journal article·2019-12

Influencing factors, repeatability and correlation of chamber methods in measuring formaldehyde emissions from fiber- and particleboards

Venla Hemmilä, Bettina Meyer, Annelise Larsen, Harald Schwab, Stergios Adamopoulos

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Abstract

Recently, there has been focus on lowering emission levels of wood-based boards. However, the accuracy and correlation between EN 717-1 and ASTM D 6007 chamber methods at emission levels below 0.05 ppm are not well investigated, and information about their correlation to the EN 16516 method is limited. In this paper, the low emission level of interest was determined by measuring emissions from particles, fibers and pressed boards without glue. The effect of analytical methods and edge-sealing on chamber emissions was determined, and accuracies and correlations of the EN 717-1 and ASTM D 6007 chambers were defined at low emission levels (<0.05 ppm). In addition, some emission values were compared to those obtained with EN 16516. The EN 717-1 and ASTM D 6007 methods had high accuracy. The acetyl acetone and 2.4-dinitrophenylhydrazine analytical methods showed low standard deviations (<5%), except at emission levels below 0.02 ppm. This could be counteracted by using a direct reagent absorber solution. Opening 5% of the edge of boards affected emissions and was dependent on board type. ASTM D 6007 and EN 717-1 methods were highly correlated for both particleboards (r 2 = 0.9167) and fiberboards (r 2 = 0.9443) at emission levels below 0.05 ppm. EN 16516 emissions were 2.6 times greater than those of EN 717-1 at emission range <0.05 ppm, exceeding the conversion factor of two given in the German legislation. The EN 717-1 to EN 16516 correlation needs to be further evaluated for different board types and emission ranges.
Source: expertdata · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Indoor Air Quality and Microbial Exposure · Air Quality and Health Impacts · Odor and Emission Control Technologies

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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