Introduction
Global trends of increasing gas-phase ammonia (NH3) concentrations
(Erisman et al., 2008) have multiple environmental implications. As part of
the global nitrogen cycle (Fowler et al., 2013), excessive NH3
deposition promotes alga blooms, degrades water quality, and may be toxic for
ecosystems (Krupa, 2003; Camargo and Alonso, 2006). NH3 is one of
the most important atmospheric alkaline species, as it influences the pH of
clouds, fogs, precipitation (Wells et al., 1998), and fine particles
(PM2.5) (Guo et al., 2017c). Agricultural practices, including use of
synthetic nitrogen-based fertilizer and domesticated animal manure, are the
major anthropogenic NH3 sources (Galloway et al., 2003; Aneja et
al., 2009; Zhang et al., 2018). Minor contributions include biomass burning
(e.g., forest fires), fossil fuel combustion, and vehicle catalytic
converters (Perrino et al., 2002; Behera et al., 2013). Higher temperatures
resulting from global warming can also potentially enhance NH3
emissions (Skjøth and Geels, 2013). Given that fertilizer usage supports
food production for about half the global population (Erisman et al., 2008),
NH3 emissions are linked to world population and so expected to
increase into the 21st century (Gerland et al., 2014). Compared to the
limited regulation of NH3 emissions, other anthropogenic air
pollutants that are linked to acidic atmospheric species, such as sulfur
dioxide (SO2) and nitrogen oxide (NOx), are
regulated through air quality standards, which accounts for their observed
decreasing gas and aerosol concentrations in the United States (Hand et al., 2012;
Russell et al., 2012; Hidy et al., 2014), western Europe, and China (Warner
et al., 2017). Decreasing trends of SO2 and NOx
emissions are expected to continue on global scales throughout the century
(IPCC, 2013). The contrast between increasing NH3 and decreasing
SO2 and NOx leads to changes in aerosol
composition and mass concentration. NH3 reacts rapidly with the
oxidized products of SO2 and NOx, sulfuric
(H2SO4) and nitric (HNO3) acids, to form ammonium
sulfate ((NH4)2SO4, or other forms such as
NH4HSO4, (NH4)3H(SO4)2), and ammonium
nitrate (NH4NO3) aerosols, which globally constitute an
important fraction of ambient PM2.5 mass (Kanakidou et al., 2005; Sardar
et al., 2005; Zhang et al., 2007). These reaction pathways link NH3
to PM2.5 mass and its subsequent impacts on human health (Pope et al.,
2004; Lim et al., 2012; Lelieveld et al., 2015; Cohen et al., 2017) and the
climate system (Haywood and Boucher, 2000; Bellouin et al., 2011; IPCC,
2013).
A number of studies using regional- or global-scale models have investigated
NH3 controls as a way to reduce PM2.5 mass to meet air quality
standards (Erisman and Schaap, 2004; Pinder et al., 2007, 2008; Paulot and
Jacob, 2014; Bauer et al., 2016; Pozzer et al., 2017). The fundamental
premise is that reducing NH3 will increase aerosol acidity (i.e.,
lower aerosol pH) and prevent the formation of NH4NO3, reducing
overall PM2.5 mass. As a secondary effect, lower pH can also reduce the
sulfate production rate, such as the in-cloud SO2 oxidation by
O3 (Wang et al., 2011; Cheng et al., 2016; Paulot et al., 2017).
Use of large-scale models to assess effectiveness of NH3 controls
requires (i) good predictions of a range of pertinent emissions and sinks
(NH3; NOx; SO2; and nonvolatile cations,
NVCs) and (ii) accurate representation of their applicable atmospheric
chemical processes. Thermodynamic modules in different levels of complexity
are then applied to determine sensitivities to the precursors (e.g.,
NH3, HNO3). In some cases (Pozzer et al., 2017), the
aerosol pH is explicitly determined with an embedded thermodynamic model,
such as ISORROPIA-II (Fountoukis and Nenes, 2007). Due to the complexities
from all these factors, chemical-transport-model-predicted responses to
changing emissions may not align with observations. For example, the
sensitivity of PM2.5 pH in the Community Multiscale Air Quality Modeling
System (CMAQ) simulations to the mass of crustal material apportioned to the
PM2.5 size range can have important effects on anticipated responses to
these changing emission trends. Vasilakos et al. (2018) have shown that
including too much crustal material in PM2.5 results in a predicted
increasing trend in both aerosol pH and concentrations of
NH4NO3, which is counter to observations (Weber et al., 2016).
Overall, calculating aerosol pH is a more accurate approach that provides a
fundamental understanding of the factors controlling
HNO3–NO3- partitioning and therefore enables a direct
evaluation of different studies. Furthermore, it is also useful to determine
aerosol pH since it has broad application to many other important aerosol
processes. For instance, pH is a mediator of many heterogeneous chemical
processes, including various acid-catalyzed reactions (Jang et al., 2002;
Eddingsaas et al., 2010; Surratt et al., 2010); gas–particle partitioning of
species other than HNO3 and NH3, such as organic acids
and halogens (Fridlind and Jacobson, 2000; Young et al., 2013; Guo et al.,
2017a; Nah et al., 2018); and solubility of metals and other nutrient species
(Meskhidze et al., 2003; Nenes et al., 2011; Longo et al., 2016; Stockdale et
al., 2016; Fang et al., 2017).
In this study, we apply a more direct approach, where measured gas and
particle concentrations and the thermodynamic model ISORROPIA-II are used directly in a sensitivity analysis to evaluate the
effectiveness of NH3 emission controls on fine particle mass
relative to NOx control. Contrasts are made between sites
that have a wide range in NH3 concentrations and aerosol
composition, with a focus on a 1-year data set collected in Cabauw, the
Netherlands (Schlag et al., 2016). This site had year-round high
NH3 concentrations (average 7.3±6.0 µg m-3,
∼10 ppbv), with nitrate comprising a significant fraction of the fine
particle mass (30 % NO3- of PM1), and there was a
strong seasonal temperature variation. The goal is to establish a transparent
and fundamental understanding on when NH3 emission controls could
be an effective way to alter aerosol pH to reduce ammonium nitrate aerosol
concentrations, without the use of a full chemical transport model.
Methods
Sampling sites
Cabauw. One year (July 2012 to June 2013) of online aerosol and
gas measurements of inorganic species were performed at the Cabauw
Experimental Site for Atmospheric Research (CESAR; 51.970∘ N,
4.926∘ E), near the village of
Cabauw, the Netherlands. Cabauw is a rural site situated approximately 45 km
from the Atlantic Ocean and surrounded by
agricultural land. Northwestern Europe has fairly high NH3
concentrations with yearly averages ranging from 1 to
14 µg m-3 (median as 4.2 µg m-3) for the
Netherlands in 2013, reported by the Measuring Ammonia in Nature (MAN)
network (Lolkema et al., 2015). Satellite-derived 14-year average for western
Europe is 3 ppbv (∼2.3 µg m-3) (Warner et al., 2017).
Cabauw was somewhat higher due to intensive agriculture in the region with
observed yearly NH3 average of 7.3±6.0 µg m-3
(∼10 ppbv). Site details, instrumentation, and measurement
intercomparisons can be found in Schlag et al. (2016). The data used in this
analysis are from a monitor for aerosols and gases (MARGA, Applikon
Analytical BV) that was operated by the Energy Research Centre of the
Netherlands (ECN). The instrument performs online measurements of soluble
inorganic gases collected in a continuously wetted-wall denuder, followed by
a steam–condensation system for collection of particles. Both the aqueous
samples of gases and particles are measured via ion chromatography (Schaap et
al., 2011; Rumsey et al., 2014), including NH3, HNO3, and
HCl, and particle-phase NO3-, SO42-, Cl-,
NH4+, Na+, K+, Ca2+, and
Mg2+ alternatively between PM1 and PM2.5, with each size
sampled hourly (i.e., a 2 h interval for one size and a 1 h interval for
gas). Measurement uncertainties were below 10 % (Schaap et al., 2011).
The detection limits were 0.05, 0.10, 0.08, and 0.01 µg m-3
for aerosol ions NH4+, NO3-, SO42-,
and Cl-, respectively, and 0.10 and 0.05 µg m-3 for
the gases HNO3 and NH3 (Rumsey et al., 2014). Relative
humidity (RH) and temperature (T) data were collected at the 2 m level
from the CESAR tower and used to represent ground level meteorological
conditions (for an overview see Fig. S7 in Schlag et al., 2016).
Other sites. In addition to the Cabauw site, we analyze the
effectiveness of NH3 reduction for a number of contrasting sites
where we have already reported on aerosol pH in detail. This includes data
from the Southern Oxidant and Aerosol Study (SOAS) (Guo et al., 2015);
Wintertime Investigation of Transport, Emissions, and Reactivity (WINTER)
(Guo et al., 2016); and California Research at the Nexus of Air Quality and
Climate Change (CalNex) study (Guo et al., 2017a). Briefly, the SOAS data
were collected at the Southeastern Aerosol Research and Characterization (SEARCH)
Centreville ground site, representative of the southeastern US background
conditions, from June to July 2013. The WINTER data were sampled from the
National Center for Atmospheric Research (NCAR) C-130 aircraft operating from
Feb to March 2015 mainly in the northeastern US. The CalNex data were
collected from May to June 2010 in Pasadena, California, an urban site that
is part of the greater Los Angeles region. As a further contrast for regions
of very high NH3 concentrations, we include an analysis from
published data in Beijing during winter haze events in 2015 (Wang et al.,
2016), for which pH has also been investigated (Guo et al., 2017c). Table S1
in the Supplement summarizes the conditions at the various sites.
Thermodynamic modeling
The ISORROPIA-II thermodynamic model (Fountoukis and Nenes, 2007) was used to
determine the composition and phase state of an NH4+,
SO42-, NO3-, Cl-, Na+,
Ca2+, K+, Mg2+, and water inorganic aerosol and
its partitioning with corresponding gases. Thermodynamic equilibrium is
assumed between fine particles and gases for all semivolatile inorganic
species, including particle water and water vapor. Timescales for submicron
particles to reach equilibrium are about 30 min (Dassios and Pandis, 1999;
Cruz et al., 2000; Fountoukis et al., 2009). The model is run in “forward
mode” to calculate gas–particle equilibrium concentrations based on the
input of total concentration of inorganic species (e.g.,
NH3 + NH4+,
HNO3 + NO3-, SO42-, and Na+).
SO42- has no gas pair as it is virtually nonvolatile in the
observed temperature ranges of this study (An et al., 2007). The forward mode
gives more accurate and robust results than the reverse mode since it is much
less sensitive to measurement uncertainties (Hennigan et al., 2015).
Inorganic ions are also assumed to be only in the aqueous phase (i.e., no
solid precipitates). This entails a number of assumptions. First, the ambient
RH and the history of the particles' exposure to RH result in a deliquesced
particle. In many cases, diurnal swings in RH (i.e., the maximum RH in early
morning) are generally sufficient to reach the deliquescent point.
Furthermore, efflorescence RHs are generally low and rarely reached by the
ambient RH (10 to 30 %) (Bertram et al., 2011). Thus, a deliquesced
particle is often a good assumption when average ambient RH is above
50 %. For Cabauw, the 1-year mean RH was 81±15 % (±SD),
with RH reaching up to 90 % during diurnal cycles (see Fig. S1a in the
Supplement), making the presence of a liquid phase a reasonable assumption. For
the other sites studied, average RHs were all above 55 % (Table S1). A
second assumption is that most ions are in an aqueous liquid inorganic phase
and only minor fractions reside dissolved in a separate liquid organic phase,
if it exists. This is supported by very good agreement between observed
ammonia gas–particle partitioning with thermodynamic model predictions that
do not consider an organic phase. (See Figs. S3 and S4 for this study;
similar results are found in other studies, e.g., Guo et al., 2015,
2017a; and Nah et al., 2018.) Pye et al. (2018) found only minor differences in
the predicted ammonia partition when an organic phase was considered. It is also
assumed that the particles were internally mixed and that pH did not vary
with size. Mixing state of the nonvolatile cations can affect particle
composition and pH (Zhu et al., 2016), but the effect on predicted fine
particle pH is small if a minor fraction of nonvolatile sulfate is internally
mixed with the nonvolatile cations (Guo et al., 2017b); however, it can add
uncertainty to predicted nitric acid partitioning (discussed below in
Sect. 2.3). Since there are no data on the mixing state and the mass
concentrations (or mole fractions) of nonvolatile cations are generally small
(discussed below in Sect. 3.2 and also see Table S1), internal mixing is
assumed in the following analysis.
With increasing pH (e.g., above 2 for oxalate), organic acids can be found at
increasing quantities in the particle phase (Nah et al., 2018). However,
organic acids are not considered in the ISORROPIA-II pH calculations. In
Cabauw, it has been reported that excess NH4+ (i.e.,
NH4+ not paired with SO42-, NO3-, and
Cl-) was correlated with (di-)carboxylic organic acids. Excess
NH4+ on average constituted only 5 % of the
NH4+ reported by an aerosol mass spectrometer (AMS), so it is
likely to have a small effect on predicted pH (Schlag et al., 2017). This is
confirmed by the good agreement between measured and ISORROPIA-II-predicted
NH3–NH4+ partitioning without considering organic
acids or other organic species (see Sect. 3.2). Although a recent modeling
study has suggested that ambient NH3 concentration can be decreased
by as much as 31 % in winter and 67 % in summer in the US, due to the
reactive uptake of NH3 by secondary carbonyl compounds (Zhu et al.,
2018), this process does not appear to have an impact on
NH3–NH4+ partitioning and predicted pH for the
locations in this study. For the winter haze conditions in Beijing, which had the
highest pH among the sampling sites, including organic acids (i.e., oxalate) in the model calculation of pH
is reported to only reduce pH by at most 0.07 (Song
et al., 2018).
NOx vs. NH3 control
to limit PM2.5 ammonium nitrate?
Following the various assessments of NH3 control on PM2.5 mass
(Erisman and Schaap, 2004; Pinder et al., 2007, 2008; Paulot and Jacob, 2014;
Bauer et al., 2016; Pozzer et al., 2017), we assume the PM2.5 inorganic
nitrate is mainly in the form of semivolatile ammonium nitrate and negligible
in nonvolatile forms, such as Ca(NO3)2, NaNO3, and
similar species, which are generally not found to a large extent in particles
smaller than 1 µm. However, it is noted that in locations where
concentrations of minerals or sea-salt particle components are high, and the
aerosol has aged, formation of semivolatile NH4NO3 will be
perturbed as the HNO3 will evolve over time to the more stable
largely coarse-mode salts (e.g., CaCl2 and NaCl) at the expense of
fine-mode NH4NO3 (see Guo et al., 2017a, for example).
Aerosol organic nitrate species can also contribute to aerosol mass (Farmer
et al., 2010; Perring et al., 2013; Xu et al., 2015), and may respond to
NOx control, but are not considered here. For the 1-year
Cabauw data set analyzed here, 9 % of the aerosol nitrate was inferred to
be organic nitrate, calculated from the difference in Aerosol Chemical
Speciation Monitor (ACSM) nitrate and MARGA-measured nitrate (Schlag et al.,
2016). Higher fractions (34 % to 44 %) have been reported for
European submicron aerosols (Kiendler-Scharr et al., 2016).
NOx emission controls could lead to a change in the relative
importance of inorganic and organic nitrate (Edwards et al., 2017).
Focusing just on ammonium nitrate, there are two fundamental ways to control
PM2.5 nitrate: (i) limit the precursors of nitrate aerosol, that is
HNO3, or (ii) move the nitrate out of the aerosol by reducing the
aerosol pH (increasing the particle acidity). The equilibrium aerosol nitrate
concentration is given by
NO3-=εNO3-×NO3T,
where NO3- is the concentration in
air of semivolatile aerosol nitrate and ε(NO3-) is
the fraction of NO3- in the particle phase relative to gas plus
particle nitrate (HNO3 + NO3-), which is defined as total
nitrate, NO3T. Equation (1) is the definition of ε(NO3-). Because ε(NO3-) depends on
pH, the premise of NH3 control is to reduce ε(NO3-) through decreasing particle pH, whereas
NOx emission controls will mainly reduce NO3T,
although this can also slightly affect pH through aerosol water uptake
(discussed below; see Fig. 4 for example).
NOx control. Emitted NOx can
undergo a variety of reactions that produce a range of compounds
(NOz), including HNO3, peroxynitric acid
(HO2NO2), the nitrate radical (NO3), nitrous acid
(HONO), dinitrogen pentoxide (N2O5), and both gas-phase (e.g.,
peroxyacetyl nitrate – PAN)
and particle-phase nitrate and organic nitrate species. Once gas-phase
HNO3 or particle-phase NO3- is formed, equilibrium
between the phases will re-establish gas and particle concentrations.
HNO3 is largely formed by NO2 reaction with the hydroxyl
radical (OH) and at night through the nitrate-radical–N2O5
pathway. Modeling studies show that HNO3 can be the most
significant of NOz species (Atkinson, 2000) and is
correlated to NOx emissions (Shah et al., 2018). Here we
assume, to a first approximation, that NOx mainly produces
HNO3 (either directly through reaction with OH or indirectly
through production of N2O5) that partitions to the particle to
form semivolatile aerosol nitrate and rapidly reaches equilibrium.
NO3T concentrations are then directly related to
NOx control. Use of more detailed modeling approaches can
better assess the relationship between NOx emissions and
NO3T. For example, we are not considering competing chemical
pathways that lead to organic nitrates versus inorganic nitrate that is in
equilibrium with gas-phase HNO3.
NH3 control. The effectiveness of ammonia control in
reducing NH4NO3 burdens depends on ε(NO3-) and how it varies with pH, actual pH of the ambient
aerosol, and the sensitivity of ambient aerosol pH to changes in
NH3 concentration. From thermodynamic equilibrium, ε(NO3-) can be derived from the solubility, Reaction (R1), and dissociation,
Reaction (R2), of HNO3:
HNO3g↔HNO3(aq),HHNO3HNO3(aq)↔NO3(aq)-+H(aq)+,Kn1.
Assuming the solution is ideal, ε(NO3-) as a
function of pH can be predicted solely based on known properties of
HNO3; the HNO3 Henry's constant, HHNO3,
and the acid dissociation constant, Kn1 (HHNO3 and
Kn1 are T dependent); ambient T; and particle liquid water content.
The latter is often estimated by only considering water associated with
inorganic species (Wi; µg m-3), determined from
measured inorganic aerosol components and relative humidity. Liquid water
associated with organic species can also be included but normally has a minor
influence on pH of much lower hygroscopicity and the logarithmic nature of pH
(Guo et al., 2015). A more accurate result may be achieved by using measured
particle water concentrations.
By combining the equilibrium of Reactions (R1) and (R2),
εNO3-=HHNO3∗WiRT0.987×10-14γNO3-γH+10-pH+HHNO3∗WiRT0.987×10-14,
where 0.987×10-14 is a unit conversion factor (from converting atm
and µg to SI units), R (J mol-1 K-1) is the gas
constant and HHNO3∗=HHNO3Kn1
(mol2 kg-2 atm-1) is the combined molality-based equilibrium
constant of HNO3 dissolution and deprotonation, and γ
represents the activity coefficients (equal to 1 if assuming an ideal
solution). Derivation of Eq. (2) and references for the temperature-dependent
equilibrium constants, and similar equations for NH3 and HCl
partitioning, can be found in the Supplement of Guo et al. (2017a).
Predicted particle-phase fraction of total nitrate, ε(NO3-), versus pH for 1-year average condition in Cabauw based
on Eq. (2). The blue-color zone denotes where
HNO3–NO3- (nitric acid–nitrate) partitioning is not
affected by changes in pH, while the red-color zone shows the region where
adjusting pH will change HNO3–NO3- partitioning and
hence NO3- concentration.
NO3- is most sensitive to pH at ε(NO3-) = 50 %,
which corresponds to a pH defined as pH50.
Results and discussions
The nitrate partitioning S curve
The S curve given by Eq. (2) provides a conceptual basis for the effect of
ammonia control, through changes in aerosol pH, on particle nitrate. Figure 1
shows the characteristic S-shaped curve of ε(NO3-) plotted as a function of pH using Eq. (2), for the
yearly average conditions in Cabauw and with activity coefficients extracted
from ISORROPIA-II (γNO3-γH+=0.24).
Including nonideality shifts the ε(NO3-) S curve
to lower pH by approximately 0.6 units (shown as Fig. S2).
Figure 1 shows that there are three pertinent pH regions: (1) low pH, where
ε(NO3-) asymptotically approaches 0 and
practically all NO3T is in the gas phase; (2) ε(NO3-) varies between approximately 0 and 1 and is highly
sensitive to pH variations; and (3) higher pH, where ε(NO3-) approaches 1 and practically all NO3T is in
the particle phase. This demonstrates that for the 1-year average
conditions in Cabauw, there is a certain range in ambient pH where
NH3 control to alter ambient pH will result in a change in
NO3- (i.e., region 2 where pH is between 0 and 3). The greatest
change in NO3- to a lowering of pH occurs when ε(NO3-) is near 50 % (referred to as pH50).
It follows that NH3 control will only lead to reduction in
NO3- if ambient particle pH is within region 2 of Fig. 1. If pH
is in region 1 there is no need for NH3 control since pH is
sufficiently low that little NO3- exists, and if pH is in
region 3 the sensitivity of pH to reducing NH3 will determine the
effectiveness of NH3 controls. For example, NH3 first
needs to be reduced to move particle pH to the transition point between
region 2 and 3, where ε(NO3-) starts to drop. (Note
that NH3 control also affects particle mass by changing
NH4+ concentrations; this is discussed more below in Sect. 3.4.)
The S curve of Fig. 1 applies for a given situation (see Eq. 2), which
changes as the particle composition or ambient conditions (RH, T) change.
For example, if NH3 concentrations change, the inorganic particle
composition changes, which affects particle water and activity coefficients
in Eq. (2), resulting in a shift in the ε(NO3-)
curve. Thus, these curves provide only a sense of the general state of how
NO3- responds to changes in NH3. A full thermodynamic
model needs to be run to actually determine the new ε(NO3-) when conditions change. This analysis is provided in the
later part of the paper. The S curve, however, provides valuable insight on
sensitivity of ε(NO3-) to pH for a given situation
(i.e., what region of Fig. 1).
pH predicted in Cabauw
High concentrations of aerosol inorganic species were observed during the
1 year of observations at the CESAR tower. The mass fractions of
NO3-, SO42-, NH4+, and Cl-
were on average 30 %, 15 %, 14 %, and 1 %, respectively, of
the 9.5 µg m-3 particle mass (PM1) (Schlag et al.,
2016). The gas–particle partitioning of three semivolatile pairs,
NH3–NH4+, HNO3–NO3-, and
HCl–Cl-, measured with MARGA are compared with the thermodynamic
model predictions (see Sect. 3 in the Supplement for plots). PM2.5 and
PM1 MARGA data sets produce similar results (Fig. S3 versus Fig. S4);
here we mainly discuss predictions based on PM2.5. Measured and
ISORROPIA-predicted partitioning of ammonia was in agreement (NH3:
slope = 1.02, R2= 0.997; NH4+: slope = 0.97,
R2 = 0.96) (Fig. S3). NO3- (slope = 1.01, R2= 0.987) and Cl- (slope = 0.98, R2= 0.91) were also in
agreement. However, for unknown reasons, gas-phase components of these two
species showed significant discrepancies (R2 of 0.13 to 0.17). We note
that these discrepancies may be associated with the very low gas-phase concentrations of
these species, in contrast to NH3.
HNO3–NO3- and HCl–Cl- were dominated by
particle phases, ε(NO3-)= NO3- / NO3T =88±11 % and
ε(Cl-) = Cl- / (Cl- + HCl) =66±33 %. The opposite was found for NH3–NH4+:
the gas phase dominated with ε(NH4+) = NH4+ / NHx =19±15 % (total ammonium is referred to as
NHx = NH3 + NH4+), which is
consistent with particle artifacts in the gas collection system possibly
affecting HNO3 and HCl, but having less effect on NH3.
Furthermore, a generally better prediction of
NH3–NH4+ compared to
HNO3–NO3- and HCl–Cl- partitioning has
been observed in our previous studies (Guo et al., 2017a) and is consistent
with the lack of a coarse-mode sink for NH3, in contrast to
HNO3 and HCl, which can react with sodium and other nonvolatile
cations and bias the equilibrium states between fine particles and gases. In
summary, all the semivolatile inorganic species in the particle phase
(NO3-, NH4+, and Cl-) are predicted with
high accuracy, as well as NH3–NH4+ partitioning;
therefore, particle water and pH predictions by ISORROPIA-II are expected to
be reasonable.
ε(NO3-) versus pH for various field
studies based on the average temperature, liquid water, and activity
coefficients for each study, according to Eq. (2). The WINTER study curve
overlaps completely with the Cabauw 1-year average curve in red color. The
input can be found in Table S1. Vertical lines are the study average ambient
fine particle pH calculated with ISORROPIA-II and error bars show the
variability in pH as 1 standard deviation. S curves and ambient pH for each
site or season can be matched by color. For a more direct comparison between
seasons at a specific region, Fig. S5 shows separate curves and ambient pH
plots.
As noted above, the presence of water-soluble nonvolatile cations (which here
include Na+, K+, Ca2+, and Mg2+) can
affect the bulk pH analysis. In Cabauw, NVC effects can be assessed by
comparing hourly PM1 and PM2.5 data, since these mechanically
generated species are largely found in particles larger than 1 µm
in diameter. Average NVC mole fractions (i.e., NVCs divided by the total
inorganic species, not including liquid water) were consistently small:
5.7 % for PM1 and 5.9 % for PM2.5. However, Na+
was slightly higher in PM2.5 at 0.14±0.25 µg m-3,
compared to 0.05±0.09 µg m-3 for PM1. The small
and nearly identical fractions of NVCs result in the same predicted pH for
PM1 and PM2.5; in both cases pH = 3.7±0.6. Therefore, we
focus on the PM2.5 in the following discussion due to the similar
partitioning predictions and pH for PM1 and PM2.5 (Fig. S3 and S4).
A diurnal pattern of ambient particle pH is observed in Cabauw, similar to
other studies (Guo et al., 2015). For example, for the nighttime period from
01:00 to 07:00, the average pH is 3.9, whereas for the daytime period
of 13:00 to 18:00, the pH is ∼3.5. The difference is mainly
driven by the diurnal variation in liquid water content (see Fig. S1).
Prediction of (a) particle pH; (b) particle-phase
fractions of total nitrate, ε(NO3-); (c)
ammonium and nitrate mass concentration; and (d) particle-phase
fractions of total ammonium, ε(NH4+), for a wide
range of ammonia. The simulations are based on the 1-year
(July 2012–June 2013), summer (June–August 2012), and winter
(December 2012–February 2013) average conditions at the Cabauw site with
NHx (NH4+ + NH3) left as a free
variable. The measured NH3 ranges for the 1-year span are also
shown as the lighter (min–max) and darker (25 %–75 % percentiles)
orange-color zones. Plot (a) also includes the predicted pH versus
measured NH3 data for the entire study and colored by ambient
temperature.
Contrasts in pH and ε
(NO3-) between studies
Figure 2 includes a comparison of ε(NO3-) versus pH
for the different locations and seasons (Fig. S5 shows separate plots for
each region). The ε(NO3-) curves are plotted based
on the campaign average conditions (i.e., T, Wi, and γNO3-γH+; all listed in Table S1). Two sub-data sets in
Cabauw, summer (June–August 2012) and winter (December 2012–February 2013),
are shown together with the 1-year whole data set. As seen for Cabauw,
lower temperatures (dark blue vs. red vs. orange lines in Fig. 2) shift
HNO3–NO3- partitioning to favor the particle phase due
to (i) the effect of T on nitric acid Henry's law and dissociation constants and
(ii) the explicit effect of T in Eq. (2). For example, at given activity
coefficients and liquid water levels, a decrease from 20
(∼ summer) to 0 ∘C (∼ winter) shifts ε(NO3-) to lower pH by roughly one unit. The differences between
the ε(NO3-) curves are also caused by variations in
liquid water and to a lesser degree by variation in activity coefficients.
In general, the summer curves (the right three curves) are at higher pH and
the winter curves are at lower pH.
In addition to the S curves, Fig. 2 shows the average ambient particle pH
predicted by ISORROPIA-II for each of the studies. Note that pH could also be
inferred from the S curve and measured ε(NO3-) but
is more uncertain and requires activity coefficients for nonideality
effects. A comparison between Eq. (2)-predicted ε(NO3-) versus pH and observed ε(NO3-)
versus ISORROPIA-predicted pH is shown in Fig. S6 and confirms the consistency
between the ISORROPIA-predicted pH and S curve given by Eq. (2). (A plot of
ε(NH4+) vs. pH is also shown in Fig. S6.) Fine
ambient particle pH varies among the sites. The pH of 3.7±0.6 in
Cabauw is higher than several other regions, such as the SE US (pH = 0.9±0.6), the NE US (0.8±1.0), and the SW US (1.9±0.5), but
slightly lower than the China haze ambient particle pH of 4.2. The higher
ambient particle pH is generally associated with higher concentrations of
NH3 and NO3-. Particle pH is affected by coupling
between many variables, hence the need for a thermodynamic model.
ISORROPIA-II predicts the overall resulting equilibrium values and associated
pH. Particle nitrate has a secondary effect on pH by increasing particle
liquid water and diluting H+ aqueous concentrations, resulting in
slightly higher pH. This effect is less pronounced when SO42-
levels exceed NO3-, meaning that liquid water is mainly
controlled by nonvolatile SO42-. Thus, NH3,
NO3-, and particle pH are coupled. Regions of higher
NH3 will have higher pH, which can lead to higher NO3-
(when in region 2 of Fig. 1). The highest observed NH3
(12.8 µg m-3) and NO3-
(26 µg m-3) concentrations were found for the Beijing haze
condition. The Cabauw 1-year average NH3 was lower at
7.3 µg m-3, and NO3- was on average
4.7 µg m-3. The lowest NH3 and NO3-
levels were observed in the US studies: for example,
1.37 µg m-3 NH3 and 3.58 µg m-3
NO3- in the SW US, and only 0.39 µg m-3
NH3 and 0.08 µg m-3 NO3- in the SE US,
both in summer.
The intersection of the ε(NO3-) S curves with
ambient particle pH in Fig. 2 (i.e., intersection of vertical line and
corresponding site S curve) provides contrast in the average ε(NO3-) at each site and hence how much NH3
control will be needed to shift ε(NO3-) to 50 %
and a corresponding pH of pH50. The lowest ε(NO3-) was found in the SE US at 22 % in summer and a
higher ε(NO3-) in the NE US in winter at 39 %.
The Cabauw site also had higher ε(NO3-) in winter
(91 %) than summer (84 %). Additionally, the SW US site observed on
average 54 % ε(NO3-) in summer and the China haze
had ∼100 % ε(NO3-) in winter. These data
show that, in the SE US in summer, ε(NO3-) is
generally so low that shifting pH by changing NH3 emissions will
not greatly influence NH4NO3 concentrations since most of it is
already in the gas phase. Higher NH3 can increase
NH4NO3, but large changes in NH3 are needed in these
regions to change pH (Weber et al., 2016). For the SW US summer,
NO3- partitioning is sensitive to changes in pH with a 54 %
ε(NO3-). In the Beijing winter, substantial decrease in
pH is needed to evaporate NH4NO3, even more so than Cabauw in
winter. For Cabauw, a substantial reduction in ambient pH would be needed to
evaporate NO3- since the current pH is on the flat zone of the S
curve (region 3), where ε(NO3-) is near 100 %.
In summer, however, a much smaller reduction in ambient particle pH would
result in a decrease in NO3-.
ISORROPIA-predicted PM2.5 pH (first column), liquid water content
(Wi, second column), ε(NO3-) (third
column), ammonium and nitrate (fourth column), and aerosol inorganic mass
concentrations (fifth column) as a function of changes in NHx
(NH4+ + NH3, first row), NO3T
(NO3- + HNO3, second row), and SO42-
(third row). Simulations are based on average conditions of 1-year, summer,
and winter observational data in Cabauw, the Netherlands, and changing only
NHx, NO3T, and SO42- from the
average conditions. The black dashed lines in the pH figures identify the
critical pH value of 3.
Simulation of particle mass reduction with a thermodynamic model
Sensitivities of pH and nitrate partitioning to NH3
concentration
In the above analysis, ε(NO3-) versus pH curves
relative to ambient particle pH are used to provide insight on how
ε(NO3-) is expected to change with small changes in
pH. The S curves are based on the average ambient conditions for each time
period, and variables, such as particle water and activity coefficients, are
held constant. But changes in NH3 concentration will vary aerosol
composition, liquid water content, and the activity coefficients, which in
turn modulates the S curve, Eq. (2). To address this, in the following
analysis, we run ISORROPIA-II for various input NHx
concentrations, while T, RH, NO3T, and SO42- are
held constant, and plot various parameters of interest. This takes into
account the various aerosol composition and gas-phase species concentrations
through the consideration of the partitioning of all semivolatile species, including
water, and how this affects thermodynamic properties, such as activity
coefficients.
First, we consider the extent of NH3 control needed to reduce
NH4NO3, which depends on the response of pH to changes in
ambient NH3 concentration, which in turn is related to
NH3 emissions (i.e., changes in NHx). In a
previous study, we show that for average conditions at the various sites
discussed above, a general rule is that an order of magnitude reduction in
NH3 lowers pH by about one unit (Guo et al., 2017c) (ΔpH / Δ(log10NH3) are listed in Table S1). At the
Cabauw site, the responses in pH to changes in NH3 are similar to
these other locations; the linear fitted curves for the semi-log plot in
Fig. 3a give slopes of 1.00 in winter, 1.16 in summer and 1.05 for the
1-year average (all R2 > 0.99). Figure 3a also shows
predicted pH versus measured NH3 based on hourly average data. How
pH changes with temperature for a constant NH3 can also be seen in
Fig. 3a; higher temperature leads to lower particle pH due to volatilization
of semivolatile NH4+, NO3-, and particle water. The
physical explanation for this is that, with higher temperature,
NH4+ is converted to NH3 and releases one H+ to
the particle phase, whereas NO3- is converted to HNO3
and results in loss of one H+ from the particle phase. The former
process dominates over the latter due to the differences in the temperature
dependency of equilibrium constants (see Fig. S7) and the greater loss of
NH4+ from NH4NO3 and (NH4)2SO4
compared to less loss of NO3- only from NH4NO3,
leading to a net increase in particle H+ and lower pH. The loss of water
associated with NH4+ and NO3- further reduces pH,
as the H+ becomes more concentrated. The water effect is also seen in
the diurnal pH trends (see Fig. S1b).
This analysis also permits assessing how ε(NO3-),
the sum of NH4+ and NO3-
(NH4+ + NO3-), and ε(NH4+) responds to changes in NH3. Figure 3b shows
that it takes a factor of 1000 change in NH3 concentration (∼3
pH units) to reduce ε(NO3-) from ∼100 % to
∼0 % (i.e., from complete particle phase to complete gas phase).
Also, a change temperature of ∼8 ∘C shifts ε(NO3-) equivalent to roughly an order of magnitude change in
NH3 concentration. (For reference, ΔT between winter and
1-year averages is 6.6 ∘C and ΔT between 1-year average
and summer averages is 8.8 ∘C). Figure 3b and c again show that
larger reductions in NH3 are needed in winter compared to summer to
reduce NO3-. In Cabauw, only during the highest temperature
periods is a NH3 control policy immediately effective.
The response of ε(NH4+) to changes in NH3
is shown in Fig. 3d. The S curves are reversed compared to ε(NO3-) due to opposite base and acid partitioning responses to
changes in pH. Thus, lowering NH3 reduces ε(NO3-), reducing NO3- for constant
NO3T, but raises ε(NH4+) as the
particles become more acidic, resulting in relatively more NH4+
in the particle phase and less NH3 in the gas phase. This is
important since although we discuss NH3 emissions, changes in
particle pH also affect NH3 concentrations through changes in
gas–particle partitioning (i.e., ε(NH4+)), but it
is NHx that is really changing through emission controls.
Finally, Fig. 3d shows that temperature has little effect on the ε(NH4+) versus NH3 curves. This is because for
constant Wi and activity coefficients, the ε(NH4+) versus pH S curves move in the opposite direction with
change in temperature than the ε(NO3-) versus pH S
curves; ε(NH4+) shifts to a lower pH region and
ε(NO3-) shifts to a higher pH region with
increasing temperature. This tends to bring the
NH3–NH4+ partitioning versus NH3 curves
together and separate the HNO3–NO3- partitioning
versus NH3 curves for different seasons, considering an increase in
pH at lower temperature and constant NH3 shown in Fig. 3a or vice
versa.
Effects of NH3, NOx, and SO2 emission
control in Cabauw
Here we assess the relative merits of NH3, NOx,
and SO2 control on various aspects of PM2.5 in Cabauw, again
using the full thermodynamic model. Changes in pH, particle water
(Wi), ε(NO3-), mass of
NH4+ plus NO3-, and overall PM2.5 ion mass are
assessed when changes are made to NHx
(NH3 + NH4+), NO3T
(HNO3 + NO3-), and SO42-,
representing control of NH3, NOx, and
SO2 emissions, respectively. Each are reduced in steps starting
from 0 % to a 90 % reduction, while holding the other model inputs
constant. The results are shown in Fig. 4. The base values are the 1-year,
summer, and winter average conditions and correspond to 0 % reduction in
all plots.
The first row in Fig. 4 shows that all parameters respond nonlinearly to
NHx reduction, remaining relatively constant until
∼70 % NHx reduction, at which point they start to
rapidly decrease. This is a result of the ε(NO3-)
versus pH S curve of Fig. 1, where little effect is realized until pH reaches
a critical value of about 3 (the horizontal dashed line in Fig. 4 pH plots).
Once pH drops below this, the balance between HNO3 and
NO3- is sharply shifted towards the gas phase due to the
combined effects of reduced particle pH and also reduced particle water
(Wi). An approximate 70 % reduction in NHx
is required in Cabauw, in winter or based on the yearly average data, to
achieve effective reductions in (NH4+ + NO3-)
and particle ion mass. In summer, some minor reductions in the mass
concentrations occur for small NHx reductions, since pH is
slightly lower in summer (3.3) compared to winter (3.9). Despite the seasonal
variations in gas and particle composition, RH, and T, all three pH curves
(1 year, summer, winter) appear to be similar and show a critical pH of
approximately 3; NHx reduction is more effective for pH below 3 but far less effective for pH above 3, consistent with the simplified
analysis above (see Fig. 1).
Response of predicted PM2.5 inorganic mass concentration (first
row) and pH (second row) to reduced levels of NHx (NH3 + NH4+), NO3T
(HNO3 + NO3-), and SO42- for several
studies including (a) the southeastern US summer at a rural ground
site in Centreville, AL (SOAS study); (b) the northeastern US during
winter (WINTER aircraft study); (c) the southwestern US summer at an
urban site in Pasadena, CA (CalNex study); (d, e) the Netherlands
summer and winter conditions at a rural site in Cabauw from this study; and
(f) polluted winter conditions (haze) in Beijing, China. For each
case, the average fine ambient particle pH and ε(NO3-), prior to the reductions, are shown above the figures,
with the columns ordered with increasing ambient particle pH from left to
right. PM2.5 mass fractions of
NH4+–SO42-–NO3- based on study
averages are shown as pie graphs along the bottom.
Effects of reducing NO3T (the second row, Fig. 4b, i.e.,
NOx control) and SO42- (the third row, i.e.,
SO2 control) show different responses. For NOx
control, holding NHx and SO42- constant, a
linear reduction in NO3T causes a linear decrease in
Wi, (NH4+ + NO3-), and PM2.5
ion concentrations simply because ε(NO3-) remains
close to 1 so that NO3- ∼ NO3T. Then a
reduction NO3T is just transmitted directly to Wi
(SO42- is constant so particle hygroscopicity is controlled by
NO3-), (NH4+ + NO3-), and
PM2.5 ions. ε(NO3-) is relatively constant
(more so in winter) because it is ∼100 % and so not sensitive to the
changes in Wi. Lower Wi does shift the
HNO3–NO3- S curve towards a higher pH, but since pH is
affected little, and never drops below the critical value of 3,
HNO3–NO3- partitioning is barely affected by reducing
NO3T (i.e., remains in region 3 in Fig. 1)
In the case of SO42- reduction, particle pH only increases
slightly with substantial SO42- reduction due to buffering by
NH3–NH4+ partitioning (i.e., NH4+
volatility) (Weber et al., 2016; Guo et al., 2017c).
(NH4+ + NO3-) decreases slightly due to the
loss of associated NH4+ due to both the drop in
SO42- and volatilization caused by reduced particle water. Since
SO42- is nonvolatile and no gas–particle partitioning is
involved, the SO42- reduction results in a linear reduction in
particle ionic mass, while model input of NHx and
NO3T is constant.
Sensitivity tests were also performed to investigate the robustness of these
results. Considering the observed decreasing trends of SO2
emissions in many regions (Hand et al., 2012; Hidy et al., 2014; Warner et
al., 2017), we tested a cleaner future with less sulfate (20 % of the
current level; see Fig. S8). Also, since significant changes in global
climate and surface land cover can result in a dustier future with more NVCs,
we investigated the effect of a 400 % increase in NVCs above the Cabauw
levels (see Fig. S9). These two assumed scenarios produce a similar
conclusion as the base simulation discussed above, including our finding of a
critical pH of 3 and nonlinear response to a NHx reduction.
However, we note that, in the reduced SO42- case (i.e., the cleaner future scenario),
SO42- control had nearly no effect on particle ion mass because
the SO42- concentrations were already very low.
In summary, the optimal strategy to reduce ammonium nitrate or particle total
inorganic ion mass for the current conditions in Cabauw is to control
NO3T (NOx emission) since it results in a
linear response. Even SO42- control is superior over
NHx control to reduce particle ion mass, unless over
70 % reduction in NHx could be achieved. If
NHx is reduced, the effects will be greatest in warmer
periods. These are also the times when NH3 emissions and
concentrations are largest regionally or globally (Yamamoto et al., 1988;
Warner et al., 2016, 2017; Zhang et al., 2018) (see Table S1 for Cabauw
NH3 levels), and so there may be other benefits to controlling
NH3 emissions at these times, for example, minimizing
eutrophication in surface aqueous systems.
The above findings in Cabauw are in contrast to results of a global model,
which also utilized ISORROPIA-II (Pozzer et al., 2017). They find the impacts
of NH3 emissions on PM2.5 mass are strongest in winter for
Europe (along with North America and Asia). Some of the differences are
likely attributed to our higher predicted pH in Cabauw of ∼3.7 compared
to the average pH of Europe predicted in the global model to be near 2
(Pozzer et al., 2017). Thus, we predict conditions above the critical pH of
3, and Pozzer et al. (2017) predicts pH below this value. Difference in pH
may be due to meteorological conditions or the concentration of aerosol and
gas inorganic species, but it does demonstrate the sensitivity of responses
to what the local ambient pH is and that care should be taken to evaluate
predicted particle pH against inferences from ambient measurements. Another
thermodynamic model may give a different pH compared to ISORROPIA-II, which
may result in a slightly different critical pH (i.e., ∼3 in this study).
Next, we explore the outcomes of NHx reductions in other
locations and show that NH3 emission control is more effective in
winter than summer.
Effects of NH3, NOx, and SO2 emission
control for other locations
NHx, NO3T, and SO42- reduction
tests were also run for the other sampling sites following the same approach
as described above for Cabauw. The model input (period averages) can be found
in Table S1 and the results summarized in Fig. 5. The Cabauw simulations are
included in Fig. 5 for direct comparison with the other studies, despite
being also plotted in Fig. 4. The average fine particle pH and ε(NO3-) in each study are listed at the top of each plot in
Fig. 5 and the plots for the different studies are arranged with increasing
ambient pH from left to right. This order is followed in the following
discussion.
Fine particles in the eastern US (SOAS and WINTER studies, Fig. 5a and b) are
the most acidic among the sites, with an average pH of approximately 1 due to
the lowest NH3 (and to some minor extent due to small
NO3-, through its effect on liquid water). In the NE US in
winter, NHx control is most efficient in decreasing
PM2.5 ion mass since the particle pH and ε(NO3-) (37 %) indicate a sweet spot, where the change
in NHx emission affects NO3- immediately.
PM2.5 ion mass reductions from NO3T control and
SO42- control are similar, since aerosol NO3- and
SO42- are comparable in mass. In the SE US in summer,
NO3T control is not effective because NO3- only
contributed 4 % to the
NH4+–SO42-–NO3- aerosols (Fig. 5a). A
small fraction of nitrate aerosol is typically observed in the southeast in
summer (Hidy et al., 2014) due to the high temperature and low particle pH.
Because of the small NO3- fraction and already low pH in summer,
NHx control only leads to minor reductions in particle ionic
mass. In contrast, SO42- control produces the highest reduction
of particle ionic mass since it is the dominant inorganic species (76 %)
in this region. Therefore, it is more effective to control
NHx in winter in the NE US and SO42- in summer
in the SE US, a finding consistent with previous studies (Duyzer, 1994;
Tsimpidi et al., 2007).
For the southwestern US summer (CalNex
study, Fig. 5c), since NO3- was the most abundant among
NH4+–SO42-–NO3- aerosol components,
reducing NHx is the most effective way to reduce PM2.5
ion mass as the ambient particle pH is within the range where ε(NO3-) is sensitive to pH. NO3T control follows
closely in effectiveness, whereas reducing SO42- is the least
effective. In the WINTER (NE US winter) and CalNex (SW US summer) studies,
PM2.5 ion mass decreases at a lower rate towards higher levels in
NHx reduction (see Fig. 5b and c) due to the nonlinear
response in ε(NO3-) to NH3 concentration
(as shown in Figs. 3b or 2). For instance, when ε(NO3-) drops from 50 % to 0 %, the sensitivities to
NH3 keep decreasing asymptotically towards zero. The pH stays
nearly flat for the NO3T control and SO42- control
and decreases with the NHx control.
Cabauw winter and Beijing winter haze conditions (see Fig. 5e and f) are
similar in terms of benefits in reducing particle ionic mass from
NHx, NO3T, or SO42- controls. This
is because of similarities in pH and ε(NO3-)
between these sites. For the haze condition in Beijing, NHx
control does not produce as much PM2.5 ion mass reduction as
NO3T and SO42- controls, unless more than a
60 % reduction in NHx is reached. However, after that PM
mass reduction is fast. At 90 % NHx reduction, a
decrease of more than half of the particle ionic mass is predicted.
NO3T and SO42- controls produce equivalent results
due to the same mass fractions of NO3- and SO42-
(both equal to 36 %) and linear response in particle ionic mass.
Comparing the pH profiles, the largest reduction in pH is predicted for
Beijing haze if reducing NHx. At 50 %
NHx reduction, pH changes from 4.1 to 2.5 in Beijing,
whereas pH only changes from 3.9 to 3.3 in Cabauw. This can be explained by
differences in ε(NH4+), which are at 60 % in
Beijing versus 27 % in Cabauw.
Other implications of lowering pH by NH3 emission
control
The benefit of reducing NH3 emission to reduce ambient PM2.5
mass concentrations depends on the conditions at a specific site. While
particle pH is lowered during the process, other pH-related atmospheric
processes are affected. One potentially unintended effect is nitrogen
deposition. Nitrogen dry deposition rates depend on particle versus gas-phase
fractions since there are large differences between gas and particle
deposition velocities. For example, the dry deposition velocity of
NH3 is about 1–2 cm s-1 over forest, agricultural, or
mixed-use land and 10 times that of NH4+ (Duyzer, 1994;
Schrader and Brummer, 2014). Also, the dry deposition velocity of
HNO3 is similar to that of NH3 (Huebert and Robert,
1985). Lowering particle pH through NH3 reductions will decrease
overall reduced nitrogen deposition, but may result in more localized
oxidized nitrogen dry deposition, if the lower pH results in
NO3- evaporation and higher HNO3 concentrations.
Deposition due to wet removal processes is not considered here.
An additional consequence of lowering particle pH is that it can increase
aerosol toxicity. Many studies have identified links between strong particle
acidity and adverse health endpoints (Koutrakis et al., 1988; Thurston et
al., 1994; Raizenne et al., 1996; Gwynn et al., 2000; Lelieveld et al.,
2015). We recently showed one way this can happen is due to increased
conversion of PM2.5 insoluble transition metals to soluble forms by
strong acidity (Fang et al., 2017), which increases the particles' ability to
induce oxidative stress (Ghio et al., 2012). Lowering pH may reduce
PM2.5 mass but increase the overall potential for adverse health effects due
to significantly greater toxicity of soluble metals relative to ammonium
nitrate. Finally, lowering pH can also impact the deposition pattern and
bioavailability of trace-limiting nutrients such as Fe, P, and other metals
(Meskhidze et al., 2003; Nenes et al., 2011) with important implications for
primary productivity (Meskhidze et al., 2005) and even the oxygen state of
the subsurface ocean (Ito et al., 2016).
Summary
In this study, we assess the effectiveness of NH3 control as a way
to lower inorganic PM2.5 mass based on observational data sets from the
US, the Netherlands, and China during different seasons. These sites
encompass a diverse range in (i) NH3 and inorganic aerosol
concentrations and (ii) thermodynamic conditions. In all cases, the relative
humidities are sufficiently high (average RH > 55 %) that a
completely deliquesced inorganic phase is a reasonable assumption, which is
implicit to the thermodynamic calculations (metastable mode). Focusing on
Cabauw, the Netherlands, a site in a region highly impacted by agricultural
emissions, we show that the effectiveness of NH3 control changes
with season. In winter, a much larger reduction in NH3 is required
to reduce NO3- than in summer, making NOx
control more effective in winter. This is explained by a shift in the
HNO3–NO3- partitioning (ε(NO3-)) curve to lower pH in winter and pH50 (where
ε(NO3-) = 50 %) further from the actual
ambient particle pH. A similar situation is seen in Beijing in winter, where
NH3 emission control would also be less effective. In most other
sites investigated, NH3 control is effective in reducing PM2.5
mass in regions with reasonably high ammonium nitrate concentrations.
The analysis presented here provides a conceptual and direct evaluation of
how the inorganic gas–particle system can be expected to respond to changes
in NH3 emissions and how it contrasts to NOx
control. The approach relies on the single HNO3–NO3-
partitioning equation and the use of a thermodynamic model to predict pH.
Other approaches are also often used to address this question. Chemical
transport models with imbedded thermodynamic sub-modules (such as ISORROPIA)
can provide a more detailed analysis that includes other possible impacts of
the emission controls, such as ammonia and nitrate deposition and associated
environmental impacts. However, the various uncertainties associated with the
many simulated processes involved in these models (e.g., emissions and
processing) can affect the predicted results and obscure the fundamental
partitioning processes. With the more transparent and accessible approach
presented here, this is less of an issue. Both approaches have benefits, but,
regardless of which analysis is utilized, it is always useful to explicitly report
estimated particle pH as it allows assessment of the predictions and provides
contrasts between studies at specific sites.