Introduction
Gold amalgamation preconcentration, followed by thermal desorption (TD) in
Ar carrier gas and detection via atomic fluorescence spectrometry (AFS), is a
commonly used method for quantifying atmospheric elemental mercury vapor,
Hg0(g) (hereafter referred to as gaseous elemental mercury, GEM)
(Schroeder et al., 1995; Gustin and Jaffe, 2010; Pandy et al., 2011). Coupled
with various sample capture and pretreatment methods, the above measurement
scheme is also used for quantitative analysis of atmospheric gaseous oxidized
mercury (GOM) (Stratton and Lindberg, 1995; Landis et al., 2002; Lyman et
al., 2007), total gaseous mercury (TGM ≡ GEM + GOM) (Ambrose et
al., 2015), atmospheric particle-bound mercury (PBM) (Landis et al., 2002),
atmospheric total mercury (THg ≡ GEM + GOM + PBM) (Jaffe et
al., 2005), and total aqueous Hg (USEPA, 2002). Most AFS-based atmospheric Hg
measurements employ Tekran® Instruments
Corporation's model 2537 Hg vapor analyzers (versions A and B; hereafter
referred to collectively as “the Tekran®
analyzer”) (Schroeder et al., 1995; Landis et al., 2002; Tekran Corporation,
2006, 2007).
Several previous reports identified analytical uncertainties as critical in
limiting scientific understanding of environmental Hg cycling (Jaffe and
Gustin, 2010; Pirrone et al., 2013; Jaffe et al., 2014). An often overlooked
source of analytical uncertainty is the method by which the raw Hg atomic
fluorescence (AF) signal is processed by the
Tekran® analyzer. Although most researchers
rely on the Tekran® analyzer's embedded
software to automatically integrate the Hg TD peaks, Swartzendruber et
al. (2009) and Slemr et al. (2016) demonstrated that the accuracy and
precision of the Tekran® peak integration
method can significantly decline at low Hg loadings.
To better characterize the analytical uncertainty associated with atmospheric Hg
measurements made with the Tekran® analyzer, and
in an attempt to improve upon existing measurement methods for atmospheric
Hg, I developed new software-based methods for offline processing of the raw
Hg AF signal from the Tekran® analyzer. Here I
describe the key features of the new signal processing methods and
characterize their performances, together with that of the standard
Tekran® signal processing method.
Experimental
Using National Instruments™ LabVIEW software (version
12.0), I developed a virtual instrument (VI) for offline processing of the
serial data output from Tekran® models 2537A
and 2537B mercury vapor analyzers. The VI is characterized and validated
using data collected as part of the Nitrogen, Oxidants, Mercury, and Aerosol
Distributions, Sources, and Sinks (NOMADSS) campaign
(https://www.eol.ucar.edu/field_projects/nomadss) using a
Tekran® 2537A instrument and a 2537B
instrument incorporated in the University of Washington's Detector for
Oxidized Hg Species (Lyman and Jaffe, 2012; Ambrose et al., 2013, 2015;
Ambrose, 2017). The instrument configurations are described in Ambrose et
al. (2015). Instrument operating parameters are given in Table S1 in the
Supplement. The most significant difference between the two instruments
tested is that the 2537B instrument was modified to improve its
signal-to-noise ratio. The instrument's sample cuvette and detector bandpass
filter were replaced with a mirrored cuvette and an improved bandpass filter,
respectively (Ambrose et al., 2013). The cuvette and bandpass filter were
obtained from Tekran® (part numbers
40-25105-00 and 40-25200-02, respectively).
All linear regression equations reported herein were calculated by the
bisquare method (unweighted) in LabVIEW. All stated uncertainties represent
95 % confidence intervals, unless otherwise specified.
Virtual instrument design overview
Operation
The VI's Hg atomic fluorescence signal processing method parses the
Tekran® analyzer's serial data output
(RAWDUMP format; see Sect. S1 in the Supplement) using text delimiters
associated with each component (Figs. S1 and S2 in the Supplement).
At the start of an analysis, the VI computes the overall mean baseline
standard deviation, σbl, for all samples in the data file to
be analyzed. The baseline standard deviation for each sample is first
calculated as the 10-point (1 s) running mean of the baseline measurements
made from 1 s after the start of the AF signal recording interval to 2 s
prior to the approximate start of the Au trap desorption cycle (i.e., the
value of the “BL time” parameter in Table S1 in the Supplement). The value of σbl is then calculated from the values for all individual samples.
(a) Example Hg thermal desorption profile during a
calibration gas analysis cycle on a Tekran®
2537A instrument. Also shown is the corresponding 150-point exponential
bisquare (unweighted) regression fit (Eq. 1; r2=0.998) used to derive
the decay constant (b=-0.041±0.004 ds-1) during initialization
of the VI's signal processing method. (b) Comparison between the
calculated (fit) and observed Hg atomic fluorescence signal values in
panel (a). The slope and intercept of the linear fit are
0.921 ± 0.007 and 7.2 ± 0.8 mV, respectively (uncertainties are
95 % confidence intervals). (Analogous results obtained with a 2537B
instrument are shown in Fig. S4.)
For each sample analysis cycle, the VI carries out the following operations:
The “raw data” string (Fig. S1) is
converted to an array of 10 Hz AF signal values.
The timestamp, cycle-type flag, and trap identity are extracted from the “final data” string
(Fig. S1).
An x–y plot is generated from the data array created in
step 1.
Unless the VI is set to automatically define the Hg thermal
desorption peaks, the user is prompted to identify the start time,
tstart, of the TD peak. This selection is accomplished by first
manually setting the placement of a cursor on the x–y plot generated in
step 3 and then using a control to extract the associated coordinates.
Alternatively, values of tstart can be set to values defined
automatically during the initialization procedure (see below). The mean
baseline voltage at the start of the TD peak is calculated over the interval
from tstart to tstart-9 ds (n=10 data points).
The coordinates of the Hg TD peak maximum are identified automatically
from the data array created in step 1. In my experience, the
Tekran® analyzer's baseline generally
decreases (slopes downward) across the Hg TD peak. I parameterized the VI for
such a condition by identifying the TD peak maximum as the largest AF signal
value recorded after tstart.
A preliminary TD peak height value is calculated from the mean baseline
voltage at the start of the peak (step 4) and the maximum voltage (step 5).
In cases when the preliminary peak height is a negative number, the VI sets
the value to σbl. (See Sect. S3 for further details.)
Based on user-defined settings on the VI's front panel, the TD peak end
coordinates are either selected manually (as for the peak start time in step
4) or calculated automatically from the peak maximum in step 5 and the
initialization parameters (as described below).
The baseline beneath the Hg TD peak is calculated. For this purpose, the
baseline at the beginning of the peak is defined by the coordinates of
tstart (step 4) and the preceding nine AF signal values. The
baseline at the peak end is similarly defined by the peak end coordinates in step
7 and the trailing nine AF signal values. The baseline coordinates beneath
the peak are calculated by linear regression (n=20 data points).
The baseline standard deviation at the Hg TD peak is estimated from the
mean residual of the regression in step 8 (see Sect. S4 for further details).
I denote this value as σbl,fit to differentiate it from
σbl defined above.
The baseline-slope-corrected Hg TD peak height is calculated from the
peak maximum voltage (step 5) and the calculated baseline voltage at the
time of the peak maximum (step 8).
At the end of an analysis, an output data file is created, which contains,
among other parameters, the baseline-slope-corrected Hg TD peak height (step
10) for each sample in the data file that was processed.
Hereafter I refer to three different configurations of the VI:
VIm,m when the peak start and end times are both identified
manually, VIm,a when only the peak start time is identified
manually, and VIa,a when the peak start and end times are both
identified automatically. I abbreviate the Tekran®
peak integration method as “the Tekran®
method”.
Test dataset collected with a
Tekran® 2537A instrument, represented as Hg
loadings derived from my VI-based manual, semiautomated, and automated Hg
thermal desorption peak height determination methods (the VIm,m,
VIm,a, and VIa,a methods, respectively) and by the
Tekran® method. Peaks not detected by the
Tekran® method are assigned a value of
0.01 pg. The two pairs of data points at > 100 pg correspond with
calibration gas analysis cycles (SPAN samples). I use the first pair of SPAN
samples to initialize the VI (as described in Sect. 2.2). I use response
factors calculated from the second pair of SPAN samples (and the preceding
pair of blanks) to calculate Hg loadings for all other samples in the
dataset. The mean value of the baseline standard deviation,
σbl (defined in Sect. 2.1), is ∼ 0.03 mV (equal to
∼ 0.03 pg). The corresponding estimated lower-limit f value (mean
±2σ; Sect. 2.3) is 1.86(1)×10-4.
Initialization
The VIm,a and VIa,a methods are “initialized” by fitting
Eq. (1) to the Hg thermal desorption peaks recorded for a pair of calibration gas
analysis cycles (i.e., one A SPAN and one B SPAN; example fits are shown in
Fig. 1 and in Fig. S4; see Sect. S1 for further details on cycle-type flags).
S(t)=A×eb×t+Soffset
Here, S(t) is the time-dependent 10 Hz Hg fluorescence signal along the
tail of the Hg TD peak, A is the peak amplitude (which is approximately
equal to the peak height, H), b is the decay constant, Soffset
is the baseline offset (i.e., the difference between lim[S(t)]t→∞ and zero), and t is expressed in units of deciseconds (ds). The
fit includes 150 S(t) values, starting with the peak maximum, Smax
(Figs. 1 and S4). The values of Soffset and A are constrained to
the baseline minimum, Smin, and Smax-Soffset,
respectively. (For the data shown in Fig. 1, the values of Soffset
and A are approximately equal to 70 and 170 mV, respectively.)
The two decay constants (one for each Au trap) are the key parameters derived
from the initialization procedure (see Sect. 2.3). I estimate the uncertainty
in each b value from the linear sum of two terms: (1) the relative
difference between unity and the slope of a linear regression fit to a plot
of the calculated versus measured Hg TD peak decays (Figs. 1b and S4b) and
(2) the 95 % confidence interval in the slope of the fit. For the data
shown in Fig. 1, the uncertainty in b is estimated to be 9 %.
During initialization, peak start times, tstart, are also
calculated for the pair of calibration gas analysis cycles. For this purpose,
the VI defines tstart as the first 10 Hz Hg fluorescence signal
value in a series of seven consecutively increasing signal values. If the
operator chooses (via a control on the VI's front panel) to automatically
define the Hg TD peak start times, the values of tstart calculated
during initialization are assigned (paired by Au trap identity) to all
samples in the data file to be processed.
Automatic determination of the Hg thermal desorption peak end
time
It is necessary to define each Hg thermal desorption peak's end time,
tend, within the interval during which the
Tekran® analyzer's Hg atomic fluorescence
signal is recorded (38.9 s in Figs. 1a and S4a). Therefore, for each sample
the VI defines tend as the time at which S(t) decays to a value
equal to, or less than, a fraction, f, of the amplitude, ASPAN
(derived as in Sect. 2.2), determined from a calibration gas analysis cycle
on the same Au trap:
Stend≤f×ASPAN+Soffset.
Substituting the right-hand side of Eq. (2) for S(t) in Eq. (1) and
solving for t(=tend) yields an analytical expression for the
upper-bound value of tend:
tend=lnf×ASPANAi×b-1≅lnf×ASPANHi×b-1.
Here, Ai and Hi are the TD peak amplitude and the initial
peak height (from step 6 in Sect. 2.1), respectively, of the sample for which
the baseline-slope-corrected peak height is to be quantified. The partial
equality in Eq. (3) reflects the facts that ASPAN/Ai≅HSPAN/Hi and ASPAN≅HSPAN. The
Au-trap-dependent value of the decay constant, b, is derived as in
Sect. 2.2.
(a) Comparison of Hg loadings derived from measurements
made with a Tekran® 2537A instrument using
the Tekran® method and my VI-based Hg thermal
desorption peak height determination method (dataset shown in Fig. 2,
excluding SPANs),
with the peaks defined manually (the VIm,m
method). The equation of the linear regression is y=1.00(1)x-0.45(8) pg (r2=0.997, n=74). The fit excludes data derived from
peaks not detected by the Tekran® method
(represented by the open symbols). (b) Absolute and relative biases
in the Tekran®-derived Hg loadings, based on
the fit in panel (a). The grey bands represent propagated
uncertainties (95 % confidence intervals) in the fit parameters.
(c) Distribution of residuals from panel (a), including
only data derived from detected peaks. (Analogous results for the 2537B
instrument are presented in Fig. S5.)
Bias in Hg loadings derived by applying automated and semiautomated
Hg atomic fluorescence signal processing methods to measurements made with a
Tekran® 2537A instrument.
Method
Hg (pg, ng m-3)1,2
7.5, 1.5
3.75, 0.75
2.5, 0.5
1.25, 0.25
0.5, 0.1
0.25, 0.05
0.125, 0.025
Bias (%)
Tekran®3
-6±2
-12±2
-18±3
-36±6
-1006
-1006
-1006
VIa,a4
0.1 ± 0.2
0.1 ± 0.2
0.1 ± 0.3
0.2 ± 0.5
0.3 ± 1.2
0.6 ± 2.3
1 ± 5
VIm,a5
0.15 ± 0.08
0.2 ± 0.1
0.3 ± 0.1
0.5 ± 0.3
1.0 ± 0.6
2 ± 1
4 ± 2
1 Bias values for the Tekran®
and VIa,a methods are calculated from the equations of the linear
regressions in Figs. 3a and 5a, respectively. Bias values for the
VIm,a method are similarly calculated from the linear regression
equation given in Table S4 (“standard” configuration). All bias values are
expressed relative to Hg loadings derived by processing the data using manual
peak definition (the VIm,m method); 2 Hg loadings are also
expressed in terms of concentrations under the typical
Tekran® operating parameters;
3 Tekran® operating and peak integration
parameters are defined in Table S1; 4 my VI-based peak height
determination method, with peak start and end times determined automatically
(VIa,a); 5 my VI-based peak height determination method, with
peak start times determined manually and peak end times determined
automatically (VIm,a); 6 for Hg < the estimated 0.8 pg
Tekran® limit of detection, the true bias is
-100 %. For clarity, the true bias is substituted for the calculated
values.
The value of f is chosen such that tend is ≥ 0 at the
smallest expected value of Hi, which the VI approximates as
σbl (Sect. 2.1). The upper-bound value of f is estimated
separately for each Au trap by solving Eq. (3) for f, with tend=0 and Hi=σbl:
f=σblASPAN.
The Au-trap-dependent f values are then averaged prior to application in
Eq. (3). I estimate the uncertainty in f to be twice the standard deviation
in the mean for the two Au traps. Uncertainty in the value of Hi is
estimated to be equal to twice the baseline standard deviation calculated in
step 9 of Sect. 2.1. Uncertainty in the automatically derived value of
tend, δtend, is estimated by propagating
uncertainties in b, f, and Hi through Eq. (3) (see below).
It is necessary to further constrain tend such that for large TD
peaks (e.g., those recorded for SPAN samples) the automatically determined
value of tend is at least 10 ds before the upper-bound time,
tn, of the interval during which the instrument's Hg AF signal is
recorded. The VI therefore defines tend as the smaller of the
results of Eq. (3) and tn-10, where n represents the number of AF
signal values recorded (n=389 in Figs. 1a and S4a). A minimum value of
10 ds is also prescribed for tend such that for very small TD
peaks the automatically determined peak end does not occur at or before the
peak max time (e.g., when f≥Hi/ASPAN in Eq. 3). (See
Sect. S3 for further details.)
Evaluation
I evaluate the performances of the VIm,a and VIa,a
methods by applying both methods to laboratory data collected with two
Tekran® analyzers (one model 2537A and one
model 2537B). One dataset is processed for each analyzer. The test dataset
collected with the 2537A instrument is shown in Fig. 2. (The test dataset for
the 2537B instrument is shown in Fig. S3.)
Comparison of Hg fluorescence baselines calculated by applying
manual (VIm,m method), automated
(Tekran® and VIa,a methods), and
semiautomated (VIm,a method) Hg thermal desorption peak definition
methods to samples with Hg loadings of approximately (a) 0.5,
(b) 1, (c) 2, and (d) 4 times the limit of
detection of the Tekran® method
(∼ 0.8 pg). Measurements were made with a 2537A instrument. Biases in
the Hg loadings derived from the Tekran®
method and the VI-based methods are indicated. Biases are expressed relative
to the loadings derived from the VIm,m method and are negative when
the VIm,m-based loadings are higher. The
Tekran® baselines are missing from
panels (a) and (b) because the peaks are not detected by
the Tekran® method.
I consider the manual definition of the Hg thermal desorption peaks (the
VIm,m method) to be the benchmark for signal processing accuracy, and I
assess the accuracies of the VIm,a and VIa,a methods by comparing
Hg sample loadings derived from both methods with loadings derived from the
VIm,m method. A similar comparison is used to evaluate the accuracy of
the Tekran® method. The performances of all
methods are further evaluated and compared based on the Hg limits of
detection (LODs) they achieve.
Results and discussion: performance evaluations
The Tekran® Hg thermal desorption peak
integration method
For Hg loadings derived from the Tekran®
method, HgTekran, I define absolute bias as HgTekran-Hgbenchmark, where Hgbenchmark represents
loadings derived from the VIm,m method. I define relative bias as
100×(HgTekran-Hgbenchmark)/Hgbenchmark. As illustrated in Fig. 3 (and Fig. S5), Hg
loadings derived using the Tekran® method
tend to be biased low, with the relative bias becoming more negative with
decreasing loading. I present in Table 1 relative bias values from Fig. 3b at
several discrete Hg sample loadings (analogous results for the 2537B dataset
are presented in Table S2). The corresponding Hg concentrations
(ng m-3) under typical operating conditions for the
Tekran® analyzer (5 L sample volumes) are
also shown. My results are consistent with those of Swartzendruber et
al. (2009) and Slemr et al. (2016) but also demonstrate that the
Tekran® method can produce significant low
biases (≥ 5 %; see Tables 1 and S2) at tropospheric background GEM
and THg concentrations (∼ 1 to 2 ng m-3; sample loadings of
5–10 pg under typical Tekran® operating
conditions).
To further characterize the performance of the
Tekran® method, Fig. 4 compares Hg atomic
fluorescence baselines calculated by the
Tekran® method and by my VI-based peak height
determination methods for samples with Hg loadings of approximately 0.5, 1,
2, and 4 times the estimated ∼ 0.8 pg LOD achieved with the
Tekran® method (see below). (See Sect. S6 for
details on how I reproduced the baselines calculated by the
Tekran® method.) Figure 4 illustrates the
tendency of the Tekran® method to truncate
the Hg thermal desorption peaks. The peaks tend to become more severely
truncated as they become smaller, and as a result, the relative biases in the
corresponding Hg loadings tend to become more negative as the loadings
decrease, as shown in Figs. 3b and S5b (see also Fig. 2 in Slemr et al.,
2016).
It is possible that the bias introduced by the
Tekran® method can be made smaller by
calibrating at loadings more similar to the loadings in the samples of
interest. The measurements in Figs. 3a and S5a are calibrated with SPAN
loadings > 10 times higher (see Figs. 2 and S3, respectively). Calibrating
the measurements in Fig. S5a using the external SPANs (Fig. S3; loadings
> 5 times higher) yields the linear regression equation (as in Fig. S5a)
y=0.96(1)x-0.09(5) pg. The slope of the latter equation is closer to
unity (though not significantly) than that of the equation derived from
Fig. S5a.
Estimated signal processing uncertainties and Hg limits of
detection (LODs) achieved with the Tekran® peak
integration method and my VI-based peak height determination methods as
applied to measurements made with Tekran® 2537A
and 2537B instruments.
Method1
Signal processing
Hg LOD (pg)
uncertainty2
2537A dataset3
Tekran®
±[1 % + 1.2 pg]
0.804
VIm,m
±0.053 pg
0.105
VIa,a
±[0.2 % + 0.053 pg]
0.125
VIm,a
±[0.2 % + 0.053 pg]
0.105
2537B dataset6
Tekran®
±[6 % + 0.21 pg]
0.204
VIm,m
±0.030 pg
0.0857
VIa,a
±[2 % + 0.080 pg]
0.137
VIm,a
±[0.6 % + 0.030 pg]
0.107
1 The Tekran® method is
the Tekran® analyzer's internal automated Hg
thermal desorption peak integration method, parameterized as indicated in
Table S1. The VIm,m, VIa,a, and
VIm,a Hg TD peak height determination methods were developed in
this work and are described in Sect. 2. The operating parameters of the
Tekran® analyzers are presented in Table S1;
2 estimated as described in Sect. 3.4; 3 the 2537A dataset is shown in
Fig. 2; 4 estimated as the highest Hg loading derived from the
VIm,m method for samples for which the
Tekran® method failed to detect the Hg TD
peak; 5 estimated as twice the standard deviation of blank loadings (n=62). 6 The 2537B dataset is shown in Fig. S3. 7 Estimated as twice the
standard deviation of blank loadings (n=37).
Table 2 presents Hg LODs for the Hg fluorescence signal processing methods
and datasets I tested. The nominal Hg limit of detection of the
Tekran® analyzer is 0.5 pg (see Sect. S7 for
further details). However, some Hg thermal desorption peaks in the 2537A
dataset are undetected by the Tekran® method
for Hg loadings ≤ 0.8 pg (Figs. 2 and 3a). My results suggest that the
actual Hg LOD achieved with the Tekran®
method is ∼ 60 % higher than the nominal value. By comparison, the
LOD achieved with the VIm,m method (estimated as twice the standard
deviation of blank samples, n=62) is 0.10 pg.
For the 2537B dataset, all Hg thermal desorption peaks are detected by the
Tekran® method for Hg loadings > 0.2 pg
(Figs. S3 and S5), suggesting the LOD is ∼ 60 % lower than the
nominal value. The lower Hg LOD achieved with the
Tekran® method when applied to the 2537B
dataset is attributable to the hardware modifications that were made to the
2537B instrument to increase its signal-to-noise ratio (Sect. 2). The
0.085 pg LOD achieved with the VIm,m method is 56 % lower than
the Tekran®-based LOD (Table 2).
The automated VI-based Hg thermal desorption peak height
determination method
By comparison with the Tekran® method, the
VIa,a method usually identifies the Hg thermal desorption peak
baseline with good accuracy, even at Hg loadings near to or below the LOD
achieved with the Tekran® method (Fig. 4). As
a result, most Hg loadings derived from the VIa,a method are quite
accurate (Figs. 2 and 5; Table 1). For samples with Hg loadings below the
estimated 0.8 pg LOD of the Tekran® method,
the mean absolute and relative unsigned biases in the loadings derived from
the VIa,a method are 0.028 ± 0.005 pg and
15 ± 3 %, respectively (n=78). The biases are very small and
much smaller in magnitude than those for the
Tekran® method (estimated at -0.80 pg and
-100 %). Based on the equations of the linear regressions shown in
Figs. 3a and 5a (Hg loadings ≤ 10 pg), the VIa,a method
achieves ≥ 94 % reduction in absolute unsigned bias in calculated
Hg loadings when compared with the Tekran®
method.
(a) Comparison of Hg loadings derived from measurements
made with a Tekran® 2537A instrument using my
VI-based automated and manual peak height determination methods (the
VIa,a and VIm,m methods, respectively; dataset shown in
Fig. 2, excluding SPANs). The equation of the linear regression is y=1.001(1)x+0.000(6) pg (r2=0.99992, n=152).
(b) Absolute and relative biases in the VI-based Hg loadings, based
on the fit in panel (a); grey bands represent propagated
uncertainties (95 % confidence intervals) in the parameters of the fit in
panel (a). (c) Distribution of residuals from
panel (a).
The Hg LOD achieved with the VIa,a method is 0.12 pg (estimated as
twice the standard deviation of blank values; n=62). That value is
20 % higher than the LOD achieved with the VIm,m method but
85 % lower than the LOD achieved with the
Tekran® method (Table 2). Similarly, the
width of the residual distribution in Fig. 5c is 78 % narrower than that
in Fig. 3c, which reflects the improved analytical precision achieved with
the VIa,a method in comparison with the
Tekran® method.
Evaluation of the VIa,a method as in Fig. 5 for the 2537B dataset
(Fig. S6, Table S2) yields the linear regression equation y=1.015(2)x-0.017(8) pg (r2=0.9998, n=132). For samples with Hg loadings
below 0.8 pg, the observed mean absolute and relative unsigned biases in the
loadings derived from the VIa,a method are 0.031 ± 0.007 pg
and 33 ± 9 %, respectively (n=41). The VIa,a method
yields a larger relative bias when applied to the 2537B dataset than when
applied to the 2537A dataset. However, absolute unsigned biases are
equivalent (at the 95 % confidence interval) for the two datasets. Based
on the equations of the linear regressions shown in Figs. S5a and S6a, the
VIa,a method achieves ≥ 82 % reduction in absolute
unsigned bias in calculated Hg loadings when compared with the
Tekran® method. The Hg LOD is 0.14 pg
(Table 2), which is 59 % higher than the value achieved with the
VIm,m method but 31 % lower than the value achieved with the
Tekran® method. The VIa,a method
appears to yield a comparable improvement in analytical precision over the
Tekran® method when applied to the 2537A and
2537B datasets (the width of the residual distribution in Fig. S6c is
72 % narrower than that in Fig. S5c).
The semiautomated VI-based peak height determination method
The VIa,a method poorly identifies the start of the Hg thermal
desorption peak for some blank samples with the lowest Hg loadings. As a
result, the Hg TD peak height and the calculated Hg loading tend to be
underestimated for those samples (Figs. 2 and S3).
I developed the VIm,a method as a compromise between the Hg TD peak
definition accuracy achieved with the VIm,m method and the data
processing speed achieved with the VIa,a method. (Peak height
determination for a single sample requires from < 1 s with the
VIa,a method to several seconds for the VIm,m method; the
data processing time for the VIm,a method is approximately half
that for the VIm,m method.) Comparison of the VIm,a and
VIa,a results in Fig. 2 (and Fig. S3) shows that defining
tstart manually instead of automatically yields more accurate
measurements for samples with the lowest Hg loadings.
Evaluation of the VIm,a method as in Fig. 5 yields the linear
regression equation y=1.001(1)x+0.004(3) pg (r2=0.99998, n=152). Comparison between the latter equation and that of the regression in
Fig. 5a suggests that biases in Hg loadings derived from the VIa,a
and VIm,a methods are not significantly different over the range of
loadings in the 2537A dataset (see also Table 1). However, biases in Hg
loadings derived from the VIm,a method are lower at low loadings
than those determined for the VIa,a method (Sect. 3.2): for samples
with Hg loadings below 0.8 pg, the mean absolute and relative unsigned
biases in the loadings derived from the VIm,a method are
0.012 ± 0.003 pg and 6 ± 1 %, respectively (n=78). The
estimated 0.10 pg Hg LOD achieved with the VIm,a method is
equivalent to that achieved with the VIm,m method
(Table 2).
The VIm,a method performs consistently better for the 2537B dataset
than does the VIa,a method. The equation of the linear regression
(as in Fig. 5a) is y=1.005(1)x+0.009(5) pg (r2=0.99994, n=132). For samples with Hg loadings below 0.8 pg, the observed mean absolute
and relative unsigned biases in the loadings derived from the VIm,a
method are 0.010 ± 0.003 pg and 6 ± 2 %, respectively (n=41). The estimated LOD achieved with the VIm,a method is 0.10 pg
and falls between the LODs achieved with the VIm,m and
VIa,a methods (Table 2).
Sensitivity analyses and uncertainties
To test the sensitivity to initialization parameters of Hg loadings derived
from the VIa,a and VIm,a methods, I recalculated those
loadings after making a series of modifications to the method initialization
parameters. After each modification I recalculated the parameters of the Hg
regression (e.g., Fig. 5a). The modifications tested for the 2537A dataset
(described further in Tables S3 and S4) include shifts in tend by
±δtend (as defined in Sect. 2.3), shifts in
tstart to the values determined (as described in Sect. 2.2) for the
second pair of SPAN samples in Fig. 2 (applicable only to the VIa,a
method), and initialization of the VI using the second pair of SPAN samples.
(Details on the sensitivity tests applied to the 2537B dataset are described
in Tables S5 and S6.)
The above modifications result in insignificant changes (at the 95 %
confidence interval) to the bias parameters derived (as in Fig. 5a) for the
VIa,a method and the 2537 dataset (Table S3). Two sensitivity tests
applied to the VIa,a method and the 2537B dataset result in
significant changes (at the 95 % confidence interval) to the calculated
bias parameters (Table S5). Results obtained with the VIa,a method
therefore appear to be sensitive to initialization parameters in some cases,
although signal processing uncertainties remain quite low and well below
those estimated for the Tekran® method
(Table 2). It is possible that a large shift in tstart for SPAN
samples over the course of an analysis would increase the sensitivity of the
VIa,a method to initialization parameters. Results obtained with
the VIm,a method are insensitive (at the 95 % confidence
interval) to initialization parameters (Tables S4 and S6).
I estimate signal processing uncertainties in the Hg thermal desorption
peak heights derived from the VIm,m method to be equal to twice the
mean baseline standard deviation (σbl, Sect. 2.1),
corresponding with a Hg loading of 0.053 pg for the dataset in Fig. 2
(0.030 pg for the dataset in Fig. S3). I estimate signal processing
uncertainties in the Hg loadings derived from the
Tekran®, VIa,a, and VIm,a
methods as the sum of the biases in those methods (derived as in Fig. 3) and
the resulting increase (relative to the VIm,m method) in the Hg
limit of detection (Table 2). For the VIa,a and VIm,a
methods, I estimate a conservative threshold uncertainty value of
±2σbl.
Signal processing uncertainty attributable to my VIa,a method, as
applied to the 2537A dataset, is estimated to be within
±[0.2 % + 0.053 pg]. (The first term in the latter expression
represents the slope of the regression in Fig. 5a; the second term represents
2σbl, which in this case is larger than the sum of the
0.000 ± 0.006 pg intercept of the fit in Fig. 5a and the 0.02 pg
difference in Hg LODs achieved with the VIa,a and VIm,m
methods). Estimated signal processing uncertainty attributable to the
VIm,a method is also within ±[0.2 % + 0.053 pg]. By
comparison, signal processing uncertainty attributable to the
Tekran® method is within
±[1 % + 1.2 pg]. The above uncertainty results, together with
estimated Hg LODs and analogous results for the 2537B dataset, are summarized
in Table 2. For both test datasets, signal processing uncertainties estimated
for my VI-based methods are significantly lower than those for the
Tekran® method. Signal processing uncertainty
ranks as follows for the VI-based methods: VIm,m< VIm,a ≤ VIa,a.
Conclusions and implications
I describe three improved methods for processing the raw Hg atomic
fluorescence signal from Tekran® 2537A and
2537B Hg vapor analyzers. The methods incorporate manual, semiautomated, or
fully automated Hg thermal desorption peak identification processes. I
implement my methods through a virtual instrument in National
Instruments™ LabVIEW and evaluate them,
together with the Tekran® internal Hg TD peak
integration method, using test datasets from two
Tekran® instruments (one 2537A and one
2537B).
Consistent with previous work (Swartzendruber et al., 2009; Slemr et al.,
2016), my results demonstrate that Hg loadings derived from the
Tekran® method tend to be biased low, with
the relative bias becoming more negative with decreasing loading. It follows
that the magnitude of the bias in
Tekran®-based Hg measurements will depend
significantly on sampling conditions (e.g., sample concentration and volume).
Therefore, I recommend that signal processing bias be examined, and
associated uncertainties be quantified, in all future applications of the
Tekran® instruments, regardless of sampling
arrangement.
With respect to atmospheric GEM and THg measurements, my results demonstrate
that the Tekran® method can produce
significant low biases (≥ 5 %) at background concentrations
(∼ 1 to 2 ng m-3) under typical operating conditions (Hg
loadings of 5–10 pg). My results therefore indicate that post-processing
the raw Tekran® data can yield significant
improvements in the accuracy of the derived Hg concentrations under much
broader environmental conditions than previously recognized. Such conditions
should not be assumed to be limited to those where GEM concentrations can be
significantly depleted, as can occur in the free troposphere and lower
stratosphere (e.g., Talbot et al., 2007; Lyman and Jaffe, 2012; Timonen et
al., 2013; Gratz et al., 2015), as well as at the surface in polar and midlatitude
regions under special photochemical conditions (e.g., Schroeder et al., 1998;
Obrist et al., 2011).
Most measurements of atmospheric GOM and PBM made with the
Tekran® 1130/1135 Hg speciation system (Landis et al., 2002) may be significantly biased low. For instance,
typical median GOM and PBM concentrations measured at 21 Atmospheric
Mercury Network (AMNet) sites in the United States and Canada during the
years 2009–2011 were in the ranges 1.2–2.5 and 2.5–5.0 pg m-3,
respectively (Gay et al., 2013). The corresponding Hg loadings are 1.4–3.0 and 3.0–6.0 pg.
The median signal processing bias, estimated as 100×(HgTekran-Hgbenchmark)/HgTekran, would be within -39 and -19 %,
respectively, based on the fit in Fig. 3a, and within -17 and -12 %,
respectively, based on the fit in Fig. S5a. Similarly, median concentrations
of GOM and/or PBM measured at 10 sites in Canada during the years 2002–2011
were typically < 5 pg m-3 (Cole et al., 2014), corresponding with
sample loadings of 3–9 pg. The corresponding median signal processing bias
would be within -19 and -8 %, respectively, based on the fit in
Fig. 3a, and within -12 and -8 %, respectively, based on the fit in
Fig. S5a.
Signal processing uncertainties can represent a significant fraction of the
overall uncertainty in Tekran®-based
atmospheric Hg measurements. I estimate that signal processing uncertainties
in Hg loadings derived from the Tekran®
method are within ±[1 % + 1.2 pg] for the 2537A dataset and
within ±[6 % + 0.21 pg] for the 2537B dataset. By comparison,
non-signal-processing-related uncertainties in
Tekran®-based GEM, TGM, and THg measurements
were previously estimated to be on the order of ∼ 10 to 15 % for
loadings typically < 10 pg (Ambrose et al., 2011, 2013, 2015).
Quantification of signal processing uncertainties in future applications of
the Tekran® 2537 instrument will
substantially improve the quality of the resulting measurements.
My results suggest that the performance of the
Tekran® method may be improved with hardware
modifications that increase the instrument's signal-to-noise ratio. The
Tekran® method performs better at low Hg
loading when applied to the 2537B dataset than when applied to the 2537A
dataset. The primary difference between the 2537A and 2537B instruments I
tested is that the 2537B instrument was modified to improve its
signal-to-noise ratio by replacing the sample cuvette and detector bandpass
filter with a mirrored cuvette and an improved filter, respectively (Ambrose
et al., 2013). It is possible that the performance of the
Tekran® method can also be improved through
modification of the method's integration parameters, though I tested only the
default parameters (Table S1). Additionally, it is possible that measurement
bias introduced by the Tekran® method can be
made smaller by calibrating at loadings more similar to the loadings in the
samples of interest. My results demonstrate a minor reduction in bias when
measurements made with the 2537B instrument are calibrated at loadings that
are > 5 times higher rather than at loadings > 10 times higher.
Measurement bias and precision can in principle both be improved by employing
longer sample preconcentration times and/or higher sample flow rates to
achieve higher sample loadings.
Estimated signal processing uncertainties in Hg loadings derived from my
methods range from within ±0.053 pg when the Hg thermal desorption peaks
are defined manually to within ±[0.2 % + 0.053 pg] (2537A
dataset) and ±[2 % + 0.080 pg] (2537B dataset) when Hg TD peak
definition is fully automated. Biases in Hg loadings derived from my methods
are lower by > 80 % than biases derived from the
Tekran® method. Limits of detection for Hg
decrease by 31 to 88 % when my methods are used in place of the
Tekran® method.