Atmospheric chemistry
; Atmospheric movements
; Cadmium
; Copper
; Decision trees
; Deposition
; Ecology
; Heavy metals
; Land use
; Lead
; Mapping
; Mercury (metal)
; Meteorological problems
; Nickel
; Nitrogen
; Population statistics
; Soil pollution
; Zinc
; Atmospheric depositions
; Biomonitoring
; Chemical transport models
; Classification and regression tree
; Ecological land classifications
; Element concentrations
; Nitrogen concentrations
; Spatial reference systems
; Pollution
; arsenic
; cadmium
; chromium
; copper
; heavy metal
; lead
; mercury
; nickel
; nitrogen
; vanadium
; zinc
; atmospheric deposition
; biomonitoring
; boundary condition
; concentration (composition)
; ecological approach
; heavy metal
; land classification
; moss
; nitrogen
; pollutant transport
; spatial analysis
; spatiotemporal analysis
; statistical analysis
; Article
; atmospheric deposition
; biological monitoring
; clay
; Europe
; explanatory variable
; land use
; moss
; nitrogen concentration
; nonhuman
; population density
; precipitation
; priority journal
; random forest
; Europe
; Bryophyta
Scopus学科分类:
Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要:
Objective This study explores the statistical relations between the concentration of nine heavy metals (HM) (arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), vanadium (V), zinc (Zn)), and nitrogen (N) in moss and potential explanatory variables (predictors) which were then used for mapping spatial patterns across Europe. Based on moss specimens collected in 2010 throughout Europe, the statistical relation between a set of potential predictors (such as the atmospheric deposition calculated by use of two chemical transport models (CTM), distance from emission sources, density of different land uses, population density, elevation, precipitation, clay content of soils) and concentrations of HMs and nitrogen (N) in moss (response variables) were evaluated by the use of Random Forests (RF) and Classification and Regression Trees (CART). Four spatial scales were regarded: Europe as a whole, ecological land classes covering Europe, single countries participating in the European Moss Survey (EMS), and moss species at sampling sites. Spatial patterns were estimated by applying a series of RF models on data on potential predictors covering Europe. Statistical values and resulting maps were used to investigate to what extent the models are specific for countries, units of the Ecological Land Classification of Europe (ELCE), and moss species. Results Land use, atmospheric deposition and distance to technical emission sources mainly influence the element concentration in moss. The explanatory power of calculated RF models varies according to elements measured in moss specimens, country, ecological land class, and moss species. Measured and predicted medians of element concentrations agree fairly well while minima and maxima show considerable differences. The European maps derived from the RF models provide smoothed surfaces of element concentrations (As, Cd, Cr, Cu, N, Ni, Pb, Hg, V, Zn), each explained by a multivariate RF model and verified by CART, and thereby more information than the dot maps depicting the spatial patterns of measured values. Conclusions RF is an eligible method identifying and ranking boundary conditions of element concentrations in moss and related mapping including the influence of the environmental factors. � 2017 Elsevier Ltd
Chair of Landscape Ecology, University of Vechta, POB 15 53, Vechta, Germany; University of Bremen, POB 330 440, Bremen, Germany; ICP Vegetation Programme Coordination Centre, Centre for Ecology and Hydrology, Bangor, Gwynedd, United Kingdom; Moss Survey Coordination Centre, Frank Laboratory of Neutron Physics, Moscow Region, Russian Federation; Environmental Agency of Bolzano, Laives, Italy; International Sakharov Environmental University, Minsk, Belarus; Ss. Cyril and Methodius University, Skopje, Macedonia; National Botanical Garden, Academy of Science of Ukraine, Kiev, Ukraine; IVL Swedish Environmental Research Institute, G�teborg, Sweden; Veterinary and Agrochemical Research Centre CODA-CERVA, Tervuren, Belgium; Ivanovo State University of Chemistry and Technology, Ivanovo, Russian Federation; Norwegian Meteorological Institute, Oslo, Norway; W. Szafer Institute of Botany, Polish Academy of Sciences, Krak�w, Poland; Meteorological Synthesizing Centre East, Moscow, Russian Federation; TNO, Utrecht, Netherlands; Jožef Stefan Institute, Ljubljana, Slovenia; University of Tirana, Tirana, Albania; National Museum of Natural History, Paris, France; Tallinn Botanic Garden, Tallinn, Estonia; Icelandic Institute of Natural History, Gar�ab�r, Iceland; Institute of Landscape Ecology, Slovak Academy of Sciences, Bratislava, Slovakia; University of La Rioja, Logro�o, Spain; Natural Resources Institute Finland (Luke), Oulu, Finland; Valahia University of Targoviste, Targoviste, Romania; University of Vlora, Vlor�, Albania; University of Novi Sad, Faculty of Sciences, Novi Sad, Serbia; University of NavarraNavarra, Spain; Slovenian Forestry Institute, Ljubljana, Slovenia; Green Infrastructure Ltd, Zagreb, Croatia; Norges Teknisk-Naturvitenskapelige Universitet, Trondheim, Norway; Silva Tarouca Research Institute for Landscape and Ornamental Gardening, Průhonice, Czech Republic; FUB-Research Group for Environmental Monitoring, Rapperswil, Switzerland; Norwegian Institute for Air Research, Kjeller, Norway; University of Vienna, Vienna, Austria
Recommended Citation:
Nickel S,, Schr�der W,, Wosniok W,et al. Modelling and mapping heavy metal and nitrogen concentrations in moss in 2010 throughout Europe by applying Random Forests models[J]. Atmospheric Environment,2017-01-01,156