Chuanjie Lin1, Tingting Huo1, Fumo Yang2, Bin Wang1, Yang Chen3, Huanbo Wang This email address is being protected from spambots. You need JavaScript enabled to view it.1 

1 School of Environment and Resource, Southwest University of Science and Technology, Mianyang 621010, China
2 National Engineering Research Center for Flue Gas Desulfurization, Department of Environmental Science and Engineering, Sichuan University, Chengdu 610065, China
3 Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China


Received: August 9, 2020
Revised: December 8, 2020
Accepted: December 8, 2020

 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.200513  


Cite this article:

Lin, C., Huo, T., Yang, F., Wang, B., Chen, Y., Wang, H. (2021). Characteristics of Water-soluble Inorganic Ions in Aerosol and Precipitation and their Scavenging Ratios in an Urban Environment in Southwest China. Aerosol Air Qual. Res. 21, 200513. https://doi.org/10.4209/aaqr.200513


HIGHLIGHTS

  • Aerosol and precipitation samples were collected simultaneously over one-year period.
  • Dynamic characteristics of particulate matter during rainfall processes were investigated.
  • Scavenging ratios of major inorganic ions and gas pollutants were estimated.
  • The relative wet scavenging contributions of gases and particles were calculated.
 

ABSTRACT


Daily fine particulate matter (PM2.5) and precipitation samples were collected simultaneously at an urban site in southwest China in four segregated months in 2015 for measuring major water-soluble inorganic ions (WSIIs). Online hourly concentrations of PM10 and PM2.5 were also monitored, which showed annual mean concentrations of 67.8 and 41.6 µg m–3, respectively. PM2.5 showed the highest concentration in winter and lowest in summer. The annual mean concentration of the total WSIIs was 20.3 µg m–3, accounting for about 48.7% of PM2.5. Among the total WSIIs in ambient PM2.5, SO42– was the predominant component (49.7%), followed by NH4+ (24.1%) and NO3 (21.4%). NH4+ and SO42– were the two most abundant ions in precipitation, followed by Ca2+ and NO3. Seasonal patterns of the major inorganic ions in precipitation were similar to those in PM2.5, with the highest concentration in winter and lowest in summer. The mean scavenging ratios were 454, 445, 364, 456, and 394 for SO42–, NO3, NH4+, Cl, and K+, and 116, 353, and 18 for gas SO2, HNO3, and NH3, respectively. The higher scavenging ratios of particulate ions than their gaseous precursors suggest the higher contributions of particles than gases to the total wet deposition.


Keywords: Wet scavenging, Water-soluble inorganic ions, PM2.5, Precipitation chemistry


1 INTRODUCTION


Wet scavenging efficiently removes particulate matter and their gaseous precursors from the atmosphere (Wang et al., 2018a). In-cloud (rainout) and below-cloud (washout) scavenging are the two components of wet scavenging processes for air pollutants. For particulate matter, rainout and washout mechanisms are different, with rainout involving mostly nucleation of aerosols (acting as cloud condensation nuclei) and to a much less extent impaction scavenging, whereas washout involving mainly impaction scavenging (collected by falling raindrops) (Zhang and Vet, 2006). For gases, dissolution in liquid water droplets is the mechanism for both rainout and washout processes. The relative contributions of rainout and washout to the total wet deposition could be estimated based on the sequential precipitation sampling measurements (Aikawa and Hiraki, 2009). For example, using this method, Xu et al. (2017) estimated that washout contributed about 56%, 61%, and 47% to total wet scavenging of SO42–, NO3, and NH4+ respectively, in Beijing, and Ge et al. (2016) obtained 88%, 92%, and 84% in Dalian and 56%, 50%, and 46% in Dandong in North China. Furthermore, washout accounted for about two-thirds of total wet scavenging for NO3 and half for SO42– in Japan using the same method (Aikawa et al., 2014; Kajino and Aikawa, 2015).

Scavenging ratio (W), defined as the ratio of a pollutant concentration in precipitation to that in air, can be a useful indicator for wet scavenging efficiency (Engelmann, 1971). This parameter depends on the physical and chemical characteristics of both particles and precipitation, and can vary by up to two orders of magnitude with different chemical species. Budhavant et al. (2020) estimated the scavenging ratios of ten major chemical species in PM10 over the Northern Indian Ocean, and obtained values of less than 50 for black carbon and up to 3000 for Cl and NO3. Cheng and Zhang (2017) investigated the scavenging ratios of eight inorganic ions at 13 monitoring sites across Canada and obtained values varying by a factor of up to 6, with larger values for chemical species with higher fractions in coarse particles, e.g., Ca2+ and NO3. The important role that below-cloud scavenging plays on ambient aerosol concentration under different rain conditions has also been demonstrated by field experiments and numerical studies (Zhang et al., 2004; Gao et al., 2019; Lu et al., 2019; Luan et al., 2019).

Due to the strong dependence of scavenging ratio on the characteristics of particles and precipitation, existing database are subject of large uncertainties. Scavenging ratio obtained from parallel measurements of ambient particulate matter and precipitation chemistry over long sampling periods may reduce such uncertainties. Furthermore, it is even more complicated for wet-scavenged chemical species involving both particulate- and gaseous-phase pollutants in air, such as SO42–, NO3, and NH4+. Kasper-Giebl et al. (1999) estimated that 89–96%, 4–12%, and 49–79% of SO42–, NO3, and NH4+ in precipitation were from the scavenging of their ambient particulate-phase ions, respectively, with the rest from the gaseous pollutants (SO2, HNO3, and NH3). Similarly, Cheng and Zhang (2017) suggested that HNO3 dominated the particulate NO3 in wet scavenging with a percentage contribution of about 72%, whereas the contributions from particulate SO42– and NH4+ were greater than the corresponding gas SO2 and NH3, with an average percentage contribution of 63% and 70%, respectively. Comprehensive investigation on the relative contributions from gases and particles to the total wet deposition may improve the wet scavenging models of SO42–, NO3, and NH4+, and consequently reduce the uncertainties in prediction of particle concentrations during precipitation periods.

Wanzhou is a small urban city, situated in southwest China. The climate in Wanzhou is characterized by high relative humidity, extremely low wind speeds all year round, and abundant rainfall with annual precipitation of about 1200 mm. Thus, wet scavenging is expected to play an important role in removing pollutants from the atmosphere. Previous studies have focused on the dry and wet deposition fluxes of nitrogen or other pollutants in southwest China (Wang et al., 2016; Wang et al., 2018a), but provided no information on the wet scavenging processes or scavenging ratios of water-soluble inorganic ions. The present study aims to fill this knowledge gap by simultaneously measuring PM2.5 and precipitation chemistry in different seasons of a year. The main objectives are to (1) investigate the dynamic characteristics of PM2.5 and PM10 during rainfall processes, (2) characterize major water-soluble inorganic ions in PM2.5 and precipitation, and (3) estimate the scavenging ratios of major inorganic ions and gaseous precursors as well as their relative contributions.

 
2 METHODS


 
2.1 Sampling and Chemical Analysis

PM2.5 and precipitation samples were collected in parallel in Wanzhou. The sampling site is located on the roof of a building inside the Chongqing Three Gorges University (108°13′E, 30°48′N), about 28 m above the ground, which is influenced by local vehicular emission and point sources (Wang et al., 2016). 23-h PM2.5 samples were collected by an air pollutant sampler (URG Corp., URG-3000K, North Carolina, USA) from 11:00 a.m. to 10:00 a.m. the next day at a flow rate of 15 L min–1. Two channels of the air pollutant sampler were used to collect PM2.5 samples in parallel. The left channel was preceded by two annual denuders coated with glycerol/Na2CO3 and glycerol/citric acid solution for trapping HNO3 and NH3 sequentially, and then a Teflon filter was equipped for mass weighting. The right channel was loaded with quartz filter for water-soluble inorganic ions analysis. Daily precipitation samples were collected using an automatic atmospheric deposition sampler equipped with a rain gauge sensor (APS-3A, Xianglan Scientific Instruments Co., Ltd., China). Wanzhou has a subtropical monsoon climate with temperature above 0°C all year round, hence, precipitation at this location is almost always in rain. Although rainfall samples were collected on a daily basis in order to match PM2.5 sampling campaign, the rain gauge sensor could automatically record the detailed information of each rainfall event, including the beginning and ending time, besides the rainfall amount. Sampling campaigns were conducted in four months in 2015: from 2nd to 29th in April, 2th to 30th in July, 16th October to 13th November, 16th December to 14th January in 2016, representing spring, summer, autumn, and winter, respectively. Note that PM2.5 samples were not available from 2nd to 7th July due to the sampler maintenance. PM2.5 samples were stored in the dark and at –18°C until analysis to prevent the evaporation of volatile compounds, while rainfall samples were filtered through a 0.45 µm filter after collection and refrigerated at 4°C.

Five cations (Na+, NH4+, K+, Mg2+, Ca2+) and three anions (Cl, SO42–, NO3) were determined by an ion chromatography (DX-600, Dionex, Sunnyvale, CA) for both PM2.5 and rainfall samples. Cations were measured using a CS12A column with 20 mM methanesulfonic acid (MSA) as an eluent while anions were analyzed using an AS11-HC column with 30 mM KOH as an eluent. Ions in rainfall samples were measured directly, while PM2.5 samples should be extracted using ultrapure water before ion chromatography analysis. HNO3 and NH3 captured by denuders were extracted using ultrapure water first, and then measured by an ion chromatography. A detailed description of PM2.5 and rainfall sampling as well as water-soluble inorganic ions analysis were presented in Wang et al. (2016, 2018b). For Na+ in PM2.5, the field blanks varied largely and might result in large uncertainties. For Mg2+ and Ca2+, most of PM2.5 samples were below the detection limits due to their relatively low concentrations. Hence, only five water-soluble inorganic ions in PM2.5 including SO42–, NO3, NH4+, Cl, and Kwere discussed in the following section.

Hourly concentrations of PM2.5 and PM10 were measured using online PM2.5/PM10 analyzer (FH62C14, Thermo Fisher, U.S.). Hourly concentrations of SO2 were not available at the sampling site and obtained from an air quality monitoring station about 6 km away.

 
2.2 Data Analysis

The rate of change in particle concentration (ΔC) for each rainfall event was defined as follows:

 

where Cb is the average concentration of PM2.5 or PM10 within 3 h before the rain, Cd is the average concentration during the rain. If the value of ΔC is positive, it means that PM2.5 or PM10 decreases when rainfall occurs, otherwise, it indicates that PM2.5 or PM10 increases during rain. Calculation of ΔC requires that the free-rainfall period between two events is higher than 3 h, otherwise, consecutive rainfall events are combined (Aikawa et al., 2014). As a consequence, a total of 7, 8, 14, and 13 rainfall events were obtained in spring, summer, autumn, and winter, respectively (Table S1).

Scavenging ratio of major inorganic ions in each month was calculated based on monthly mean concentration as follows (Kasper-Giebl et al., 1999; Cheng and Zhang, 2017):

  

where Cprec is the volume weighted mean (VWM) concentration of inorganic ions in precipitation (mg L–1), Cair is the mean concentration of water-soluble inorganic ions in the air (µg m–3), and ρa and ρw are the density of air (1200 g m–3) and water, respectively.

SO42–, NO3, and NH4+ in rainfall originated from both particles and gaseous precursors. Hence, it is assumed that the difference between the total and particulate wet scavenging was from the gaseous precursor. Scavenging ratio of gaseous precursor was calculated followed by Cheng and Zhang (2017). The procedure for estimating the scavenging ratio of HNO3 was described in detail, and the calculation for the scavenging ratio of SO2 and NH3 was done similarly.

The particulate NO3 concentration in rainfall was first estimated as follows:

 

where [pNO3]prec is the part of NO3 wet scavenging from particulate NO3. WfPM and WcPM are monthly scavenging ratio of fine and coarse particles, respectively. WfPM could be calculated from the scavenging ratio of K+, while WcPM could be calculated based on the average scavenging ratio of Ca2+, Mg2+, and Na+. In this study, the coarse particles data were not available, thus the contributions from coarse particles were neglected. [pNO3]fPM and [pNO3]cPM are monthly mean concentration of NO3 in fine and coarse particles, respectively. Note that the concentrations of major inorganic ions in coarse particles were not available, alternative data assumptions were made, which were discussed in the following section in detail. Then, the scavenging ratio of HNO3 and its contribution were calculated as follows:

 

where [total NO3]prec means the monthly VWM concentrations of NO3 in rainfall, [HNO3]prec is the concentration of NO3 in rainfall from gas contribution, and [HNO3]air is ambient concentration of gas HNO3. %HNO3 and %NO3 are the contributions of HNO3 and particulate NO3 to the total NO3 wet scavenging, respectively. In some cases, the calculated [HNO3]prec value was negative, then we assumed that only particulate NO3 contributed to the wet scavenging and the contribution from gas HNO3 was negligible.

 
3 RESULTS AND DISCUSSION


 
3.1 Variations of Hourly Particulate Matter Concentrations during Rain Events

During the observation periods, the accumulated rainfall amounts were 79.2, 152.8, 85.8, and 31.7 mm in spring, summer, autumn, and winter, respectively, while the corresponding total rainfall hours were 57.1, 61.7, 38.2, and 53.3 h. Note that the rainfall data were obtained in a typical month rather than three months in each season. In Wanzhou, rainfall was the most abundant in summer and least in winter. Although the total rainfall hours were similar in spring, summer, and winter, the accumulated amounts varied about five times due to the different rainfall intensities. Rain could be classified into three categories based on the rainfall intensity (Luan et al., 2019): light rain (0.1–2.5 mm h–1), moderate rain (2.6–7.6 mm h–1), and heavy rain (> 7.6 mm h–1). Regarding the rain events recorded by rain gauge sensor, the predominant rain category was light rain in all seasons, in particular in spring and winter where the numbers of light rain events accounted for about 96% and 100% of the total events, respectively. Moderate and heavy rain events contributed to about 15.8% and 17.9% of the total events in summer, and 5.3% and 3.6% in autumn, respectively.

Particulate matter concentrations were affected by many factors, such as source emissions and wet scavenging effects. Fig. 1 shows the hourly concentrations of PM2.5, PM10, and rain intensities during the observation periods. For those rainfall events with relatively long durations and/or high rainfall intensities, PM2.5 and PM10 concentrations decreased greatly during rain, e.g., on 19th April, 15th July, and 25th October in 2015, and 7th January 2016, indicating that wet scavenging played an important role on the decreases of the PM2.5 and PM10 concentrations. However, for those rainfall events with short durations or low intensities, the scavenging effects exhibited large variations, implying that other factors would primarily contribute to the variations of particle concentrations. For example, on 21st July with the rain duration of 0.8 h and rain intensity of 0.13 mm h–1, PM2.5 and PM10 concentrations decreased by 39% and 48% during rain, respectively. We found that this phenomenon was caused by the increased PM2.5 and PM10 concentrations during the rush hours in the evening, and when the rush hours ended after 21:00 local time, the air pollutants concentrations began to decrease. This can be supported by the increased NO2 concentrations during the rush hours and the decreased concentrations one or two hours before the rain. That means the decreases of PM2.5 and PM10 concentrations on this day were ascribed to the emission reduction after rush hours rather than the wet scavenging effects. On 24th December, PM2.5 and PM10 concentrations increased by about 27% during rain period although it lasted about 8 hours from 7:00 to 17:00 local time. The reason for this phenomenon was similar to the rain event on 21st July, which was caused by the high emissions during the rush hours when rain occurred, resulting in relatively high average PM2.5 and PM10 concentrations. Furthermore, no evident variations of PM2.5 and PM10 concentrations were observed before and during rain on 7th April, in which the duration was about 5 h and rainfall intensity was greater than 1.0 mm h–1. In total, those distinct scavenging effects for different rainfall events indicated that other factors also affected the overall scavenging effects besides rainfall duration and intensities.

Fig. 1. Hourly concentrations of PM2.5, PM10, and rainfall intensity in spring, summer, autumn, and winter.Fig. 1. Hourly concentrations of PM2.5, PM10, and rainfall intensity in spring, summer, autumn, and winter.

The variations of PM2.5 and PM10 concentrations were not uniform for each event, showing increasing or decreasing trends when rainfall occurred (Fig. 2). For PM10, almost all events showed decreasing trends in spring. In other seasons, the number of events with decreasing trends accounted for about 62.5–71.4% of the total rain events. For PM2.5, the number of events presenting decreasing trends accounted for 50–64.3% of the total rainfall events, implying the important role of wet scavenging effects on decreasing particle concentrations.

Fig. 2. The variations of PM10 (a) and PM2.5 (b) concentrations for each rainfall event.
Fig
. 2The variations of PM10 (a) and PM2.5 (b) concentrations for each rainfall event.

Generally, the decreasing rates of PM10 by rainfall were higher than PM2.5 for every event (Fig. 2), which can be explained by the size-dependent below-cloud impaction scavenging efficiency (Wang et al., 2010). Besides particle size distribution, removal rates are also related to the concentrations of particles before rain. The rates of change in particle concentrations were grouped with PM10 or PM2.5 concentrations before rain at 20 µg m–3 interval bins. As shown in Fig. 3(a), ΔC typically increased with the increases of PM10 and PM2.5 concentrations, and the highest change rate in PM10 and PM2.5 concentrations appeared in the range of 80–100 µg m–3 and 60–80 µg m–3, respectively. This trend was also observed by Olszowski (2016), in which the scavenging effect was well correlated with higher particle concentrations before rain. Note that ΔC showed decreasing trends when PM10 and PM2.5 concentrations were higher than 100 µg m–3. This phenomenon indicated that other meteorological conditions might govern the severe PM10 and PM2.5 pollution and then the wet scavenging effect was overwhelmed.

Fig. 3. The variations of PM10 and PM2.5 grouped by particle concentrations before rain (a), and rain intensity bins (b).Fig. 3The variations of PM10 and PM2.5 grouped by particle concentrations before rain (a), and rain intensity bins (b).

It has been reported that the rainfall intensity also influenced the variations of particle concentrations (He and Balasubramanian, 2008; Luan et al., 2019). As shown in Fig. 3(b), ΔC was highest (higher than 50% for both PM10 and PM2.5) when rainfall intensities were higher than 2 mm h–1, indicating that moderate or heavy rain exhibited relatively high scavenging efficiency on particulate matter. This can also be explained by the precipitation-intensity dependent below-cloud scavenging efficiency (Wang et al., 2010). Scavenging effects were positive for each moderate or heavy rainfall event with the value of ΔC in the range of 8.3–90.9% for PM10 and 2.6–93.5% for PM2.5, respectively. However, no obvious trends of ΔC were observed for light rains, likely because other factors overwhelmed the below-cloud scavenging effect.

 
3.2 Gaseous Precursors and Water-soluble Inorganic Ions in PM2.5 and Rainfall

Among the three gaseous precursors discussed here, the annual mean concentrations were the lowest for HNO3, followed by SO2, and highest for NH3 (Table 1). From a seasonal perspective, SO2 showed the highest concentration in spring and lowest in summer. SO2 is primarily emitted from coal combustion for residential heating, power plants and industrial use in China. Wanzhou is located in southwest China with a temperature of above 0°C all year round, and residential heating using coal may not be extensive in this region. Thus, the seasonal patterns of SO2 were mainly influenced by the meteorological conditions and chemical transformations rather than source emissions. High temperature and strong solar radiation in summer could enhance photochemical reactions, leading to the decrease of gaseous precursor concentration such as SO2 and the increase of the secondary aerosol such as SO42–. In addition, the high planetary boundary layer height in summer favors the pollutants dispersion and then leads to the low SO2 concentration. Contrary to the case of SO2, HNOand NH3 exhibited the maximum concentrations in summer and lowest values in winter and autumn, which were likely associated more with the source emission and thermodynamic behavior of NH4NO3 than other conditions (Wang et al., 2018b).

Table 1. Seasonal and annual concentrations of gaseous precursors and major inorganic ions in PM2.5 and precipitation.  

The annual mean concentration of PM2.5 was 41.6 ± 24.5 µg m–3, with the highest concentration in winter and lowest in summer. The total concentration of the five major water-soluble inorganic ions (WSIIs) was 20.3 µg m–3, accounting for about 48.7% of PM2.5. All five water-soluble inorganic ions displayed the highest concentrations in winter (Fig. 4 and Table 1), which were partly related to the unfavorable diffusion conditions, i.e., extremely weak wind (< 1 m s–1) and low precipitation amount. In addition, the relative humidity was generally higher than 80% in winter, which was conducive to secondary inorganic aerosol formation through heterogeneous reactions and thus increased the concentrations of SO42–, NO3, and NH4+. Biomass burning for residential heating in winter probably contributed to the higher concentrations of Cl and K+. The lowest concentrations of the five inorganic ions appeared in summer due to the favorable diffusion conditions and abundant precipitation with the exception of SO42–, which was in autumn. Furthermore, the pronounced high concentrations of NO3 in winter and extremely low concentrations in summer were governed by the thermodynamic equilibrium of NH4NO3, since the low temperature and high relative humidity in winter were conducive to the formation of particulate NO3, whereas high temperature in summer enhanced the volatilization of NH4NO3 (Wang et al., 2018b). Compared with the other inorganic ions, the relatively higher concentration of SO42– in summer was mainly ascribed to the intense photochemical reactions as mentioned above. In PM2.5, the winter/summer ratios were 1.7, 6.7, 2.3, 25, and 2.5 for SO42–, NO3, NH4+, Cl, and K+, respectively, indicating that NO3 and Cl presented the largest seasonal variations.

Fig. 4. Seasonal patterns of (a) PM2.5, (b) Cl–, (c) K+, (d) SO42–, (e) NO3–, and (f) NH4+ in PM2.5 as well as the seasonal VWM concentrations of inorganic ions in rainfall.Fig. 4Seasonal patterns of (a) PM2.5, (b) Cl, (c) K+, (d) SO42–, (e) NO3, and (f) NH4+ in PM2.5 as well as the seasonal VWM concentrations of inorganic ions in rainfall.

Among the five major water-soluble inorganic ions in PM2.5, SO42– is the most abundant ions, showing seasonal mean contributions of 46–64% to WSIIs during the sampling periods, implying the important contributions of coal combustion to PM2.5 pollution. Besides SO42–, NO3 and NH4+ were also predominant components of WSIIs. In autumn and winter, NO3 and NH4+ showed comparable contributions, each of which was about half of that of SO42–. However, SO42–, NO3, and NH4+ presented distinct contributions to WSIIs in summer, where the percentage of SO42– was about 3 times that of NH4+ and even 7 times higher than that of NO3. The relatively high contribution of SO42– and quite low contribution of NO3 were consistent with the patterns of their concentrations. The fractions of Cl and Kin WSIIs were minor, ranging from 0.37% in summer to 3.8% in winter for Cl and from 1.6 to 2.6% for K+.

As shown in Fig. 4 and Table 1, the seasonal variations of eight inorganic ions in rainfall followed the patterns of those in PM2.5, showing the highest VWM concentrations in winter and lowest in summer. Besides the relatively low concentrations of pollutants in the air, dilution effect due to the abundant rainfall was also responsible for the lower concentrations of those inorganic ions in summer. Compared the winter/summer ratios of the inorganic ions in PM2.5 and rainfall, it could be found that the seasonal variations were more pronounced in PM2.5 than in rainfall except for SO42–, implying the sensitivity of the air pollutants to the meteorological conditions. Although Ca2+ concentrations were very low in PM2.5, they were almost comparable to NH4+ in rainfall in spring and autumn, highlighting a high contribution from coarse particles to rainfall. On an equivalent amount basis, rainfall was dominated by NH4+ and SO42–, followed by Ca2+ and NO3 (Table 1). The distribution of inorganic ions abundance in rainfall at Wanzhou was similar to Hangzhou (Han et al., 2019), but slightly different from the mean value across China which showed SO42– and Ca2+ as the most abundant species (Li et al., 2019b).

 
3.3 Scavenging Ratios of Major Inorganic Ions

Calculating scavenging ratios of inorganic ions and gaseous pollutants required more parameters, including the concentrations of major inorganic ions in fine particles, coarse particles and rainfall, and the concentrations of SO2, HNO3, and NH3. Fine/coarse fractions of these ions were not measured in the present study, but were previously reported for urban sites in Chongqing (Li et al., 2018a, b), which can be considered to be representative of the location of the present study. Briefly, Mg2+ and Ca2+ were mainly distributed in coarse particles, SO42–, NH4+, and Kwere primarily in fine particles, and Na+, NO3, and Cl were distributed in both fine and coarse particles (Table S2). In this study, the concentrations of inorganic ions in coarse particles were not available. Considering the relatively high fractions of coarse particles for Mg2+, Ca2+, and Na+, the scavenging ratios of these ions will be highly uncertain if their concentrations in coarse particles were ignored. Meanwhile, the scavenging ratio of coarse particles (WcPM) cannot be obtained due to the lack of coarse particles data, since WcPM was determined by averaging scavenging ratios of Na+, Mg2+, and Ca2+ (Cheng and Zhang, 2017). Nevertheless, it is assumed that the influences due to the lack of coarse particles data would be minor on those inorganic ions dominated in fine particles. Thus, the scavenging ratios were only analyzed for SO42–, NO3, NH4+, Cl, and K+ using their concentrations in PM2.5. Taking into account the fractions of Kin fine particles around 80%, the scavenging ratio of K+ multiplied by 0.8 was assumed to be the scavenging ratio of fine particle (WfPM).

Seasonal and annual mean scavenging ratios of five inorganic ions and three gases are summarized in Table 2. On an annual basis, SO42–, NO3, and Cl showed comparable scavenging ratios at around 450, which was about 13–25% higher than the values for NH4+ and K+. As shown in Fig. S1, positive correlations of equivalent concentrations between [NH4+] and [SO42– + NO3] were observed in the four seasons, with the correlation coefficients higher than 0.90 and the slopes of linear regressions being around 1.0, suggesting that SO42– and NO3 were fully neutralized by NH4+, and (NH4)2SO4 and NH4NO3 were the two major chemical forms in PM2.5. Consequently, it seems that SO42–, NO3, and NH4+ should have consistent scavenging ratios due to their coexistence in fine particles. However, slightly higher values of SO42– and NO3 than NH4+ were observed. As mentioned above, about 30%–40% of Cl and NO3 were found in coarse particles, thus the relatively higher scavenging ratio of NO3 was in part ascribed to the contributions from coarse particles. The slightly higher scavenging ratio of SO42– was probably attributed to the in-cloud rainout process, since SO42– could be formed efficiently through in-cloud oxidation of SO2 by H2O2 or O3, and then the cloud droplets including SO42– could be removed as the raindrop falls. Based on Eqs. (2)–(5), the annual mean scavenging ratio was 116, 353, and 18 for SO2, HNO3, and NH3, respectively. The different scavenging ratios of the three gases could be partly explained by their different solubilities. HNO3 is the most soluble species, followed by NH3, and SO2 is the least soluble gas, with a Henry’s law constant of around 2.1 × 103, 6.1 × 10–1, and 1.2 × 10–2 mol m–3 pa–1, respectively (Sander, 2015). NH3 solubility is higher than SO2, their relative low scavenging ratio might be responsible for the short lifetime of NH3. The air pollutants incorporated into precipitation occurs at cloud level, however, the ambient concentrations were measured at the ground. Over this vertical distance, NHmay transform into particulate NH4and consequently lead to the wet scavenging mainly from particulate NH4+ rather than gas NH3.

Table 2. Scavenging ratios of inorganic ions and gaseous precursors on mass basis in literatures.

Different seasonal patterns of the scavenging ratios were observed for different inorganic ions and gases. Seasonal variations in scavenging ratios are caused by many factors, such as ambient concentrations of the ions, rain intensities and associated precipitation chemistry and droplet spectra. These factors would affect the dissociate rate of related pollutants between ambient air and rain droplets, below-cloud evaporation of droplets, etc., all of which would contribute to the overall calculated scavenging ratio. The maximum scavenging ratio was observed in autumn for both SO42– and NH4+, and the minimum value appeared in summer and winter, respectively. In contrast, the highest scavenging ratio was found in summer for Cl and NO3 and lowest in autumn and winter, respectively. The highest scavenging ratios of SO2, HNO3, and NH3 were observed in autumn, spring, and summer, respectively.

The scavenging ratio values of SO42–, NH4+, and K+ obtained in the present study generally agreed well with existing data reported in literature (Table 2); however, those of NO3 were slightly lower than reported in the other regions, which was probably related to the different size distributions among different regions, among other factors. Contrary to the predominant coarse mode distribution of NO3 influenced by natural sources or sea salt in other regions (Zhang et al., 2008), about 70%–80% of NO3 was found in fine mode in the study region, which might result in lower scavenging ratio since rainfall scavenges coarse particles more efficiently. Additionally, the sampling site in this study is an urban site, which is severely affected by the vehicle emissions and hence results in relatively high ambient concentrations of NO2 and NO3. For the three gases, limited NH3 scavenging ratios were reported in the previous studies. The scavenging ratio of SO2 in this study was within the range of those values in the literatures, while the value for HNO3 was somewhat lower in this study (Table 2).

As expected, particulate SO42– was the dominant contributor to rainfall SO42–, accounting for 69.4%, while gas SO2 contributed about 30.6% (Fig. 5), which were consistent with those conducted at rural sites in Canada (Cheng and Zhang, 2017). Similar to SO42–, particulate NO3 accounted for about 70% of the rainfall NO3, which was much higher than those reported by Cheng and Zhang (2017) (around 28%) and Kasper-Giebl et al. (1999) (about 4–12%). The discrepancies between our results and other literatures were probably attributed to the different characteristic of sampling location, where the sampling site with higher ambient NO3 concentrations exhibited high relative contributions of particulate NO3. The relative contribution of particulate NH4+ to total wet scavenging was 86.6%, which was higher than those in Cheng and Zhang (2017) (70%) and Kasper-Giebl et al. (1999) (48–79%).

Fig. 5. Relative contributions of particulate and gas-phase to the total wet scavenging of SO42–, NO3–, and NH4+ during the sampling periods.Fig. 5Relative contributions of particulate and gas-phase to the total wet scavenging of SO42–, NO3, and NH4+ during the sampling periods.

 
4 CONCLUSIONS


Water-soluble inorganic ions in ambient fine particles and in precipitation were characterized, and relative contributions from particulate and gaseous pollutants to inorganic ions in precipitation were quantified. PM2.5 and PM10 concentrations were generally decreased after occurrence of moderate or heavy rain event, but this was not always the case after light rain event due to too many other factors affecting the ambient concentration. The scavenging ratio values derived for the five inorganic ions that were mostly distributed in fine particles varied by 25% on average, e.g., about 450 for SO42–, NO3, and Cl, 364 for NH4+, and 395 for K+, likely due to their slightly different fine/coarse fractions. Scavenging ratios for gaseous species investigated here differed greatly, e.g., 353 for HNO3, 116 for SO2, and 18 for NH3, likely related to their different solubility or lifetime in air. Besides, major sources and chemical processes might have contributed to the ambient concentrations of some chemical species and thus substantially lowered the scavenging ratio, e.g., for the case of NH3. Seasonal variations of the scavenging ratios were a factor of 1.7–8 for different air pollutants. Wet scavenging of SO42–, NO3, and NH4were primarily from particulate forms (> 69%), especially considering that only fine particles were considered in this estimation due to the lack of the coarse particles data. The scavenging ratio data obtained in this study may be used for improving the prediction of particle concentrations during rain periods using air quality models.

 
ACKNOWLEDGEMENTS


This work was supported by the National Natural Science Foundation of China (grant numbers 41405027, 41831285), Key Research and Development Projects of Sichuan Province (grant number 2020YFS0056), and the National Key R&D Program of China (grant numbers 2016YFC0200400, 2018YFC0214002).


REFERENCES


  1. Aikawa, M., Hiraki, T. (2009). Washout/rainout contribution in wet deposition estimated by 0.5 mm precipitation sampling/analysis. Atmos. Environ. 43, 4935–4939. https://doi.org/10.1016/j.atmosenv.2009.07.057

  2. Aikawa, M., Kajino, M., Hiraki, T., Mukai, H. (2014). The contribution of site to washout and rainout: Precipitation chemistry based on sample analysis from 0.5 mm precipitation increments and numerical simulation. Atmos. Environ. 95, 165–174. https://doi.org/10.1016/j.atmosenv.2014.06.015

  3. Budhavant, K., Andersson, A., Holmstrand, H., Bikkina, P., Bikkina, S., Satheesh, S.K., Gustafsson, O. (2020). Enhanced light-absorption of black carbon in rainwater compared with aerosols over the northern Indian Ocean. J. Geophys. Res. 125, e2019JD031246. https://doi.org/10.1029/2019JD031246

  4. Cheng, I., Zhang, L. (2017). Long-term air concentrations, wet deposition, and scavenging ratios of inorganilc ions, HNO3, and SO2 and assessment of aerosol and precipitation acidity at Canadian rural locations. Atmos. Chem. Phys. 17, 4711–4730. https://doi.org/10.5194/acp-17-4711-2017

  5. Encinas, D., Casado, H. (1999). Rain-Aerosol coupling in a rural area in the Basque Country (Spain): Scavenging ratios. Aerosol Sci. Technol. 30, 411–419. https://doi.org/10.1080/027868299304462

  6. Encinas, D., Calzada, I., Casado, H. (2004). Scavenging ratios in an urban area in the Spanish Basque Country. Aerosol Sci. Technol. 38, 685–691. https://doi.org/10.1080/02786820490460716

  7. Engelmann, R.J. (1971). Scavenging prediction using ratios of concentrations in air and precipitation. J. Appl. Meteorol. 10, 493–497. https://doi.org/10.1175/1520-0450(1971)0102.0.CO;2

  8. Gao, B., Ouyang, W., Cheng, H.G., Xu, Y., Lin, C.Y., Chen, J. (2019). Interactions between rainfall and fine particulate matter investigated by simultaneous chemical composition measurements in downtown Beijing. Atmos. Environ. 218, 117000. https://doi.org/10.1016/j.atmosenv.2019.117000

  9. Ge, B.Z., Wang, Z.F., Gbaguidi, A.E., Zhang, Q.X. (2016). Source Identification of acid rain arising over northeast China: Observed evidence and model simulation. Aerosol Air Qual Res. 16, 1366–1377. https://doi.org/10.4209/aaqr.2015.05.0294

  10. Han, Y., Xu, H., Bi, X.H., Lin, F.M., Jiao, L., Zhang, Y.F., Feng, Y.C. (2019). The effect of atmospheric particulates on the rainwater chemistry in the Yangtze River Delta, China. J Air Waste Manage. 69, 1452–1466. https://doi.org/10.1080/10962247.2019.1674750

  11. He, J., Balasubramanian, R. (2008). Rain-aerosol coupling in the tropical atmosphere of southeast Asia: Distribution and scavenging ratios of major ionic species. J. Atmos. Chem. 60, 205–220. https://doi.org/10.1007/s10874-008-9118-x

  12. Hicks, B.B. (2005). A climatology of wet deposition scavenging ratios for the United States. Atmos. Environ. 39, 1585–1596. https://doi.org/10.1016/j.atmosenv.2004.10.039

  13. Kajino, M., Aikawa, M. (2015). A model validation study of the washout/rainout contribution of sulfate and nitrate in wet deposition compared with precipitation chemistry data in Japan. Atmos. Environ. 117, 124–134. https://doi.org/10.1016/j.atmosenv.2015.06.042

  14. Kasper-Giebl, A., Kalina, M.F., Puxbaum, H. (1999). Scavenging ratios for sulfate, ammonium and nitrate determined at Mt. Sonnblick (3106 M Asl). Atmos. Environ. 33, 895–906. https://doi.org/10.1016/S1352-2310(98)00279-9

  15. Kulshrestha, U.C., Reddy, L.A.K., Satyanarayana, J., Kulshrestha, M.J. (2009). Real-time wet scavenging of major chemical constituents of aerosols and role of rain intensity in Indian Region. Atmos. Environ. 43, 5123–5127. https://doi.org/10.1016/j.atmosenv.2009.07.025

  16. Li, Q.K., Yang, Z., Li, X.D., Ding, S.Y., Du, F. (2019a). Seasonal characteristics of sulfate and nitrate in size-segregated particles in ammonia-poor and -rich atmospheres in Chengdu, Southwest China. Aerosol Air Qual Res. 19, 2697–2706. https://doi.org/10.4209/aaqr.2019.07.0368

  17. Li, R., Cui, L.L., Zhao, Y.L., Zhang, Z.Y., Sun, T.M., Li, J.L., Zhou, W.H., Meng, Y., Huang, K., Fu, H.B. (2019b). Wet deposition of inorganic ions in 320 cities across China: Spatio-temporal variation, source apportionment, and dominant factors. Atmos. Chem. Phys. 19, 11043–11070. https://doi.org/10.5194/acp-19-11043-2019

  18. Li, Y.P., Hao, Q.J., Wen, T.X., Ji, D.S., Liu, Z.R., Wang, Y.S., Jiang, C.S. (2018a). Mass concentrations and size distributions of water-soluble inorganic ions in atmospheric aerosols in Beibei district, Chongqing. Huan Jing Ke Xue. 39, 4002–4013. https://doi.org/10.13227/j.hjkx.201712077

  19. Li, Y.P., Hao, Q.J., Wen, T.X., Ji, D.S., Liu, Z.R., Wang, Y.S., Li, X.X., He, X.H., Jiang, C.S. (2018b). Pollution characteristics of water-soluble ions in aerosols in the urban area in Beibei of Chongqing. Aerosol Air Qual Res. 18, 1531–1544. https://doi.org/10.4209/aaqr.2017.11.0500

  20. Lu, X.C., Fung, J.C.H. (2018). Sensitivity assessment of PM2.5 simulation to the below-cloud washout schemes in an atmospheric chemical transport model, Tellus B70, 1476435. https://doi.org/10.1080/16000889.2018.1476435

  21. Lu, X.C., Chan, S.C., Fung, J.C.H., Lau, A.K.H. (2019). To what extent can the below-cloud washout effect influence the PM2.5? A combined observational and modeling study. Environ Pollut. 251, 338–343. https://doi.org/10.1016/j.envpol.2019.04.061

  22. Luan, T., Guo, X.L., Zhang, T.H., Guo, L.J. (2019). Below-Cloud aerosol scavenging by different-intensity rains in Beijing City. J. Meteorol. Res. 33, 126–137. https://doi.org/10.1007/s13351-019-8079-0

  23. Olszowski, T. (2016). Changes in PM10 concentration due to large-scale rainfall. Arabian J. Geosci. 9, 160. https://doi.org/10.1007/s12517-015-2163-2

  24. Sander, R. (2015). Compilation of Henry's law constants (version 4.0) for water as solvent. Atmos. Chem. Phys. 15, 4399–4981. https://doi.org/10.5194/acp-15-4399-2015

  25. Wang, H.B., Yang, F.M., Shi, G.M., Tian, M., Zhang, L.M., Zhang, L.Y., Fu, C.A. (2016). Ambient concentration and dry deposition of major inorganic nitrogen species at two urban sites in Sichuan Basin, China. Environ Pollut. 219, 235–244. https://doi.org/10.1016/j.envpol.2016.10.016

  26. Wang, H.B., Shi, G.M., Tian, M., Chen, Y., Qiao, B.Q., Zhang, L.Y., Yang, F.M., Zhang, L.M., Luo, Q. (2018a). Wet deposition and sources of inorganic nitrogen in the Three Gorges Reservoir Region, China. Environ Pollut. 233, 520–528. https://doi.org/10.1016/j.envpol.2017.10.085

  27. Wang, H.B., Tian, M., Chen, Y., Shi, G.M., Liu, Y., Yang, F.M., Zhang, L.M., Deng, L.Q., Yu, J., Peng, C., Cao, X.Y. (2018b). Seasonal characteristics, formation mechanisms and source origins of PM2.5 in two megacities in Sichuan Basin, China. Atmos. Chem. Phys. 18, 865–881. https://doi.org/10.5194/acp-18-865-2018

  28. Wang, X., Zhang, L., Moran, M.D. (2010). Uncertainty assessment of current size-resolved parameterizations for below-cloud particle scavenging by rain. Atmos. Chem. Phys. 10, 5685–5705. https://doi.org/10.5194/acp-10-5685-2010

  29. Xu, D.H., Ge, B.Z., Wang, Z.F., Sun, Y.L., Chen, Y., Ji, D.S., Yang, T., Ma, Z.Q., Cheng, N.L., Hao, J.Q., Yao, X.F. (2017). Below-cloud wet scavenging of soluble inorganic ions by rain in Beijing During the summer of 2014. Environ Pollut. 230, 963–973. https://doi.org/10.1016/j.envpol.2017.07.033

  30. Zhang, L., Michelangeli, D.V., Taylor, P.A. (2004). Numerical studies of aerosol scavenging in low-level, warm stratiform clouds and precipitation. Atmos. Environ. 38, 4653–4665. https://doi.org/10.1016/j.atmosenv.2004.05.042

  31. Zhang, L., Vet, R. (2006). A review of current knowledge concerning size-dependent aerosol removal. China Particuology. 4, 272–282. https://doi.org/ 10.1016/S1672-2515(07)60276-0

  32. Zhang, L., Vet, R., Wiebe, A., Mihele, C., Sukloff, B., Chan, E., Moran, M.D., Iqbal, S. (2008). Characterization of the size-segregated water-soluble inorganic ions at eight canadian rural sites. Atmos. Chem. Phys. 8, 7133–7151. https://doi.org/10.5194/acp-8-7133-2008


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