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Article

Partitioning of NH3-NH4+ in the Southeastern U.S.

1
Oak Ridge Institute for Science and Education (ORISE), Postdoctoral Research Participant at U.S. EPA, Research Triangle Park, Oak Ridge, NC 27709, USA
2
Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC 27695, USA
3
Department of Marine Earth and Atmospheric Science, North Carolina State University, Raleigh, NC 27695, USA
4
Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA
*
Author to whom correspondence should be addressed.
Submission received: 3 November 2021 / Revised: 11 December 2021 / Accepted: 13 December 2021 / Published: 15 December 2021
(This article belongs to the Special Issue Ammonia in a Changing Atmosphere)

Abstract

:
The formation of inorganic fine particulate matter (i.e., iPM2.5) is controlled by the thermodynamic equilibrium partitioning of NH3-NH4+. To develop effective control strategies of PM2.5, we aim to understand the impacts of changes in different precursor gases on iPM2.5 concentrations and partitioning of NH3-NH4+. To understand partitioning of NH3-NH4+ in the southeastern U.S., responses of iPM2.5 to precursor gases in four seasons were investigated using field measurements of iPM2.5, precursor gases, and meteorological conditions. The ISORROPIA II model was used to examine the effects of changes in total ammonia (gas + aerosol), total sulfuric acid (aerosol), and total nitric acid (gas + aerosol) on iPM2.5 concentrations and partitioning of NH3-NH4+. The results indicate that reduction in total H2SO4 is more effective than reduction in total HNO3 and total NH3 to reduce iPM2.5 especially under NH3-rich condition. The reduction in total H2SO4 may change partitioning of NH3-NH4+ towards gas-phase and may also lead to an increase in NO3 under NH3-rich conditions, which does not necessarily lead to full neutralization of acidic gases (pH < 7). Thus, future reduction in iPM2.5 may necessitate the coordinated reduction in both H2SO4 and HNO3 in the southeastern U.S. It is also found that the response of iPM2.5 to the change in total H2SO4 is more sensitive in summer than winter due to the dominance of SO42− salts in iPM2.5 and the high temperature in summer. The NH3 emissions from Animal Feeding Operations (AFOs) at an agricultural rural site (YRK) had great impacts on partitioning of NH3-NH4+. The Multiple Linear Regression (MLR) model revealed a strong positive correlation between cation-NH4+ and anions-SO42− and NO3. This research provides an insight into iPM2.5 formation mechanism for the advancement of PM2.5 control and regulation in the southeastern U.S.

1. Introduction

Particulate matter (PM) with aerodynamic diameter less than or equal to 2.5 µm (i.e., PM2.5) causes adverse impacts on the environment and human health [1,2,3,4,5]. In general, PM2.5 consists of inorganic ions, organic carbon (OC), elemental carbon (EC), various elements, and unclassified components [6,7,8,9,10]. Particulate matter can be classified as primary and secondary aerosol based on formation processes. Primary PM2.5 is directly emitted from emission sources while secondary PM2.5 is mainly formed through various chemical reactions and atmospheric processes [11,12,13]. The formation of the secondary inorganic PM2.5 (iPM2.5) is largely controlled by the chemical reactions between various precursor gases [14]. Ammonia (NH3) neutralizes acidic species (e.g., nitric acid (HNO3), sulfuric acid (H2SO4), and hydrochloric acid (HCl)) to form ammonium (NH4+) salts, and this dynamic process is called gas-particle partitioning of NH3-NH4+ [15]. In the atmosphere, secondary iPM2.5 mainly includes ammonium nitrate (NH4NO3), ammonium sulfate ((NH4)2SO4), and ammonium chloride (NH4Cl) and may account for a large portion of total PM2.5 [16,17,18,19,20,21]. Depending upon the availability of NH3, H2SO4 may be partially or fully neutralized to form bisulfate (HSO42−) or sulfate (SO42−) salts and NH3 may also react with HNO3 to form NH4NO3. As a semi-volatile compound, the formation of NH4NO3 is also impacted by the ambient condition such as temperature (T) and relative humidity (RH); low T and high RH tend to favor the formation of NH4NO3 [22,23,24].
Studies on the formation of the iPM2.5 as impacted by the changes in the concentrations of precursor gases have been carried out through modeling approaches [25,26,27]. ISORROPIA II is a commonly used thermodynamic equilibrium model to simulate the dynamics of phase changes (e.g., gas, liquid, and solid) and interaction of different chemical species including NH4+, nitrate (NO3), SO42−, chloride (Cl), potassium (K+), calcium (Ca2+), magnesium (Mg2+), and sodium (Na+) in ambient air [26,28]. This model simulates the gas-particle partitioning phenomenon and impacts of T and RH on such partitioning [29,30,31,32]. The relationship of iPM2.5 and its precursor gases at an agricultural site located in eastern North Carolina (NC) was studied using field measurements and ISORROPIA model simulation [33]. The research examined the impacts of the 50% reduction in total NH3 (gas + aerosol), total HNO3 (gas + aerosol), and total H2SO4 (aerosol) concentrations on the changes in iPM2.5 concentrations in winter and summer. It was found that the 50% reduction in total NH3 concentration may not lead to a significant reduction in iPM2.5 concentration. This may suggest that NH3 emissions from Animal Feeding Operations (AFOs) at agricultural sites led to elevated atmospheric NH3 concentration and NH3-rich conditions dominated; thus, the change in iPM2.5 concentration was not sensitive to the change in NH3 concentrations. To understand the formation of iPM2.5 as impacted by AFOs NH3 emissions, the response of iPM2.5 to NH3 concentrations near an egg production farm in the southeastern U.S. was studied [34]. The NH3 concentrations and iPM2.5 chemical compositions measured at in-house and ambient locations were used as inputs in ISORROPIA II model to simulate the responses of iPM2.5 to the concentrations of precursor gases, T, and RH. It was confirmed that the most significant reduction in iPM2.5 could be achieved by the reduction in total H2SO4 instead of total NH3. It was also suggested that in NH3-rich areas, NH3 was in excess to neutralize the acidic gases and the formation of the iPM2.5 was limited by the availability of acidic gases [34]. The changes in the partitioning of NH3-NH4+ caused by the changes in precursor gases may vary under different ambient conditions in response to the unique atmospheric chemical conditions and local meteorology; thus, more efforts are needed to investigate the partitioning of NH3-NH4+ [35,36,37].
The effects of changes in total H2SO4 (aerosol), total NH3 (gas + aerosol), and total HNO3 (gas + aerosol) on iPM2.5 concentrations have been studied in the southeastern U.S. from 1998 to 2004 under the Southeastern Aerosol Research and Characterization (SEARCH) network. It was reported that the formation of NO3 was limited by the availability of NH3 in 1998–1999 [38]. Another study also indicated that the combination of the reductions in total H2SO4 and total HNO3 was more effective to decrease iPM2.5 mass concentration in 1998–2001 [39]. Reduction in total H2SO4 was more effective in decreasing iPM2.5 concentrations in 2004 [40]. Formation of iPM2.5 was limited by the availability of NH3 in rural-forest and coastal areas of the southeastern U.S. in 2004 [41]. While the above research provides fundamental understanding of secondary iPM2.5 formation, the implementations of new regulations [42,43,44] led to the temporal changes in precursor gases emissions in the southeastern U.S.; thus, the responses of iPM2.5 concentrations and partitioning of NH3-NH4+ to the changes in total H2SO4, total NH3, and total HNO3 may also change over time [45]. The objective of this research is to investigate the partitioning of NH3-NH4+ in urban and rural areas of the southeastern U.S. under different meteorological conditions using the latest field measurements of iPM2.5 and precursor gases. The research findings may provide further insights to develop effective PM2.5 control strategies.

2. Materials and Methods

2.1. Data Acquisition and Processing

This research utilized the 24 h particle-phase measurements and 1 h average gas-phase measurements from the SEARCH network [46] (Figure 1). For 24 h measurements, the chemical compositions of PM2.5 were measured using filter-based Federal Reference Method (FRM), and 1 h average measurements were converted from 1 min or 5 min continuous measurements (Table S1); the detailed information about measurement techniques and detection limits can be found in SEARCH network literature [46]. The NH3 gas concentration measurements were available at five sites named YRK, JST, CTR, BHM, and OLF in 2012–2016; thus, the responses of the partitioning of NH3-NH4+ to the changes in precursor gases were investigated at these five sites. The dataset includes some NH3 values that are either negative or below the detection limit. The negative values were excluded from data analysis, while the values below the detection limit were replaced with half of the detection limit [47,48].

2.2. Investigation of the Partitioning of NH3-NH4+

The partitioning of NH3-NH4+ was investigated using ISORROPIA II [27]. The model performance evaluation was performed in another study; thus, it will not be further elaborated on. In this research, 10% to 90% reductions in total NH3, total H2SO4, and total HNO3 at the five sites in four seasons were used to investigate the responses of iPM2.5 (NH4+ + NO3 + SO42−) to the changes in precursor gases in 2012–2016, spring and fall results are the transition case scenarios between summer and winter; thus, only summer and winter results are reported and discussed here. Moreover, only the gas-particle partitioning processes are considered, other processes such as emissions, dispersion, and dry and wet depositions are thus not included in this research.
The concentrations of iPM2.5 and NH4+ under different total NH3, total H2SO4, and total HNO3 concentrations in four seasons were simulated using 24 h average data at the five sites. The gas-phase NH3 molar fraction (NH3/NHx) in Equation (1) [49,50] was used to study the effects of changes in precursor gases on the partitioning of NH3-NH4+.
NH3/NHx = [NH3]/([NH3] + [NH4+])
Gas ratio (GR) in Equation (2) [25,51] was calculated to study the effects of changes in precursor gases concentrations on the atmospheric chemical conditions, diurnal variation of iPM2.5 and partitioning of NH3-NH4+.
GR = ([TA] − 2 × [TS])/[TN]
where [TA] is the sum of molar concentrations of NH3 and ammonium (NH4+) (in the unit of µmol m−3); [TS] is the sum of molar concentrations of SO42−, bisulfate (HSO4), and H2SO4 (in the unit of µmol m−3); and [TN] is the sum of molar concentrations of HNO3 and nitrate (NO3) (in the unit of µmol m−3).
The pH [52] was calculated to study the acidity of the inorganic aerosol.
pH = log 10 1000 γ H + H air + W
where γH+ is the hydronium ion activity coefficient, which is set as unity; Hair+ (µg m−3) is the hydronium ion concentration in volume of air; and W (µg m−3) is particle water concentration associated with inorganic aerosol. Both Hair+ and W are from ISORROPIA II model output.

2.3. ISORROPIA II Model

The performance of ISORROPIA II for predicting inorganic aerosols in the southeastern U.S. was investigated in another research [53] and the ISORROPIA II model predicted the concentrations of various compositions of iPM2.5 well.
For this study, the iPM2.5 was assumed to be internally mixed, and the thermodynamic equilibrium was also assumed to be established instantaneously [29]. The ISORROPIA II allows the user to specify the problem type (forward or reverse) and thermodynamic state (stable or metastable). In this study, ISORROPIA II is set as forward type, which requires the concentrations of total NH3 (gas + aerosol), total HNO3 (gas + aerosol), and total H2SO4 (aerosol) as the model input. The metastable thermodynamic state was selected in this research [15,32].

2.4. Multiple Linear Regression Model

The multiple linear regression (MLR) model was constructed to examine the response of NH4+ to various factors. Step-wise model selection method based on the Bayesian information criterion (BIC) was used to select the best fitting model from Equation (4):
NH4+ = β0 + βi × xi + interaction terms + quadratic terms + εi
where xi are iPM2.5 chemical components and gaseous pollutants including SO42−, NO3, Ca2+, Mg2+, K+, Na+, Cl, NH3, and HNO3, ambient T, and RH. Interaction terms include up to two factors. All the gas- and particle-phase pollutants were converted in the unit of µg m−3, T was in °C, RH was in %. The 24 h average Cl, K+, Na+, Mg2+, Ca2+, NH4+, SO42−, and NO3 data and 1 h average T, RH, NH3, and HNO3 data were available at the BHM site (2011–2016), CTR site (2012–2016), JST site (2010–2016), YRK site (2008–2016), OLF site (2013–2016), and OAK site (2010). The MLR model was built in two periods: 2008–2011 and 2012–2016. The best fitting MLR models vary in space and time and are only used to aid in the investigation of partitioning of NH3-NH4+.

3. Results and Discussion

3.1. Statistical Characterization of the Field Measurement Data

The statistical summaries of iPM2.5 precursor gases, nonvolatile cations (NVCs), T and RH at six sites in two periods (2008–2011 and 2012–2016) are shown in Tables S2–S10. Tables S2–S10 reveal the seasonal variations of different precursor gases such as H2SO4, HNO3, HCl, NH3, and NVCs such as Na+, K+, Mg2+, and Ca2+ as well as T and RH at the six sites of the SEARCH network. In general, T and RH were both lowest in winter and highest in summer (see the Supplementary Materials). The concentration of total H2SO4 was higher in summer than the other seasons at the six sites. The concentrations of total NH3 and total HNO3 did not exhibit a distinct seasonal pattern, which may be caused by spatial variation of emissions sources at the six sites of the southeastern U.S.
The concentrations of iPM2.5 chemical compositions and precursor gases were also measured in different locations of the world. The aerosol composition measurements and source apportionment studies in a coastal city of eastern China during 2018–2019 indicated that inorganic aerosols accounted for a large portion of PM2.5 mass concentration and local steel plant emissions were dominated by NH4)2SO4 and ammonium bisulfate (NH4HSO4); in addition, the iPM2.5 concentrations at the coastal city of China were much higher than that measured in the southeastern U.S. [54]. The inorganic composition of PM2.5 and precursor gases were measured at Seoul and Deokjeok Island of South Korea in 2014, where the haze aerosols mainly consisted of inorganics (e.g., NH4+ salts); ISORROPIA II model simulations implicated that the addition of SO42− into the aerosols during the transport process increased the mass concentrations of NH4NO3, and another finding is that the concentrations of total NH3, total HNO3, and total H2SO4 were also higher than the measurement values in the southeastern U.S. during the same period of time [55]. Moreover, a newly developed method was used in Brno, Czech in 2018, to simultaneously measure the concentrations of gaseous NH3 and aerosol NH4+ with a time resolution of 1 s; the measurement results indicated a seasonal variation for NH3 and NH4+ with higher NH3 concentrations in summer, and higher NH4+ concentrations in winter; the ratio of NH3/NH4+ indicated the dominance of NH3 and NH4+ in summer and winter, respectively [56]. The difference in local to regional emissions sources contributed to the spatial and temporal variations of iPM2.5 and partitioning of NH3-NH4+ across the world.

3.2. Seasonal Simulation of Partitioning of NH3-NH4+

The responses of iPM2.5, NH4+, and NH3/NHx to the changes in total NH3 and total HNO3 in 2012–2016 are presented in Figure 2 and Figures S1–S4.
As can be seen in Figure 2 and Figures S1–S4, the formation of iPM2.5 and the partitioning of NH3-NH4+ were sensitive to the changes in total NH3 concentration when total NH3 concentration was reduced at least 20% or when total HNO3 concentration was not reduced in 2012–2016. Although the YRK, JST, BHM, and OLF sites were all under NH3-rich condition, the pH analysis indicated that inorganic aerosols were still acidic (Table S11) instead of full neutralization. The reduction in total NH3 concentration could decrease the gas-phase NH3 concentration but could not decrease the formation of iPM2.5. When enough reduction in total NH3 was achieved or acidic gases (total H2SO4 and total HNO3) were in excess to react with NH3 gas, the subsequent reduction in total NH3 can lead to the decrease in iPM2.5.
The formation of iPM2.5 and the partitioning of NH3-NH4+ were more sensitive to the changes in total NH3 and total HNO3 in winter than in the other seasons. The semi-volatile characteristics of NH4NO3 may explain the seasonal variation of the responses of iPM2.5 to the change in total NH3 and total HNO3. The lower T in winter favored the formation of NH4NO3; if there was adequate NH3 reacting with acidic gases, then most of the HNO3 stayed in particle-phase [22,57]. Thus, as observed in Figure 2 and Figures S1–S4, the formation of iPM2.5 was sensitive to the change in total HNO3 when total NH3 was not reduced in winter of 2012–2016.
The responses of iPM2.5, NH4+, and NH3/NHx to the changes in total NH3 and total H2SO4 are presented in Figure 3 and Figures S5–S8.
As can be seen in Figure 3 and Figures S5–S8, the formation of iPM2.5 was very sensitive to the change in total H2SO4 concentration in summer and winter. The reduction in total H2SO4 can effectively decrease the concentration of iPM2.5, and more NH3 stayed in the gas-phase in this process. The responses of NH4+ to the change in total H2SO4 may exhibit two different regions. The less reduction in total NH3 and the more reduction in total H2SO4 were achieved, the more sensitive the NH4+ responded to the change in total H2SO4. This can be explained that when NH3 was not adequate to react with both HNO3 and H2SO4, the reduction in H2SO4 may free some NH3 associated with SO42−, and the available NH3 can react with HNO3 to form NH4NO3, which lead to the decrease in SO42− salts and increase in NO3 salts. Thus, NH4+ concentration may remain at approximately the same level. Furthermore, when greater than 80% reduction in total H2SO4 was achieved, the reduction in total H2SO4 may lead to the increase in iPM2.5 at the JST (Figure S5), CTR (Figure S6), and OLF (Figure S8) sites in winter.
The formation of iPM2.5 was more sensitive to the change in total H2SO4 in summer than in winter. This can be explained by the dominance of SO42− salts in iPM2.5 in summer. The more intense summer solar radiation enhanced the transformation of SO2 to SO42− [24]. Moreover, as the high T in summer did not facilitate the formation of NH4NO3, the decrease in SO42− salts caused by the reduction in total H2SO4 will not be offset by the increase in the NO3 salts.

3.3. Diurnal Simulation of the Partitioning of NH3-NH4+

In addition to the investigation of the partitioning of NH3-NH4+ in four seasons, the partitioning of NH3-NH4+ was also studied in different time of the day at the five sites in 2012–2016, the results of YRK site are shown in Figure 4, Figure 5 and Figure 6, the results at the other sites are shown in Figures S9–S20.
Figure 4, Figure 5 and Figure 6 indicate that the reduction in total NH3 and total HNO3 may not be effective in reducing the concentration of iPM2.5 unless more than a 60% reduction can be achieved. The reduction in total NH3 and total HNO3 can only lead to a decrease in NH4NO3, while the SO42− concentration remained at approximately the same level. The reduction in total NH3 concentration reduced both NH3/NHx and GR, and more NH3 partitioned towards particle-phase. The reduction in total HNO3 led to the increase in both NH3/NHx and GR, and more NH3 remained in the gas-phase. Overall, Figure 4, Figure 5 and Figure 6 illustrate that the reductions in total NH3 and total HNO3 are ineffective for controlling iPM2.5 concentration.
Figure 6 shows that the reduction in total H2SO4 was more effective in reducing the concentrations of iPM2.5; however, the reduction in total H2SO4 may lead to the increase in NO3 concentration, especially at the CTR and OLF sites (Figures S14 and S20) (e.g., at 12:00 p.m., 80% total H2SO4 reduction at the CTR site (1.50→0.30 µg m−3‪) resulted in the decrease in iPM2.5 (2.11→0.66 µg m−3) and increase in NO3 (0.03→0.16 µg m−3)). The YRK, JST, and BHM sites were all in NH3-rich area, and the reduction in total H2SO4 may free some NH3 associated with H2SO4, however, the increase in gas-phase NH3 was not able to transform more HNO3 into particle-phase at NH3-rich sites (Figure 6, Figures S11 and S17). While at the CTR and OLF sites (Figures S14 and S20), the increase in gas-phase NH3 may change the partitioning of HNO3-NO3 toward particle-phase when the NH3 is not in excess to neutralize both HNO3 and H2SO4. The reduction in total H2SO4 can also increase both NH3/NHx and GR, which indicates that more NH3 stayed in the gas-phase rather than in the particle-phase in this process.
Reduction in total H2SO4 concentration may lead to a significant reduction in iPM2.5; thus, it was more effective than reducing total HNO3 and total NH3 concentrations to reduce iPM2.5 concentration. However, the reduction in total H2SO4 concentration may also increase the concentration NO3 at the CTR and OLF sites (Figures S14 and S20). Thus, the future reduction in iPM2.5 may necessitate the coordinated reduction in both H2SO4 and HNO3 in the southeastern U.S.
The YRK site was located in a rural area impacted by the NH3 emissions from AFOs, while BHM and JST sites were located in the area impacted by industrial emission sources. The CTR site was located in a forest area and the OLF site was located in a suburban area. The spatial variation of the responses of the partitioning of NH3-NH4+ to the reductions in precursor gases implicated the important impact of AFOs NH3 emissions. At the agricultural rural site—YRK site, the NH3 emissions from AFOs led to elevated NH3 concentration, which was in excess to neutralize acidic gases, and the formation of NH4NO3 was not affected by the reduction in total H2SO4 [36].

3.4. Multiple Linear Regression Model

The effects of the various predictor variables (e.g., SO42−, NO3, NH3, etc.) on the response variable (NH4+) were estimated using regression analysis. The MLR models for the responses of NH4+ to various factors in two periods (2008–2011 and 2012–2016) at the six sites were shown in Table 1, Table 2 and Tables S12–S18.
In the linear regression analysis, the interaction terms may cause serious multi-collinearity problem, which will provide redundant information [58]; thus, the model diagnostics may exclude the interaction terms when the variance inflation factor (VIF) for interaction term is greater than 10. The selection of predictor variables varied at different sites in different periods.
Both SO42− and NO3 were included in the regression models at the six sites in two periods. The iPM2.5 mainly consisted of NH4+ salts, most of the NH4+ cations were associated with SO42− and NO3 anions. The coefficients for both SO42− and NO3 were positive, which indicated the positive correlation between cation-NH4+ and anions-SO42− and NO3. The positive regression coefficients (0.29–0.38) for SO42− were greater than the coefficients for all the other predictor variables. The dominance of particle-phase SO42− salts led to the significant relationship between NH4+ and SO42−; the changes in SO42− can cause corresponding changes in NH4+. Some centered quadratic terms were included in the model as well, the quadratic terms indicated that the direction of the relationship between NH4+ and SO42−, NO3 may change as SO42− and NO3 concentrations changed. The complex relationship between NH4+ and SO42−, NO3 may be caused by reactions between NH3 and H2SO4, HNO3, the dynamic changes in particle-phase SO42−; NO3 may also change the dynamic reactions of NH3 and various acidic gases (e.g., the free NH3 from the reduction in SO42− may react with HNO3 to form NH4NO3).
As for the gas-phase NH3, the BIC step-wise model selection method did not include NH3 in the MLR model at the JST site in 2010–2011, at the YRK site in 2008–2011 and 2012–2016, and at the BHM site in 2012–2016. The exclusion of NH3 indicated that NH3 may not limit the formation of NH4+ salts at these three sites. Specifically, NH3 was excluded from the regression model from 2008 to 2016 at the YRK site. The NH3 emissions from AFOs contributed to the abundant NH3 gas at the YRK site; thus, the NH3-rich conditions dominated. While for the CTR site (Table S16) in 2012–2016, OAK site (Table S17) in 2010, and OLF site (Table S18) in 2013–2016, the NH3 was included in the regression model and the coefficients were positive. Especially, at the OAK site, coefficient of NH3 was 0.16, which was higher than the other sites. The positive coefficients suggested that the higher NH3 led to increased formation of NH4+ salts at these sites.
As for the gas-phase HNO3, it was included in the regression model at the YRK site in 2012–2016, and at the BHM site in 2011, and the regression coefficients for HNO3 in these two models were negative. The semi-volatile characteristic of NH4NO3 may explain the negative coefficient. Under ambient conditions, such as high T and low RH, the NH4NO3 may decompose into gas-phase NH3 and HNO3, the increase in gas-phase HNO3 leads to a decrease in NH4+. The interaction term—T:HNO3 at the YRK site, may indicate the dependence of the formation of NH4NO3 on ambient conditions.
As for ambient meteorological conditions—T and RH, only T was included in the regression models at the JST site in 2012–2016 (Table S13), at the YRK site in 2012–2016 (Table 2), at the BHM site in 2011 (Table S14) and 2012–2016 (Table S15), and at the OAK site in 2010 (Table S17). The RH was excluded from all the regression models. The coefficients for T were all negative, which indicated that the increase in T led to the decrease in NH4+, but the coefficients for T were smaller compared to the coefficients for the other predictor variables. The smaller coefficient for T may indicate the relatively weak impact of T on the NH4+.
As for the NVCs and Cl, although the concentrations were lower compared with the other gas- and particle-phase species, one of Mg2+, Na+, or Cl was included in the regression models, this indicated that the NVCs and Cl were important factors affecting the NH4+ concentration. The coefficients for Mg2+ may exhibit some large values (e.g., −6.69 and −3.83), which indicated that there is a strong negative correlation between Mg2+ and NH4+. However, ISORROPIA II model simulation implicated that the low concentration of Mg2+ may not lead to a significant change in NH4+ concentration, which is against MLR model results.

4. Conclusions

In this research, the effects of changes in precursor gases on the formation of iPM2.5 as well as the partitioning of NH3-NH4+ were investigated using ISORROPIA II modeling approach with inputs of field measurements of gas-phase and particle-phase pollutants and meteorological data in the SEARCH network. The results indicated that the reduction in total H2SO4 was more effective to decrease the formation of iPM2.5, especially under NH3-rich conditions. In addition, the reduction in total H2SO4 may change the partitioning of NH3-NH4+ towards gas-phase. Moreover, the reduction in total H2SO4 may lead to an increase in NO3 when NH3 was not in excess to neutralize the acidic gases. Thus, the future reduction in iPM2.5 may necessitate the coordinated reduction in both H2SO4 and HNO3 in the southeastern U.S. It was also discovered that the response of iPM2.5 to the change in total H2SO4 was more sensitive in summer than winter. The dominance of SO42− salts in iPM2.5 and high T in summer did not facilitate the formation of NH4NO3, the decrease in SO42− salts caused by the reduction in total H2SO4 will not be offset by the increase in the NO3 salts. The significant NH3 emissions from AFOs in the agricultural rural area had great impact on the partitioning of NH3-NH4+, and the NH3 emissions from the AFOs led to the elevated NH3 concentration, which was in excess to neutralize acidic gases. The formation of NH4NO3 was not affected by the reduction in total H2SO4 in an agricultural rural area. The BIC stepwise model selection determined the MLR model to predict NH4+ at six sites, there was a strong positive correlation between cation-NH4+ and anions-SO42− and NO3. The NH3 was excluded from the regression model at the YRK site due to the abundant NH3 emitted from AFOs, and the NVCs and Cl were the significant impact factors affecting NH4+ concentrations.

Supplementary Materials

The following are available online at https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/atmos12121681/s1, Table S1: Field measurements at the eight sites, Table S2: The statistics of different precursor gases of iPM2.5 by season at the YRK site in 2008–2011, Table S3: The statistics of different precursor gases of iPM2.5 by season at the YRK site in 2012–2016, Table S4: The statistics of different precursor gases of iPM2.5 by season at the JST site in 2010–2011, Table S5: The statistics of different precursor gases of iPM2.5 by season at the JST site in 2012–2016, Table S6: The statistics of different precursor gases of iPM2.5 by season at the CTR site in 2012–2016, Table S7: The statistics of different precursor gases of iPM2.5 by season at the BHM site in 2011, Table S8: The statistics of different precursor gases of iPM2.5 by season at the BHM site in 2012–2016, Table S9: The statistics of different precursor gases of iPM2.5 by season at the OAK site in 2010, Table S10: The statistics of different precursor gases of iPM2.5 by season at the OLF site in 2013–2016, Table S11: The summary of aerosol pH at five sites in 2012 to 2016, Table S12: The summary of final MLR model coefficients at the JST site from 2010 to 2011, Table S13: The summary of final MLR model coefficients at the JST site from 2012 to 2016, Table S14: The summary of final MLR model coefficients at the BHM site in 2011, Table S15: The summary of final MLR model coefficients at the BHM site from 2012 to 2016, Table S16: The summary of final MLR model coefficients at the CTR site from 2012 to 2016, Table S17: The summary of final MLR model coefficients at the OAK site in 2010, Table S18: The summary of final MLR model coefficients at the OLF site from 2013 to 2016, Figure S1: Responses of iPM2.5, NH4+, and NH3/NHx to the changes of TNH3 and THNO3 at the JST site in summer and winter of 2012–2016, Figure S2: Responses of iPM2.5, NH4+, and NH3/NHx to the changes of TNH3 and THNO3 at the CTR site in summer and winter of 2012–2016, Figure S3: Responses of iPM2.5, NH4+, and NH3/NHx to the changes of TNH3 and THNO3 at the BHM site in summer and winter of 2012–2016, Figure S4: Responses of iPM2.5, NH4+, and NH3/NHx to the changes of TNH3 and THNO3 at the OLF site in summer and winter of 2013–2016, Figure S5: Responses of iPM2.5, NH4+, and NH3/NHx to the changes of TNH3 and TH2SO4 at the JST site in summer and winter of 2012–2016, Figure S6: Responses of iPM2.5, NH4+, and NH3/NHx to the changes of TNH3 and TH2SO4 at the CTR site in summer and winter of 2012–2016, Figure S7: Responses of iPM2.5, NH4+, and NH3/NHx to the changes of TNH3 and TH2SO4 at the BHM site in summer and winter of 2012–2016, Figure S8: Responses of iPM2.5, NH4+, and NH3/NHx to the changes of TNH3 and TH2SO4 at the OLF site in summer and winter of 2013–2016, Figure S9: Responses of iPM2.5, SO42−, NH4+, NO3, NH3/NHx, and GR to the reductions in TNH3 at the BHM site in 2012–2016, Figure S10: Responses of iPM2.5, SO42−, NH4+, NO3, NH3/NHx, and GR to the reductions in THNO3 at the BHM site in 2012–2016, Figure S11: Responses of iPM2.5, SO42−, NH4+, NO3, NH3/NHx, and GR to the reductions in TH2SO4 at the BHM site in 2012–2016, Figure S12: Responses of iPM2.5, SO42−, NH4+, NO3, NH3/NHx, and GR to the reductions in TNH3 at the CTR site in 2012–2016, Figure S13: Responses of iPM2.5, SO42−, NH4+, NO3, NH3/NHx, and GR to the reductions in THNO3 at the CTR site in 2012–2016, Figure S14: Responses of iPM2.5, SO42−, NH4+, NO3, NH3/NHx, and GR to the reductions in TH2SO4 at the CTR site in 2012–2016, Figure S15: Responses of iPM2.5, SO42−, NH4+, NO3, NH3/NHx, and GR to the reductions in TNH3 at the JST site in 2012–2016, Figure S16: Responses of iPM2.5, SO42−, NH4+, NO3, NH3/NHx, and GR to the reductions in THNO3 at the JST site in 2012–2016, Figure S17: Responses of iPM2.5, SO42−, NH4+, NO3, NH3/NHx, and GR to the reductions in TH2SO4 at the JST site in 2012–2016, Figure S18: Responses of iPM2.5, SO42−, NH4+, NO3, NH3/NHx, and GR to the reductions in TNH3 at the OLF site in 2013–2016, Figure S19: Responses of iPM2.5, SO42−, NH4+, NO3, NH3/NHx, and GR to the reductions in THNO3 at the OLF site in 2013–2016, Figure S20: Responses of iPM2.5, SO42−, NH4+, NO3, NH3/NHx, and GR to the reductions in TH2SO4 at the OLF site in 2013–2016.

Author Contributions

Conceptualization, B.C. and L.W.-L.; methodology, B.C. and L.W.-L.; project administration, L.W.-L.; data analysis, B.C. and L.W.-L., data interpretation, B.C., L.W.-L., N.M., J.C. and P.B.; writing—original draft preparation, B.C.; writing—review and editing, B.C., L.W.-L. and N.M.; funding acquisition, L.W.-L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported in part by NSF Award No. CBET-1804720.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Publicly available datasets were analyzed in this study. This data can be found here: (https://www.dropbox.com/sh/o9hxoa4wlo97zpe/AACbm6LetQowrpUgX4vUxnoDa?dl=0 (accessed on 3 October 2021)).

Acknowledgments

Great thanks to Eric Edgerton from ARA, Inc. for providing the SEARCH network data.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Figure 1. The geographical locations of the eight monitoring sites of the SEARCH network [46].
Figure 1. The geographical locations of the eight monitoring sites of the SEARCH network [46].
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Figure 2. Responses of iPM2.5, NH4+, and NH3/NHx to the changes in TNH3 and THNO3 at the YRK site in summer and winter of 2012–2016, the average total NH3 concentrations were reduced from 1.53 µg m−3 (0% reduction) to 0.153 µg m−3 (90% reduction) and from 2.66 µg m−3 (0% reduction) to 0.266 µg m−3 (90% reduction) in winter and summer, respectively; the average total HNO3 concentrations were reduced from 1.41 µg m−3 (0% reduction) to 0.141 µg m−3 (90% reduction) and from 1.11 µg m−3 (0% reduction) to 0.111 (90% reduction) in winter and summer, respectively.
Figure 2. Responses of iPM2.5, NH4+, and NH3/NHx to the changes in TNH3 and THNO3 at the YRK site in summer and winter of 2012–2016, the average total NH3 concentrations were reduced from 1.53 µg m−3 (0% reduction) to 0.153 µg m−3 (90% reduction) and from 2.66 µg m−3 (0% reduction) to 0.266 µg m−3 (90% reduction) in winter and summer, respectively; the average total HNO3 concentrations were reduced from 1.41 µg m−3 (0% reduction) to 0.141 µg m−3 (90% reduction) and from 1.11 µg m−3 (0% reduction) to 0.111 (90% reduction) in winter and summer, respectively.
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Figure 3. Responses of iPM2.5, NH4+, and NH3/NHx to the changes in TNH3 and TH2SO4 at the YRK site in summer and winter of 2012–2016, the average total NH3 concentrations were reduced from 1.53 µg m−3 (0% reduction) to 0.153 µg m−3 (90% reduction) and from 2.66 µg m−3 (0% reduction) to 0.266 µg m−3 (90% reduction) in winter and summer, respectively; the average total H2SO4 concentrations were reduced from 1.56 µg m−3 (0% reduction) to 0.156 µg m−3 (90% reduction) and from 2.05 µg m−3 (0% reduction) to 0.205 (90% reduction) in winter and summer, respectively.
Figure 3. Responses of iPM2.5, NH4+, and NH3/NHx to the changes in TNH3 and TH2SO4 at the YRK site in summer and winter of 2012–2016, the average total NH3 concentrations were reduced from 1.53 µg m−3 (0% reduction) to 0.153 µg m−3 (90% reduction) and from 2.66 µg m−3 (0% reduction) to 0.266 µg m−3 (90% reduction) in winter and summer, respectively; the average total H2SO4 concentrations were reduced from 1.56 µg m−3 (0% reduction) to 0.156 µg m−3 (90% reduction) and from 2.05 µg m−3 (0% reduction) to 0.205 (90% reduction) in winter and summer, respectively.
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Figure 4. Responses of iPM2.5, SO42−, NH4+, NO3, NH3/NHx, and GR to the reductions in TNH3 at the YRK site in 2012–2016.
Figure 4. Responses of iPM2.5, SO42−, NH4+, NO3, NH3/NHx, and GR to the reductions in TNH3 at the YRK site in 2012–2016.
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Figure 5. Responses of iPM2.5, SO42−, NH4+, NO3, NH3/NHx, and GR to the reductions in THNO3 at the YRK site in 2012–2016.
Figure 5. Responses of iPM2.5, SO42−, NH4+, NO3, NH3/NHx, and GR to the reductions in THNO3 at the YRK site in 2012–2016.
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Figure 6. Responses of iPM2.5, SO42−, NH4+, NO3, NH3/NHx, and GR to the reductions in TH2SO4 at the YRK site in 2012–2016.
Figure 6. Responses of iPM2.5, SO42−, NH4+, NO3, NH3/NHx, and GR to the reductions in TH2SO4 at the YRK site in 2012–2016.
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Table 1. The summary of final MLR model coefficients at the YRK site from 2008 to 2011.
Table 1. The summary of final MLR model coefficients at the YRK site from 2008 to 2011.
PredictorsCoefficientsSEt ValuePr > |t|
Intercept (β0)0.080.0332.390.018
SO42−1)0.330.00937.11<2 × 10−16
NO32)0.250.01813.64<2 × 10−16
(SO42−-3.27)23)−0.0080.001−5.744.5 × 10−8
Mg2+4)−3.831.45−2.650.0089
Residual standard error: 0.1672 on 161 degrees of freedom. Multiple R-squared: 0.94. Adjusted R-squared: 0.94. F-statistic: 636.3 on 4 and 161 DF, p-value: <2.2 × 10−16.
Table 2. The summary of final MLR model coefficients at the YRK site from 2012 to 2016.
Table 2. The summary of final MLR model coefficients at the YRK site from 2012 to 2016.
PredictorsCoefficientsSEt valuePr > |t|
Intercept (β0)0.0280.0271.030.31
SO42−1)0.380.01329.58<2 × 10−16
NO32)0.0670.0322.120.036
(NO3-0.41)23)0.1550.0285.621.23 × 10−7
Na+4)−0.540.132−4.17.48 × 10−5
T (β5)−0.00250.00149−1.670.097
(Na+-0.04)26)0.710.292.430.0166
HNO37)−0.0450.027−1.660.0995
K+8)0.870.382.310.0226
SO42−:T (β9)−0.00290.000684−4.244.39 × 10−5
T:HNO310)0.00450.00143.250.00149
Residual standard error: 0.05419 on 122 degrees of freedom. Multiple R-squared: 0.97. Adjusted R-squared: 0.97. F-statistic: 443.7 on 10 and 122 DF, p-value: <2.2 × 10−16.
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Cheng, B.; Wang-Li, L.; Meskhidze, N.; Classen, J.; Bloomfield, P. Partitioning of NH3-NH4+ in the Southeastern U.S. Atmosphere 2021, 12, 1681. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12121681

AMA Style

Cheng B, Wang-Li L, Meskhidze N, Classen J, Bloomfield P. Partitioning of NH3-NH4+ in the Southeastern U.S. Atmosphere. 2021; 12(12):1681. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12121681

Chicago/Turabian Style

Cheng, Bin, Lingjuan Wang-Li, Nicholas Meskhidze, John Classen, and Peter Bloomfield. 2021. "Partitioning of NH3-NH4+ in the Southeastern U.S." Atmosphere 12, no. 12: 1681. https://0-doi-org.brum.beds.ac.uk/10.3390/atmos12121681

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