Radionuclide export by deer mice at a solid radioactive waste disposal area in southeastern Idaho.
Concentrations of 90Sr, 137Cs, 238Pu, 239+240Pu, and 241Am in deer mice tissues collected from a radioactive waste disposal area in southeastern Idaho were significantly (p less than 0.05) higher than those from a control area. The highest concentrations of Pu and Am occurred in pelts of deer mice inhabiting an area which had elevated surface and subsurface soil concentrations of these nuclides as compared to other Subsurface Disposal Area locations. Therefore, transuranic contamination in tissues likely originated from both soil depths. However, 137Cs and 90Sr in tissues likely originated from subsurface areas, since surface soils were below background concentrations for these nuclides. Based on a minimum of 6160 deer mice inhabiting the 36-ha waste disposal area over a 1-yr period, a total minimum inventory of 22.8 mu Ci radioactivity was contained in deer mice tissues. Of this estimate, 22.7 mu Ci activity was due to the radionuclides 90Sr and 137Cs. An estimated total of 8.4 mu Ci was transported from the disposal area in mice dispersing from the area. A calculated annual radionuclide inventory of 28.8 mu Ci in deer mice feces was deposited in and around the radioactive waste disposal area. Deer mice inhabiting the SDA are a mode of radionuclide uptake and transport; however, the environmental consequences of this transport mechanism are likely minimal. The results for deer mice, which make up 69% of the small mammal biomass, are discussed in relation to other small mammals within the disposal area. Other modes of transport associated with the deer mice, such as radionuclides in excavated soils associated with burrowing activities and predation, are also discussed.
Plutonium and Americium Contamination near a Transuranic Storage Area in Southeastern Idaho
From 1954 through 1970, transuranic waste from the Rocky Flats facility near Golden, Colorado, was shipped to the Idaho National Engineering Laboratory and buried in the Subsurface Disposal Area (SDA) at the Radioactive Waste Management Complex. Soil samples collected near the SDA indicate that this storage has resulted in transuranic contamination outside the SDA perimeter. Maximum concentrations in surface soils (0–4 cm) occurred in the drainage depression near the perimeter of the SDA and were 2,048 nCI ²⁴¹Am/m², 1,377 nCi ²³⁹Pu/m², and 32 nCi ²³⁸Pu/m². Contamination outside this drainage channel was lower and has primarily spread in the northeast-southwest directions. The maximum distances from the SDA perimeter that above background concentrations of ²⁴¹Am, ²³⁹Pu, and ²³⁸Pu could be detected were approximately 2,500, 2,400, and 1,000 m, respectively. Surface water runoff in 1962 and 1969 and wind transport appear to be the primary mechanisms which transported these nuclides out of the SDA. The vertical soil migration of ²³⁸Pu from 0–4 cm to the 4–8 cm depth was significantly greater than that for ²³⁹Pu (P = 0.001). Hides and gastrointestinal tracts of deer mice (Peromyscus maniculatus) had higher concentrations of transuranics than lungs or carcass. Ingestion appeared to be a more important mechanism than inhalation in the intake of transuranics into the deer mice. The ²⁴¹AM/²³⁹Pu ratio in the carcass was significantly (P = 0.02) higher than the ratio in soil indicating a greater uptake of ²⁴¹Am into deer mice. The data indicate that ²³⁸Pu, ²³⁹Pu, and ²⁴¹Am may behave differently in the terrestrial environment.
Benchmark database for fine-grained image classification of benthic macroinvertebrates
Abstract Managing the water quality of freshwaters is a crucial task worldwide. One of the most used methods to biomonitor water quality is to sample benthic macroinvertebrate communities, in particular to examine the presence and proportion of certain species. This paper presents a benchmark database for automatic visual classification methods to evaluate their ability for distinguishing visually similar categories of aquatic macroinvertebrate taxa. We make publicly available a new database, containing 64 types of freshwater macroinvertebrates, ranging in number of images per category from 7 to 577. The database is divided into three datasets, varying in number of categories (64, 29, and 9 categories). Furthermore, in order to accomplish a baseline evaluation performance, we present the classification results of Convolutional Neural Networks (CNNs) that are widely used for deep learning tasks in large databases. Besides CNNs, we experimented with several other well-known classification methods using deep features extracted from the data.