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Modelling Cs-137 concentrations in moose (1986-2012) from areas highly contaminated by the Chernobyl fallout

机译:模拟来自切尔诺贝利尘埃污染严重地区的驼鹿(1986-2012)中Cs-137的浓度

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摘要

Data from long term annual monitoring of Cs-137 concentrations in harvested moose (Alces alces) were empirically modelled by applying multivariate data analysis that is able to from relatively raw datasets show how the many various impact variables are related (Principal component analysis, PCA). In the later stage regression modelling (Partial least squares, PLS) was applied to analyse which environmental and physiological factors were significant (i.e. of predictive value) based on the measured (X) activity concentrations in moose meat. The data sets originate from two different forest dominated areas in Sweden. One area is located inland (Heby municipality) and the other borders to the Baltic Sea (Gavle municipality). In inland with 20% farmland, GIS-software was used to calculate the proportion of different habitat types and Cs-137 deposition around individual killing spots. This model reveals that the proportions of farmland and forest around the killing spot were significant parameters, second to deposition and years since fallout. Significance was also obtained for the proportions of mire and water bodies, the amount of rain in summer and the age of the moose. In the other model based on data from the coastal area with only about 4% farmland, the coordinates of the moose killing spots were not recorded in the data sets. In the resulting model the temperature in July was the most important parameter, second to years since fallout. Significance was also found for the following parameters: temperature and rainfall in several summer months, the approximate north- and eastward location of the killing spot and to which age category (adult/calf) the harvested moose belonged. (C) 2016 Elsevier Ltd. All rights reserved.
机译:通过应用多元数据分析,对来自采集的驼鹿(Alces alces)中Cs-137浓度的长期年度监测数据进行经验建模,该数据能够从相对原始的数据集中显示许多不同的影响变量之间的关系(主成分分析,PCA) 。在后期阶段,基于在驼鹿肉中测得的(X)活性浓度,使用回归建模(偏最小二乘,PLS)来分析哪些环境和生理因素是重要的(即具有预测价值)。数据集来自瑞典两个不同的森林为主地区。一个地区位于内陆(赫比市),另一地区与波罗的海接壤(加维尔市)。在拥有20%农田的内陆地区,使用GIS软件来计算不同生境类型的比例以及单个杀伤点周围Cs-137的沉积。该模型表明,杀害点周围的农田和森林比例是重要的参数,仅次于沉积和沉降的年限。泥泞和水体的比例,夏季的降雨量和驼鹿的年龄也具有重要意义。在另一种基于仅约4%农田的沿海地区数据的模型中,未在数据集中记录驼鹿杀害点的坐标。在生成的模型中,7月的温度是最重要的参数,仅次于余尘。还发现以下参数具有重要意义:几个夏季月份的温度和降雨量,杀害点的大约北和东位置以及所收获的驼鹿属于哪个年龄类别(成人/小腿)。 (C)2016 Elsevier Ltd.保留所有权利。

著录项

  • 来源
    《Journal of Environmental Radioactivity》 |2016年第8期|112-122|共11页
  • 作者单位

    Swedish Univ Agr Sci, Dept Aquat Sci & Assessment, Box 7050, SE-75007 Uppsala, Sweden|Askmolnsvagen 73, SE-74335 Storvreta, Sweden;

    Swedish Univ Agr Sci, Dept Soil & Environm, Box 7014, SE-75007 Uppsala, Sweden;

    Swedish Univ Agr Sci, Dept Aquat Sci & Assessment, Box 7050, SE-75007 Uppsala, Sweden;

    Swedish Univ Agr Sci, Dept Ecol, Grimso Wildlife Res Stn, SE-73091 Riddarhyttan, Sweden;

    Swedish Univ Agr Sci, Dept Soil & Environm, Box 7014, SE-75007 Uppsala, Sweden|Rodhakevagen 15, SE-75652 Uppsala, Sweden;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Modelling; Radiocaesium; Moose; PLS; Forest; Long-term;

    机译:建模;放射性碳;麋;PLS;森林;长期;

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