Atmospheric Transport Patterns from the Kola Nuclear Reactors

Alexander Mahura University of Alaska, Fairbanks, USA University of Washington, Seattle, USA Kola Science Center, Apatity, Russia

Robert Andres University of Alaska, Fairbanks, USA University of North Dakota, Grand Forks, USA

Daniel Jaffe University of Alaska, Fairbanks, USA University of Washington-Bothell, USA

Northern Studies Working Paper cerum, Centre for Regional Science No. 24:2001 se-90187 Umeå Sweden The Project “Nuclear Problems, Risk Perceptions of, and Societal Responses to, Nuclear Waste in the Barents Region” - an Acknowledgement

Since the late eighties, CERUM has developed research with a focus on the shaping of and development within the Barents Region. Two specific features have characterised this research. First of all our ambition has been to develop research projects in close collaboration with international and especially Russian researchers. This has materialised as an exchange of researchers at conferences both in Sweden and in Russia. Secondly, our view has been that the Barents region must be analysed by researchers that represent a broad set of competences. Especially our ambition is to develop a deeper and more integrated collaboration between researchers from social sciences and arts on one hand and natural sciences on the other. With the Swedish Board for Civil Emergency Preparedness (ÖCB) as the main finacier, CERUM has for a couple of years developed research within the project “Nuclear Problems, Risks Perceptions of, and Social Responses to, Nuclear Waste in the Barents Region”. This report is produced within the afore-mentioned project. The project deals with vulnerability as a response to the latent security questions associated with the existence of nuclear power and nuclear waste in the Barents region. Clearly there is within the project a large scoop for an analysis with its roots in natural sciences of the size and dispersion of various types of waste from the region. The project also has produced a set of such papers. Those papers raise questions that immedi- ately lead to other papers and a discussion with its roots in social sciences, of civil emergency preparedness in a broad and spatially delim- ited sense as well as a discussion of the need for an enlarged concept of safety. The pattern of spatial risk dispersion, which in this case not halts at the national borders, and the associated construction of governance in the Barents region also imply that trans-border negotiation, conflict, and cooperation become key words in the discourse.

Gösta Weissglas Lars Westin Project leader Director of CERUM 2

List of Figures and Tables

Figure 1.2.1 Geographic relationship of KNPP 5 Figure 1.3.1 Position of the Arctic front 6

Figure 2.1.1 Geographical regions of interest and model grid domain 8 Figure 2.2.1 Examples of trajectories showing consistent air flow 9 Figure 2.2.2 Examples of complex trajectories 1 Figure 2.3.1 5-day isentropic forward trajectories originated over the KNR region during 11 1992 Figure 2.3.2 Cluster mean trajectories or transport pathways from KNR during 1992

Figure 3.1.1 Monthly variations in the average transport time (in days) from the KNR region 14 to geographical regions based on the forward trajectories during 1991-1995 Figure 3.1.2 Monthly variations in the average percentage of trajectories originated over the KNR region at lower altitudes within the boundary layer and reached the geographical regions during 1991-1995 Figure 3.2.1 Atmospheric transport pathways (cluster mean trajectories) from the KNR 15 region based on the forward trajectories during 1991-1995 Figure 3.3.1 Airflow probability field within the boundary layer for the trajectories, originated over the KNR region during 1991-1995 (isolines are shown every 10%) Figure 3.4.1 Precipitation factor probability field for the boundary layer based on the relative humidity fields for the trajectories, originated over the KNR region during 1991-1995 (isolines are shown every 5%) Figure 3.5.1 Fast transport probability field within the boundary layer for the trajectories, originated over the KNR region during April of 1991-1995 (isolines are shown every 10%)

Figure A1 Atmospheric transport pathways from KNR during spring Figure A2 Atmospheric transport pathways from KNR during summer Figure A3 Atmospheric transport pathways from KNR during fall Figure A4 Atmospheric transport pathways from KNR during winter

Figure B1 Spring airflow probability field within the boundary layer Figure B2 Summer airflow probability field within the boundary layer Figure B3 Fall airflow probability field within the boundary layer Figure B4 Winter airflow probability field within the boundary layer

Figure C1 Spring precipitation probability field for the surface-1.5 km asl layer Figure C2 Summer precipitation probability field for the surface-1.5 km asl layer Figure C3 Fall precipitation probability field for the surface-1.5 km asl layer Figure C4 Winter precipitation probability field for the surface-1.5 km asl layer

Figure D1 Spring fast transport probability field within the boundary layer Figure D2 Summer fast transport probability field within the boundary Figure D3 Fall fast transport probability field within the boundary layer Figure D4 Winter fast transport probability field within the boundary layer

Table 1.3.1 Seasonal average meteorological variables (Meteorological station at Zasheek) Table 3.1.1 Transport summary from KNR region to geographical regions

EXECUTIVE SUMMARY

This report has a purpose to show a methodology and possibilities for future studies of the possible impact to different geographical areas of the Barents Euro-Arctic Region from the numerous nuclear risk objects on the Kola Peninsula.

The Kola Peninsula (Murmansk region, Russia), being the nearest neighbor to the Nordic countries, plays a considerable role in the airborne pollution problem in the Barents Euro-Arctic region. There are two main types of airborne pollution sources in this area. From one side, it is an airborne pollution from largest Cu-Ni metallurgical companies of the Kola Peninsula. From another side, there is a potential risk of radioactive pollution under hypothetical accidents at numerous objects of the radiation risk in the region.

It is known that among the reactors of the Former Soviet Union and Eastern Europe, the 15 RBMK and 6 VVER-440/230 are reactors of the greatest concern. Beside that, the high number of naval and commercial nuclear reactors on and along the Kola Peninsula in the Northwest Russia exceeds by far the concentration in any other region of the world. There are more than 170 nuclear reactors in operation, and about 140 reactors waiting to be decommissioned. Finally, the Russian government plans additional reactor units, despite public opposition and limited financial resources. The Kola nuclear power plant (KNPP) on the Kola Peninsula is the closest Russian nuclear power plant to the Scandinavian countries. It is located just 100 km from the Finnish and 200 km from the Norwegian border. Because this plant uses an older VVER-440/230 design reactors, has problems with storage and disposal of existing radioactive wastes and it was built in an area of the medium seismic activity, there are concerns about potential accidents at this facility and a subsequent release of radionuclides.

For these reasons, we have examined atmospheric transport patterns, airflow, precipitation factor, fast transport probabilities, and possible impact from the plant’s reactors based on 1991- 1995 meteorological data sets on several geographical regions -- Scandinavian, European, Central Former Soviet Union (FSU), and Taymyr -- using an isentropic trajectory model, cluster analysis, and probability fields techniques.

We found, that highly populated regions, Scandinavian and Central FSU, are at the greatest risk in comparison with the European region. Radionuclide transport to the Central FSU region can occur in one day, but averages 2.7 days with 45% of the cases of the free troposphere transport. To the Scandinavian region it can occur in 0.5 days, but averages 1.2 days with most of occurrences (up to 70%) resulting from the transport in the boundary layer. Since rapid transport (1 day) could bring relatively undiluted air parcels containing significant concentrations of radionuclides, these events were considered in greater detail by analyzing probabilistic fields.

1. INTRODUCTION

1.1. Introduction Background An accident at the Chernobyl nuclear power plant (51°17' N, 30°15' E), located in Ukraine (30 km south of the Belorussian border), was the most severe in the nuclear industry. The emissions continued from 26 April until 6 May 1986. This accident caused the rapid death of 31 plant employees and brought the evacuation of 116,000 people within a few weeks. About half a million workers and four million others have been exposed, to some extent, to radiation doses resulting from the Chernobyl accident (Bouville, 1995). During the weeks after the initial explosion, radioactive material was observed almost everywhere in the Northern Hemisphere due to long- range atmospheric transport. The main sources of radioactive pollution in northern Russia are similar to those in many western countries (Bridges and Bridges, 1995; Nuclear Wastes in the Arctic, 1995). The most common source of anthropogenic radioactivity has been the global fallout from nuclear weapons testing in the atmosphere (Aarkrog, 1994; Bocharov et al., 1995). The ecosystems may contain relatively high levels of radioactive contamination. Beside global fallout they have received significant amounts of radioactivity from a number of other sources. These sources were studied in detail by Baklanov et al., 1993; Nilsen and Bohmer, 1994; Bergman et al., 1996; Nilsen et al., 1996. Nuclear power plants (NPPs) are one of these sources and they have a rank of relatively high radiological consequences in terms of doses to total population and influence on environment, if substantial release occurs (Baklanov et al., 1996). There are 29 operating nuclear power reactors, including 4 at the Kola Nuclear Power Plant (KNPP), situated at 9 sites within Russia (Improving Safety of Soviet-Designed Nuclear Power Plants, 1996). All sites, except Bilibino, are in the European part of the former Soviet Union (FSU) and in areas with high population density. An accident at any of them could have an effect on both far and near territories. The KNPP is a potential radioactive risk site that could have impact on environment. There are other nuclear power plants in Scandinavia, Russia and England located within 1000 km of the Arctic Circle. Routine discharges from these plants are small. The main concern regarding NPPs is the possibility of accidents. Although construction and operation of nuclear power plants are monitored and regulated, accidents are possible. The possible ways of radionuclide releases are accidents: at nuclear reactors, storage facilities for radioactive wastes and during transport of the spent nuclear fuel and waste to or from plants (Shvyryaev et al., 1992, Baklanov et al., 1994; Elokhin and Solovei, 1994). Human error or natural hazardous conditions can cause the releases. The most immediate danger from an accident is exposure to high levels of radiation. There may be radiation hazard in the surrounding areas both near and far, depending on the type of accident, amounts of radiation released, and weather conditions. The influence on the environment will vary with temporal and spatial conditions. In this study the atmospheric transport patterns, probabilistic fields, possible impact to different geographical regions -- Scandinavian, European, Central FSU and Taymyr -- are examined from the Kola Nuclear Reactors (KNR). Atmospheric transport pathways are analyzed using 5-day forward isentropic trajectories originated over the KNR region during 1991-1995. We assume that obtained results can be used in the event of an accident to estimate the probability of the radionuclide transport from KNR. It is very important to have a probabilistic assessment of a possible long transport of pollutants, especially for further study of social and economical consequences of the radioactive risk for the Euro-Arctic region.

1.2. Reactors of the Kola Nuclear Power Plant – Source of the Radiation Risk The Kola nuclear power plant (67° 28' N, 32° 28' E; 131 m asl) is located 15 km north-west of the urban settlement Polyarnye Zori on the shore of Iokostrovskaya Imandra lake. It is 5 the first NPP in the FSU to be built north of the Arctic Circle. The distance to the Finnish border is about 100 km, to the Norwegian border is 220 km and to the Swedish border is 330 km. The geographic relationship of the Kola NPP is shown in Figure 1.2.1.

Figure 1.2.1. Geographic relationship of KNPP

The main road from St.-Petersburg to Murmansk follows the Kola Peninsula and crosses Imandra Lake close to the plant. A railway line passes a few kilometers south of the plant along the southern part of the lake with a sidetrack connecting the plant and main line. The closest airport, Khibiny, is located about 40 km north of the plant. The plant’s personnel live at Polyarnye Zori. The town is situated 10 km south of Imandra lake on the Niva river. The population is more than 20000 inhabitants with nearly 3000 people attached to KNPP. The operational parts of KNPP are situated on the cape of the narrow peninsula, which juts into the Imandra lake. Imandra is the largest lake in the center of the Kola Peninsula with an average depth around 13 m. The surrounding area is mountainous with ridges up to 1000 m above sea level. The immediate area around the plant is marshy and the nearest hills reach heights of about 200 m. The Kola Peninsula belongs to a stable geological formation (Baltic Shield), but there is low to medium probability for seismic activity (Bune et al., 1980). The plant was built to supply power to a growing mining and metallurgical industry and to support the increasing population due to buildup of activities in the Murmansk Region. The KNPP has two older reactors of type VVER-440/230 and two reactors of type VVER-440/213. First two units (VVER-440/230) were put into operation between 1973-1974, third and forth (VVER- 440/213) -- between 1981-1984. The electric capacity of each unit is 411 MW. The expected service time is 30 years. The plant produces 1644 MW with all units on full load. It is connected by four high-tension 330 kW lines to the network of the "Kolenergo" Power Company. Approximately 60% of the electric energy produced at the plant are supplied to local heavy industry. The cooling water is discharged into the western part of Babinskaya Imandra lake through a one-kilometer long channel. As a result, the vast water surface of Molochnaya bay does not freeze throughout the winter. Due to the cool climate and correspondingly low temperature of lake water, the plant has access to a particularly good supply of water. Parts of the cooling water are used for heating greenhouses and were used for a fish farm. Radioactive wastes of low and medium level 6 radiation are stored by the plant (Nilsen and Bohmer, 1994). Used fuel assemblies are stored up to 3 years and then transported for reprocessing to South Ural (Mayak) by railway. There are plans for the construction of three 640 MW NP-500 reactor units near Imandra lake. The lake’s water supply is sufficient for additional energy units. Russian authorities have announced plans to build two or three new NP-500s (a 640-megawatt VVER with enhanced safety features). The first of the units is scheduled to begin operating in 2003, when the first two units of the existing plant, which are VVER-440/230s, will be decommissioned (“Soviet-Designed Nuclear Power Plants in Russia, Ukraine, Lithuania, the Czech Republic, the Slovak Republic, Hungary, and Bulgaria”, 1996).

1.3. Conditions Affecting Atmospheric Transport on the Kola Peninsula The complexity of the Arctic climate derives from the varying radiative effects of the underlying surface, non-equal distribution of land and water, horizontal transport of the heat from lower latitudes. These regions are synoptically active (Walsh and Chapman, 1990). The Aleutian and Icelandic Lows and Siberian High have influence on the transport of air masses within the Arctic regions. The Kola Peninsula lies on the boundary of the Arctic front (Krebs and Barry, 1970). Its mean position, as shown in Figure 1.3.1, varies as a function of season over the Kola Peninsula: from Greenland to the middle territories of the Scandinavian Peninsula (in winter) and from Spitsbergen Islands to the Barents Sea (in summer) (Shaw, 1988). The front divides two air masses with different characteristics: cold and dense Arctic air and the warmer and moister maritime air of the Northern Atlantic. On the synoptic time scales, the front in the Kola and Scandinavian Peninsula sector may be located anywhere from the Arctic to the inland at any season.

Figure 1.3.1. Position of the Arctic front

For most of the Arctic territories, summer precipitation is up to three times the amount of winter precipitation. Increased precipitation favors the washout of pollutants and levels of the deposition. Atmospheric circulation over these regions is affected by strong Coriolis effects. During winter, cold air temperatures, limited low-level moisture supply and high static stability characterize these areas. Such conditions will limit the intensity of the vertical circulation. During summer-fall, when the background stratification is not excessively strong, intensity of cyclone development over 7 the regions will depend critically on the conditions of the underlying surface (e.g. water surface or land, open or covered by snow or ice). The main cyclone and anticyclone pathways are associated with activity of the Icelandic Low throughout the year (Klein, 1957). Although KNPP is located north of the Polar Circle, the climate is rather mild. Observations for wind direction and velocity, humidity and temperature are conducted and recorded at the plant, but not available. The nearest meteorological station is situated at Zasheek (67° 42' N, 32° 45' E; 151m asl), 7 km from the plant. Average monthly temperature is from -13.7° C (January) to 13.9° C (July), humidity from 86% (January) to 72% (July). During winter south and southwest winds prevail in the area, in summer time north and northeast winds. The extreme atmospheric phenomena for the Kola Peninsula are snowstorms, thunderstorms, hails and fogs. Only one tornado was observed in February 2, 1993 with a wind speed more than 40 m/s, which had impact on the normal operation of the plant. A summary of meteorological variables by season is presented in Table 1.2.1.

Season vs. Winter Spring Summer Fall Year Meteorological variable Temperature, ° C -12.8 -2.0 +12.1 +0.3 -0.6 Humidity, % 86 75 72 85 80 Cloudiness, units of 10 total 7.4 7.3 7.2 8.0 7.5 low 6.1 5.2 5.5 6.8 5.9 Velocity of wind, m/s 3.4 4.0 3.8 3.8 3.8 Direction of wind, % N 10 17 23 10 15 NE 7 9 18 7 10 E 8 5 7 6 7 SE 9 6 6 7 7 S 19 15 15 19 17 SW 28 24 16 24 23 W 9 9 6 17 10 NW 10 15 9 10 11

Table 1.3.1. Seasonal average meteorological variables (Meteorological station at Zasheek)

2. METHODOLOGY

2.1. Specification of the Impact Regions As we mentioned in Chapter 1, the Kola Nuclear Power Plant (KNPP or Kola NPP) located at 67.45°N and 32.45°E; and, in particular, its reactors of the older generation (PWR-440/230 design) are subject to possible severe accidents. Bergman et al., 1996 mentioned that KNPP is one of the main potential radiation risk sites in the Russian European North. An accident at this plant, following by the subsequent radionuclide’s releases, may have a significant impact on the environment and population of the Barents region. In this part of the study, we had examined the probabilistic atmospheric transport patterns from the Kola Nuclear Reactors (KNR) region. These patterns, we assumed, may be useful in estimation of the possible radiological impact on different geographical regions. For this study, we selected (as shown in Figure 2.1.1) the following impact regions: Scandinavian, European, Central Former Soviet Union (FSU) and Taymyr regions. Scandinavian region covers the Scandinavian Peninsula and surrounding seas (60-72° N and 0-30° E). European region includes practically territories of all European countries and attaches surrounding seas (35-55° N and 10° W-25° E). Central FSU region is situated between 45-60° N and 25-70° E and covers the European part of Russia and territories of the FSU republics -- Ukraine, Belorussia and north-western areas of Kazakhstan. Taymyr region has boundaries between 60-80° N and 70-120° E and includes the Taymyr Peninsula, the land south and seas north of the peninsula. The boundaries of the impact regions were chosen based on the selection’s assumption of the most populated geographical areas and various climatic regimes.

Figure 2.1.1. Geographical regions of interest and model grid domain

2.2. Isentropic Trajectory Calculation Although atmospheric trajectory models use an assumption of adiabatically moving air parcels and neglect various physical effects, they are a useful tool for evaluating common air flow patterns within meteorological systems on various scales (Merrill et al., 1986; Harris & Kahl, 1990; Harris & Kahl, 1994; Jaffe et al., 1997a; Mahura et al., 1997a; Jaffe et al., 1997b; Mahura et al., 1997b). Some uncertainties in these models are related to the interpolation of meteorological data, applicability of the considered horizontal and vertical scales, and assumptions of vertical transport (Merrill et al., 1985; Draxler, 1987; Kahl, 1996). In general, the computed atmospheric 9 trajectory represents the pathway of an air parcel motion in time and space and is considered as an estimation of the mean motion of a defusing cloud of material. In our study we interpolated the original National Center for Environmental Prediction (NCEP) gridded wind fields from the database DS.082 (NCEP Global Tropo Analysis, daily, from July 1976 to present) to potential temperature (isentropic) surfaces. Interpolation procedure has been performed for the period of 1991-1995 and applying a technique described by Merrill et al., 1985. Then, we used the wind fields on these surfaces to calculate trajectories in the model domain. The model grid domain is located between 20°-80°N and 60°W-127.5°E as shown in Figure 2.1.1. All forward isentropic trajectories for the KNR region we computed twice per day (at 00 and 12 UTC, Universal Coordinated Time) at different potential temperature levels. These levels ranged from 255°K to 330°K with a step of 5°K. The National Center for Atmospheric Research (NCAR, Boulder, Colorado) and Arctic Region Supercomputing Center (ARSC, Fairbanks, Alaska) CRAY computer resources were used to compute more than 200 thousand trajectories. Less than one percent of the trajectories were missing because of the absence of archived meteorological data and processing problems. In this study, we used for every calculation four trajectories. The initial points of trajectories are located on the corners of a 1° by 1° of latitude-longitude box, where KNR is in the center of the box. Only cases, when the wind field was reasonably consistent along the transport pathway (i.e. all four trajectories have shown a similar direction of transport for one time period) were included in further analysis (as shown in Figure 2.2.1). Trajectories, showing a strong divergence of flow, were assigned to the category of the complex trajectories (as shown in Figure 2.2.2). Such trajectories reflect more uncertainties in the air parcel transport and, therefore, we excluded them from our further analysis.

Figure 2.2.1. Examples of trajectories showing consistent air flow

As shown in Figure 2.2.1 (left), the air mass originated over the KNR region at February 3, 1991, 12 UTC. Beginning at 60° N it moves to west crossing the Karelia, Baltic Sea and the northern territories of the Baltic states during next day. Air parcel reached the Great Britain Islands on February 7, 1991. During all this time the air parcel traveled within the boundary layer. 10

Figure 2.2.2. Examples of complex trajectories

Baklanov et al., 1996 performed analysis of different scenarios for the hypothetical KNPP accidents. Their study noted that the height of radionuclide releases and its duration may vary from 100 to 500 m and from several hours to several days. Therefore, from all trajectories we decided to choose only less than 13 thousand trajectories (from original more than 200 thousand trajectories). All chosen trajectories start in the range of these heights and have duration of 5 days or longer. Additionally, to study altitudinal variations in flow patterns (in particular, within the boundary layer and free troposphere), we also considered trajectories originated over the KNR region at 1.5 and 3 km asl.

2.3. Cluster Analysis Technique Cluster analysis refers to a variety of multivariate statistical analysis techniques used to explore the structure within data sets (Romesburg, 1984). The specific purpose is to divide a data set into clusters or groups of similar variables or cases. Miller (1981) initiated the use of trajectories to determine the flow climatology, particularly over long time periods. Studies using cluster analysis techniques on trajectories were conducted by Moody (1986), Moody and Galloway (1988), and later by Harris and Kahl (1990), Harris (1992), Harris and Kahl (1994), Moody et al. (1994); Dorling and Davies (1995), Jaffe et al. (1997a), Mahura et al. (1997a) and Mahura et al. (1997b). SAS/STAT software has tools for many types of statistical analysis including cluster analysis. We used FASTCLUS procedure, which performs a disjoint cluster analysis on the basis of Euclidean distances computed from one or more quantitative variables. The observations are divided into clusters such that every observation belongs to, at least, one cluster. In the case of separate analyses for different numbers of clusters we run FASTCLUS once for each analysis. This procedure is intended for use with large data sets, up to 100000 observations. It is possible to print brief summaries of the clusters it finds. For more extensive examination of the clusters it was necessary to request output data sets containing a cluster membership variable. Hence, we decided that FASTCLUS procedure is more useful for our analysis. Figure 2.3.1 shows all trajectories starting at KNR region during 1992. Another Figure 2.3.2 shows obtained after the cluster analysis the atmospheric transport pathways from the KNR region 11 for 1992. The mean trajectory for each cluster is given with the points indicating 12-hour intervals between points. The clusters are marked by 2 corresponding numbers. The first number is the number or identifier of the cluster. The second is the percentage of trajectories within that cluster. The cluster numbers are used to separate the possible types of transport and are arbitrary.

Figure 2.3.1. 5-day isentropic forward trajectories originated over the KNR region during 1992

We used cluster analysis to divide the calculated trajectories into groups. The following criteria were used: latitude and longitude values for each time step, which represent the direction and velocity of air parcel motion. Similarity among trajectories in each cluster is maximized considering the full length of each 5-day forward trajectory. Within each cluster, individual trajectories may be averaged to obtain the mean cluster trajectory (or transport pathway). Thus, the initial large data set may be reduced to a small number of mean cluster plots. These plots then could be interpreted, based on common synoptic conditions and features.

Figure 2.3.2. Cluster mean trajectories or transport pathways from KNR during 1992

Using the cluster analysis technique we summarized the flow climatology for the KNR region for 1991-1995, each year, season and month. Details of the clustering and discussion of the results are presented in consequent Section of the “Results and Discussion” Chapter, and Appendix 12 A. We should also note that trajectories, which cross the top boundary at 80°N (i.e. showing transport to the Central Arctic territories) of the model grid domain, were not used in cluster analysis. It is due to their short duration and termination at the border. In particular, these trajectories did not pass the most populated regions, and hence, have no interest for our study conditions.

2.4. Probability Field Analysis Probabilistic analysis is one of the ways to estimate the likelihood in occurrence of one or another phenomena or event. As we mentioned, in this study we calculated a large number of trajectories passed other various geographical regions. Each calculated trajectory contained information about longitude, latitude, altitude, pressure, temperature, relative humidity and other variables on each time step. The probability fields for these characteristics, individual or combined, may be represented by a superposition of probabilities for the air parcels reaching each grid region in the chosen domain or on the geographical map. The most interest for an analysis have the following probabilistic fields: a) airflow patterns, b) contribution of precipitation, and c) fast transport. The first type of the fields shows the general common features in the atmospheric transport patterns, i.e. it may provide a general insight on the possible main direction of the radioactive cloud’s transport as well as probability to reach or pass any geographical area. The second type describes a possibility for removal processes from the polluted air mass when air parcels passed over the particular geographical area. The last type has indication for probability of the rapid air parcels’ movement during the first day of transport. It is important information, especially, for the estimation of the short-lived radionuclides impact. I.e. it shows, which territories may be reached during the first day, and which areas are at the most danger due to higher transport probability. In our study probabilistic fields were constructed for the mentioned above three areas of interest. Fields for the airflow and fast transport were constructed using latitude, longitude, altitude, and time step for 5 and 1 day trajectories, respectively. These probability fields reflected 1991-1995, individual year, seasonal, and monthly variations for the boundary layer. Fields for the precipitation factor were constructed based on the values of the relative humidity at trajectory’s latitude, longitude, and altitude on each time step. As the previous fields, these fields have seasonal variations, but additionally it was calculated for several atmospheric layers between situated between surface, 1.5, 3, 5 km asl, and higher. Results of the probability field analysis are presented in consequent Section of the “Results and Discussion” Chapter as well as in Appendixes B,C, and D.

3. RESULTS AND DISCUSSION

3.1. KNR Possible Impact In this study we analyzed all forward trajectories originated over the nuclear reactors locations to investigate the likelihood of the reactors’ impact on several remote geographical regions. We assumed that any isentropic trajectory, which crosses into chosen geographical region, could bring relatively undiluted air parcels containing radionuclides. Therefore, only trajectories crossed boundaries of these regions were used for further analysis. To analyze the probability of the plant’s impact, we estimated several parameters: 1) number and percentage of trajectories reached the boundaries of the geographical regions; 2) number and percentage of days if at least one trajectory had reached the region; 3) average transport time of air parcels; 4) probability of transport within different atmospheric layers; 5) likelihood of very rapid (fast) transport - i.e. transport in one or less days. Such evaluation was performed over the studied period, by individual year, season and month. A summary of transport from the region of interest to four chosen geographical regions is presented in Table 3.1.1. Monthly variations in the average time of transport (in days) and number of trajectories reached regions during 1991-1995 are shown in Figures 3.1.1 and 3.1.2.

Parameter vs. Region Scandinavia Europe Central FSU Taymyr Trajectories reached the regions # vs. % of trajectories 3834 (28.4) 363 (2.7) 3192 (23.7) 4535 (33.6) Days when trajectories reached the regions # vs. % of days 812 (44.5) 148 (8.1) 788 (43.2) 1014 (55.5) Transport time: average ± std.dev. 1.3 ± 1.2 4.5 ± 2.2 2.7 ± 1.8 3.4 ± 2.0 (in days) Highest occurrence of transport Apr, Jul-Aug, Aug-Feb Sep-Mar (months) Jun-Sep Nov-Feb Fast transport events 72.8 0.8 16.0 1.9 % trajectories Highest occurrence of fast transport Mar-Nov Dec Dec-Mar, Dec-Jan, (months) May Apr-May Transport within boundary layer 70 60 55 27 (% trajectories)

Table 3.1.1. Transport summary from KNR region to geographical regions

The winter shows the lowest probability of transport (less than 20% of trajectories) from the KNR area to the Scandinavian region with the highest during spring and summer. The Taymyr region reflects the higher occurrence of the transport during October-January (more than 40%), and lowest occurrence during May-August with a minimum in June (19.1%). For the European region, there are two periods when probability of transport is higher: during November-February (approximately 4%) and during July-August (3.5%). The lowest probability – less than 1% - is observed during May and October. The Central FSU region is characterized by the lower probability during spring and summer (on average less than 20%). Then this probability gradually increases during fall and winter reaching absolute maximum in February (36.2%). It is well known that in the cases of the transport within the boundary layer the higher concentrations of radionuclides are more likely at the surface. As seen from Table 3.1.1, the boundary layer transport, on a yearly basis, is predominant for the Scandinavian, European and Central FSU regions. The free troposphere transport prevails for the Taymyr region. It should be noted also that the European region is characterized by the boundary layer transport during all 14 seasons, except in summer, when the transport occurs half of the time on the border between the boundary layer and free troposphere. Although the Taymyr region shows transport on the border between layers and in the free troposphere throughout the year, during summer the transport pattern is shifted completely to the higher layers in the free troposphere.

Figure 3.1.1. Monthly variations in the average transport time (in days) from the KNR region to geographical regions based on the forward trajectories during 1991-1995

Figure 3.1.2. Monthly variations in the average percentage of trajectories originated over the KNR region at lower altitudes within the boundary layer and reached the geographical regions during 1991-1995

The cases of the fast transport (1 day or less), as shown in Table 2.3.2.1, account on average for approximately 73, 16, 2.1 and less than 1% for Scandinavian, Central FSU, Taymyr and European regions, respectively. In this study we found that the lowest probability of the fast transport events is observed for the European region, and, in particular, all these events took place during December. In comparison with other regions, the Scandinavian region is characterized by the highest probability of the fast transport due to its proximity to the KNR area. On average this probability is 72.8% of the total number of trajectories reaching the region, and it decreases during winter to 61%. For the Central FSU region, the probability of such transport is higher during winter (up to 30%) and it decreases by factor of 1.5-2 in other seasons. The Taymyr region as well as European has the lower probability of the fast transport events. Although on average this probability is approximately 1.8%, during fall months it is less than 1%, and there is a flat maximum of 4% during December-January. In this study, we found that highly populated Scandinavian and Central FSU regions are at the greatest risk in comparison with the European region. Moreover, the transport of radionuclides in the case of an accident to the Central FSU region could occur within a day, but it averages 2.7 days, and almost half of the time resulting from the free troposphere transport. To the Scandinavian region this transport may occur within 0.5 days, but it averages 1.2 days, and 70% of the time resulting from boundary layer transport.

3.2. Atmospheric Transport Patterns In this study we had an interest in the fast transport from KNR region. We found also that the average transport times to chosen geographical regions (as shown in Table 3.1.1) are less than 5 days. Thus, we decided to use in the cluster analysis only forward trajectories of this duration. Figure 3.2.1 shows the atmospheric transport pathways from the KNR region using trajectories during 1991-1995, which had an origin within the boundary layer. The mean trajectory for each cluster is given with points indicating 12-hour intervals. Two numbers were used for each cluster. 15 The first is the identifier of a cluster. The second is the percentage of trajectories within a cluster. The cluster numbers are only used to separate the possible types of transport and are arbitrary. In our study six clusters were identified for the KNR region (Figure 2.3.2.3). Four of them (#1, 2, 3 and 4 with 27, 22, 10 and 16% of occurrence, respectively) show westerly flow. These observed about 75% of the time. Cluster #6 (7%) shows easterly flow toward the Northern Atlantic both within the boundary layer and free troposphere. Cluster #5, which occur 18% of the time, is transport to southwest through the Scandinavian Peninsula into the Baltic Sea. As was noted previously, we did not consider trajectories, which cross the top of model grid domain and move into the Central Arctic.

Figure 3.2.1. Atmospheric transport pathways (cluster mean trajectories) from the KNR region based on the forward trajectories during 1991-1995

Throughout the year, westerly flow is predominant for the KNR region. Transport from the west varies from 68% (in fall) to 94% (in spring) of the time. Transport from the east occurs from 3% (in winter) to 26% (in summer) of the cases. Transport with the southward component take place 15% of the time (in winter) increasing up to 25% (in fall). Analyzing trajectories at the higher altitudes - 1.5 and 3 km asl - we also found that within the free troposphere the probability of transport from west increases up to 90%. Detail seasonal variations in the mean transport pathways within the boundary layer are shown in Appendix A.

3.3. Airflow Probability Fields To test and compare the results of clustering we calculated the airflow probability field using all forward trajectories during 1991-1995. Such probability fields show geographical variations of airflow patterns from the chosen site. In a climatological sense, the pathway of airflow from the chosen site could be represented by a superposition of the probability of air parcels reaching each grid region on a geographical map (Merrill, 1994). The regions with higher occurrence of trajectory passages are areas where the probability of KNPP impact will be higher. Figure 3.3.1 shows the airflow probability field for KNR constructed using 1991-1995 trajectories. Each probabilistic field is presented using isolines, given with interval of 5%, on the background of the geographical territories. The areas of higher probability, which are located close to the KNR region, indicate that trajectories have spent more time in this geographical area. Because all trajectories start near the site, the cumulative probability is 100% there. Thus, the field was altered using a correction factor. This factor accounts the increase with distance from the site therefore it takes into account the contribution at greater distances. The airflow probability field 16 also shows that westerly flow is predominant for the KNR region. It is in agreement with the results of the trajectory’s cluster analysis. Our detail analysis showed that transport toward east is clearly dominates throughout the year. The southward components of flow reveal themselves starting in January and continued until September. The westward flow components show up in April and summer months. Detail seasonal airflow probability fields within the boundary layer are shown in Appendix B.

Figure 3.3.1. Airflow probability field within the boundary layer for the trajectories, originated over the KNR region during 1991-1995 (isolines are shown every 10%)

3.4. Precipitation Factor Probability Fields To account for the contribution of radionuclide wet removal during transport of an air parcel we calculated simultaneously for each trajectory point (i.e. at latitude, longitude and pressure level point) the value of relative humidity. Based on calculated temporal and spatial distribution of the relative humidity we constructed the relative humidity (we named it “precipitation factor”) probability fields over the studied geographical areas. Several atmospheric layers - surface - 1.5, 1.5-3, 3-5, and above 5 km asl - were examined to determine altitudinal differences in the possibility of removal processes. We assumed, that areas with the relative humidity above 65% were areas, where water vapor could be condensed and later removed in the form of precipitation. Figure 3.4.1 shows the relative humidity probability field for the boundary layer. Appendix B shows the seasonal distribution of the precipitation factor within the boundary layer. Detail seasonal variations in the precipitation factor probability field for various layers are shown in Appendix C. Our detail analysis of the probabilistic fields concluded that contribution of the precipitation factor dominates within the low troposphere layers in the areas associated with the activity of the Icelandic low and along main tracks of the cyclone systems.

Figure 3.4.1. Precipitation factor probability field for the boundary layer based on the relative humidity fields for the trajectories, originated over the KNR region during 1991-1995 (isolines are shown every 5%)

3.5. Fast Transport Probability Fields Although atmospheric transport from the radiation risk site or region to another geographical area may occur at any time, the fast transport is the greatest concern. It is especially valid and important issue for the measures of the emergency response and preparedness. To study the contribution of the fast transport we evaluated all available trajectories during the first day of their transport. I.e. all trajectories were terminated routinely after 1 day of transport. In a similar way, as for the flow probability, we constructed the fast transport probability fields. All these fields show the probability of air transport from the region of interest during the first day. An analysis has been done for the entire period and each year, as well as by season and month. We analyzed the lowest altitude of trajectories, i.e. trajectories starting below 0.5 km asl. This is due to major importance of the boundary layer transport, especially for the short-lived radionuclides, during the first several hours after an accident. Our analysis showed that on a yearly basis the highest probability of the fast transport is with the eastward and southward components. During summer the southwestern component of the fast transport dominates within the boundary layer. During fall and winter the eastward and southeastward components are predominant, which show transport toward less populated territories of the Northern Arctic Russia. February is associated with the highest probability of transport toward the Central Russia. During spring the southward components exist. The southwestward component toward the Scandinavian countries is well pronounced during April (as shown in Figure 3.5.1). November and December represent the clear eastward component in the fast transport. Appendix D shows seasonal variations in the fast transport pattern. 18

Figure 3.5.1. Fast transport probability field within the boundary layer for the trajectories, originated over the KNR region during April of 1991-1995 (isolines are shown every 10%) 19

CONCLUSIONS AND RECOMMENDATIONS

The main purpose of this study was the testing of the methodology to examine probabilistic atmospheric transport patterns, precipitation factor’s contribution, fast transport, and possible impact of the Kola Nuclear Reactors, located in the North Western Russia region, on several geographical regions: Scandinavia, Europe, Central FSU and Taymyr – following a hypothetical accident. The period studied is 1991-1995. An isentropic trajectory model has been used to calculate forward isentropic trajectories which originated over the studied region. Atmospheric transport patterns were identified using a cluster analysis of the 5-day forward trajectories originating over the KNR region. Probabilities for the airflow, precipitation, and fast transport patterns were constructed using probabilistic field’s analysis.

The main results of this part of the study are the following:

1. The methodology to estimate potential nuclear risk and vulnerability levels – i.e. examination of the atmospheric transport patterns, contribution of the precipitation factor, fast transport, and possible impact - was applied and tested. As an example, we selected the Kola Nuclear Reactors in the North Western Russia. Examination has been done for the following geographical regions: Scandinavia, Europe, Central FSU and Taymyr.

2. Scandinavian and Central FSU regions, as the most highly populated regions, are at the greatest risk of the radioactive pollution in the case of an accident comparing to the European region. Although transport from these regions could occur in 1-1.5 days, it averages in 1.3 and 2.7 days for the Scandinavian and Central FSU regions, respectively. It should be noted also that for these regions transport mostly (65-70% of the time) occurs within the boundary layer.

3. For the studied region, during all seasons the westerly flows are predominant within the boundary layer, and on average it occurs 75% of the time throughout the year. In the free troposphere it could occur about 90% of the time.

4. Precipitation factor’s contribution dominates in the low troposphere layers, and in the areas associated with the Icelandic Low activity as well as along the main tracks of the cyclone systems.

5. The fast transport pattern shows the dominance of the eastward and southward components throughout the year. February is associated with the highest probability of fast transport toward the Central Russia regions. April has a clear dominance of the fast transport toward the Scandinavian countries.

Our results has been used for the further analysis of the possible consequences as a result of the hypothetical accident at the Kola nuclear power plant. Our obtained results could be used in the event of an accident to estimate, and especially at the first stages, the probability of the radionuclide transport from the accident location. They may be used as input data for the high-level models in order to estimate detail impact on the environment and population within the Barents region territories. They also could be important for the probabilistic assessment of the pollutant’s long- range transport as well as in the studies of the social and economical consequences from the radioactive risk sites in the Barents Euro-Arctic region.

From our point of view, several areas need continue attention and recommended for an additional study:

20 1. Due to the possibility of rapid transport of radionuclides to Central FSU and Scandinavian regions, current and future response plans should include provisions for rapid event notification.

2. Modeling of transport, deposition, removal processes and an estimation of consequences should be continued, especially focusing on the rapid transport events which could bring radionuclides to Scandinavian and Central FSU regions as quickly as 24 hours following an accident.

3. For a future study, it will important to develop a method for calculation of probabilistic trajectories and direct impact, taking into account the precipitation factor, the estimation of the boundary layer influence and other parameters.

4. For a future study, it will important to conduct a probabilistic estimation of possible effects on the population in the Barents Euro-Arctic Region and territories of Central Arctic from KNPP, other radioactive risk objects and industrial pollution source regions.

5. We suppose that plant safety at these reactors must be improved to reduce the likelihood of a serious accident as well as monitoring for radionuclide release should be continued in the KNPP region as well as surrounding territories of Russia and Scandinavian countries.

We believe that the use of mentioned methodology -- trajectory modeling with high-resolution grid and cluster analysis technique -- might be successively applied for similar studies.

ACKNOWLEDGMENTS

The authors are very grateful for collaboration with Drs. Alexander Baklanov (Danish Meteorological Institute, Copenhagen, Denmark), Sergey Morozov (Institute of the Northern Ecological Problems, Kola Science Center, Russian Academy of Sciences, Apatity, Russia), and John Merrill (Center for Atmospheric Chemistry, Graduate School of Oceanography, University of Rhode Island). Thanks to Glenn Shaw and Sue-Ann Bowling (Geophysical Institute, University of Alaska, Fairbanks) for constructive discussions. Thanks to Robert Huebert and Dennis Fielding (ARSC) for computer assistance.

The computer facilities from the National Center for Atmospheric Research (NCAR, Boulder, Colorado, USA), Arctic Region Supercomputing Center (ARSC, Fairbanks, Alaska, USA), University of Washington (UW) of the Atmospheric Sciences Department, and the Institute of the North Ecological Problems (INEP, Apatity, RUSSIA), as well as databases of NCAR and European Center for Medium-Range Weather Forecast (ECMWF, Reading, UK) have been used in this work.

The isentropic trajectory model has been developed and modified using FORTRAN-77 and 90 languages. Partially, some model blocks and blocks for data statistical analysis were developed in FORTRAN and C++. Trajectory’s visualization was done using NCAR Graphics and General Mapping Tools (GMT) software packages. Cluster analysis of trajectories we performed using SAS software. Programs for analysis were written in SAS language. Probabilistic fields were constructed using MATLAB-v.5 software, which includes the geographical maps’ addition.

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24 APPENDIX A

Seasonal Atmospheric Transport Pathways from KNR

Figure A1. Atmospheric transport pathways from KNR during spring

Figure A2. Atmospheric transport pathways from KNR during summer

Figure A3. Atmospheric transport pathways from KNR during fall

Figure A4. Atmospheric transport pathways from KNR during winter 26 APPENDIX B Seasonal Airflow Probability Fields within the Boundary Layer

Figure B1. Spring airflow probability field within the boundary layer

Figure B2. Summer airflow probability field within the boundary layer 27

Figure B3. Fall airflow probability field within the boundary layer

Figure B4. Winter airflow probability field within the boundary layer 28 APPENDIX C

Seasonal Precipitation (Relative Humidity) Probability Fields

Figure C1. Spring precipitation probability field for the surface-1.5 km asl layer

Figure C2. Summer precipitation probability field for the surface-1.5 km asl layer

Figure C3. Fall precipitation probability field for the surface-1.5 km asl layer

Figure C4. Winter precipitation probability field for the surface-1.5 km asl layer

30 APPENDIX D

Seasonal Fast Transport Probability Fields within the Boundary Layer

Figure D1. Spring fast transport probability field within the boundary layer

Figure D2. Summer fast transport probability field within the boundary layer 31

Figure D3. Fall fast transport probability field within the boundary layer

Figure D4. Winter fast transport probability field within the boundary layer 8 Northern Studies Working Papers

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