Assessment of the seasonal cycle of nitrate in PM2.5 using chemical compositions and stable nitrogen and oxygen isotopes at Nanchang, China

Atmospheric Environment 225 (2020) 117371

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Atmospheric Environment journal homepage: http://www.elsevier.com/locate/atmosenv

Assessment of the seasonal cycle of nitrate in PM2.5 using chemical compositions and stable nitrogen and oxygen isotopes at Nanchang, China Li Luo a, b, Yuan-Yuan Pan a, Ren-Guo Zhu a, Zhong-Yi Zhang a, Neng-Jian Zheng a, Yong-Hui Liu a, Cheng Liu a, Hong-Wei Xiao a, b, **, Hua-Yun Xiao a, b, * a b

Jiangxi Province Key Laboratory of the Causes and Control of Atmospheric Pollution, East China University of Technology, Nanchang, 330013, China School of Water Resources and Environmental Engineering, East China University of Technology, Nanchang, 330013, 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

� NO3 concentration and its dual isotopes in PM2.5 were observed at Nanchang for one year. � Seasonal variations of NO3 were mainly affected by NOx sources. � Production of NO3 by NO2 þ OH pathway and other pathways change seasonally.

A R T I C L E I N F O

A B S T R A C T

Keywords: PM2.5 Nitrate Stable nitrogen and oxygen isotopes Chemical compositions Seasonal cycle Nanchang

Nitrate (particulate NO3 and gas-phase HNO3) are the primary acidic ions in the atmosphere and play important roles in regional air pollution. However, quantification of the formation pathways of atmospheric nitrate are still poorly understood. Samples of PM2.5 were collected at Nanchang, China, from September 2017 to August 2018. The concentrations of water-soluble ions and the stable isotopic compositions of δ18O-NO3 and δ15N-NO3 were measured. Seasonal values of δ15N-NO3 (autumn:4.4 � 2.8‰; winter: 6.6 � 2.4‰; spring: 4.4 � 2.0‰; summer: 3.1 � 2.4‰) suggest that seasonal cycles of NO3 in PM2.5 are mainly affected by NOx sources. The highest concentrations of NO3 , aerosol liquid water content (ALWC), Cl , NHþ 4 , and the lowest concentration of OH were showed during winter. These parameters exhibited opposite trends during summer. δ18O-NO3 values in PM2.5 during autumn (79.2 � 6.7‰) and spring (73 � 6.3‰) were between those observed in winter (86.4 � 5.9‰) and summer (60.7 � 3.9‰). Values of δ18O-HNO3 endmembers produced via pathway of NO2 þ OH (PNO2þOH), pathway of N2O5 þ H2O (PN2O5þH2O) and pathways of NO3 þ VOCs, N2O5 þ Cl , ClNO3 þ H2O and 2NO2 þ H2O (Pother) were evaluated during four seasons. The possible fractional contributions of different HNO3 formation pathways to PM2.5 NO3 were assessed using the Bayesian isotope mixing model. The results show that the PNO2þOH contributes to 59 � 5% and 12 � 6% of HNO3 production during summer and winter, respectively. The possible fractional contributions of PN2O5þH2O in all seasons are comparable, but the contributions of Pother in

* Corresponding author. Jiangxi Province Key Laboratory of the Causes and Control of Atmospheric Pollution, East China University of Technology, Nanchang, 330013, China. ** Corresponding author. Jiangxi Province Key Laboratory of the Causes and Control of Atmospheric Pollution, East China University of Technology, Nanchang, 330013, China. E-mail addresses: [email protected] (H.-W. Xiao), [email protected] (H.-Y. Xiao). https://doi.org/10.1016/j.atmosenv.2020.117371 Received 8 December 2019; Received in revised form 15 February 2020; Accepted 23 February 2020 Available online 26 February 2020 1352-2310/© 2020 Elsevier Ltd. All rights reserved.

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winter are higher than those during summer. This study suggested that Pother may be important for wintertime NO3 formation and provides potential information for using chemical models to simulate atmospheric NO3 formation.

value is equal to 2/5δ18O–NO2 þ 3/5δ18O–NO3. The hydrolysis of N2O5 result in the formation of HNO3, in which δ18O–HNO3 value is lower than that of δ18O–N2O5 due to the low value of δ18O–H2O (global ranges: –36 ~ þ4‰). Similarly, according to these formation pathways (Table 1), values of δ18O–HNO3 formed by pathways of NO3 þ volatile organic carbons (VOCs), N2O5 þ Cl , ClNO3 þ H2O, and 2NO2 þ H2O are equal to the values of δ18O–NO3, δ18O–N2O5, δ18O–NO3 and δ18O–NO2, respectively. Therefore, δ18O-NO3 value is a useful tool to explore the NO3 formation pathway. Nanchang is located in the southeast of China, the yearly average number of haze pollution days at Nanchang has been approximately 100 days over the past ten years (Chen et al., 2016). Compared with region of notoriously polluted air, such as the North China Plain, where the NO3 formation mechanisms have been widely discussed (Pan et al., 2016; Li et al., 2018), the formation mechanisms of haze pollution and aerosol NO3 have not been well studied. In this study, water-soluble ions and the dual isotopic compositions of NO3 in PM2.5 were analyzed from September 2017 to August 2018. These data provide valuable infor­ mation for understanding the seasonal variations in concentrations, sources, and formation mechanisms of NO3 .

1. Introduction Aerosol nitrate (NO3 ) levels are frequently enhanced during air pollution days, and aerosol NO3 concentration during air polluted days has been approximately 10 times higher than that of non-polluted days in many Chinese cities in recent years (Liu et al., 2015; Pan et al., 2016; Tian et al., 2014; Hu et al., 2016; Shen et al., 2017; Sun et al., 2013; Wang et al., 2006; Zhang et al., 2016; Ma et al., 2017; Yang et al., 2017; Zou et al., 2018). The NO3 mass concentration percentage of particu­ lates during air polluted periods was two to six times higher than that during non-polluted periods (Pan et al., 2016; Zou et al., 2018). More­ over, nitrate could be a trigger of haze pollution in Beijing (Pan et al., 2016; Li et al., 2018). These studies suggest that NO3 is an important ionic contributor to aerosols during haze pollution. Previous studies found that it was important to examine the aerosol water-soluble ions compositions to understand the importance of aerosol NO3 and its for­ mation pathways. For example, the ionic stoichiometry among NO3 ,

2 NHþ 4 and SO4 can indicate the presence of NH4NO3 (Zhang et al., 2013; Luo et al., 2016; Lin et al., 2019). The stable isotopic compositions of δ15N-NO3 and δ18O-NO3 values can be useful for exploring the sources and formation pathways of NO3 in PM2.5. For example, values of δ15N-NOx from coal combustion (13.5 � 4.9‰; Heaton, 1990; Felix et al., 2012) were higher than those from vehicle exhausts ( 4.3 � 4.5‰; Felix and Elliott, 2014; Walters et al., 2015; Miller et al., 2017) and biomass burning (1.0 � 4.1‰; Fibiger and Hastings, 2016). The lowest observed δ15N-NOx values of 28.9 � 8.2‰ originated from soil microbial activity (Li and Wang, 2008; Felix and Elliott, 2013; 2014). The terminal product of atmospheric NOx is par­ ticulate NO3 , thus, value of δ15N-NO3 in PM2.5 can be used to qualita­ tively illustrate the sources of NO3 . Value of δ18O-NO3 can be used to assess the formation pathways of atmospheric NO3 (Hastings et al., 2003; Fang et al., 2011; Michalski et al., 2012; Altieri et al., 2013; Xiao et al., 2015). During the photo­ chemical cycle of NOx with O3 and peroxy radicals (HO2 and RO2), oxygen atoms in O3 and HO2/RO2 are integrated into the product NO2 by pathways of NO þ O3 and NO þ RO2/HO2 (Table 1). Previous studies have shown that value of δ18O–O3 (90–120‰; Thiemens and Jackson, 1990) exceed those of δ18O–O2 (23.5‰; Kroopnick and Craig, 1972), thus, δ18O–NO2 value better represent the photochemical cycling pathways of NOx-O3-HO2/RO2 (Walters and Michalski, 2016). When NO2 is further oxidized into HNO3 by OH during the daytime, δ18O–HNO3 value be decreased due to the negative value of δ18O–OH (Michalski et al., 2012). At night, NO2 is oxidized into NO3 by O3, and value of δ18O–NO3 is equal to that of 2/3δ18O–NO2 þ 1/3δ18O–O3 (Table 1). As NO3 further reacts with NO2 to produce N2O5, δ18O–N2O5

2. Materials and methods 2.1. Sampling location Samples were collected at the campus of the East China University of Technology (115.8� E, 28.7� N), Nanchang, China. Meteorological and air quality parameters at Nanchang are shown in Fig. 1. Ambient tem­ perature gradually decreased from September to January, followed by a steady increase from January to June (Fig. 1a). Relative humidity ranged from 40% to 100% throughout the year, with large fluctuations during the colder months. The insolation duration is negatively corre­ lated with the solar zenith angle, with the lowest insolation duration and highest solar zenith angle occurring in winter whereas the highest insolation duration and lowest solar zenith angle occurring during summer (Fig. 1b). Concentration of O3 in September is comparable to that in October, but rapidly decreased in November and remained low throughout the remaining winter months. The O3 concentration increased from March to June, reaching its maximum concentration in the summer (Fig. 1c). The temperature, insolation duration, solar zenith angle, and O3 concentration followed clear seasonal patterns. Concen­ trations of NO2, SO2, and PM2.5 suddenly increased in November, and gradually decreased from April to July (Fig. 1d and e). 2.2. Sampling and chemical analysis PM2.5 samples were collected using a high-volume air sampler (model KC-1000, Qingdao Laoshan Electronic Instrument General Fac­ tory Co., Ltd, China) equipped with a PM2.5 impactor at a flow rate of 1.05 m3/min. Before sampling, the TISSUQUARTZ-2500QAT-UP filters (Pall Corporation) were pre-combusted at 450 � C for 4 h. Individual PM2.5 samples were sampled over a 23.5 h period from September 20, þ 2þ þ 2017 to August 31, 2018. Water-soluble ions (NHþ 4 , Na , Mg , K ,

Table 1 Calculated δ18O values of NOy for each nitrate production pathway. NO.

Pathway

Expression

P1 P2 P3 P4 P5 P6

NO þ O3 → NO2 þ O2 NO þ RO2/HO2 → NO2 þ O2 NO2 þ O3 → NO3 NO2 þ NO3 → N2O5 NO2 þ OH → HNO3 N2O5 þ H2O → HNO3

δ18O–NO2 ¼ ϕ δ18O–O3 þ (1-ϕ) δ18O-R/HO2

P7 P8

NO3 þ VOCs → HNO3 N2 O5 ​ þ ​ Cl ​ →NO3

P9 P10

ClNO3 þ H2O → HNO3 2NO2 þ H2O → HNO3

δ18O–NO3 ¼ 2/3 δ18O–NO2 þ 1/3 δ18O–O3 δ18O–N2O5 ¼ 2/5 δ18O–NO2 þ 3/5 δ18O–NO3 δ18O–HNO3 ¼ 2/3 δ18O–NO2 þ 1/3 δ18O–OH δ18O–HNO3 ¼ 5/6 δ18O–N2O5 þ 1/6 δ18O–H2O δ18O–HNO3 ¼ δ18O–NO3 δ18O–HNO3 ¼ δ18O–N2O5

Ca2þ, NO3 , SO24 and Cl ) and stable isotopic compositions of δ15N-NO3 and δ18O-NO3 were analyzed. Detailed information regarding the watersoluble ionic analyses can be found in previous studies (Luo et al., 2019a). Briefly, one-eighth pieces of each filter were extracted in 50 mL of Milli-Q water with a specific resistivity of 18.2 MΩ cm 1, and then filtered using 0.22 μm filter and frozen at 20 � C until ionic analysis.

δ18O–HNO3 ¼ 2/3 δ18O–NO2 þ 1/3 δ18O–O3 δ18O–HNO3 ¼ δ18O–NO2

2

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Concentrations of water-soluble ions were analyzed using an ion chro­ matograph system (model Dionex ICS-2100, Thermo Fisher Scientific, þ 2þ þ Inc., USA). The detection limits of NO3 , SO24 , Cl , NHþ 4 , Na , Mg , K , 2þ Ca were 0.35 μmol/L, 0.12 μmol/L, 0.14 μmol/L, 0.07 μmol/L, 0.0001 μmol/L, 0.1 mol/L, 0.05 μmol/L, and 0.002 μmol/L, respectively. Values of δ18O-NO3 (δ18O-NO3 (‰) ¼ (Rsample/Rstandard 1) � 1000, where R is the ratio of 18O/16O in the sample and standard) and δ15NNO3 (δ15N-NO3 (‰) ¼ (Rsample/Rstandard 1) � 1000, where R is the ratio of 15N/14N in the sample and standard) were analyzed following the denitrifier method (Sigman et al., 2001; Casciotti et al., 2002). In brief, NO3 in the extract was converted to N2O by cultured denitrifying bacteria (Pseudomonas aureofaciens, ATCC 13985) that lack the N2O reductase enzyme and can quantitatively convert NO3 into N2O. Masses 44, 45 and 46 from N2O were then measured to determine δ15N and δ18O

δ18 O 18

δ O

NO3

� PM2:5

​ ¼ ​ f Pð5Þ ​ � δ18 O 18

ðHNO3 ÞPð7Þ ​ þ ​ f Pð8Þ � ​ δ O

þ ​ f Pð10Þ ​ � δ18 O

ðHNO3 ÞPð5Þ ​ þ ​ f Pð6Þ ​ � δ18 O ðHNO3 ÞPð8Þ ​ þ ​ f Pð9Þ � ​ δ18 O

values of gaseous N2O using a GasBench-II coupled with a continuous-flow isotope ratio mass spectrometer (IRMS; Thermo Fisher DELTA V advantage, Thermo Fisher Scientific, Inc., USA). International nitrate reference materials, USGS32, USGS34, USGS35, IAEA-N3 €hlke et al., 2003) and a laboratory working nitrate standard were (Bo used for data calibration. The standard deviations for the replicates of the standards in our method were better than 0.2‰ for δ15N-NO3 and 0.5‰ for δ18O-NO3 . The steps of this method were detailed by Luo et al. (2018; 2019b). 2.3. δ18O-NO3 to quantify nitrate formation pathway Value of δ18O-NO3 in PM2.5 was determined by the contributions of different HNO3 formation pathways, δ18O-NO3 value in PM2.5 can be represented by equation (1):

ðHNO3 ÞPð6Þ ​ þ ​ f Pð7Þ � ðHNO3 ÞPð9Þ ​

(1)

ðHNO3 ÞPð10Þ

Fig. 1. Seasonal variations of (a) temperature and relative humidity (RH), (b) insolation duration and solar zenith angle, (c) concentrations of O3 and CO, (d) concentrations of NO2 and SO2, and (f) concentration of PM2.5 at Nanchang from September 2017 to August 2018. Temperature and relative humidity were downloaded from http://www.weatherandclimate. info, and the concentrations of O3, CO, NO2, SO2, and PM2.5 were downloaded from www.aqistudy.cn. 3

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cik ​ � ​ N ​ μck ; Σ ck

where δ18O-(HNO3)P(5), δ18O-(HNO3)P(6), δ18O-(HNO3)P(7), δ18O(HNO3)P(8), δ18O-(HNO3)P(9), and δ18O-(HNO3)P(10) are the δ18O–HNO3 values produced by pathways of NO2 þ OH, N2O5 þ H2O, NO3 þ VOCs, N2O5 þ Cl , ClNO3 þ H2O and 2NO2 þ H2O (Table 1), respectively, and fP(5), fP(6), fP(7), fP(8), fP(9), and fP(10) are the fractional contributions of HNO3 generated from these formation pathways, with fP(5) þ fP(6) þ fP(7) þ fP(8) þ fP(9) þ fP(10) ¼ 1. Given the overlap of δ18O–HNO3 values formed by P(7)–P(10) (detailed discussion in Section 4.3), pathways of NO3 þ VOCs, N2O5 þ Cl , ClNO3 þ H2O and 2NO2 þ H2O are defined as the Pother in this study. Therefore, equation (1) can be simplified to fP(5) � δ18O-(HNO3)P(5) þ fP(6) � δ18O-(HNO3)P(6) þ fPother � δ18O(HNO3)Pother ¼ 1. Values of f can be derived by the Bayesian isotope mixing model, which has been widely used in the field of stable isotopes to determine the distribution probability for the contribution of different sources to a mixture (Parnell and Jackson, 2013). Owing to lack observational data regarding the NO, HO2 and RO2 concentrations, and based on previous studies (Alexander et al., 2009, 2019; Morin et al., 2011; He et al., 2018), the annual ϕ (Table 1) was assumed to be 0.85 in this study.

εi~N (0, Σ

Daily air mass backward trajectories were analyzed using the Na­ tional Oceanic and Atmospheric Administration (NOAA) Hybrid Singleparticle Lagrangian Integrated Trajectory (HYSPLIT) model with a reg­ ular 1� � 1� longitude-latitude grid. The meteorological database used in the HYSPLIT model included data from the National Center for Envi­ ronmental Prediction and the National Center for Atmospheric Research Global Reanalysis gridded meteorological data archives. Three-days backward trajectories were modeled at an elevation of 500 m above sea level. Details about the HYSPLIT model can be found at https://r eady.arl.noaa.gov/HYSPLIT.php.

(2) 2.7. ISORROPIA-II model

5

where J1: J0 ¼ 8 � 10 , ζ ¼ 2.2, χ ¼ 0.85 � SZA; J2: J0 ¼ 6.4 � 10 , ζ ¼ 0.67, χ ¼ 0.86 � SZA, with J0 in s 1; C1 ¼ 1.5 � 1010 molecules cm 3 s, C2 ¼ 1.75 � 1010 molecules cm 3 s, and SZA is the solar zenith angle at Nanchang. Value of δ18O–OH were calculated following the method of Walters and Michalski (2016), δ18O–OH ¼ δ18O–H2O(g) þ 1000(18αX/Y – 1) 1000

18

αX=Y

� A B C D � 108 þ ​ � 106 þ � 104 1 ¼ � 1010 ​ þ T4 T3 T2 T

where X is H2O(g), Y is OH, A, B, C and D are equal to 2.1137, 2.5653 and 0.5941 respectively. 18

18

18

δ O–H2O(g) ¼ δ O–H2O(l) þ 1000( αX/Y – 1)

ISORROPIA-II, is a thermodynamic equilibrium model for the Kþ2 þ Ca -Mg2þ-NHþ 4 - Na -SO4 -NO3 -Cl -H2O aerosol system (Fountoukis and Nenes, 2007) and was utilized to calculate the aerosol liquid water content (ALWC) (Guo et al., 2015). Input parameters for the ISO­ RROPIA-II model were the concentrations of water-soluble ions, relative humidity, and ambient temperature. In this study, the ISORROPIA-II model was computed as a “forward problem” and determined using the “metastable” mode. The detailed information regarding the ISO­ RROPIA-II model can be found in Fountoukis and Nenes (2007). 2þ

(3) (4) 3.8026,

3. Results

(5)

3.1. Concentrations of water-soluble ions, ALWC, NOR and OH

where δ18O–H2O(l) is the observed value of δ18O–H2O from Changsha (Huang et al., 2015; Zhou et al., 2019) which on the same latitude with Nanchang. Here, 1000(18αX/Y – 1) is calculated following Michalski et al. (2012). 1000

18

αX=Y

� 0:35041 � ​ 109 1 ¼ T3

1:6664 6:7123 � 106 þ � ​ 103 T2 T2

The daily concentrations of water-soluble ions (NO3 , SO24 , Cl , þ 2þ þ 2þ NHþ 4 , Na , Mg , K and Ca ) in PM2.5 at Nanchang were obtained from September 2017 to August 2018 at Nanchang. The total concen­ trations of water-soluble ions ranged from 3.5 to 113.2 μg/m3 throughout the year (Fig. 2). Concentrations of NO3 (ranging from 0.6 μg/m3 to 62.5 μg/m3, with an annual average of 9.4 � 10.3 μg/m3), SO24 (varying from 1.9 μg/m3 to 29.5 μg/m3, with a mean value of 10.3 3 3 � 4.8 μg/m3) and NHþ 4 (fluctuating between 0.4 μg/m to 28.2 μg/m , averaging 5.2 � 3.7 μg/m3) are the most abundant species. Annual av­ erages of NO3 , SO24 and NHþ 4 contents accounted for 26.6 � 14.6%, 41.3 � 13.0% and 18.4 � 4.2% of the total water-soluble ions for the year, respectively (Fig. S1). Ion stoichiometry results indicate a strong correlation between total anions (NO3 , SO24 and Cl ) and total cations þ 2þ þ 2þ (NHþ 4 , Na , Mg , K and Ca ) during all seasons (Fig. S2). The ratios of total anions to total cations follow an approximately linear correlation of 1:1, which indicate a strong ionic charge balance, thereby validating our data quality. In addition, excluding SO24 and Mg2þ, water-soluble þ þ 2þ ions (NO3 , Cl , NHþ 4 , Na , K and Ca ) exhibited similar seasonal patterns, with high concentrations during winter and low concentrations during summer (Fig. S3). ALWC show seasonal variation, with noticeable increases from autumn to winter, and decreases from spring through summer (Fig. 3a).

7:685 (6)

where X is H2O(g) and Y is H2O(l). 2.5. Bayesian isotope mixing model The Bayesian isotope mixing model (Stable Isotope Analysis in R, or SIAR) can be used to determine the probability distribution of different source contributions to a mixture and can explicitly incorporate the uncertainties associated with multiple sources and measured isotope signatures (Parnell and Jackson, 2013). The model formulation of the Bayesian, N-mixture measurements on J isotopes with k source con­ tributors involves the following formulas: � Yi ​ � ​ N ​ pTi ðsi ​ þ ​ ciÞ; ​ Σ ​ (7) sik ​ � ​ N μsk ; Σ sk

�

(10)

2.6. Air mass backward trajectory

Concentrations of OH were evaluated following the method of Hanisco et al. (2001), 5

(9)

where Yi is the J-vector of the isotope values for mixture i, which rep­ resents the isotope measurement in mixture i for isotope j; sik is the Jvector of the isotope sources values for mixture i on source k; cik is the Jvector of the trophic enrichment factor values for mixture i on source k; and εi is the J-vector of the residual terms for mixture i with a covariance matrix. The information regarding the SIAR package is available from the CRAN website (http://cran.r-project.org/web/packages/siar/siar. pdf).

2.4. Concentration of hydroxyl radicals (OH) and δ18O–OH value

[OH] ¼ C1J1 þ C2J2, and J ¼ J0 exp[-ζ(sec(χ) – 1]

�

(8) 4

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2þ þ þ 2þ Fig. 2. Daily concentrations of NO3 , SO24 , NHþ at Nanchang from September 2017 to August 2018. 4 , Ca , K , Cl , Na , and Mg

A similar pattern is also for the nitrogen oxidation ratio (NOR, NOR ¼ mNO3 /(mNO3 þ nNO2), m and n refer to molar concentration), have been widely used to evaluate the degree of the conversion capability of NO2 to NO3 (Sun et al., 2006; Zhao et al., 2013; Zheng et al., 2015; Zhang et al., 2016; Ma et al., 2017). Values of NOR are ranged from 0.01 to 0.55, with an average value of 0.13 � 0.10 during the entire year observation. These values are comparable to that from cities in central China (e.g., 0.14 � 0.09 in Nanjing; Lin et al., 2019), but is lower than that (0.32 � 0.18) in Beijing (Luo et al., 2019b). In addition, NOR also displays seasonal cyclicity, with the highest value in winter and lowest value in summer (Fig. 3b). The OH concentration during winter is 1.4 � 106 molecules/cm3 (Fig. 3c), which is similar to wintertime in-situ observed concentration of OH at Tokyo (Kanaya et al., 2007). Howev­ er, during summer, the calculated OH concentration (2.2 � 106 mole­ cules/cm3, Fig. 3c) is two to five times lower than the observed data in the North China Plain (Tan et al., 2017). The estimated values of δ18O–OH at Nanchang ranges from 54.6‰ to 46.5‰ (Fig. 3d), which is similar to those over the United States (Michalski et al., 2012).

2013), and the Antarctica ( 60.8–13‰; Frey et al., 2009; Walters et al., 2019). Monthly δ15N-NO3 values also exhibite seasonal variations, with visible increase from autumn to winter, then gradual decreases from winter to spring and summer (Fig. 4b); similar seasonal patterns also be reported in previous studies (Elliott et al., 2009; Zong et al., 2017; Song et al., 2019). Monthly δ18O-NO3 values gradually increase from October (75.2 � 5.5‰) to December (89.1 � 3.6‰), then slowly decrease from January (86.9 � 7.0‰) to August (59.8 � 3.5‰) (Fig. 4c). The strong correlation between δ18O-NO3 value and temperature (R2 ¼ 0.72), and between δ18O-NO3 value and solar zenith angle (R2 ¼ 0.73) (Fig. S4) may be linked to seasonal variations in δ18O-NO3 . This seasonal variability in δ18O-NO3 values (highest during winter and lowest during summer) is similar to those observed in previous studies conducted in the Northern Hemisphere (Fig. S5). 4. Discussion 4.1. Seasonal cycles of NO3 in PM2.5

3.2. NO3 concentration

The correlations between NO3 concentrations and δ15N-NO3 values (R2 ¼ 0.78; Fig. 5a) indicate that the sources of NOx play an important role in the seasonal cycle of NO3 . Observed winter δ15N-NO3 values (6.6 � 2.4‰) at Nanchang in this study were significantly lower than those previous observed aerosol δ15N-NO3 values over the northern cities of China during winter ranged from 10.9 � 3.5–15.5 � 2.5‰ (Song et al., 2019; Luo et al., 2019b). The air mass backward trajectory anal­ ysis indicates that most of the winter air masses at Nanchang originate from areas of northwestern China where heating is reliant on coal (Fig. S6), together with high δ15N-NOx value from coal combustion (5.2–25.6‰; Heaton, 1990; Felix et al., 2012), suggesting that the air mass transport of NOx from northern China to Nanchang could be an important source of NO3 during winter. During summer, the low values of δ15N-NO3 indicate NOx sources with low δ15N signal. The air mass backward trajectories show that air masses during summer at Nanchang are mainly derived from areas located southeast of China (Fig. S6) where with low δ15N-NO3 value. For example, summertime δ15N-NO3 values from Guangdong Province ranged from 0.4–4.1‰ (Fang et al., 2011; Chen et al., 2019), considerably lower than those in north China (Song et al., 2019). In addition, more negative δ15N-NOx from soil emissions during summer months may lower the δ15N-NO3 in PM2.5 (Zong et al., 2017; Song et al., 2019). The well correlations between NO3 concentrations and values of NOR (R2 ¼ 0.87; Fig. 5b) indicate that the secondary NO3 formation may be responsible for the PM2.5 NO3 seasonal cycle. During winter, values of NOR range from 0.19 � 0.09 to 0.26 � 0.13, which are significantly higher than those during summer (0.05 � 0.02 to 0.08 � 0.05; Fig. 3b). These data suggest that the conversion capability of NO2 to NO3 at Nanchang was higher during the winter than the summer.

In autumn, NO3 concentrations sharply increase from 2.8 � 1.7 μg/ m3 in September to 23.4 � 12.5 μg/m3 in November, maintain high values throughout winter (15.5 � 8.7–22.3 � 8.4 μg/m3), then gradually decrease from spring (9.9 � 6.2–3.4 � 2.7 μg/m3) to summer (1.4 � 0.5–2.3 � 1.2 μg/m3) (Fig. 4a). Concentration ranges and seasonal variations of NO3 in this study were similar to previously reported for aerosol NO3 sampled at Beijing (Song et al., 2019), Ningbo (Zhang et al., 2018) and Wuhan (Huang et al., 2016). A similar pattern is also observed for the monthly percentages of NO3 in the total water-soluble ions, with ranges of 14.6 � 6.9–46.4 � 7.4% in autumn, increasing to 37.1 � 7.4–45.4 � 4.3% in winter, then beginning to decrease in spring to 16.6 � 8.0–33.5 � 9.0%, and finally reaching lower levels of 9.4 � 3.7–15.6 � 5.3% during summer (Fig. S1). Such variation in the seasonal percentages of NO3 to the total water-soluble ions was also observed at Nanjing (Lin et al., 2019). 3.3. Dual isotopic compositions Values of δ15N-NO3 range from 1.9‰ to 13.8‰ (Fig. 4b), which are approximately consistent with previous aerosol δ15N-NO3 measure­ ments sampled at Beihuangcheng Island ( 1.7–24.0‰; Zong et al., 2017), Beijing ( 2.3–21.2‰; Song et al., 2019; Luo et al., 2019b), Gosan, Jeju Island (1.7–21.6‰; Kundu et al., 2010), and the north­ eastern United States ( 9.5–14.1‰ with a mean value of 6.8‰; Elliott et al., 2009). These values are significantly higher than δ15N-NO3 values observed in remote regions, such as the Gulf of Aqaba ( 6.9–1.9‰; Wankel et al., 2010), the west coast of Spitsbergen, Svalbard ( 17 � 11‰; Morin et al., 2009), the North Atlantic ( 12–5.1‰; Gobel et al., 5

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Fig. 3. Calculated monthly (a) aerosol liquid water content (ALWC), (b) nitrogen oxidation ratio (NOR), (c) OH concentration and (d) δ18O–OH value from September 2017 to August 2018 at Nanchang. The large boxes represent the inter-quartile range from the 25th to 75th percentiles. The whiskers extend upward to the 90th and downward to the 10th percentiles.

Spring and autumn are transitional seasons with air mass backward trajectories indicating contributions from both the southern and north­ ern of China (Fig. S6), resulting in values of NOR and δ15N-NO3 are between those observed in winter and summer (Figs. 3b and 4b).

4.2. Exploration of NO3 seasonal formation based on the chemical compositions The positive relationships between NOR and ALWC (R2 ¼ 0.84; Fig. 5c), and between NO3 and ALWC (R2 ¼ 0.88; Fig. 5d) imply the 6

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Fig. 4. Monthly concentration of (a) NO3 , values of (b) δ15N-NO3 and (c) δ18O-NO3 in PM2.5 at Nanchang from October 2017 to August 2018. The large boxes represent the inter-quartile range from the 25th to 75th percentiles. The whiskers extend upward to the 90th and downward to the 10th percentiles.

increasing importance of HNO3 formation via pathways that involve H2O, such as N2O5 þ H2O, 2NO2 þ H2O and ClNO3 þ H2O. A study comparing haze-polluted and non-polluted days indicated that NOR was positively related to the relative atmospheric humidity, and the authors of the study attributed the high NO3 concentrations during hazepolluted days to the heterogeneous formation of NO3 (Zheng et al., 2015). In our study, the noticeably higher ALWC during winter than those during summer (Fig. 3a) suggest that more NO3 was produced by formation pathways that involved H2O during the cooler months. Be­ sides, seasonal concentrations of Cl also exhibite positive relationships with NOR (R2 ¼ 0.76; Fig. 5e) and NO3 (R2 ¼ 0.93; Fig. 5f), implying that HNO3 formation pathways that involved Cl , such as N2O5 þ Cl and ClNO3 þ H2O, could have produced NO3 at Nanchang. By simul­ taneously measuring concentrations of N2O5 and ClNO2 using a high-resolution time-of-flight chemical ionization mass spectrometer at Beijing, Zhou et al. (2018) found high yields of ClNO2 (0.1–0.35) which can indicate the reaction of N2O5 þ Cl →NO3 þ ClNO2. A study quantifying biomass-burning aerosol during the night found that approximately 10% of N2O5 was converted into NO3 via the N2O5 þ Cl pathway (Ahern et al., 2018). These findings indicate that HNO3 for­ mation pathways involving Cl can not be ignored during winter at

Nanchang. In addition, the negative relationships between observed NOR and OH (R2 ¼ 0.96; Fig. 5g), and between NO3 and OH (R2 ¼ 0.80; Fig. 5h) indicate that pathway of NO2 þ OH is likely to dominate HNO3 formation during summer when the OH concentration is highest (Fig. 3c) and the insolation duration is longest (Fig. 1b). HNO3 formed by various pathways may react with the gas phase NH3 to form NH4NO3. NH3 is the major alkaline species that neutralizes HNO3 and H2SO4 in the atmosphere. As SO24 is fully neutralized by 2 2 þ þ þ NHþ 4 , the excess NH4 (excess-NH4 ¼ (NH4 /SO4 – 1.5) � SO4 ; Pathak et al., 2009) can be used to further neutralize NO3 . Therefore, the

2 relationship between the molar ratios of NO3 /SO24 and NHþ 4 /SO4 may indicate the degree of particulate NO3 formation under different NHþ 4 levels (Pathak et al., 2009; Tao et al., 2016). Fig. 6a shows a scatter plot 2 2 þ of NHþ 4 against with SO4 , where all ratios of SO4 /NH4 tend to be below 1:2 during autumn, winter and spring, indicative of the presence 2 2 þ of excess NHþ 4 . The molar ratios of NO3 /SO4 against NH4 /SO4 are shown in Fig. 6b. The positive relationship between the molar ratios of 2 þ NO3 /SO24 and NHþ 4 /SO4 under NH4 -rich regimes suggests that aero­ 2 sol NO3 began to form when the molar ratio of NHþ 4 /SO4 exceeded 1.5 (Pathak et al., 2009). Fig. 6c displays NO3 against excess-NHþ 4 . The

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4.3. Partitioning NO3 seasonal formation using δ18O The seasonal variations in δ18O-NO3 values observed in this study are consistent with the previous observations of atmospheric δ18O-NO3 values in the northern hemisphere (Fig. S5). These variations are largely attributed to the differences in NO3 formation pathways during different seasons (Hastings et al., 2003; Fang et al., 2011; Michalski et al., 2012; Altieri et al., 2013; Xiao et al., 2015). Atmospheric δ18O-NO3 value reflects the NOx (NO þ NO2) photochemical cycle with O3 and RO2/HO2, where NO2 is subsequently oxidized into HNO3 by different formation pathways that are contained different δ18O signals from O3, H2O and OH. Based on the formation pathways (Table 1), the estimated values of δ18O–NO2, δ18O–NO3, and δ18O–N2O5 are 92.7 � 7.7‰, 97.0 � 8.2‰ and 95.0 � 8.0‰, respectively (Fig. S7), which are close to the upper limit of observed δ18O-NO3 values in PM2.5. Owing to the low value of δ18O–OH (Fig. 3d) result in the HNO3 formed by pathway of NO2 þ OH with the lowest value of δ18O–HNO3 (Fig. 7a). The hydrolysis of N2O5 to form HNO3 involved the oxygen atom in H2O, the values of δ18O–HNO3 formed by pathway of N2O5 þ H2O (PN2O5þH2O) range from 66.7‰ to 89.2‰, with an annual average of 78.2 � 6.9‰ (Fig. 7b), which are lower than the values of δ18O–N2O5. HNO3 is formed by pathways of NO3 þ VOCs, N2O5 þ Cl , ClNO3 þ H2O and 2NO2 þ H2O inheriting oxygen atoms from NO2, NO3 and N2O5, thus, values of δ18O–HNO3 formed by pathways NO3 þ VOCs, N2O5 þ Cl , ClNO3 þ H2O, and 2NO2 þ H2O could be equal to values of δ18O–NO2, δ18O–NO3 and δ18O–N2O5. δ18O-NO3 values during summer range from 50.7‰ to 70.5‰ (with an average value of 60.7 � 3.9‰; Fig. 4c), which are close to value of δ18O–HNO3 formed by pathway of NO2 þ OH (Fig. 7a), indicating that NO3 was mainly formed by NO2 þ OH pathway during sumemr. During winter, values of δ18O-NO3 vary from 73.4‰ to 97.7‰ (with an average value of 86.4 � 5.9‰), which are within the ranges of δ18O–HNO3 formed by pathway of N2O5 þ H2O (PN2O5þH2O) and pathways of NO3 þ VOCs, N2O5 þ Cl , ClNO3 þ H2O, and 2NO2 þ H2O (Pother), suggesting that NO3 were largely produced by these formation pathways. Based on the estimated δ18O–HNO3 values produced by pathways of PNO2þOH, PN2O5þH2O and Pother, we evaluated the possible fractional contributions of HNO3 formed by PNO2þOH, PN2O5þH2O and Pother to NO3 in PM2.5 using the Bayesian isotope mixing model. The possible frac­ tional contributions of HNO3 formation pathways to NO3 in PM2.5 are shown in Fig. 8. There are clear seasonal patterns in the fractional contributions of the NO2 þ OH pathway, fNO2þOH, with the lowest fNO2þOH occurring in winter (3 � 3 to 12 � 6%, with an average 7.5 � 3%) and highest occurring in summer (51 � 6% to 64 � 4%, with an average 59 � 3%; Fig. 8a). The similar patterns of fNO2þOH (Fig. 8a), insolation duration (Fig. 1b) and concentration of OH (Fig. 3c) indicate that NO2 þ OH pathway was the main HNO3 formation pathway during summer. The possible fractional contributions of N2O5 þ H2O, fN2O5þH2O, are 39 � 18% in autumn, 34 � 13% in winter, 35 � 18% in spring, and 28 � 14% in summer, with an annual mean of 33 � 15% (Fig. 8b), which are similar to those at Beijing (41 � 10%) assessed based on the Δ17O-NO3 in PM2.5 and the Bayesian isotope mixing model (Wang et al., 2019). Moreover, no significant seasonal variation in fN2O5þH2O was observed in our study (Fig. 8b) and a previous study (Wang et al., 2019). These results suggest that the N2O5 þ H2O pathway is likely an important at­ mospheric NO3 formation pathway in every season. The monthly possible fractional contributions produced by pathways of NO3 þ VOCs, N2O5 þ Cl , ClNO3 þ H2O and 2NO2 þ H2O (Pother) are 12 � 8% ~ 66 � 7% at Nanchang (Fig. 8c). Surprisingly, the fPother in December (66 � 7%) and January (64 � 11%) are double value of fN2O5þH2O. A study on haze pollution events in Beijing reported that the NO3 þ VOCs and N2O5 þ Cl pathways contribute to 0–70% (average 16–56%) of the HNO3 formation (He et al., 2018). This suggests that Pother may be important for NO3 formation during periods of air

Fig. 5. Scatter plots of monthly (a) NO3 versus δ15N-NO3 , (b) NOR versus NO3 , (c) ALWC versus NOR, (d) ALWC versus NO3 , (e) Cl versus NOR, (f) Cl versus NO3 , (g) OH versus NOR and (h) OH versus NO3 . 3 excess-NHþ 4 concentrations range from 105.8 to 1106.7 nmol/m , and þ only 10% of the excess-NH4 data are below zero throughout the year, suggesting that aerosol NO3 was mainly produced under excess-NHþ 4 at Nanchang. In particular, NHþ 4 was available for NO3 formation in all samples during winter; however, during summer, there was only 76% þ excess-NHþ 4 , indicating a somewhat deficient NH4 reservoir for NH4NO3 formation. Additionally, the relationship between NO3 and excess-NHþ 4 is close to a 1:1 linear correlation (Fig. 6c), suggesting that a neutrali­ zation reaction between NH3 and HNO3 likely occurred under NHþ 4 -rich regimes. For the transition seasons (spring and autumn), the neutrali­ zation reaction between NH3 and HNO3 is expected to be a combination of summer and winter processes.

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2 2 2 þ Fig. 6. Scatter plots of (a) daily concentrations of NHþ 4 versus SO4 , (b) molar ratios of NO3 /SO4 versus NH4 /SO4 , and (c) concentrations of NO3 against with excess-NHþ . 4

Fig. 7. Evaluated values of (a) δ18O–HNO3 formed by NO2 þ OH pathway (PNO2þOH) and (b) δ18O–HNO3 formed by N2O5 þ H2O pathway (PN2O5þH2O) at Nanchang from September 2017 to August 2018. The large boxes represent the inter-quartile range from the 25th to 75th percentiles. The whiskers extend upward to the 90th and downward to the 10th percentiles.

pollution. Additionally, the fractional contributions of fPother in December and January are five times higher than those in June and July (Fig. 8c), further suggesting that Pother can not be ignored when analyzing NO3 formation at Nanchang during winter. However, this conclusion requires caution. First, the value of the gas-phase δ18O–NO2 cannot be directly measured under current analytical techniques, thus there are uncertainties in δ18O–NO2 value calculated using the δ18O–NO2 ¼ ϕ δ18O–O3 þ (1 ϕ) δ18O–O2 equation (Michalski et al., 2012). Observations of NO, NO2, and O3 concentrations, together with oxygen isotope fractionation equilibrium among NO–O3–NO2, may improve this issue (Walters and Michalski, 2016). Second, the formation mechanisms of 2NO2 þ H2O are still unclear (Finlayson-Pitts et al., 2003; Yabushita et al., 2009; Gustafsson et al., 2008) and may affect the calculation of the δ18O–HNO3 value. Third, the overlap of δ18O-NO3 restricts the further investigation of the fractional contributions of NO3 þ VOCs, N2O5 þ Cl , ClNO3 þ H2O and 2NO2 þ H2O pathways. More

studies combining stable oxygen isotopic composition and chemical models may provide new insight in the future. 5. Conclusion PM2.5 samples were collected from September 2017 to August 2018 at Nanchang. Water-soluble ions and dual isotopic compositions of NO3 were analyzed to evaluate the seasonal cycling of NO3 in PM2.5. Sea­ sonal variations of NO3 are mainly affected by NOx sources, however, secondary NO3 formation is also important for seasonal NO3 produc­ tion. Values of δ18O–HNO3 formed by different pathways were evalu­ ated, and the Bayesian isotope mixing model results indicate that pathway of NO2 þ OH may contribute to 59 � 3% of the resultant NO3 formation during summer. During winter, pathways of NO3 þ VOCs, N2O5 þ Cl , ClNO3 þ H2O and 2NO2 þ H2O likely dominate NO3 for­ mation at Nanchang. This may be an important finding that HNO3 9

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Fig. 8. Estimated possible fractional contributions of NO3 formed by (a) pathway of NO2 þ OH (PNO2þOH), (b) pathway of N2O5 þ H2O (PN2O5þH2O), and (c) pathways of NO3 þ VOCs, N2O5 þ Cl , ClNO3 þ H2O and 2NO2 þ H2O (Pother) from October 2017 to August 2018. Grey points are the percentage data (n ¼ 30000) from the SIAR model. The large boxes represent the inter-quartile range from the 25th to 75th percentiles. The whiskers extend upward to the 90th and downward to the 10th percentiles.

formation pathways of NO3 þ VOCs, N2O5 þ Cl , ClNO3 þ H2O and 2NO2 þ H2O could be considered when modeling atmospheric NO3 formation during winter at Nanchang. Additionally, the uncertainties of possible fractional contributions estimated by the Bayesian isotope mixing model nend to be improved.

Acknowledgments This research was supported by the National Natural Science Foun­ dation of China (41763001 and 41425014), Doctoral Scientific Research Foundation of East China University of Technology (DHBK2016105), Science and technology project of the Jiangxi Provincial Department of Education (GJJ160580).

Declaration of competing interest

Appendix A. Supplementary data

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.

Supplementary data to this article can be found online at https://doi. org/10.1016/j.atmosenv.2020.117371.

CRediT authorship contribution statement

References

Li Luo: Conceptualization, Writing - original draft, Writing - review & editing. Yuan-Yuan Pan: Writing - original draft. Ren-Guo Zhu: Writing - original draft. Zhong-Yi Zhang: Methodology, Writing original draft. Neng-Jian Zheng: Methodology, Writing - original draft. Yong-Hui Liu: Writing - original draft. Cheng Liu: Writing - original draft. Hong-Wei Xiao: Conceptualization, Writing - original draft. HuaYun Xiao: Conceptualization, Writing - original draft.

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