Subscriber access provided by University of Newcastle, Australia
Article
Uncertainty Assessment of Gaseous Oxidized Mercury Measurements Collected by Atmospheric Mercury Network Irene Cheng, and Leiming Zhang Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b04926 • Publication Date (Web): 06 Dec 2016 Downloaded from http://pubs.acs.org on December 14, 2016
Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.
Environmental Science & Technology is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.
Page 1 of 30
Environmental Science & Technology
Uncertainty Assessment of Gaseous Oxidized Mercury Measurements Collected by Atmospheric Mercury Network Irene Cheng* and Leiming Zhang* Air Quality Research Division, Science and Technology Branch, Environment and Climate Change Canada, Toronto, Ontario M3H 5T4 Canada *Corresponding authors: Irene Cheng Air Quality Research Division Science and Technology Branch Environment and Climate Change Canada 4905 Dufferin Street Toronto, Ontario M3H 5T4 Canada Tel: 1 416-739-4455; Fax: 1 416-739-4281 Email:
[email protected] Leiming Zhang Air Quality Research Division Science and Technology Branch Environment and Climate Change Canada 4905 Dufferin Street Toronto, Ontario M3H 5T4 Canada Tel: 1 416-739-5734; Fax: 1 416-739-4281 Email:
[email protected] ACS Paragon Plus Environment
Environmental Science & Technology
1
ABSTRACT
2
Gaseous oxidized mercury (GOM) measurement uncertainties undoubtedly impact the
3
understanding of mercury biogeochemical cycling; however, there is a lack of consensus on the
4
uncertainty magnitude. The numerical method presented in this study provides an alternative
5
means of estimating the uncertainties of previous GOM measurements. Weekly GOM in
6
ambient air was predicted from measured weekly mercury wet deposition using a scavenging
7
ratio approach, and compared against field measurements of 2-4 hourly GOM to estimate the
8
measurement biases of the Tekran speciation instruments at thirteen Atmospheric Mercury
9
Network (AMNet) sites. Multi-year average GOM measurements were estimated to be biased
10
low by more than a factor of 2 at six sites, between a factor of 1.5 and 1.8 at six other sites, and
11
below a factor of 1.3 at one site. The differences between predicted and observed were
12
significantly larger during summer than other seasons potentially because of higher ozone
13
concentrations that may interfere with GOM sampling. The analysis of multi-year data collected
14
at multiple sites provides a consensus of the systematic bias in GOM measurements, suggesting
15
the need for further development of new measurement technologies and identifying the chemical
16
composition of GOM.
17
2 ACS Paragon Plus Environment
Page 2 of 30
Page 3 of 30
Environmental Science & Technology
18
1. INTRODUCTION
19
Accurate measurements of gaseous oxidized mercury are indispensable to the study of mercury
20
cycling in the environment, including identifying mercury transformation mechanisms, modeling
21
its dry deposition, and evaluating mercury chemical transport models. The Tekran speciation
22
system (Tekran® Instruments Corporation) is a widely used set of automated instruments for
23
measuring three operationally-defined forms of atmospheric mercury, including gaseous
24
elemental mercury (GEM), gaseous oxidized mercury (GOM) and particle-bound mercury in fine
25
particles (< 2.5µm) (PBM), at high temporal resolution. The instruments have been deployed
26
around the world by researchers and large-scale mercury monitoring networks, such as the
27
Atmospheric Mercury Network (AMNet)1,2 and Global Mercury Observation System3,4. Despite
28
the prevalent use of the Tekran speciation system, the accuracy of GOM measurements remains
29
a very polarizing issue; however, researchers increasingly acknowledge that GOM and PBM
30
have large measurement uncertainties5. This is because the exact chemical compositions of
31
GOM and PBM are unknown and have not been quantified which hinders the development of
32
calibration standards, and recent studies show there are potential sampling artifacts and
33
discrepancies in GOM concentrations among various measurement methods5-9.
34
Studies suggest elevated ozone can cause a positive artifact on PBM measurements10 and loss of
35
GOM11. Water vapor can also interfere with GOM measurements and cause denuders to become
36
passivated10,12,13. There may be chemical reactions between atmospheric mercury and other
37
gases and aerosols during sampling that can result in GOM loss or positive artifacts on PBM6,14.
38
However, this may only be an issue during long sampling duration (12 h)14. The mechanisms
39
causing the aforementioned issues are also not well established. More recently, it was revealed
40
that denuders do not capture all forms of GOM15, contradictory to an earlier study demonstrating 3 ACS Paragon Plus Environment
Environmental Science & Technology
41
high collection efficiency of GOM by denuders16. Other measurement techniques, such as mist
42
chambers, nylon and cation exchange membranes, and Detector for Oxidized Hg, were capable
43
of collecting more GOM than KCl-denuders6,15,17,18. However similar to the Tekran instrument,
44
these alternative methods are not immune to sampling artifacts caused by high water vapor15,18
45
and other gases and aerosols17. Studies suggest the temperature settings of the Tekran instrument
46
favour GOM deposition in the sampling lines and gas-particle partitioning and evaporative losses
47
of PBM within the instrument6,7,11,19,20. Slemr et al.21 also reported that the Tekran Hg analyzer
48
was unable to quantify small signal to noise peaks resulting in very low or non-detectable total
49
gaseous mercury concentrations.
50
With the growing number of studies raising issues regarding GOM and PBM uncertainties and
51
their potential consequences on analysis and modeling of mercury, further studies are needed to
52
determine the magnitude of the GOM and PBM uncertainties, improve the instrumentation
53
detection of Hg species, and develop calibration technologies. Current estimates of GOM
54
measurement uncertainties are a factor of 1.3-5.06,15,18 and up to a factor of 127 based on
55
intercomparisons of various sampling and analysis methods. However, consensus has yet to be
56
reached on the uncertainty magnitude. In this study, an indirect numerical modeling approach
57
was developed to quantify the uncertainties of Tekran measurements of GOM at multiple
58
AMNet sites, taking advantage of the large amount of speciated data that have been made
59
available in the past several years (2009-2014). This dataset is longer than those collected from
60
previous intercomparison studies of different measurement techniques, which typically span over
61
a one month period. The approach predicts GOM concentrations from Hg wet deposition since
62
Hg in precipitation originates from GOM and PBM, and a previous study has shown a strong
63
empirical relationship between atmospheric oxidized mercury and mercury wet deposition22.
4 ACS Paragon Plus Environment
Page 4 of 30
Page 5 of 30
Environmental Science & Technology
64
Predicted GOM are then compared to observed GOM to estimate the potential measurement
65
biases.
66
2. METHODS
67
2.1 Site and data description
68
GOM concentrations were predicted at 13 AMNet sites (co-located instruments at MD98/99)
69
located in the U.S. and Canada (Table 1). Ambient monitoring data from 2009-2014 near the
70
AMNet sites were obtained from various networks (Table S1 of the SI). GEM, GOM and PBM
71
measured using the Tekran speciation system and mercury wet deposition fluxes were obtained
72
from the AMNet and Mercury Deposition Network, respectively 23,24. Trace gases and inorganic
73
ions in air and precipitation were obtained from the Clean Air Status and Trends Network
74
(CASTNET25), National Trends Network26, and the Canadian Atmospheric and Precipitation
75
Monitoring Network (CAPMoN27) for the NS01 site (Sect. 1 of SI). PM2.5 and PM10
76
concentrations were obtained from AirData28 and CAPMoN. Meteorological data and ground-
77
level ozone were obtained from CASTNET and CAPMoN. Due to the different sampling
78
intervals of the networks, the datasets were averaged to a weekly interval. The datasets were
79
also averaged to monthly intervals to examine differences between weekly and monthly data
80
resolution and to estimate the method uncertainties. The data was quality controlled according to
81
the criteria in Tables S2 and S3.
82
2.2 Determination of the HNO3 scavenging ratio
83
The premise behind the method for predicting GOM is based on the knowledge of the mercury
84
wet deposition and assumptions on GOM and PBM wet scavenging efficiencies. The wet
85
scavenging of GOM and PBM were quantified using the scavenging ratios of other pollutants. 5 ACS Paragon Plus Environment
Environmental Science & Technology
Page 6 of 30
86
Scavenging ratios (equation (1)) are typically determined for particulate pollutants, whereas
87
scavenging ratios of gaseous pollutants are rare because the total pollutant (dissolved +
88
particulate) concentration in precipitation is typically measured like in the case of mercury and
89
ammonium, nitrate and sulfate. In the absence of direct measurements, gaseous HNO3 was used
90
as a surrogate for the scavenging ratio of GOM based on the similarities in their physicochemical
91
properties. Both HNO3 and GOM are water soluble and easily scavenged by precipitation. Both
92
gases have low surface resistance and high dry deposition velocity (1-5 cm s-1)16; thus, GOM is
93
modeled like HNO3 in dry deposition models 29-32.
94
Since the scavenging ratio of HNO3 (WHNO3) cannot be determined directly from field
95
measurements, the approach for determining WHNO3 required calculating the scavenging ratios
96
(W) of the particulate-phase inorganic ions Ca2+, Mg2+, Na+, and K+ using equation (1).
97
W =
(1)
98
Cprec and Cair are the precipitation and air concentrations, respectively. The scavenging ratio of
99
coarse PM (WcPM) was determined by averaging WCa, WMg, and WNa since these ions dominate
100
the coarse PM fraction33. WK was used as a surrogate for the scavenging ratio of fine PM (WfPM)
101
for inland sites, whereas WK/2 was assumed for coastal sites (NS01, NY06) following the
102
methodology in Cheng et al.33
103
The wet scavenging of particulate nitrate (pNO3-) is then determined using equation (2):
104
[pNO3-] prec = WfPM [pNO3-] air Pf + WcPM [pNO3-] air (1-Pf)
(2)
105
WfPM and WcPM are the scavenging ratios of fine and coarse PM. [pNO3-]air is the NO3-air
106
concentration. Pf is the fine mass fraction of NO3-. Similar to the methodology in Cheng and 6 ACS Paragon Plus Environment
Page 7 of 30
Environmental Science & Technology
107
Zhang34, a Pf of 0.84 was assumed for the winter months (DJF), while 0.29 was used for all other
108
months since lower temperatures favor ammonium nitrate production in fine particles.
109
Equation (3) is subsequently used to determine WHNO3.
110
WHNO3 =
[ ] [ ]
=
[ ] [ ] [ ]
(3)
111
[Total NO3-]prec is the total NO3- precipitation concentration and [pNO3-]prec is from equation (2).
112
If [HNO3]prec< 0, it is assumed that only pNO3- contributed to total NO3- in precipitation and
113
WHNO3 is not determined.
114
2.3 Determination of GOM wet deposition and prediction of GOM
115
The detailed methodology for determining the wet deposition flux of GOM is described in Cheng
116
et al.33. A synopsis of the methodology is provided here. First, the wet deposition fluxes of fine
117
PBM and coarse PBM are determined using scavenging ratios, air concentrations of PBM, and
118
precipitation amount. The difference between total Hg wet deposition and the wet deposition
119
fluxes of fine and coarse PBM results in the wet deposition flux of GOM. Given the GOM wet
120
deposition flux (FGOM), the assumption that WHNO3 ≈ WGOM from the previous section, and the
121
precipitation amount (P), the GOM concentration (CGOM) can be predicted using equation (4).
122
123
FGOM = WGOMCGOMP (4)
CGOM =
124
2.4 Comparison of predicted and observed GOM
125
The predicted GOM were compared to the observed GOM to assess the bias in the observed
126
GOM measured by the Tekran system. Bias and normalized mean bias (NMB), which are model 7 ACS Paragon Plus Environment
Environmental Science & Technology
127
evaluation metrics reported in chemical transport modeling studies35, were used to quantify the
128
bias in GOM (equations (5) and (6)). Bias = Pi - Oi (5)
129
∑ !
130
NMB =
131
BiasF = 1.0+NMB
∑ !
(6)
(7)
132
Pi and Oi are the predicted GOM and observed GOM, respectively. The observed GOM is a
133
weekly or monthly average value calculated from the 2-4 h concentrations. N is the number of
134
weekly or monthly cases. Bias is the difference between predicted and observed GOM, while
135
NMB represents an average percent difference between predicted and observed GOM from all
136
the data35. BiasF is defined here as a bias factor, which can be simply used to adjust measured
137
data by multiplying this factor. The scavenging ratio method uncertainties are described in Sect.
138
2 of the SI and shown in Table S4.
139
Initially, the comparison of predicted and observed GOM was performed on all cases. However,
140
this comparison resulted in an unreasonably large NMB (Table S5) and a very low correlation
141
between predicted and observed GOM (r = 0.07, p = 0.071). It was speculated that outliers in
142
certain parameters caused the extremely high predicted GOM. Therefore, data quality control
143
was applied on specific parameters, such as PBM and GOM concentrations and WHNO3. PBM
144
and GOM were constrained to concentrations ≥ 1 pg m-3, which is an estimate of the PBM and
145
GOM detection limits of the Tekran system. The exclusion of highly uncertain PBM and GOM
146
(i.e. low concentrations) would likely result in a more reliable NMB. The range in WHNO3 were
147
constrained between 372 and 6612 (mass basis), which represents the interquartile range (25th-
8 ACS Paragon Plus Environment
Page 8 of 30
Page 9 of 30
Environmental Science & Technology
148
75th percentile) of the WHNO3 from our previous study of Canadian rural sites34. Limited field
149
data on scavenging ratios of HNO3 and other gases are available; therefore, the WHNO3 predicted
150
from our previous long-term multi-site study was used to constrain WHNO3 in this study. The
151
constraints on PBM and GOM concentrations had little effect on the NMB and correlation
152
between predicted and observed GOM. However, narrowing the WHNO3 range led to a more
153
reasonable NMB (Table S5) and correlation coefficient (r = 0.2, p
202
predicted GOM. The positive and negative biases suggest that the low GOM bias associated
203
with the Tekran instrument cannot be generalized to all locations and measurements. More field
204
intercomparison studies of different GOM measurement methods are needed to assess the spatial
205
variability of the GOM bias and confirm whether there are positive and/or negative biases.
206
3.2 Seasonal variation in bias
207
Aggregating all the sites together, BiasF of weekly GOM was 1.9 in winter, 1.8 in spring, 2.9 in
208
summer, and 2.0 in fall, and the corresponding method uncertainties were a factor of 1.7, 1.5,
209
1.01, and 1.3, respectively. These results indicate that the biases in GOM measurements were
210
the largest in summer (> factor of 2 after considering the method uncertainties) (Fig. 3). The
211
seasonal trend of the predicted GOM was similar to that of the observed GOM with the
212
exception of the higher predicted GOM during summer (Fig. 3). The larger bias in GOM during
213
summer may be attributed to ozone interferences on GOM measurements as reported in
11 ACS Paragon Plus Environment
Environmental Science & Technology
214
experimental studies11, since ozone concentrations are frequently elevated in the summer (Fig.
215
3). However, the mechanisms causing the loss of GOM when KCl-coated denuders are exposed
216
to elevated ozone during sampling are not yet known.
217
218
3.3 The case of GA40 in southeastern U.S.
219
It is noted that 11 of the 13 sites have BiasF values smaller than 3.0 and one site (OK99) has a
220
value of 5.4. However, an extremely high BiasF value of 14.3 was found at GA40, a
221
southeastern U.S. site. This is because AMNet sites in the southeastern U.S observed much
222
higher mercury wet deposition, although similar ambient GOM concentration, than sites
223
elsewhere in the U.S.36-38. Elevated Hg wet deposition in southeastern U.S. and Puerto Rico has
224
been attributed to the efficient scavenging of GOM from the free troposphere by deep convective
225
storms38-41. Elevated GOM was indeed observed in the free troposphere by aircraft
226
measurements. 42-44 About 60% of the total Hg wet deposition was estimated to be from upper-
227
level (or in-cloud) scavenging in southeastern U.S. based on model simulations40,41. In this case,
228
the predicted GOM represents more of the upper altitude than surface concentration. For GA40,
229
if the total wet deposition was reduced by 60%, then the revised BiasF would be 5.7, which is
230
more in line with values at other sites. Thus, the discrepancy between the predicted and
231
observed GOM at GA40 should be a combination of the enhanced wet deposition due to deep
232
convection and the instrument measurement uncertainties, the latter was the focus of the present
233
study. Note that the exact contributions from these two sources to the total BiasF cannot be
234
quantified in this study.
12 ACS Paragon Plus Environment
Page 12 of 30
Page 13 of 30
Environmental Science & Technology
235
The higher BiasF during summer than other seasons can also be explained by the higher
236
predicted wet deposition of GOM aside from elevated ozone during summer. The higher wet
237
scavenging of GOM during summer is not due to the higher precipitation amount, since it was
238
lower than the precipitation amount in spring (Fig. S2 of SI). It was due to the higher
239
precipitation Hg concentration during summer. Figure 4 illustrates the strong relationship
240
between the predicted- observed GOM difference and the precipitation Hg concentration. This
241
relationship can be used to predict the discrepancy between predicted and observed GOM given
242
the precipitation Hg concentration. With the precipitation Hg concentration < 20 ng l-1 at most
243
Mercury Deposition Network (MDN) locations in the U.S. and Canada36, the expected difference
244
between the predicted and observed GOM concentrations would be up to ~15 pg m-3. Based on
245
the geographical distribution of precipitation Hg concentrations36, this difference is expected to
246
be larger in southeastern U.S. which further supports the high BiasF at GA40.
247
3.4 Uncertainties of PBM measurements on GOM bias - a sensitivity analysis
248
The method of predicting GOM in this study relies on PBM (
