Abstract
Abstract. Closed-path eddy-covariance (EC) systems are used to monitor exchanges of trace gases (e.g., carbon dioxide [CO2], water vapor [H2O], nitrous oxide and methane) between the atmosphere and biosphere. Traditional EC-intake systems are equipped with inline filters to prevent airborne dust particulate from contaminating the optical windows of the sample cell which causes measurement degradation. The inline filter should have a fine pore size (1 to 20 µm is common) to adequately protect the optics and a large filtration surface area to extend the time before it clogs. However, the filter must also have minimal internal volume to preserve good frequency response. This paper reports test results of the field performance of an EC system (EC155, Campbell Scientific, Inc., Logan Utah, USA) with a prototype vortex intake replacing the inline filter of a traditional EC system. The vortex-intake design is based on fluid dynamics theory. An air sample is drawn into the vortex chamber, where it spins in a vortex flow. The initially homogenous flow is separated when particle momentum forces heavier particles to the periphery of the chamber, leaving a much cleaner airstream at the center. Clean air (75 % of total flow) is drawn from the center of the vortex chamber, through a tube, to the sample cell where it is exposed to the optical windows of the gas analyzer. The remaining 25 % of the flow carries the heavier dust particles away through a separate bypass tube. An EC155 system measured CO2 and H2O fluxes in two urban-forest ecosystems in the megalopolis of Beijing, China. These sites present a challenge for EC measurements because of the generally poor air quality which has high concentrations of suspended particulate. The closed-path EC system with vortex intake significantly reduced maintenance requirements by preserving optical signal strength and sample-cell pressure within acceptable ranges for much longer periods. The system with vortex intake also maintained an excellent frequency response. For example, at the Badaling site, the amount of system downtime attributed solely to clogged filters was reduced from 26 % with traditional inline filters to 0 % with the prototype vortex intake. The use of a vortex intake could extend the geographical applicability of the EC technique in ecology and allow investigators to acquire more accurate and continuous measurements of trace-gas fluxes in a wider range of ecosystems.
Highlights
Eddy-covariance (EC) technology provides an opportunity to evaluate the fluxes of energy, momentum, water vapor, carbon dioxide and other scalars between the earth’s surface and the turbulent atmosphere (Aubinet et al, 2016; Baldocchi, 2003; Montgomery, 1948)
The normalized cospectra for both systems were consistent at all frequencies, with no significant difference (P > 0.05); the frequency response of the EC155 sampling system with either the inline filter or a vortex intake could not be distinguished from that of the sonic anemometer
The corresponding cutoff frequencies for the sonic anemometer are 2 to 10 Hz (Massman, 2000), which bracket the EC155 cutoff frequencies measured in the laboratory (5.1 and 4.3 Hz for the inline filter and vortex intake, respectively, Burgon et al, 2016)
Summary
Eddy-covariance (EC) technology provides an opportunity to evaluate the fluxes of energy, momentum, water vapor, carbon dioxide and other scalars between the earth’s surface and the turbulent atmosphere (Aubinet et al, 2016; Baldocchi, 2003; Montgomery, 1948). J. Ma et al.: Measuring carbon dioxide and water fluxes of ecosystems because of polluted air that contaminates the optical windows of the gas analyzer. Optical signal strength is reduced and gas concentration measurements degrade as dust and debris are deposited on the optical windows of the analyzer. This problem occurs in both open-path and closed-path systems. Using intakes with inline filters in closed-path systems can help keep the analyzer’s windows free of debris for a longer time. Dirty sample air can contaminate other parts of the EC system, leading to underestimated fluxes and data gaps (Jia et al, 2013; Xie et al, 2015)
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