Auxiliary Material for
Origins of aerosol chlorine during winter over north central Colorado, USA
C.E. Jordan1, A.A.P. Pszenny1, W.C. Keene2, O.R. Cooper3,
B. Deegan4, J. Maben2, M. Routhier1, R. Sander5, A.H. Young1,*
1
Earth System Research Center, University of New Hampshire, Durham, NH 03824,
USA
2
Department of Environmental Sciences, University of Virginia, Charlottesville, VA
22904, USA
3
Chemical Sciences Division, NOAA Earth System Research Laboratory, Boulder, CO
80303, USA
4
97 Raymond St., Fairhaven, MA 02719, USA
5
Air Chemistry Department, Max Planck Institute for Chemistry, Mainz, Germany
*
415 East 75th St., Apt 10, New York, NY 10021
Submitted to: Journal of Geophysical Research, Atmospheres, 7 July 2014
Revised 2 October 2014
Revised 18 December 2014
I. INTRODUCTION
The auxiliary information provided for this manuscript includes:
1) Additional text related to methods.
2) Tables (Auxiliary_Material-ts) with information useful to demonstrate data quality and
consistency among various sampling and analysis methods (Tables S1 - S4), along with
site information for meteorological station data used in the manuscript (Table S5-S8).
3) Figures (Auxiliary_Material-fs) to illustrate data quality and consistency among
various sampling and analysis methods (Figures S1 and S2), to show the time series of
total Br measured in BulkFP samples (Figure S3), to provide the time series of observed
sub-µm and super-µm aerosols (Figure S4), and to provide all meteorological data for the
study (Figures S5 and S6), of which only a subset are included in the manuscript.
The tables and figures are all cited in the manuscript text, such that additional
information is not provided here about these data. However, the figure captions for the
auxiliary figures are below (following the methodology text).
II. ADDITIONAL METHODOLOGY INFORMATION
II.1. Aerosol sampling and chemical analysis
The quality of data on ionic concentration measurements generated by the UVA
laboratory was verified as part of an on-going rigorous quality assessment program that
includes: (1) Routine analysis of audit solutions provided by the World Meteorological
Organization, (2) routine analysis of laboratory-prepared standard solutions from
previous analytical runs, (3) routine extraction and analysis of replicate sections of
sample substrates and filters, (4) periodic analysis of spike additions to samples, (5)
routine evaluation of ion-balance calculations and diagnostic ratios, and (6) periodic
intercomparisons of opportunity with other laboratories. These evaluations indicate that
the data on ionic concentration measurements are unbiased and precise to specified levels
(typically better than ±5% or ±0.5 * the detection limit, whichever is the greater absolute
value). Detection limits for each analyte estimated following Keene et al. [1989] are
summarized in Table S1. In addition, mass-flow meters were factory calibrated prior to
the campaign and intercompared (via plumbing and operating in line) in the field
immediately before and after deployment. All flow rates agreed within better than ±2%.
An accuracy and precision assessment of the NAA data is presented in Table S2.
Recoveries of standard spikes were close to 100% for Na, Mg, Cl, Mn and Br but
significantly lower than 100% for Al (~60%) and V (~75%) for both filter types. Similar
assessments of standards run with sample sets from previous field campaigns [e.g.,
Lawler et al., 2009; Sander et al., 2013] did not show low recoveries for Al or V. This
observation combined with the near quantitative recoveries of the other five elements
suggests that the standard solution purchased new for this set of analyses did not contain
the expected amounts of Al and V. Therefore no recovery corrections were applied to the
Al and V data for samples or field blanks. Counting statistics usually controlled the
precision of an individual determination. This precision was taken as equivalent to the
standard error of counts under each element’s photopeak as calculated by the gamma ray
spectrometer manufacturer’s software (Genie™ 2000, Canberra Industries, Inc., Meriden,
CT); it was not greater than 10% and typically less than 5%
II.2. Cl comparisons: Impactor vs. BulkHV vs. BulkFP
Comparisons of the results of total Cl and Cl– analyses in the three different
sample types are presented in Table S4 and Figure S2. To facilitate direct comparisons,
Cl- concentrations for each cascade impactor sample were summed over all size fractions
to yield an equivalent bulk concentration (ImpactorSum). In addition, total Cl
concentrations measured with the filter packs (nominal 3-hr duration) were averaged over
the corresponding intervals for the paired high-volume bulk and cascade impactor
samples (nominal 12-hr duration). Figure S2a depicts a scatter plot and RMA regression
of the summed Cl– from the cascade impactor versus total Cl in paired high-volume bulk
filter samples. The RMA correlation is significant (r2 = 0.81) but the slope of 0.60 is
significantly less than 1.0. Figure S2b depicts a scatter plot and RMA regression of total
Cl based on the FP samples versus the paired high-volume bulk samples. The regression
slope is close to 1 and the correlation is weak (r2 = 0.13) but significant (p < 0.05). Figure
S2c depicts a scatter plot and RMA regression of the summed Cl– for the cascade
impactor versus the paired total Cl based on the FP samples. The correlation is weak (r2 =
0.21) but significant (p < 0.05). Note, the significance tests used here were based on tests
of null hypotheses of zero correlation done with the StatsLinearCorrelationTest procedure
in Igor Pro Version 6.35. (www.wavemetrics.com).
These results coupled with the fact that virtually all Cl was in the form of Cl(e.g., Figure S1a) suggest that the collection efficiency for the cascade impactor was
lower than those for the high-volume bulk and FP samplers. These differences did not
vary systematically with respect to wind speed and direction (not shown). Young et al.
[2013] noted that NO3-, NH4+, and SO42- summed over the impactor size bins agreed well
with corresponding concentrations in the paired high-volume bulk samples. These
analytes were associated primarily with sub-μm size fractions indicating that the impactor
and high-volume bulk sampler collected sub-μm fractions of the aerosol population with
similar efficiencies. In contrast, comparisons between Na+ and Cl-, which were associated
primarily with super-μm-diameter size fractions, suggested that the impactor sampled
larger aerosol size fractions at lower efficiency than the high-volume bulk sampler. The
MSP nozzle technology suffers from significant internal losses of both liquid and solid
particles greater than about 4-μm aerodynamic diameter due to the combined influences
of bounce and inertial effects [Marple et al., 1991]. Available evidence supports the
hypothesis that internal losses of super-μm-diameter aerosols within the impactor was the
primary explanation for systematic differences between particulate Na+ and Cl- collected
in parallel with the impactor relative to the high-volume bulk and FP samplers [Young et
al., 2013].
The bulk filter, FP, and cascade impactor data are available for download at
http://www.esrl.noaa.gov/csd/groups/csd7/measurements/2011NACHTT/.
II.3. Land surface data
SSURGO is a 1:24,000 scale data set that includes tabular soil electrical
conductivity (EC) class and wind erodibility index (WEI) information for polygons with
boundaries downloaded from the Geospatial Data Gateway
(http://datagateway.nrcs.usda.gov/). These data were mosaiced into one data layer using
ArcGIS. SSURGO tabular data were joined to polygon data via a key field (MUKEY)
used in both tables.
Since soil properties over a given area are generally mixed, there can be multiple
entries for soil types, EC, WEI, and other parameters present in any given polygon, with
each entry assigned a percent contribution to the total within the polygon. Here, only
moderately (8.1 mS/cm ≤ EC ≤ 16 mS/cm) and strongly (EC > 16 mS/cm) saline soils are
mapped (Figure 1), with the highest WEI component present in the polygon mapped.
This may not represent the largest percentage contributor to the mixture of soils in the
polygon, but it does represent the most erodible component in that polygon. For example,
if in a given polygon there is a moderately saline soil component with a WEI of 48 that
comprises 12% of the mixture of soils in that polygon, along with a strongly saline
component with a WEI of 86 that comprises 5%, then the strongly saline soil with WEI
86 will be mapped (Figures 1 and 2). Wind erosion of dust often occurs from essentially
point sources within broader regions [Boyer, 2003], so this mapping choice best identifies
susceptibility to wind erosion for each map unit.
There are 11 wind erodibility indices (0, 38, 48, 56, 86, 134, 160, 180, 220, 250,
and 310 tons/acre/year, abbreviated as t/a/y) that provide a value indicating the mass of
soil eroded by wind per acre per year. For the moderately and strongly saline surface soils
here, the majority (49%) are classified with WEI = 86 t/a/y, followed by 16% with 48
t/a/y, 12% with 0 t/a/yr (not susceptible to wind erosion), 8% with 56 t/a/y, 5% of the
polygons had no WEI information, with the remaining indices contributing 3% or less to
the total for these soils. Due to this distribution, and to simplify the color representation
in the map (Figure 2), the two smallest indices (0 and 38) and the five largest indices
(160-310) are lumped together, with the remainder shown individually.
Note, not all lands are mapped uniformly. There are significant data gaps,
principally on national lands, where there is limited, if any, information in the soil
database (Figure 2).
REFERENCES
(see main text for references; additional references cited only in supplement are below)
Keene, W. C., R. W. Talbot, M. O. Andreae, K. Beecher, H. Berresheim, M. Castro, J. C.
Farmer, J. N. Galloway, M. R. Hoffman, S.‑M. Li, J. R. Maben, J. W. Munger, R.
B. Norton, A. A. P. Pszenny, H. Puxbaum, H. Westberg, and W. Winiwarter
(1989), An intercomparison of measurement systems for vapor‑ and
particulate‑phase concentrations of formic and acetic acids, J. Geophys. Res., 94,
6457‑6471.
Lawler, M. J., B. D. Finley, W. C. Keene, A. A. P. Pszenny, K. A. Read, R. von Glasow,
and E. S. Saltzman (2009), Pollution-enhanced reactive chlorine chemistry in the
eastern tropical Atlantic boundary layer, Geophys. Res. Lett., 36, L08810,
doi:10.1029/2008GL036666.
Marple, V. A., K. L. Rubow, and S. M. Behm (1991), A microorifice uniform deposit
impactor (MOUDI): Description, calibration, and use, Aerosol Sci. Technol., 14,
434-446.
III. AUXILIARY MATERIAL FIGURE CAPTIONS
Figure S1. Scatter plots of ionic versus total concentrations (nmol m-3 at STP) of
analytes based on paired subsamples of high-volume bulk filters (BulkHV); (a) Cl- versus
total Cl; (b) Na+ versus total Na; (c) Mg2+ versus total Mg; and (d) Br- versus total Br.
Solid lines depict RMA regressions (see Table S3) and dashed lines depict 1:1 lines.
Figure S2. Scatter plots of (a) particulate Cl- sampled with the cascade impactor and
summed over all size fractions (Cl-, ImpactorSum) versus total Cl sampled in parallel with
the high-volume bulk sampler (Total Cl, BulkHV,NAA); (b) total Cl sampled in bulk with
the filter packs and averaged over sampling intervals for the corresponding high-volume
bulk samples (Average Total Cl, BulkFP) versus total Cl sampled in parallel with the
high-volume bulk sampler (Total Cl, BulkHV,NAA); and (c) Cl-, ImpactorSum versus
Average Total Cl, BulkFP. Solid lines depict RMA regressions (see Table S4) and dashed
lines depict 1:1 lines. All units are in nmol m-3 at STP.
Figure S3. Time series of total Br measured in BulkFP samples. Vertical gray bars
indicate nighttime.
Figure S4. Time series of major ionic aerosol constituents collected in bulk with the
high-volume sampler (BulkHV,IC); ions associated primarily with super-μm size fractions
are plotted in the left panel and those associated primarily with sub-μm size fractions
(plus Cl-) are plotted in the right panel. Cl- is plotted in both panels to facilitate direct
comparisons with all analytes. The four case studies (numbered sequentially from left to
right) are indicated with black arrows.
Figure S5. Wind speeds at meteorological stations within the peak FLEXPART
footprint for (a and b) Case 1, (c, d, and e) Case 2, (f) Case 3, and (g and h) Case 4. All
plots start 48 hours before sampling commenced at BAO. The 24 hr point before
sampling started is indicated in all plots with a black vertical line. The sampling period is
highlighted in the gray section of each panel. Two threshold wind speeds are indicated in
all panels by horizontal lines (dashed line, 8 m s-1, and solid line, 14 m s-1). Wind speed
data (SPD) are shown in blue, wind gust data (GUS), where available, are shown in
black. Individual meteorological station ids (2-4 letter codes in legends) may be found in
supplementary tables S5-S8. The panels are arranged in columns such that the top left
panel is for the station nearest BAO, the bottom right panel is for the station farthest from
BAO.
Figure S6. Wind speeds (WSPD) and wind gusts (GST) in m s-1 measured at National
Data Buoy Center buoys (identified by 5 digit numbers) located along California coast
near the peak FLEXPART footprint for Case 2. Panels arranged with northernmost buoy
at top, southernmost at bottom (see Table S6b for latitude and longitude of buoys). The
panels follow the same format as in figures S5, i.e., the Case 2 sampling period
highlighted in gray, shown with the preceding 48 hours of data. The 24 hr point before
sampling started is indicated with a black vertical line. Dotted lines at 4 m s-1 indicate a
threshold for sea-salt aerosol generation.