The primary pathway by which radionuclides can move off the INL Site is through the air and for this reason the air pathway is the primary focus of monitoring on and around the INL Site. Samples for particulates and iodine-131 (131I) gas in air were collected weekly for the duration of the quarter at 16 locations using low-volume air samplers. The sampler in Jackson resumed operation at the end of 2016 following completion of the construction at a new sampling location. Moisture in the atmosphere was sampled at four locations around the INL Site and analyzed for tritium. Air sampling activities and results for the first quarter of 2017 are discussed below. A summary of approximate minimum detectable concentrations (MDCs) for radiological analyses and DOE Derived Concentration Standard (DCS) (DOE 2011b) values is provided in Appendix B.
Radioactivity associated with airborne particulates was monitored continuously by 18 low-volume air samplers (two of which are used as replicate samplers) at 16 locations during the third quarter of 2017 (Figure 2). Three of these samplers are located on the INL Site, seven are situated off the INL Site near the boundary, and eight have been placed at locations distant to the INL Site. Samplers are divided into INL Site, Boundary, and Distant groups to determine if there is a gradient of radionuclide concentrations, increasing towards the INL Site. Each replicate sampler is relocated every other year to a new location. At the start of 2017, one replicate sampler was moved to Blue Dome (a Boundary location) and one was moved to Atomic City (also a Boundary location). An average of 18,598 ft3 (527 m3) of air was sampled at each location, each week, at an average flow rate of 1.85 ft3/min (0.05 m3/min). Particulates in air were collected on membrane particulate filters (1.2-µm pore size). Gases passing through the filter were collected with an activated charcoal cartridge.
Filters and charcoal cartridges were changed weekly at each station during the quarter. Each particulate filter was analyzed for gross alpha and gross beta radioactivity using thin-window gas flow proportional counting systems after waiting about four days for naturally-occurring daughter products of radon and thorium to decay.
The weekly particulate filters collected during the quarter for each location were composited and analyzed for gamma-emitting radionuclides. Selected composites were also analyzed by location for 90Sr, 238Pu, 239/240Pu, and 241Am as determined by a rotating quarterly schedule.
Charcoal cartridges were analyzed for gamma-emitting radionuclides, specifically for iodine-131 (131I). Iodine-131 is of particular interest because it is produced in relatively large quantities by nuclear fission, is readily accumulated in human and animal thyroids, and has a half-life of eight days. This means that any elevated level of 131I in the environment could be from a recent release of fission products.
Gross alpha results are reported in Table C-1 and shown in Figures 3 through 6. Gross alpha data are tested for normality prior to statistical analyses, and generally show no consistent discernible distribution. Because there is no discernible distribution of the data, the nonparametric Kruskal-Wallis test of multiple independent groups was used to test for statistical differences between INL Site, Boundary, and Distant locations. The use of nonparametric tests, such as Kruskal-Wallis, gives less weight to outlier and extreme values thus allowing a more appropriate comparison of data groups. A statistically significant difference exists between data groups if the (p) value is less than 0.05. Values greater than 0.05 translate into a 95 percent confidence that the medians are statistically the same. Comparisons of gross alpha concentrations were made for the quarter and for each month of the quarter using this methodology. The p-value for each comparison is shown in Table D-1. In the third quarter, there was not a statistical difference between INL Site, Boundary, and Distant groups for the quarter as a whole. For the months of July and August the Boundary group showed the highest concentration, followed by the INL Site group. Although the Distant group was found with the lowest gross alpha concentrations, the differences between the groups was very small, as shown in Figure 3.
As an additional check, comparisons between gross alpha concentrations measured at Boundary and Distant locations were made on a weekly basis. The Mann-Whitney U test was used to compare the Boundary and Distant data because it is the most powerful nonparametric alternative to the t-test for independent samples. INL Site sample results were not included in this analysis because the onsite data, collected at only three locations, are not representative of the entire INL Site and would not aid in determining offsite impacts. There were two weeks where a statistical difference existed between the two sample groups (Table D-2). There were five weeks where a statistical difference existed between the two sample groups (Table D-2). These were the weeks of July 5, July 26, August 2, August 23, and August 30. However, the concentrations were well within the range seen historically (the maximum background concentration from 2007-2016 was 8.26 x10-15 µmCi/ml).
Gross alpha data typically demonstrate seasonality during the year, with highest concentrations occurring in the third quarter. Figure 7 presents box plots of the gross alpha results grouped by month during the third quarter. The figure shows that the highest concentrations of gross alpha activity occurred in August and September when agricultural activities and particulate concentrations tend to be elevated around the INL Site (Figure 8). The wide range of gross alpha concentrations in September appears to reflect the high particulate concentrations in the beginning of the month, followed by a large decrease at the end. A scatterplot of weekly gross alpha concentrations appears to show a pattern similar to particulate concentrations (Figure 9). The relationship between particulate weight and gross alpha activity was tested statistically using a linear regression analysis. The regression showed a positive correlation between particulate concentration that is weak (the correlation coefficient [r2] in this case is 0.23, which is far less a perfect correlation of 1). The data appear as a cluster and are best visualized using a bagplot (Figure 10). A bagplot is a bivariate (i.e., two variables) generalization of a boxplot and therefore provides insight in the distribution of data points in both axes. A bagplot adds three features to a scatterplot. First is the depth median, which is the point with the highest possible Tukey depth. It is analogous to (but not identical to) to the common univariate median. Second is a polygon that encloses 50% of the points around the depth median. This is called the 'bag', and is analogous to the box in a box and whisker plot. Third is a polygon that is a convex hull around the points inside a region that is 3 times the size of the bag. This is called the 'loop', and is analogous to the whiskers on a box and whisker plot. Points located outside of the loop are outliers similar to outliers on a box and whisker plot.
Figure 10 shows that while gross alpha activity tends to increase as a function of particulate concentration there is considerable variability and a few outliers. There are obviously some other variables, besides particulate concentration, which explain the variability in the gross alpha results. One such variable may be the presence of smoke in the air from regional wildfires observed at most or all stations during the period from August 2 through September 6, 2017. Everhart (2010) demonstrates that gross alpha activity can increase during wildfire events. During the Cerro Grande fire that burned part of Los Alamos National Laboratory in May 2000, samples were collected more frequently (weekly) than normal (biweekly) because buildup of smoke particles on the filters was decreasing the air flow. To evaluate potential human exposure to air contaminants, the samples were analyzed as soon as possible and for additional specific radionuclides. Analyses showed that the smoke from the fire included resuspended radon decay products that had been accumulating for many years on the vegetation and the litter that burned. Gross alpha appeared to have increases at all stations during the fire due to increases in concentrations of polonium-210 (an alpha-emitting radon decay product). Site-to-site variability also peaked during the fire because resuspended radon decay products did not equally impact all of the sampling locations. This may help to explain some of the high concentrations and variability of gross alpha activity at the INL Site and vicinity during August and the first week of September.
Gross beta results are presented in Table C-1 and displayed in Figures 11 through 14. The data are tested quarterly and generally are found to be neither normally nor log-normally distributed.Outliers and extreme values were retained in subsequent statistical analyses because they are within the range of background measurements made in the past ten years (a maximum concentration of 127 x 10-15 µCi/ml was measured during the period from 2007-2016), and because these values could not be attributed to mistakes in collection, analysis, or reporting procedures. No statistical differences were noted in the quarterly data or during any month of the quarter (Table D-1). Weekly comparisons were also made using the same method as for the gross alpha data and no statistical differences were found during any week of the quarter (Table D-2).
The boxplot of data categorized by month (Figure 15) shows higher concentrations in August and September with the highest variability in September. Weekly beta concentrations (Figure 16) are similar to weekly particulate concentrations (Figure 8) in that concentrations peak in August and early September and show the largest range of values in the second half of September. A linear regression was performed of gross beta activity versus particulate concentrations and shows a positive, weak, linear relationship (r2 = 0.18). A bagplot of the two variables confirms that gross beta activity generally increases as a function of particulate concentration but that there is considerable variability and a few outliers. As with gross alpha activity, there are other variables, besides particulate concentration, influencing gross beta activity measured in air. Again, the presence of smoke in the air from regional wildfires observed at most or all stations during the period from August 2 through September 6, 2017, may be one such variable. Everhart (2010) observed that gross beta activity increased during the wildfire at Los Alamos National Laboratory in May 2000 and attributed it to the presence of radon decay products in smoke from the fire. Bismuth-210 (a beta emitter and a radon decay product) was measured in the filters during the fire. Variability of gross beta concentrations within stations was attributed in variability of radon decay products in vegetation that was burned.
Iodine-131 was not detected in any of the 26 sets of charcoal cartridges measured during the third quarter. Weekly 131I results for each location are listed in Table C-2 of Appendix C.
No 137Cs or other human-made gamma-emitting radionuclides were found in quarterly composites. No 90Sr or was found either. Amercium-241 and Plutonium-239/240 were detected just above the 3s uncertainty level in the composite from the duplicate sampler at Blackfoot (but not in the composite from the regular sampler in Blackfoot) Amercium-241 was also found slightly above the 3s concentration at the Mud Lake sampler. In comparison to the Derived Concentration Standard, the 241Am result was 0.005 percent of the DCS. Plutonium-238 was also reported in a composite from Blackfoot and Jackson Hole, also just above the detection limit. The detected value was for Blackfoot was 0.006 percent of the DCS and Jackson Hole was 0.007 percent of the DCS. A lower detection limit achieved by the current laboratory performing these analyses has resulted in a few results near the detection limit in 2016 and 2017. Results for these analyses are found in Table C-3 of Appendix C.
Atmospheric moisture is collected by pulling air through a column of absorbent material (molecular sieve material) to absorb water vapor. The water is then extracted from the absorbent material by heat distillation. The resulting water samples are then analyzed for tritium using liquid scintillation.
Results were available for 16 atmospheric moisture samples collected during the third quarter of 2017. Six of the 12 results exceeded the 3s uncertainty level for tritium, with similar results to those reported previously. Results also remain similar between the four sampling locations. All samples were significantly below the DOE DCS for tritium in air of 1.4 x 10-8 µCi/mLair with a maximum reported value of 9.69 x 10-13 µCi/mLair at EFS. Results are shown in Table C-4, Appendix C.
Radiation has always been a part of the natural environment in the form of cosmic radiation, cosmogenic radionuclides [carbon-14 (14C), Beryllium-7 (7Be), and tritium (3H)], and naturally occurring radionuclides, such as potassium-40 (40K), and the thorium, uranium, and actinium series radionuclides which have very long half lives. Additionally, human-made radionuclides were distributed throughout the world beginning in the early 1940s. Atmospheric testing of nuclear weapons from 1945 through 1980 and nuclear power plant accidents, such as the Chernobyl accident in the former Soviet Union during 1986, have resulted in fallout of detectable radionuclides around the world. This natural and manmade global fallout radioactivity is referred to as background radiation. MORE
The primary concern regarding radioactivity is the amount of energy deposited by particles or gamma radiation to the surrounding environment. It is possible that the energy from radiation may damage living tissue. When radiation interacts with the atoms of a given substance, it can alter the number of electrons associated with those atoms (usually removing orbital electrons). This is called ionization. MORE