Investigating Dual Character of Atmospheric Ammonia on Particulate NH₄NO₃: Reducing Evaporation Versus Promoting Formation

Abstract

Ammonium nitrate (NH₄NO₃) is a major constituent of fine particulate matter (PM_2.5), playing a critical role in air quality and atmospheric chemistry. However, the dual regulatory role of ammonia (NH₃) in both the formation and volatilization of NH₄NO₃ under ambient atmospheric conditions remains inadequately understood. To address this gap, we conducted high-resolution field measurements at a clean tropical coastal site in China using an integrated system of Aerosol Ion Monitor-Ion Chromatography, a Scanning Mobility Particle Sizer, and online OC/EC analyzers. These observations were complemented by thermodynamic modeling (E-AIM) and source apportionment via a Positive Matrix Factorization (PMF) model. The E-AIM simulations revealed persistent thermodynamic disequilibrium, with particulate NO₃⁻ tending to volatilize even under NH_3gas-rich conditions during the northeast monsoon. This suggests that NH₄NO₃ in PM_2.5 forms rapidly within fresh combustion plumes and/or those modified by non-precipitation clouds and then undergoes substantial evaporation as it disperses through the atmosphere. Under the southeast monsoon conditions, reactions constrained by sea salt aerosols became dominant, promoting the formation of particulate NO₃⁻ while suppressing NH₄NO₃ formation despite ongoing plume influence. In scenarios of regional accumulation, elevated NH₃ concentrations suppressed NH₄NO₃ volatilization, thereby enhancing the stability of particulate NO₃⁻ in PM_2.5. PMF analysis identified five source factors, with NO₃⁻ in PM_2.5 primarily associated with emissions from local power plants and the large-scale regional background, showing marked seasonal variability. These findings highlight the complex and dynamic interplay between the formation and evaporation of NH₄NO₃ in NH_3gas-rich coastal atmospheres.

1. Introduction

Ammonium nitrate (NH₄NO₃), a significant water-soluble inorganic component in atmospheric particles, plays a critical role in influencing regional visibility, cloud condensation nuclei (CCN) activity, and radiative forcing through its spatial distribution and gas–particle partitioning behavior [1,2,3,4,5,6,7,8,9,10,11,12]. The thermodynamic properties of NH₄NO₃ make its concentration highly sensitive to changes in ambient temperature (T) and relative humidity (RH) [2,13,14,15,16,17,18,19,20]. For example, elevated RH enhances the partitioning of both HNO_3gas and NH_3gas into the submicron particle phase, as well as the transfer of HNO_3gas to supermicron particles through the neutralization of negative charges by non-volatile metal ions, thereby further regulating the formation and stability of NH₄NO₃ and nitrate metal salts [2,14,15,17,18,19]. Conversely, elevated T and low RH conditions tend to drive the dissociation of NH₄NO₃ back into the gas phase [17,18,19]. These coupled thermodynamic and chemical interactions underscore the importance of elucidating the formation and depletion mechanisms of NH₄NO₃ in the atmosphere [9,11,21,22,23].

In the formation of submicron NH₄NO₃ particles, NH_3gas, the primary component of atmospheric alkaline gases, serves as both a key precursor and a stabilizing agent [24,25,26,27,28,29,30]. Additionally, N₂O_5gas contributes to nighttime nitrate formation through particle-phase condensation processes, during which NH_3gas further enhances the stability of NH₄NO₃ [31,32,33,34,35,36]. This is particularly true under conditions of low alkali metal ion concentrations in submicron particles such as Na⁺ and K⁺. Globally, the primary source of NH₃ is agricultural activities, although transportation and industrial emissions in urban areas also contribute to NH₃ emissions [37,38,39,40,41,42,43]. Thus, the regulatory effect of NH₃ on NH₄NO₃ demonstrates significant spatial heterogeneity and concentration dependence [12,24,44,45,46,47,48]. Observational evidence suggests that when the NH₃ concentration exceeds the stoichiometric threshold required for the complete neutralization of sulfate, its role shifts from “precursor-limited” to “thermodynamic-limited” [10,48,49,50,51,52,53]. The shift fundamentally alters the pathways of NH₄NO₃ formation and its phase stability.

In contrast to previous studies that primarily focused on the formation pathways, environmental impacts, climatic effects, and policy implications of NH₄NO₃, Xiao et al. [54] used isotopic traces and found that nitrate in PM_2.5 (atmospheric particles with diameter less than 2.5 μm) primarily originated from primary combustion in a central Chinese city. Shen et al. [23] provided comprehensive evidence showing that high concentrations of NH₄NO₃ during severe PM_2.5 air pollution events are primarily due to prolonged, rapid conversion within combustion plumes under cold, humid, and stable meteorological conditions, followed by subsequent evaporation along the plume dispersion track in ambient air. Furthermore, two independent thermodynamic modeling studies in Beijing showed that thermodynamic force did not support the formation of NH₄NO₃, even under NH_3gas-rich atmospheres [49,55]. More recently, Sun et al. [8] and Yang et al. [44] identified high concentrations of NH₄NO₃ at approximately 400 m above ground level in northern and eastern China, consistent with emission heights and plume rise from strong industrial sources. If NH₄NO₃ aerosols in ambient air primarily originated from primary emissions and prolonged combustion plumes, NH₄NO₃ evaporation would generally be expected to occur [56,57]. This raises the following question: does atmospheric NH₃ play a role in reducing NH₄NO₃ evaporation or in promoting NH₄NO₃ formation in ambient air?

This paper aims to comprehensively investigate the dual regulatory roles of ammonia in both the formation and evaporation of NH₄NO₃, using high-resolution data collected at a clean coastal site on a tropical island in China [58]. The simplicity and clarity of the pollution sources at this location offer an ideal environment for isolating the regulatory effects of NH₃, thereby enhancing the robustness of the analysis. By combining field observational data analysis, thermodynamic equilibrium modeling, and source apportionment analysis, the study objectives are (1) to determine whether NH₃ not only serves as a precursor facilitating NH₄NO₃ formation, but also suppresses its evaporation by shifting the gas–particle equilibrium toward the particulate phase; (2) to investigate whether the regulatory effect of NH₃ on nitrate partitioning evolves under varying meteorological and chemical conditions—including differences in monsoonal air masses, ambient T, RH, and NH₃ availability; (3) to characterize the episodic behavior of NO₃⁻ in PM_2.5 and explore the possible contributions from local primary sources versus secondary formation. This work provides novel scientific insights into the synergistic regulation of nitrate aerosols in PM_2.5. The findings are expected to offer theoretical foundations for improved air pollution mitigation strategies and for advancing our understanding of aerosol–climate interactions.

2. Materials and Methods

2.1. Study Area

The sampling site for this study is situated on the third floor of a research building within a high-tech industrial park in southeastern Hainan Province (18.328° N, 109.169° E, Figure 1a,b). This region represents a land–sea transition zone, positioned approximately 1.9 km west of the South China Sea coastline [58]. It is seasonally influenced by alternating southeasterly and northeasterly monsoons under the overarching control of the East Asian monsoon system. To the south of the site, a 487 m high hill situated 1.3 km away acts as a local topographic barrier, fostering local circulation patterns under weak synoptic forcing. Between the sampling site and the hill lies the Huaneng Nanshan Power Plant—a natural gas-fired facility located 1.2 km to the south at the hill’s base (Figure 1c,d). The plant has a total installed capacity of 2 × 46 MW (new generators are visible on the left side of Figure 1c) and 132 MW (original units largely obscured on the right side of Figure 1c) and serves as a major source of industrial air pollutant emissions in the area. Plumes from the facility are occasionally observed traversing the sampling location. Additionally, within a 5 km radius of the park, the surrounding landscape comprises undeveloped wastelands and agricultural land, with approximately 2.3 km² designated for future development. A major traffic road is located 50 m east of the sampling point, with traffic volumes reaching 810 vehicles per hour during peak periods (07:00–09:00 and 17:00–19:00), 24% of which are electric vehicles (zero-emission models), classifying it as a low mobile pollution source area.

2.2. Study Data

The study utilized an Aerosol Ion Monitor-Ion Chromatography system (AIM-IC) (Thermo Fisher, Waltham, MA, USA) for the continuous monitoring of both gaseous and particulate matter in the atmosphere. The detection limits for NO₃⁻ and NH₄⁺ were 5 × 10⁻² μg m⁻³ and 4 × 10⁻⁴ μg m⁻³, respectively. The AIM-IC system was installed within the laboratory and connected to ambient air via a 2.5 m long stainless steel inlet tube (3.5 cm inner diameter), with the sampling probe positioned approximately 10 m above ground level. Regular maintenance and calibration procedures were carried out throughout the observation period to ensure the accuracy and instrument stability. Data collected during two periods—November to December 2023 and June 2024—were used for analysis, capturing atmospheric composition under varying meteorological conditions. Detailed operational information for the AIM-IC system is available in our previous publications [18,58,59]. The AIM-IC system is equipped with two analytical columns, one for cations (Ion Pac CS20, 2 × 250 mm) and one for anions (Ion Pac AS 11-HC, 2 × 250 mm), along with two guard columns, one for cations (CG20, 2 × 50 mm) and one for anions (AG 11-HC, 2 × 50 mm). Additionally, the system is equipped with a PM_2.5 cyclone separator to ensure accurate sampling of fine particulates, operating at a flow rate of 3 L min⁻¹. The system provides hourly concentration data for reactive gases (NH₃, SO₂, HNO₃, etc.) and water-soluble ions (NH₄⁺, NO₃⁻, etc.). It is important to note that the HNO₃ signal measured by AIM-IC includes not only gaseous HNO₃ but also contributions from its atmospheric precursors, such as N₂O₅, a fraction of organic nitrate, and trace amounts of NO₂ in ambient air, similar to other acidic vapor species measured by the system [59]. Therefore, HNO₃* was used instead of HNO₃ in the subsequent analysis.

Particle size spectrum data were measured simultaneously using a Scanning Mobility Particle Sizer (SMPS, Grimm, Germany). During sampling, the air was dried before entering a differential analyzer, where the number concentration size distribution from 11 to 1110 nm was obtained at a sampling flow rate of 0.3 L min⁻¹, with a 4 min resolution and 112 sampling channels, providing high-precision particle size distribution data. Additionally, an OC-EC online analyzer (Model 4, Sunset, Tigard, OR, USA) was employed for the continuous monitoring of organic carbon (OC) and elemental carbon (EC) in PM_2.5. Owing to the relatively clean atmospheric conditions at the sampling site (annual mean PM_2.5 < 10 μg m⁻³), OC and EC concentrations were monitored with a time resolution of 2 h to improve signal-to-noise reliability. However, the OC-EC analyzer experienced frequent malfunctions. Data points identified as outliers due to instrument malfunction or external contamination events were excluded during quality control to ensure the accuracy and integrity of the dataset.

2.3. Modeling Method

For model applications, this study utilized the Extended AIM Aerosol Thermodynamics Model (E-AIM, versions Model II and Model IV, available at: http://www.aim.env.uea.ac.uk/aim/aim.php, accessed on 13 February 2025) to simulate the gas–particle partitioning equilibrium of NH₄NO₃. E-AIM remains the only publicly available thermodynamic model capable of simultaneously resolving multiphase equilibrium processes involving both inorganic and organic aerosols. The core assumptions of E-AIM are as follows: (1) homogeneous internal mixing of aerosol components and (2) thermodynamic equilibrium between aerosols and ambient gases [58,59]. In this study, both Model II and Model IV were used to calculate equilibrium partitioning among the gaseous, aqueous, and solid phases. Model II is suitable across a broad range of relative humidity (0.1 < RH < 1.0) and accounts for the multiphase behavior of the NH₃-HNO₃-H₂SO₄-NH₄⁺-NO₃⁻-SO₄²⁻ (including solid salt precipitation and liquid water dissolution), resolving the distribution of species such as (NH₄)₂SO₄, NH₄NO₃, etc. Model IV extends the capabilities of Model II by incorporating Cl⁻ and Na⁺ to represent the influence of sea salt aerosols. Meteorological data were obtained from the China Meteorological Data Service Center (http://data.cma.cn/, accessed on 10 January 2025), and hourly PM_2.5 mass concentration data were retrieved from three national monitoring sites in Sanya. Using the NOAA HYSPLIT model (https://www.ready.noaa.gov/HYSPLIT.php, accessed on 10 January 2025) and adopting the MeteoInfo (version 3.9.9) software with GDAS meteorological data (1° × 1° spatial resolution, 3 h temporal resolution), 24 h backward trajectories were calculated at a height of 500 m, reflecting air mass transport characteristics at the top of the boundary layer during the day or above it at night, with a time resolution of 1 h. A 24 h backward trajectory is selected because it corresponds well with the atmospheric lifetime of NO_x, which typically undergoes oxidation and transformation within a day. In addition, both NH₃ and HNO₃ have relatively high dry deposition rates, and their atmospheric residence times are generally short. Therefore, a 24 h period is appropriate for capturing the relevant air mass transport influencing local nitrate formation. The 500 m arrival height was chosen to reflect regional-scale transport while avoiding the limitations of near-surface trajectories, which can be highly uncertain due to local topographic influences and boundary layer turbulence. This altitude is generally above the marine mixing layer, providing a more stable representation of air mass pathways.

To apportion the source contributions of NO₃⁻ in PM_2.5 ([NO₃⁻]_PM2.5), the study integrated the modified particle number concentration spectrum (11–700 nm, with 39 size segments reduced from the original ones) with the [NO₃⁻]_PM2.5 composition matrix using the Positive Matrix Factorization (PMF) model for positive matrix decomposition. This method clarified the contributions of various source aerosols to the formation and evolution of NO₃⁻. Strong new particle formation (NPF) events were excluded during the PMF analysis to minimize their dominant effect on the dataset. The study used the Q-value (Q/Qexp) as a fitting evaluation index, comparing changes in Q-values across different factor solutions and considering residual distribution and the physical significance of factor spectra to determine the optimal solution [60]. After running the model 20 times with data from 2023 and 2024, the five best factors were identified, with a Q/Q_exp ratio of 1.1.

3. Results and Discussion

3.1. Overview of Observational Results at the Coastal Site

Figure 2 illustrates time series of concentrations of major ions (including NO₃⁻, NH₄⁺, SO₄²⁻, and Na⁺) and carbonaceous components (OC and EC) in PM_2.5, along with hourly measurements of NH₃ and HNO₃* gases during the observation periods (14 November to 12 December 2023 and 7–20 June 2024). During the continuous observation in November 2023 (excluding 27–30 November), the [NO₃⁻]_PM2.5 remained relatively low, fluctuating around 0.6 ± 0.3 μg m⁻³, with a maximum value of only 1.7 μg m⁻³. However, between 27 and 30 November, several peaks in [NO₃⁻]_PM2.5 were observed, with the highest concentration reaching 3.9 μg m⁻³. A weak correlation was observed between [NO₃⁻]_PM2.5 and the corresponding SO₄²⁻ and NH₄⁺ concentrations (R² = 0.42 and 0.52, respectively; p-value < 0.01), whereas SO₄²⁻ and NH₄⁺ were strongly correlated (R² = 0.91; p-value < 0.01). These results suggested that NO₃⁻ in PM_2.5 partly existed as NH₄NO₃, while SO₄²⁻ primarily existed as NH₄⁺ salts. Furthermore, [NO₃⁻]_PM2.5 showed no correlation with Na⁺, indicating that aged sea salts, such as NaNO₃, were not major contributors to the observed NO₃⁻ in PM_2.5. Additionally, NO₃⁻ in PM_2.5 and other ions exhibited significant diurnal variations, though their patterns varied inconsistently.

In contrast, the diurnal variations in the concurrently observed HNO₃*_gas were consistently clear, with peaks around noon and before sunset, and troughs before sunrise (Figure 2c). Similarly, NH_3gas also showed a consistent diurnal pattern, with peaks shortly after sunrise and around midnight, and minima before and after sunset. The comparison with ion variations in PM_2.5 suggests that diurnal meteorological factors (such as T, RH, wind speed, wind direction, sunlight, and planetary boundary layer height) may significantly influence the concentration variations of each component. The sources of ions in PM_2.5 appeared more variable than HNO₃*_gas and NH_3gas.

Furthermore, during the one-month observation period, atmospheric NH_3gas concentrations fluctuated around 8.2 ± 3.3 μg m⁻³, with a maximum of 23 μg m⁻³ and a minimum of 2.5 μg m⁻³. In contrast, HNO₃*_gas concentrations remained much lower, fluctuating around 0.1 ± 0.04 μg m⁻³, with the maximum of 0.3 μg m⁻³. Despite the fact that the formation of particulate [NO₃⁻]_PM2.5 is governed by the product of NH₃ and HNO₃ concentrations [13], the markedly lower levels of HNO₃*_gas suggest that it was a more significant limiting factor for [NO₃⁻]_PM2.5 formation in this environment. The carbonaceous components in PM_2.5, particularly OC, significantly exceeded the concentrations of inorganic ions, with concentrations ranging from 4.7 to 12 μg m⁻³ C (6.7 ± 1.3 μg m⁻³ C). The corresponding EC concentrations ranged from 0.2 to 1.5 μg m⁻³ C (0.4 ± 0.1 μg m⁻³ C), which were slightly lower than [NO₃⁻]_PM2.5. The high OC and OC/EC ratios might be related to nearby cooking emissions and/or transported biomass burning, as suggested by the concurrently low SO₄²⁻ and NO₃⁻ concentrations [61].

Consistent with the November-December 2023 data, [NO₃⁻]_PM2.5 in June 2024 remained low at 0.2 ± 0.1 μg m⁻³, except on June 16. The highest value during the period, excluding June 16, was 0.7 μg m⁻³. However, on June 16, [NO₃⁻]_PM2.5 spiked to 1.8 μg m⁻³, markedly exceeding concentrations before and after that day. During the two-week observation period, [NO₃⁻]_PM2.5 exhibited clear diurnal variation, though the timing and width of the daily peaks were inconsistent, suggesting the complexity of its influencing factors. A weak correlation was observed between [NO₃⁻]_PM2.5 and the corresponding SO₄²⁻ and NH₄⁺ concentrations (R² = 0.46 and 0.51, respectively; p-value < 0.01), whereas SO₄²⁻ and NH₄⁺ were strongly correlated (R² = 0.88, p-value < 0.01). These results suggest that NO₃⁻ in PM_2.5 partly existed as NH₄NO₃, while SO₄²⁻ primarily formed NH₄⁺ salts. A significant correlation was found between NO₃⁻ and Na⁺ in PM_2.5 (R² = 0.64, p-value < 0.01), indicating that aged sea salts, such as NaNO₃, may have contributed to nitrate formation.

The diurnal variation in the concurrently observed NH_3gas concentrations exhibited strong fluctuations, but peak timing and shape varied considerably. During the period, NH₃ ranged from 0.5 to 13 μg m⁻³. Concentrations below 1 μg m⁻³ predominantly occurred between midnight and sunrise. The diurnal variation in HNO₃*_gas generally showed an inverse pattern to NH_3gas, except on 16 June. HNO₃*_gas concentrations fluctuated around 0.06 ± 0.06 μg m⁻³, with the maximum of 0.6 μg m⁻³. However, nighttime concentrations remained below 0.1 μg m⁻³. On 16 June, between 06:00 and 12:00, HNO₃*_gas concentrations exceeded 2 μg m⁻³ during four separate intervals. The impact of high concentrations of HNO₃*_gas on [NO₃⁻]_PM2.5 is discussed later. The carbonaceous components in PM_2.5 exhibited relatively low concentrations. Specifically, OC ranged from 0.5 to 2.7 μg m⁻³ C (1.1 ± 0.4 μg m⁻³ C), while EC ranged from 0.01 to 0.4 μg m⁻³ C (0.1 ± 0.1 μg m⁻³ C). These values ranked among the lowest compared to recent U.S. measurements [62]. The low OC and EC levels were likely due to the southwest monsoon, which transported clean oceanic air masses.

3.2. Thermodynamic Equilibrium Simulations for the Four Cases

To understand the causes for NO₃⁻ peaks in PM_2.5, thermodynamic equilibrium simulations were conducted to examine the gas–aerosol partitioning states. Figure 3 shows a comparison between the thermodynamic equilibrium simulations and the observations for HNO_3gas, NH_3gas, and HCl_gas. During 27–29 November 2023, simulated HNO_3gas concentrations deviated from the 1:1 line in over 90% of the time, indicating that NO₃⁻ in PM_2.5 had not reached equilibrium with HNO_3gas and was undergoing volatilization into the gas phase. The degree of deviation was independent of [NO₃⁻]_PM2.5 levels, as shown by the color scale in Figure 3. However, the simulation did not include K⁺, which may bind with a fraction of NO₃⁻. K⁺ had an appreciable contribution in PM_2.5, as analyzed in Section 3.3, Section 3.4, Section 3.5 and Section 3.6. This omission could partially explain the positive bias of the simulated HNO_3gas. Nevertheless, due to the elevated atmospheric NH₃ levels during this period, the impact of this omission is likely minimal. Moreover, on 27 and 29 November, approximately 10% of the data points aligned with the 1:1 line, suggesting that NO₃⁻ in PM_2.5 and gaseous HNO₃ reached equilibrium during these periods. In contrast, no negative deviations from the 1:1 line were observed, providing strong evidence against nitrate formation. It should be noted that observed HNO₃* includes not only gaseous HNO₃ but also N₂O₅, organic nitrate, and minor hydrolyzed NO₂. This may introduce a positive bias in the observed HNO_3gas, which could theoretically result in a negative deviation in the simulation. This effect is likely more pronounced at night, when N₂O₅ concentrations may exceed those of HNO₃. However, no such negative deviations were observed. Additionally, excluding NaCl from the model led to a more pronounced overestimation of HNO_3gas on 28 and 29 November, especially under high [NO₃⁻]_PM2.5 conditions (Figure S1). This suggests that some HNO_3gas may undergo heterogeneous reactions with NaCl to form particulate NaNO₃. However, the difference was negligible on 27 November.

On 16 June 2024, the modeled HNO_3gas concentrations closely aligned with the 1:1 line for case with higher-[NO₃⁻]_PM2.5 and were positively deviated from the 1:1 line to some extent for other cases. Again, excluding NaCl resulted in greater overestimation of HNO_3gas, even in some high-[NO₃⁻]_PM2.5 cases. Thus, it can be inferred that 1) NO₃⁻ associated with NH₄⁺ in PM_2.5 was undergoing volatilization to HNO_3gas; 2) NO₃⁻ associated with Na⁺ had reached gas–aerosol equilibrium on that day.

The simulated equilibrium NH_3gas concentration slightly exceeded the observed values, primarily because the observed NH_3gas concentration was sufficiently high, minimizing the impact of gas–particle equilibrium. The simulated HCl_gas concentration showed both positive and negative deviations from the 1:1 line. These inconsistencies may stem from sea salt and ammonium salts in externally mixed forms, which are treated as internally mixed in the thermodynamic simulation. Fresh sea salt tends to produce a positive deviation, whereas aged sea salt may lead to a negative one. Additionally, the observed HCl*_gas includes contributions not only from pure HCl_gas but also from Cl_2gas and ClNO_2gas [63], potentially introducing a positive measurement bias that could account for negative deviations in the simulated HCl_gas concentrations.

From a theoretical perspective, the elevated atmospheric T typical of tropical regions facilitates the attainment of gas–particle equilibrium for NH₄NO₃, with timescales on the order of minutes [64,65]. The simulation results for 16 June also corroborated this behavior. However, in particles produced by fresh combustion, NH₄NO₃ may be encapsulated by organic material [66,67,68], significantly prolonging the time required to reach equilibrium.

3.3. Case 1: Synergistic Effects of Primary Emissions and Meteorological Conditions on the Rapid In-Plume Formation and Subsequent Volatilization of Fresh Ammonium Nitrate

The case analysis was further conducted to characterize the NO₃⁻ peaks. Case 1 is based on the observations obtained on 27 November 2023. Figure 4a–e display the concentrations of NO₃⁻, NH₄⁺, and Na⁺ in PM_2.5, along with the concurrent atmospheric NH_3gas concentrations, particle number size distributions, and PM_2.5 mass concentrations at three national control sites in Sanya. They also included the correlation between the normalized [NO₃⁻]_PM2.5 and the sum of the particle number concentrations larger than 100 nm (N_>100) (as defined in the caption of Figure 4), as well as 24 h air mass back trajectories. Figure S2a,b illustrate the correlations between NH₄⁺ and NO₃⁻, and between NH₄⁺ and the equivalent concentrations of (NO₃⁻ + SO₄²⁻) in PM_2.5. Figure 4a shows a bimodal distribution of [NO₃⁻]_PM2.5, with a lower peak at 09:00 and a higher peak at 20:00. The PM_2.5 mass concentrations at the three national control sites also exhibited a similar bimodal distribution, except for a trough observed at the Junyue Sea-Beach station, likely related to localized showers. This suggested that the increase in [NO₃⁻]_PM2.5 may have been a widespread phenomenon. However, the air mass back trajectories indicated that (1) the sampling site was neither downwind nor upwind of the national control sites, and (2) the trajectories nearly overlapped during both the peak and the non-peak periods (Figure 4e). Therefore, it can be inferred that the atypical diurnal variations observed on that day were more likely due to local atmospheric processes rather than regional pollutant transport.

The concentrations of NH₄⁺ and Na⁺ in PM_2.5 exhibited unidirectional increases and decreases in their diurnal variations, respectively, and neither correlated significantly with NO₃⁻ (Figures S2 and S3a). This indicates that sea salt and atmospheric NH_3gas were not the dominant factors driving the [NO₃⁻]_PM2.5 increase. However, atmospheric NH_3gas also displayed a bimodal distribution and was weakly correlated with NO₃⁻ (R² = 0.32, p-value < 0.01). A more detailed analysis revealed that NH_3gas concentrations lagged behind changes in [NO₃⁻]_PM2.5 (Figure 4a,b). Additionally, the strong correlation between the equivalent concentration of NH₄⁺ and that of (NO₃⁻ + SO₄²⁻) (Figure S2) suggested that NH₄⁺ in PM_2.5 predominantly existed as inorganic ammonium salts. Thus, the delayed peaks of atmospheric NH_3gas are more likely to be attributable to the volatilization of NH₄NO₃ [8,23,56], as supported by the thermodynamic equilibrium stimulation in Section 3.2. In this specific case, atmospheric NH_3gas likely played a role in restricting its volatilization as gaseous (HNO₃ + NH₃).

Figure 4c shows that during the [NO₃⁻]_PM2.5 peak period, the median accumulation mode particle diameter (0.1 μm–1 μm) was notably smaller than during the lower [NO₃⁻]_PM2.5. Additionally, the normalized [NO₃⁻]_PM2.5 exhibited a strong positive correlation with the normalized N_>100, with a regression slope of 0.7. Theoretically, when an increase in particle number concentration is observed without a corresponding increase in median mode diameter, this suggests that particulate matter mainly originates from primary emissions, rather than particle growth through secondary formation. Thus, the observed NH₄NO₃ at the moment should primarily originate from primary sources [23,54,63,64]. The slope below unity further supports that NH₄NO₃ was undergoing volatilization (Figure 4d). Furthermore, the number concentration spectra fluctuated dramatically during the NO₃⁻ peak periods (Figure 4c); this a typical feature that is indicative of the influence of nearby pollution sources.

3.4. Case 2: Biomass Burning-Derived Potassium Salts and Cloud Processing of KNO₃ and NH₄NO₃ by Mixing with Additional Anthropocentric Sources, Followed by NH₄NO₃ Volatilization

The second case occurred on 28 November 2023, with a diurnal [NO₃⁻]_PM2.5 pattern distinct from that observed on 27 November (Figure 4 and Figure 5). Between 00:00 and 20:00, [NO₃⁻]_PM2.5 remained relatively stable at 1.3 ± 0.2 µg m⁻³ (Figure 4a), followed by a statistically significant increase to 1.5–3.0 µg m⁻³ (p < 0.05). Notably, no corresponding increase in PM_2.5 mass concentration was recorded at the three national control sites (Figure 5b). This suggested that the increase in [NO₃⁻]_PM2.5 was a localized phenomenon rather than a regional event. Simultaneous particle number size distributions revealed that, during the [NO₃⁻]_PM2.5 increase, the median diameter of the accumulation mode particles decreased rather than increased (Figure 5c). Furthermore, a strong positive correlation was identified between the normalized [NO₃⁻]_PM2.5 and the normalized N_>100 (Figure 5d), with the regression slope of 2.2, which significantly exceeded unity. These results suggested that while primary sources contributed to the NO₃⁻ increase, they cannot fully explain the observed pattern.

On 28 November, the NH₄⁺ and Na⁺ concentrations in PM_2.5 exhibited diurnal variations similar to those observed on 27 November, but with larger fluctuations. Although no statistically significant correlation was identified between the NO₃⁻ and NH₄⁺ concentrations in PM_2.5 (p-value > 0.05, Figure S2c), NH₄⁺ showed a strong correlation with the equivalent concentrations of (NO₃⁻ + SO₄²⁻), with a regression slope close to unity. Based on this, using 20:00–23:00 as the analysis window (t₀, t₁, t₂, t₃), the net increases in NH₄⁺ concentrations at t₁–t₃ relative to t₀ can be calculated by the difference. Assuming NH₄⁺ was fully associated with (NO₃⁻ + SO₄²⁻) at a 1:1 equivalent concentration ratio, the net increase in NO₃⁻ from NH₄NO₃ at t₁-t₃ was 0.02 µg m⁻³, 0.04 µg m⁻³, and 0.05 µg m⁻³, accounting for 3.0%, 2.4%, and 4.2% of the total [NO₃⁻]_PM2.5 increase, respectively. Recall that thermodynamic equilibrium stimulation analysis indicated the volatilization of NH₄NO₃ into a gas phase. The significant increase in [NO₃⁻]_PM2.5 between 21:00 and 23:00 was likely related to the formation of other nitrates, such as KNO₃ and NaNO₃. However, the minimal increase in Na⁺ concentration in PM_2.5 during this period (Figure 5a) suggested that NaNO₃ may have accounted for only a small fraction. In contrast, a marked increase in K⁺ concentration was observed in PM_2.5 between 21:00 and 23:00 (Figure S4), indicating that biomass burning emissions may be a major contributor [69,70]. Concurrently, atmospheric NH₃ also increased (Figure 5b), probably from biomass burning emissions [33], while other anthropogenic or natural nighttime NH₃ sources were considered unlikely [18,33,71]. It is noteworthy that, compared with 27 November, the nucleation mode in the particle number size spectrum nearly disappeared during the NO₃⁻ increase, implying that the biomass burning aerosols may have undergone atmospheric processing, such as cloud modification under non-precipitating conditions [60]. Therefore, the significant increase in [NO₃⁻]_PM2.5 during 21:00–23:00 was more likely driven by secondary formation processes within non-precipitating clouds, rather than direct primary emissions. Interestingly, a secondary increase in K⁺ also occurred between 01:00 and 03:00 on the same day (Figure S4), accompanied by a pronounced rise in particle number concentration within the accumulation mode (Figure 5c). Surprisingly, the NO₃⁻ in PM_2.5 and the atmospheric NH_3gas concentration showed almost no change during this period. Therefore, primary biomass burning aerosols and non-precipitation modification alone cannot fully explain the increase in [NO₃⁻]_PM2.5 between 21:00 and 23:00, leaving additional anthropogenic sources as co-contributors. Importantly, no significant shift was observed in the air mass back trajectories before and after the [NO₃⁻]_PM2.5 increase (Figure 5e).

3.5. Case 3: Transition from Regional Transport to Local Combustion Sources Induces Strong Phase Instability and NH₄NO₃ Volatilization

On 29 November 2023, [NO₃⁻]_PM2.5 exhibited two distinct periods: from 00:00 to 18:00 and after 18:00. During the first period, the variations in NO₃⁻ and NH₄⁺ concentrations were consistent with the PM_2.5 trends at the national monitoring stations (Figure 6a,b), suggesting a common regional source. In contrast, after 18:00, [NO₃⁻]_PM2.5 rapidly increased from 1.4 µg m⁻³ to 3.9 µg m⁻³, without a corresponding rise in PM_2.5 concentrations at the national stations. Furthermore, the correlation between the NO₃⁻ and NH₄⁺ concentrations in PM_2.5 over the full day (R² = 0.34) was significantly weaker than that during the first period (R² = 0.92) (Figure S2e). These results support the hypothesis that the increase in [NO₃⁻]_PM2.5 after 18:00 was primarily attributable to local sources. Moreover, air mass trajectories during the period of elevated [NO₃⁻]_PM2.5 lay between two slightly divergent paths observed before 18:00 (Figure 6e).

However, whether the equivalent concentrations were calculated using data from 00:00 to 23:00 or from 00:00 to 18:00, the results consistently showed that [NH₄⁺] = 0.9 × ([SO₄²⁻] + [NO₃⁻]) + 0.02 (R² = 0.99, p-value < 0.01) (Figure S1f). This suggested that NO₃⁻ in PM_2.5 both before and after 18:00 primarily existed as NH₄NO₃. Consequently, the observed decrease in [NO₃⁻]_PM2.5 between 00:00 and 18:00, along with the reduced NH₄NO₃ fraction among ammonium salts, may be attributed to the volatilization of NH₄NO₃, which is consistent with previous thermodynamic simulations. However, the NH_3gas released through this process was much smaller than the total reduction in ambient NH_3gas concentrations. The concurrent decline in atmospheric NH_3gas levels may have further promoted NH₄NO₃ volatilization (Figure 6b).

Additionally, the diurnal variations in Na⁺ and NO₃⁻ in PM_2.5 were nearly opposite (Figure S3c), indicating that NO₃⁻ associated with NaNO₃ in PM_2.5 was likely negligible. After 18:00, the particle number size distributions showed that the smaller Aitken mode dominated the total concentration (Figure 6c). The median Aitken mode diameters fluctuated significantly on a minute scale and exhibited an overall increasing trend, although it remained much smaller than the accumulation mode diameter dominant before 06:00. According to particle emission fingerprints from different sources reported in the literature [72], the particles observed after 18:00 were most likely emitted from combustion sources rather than ambient nucleation processes [73].

Further analysis of the normalized [NO₃⁻]_PM2.5 and N_>100 revealed a strong correlation from 00:00 to 18:00, with a regression slope of 1.5. By forcing the intercept to be zero, the slope decreased to 1.2 with the R² values slightly down to 0.88. The slope was close to the unity, implying that primary emissions dominated and accounted for the observed [NO₃⁻]_PM2.5 with a minor fraction from secondary NO₃⁻ formation. However, it is likely that secondary NO₃⁻ formation occurred earlier during air mass transport to the observation site, as NH₄NO₃ appeared to have been predominantly volatilized from the particle phase; this finding is supported by the preceding thermodynamic analysis. In contrast, after 18:00, the correlation between normalized [NO₃⁻]_PM2.5 and N_>100 weakened significantly, and the regression slope increased to 8.1. The slope value alone implied that the NO₃⁻ in PM_2.5 was mainly derived from secondary sources, despite the fact that the observed HNO₃*_gas concentration remained relatively low (0.13–0.21 µg m⁻³). Therefore, the pathway of large-scale N₂O₅ production, which could promote NH₄NO₃ formation, is ruled out. On the contrary, NH₄NO₃ may have formed rapidly during the cooling of combustion plumes, followed by dilution and NH₃-limited volatilization [23,54].

3.6. Case 4: Primary Emissions and Marine Aerosol Interactions Drive Diurnal Sea Salt-Restricted Nitrate Formation Under Southeast Monsoon Influence

Figure 7a–h show a unique case observed under the influence of the southeast monsoon. During this period, the nearby natural gas-fired power plant was intermittently located directly upwind of the sampling site. On 16 June 2024, the concentrations of NO₃⁻, Na⁺, and NH₄⁺ in PM_2.5 exhibited distinct temporal patterns before and after 12:00 (Figure 7a). After 12:00, [NO₃⁻]_PM2.5 exhibited a bell-shaped temporal pattern closely resembling that of Na⁺ in PM_2.5. The strong correlation between [NO₃⁻]_PM2.5 and Na⁺ in PM_2.5 supported the heterogeneous formation of NaNO₃ through the reaction between HNO_3gas and sea salt aerosols [74]. In contrast, the low concentrations of HNO_3gas and the results from thermodynamic simulations did not support the formation of NH₄NO₃ during the moments. Note that the diurnal variation in Na⁺ concentrations in PM_2.5 was reasonably consistent with the tide height (Figure S5).

Before 12:00, the concentrations of NO₃⁻, Na⁺, and NH₄⁺ all increased synchronously, exhibiting a bimodal distribution with peaks at 07:00 and 09:00. Three peaks of HNO₃*_gas were observed before 12:00 (2.1–2.9 µg m⁻³) and were approximately one order of magnitude higher than the other values recorded throughout the study. However, low concentrations of OC were recorded at only 1.3–1.6 µg m⁻³ during the peak periods. Although the concentration of EC increased to 0.7 μg m⁻³ between 08:00 and 10:00, the ratio of OC/EC was only 2.1, a typical value for primary particulate carbonaceous emissions [75]. Moreover, the PM_2.5 levels at the three national sites did not increase proportionally during the peaks, indicating no large-scale influx of air pollutants. Therefore, the large sawtooth-pattern increases in [HNO₃*_gas] and [NO₃⁻]_PM2.5 were more likely associated with primary emissions and chemical conversion in fresh plumes from the nearby natural gas-fired power plant, rather than the secondary formation of HNO₃*_gas in low-concentration ambient air, followed by the heterogeneous reaction with sea salt aerosols [74]. In this case, the amount of sea salt aerosols appeared to be a controlling factor for [NO₃⁻]_PM2.5, as no corresponding peak in [NO₃⁻]_PM2.5 occurred concurrently with the third peak of HNO₃*_gas in the absence of a [Na⁺]_PM2.5 peak. The [Na⁺]_PM2.5 trough at 08:00 was likely caused by wet scavenging as low clouds passed over the hill (Figure S5).

Unlike the patterns observed on 27–29 November 2023, no significant correlation (p-value > 0.05) was found between normalized [NO₃⁻]_PM2.5 and N_>100 on 16 June. The particle number size distributions showed an increase in the concentrations of accumulation mode particles after 08:00, which coincided with the increase in EC and NH₄⁺ in PM_2.5. These findings indicate that the power plant’s strongest emissions likely occurred between 08:00 and 12:00 and were consistent with the elevated HNO₃*_gas levels.

3.7. Source Apportionment of NO₃⁻ in PM_2.5 During the Four Cases

The analysis of the observation cases from 2023 and 2024 highlights the critical role of both primary and secondary sources in partitioning the gas–particle distribution of nitrate. Therefore, this study quantitatively evaluated the contributions of aerosols from various sources to nitrate using the PMF model (Figure 8). In November, NO₃⁻ in PM_2.5 was predominantly attributed to Factor 2 and Factor 5 only. Factor 5 contributed between 52% and 73%, while Factor 2 contributed between 26% and 46%. By contrast, on 16 June, Factor 1 made a limited contribution (4.6%), whereas the contribution from Factor 5 increased to 79%, and Factor 2 decreased to 16%.

Factor 1 exhibited a unimodal particle size distribution, with a median diameter centered at approximately 40 nm. This source factor displayed distinct peaks during both morning and evening rush hours, suggesting a potential association with non-electric vehicular emissions [72,73,76,77]. The relatively weaker morning peak may be attributed to the widespread use of electric motorcycles for commuting in tropical regions. In contrast, Factor 2 presented a complex trimodal size distribution, with the dominant mode characterized by a median particle diameter exceeding 200 nm. Despite this, its total number concentration was the lowest among all five factors, approximately an order of magnitude lower than those of Factors 1, 3, and 4, indicating that it likely represents a regional background source, particularly of marine origin [72,78]. Factor 3 demonstrated a bimodal distribution, with prominent peaks at approximately 70 nm and a minor mode below 20 nm. This factor was likely attributable to emissions from Chinese-style cooking, where high-temperature oil use leads to substantial particle generation, particularly between 18:00 and 20:00. However, its contribution to NO₃⁻ in PM_2.5 was found to be negligible. Factor 4 was dominated by nucleation-mode particles, indicative of short atmospheric nucleation times. Its occurrence was more frequent during nighttime, implying that these events were likely influenced by transient coastal atmospheric conditions [78,79]. Correspondingly, this factor also exhibited a minimal contribution to NO₃⁻ levels in PM_2.5. Lastly, Factor 5 displayed a bimodal particle size distribution and was primarily associated with emissions from a nearby natural gas-fired power plant (~1 km distance), with additional influence from regional background sources. Factor 5 exhibited a distinct trough in the particle size distribution of around 100 nm, a feature resembling the Hoppel effect modified distribution under an environmental supersaturation of 0.2%. This suggests that the gas-fired power plant plume, when emitted into an environment with a substantial temperature contrast, may promote the formation of microdroplets, thereby leading to an aerosol size distribution akin to that observed in non-precipitating cloud processing. Consequently, it is anticipated that Factor 5 will contribute a higher mass concentration of nitrate in winter compared to summer (Figure 8).

4. Conclusions

This study systematically investigates the dual regulatory effects of atmospheric NH₃ on the formation and evaporation of NH₄NO₃ at a clean tropical coastal site. By integrating high-resolution field measurements, E-AIM modeling, and PMF source apportionment analysis, we demonstrate that NH₃ plays a role in suppressing the volatilization of NH₄NO₃ under varying atmospheric conditions rather than acting solely as a precursor promoting its formation.

Multiple case studies revealed that the NH₄NO₃ in PM_2.5 originated from both primary emissions—particularly from local combustion plumes and in-cloud processing of KNO₃ and/or NH₄NO₃, especially under the influence of the northeast monsoon. However, this newly formed NH₄NO₃ is often thermodynamically unstable and undergoes rapid volatilization as plumes age and disperse. In contrast, during the southeast monsoon, the formation of particulate NO₃⁻ was primarily driven by heterogeneous reactions with sea salt aerosols, while NH₄NO₃ formation was suppressed due to limited HNO_3gas availability and unfavorable thermodynamic conditions. The analysis showed that under regional accumulation scenarios, elevated NH₃ concentrations significantly inhibited the volatilization of NH₄NO₃, enhancing the stability and persistence of particulate nitrate in PM_2.5. E-AIM modeling consistently indicated persistent gas–particle partitioning disequilibrium. Even under NH₃-rich conditions, particulate nitrate was often thermodynamically driven toward volatilization. Moreover, interactions with sea salt aerosols further diverted NO₃⁻ partitioning toward NaNO₃ formation, particularly during marine air mass intrusion. PMF analysis identified five source factors, with regional background transport and emissions from a nearby natural gas power plant being the dominant contributors to nitrate in PM_2.5. The nitrate associated with power plant emissions showed greater instability and higher susceptibility to volatilization, while marine background sources contributed more stable nitrate, especially under southeast monsoon conditions. This seasonal shift highlights how the interaction between source profiles and meteorological regimes shapes nitrate aerosol behavior.

These findings challenge the conventional understanding of nitrate formation as purely precursor-limited, highlighting the importance of simultaneously accounting for formation and volatilization dynamics. This study refines the understanding of NH₄NO₃ behavior in ammonia-rich marine boundary layers and has important implications for optimizing future nitrate control strategies in coastal and urban environments.