The dry component of total nitrogen and sulfur atmospheric deposition
remains uncertain. The lack of measurements of sufficient chemical
speciation and temporal extent make it difficult to develop accurate mass
budgets and sufficient process level detail is not available to improve
current air–surface exchange models. Over the past decade, significant
advances have been made in the development of continuous air sampling
measurement techniques, resulting with instruments of sufficient sensitivity
and temporal resolution to directly quantify air–surface exchange of
nitrogen and sulfur compounds. However, their applicability is generally
restricted to only one or a few of the compounds within the deposition
budget. Here, the performance of the Monitor for AeRosols and GAses in
ambient air (MARGA 2S), a commercially available online ion-chromatography-based analyzer is characterized for the first time as applied
for air–surface exchange measurements of HNO
Development of risk assessments and mitigation strategies such as critical
load frameworks (Burns et al., 2008) to protect ecosystems from nutrient and
acidic deposition requires accurate speciated deposition budgets of nitrogen
(N) and sulfur (S) compounds. In the United States, wet deposition has been
well characterized by the National Atmospheric Deposition Program (NADP).
The US Environmental Protection Agency's (US EPA's) Clean Air Status and
Trends Network (CASTNet) was established in 1991 to characterize temporal
and spatial trends in atmospheric concentrations and dry deposition of
select N and S compounds in rural locations. Air
concentrations of sulfur dioxide (SO
Over the past decade, significant advances have been made in the development
of continuous air sampling measurement techniques with sufficient
sensitivity and temporal resolution to directly quantify air–surface
exchange of N and S compounds. With respect to N, these
include bulk measurements of groups of compounds, such as fast
chemiluminescence with thermal conversion for total reactive nitrogen (
NH
The Monitor for AeRosols and GAses in ambient air (MARGA, Metrohm-Applikon, the Netherlands) is a commercially available online ion-chromatography-based analyzer that semi-continuously measures gases and soluble ions in aerosols (ten Brink et al., 2007; Makkonen et al., 2012; Rumsey et al., 2014) The MARGA is quasi-similar to the GRAEGOR system described by Thomas et al. (2009) and Wolff et al. (2010), which has been used for flux measurements. The major difference between the MARGA 2S and GRAEGOR systems is that the MARGA employs ion chromatography for analysis of both anions and cations whereas the GRAEGOR employs ion chromatography for anions and flow injection analysis for cations. The MARGA also employs mass flow control to regulate air sampling flow rates as opposed to control by critical orifice, as in the GRAEGOR. Another difference between the GRAEGOR and the MARGA that may influence the performance of the instruments is the integration of instrument control and chromatography in the MARGA software, which includes real-time instrument performance and data quality indicators for air and liquid flows, sample collection device conditions and chromatography. The performance of the GRAEGOR in measuring air–surface fluxes has been described by Thomas et al. (2009) and Wolff et al. (2010); however, there has been no evaluation of the performance of the MARGA in measuring air–surface fluxes. Furthermore, neither the Thomas et al. (2009) nor Wolff et al. (2010) studies assessed the performance of the GRAEGOR for S compounds in comparison to empirical gradient flux data.
In this study, the performance of the MARGA in measuring gradient flux of speciated N and S is evaluated and described for the first time. This study uses a MARGA 2S system, which is different from the MARGA 1S system described by Rumsey et al. (2014) in two key ways. First, the 2S system employs two sampling boxes interfaced to a single analytical system. The two sampling boxes in this case are positioned at two heights above the terrestrial surface to simultaneously measure the vertical concentration gradient. Second, the MARGA 2S, as configured for this work, draws the air sample through a much shorter length of tubing (30 cm) relative to the 1S configuration described by Rumsey et al. (2014).
The objective of this paper is to comprehensively evaluate and describe the
performance of the MARGA in the measurement for air–surface exchange
measurements of HNO
Measurements were conducted in an unfertilized 15 ha grass field in the
Blackwood Division of Duke Forest, Orange County, North Carolina, USA
(35.58
As previously mentioned, the MARGA is a commercially available online ion-chromatography-based analyzer that semi-continuously measures gases and
soluble ions in aerosols. The 2S version used in this study employs two
sampling boxes interfaced to a single analytical system. The two sampling
boxes (SB1 and SB2) are positioned at two heights above the surface to
measure simultaneous concentration gradients from which the vertical
chemical fluxes are calculated. Air is sampled through a short length (30 cm, 0.5
Each sample box contains a wet rotating denuder (WRD) and steam jet aerosol
collector (SJAC). The sample air first flows (as laminar flow) into the WRD
(Wyers et al., 1993; Keuken et al., 1998), which rotates continuously so that
the walls of the denuder are coated with absorption solution (double
de-ionized water with 10 ppm hydrogen peroxide), ensuring that the
gases diffuse into the liquid film. The level of the bulk liquid within the
WRD is kept constant using a level sensor and pump connected to the
absorbance solution. Particles pass through the WRD and are collected
directly downstream in the SJAC (Khlystov et al., 1995). Within the SJAC, a
supersaturated environment is created in which particles grow by
deliquescence, allowing them to be collected by inertial separation. The
supersaturated environment is created using a temperature-controlled steamer
continuously supplied with absorbance solution. Air is drawn through the WRD
and SJAC at 16.7 L min
Software integrated within the MARGA calculates atmospheric concentrations based on air sample flow rate, syringe speed during injection (relatively constant) and ion concentrations (corrected for internal standard) in the collected solutions. These results, as well as the anion and cation chromatograms and various hardware parameters, are recorded by the MARGA software.
To verify the analytical accuracy of the MARGA as controlled by the internal
LiBr standard and to assess potential contamination in the liquid solutions
and in the liquid flow path of the MARGA system, an experiment was conducted
during field deployment using a liquid blank and four liquid external
standards with different concentrations. Furthermore, the relationship
between the expected and observed external standard concentrations as well
as the blank concentrations were used to adjust the raw concentration data
prior to flux calculations. Both the blanks and the external standards
experiments were conducted with the air pumps disconnected and denuder
inlets sealed. A blank was assessed using the absorption solution for a
period over 24 h. The external standard test was conducted by replacing
the absorption solution with a known liquid standard containing
SO
It is acknowledged that the liquid external standards used to determine
accuracy do not take into account all uncertainties associated with the
MARGA measurement system. In this study, it is assumed that the performance
of the WRD and SJAC for collecting gases and aerosols is similar to that
reported by Keuken et al. (1988), Wyers et al. (1993) and Khylstov et al. (1995),
respectively. The inlet associated with the MARGA sampling system
may also affect measurement accuracy, particularly for “sticky” gases such
as NH
The detection limit of an analytical instrument is defined as the lowest
concentration that can be determined to be statistically different from a
blank at a certain level of statistical confidence. In this study, the MARGA
detection limit is calculated using a method from Currie (1999):
The detection limit was determined by combining data from all analytical channels (in this study, there are four different channels including denuder and SJAC samples from both sample boxes) into a single data set. From this single data set, the standard deviation and number of analyses are used to determine the detection limit. However, using this approach means that the standard deviation may reflect a combination of random error plus systematic error between channels. To investigate this possibility, the detection limit methodology was conducted in conjunction with the Dunn's test (Dunn, 1964) and the Brown–Forsythe test (Brown and Forsythe, 1974) to compare differences across channels. Additional information on the detection limit methodology, which includes descriptions of the Dunn's test and Brown–Forsythe test methodologies, is provided in Sect. S2.3.2 in the Supplement.
When quantifying the detection limit using an external standard, the aim is
to use a concentration that is slightly above the detection limit as
variance may increase with increasing concentration. Therefore an
appropriate external standard level was selected for each compound. In
addition, two different solutions used to determine SO
Air–surface exchange fluxes were quantified using the AGM. The AGM is based on the application of Fick's Law to turbulent
diffusion, which relates the flux of heat, mass and momentum to the
vertical gradient and turbulent transfer coefficient (eddy diffusivity) of
the particular scalar of interest, in this case air concentration (
AGM fluxes were calculated from hourly average concentration gradients and
hourly EC momentum and sensible heat fluxes measured above the canopy. EC
momentum and sensible heat fluxes were measured with a sonic anemometer
(R.M. Young model 81000V, Traverse City, MI) placed approximately 2.6 m
above the ground. EC fluxes were calculated offline from 10 hz data using
SAS (SAS Institute, Cary, NC) software following standard approaches for EC.
Hourly average concentration gradients were based on adjusted air
concentration data. Air concentration measurements were adjusted using the
internal LiBr standard, external liquid standard
calibrations and air flow quality control (QC) checks. Air flow rates were
independently measured at the denuder inlet at least weekly using a National
Institute of Standards and Technology (NIST)-traceable primary standard
(Bios DryCal DC-Lite flowmeter, Mesa Laboratories, Inc., Lakewood, CO). If
the measured airflow rate was > 5 % different from the target
airflow rate of 16.7 L min
The flux uncertainty
The uncertainty of the gradient concentration is also quantified during
co-location tests. Again, the concentrations from the two sample boxes are
regressed against each using a slope and intercept from orthogonal least
squares regression. The residuals of the regression represent the random
error of the concentration difference between the two boxes. The standard
deviation of the residuals provides a measure of the precision of the
denuder and SJAC measurements for individual analytes, which also represents
the precision of the concentration gradient (
Air–surface exchange fluxes and their associated concentration gradient uncertainty and flux uncertainty were determined over 3-week representative period (23 September–14 October 2012) at the sampling site.
A variety of meteorological parameters and surface characteristics were measured during sampling. The influence of these factors on air–surface exchange flux will be examined in the companion paper. In this paper, the meteorological parameters, wind speed, air temperature and global radiation will be presented to provide basic information on meteorological conditions during the 3-week representative period. Wind speed and air temperature were measured using the sonic anemometer (R.M. Young model 81000V, Traverse City, MI) at a height of 2.6 m. Global radiation was measured using the REBS Q7.1 net radiometer (Campbell Scientific, Logan, UT). Other surface characteristics reported in this paper include canopy height, which was measured manually and leaf area index (LAI). Single-sided LAI was measured by destructive (prior to canopy closure) and optical methods (LICOR model LAI-2000, LICOR Biosciences, Lincoln, NE)
In the following analysis, results from liquid standard tests are expressed
as equivalent air concentration unless otherwise noted. Also, though liquid
standards obviously only contain dissolved ionic forms (i.e.,
NO
For the sulfur compounds (SO
Detection limit (liquid and air equivalent) results incorporating data from all four channels for each analyte.
A summary of the detection limit analysis for each analyte is provided in
Table 1. Detection limits in Table 1 were determined by incorporating data
from all four channels for each analyte. Calculated detection limits were
low for all compounds ranging from (in equivalent air concentration) 0.020
As previously described, the concentration gradient precision, which can
also be defined as the gradient detection limit is the standard deviation of
the residuals of the orthogonal least squares regression of SB1 (
Summary of colocation results.
Results of the orthogonal least squares analysis by colocation period are
summarized in Table S6 provided in the Supplement. Slopes are
within
Results of the combined colocation experiments are summarized in Table 2. In
general, concentrations during the three experiments were low, < 0.65
When comparing gradient detection limits, it is important to consider the
relationship between concentration gradient precision and concentration. As
discussed by Wolff et al. (2010), for some species the standard deviation of
the orthogonal least squares residuals tends to increase with air
concentration. Thus, the gradient detection limit varies with air
concentration. The relationship between gradient detection limit and air
concentration observed during our experiments is provided in Fig. S4. For this analysis, orthogonal least squares
residuals were combined for the three colocation experiments and sorted into
seven bins defined by air concentration (see Fig. S5). Within each bin, which individually
contained
Of 504 possible hourly observations (during a period of 3 weeks), there were
Over the period of fluxes analyzed, air temperatures generally ranged from 5
to 10
As mentioned, the concentrations used to determine concentrations gradients and thus fluxes were adjusted for the systematic difference between concentration measurements during co-location sampling. An analysis of the influence of the co-location concentration adjustment on calculated fluxes using the 3 weeks of flux values presented in this manuscript as an example is provided in Sect. S3.2.2b in the Supplement.
Individually, percentages of hourly chemical concentration gradients larger
than the corresponding gradient detection limit were 86, 42, 82,
72, 74 and 69 % for NH
Diurnal profiles of uncertainty in chemical concentration
gradients and transfer velocity expressed as a percentage of the
corresponding concentration gradient (
Summary of flux error (Eq.
Patterns of flux uncertainty are summarized in Figs. 1 and 2. Overall
uncertainty in the transfer velocity (
Total flux uncertainty is summarized in Fig. 2. When both day and night
periods are considered, median total flux uncertainties are 31, 121,
42, 43, 67 and 56 % for NH
This paper presents for the first time an assessment of the performance of a
MARGA 2S instrument as applied for the measurement of air–surface exchange
of N and S compounds. Analytical detection limits were low for
all compounds ranging from 0.02 g m
While the characteristics of the analytical system reported here should be generally applicable to the MARGA 2S, the assessment of gradient precision and flux uncertainty will vary to some extent for different meteorological and atmospheric chemical conditions, though not dramatically. Overall, we find that the flux uncertainties are similar to other wet chemical methods and that the instrument is sufficiently precise for flux gradient applications. It is recommended that colocation experiments be conducted regularly for long-term deployments (e.g., monthly) or for each short-term intensive deployment to properly account not only for any short-term systematic bias that may develop between the two sample boxes but also to assess the relationship between concentration gradient precision and concentration. A companion paper focusing on the air–surface exchange processes of individual compounds over a longer period of study at our site is forthcoming.
We would like to acknowledge Rob Proost (Metrohm Applikon) and Rene Otjes (Energy Research Center, the Netherlands) for technical assistance as well as Aleksandra Djurkovic (EPA) and David Kirchgessner (EPA) for laboratory and field support. We also acknowledge Gary Lear (EPA) and Gregory Beachley (EPA) for helpful discussions regarding the MARGA instrument. This manuscript has been reviewed by EPA and approved for publication. Mention of trade names does not constitute endorsement or recommendation of a commercial product by US EPA. Edited by: G. Phillips