Cargando…
Estimation of plant sampling uncertainty: an example based on chemical analysis of moss samples
In order to estimate the level of uncertainty arising from sampling, 54 samples (primary and duplicate) of the moss species Pleurozium schreberi (Brid.) Mitt. were collected within three forested areas (Wierna Rzeka, Piaski, Posłowice Range) in the Holy Cross Mountains (south-central Poland). During...
Autor principal: | |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5101290/ https://www.ncbi.nlm.nih.gov/pubmed/27557965 http://dx.doi.org/10.1007/s11356-016-7477-4 |
_version_ | 1782466261518647296 |
---|---|
author | Dołęgowska, Sabina |
author_facet | Dołęgowska, Sabina |
author_sort | Dołęgowska, Sabina |
collection | PubMed |
description | In order to estimate the level of uncertainty arising from sampling, 54 samples (primary and duplicate) of the moss species Pleurozium schreberi (Brid.) Mitt. were collected within three forested areas (Wierna Rzeka, Piaski, Posłowice Range) in the Holy Cross Mountains (south-central Poland). During the fieldwork, each primary sample composed of 8 to 10 increments (subsamples) was taken over an area of 10 m(2) whereas duplicate samples were collected in the same way at a distance of 1–2 m. Subsequently, all samples were triple rinsed with deionized water, dried, milled, and digested (8 mL HNO(3) (1:1) + 1 mL 30 % H(2)O(2)) in a closed microwave system Multiwave 3000. The prepared solutions were analyzed twice for Cu, Fe, Mn, and Zn using FAAS and GFAAS techniques. All datasets were checked for normality and for normally distributed elements (Cu from Piaski, Zn from Posłowice, Fe, Zn from Wierna Rzeka). The sampling uncertainty was computed with (i) classical ANOVA, (ii) classical RANOVA, (iii) modified RANOVA, and (iv) range statistics. For the remaining elements, the sampling uncertainty was calculated with traditional and/or modified RANOVA (if the amount of outliers did not exceed 10 %) or classical ANOVA after Box-Cox transformation (if the amount of outliers exceeded 10 %). The highest concentrations of all elements were found in moss samples from Piaski, whereas the sampling uncertainty calculated with different statistical methods ranged from 4.1 to 22 %. |
format | Online Article Text |
id | pubmed-5101290 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-51012902016-11-21 Estimation of plant sampling uncertainty: an example based on chemical analysis of moss samples Dołęgowska, Sabina Environ Sci Pollut Res Int Research Article In order to estimate the level of uncertainty arising from sampling, 54 samples (primary and duplicate) of the moss species Pleurozium schreberi (Brid.) Mitt. were collected within three forested areas (Wierna Rzeka, Piaski, Posłowice Range) in the Holy Cross Mountains (south-central Poland). During the fieldwork, each primary sample composed of 8 to 10 increments (subsamples) was taken over an area of 10 m(2) whereas duplicate samples were collected in the same way at a distance of 1–2 m. Subsequently, all samples were triple rinsed with deionized water, dried, milled, and digested (8 mL HNO(3) (1:1) + 1 mL 30 % H(2)O(2)) in a closed microwave system Multiwave 3000. The prepared solutions were analyzed twice for Cu, Fe, Mn, and Zn using FAAS and GFAAS techniques. All datasets were checked for normality and for normally distributed elements (Cu from Piaski, Zn from Posłowice, Fe, Zn from Wierna Rzeka). The sampling uncertainty was computed with (i) classical ANOVA, (ii) classical RANOVA, (iii) modified RANOVA, and (iv) range statistics. For the remaining elements, the sampling uncertainty was calculated with traditional and/or modified RANOVA (if the amount of outliers did not exceed 10 %) or classical ANOVA after Box-Cox transformation (if the amount of outliers exceeded 10 %). The highest concentrations of all elements were found in moss samples from Piaski, whereas the sampling uncertainty calculated with different statistical methods ranged from 4.1 to 22 %. Springer Berlin Heidelberg 2016-08-24 2016 /pmc/articles/PMC5101290/ /pubmed/27557965 http://dx.doi.org/10.1007/s11356-016-7477-4 Text en © The Author(s) 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Research Article Dołęgowska, Sabina Estimation of plant sampling uncertainty: an example based on chemical analysis of moss samples |
title | Estimation of plant sampling uncertainty: an example based on chemical analysis of moss samples |
title_full | Estimation of plant sampling uncertainty: an example based on chemical analysis of moss samples |
title_fullStr | Estimation of plant sampling uncertainty: an example based on chemical analysis of moss samples |
title_full_unstemmed | Estimation of plant sampling uncertainty: an example based on chemical analysis of moss samples |
title_short | Estimation of plant sampling uncertainty: an example based on chemical analysis of moss samples |
title_sort | estimation of plant sampling uncertainty: an example based on chemical analysis of moss samples |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5101290/ https://www.ncbi.nlm.nih.gov/pubmed/27557965 http://dx.doi.org/10.1007/s11356-016-7477-4 |
work_keys_str_mv | AT dołegowskasabina estimationofplantsamplinguncertaintyanexamplebasedonchemicalanalysisofmosssamples |