Interactions between rainfall and fine particulate matter investigated by simultaneous chemical composition measurements in downtown Beijing

Atmospheric Environment 218 (2019) 117000

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Interactions between rainfall and fine particulate matter investigated by simultaneous chemical composition measurements in downtown Beijing Bing Gao a, Wei Ouyang a, *, Hongguang Cheng a, Yi Xu a, Chunye Lin a, Jing Chen a, b a b

School of Environment, State Key Laboratory of Water Environment Simulation, Beijing Normal University, Beijing, 100875, China Center of Atmospheric Environmental Studies, Beijing Normal University, Beijing, 100875, China

H I G H L I G H T S

G R A P H I C A L A B S T R A C T

� Disparities in ionic proportions of PM2.5 and rainfall were revealed. � Rainfall reduce PM2.5 and its ions with simultaneous in situ measurement. � PM2.5 posed a more impacts on Ca2þ and Kþ in rainfall. � Key factors affecting the ions in PM2.5 and rainfall were identified.

A R T I C L E I N F O

A B S T R A C T

Keywords: PM2.5 Rainwater Washing effect Correlation analysis Diffuse pollution

Rainfall can directly remove atmospheric pollutants through the below-cloud scavenging process, and the at­ mospheric particles also affect the chemical composition of rainfall. Therefore, the interactions between rainfall properties and fine particulate matter (PM2.5) need deep investigation. Based on simultaneous in situ mea­ surements of PM2.5 and rainfall samples during May–October in 2017 and 2018, their chemical composition dynamics and the relationship were identified. It was found that NO3 and SO24 were predominant in PM2.5, whereas NO3 and Ca2þ were the main ionic species in rainfall. The characteristics of ions in rainfall and PM2.5 showed that Beijing was heavily affected by mobile sources and by anthropogenic pollution. The PM2.5 was most effectively removed from the atmosphere by rainfall and the responses of the ionic compositions of PM2.5 to the washing effect were much different. Simultaneously, PM2.5 posed a significant impact on Ca2þ and Kþ in rain­ water. The effect of PM2.5 1 day before rain on the Ca2þ in rainwater was more prominent than the effect of the chemical species in PM2.5 on the day of the rainfall, while the trend was the opposite for Kþ. The key factors affecting the characteristics of ions in PM2.5 and rainfall were rainfall amount, duration and relative humidity. The wind direction also have big impacts on NHþ 4 . The findings provide more scientific supports the rainfall pollution and PM2.5 management in the urban.

* Corresponding author. E-mail addresses: [email protected] (B. Gao), [email protected] (W. Ouyang), [email protected] (H. Cheng), [email protected] (Y. Xu), [email protected] (C. Lin), [email protected] (J. Chen). https://doi.org/10.1016/j.atmosenv.2019.117000 Received 1 April 2019; Received in revised form 19 September 2019; Accepted 20 September 2019 Available online 23 September 2019 1352-2310/© 2019 Elsevier Ltd. All rights reserved.

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1. Introduction

2. Materials and methods

Fine particulate matter (PM2.5) pollution has received intense at­ tentions in recent years due to its adverse impacts on public health and global climate (Apte et al., 2015; Lu et al., 2019). PM2.5 directly cause damage to human health through respiratory intake and skin exposure, which also pollute the surface water bodies through dry and wet depo­ sition (Li et al., 2014). Some studies have suggested that rainfall can effectively remove pollutants from the air by the manner of wet depo­ sition (Negrel et al., 2007; Luan et al., 2019). Simultaneously, the scavenging process of rainfall on fine particulate matter in the atmo­ sphere can also affect the composition of rainfall (Xie et al., 2009; Ouyang et al., 2019). The deposition characteristics of PM2.5 under rainfall conditions and its correlation with rainfall are area of key advancing direction. Previous research has studied the chemical composition character­ istics of PM2.5 (Philip et al., 2014; Gao et al., 2016) and rainfall (Ouyang et al., 2015; Rajeev et al., 2016) at different sites and time scales. In general, ionic species account for approximately 33% of particulate matter in urban atmospheres (Wang et al., 2005). Among all water soluble inorganic ions in PM2.5, the percentages of SO24 , NO3 , and NHþ 4 can reach about 80% (Chang et al., 2013; Yin et al., 2014). Several 2þ are the predominant studies also show that SO24 , NO3 , NHþ 4 , and Ca ions in rainfall (Xu et al., 2015). In the downtown area of Beijing, the proportions of the two main anions (namely, SO24 and NO3 ) and the 2þ two main cations (namely, NHþ 4 and Ca ) can comprise more than 80% of the total anionic and cationic species, respectively (Yang et al., 2012). Based on the chemical composition characteristics of rainfall and PM2.5, the composition disparities between PM2.5 and rainwater (Rajeev et al., 2016) and the scavenging effect on PM2.5 (Ikeuchi et al., 2015) have been identified. The PM2.5 concentrations can decrease to 10–30 μg/m3 from more than 400 μg/m3 with 5 mm of rainfall, indicating a good scavenging effect of rainfall on the concentration of PM2.5 (Ouyang et al., 2015). Some studies also investigate the impacts of rainfall in­ tensity, rainfall duration and different kinds of rain events on PM2.5 concentration (Feng and Wang, 2012; Luan et al., 2019). Rainfall can effectively reduce water-soluble ions in atmospheric particulate matter (Chakraborty et al., 2016). A previous study has 2þ shown that the scavenging effects of cations (namely, NHþ 4 and Ca ) 2 and anions (namely, SO4 and NO3 ) in PM2.5 were obvious, and the removal rates were 53.37%, 42.94%, 19.21%, and 38.84%, respectively (Li and Zhang, 2013). The removal of particulate matter from air by rainfall is an important source of ionic species in rainfall runoff (Tang and Hu, 2018). Few studies have simultaneously collected PM2.5 and rainwater samples to investigate their chemical composition dynamics and relationships. The effect of rainfall scavenging on the mass con­ centration of PM2.5 and its ionic species could be an effective indicator to mitigate PM2.5 pollution in downtown Beijing. In addition, chemical composition characterization in rainfall can help to clarify the interac­ tion between rainfall and PM2.5. Based on the independent observations of chemical composition dynamics in rainfall or fine particulate matter (Philip et al., 2014; Rajeev et al., 2016), the PM2.5 samples before, during and after rain events and rainwater were simultaneously collected. Compositions analysis of ion in rainfall and PM2.5 can determine the dynamic characteristics of ion species in PM2.5 during the rainfall process. The key factors influencing the ion concentrations in PM2.5 and in rainfall were identified by redundancy analysis. Then, the relationship between PM2.5 and rainfall was explored through correlation analysis. The findings will facilitate further understanding of the dynamic of the chemical components of rainfall and the interaction with PM2.5, which is important for the improvement of air quality and the prevention of diffuse pollution.

2.1. Sampling site Fine particulate matter (PM2.5) and rainwater samples were collected simultaneously from the roof of the School of Environment Building (116� 220 E, 39� 580 N, 20 m above ground level) at Beijing Normal Uni­ versity, a typical urban area without major pollution sources nearby. The monitoring site is located inside the 3rd road of Beijing, which represents the air quality and rainfall characteristics of the downtown area (Yang et al., 2016). The data used for this study was during May­ –October in 2017 and 2018. 2.2. Sampling process The PM2.5 concentrations with 1 h (h) resolution during rain events were obtained by a continuous ambient particulate monitor (TEOM 1405, Thermo Scientific, USA) with filter dynamics measurement sys­ tem (FDMS). According to the interval between two rain events, if the interval is more than 3 h (including 3 h), any rainfall is considered to be a new rain event (Aikawa et al., 2014). During May–October in 2017 and 2018, 54 rain events occurred. The meteorological data with 1 h reso­ lution were obtained from an automatic weather monitoring station (HOBO U30, Onset Corp., Pocasset, MA, USA). To investigate the variation of the overall mass concentration of PM2.5 ions before, during, and after rain, fine particulate matter (PM2.5) samples were collected by high volume particle samplers manufactured by Qingdao Hengyuan S.T. Development Co., Ltd. (model: HY1000; flow rate: 1.05–1.18 m3/min). PM2.5 samples were collected for 23 h (10:00 a.m. to 9:00 a.m. the next day) on 20.32 � 25.40 cm2 Tissuquartz filters (Pall Laboratory, NY, U.S.). The average flow rate was 1.13 m3/min. Before sampling, all filters were placed in a Muffle furnace at 500 � C for 5 h to remove volatile components. Then, each filter was packaged using sterilised aluminium foil. The filters were weighed both before and after sampling after a 24 h equilibration period to calculate the mean con­ centration of PM2.5. After sampling, the filters were packaged using sterilised aluminium foil and stored at 4 � C to measure the concentration of water-soluble ions. Rainfall samples were collected with an ISCO Full-size Portable

Table 1 The detailed information of the 21 rainfall events. Date

Duration

Amount (mm)

Date

Duration

Amount (mm)

May. 22, 2017 Jun. 6, 2017 Jun. 18, 2017 Jun. 22, 2017 Aug. 8, 2017 Aug. 11, 2017 Aug. 16, 2017 Aug. 22, 2017 May. 17, 2018 May. 21, 2018 Jun. 12, 2018

08:29–14:19

15.8

05:33–08:53

29.2

08:39–11:19

21.6

17:23–19:23

4.6

16:29–16:59

1.2

16:13–21:13

12.2

09:06–15:46

10.2

08:23–14:23

7.2

21:39–22:09

12.4

02:59–09:19

36.0

18:59–22:09

20.0

04:39–12:09

20.6

15:49–16:19

3.8

05:39–07:49

2.8

01:59–22:39

21.0

05:09–06:09

9.6

09:34–11:24

2.6

09:02–10:32

2.0

03:44–08:14

2.6

22:02–22:52

1.2

15:53–17:33

7.2

Jun. 17, 2018 Jun. 30, 2018 Jul. 7, 2018 Jul. 11, 2018 Jul. 16, 2018 Jul. 24, 2018 Aug. 6, 2018 Aug. 11, 2018 Aug. 30, 2018 Sep. 1, 2018

Note: PM2.5 samples were collected one day before the rain, on the day of the rain, and one day after the rain. 2

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Sampler (ISCO 6712, TELEDYNE ISCO, Nebraska, USA), which is covered with 24 polyethylene plastic bottles of 1000 ml volume. The polyethylene board (2 m � 4 m) was used to guide the rainwater to the ISCO equipment. The instrument automatically changed the bottles every 5 min, and collected samples for 2 h during a rain episode. The collected rainfall samples were immediately taken to the laboratory for chemical component analyses. The 21 rainfall events that did not go across 24 h were selected for the interaction analysis (Table 1), and PM2.5 samples were collected one day before rain, on the day of the rain, and one day after rain. The 63 PM2.5 samples and 403 valid rainfall runoff samples were totally collected. All the PM2.5 samples were entirely before, during and after rain.

neutralization status (Fig. 1). The results showed PM2.5 and rainwater samplers were closed to neutral. To investigate the dynamic characteristics of eight ion species in rainwater during rainfall process, the averaged ion concentration at 5 min intervals presented their temporal patterns in 2017 and 2018 by a pollution rose figure (Gustavo et al., 2010; Ouyang et al., 2018, 2019). Each arrow represented a pollution indicator, the labelled value was the initial concentration for the first 5 min, and the concentration dynamic varied counter clockwise. To identify the key factor of influencing the ion species concentration in PM2.5 and rainfall during rainfall, redun­ dancy analysis was employed (Angeler et al., 2008). The redundancy analysis of PM2.5 and rainfall with meteorological factor was conducted by Canoco 5.0 software (Leps and Smilauer, 2003). The ion species in PM2.5 and rainfall were set as dependent variables, respectively. The meteorological factors were set as explanatory variables. The dependent variables and explanatory variables were imported into the Canoco 5 software to generate the result figure. The cosine value of the angle between species and environmental variables represents the relationship between them. The statistical significance was showed through permu­ tation test. The distribution of some ions in PM2.5 and rainfall were not normal through normality test in IBM SPSS Statistics 20, so Spearman correlation analysis were used. The correlation analysis of the ionic composition of PM2.5 and rainfall were conducted by R software.

2.3. Sample pretreatment and chemical analysis Each PM2.5 and blank sample filter was cut into debris and put into 50 ml centrifuge tubes. The tubes were filled with 50 ml distilleddeionised water and then ultrasonicated for 40 min. The solution and rainfall samples were filtered through a 0.45 μm glass fibre filter (dried at 400 � C before using) into 10 ml centrifuge tubes. Eight inorganic ions 2þ þ þ 2þ (SO24 , NO3 , Cl , NHþ 4 , Ca , Na , K and Mg ) were analysed by ion chromatography (IC, Dionex 600) (Yuan et al., 2003). For each 10 samples tested, one sample was randomly selected for repeated experi­ ments, and the results were compared to ensure that the error of the two monitored values was within 5%. The background values of blank 2þ þ þ 2þ sample filters for SO24 , NO3 , Cl , NHþ were 4 , Ca , Na , K and Mg 0.16, 0.22, 0.09, 0.23, 0.40, 0.09, 0.07 and 0.03 mg/L, respectively.

3. Results 3.1. Variation characteristics of PM2.5 and ion species during rainfall events A total of 237 rain hours were observed during the rain events. Ac­ cording to rainfall standards of American Meteorological Society (2019), the rain was classified into three categories: rainfall � 2.5 mm/h (level I: light rain), 2.6 � rainfall < 7.6 mm/h (level II: moderate rain), rainfall �7.6 mm/h (level III: heavy rain). The numbers for light rain, moderate rain and heavy rain accounted for 75.1%, 16.5% and 8.4% of the total rain hours, respectively. The mean rainfall intensity and standard de­ viation (SD) for these three rain levels were 0.9 � 0.7 mm/h, 4.3 � 1.6 mm/h, and 12.9 � 5.2 mm/h, respectively. The average wind speed and SD were 0.56 � 0.62 m/s during all the rainfall events, which belonged the breeze level. The circle size and colour represented the concentration of PM2.5 per hour during rain events. The concentration of PM2.5 during light rain events ranged from 3 μg/m3 to 132 μg/m3, with an average of 35.8 μg/m3 (Fig. 2a). The highest PM2.5 concentration occurred during light rain event. The average PM2.5 concentration and

2.4. Data analysis The event mean concentration (EMC) of the rainfall was adopted to characterise the concentration of each pollutant in rainfall (Smullen et al., 1999). The EMC of each pollutant in the rainfall can be calculated as follows: Pn i¼1 ðCi Vi Þ EMC ¼ P n i¼1 Vi where i is the sample number each 5 min interval, Ci is the concentration of pollutant in i (mg/L), and Vi is corresponding total rainfall volume (m3). According to the concentration of ionic species in PM2.5 and rain­ water samples, the ion balance were calculated to investigate their

Fig. 1. Ion balance of cations and anions in PM2.5 (a) and rainwater (b) samples. 3

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Fig. 2. Variation in the PM2.5 concentration with different rainfall levels (a) and characteristics of ion species in PM2.5 during the different rain periods (b).

Fig. 3. Variations of total mass concentration of each ion composition in PM2.5 before, during and after rainfall events (n ¼ 21).

4

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SD for the moderate and heavy rain events were 29.7 � 20.7 μg/m3 and 26.1 � 17.5 μg/m3, respectively. The mean PM2.5 concentration decreased by 17%–27% with the increase in rain intensity. The con­ centration of PM2.5 displayed an overall downward trend with an extension of hours into the rain. The concentration of daily PM2.5 and eight ionic species before, during and after rainfall event were measured (Fig. 2b). The value during all periods represented the mean value of before, during and after rain periods. When rain occurred, PM2.5 concentration underwent a continuous decline and decreased by about 29% on average. Compared with the concentration of PM2.5 during rainfall, the values of PM2.5 after rain showed little variation. The total mass concentration during all the periods was 12.27 μg/m3, accounting for 20% of the PM2.5 concentra­ tion (61.18 μg/m3). The total mass concentrations of eight ions during three periods exhibited similar patterns to the PM2.5. The values continuously decreased from 17.25 μg/m3 to 8.79 μg/m3 with a removal rate of 49%. The removal rates for individual events were primarily distributed from 32.48% to 86.87%. Among 21 rain events, only two cases of light rain event presented negative value. The removal rates for light, moderate and heavy rainfall were in the range of 32.48%– 83.12%, 4.77%–86.87% and 58.00%–68.28%, respectively. The results indicated that rainfall can efficiently remove the ion species in PM2.5. However, the proportion of ions in PM2.5 did not change significantly during different rain periods. There were significant differences in the variation of different ion components in PM2.5 before, during and after rainfall (Fig. 3). During the rainfall process, the concentration of all the ions was much lower than before rain period, and the removal varied between 20% and 60%. Ca2þ, in particular, had the biggest removal rate during the rainfall process, þ reaching 56%. The concentrations of NO3 , SO24 , NHþ 4 , K and Cl consistently decreased after rain, while the concentrations of Naþ, Ca2þ and Mg2þ increased by 7%, 63% and 21%, respectively. The removal rates of SO24 and Kþ during rain periods reached more than 40%, but the removal rate decreased after rain to below 20%. The removals of NO3 and NHþ 4 after rain were more than 30%, which was higher than during rain. The concentrations of Cl during and after rain periods steadily declined by 20%. For individual rain events, the removal rates for cations and anions during rain period were in the range of 34.24%– 87.96% and 42.80%–72.83%, respectively, while the values after rain were in the range of 108.92%–67.59% and 65.99%–82.77%,

Fig. 5. Redundancy analysis of the ion species in PM2.5 with driving forces during rain evens.

Fig. 6. Percentage of ions composition in PM2.5 (a) and rainfall (b).

respectively. Overall, the removal rate of cations (37.60%) during rainfall was higher than for anions (33.98%), and the rate of anions (24.31%) after rain periods was greater than for cations (14.92%). 3.2. Chemical composition characterisation in rainfall Based on the samples in two years, the pollution characteristics of the ion components in rainfall at 5-min interval were compared (Fig. 4). The ion species in rainwater showed similar variations and clearly decreased within 30 min and then fluctuated minimally. The concentration of each ion in 2018 was lower than that in 2017, particularly NHþ 4 . During the first 5 min, the NHþ 4 concentration decreased from 9.31 mg/L in 2017 to 7.08 mg/L in 2018. At 30 min later, it decreased by more than 60%. The concentration of NO3 during the first 5 min decreased from 55.88 mg/L in 2017 to 41.90 mg/L in 2018. The reduced proportion of NHþ 4 con­ centration within these two years was greater than NO3 . For Naþ, Kþ, Mg2þ and Ca2þ, the disparities in concentrations over the two years decreased in the latter period of the rainfall process. To identify the key meteorological factors affecting ion concentra­ tions in PM2.5 during rainfall, a redundancy analysis with statistical significance (p < 0.003) was conducted with the 24 h data including “before”, “during”, and “after” rain periods (Fig. 5). The relationship between species and environmental variables was represented by the cosine value of the angle between them. The longer the arrow of the environmental variable meant the stronger the impact. The concentra­ tions of NO3 , SO24 , NHþ 4 and Cl had strongly positive correlations with

Fig. 4. Temporal concentration patterns of the ionic species in rainfall in 2017 and 2018. 5

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Fig. 7. Correlation coefficients among the ionic compositions in the PM2.5 (a) and rainfall (b).

wind direction and strongly negative associations with rainfall amount, rainfall duration and relative humidity. The correlations of the con­ centrations of Kþ, Naþ and Mg2þ with pressure and wind direction were relatively weak, and their correlations with rainfall amount, rainfall duration and relative humidity were relatively strong. The concentra­ tion of Ca2þ showed a strongly positive correlation with pressure but had a poor association with rainfall, rainfall duration and relative hu­ midity. The wind speed, rainfall intensity and rainfall interval had weak influences on the ion concentration in PM2.5 during rain processes. Overall, the concentrations of ionic species in PM2.5 during rainfall events were mainly affected by rainfall amount and duration.

NO3 , and NHþ 4 ) in the PM2.5 and rainfall samples. The correlation co­ efficient among these three ions was greater than 0.73 (p < 0.01), indicating that the sources and reaction mechanisms of SO24 , NO3 and NHþ 4 were similar. The precursor substances (namely, SO2 and NOx) of SO24 and NO3 had been emitted together into the atmosphere and had the same entrance way to rainfall to generate sulfuric acid and nitric acid. The sulfuric acid and nitric acid reacted with NH3 in the air, and (NH4)2SO4 and NH4NO3 were the main bonding modes. However, the 2 correlations of NHþ 4 with SO4 and NO3 were relatively poor in the rainwater, indicating that their sources o rainwater were more compli­ cated. There was also a significantly positive correlation among Ca2þ, Mg2þ and Naþ, showing that they had similar sources and might be related to soil resuspension. The correlation coefficients of Cl with Naþ and Kþ were greater than 0.75 (p < 0.01), indicating that they had similar sources. In contrast to PM2.5, the correlation coefficients of Ca2þ with Cl , SO24 and NO3 were greater, showing that Ca2þ was mainly in the form of CaCl2, CaSO4 and Ca(NO3)2 in rainfall. Overall, the corre­ lation coefficients among the ionic species in the rainfall presented better than PM2.5. The close correlations between cations and anions showed that these ions played an important role in the neutralising effect during the rainfall process. The cluster analysis for ionic species in PM2.5 and rainfall were distinct. The ions in the rainwater were clearly clustered þ into two categories: one category included SO24 , NO3 , NHþ 4 and K , and the other category included Cl , Ca2þ, Naþ and Mg2þ. The ions in the PM2.5 samples were clustered into three categories: one category included Ca2þ, Naþ and Mg2þ, the second category included Kþ and Cl , and the third category included SO24 , NO3 , NHþ 4. To investigate the effects of ion components in rainfall, a correlation analysis of the ions on day before rainfall with on the day of rainfall was

3.3. Correlation analysis of main composition in PM2.5 and rainfall Based on the characteristics of ions in PM2.5 and rainfall samples, the disparities in the proportions of ions in PM2.5 and rainwater samples were analysed (Fig. 6). In the PM2.5 sample, the main ionic compositions 2þ included NO3 (34.73%) > SO24 (33.02%) > NHþ 4 (20.27%) > Ca (6.54%). These four ions accounted for 95% of the total ion concentra­ 2þ in tion in PM2.5. However, the percentages of NO3 , SO24 , NHþ 4 , and Ca the rainfall samples were 31.03%, 22.25%, 12.00% and 25.56%, respectively. The percentage of NO3 was the highest, followed by Ca2þ. The percentage of NO3 in rainwater was similar to that in PM2.5. The percentage of Ca2þ in rainwater samples was about 4 times that in PM2.5, indicating that coarse-grained mineral dust exhibited a significant 2 contribution to rainwater. Conversely, the percentages of NHþ 4 and SO4 in PM2.5 were 1–2 times higher in PM2.5. The correlations between the ionic components in PM2.5 and in the rainfall sample were then compared (Fig. 7). The close correlations were observed among the typical secondary inorganic ions (namely, SO24 ,

Table 2 Correlation analysis for ion species in rainfall and in PM2.5 samples. Index PMb2.5 PMd2.5 a b

SO24 0.38 0.02

NO3 0.22 0.16

Cl 0.06 0.42

NHþ 4 0.22 0.23

Ca2þ a

0.87 0.72a

Kþ

Naþ

Mg2þ

0.67 0.86b

0.62 0.46

0.44 0.60

The chemical species in PM2.5 one day before rain are represented by PMb2.5, and the chemical species in PM2.5 on the day of rain are represented by PMd2.5. Significant correlation at 0.01 level (bilateral), * Significant correlation at 0.05 level (bilateral). 6

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further conducted (Table 2). There was close correlation between Ca2þ in PM2.5 and rainfall. The correlation coefficient of Ca2þ in rainfall with that in PMb2.5 (r ¼ 0.87, p < 0.05) was more significant than for PMd2.5 (r ¼ 0.72, p < 0.05). This result indicated that the effect of PMb2.5 on Ca2þ in rainwater was more prominent than for PMd2.5. The Kþ concentration between PM2.5 and rainfall samples showed a good correlation, indi­ cating that part of the Kþ in the rainwater samples came from PM2.5. Furthermore, the correlation coefficient of Kþ in rainfall with that in PMd2.5 (r ¼ 0.86, p < 0.01) was bigger than PMb2.5 (r ¼ 0.67, p > 0.05). This result indicated that the effect of PMd2.5 on Kþ in rainwater was more prominent than for PMb2.5. In addition, the correlation coefficients of 2 NHþ 4 , NO3 and SO4 between PM2.5 and rainfall samples were quite poor 2 (r < 0.40, p > 0.05). This analysis indicated that NHþ 4 , NO3 and SO4 in rainwater were not mainly derived from the scavenging process of PM2.5 during the rain events.

PM2.5 were 0.70 and 0.40, respectively. The results show Beijing downtown is greatly influenced by anthropogenic pollution. 4.2. Scavenging effect of rainfall on PM2.5 and its compositions Rainfall with greater intensities tend to be more efficient in removing pollutants from the atmosphere (Ikeuchi et al., 2015). In this study, the mean PM2.5 concentration decreased by about 17%–27% with bigger rain intensity. This finding is attributed to the fact that the higher rainfall rates tends to have a greater number of raindrops, which im­ proves the chance of collision with particulate matter (Luan et al., 2019). However, the PM2.5 concentrations did not always decrease during rainfall and even slightly increased in some cases. The transport of external pollution also cause the increase in PM2.5 concentration. Rainfall is an effective way to reduce the PM2.5concentration and its components in the air (Chang et al., 2018). However, the washing effects of rainfall on different ionic species in PM2.5 show notable differences, which is related to the nature of the species and the different discharged load. Fig. 3 shows a good scavenging effect of rainfall on ionic species þ (20%–60%) in PM2.5. The removal rates of SO24 , NO3 , NHþ 4 and K are significant due the close relation with rainfall amount and duration. However, the characteristics of Naþ, Ca2þ and Mg2þ concentration presented a “V” shape and increase slightly after rain, which may be due to soil resuspension and slightly increased wind speed after rain. Fig. 7 shows that Naþ, Ca2þ and Mg2þ had similar source. Furthermore, these three ions were prominently influenced by the rainfall interval rather than by rainfall and rain duration. Though the concentrations of Naþ, Ca2þ and Mg2þ exhibited a rebound phenomenon, the concentration levels were still lower than before rain. The removal effect of rainfall on PM2.5 is a complicated process, affecting by the chemical properties of the different components themselves, rainfall amount, intensity, dura­ tion, interval and relative humidity (Kawamura and Sakaguchi, 1999). Therefore, the removal effects and mechanisms of rainfall on various pollutants in the air need to be further studied.

4. Discussion 4.1. Chemical species dynamics and disparities in PM2.5 and rainfall 2þ The dominant ions in PM2.5 included NO3 > SO24 > NHþ but 4 > Ca 2þ the proportion of NO3 and Ca was dominant in rainwater samples. This result is consistent with the results of previous scholars (Zhou et al., 2016). There was a great difference in the proportions of ions in the PM2.5 and rainwater samples (Fig. 6). It is worth noting that the per­ centage of Ca2þ in rainfall was 4 times higher than in PM2.5, which is mainly due to the scavenging effect of rainwater on coarse particulate matter in the atmosphere (Rajeev et al., 2016). Moreover, the increase in Ca2þ in rainfall suppress the proportions of SO24 and NHþ 4 due to the wash-out contribution of both coarse and fine particles in rainfall. The average mass concentrations of total ions in the PM2.5 samples in the sampling period are much lower than the values for 2014 in Beijing (Gao et al., 2016), which are due to the synergistic effects of meteorological factors and the air pollution control measures. For rainwater samples, the concentrations of NHþ 4 and NO3 in 2017 and 2018 exhibited dra­ matic variations compared to the concentrations in 2013 and 2014 (Ouyang et al., 2019). The NHþ 4 concentration decreased from 11.57 mg/L in 2013 to 2.45 mg/L in 2018. The ammonia emission sur­ vey in Beijing suggested that the contribution values of NH3 from the fertilizers, animal waste, human, industrial facilities, and wastewater treatments are 41%, 34%, 22%, 2%, and 1% respectively (Peng et al., 2000). According to the Beijing statistical yearbook published in 2018 (http://data.cnki.net/), the usage of fertilizers and the total number of animals in Beijing present an obvious decrease (more than 33%) from 2013 to 2017. In addition, the output of industrial products during 2013–2017 period has also decreased evidently, particularly raw coal, diesel oil and fuel oil that have relatively high emission factors (0.11 kg⋅t-fuel 1) among the ammonia emission sources (Roe et al., 2004). Moreover, the output of raw coal, diesel oil and fuel oil decreased around by 49%, 20% and 77% from 2013 to 2017, respectively. Therefore, this result also indicates that NH3 pollution has been effec­ tively controlled in Beijing, but that NOx pollution controls need to be further improved. The ratio of NO3 /SO24 has been referred to as an important indicator for measuring the relative contribution of mobile sources and coal combustion sources (Wang et al., 2005). The average ratios of NO3 /SO24 in rainwater and in PM2.5 samples retained a value more than 1 in this study, which implies that mobile sources continue to be a prominent source in Beijing downtown. Due to the change of energy structure and big number of automobiles, the SO2 emissions caused by the burning of fossil fuels decreased and NOx emissions increased significantly. The nitrogen pollution has replaced the sulphur pollution 2þ 2 as the priority concern. The equivalent ratio of (NHþ 4 þ Ca )/(SO4 þ NO3 ) has been used as an indicator of anthropogenic activity (Tang et al., 2005) and the ratios in the rainwater of Beijing was 0.73. In this 2þ 2 research, the ratios of (NHþ 4 þ Ca )/(SO4 þ NO3 ) in rainwater and

4.3. Effect of PM2.5 on chemical species in rainfall Below-cloud scavenging and in-cloud scavenging, as the two primary removal mechanisms of pollutants in the air, causing impacts on rain quality (Olszowski, 2016). Previous studies have reported the relation­ ship between chemical components in coarse particulate matter (PM10) and rainfall (Farahmandkia et al., 2010; Han et al., 2013). During the same rain event, the concentrations of ions in the initial rainwater runoff were highest and gradually decreased with rainfall duration due to the first flush effect (Fig. 4). When rainfall occurred, the washing effect caused accumulated pollutants entering the rainwater with high initial values. With the continuous scavenging effect, the pollutants accumu­ lated in the atmosphere are effectively removed and the concentration gradually declined. The concentration of ions fluctuated during the last portion of rain due to changes of the meteorological factors and trans­ port of the external pollution sources. The effect of PM2.5 on chemical species in rainwater were analysed with correlation analysis between chemical components in rainwater and PM2.5 concentrations before and during rain (Table 2). The close correlations between Ca2þ in the rainwater and PM2.5 indicates that the Ca2þ in the rainwater samples is mainly derived from the scavenging effect of rainfall on PM2.5. The effect of PMb2.5 on Ca2þ on rainwater samples is more prominent than that of PMd2.5, which is in contrast to previous research about the relationships between PM10 and rainwater (Han et al., 2013). These findings may relate to the different particle size and the disparities in scavenging effect of rain on different particulate matters. The high concentrations of Ca2þ mainly exist coarse particulate matters (Rajeev et al., 2016). Conversely, the correlation coefficient between Kþ in PM2.5 and rainwater showed the effect of PMd2.5 on Kþ in rainwater is more prominent than that of PMb2.5. The poor correlations 7

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B. Gao et al. 2 between NHþ 4 , NO3 and SO4 in rainwater and PM2.5 indicate that they are mainly from the scavenging effect of rainfall on gaseous pollutants (NH3, NO2 and SO2) rather than PM2.5.

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5. Conclusions In this study, we investigated the chemical characteristics dynamics and interaction between rainfall and PM2.5 in downtown Beijing. It was found that the concentrations and following orders of ions in PM2.5 and rainwater had obvious differences. The proportion of Ca2þ was 4 times higher in rainfall than in PM2.5, and the increase in Ca2þ in rainwater suppressed the percentage of composition of SO24 and NHþ 4 . The vari­ ations of NHþ 4 and NO3 concentrations indicated that NOx pollution need to be further controlled in Beijing. Moreover, the ratios of NO3 / 2þ 2 SO24 and (NHþ 4 þ Ca )/(SO4 þ NO3 ) in rainwater and in PM2.5 also indicated that Beijing downtown was mainly affected by mobile sources and by anthropogenic pollution. The rainfall can remove about 17%–27% PM2.5 from the air, but the removal effects of ions in PM2.5 were different. The scavenging effects on þ SO24 , NO3 , NHþ 4 and K were significant, which was mainly influenced by rainfall amount and duration. The Naþ, Ca2þ and Mg2þ concentra­ tions presented a “V” shape with the influence of rainfall interval. The PM2.5 posed a potential impact on the chemical components in rain­ water. The concentrations of ions in rainwater showed a good first flush effect. The effect of PMb2.5 on Ca2þ in rainwater samples was more prominent than that of PMd2.5, while the effect of PMd2.5 on Kþ was more 2 prominent. The NHþ 4 , NO3 and SO4 in rainwater mainly come from the scavenging effect of rainfall on gaseous pollutants (NH3, NO2 and SO2) rather than from PM2.5. This study provides evidence for the relationship between PM2.5 and rainfall, which could facilitate for air quality and diffuse pollution management in urban cities. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The research discussed in this paper obtained financial support from the National Key Research and Development Programs of China (Grant Nos. 2016YFC0206202 and 2016YFD0800503), the National Natural Science Foundation of China (Grant Nos. 41622110 and 91647105) and “the Fundamental Research Funds for the Central Universities”. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.atmosenv.2019.117000. References Apte, J.S., Marshall, J.D., Brauer, M., Cohen, A.J., 2015. Addressing global mortality from ambient PM2.5. Environ. Sci. Technol. 49, 8057–8066. 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. Angeler, D.G., Viedma, O., Sanchez-Carrillo, S., Alvarez-Cobelas, M., 2008. Conservation issues of temporary wetland Branchiopoda (Anostraca, Notostraca: Crustacea) in a semiarid agricultural landscape: what spatial scales are relevant? Biol. Conserv. 141 (5), 1224–1234. American Meteorological Society, 2019. “Rain”. Glossary of meteorology. Available online at. http://glossary.ametsoc.org/wiki/Rain. Chang, S.Y., Chou, C.C.K., Liu, S., Zhang, Y.H., 2013. The characteristics of PM2.5 and its chemical compositions between different prevailing wind patterns in Guangzhou. Aerosol Air Qual. Res. 13, 1373–1383. Chakraborty, A., Gupta, T., Tripathi, S.N., 2016. Chemical composition and characteristics of ambient aerosols and rainwater residues during Indian summer monsoon: insight from aerosol mass spectrometry. Atmos. Environ. 136, 144–155.

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