2012
DOI: 10.5194/amt-5-2115-2012
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A disjunct eddy accumulation system for the measurement of BVOC fluxes: instrument characterizations and field deployment

Abstract: Abstract. Biological volatile organic compounds (BVOCs), such as isoprene and monoterpenes, are emitted in large amounts from forests. Quantification of the flux of BVOCs is critical in the evaluation of the impact of these compounds on the concentrations of atmospheric oxidants and on the production of secondary organic aerosol. A disjunct eddy accumulation (DEA) sampler system was constructed for the measurement of speciated BVOC fluxes. Unlike traditional eddy covariance (EC), the relatively new technique o… Show more

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Cited by 4 publications
(4 citation statements)
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References 66 publications
(108 reference statements)
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“…Modeled mid-day canopy-scale emission rates of total MTs and SQTs (0.23 [0.04-0.61] mg m −2 h −1 and 0.03 [0.001-0.09] mg m −2 h −1 , respectively) were close to previous estimates (0.21 ± 0.06 mg m −2 h −1 and 0.10 ± 0.05 mg m −2 h −1 , respectively) based on PTR-MS measurements at UMBS (Kim et al, 2009). Considering uncertainties and variability, modeled α-and β-pinene emission rates were also in agreement with measured fluxes at UMBS using a disjunct eddy accumulation system (Edwards et al, 2012); these new speciated MT flux measurements by Edwards et al (2012) will help constrain emissions in future modeling efforts. Comparison between measured (PTR-LIT) and base modeled total MT concentrations ranged from agreement to an under-prediction of up to a factor of ∼ 3; however, these values were still within the range of measured variability (Fig.…”
Section: Bvoc Emissions and Concentrationssupporting
confidence: 60%
See 1 more Smart Citation
“…Modeled mid-day canopy-scale emission rates of total MTs and SQTs (0.23 [0.04-0.61] mg m −2 h −1 and 0.03 [0.001-0.09] mg m −2 h −1 , respectively) were close to previous estimates (0.21 ± 0.06 mg m −2 h −1 and 0.10 ± 0.05 mg m −2 h −1 , respectively) based on PTR-MS measurements at UMBS (Kim et al, 2009). Considering uncertainties and variability, modeled α-and β-pinene emission rates were also in agreement with measured fluxes at UMBS using a disjunct eddy accumulation system (Edwards et al, 2012); these new speciated MT flux measurements by Edwards et al (2012) will help constrain emissions in future modeling efforts. Comparison between measured (PTR-LIT) and base modeled total MT concentrations ranged from agreement to an under-prediction of up to a factor of ∼ 3; however, these values were still within the range of measured variability (Fig.…”
Section: Bvoc Emissions and Concentrationssupporting
confidence: 60%
“…BVOC emission rates are difficult to determine due to tree-to-tree variability and sampling challenges during branch enclosure BVOC emission measurements (e.g., Ortega et al, 2008), leading to large ranges in estimated production rates, shown here to result in a range of oxidation product concentrations. In the future, improved canopy-level flux measurements of speciated monoterpenes, such as those recently completed by Edwards et al (2012) at UMBS, will improve modeling of individual monoterpenes at the canopy level.…”
Section: Discussionmentioning
confidence: 99%
“…The ∼23 m forest includes bigtooth aspen, quaking aspen, and paper birch (upper canopy); white pine, red maple, and American beech (lower canopy); and red oak (both upper and lower canopy). , There are minimal sources of pollution in the surrounding area (median daytime [10:00–20:00 EDT] NO during PROPHET-AMOS was 23 ppt). There has been a long history of atmospheric measurements at the tower since its construction in 1997, including studies focusing on individual VOCs or classes of VOCs such as isoprene and monoterpenes, sesquiterpenes, oxygenated VOCs, , and organic nitrates. , …”
Section: Methodsmentioning
confidence: 99%
“…Observed fluxes for these species are also within the range of what has been observed previously at PROPHET. 14,21 We see in Figure S5 that the GEOS-Chem simulation underestimates isoprene emissions by 30−40% around mid-day, even after correcting the model temperature fields as described in Section 2.5. The decline from the midday peak also occurs too soon in the model, so that a much larger underestimate is seen in the afternoon.…”
Section: Most Detected Peaks Exhibitmentioning
confidence: 96%