Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 2 de 2
Filter
Add more filters











Database
Language
Publication year range
1.
Environ Monit Assess ; 184(7): 4517-38, 2012 Jul.
Article in English | MEDLINE | ID: mdl-21833733

ABSTRACT

Assessment of trace element contents in soils is required in Germany (and other countries) before sewage sludge application on arable soils. The reliability of measured element contents is affected by measurement uncertainty, which consists of components due to (1) sampling, (2) laboratory repeatability (intra-lab) and (3) reproducibility (between-lab). A complete characterization of average trace element contents in field soils should encompass the uncertainty of all these components. The objectives of this study were to elucidate the magnitude and relative proportions of uncertainty components for the metals As, B, Cd, Co, Cr, Mo, Ni, Pb, Tl and Zn in three arable fields of different field-scale heterogeneity, based on a collaborative trial (CT) (standardized procedure) and two sampling proficiency tests (PT) (individual sampling procedure). To obtain reference values and estimates of field-scale heterogeneity, a detailed reference sampling was conducted. Components of uncertainty (sampling person, sampling repetition, laboratory) were estimated by variance component analysis, whereas reproducibility uncertainty was estimated using results from numerous laboratory proficiency tests. Sampling uncertainty in general increased with field-scale heterogeneity; however, total uncertainty was mostly dominated by (total) laboratory uncertainty. Reproducibility analytical uncertainty was on average by a factor of about 3 higher than repeatability uncertainty. Therefore, analysis within one single laboratory and, for heterogeneous fields, a reduction of sampling uncertainty (for instance by larger numbers of sample increments and/or a denser coverage of the field area) would be most effective to reduce total uncertainty. On the other hand, when only intra-laboratory analytical uncertainty was considered, total sampling uncertainty on average prevailed over analytical uncertainty by a factor of 2. Both sampling and laboratory repeatability uncertainty were highly variable depending not only on the analyte but also on the field and the sampling trial. Comparison of PT with CT sampling suggests that standardization of sampling protocols reduces sampling uncertainty, especially for fields of low heterogeneity.


Subject(s)
Environmental Pollution/statistics & numerical data , Soil Pollutants/analysis , Soil/chemistry , Trace Elements/analysis , Environmental Monitoring , Germany , Metals, Heavy/analysis , Reference Values , Sewage/analysis , Soil Pollutants/standards , Trace Elements/standards , Uncertainty , Waste Disposal, Fluid/methods
2.
Environ Manage ; 45(5): 1201-22, 2010 May.
Article in English | MEDLINE | ID: mdl-20306042

ABSTRACT

Diffuse Nitrogen (N) loss from agriculture is a major factor contributing to increased concentrations of nitrate in surface and groundwater, and of N(2)O and NH(3) in the atmosphere. Different approaches to assess diffuse N losses from agriculture have been proposed, among other direct measurements of N loads in leachate and groundwater, and physically-based modelling. However, both these approaches have serious drawbacks and are awkward to use at a routine base. N loss indicators (NLIs) are environmental management tools for assessing the risk of diffuse N losses from agricultural fields. They range in complexity from simple proxy variables to elaborate systems of algebraic equations. Here we present an overview of NLIs developed in different parts of the world. NLIs can be categorized into source-based, transport-based, and composite approaches. Several issues demand more attention in future studies. (1) Is incorporation of leaching losses and gaseous losses into one single NLI warranted? (2) Is it sufficient to restrict the focus on the rooted soil zone without considering the vadose zone and aquifer? (3) Calibration and validation of NLIs using field data of N loss seems not sufficient. Comparisons of several different NLIs with each other needs more attention; however, the different scaling of NLIs impedes comparability. (4) Sensitivity of input parameters with regard to the final NLI output needs more attention in future studies. (5) For environmental management purposes, factors addressing management decision by farmers deserve more attention.


Subject(s)
Agriculture , Environmental Monitoring/methods , Nitrogen/analysis , Water Pollutants, Chemical/analysis , Agriculture/standards , Models, Theoretical , Multivariate Analysis , Risk Assessment , Seasons , Soil/analysis , Weather
SELECTION OF CITATIONS
SEARCH DETAIL