Atmospheric transport pathways from the Bilibino nuclear power plant to Alaska

Atmospheric Environment 33 (1999) 5115}5122

Atmospheric transport pathways from the Bilibino nuclear power plant to Alaska A.Gr. Mahura!,",*, D.A. Ja!e#,1, R.J. Andres$, J.T. Merrill% !Geophysical Institute, Department of Chemistry, University of Alaska Fairbanks, AK 99775, USA "Institute North Ecological Problems, Kola Science Center, Russian Academy of Sciences, Apatity, Murmansk region, 184200, Russia #Geophysical Institute, Department of Chemistry, University of Alaska Fairbanks, AK 99775, USA $Institute of Northern Engineering, University of Alaska Fairbanks, AK 99775, USA %Center for Atmospheric Chemistry Studies, University of Rhode Island, RI 02882, USA Received 15 December 1997; received in revised form 13 February 1999; accepted 15 February 1999

Abstract The Bilibino nuclear power plant (68303@N, 166320@E, 340 m asl) in northeastern Siberia is the closest Russian nuclear power plant to the USA. We used an isentropic trajectory model to estimate the probability that air in the Bilibino region would be transported to Alaska following a hypothetical accident. This estimate is based on the meteorological data from 1991 to 1995. Our calculations indicate that the probability that air in the Bilibino region will be transported to Alaska is approximately 6}16%, averaged over the entire year. This probability doubles in the summer and early fall with a maximum in August of 12}33%. For the entire year the mean, median, and minimum transport times from the plant to Alaska are 4, 3.5 and 1 d, respectively. Since rapid transport (1}2 d) could bring air parcels containing short-lived radionuclides, these events potentially represent the greatest risk to inhabitants of Alaska. ( 1999 Elsevier Science Ltd. All rights reserved. Keywords: Isentropic trajectory; Cluster analysis; Direct impact; Radionuclides; Nuclear power plant

1. Introduction There are 29 operating nuclear power reactors (including 4 at the Bilibino Nuclear Power Plant } BNPP) which are situated at 9 sites within Russia. All sites, except Bilibino in northeastern Siberia, are in the European part of the former Soviet Union (FSU) and in areas with high population density. One of main concern regarding nuclear power plants (NPPs) is the possibility of accidents. The possible ways of radionuclide releases are accidents at nuclear reactors, storage facilities for radioactive wastes and during transport of the fuel and waste to/from plants (Baklanov et al., 1996).

* Corresponding author. Atmospheric Sciences Department, University of Washington, WA 98195, USA. 1 Present address: University of Washington-Bothell, WA 98021, USA.

Although the reactors are relatively small (12 MW each), the BNPP is a site that could have the greatest impact on the environment of Alaska (Fig. 1). The plant has four reactors, which are graphite-moderated, cooled by boiling light water, and similar to the RBMKs. All units were put into operation between 1974 and 1976 and are expected to be in service for 25}30 yr. Radioactive wastes are saved for long-term storage in facilities near the station, including spent fuel, "lters and reactor components. BNPP and surrounding territories are within the arctic continental climate. Meteorological observations are conducted at the plant, but not recorded (personal communication, Mark Tumeo). The relief in the BNPP region is complex with heights varying from 300 to 1400 m. In the event of an accident, local meteorological conditions will have an important in#uence on the distribution and transport of the radionuclides released.

1352-2310/99/$ - see front matter ( 1999 Elsevier Science Ltd. All rights reserved. PII: S 1 3 5 2 - 2 3 1 0 ( 9 9 ) 0 0 1 6 8 - 5

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Fig. 1. Geographic relationship of BNPP to Alaska and model grid domain.

Among the reactors of FSU and Eastern Europe, RBMKs and VVERs-440/230 are the greatest concern (Improving the safety of Soviet-designed nuclear power plants, 1996). For BNPP, which uses the RBMK design, the reactors do not meet modern safety standards. This facility also has problems with storage and disposal of existing radioactive wastes and was built in an area of medium seismic activity. Finally, the Russian government plans to continue to rely on nuclear power to meet the electricity needs of the region. For these reasons, we have examined atmospheric transport pathways from BNPP to Alaska using an isentropic trajectory model and cluster analysis technique. This examination was used to determine: (1) the probability that air in the Bilibino region will be transported to Alaska; (2) the number of days this transport would take; (3) the regions in Alaska most likely to be a!ected, and (4) the seasonal distribution of each of these parameters. Chosen approach is based on the evaluation of a direct impact by an air parcel traveling from the source region to chosen geographical area. Our model output could serve as the input for speci"c e!ect analysis, which is a function of the exact constituents and the processes which a!ect it, e.g. dispersion, wet and dry deposition, radioactive decay, etc. Atmospheric transport pathways were analyzed using 5-d backward isentropic trajectories arriving at Barrow (71317@N, 156347@W), Nome (64330@N, 165325@W) and Anchorage (61313@N, 149353@W) and forward trajectories

from BNPP during 1991}1995. Backward trajectories are used to determine the history of air parcels arriving at these sites. Forward trajectories are used to trace air parcels, which originate from the BNPP region. Barrow, Nome and Anchorage were chosen to cover the various climatic and transport regimes and most populated areas within Alaska. The results can be used to estimate the probability of atmospheric transport of radionuclides from BNPP to Alaska.

2. Methodology 2.1. Trajectory calculations An atmospheric trajectory is the modeled pathway of an air parcel advected by a wind "eld backward or forward in time from a chosen site. Atmospheric trajectory models can be used to diagnose a source}receptor relationship for air pollutants, assess transport pathways of tracers and evaluate air #ow patterns within meteorological systems (Merrill et al., 1985; Harris and Kahl, 1990,1994; Ja!e et al., 1997a; Mahura et al., 1997). The NCEP-gridded wind "elds were interpolated to potential temperature surfaces (isentropic surfaces) using the technique described by Merrill et al. (1986). The wind "elds on these surfaces were used to calculate the trajectories. The isentropic model includes an extended grid domain of 20}803N and 903E}82.53W for the Alaska

A.Gr. Mahura et al. / Atmospheric Environment 33 (1999) 5115}5122

Arctic area (Fig. 1). The assumption used in the isentropic model is that an air parcel moves about free of diabatic e!ects. The e!ects of phase changes of water, radiative transfer and mixing of energy or mass are not considered. This assumption can be reasonably satis"ed in the free troposphere during large-scale motions. However, in regions with strong mixing, cloudiness and precipitation this assumption does not work well. An air parcel cannot always travel isentropically because the isentropic surface may intersect the land or be multiply de"ned in the boundary layer. All trajectory models are subject to uncertainty arising from interpolation of sparse meteorological data and to assumptions regarding vertical transport (Haagenson et al., 1987; Draxler, 1987; Kahl, 1996). The errors in the computation of trajectories can vary between 200 and 400 km depending on the distance (in the range 800}1500 km) and time 1}2 d (Stunder, 1996). In this study, the backward isentropic trajectories for three sites (Barrow, Nome, and Anchorage) and forward for BNPP were computed twice per day (00 and 12 UTC) for the period 1991}1995. Trajectories were calculated at di!erent potential temperature levels ranging from 255 to 330 K with a step of 5 K. Because the use of an individual trajectory gives no indication of possible wind shear uncertainties, we used 4 trajectories in this study. The initial points of these trajectories were on the corners of 13 latitude]13 longitude box with the site in the center. If all 4 trajectories showed similar transport pathways for one time period then the wind "eld was considered reasonably consistent along the transport pathway. We computed 934 912 trajectories for this study. Less than 1% of the trajectories could not be calculated due to missing archived meteorological data. Approximately 2% of the trajectories were discounted because of divergence of #ow (complex trajectories), which leads to more uncertainties in the air transport. All backward and forward trajectories ending within 1 d were excluded from our analysis. Then from trajectories at the several potential temperature levels we chose the trajectories which arrive closest to surface pressure levels at these three sites. Hence, 48 730 trajectories were used for further analysis. 2.2. Cluster analysis Cluster analysis divides a data set into clusters or groups of similar variables or cases (Romesburg, 1984). Miller (1981) initiated the use of trajectories to determine the #ow climatology, particularly over long time periods. Studies using cluster analysis techniques on trajectories were conducted by Moody (1986), Moody and Gallow (1988) and later by Harris and Kahl (1990,1994), Harris (1992), and Dorling and Davies (1995). We used the FASTCLUS procedure in SAS/STAT software, which performs a disjoint cluster analysis on the basis of Euclidean distances computed from one or

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more quantitative variables (SAS Language and Procedures: Introduction, 1991). To divide the calculated trajectories into groups we used the following criteria: latitude and longitude values of segment endpoints at 12-h intervals along each 5-d trajectory. Hence, each case consisted of 22 variables, where 11 latitude}longitude pairs describe air parcel positions during its travel time. Within each cluster, individual trajectories may be averaged to obtain the mean cluster trajectory (or transport pathway). Thus, the initial large data set is reduced to a small number of mean cluster plots. These plots can be interpreted in terms of synoptic conditions. For backward trajectories, we systematically increased the number of clusters until a cluster mean trajectory located close to the BNPP region was found. This always occurred between 5 and 8 clusters. If adjacent cluster mean trajectories were climatologicaly equivalent, then they were combined. An example is presented in Fig. 2 showing transport pathways to Anchorage for 1991}1995. The mean trajectory for each cluster is given with points indicating 12-h intervals. The clusters are marked by 2 numbers: "rst, the number or identi"er of the cluster; second, the percentage of trajectories within that cluster. The cluster numbers are used to separate the possible types of transport and are arbitrary. To summarize the #ow climatology we conducted the clustering of all backward and forward trajectories for 1991}1995, by year and by season. A similar procedure was performed for each site. It should be noted that trajectories which cross the top boundary of the model grid domain (803N) are not considered in our clustering due to their short duration and termination at the border. Therefore, clusters showing transport from/to the Central Arctic are not evaluated in our study. These trajectories do not cross the BNPP region and hence have no bearing on this study.

3. Results and discussion 3.1. Direct impact Although transport from the BNPP region to Alaska can occur at any time of the year, there is rapid transport, which is the greatest concern. We suggest that rapid transport of a radioactive cloud is most likely to occur in summer}early fall by low-pressure systems. These systems are common at this time over the BNPP region and Alaska and they could transport radionuclides within 1.5}2 d to the western shore of Alaska. At the same time these low-pressure systems will also have signi"cant dispersion and washout e!ects. Therefore, the net e!ect on the deposition of the radionuclides should be considered by more complex model. To study the BNPP's impact as a function of the transport time we have tested the entire data set of

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Fig. 2. Cluster mean trajectories (or transport pathways) to Anchorage based on trajectories 1991}1995.

Table 1 Monthly variations of BNPP forward trajectories (trajectories reaching the regions, transport time, direct impact and transport within the boundary layer), which reached Alaska during 1991}1995 Months

Jan

Feb

Mar

Apr

May Jun

Jul

Aug

Sep

Oct

Nov

Dec

Ann

Trajectories reaching Alaska d trajectories % trajectories within month

64 5.4

33 3.2

99 8.7

21 2

29 2.9

88 8.4

72 8.2

127 94 11.9 9.1

63 5.4

81 7.3

20 1.7

791 6.1

1.5 3.8 3

1.5 4.7 5

1.5 3.3 2.5

1.5 3.3 2.5

1 4.2 3.5

1.5 3.6 3.5

1.5 4.2 4

1.5 4.1 3

1 3.9 3.5

2 3.8 2.5

1 4 3.5

Direct impact d days of impact % days within month

25 16.1

15 10.7

26 16.8

9 6

12 7.7

32 21.3

31 20

51 37 32.9 24.7

22 14.2

28 18.7

9 5.8

297 16.3

Boundary layer transport d trajectories % trajectories within month

35 3

21 2

62 5.5

10 0.9

23 2.3

18 1.7

24 2.7

36 51 3.4 4.9

49 4.2

58 5.2

13 1.1

400 3.1

Transport time (d) Minimum Average Median

1.5 5.2 4

forward trajectories beginning at the plant. The area considered was a box (60}703N and 145}1653W) which covered most of Alaska. All trajectories reaching this box were extracted into a separate data set. Where the trajectory intersects the relevant boundary we noted the following parameters: the time of transport from the plant (d), potential temperature (K) and pressure (hPa). These data were statistically examined by month, season and year (Table 1). To estimate the probability of impact, the number of trajectories and number of days of impact were cal-

1 4.1 3.5

culated. If one or more trajectories crossed the area during the day (00 and 12 UTC), we assume this day is a day of impact. Taking into account that the average thickness of the boundary layer is close to 850 hPa, the trajectories were divided into two categories: "rst, those which transport air within the boundary layer; and second, transport in the free troposphere. In the cases of boundary layer transport, higher ground-level concentrations of radionuclides are likely, compared to the free troposphere. About 15% of the cases show free troposphere transport from BNPP across the Arctic shore of

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Alaska into the Canadian Arctic. In these cases contaminated air could be transported to northern Alaska and Canadian territories. We found that the percent of trajectories from BNPP, which reach Alaska, varies from 4.3 to 8.1%, with an average of 6.1% through 1991}1995. The percentage of days with impact ranges from 13.2 to 19.2%, with an average of 16.3%. Roughly 50% of these trajectories arrive at Alaska within the boundary layer. Summer and beginning of fall are the times when most days of impact occur (Table 1). This approach yields two values to estimate the risk that air in the BNPP region will be transported to Alaska. If one considers all forward trajectories starting at BNPP, then 6.1% of these reach Alaska. If instead one considers the percentage of days when one or more trajectories reach Alaska, then an annual average value of 16.3% is obtained. Since there are 8 forward trajectories from BNPP calculated per day (4 each at 00 and 12 UTC), then on average, 3 trajectories will reach Alaska if any do on that day. To some extent our understanding of the risk is related to the duration of any release that may occur. The 6.1% value represents the probability that air in the BNPP region is transported to Alaska at any given moment. However, if an accident release was to occur for 24 h, then the 16.3% value is probably more appropriate to consider. We interpret the 16.3 and 6.1% values as upper and lower bounds to the risk. The percentage of days of impact varies from 6 to 33% by month. On average through the year it was estimated to be 16.3%. On average, only about 3% of the trajectories arrive in Alaska within the boundary layer. More frequent boundary layer transport from BNPP occurs during fall. Although September is the "rst month of the fall, it resembles the last month of the summer in many aspects of its cyclone activity. During October} November the main belt of westerlies moves south and a corresponding southward displacement in the cyclone pathways occurs for eastern Siberia. A second, wellde"ned boundary layer transport from BNPP occurs in the beginning of the spring (March). At this time a great increase in cyclone frequency is observed in Siberia. Our results show that the transport of air from the BNPP region is possible during all seasons. Taking into account that the greatest danger is associated with the short-lived radionuclides and with lifetimes up to 1}2 d, we believe that the most serious risk to Alaska is during summer and early fall. This period is associated with low-pressure systems passing the Bering and Chukchi Seas, which have the ability to rapidly transport air from the accident location. It takes a minimum of 1 d with an average of 4 d for transport from BNPP. Radionuclides with a lifetime of several days and longer can be transported over large distances, depending on the meteorological conditions, and deposited. During winter Alaska is in#uenced by high-pressure systems.

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Anticyclones are slowly moving to the east, covering interior and northern areas of the state. The atmospheric conditions are stable at that time. There are no large removal processes, especially in the interior area of Alaska, and high aerosol deposition at this time is not usually observed (personal communication, Glenn Shaw). In the case of an accident the radioactive cloud will not be diluted very quickly and can be transported over large distances. 3.2. Direct impact by region in the state The mountain barriers } Alaska and Brooks ranges } have signi"cant in#uence on the formation of atmospheric transport over Alaska. We divided Alaska into three regions (Fig. 1). Region I is situated north of the Brooks range and covers the Arctic tundra and northern coastal waters of Alaska (68}713N). Region II (63}683N) lies between the Alaska and Brooks ranges and includes western and interior Alaska. Region III is situated south of the Alaska range (57}633N). Using the forward trajectories from BNPP we study the transport time and direct impact to each region. Northern and interior parts of the state are more likely to be impacted by radioactive pollution from BNPP compared to southern Alaska (Table 2). The transport occurs 46 and 57% of the time within the boundary layer for the "rst two regions and 68% for region III. Fig. 3 shows the transport time of the trajectories reaching the three regions. For regions I and II most of the transport (78.4 and 74.6%) occurs when the trajectories are less than 5 d, with a maximum at 2 d of transport (14.6 and 13.1%), respectively. In about 50% of the cases the transport occurs within 3 d for both regions. Only a few cases of very rapid transport (1 d) were identi"ed. All of them are associated with region I. For region III most of the transport occurs within 6.5}7 d (76}81%) with a maximum at 3.5 d (8.6%). Ja!e et al. (1997b) reported that the average time of transport to western and northern Alaska is 3.8 and 4 d, with a minimum of 1 and 1.5 d, respectively. Cases of very rapid transport occur most often during summer}fall. To southern Alaska the average time of transport is 5.1 d, with a minimum of 1.5 d. Seasonally the rapid transport mainly occurs during summer}fall and is associated with cyclonic activity in the region. 3.3. Transport pathways to Alaska Analyzing the forward trajectories from BNPP, we found that most of the trajectories, which reach Alaska (76.5% of all 791 trajectories), do so in less than 5 d. Taking into account that we were interested in the rapid transport from BNPP and the average time of transport is equal to 4 d, we decided to use only 5-d trajectories for clustering.

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Table 2 Summary of transport (trajectories reaching the regions, direct impact and transport within the boundary layer/free troposphere) from BNPP to Alaska by region for 1991}1995 Regions

Region I

Region II

Region III

Trajectories reaching region d trajectories % trajectories

528 4.1

503 3.9

361 2.8

Direct impact d days % days

233 12.8

203 11.1

153 8.4

Transport within (d traj/%) Boundary layer Free troposphere

243/46 285/54

288/57 215/43

246/68 115/32

Fig. 3. Variation of the number of days for transport from BNPP to Alaska by region (I } North, II } Central, III } South).

For Anchorage, on the south side of the Alaska Range, most of the transport is from the south and southwest (Fig. 2). This situation occurs more than 80% of the time. Clusters (d1, 2, 3, and 5) have a signi"cant cyclonic curvature and correspond with transport around the Aleutian Low. These regions are also the main cyclone pathways. Approximately 8% of the trajectories are in cluster d4, which shows northeasterly #ow with a 5-d origin over northern Canada and has an anticyclonic curvature. In about 7% of the cases the transport is associated with the BNPP region, as shown by cluster

d6. Seasonally, cluster from the BNPP region occurs 2}7% of the time, with a maximum in spring}winter and a minimum in summer. Nome is the closest site to BNPP. Up to 40% of the time (clusters d2 and 3) #ow from the southwest and west occurs (Fig. 4). Both clusters have a cyclonic curvature. Cluster d1 also has a cyclonic curvature and shows southerly air#ow up to 17% of the time. All these clusters are associated with cyclonic activity in the Bering Sea and Gulf of Alaska throughout the year. Clusters d4 and 6 have northerly and northwesterly components of #ow.

A.Gr. Mahura et al. / Atmospheric Environment 33 (1999) 5115}5122

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Fig. 4. Cluster mean trajectories (or transport pathways) to Nome based on trajectories 1991}1995.

Both represent transport from the Arctic regions. About 9% of the time the transport corresponds to cluster-mean d5, which crosses close to the BNPP region. The occurrence of this cluster varies from 7 to 18% of the time throughout the year, with a maximum in summer}fall and minimum in spring. For Barrow, we identi"ed "ve clusters. Up to 80% of the time the transport is from the Canadian and Russian Arctic which agrees with the results presented by Harris and Kahl (1994). The southerly #ow with a cyclonic curvature occurs 23% of the time and results from the activity of the Aleutian Low. The occurrence of the cluster, laying close to the BNPP region, varies from 21}23 to 9}13%, for winter}fall and spring}summer, respectively.

4. Summary and conclusions Forward isentropic trajectories for BNPP have been calculated, using an isentropic trajectory model. Backward trajectories were also calculated for three sites: Anchorage, Nome, and Barrow. The forward trajectories have been used to study the possibility of direct impact from BNPP on Alaska following a hypothetical accident. The analyses have been done using 1991}1995 meteorological data for three regions in the state and for Alaska as a whole. Cluster analysis of 5-d backward trajectories arriving at sites in Alaska was used to group trajectories into patterns. The patterns describe possible air transport pathways from the region near BNPP. The clustering was made for each site separately for entire data set, yearly and by season to "nd the synoptic #ow variations.

The main results of our study are as follows. f We found that 6.1% of all forward trajectories from BNPP reached Alaska during 1991}1995. These trajectories cross Alaska on 16.3% of the days during the 5 yr studied, with most of these days concentrated in summer}early fall. f While transport is possible throughout the year, this study revealed the importance of summer}early fall activity of low-pressure systems, which have the ability to transport rapidly polluted air from the accident location. In the cases of rapid transport, a radioactive cloud could reach the western shore of Alaska in 1.5 d. f The regions of northern and western Alaska are at greatest risk in comparison with southern areas. Radionuclide transport to northern and western Alaska can occur in 1 d, but averages 3.9 d. To southern Alaska it can occur in 1.5 d, but averages 5.1 d. f For Anchorage, southerly and southwesterly #ows are predominant, with a maximum occurrence up to 85% during spring}summer, and decreasing to 62% in fall}winter. The probability of the transport pathway from BNPP varies from 2 to 7%, with a maximum in spring}winter and a minimum in summer. The time of transport has a minimum of 2.5 d with an average of 5.9 d. The probability of direct impact is 3.4% throughout the year with most of the occurrence (up to 90%) resulting from transport in the free atmosphere. f During spring and summer, southerly and southeasterly #ows to Nome occur 34}70% of the time, decreasing to 16}25% in winter. This is determined by the cyclonic activity in the Bering Sea and Gulf of Alaska. Air masses from the Arctic regions arrive

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during fall}winter, up to 60% of the time. The occurrence of the transport pathway to Nome from BNPP changes through the year from 7 to 18% of the time, with the maximum during summer}fall and a minimum in spring. The time of transport is on average 4.7 d, and with a minimum of 1.5 d. The probability of direct impact is 4.4% throughout the year with more than 60% of the transport within the boundary layer. f About 80% of the time the #ows to Barrow are from the Canadian, Russian and Central Arctic. The probability of the transport pathway from BNPP varies from 22 to 11%, for winter}fall and spring}summer, respectively. The time of transport is minimum 1-d with an average of 5.1 d. The probability of direct impact is 3% throughout the year with roughly 50% as a result of boundary layer transport. These results should be helpful for preliminary evaluation of the possibility of the atmospheric radionuclide transport in case of an accident at BNPP. They are a necessary input into more speci"c e!ect analysis for particular release scenarios.

Acknowledgements Support for this research has come from the Alaska Department of Environmental Conservation (UAFADEC joint project 96-001). The computer facilities of National Center for Atmospheric Research (NCAR, Boulder, Colorado) and Arctic Region Supercomputing Center (ARSC, Fairbanks, Alaska) have been used extensively in this work. This work was supported in part by a grant of HPC time from the Arctic Region Supercomputing Center. The authors are very grateful for collaboration with Ruth Platner (Center for Atmospheric Chemistry, University of Rhode Island), Richard Honrath (Michigan Technological University, Michigan), Doug Dasher (Department of Environmental Conservation, Fairbanks), Alexander Baklanov and Sergey Morozov (Institute of North Ecology Problems, Kola Science Center, Russian Academy of Sciences), Tom Sullivan, Jim Ellis, and Brent Bowen (Lawrence Livermore National Laboratory, California). Thanks to Sue-Ann Bowling, Glenn Shaw and Jennifer Kelley (Geophysical Institute, University of Alaska Fairbanks) for constructive discussions. Thanks to Robert Huebert (ARSC) for computer assistance.

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