Eddy correlation fluxes of trace gases using a tandem mass spectrometer

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Atmospheric Environment Vol. 32, No. 17, pp. 2887—2898, 1998 ( 1998 Published by Elsevier Science Ltd. All rights reserved Printed in Great Britain S1352-2310(98)00036-3 1352—2310/98 $19.00#0.00

EDDY CORRELATION FLUXES OF TRACE GASES USING A TANDEM MASS SPECTROMETER WILLIAM J. SHAW,*,- CHESTER W. SPICER‡ and DONALD V. KENNY‡ Battelle, Pacific Northwest Laboratories, PO Box 999, MS K9-30, Richland, WA 99352, USA ‡ Battelle Memorial Institute, Columbus, OH, USA

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(First received 2 July 1997 and in final form 16 December 1997. Published June 1998) Abstract—This paper describes a field evaluation of a tandem mass spectrometer (TAGA) for use in measuring turbulence fluxes of trace gases. Measurements were made over a two-day period in a fallow farm field west of Columbus, OH. The results show that for weakly adsorbing species such as acetone, the effective time constant for the TAGA is (0.1 s, and the device can be used for eddy correlation flux measurements with little need for corrections. The effective time constant is longer for adsorbing species, such as ammonia, but spectral corrections can permit flux estimates for these species as well. This paper presents the first reported measurements of fluxes of acetone, formic acid, and ammonia using the eddy correlation technique. ( 1998 Published by Elsevier Science Ltd. All rights reserved Key word index: Trace gas fluxes, eddy correlation, acetone flux, ammonia flux, formic acid flux, dry deposition.

the flux of trace chemical species in the atmosphere and f to present first observations of surface turbulent fluxes of acetone and other trace chemical species.

INTRODUCTION

One of the fundamental measurements made in studies of meteorological or atmospheric chemical processes near the Earth’s surface is the turbulent transfer of momentum, heat, or trace species between the surface and the atmosphere. The generally preferred method of measuring these fluxes in the turbulent atmospheric boundary layer is the eddy correlation technique, which requires instrumentation capable of rapid response to fluctuations in the variable of interest. This has been a particularly difficult challenge for the measurement of trace chemical species. For the past several years we have operated a triple quadrupole mass spectrometer that is capable of rapid sensing of trace chemical species at high sensitivity. This instrument has proved to be applicable to numerous chemicals of atmospheric interest (Chapman et al., 1995; Spicer et al., 1994b, 1996). The instrument is known as the Trace Atmospheric Gas Analyzer (TAGA). Based on preliminary laboratory studies, this technique appears to have potential for use in eddy correlation measurements. As a consequence, we have carried out a small field measurement program to investigate the suitability of the TAGA for use in turbulence flux measurements. The purposes of this paper are f to provide an assessment of the TAGA’s potential as a detector suitable for measuring

* Author for correspondence.

BACKGROUND

There are numerous techniques for measuring turbulence fluxes between the Earth’s surface and the atmosphere. Direct measurements effectively fall into two categories: budget methods and the eddy correlation technique. Budget methods rely upon the construction of some sort of measurement ‘‘box’’. The transfer of mass through the walls and top of the box are measured together with the time rate of change of concentration within the box. The imbalance between the measured mass transfer and the change in concentration yields the surface flux. Examples of flux measurements using this approach range from largescale computations using radiosondes (e.g., Taylor et al. 1983; Bru¨mmer 1978; Holland and Rasmussen, 1973) to weighing lysimeters for water vapor flux (e.g. Brutsaert, 1982) to flux chambers that are placed directly on the ground (e.g. Cicerone and Shetter, 1981). In contrast to the budget method, which produces an area-averaged flux, the eddy correlation method produces a point measurement of the flux. In this approach, velocity and concentration variables are sampled rapidly (usually 10 s~1 or faster). The covariance between fluctuations of vertical velocity and concentration then yields a measure of the average mass flux for the species of interest.

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Because the theoretical framework for turbulent flow generally expresses turbulence fluxes in terms of covariances, eddy correlation is considered by many to be the preferred method for measuring the flux. Apart from the ergodic hypothesis that time (or in some cases spatial) averages can represent statistical ensemble averages, there are no assumptions inherent in the technique. Further, eddy correlation measurements can be made not only near the surface but also at any height in the atmosphere. The eddy correlation method has its own challenges. The most important of these is finding instruments that have a sufficiently fast response to measure all of the frequencies of turbulence fluctuations that significantly contribute to the overall flux. This problem was essentially solved for velocity and temperature fluctuations in the late 1960s [see summaries by Kaimal (1980) and Larsen et al. (1980)], a time that also marked the advent of data acquisition systems which were capable of managing the large volume of data generated by rapid sampling. The development of sensors for measuring turbulent fluctuations of water vapor also occurred in this period (Hay, 1980; Friehe, 1986). Progress has been much slower, however, in the development of capabilities for measuring turbulent fluctuations of other trace gases in the atmosphere. To date, only a few trace species besides water vapor are readily measured fast enough for eddy correlation calculations. These include ozone and nitrogen oxides (e.g. Eugster and Hesterberg, 1996; Stocker et al., 1995; Ridley et al., 1992; Wesely et al., 1982; Lenschow et al., 1981), carbon dioxide (e.g. Auble and Meyers, 1992), and carbon monoxide and methane (e.g. Ritter et al., 1990).

THE TRACE ATMOSPHERIC GAS ANALYZER (TAGA)

The Sciex Model 6000E Trace Atmospheric Gas Analyzer (TAGA) allows such fast measurements to be made. The TAGA is a tandem, or triple quadrupole, mass spectrometer that can be used to identify unknown chemicals by analysis of the fragments (daughter ions) produced by collisionally activated dissociation (CAD) of the parent ion of the unknown compound. It can also be used for highly sensitive and specific continuous monitoring of chemicals in air, as required for eddy correlation measurements. The mass spectrometer samples air directly into its inlet at atmospheric pressure. Trace contaminants in the air stream are ionized by a corona discharge at atmospheric pressure. This is a relatively gentle ionization process, so most chemicals form molecular ions rather than decomposing. The ionized molecules are electrically accelerated through a countercurrent flow of dry nitrogen gas toward a small orifice, where they are carried into the vacuum system. The first mass analyzer is normally operated as a mass filter, eliminating all but those ions of a specific mass of interest. The mass of interest is

selected to correspond to the molecular ion of a particular contaminant. For many compounds, high selectivity can be achieved through the use of the second and third quadrupoles. Ions passing through the first quadrupole are accelerated into the second quadrupole, where they are intercepted by a beam of neutral argon atoms. Collision with argon results in fragmentation of the ions in a predictable manner characteristic of their molecular structure. Fragments resulting from the molecular ion of interest are then sorted out by the third quadrupole mass analyzer.

CONCEPTUAL FRAMEWORK FOR ANALYSIS

The ideal method for evaluating the performance of any measurement system is to compare its output with that from an accepted standard. Our aim, however, is to use the TAGA to measure trace species at sufficiently high frequencies to perform eddy correlation flux measurements. Since there are no standard instruments with high-frequency response for any of the species that the TAGA can measure, other means of evaluation must be used. The conceptual framework that will guide our analysis derives from ideas of similarity theory, specifically Monin—Obukhov similarity theory, as it applies to the atmospheric surface layer. Monin—Obukhov theory is the foundation of current understanding of surface layer turbulence and is discussed in numerous texts (e.g. Sorbjan, 1989; Stull, 1988; Lumley and Panofsky, 1964). The basic notion is that the proper selection of scaling parameters for geometrical coordinates and statistical quantities involving velocity and scalar variables leads to universal functions describing the statistical quantities (which include spectral quantities discussed below). A key requirement of Monin—Obukhov theory is that the turbulence is statistically horizontally homogeneous. Under these conditions, passive scalar variables, which include temperature, moisture, and trace chemical species whose reaction times are longer than turbulence time scales (Vila`-Guerau de Arellano et al., 1995), are transported identically by turbulent eddies. It is this aspect of turbulent transfer that will allow us to infer the performance of the TAGA by comparing its time series and statistics with, in our case, the well understood virtual temperature measured by an acoustic anemometer.

THE FIELD MEASUREMENT PROGRAME

Field experiments were performed at Battelle’s facilities near West Jefferson, OH, approximately 25 km west of Columbus. The experiment was performed during the period 6—14 June 1995 near the eastern side of a large field that was not under cultivation at the time of the experiment. The vegetation

Eddy correlation fluxes of trace gases

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mirror dew point hygrometer were also mounted on the tower around the 5.0 m level. The signals from these instruments and the TAGA were recorded at a rate of 10 samples per second by a Science Engineering Associates (SEA) data acquisition system located in the mobile laboratory. The acoustic anemometer was mounted so that the positive x-direction was toward the west, the direction of climatological mean wind. Instruments mounted on the tower are listed in Table 1, together with the sampling rates for each. Some of the instruments were intended to provide a measure of the mean gradient of trace species; however, those data have yet to be analyzed, and we will not discuss them in this paper. ¹AGA sampling

Fig. 1. Map of Battelle’s West Jefferson, OH, facilities showing the location of the eddy correlation experiment.

covering the field was primarily Canadian thistle, aster, and timothy. A map of the site is shown in Fig. 1. Measurements were performed from a small meteorological tower erected on site for this experiment. The TAGA mass spectrometer and data acquisition system were housed in Battelle’s 40-foot mobile environmental laboratory. The mobile lab was positioned approximately 17 m east (climatologically downwind) of the tower. The tower was located approximately 615 m east of a state road, and about 800 m south of the Battelle Lake shown in Fig. 1. Although the prevailing winds in this area during the summer are from the west, wind directions ranged from northeast to northwest during the measurement period. All runs selected for analysis had a mean westerly wind component to ensure acceptably low flow distortion effects (e.g. Wyngaard, 1986) in the sonic anemometer data. The probe for the TAGA consisted of a 17.3 m Teflon tube (4 mm i.d.) which was mounted at the 5.0 m level of the tower. A carbon vane pump (GAST) was used to draw air through the tube, into the TAGA inlet, and into the ionization region. The flow rate was measured with a mass flow meter at the inlet end of the Teflon tube. Based on periodic flow measurements, the sample transfer time is estimated to be 0.5—0.7 s for these experiments. We will infer transfer times from the spectral analysis in subsequent sections of the paper that are in agreement with the times obtained from the flow meter. An acoustic anemometer, a krypton hygrometer, wet-bulb and dry-bulb thermistors, and a chilled-

We selected four target chemicals for this experiment: acetone, ammonia, formic acid, and nitric acid. These species represent a diverse challenge for the TAGA. The TAGA was operated in the positive ion mode for acetone and ammonia, and in the negative mode for formic acid and nitric acid. For the nitric acid measurement, we employed chloroform as a reagent in the ion source to generate a chlorine adduct of HNO . This procedure allowed us to monitor ad3 ducts with HNO of both chlorine isotopes 35Cl and 3 37Cl and has been shown to improve the specificity of the nitric acid measurement (Davidson et al., 1980; Spicer et al., 1994a). The TAGA was calibrated for acetone, ammonia, and formic acid using permeation tubes and a permeation tube calibrator (VICI Metronics). Nitric acid calibration made use of a nitric acid diffusion tube in a calibrated oven (Kintek). Baseline signals from the TAGA for zero concentrations of ammonia were established with a denuder tube coated with citric acid. For nitric acid and formic acid, the denuder tube was one that was coated with NaCO . Table 2 3 provides a summary of results of the laboratory calibrations and of the parent/daughter ion mass combinations used in the measurements.

RESULTS

¹ime series Earlier we noted that a requirement of Monin— Obukhov similarity theory is that turbulence fields, including those of scalar variables, are horizontally homogeneous. This is most likely to occur over relatively flat sites, such as the West Jefferson site, with widely distributed surface sources or sinks of trace species. Species with localized sources (e.g. auto exhaust emitted along a highway) will violate this requirement. For variables that meet the condition of horizontal homogeneity, however, a pair of time series will display a close correspondence in structure at all scales, even if the ground is a source for one and a sink for the other.

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Table 1. Suite of instrumentation deployed for field measurements at the West Jefferson site in Ohio Variable

Sensor

Wind components and virtual temperature (u, v, w, ¹ ) 7 Absolute humidity Fluctuating trace chemical species Mean trace chemical species Temperature Wet-bulb temperature

Dew point temperature Mean trace chemical species Temperature Wet-bulb temperature

Dew point temperature

Manufacturer

Sampling height (m)

Sampling interval

Acoustic anemometer

Applied Technologies, Inc.

5.0

0.1 s

Krypton hygrometer Teflon tube to TAGA (17.3 m, 4 mm o.d.) TAGA analysis of 30 min integrated sample Shielded, aspirated thermistor and bridge Wicked thermistor and bridge (also shielded and aspirated) Chilled mirror hygrometer TAGA analysis of 30 min integrated sample Shielded, aspirated thermistor and bridge Wicked thermistor and bridge (also shielded and aspirated) Chilled mirror hygrometer

Campbell Scientific, Inc. Sciex Corp.

4.7 5.0

0.1 s 0.1 s

Sciex Corp.

5.0

In-house

5.6

In-house

5.6

Campbell Scientific, Inc. Sciex Corp.

5.6 1.2

In-house

1.2

In-house

1.2

1s

Campbell Scientific, Inc.

1.2

1s

30 min (bag fill) 1s 1s

1s 30 min (bag fill) 1s

Note. Virtual temperature is a meteorological convention that accounts for water vapor in the equation of state so that the average molecular weight of air is treated as a constant. It differs only slightly from temperature.

Table 2. Ions used by the TAGA for detection of the target chemicals together with laboratory calibration results Trace gas

Parent/daughter ion mass (amu)

Sensitivity (icps/ppt)

Detection limit (ppt)

Precision (%)

Acetone Ammonia Nitric acid

59/31 18/98/62 (35Cl) 100/62 (37Cl) 45/-

39 12 0.99

52 330 300

2.0 2.7 10.6

30

—

Formic acid

48

Note. The absence of a second ionization mass indicates that the TAGA was operated in a single ionization mode.

Figure 2 shows time series of virtual temperature and acetone fluctuations for a five minute period from the morning of June 13. Acetone is the most weakly adsorbing chemical that we measured and is therefore likely to demonstrate the best response that is possible from the TAGA. The acetone time series is shown inverted to make it easier to compare the two. Several features are apparent. First, the same variations appear in both time series. The gray bar in each figure highlights the time of passage of a turbulent plume by the measurement tower, with its characteristic ramp structure (see, e.g. Kaimal and Businger, 1971). Inspection shows that this and other features with a time scale of tens of seconds correspond closely. Further inspection shows that many features lasting on the order of only a second also correspond closely. An example is indicated by the vertical arrow in each graph. The temperature ramps in Fig. 2 are a reflection of upward heat transfer by turbulence in the presence of a light wind. The fact that all of this

structure is inverted for acetone indicates that turbulence was at the same time transferring acetone downward. While there is much correspondence between the virtual temperature and acetone time series, it is also apparent that the acetone time series is noisier. This will be illustrated later in the paper through spectral analysis. Typical time series for turbulent fluctuations of other species are shown in Fig. 3. Figure 3a illustrates the behavior of formic acid compared with virtual temperature from the TAGA for a five-minute period. The signal-to-noise ratio for formic acid is lower than for acetone, but similar features can be found in both formic acid and ¹ for periods as short as a few 7 seconds. There is a clear positive correlation between the two time series. Figure 3b compares ammonia and virtual temperature for a different five-minute period. The character of the ammonia time series is markedly different from those for acetone and formic acid. There is an obvious correspondence between

Eddy correlation fluxes of trace gases

Fig. 2. Example time series of departures from the mean of virtual temperature from the sonic anemometer and acetone measured by the TAGA. Both time series show a ramp structure (section spanned by the gray bar) characteristic of turbulent plumes in the surface layer. They also correspond closely in details, an example of which is illustrated by the vertical arrow. The acetone time series has been inverted to facilitate comparison.

Fig. 3. Virtual temperature deviations from the mean compared with those for time series of other variables measured by the TAGA for this work. Nitric acid has been inverted to facilitate comparison with ¹ . Note 7 (a), (b), and (c) are not simultaneous. The bottom curve in each panel is virtual temperature.

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variations in ammonia and virtual temperature over periods of several tens of seconds, and the noise level in the ammonia signal is low. However, higher frequency features in the virtual temperature trace do not appear in the ammonia signal. Further, fluctuations in ammonia lag those in ¹ . Figure 3c com7 pares nitric acid with ¹ , where there is a negative 7 correlation between the two variables. While some features seem to correspond, other features seem unrelated (e.g. ¹ and the drift in nitric acid approximately 7 150 s into to the segment). There is noticeable highfrequency noise in the nitric acid time series. Power density spectra Power density spectra of acetone and virtual temperature are shown in Fig. 4a. These spectra have been computed via the Fast Fourier Transform (FFT) method after applying a high-pass filter to the data to reduce variance from low frequencies. The specific

filter used was a compound running mean subtraction filter that passed all of the signal variance for periods shorter than 10 min and less than 5% of the variance for periods longer than 30 min. Spikes were also removed from the data prior to spectral computations. The raw spectra were averaged to produce 100 equally spaced spectral estimates in log frequency. Each spectrum has also been normalized by dividing by the variance of its time series to facilitate the comparison between variables of different units. Inspection of Fig. 4a shows a clear correspondence between the spectra of acetone and ¹ to a frequency 7 of about 1 Hz. Above 1 Hz the noise floor is apparent in the acetone spectrum, while the much less noisy virtual temperature spectrum follows the !5/3 slope characteristic of the inertial subrange of turbulence spectra. While the spectra diverge at the higher frequencies, they also show that this is a range in which the natural fluctuations of turbulent scalar variables

Fig. 4. (a) Example power density spectra of acetone and virtual temperature, normalized for comparison. The overall shape and even the fine structure of the spectra are quite similar. Noise prevents the acetone spectrum from reaching the -5/3 slope characteristic of turbulence spectra at higher frequencies, but it does not cause a significant error in integrated turbulence statistics. (b) Power density spectra for ammonia and virtual temperature. The ammonia spectrum decays much faster at higher frequencies than the acetone spectrum in (a), reflecting its longer time constant.

Eddy correlation fluxes of trace gases

are relatively small. As a result, there is a negligible error in acetone variance measurements by the TAGA as a result of high-frequency noise. The power spectrum for ammonia, derived from the single run on 14 June, is shown in Fig. 4b. Because ammonia adsorbs on and desorbs from the walls of the inlet tubing and other surfaces in response to concentration fluctuations, we expected the frequency response of this species to be poorer than for acetone. The figure clearly shows that this is the case in the pronounced spectral rolloff at higher frequencies. Noise is also evident for this species at the highest frequencies, although its level is relatively low compared to the other species measured. This is consistent with the very smooth appearance of the ammonia time series in Fig. 3. Spectra for formic acid and nitric acid, calculated but not shown, were consistent with their time series shown in Fig. 3. Formic acid does not adsorb appreciably on system surfaces, and its spectra showed good high-frequency response comparable to that for acetone. Its noise level, however, was relatively high. Of the four species measured, nitric acid adsorbs most strongly on the system surfaces, and its spectrum showed poorest response. Its rolloff began at very low frequencies, and its signal reached the spectral noise floor at a frequency of about 0.2 Hz. ¹ime constants and delays A time constant is strictly appropriate only when describing a linear system characterized by a firstorder differential equation. It is not a priori obvious that the TAGA is such a system. However, if the behavior of the TAGA is approximately first-order linear, then an effective time constant can be calculated that gives an indication of the overall response of the TAGA system for each species. There are several ways to approach this calculation. One is to make laboratory measurements of the time response of the TAGA when a sudden change in concentration is introduced in the flow through the TAGA. However, it is difficult to generate a good approximation to a concentration step change in the flow, and it is not clear how well such a measurement will represent the TAGA response in the field environment. If we assume that the sonic anemometer has perfect frequency response in measuring virtual temperature, we can also measure an effective time constant by comparing spectral representations of the TAGA data with those from the anemometer. There are two ways to infer the effective time constant from the frequency domain. The first is to use the normalized ratio of TAGA spectra to virtual temperature spectra from the sonic anemometer as a measure of the TAGA transfer function. Because of the high-frequency noise in the TAGA data, however, these spectral ratios tend to be too distorted to be useful for some variables. A more effective technique is to use the frequency-dependent phase shift inherent in a system with a time constant.

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The phase shift / ( f ) due to a time constant (e.g. q Bendat and Piersol, 1986) is given by / ( f )"tan~1(!2nfq), and 0*/ ( f )'!n/2 q q for f*0 (1) and the phase shift / ( f ) due to a time delay *t can be T shown to be / ( f )"!2nf *t, and 0*/ ( f )'!R T q for f*0 (2) Here a negative phase shift implies a delay. These processes add linearly to produce a total phase shift from both a time constant and a time delay, that is /( f )"/ ( f )#/ ( f )"tan~1(!2nfq)!2nf *t. (3) q T The phase shift as a function of frequency is obtained from the inverse tangent of the ratio of the imaginary and real parts of the cross-spectrum (e.g. Bendat and Piersol, 1986) between the species of interest and virtual temperature. One can then use generally available non-linear curve-fitting routines to find q and *t. As a practical matter, the computed inverse tangent function returns a value between !n and n, but equation (3) shows that the phase function decreases linearly without bound for large values of frequency. In processing our data, we wrote an algorithm to detect sharp changes in phase and subtract appropriate multiples of 2n so that this unbounded decrease in phase was preserved. Example fits for each of the species are shown in Fig. 5. Each of the phase functions appears to be fairly well described by the time constant-time delay model. At low frequencies acetone and nitric acid are basically p out of phase with virtual temperature, while in the same frequency range formic acid and ammonia are in phase. Note also that the phase spectra for ammonia and nitric acid depart from 0 and !n, respectively, more rapidly as frequency increases than do the phase spectra for acetone and formic acid. This latter behavior corresponds to the larger time constants for the former species. Table 3 shows the time constants and intake delays calculated for each run using the method above. Cospectra for fluxes of heat and trace species The flux cospectrum provides an additional means to identify frequency-dependent behavior in a system that can lead to systematic errors in flux measurements. This is because the cospectrum, the form of which is well-established in micrometeorological measurements, reflects the phase relationship between two variables (e.g. vertical velocity and temperature) as well as their respective amplitudes as a function of frequency. Further, the integrated area under the cospectral curve is mathematically identical to the covariance between the two variables. In this paper the covariances represent fluxes of various scalars due to turbulence.

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Fig. 5. Example phase shifts (open circles) of the various species relative to virtual temperature. Also shown are fits (solid lines) to the data using the time constant-time delay model described in the text.

Table 3. Time constants, time delays, mean mixing ratios, and turbulence fluxes for each of the runs discussed in this paper Chemical species

Date and time Mean Effective of run mixing ratio time (lg kg~1) constant (s)

Acetone

13 13 13 13 14 13 13 14 13

Formic acid Ammonia Nitric acid

June June June June June June June June June

0915 1020 1520 1645 1030 0800 1130 0800 1410

499 560 547 462 406 680 731 1350 2.62

0.09 0.05 0.13 0.10 1.43 0.23 0.02 2.95 9.11

Intake delay (s)

Uncorrected flux (lg m~2 s~1)

0.46 0.48 0.45 0.45 0.44 0.41 0.49 0.62 0.63

!13.8 !16.1 !12.3 !6.82 !20.1 !3.71 2.00 75.7 !0.09

Corrected Percentage Flux underestimate of (lg m~2 s~1) uncorrected flux magnitude (%) !14.4 !16.3 !12.5 !6.93 !26.9 !3.44 2.50 145 —

3.6% 1.2% 1.5% 1.7% !25% !7.7% 20% 48% —

Note. The last column indicates the percentage by which the true flux is underestimated if no correction is made for the time constant.

Sample cospectra of vertical velocity with virtual temperature and with acetone are shown in Fig. 6a. The cospectra have been normalized so that the area under both curves is 1, and the acetone time series from the TAGA has been corrected for the measured time delay caused by the inlet tube. The correspondence of the two curves is heartening, especially at higher frequencies where statistical significance of each cospectral estimate has been increased by band averaging. Not only do individual features of the curves correspond, but there is also no apparent distortion of the acetone cospectrum at the highest frequencies. This is consistent with the small effective

time constant for the TAGA’s acetone measurement. It is also significant that the noise apparent in the acetone power density spectrum (Fig. 4) exerts a negligible influence on the acetone flux cospectrum. This is because the noise in the acetone signal is uncorrelated with the vertical velocity measured by the sonic anemometer. As a result, there is no systematic phase shift, and no cospectral distortion. This result clearly shows that the TAGA is fully capable of measuring turbulence fluxes of acetone via the eddy correlation method. The situation is different, however, for the species with longer time constants. The flux cospectrum for

Eddy correlation fluxes of trace gases

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Fig. 6. (a) Example cospectra of vertical velocity with virtual temperature and vertical velocity with acetone, normalized for comparison. The acetone cospectrum has been corrected for the time delay through the inlet tube, but not for time constant effects. Consistent with the small effective time constant for acetone, the general shape and the details of the two cospectra correspond closely over the entire range of frequencies measured. (b) Example cospectra of vertical velocity with virtual temperature and with ammonia. The loss of high-frequency response to ammonia is obvious at the higher frequencies.

ammonia and the associated temperature flux cospectrum are shown in Fig. 6b. In this case, the ammonia flux cospectrum rolls off much too rapidly beyond approximately 0.1 Hz. It is clear that the long time constant causes a serious underestimate of the ammonia flux at higher frequencies. At lower frequencies, though, the spectrum is well-behaved. This means that even with the loss of high-frequency response, the TAGA will at least get the sign of the flux right. A means of accounting for lost flux in the case of species that have longer time constants will be discussed below. Accounting for missing flux There are a number of methods by which ‘‘missing’’ flux can be accounted for in eddy correlation sensors. These generally require either accurate knowledge of the measurement system transfer function or a priori knowledge of the spectral characteristics of the vari-

able being measured. Because the TAGA response at the higher frequencies required for eddy correlation flux measurements depends on many factors (e.g. ‘‘stickiness’’ of the species of interest, length of inlet tubing), it is not currently practical to establish a general system transfer function. However, it is practical to determine the spectral characteristics a priori if sampling conditions are horizontally homogeneous. This is accomplished by invoking the same scalar similarity assumption that we have used to evaluate the TAGA in this paper. The method requires measuring a reference scalar, such as temperature or water vapor, with well understood, fast-response instrumentation simultaneously with the TAGA measurements. It is then assumed that, because of similarity, the ratio of the two resulting flux cospectra is the same at higher frequencies as at lower frequencies. To obtain a flux using this assumption, one integrates the cospectra for both the well measured scalar

W. J. SHAW et al.

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flux (temperature or water vapor) and the TAGA species flux over a band in which the TAGA response is nearly perfect. So far as the TAGA is well described by an effective time constant, an adequate band could be chosen as the decade of frequencies lower than about 0.1/2nq, where the effects of signal amplitude reduction and phase shifts are small. The required ratio is then

P

R"

fU

NP

fU

Co ( f ) df (4) wT fL where f and f are the upper and lower limits of the L U frequency decade. The flux of the chemical species measured by the TAGA is then fL

CP

Co ( f ) df wa

fU

P

D

= Co ( f ) df (5) wT 0 fL where we have taken advantage of cospectral symmetry about the origin. This method is similar to that described by Horst et al. (1997), although we have used the effective time constant only in setting the reference frequency band. The cospectra have been represented here as continuous functions. In practice, the computations are performed with the discrete Fourier transform, and the integration limits range from the fundamental frequency to the Nyquist frequency. Note that the faster the TAGA responds, the smaller is the part of the cospectrum that is affected by this procedure. w@a@"2

Co ( f ) df#R wa

Summary statistics Table 3 also shows the turbulence fluxes of the various species measured by the TAGA, both uncorrected (except for time delay) and corrected according to the method described above. Consistent with the generally small time constants for the acetone measurement, the uncorrected flux is within 5% of the corrected value. The exception is the last acetone run, for which the effective time constant was an order of magnitude larger than for all other acetone measurements. We do not currently have an explanation for this run. In general, though, the acetone data show that the inherent TAGA frequency response is fast enough to measure turbulence fluxes of trace chemical species with very little correction. The negative acetone fluxes indicate that the soil or the vegetation was a sink for acetone. The formic acid behavior is more irregular than that for acetone. While the corrected fluxes are not very different from the uncorrected values, it is notable that the correction procedure on the first run yields a flux magnitude that is smaller than the uncorrected value. This is inconsistent with the expected behavior for a time constant and may reflect a breakdown of the similarity assumption for this species. This would happen if the sources or sinks of formic acid are localized rather than spread over a broad area as the surface heating is. This possibility is further suggested by the fact that the sign of the formic

acid flux changes between 0800 and 1130 on the same day. Table 3 shows that the time constant for ammonia at nearly 3 s is significantly larger than for acetone or formic acid. Consistent with this value, the uncorrected flux misses nearly half of the turbulent transfer of ammonia. Nevertheless, the ammonia flux is suitable for correction with the method described above. The noise level in the ammonia signal is very low. Further, the surface is almost certainly a distributed ammonia source similar to that for heat because of the historical application of fertilizers to the farmland at the West Jefferson site. Therefore, the similarity assumption for this species is quite likely to be valid. The positive ammonia flux (away from the surface) is consistent with a surface source of ammonia due to fertilizer applications or organic decomposition. The time constant for a variable can be too long to apply the flux correction scheme that we have used. This apparently was the case for nitric acid, and no corrected flux is provided in Table 3. For a time constant of nearly 10 s, the band from which the correction ratio would be calculated is at such low frequencies that statistical uncertainties and the breakdown of similarity due to large non-turbulent contributions to the species time series invalidate the method. DISCUSSION

We have executed a small field measurement campaign to examine the suitability of the TAGA for the purpose of measuring the eddy correlation flux of trace gases with the following results: f We have used the concept of scalar similarity in turbulent transport to determine species-dependent effective time constants for the TAGA. These range from (0.1 s for weakly adsorbing acetone to several seconds or more for ammonia and nitric acid. f We have shown that the response of the TAGA for weakly adsorbing species is sufficient to capture 95% or more of the turbulent flux from a sampling configuration 5 m above the surface. f We have demonstrated how similarity arguments may be used to recover the part of the flux that is ‘‘lost’’ due to imperfect TAGA response. f We have reported what we believe are the first eddy correlation flux values of acetone, ammonia, and formic acid, finding that the soil or vegetation is a sink for acetone at our farm location and (not surprisingly) that ammonia is transferred away from the earth’s surface. The fundamental challenge for the TAGA in making eddy correlation measurements of the turbulent transfer of trace chemicals is the requirement to measure the high-frequency fluctuations of the concentrations of these species. The frequency-response requirements for sensors used in eddy correlation flux

Eddy correlation fluxes of trace gases

measurements vary depending on the height of measurement and the structure of the atmosphere. The time scale of the dominant eddies involved in turbulent transfer in the atmospheric surface layer (within the first hundred or so meters of the surface) is of the order z/º (e.g. Sorbjan, 1989), where z is the measurement height and º is the average wind speed at the measurement height. Therefore, a measurement close to the ground places a greater demand on frequency response than, say, a measurement on a tall tower. Thermodynamic stratification also plays a role, with turbulent transfer occurring at higher frequencies during nocturnal stable conditions than during the unstable conditions of a convective afternoon at the same wind speed. Under daytime convective conditions above the surface layer, when aircraft are used for sampling, time series are dominated by frequencies of the order z /º , where z is the depth of the turbu* ! * lent boundary layer, and º is the sampling speed of ! the aircraft. For typical surface layer measurements at a height of 5 m and a wind speed of 5 m s~1, the time scale of dominant eddies is of order 1 s. For a 1 km deep boundary layer and an aircraft sampling speed of 100 m s~1, the time scale is 10 s. While these two time scales cannot be precisely compared, this does indicate that the frequency-response requirements for airborne flux measurements above the surface layer are more relaxed than for measurements near the surface. As a result, the TAGA’s success in eddy correlation measurements near the ground indicates that it should also be successful in airborne use for measuring turbulence fluxes. Finally, our evaluation has depended on the availability of a well understood fast-response scalar sensor against which to compare the TAGA. Until TAGA characteristics for a particular species and measurement configuration are established and clearly stable, such a sensor should always be part of the flux measurement suite. In practice, this will probably be the case anyway, since heat (and generally water vapor) flux measurements are fundamental to any boundary layer measurement program.

CONCLUSION

We believe that the evaluation reported in this paper establishes the TAGA as a suitable instrument for providing previously unavailable eddy correlation fluxes of trace chemical species. The system can be used on the ground or flown in an aircraft. Thus it can be used to obtain high frequency trace gas measurements in support of studies addressing topics ranging from air quality to global climate change. Examples of compounds that are amenable to highly sensitive monitoring by the TAGA include dimethyl sulfide, carboxylic acids, molecular halogens, amines, inorganic acids, nicotine, many oxygenated organic species, and many pesticides.

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Acknowledgements—We are grateful to John M. Hubbe at Pacific Northwest Laboratories for his assistance in preparing equipment for the field and reading data tapes afterward and for his earlier analysis of TAGA data that suggested its response might be fast enough for eddy correlation work. We also thank Bob Plastridge for his assistance in the field. This work was carried out under the auspices of the internal research and development program of Battelle Memorial Institute’s Environmental Systems and Technology Division, for which we are grateful.

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