Nickel Stefan, Schröder Winfried, Schmalfuss Roman, Saathoff Maike, Harmens Harry, Mills Gina, Frontasyeva Marina V, Barandovski Lambe, Blum Oleg, Carballeira Alejo, de Temmerman Ludwig, Dunaev Anatoly M, Ene Antoaneta, Fagerli Hilde, Godzik Barbara, Ilyin Ilia, Jonkers Sander, Jeran Zvonka, Lazo Pranvera, Leblond Sebastien, Liiv Siiri, Mankovska Blanka, Núñez-Olivera Encarnación, Piispanen Juha, Poikolainen Jarmo, Popescu Ion V, Qarri Flora, Santamaria Jesus Miguel, Schaap Martijn, Skudnik Mitja, Špirić Zdravko, Stafilov Trajce, Steinnes Eiliv, Stihi Claudia, Suchara Ivan, Uggerud Hilde Thelle, Zechmeister Harald G
1Chair of Landscape Ecology, University of Vechta, Vechta, Germany.
2ICP Vegetation Programme Coordination Centre, Centre for Ecology and Hydrology, Bangor, Gwynedd LL57 2UW UK.
Environ Sci Eur. 2018;30(1):53. doi: 10.1186/s12302-018-0183-8. Epub 2018 Dec 21.
This paper aims to investigate the correlations between the concentrations of nine heavy metals in moss and atmospheric deposition within ecological land classes covering Europe. Additionally, it is examined to what extent the statistical relations are affected by the land use around the moss sampling sites. Based on moss data collected in 2010/2011 throughout Europe and data on total atmospheric deposition modelled by two chemical transport models (EMEP MSC-E, LOTOS-EUROS), correlation coefficients between concentrations of heavy metals in moss and in modelled atmospheric deposition were specified for spatial subsamples defined by ecological land classes of Europe (ELCE) as a spatial reference system. Linear discriminant analysis (LDA) and logistic regression (LR) were then used to separate moss sampling sites regarding their contribution to the strength of correlation considering the areal percentage of urban, agricultural and forestry land use around the sampling location. After verification LDA models by LR, LDA models were used to transform spatial information on the land use to maps of potential correlation levels, applicable for future network planning in the European Moss Survey.
Correlations between concentrations of heavy metals in moss and in modelled atmospheric deposition were found to be specific for elements and ELCE units. Land use around the sampling sites mainly influences the correlation level. Small radiuses around the sampling sites examined (5 km) are more relevant for Cd, Cu, Ni, and Zn, while the areal percentage of urban and agricultural land use within large radiuses (75-100 km) is more relevant for As, Cr, Hg, Pb, and V. Most valid LDA models pattern with error rates of < 40% were found for As, Cr, Cu, Hg, Pb, and V. Land use-dependent predictions of spatial patterns split up Europe into investigation areas revealing potentially high (= above-average) or low (= below-average) correlation coefficients.
LDA is an eligible method identifying and ranking boundary conditions of correlations between atmospheric deposition and respective concentrations of heavy metals in moss and related mapping considering the influence of the land use around moss sampling sites.
本文旨在研究欧洲生态土地类别中苔藓体内九种重金属浓度与大气沉降之间的相关性。此外，还考察了苔藓采样点周围土地利用对统计关系的影响程度。基于2010/2011年在欧洲各地收集的苔藓数据以及由两个化学传输模型（EMEP MSC-E、LOTOS-EUROS）模拟的总大气沉降数据，针对以欧洲生态土地类别（ELCE）作为空间参考系统定义的空间子样本，确定了苔藓中重金属浓度与模拟大气沉降中重金属浓度之间的相关系数。然后，使用线性判别分析（LDA）和逻辑回归（LR），根据采样地点周围城市、农业和林业土地利用的面积百分比，将苔藓采样点按其对相关强度的贡献进行分类。在通过LR对LDA模型进行验证后，使用LDA模型将土地利用的空间信息转换为潜在相关水平图，适用于欧洲苔藓调查未来的网络规划。
发现苔藓中重金属浓度与模拟大气沉降中重金属浓度之间的相关性因元素和ELCE单元而异。采样点周围的土地利用主要影响相关水平。所考察的采样点周围较小半径（5公里）对镉、铜、镍和锌更为重要，而较大半径（75 - 100公里）内城市和农业土地利用的面积百分比对砷、铬、汞、铅和钒更为重要。对于砷、铬、铜、汞、铅和钒，发现了大多数有效LDA模型模式，错误率<40%。基于土地利用的空间模式预测将欧洲划分为调查区域，显示出潜在的高（=高于平均水平）或低（=低于平均水平）相关系数。
LDA是一种合适的方法，可识别和排列大气沉降与苔藓中重金属各自浓度之间相关性的边界条件，并考虑苔藓采样点周围土地利用的影响进行相关制图。
