Mingming Zheng1, Ke Xu2, Lianxin Yuan2, Nan Chen This email address is being protected from spambots. You need JavaScript enabled to view it.2, Menghua Cao This email address is being protected from spambots. You need JavaScript enabled to view it.3

1 School of Chemical and Environmental Engineering, Wuhan Polytechnic University, Wuhan 430023, China
2 Hubei Environmental Monitoring Center, Wuhan 430072, China
3 College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China

Received: December 28, 2021
Revised: March 14, 2022
Accepted: April 11, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.4209/aaqr.210394  

Cite this article:

Zheng, M., Xu, K., Yuan, L., Chen, N., Cao, M. (2022). Fine Particle pH and its Impact on PM2.5 Control in a Megacity of Central China. Aerosol Air Qual. Res. 22, 210394. https://doi.org/10.4209/aaqr.210394


  • NHx and RH dominated the PM2.5 pH changes in Central China.
  • Low pH was beneficial for PM2.5 reduction through SO2 control.
  • NOx control was more effective for PM2.5 reduction at high pH.
  • PM2.5 reduction by NHx control was nonlinearly affected by particulate pH.


Fine particle (PM2.5) acidity greatly affects the formation of secondary aerosol, and the drivers of PM2.5 pH variation are vital in understanding its effects. Moderate PM2.5 acidity was found in Wuhan, a megacity of Central China, wherein 80% of PM2.5 hold pH values ranging from 2–4. Total ammonia (NHx) and sulfate contributed 79.1–93.7% to pH changes in spring and winter, while relative humidity was the largest contributor (33.7–36.3%) in summer and fall. By sensitivity simulations, PM2.5 remained acidic with pH changes less than 0.5 units in spring, summer and fall in foreseeable future even when the concentration varied by two orders of magnitude. While pH changes in winter were three times those in the other seasons, and NHx changes was suggested as the indicator of PM2.5 pH variation in winter. Furthermore, the impact of pH on PM2.5 responses to emissions control was evaluated. The pH has opposite influences on the effect of SO2 and NOx control in reducing PM2.5, the former being more effective at low pH and the latter being more effective at high pH due to ammonium and nitrate gas-particle partitioning. The effect of NHx control on PM2.5 reduction is nonlinearly affected by pH. It is directly effective at low pH, but more ammonia control is required before achieving effectiveness at high pH. For the current particulate pH of 3–4 in Wuhan, both SO2 and NOx control are beneficial for PM2.5 reduction. However, NHx control is less effective before it is reduced by approximately 20%.

Keywords: Fine particle pH, Gas-particle partitioning, PM2.5 reduction, ISORROPIA


Fine particulate (PM2.5) pH plays great influence on secondary aerosol formation and transformation through chemical kinetics and thermodynamics of gas-particle partitioning (Zheng et al., 2020; Cheng et al., 2016; Wang et al., 2016; Manktelow et al., 2010; Keene et al., 2004; Grassian, 2001; Underwood et al., 2001). Exploring the variations in atmospheric particle pH is essential for understanding the properties of atmospheric particles and their effects on the climate (Pye et al., 2020; Crumeyrolle et al., 2008; Watson et al., 2002), ecosystem (Johnson et al., 2008; Larssen et al., 2006; Schindler, 1988), and human health (Dockery et al., 1996). The PM2.5 pH has garnered attention owing to its important role in haze formation in China (Zhou et al., 2018; Tian et al., 2018; Liu et al., 2017).

Fine particle pH is affected by precursor emissions (Shi et al., 2017) and meteorological conditions (He et al., 2012), resulting in different acidities at different sites. Many regions in China, such as Beijing (Ding et al., 2019; Wang et al., 2016; Cheng et al., 2016; Yang et al., 2015; He et al., 2012), Shanghai (Pathak et al., 2009), Guangzhou (Jia et al., 2018; Huang et al., 2011), Tianjin (Shi et al., 2017), Xi’an (Wang et al., 2018), Lanzhou (Pathak et al., 2009), Chongqing (He et al., 2012), Hong Kong (Yao et al., 2007, 2006; Pathak et al., 2004, 2003), and Tai mountains (Zhou et al., 2012), have reported the fine particle acidity characteristics. There is a wide range of PM2.5 pH in China, with values ranging between –2.5 (Yao et al., 2007) and 6.2 (Cheng et al., 2016), respectively. Previous studies investigated the impacts of particle chemical components (i.e., sulfate, nitrate and ammonium) on pH (Kakavas et al., 2021; Chen et al., 2019; Zheng et al., 2019; Ding et al., 2019; Liu et al., 2017; Guo et al., 2015), and highlighted the role of gas-particle partitioning in understanding the PM2.5 pH (Guo et al., 2018, 2017; Liu et al., 2017). Additionally, aerosol water content (AWC) can also influence the particulate pH by H+ production from aqueous reactions and the dilution of proton concentrations (Zhou et al., 2012; Pathak et al., 2004). Few studies have quantified the contributions of different factors to the PM2.5 acidity changes in different seasons.

The reduction of atmospheric particulate matter has always been a concern, especially after frequent haze pollution events occurring in China (Zhou et al., 2022; An et al., 2019; Bai et al., 2019; Tian et al., 2018). Many studies have explored the responses of PM2.5 to precursor emissions control, such as SO2, NOx, and NH3 (Pinder et al., 2008; Blanchard et al., 2000; Ansari and Pandis, 1998). Recently, the sensitivity of particulate nitrate to ammonia or nitric acid has become a research hotspot (Xu et al., 2019; Guo et al., 2018; Wu et al., 2016). Nitrate in PM2.5 could be effectively reduced by ammonia emission control in North China (An et al., 2019; Liu et al., 2019), but unsensitive in other regions such as South China (Liu et al., 2019), Sichuan Basin (Liu et al., 2019), and East China (Xu et al., 2020). Moreover, studies have also focused on the evaluation methods and indicators of the effectiveness of PM2.5 reduction in emission control (Nenes et al., 2020; Zheng et al., 2019; Xu et al., 2019; Wu et al., 2016). However, the effect of particulate pH on the sensitivity of PM2.5 to precursor emissions have not been considered widely. This may lead to the ineffectiveness of emission control measures for particles reduction.

Based on the hourly observations of water-soluble inorganic ions in PM2.5 and precursor gases in 2014 and 2018 in Wuhan, the driving factors of particulate pH changes during this period were identified and their contributions were quantified using a thermodynamic model. The variations of future PM2.5 pH for each season were predicted. Besides, the effect of particulate pH on the response of PM2.5 to precursor reductions was explored. Results here can deepen the understanding of aerosol acidity properties and provide a reference for fine particle reduction policy making.


2.1 Observations

The observation site (114.36°N, 30.53°E) is approximately 20 m above ground and is located in a commercial/residential mixed area without obvious industrial emission sources (Fig. 1). Water-soluble ions (WSI), including NH4+, K+, Ca2+, Na+, Mg2+, SO42–, NO3, and Cl in PM2.5, and atmospheric HNO3, NH3, and HCl were synchronously observed hourly using an online ions analyzer (Marga ADI 2080). The details about the instrument can be found in Text S1 and previous research (Zheng et al., 2019). Continuous monitoring was conducted from January to December in 2014 and 2018, except for data missing due to equipment maintenance. Hourly temperature (Temp) and relative humidity (RH) were obtained from the local observatory (Text S1) (Zheng et al., 2019). At this observation site, SO2 and NO2 were monitored hourly by ultraviolet fluorescence and chemiluminescence online monitoring equipment from 2013, respectively.

Fig. 1. Location of the observation site in Wuhan, a megacity of Central China.
Fig. 1. Location of the observation site in Wuhan, a megacity of Central China.

2.2 Simulations

The fine particle pH and AWC were calculated using the thermodynamic model ISORROPIA-II (Fountoukis and Nenes, 2007; Nenes et al., 1998) (http://nenes.eas.gatech.edu/ISORROPIA), wherein the inputs were NH4+, K+, Ca2+, Na+, Mg2+, SO42–, NO3, Cl, gaseous precursors (HNO3, HCl and NH3) (Song et al., 2018), ambient RH, and Temp. Concentrations and existing forms of species (gas or particle phase) under chemical equilibrium can be predicted by the model (Guo et al., 2018). The metastable state with forward mode was applied in this study because of its better performance (Weber et al., 2016; Guo et al., 2016; Hennigan et al., 2015; Guo et al., 2015; Fountoukis and Nenes, 2007). Similar to previous studies at this observation site (Zheng et al., 2019), there were good agreements between the predicted and observed concentrations for major species, as shown in Fig. 2. The minimum observed RH was 30%, satisfying the model assumptions of inorganic ions in the liquid phase (Guo et al., 2017; Bertram et al., 2011Fountoukis and Nenes, 2007; Ansari and Pandis, 2000). Data with RH > 95% were excluded owing to the exponential changes in AWC with RH, which could lead to huge pH uncertainty due to the propagation of RH uncertainties (Guo et al., 2016, 2015; Malm and Day, 2001) and issues with inlet transmission losses (Guo et al., 2016).

Fig. 2. Comparisons of the measured SO42–, NO3–, NH4+ and NH3 with the predictions by thermodynamic model ISORROPIA-II.Fig. 2. Comparisons of the measured SO42–, NO3, NH4+ and NH3 with the predictions by thermodynamic model ISORROPIA-II.

In Section 3.2, a series of sensitivity simulations were conducted to explore the key factors and their contribution in each season. The chemical and meteorological factors, including SO42–, NHx (NH3 + NH4+), TNO3 (NO3 + HNO3), TCl (Cl + HCl), RH, and temperature were evaluated. Each factor was subjected to interannual replacement. For instance, firstly based on 2014 observation, a factor was imported by 2014 and 2018 seasonal average, respectively, while the other model inputs were unchanged with 2014, obtaining pH2014-4 and pH2014-8. Then based on 2018 observation, the similar simulation was conducted, obtaining pH2018-4 and pH2018-8. The DpH (pH changes) was the average of the difference between pH2014-4 and pH2014-8 and the difference between pH2018-4 and pH2018-8. The same approach was applied to each factor and the simulation of AWC changes (and DAWC).


3.1 Fine Particle pH in 2014 and 2018

The annual average PM2.5 concentration significantly decreased by 45.7% in Wuhan from 2014 (92.2 ± 57.6 µg m–3) to 2018 (50.4 ± 33.6 µg m–3), while both of them still exceeded the PM2.5 annual value of the secondary air quality standard in China (35 µg m–3). The proportion of SNA (sulfate, nitrate and ammonium) in PM2.5 increased from 34% in 2014 to 47% in 2018, indicating the gradual dominance of inorganic components in fine particles, which is consistent with other studies (Li et al., 2019). The strong relationship among SO42–, NO3, and NH4+ in 2014 (r2 (square of correlation coefficient) of SO42––NO3 = 0.73, r2 of NH4+–NO3 = 0.91, r2 of SO42––NH4+ = 0.86) (Table S1) exhibited partial homology (e.g., combustion emission). While the correlation among SO42–, NO3, and NH4+ decreased in 2018 (r2 values of SO42–NO3, NH4+–NO3, and SO42––NH4+ were 0.40, 0.87 and 0.59, respectively) (Table S1). The variation of dominant emission sources was identified from the changes in the [NO3]/[SO42–] mass ratio (Xing et al., 2021), which increased from 0.85 in 2014 to 1.17 in 2018, implying the decrease in stationary source (mainly coal combustion) or increase in mobile source (mainly traffic emissions). Compared to SNA, the contributions of non-volatile cations (NVCs, including K+, Na+, Ca2+, and Mg2+) to PM2.5 were only 3.0% and 4.0% in 2014 and 2018, respectively.

The aerosol acidity was moderate in Wuhan, exhibiting a decline of 0.25 units from 2014 (3.63) to 2018 (3.38). The difference of pH between 2014 and 2018 was 0.3 when NVCs were not included in the thermodynamic analysis. The largest component of NVCs is Ca2+ (Table 1), which has the greatest effect on pH among NVCs and was mainly originated from sand or road dust and construction activities (Hegde et al., 2016). Considering the weak effect of NVCs on the aerosol acidity in Wuhan and the randomness of sand-dust event, the NVCs effect was subtracted from fine particle pH and AWC in the subsequent discussion.

Table 1. Comparisons of the particulate components and meteorological parameters in Wuhan in 2014 and 2018.

Fig. 3 showed the frequency proportions in different particle pH ranges during the four seasons. In both 2014 and 2018, the fine particle pH distribution was in accordance with the norm, and both of the peaks were at pH values of 3–3.5 with frequencies of 27.4% and 24.6%, respectively. In Wuhan, over 80% of the fine particulate was at a pH of 2–4. Higher pH mainly appeared in winter (3.5–4.5) and lower particulate pH was observed in summer (below 3.5) (Fig. 3). From the pH frequency distribution between 2014 and 2018, the particulate pH in each season dropped in Wuhan. Fine particle pH also changed hourly, with an early morning peak at approximately 07:00 (local time, pH up to 3.67 in 2014 and 3.27 in 2018), and a subsequent decrease during the daytime, reaching a minimum in the afternoon at around 16:00 (local time) (Fig. S1), which was similar to the results of Guo et al. (2015). There was a great consistency in the diurnal variation trend between particulate pH and AWC both in 2014 and 2018 (Fig. S1). The following section explored the driving factors of pH variation in Wuhan during 2014–2018.

Fig. 3. Frequency proportions of different pH ranges in four seasons (the number of data n = 4173 in 2014 and n = 5123 in 2018).Fig. 3. Frequency proportions of different pH ranges in four seasons (the number of data n = 4173 in 2014 and n = 5123 in 2018).

3.2 Driving Factors of pH Decline during 2014–2018

The annual average concentrations of atmospheric SO2 and NOx (Fig. S2) obviously decreased from 2014 to 2018 due to the implementation of control measures (Jin et al., 2016), while the particle pH still continued to decline. There were other driving factors affecting the pH variation during 2014–2018.

Fig. 4 showed the sensitivity results of the driving factors affecting the pH variation in Wuhan between 2014 and 2018 (please refer to Section 2.2 for the simulation details). NHx mainly contributed to the pH decline during 2014–2018 both in spring and winter. RH was the most important factor leading to the pH decreases in summer and fall. The contribution of NHx to pH variation were 44.2%–48.9% in spring and winter, and the RH contribution to pH changes was 33.7–36.3% in summer and fall (Fig. 4, pie chart). This differed from the North China Plain, where SO42– + Ca2+, NHx + RH, NHx + Temp, and SO42– + NHx were the driving factors of pH corresponding to spring, summer, fall and winter, respectively (Ding et al., 2019). Compared with North China, the higher RH (NBS, 2019) and relatively lower ammonia emission (Huang et al., 2012) in Central China resulted in the higher sensitivity of RH and NHx to particulate pH (Liu et al., 2019; Ding et al., 2019).

Fig. 4. Factors contributing to pH changes during 2014–2018. Notably, the pie chart does not consider positive or negative changes, but represents the amplitude of pH change. The factors include SO42–, TNO3 (NO3– + HNO3), NHx (NH3 + NH4+), TCl (Cl– + HCl), RH, and Temp, respectively.Fig. 4. Factors contributing to pH changes during 2014–2018. Notably, the pie chart does not consider positive or negative changes, but represents the amplitude of pH change. The factors include SO42–, TNO3 (NO3 + HNO3), NHx (NH3 + NH4+), TCl (Cl + HCl), RH, and Temp, respectively.

Sulfate decline in Wuhan raised the particulate pH as expected (Fu et al., 2015), wherein the pH increased by 0.07–1.0 units corresponding to the 12–63% decrease in sulfate. Similar to previous research (Ding et al., 2019), the sensitivity of fine particulate pH to TNO3 variation was less than that of SO42–, owing to the low volatility of the latter. In spring, summer, and winter, the particulate pH almost unchanged when TNO3 decreased (7–12%). The pH in fall slightly decreased by 0.05 units with 61% TNO3 increase. The insensitivity of pH to TNO3 reduction partly resulted from the decrease in AWC (Fig. S3) and the gas-particle partitioning. Moreover, the removal of nitrate released ammonium to the gas phase, leading to hydroxyl decline and buffering changes in PM2.5 pH (Blanchard et al., 2000; Dennis et al., 2008).

NHx decreased by 22.3% and 44.1% in spring and winter, respectively, resulting in pH decrease by 0.2 and 1.0 units, respectively. The difference of pH decline between the two seasons was partly due to the discrepancy in AWC changes (Fig. S3). Lower particle phase distribution in spring (below 0.4 in both 2014 and 2018) compared to winter (about 0.7 in both 2014 and 2018) contributed to the low AWC changes in former. NHx reduction in the season with higher particle phase distribution caused a greater decline in NH4+ water uptake (Guo et al., 2018). The negligible effect of NHx on pH changes in summer and fall was mainly due to the small NHx concentration variation and relatively low NHx particle phase distribution (Table 1).

Unlike SO42– and TNO3, decreasing TCl in summer and fall reduced the particle pH, partly due to the decline in AWC (Fig. S3). TCl showed a negligible impact on the pH changes in winter, even though the AWC in winter also obviously decreased. Compared to the other seasons, TCl in winter was mostly distributed in the particle phase, with ε(Cl) (particulate Cl fraction, ε(Cl) = Cl/TCl) of 0.95 in 2014 (Table 1). Cl in particle phase was in the form of NH4Cl, owing to excessive ammonia in the observation city (Zheng et al., 2019). Reduction of TCl released associated ammonium to the gas phase, buffering the particulate pH changes.

RH hold different impacts on the fine particulate pH in different seasons. Decreased RH reduced the PM2.5 pH in summer and fall, but nearly exhibited no effect in spring. Moreover, increased RH in winter also presented a negligible impact on the pH changes. The effect of RH on fine particulate pH was determined by the competition of RH’s impact on protons and AWC (Ding et al., 2019 ACP), including the process of water uptake, gas-to-particle conversion and liquid phase reaction (Seinfeld and Pandis, 2016; Guo et al., 2018). The Temp in summer and autumn in 2018 increased by 1.5–1.7°C compared with 2014, contributing to the decline in particulate pH. High Temp promote the conversion of semi-volatile components, such as ammonium nitrate and ammonium chloride, to the gas phase (Seinfeld and Pandis, 2016). Additionally, high Temp could also reduce the AWC (Fig. S3), further leading to the decrease in PM2.5 pH (Guo et al., 2015).

3.3 Future pH

Based on the most significant positive and negative drivers of particulate pH changes, we expanded the ranges of NHx and SO42– in spring and winter, and RH and SO42– in summer and fall, for more sensitivity analyses by ISORROPIA-II. Total ammonia and sulfate were independently varied in steps of 0.1 µg m–3 in spring and winter. In summer and fall, RH and sulfate independently changed in steps of 1% and 0.1 µg m–3, respectively. Other inputs were under the seasonal average conditions. The corresponding prediction results were shown in Fig. 5.

Fig. 5. Sensitivity of the pH to total ammonia (NHx) and sulfate (SO42–) concentrations in spring and winter, and that to RH and SO42– in summer and fall.Fig. 5. Sensitivity of the pH to total ammonia (NHx) and sulfate (SO42–) concentrations in spring and winter, and that to RH and SO42– in summer and fall.

It showed that PM2.5 remained acidic even with the significant reduction of sulfate (from 35 to 0.1 µg m–3), in consistent with Weber et al. (2016)’s study in the U.S. Moreover, the pH variations in summer and fall were smaller than those in spring and winter, which agreed well with the above section.

According to the changes during 2014–2018, the sulfate concentration decreased in each season, and all values were lower than 10 µg m–3 in 2018. In the springs of foreseeable future, within a sulfate concentration of 10 µg m–3, the particulate pH will still be above 2.5, even if NHx is reduced by half from 2018. Furthermore, assuming an increase of sulfate in future spring, although this probability is low due to the continuous SO2 control policy in China, the particulate pH would still be close to 2 (1.83) with a 50% increase in sulfate and a 50% NHx reduction. Additionally, assuming that the NHx increases by half in the future and the sulfate changes by +50%, 0% and –50%, the changes in pH are +0.19, +0.47, and +0.62, respectively. As mentioned in last section, sulfate and NHx were the most important factors affecting the pH in spring. While the particulate pH in future spring is generally less affected by sulfate and NHx. This shows that in the foreseeable future, the pH of aerosols in Wuhan will not change considerably (within 0.5) in spring.

In winter, at the sulfate level observed in 2018, the pH decreased by 1.7 units when NHx decreased by half. Even if NHx drops by 20%, the pH changes will still be more than 0.5 units. However, if NHx remains at its 2018 level, the pH only increases by 0.29 units when the sulfate decreases by half. The pH decreased by 1.28 units when both sulfate and NHx decreased by half. Clearly, the particulate pH was sensitive to NHx changes in winter, and NHx variation might be an indicator of fine particle pH in winter. In the foreseeable future, one may directly deduce the decline in wintertime particulate pH from the reducing NHx in Wuhan.

In fall, the pH value decreased by 0.7units when the RH decreased to 30%, and it increased by 0.27 units when the RH increased to 95%. When RH was at the 2018 annual value, the pH increased and decreased by 0.3 and 0.46 units when sulfate was halved and increased by half, respectively. The results showed that although sulfate and RH were the most important components and meteorological parameters affecting the particulate pH in fall, fine particle pH in future fall was less affected by them. In the foreseeable future, the particulate pH in this megacity of Central China will not change significantly (within 0.5 units), similar to that in spring.

In summer, Fig. 5 showed that the changes in RH could lead to significant changes in particulate pH. The pH decreased by 1.05 units when RH dropped to 30%, and it increased 0.63 units when RH increased to 95%. A decrease and increase by half in sulfate resulted in a pH increase of 0.15 and a decrease of 0.22 units, respectively. Compared to RH, the effect of sulfate on summertime PM2.5 pH was less than that in the former. The pH changed by 0.2–0.3 units when the summertime RH changed by 10%. This implies that RH can be used as an indicator of fine particle pH in summer. However, considering the modest interannual variation of atmospheric RH, the future summer pH will not deviate considerably from the current levels. For the diurnal and hourly PM2.5 variations in summer, the RH indicator might be more useful.

3.4 Effect of pH on PM2.5

Here, we assessed the effect of pH on PM2.5 response to SO2, NOx, and NH3 control in Wuhan using a thermodynamic model. The observation data were divided into four groups according to particulate pH: pH > 4, pH 3–4, pH 2–3 and pH < 2. Changes in the SNA were assessed when the SO42–, TNO3, and NHx were changed in each pH group, representing the control of SO2, NOx, and NH3 emissions, respectively (Guo et al., 2018). Each of them was individually changed in steps of 20%, while the other inputs remained constant.

The effect of SO42– reduction on SNA changes at different pH groups in Fig. 6 indicated that SO2 control was more beneficial to SNA reduction at lower particulate pH. This was mainly contributed from the increasing NH4+ decrease owing to relatively higher ammonia particle phase partitioning at low pH. The loss of associated NH4+ was attributed to both the decrease in sulfate and the volatilization caused by reduced AWC (Guo et al., 2018). This implied that the efficiency of reducing SNA by SO2 control in winter was lower than that in other seasons. It also showed that the particulate pH changed slightly (within 0.5 units) when SO42– changed by 80% (Fig. S4), partly due to buffering by ammonia gas-particle partitioning (Guo et al., 2017; Weber et al., 2016).

Fig. 6. The effect of fine particle pH on the SNA response to sulfate, total nitrate and total ammonia changes.Fig. 6. The effect of fine particle pH on the SNA response to sulfate, total nitrate and total ammonia changes.

Compared to SO42–, there was a contrary effect of pH on the reduction of SNA from TNO3 control. At higher particulate pH, TNO3 control was more effective for SNA reduction. A linear reduction in TNO3 at high pH caused a linear decrease in SNA concentrations, owing to ε(NO3) (ε(NO3) = [NO3]mol/([NO3]mol + [HNO3]mol)) close to 1. Thus, NO3 was approximately equal to TNO3 (Fig. 7). Additionally, more ammonia was released with the reduction of nitrate at higher pH, from the significant ε(NH4+) (ε(NH4+) = [NH4+]mol/([NH4+]mol + [NH3]mol) ) decline (Fig. 7). At low pH (lower than 2, Fig. 6), TNO3 reduction slightly influenced the SNA concentration, since most of the TNO3 was in the gas phase, implying that NOx control might barely reduce strong acidic inorganic aerosols.

Fig. 7. Changes of ε(NO3–) and ε(NH4+) as functions of DTNO3 at different pH ranges (particulate NO3– fraction ε(NO3–) = [NO3–]/([NO3–] + [HNO3]), NH4+ fraction ε(NH4+) = [NH4+]/([NH4+] + [NH3])).Fig. 7. Changes of ε(NO3) and ε(NH4+) as functions of DTNO3 at different pH ranges (particulate NO3 fraction ε(NO3) = [NO3]/([NO3] + [HNO3]), NH4+ fraction ε(NH4+) = [NH4+]/([NH4+] + [NH3])).

In the case of NHx reduction, at low pH (nearly pH < 3, Fig. 6), NHx reduction was directly effective for the decline in SNA. At higher particulate pH, NHx control was not immediately effective for SNA reduction, and more NHx reduction was required. For instance, considering a pH greater than 4, it could be seen that a 20% reduction in NHx had little effect on the SNA concentration. While the reduction of SNA was almost linear with NHx control when the NHx reduction reduced by more than 40%. At low pH, NHx reduction resulted in more NO3 shifting to the gas phase, partly due to the decrease in ε(NO3). At high pH, TNO3 remained in the particle phase at 40% reduction of NHx. However, once the partitioning between NO3 and HNO3 was noticeably toward the gas phase due to the decline in pH and AWC since more NHx reduction, NO3 sharply decreased (https://aaqr.org/articles/aaqr-21-12-oa-0394_suppl.pdfFig. S5). Simultaneously, NH4+ also rapidly declined as the ε(NH4+) value close to 1.

Fine particle pH is an objective condition that cannot be ignored when implementing strategies to reduce inorganic fine particles. For current observation with a pH of 3–4, both SO2 and NOx control are effective for fine particle reduction. However, NHx control is less effective before approximately 20% NHx reduction in Wuhan, which almost consistent with previous studies (Zheng et al., 2019) suggesting a 25% effectiveness critical point of ammonia control on PM2.5.


Using hourly chemical composition observation data collected in a megacity of Central China, the driving factors of fine particle pH changes between 2014 and 2018 were analyzed. Moderate aerosol acidity was observed in Wuhan, with mean pH values of 3.63 and 3.38 in 2014 and 2018, respectively, showing a slight decline. In both 2014 and 2018, over 80% of particulate pH was at 2–4, with the largest frequency proportion occurring at a pH of 3–3.5. NHx mainly contributed to the pH decline during 2014–2018 in both spring and winter. RH was the most important factor leading to a decrease of pH in summer and fall. Further evaluation of the contribution of each factor to particulate pH changes showed that the contribution of NHx + SO42– was 79.1–93.7% in spring and winter, and RH was the largest contributor (33.7–36.3%) in summer and fall.

Sensitivities simulations of exploring the future particulate pH were conducted by expanding the concentration ranges of driving factors in each season. PM2.5 will remain acidic even when concentrations varied by two orders of magnitude. The interannual pH fluctuation in future spring, summer and fall (within 0.5 units in the foreseeable future) is less than that in winter. NHx can be suggested as an indicator of the changes of PM2.5 pH in winter. The decline of wintertime particulate pH can be inferred from the decrease of NHx in Wuhan.

Owing to higher NHx particle phase partitioning at low pH leading to a higher NH4+ decrease, SO2 control is more beneficial to SNA reduction at lower particulate pH. In Contrast, NOx control is more effective for SNA reduction at higher particulate pH owing to high ε(NO3) (close to 1). NHx reduction is directly effective for SNA decline at low pH (nearly pH < 3). At higher particulate pH, more NHx reduction will be required before effectiveness is achieved. For the observation city with a pH of 3–4, both SO2 and NOx control are effective for fine particle reduction, while NHx control is less effectiveness before approximately 20% NHx reduction.


This study was financially supported by Research and Innovation Initiatives of Wuhan Polytechnic University (2021Y17) and the National Natural Science Fund (21976065).


  1. An, Z.S., Huang, R.J., Zhang, R.Y., Tie, X.X., Li, G.H., Cao, J.J., Zhou, W.J., Shi, Z.G., Han, Y.M., Gu, Z.L., Ji, Y.M. (2019). Severe haze in Northern China: A synergy of anthropogenic emissions and atmospheric processes. PNAS 116, 8657–8666. https://doi.org/10.1073/pnas.1900125116

  2. Ansari, A.S., Pandis, S.N. (1998). Response of inorganic PM to precursor concentrations. Environ. Sci. Technol. 32, 2706–2714. https://doi.org/10.1021/es971130j

  3. Ansari, A.S., Pandis, S.N. (2000). The effect of metastable equilibrium states on the partitioning of nitrate between the gas and aerosol phases. Atmos. Environ. 34, 157–168. https://doi.org/​10.1016/S1352-2310(99)00242-3

  4. Bai, Z., Winiwarter, W., Klimont, Z., Velthof, G., Misselbrook, T., Zhao, Z., Jin, X., Oenema, O., Hu, C., Ma, L. (2019). Further improvement of air quality in china needs clear ammonia mitigation target. Environ. Sci. Technol. 53, 10542–10544. https://doi.org/10.1021/acs.est.9b04725

  5. Bertram, A.K., Martin, S.T., Hanna, S.J., Smith, M.L., Bodsworth, A., Chen, Q., Kuwata, M., Liu, A., You, Y., Zorn, S.R. (2011). Predicting the relative humidities of liquid-liquid phase separation, efflorescence, and deliquescence of mixed particles of ammonium sulfate, organic material, and water using the organic-to-sulfate mass ratio of the particle and the oxygen-to-carbon elemental ratio of the organic component. Atmos. Chem. Phys. 11, 10995–11006. https://doi.org/​10.5194/acp-11-10995-2011

  6. Blanchard, C., Roth, P., Tanenbaum, S., Ziman, S.D., Seinfeld, J.H. (2000). The use of ambient measurements to identify which precursor species limit aerosol nitrate formation. J. Air Waste Manage. Assoc. 50, 2073–2084. https://doi.org/10.1080/10473289.2000.10464239

  7. Chen, Y.L., Shen, H.Z., Russell, A.G. (2019). Current and future responses of aerosol ph and composition in the U.S. to declining SO2 emissions and increasing NH3 emissions. Environ. Sci. Technol. 53, 9646–9655. https://doi.org/10.1021/acs.est.9b02005

  8. Cheng, Y., Zheng, G., Wei, C., Mu, Q., Zheng, B., Wang, Z., Gao, M., Zhang, Q., He, K., Carmichael, G., Pöschl, U., Su, H. (2016). Reactive nitrogen chemistry in aerosol water as a source of sulfate during haze events in China. Sci. Adv. 2, 1601530. https://doi.org/10.1126/sciadv.1601530

  9. Crumeyrolle, S., Gomes, L., Tulet, P., Matsuki, A., Schwarzenboeck, A., Crahan, K. (2008). Increase of the aerosol hygroscopicity by cloud processing in a mesoscale convective system: A case study from the AMMA campaign. Atmos. Chem. Phys. 8, 6907–6924. https://doi.org/10.5194/​acp-8-6907-2008

  10. Dennis, R.L., Bhave, P.V., Pinder, R.W. (2008). Observable indicators of the sensitivity of PM2.5 nitrate to emission reductions-Part II: Sensitivity to errors in total ammonia and total nitrate of the CMAQ-predicted non-linear effect of SO2 emission reductions. Atmos. Environ. 42, 1287–1300. https://doi.org/10.1016/j.atmosenv.2007.10.036

  11. Ding, J., Zhao, P., Su, J., Dong, Q., Du, X., Zhang, Y.F. (2019). Aerosol pH and its driving factors in Beijing. Atmos. Chem. Phys. 19, 7939–7954. https://doi.org/10.5194/acp-19-7939-2019

  12. Dockery, D.W., Cunningham, L., Neas, L.M., Spengler, J.D., Koutrakis, P., Ware, J.H., Raizenne, M., Speizer, F.E. (1996). Health effects of acid aerosols on North American children: Respiratory symptoms. Environ. Health Perspect. 104, 500–505. https://doi.org/10.1289/ehp.96104500

  13. Fountoukis, C., Nenes, A. (2007). ISORROPIA II: A computationally efficient thermodynamic equilibrium model for K+-Ca2+-Mg2+-NH4+-Na+-SO42--NO3--Cl--H2O aerosols. Atmos. Chem. Phys. 7, 4639–4659. https://doi.org/10.5194/acp-7-4639-2007

  14. Fu, X., Guo, H., Wang, X., Ding, X., He, Q., Liu, T., Zhang, Z. (2015). PM2.5 acidity at a background site in the Pearl River Delta region in fall-winter of 2007-2012. J. Hazard. Mater. 286, 484–492. https://doi.org/10.1016/j.jhazmat.2015.01.022

  15. Grassian, V. H. (2001). Heterogeneous uptake and reaction of nitrogen oxides and volatile organic compounds on the surface of atmospheric particles including oxides, carbonates, soot and mineral dust: Implications for the chemical balance of the troposphere. Int. Rev. Phys. Chem. 20, 467–548. https://doi.org/10.1080/01442350110051968

  16. Guo, H., Bougiatioti, A., Cerully, K.M., Capps, S.L., Hite Jr, J.R., Carlton, A.G., Lee, S., Bergin, M.H., Ng, N.L., Nenes, A., Weber, R.J. (2015). Fine-particle water and pH in the southeastern United States. Atmos. Chem. Phys. 15, 5211–5228. https://doi.org/10.5194/acp-15-5211-2015

  17. Guo, H., Sullivan, A.P., Campuzano-Jost, P., Schroder, J.C., Lopez-Hilfiker, F.D., Dibb, J.E., Jimenez, J.L., Thornton, J.A., Brown, S.S., Nenea, A., Weber, R.J. (2016). Fine particle pH and the partitioning of nitric acid during winter in the northeastern United States. J. Geophys. Res. 121, 10355–10376. https://doi.org/10.1002/2016JD025311

  18. Guo, H., Liu, J., Froyd, K.D., Roberts, J.M., Veres, P.R., Hayes, P.L., Jimenez, J.L., Nenes, A., Weber, R.J. (2017). Fine particle pH and gas–particle phase partitioning of inorganic species in Pasadena, California, during the 2010 CalNex campaign. Atmos. Chem. Phys. 17, 5703–5719. https://doi.org/10.5194/acp-17-5703-2017

  19. Guo, H., Otjes, R., Schlag, P., Kiendler-Scharr, A., Nenes, A., Weber, R. J. (2018). Effectiveness of ammonia reduction on control of fine particle nitrate. Atmos. Chem. Phys. 18, 12241–12256. https://doi.org/10.5194/acp-18-12241-2018

  20. He, K., Zhao, Q., Ma, Y., Duan, F., Yang, F., Shi, Z., Chen, G. (2012). Spatial and seasonal variability of PM2.5 acidity at two Chinese megacities: Insights into the formation of secondary inorganic aerosols. Atmos. Chem. Phys. 12, 1377–1395. https://doi.org/10.5194/acp-12-1377-2012

  21. Hegde, P., Kawamura, K., Joshi, H., Naja, M. (2016). Organic and inorganic components of aerosols over the central Himalayas: Winter and summer variations in stable carbon and nitrogen isotopic composition, Environ. Sci. Pollut. Res. 23, 6102–6118. https://doi.org/10.1007/​s11356-015-5530-3

  22. Hennigan, C.J., Izumi, J., Sullivan, A.P., Weber, R.J., Nenes, A. (2015). A critical evaluation of proxy methods used to estimate the acidity of atmospheric particles. Atmos. Chem. Phys. 15, 2775–2790. https://doi.org/10.5194/acp-15-2775-2015

  23. Huang, X., Qiu, R., Chan, C.K., Ravi Kant, P. (2011). Evidence of high PM2.5 strong acidity in ammonia-rich atmosphere of Guangzhou, China: Transition in pathways of ambient ammonia to form aerosol ammonium at [NH4+]/[SO42–]=1.5. Atmos. Res. 99, 488–495. https://doi.org/​10.1016/j.atmosres.2010.11.021

  24. Huang, X., Song, Y., Li, M., Li, J., Huo, Q., Cai, X., Zhu, T., Hu, M., Zhang, H. (2012). A high-resolution ammonia emission inventory in China. Global Biogeochem. Cycles. 26, GB1030. https://doi.org/​10.1029/2011GB004161

  25. Jia, S.H., Sarkar, S., Zhang, Q., Wang, X.M., Wu, L.L., Chen, W.H., Huang, M.J., Zhou, S.Z., Zhang, J.P., Yuan, L., Yang, L.M. (2018). Characterization of diurnal variations of PM2.5 acidity using an open thermodynamic system: A case study of Guangzhou, China. Chemosphere 202, 677–685. https://doi.org/10.1016/j.chemosphere.2018.03.127

  26. Jin, Y., Andersson, H., Zhang, S. (2016). Air pollution control policies in China: A retrospective and prospects. Int. J. Environ. Res. Public Health 13, 1219. https://doi.org/10.3390/ijerph13121219

  27. Johnson, A.H., Moyer, A., Bedison, J. L., Richter, S., Andersen Willig, S. (2008). Seven decades of calcium depletion in organic horizons of adirondack forest soils. Soil Sci. Soc. Am. J. 72, 1824–1830. https://doi.org/10.2136/sssaj2006.0407

  28. Kakavas, S., Patoulias, D., Zakoura, M., Nenes, A., Pandis, S.N. (2021). Size-resolved aerosol pH over Europe during summer. Atmos. Chem. Phys. 21, 799–811. https://doi.org/10.5194/acp-21-799-2021

  29. Keene, W.C., Pszenny, A.A.P., Maben, J.R., Stevenson, E., Wall, A. (2004). Closure evaluation of size-resolved aerosol pH in the New England coastal atmosphere during summer. J. Geophys. Res. 109, D23307. https://doi.org/10.1029/2004jd004801

  30. Larssen, T., Lydersen, E., Tang, D., He, Y., Gao, J., Liu, H., Duan, L., Seip, H., Vogt, R., Mulder, J., Shao, M., Wang, Y., Shang, H., Zhang, X., Solberg, S., Aas, W., Okland, T., Eilertsen, O., Angell, V., Li, Q., Zhao, D., Xiang, R., Xiao, J., Luo, J. (2006). Acid Rain in China. Environ. Sci. Technol. 40, 418–425. https://doi.org/10.1021/es0626133

  31. Li, H., Wang, D., Cui, L., Gao, Y., Huo, J., Wang, X., Zhang, Z., Tan, Y., Cao, J., Chow, J., Lee, S., Fu, Q. (2019). Characteristics of atmospheric PM2.5 composition during the implementation of stringent pollution control measures in shanghai for the 2016 G20 summit. Sci. Total. Environ. 648, 1121–1129. https://doi.org/10.1016/j.scitotenv.2018.08.219

  32. Liu, M., Song, Y., Zhou, T., Xu, Z., Yan, C., Zheng, M., Wu, Z., Hu, M., Wu, Y., Zhu, T. (2017). Fine particle pH during severe haze episodes in northern China. Geophys. Res. Lett. 44, 5213–5221. https://doi.org/10.1002/2017GL073210

  33. Liu, M., Huang, X., Song, Y., Tang, J., Cao, J., Zhang, X., Zhang, Q., Wang, S., Xu, T., Kang, L., Cai, X., Zhang, H., Yang, F., Wang, H., Yu, J., Lau, A., He, L., Huang, X., Duan, L., Ding, A., Xue, L., Gao, J., Liu, B., Zhu, T. (2019). Ammonia emission control in China would mitigate haze pollution and nitrogen deposition, but worsen acid rain. PNAS 116, 7760–7765. https://doi.org/10.1073/​pnas.1814880116

  34. Malm, W.C., Day, D.E. (2001). Estimates of aerosol species scattering characteristics as a function of relative humidity. Atmos. Environ. 35, 2845–2860. https://doi.org/10.1016/S1352-2310(01)00077-2

  35. Manktelow, P.T., Carslaw, K.S., Mann, G.W., Spracklen, D.V. (2010). The impact of dust on sulfate aerosol, CN and CCN during an East Asian dust storm. Atmos. Chem. Phys. 10, 365–382. https://doi.org/10.5194/acp-10-365-2010

  36. National Bureau of Statistics of China (NBS) (2019). China Statistical Yearbook. China National Bureau of Statistics. http://www.stats.gov.cn/tjsj/ndsj/ (accessed 6 March 2022).

  37. Nenes, A., Pandis, S.N., Pilinis C. (1998). ISORROPIA: A new thermodynamic equilibrium model for multiphase multicomponent inorganic aerosols. Aquat. Geochem. 4, 123–152. https://doi.org/​10.1023/A:1009604003981

  38. Nenes, A., Pandis, S.N., Weber, A., Russell, A. (2020). Aerosol pH and liquid water content determine when particulate matter is sensitive to ammonia and nitrate availability. Atmos. Chem. Phys. 20, 3248–3258. https://doi.org/10.5194/acp-20-3249-2020

  39. Pathak, R.K., Yao, X.H., Lau, A.K.H., Chan, C.K. (2003). Acidity and concentrations of ionic species of PM2.5 in Hong Kong. Atmos. Environ. 37, 1113–1124. https://doi.org/10.1016/S1352-2310(02)00958-5

  40. Pathak, R.K., Louie P.K.K., Chan, C.K. (2004). Characteristics of aerosol acidity in Hong Kong. Atmos. Environ. 38, 2965–2974. https://doi.org/10.1016/j.atmosenv.2004.02.044

  41. Pathak, R.K., Wu, W.S., Wang, T. (2009). Summertime PM2.5 ionic species in four major cities of China: Nitrate formation in an ammonia-deficient atmosphere. Atmos. Chem. Phys. 9, 1711–1722. https://doi.org/10.5194/acp-9-1711-2009

  42. Pinder, R.W., Dennis, R.L., Bhave, P.V. (2008). Observable indicators of the sensitivity of PM2.5 nitrate to emission reductions-Part I: Derivation of the adjusted gas ratio and applicability at regulatory-relevant time scales. Atmos. Environ. 42, 1275–1286. https://doi.org/10.1016/j.​atmosenv.2007.10.039

  43. Pye, H.O.T., Nenes, A., Alexander, B., Ault, A.P., Barth, M.C., Clegg, S.L., Collett Jr., J.L., Fahey, K.M., Hennigan, C.J., Herrmann, H., Kanakidou, M., Kelly, J.T., Ku, I.T., McNeill, V.F., Riemer, N., Schaefer, T., Shi, G., Tilgner, A., Walker, J.T., Wang, T., et al. (2020). The acidity of atmospheric particles and clouds. Atmos. Chem. Phys. 20, 4809–4888. https://doi.org/10.5194/acp-20-4809-2020

  44. Rumsey, I.C., Cowen, K.A., Walker, J.T., Kelly, T.J., Hanft, E.A., Mishoe, K., Proost, R., Beachley, G., Lear, G., Frelink, T., Otjes, R. (2014). An assessment of the performance of the Monitor for AeRosols and GAses in ambient air (MARGA): A semi-continuous method for soluble compounds. Atmos. Chem. Phys. 14, 5639–5658. https://doi.org/10.5194/acp-14-5639-2014

  45. Schindler, D.W. (1988). Effects of acid rain on freshwater ecosystems. Science 239, 149–157. https://doi.org/10.1126/science.239.4836.149

  46. Seinfeld, J.H., Pandis, S.N. (2016). Atmospheric chemistry and physics: From air pollution to climate change, Third ed., John Wiley & Sons, Inc., Hoboken, New Jersey.

  47. Shi, G., Xu, J., Peng, X., Xiao, Z., Chen, K., Tian, Y., Guan, X., Feng, Y., Yu, H., Nenes, A., Russell, A. (2017). pH of Aerosols in a polluted atmosphere: Source contributions to highly acidic aerosol. Environ. Sci. Technol. 51, 4289–4296. https://doi.org/10.1021/acs.est.6b05736

  48. Song, S., Gao, M., Xu, W., Shao, J., Shi, G., Wang, S., Wang, Y., Sun, Y., McElroy, M. (2018). Fine-particle pH for Beijing winter haze as inferred from different thermodynamic equilibrium models. Atmos. Chem. Phys. 18, 7423–7438. https://doi.org/10.5194/acp-18-7423-2018

  49. Tian, S., Pan, Y., Wang, Y. (2018). Ion balance and acidity of size-segregated particles during haze episodes in urban Beijing. Atmos. Res. 201, 159–167. https://doi.org/10.1016/j.atmosres.​2017.10.016

  50. Underwood, G.M., Song, C.H., Phadnis, M., Carmichael, G.R., Grassian, V.H. (2001). Heterogeneous reactions of NO2 and HNO3 on oxides and mineral dust: A combined laboratory and modeling study. J. Geophys. Res. 106, 18055–18066. https://doi.org/10.1029/2000JD900552

  51. Wang, G., Zhang, R., Gomez, M.E., Yang, L., Levy Zamora, M., Hu, M., Lin, Y., Peng, J., Guo, S., Meng, J., Li, J., Cheng, C., Hu, T., Ren, Y., Wang, Yuesi, Gao, J., Cao, J., An, Z., Zhou, W., Li, G., et al. (2016). Persistent sulfate formation from London Fog to Chinese haze. PNAS 113, 13630–13635. https://doi.org/10.1073/pnas.1616540113

  52. Wang, G.H., Zhang, F., Peng, J., Duan, L., Ji, Y., Marrero-Ortiz, W., Wang, J., Li, J., Wu, C., Cao, C., Wang, Y., Zheng, J., Secrest, J., Li, Y., Wang, Y., Li, H., Li, N., Zhang, R. (2018). Particle acidity and sulfate production during severe haze events in China cannot be reliably inferred by assuming a mixture of inorganic salts. Atmos. Chem. Phys. 18, 10123–10132. https://doi.org/10.5194/​acp-18-10123-2018

  53. Watson, J.G. (2002). Visibility: Science and regulation. J. Air Waste Manage. Assoc. 52, 628–713. https://doi.org/10.1080/10473289.2002.10470813

  54. Weber, R.J., Guo, H., Russell, A.G., Nenes, A. (2016). High aerosol acidity despite declining atmospheric sulfate concentrations over the past 15 years. Nat. Geosci. 9, 282–285. https://doi.org/10.1038/ngeo2665

  55. Wu, Y., Gu, B., Erisman, J W., Fang, Y.F., Lu, X., Zhang, X. (2016). PM2.5 pollution is substantially affected by ammonia emissions in China. Environ. Pollut. 218, 86–94. https://doi.org/10.1016/​j.envpol.2016.08.027

  56. Xing, W., Yang, L., Zhang, H., Zhang, X., Wang, Y., Bai, P., Zhang, L., Hayakawa, K., Nagao, S., Tang, N. (2021). Variations in traffic-related water-soluble inorganic ions in PM2.5 in Kanazawa, Japan, after the implementation of a new vehicle emission regulation. Atmos. Pollut. Res. 12, 101233. https://doi.org/10.1016/j.apr.2021.101233

  57. Xu, G.Y., Zhang, Q.Q., Yao, Y., Zhang, X. (2020). Changes in PM2.5 sensitivity to NOx and NH3 emissions due to a large decrease in SO2 emissions from 2013 to 2018. Atmos. Ocea. Sci. Lett. 13, 210–215. https://doi.org/10.1080/16742834.2020.1738009

  58. Xu, Z., Liu, M., Zhang, M., Song, Y., Wang, S., Zhang, L., Xu, T., Wang, T., Yan, C., Zhou, T., Sun, Y., Pan, Y., Hu, M., Zheng, M., Zhu, T. (2019). High efficiency of livestock ammonia emission controls in alleviating particulate nitrate during a severe winter haze episode in northern China. Atmos. Chem. Phys. 19, 5605–5613. https://doi.org/10.5194/acp-19-5605-2019

  59. Yang, Y., Zhou, R., Wu, J., Yu, Y., Ma, Z., Zhang, L., Di, Y. (2015). Seasonal variations and size distributions of water-soluble ions in atmospheric aerosols in Beijing, 2012. J. Environ. Sci. 34, 197–205. https://doi.org/10.1016/j.jes.2015.01.025

  60. Yao, X., Ling, T.Y., Fang, M., Chan, C.K. (2006). Comparison of thermodynamic predictions for in situ pH in PM2.5. Atmos. Environ. 40, 2835–2844. https://doi.org/10.1016/j.atmosenv.2006.​01.006

  61. Yao, X., Ling, T.Y., Fang, M., Chan, C.K. (2007). Size dependence of in situ pH in submicron atmospheric particles in Hong Kong. Atmos. Environ. 41, 382–393. https://doi.org/10.1016/j.​atmosenv.2006.07.037

  62. Zheng, G.J., Su, H., Wang, S.W., Andreae, M.O., Pöschl, U., Cheng, Y.F. (2020). Multiphase buffer theory explains contrasts in atmospheric aerosol acidity. Science 369, 1374–1377. https://doi.org/10.1126/science.aba3719

  63. Zheng, M.M., Wang, Y.H., Bao, J.G., Yuan, L., Zheng, H., Yan, Y., Liu, D., Xie, M., Kong, S. (2019). Initial cost barrier of ammonia control in central China. Geophys. Res. Lett. 46, 14175–14184. https://doi.org/10.1029/2019GL084351

  64. Zhou, M., Zhang, Y., Han, Y., Wu, J., Du, X., Xu, H., Feng, Y., Han, S. (2018). Spatial and temporal characteristics of PM2.5 acidity during autumn in marine and coastal area of Bohai Sea, China, based on two-site contrast. Atmos. Res. 202, 196–204. https://doi.org/10.1016/j.atmosres.​2017.11.014

  65. Zhou, X., Strezov, V., Jiang, Y., Kan, T., Evans, T. (2022). Temporal and spatial variations of air pollution across China from 2015 to 2018. J. Environ. Sci. 112, 161–169. https://doi.org/​10.1016/j.jes.2021.04.025

  66. Zhou, Y., Xue, L., Wang, T., Gao, X., Wang, Z., Wang, X., Zhang, J., Zhang, Q., Wang, W. (2012). Characterization of aerosol acidity at a high mountain site in central eastern China. Atmos. Environ. 51, 11–20. https://doi.org/10.1016/j.atmosenv.2012.01.061 

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