Introduction
As the most abundant basic gas in the atmosphere, ammonia (NH3) can
neutralize ambient acidic species, such as sulfuric acid (H2SO4)
and nitric acid (HNO3), to form ammonium salts, which are the dominant
inorganic compounds in ambient PM2.5 (particulate matter with
aerodynamic diameter less than 2.5 µm). PM2.5 has been linked
to adverse effects on human health (Davidson et al., 2005; Schwartz and Neas,
2000; Lelieveld et al., 2015) and regional visibility reduction (Park et al.,
2006) and also impacts climate via direct and indirect changes in radiative
forcing (Langridge et al., 2012; Parry et al., 2007). While the atmospheric
lifetime of NH3 is short (on the order of hours to days due to rapid dry
deposition and particle-forming chemical reactions), ammonium (NH4+)
salts are mainly found in submicron aerosol particles and have longer
atmospheric lifetimes (on the order of several days) so that they can be
transported to remote areas away from NH3 sources (Aneja et al.,
2001; Fowler et al., 1998; Ianniello et al., 2011). Dry and wet deposition of
NH3 and NH4+ also play an important role in the adverse effects
of increased nitrogen deposition to sensitive ecosystems (Asman et al.,
1998; Beem et al., 2010; Benedict et al., 2013b; Horii et al., 2006; Paulot et
al., 2013). Li et al. (2016) analyzed wet and dry deposition of reactive
nitrogen across the US and found that reduced nitrogen, derived from
NH3 emissions, now constitutes the majority of inorganic nitrogen
deposition in most regions.
Summary of sampling site locations and dates.
ID
Site name
Type
Latitude
Longitude
Elevation (m)
Year1
Sampler type
LE
Louisville
Suburban
39.987
-105.151
1698
11
Passive
FC_W
Fort Collins_West
Suburban
40.589
-105.148
1570
10, 11, 12,
Passive/URG
13, 14, 15
LD
Loveland
Suburban
40.438
-105.127
1582
10, 11, 12,
Passive
13, 14, 15
BAO
BAO Tower
Suburban
40.050
-105.004
1584
122
Passive/URG
GC
Golf Course
Golf course
40.426
-105.107
1551
10,11, 12, 13, 14, 15
Passive
13, 14, 15
FC_E
Fort Collins_East
Suburban-agricultural
40.591
-104.928
1562
12, 13, 14
Passive
SE
Severance
Suburban-agricultural
40.572
-104.836
1550
12, 13, 14, 15
Passive
GY
Greeley
Suburban-agricultural
40.389
-104.751
1492
10, 11, 12, 13, 14, 15
Passive
13, 14, 15
NN
Nunn
Rural
40.821
-104.701
1644
11, 12, 13, 14, 15
Passive
14, 15
BE
Briggsdale
Rural
40.635
-104.330
1481
10, 11, 12,
Passive
13, 14, 15
RH
Ranch
Rural
40.473
-104.317
1475
10
Passive
AT
Ault
Rural-agricultural
40.612
-104.709
1514
11, 12, 13, 14, 15
Passive
14, 15
KY
Kersey
Rural-agricultural
40.377
-104.532
1403
10, 11, 12, 13, 14, 15
Passive
13, 14, 15
BH
Brush
Rural-agricultural
40.313
-103.602
1286
10, 11, 12,
Passive/URG
13, 14, 15
1 Sampling period: 20 May–2 September 2010; 2 June–31 August 2011;
21 June–29 August 2012; 30 May–29 August 2013; 29 May–28 August 2014;
26 May–1 September 2015. 2 Even though one full year of measurements was conducted at the BAO site
(13 December 2011–9 January 2013), only the summer average NH3 concentration
(19 June–30 August 2012) was reported in Fig. 1 to compare with the NH3
concentrations at other sites.
It is widely believed that agriculture represents the largest source of
atmospheric NH3 globally, but at smaller spatial scales the influence
of agriculture varies greatly. Sutton et al. (2013) estimated that 57 % of
global atmospheric NH3 is emitted from livestock and crops, while the
US Environmental Protection Agency (EPA) attributed over 82 % of
NH3 emissions in the US to the agricultural sector in the 2014
National Emissions Inventory (NEI,
https://www.epa.gov/air-emissions).
Hertel et al. (2006) also found that deposition of atmospheric NH3 near
an intensive agricultural area would dominate the overall load of reactive
nitrogen (N) from the atmosphere. Agricultural NH3 emissions have
become one of the most prominent air pollution problems in recent years and
have given rise to growing concerns (Aneja et al., 2006; Pan et al., 2012;
Bauer et al., 2016). Within the US, efforts to routinely monitor NH3
concentrations have been growing via the Ammonia Monitoring Network (AMON;
http://nadp.sws.uiuc.edu/). NH3 can now be considered
as a precursor to PM2.5 in the state implementation planning process
for meeting the national ambient air quality standards, and voluntary
reductions in agricultural NH3 emissions have been prioritized as part
of efforts to reduce reactive nitrogen deposition in Rocky Mountain National
Park (http://www.rmwarningsystem.com/Reducing). Besides the
dominant contributions from agricultural sources, ambient NH3 also
originates from other sources such as vehicles with three-way catalysts
(Shelef and Gandhi, 1974; Chang et al., 2016). Biomass burning (such as
wildfires) is another important source of NH3 (Benedict et al., 2017):
in the 2014 US NEI, wildfires make up nearly 4.3 % of national NH3
emissions.
The northeastern plains of Colorado include the Denver–Fort Collins urban
corridor along the Front Range and a large agricultural region reaching
eastward toward the border with Nebraska. This area has been recognized as
an important NH3 emission source region, and the largest reduced
nitrogen source near Rocky Mountain National Park (RMNP; Benedict et al.,
2013c; Ellis et al., 2013). According to the 2002 Front Range NH3
emission inventory, NH3 emissions from the Front Range were 10 288 t yr-1 from livestock and 5183 t yr-1 from fertilizer application,
which accounted for 30 and 27 % of Colorado's NH3 emissions,
respectively (according to RMNP Initiative – Nitrogen Deposition Reduction
Contingency Plan, 2010). The Rocky Mountain Atmospheric Nitrogen and Sulfur
(RoMANS) studies (https://www.nature.nps.gov/air/; Beem et al.,
2010; Benedict et al., 2013c; Malm et al., 2013, 2016; Thompson et al., 2015), conducted in 2006 and 2009, showed that together NH3 and
NH4+ contributed approximately 50 % of the total reactive
nitrogen deposition (both wet and dry) in RMNP, with the remainder coming
from dry and wet deposition of nitrate and organic nitrogen (Benedict et
al., 2013a). The highest concentrations of particulate NH4+
measured during RoMANS were associated with upslope transport from the east
side of RMNP, indicating major sources of NH3 to RMNP are located in
the northeastern plains of Colorado (Benedict et al., 2013c; Beem et al.,
2010; Eilerman et al., 2016). In 2010, an effort was initiated to map the
NH3 concentrations in northern Colorado and significant NH3
spatial differences were found, with averages ranging from 3.43 µg m-3 at rural grasslands to 10.7 µg m-3 at suburban-urban
sites and 31.5 µg m-3 near an area of concentrated animal feeding
operations (CAFOs; Day et al., 2012).
Due to the short atmospheric lifetime and high dry deposition velocity of
NH3, there are many factors, such as the height of the boundary layer,
surface properties, location of sources, local advection, and the vertical
mixing rate, that influence spatial (horizontal and vertical) distributions
of NH3 concentrations. This complex dependence of NH3
concentrations on atmospheric conditions and deposition variability results
in great uncertainties of NH3 concentrations in global and regional
atmospheric chemistry models (Sutton et al., 2008; Zhu et al., 2013). Several
model performance evaluations (MPEs) have found model predictions of
NH3 concentrations in the western US to be low (Rodriguez et al.,
2011; Thompson et al., 2015; Battye et al., 2016). Rodriguez et al. (2011) and
Thompson et al. (2015) utilized the Comprehensive Air Quality Model with
Extensions (CAMx); Battye et al. (2016), meanwhile, ran a different
photochemical model (CMAQ), and utilized emissions inventories generated
with less focus on the precise spatial positioning of agricultural sector
emissions in the Inter-Mountain West. Evaluation of NH3 concentration
prediction performance in larger scale models has suggested that uncertainty
in emissions inventories is a cause of NH3 concentration
underestimation in the west (Zhu et al., 2013; Heald et al., 2012). Van
Damme et al. (2015) used measured NH3 data from the US, China,
Africa, and Europe (ground-based and airborne observations) and compared
these data with IASI-NH3 columns. During the DISCOVER-AQ campaign, Sun
et al. (2015) also compared in situ observations (airborne and vehicle-based) with
Tropospheric Emission Spectrometer (TES) NH3 columns. Both comparisons
demonstrated fair agreement between in situ measurements and satellite total
columns, indicating that NH3 data from in situ measurements and satellite
retrievals are reliable. The discrepancy between model predictions and
observations of NH3 concentrations suggests that variability in the
spatial and/or temporal distribution of NH3 is not captured by current
emissions inventories or model inputs, and additional understanding of
atmospheric NH3 distributions, for example, with height above ground
level, is needed. Vertical NH3 profiles have previously been reported
from airborne studies such as CalNex (Nowak et al., 2012; Schiferl et al.,
2014), the DISCOVER-AQ campaign (Sun et al., 2015; Müller et al., 2014),
and from measurements made at the Canadian oil sands (Shephard et al.,
2015). These studies have found strong variation of NH3 concentration
above ground, but do not provide a sufficient basis to characterize the
general vertical distribution of NH3 with limited sampling periods.
The primary goal of this study is to investigate the spatial and temporal
variability of NH3 concentrations in the northeastern plains of
Colorado. This effort builds upon the earlier efforts of Benedict et al. (2013c), Day et al. (2012), and Battye et al. (2016) to look at patterns of
spatial variability across several years with different meteorology and
source strength (e.g., years with and without active fire seasons) and to
identify any multi-year trends in regional NH3 concentrations.
Year-round measurements of the vertical profile of NH3 measured using a
300 m tower near Erie, Colorado, will also provide new insight into the
vertical profile of NH3 concentrations in the lower atmosphere and its
change with season. The in situ surface and tower measurements will also be
compared to NH3 remote sensing measurements from the Infrared
Atmospheric Sounding Interferometer (IASI) satellite (Whitburn et al.,
2016; Van Damme et al., 2015) and predictions from CAMx to provide insight
into the regional performance of each. Many recent and past MPEs have
utilized special studies, such as the one presented in this paper, to
evaluate photochemical model performance with respect to NH3. Overall,
our results are useful for determining important sources contributing to
regional nitrogen deposition, validating emission inventories and
concentration predictions for atmospheric chemistry models, and setting a
baseline against which concentration changes resulting from future emission
changes can be assessed.
Methodology
Site descriptions
The northeastern plains of Colorado are an intensive agricultural area with
many CAFOs, including beef cattle feedlots and dairy operations. The densely
populated Front Range urban corridor is located just west of this area, and
just east of the Rocky Mountains. In order to gain information about spatial
variability of northeast Colorado NH3 concentrations, fourteen
monitoring sites were selected in the region according to land use
categories and distance from known, major NH3 sources (Table 1). Five
suburban monitoring sites located in the Front Range urban corridor are
representative of areas with little local agricultural influence, especially
from animal feeding operations: Louisville (LE), western Fort Collins
(FC_W), Loveland (LD), Loveland Golf Course (GC), and the
Boulder Atmospheric Observatory (BAO) tower. Three rural sites (Nunn, NN;
Briggsdale, BE; and Ranch, RH), located close to the northern boundary of
Colorado with Wyoming, are grassland sites with minimal local agricultural
influence. Three suburban sites (eastern Fort Collins, FC_E;
Severance, SE; and Greeley, GY) as well as three rural sites (Ault, AT;
Kersey, KY; and Brush, BH) represent areas close to and likely significantly
influenced by agricultural activities, including animal feeding operations.
For example, the KY site is located approximately 0.4 km from a large beef
cattle feedlot (about 100 000 cattle capacity).
The BAO tower is a 300 m meteorological tower situated in the southern part
of the sampling area (40.050∘ N, 105.004∘ W). It has been owned and operated by
the National Oceanic and Atmospheric Administration (NOAA) for more than 25
years (http://www.esrl.noaa.gov/psd). The tower is
surrounded by natural grass and wheat fields, and is approximately 400 m
west of Interstate 25 and 30 km north of downtown Denver.
Sample collection and validation
In order to obtain spatial and vertical distributions of NH3
concentrations, two sampling campaigns were carried out in the northeastern
plains of Colorado using Radiello passive NH3 samplers and URG
(University Research Glassware, Inc.) denuder/filter-pack systems. The
Radiello passive NH3 sampler consists of a cartridge adsorbent (part
number: RAD168), a blue microporous cylindrical diffusive body (part number:
RAD1201), and a vertical adapter (part number: RAD 122). All Radiello sampler
components were obtained from Sigma Aldrich (http://www.sigmaaldrich.com). Measurements of the spatial NH3
distribution were conducted each summer from 2010 to 2015. During the first
summer (2010), measurements were made at nine sites; in 2011, the Ranch (RH)
site was removed and the LE and NN sites were added; in 2012, the LE site
was removed; and two sites, FC_E and SE, were added in 2013. The
two site removals in 2013 (RH and LE) and FC_E removal in
2015 were both due to property access issues. In a second campaign,
measurements of vertical NH3 concentration profiles were conducted at
the BAO tower from December 2011 to January 2013.
Passive sampler
Passive ammonia samplers have been used in several previous studies because
of their reliability, low labor intensity, simplicity and lack of power
requirement (Cisneros et al., 2010; Day et al., 2012; Meng et al., 2011; Reche
et al., 2012; Puchalski et al., 2011). During sample collection, the sampler
was protected from precipitation and direct sunlight by an inverted plastic
bucket. Ambient NH3 diffuses through a microporous diffusive body
surface and is captured as NH4+ by a cartridge impregnated with
phosphoric acid (H3PO4). A weekly sampling campaign period was
implemented in each summer during the study: 20 May to 2 September
2010, 2 June to 31 August 2011, 21 June to
29 August 2012, 30 May to 29 August 2013, and 29 May
29 to 28 August 2014. Biweekly samples were collected from
26 May to 1 September 2015. At the BAO tower, NH3 was
sampled at nine heights: 1, 10, 22, 50, 100, 150, 200, 250,
and 300 m. Vertical profiles were measured across 2-week sampling periods
from 13 December 2011 to 9 January 2013, except when weekly
measurements were conducted from 19 June to 30 August 2012,
when higher concentrations were anticipated. Passive samplers were prepared
in an NH3-free laminar flow hood (Envirco Corporation) and sealed for
transport to the field. More detailed information regarding sampler
preparation can be obtained in Day et al. (2012).
The ambient NH3 concentration was calculated based on the
characteristics of the passive sampler and the diffusivity of NH3 in
the atmosphere (DNH3), which is a function of local temperature (T) and
ambient pressure (P), and can be expressed using Eq. (1):
DNH3(T,P)=D0,1×P0P×TT01.81,
where D0,1= 0.1978 cm2 s-1 at T0= 273 K (0 ∘C)
and P0= 1 atm (Massman, 1998). Then, the diffusional flow rate
through the NH3 passive sampler (QNH3) is given by Eq. (2):
QNH3=DNH3(T,P)×AΔX,
where A is the passive sampler effective cross-sectional area and ΔX
is the passive sampler diffusion distance. For the Radiello NH3 passive
sampler, A/ΔX represents the geometric constant for radial flow and
has been reported to be 14.2 cm, based on actual physical measurements (Day
et al., 2012; Puchalski et al., 2011), which differs from the manufacturer's
description (http://www.radiello.com/english).
Each diffusional flow rate (QNH3) was calculated for the averaged T and
P for each interval sampling period. Finally, the NH3 concentration in
the air (CNH3) is calculated from the diffusional flow rate
(QNH3), the duration of sampling time (t) and the mass of NH3
collected on the cartridge (mNH3) as shown in Eq. (3):
CNH3=mNH3t×QNH3.
For the northeastern plains network, hourly temperature data were obtained
from nearby CoAGMET weather stations (http://www.coagmet.com/; Table S1 in the Supplement). The distance between the NH3 measurement sites and the
nearby meteorological stations referenced in the paper were from 0.1 km
(KSY01 to KY) to 68.1 km (BRG01 to BH), with an average value of 16.5 km.
The average meteorological record was fairly consistent from year to year.
The ambient pressure was calculated based on the elevation of each site. At
the BAO tower, temperature, and relative humidity were measured by
battery-powered sensors (EBI20-TH1, EBRO Inc. Ingolstadt, Germany;
http://shop.ebro.com/) co-located with the NH3
passive samplers at each sampling height.
URG denuder/filter-pack sampler
During the same sampling periods as the NH3 passive samplers, URG
denuder/filter-pack sampling systems were also installed during select
campaign years at the FC_W, GY, and BAO tower sites to
measure the concentrations of gaseous NH3 and HNO3, as well as
fine particulate inorganic ions. Air was drawn first through a Teflon-coated
PM2.5 cyclone (D50= 2.5 µm) at the inlet, followed by
two annular denuders connected in series. The first denuder was coated with
sodium carbonate (Na2CO3) solution (10 g of Na2CO3 and
10 g of glycerol dissolved in 500 mL of 18.2 MΩ cm deionized water
and 500 mL methanol) to collect gaseous HNO3 and sulfur dioxide
(SO2). The second denuder was coated with a phosphorous acid
(H3PO3) solution (10 g of H3PO3 dissolved in 100 mL of
deionized water and 900 mL methanol) to collect gaseous NH3 in the
atmosphere. The air was then drawn through a filter pack containing a 47 mm
nylon filter (Nylasorb, pore size 1 µm, Pall Corporation) to collect
fine particles, followed by a backup H3PO3-coated denuder to
capture any NH3 re-volatilized from NH4+ salt particles
collected on the nylon filter. The URG samplers were changed at the same
time as the passive samplers during each site visit. The air flow rate was
controlled by a URG mass flow-controlled pump; the total flow rate through
the system was nominally 3 L min-1 at FC_W, GY, and BAO. The
URG sampling system has been used widely in previous studies because of its
validated performance in sampling gases and particles (Bari et al.,
2003; Beem et al., 2010; Benedict et al., 2013b; Lee et al., 2008; Li et al.,
2014; Lin et al., 2006) and was used as a reference method for evaluating the
performance of the NH3 passive samplers.
Summary of summer NH3 concentrations (units: µg m-3) measured from 2010 to 2015.
2010
2011
2012
2013
2014
2015
Site
All years
20 May–2 September
2 June–31 August
21 June–29 August
30 May–29 August
29 May–28 August
26 May–1 September
Avg
Max
Min
Avg
Max
Min
Avg
Max
Min
Avg
Max
Min
Avg
Max
Min
Avg
Max
Min
Avg
Max
Min
LE
3.33
5.23
2.27
–
–
–
3.33
5.23
2.27
–
–
–
–
–
–
–
–
–
–
–
–
FC_W
4.09
8.55
1.95
4.13
5.88
3.02
3.76
4.72
2.79
4.63
8.55
2.92
4.45
6.13
1.95
3.78
4.98
2.39
3.83
4.62
2.54
LD
4.40
10.37
2.29
4.17
6.29
2.67
4.81
6.94
3.61
4.57
10.37
2.55
5.08
7.16
2.29
3.68
5.82
2.83
3.99
4.74
2.60
BAO
5.09
7.84
2.85
–
–
–
–
–
–
5.09
7.84
2.85
–
–
–
–
–
–
–
–
–
GC
5.14
7.87
1.81
4.85
7.68
3.01
5.30
7.87
3.87
5.22
7.27
3.74
5.34
7.11
1.81
4.92
6.18
4.07
5.31
7.69
3.33
FC_E
8.56
11.38
5.52
–
–
–
–
–
–
8.36
10.84
5.52
8.30
11.25
5.80
8.99
11.38
6.92
–
–
–
SE
9.10
13.79
4.52
–
–
–
–
–
–
9.34
13.14
6.24
8.52
12.67
4.52
9.70
13.79
7.10
8.66
10.13
6.18
GY
11.34
19.02
5.19
10.39
13.11
7.94
12.90
19.02
8.40
11.07
14.51
6.68
10.52
12.54
5.19
11.72
14.95
9.35
11.63
13.75
7.00
NN
2.66
4.01
0.35
–
–
–
2.78
3.88
1.51
2.59
3.54
1.68
3.01
3.95
1.69
2.84
4.01
1.43
1.60
2.70
0.35
BE
3.07
5.40
1.09
3.18
4.48
1.90
3.33
4.90
2.55
2.99
4.58
2.12
3.00
3.62
1.42
3.15
5.40
2.24
2.43
3.02
1.09
RH
3.27
5.01
1.90
3.27
5.01
1.90
–
–
–
–
–
–
–
–
–
–
–
–
–
–
–
AT
13.75
20.47
6.56
12.55
16.16
9.13
13.78
18.61
8.82
13.70
19.27
9.25
15.13
20.47
6.56
14.49
19.03
10.44
12.08
14.11
6.89
KY
42.73
73.78
23.30
31.05
42.82
23.30
45.96
73.78
30.32
41.65
53.55
25.93
42.67
68.61
25.20
46.57
68.82
29.22
55.14
64.21
47.31
BH
6.17
10.83
3.59
6.54
9.67
3.67
7.26
10.83
5.09
5.45
8.52
3.80
5.99
7.80
3.59
5.62
6.79
4.47
5.07
7.66
4.24
NH3 concentrations (unit: µg m-3) and feedlot
emissions (unit: t yr-1) in northeast Colorado. All sites indicated by
circles include at least 3 years of measurements in summer. NH3
concentrations at the RH, LE, and BAO sites (squares) were only measured in
the summers of 2010, 2011, and 2012, respectively. The predicted annual
NH3 emissions are calculated based on Eq. (4).
Sample analysis and evaluation
Passive samplers and URG denuders were extracted on arrival in the lab at
Colorado State University (CSU). The URG denuders were extracted with 10 mL
deionized water; the Nylon filters and passive sampler cartridges were
ultrasonically extracted for 55 min in 6 and 10 mL deionized water,
respectively. Passive sampler and H3PO3-coated denuder extracts
were analyzed by ion chromatography for NH4+,
Na2CO3-coated denuder extracts were analyzed for NO3-
and SO42-, and Nylon filter extracts were analyzed for cations
(Na+, NH4+, K+, Mg2+, and Ca2+) and anions
(Cl-, NO3-, SO42-). Cations in the samples were
separated with a 20 mM methanesulfonic acid eluent (0.5 mL min-1) on a Dionex
CS12A ion exchange chromatography column configured with a CSRS ULTRA II
suppressor and detected using a Dionex conductivity detector. Anions in the
samples were separated with an 8 mM carbonate/1 mM bicarbonate eluent (1 mL min-1) on a Dionex AS14A column followed by an ASRS ULTRA II suppressor and
detected using a Dionex conductivity detector (Li et al., 2014).
Replicate Radiello passive samples were collected at FC_W
(2011, weekly), BH (2012, 2013, and 2014, weekly), GY (2014, weekly; 2015,
biweekly), KY (2014, weekly) and three different heights (1, 100, and
300 m) of the BAO tower (biweekly; weekly in summer) during the campaign to
evaluate the performance of NH3 passive samplers under different
NH3 concentrations and sampling periods. Comparison of replicate
samples yielded good precision (see Fig. S1 in the Supplement) with a pooled relative standard
deviation of 8.9 % (n= 288). The weekly and biweekly NH3
concentrations collected by passive samplers were also in good agreement
with measurements by co-located URG denuder samplers for the same sampling
durations (a linear least-squares regression fit yielded a correlation
coefficient (R2) between the two methods of 0.92 with a slope of 0.98
and a small positive intercept (0.25 µg m-3) with n= 136
collocated measurements; Fig. S2). These findings are consistent with
previous studies (Benedict et al., 2013b; Day et al., 2012; Puchalski et al.,
2011). Field and laboratory blanks were collected throughout the research
campaign and used to blank correct sample results and determine the minimum
detection limits (MDLs). From the field blanks, the MDL was calculated to be
0.27 µg m-3 for a 1-week Radiello passive NH3 sample.
Satellite retrievals of ammonia
The Infrared Atmospheric Sounding Interferometer (IASI) is a passive
infrared Fourier transform spectrometer onboard the MetOp platforms,
operating in nadir (Clerbaux et al., 2009). IASI provides a quasi-global
coverage twice a day with overpass times at around 09:30 and 21:30 (when
crossing the Equator) at a relatively small pixel size (circle with 12 km
diameter at nadir, distorted to ellipse-shaped pixels off-nadir). The
combination of low instrumental noise (∼ 0.2 K at 950 cm-1 and 280 K), a medium spectral resolution (0.5 cm-1 apodized)
and a continuous spectral coverage between 645 and 2760 cm-1 makes
IASI a suitable instrument to measure various constituents of the atmosphere
(Clarisse et al., 2011).
The IASI-NH3 data set used in this work is based on a recently
developed retrieval scheme presented in detail in Whitburn et al. (2016).
The first step of the retrieval scheme is to calculate a so-called
hyperspectral range index (HRI) for each IASI spectrum, which is
representative of the amount of NH3. This HRI is subsequently converted
into NH3 total columns using a neural network (NN) approach. It is an
extension of the HRI method presented in Van Damme et al. (2014), who used
two-dimensional look-up tables (LUTs) for the radiance–concentration
conversion. The new NN-based method inherits the advantages of the LUT-based
HRI method whilst providing several significant improvements such as (1) better sensitivity at low concentrations due to the large variation in
temperature, pressure, and humidity vertical profiles in the retrieval; (2) a
reduction of the reported positive bias of LUT retrieval at low
concentrations; (3) the possible consideration of NH3 vertical profile
information from third-party sources; and (4) a full uncertainty
characterization of the retrieved column variables (Whitburn et al., 2016).
The IASI sensitivity to NH3 is dependent on the thermal contrast (TC),
defined as the temperature difference between the surface and the air at the
surface. With a TC of 5, 10, and 15 K, the detection limit at one sigma is
respectively 6.3 × 1015, 3.3 × 1015, and
2 × 1015 molec. cm-2. In northern Colorado, the
TC during the summer period for the morning overpass of IASI is around 10 K.
Ammonia modeling
Chemical transport models are valuable tools for evaluating how various
processes influence ambient air quality and pollutant deposition. They can
be especially helpful in designing effective source control strategies for
air quality improvement. Unfortunately, current models frequently have
difficulties accurately simulating spatial concentrations of NH3 (Battye et al., 2016; Adelman et al., 2015). In addition to the typical
model difficulties in accurately simulating transport, NH3 emissions
are not well constrained (Zhu et al., 2013) and the parameterization of
NH3 deposition is challenging (Bash et al., 2013; Pleim et al., 2013).
In order to examine some of these issues, NH3 measurements from this
study are compared to modeled concentrations from the Comprehensive Air
Quality Model with Extensions (CAMx,
http://www.camx.com/files/). CAMx, a
photochemical model that simulates the emissions, transport, chemistry, and
removal of chemical species in the atmosphere, is one of US EPA's
recommended regional chemical transport models and is frequently used for
air quality analysis (EPA, 2007, 2011). The 2011 modelled period presented
here (version base_2011a), including inputs representing
emissions and meteorology, was developed for the Intermountain West Data Warehouse (IWDW; Adelman et al., 2015); details on modeling protocol and model
performance are available on the IWDW website
(http://views.cira.colostate).
Results and discussion
Spatial distributions of ammonia
Large spatial differences in NH3 concentrations were found in the
northeastern plains of Colorado with mean NH3 concentrations ranging
from 2.66 to 42.7 µg m-3 as illustrated in Fig. 1. Also included in Fig. 1 are, for qualitative comparisons, estimated
NH3 emissions from major feedlots in northeastern Colorado. The
feedlots were classified into categories based on the type of animals raised
(data were provided by the Colorado Department of Public Health and
Environment) and NH3 emissions were calculated following Eq. 4:
NH3Emission=∑Population×Emission
Factor,
where the NH3 emissions are the total NH3 emitted from each
feedlot in tons per year (converted from kilograms to tons for Fig. 1), population
is the animal population in each feedlot and the emission factor was
specified for each kind of animal: 44.3, 38.1, 3.37, 0.27, 6.50, and
12.2 kg NH3 head yr-1, for beef cattle, dairy cows, sheep, poultry, swine, and
horses, respectively (USEPA, 2004; Todd et al., 2013). In total, 73 % of the total
regional feedlot emissions are contributed by beef feedlots. Many large
sources are located within several tens of km to the south, east, and north
of Greeley. Other large sources are located further east along the South
Platte River with some smaller sources (mostly dairies) located further west
in the sampling region, closer to the urban corridor. The lowest average
ambient NH3 concentrations from 2010 to 2015 in the sampling network
were found at remote grassland sites such as NN and BE: 2.66 and 3.07 µg m-3, respectively (Table 2). Concentrations
of NH3 at suburban sites were somewhat higher than at these remote,
rural sites, indicating possible impacts of human activities such as
emissions from vehicles equipped with three-way catalytic converters, local
waste treatment, and fertilization of yards and parks on local NH3
concentrations. The measured weekly average NH3 concentration at the
Loveland Golf Course (GC) site was 5.14 µg m-3 with a range of
1.81 to 7.87 µg m-3, showing only slightly
elevated values compared to NH3 concentrations at other nearby
suburban sites (FC_W and LD), suggesting that golf course
fertilization at this location is probably not a major, regional NH3
source. However, the NH3 concentrations at the GC were modestly higher
(17 % on average) than NH3 sampled at the LD site during each summer
measurement campaign (Table 2), suggesting that the contributions from
fertilization of the golf lawn cannot be neglected. The highest ambient
NH3 concentrations were consistently observed at sites near extensive
animal feeding operations. Compared to the remote sites (NN and BE), an
approximately 2- to 5-fold increase in NH3 concentrations was
observed at rural sites BH and AT (6.17 and 13.8 µg m-3), which
were under the influence of nearby animal feeding operation emissions. A
15-fold increase in mean NH3 concentrations was observed from the
grassland NN and BE sites (2.66 and 3.07 µg m-3) to KY (42.73 µg m-3), 0.4 km from a feedlot with almost 100 000 cattle.
Average concentrations of NH3 in each summer (approximately
June through August) across the nine sites. In 2006 (6 July–10 August), ambient
NH3 concentrations were sampled by a URG denuder (daily) at the BH
site; in 2009 (11 June–27 August) ambient NH3 concentrations were sampled by
a URG denuder (weekly) at the GC and BH sites; in 2010 (17 June–2 September), 2011
(16 June–31 August), 2012 (21 June–29 August), 2013 (20 June–29 August), 2014 (19 June–28 August)
and 2015 (23 June–1 September), ambient NH3 concentrations were all sampled by
Radiello NH3 passive samplers across all the sites. Trend analysis
(annual concentration vs. time) was conducted at each site. The slope of the
Theil regression and p value for each site are labeled in black and blue.
The interannual variation of average summertime NH3 concentrations
sampled at each site spanning several years exhibited a statistically
significant (p < 0.1) trend (Fig. 2) at three sites; six sites showed
no significant trend. Both the GY and KY sites show increasing trends, while
BH exhibits a decreasing trend. Trend analysis was conducted using Theil
regression (Theil, 1992) and the Mann–Kendall test (Gilbert, 1987; Marchetto
et al., 2013). We define an increasing (decreasing) trend as a positive
(negative) slope of the Theil regression, while the statistical significance
of a trend was determined by the Mann–Kendall test (p value). A 90th
percentile significance level (p < 0.10) was assumed as in a previous
study (Hand et al., 2012). The power of these analyses are limited by the
relatively small number of measurement years to date; additional power for
assessing interannual trends will become available as the measurement record
lengthens. Data from the Colorado Agricultural Statistics Report (2015,
http://www.nass.usda.gov/Statistics) indicate that Weld,
Larimer, and Morgan counties (three major counties located in the
northeastern plains of Colorado) did not show significant growth in
livestock numbers between 2009 and 2014. The total annual numbers of beef
cattle, milk cows, cattle, and calves in these counties were 986, 974, 996,
1065, 955, and 936 thousand head, respectively, in the 6 years from 2009 to
2014.
Temporal variations in NH3 concentrations (unit: µg m-3) at each site from 2010 through 2015. Note the differences in the
y-axis values.
Time series of vertical distribution of NH3 concentrations and surface temperature measured at the BAO tower
from 13 December 2011 to 9 January 2013.
Comparison of seasonal average vertical profiles of (a) NH3 concentration and (b) temperature measured at the BAO tower from
13 December 2011 to 9 January 2013.
Comparison of surface NH3 concentrations with IASI satellite
retrievals and CAMx model simulations. (a) Radiello passive sampler surface
NH3 concentrations (µg m-3, left color bar) plotted on top
of IASI-NH3 satellite column retrievals (molec. cm-2,
right color bar), both averaged for the summers of 4 years (2012–2015).
The BAO site was only sampled in situ in the summer of 2012. (b) Comparison
of measured and modeled NH3 concentrations in the summer of 2011. The
circles correspond to concentrations measured; these are superimposed on the
CAMx modeled NH3 concentration field. Animal units are indicated by
the triangles.
A number of best management practices (BMPs; http://www.rmwarningsystem.com) are under
evaluation to help agricultural producers in the region to lower NH3
emissions as part of efforts to reduce reactive nitrogen deposition in Rocky
Mountain National Park. The baseline regional concentration information
gathered here will be critical in helping to evaluate the success of future
efforts to reduce NH3 emissions.
Weekly average atmospheric NH3 concentrations at each observation site
are plotted for summers 2010–2015 in Fig. 3. These observations again show
the general similarity, at a given location, of summertime concentrations
across several years. Some variation from week to week is expected due to
differences in meteorology. Emissions, for example, are dependent on the
temperature, dispersion is influenced by turbulence and mixing layer depth,
and removal is influenced by precipitation and turbulence. One clear outlier
period is the elevated NH3 concentrations observed at FC_W at the beginning of summer 2012 (Fig. 3c). The maximum weekly average
NH3 concentration at this site (8.55 µg m-3) was measured
during 21–28 June 2012 and was more than 2 times the average NH3
concentration in 2010 (4.13 µg m-3) and 2011 (3.76 µg m-3; see Table 2). This is supported by the satellite observation
reported by IASI (see Sect. 3 and Fig. 7). During this elevated
concentration period, the High Park Fire, one of the largest fires recorded
in Colorado history at 353 km2 burned, was burning in the mountains
west of Fort Collins and the city was frequently impacted by smoke. The fire
was first spotted on 9 June 2012 and declared 100 % contained on 1 July
2012 (https://inciweb.nwcg.gov). During the wildfire period, on-line instruments (Picarro NH3
analyzer and Teledyne CO analyzer) were also set up to measure CO and
NH3 concentrations near the FC_W site. A significant
correlation between CO and NH3 was found during the wildfire (Prenni et
al., 2012; Benedict et al., 2017). The FC_W was site was the
closest site to the High Park Fire and normally has relatively low ambient
NH3 concentration. The NH3 emitted from the High Park Fire may
also have reached other, more distant sites downwind; however, enhanced
NH3 concentrations at these sites from other nearby sources and the
greater dilution of the smoke plume as it travels further downwind make it
difficult to identify any impacts of the wildfire at these locations.
Elevated NH3 concentrations in the High Park Fire plume are evidence of
the importance of wild and prescribed burning as a source of atmospheric
NH3, reinforcing similar findings from previous studies (Coheur et al.,
2009; Prenni et al., 2014; Sutton et al., 2000; Whitburn et al., 2015; Luo et
al., 2015).
Vertical distribution of ammonia
While surface measurements of NH3 concentrations remain uncommon,
measurements of vertical profiles of NH3 concentrations above the
surface are more rare, with the exception of a small number of aircraft
measurements over limited time frames as mentioned in the introduction. Time
series of vertical profiles of ambient NH3 concentrations measured at
the BAO tower across the full year of 2012 are shown in Fig. 4. During most
sampling periods, the NH3 concentration exhibited a maximum at 10 m
decreasing both toward the lowest (1 m) measurement point and with height
above 10 m. The minimum concentration was observed at the highest
measurement point at the top (300 m) of the BAO tower. While the major
sources of NH3 are surface emissions, it is not surprising to see a
gradient of decreasing concentration near the surface at this location where
local emissions are expected to be small and the net local flux represents
surface deposition (Pul et al., 2009). The long time duration of the
integration period (1–2 weeks) in this study precludes a meaningful
determination of surface removal rates based on the observed concentration
gradient.
Seasonal variations in the vertical profile of NH3 are depicted in Fig. 5 with March, April, and May defined as spring; June, July, and August as
summer; September, October, and November as fall; and December, January, and
February as winter. Vertical concentration differences were greatest in
winter (from an average concentration greater than 4 µg m-3 near
the surface to approximately 1 µg m-3 at 300 m, representing a
decrease of approximately 75 %) followed by fall (1.9 µg m-3
near the surface and 4.5 µg m-3 at 300 m). Low-level temperature
inversions which trap emissions closer to the surface are common in both
seasons (fall and winter). The highest concentrations across the profile
were observed in summer, when volatility of NH3 increases due to higher
temperatures and vertical mixing is enhanced. The concentration decrease
from the surface to 300 m averaged only 44 % in summer. Increased NH3
concentrations in summer also may reflect a shift in thermodynamic
equilibrium of particulate NH4NO3 toward its gas-phase
precursors NH3 and HNO3. Previous studies have reported
increased NH3 concentrations in summer and/or reduced concentrations
in winter due to the seasonal changes of NH3 emissions and gas–particle
partitioning (Li et al., 2014; Meng et al., 2011; Plessow et al., 2005; Walker
et al., 2004; Zbieranowski and Aherne, 2012). Day et al. (2012) previously
suggested that trapping of regional NH3 emissions in a shallow winter
boundary layer can produce elevated surface concentrations. The BAO tower
observations in Fig. 5a support this hypothesis, as concentrations are
elevated near the surface but fall off quickly at heights greater than 10–20 m. Evidence of winter temperature inversions is present even in the average
winter temperature profile shown in Fig. 5b.
Several long-term measurements have shown a strong correlation between
NH3 concentration and ambient temperature, due to enhanced NH3
emissions from soil and volatilization from NH4NO3 particulate
matter (Bari et al., 2003; Ianniello et al., 2010; Lin et al., 2006; Meng et
al., 2011). Almost no correlation (R2= 0.02) between NH3
concentration and temperature was observed at 1 m height in the current
study; higher correlation (R2= 0.65) was found at the top of the tower
(Fig. S3a). The correlation coefficients increase substantially with height
(Fig. S3b), particularly above 50 m, suggesting that temperature might
influence ambient NH3 concentrations at this location at higher
altitude but is not a dominant factor at the surface (Fig. S3b). This
pattern likely reflects greater vertical mixing during warmer periods, as
discussed above. In order to investigate the possible influence of changes
in NH4NO3 aerosol–gas partitioning on vertical NH3
concentration profiles, thermodynamic simulations were performed using the
ISORROPIA II model (Fountoukis and Nenes, 2007; Fig. S4). Model inputs
included BAO site URG denuder/filter-pack surface measurements of key
species (gaseous NH3 and HNO3 and PM2.5 NH4+,
NO3-, and SO42-) and measurements of temperature and
relative humidity at each tower measurement height. Because vertical
differences in temperature and relative humidity were generally small,
little change was predicted with height in the thermodynamic partitioning of
the NH3-HNO3-NH4NO3 system. Consequently, a shift in
partitioning toward the particle phase as temperatures cool at higher
altitudes appears not to account for much of the observed decrease in
NH3 concentration with height. For this location and for the lowest 300 m of the atmosphere, the vertical thermal structure of the atmosphere and
associated mixing, ambient dilution, and NH3 surface deposition appear
to be the major factors determining vertical distributions of atmospheric
NH3.
Time series of (bi)weekly averaged IASI-NH3 satellite column
(red, × 1016 molec. cm-2) and surface
concentrations measured by Radiello passive sampler (blue, µg m-3) at FC_W site. The error bars
represent the standard deviation of the mean satellite column retrievals.
Time series of weekly NH3 concentrations measured (red) and
modeled (green) in the summer of 2011 (2 June–31 August 2011) at all the
sites.
Comparison with satellite observations
Several recent studies have used surface NH3 measurements to evaluate
or improve remote sensing techniques for retrieving NH3 concentrations
and determining distributions (Heald et al., 2012; Pinder et al., 2011; Zhu et
al., 2013; Van Damme et al., 2015). The first version of the IASI-NH3
data set has been evaluated against model simulations over Europe and has
demonstrated consistency between model output and satellite-retrieval-derived NH3 concentrations (Van Damme et al., 2014). These initial
validation steps highlighted the need to expand the NH3 monitoring
network to achieve a more complete validation of the NH3 satellite
observations (Van Damme et al., 2015). The comparison here is a contribution
to that effort and benefits from a relatively high spatial density of
monitoring sites in a region with substantial NH3 emission and
concentration gradients.
In Fig. 6a IASI-retrieved column distributions averaged over the
ground-based measurement period from 2012 to 2015 are compared with the
Radiello passive NH3 surface concentration measurements in northeastern
Colorado. Only IASI observations with a relative error below 100 % or an
absolute error below 5 × 1015 molec., cm-2 were used for
comparison in the latitude range from 39 to 42∘ N
and longitude range from 102 to 106∘ W. This
combined filtering using relative and absolute thresholds on the error
avoids introducing a bias when averaging and results in considering 98.5 %
of the IASI cloud-free morning observations for this area. Overall, the IASI
observations and Radiello passive measurements show similar spatial
patterns. The IASI columns exceed 2 × 1016 molec. cm-2
around the KY site and decrease moving away from concentrated agricultural
areas.
In order to further explore the temporal concentration variability,
including the postulated contributions from wildfire to local ambient
NH3 concentrations, averages of IASI measurements (based on weekly or
biweekly Radiello passive sampling periods) above the FC_W
site are shown in Fig. 7. In general, similar temporal trends are found
between the Radiello passive measurements (blue) and IASI observations
(red). Elevated NH3 concentrations during the High Park Fire period in
June 2012 are seen in both the satellite and surface measurements. It is
also interesting to note the relatively high IASI-NH3 total column
measured at the beginning of June 2011 (8.5 × 1015 molec. cm-2 ), which could be linked with transported wildfire
plumes at higher altitude (Fig. S5) not captured by surface measurements.
The similar spatial and temporal patterns captured show the respective
consistency of the IASI measurements and the Radiello network to monitor
regional NH3 variations in northeast Colorado. The passive measurements
provide an accurate, long-term record of spatial variability and surface
concentration trends, while the IASI satellite NH3 columns provide
higher time resolution snapshots of conditions over the region, including
plumes elevated above the surface.
Comparison with CAMx model simulations
Simulations with CAMx version 6.1 were performed with two-way nested domains
and horizontal grid size resolutions of 36, 12, and 4 km (Fig. S6).
The outermost domain includes the continental US, southern Canada, and
northern Mexico; the 12 km domain extends over the western states; and the 4 km domain extends over Colorado, Wyoming, and Utah. The Weather Research
and Forecasting Model (WRF), Advanced Research WRF (ARW) v3.5.1, was used to
develop meteorological inputs to the air quality model (Skamarock et al.,
2005). The input meteorological data represent conditions as they occurred
in 2011. A performance evaluation of the WRF simulations was conducted by
The University of North Carolina at Chapel Hill (Three-State Air Quality
Modeling Study (3SAQS) – Weather Research Forecast 2011 Meteorological
Model Application/Evaluation available at: http://vibe.cira.colostate.edu).
Model performance was evaluated by the Intermountain West Data Warehouse
team (Adelman et al., 2015). The model met performance standards as
recommended by the US EPA for regulatory photochemical modeling purposes
(https://www3.epa.gov/scram001/guidance/guide). In
general, model performance statistics for ambient concentrations of ozone
and many individual species of fine particles fell within the recommended
ranges. However, concentrations of organic and elemental carbon (two
particulate matter species) are overpredicted by the model and performance
criteria fall outside the recommended range. Additionally, modeled
particulate NO3- concentrations are overpredicted in the winter,
and underpredicted in the summer in most locations. Model performance with
respect to NH3 can be best evaluated using the measurement data
presented in this report.
(a) Comparison of seasonal 2012 NH3 concentrations
(µg m-3): passive measurements (solid lines) and 2011 CAMx modeling
results (dashed lines); (b) comparison of seasonal NH3 passive
measurements normalized by surface concentrations (solid lines) and CAMx
modeling results (dashed lines). Each profile is normalized such that the
concentration at the lowest level is set to 100.
The Sparse Matrix Operator Kernel Emissions (SMOKE) processing system
(https://www.cmascenter.org/smoke/documentation/3.1/html/;
Houyoux et al., 2000) was used to prepare the emissions inventory data in a
format that reflects the spatial, temporal, and chemical speciation
parameters required by CAMx. The emissions inventory is based on the 2011
NEI v1 (http://www.epa.gov/ttn/chief/net/2011).
Important updates to the 2011 NEI included a detailed oil and gas inventory,
and the spatial allocation of livestock emissions using latitude/longitude
location data of livestock facilities (IWDW; Adelman et al., 2015). Boundary conditions were
developed using the Model for Ozone and Related chemical Tracers (MOZART) and
represent the 2011 modeling period (Emmons et al., 2010).
Figure 6b illustrates an evaluation of CAMx simulated NH3 concentrations
both spatially and across time. Generally speaking, CAMx reasonably
reproduces average observed NH3 in the northeastern plains of Colorado,
with a model / measurement ratio of 91 % averaged across all measurement
locations. This is a much closer match than a separate 12 km resolution CMAQ
summer 2014 model comparison to surface passive ammonia measurements
(including some of the observations collected in the current study) reported
by Battye et al. (2016), who found that the average measured concentration
was 2.7 times higher than the modeled concentration. Despite the better
average comparison of measurements with the CAMx prediction reported here,
however, the CAMx simulation tends to overestimate concentrations near
major NH3 sources (e.g., at the KY monitoring site), while
underestimating NH3 concentrations at sites further away from feedlot
locations (Fig. 8). Across our measurement locations, the model performance
is best at GY, a site surrounded by, but not immediately adjacent to, large
NH3 sources. The modest overestimation of NH3 concentration at the
KY site is likely an artifact of model resolution and the assumption that
emissions are immediately and homogeneously dispersed throughout the grid
cell in which they are emitted. A model–measurement mismatch moving farther
away from NH3 source locations could result from a number of factors,
including smaller and/or non-agricultural sources (e.g., suburban
N fertilization or transportation) underrepresented in the emissions
inventory, possible overestimation of NH3 deposition in the model,
which does not account for the bidirectional nature of NH3 exchange
with the surface, or a tendency for the model to more actively move surface
NH3 emissions aloft during downwind transport than occurs in the real
atmosphere.
Figure 9 shows both measured (measurements taken in 2012) and modeled (2011)
vertical concentrations of NH3 at the BAO tower location. Although
these comparisons are for two adjacent years, the results presented earlier
demonstrate that seasonal average concentrations across the region are
typically similar from year to year. Modeled vertical NH3
concentrations are reported from the lowest 6 levels of the model, up to
approximately 325 m above the surface. The model height represented by the
value plotted on the y axis in Fig. 9a represents the top of the layer from
which the corresponding concentration is reported (i.e., the surface or
lowest model layer is reported at 24 m – the approximate height of the
surface layer). Model layer height is based on the meteorological model and
modeled pressure and is not fixed (http://vibe.cira.colostate.edu/wiki/Attachments/Modeling/3SAQS_2011_WRF_MPE_v05Mar2015.pdf).
The vertical concentrations are homogeneous within each model layer.
Therefore, the model is not able to capture the detailed vertical pattern
shown from 0 to 10 to 20 m by the observations. The model–measurement
comparisons of vertical profiles demonstrate a significant underprediction
by the model at all elevations in all four seasons. The underprediction at
the surface is consistent with the observation above that the model tends to
underestimate NH3 concentrations farther from the major regional
feedlot sources. The fact that the model also underpredicts NH3 aloft
suggests that the surface mismatch is not simply a result of excess vertical
transport of NH3 in the model. Model vertical NH3 concentration
profiles normalized for surface concentration are shown in Fig. 9b and
compared to similarly normalized measurements. These profiles suggest that
the model does a reasonable job of capturing the shape of the observed
vertical concentration gradient, although the relative concentration
decrease with height in the model is a bit stronger than observed via
passive sampler measurements in each season.
Conclusions
Six years of passive sampler measurements revealed strong spatial
differences in NH3 concentrations in northeastern Colorado. Summer
average weekly NH3 concentrations ranged from 2.7 to
42.7 µg m-3. The lowest average NH3 concentration always
occurred at a remote prairie site, while average NH3 concentrations
nearly a factor of 15 greater were observed at a site near a large animal
feeding operation. Based on 6 years of available data, no significant
regional long-term trends were detected in NH3 concentrations at six of
the nine study sites, consistent with similar seasonal meteorological
conditions and relative stability in regional livestock head counts over the
period. Two sites near animal feeding operations (GY and KY) showed evidence
of an increasing NH3 concentration trend, while a decreasing trend was
evident at a third site (BH). Further effort is warranted to see whether
changes in local animal feeding operations might explain these trends. The
NH3 concentration levels observed in this study provide an important
reference point for evaluating the success of future efforts to mitigate
regional NH3 emissions through voluntary implementation of BMPs as part
of a strategy to reduce nitrogen deposition levels and impacts in nearby
Rocky Mountain National Park.
Measurements of NH3 at the BAO meteorological tower near Erie, Colorado,
provide the first long-term insights into vertical gradients of NH3
concentrations in the region and some of the first long-term measurements of
this type anywhere in the world. A general pattern of decreasing NH3
concentrations with height above 10 m was observed in all seasons, as was a
decreasing concentration below 10 m height. The lowest average
concentrations were observed in winter at the surface along with a steeper
vertical concentration gradient. Higher average concentrations were observed
in summer at all altitudes along with a shallower vertical concentration
gradient. Surface deposition, vertical dilution, and the formation of
thermal inversions that limit the vertical mixing of regional, surface-based
NH3 emissions appear to have greater influence than temperature and
humidity-driven changes in NH4NO3 gas–particle partitioning on the
observed vertical concentration profiles.
Comparison of measured NH3 spatial distributions with IASI satellite-retrieved NH3 columns reveals that both monitoring techniques capture
similar spatial and temporal variability in northeastern Colorado. These
comparisons lend additional weight to the growing body of evidence
suggesting that satellite retrievals of NH3 columns can provide useful
information about spatial and temporal concentration variability of this key
species, even in regions with strong sources and sharp spatial concentration
gradients. Some temporal differences between satellite and in situ measurements at
the FC_W site appear to reflect NH3 in elevated wildfire
plumes that are observed from the satellite but are not sampled at the
surface.
Measured spatial distributions of NH3 concentrations also provide a
good basis for comparison to regional air quality model simulations. A
comparison with CAMx simulations finds that the model captures average
NH3 concentrations across the study, but tends to overpredict
concentrations close to sources and underpredict concentrations at
locations further away. A comparison of measured and modeled vertical
profiles in a non-source region reveals an underprediction of modeled
NH3 from the surface up to 300 m in all seasons. The mismatch aloft
provides evidence that the difficulty for the model in reproducing surface
observations away from sources is not a simple result of excess vertical
mixing of NH3 emissions in the model. Rather, the model emission
inventory may be missing or underpredicting smaller or non-agricultural
NH3 sources or, perhaps more likely, the model may be overpredicting
surface NH3 deposition due to the absence of bidirectional treatment of
NH3 atmosphere–surface exchange. Although additional research is
definitely needed, we expect the NH3 concentrations and
spatial/vertical differences presented here to be useful in constraining
future simulated concentrations of atmospheric NH3 in chemical
transport models.