Seasonal Variations and Source Apportionment of Water-Soluble Inorganic Ions in PM2.5 in Nanjing, a Megacity in Southeastern China

Journal of Atmospheric Chemistry https://doi.org/10.1007/s10874-019-09388-z

Seasonal variations and source apportionment of water-soluble inorganic ions in PM2.5 in Nanjing, a megacity in southeastern China

Xiaoyu Zhang1 & Xin Zhao 1 & Guixiang Ji1 & Rongrong Ying1 & Yanhong Shan1 & Yusuo Lin1

Received: 17 September 2018 /Accepted: 8 February 2019/ # Springer Nature B.V. 2019

Abstract Daily PM2.5 samples were collected in Nanjing, a megacity in southeastern China, for a period of one-half of a month during every season from 2014~2015. Mass concentrations of nine − − 2− − + + + 2+ 2+ water soluble inorganic ions (F ,Cl,SO4 ,NO3 ,Na,NH4 ,K,Mg and Ca )were determined using ion chromatography to identify the chemical characteristics and potential −3 sources of PM2.5. The mass concentrations of daily PM2.5 ranged from 31.0 to 242.9 μgm , with an annual average and standard deviation of 94.4 ± 31.1 μgm−3. The highest seasonal −3 average of PM2.5 concentrations was observed during winter (108.5 ± 31.8 μgm ), and the lowest average was observed during summer (85.0 ± 22.6 μgm−3). The annual average concentration of total water soluble inorganic ions was 39.82 μgm−3, accounting for 44.4% of the PM2.5. The seasonal variation in water soluble inorganic ions in PM2.5 reached its maximum during autumn and reached its minimum during spring. Sulfate, nitrate and ammonium were the dominant water soluble inorganic species, with their combined proportion of 82.0% of the total water soluble inorganic ions and 36.8% of the fine particles. Seasonal variations in aerosol acidity and chemical forms of secondary inorganic ions were discussed. − 2− The average ratio of NO3 /SO4 was 0.95. According to the results of principal component analysis, secondary sources, burning processes, and airborne dust were the dominant potential sources of PM2.5 in Nanjing.

Keywords Fineparticles.Watersolubleinorganicions.Seasonalvariation.Principalcomponent analysis . Nanjing FOR APPROVAL

* Xin Zhao [email protected]

1 Nanjing Institute of Environmental Science, Ministry of Ecology and Environment, Nanjing 210042, China Journal of Atmospheric Chemistry

1 Introduction

Rapid urbanization and industrialization during recent decades have caused a deterioration of atmospheric particle pollution in China. Artificial factors, such as urban construction, energy consumption and vehicle exhaust, combined with natural factors, such as sandstorms from north of China and a monsoonal climate, have jointly caused a serious air quality pollution problem (Yin et al. 2014). Particles with aerodynamic diameters of less than 2.5 μm(PM2.5), recognized as an important indicator of fine particulates, have been found to adversely impact human health and the atmospheric environment. Because of their smaller size and strong ability to absorb harmful pollutants, fine particles can easily penetrate deeply into lungs and are therefore likely to cause respiratory and cardiovascular disease (Rupp 2009; Vedal et al. 2009). The chemical compositions of fine particles are very complex, and mainly consist of many inorganic species and hundreds of organic compounds (Li et al. 2013a). The chemical compositions of aerosol particles influence their hygroscopicity, solubility and particle light extinction efficiency (Lin et al. 2013). Water soluble inorganic ions (WSIIs) are among the most important compounds in PM2.5 (Xiang et al. 2017). Li et al. (2013b) conducted research on the characteristics of size- segregated water-soluble inorganic ions in Jing-Jin-Ji and found that approximately 61% of the total water soluble inorganic ions were distributed in fine particles. Shen et al. (2009)found that water soluble inorganic ions composed 50~60% of the mass of fine particles in a hazy climate. Sulfate, nitrate and ammonium are the major components of secondary particles in the atmosphere that make important contributions to the aerosol extinction coefficient and thus can degrade atmospheric aerosol visibility (An et al. 2018). Deng et al. (2016a) revealed that secondary inorganic aerosol and organic carbon (OC) were the main contributors of aerosols light scattering in Xiamen. In addition, water soluble components increase the solubility of toxic organic compounds, such as polycyclic aromatic hydrocarbons (PAHs), by acting as surface active reagents and therefore increase their toxicity in regard to human health (Wang et al. 2003). Therefore, it is crucial to investigate the concentrations, relationships and behaviors of water soluble inorganic ions in fine particle sources and the atmospheric aerosol formation process and its adverse effect on humans and the environment (Tian et al. 2013). Nanjing is among the largest cities in China, with a population of more than 8 million people, and is situated in the Yangtze River Delta (YRD) of southeastern China. Serious ambient particulate pollution has become a persistent problem in the region (Wang et al. 2012). Several related studies have been performed in Nanjing. Wang et al. (2003) indicated that the water soluble fraction of atmospheric aerosols in Nanjing was acidic and non-sea-salt ions as dominating components accounted for approximately 80% of the total water-soluble ions. Yang et al. (2005) showed that coal combustion, secondary aerosols, vehicular exhaust and road/seaFOR salt were the main contributors APPROVAL affecting fine particle pollution in Nanjing. Wang et al. (2015) found that ammonium sulfate and ammonium nitrate in PM2.5 were the major species causing the light extinction of particulate matter. Wang et al. (2016) showed that different water soluble inorganic ions had distinct diurnal variations. In this study, the concentrations of water soluble inorganic ions in Nanjing were obtained using middle volume samplers and ion chromatography during the period of December 2014 to November 2015. This paper reports on the concentrations and seasonal variations in the major water soluble inorganic species. In addition, the major sources of water soluble inorganic ions (WSIIs) during the four different seasons are discussed in detail as determined via principal component analysis (PCA). The obtained data may improve our knowledge of the fine particle properties in Nanjing and help Journal of Atmospheric Chemistry the Ministry of Environmental Protection develop reasonable strategies to alleviate fine particulate pollution.

2 Methodology

2.1 Site description and sampling

PM2.5 samples were simultaneously collected on the rooftops of the Nanjing Institute of Environmental Sciences, Ministry of Environmental Protection (NIES-MEP; 32°04′54.53″N, 118°49′28.65″E) and the Ninghai Middle School (NHMS; 31°58′18.95″N,118°47′21.68″E) (Fig. 1). NIES, in a residential area, is 0.5 km west of Purple Mountain and 1.5 km east of the Long Distance Bus Station. Purple Mountain results in the accumulation of air pollutants by reducing air flow. The bus station increases vehicular exhaust emissions. NHMS is east of Ninghai Road, which is surrounded by universities and residential buildings. Daily 22 ± 1-h (starting at 9:00 am, local time) PM2.5 samples were collected using 90-mm Whatman quartz filters and two middle volume samplers (Zhonglao 1108A-1, China) operat- ing at a flow rate of 100.0 L min−1. Considering the weather change and the shortage of samplers, 32, 32, 33 and 30 samples were collected discontinuously during the periods of 11 December to 27 December (winter) in 2014 and 17 April to 21 May (spring), 16 August to 2 September (summer), and 21 October to 29 November (autumn) in 2015, respectively. A set of field blank filters were also collected. Prior to sampling, quartz filters were prebaked at 450 °C for at least 6 h to eliminate the influence of background values. Filters were weighed for particle mass concentrations using a Sartorius electronic microbalance (CPA225D, Germany) with an accuracy of 10 μg after 24 h

FOR APPROVAL

Fig. 1 Sampling sites in Nanjing Journal of Atmospheric Chemistry of equilibration at a temperature of 25 °C and a relative humidity (RH) of 50% before and after sampling. The exposed filters and field blank filters were placed into polyethylene zip-lock bags and stored in a freezer at −40 °C before chemical analysis to minimize evaporation of volatile components.

2.2 Determination of WSIIs

One-fourth of each quartz filter sample was cut into pieces and then ultrasonically extracted two times, each time for 40 min, using approximately 10 mL of deionized water (conductivity: 18.2 MΩ·cm; temperature: 25 °C). The combined extracts were filtered via microporous membrane filters (pore size: 0.22 μm; diameter: 13 mm) and then diluted with deionized water to 25 mL. The extract solutions were stored in a freezer (4 °C) until subsequent analysis. + + + 2+ 2+ − 2− − Cations (Na ,NH4 ,K,Mg , and Ca ) and anions (Cl ,SO4 , and NO3 ) were quantified using an Ion Chromatography System (ICS-3000, Dionex, USA) equipped with an AS-type automatic injector and conductivity detector. Cations were detected using an Inopac CS12A column with 20 mM MSA eluent. Anions were separated on an Inopac AS11-HC column, using 25 mM NaOH as an eluent. Other analytical parameters were as follows: (1) guard: Inopac AS11-HC guard (anions) and Inopac CS12A guard (cations); (2) suppressor: Dionex 4 mm AERS 500 (anions) and 4 mm CERS 500 (cations); (3) column temperature: 30 °C; (4) flow rate: 1.0 mL min−1; (5) injection volume: 25 μL.

2.3 Quality assurance and control

All analytical procedures complied with strict quality assurance and control. Standard reference materials were obtained from the National Center of Analysis and Testing for Nonferrous Metals and Electronic Materials, China. Ultrapure water was maintained under a condition of 18.2 MΩ·cm conductivity and 25 °C temperature. The correlation coefficients of the standard working curves for the 8 WSIIs were all greater than 0.9990. The method detection limits of − 2− − + + + 2+ 2+ Cl ,SO4 ,NO3 ,Na,NH4 ,K,Mg and Ca were 0.003, 0.007, 0.006, 0.010, 0.009, 0.006, 0.002 and 0.002 μgm−3, respectively. A blank concentration was also measured using field blank filters via similar procedures as those followed for the extraction and analysis of the exposed particulate samples. All the WSII concentrations had already been corrected by eliminating field blank values to eliminate the error caused by gas adsorption artifacts.

3Results

3.1 ConcentrationsFOR and seasonal APPROVAL variations in PM2.5

Daily concentrations of PM2.5 in Nanjing from December 2014 to November 2015 were in a range of 31.0~242.9 μgm−3, with a mean and standard deviation of 94.4 ± 31.1 μgm−3, exceeding the China National Ambient Air Quality Standards (GB3095–2012) (gradeII, −3 35 μgm ) by a factor of 2.7. In terms of the standard, 88.6% of the PM2.5 samples during the study period exceeded the guideline, suggesting that the PM2.5 pollution in Nanjing was serious and some measures should be implemented to alleviate the pollution. In order to recognize the status of fine particulate pollution in Nanjing, PM2.5 levels were compared with measurements conducted in other cities over China and foreign countries. As showed in Journal of Atmospheric Chemistry

Table 1, compared with other cities in China, PM2.5 concentration in Nanjing was higher than those values observed in other south China cities, such as Ningbo(77 μg/m3), Chongqing(67.5 μg/m3), Wuhan(65 μg/m3), Zhuhai(36.4 μg/m3), but much lower than those in north China cities, such as Baoji(132 μg/m3), Xi’an(182 μg/m3), Zibo(164.4 μg/m3), Handan(139.4 μg/m3), Beijing(125 μg/m3). In addition, it was much higher than that in Lin’an, a regional background site located in the Yangtze River Delta (YRD) in East China. Besides, the PM2.5 concentration in Nanjing was comparable with that in Shanghai(94.6 μg/ 3 m ). The annual PM2.5 concentration in Nanjing was much higher by a factor of 2.1~5.4 compared with those developed countries, such as Korea (Seoul), Japan(Nagasaki), Italy(Veneto), Hungary(Budapest), Greece(Thessaloniki). Similarly as a developing large country, PM2.5 concentration in India (Delhi) was significantly higher than that in Nanjing. The seasonal concentration of PM2.5 ranked in an order of winter (108.5 ± 31.8 μgm−3) > spring (99.4 ± 35.0 μgm−3) > autumn (89.0 ± 26.9 μgm−3) > summer (85.0 ± 22.6 μgm−3). Emission sources and meteorological conditions could explain the seasonal variations in PM2.5. Higher PM2.5 concentrations during winter might be caused by the combined effects of elevated emissions from fossil fuels and coal burning and because of the prevailing meteorological conditions such as lower temperatures, wind speed and mixing height (Masiol et al. 2015). The lower inversion caused by a low mixing height limits the dilution and dispersion of PM2.5 pollutants during the winter season (Deshmukh et al. 2013). Furthermore, the lower temperatures during winter favor a shift from gaseous semi-volatile substances to particle phases, causing higher PM2.5 concentrations (Verma et al. 2010). Frequent precipitation helps remove particles from the atmosphere, as well as a higher mixing height favoring particle dispersion, results in the lowest PM2.5 concentrations occurring during summer.

3.2 Characteristics of WSIIs

The annual and seasonal average concentrations of WSIIs in PM2.5 in Nanjing are listed in Table 2. The annual average concentration of total WSIIs was 39.82 μgm−3, accounting for 2− − 44.4% of the PM2.5 mass. The percentages of each WSII ranked in an order of SO4 >NO3 > + + 2+ − + 2+ − NH4 >Na >Ca >Cl >K >Mg >F (Fig. 2). Sulfate, nitrate and ammonium were the dominant water soluble inorganic species, with their combined proportions accounting for 2− − + 82.0% of the total WSII concentrations. SO4 ,NO3 and NH4 as typical secondary inorganic aerosols (SIAs) usually formed from the direct emission of sulfur dioxides (SO2), nitrogen oxides (NOx), and ammonia (NH3) gases into the atmosphere (Xu et al. 2017). The combined percentages of SIAs in fine particles were 36.8%, suggesting that secondary inorganic ions had great contributions to the mass concentration of fine particles in Nanjing. Table 2 showed comparisonFOR with our SIAs results APPROVAL with those of previous studies. By comparison, spatial characteristics of each SIA were basically consistent with that of PM2.5 concentration. More- over, the concentration of total three SIA generally accounted for about 50% of PM2.5 mass concentration in China cities, such as Chongqing(57%), Ningbo(53.7%), Lin’an (53.3%), Zibo(47.5%), Handan(45.3%), Nanjing (44.4%,this study). The annual concentration of 2− − SO4 and NO3 in Nanjing was higher by a factor of 2.2~6.6 and 1.7~7.6 respectively compared with developed countries, such as Korea(Seoul), Japan(Nagasaki), Italy(Veneto), Hungary(Budapest), Greece(Thessaloniki). The differences in contribution of WSIIs to PM2.5 among cities might be due to variations in economic structure, energy structure, population density and meteorological parameters. Journal of Atmospheric Chemistry ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) ) 2017 2015 2015 2016 2018 2017 2016 2018 2018 2017 2016b 2015 2018 2018 2018 2017 2018 2016 2017 (Niu et al. (Liang et al. (Park et al. (Ming et al. Deng et al. ( (Huang et al. (Park et al. (Park et al. (Szigeti et al. (Tolis et al. (%) Reference 2.5 – – – – – – – – – – )WSIIs/PM 3 g/m μ ( + 4 )NH 3 g/m μ ( − 3 na and those from foreign countries )NO 3 g/m μ ( − 2 4 )SO 3 g/m μ ( 2.5 concentrations in other cities of Chi + 4 NH -, 3 ,NO 015.4 125 5.11 8.31 3.81 − 2 FOR4 APPROVAL ,SO 2.5 Comparisons of PM represented unknown values an, China 2014.7~2015.4 68.9 9.2 9.2 5.8 53.3 (Zhang et al. ’ an, China 2016.11~2016.12 182 33.01 31.63 22.18 50.1 (Zhang et al. ’ ^ – Nanjing, ChinaBaoji, ChinaXi 2014.12~2015.11 2012.3~2013.3 94.4 132 14.87 19.8 11.78 14.8 6.8 7.5 44.4 this study Ningbo, China 2014.11~2015.2 77 11.2 14.5 9.1 53.7 (Wang et al. Lin Zhuhai, China 2015.1~2016.1 36.4 4.19 3.45 9.78 Delhi, India 2014 190 16.16 16.46 12.91 38.7 (Saxena et al. Zibo, ChinaHandan, ChinaHeze, ChinaShanghai, China 2006.3~2007.2 2013~2014 2013.9~2014.8 164.4 2015.8~2016.4 139.4 94.6 100.9 32.7 25.2 23.0~33.5 14.5 11.53 16.0~21.2 20.6 18 7.1~8.8 14.94 13 32.7~51.7 8.13 47.5 45.3 (Liu et al. (Luo et al. (Meng et al. Beijing, ChinaHefei, China 2014.11~2 2012.9~2013.8 86.29 15.56 15.14 7.82 Chongqing, China 2015.12~2016.3 67.5 17.5 10.9 6.56 57 (Tian et al. Wuhan, China 2013.1~2013.12 65 16.78 11.28 9.67 Seoul, KoreaNagasaki, JapanVeneto, ItalyBudapest, Hungary 2013.9~2015.5 2014.2~2015.5 2010.6~2013.5 2012.4~2013.3 44.6 17.4 21.0 25.0 6.67 2.24 2.8 2.6 6.92 1.55 2.1 3.6 4.21 0.9 1.3 1.9 3 (Masiol et al. Thessaloniki, Greece 2011.6~2012.5 27.7 4 2.4 3.8 Study area Sampling period PM Table 1 B Journal of Atmospheric Chemistry

Table 2 Mean mass concentrations and standard deviation of WSIIs in PM2.5 for annual and seasonal samples in Nanjing (μgm−3)

Species Annual (n = 127) Winter (n = 32) Spring (n = 32) Summer (n = 33) Autumn (n =30)

F− 0.11 ± 0.08 0.19 ± 0.09 0.09 ± 0.05 0.04 ± 0.05 0.09 ± 0.04 Cl− 1.10 ± 1.35 3.05 ± 1.26 0.33 ± 0.21 0.16 ± 0.09 0.88 ± 0.53 2− SO4 14.87 ± 8.22 9.60 ± 5.20 12.56 ± 5.20 22.13 ± 8.00 14.98 ± 8.03 − NO3 11.78 ± 8.46 14.39 ± 7.50 11.15 ± 6.78 5.35 ± 3.17 16.76 ± 10.27 Na+ 2.22 ± 1.66 1.76 ± 0.28 2.31 ± 0.42 3.53 ± 2.55 1.17 ± 1.02 + NH4 6.80 ± 4.22 5.72 ± 2.60 4.67 ± 2.11 6.55 ± 3.54 10.52 ± 5.45 K+ 0.92 ± 0.40 1.08 ± 0.40 0.72 ± 0.22 0.97 ± 0.48 0.89 ± 0.35 Mg2+ 0.13 ± 0.06 0.13 ± 0.05 0.10 ± 0.04 0.13 ± 0.06 0.15 ± 0.06 Ca2+ 2.00 ± 1.10 1.63 ± 0.60 1.36 ± 0.53 2.96 ± 1.44 2.00 ± 0.78 ∑WSIIs 39.82 ± 17.45 37.55 ± 15.86 33.31 ± 12.70 41.82 ± 14.86 47.43 ± 22.20 ∑SIA 33.46 ± 16.47 29.71 ± 14.43 28.38 ± 12.64 34.02 ± 12.79 42.25 ± 21.34 ∑SIA/∑WSIIs(%) 82.0 ± 7.7 77.0 ± 6.0 83.2 ± 6.2 81.2 ± 8.7 87.1 ± 5.9 ∑WSIIs/PM2.5(%) 44.4 ± 18.9 34.8 ± 11.1 35.0 ± 12.5 52.6 ± 20.8 55.7 ± 18.5

2− − + ∑WSIIs: sum of all the analyzed water soluble inorganic ions; ∑SIA: sum of SO4 ,NO3 and NH4

The average WSII concentrations were higher during autumn and summer and lower during winter and spring. The contribution of WSIIs in PM2.5 was observed in the order of autumn (55.7%) > summer (52.6%) > spring (35.0%) ≈ winter (34.8%). The proportions of the concentrations of SIA to the total WSII mass concentrations were ranked in an order of autumn (87.1%) > spring (83.2%) > summer (81.2%) > winter (77.0%). The seasonal trends in SIAs were obviously different. Biomass burning, microbe metabo- lism and agricultural activity are the main contributors to ambient ammonia (Yin et al. 2014). + The highest proportion of NH4 to the total WSIIs was observed during autumn, resulting from more frequent biomass burning activities, such as straw burning during the harvest. In addition, NH4NO3 easily forms and is enriched in fine particles at lower temperatures (Zhao − et al. 2011). Seasonal variations in NO3 exhibited the trend of winter > autumn > spring > summer. Temperature was among the dominant factors that impacted the nitrate distributions in the fine particles; temperatures lower than 15 °C favor nitrate existing in the form of fine

59.0 0.6 0.5 49.0 0.4 0.3 0.2 39.0 0.1 0.0 FOR29.0 APPROVALMg

19.0 Percentage(%) 9.0

-1.0 FClSONONaNHKMgCa winter spring summer autumn Annual Fig. 2 Seasonal variations in individual water soluble inorganic ions Journal of Atmospheric Chemistry

particles while temperatures above 30 °C result in the formation of gaseous HNO3 because of the NH4NO3 dissociation (Chen et al. 2014). Nitrate was affected by the monsoon as well. During winter, winds from the northwest bring polluted air masses from Henan Province, Anhui Province and Jing-Jin-Ji, aggravating nitrate pollution in Nanjing, while winds from the southeast during summer bring clean air masses from the sea causing low nitrate concentrations (Wang et al. 2016). Sulfate mainly forms from the photochemical oxidation of sulfur- containing precursors, such as SO2 and H2S, by gas phase, aqueous phase and multi-phase reaction (Kleeman et al. 2000). Intense photochemical activity because of abundant O3 and strong optical radiation during summer increases the amount of sulfate formation (Zhang et al. 2015), whereas the minimum ratio of sulfate during winter is associated with a stable boundary layer. Cl− in fine particles is mainly from marine source, coal burning and the resuspension of road salt (Xu et al. 2017; Liu et al. 2017). Frequent coal burning and industrial emissions generate the highest Cl− proportion during winter. Furthermore, fog during winter avails a shift from gaseous HCl to Cl− particles (Aikawa et al. 2007).

3.3 Aerosol acidity

Acidic aerosols do harm to human health and damage ecological systems. The validity of WSII measurement is verified via anion (A) and cation (C) balance. The equivalent molar ratio of cations to anions (C/A) is an indicator to evaluate aerosol acidity (Chow et al. 1994); when the value is near 1.0 most of the WSIIs have been neutralized. As shown in Fig. 3,strong correlations between cation and anion micro-equivalents were found during the four seasons, showing that most of the important WSII species were identified and the measurements were reliable. The C/A ratio during winter and spring ranged from 0.50 to 1.77 and from 0.70 to 1.75, with an average and standard deviation of 1.05 ± 0.24 and 1.09 ± 0.24, respectively. The average C/A ratios during winter and spring were both near 1.0, showing that WSIIs charged during winter and spring were basically balanced and fine particles were electrically neutral. The mean ratios of C/A during summer and autumn were 1.31 ± 0.18 and 1.33 ± 0.21, respectively, suggesting a deficiency in anions in the summer and autumn samples, probably because of a lack of measurements of carbonate and water soluble organic ions or some analytical uncertainties (Contini et al. 2014).

1.40

1.20

1.00 -3 FOR0.80 APPROVAL

μmol m μmol 0.60

AE winter 0.40 y = 1.174x - 0.092; R² = 0.8738 spring y = 1.601x - 0.273 R2=0.902 0.20 summer y = 0.888x - 0.068 R² = 0.967 autumn y = 0.914x - 0.091 R² = 0.974 0.00 0.00 0.40 0.80 1.20 1.60 2.00 CE μmol m-3 Fig. 3 Ion balance for the four seasons in Nanjing Journal of Atmospheric Chemistry

2− − 3.4 Sulfate-nitrate (SO4 /NO3 )

− 2− The mass ratio of NO3 /SO4 has been used as an indicator of the relative importance of stationary sources (coal burning) versus mobile sources (vehicular emission). The sulfur contents in gasoline and diesel in China were 0.12% and 0.2%, respectively. The NOx/SOx ratios from comburent of gasoline and diesel fuel were approximately 13:1 and 8:1, respectively. Coal’s sulfur content is 1%; the ratio of NOx/SOx from coal’s combustion is approximately 1:2. Therefore, NOx and SOx can act as tracers of mobile sources and stationary sources separately (Huang et al. 2016;Xuetal.2016). Previous studies have indicated that a − 2− ratio of NO3 /SO4 greater than 1.0 suggests that mobile sources make a greater contribution than that of stationary sources (Arimoto et al. 1996). The results have showed that the annual − 2− average ratio of NO3 /SO4 was 0.95 ± 0.63, indicating that stationary sources and mobile sources make equally important contributions to fine particle pollution. The rapidly increasing number of cars and vigorous promotion of clean energy during recent decades have changed − 2− the NO3 /SO4 ratio. As listed in Table 3, the ratio in our study was higher than those previously recorded in China, such as at Zibo, Zhuhai, Lin’an, Wuhan, Xi’an, Zhengzhou, or that recorded in India(Kadapa). However, the ratio was lower than the values in Beijing, Shanghai,Hefei,Seoul,Nagasaki. − 2− Significantly seasonal variations in the NO3 /SO4 ratio have been observed, with a mean and standard deviation of 1.55 ± 0.43, 1.16 ± 0.54, 0.91 ± 0.44 and 0.24 ± 0.10 during winter, autumn, spring and summer, respectively. It is suggested that vehicle emissions are the dominate source during winter and autumn, while coal burning dominates during spring and − summer. Lower temperatures during winter and autumn suppress the volatilization of NO3 − 2− from the particle phase to gas phase, resulting in a higher ratio of NO3 /SO4 . In contrast, higher temperatures and stronger optical radiation during summer and spring favors the 2− − formation of SO4 and promotes the volatilization of NO3 (Li et al. 2014), causing a − decreased ratio. It is noted that the concentrations of NO3 measured in this study were much lower than their actual values because of the loss of NH4NO3 via the heterogeneous process − 2− during sampling, and therefore, the actual NO3 /SO4 ratio was greater than that measured (Sun et al. 2013).

Table 3 Literature data of ratio of nitrate to sulfate over China and foreign countries

− 2− Study area Sampling period NO3 /SO4 References

Nanjing, China 2014.12~2015.11 0.95 this study Beijing, China 2014.11~2015.4 2.14 (Park et al. 2018) Shanghai, China 2013.9~2014.8 1.05 (Ming et al. 2017) Hefei, ChinaFOR 2012.9~2013.8 APPROVAL 1.1 (Deng et al. 2016b) Zhengzhou, China 2014.10~2015.7 0.9 (Jiang et al. 2018) Xi’an, China 2016.11~2016.12 0.88 (Zhang et al. 2018) Wuhan, China 2013.1~2013.12 0.64 (Huang et al. 2016) Heze, China 2015.8~2016.4 0.63 (Liu et al. 2017) Lin’an, China 2014.7~2015.4 0.53 (Zhang et al. 2017) Zhuhai, China 2015.1~2016.1 0.09~0.50 (Liang et al. 2018) Zibo, China 2006.3~2007.2 0.37 (Luo et al. 2018) Kadapa, India 2013.3~2015.2 0.39 (Begam et al. 2017) Seoul, Korea 2013.9~2015.5 1.23 (Park et al. 2018) Nagasaki, Japan 2014.2~2015.5 1 (Park et al. 2018) Journal of Atmospheric Chemistry

3.5 Spearman correlation analysis

The correlation coefficients (r) between individual WSIIs are shown in Table 4. The forms and sources of WSIIs could be initially determined by analyzing the correlations between ions. Highly correlated WSII species show identical sources of origin while those species that are not significantly correlated with each other suggest diverse sources of origin. As shown in 2− − 2− + Table 4, there were significant high correlations between species SO4 -NO3 ,SO4 -NH4 , 2− + 2− 2+ − + − + + + 2+ 2+ SO4 -K ,SO4 -Mg ,NO3 -NH4 ,NO3 -K ,NH4 -K and Mg -Ca during winter, as well 2− + 2− + − + 2+ 2+ 2− + + as between SO4 -NH4 ,SO4 -K ,NO3 -NH4 and Mg -Ca during spring, SO4 -K ,Na - + + 2+ + 2+ + 2+ + 2+ 2+ 2+ 2− K ,Na-Mg ,Na-Ca ,K-Mg and K -Ca and Mg -Ca during summer, and SO4 - + − + − + + 2+ NH4 ,NO3 -NH4 ,NO3 -K and Na -Mg during autumn. The close relationship between Mg2+-Ca2+ during winter, spring and summer indicated their similar origins from crustal resuspension, such as road dust, construction dust and soil dust. The poor correlation between

Table 4 Spearman correlation analysis for WSIIs in PM2.5

− − 2− − + + + 2+ 2+ Seasons Species F Cl SO4 NO3 Na NH4 K Mg Ca

Winter F− 1.00 Cl− 0.12 1.00 2− SO4 0.09 0.54** 1.00 − NO3 0.04 0.67** 0.79** 1.00 Na+ −0.10 −0.13 0.05 −0.05 1.00 + NH4 0.00 0.54** 0.92** 0.81** 0.21 1.00 K+ 0.12 0.59** 0.85** 0.76** 0.17 0.90** 1.00 Mg2+ 0.42* 0.25 0.74** 0.41* 0.28 0.66** 0.67** 1.00 Ca2+ 0.53** 0.05 0.45* 0.21 0.22 0.37* 0.55** 0.80** 1.00 Spring F− 1.00 Cl− 0.07 1 2− SO4 −0.06 0.28 1 − NO3 −0.34 0.41* 0.56** 1 Na+ 0.45** 0.28 −0.08 −0.1 1 + NH4 −0.29 0.23 0.84** 0.86** −0.15 1 K+ 0.26 0.24 0.74** 0.42* 0.31 0.65** 1 Mg2+ 0.23 0.63** 0.1 −0.09 0.37* −0.11 0.18 1 Ca2+ 0.31 0.36* −0.09 −0.32 0.31 −0.36 −0.04 0.82** 1 Summer F− 1.00 Cl− −0.03 1.00 2− SO4 0.02 0.03 1.00 − NO3 0.06 0.45** 0.63** 1.00 Na+ −0.53 0.11 0.50** 0.28 1.00 + NH4 0.47** 0.14 0.55** 0.63** −0.31 1.00 K+ −0.33 0.13 0.83** 0.57** 0.86** 0.13 1.00 Mg2+ −0.28 −0.02 0.68** 0.33 0.85** −0.17 0.89** 1.00 FOR2+ APPROVAL Ca −0.33 −0.12 0.53** 0.15 0.75** −0.35 0.73** 0.93** 1.00 Autumn F− 1.00 Cl− 0.44* 1.00 2− SO4 0.07 0.38* 1.00 − NO3 0.16 0.48** 0.53** 1.00 Na+ 0.10 0.32 0.29 0.03 1.00 + NH4 0.17 0.47** 0.85** 0.85** 0.01 1.00 K+ 0.24 0.68** 0.65** 0.72** 0.48** 0.68** 1.00 Mg2+ 0.07 0.23 0.46* 0.03 0.88** 0.13 0.47** 1.00 Ca2+ −0.16 −0.21 0.23 0.05 0.34 0.05 0.07 0.55** 1.00

**: correlation is significant at the 0.01 level (2-tailed), *: correlation is significant at the 0.05 level (2-tailed) Journal of Atmospheric Chemistry

Na+ and Cl− during the four seasons suggested their origin from non-sea-salt substances. Only 2− − 2− with an exception during summer, high correlation coefficients between SO4 -NO3 ,SO4 - + − + NH4 , and NO3 -NH4 implied similar sources of anthropogenic origin. Because SIAs are the dominant water soluble ionic species in PM2.5,inthisstudy,we discuss the formation process among the three ions in detail. Due to the much higher vapor pressure of ammonium nitrate than that of ammonium sulfate, ammonium exists in the form of ammonium sulfate or ammonium bisulfate first, then ammonium nitrate under a condition of abundant NH3 and high humidity, and lastly ammonium chloride (Robarge et al. 2002). + 2− Ammonium-rich and -poor are defined with a threshold of 2 (NH4 /SO4 > 2: ammonium- + 2− + 2− rich; NH4 /SO4 < 2: ammonium-poor) (Wang et al. 2015). The molar ratios of NH4 /SO4 were 3.4, 3.8, 2.0 and 1.7 during winter, autumn, spring and summer, respectively. By comparing the calculated ammonium with the measured ammonium, the formation among the three SIAs can be confirmed. When all nitrate is in the form of NH4NO3 and sulfate is in 2− − the form of (NH4)2SO4, the calculated ammonium = 0.19 × [SO4 ] + 0.29 × [NO3 ], and when 2 − in the form of NH4HSO4, the calculated ammonium = 0.38 × [SO4 ] + 0.29 × [NO3 ]. As shown in Fig. 4, the calculated ammonium showed reasonable agreement with the actual measured ammonium and the slope of curves was near 1.0 during winter and spring when + NH4HSO4 was assumed, suggesting that NH4 was mainly in the form of NH4HSO4 and + NH4NO3, while during summer and autumn, NH4 was in the form of (NH4)2SO4.

3.6 Principal component analysis

Principal component analysis (PCA, SPSS version 17.0) was performed to study preliminary sources of PM2.5 in Nanjing. In this study, a few of the principal components were extracted from the concentration data of each WSII by dimension reduction to explain the relationships

20.00 20.00 Winter Spring 16.00 16.00 y = 1.3754x - 0.0489 y = 1.6003x + 0.5312 R² = 0.8295 R² = 0.9392 12.00 12.00

8.00 8.00 y = 1.0301x + 0.1215 y = 1.2077x + 0.0031 R² = 0.7841 4.00 4.00 R² = 0.9185

0.00 0.00 0.00 3.00 6.00 9.00 12.00 0.00 5.00 10.00 15.00

20.00 Summer 25.00 Autumn

y = 0.6372x + 5.7882 FOR16.00 APPROVAL20.00 y = 0.9401x + 0.6655 R² = 0.373 R² = 0.9488 12.00 15.00

8.00 10.00 y = 0.7056x + 0.3157 R² = 0.9217 4.00 y = 0.4028x + 3.1629 5.00 R² = 0.4071 0.00 0.00 0.00 5.00 10.00 15.00 0.00 5.00 10.00 15.00 20.00 25.00 30.00

Fig. 4 Comparison between calculated and measured ammonium. ○: (NH4)2SO4, ●:NH4HSO4; X-axis: + −3 + −3 measured NH4 (μmole m ), Y-axis: calculated NH4 (μmole m ) Journal of Atmospheric Chemistry among the measured variables and deduce the sources of fine particles. A Kaiser-Meyer-Olkin (KMO) test and Bartlett’s test were performed to examine the suitability of the measured data. The KMO threshold of 0.5 was recognized as an indicator of whether factor analysis is useful or not (Parinet et al. 2004). In this work, the KMO values were 0.70 (winter), 0.67 (spring), 0.69 (summer) and 0.74 (autumn), indicating the validity of the factor analysis. The results of PCA for PM2.5 are shown in Table 5. During winter, the variables in the WSIIs were explained on the basis of three component factors, accounting for 85.7% of the 2− − + + 2+ total variance. Factor 1 had high a loading of SO4 ,NO3 ,NH4 ,K and Mg and explained 52.8% of the total variance. This factor could be attributed to secondary sources and biomass burning. In addition, Mg2+ was used as an indicator of crustal resuspension (soil particles or falling dust) and municipal construction. Factor 2 should stand for industrial source with highly loading of F−. Factor 3 was responsible for 12.8% of the total variance and was strongly associated with Na+, which could be attributed to coal combustion. During spring, three components were extracted, accounting for 80.9% of the total variance. Factor 1 was highly 2− − + + loaded with SO4 ,NO3 ,NH4 and K , accounting for 36.5% of the total variance, suggesting secondary sources and biomass burning. Factor 2 with 30.3% of variables, was affected by Mg2+,Ca2+,Na+,Cl−, indicating the contributions of suspending dust and coal combustion to the fine particle mass. Factor 3 explained 14.1% of the variance with loading of F−, which were likely from industrial source. During summer, three component factors accounted for 88.0% of the total variance. Factor 1 was dominated by Na+,K+,Mg2+ and Ca2+, which could be − explained by coal combustion, biomass burning and dust. Factor 2 with a high loading of NO3 + − and NH4 was affected by secondary sources. Factor 3 was loaded with Cl ,suggestinga source of coal combustion. During autumn, three factors were extracted and accounted for 2− − + + 81.7% of the total variance. Factor 1 was loaded with SO4 ,NO3 ,NH4 and K ; factor 2 was loaded with Na+,Mg2+ and Ca2+; and factor 3 was loaded with F−, indicating their origins from secondary sources and biomass burning (factor 1), industry emissions and crustal resuspension (factor 2), and industrial emissions (factor 3). In conclusion, principal component analysis identified three sources in Nanjing, that is, secondary sources, burning processes and airborne dust, which greatly contribute to fine particle pollution.

4Conclusions

The analytical results of the fine particle samples collected in Nanjing during the period of December 2014 to November 2015 were discussed. The annual average PM2.5 concentrations exceeded the new Chinese ambient air quality standard by a factor of 2.7, suggesting that the fine particleFOR pollution was severe APPROVAL in Nanjing. Seasonal variations in the total WSIIs were inconsistent with those of PM2.5. Sulfate, nitrate and ammonium were the most abundant WSIIs, with their combined proportion of 82.0% of the total WSIIs. Three secondary inorganic aerosols had different temporal variations, which were mainly attributed to meteorological 2− conditions. SO4 was highest during winter and lowest during summer as a result of − + photochemical activity. The highest proportions of NO3 and NH4 were observed during winter and autumn because of the thermal stability of the NH4NO3 in the fine particles. PM2.5 particles were mostly neutralized during winter and spring and were alkaline during summer and autumn. The ratio of sulfate to nitrate showed that mobile sources (traffic emission) have played an increasingly important role in air pollution, because of the comprehensive effects of Journal of Atmospheric Chemistry 0.258 0.352 0.344 0.683 0.325 0.107 0.429 − − − − 0.441 0.415 0.2490.025 0.475 0.246 0.059 0.083 0.709 0.761 0.673 − − − − − − 0.750 0.816 0.712 0.807 0.917 0.248 0.165 0.4270.852 0.277 0.298 0.150.204 0.600 0.231 − − − − − 0.726 0.951 0.122 0.001 0.290 0.1620.160 0.343 0.541 − − − 0.383 0.562 0.005 0.892 0.973 0.948 0.844 − − 0.215 0.519 0.65 0.432 0.108 0.359 0.406 0.005 0.353 0.296 − − − − − 0.072 0.661 0.166 0.103 0.784 0.492 0.652 0.087 0.3850.883 0.781 0.47 − − ing Summer Autumn 0.2510.868 0.537 0.867 0.094 0.980 0.716 0.050 0.335 − − − − 0.466 0.1550.018 0.118 0.349 0.767 0.046 − − − − − 0.707 0.546 0.120 0.390 0.1890.0680.478 0.140 0.050 0.041 − − − − − 0.211 0.612 0.937 0.818 0.1630.929 0.940 0.311 0.808 0.603 0.694 FOR ComponentAPPROVAL123123123123 Component Component Component Component matrix for WSIIs for the four seasons − − + 2 3 4 2+ 4 + 2+ − + − Na K NO SO Cl Mg NH Ca Variance explainedCumulative variance explained 52.8% 72.9% 52.8% 85.7% 20.1% 36.5% 12.8% 66.8% 36.5% 80.9% 30.3% 48.9% 14.1% 75.1% 48.9% 88.0% 26.2% 44.7% 12.9% 67.2% 44.7% 81.7% 22.5% 14.5% F The bold emphasis in the table reprented a high loadof the ions in its component column Variables Winter Spr Table 5 Journal of Atmospheric Chemistry a rapid increased in the number of cars and vigorous promotion of clean energy during recent decades. However, at present, stationary sources (coal combustion) still dominate in impacting air deterioration. Spearman correlation analysis showed good correlations among the three secondary inorganic ions during winter, spring and autumn, indicating their identical anthropogenic sources. The secondary inorganic ions exist in the form of NH4HSO4 and NH4NO3 during winter and spring and (NH4)2SO4 during summer and autumn. The analytical results of PCA indicated that secondary sources, burning processes and airborne dust greatly contribute to fine particle pollution in Nanjing.

Acknowledgements This work was supported by the Major Special Program of the Ministry of Ecology and Environmental of China (GYZX180104), Basic Research Business Fees of Central-Level Public Welfare Research Institutes Project (GYZX170204), and the National Natural Science Foundation of China (No.201406116).

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