Literature DB >> 17241725

Use of handheld X-ray fluorescence spectrometry units for identification of arsenic in treated wood.

Colleen N Block1, Tomoyuki Shibata, Helena M Solo-Gabriele, Timothy G Townsend.   

Abstract

The objective of this study was to evaluate the performance of handheld XRF analyzers on wood that has been treated with a preservative containing arsenic. Experiments were designed to evaluate precision, detection limit, effective depth of analysis, and accuracy of the XRF arsenic readings. Results showed that the precision of the XRF improved with increased sample concentration and longer analysis times. Reported detection limits decreased with longer analysis times to values of less than 1mg/kg or 18 mg/kg, depending on the model used. The effective depth of analysis was within the top 1.2 cm and 2.0 cm of sample for wood containing natural gradients of chemical preservative and concentration extremes, respectively. XRF results were found to be 1.5-2.3 times higher than measurements from traditional laboratory analysis. Equations can be developed to convert XRF values to results which are consistent with traditional laboratory testing.

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Year:  2007        PMID: 17241725      PMCID: PMC2556294          DOI: 10.1016/j.envpol.2006.11.013

Source DB:  PubMed          Journal:  Environ Pollut        ISSN: 0269-7491            Impact factor:   8.071


  13 in total

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Authors:  D A Sterling; R D Lewis; D A Luke; B N Shadel
Journal:  Environ Res       Date:  2000-06       Impact factor: 6.498

2.  Evaluation of XRF and LIBS technologies for on-line sorting of CCA-treated wood waste.

Authors:  Helena M Solo-Gabriele; Timothy G Townsend; David W Hahn; Thomas M Moskal; Naila Hosein; Jenna Jambeck; Gary Jacobi
Journal:  Waste Manag       Date:  2004       Impact factor: 7.145

3.  Energy-dispersive X-ray fluorescence systems as analytical tool for assessment of contaminated soils.

Authors:  Chris Vanhoof; Valère Corthouts; Kristof Tirez
Journal:  J Environ Monit       Date:  2004-02-20

4.  Galvanic sludge metals recovery by pyrometallurgical and hydrometallurgical treatment.

Authors:  Gustavo Rossini; Andréa Moura Bernardes
Journal:  J Hazard Mater       Date:  2005-11-16       Impact factor: 10.588

5.  Contamination of soil with copper, chromium, and arsenic under decks built from pressure treated wood.

Authors:  D E Stilwell; K D Gorny
Journal:  Bull Environ Contam Toxicol       Date:  1997-01       Impact factor: 2.151

6.  Leaching of CCA-treated wood: implications for waste disposal.

Authors:  Timothy Townsend; Thabet Tolaymat; Helena Solo-Gabriele; Brajesh Dubey; Kristin Stook; Lakmini Wadanambi
Journal:  J Hazard Mater       Date:  2004-10-18       Impact factor: 10.588

7.  Dislodgeable copper, chromium and arsenic from CCA-treated wood surfaces.

Authors:  David Stilwell; Michael Toner; Brij Sawhney
Journal:  Sci Total Environ       Date:  2003-08-01       Impact factor: 7.963

8.  Arsenic speciation of solvent-extracted leachate from new and weathered CCA-treated wood.

Authors:  Bernine I Khan; Helena M Solo-Gabriele; Brajesh K Dubey; Timothy G Townsend; Yong Cai
Journal:  Environ Sci Technol       Date:  2004-09-01       Impact factor: 9.028

9.  Children's exposure to arsenic from CCA-treated wooden decks and playground structures.

Authors:  Harold F Hemond; Helena M Solo-Gabriele
Journal:  Risk Anal       Date:  2004-02       Impact factor: 4.000

10.  Pilot scale evaluation of sorting technologies for CCA treated wood waste.

Authors:  Monika Blassino; Helena Solo-Gabriele; Timothy Townsend
Journal:  Waste Manag Res       Date:  2002-06
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  3 in total

1.  Quantities of arsenic-treated wood in demolition debris generated by Hurricane Katrina.

Authors:  Brajesh Dubey; Helena M Solo-Gabriele; Timothy G Townsendt
Journal:  Environ Sci Technol       Date:  2007-03-01       Impact factor: 9.028

2.  Variation of arsenic concentration on surfaces of in-service CCA-treated wood planks in a park and its influencing field factors.

Authors:  Ya Tang; Wei Gao; Xiuli Wang; Shiming Ding; Taicheng An; Weiyang Xiao; Ming H Wong; Chaosheng Zhang
Journal:  Environ Monit Assess       Date:  2014-12-16       Impact factor: 2.513

3.  Rapid identification of wood species using XRF and neural network machine learning.

Authors:  Aaron N Shugar; B Lee Drake; Greg Kelley
Journal:  Sci Rep       Date:  2021-09-02       Impact factor: 4.996

  3 in total

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