was carried out in 2009 by Faunatica Oy. The aim was to repeat the previous year s inventory of species composition and abundances of small mammals in various habitat types in Olkiluoto (Nieminen & Saarikivi 2008). The methods used followed those in earlier inventories (Ranta et al. 2005, Roivainen 2006, Nieminen & Saarikivi 2008). Results have been presented by Nieminen et al. (2009) and are summarised here. Only half (nine; Fig. 24) of the sites sampled in 2008 were studied in 2009 with altogether 108 traps (72 mouse traps and 36 rat traps). There were four groups of traps on each trapping site, each group consisting of three individual traps. The FET codes were applied for identifying the sites. The locations of individual traps were intended to be exactly the same as in 2008, but some locations had to be changed on logged sites. Trapping took place for four days both in spring (May 25 29) and fall (August 31 September 4). The sampled individuals were identified, photographed, weighed and handed over to Posiva for storing. (Nieminen et al. 2009). The numbers of small mammals were very low in 2009, as only two bank voles were captured in the spring and a total of 20 bank or field voles in the autumn. The numbers of small mammals per trapping site varied from 0 to 7. The vole populations were at an exceptionally high level in Southern Finland in 2008, but crashed during the spring and early summer of The population declines were strong in all types of forest in Olkiluoto, but for some reason no apparent decline took place in open clear-cut and fallow field sites. Bank vole was still the most numerous species (73 % of all individuals). (Nieminen et al. 2009). The results of the study are presented in Appendix G.

40 Figure 24. Small mammal, ant, snail and earthworm trapping sites in Map layout by Jani Helin/Posiva Oy. The most recent bird inventory on Olkiluoto Island was carried out in summer 2008 (Yrjölä 2009), repeating the study conducted in 1997 (Yrjölä 1997) with some augmentations. In 2009, monitoring of island birds was started on the sea areas off Olkiluoto during the breeding season. The aim is to produce long-term and comparable observation series as standardised bird monitoring is a good indicator of environmental changes. The island bird monitoring is coordinated by the Finnish Museum of Natural History (University of Helsinki) and it is implemented according to the museum s guidelines ( The bird fauna on the sea area close to Olkiluoto is rather diverse. Important nesting islets and breeding species are abundant. Based on the experience of the counting in early summer 2009, the area will be divided in two parts: the area of Eurajoki and the area of the northern archipelago of Rauma. These areas will be monitored on alternating years, with the exception of Puskakari just at the margin of Rauma, where the great cormorant population should be monitored annually. Based on the countings of summer 2009, numerous important species and large communities protected by the European Union bird directive nest on the study area. The most important and surprising finding was one or two pairs of nesting Calidris alpina schinzii, a subspecies of dunlin, classified as critically endangered in the national

41 classification of endangered species. It breeds in the Baltic Sea region, but its population has collapsed in the whole area. Greater scaup is another rare breeding bird observed (one nesting couple; Fig. 25). Its breeding area has diminished continually for an unknown reason. A few decades ago, the species nested on the coastal area between Uusikaupunki and Pori. Lately, nesting birds are only found in some years. Figure 25. Nest of a rare species, greater scaup (Aythya marila), on a small islet near Olkiluoto (photo by Pekka Alho/Varsinais-Suomen luonto- ja ympäristöpalvelut on June 18, 2009). Herpetofauna Reptiles and amphibians were surveyed in spring 2008 (Nieminen & Saarikivi 2008) and the results were summarised in the previous version of this report (Haapanen 2009). Insects A study on ground beetles and ants was carried out in summer 2008 (Santaharju et al. 2009) and the results summarised in the previous version of this report (Haapanen 2009). In 2009, a line transect sampling of ants was conducted on August 31 September 2 by Faunatica Oy. The aim was to survey the species composition and amount of ants in various habitat types. The methods were planned so as to allow

42 comparisons between Swedish sites (Persson et al. 2007) and Olkiluoto. The results have been reported by Nieminen et al. (2009) and the major findings are summarised below. In the study, 11 transects were sampled. The size of each transect was ca. 50 x 3 m. The midpoint of each transect was the fixed point of a FET plot, if the whole transect fitted in the same habitat type. If not, then transect was moved a distance necessary to place it within the focal habitat type. Potential nest sites were checked and a maximum of 20 individuals sampled from each nest. The transects were photographed and the aboveground sizes of anthills measured. The samples were identified to species level. The fresh weights were measured with individuals from the same nest combined, the number of individuals per sample counted and samples handed over to Posiva for storing. (Nieminen et al. 2009). A total of 104 nests and 9 ant species were found in the study (Table 14). The numbers of nests varied from zero in two fallow field sites with no available nesting places to 20 and 24 in a mixed forest and a birch-dominated forest site. The estimated number of nests per ha varied between ca. 200 and 1,600 on occupied transects. The most abundant species were Myrmica ruginodis and M. rubra, which accounted for more than 80% of all nests. This is an expected outcome in a forested environment. The absence of Lasius niger was somewhat unexpected. All Formica species were very scarce. (Nieminen et al. 2009). The species diversity was rather low. On seven transects, there were only 0 2 species, and the maximum number of species was five. The low diversity most probably results from the small size of the sampled area and relatively homogeneous or unfavourable habitat structures. There were generally no apparent differences within species in average weights of individuals from different nests and from different transects. (Nieminen et al. 2009). Table 14. Ant observations in Olkiluoto in Number of nests on each transect. No nests observed on transects FET and FET (Nieminen et al. 2009). See monitoring locations in Fig. 24. FET Camponotus herculeanus Formica exsecta 1 Formica fusca 1 Formica lugubris 1 Formica sanguinea 2 Lasius platythorax Myrmica rubra Myrmica ruginodis Myrmica scabrinodis 1

43 Snails A study on snails was conducted concurrently with the ant study by Faunatica Oy. Terrestrial snails met on the same line transects were sampled (hand-pick samples). In order to collect more comprehensive samples, sifting was used as well. The aim was to survey the species composition and amount of snails in various habitat types. The methods were planned so as to allow comparisons between Swedish sites and Olkiluoto. The results have been reported by Nieminen et al. (2009) and the major findings are summarised below. The sample volume was measured, the samples dried and sifted. Snails were picked using a binocular microscope and identified to species level. Seven individuals (0.85%) could be identified to genus level only. Individuals were divided into two categories: living when collected and dead ones (empty shells). Dry weights of living individuals were measured and the number of individuals counted. (Nieminen et al. 2009). Slugs were also collected during the hand-pick sampling, and from sifting samples also earthworms, slugs, centipedes, pseudoscorpions, harvestmen, spiders, cockroaches, bugs, cicada larvae, beetles, moth larvae, dipteran larvae and sawfly larvae were sorted out and conserved, but not identified. The samples are stored (snails as dry, other groups in 70% ethanol) by Posiva. (Nieminen et al. 2009). The snail observations are presented in Appendix H. A significantly larger number of samples was collected by sifting (788 individuals and 14 species) than by hand-picking (35 individuals and 5 species). Hand-picking was an efficient collecting method only on the shore meadow transect (midpoint at FET909272), where all samples were obtained by picking snails climbing on plants. This may be due to flooding of the brackish sea water, which keeps the numbers of terrestrial snails among the litter very low. (Nieminen et al. 2009). The highest numbers of species and individuals were found in mixed and black alder forests and the lowest in Scots pine and Norway spruce forests. This is an expected result caused by varying ph and availability of calcium. The most species-rich mixed forest (midpoint at FET920282) is a fairly good snail habitat for Finnish conditions. The site was clear-cut in summer 2009, meaning that the site will probably harbour fewer individuals in the future. On open habitats, few snails were found. Most species encountered are very common in Finland and adapted to a wide spectrum of habitats. No endangered or rare species were found. (Nieminen et al. 2009). Earthworms Earthworms were sampled from the same 11 transects as ants and snails (Fig. 24) by Faunatica Oy. The aim was to survey the species composition and amount of earthworms in various habitat types. Sampling was conducted in May 25 29, The methods were mainly adopted from the work done by Persson et al. (2007) to allow comparisons between Finnish and Swedish sites. The results have been reported by Nieminen et al. (2009) and the major findings are summarised below.

44 Four sampling plots were randomly chosen from each transect. Soil samples were taken from depths of 0 20 and cm. The numbers of large holes dug by Lumbricus terrestris were counted. The plots were photographed. The soil samples were handsorted and sieved. Earthworms and their fragments were conserved in 70% ethanol. Samples were identified to species level. The samples were measured and the biomasses calculated. Other macroscopic animals were preserved and conserved, but not identified. The samples are stored by Posiva. (Nieminen et al. 2009). A total of 117 earthworms of seven species were found (Table 15). Nine individuals could be determined to genus or family level only. The abundance of earthworms varied between individuals/m 2 ; on two transects no earthworms were observed. The highest abundance and the second highest biomass were recorded on a fallow field site, and the highest biomass on a mixed forest site. The epigeic Dendrobaena octaedra and Lumbricus rubellus, and the endogeic Aporrectodea caliginosa were most abundant. The most widespread species were D. octaedra and L. rubellus, both occupying seven partly different transects. (Nieminen et al. 2009). The numbers of earthworm species, as well as their abundances and biomasses are generally similar to those recorded by Terhivuo (1989) in corresponding habitat types in Southern Finland. The abundances and biomasses of earthworms were at around the same level as found by Persson et al. (2007), but the maximum values were higher in the Swedish study area, which can be explained by its more southern location. (Nieminen et al. 2009).. Table 15. Earthworm observations in Olkiluoto in 2009, individuals per transect. No earthworms observed on transects FET (Norway spruce-dominated forest) and FET (clear-cut) (Nieminen et al. 2009). FET Aporrectodea caliginosa Aporrectodea rosea 2 1 Aporrectodea sp. 2 Dendrobaena octaedra Dendrodrilus rubidus 1 2 Lumbricus rubellus Lumbricus terrestris 2 Lumbricus sp. 1 1 Lumbricidae sp Octolasion tyrtaeum

45 Anthropogenic and social effects Changes in land ownership, settlement and land-use are recorded annually. Historical data are also of importance. Water supply information is monitored when checking the water quality of private wells. Much of this information is available from national institutes and authorities, which also have information about potential food resources. The land register map concerning Olkiluoto area was added to Posiva's GIS database earlier, and in 2007 the grid database of Statistics Finland, as well as the population information system of the Population Register Centre were acquired. The changes in land-use due to the new nuclear power plant (OL3) and its infrastructure are also continuously monitored. 3.5 Limnic systems There are few limnic systems in Olkiluoto at the moment. The Korvensuo fresh-water reservoir is the most important, but it is artificial and heavily controlled. Its hydrogeochemistry is monitored weekly by TVO, and the results of the analyses are submitted to the water works. No further summary of the findings is compiled. In addition to the monitoring activity by TVO, Posiva started to monitor the Korvensuo reservoir in Two samples were taken in 2008, and from 2009 onwards, the sampling will be carried out three times a year. The River Eurajoki is monitored by industrial companies operating on its upper course. In addition to this mandatory monitoring, Posiva started to monitor the chemical characteristics of the river in 2008, as well. See the results of both locations in Appendix I. From 2010 on, the monitoring will be carried out according to a plan accepted with the environmental permit application. Concerning the mandatory monitoring of River Eurajoki, the discharge and the nutrients have been presented by Turkki (2010). The average discharge in 2009 (5.3 m³/s) was clearly (35 %) lower than the average discharge of , and similar to the discharge of 2004 (Table 16). The discharge was clearly at its highest in April, peak discharges occurred especially in the beginning of the month. In June September the discharges were rather low. The amounts of phosphorus, nitrogen and substance matter carried by the river to the sea were clearly lower than the year before. The amount of phosphorus and nitrogen carried by the river were approximately 60 % lower than normally. The amount of nutrients carried by the river was rather evenly distributed across the year, April being an exception with a share of 30 % of the year s nutrient load. The substance loads of River Lapinjoki have not been estimated, due to insufficient data.

46 Conductivity, μs/cm Table 16. Amounts of nutrients and substance matter carried by the River Eurajoki to the sea and the average, min and max discharges in "*"= mode of months, "**"= average for Most recent data by Turkki (2010), older monitoring data have been summarised in Haapanen (2009). P, kg N, kg Substance Discharge, m 3 /s matter, tonnes Average Min (month) Max (month) ,900** 640,000** (8*) 19.8 (4*) , ,680 3, (8) 16.4 (12) , ,080 8, (7) 26.3 (1) , ,000 8, (8) 29.4 (12) , ,000 5, (7) 23.5 (1) , ,000 7, (7) 23.3 (11) ,500 23,6000 1, (9) 13.1 (4) Posiva monitors three nearby springs: Pistola (TMA01), Kaukenpieli (TMA02) and Koivukari (TMA07). These have been managed by building a support for water sampling (concrete ring or a wooden collar and lid). Conductivity and ph results are presented in Figs. 26 and 27 and detailed results of the monitoring are compiled in Appendix I. In addition to the results presented, a few surveys on Se, I and suspended sediment are to be made in the coming years TMA01 TMA02 TMA07 0 February 2, 2008 September 15, 2008 June 3, 2009 October 27, 2009 Figure 26. Conductivity (μs/cm/25 C) in the monitored springs in

47 ph 7 TMA01 TMA02 TMA07 6 February 2, 2008 September 15, 2008 June 3, 2009 October 27, 2009 Figure 27. ph in the monitored springs in Table 17. The load into the sea caused by the sanitary waters of TVO in , kg/year. Standard deviation in brackets. (Turkki 2009, 2010). Period BOD 7ATU Total P Total N Ammonium-N Solids ,500 (908) 19 (0.99) 1,020 (320) 1, ,620 (1680) 30 (17) 1,300 (410) 1,030(500) (890) 19 (21) 1,230 (400) 950 (300) 770 (630) (95) 9.7 (1.3) 1,400 (360) 1,140 (380) 345 (65) ,738 2, ,555 1, , ,380 3, , ,222 5, , ,395 8, Treated sanitary waters of TVO are conducted to the cooling water discharge area of Olkiluoto power plants. In the last five years ( ) the nutrient, solids and BODload of sanitary waters have clearly been larger than earlier in the 2000s (Table 17). The nitrogen load has also been larger than the average load of the 1990s. The increased load of is mainly due to the construction of OL3 power plant unit, employing hundreds to thousands of workers. The high phosphorus load in 2008 was partly caused by occasional poor oxygen conditions in the purification plant. (Turkki 2009, 2010). 3.6 Marine/brackish ecosystems Of the monitoring For monitoring the sea environment, ten monitoring plots have been established in the study area to be used for a varying number of analyses. The main findings are summarised here, and the study locations are shown in Fig. 28. Result tables are presented in Appendix J.

48 The marine ecosystem has been part of TVO's mandatory monitoring programme since the 1970s. The study area mostly extends to a distance of 5 6 km from the nuclear power plant cooling water discharge site. This programme consists of seven monitoring plots (SEA03, SEA05 SEA10). Physical and chemical properties, as well as phytoplankton properties are sampled annually on all seven plots, while some more detailed monitoring is performed on sub-sets of these plots. TVO monitors aquatic macrophytes by diving a set of established transects at 4 to 5-year intervals. The ongoing Environmental Impact Assessment process by TVO has affected the schedule and some new diving lines were established and studied already in 2007 and 2008, instead of the scheduled year Bottom fauna samples are taken in TVO s monitoring programme once a year on the seawater quality monitoring sites. The species are identified and their proportions (measured in abundance), total number (individuals/m 2 ) and biomass are calculated. Baltic clams and blue mussels are sampled on one seawater quality site for radionuclide analyses, as well. TVO monitors fish stocks by test fishing every five years for determining the age and growth of trout, whitefish and perch. In addition, pike, perch, roach and Baltic herring are caught twice a year for radionuclide analysis. Detailed methods and annual results are published in the research report series of Lounais-Suomen vesi- ja ympäristötutkimus Oy (except for the macrophyte diving, fish and radionuclide studies). Results from the 2009 studies have been presented by Turkki (2010). As in 2008, Posiva ordered additional surveys on phytoplankton and zooplankton, which were carried out by Lounais-Suomen vesi- ja ympäristötutkimus Oy and reported by Saarikari (2010) and Turkki (2010). In Posiva's own monitoring programme seawater samples from four selected sites (SEA01 SEA04) are analysed for hydrochemical modelling purposes once every three years. The aim is to collect information of possible changes caused by ONKALO construction and to complete the baseline study of the disposal site. The sampling points have been monitored in 2002, 2005, and 2008, and SEA01 and SEA02 already in 1989 and The results were summarised in Haapanen (2009). In addition, Luode Consulting Oy carried out measurements of some central parameters in The results were reported by Lindfors et al. (2008) and summarised by Haapanen (2009). In 2009, a project was started with Luode Oy, which will take two years to complete. The aim is to examine the sediment load carried by rivers Eurajoki and Lapinjoki to Eurajoensalmi bay, as well as the external factors affecting the sediment transportation in the bay area. The measurements were started in the summer of The water quality, wave height, current and wind are monitored on automatic measuring stations (two stations for cloudiness and water level, two stations for cloudiness and resuspension caused by waves on the shallow coastal areas of Eurajoensalmi bay, on one of them also a weather station. Two sensors for resuspension caused by currents near the bottom of river Eurajoki mouth area in Eurajoensalmi bay; this is complemented by measuring the current during two three month periods). Also, water quality surveys by Lindfors et al. (2008) with a flow through system are repeated. The location of TVO's macrophyte diving lines is not optimal for Posiva's modelling purposes (they are meant for monitoring the effects of cooling water discharge and thus

49 situated west from Olkiluoto Island). For this and other reasons, a set of 6 new study lines was established on the shores of Olkiluoto Island in 2008 by Alleco Ltd. The results were reported by Ilmarinen et al. (2009) and summarised by Haapanen (2009). The state of the nearby waters is partly connected with the nuclear power production activity. The capacity factor of OL1 in 2009 was 97.0 % and that of OL %. Cooling water consumption was 1.82 billion m 3. In 2009, 99.7 PJ/of heat per year was conducted to the sea; the amount was slightly higher than in the previous two years. The amount of heat conducted to the sea has increased by approximately 5 % in compared to (Turkki 2010). The variation of sea water level affects some of the results presented in this report, and is shown in Fig. 29. Figure 28. Locations of established seawater sampling sites, codes according to Posiva's system. Original TVO codes used by Turkki (2010) can be found in Appendix A. Map layout by Jani Helin/Posiva Oy.

50 Sea level, m Figure 29. Sea level fluctuations at Rauma mareograph in 2009, difference from the N60 system sea level. Data by Finnish Meteorological Institute, figure by Pöyry Finland Oy Physical and chemical properties Water samples from seven observation plots (SEA03, SEA05 SEA10) were taken at four instances in February October as vertical series with 5 m distances. The samples were analysed by Lounais-Suomen vesi- ja ympäristötutkimus Oy for oxygen, ph, alkalinity, electrical conductivity and salinity, colour, cloudiness, ammonium N (NH 4 ), total N, total P and substance matter. The temperature was recorded at all physicalchemical sampling instances. Some analyses not included in the program were made on SEA09 in February and July by Southwest Finland Regional Environment Centre (No23-N, PO4-P, soluble Fe). In February, sampling plots SEA03, 07 and 10 could not be reached and the sample of SEA06 was taken ca. 50 m north from the actual plot due to poor ice conditions. In the beginning of May, SEA10 could not be reached due to bad weather conditions, and in October, no samples were obtained from SEA09 and 10. (Turkki 2010). In mid-february, the warming effect of the cooling waters was discernible on the area of SEA06 when northern/northeastern winds prevailed: water was warmer in all layers than on SEA08. A milder effect was observed on SEA08, as well, mainly in the deeper water layers. Due to lacking data, more accurate comparisons cannot be done. In the open water season, the warming effect of the cooling waters varied, much according to wind conditions: in July, it was discernible only on SEA08, in October also on SEA03 and possibly mildly on SEA07. (Turkki 2010). The temperatures at the observation plots during the open water season are presented in Appendix Table J-1. The oxygen saturation of sea water at the observation plots is presented in Appendix Table J-2. Both in late winter (end of February) and in the open water season the oxygen saturation in the area was good. As in previous years, the production maximum

51 Visible depth, m of plankton caused oversaturation of oxygen in the surface waters, with the exception of SEA10 in July. On SEA08, the oversaturation of oxygen was significant (120%). Generally, the open water season values were similar to reference values and the longtime average ( ). (Turkki 2010). The opacity of water (visible depth) varied from 2.9 m on SEA09 to 4.9 m on SEA07 (average of the open water season). SEA10 was reached only in May and July, which may have a decreasing effect on the result. SEA10 excluded, the average visible depth of open water season was clearly higher than in the past ten years. (Turkki 2010; Fig. 30). In February, the cloudiness values (average of the water column) were 40 50% lower than the long-time average ( ), due to low precipitation and discharge values. Low values were also recorded in May, July and especially in October. The open water season average values were FNU, ca % lower than the long-time average (SEA10 excluded). (Turkki 2010). The wintertime concentrations of substance matter were low. The average of the water column varied from 1.3 to 2.3 mg/l (Fig. 31, Appendix Table J-3). The average of the open water season was rather low as well, and on the same level or lower than the values of the previous year, except on SEA05, where the values were high in May. Comparisons with earlier years cannot be done, since the method was changed in 2007 (from Sartorius 0.65 μg to Nuclepore 0.4) (Turkki 2010) SEA03 SEA05 SEA06 SEA07 SEA08 SEA09 SEA10 Reference site Figure 30. Opacity of water during the open water season in The reference site is in Pyhäranta. (Turkki 2010, earlier results combined in Haapanen 2009).

52 Substance matter, mg/l SEA03 SEA05 SEA06 SEA07 4 SEA SEA09 SEA10 Winter Open water season Winter Open water season Figure 31. The concentrations of substance matter (mg/l) in water in winter and in open water season in 2008 and 2009 as an average of the vertical water column. (Turkki 2009, Turkki 2010). Wintertime N concentrations (average of the water column) varied between μg/l (Appendix Table J-4). The concentration was greatest on SEA09. The values were lower than usually in wintertime, especially on SEA09. The reference values in Kylmäpihlaja and Pyhäranta varied between 240 and 290 μg/l. The wintertime concentrations of ammonium N were also rather low or under the detection limit, with the exception of SEA09, where the ammonium N concentration of the upper layer (1 metre) was 20 μg/l. The concentrations of total N in the study locations were between μg/l during the open water season. The concentrations were ca. 10 % lower than on average in , and ca. 20 % lower on SEA09. The background concentration in coastal waters of the Bothnian Sea (Pyhäranta 300 μg/l and Kylmäpihlaja 270 μg/l) was similar or slightly higher than that of Olkiluoto area. The ammonium N concentrations were relatively small, on average 2 6 μg/l during the open water season. In May, the highest value was near the bottom on SEA06, in July on SEA03 and in October in the surface layer on SEA08. In the production layer, the total N concentrations varied between μg/l during the growing season, similar to the average or slightly lower. As an average of the growing season, the values were highest on SEA09, SEA05 and SEA08. Mineral nutrients were scarce during most of the growing season. The amounts of inorganic N compounds varied between 8 and 79 μg/l, the highest values measured from SEA05 and SEA09. (Turkki 2010). In winter (February) the average P concentrations of water columns were between μg/l (Appendix Table J-5). The values were at a normal level or above that, and ca % higher than the background concentration in coastal waters of the Bothnian Sea (Pyhäranta 19 μg/l, Kylmäpihlaja 21 μg/l). The wintertime P concentrations increased significantly between 1979 and After some fluctuation in the 2000s and

53 Total P, mg/m³ a lower value in 2007, the concentrations were the highest of the monitoring period in In 2009, the wintertime value seemed to have dropped back to a level similar to the year 2006, but it has to be noted that samples were not obtained from all the plots. The average P concentrations during the open water season have showed a periodic, irregular fluctuation. As an average of the open water season, the P concentrations (Fig. 32) were ca % lower than the average for The P concentrations in the production layer varied between 10 and 26 μg/l. Mineral nutrients were low during most of the growing season. The amounts of phosphate P varied between <2 and 13 μg/l. During the growing season (April September) the average P concentrations in the production layer were on an ordinary level. The levels were highest on SEA09, SEA05 and SEA08. (Turkki 2010) SEA03 SEA05 SEA06 SEA07 SEA08 SEA09 SEA10 Reference site Figure 32. The concentrations of total P (μg/l) during open water seasons The reference site is in Pyhäranta. (Turkki 2010, earlier results combined in Haapanen 2009) Marine vegetation Phytoplankton The primary production of phytoplankton was measured on plots SEA06 and SEA08 for seven times in April September. In addition, the water column samples were analysed for total P, phosphate P, N compounds, chlorophyll-a and the production capacity of plankton on all water quality sampling plots. No sampling could be done in the beginning of April due to poor ice conditions. SEA10 could not be reached due to weather conditions in April, in the beginning of May, in June, July or August. In order to establish the annual variation in phytoplankton, the samples taken at various sampling instances on SEA06, SEA07 and SEA08 were examined separately. On other sampling sites, the summer season phytoplankton samples were combined. (Turkki 2010).

54 % of biomass The mean temperature was above average in April and May. June was mainly chilly and rainy, in July the temperature and precipitation were average. August and September were warm and dry. The nutrient contents were on an ordinary level or slightly above that during the growing season. As in 2008, considering the weather and nutrient contents during the growing season, the conditions for phytoplankton production were on an average level or little below. Phytoplankton biomass and its composition are presented in Appendix Table J-6 and the seasonal variation of phytoplankton biomasses by species on SEA06, SEA07 and SEA08 are shown in Figs Bacillariophyceae Chrysophyceae Cryptophyta Cyanophyta Dinophyta Mesodinium Monads and flagellates Prasinophyceae Euglenophyceae Figure 33. Variation of dominating phytoplankton species by sampling date at SEA06 in The sample of May 18 is missing due to broken equipment. (Turkki 2010). Species with minor shares have been removed for clarity.

55 % of biomass % of biomass Bacillariophyceae Chrysophyceae Cryptophyta Cyanophyta Dinophyta Mesodinium Monads and flagellates Prasinophyceae Prymnesiophyceae Figure 34. Variation of dominating phytoplankton species by sampling date at SEA07 in 2009 (Turkki 2010). Species with minor shares have been removed for clarity Bacillariophyceae Chrysophyceae Cryptophyta Cyanophyta Dinophyta Mesodinium Monads and flagellates Prasinophyceae Euglenophyceae Figure 35. Variation of dominating phytoplankton species by sampling date at SEA08 in 2009 (Turkki 2010). Species with minor shares have been removed for clarity.

56 On SEA06, the sample of May 18 is missing due to broken equipment. No combined samples were made from SEA03 due to broken equipment in May. (Turkki 2010). Due to the cooling water discharge area staying open, considerable amounts of cool season planktons grow in the waters even in mid-winter. Abundant springtime production of Bacillariophyceae normally starts at least a month earlier than in other coastal areas, and the growing season is longer. The spring maximum usually takes place earlier on SEA08 than on the other stations. Since only one sample per station was obtained in April in 2009, comparisons with springtime biomasses of earlier years should be done with caution. (Turkki 2010). In 2009, the average growing season biomass was clearly lower than in 2008 and in average in the 2000s: on SEA mg/m 3, on SEA07 1,074 mg/m 3 and on SEA mg/m 3 (Fig. 36). The highest phytoplankton biomass was recorded in April due to the spring maximum of Bacillariophyceae. The dominant species was Chaetoceros wighamii. However, the average biomass of the growing season varies according to the timing of the spring maximum. If the maximum is over by the time of the first sampling, the recorded levels are lower. In 2009, the biomass was significantly higher on SEA07 than on SEA08 (5,824 and 1,448 mg/m 3, respectively). This is probably due to relatively late first sampling (April 21), and the spring maximum being partly over at the time on SEA08. (Turkki 2010). After the spring maximum of Bacillariophyceae, the total phytoplankton biomasses declined sharply and remained on a rather low level for the rest of the summer, and were lower than in No significant differences were detected between the sampling stations. Amounts of Cyanophyta were rather small on all sampling stations, the maximum was on SEA05 (59 mg/m 3 ). The highest share of Cyanophyta of total phytoplankton biomass was 15 % on SEA08, 16 % on SEA06 and 7 % on SEA07. Very small amounts of Nodularia spumigena were found in August on SEA08 (6.7 mg/m 3 ) and SEA06 (<2 mg/m 3 ). (Turkki 2010). After 1985, the yearly variation in growing season biomass has been rather large. In the 2000s, the growing season biomass has been ca 2.5 times that of This can be explained by the mild winters after the 1990s, extending the growing season, as well as the eutrophication of the Baltic Sea. As the total amount of plankton has grown on the whole sea area, the difference in biomasses with SEA08 has diminished. In many years, the average summertime biomass has been highest on SEA05 and smallest on SEA07 or SEA03. (Turkki 2010). The average summertime (June September) biomass on the sampling stations varied between 224 and 449 mg/m 3 (Fig. 37). On SEA08, it was in slightly lower than on average since the end of the 1990s. On SEA07, the summertime biomass was at the same level, on SEA06 ca. 22 % smaller and on SEA05 ca. 18 % higher than the average of the last decade. The greatest variation in summertime biomass has been on SEA05 and SEA06, the smallest on SEA08 and SEA07. (Turkki 2010).

57 Biomass, mg/m³ Biomass, mg/m³ Figure 36. The average phytoplankton biomass on SEA08 during the whole growing season in (Turkki 2010, earlier results combined in Haapanen 2009) SEA03 SEA05 SEA06 SEA07 SEA08 Figure 37. Summertime phytoplankton biomasses in (Turkki 2010). Chlorophyll-a concentrations in June September varied between 1.6 and 3.7 μg/l. On most of the area, the levels were similar to the background values in the coastal waters of the Bothnian Sea (Appendix Table J-7). On SEA09, the average chlorophyll concentration was approximately double and on SEA10 ca. 18 % higher than the

58 background value of the Bothnian Sea. The average chlorophyll concentrations of the growing season were mostly % lower than the long time averages ( ) due to cool and rainy conditions in June and July. On SEA09 the concentration was on a normal level and on SEA10 comparisons with earlier years cannot be done due to only few sampling occasions. The average primary production capacity was mg C/m 3. d during the growing season, the greatest values being on SEA09 and smallest on SEA07. The average primary production capacity in the growing season was ca % lower than the long time average ( ), apart from SEA09 and SEA10. (Turkki 2010). The average primary production of phytoplankton is shown in Appendix Table J-8. The production was on the same level on SEA06 and SEA08, 270 mg C/m 2 /d. The cumulated production of the growing season (April 1 September 30) was 49 g C/m 2. Compared to the production in the year 2008, the levels were similar on SEA06 but clearly lower on SEA08 in Compared to the long-time average ( ), the values were lower on both SEA06 and SEA08 (8 % and 18 %, respectively). Over the long term, primary production grew until the year 2002, with some annual variation. In the production has been smaller than in the late 1990s and at the turn of the millennium. (Turkki 2010). The results from the monitoring of total organic carbon (TOC) are presented in Appendix Table J-9. During the growing season (from April to September), the TOC values varied between mg/l on average. As in the previous years, the highest average value of the growing season was on SEA09 and the lowest on SEA10. On SEA10, only one sampling succeeded. (Turkki 2010). Aquatic macrophytes The latest mandatory monitoring of aquatic macrophytes by TVO took place in In connection to the ongoing Environmental Impact Assessment processes by TVO, some new diving lines were established and studied in 2007 by Ramboll Finland Oy. These results have been summarised by Haapanen (2008). The extent and vitality of the reed stands on the southern, eastern and partly also northern shores of Olkiluoto Island have been studied in 2007 and 2008 (Haapanen & Lahdenperä 2010). In 2008, Posiva set out to study shoreline areas important to terrain and ecosystem modelling by establishing six diving transects on the shores of Olkiluoto (Ilmarinen et al. 2009, summarised in Haapanen 2009). In addition, six new diving lines were established in outer sea areas (Natura 2000 area of Rauma Archipelago) by TVO on August 19 20, 2008 (Ramboll Finland Oy) in order to study the condition of algae communities on hard bottoms. These results are summarised here. The diving was carried out by M. Sc. Niko Nappu (UWC Underwater Consulting). The locations of the lines were first marked on a map and checked on the field with GPS. The lines run from shore to open sea, but were processed in opposite direction. A rope with marks at 1 m intervals was lowered to the bottom. The diver proceeded in the bottom along the line and wrote down the depth, a description of the bottom and vegetation at each metre of the rope.

59 When describing the vegetation, the emphasis was on the coverage of dominating species by each metre of depth, and borders between vegetation zones. Species that are difficult to identify were sampled and later identified. The following parameters were recorded from the Fucus vesiculosus zone: location of lowest individual, location of uniform lower border, optimum, location of uniform upper border and location of uppermost individual. The optimal locations of Fucus zones were sampled with a Fucus bag (area 2,043 cm 2 ) in order to obtain a quantitative sample. Ectocarpus siliculosus and Pilayella littoralis could not always be separated. Different Polysiphonia sp. often grew in mixed communities, making it difficult to separate them. It was sometimes impossible to estimate the amount of fluffy species such as Hildenbrandia rubra and Rhodochorton spp. under filamentous ones. Furthermore, although the aim was to survey the coverages of only those algae that were attached to the bottom, loose algae material sometimes made this difficult. Filamentous blue algae or Urospora/Ulothrix spp. most probably existed on several shores. Efforts were made to sample these species with a Kautsky device, but sampling failed due to rough sea. The depth measurements were not calibrated related to the actual sea level of the time of the study. Of the bottom, the coverages of rock outcrop, boulders, stones, gravel, sand, silt, clay and gyttja were estimated. The amount of loose sediment was estimated as well. The diving lines were located on open or relatively open shores. This was reflected in the absence of vascular plants and the dominance of species typical of open, hardbottomed shores. In all, 20 algae species were found on the diving lines (Table 18). The flora was in good condition and abundant. Near threatened (NT) Rhodocorton spp. was met on several lines, the highest coverage being 20 %. Also Furcellaria lumbricalis was abundant. Gelatinous balls of Rivularia blue alga were observed on the locations closest to the shoreline on almost all study lines. The dry mass of Fucus vesiculosus was 1,384 2,965 g/m 2. A summary of observations from each line is presented in Appendix K. Table 18. Algae species occurring in the six diving lines located in the southern and central parts of the Natura 2000 area of Rauma Archipelago in 2008 (*blue alga/cyanobacteria). Species Furcellaria lumbricalis Rhodomela confervoides Chorda filum Lithoderma/Pseudolithoderma spp. Hildenbrandia rubra Ectocarpus siliculosus Dictyosiphon foeniculaceus Coccotyllus truncatus Cladophora rupestris Tolypella nidifica Species Polysiphonia fucoides Pilayella littoralis Ceramium tenuicorne Polysiphonia fibrillosa Fucus vesiculosus Elachista fucicola Sphacelaria arctica Rhodocorton spp. Ulva intestinalis Cladophora glomerata *Rivularia blue alga

60 3.6.4 Marine fauna Zooplankton The zooplankton research was carried out by Lounais-Suomen vesi- ja ympäristötutkimus Oy. It was partly carried out together with the primary production study and partly separately. The seasonal development of zooplankton was monitored on the sampling sites SEA06 and SEA08 near the cooling water discharge area, and the zooplankton composition of the stations was compared. The sampling took place on ten occasions during the growing season (April 22, May 5, June 9, July 14 and 28, August 11 and 26, September 10 and 21 and October 26). In 2008, the study was carried out on SEA08 only. (Saarikari 2010). In the study, both mesozooplankton (0.2 2 mm, consisting of large rotifers, Cladocerans, copepods) and microzooplankton (rotifers of μm) were examined. The samples were collected with a Limnos-type tube sampler, taking two lifts from the surface to the bottom. The temperature of the water column was measured at 1 m intervals. The biomass of zooplankton (mg C/m 3 ) and the number of individuals per m 3 were calculated. (Saarikari 2010). Altogether 30 taxons were found: 16 taxons of rotifers, 8 species of Cladocera and 6 species of copepods (5 Calanoid and 1 Cyclopoid copepods). No significant differences in the number of taxons were detected between the sampling stations. Regarding the annual dynamics of zooplankton in the Baltic Sea, temperature and salinity are the most important factors. In the study, differences in the biomass development of different taxon groups between the two sampling stations were detected. According to the results, sampling station SEA08, which is closer to the cooling water discharge area, is more eutrophic than SEA06. This is based on the shares of different zooplankton groups (Fig. 38), as well as to a higher maximum and average zooplankton carbon biomass of the whole sampling season on SEA08. The carbon biomass level of SEA08 was higher than the long-time average of the Bothnian Sea, but lower than the monitoring data from the coastal areas of Helsinki. (Saarikari 2010). On SEA08, the maximum biomass occurred at the end of July (68 mg C/m 3 ) and on SEA06 in mid-august (26 mg C/m 3 ). The average carbon biomass of zooplankton of the whole sampling season was 20 mg C/m 3 on SEA08 and 12 mg C/m 3 on SEA06. On SEA08, the maximum number of individuals (266,000/m 3 ) was detected in the sample of June 9. The average number of individuals during the whole sampling season was 79,000 individuals/m 3. On SEA06, the corresponding figures were 81,000 individuals/m 3 (August 11) and 42,000 individuals/m 3. (Saarikari 2010). The most common rotifera species found in the study were Synchaeta baltica, Keratella quadrata, Keratella cochlearis and Keratella cruciformis var. eichwaldi. On SEA08, the biomass and number of individuals of rotifera was at its highest in the sample taken on June 9, due to a very strong abundance maximum of Synchaeta baltica (221,000 individuals/m 3 ). The abundance maximum of the species on SEA06 was only one sixth of that. Due to their small size, the biomass level of rotifers was not high (Fig. 38). Their share of the total biomass was highest when the amounts of Crustaceans were low: in spring, early summer and late autumn. (Saarikari 2010).

61 % Rotifera Cladocera Calanoid copepods 0 SEA06 SEA08 SEA06 SEA08 Share of total carbon biomass Share of the number of individuals Figure 38. The share (%) of different zooplankton taxons of the total carbon biomass and the number of individuals of zooplankton on the sampling stations SEA06 and SEA08. Average of the sampling season in 2009 (April October). (Saarikari 2010). Bosmina longispina maritima was the most abundant species of Cladocera on both sampling stations. The average share of the species of the total zooplankton biomass was 15 % on SEA06 and 12 % on SEA08. Podon polyphemoides and Evadne nordmanni were found regularly in the samples of mid-summer on both stations. Podon intermedius, Chydorus sphaericus and Alona affinis were found occasionally on SEA06, and Podon intermedius, Daphnia galeata and Daphnia cucullata on SEA08. (Saarikari 2010). Of the Calanoid copepods found in the samples, Acartia bifilosa, Acartia tonsa and Eurytemora affinis occurred in all stages of life cycle, whereas Temora longicornis and Limnocalanus macrurus only in the nauplius stage. Considering the whole sampling season, the biomass and number of individuals of Acartia was clearly higher than E. Affinis. Regarding temperature, A. tonsa is more demanding than A. bifilosa. A. tonsa was 2.5 times more abundant on SEA08 than on SEA06, which is located further from the cooling water discharge area. On both sampling stations, Cyclopoid copepods were only found in the nauplius stage, regularly but in small amounts. (Saarikari 2010). Small amounts of nauplius larvae of Balanus improvisus were found. In addition, there were occasional findings of crustaceans of the order Harpacticoida on SEA08. Also, veliger larvae of Gastropoda and Bivalvia, as well as nectochaeta larvae of Polychaeta were found on both sampling stations, but these were not examined further (Saarikari 2010). Temperature, nutrition and predation affect the seasonal biomasses and production of zooplankton. A rising temperature decreases the body size and thus negatively affects the biomasses, but the effect is compensated by shorter developing times. Concerning the crustacean plankton, only the length of Cladocera was measured. As a whole, the

62 Cladocerans were small; the average median length was below 0.4mm. No size differences were detected in the Cladocera between the sampling stations (Saarikari 2010). Bottom fauna Bottom fauna were inventoried in the mandatory monitoring programme of the power plant on September 10, October 26 and 27 and November 9, 2009 on six of the water quality sample plots. The bottoms of the plots varied from gravel to sulphide gyttja. On the surface layer of the sediment, there was a brown oxygen-rich layer. On SEA08, the bottom was black sulphide gyttja with a distinct odour of hydrogen sulphide. The odour was discernible on plot SEA06, as well. In the bottoms of SEA05, SEA06, SEA08 and SEA09 there were black layers or streaks. No sample was obtained from SEA07 due to an unsuitable, gravelly bottom. The station will probably be replaced by one from an area with a sediment bottom. (Turkki 2010). The number of species varied between 9 and 15: the largest number was on SEA03 and the smallest on SEA06 and SEA09. The density of individuals varied between 2,877 and 8,831 individuals/m². The largest number of individuals was on SEA08, where a large number of small clams were found. The biomasses varied between 59 and 242 g/m², being largest on SEA03, where large clams were abundant, and smallest on SEA09. On SEA08, the total biomass was similar to that in 2008, but the number of individuals had increased, mainly due to a larger number of Baltic clam (Macoma balthica). On SEA05, SEA06 and SEA03, there were no significant changes in the total biomass or number of individuals. On SEA09, the total biomass had slightly decreased due to a diminished amount of Macoma balthica. (Turkki 2010). On all sites, Macoma balthica accounted for most of the biomass (87 98 %) and individuals (37 77 %). On all plots, except SEA08, almost all size categories of Macoma balthica were found. The most even size distribution was found on SEA03. The uneven size distribution of SEA08 can be explained by an occasional lack of oxygen on the bottom. On this station, very few clams were found in In the past few years, the oxygen conditions near the bottom have ameliorated, and the bottom fauna has slowly recovered. However, there is variation between years, and the size distribution is still rather poor. (Turkki 2010). In , there have been no observations of Monoporeia affinis, which is a species typical of undisturbed bottoms. Earlier, this species has appeared occasionally. However, Monoporeia affinis has disappeared from vast areas in the Northern Baltic Sea, and the disappearance may be caused by changes in the larger area. There was no sign of a close relative, Pontoporeia femorata, either. (Turkki 2010).

63 individuals/m² g/m² Density, indiv./m² Biomass, g/m² Figure 39. The average density (number of individuals per m 2 ) and biomass of bottom fauna on gyttja bottoms in (SEA05, SEA06, SEA08 and SEA09) (Turkki 2010, earlier results have been combined in Haapanen 2009). The most common species after the Baltic clam was Oligochaeta on SEA05 and SEA09, and Hydrobia and Potamopyrgus on the other plots. On SEA08, Nereis diversicolor formed a significant share (8%) of the biomass. Marenzelleria viridis was abundant on many plots as well, but with the exception of SEA05, its relative share has diminished from the year In the beginning of the 1990s, this North-American polychaete spread rapidly in the southwestern coastal waters. The amounts of an indicator species of polluted bottoms, Tubifex coastatus, had increased compared to As in 2008, a small amount was found on SEA06, but now also a large amount on SEA03. On SEA03, small amounts of Chironomus plumosus, another indicator species of polluted bottoms, was found as well. (Turkki 2010). The numbers of species, individuals and biomass in 2009 by bottom type are presented in Appendix Table J-10. Since the sampling has often failed in hard bottoms, time series (Fig. 39) are presented for soft bottoms only. The densities of individuals on gyttja bottoms have been at their highest at the beginning of the 1980s and in the year The average biomass was highest in the beginning of the 1990s. The population sizes of Macoma balthica diminished after mid-1990s, and the standard deviation has clearly grown. This is a sign of increased instability of the bottom fauna, caused by deteriorating environmental conditions, such as occasional poor oxygen conditions. On almost all plots, rather large amounts of algal remains have usually been found. The decaying algal remains consume considerable amounts of oxygen. In some years, the clam populations have probably been destroyed due to lack of oxygen. In the years and 2009, no large amounts of algal or plant remains were detected; in 2008, there were some on SEA07. (Turkki 2010).

64 Test fishing Test fishing was last accomplished in 2006 and reported in Haapanen (2008), and the next campaign will occur in Seafloor mapping A seafloor mapping study was carried out on six study transects on the shallow areas around Olkiluoto during mid-august 2008 (Ilmarinen et al. 2009) and summarised in Haapanen (2009) Anthropogenic and social effects As part of the nuclear power plant's monitoring programme, fishing activities are followed up by interviewing fishermen every other year. In addition, five fishermen carry out continuous account fishing. The latest fishery survey was made by Ramboll Oy by Hanna Peltonen and completed in The results are summarised here. All the professional fishermen operating in the sea area off Olkiluoto were interviewed concerning their fishing activities of the year In 2009, there were 7 professional fishermen (5 full-time and 2 part-time fishers) in the area (5 in 2005, 6 in 2007). All the professional fishermen were contacted, but no information was obtained from one parttime fisherman concerning his commercial fishing. The commercial fishery in the area is for the most part done with nets (coarse-meshed nets and herring nets): 3 herring nets and 639 other nets were used by professional fishermen in The same amount of herring nets is used as in Herrings have not been caught by trap nets since the 1990s. Bait hooks, pots and salmon trap nets are used as well, but to a smaller extent. The trap amount per fisher has increased since the 1980s. In 2009, commercial fishery was practiced on the whole sea area around Olkiluoto, with the exceptions of the bays and archipelago southwest of Olkiluodonvesi and Sorkanlahti Bay. Commercial fishery is practiced around the year, but some areas cannot be reached during wintertime due to difficult ice conditions. In 2009, the number of fishermen and the amount of fishing were rather evenly distributed over the year, the most active times being in January February, May June and October. In 2007, the number of fishermen and the amount of fishing were more unevenly distributed across the year, and fishing was more active during the summer than during the winter. The total catch of professional fishermen was 13.6 tonnes in 2009 and the catch per fisherman was 2.3 tonnes. There are differences in the annual catches caused mainly by variation in the fishing effort. The greatest catches are of perch (37.7 % of total catch), as in all years during The share of perch more than doubled during , but somewhat declined in Catches of pike (15.6 %), European whitefish (12.5 %) and herring (10.0 %) were significant as well (Fig. 40). In 2009, the shares of pike and European whitefish were largest of the period Perch was

65 kg economically the most important species (32.3 % of the value of the total catch) followed by European whitefish. In the survey, the fishermen were also asked about possible changes in the fish fauna of the area during the last five years. They were particularly concerned about diminishing fish stocks: European whitefish, pike, roach, flounder and perch were mentioned. Also, harms caused to fishing by seals and great cormorants worried the fishermen. Some other changes were mentioned as well, including for example eutrophication, sliming of nets caused by algae and an increased bream population Baltic herring European whitefish Salmon and brown trout Pike Bream Ide Roach Burbot Zander Perch Flounder Rainbow trout Other Figure 40. The catch of professional fishermen on the sea areas off Olkiluoto in , kg. 3.7 Historical and future properties Knowledge of historical properties is gained either through separate research efforts or as a bi-product of continuous monitoring. The Baseline Condition Report (Posiva 2003a) summarises the results of studies carried out mainly during the environmental impact assessment process toward the end of the 1990s. The Biosphere description reports (Haapanen et al and Haapanen et al. 2009) have compiled information of historical properties. For the latter, a review was prepared of the historical land use, evolution of settlement and history of agriculture and forestry in the Satakunta region. The work continues for the next BSD version (BSD-2011). Plenty of generic GIS-data has been acquired to serve the terrain and ecosystems forecasts, in addition to the Olkiluoto specific studies. Furthermore, a GIS modelling

66 toolbox called UNTAMO has been developed for Posiva (Ikonen 2007b, Ikonen et al. 2010a, b). With the toolbox the vast amount of GIS data can be handled, processing procedures controlled and routines automated.

67 4 RESULTS II: INPUT TO ENVIRONMENTAL IMPACT ASSESSMENTS 4.1 Air quality The effects of major construction works on Olkiluoto Island (the new nuclear power plant, the disposal site for spent nuclear fuel, and the landfill site for crushed waste rock) have been monitored using a wet deposition sample plot network (MRK; Section 3.4.2) established on the island in the year Norway spruce and Scots pine needles have been collected annually or biannually from the same sample plots during 2003 to 2009 in order to follow changes in the foliar element concentrations. Special attention has been paid to assessing the effects of particulate matter originating from the construction activities on the foliar concentrations by means of different washing procedures. The analyses and reporting of the most recent sampling (which took place in March 2010) will be completed later, earlier results have been summarised in Haapanen (2009). 4.2 Noise Noise has been monitored once a year during the winter by TVO using direct measurements. Additionally, in 2005 a more comprehensive survey was performed by Insinööritoimisto Paavo Ristola Oy (on three occasions and 45 locations). In 2009, TVO staff recorded noise in October 30, November 11 and November 13. Since the noise levels were measured on three different occasions, the weather conditions varied. The noise levels are affected especially by wind direction and speed. The weather conditions are presented in Table 19. The ground was free of snow cover. The measuring device was a CESVA model SC-160 type 2 recording digital device. L AT (continuous mean noise level) was recorded at one second intervals with a recording time of ten minutes at one spot. The measuring locations for 2009 are presented in Fig. 41 and the results in Table 20. Based on the surveys, it can be said that the construction of OL3 and the amount of road traffic have a raising effect on the noise levels of the immediate surroundings. However, the weather conditions affect the results as well. The noise of the operating power plants OL1 and OL2 have no significant effect on the environmental noise levels. At the time of measuring, the power plants were operating at full power. Table 19. Noise measurement conditions in Oct. 30 Nov. 11 Nov. 13 Temperature, ºC Wind direction, º Wind speed, m/s

68 Figure 41. Noise measuring locations in Map layout by Jani Helin/Posiva Oy. Table 20. Noise levels (L AT, db). In 2007 measurements were performed on November 7, 8* and 12**; in 2008 on December 1, 5* and 7**; in 2009 on October 30, November 11* and November 13**. Location NMP ** 36.6** 39.0 NMP ** 38.1** 42.1 NMP NMP * 61.9* 55.2* NMP NMP * NMP NMP NMP ** NMP * 46.5* 45.3* 4.3 Water quality As stated above, TVO monitors seawater quality, nutrient and chemical loading and the River Eurajoki is monitored by companies operating on its upper course. Results for the quality, nutrient load and chemical loading of seawater are presented in Section 3.6 and the results for the River Eurajoki in Section 3.5. Surface runoff is presented in connection to weather (Section 3.2).