Nitrogen oxides (NOx) and ammonia (NH3) from
anthropogenic and biogenic emissions are central contributors to particulate
matter (PM) concentrations worldwide. The response of PM to changes in the
emissions of both compounds is typically studied on a case-by-case basis,
owing in part to the complex thermodynamic interactions of these aerosol
precursors with other PM constituents. Here we present a simple but
thermodynamically consistent approach that expresses the chemical domains of
sensitivity of aerosol particulate matter to NH3 and HNO3
availability in terms of aerosol pH and liquid water content. From our
analysis, four policy-relevant regimes emerge in terms of sensitivity: (i) NH3 sensitive, (ii) HNO3 sensitive, (iii) NH3 and HNO3 sensitive, and (iv) insensitive to NH3 or HNO3. For all regimes, the PM remains sensitive to nonvolatile precursors, such as nonvolatile cations and sulfate. When this framework is applied to ambient measurements or predictions of PM and gaseous precursors, the “chemical regime” of PM sensitivity to NH3 and HNO3 availability is directly determined. The use of these regimes allows for novel insights, and this framework is an important tool to evaluate chemical transport models. With this extended understanding, aerosol pH and associated liquid water content naturally emerge as previously ignored state parameters that drive PM formation.
Introduction
Gas-phase ammonia (NH3(g), hereafter “NH3”) is one of the most important atmospheric alkaline species and contributor to atmospheric fine particle mass (Seinfeld and Pandis, 2016). NH3 originates from
nitrogen-based fertilizer, animal waste (e.g., Aneja et al., 2009), biomass
burning (e.g., Behera et al., 2013), and the natural biosphere (National Academies of Sciences, Engineering, and Medicine, 2016).
NH3 emissions are also linked to world food production, so these emissions are
expected to increase with world population (NRC, 2016). Ammonia reacts with
sulfuric and nitric acids (from SO2 and NOx oxidation) to form ammonium sulfate/bisulfate and nitrate aerosol that globally constitute an important fraction of ambient PM2.5 mass (Kanakidou et al., 2005; Sardar et al., 2005; Zhang et al., 2007). SO2 and NOx emissions are expected to decrease over time due to air quality regulations (IPCC,
2013). Combined with increasing NH3 levels (e.g., Skjøth and Geels,
2013), this may lead to changes in aerosol composition and mass
concentration, with important impacts on human health (Pope III et al., 2004;
Lim et al., 2012; Lelieveld et al., 2015; Cohen et al., 2017), ecosystem
productivity (Fowler et al., 2013), and the climate system (Haywood and
Boucher, 2000; Bellouin et al., 2011; IPCC, 2013).
The abovementioned emission trends have created the expectation that atmospheric
aerosol will become significantly less acidic over time (West et al., 1999;
Pinder et al., 2007, 2008; Heald et al., 2012; Tsimpidi et al., 2007; Saylor
et al., 2015). Reductions in ammonium sulfate due to SO2 reductions can
be balanced, at least in part, by ammonium nitrate formation (e.g., West et
al., 1999; Heald et al., 2012; Karydis et al., 2016; Vasilakos et al., 2018). This behavior arises because nitrate may remain in the gas phase as
HNO3 when insufficient amounts of total ammonia (i.e., gas + aerosol)
or nonvolatile cations (NVCs) from dust and sea salt exist to “neutralize”
aerosol sulfate (i.e., completely consume any free sulfuric acid or
bisulfate salts). This conceptual model can fail, because it does not
sufficiently consider the large volatility difference between deliquesced
aerosol containing sulfate/NVCs and ammonium/nitrate, the last two of which
are strongly modulated by aerosol acidity (pH) (Guo et al., 2015, 2017; Weber et al., 2016) and aerosol water. Modeling studies explicitly considering acidity
effects may still incorrectly estimate nitrate substitution, owing to errors
in emissions of nonvolatile cations (such as Na, Ca, K, and Mg) that can bias estimates of aerosol acidity and ammonium or nitrate partitioning (Vasilakos et al., 2018). A bias in our understanding of aerosol pH can reaffirm a sometimes
incorrect conceptual model of aerosol nitrate formation, and fundamental
reasons for prediction biases in nitrate and ammonium (i.e., errors in pH
and liquid water content) are not identified, therefore inhibiting further
model improvement.
Developing an understanding of when aerosol levels are sensitive to NH3
and HNO3 concentrations requires a new approach that explicitly
considers aerosol pH and its effects on aerosol precursor volatility in a
thermodynamically consistent way. Here we present such a framework, and
demonstrate it with observational data, to understand the chemical
regimes associated with aerosol sensitivity to changes in ammonia and
nitrate availability.
The new conceptual framework
Aerosol pH needs to be sufficiently high for aerosol nitrate formation to
readily occur. Depending on the temperature and the amount of liquid water,
this threshold ranges from a pH of 1.5 to 3.5 (Meskhidze et al., 2003;
Guo et al., 2016, 2017; Fig. 1). If pH is high enough, almost all inorganic
nitrate forming from NOx oxidation mostly resides in the aerosol phase; however, when pH is low (typically below 1.5 to 2), nitrate remains almost
exclusively in the gas phase as HNO3, regardless of the amount present.
Between these “high” and “low” pH values, a “sensitivity window”
emerges, where partitioning shifts from nitrate being predominantly gaseous
to mostly aerosol bound. When acidity is below this “pH window”, aerosol
nitrate is almost nonexistent, and therefore aerosol levels are insensitive
to HNO3 availability and controls aimed solely on HNO3
reduction are unimportant since none is in the aerosol phase. When the pH is
above the sensitivity window, most nitrate resides in the aerosol phase, and aerosol
levels directly respond to HNO3 availability. A similar situation
exists for aerosol ammonium – although with an inverse dependence on pH
compared to HNO3. When aerosol pH is low enough, any inorganic ammonia
emitted mostly resides in the aerosol phase; when pH is high enough,
most of it resides in the gas phase (Fig. 1). Based on the criteria above, one can
then define characteristic levels of aerosol acidity, where aerosol becomes
insensitive to NH3 (or HNO3) concentrations and vice versa. In the following sections, we quantitatively develop these concepts and
formulate a new thermodynamically consistent framework of aerosol
formation.
Particle phase fraction of total nitrate, ε(NO3-) (blue curve), and total ammonium, ε(NH4+) (red curve), versus pH for a temperature of 288 K and an
aerosol liquid water content of (a) 10 µgm-3 and (b) 0.5 µgm-3. The blue zone denotes where aerosol responds strongly (i.e., ∂NO3-∂NO3T≈1) to the amount of total nitrate, orange is where NH3 dominates (i.e., ∂NH4+∂NH3T∼1), purple is where both NH3 and HNO3 changes affect PM concentrations, and white is where aerosol is relatively insensitive to NH3 and HNO3 fluctuations. In defining the sensitivity domains, we have assumed that a partitioning fraction of 10 % (dashed black lines), and its corresponding “characteristic” pH, defines where the aerosol becomes insensitive to changes in total NH3 and HNO3.
Determining when aerosol mass is sensitive to nitric acid and ammonia availability
For a given air mass with total nitrate NO3T (i.e., the amount of
aerosol and gas-phase nitrate), the equilibrium aerosol nitrate
concentration, NO3-, is given by NO3-=εNO3-NO3T, where ε(NO3-) is the fraction of NO3T that partitions to the particle phase.
Given that nitrate ions are associated with
semivolatile NH4+ and nonvolatile cations (NVCs) such as Na+, Ca2+, K+, and Mg2+ when they partition to aerosol, the sensitivity of aerosol mass to changes in NO3T is proportional to the changes occurring in NO3-. Therefore,
∂PM∂NO3T=ζ∂NO3-∂NO3T=ζεNO3-,
where ζ is the ratio of PM mass formed (or lost) per mole of
NO3- that condenses (or evaporates) from the particles. Therefore, if NO3- is associated with aerosol NH4NO3, then ζ=80/62=1.29. Lower values are found for particles rich in NVCs that are associated with carbonates and chlorides; for example, if nitric acid were replacing chloride in sea salt (e.g., conversion of NaCl to NaNO3), the ratio would be ζ=(85-58.4)/62=0.43.
A similar ζ is seen when alkaline dust particles rich in CaCO3 react with HNO3 to form Ca(NO3)2: ζ=(164-100)/(2×62)=0.51. Given that NVCs tend to reside in the
coarse mode aerosol, environments that are rich in NH3 and form large
amounts of NH4NO3 (e.g., in northern Europe, US Midwest,
and China) would therefore tend to exhibit ζ values of ∼1.29; in environments where there is a mixture of NVCs and NH4NO3, ζ would be a weighted average between 1.29 (pure NH4NO3 limit) and 0.5 (NVC limit), determined by the ratio of the two cation categories in the aerosol. The sensitivity of PM to changes in NO3T can therefore be expressed in terms of nitrate partitioning, so the parameters that affect εNO3- also directly impact ∂PM∂NO3T.
We now proceed with explicitly quantifying how aerosol liquid water and pH
affect nitrate partitioning and hence PM sensitivity to nitrate availability.
Meskhidze et al. (2003) and later on Guo et al. (2017) showed that, for a
deliquesced aerosol, ε(NO3-) explicitly depends on the
concentration of H+ in the aerosol phase, [H+],
and the aerosol liquid water content, Wi, as
εNO3-=Kn1HHNO3WiRTγH+γNO3-H++Kn1HHNO3WiRT,
where HHNO3 and Kn1 are the Henry's law and acid dissociation constants for HNO3, respectively; R is the universal gas constant; T is the temperature; and γH+ and γNO3- are the single-ion activity coefficients for H+ and NO3-, respectively. Temperature dependence for HHNO3 is provided by Sander (2015), while activity coefficients can be calculated using an aerosol thermodynamic model (e.g., ISORROPIA II; Fountoukis and Nenes, 2007).
Similarly, equilibrium partitioning of NH3T to the aerosol is given by NH4+=εNH4+NH3T, where εNH4+ is the fraction of NH3T (i.e., the amount of aerosol ammonium and gas-phase ammonia) that partitions to the particle phase. The sensitivity of aerosol mass to perturbations in total ammonia is ∂PM∂NH3T=λ∂NH4+∂NH3T=λεNH4+, where λ is the ratio of mass of PM that is lost or gained per mole of evaporation or loss of NH4+; λ is more variable than ζ, because the anion associated with ammonium can be an involatile or semivolatile species with relatively large molar mass. For example, if NH4+ condenses or evaporates from sulfate salts (NH4HSO4, (NH4)2SO4), then λ=18/17=1.06 and λ=4.4 for NH4NO3, λ=2.97 for NH4Cl, and λ=10 for (NH4)2(COO)2. Given that the majority of the
aerosol ammonium is associated with nitrate and sulfates, aerosol therefore
tends to exhibit a value for λ that is a weighted average of ∼1
(NH4HSO4, (NH4)2SO4 limit) or 4.4
(NH4NO3 limit), determined by the aerosol sulfate / nitrate ratio.
In regions where the aerosol is acidic, nitrate tends to reside in the gas
phase and λ≈1.
From the above criteria, the sensitivity of PM to changes in NH3T can be expressed in terms of its partitioning. εNH4+, as in Eq. (2), can be linked to aerosol liquid water and pH (Guo et
al., 2017):
εNH4+=γH+γNH4+HNH3KaH+WiRT1+γH+γNH4+HNH3KaH+WiRT,
where HNH3 and Ka are the Henry's law and dissociation constants for NH3, respectively, and γNH4+ is the single-ion
activity coefficient for NH4+. Temperature
dependence for HNH3 is provided by Sander (2015).
Defining the parameters Ψ=RTKn1HHNO3γH+γNO3-
and Φ=γH+γNH4+HNH3KaRT, Eqs. (2) and (3) can be
written as
εNO3-=ΨWiH++ΨWi;εNH4+=ΦH+Wi1+ΦH+Wi.
For given levels of Wi, the expressions in Eq. (4) yield “sigmoidal” functions
that display a characteristic “pH sensitivity window”, where the partition
fraction changes from zero to unity over a limited pH range. Equation (4)
can then be used to determine a “characteristic pH” that defines when
aerosol is insensitive to total ammonia and nitrate availability (or
emissions). For this purpose, we determine the pH for which εNO3- and εNH4+ are equal to a characteristic (small) threshold value, being α for εNO3- and β for εNH4+ (Fig. 1). When α (or β) are exceeded, the aerosol is said to be sensitive to NH3 (or NOx) emissions, because changes in NH3 and NOx levels can appreciably affect aerosol concentrations. This sensitivity may be in one direction (e.g., increase in the emissions if the corresponding particulate levels are low and decrease if they are high) or in both. Guo et al. (2018) found a “critical” pH of approximately 3, above which the εNO3- is nearly 1 and almost all nitrate (NO3T) resides in the aerosol phase. Here we generalize the approach developing relationships between the terms that depend on aerosol composition, pH, and particle water, with temperature still remaining as an independent variable.
Based on the above discussion, the characteristic acidity level for nitrate,
pH′, is computed as
α=ΨWiH+′+ΨWi⟹H+′=1-ααΨWi⟹pH′=-log1-ααΨWi,
where H+′ is the concentration where εNO3- equals the threshold value. The parameter 1-αα, which we call the “threshold factor”, adjusts pH′ to account for the threshold above which the aerosol is said to become sensitive to NO3T.
Similarly to nitrates, the characteristic acidity level for ammonium,
pH′′, is determined as
β=ΦH+Wi1+ΦH+Wi⟹H+=11-ββΦWi⟹pH′′=log1-ββΦWi.
Chemical domains of aerosol mass sensitive to nitrate and ammonia perturbations
Hereafter we consider α=β=0.1; in selecting these threshold
values, we assume that aerosol responds in an important manner to NH3/HNO3 when at least 10 % of the total precursor can partition to the aerosol phase. The threshold can be adjusted accordingly to fit any other objective, depending on the analysis required (e.g., a prescribed PM response). With these considerations, the threshold factors are 9 for both compounds and the characteristic pH values obtain the very simple formulations pH′=-log9ΨWi for nitrate and pH′′=log9ΦWi for ammonium. Apart from the value of the parameters Ψ and Φ (which vary mainly with T), pH′ and pH′′ vary only with Wi – with a logarithmic dependence. Figure 2 displays their variation for 273 and 298 K. Nitrate tends to exhibit a decrease in pH′ with increasing Wi and vice versa for ammonium and pH′′.
Characteristic pH for defining when aerosol is sensitive
to changes in available nitrate (blue lines) and ammonia (red lines) versus
Wi. Results shown for a temperature of 298 K (dotted line) and 273 K
(solid line). Note the relatively stronger effects of temperature changes on
the characteristic pH for nitrate. Calculations were carried out using the Excel spreadsheet provided in the Supplement.
Based on the values of the characteristic pH and its relation to the aerosol
pH, we can then determine whether the aerosol responds to changes in nitrate
or ammonium – as only when pH>pH′ (or pH<pH′′) does the aerosol become sensitive to changes in NO3T (or NH3T). This realization constitutes the basis of our new framework, and aerosol can belong to one of four distinct chemical regimes:
Regime 1 is not sensitive to either NH3 or HNO3: this occurs when pH>pH′′ and pH<pH′. This regime is termed “NH3, HNO3insensitive” or just “insensitive”.
Regime 2 is not sensitive to NH3, but it is sensitive to HNO3: this occurs when pH>pH′′ and pH>pH′. This regime is termed “HNO3sensitive”.
Regime 3 is sensitive to both NH3 and HNO3: this occurs when pH<pH′′ and pH>pH′. This regime is termed “NH3 and HNO3sensitive”.
Regime 4 is sensitive to NH3 but not sensitive to HNO3: this occurs when pH<pH′′ and pH<pH′. This regime is termed “NH3sensitive”.
Figure 3 shows these four regions in white (Regime 1), blue (Regime 2),
purple (Regime 3), and red (Regime 4) for 273 K (Fig. 3a) and 298 K
(Fig. 3b). Therefore, any specific set of data (from observations or a
model), based on their corresponding aerosol acidity and liquid water contents, places
them in one of the four above domains – which in turn determines the “chemical
regime” of aerosol response to NH3T and/or NO3T. What is surprising is the emergence of a region of conditions where aerosol is insensitive to either NH3 or HNO3 – which occupies an increasingly large pH–LWC (liquid water content) domain as the temperature increases (Fig. 3).
Chemical domains of aerosol response to ammonia and
nitrate emissions. Shown are results for 273 K (a) and 298 K (b). Note that there exists a fairly expansive region of acidity and liquid water content (especially for warmer temperatures) where aerosol is relatively insensitive to ammonia and nitrate emissions; here only
nonvolatiles (sulfate, NVCs) can have an appreciable impact on aerosol
mass. Also important is the role of aerosol water in helping define the
chemical regime of aerosol sensitivity to precursors.
A characteristic point on the chemical regime map corresponds to where the
two lines “crossover”, thus separating Regime 1 from Regime 3 and Regime 2 from Regime 4. This critical point corresponds to a characteristic
value of LWC, Wi*, that is easily found by equating pH′ with
pH′′:
Wi*=1-αα1-ββΦΨ-1/2.
Substitution of Wi* into either Eq. (5) or Eq (6) gives also the
characteristic pH∗ of this crossover point:
pH*=-12logΨΦ.
Both pH* and Wi* depend on temperature (Fig. 3). For
T=298 K and α=β=0.1, Ψ≈7.38×102, Φ≈1.67×107, so Wi*≈3.5µgm-3 and pH*≈2.2. Therefore, for moderately acidic
aerosol (pH*≈2) and for moderate levels of liquid water
content (a few micrograms per cubic meter, µgm-3), aerosol tends to be insensitive to emissions of either NH3T or NO3T precursors. For higher (or lower) pH levels, the aerosol transitions to Regime 2 (or 4). For liquid water above Wi*, there is a “transition pH” from an ammonia-sensitive aerosol to an exclusively nitrate-sensitive aerosol, which depends linearly on liquid water content (Fig. 3). Similarly, there is also another transition pH that defines when the aerosol becomes exclusively sensitive to NH3T.
As formulated here, the framework does not imply that the water is
associated with the species considered (ammonium, nitrate) but rather it is
treated as a variable; pH is also treated as a variable and can be modulated
from organics, NVCs, halogen ions, sulfates, carbonates, and other species.
The main requirement is that the aerosol is dominated by a single aqueous
phase, as discussed in Battaglia Jr. et al. (2019) and references therein;
therefore, the framework applies more accurately to conditions where the
relative humidity is above 40 % and the assumption of thermodynamic
equilibrium is applicable (i.e., ultra-viscosity and semisolid effects do
not considerably limit mass transfer in the aerosol phase). Given the
complexity of aerosol thermodynamics, it is remarkable that such an
apparently simple framework can be used to characterize the regions of
aerosol sensitivity to NH3T and NO3T emissions, with “coordinates” being pH and liquid water. This is illustrated in the following section.
Application of framework
The above framework requires knowledge of aerosol pH and liquid water content, which can be routinely calculated by state-of-the-art atmospheric
chemical transport models (e.g., CMAQ, CAMx) during the course of any
simulation. Thermodynamic analysis of ambient aerosol and gas-phase data
also provides aerosol pH and liquid water content; therefore, the above
framework can be used to characterize the chemical domain of ambient and
simulated aerosol.
Characteristics of the datasets used for determining the sensitivity to NH3 and HNO3 emissions. Shown is the average relative humidity (RH), temperature (K), and the concentration of major aerosol precursors (µgm-3), while in the respective standard
deviation for each parameter is shown in parenthesis. Access to the data is
described in the “Code and data availability” section.
Dataset ID, location, andRHTemp.SulfateTotal ammoniumTotal nitratereference(%)(K)(µgm-3)(µgm-3)(µgm-3)TJN, Tianjin, China56.6301.821.4637.7418.12(Shi et al., 2019)(12.4)(2.79)(10.99)(7.68)(11.50)CNX, Pasadena, CA, USA71.3291.12.863.4410.23(Guo et al., 2017)(15.5)(4.26)(1.70)(1.81)(9.74)CBW, Cabauw, the Netherlands78.2282.21.929.34.1(Guo et al., 2018)(14.8)(7.3)(1.57)(6.8)(3.9)WIN, eastern USA56.1270.81.020.532.12(Guo et al., 2016)(18.9)(6.52)(0.08)(0.44)(2.08)SAS, Centerville, AL, USA72.7297.91.810.780.12(Guo et al., 2015)(17.4)(3.45)(1.18)(0.50)(0.15)
The applicability of the chemical domain approach is demonstrated by
its application to ambient data. For this purpose, we have selected more
than 7700 data points obtained from observations over five locations worldwide:
Cabauw (CBW), Tianjin (TJN), California (CNX), SE US (SAS), and a wintertime
NE US (WIN) study (Table 1). Each dataset displays a broad range of
acidity, temperature, and relative humidity, and each has been thoroughly studied and
evaluated for the applicability of thermodynamic analysis. Each data point corresponds roughly to a 1 h measurement, meaning that the chemical domains
examined correspond to effectively the instantaneous response of PM to
ammonia and nitric acid availability. In addition to the major aerosol
species of ammonium/ammonia, sulfate, and nitrate/nitric acid, the datasets also
contain chloride/hydrochloric acid, sodium, calcium, potassium, and magnesium
(not shown in Table 1), which contribute to the pH and liquid water levels
predicted. However, not all of the data provide size-dependent composition,
so our analysis is limited, here, to looking at the bulk fine PM composition. The range of εNO3- and εNH4+ for all the data examined
is presented in Fig. 4. Noted on the figure are also indicative domains
that correspond to Regime 1 to Regime 4. It is clear that each dataset has
distinct characteristics that provide insight into the expected sensitivity of PM to NH3 and HNO3 emissions – as low εNO3- and εNH4+ correspond to a low sensitivity of PM to their respective precursor emissions. However, it is unclear, based on ε alone, where this (in)sensitivity originates from: strong or weak acidity, high or low liquid water content, or high or low temperature. The latter is important, given that those parameters in models shape the local sensitivity profiles. Much of the data are found towards the extremes in the partitioning fraction scale, leaving the central part of the diagram sparsely populated. However, this does not mean that aerosols are limited by one component or the other, as much of the data are found to be in the region sensitive to both.
Aerosol partitioning fraction for total ammonia/ammonium
and nitric acid/nitrate for the five regions examined: (a) Cabauw – CBW, (b) CalNex – CNX, (c) Tianjin – TJN, (d) SOAS – SAS, and (e) E United States (WIN).
Chemical domains of sensitivity of aerosol to NH3 and NOx emissions for five regions examined: (a) Cabauw – CBW, (b) CalNex – CNX, (c) Tianjin – TJN, (d) SOAS – SAS, and (e) E United States (WIN).
Average composition, temperature, and humidity along with their variations
(expressed by their standard deviation) are provided in Table 1.
Figure 5 presents the chemical domain classifications for each location.
These data sets are used to provide an example and may not apply to all
locations in the region. For each subplot, the characteristic curves are
calculated using the average temperature of the dataset (presented in Table 1). From each subplot it becomes clear that every location (CBW, TJN, CNX,
SAS, WIN) belongs almost exclusively to a characteristic domain for the
duration of the measurements. Cabauw, for example, is characterized by high
enough NH3 so that aerosol is not sensitive to variations of it. Nitric
acid, on the other hand, is by far a limiting factor in PM formation, and hence
CBW is in the HNO3-dominated regime throughout the year. For similar
reasons, Tianjin is also mostly in the HNO3-dominated region, although a fraction of the data points lie in the combined NH3-HNO3 region
owing to the slightly more acidic conditions compared to CBW. The southeast
US (SAS) is considerably more acidic and with an order of magnitude less
liquid water content compared to CBW and TJN; for these reasons, it belongs to the NH3-sensitive regime (i.e., there is little NH4NO3
present in summer – even if total nitrate availability may be high). The
California dataset is quite interesting, being one that partly occupies the
insensitive region and then transitions to the combined NH3-HNO3
region; in this dataset, the combination of moderate NH3 levels,
temperature, and the fraction of NVCs from sea salt that is internally mixed
with the other components makes aerosol sensitive to both NH3 and
HNO3 variations. The wintertime eastern US dataset (WIN) corresponds to
a broad region (aircraft data set), and hence the data naturally occupies
multiple domains. The lower temperatures, however, prohibit most of the data
from occupying any of the insensitive region; most of the data occupies the
NH3-sensitive regime, owing to the strong acidity and low liquid water content.
One remarkable point, however, is that regardless of location, the
transition point between NH3-dominated and HNO3-dominated
sensitivity always occurs at a pH around 2 but at variable levels of liquid
water content. The latter is important, as pH emerges as a required but not
sufficient condition to determine the type of aerosol sensitivity: too
little water (i.e., liquid water below the characteristic value Wi*) and the aerosol can be insensitive to NH3, even if the pH is as low as 2 (Fig. 5a). In the case of Cabauw conditions (Fig. 5a), where aerosol liquid water ranges from 7 to 15 µgm-3, the transition pH from an aerosol that is exclusively sensitive to NO3T precursor emissions to one that is sensitive to both NH3T and NO3T ranges from 2.8 and 3.2, which is in perfect agreement with the analysis of Guo et al. (2018). The additional insight that our framework shows is that the transition pH varies with temperature and logarithmically with aerosol liquid water content, in response to emissions and diurnal/seasonal variability and climate change. This insight, not apparent in the analysis of Guo et al. (2018), demonstrates the power and flexibility of the new framework.
Conclusions
Here we present a simple yet powerful way to understand when concentrations
of nitric acid (HNO3) and ammonia (NH3) from anthropogenic and
biogenic emissions can considerably modulate particulate matter (PM)
concentrations worldwide. The conceptual framework explicitly considers
acidity (pH), aerosol liquid water content, and temperature as the main
parameters controlling secondary inorganic PM sensitivity, and it identifies
four policy-relevant regimes: (i) NH3 dominated, (ii) HNO3 dominated, (iii) both NH3 and HNO3, and (iv) a previously unidentified domain where
neither NH3 nor HNO3 are important for PM formation (but only
nonvolatile precursors such as NVCs and sulfate). When this framework is
applied to ambient measurements and predictions of PM and gaseous
precursors, the “chemical regime” of PM sensitivity to emissions is
directly determined, allowing novel insights and eventually an important
tool to evaluate models. Given that if simulated aerosol is in the same
sensitivity regime as suggested by thermodynamic analysis of observations,
models are expected to provide plausible responses to changes in aerosol
emissions. The framework can be used to identify regions or time periods
where or when pH and liquid water content prediction errors matter for PM
sensitivity assessments. With this deeper understanding, aerosol pH and
associated liquid water content naturally emerge as policy-relevant
parameters that have not been explicitly explored until now.
Code and data availability
User access to data used in this article is described in the citations referenced for each dataset and can also be accessed from the compiled dataset by Pye et al. (2019). The ISORROPIA II thermodynamic equilibrium code is available at http://isorropia.epfl.ch (last access: 3 March 2020; EPFL, 2020).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-3249-2020-supplement.
Author contributions
AN initiated the study, developed the framework, carried out analysis of the data, and wrote the initial draft. All authors provided feedback on the analysis approach and extensively commented on the article.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
The contents are solely the responsibility of the grantee and do not necessarily represent the official views of the supporting agencies. Further, the US government does not endorse the purchase of any commercial products or services mentioned in the publication.
Acknowledgements
We thank the comments from two anonymous reviewers and Hongyu Guo and Guoliang Shi for providing access to the data used here.
Financial support
This research has been supported by the European Commission, H2020 Research Infrastructures (PyroTRACH (grant no. 726165)) and
the United States Environmental Protection Agency (grant no. R83588001). Its contents are solely the responsibility of the grantee and do not necessarily represent the official views of the supporting agencies.
Review statement
This paper was edited by Veli-Matti Kerminen and reviewed by two anonymous referees.
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