Chemical Characteristics of Water-Soluble Ions in Particulate Matter in Three Metropolitan Areas in the North China Plain

Xu Dao; Zhen Wang; Yibing Lv; Enjiang Teng; Linlin Zhang; Chao Wang

doi:10.1371/journal.pone.0113831

Abstract

PM_2.5 and PM₁₀ samples were collected simultaneously in each season in Beijing, Tianjin and Shijiazhuang to identify the characteristics of water-soluble ion compositions in the North China Plain. The water-soluble ions displayed significant seasonal variation. The dominant ions were NO₃⁻, SO₄²⁻, NH₄⁺ and Cl⁻, accounting for more than 90% and 86% to the mass of total water-soluble ions in PM_2.5 and PM₁₀, respectively. The anion/cation ratio indicated that the ion acidity of each city varied both between sites and seasonally. Over 50% of the ion species were enriched in small particles ≤1 µm in diameter. The [NO₃⁻]/[SO₄²⁻] ratio indicated that vehicles accounted for the majority of the particulate pollution in Beijing. Shijiazhuang, a city highly reliant on coal combustion, had a higher SO₄²⁻ concentration.

Citation: Dao X, Wang Z, Lv Y, Teng E, Zhang L, Wang C (2014) Chemical Characteristics of Water-Soluble Ions in Particulate Matter in Three Metropolitan Areas in the North China Plain. PLoS ONE 9(12): e113831. https://doi.org/10.1371/journal.pone.0113831

Editor: Qinghua Sun, The Ohio State University, United States of America

Received: June 9, 2014; Accepted: October 30, 2014; Published: December 1, 2014

Copyright: © 2014 Dao et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The authors confirm that, for approved reasons, some access restrictions apply to the data underlying the findings. Data used in this paper are only available upon request as they are owned by the National Environmental Monitoring Center (CNEMC). CNEMC has the authority to publish the data. Researchers can send their request and intention of using data to Dr. Guanhua Xing (Program director of atmosphere laboratory of CNEMC, email address: xinggh@cnemc.cn).

Funding: This work was supported by National Natural Science Foundation of China (Grant No. 41101567) to ZW. The authors also thank the financial award from State Scholarship Fund of China Scholarship Council (File No. 201308420277) to support the research at UCSB (to ZW). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Particulate matter (PM) in the atmosphere originates from either direct emission or physical and chemical transformation of gaseous pollutants [1]–[3]. Atmospheric aerosols have potential effects on human health, radiation balance, climate, and visibility [4], [5]. Air quality has become an increasing public health concern since a series of heavy pollutions of fine particulate matter (PM_2.5, particles ≤2.5 µm in diameter) occurred in Beijing in 2011 winter. According to the WHO air quality guidelines and interim targets for particulate matter in developing countries, the 24–hour concentration of PM₁₀ and PM_2.5 should be controlled below 150 and 75 µg/m³, respectively [6]. US EPA set the safe PM₁₀ and PM_2.5 concentration at 50 and 35 µg/m³, respectively [7]. With the increase in public awareness of the degradation of visibility or hazy weather, China released the Chinese National Ambient Air Quality Standards (NAAQS) in 2012, taking 150 and 75 µg/m³ as the 24-hour PM₁₀ and PM_2.5 limits in urban area, respectively, and 70 and 35 µg/m³ as the annual PM₁₀ and PM_2.5 limits, respectively. Previous epidemiological research has shown that high levels of particulate pollution lead to increased morbidity and mortality, as well as respiratory symptoms [8]–[11]. Smaller particles are deposited more easily in the lungs [9], and are thus more harmful to health than larger particles [12]. The chemical composition of the particles is important for understanding atmospheric visibility [13], as well as the locations of deposition in the lung [14]. Water-soluble ions are a key area of atmospheric environmental research [15]. Water-soluble ions, such as NH₄⁺, Na⁺, K⁺, Ca²⁺, Mg²⁺, SO₄²⁻, NO³⁻, F⁻ and Cl⁻, are significant components of atmospheric particles [16], [17], which varies depending upon the particle source [18], [19].

North China is one of the regions in China with the highest frequency of haze events [20], [21]. According to the PM monitoring in Beijing during 2001 and 2003,the average 12-hour PM₁₀ mass concentration reached as high as 263.2 µg/m³ and PM_2.5 ranged from 107 to 181 µg/m³ [22]. Another research showed the average PM₁₀ annual mass concentration were 124 µg/m³ in Beijing and 141 µg/m³ in Tianjin between 2009 to 2010 [23]. Both 24-hour and annual PM concentrations reported are far higher than the limits of NAAQS. Air pollution has increased as a function of population and the diversification of socio-economic activities. However, there have been few studies on PM_2.5 and PM₁₀ with respect to their chemical composition and seasonal variation [24]. The majority of research on this region has focused on the composition [25], seasonal variation [26] and source of the particulate matter [27]. Generally, carbonaceous aerosol is the most abundant component of PM_2.5 mass, and the main ions were NH₄⁺, SO₄²⁻ and NO₃⁻ [13], [26], [28]. Different dominant mechanisms for the formation of SO₄²⁻, NO₃⁻ and Cl⁻ are found in the summer and the spring [29]. Various aerosol speciations associated with PM pollution display a distinct seasonality [30]. Remote sources, which were primarily soil derived Ca²⁺, Mg²⁺ and Al, contribute a major part of the PM pollution in spring [13], [30]. Seasonal peaks of organic carbon were commonly found in winter due to the high usage of coal for heating in the region [13], [31]. Simulation showed that the PM_2.5 concentration was sensitive to NH₃ emission [32]. In general, water-soluble ion species, including Na⁺, NH₄⁺, K⁺, Mg²⁺, Ca²⁺, Cl⁻, NO³⁻, and SO₂⁴⁻, comprise 25%-50% of the aerosol mass predominantly in the form of sulfates, ammoniums and nitrates [33]. Water-soluble ions can scatter or absorb both incoming solar radiation and thermal radiation emitted from the Earth’s surface, thus directly change the radiation balance [34]. Previous studies mostly focused on a single size particle either in Beijing or in Tianjin [24], [35]. Only a few studies addressed PM concentrations across the major cities in North China Plain [36]. In addition, these studies seldom referred to the national atmospheric background status at the same period. This study, derived from a national atmospheric monitoring program in 2013, focuses on the water-soluble ion compositions and their variation in particulate matter in 3 metropolises in North China area. Also data from national atmospheric background sites are referred at the same sampling periods.

Experiment

1. Sampling

To compare the levels of the particulates and water-soluble ions in three cities (Beijing, Tianjin, Shijiazhuang) and four national atmospheric background sites, PM_2.5 and PM₁₀ samples were collected simultaneously at the seven sites in each season during 2013. Sampling locations of 3 studied cities were at the downtown area, while the background sampling sites were at remote rural area. Meanwhile samples from a control site at Datong city, which is close to the study area, were also collected. The field studies were carried out in the monitoring stations controlled or supervised by the China National Environmental Monitoring Centre (CNEMC). CNEMC has authority to permit our access to each station. Our field studies only collected atmospheric samples, which did not involve any endangered or protected species. The sampling sites are summarized in Table 1. Beijing, Tianjin and Shijiazhuang are located at the northwestern edge of the Great North China Plain, surrounded by mountains in all directions, except the south. The mountains extend several hundred kilometers and the highest peak is greater than 3000 m in elevation. The considerable emission of air pollutants in the region produces a high concentration of atmospheric particles.

Download:

Table 1. Locations of the sampling sites.

https://doi.org/10.1371/journal.pone.0113831.t001

A low-flow rate sampler, TH-16A (Tianhong, Wuhan, China) was deployed at the three city sites, and operated at a flow rate of 16.8 mL/min. A middle-flow rate sampler, TH-150 (Tianhong, Wuhan, China) was deployed at the four background sites, with a flow rate of 100 mL/min. Sampling was carried out at ∼23-h intervals, and conducted in each season for 5–7 days. The four periods were Feb 27^th to Mar 10^th in spring, Jun 19^th to 30^th in summer, Sep 10^th to 29^th in autumn, and Dec 7^th to 29^th in winter. Windy and/or rainy days were avoided collecting samples. 32 samples for National background point and 20–28 samples for each city site were sampled in each season. To assess the ion enrichment in particulate matter of various sizes, daily 23-h integrated PM₁, PM_2.5, PM₁₀, TSP (from10:00 am to 9:00 am the next day, local time) samples were collected with pure quartz fiber filters (MUNKTELL, Sweden) using a multi-stage sampler (Kalman, Hungary, Model KS-303.150.10/10/2.5/1+PAH7) in Beijing. Teflon filters (Pall International, Cat) were used to determine the concentrations by mass of the atmospheric particles, The filters were weighed with a microbalance before and after sampling under identical conditions in a temperature- and humidity-controlled room (20±1°C and 35±5% relative humidity).

2. Analysis

Each filter was transferred into a plastic jar with the particle collection side facing downwards, and then leached with 15-mL Milli-Q water (18.5 MΩ·cm⁻¹) in an ultrasonic bath for 30 min at room temperature. After ultrasonication, the leachate was filtered through a PTFE syringe filter (Fisher brand, 0.22 µm) before being introduced into an ion chromatography system (ICS-2000, Thermo Fisher). An AS19 analytical column (4×250 mm, Thermo Fisher), KOH eluent generator cartridge (EGC II KOH, Thermo Fisher) and 50-µL sample loop were employed for the determinations of anions, which included F⁻, Cl⁻, NO₃⁻, and SO₄²⁻. The cations of interest, Na⁺, NH₄⁺, K⁺, Ca²⁺, and Mg²⁺, were determined with the same IC system with a CS12A analytical column (4×250 mm, Thermo Fisher), a 50-µL sample loop and MSA (EGC II MSA, Thermo Fisher) as the eluent. The above-mentioned anions and cations were considered to be the total water-soluble ions in this study. The detection limit for major anions, including F⁻, Cl⁻, NO₃⁻, SO₄²⁻, Na⁺, NH₄⁺, K⁺, Ca²⁺, and Mg²⁺, was <2 µg/L. The recovery fell in the range 81–107%, and the precision based on seven duplicate spike samples was 3%.

All obtained data were categorized according to where and which season they were collected. Further statistical analysis including mean/variance, Pearson correlation and linear fits were calculated in the statistical package of Matlab 2013b software. Then, ion balance was used to evaluate the acid-base balance of the aerosol particles. We converted the ion concentration by mass into micro-equivalents, as follows:(1)

(2)

Micro-equivalents were also used for linear fits of secondary ions in Matlab 2013b software system.

Results and Discussion

1. Seasonal variation of total water-soluble ions

Significant seasonal variation in the water-soluble compositions of PM_2.5 and PM₁₀ were identified in each city (Table 2). In spring, autumn and winter, the ions exhibited a similar concentration distribution, with the exception of a minor difference in SO₄²⁻ in PM_2.5 during spring. The order of the total ion concentrations in the three cities was Shijiazhuang> Tianjin> Beijing. The SO₄²⁻ concentration in spring in the PM_2.5 of Tianjin was slightly higher than that of Shijiazhuang. However, an inverse distribution was found in the summer. The ion concentrations of Shijiazhuang were lower than those of Beijing and Tianjin. During the sampling period, the average PM_2.5 and PM₁₀ concentrations by mass of the whole studied region were 167 and 273 µg/m³, respectively. The concentrations of water-soluble ions in both PM_2.5 and PM₁₀ displayed a significant variation with season, in the order spring> winter> summer> autumn. This might be explained by the high proportion of coal burning for heating in spring and winter, and the frequent dust storms in spring [39].

Download:

Table 2. Concentration of water-soluble ions (mean ± standard deviation in µg/m³) at sample sites in each season.

https://doi.org/10.1371/journal.pone.0113831.t002

2. Ionic species

The proportions of each water-soluble ion also presented a clear pattern. The concentrations of the ions were: NO₃⁻> SO₄²⁻> NH₄⁺> Cl⁻> Ca²⁺> K⁺> Na⁺> F⁻> Mg²⁺ in spring and autumn; SO₄²⁻> NO₃⁻> NH₄⁺> Cl⁻> Na⁺> Ca²⁺> K⁺> Mg²⁺> F⁻ in summer; and SO₄²⁻> NO₃⁻> NH₄⁺> Cl⁻> K⁺> Ca²⁺> Na⁺> F⁻> Mg²⁺ in winter. In PM_2.5, the concentrations of NO₃⁻, SO₄²⁻, NH₄⁺ and Cl⁻ were significantly higher than those of the remaining five ions, comprising 90.3–92.3% of the total mass of water-soluble ions in each season. The total concentrations of these four dominant ions exceeded 70 µg/m³ in all samples. PM₁₀ had similar ion species characteristics to PM_2.5. Four dominant ions contributed 86.1%-88.6% of the total mass of water-soluble ions in each season, ranging from 37–195 µg/m³. The concentrations of Ca²⁺, K⁺ and Na⁺ were comparatively low, with average concentrations of 2.90, 2.27 and 1.82 µg/m³, respectively in PM_2.5 and slightly higher average concentrations in PM₁₀. Mg²⁺ and F⁻ combined contributed less than 0.5% of the total mass of ionic species. The concentration of F⁻ at the Shijiazhuang site ranged from 0.014–2.436 µg/m³ in PM_2.5 and from 0.046–3.473 µg/m³ in PM₁₀, an average concentration more than threefold greater than those at Beijing and Tianjin.

Differences in the dominant ions were found between the study area and the background sites. The NO₃⁻ concentration was similar to or even greater than the SO₄²⁻ in the study area, compared higher concentration of SO₄²⁻ than NO₃⁻ at the background sites. Cl⁻ and Ca²⁺ concentrations were one order of magnitude greater than those of K⁺ and Na⁺, while they were similar at the background sites. To give a broader picture, ion concentrations in PM_2.5 of other important city in eastern Asian were shown in Table 3. The concentrations of NO₃⁻ and SO₄²⁻ were similar between Beijing and Shanghai, while NO₃⁻ concentrations were higher in Beijing than in Guangzhou. SO₄²⁻ concentrations were similar between the two cities. The three eastern Asia cities outside China had smaller equivalent ion concentrations.

Download:

Table 3. Comparison of annual ion concentrations in PM_2.5 among major eastern Asian cities (µg/m³).

https://doi.org/10.1371/journal.pone.0113831.t003

The Datong site was used as the control point. It is located in the upwind direction of the study area, and is isolated by the Taihang Mountains. Here too, the dominant ions were NO₃⁻, SO₄²⁻, NH₄⁺, and Cl⁻. However, the concentrations were lower than those of the study area (Figure 1). The concentration of SO₄²⁻ in spring was highest in Datong, 11.69 µg/m³ in PM₁₀. The average concentration of SO₄²⁻ in the study area reached 39.26 µg/m³, almost 3.4-fold greater than in Datong. The concentrations of the remaining ions in each sample were less than 10 µg/m³, significantly lower than in the study area.

Download:

Figure 1. Seasonal variation of water-soluble ions in the study area and at the control point.

https://doi.org/10.1371/journal.pone.0113831.g001

Ion concentrations of study cities were divided by corresponding average ion concentration of 4 background sites. Namely, concentration multiples of study cities to background sites were used to calculate Pearson correlation coefficient for each site (Table 4). The water-soluble ion concentrations in PM_2.5 were similar in the 3 case study cities (correlation coefficient>0.8334, p<0.05) and displayed relatively low similarity between 3 case study cities and Datong. This implied that the North China sites might have similar sources of PM_2.5. There was a significant correlation between Datong and Shijiazhuang, and between Beijing and Tianjin in PM₁₀ (correlation coefficient>0.8047, p<0.05). This implies that fugitive dust contributed a larger share of particles to Shijiazhuang. This is supported by the significantly higher Ca²⁺ concentration in Shijiazhuang, which is derived primarily from soil sources.

Download:

Table 4. Pearson correlation among study sites and control site by using seasonal average concentration multiples of study cities to background sites.

https://doi.org/10.1371/journal.pone.0113831.t004

Correlations between the anion and cation equivalents were observed (Figure 2). Ion balance calculations stratified by season showed that summer and autumn samples were above the A/C (anion/cation) unity line in both PM_2.5 and PM₁₀, indicating that anion was excessive. In contrast, the spring anion was insufficient; i.e., below the A/C unity line. In winter, the ion balance relationships were inconsistent between PM₁₀ samples and PM_2.5 samples, indicating that the sources of PM₁₀ and PM_2.5 were different in winter. Higher regression slopes were found in PM_2.5 samples. The reason might be the precursors of SO₄²⁻ and NO_x, which enriched primarily in the small particulate matter. A steeper slope was observed in summer primarily because SO₂ and NO_x were more easily oxidized to H₂SO₄ and HNO₃ and accumulated in the aerosols under the intensive illumination and high temperature [45]. A/C also varied by size and site location. Annual A/C ratios of PM_2.5 in Beijing and in Shijiazhuang were smaller than 0.9, but larger than 1.1 in Tianjin. When coming to the PM₁₀, Beijing and Tianjin were very close to 1.0. These results demonstrate that anions tend to be distributed in small particles.

Download:

Figure 2. Total anions versus total cations. (All data were included and categorized by season, p<0.001 for all regressions.).

https://doi.org/10.1371/journal.pone.0113831.g002

3. Ion enrichment in particulate matter of various sizes

The size of the particulate matter affected its capability for ionic enrichment. The total mass concentration of the dominant ions contained in PM_2.5 was about 60%-90% of the mass concentration of the dominant ions contained in PM₁₀ (See table 2). That is, more ions were distributed in the fine particle (PM_2.5) than that in the coarse particle (PM_2.5–PM₁₀). The enrichment of non-dominant ions displayed more complex relationships. In spring and summer, ∼50 −80% of F⁻ was found in PM_2.5 at each site, while in autumn and winter the proportion dropped to 17–37%. The Na⁺ and K⁺ distributions were similar to those of the dominant ions, with an annual range of 59–96%, with the exception of Na⁺, which was present at a concentration of 43% in summer in Beijing. Greater complexity was displayed by the Ca²⁺ and Mg²⁺ distributions, both seasonally and spatially. <30% Ca²⁺ and <50% Mg²⁺ were found in PM_2.5 in Beijing. That is, the source of Ca²⁺ and Mg²⁺ in Beijing was primarily large particles. High proportions of Ca²⁺ and Mg²⁺ in PM_2.5 in spring and summer (∼49 −90%), and low proportions in autumn and winter (<25%) were found in Tianjin. The proportion of Ca²⁺ in PM_2.5 in Shijiazhuang decreased from 55% in spring to 25% in winter. The proportion of Mg²⁺ in PM_2.5 peaked in summer at ∼70%, and decreased to 23% in winter. Hence, the source of non-dominant ions might vary from site to site.

To investigate the ion enrichment of the different sizes of particulate matter, a further study was conducted in Beijing. We collected and analyzed PM₁, PM_2.5, PM₁₀ and total suspended particulate matters (TSP). Figure 3 shows the concentrations of nine ions in particulate matter of different sizes. The smaller diameter particulate matter contained the highest concentration of soluble ions. For example, >56% of the water-soluble ions were distributed in PM₁, compared to <10% in particles with a diameter>10 µm. Small particulate matter was more capable of carrying K⁺. While Cl⁻, Mg²⁺, and Ca²⁺ were present at higher concentrations in the larger sizes of particulate matter. Thus, controlling small particulate matter would substantially reduce the concentration of water-soluble ions in the atmosphere.

Download:

Figure 3. Ion enrichment in particulate matter of different sizes.

https://doi.org/10.1371/journal.pone.0113831.g003

4. Secondary ions

Secondary ions play a key role in the formation of particulate matter [46]–[48]. The non-organic secondary ions NH₄⁺, SO₄²⁻ and NO₃⁻ accumulate under stable weather conditions. Secondary ions also affect atmospheric visibility and human health [49]. The total mass of secondary ions contributed 42.4% and 31.5% of the PM_2.5 and PM₁₀, respectively. The major water-soluble ions were the secondary inorganic ions sulfate, nitrate and ammonium, which accounted for 77% of the total water-soluble ions in Beijing in 2008 [50]. The ratio of [NO₃⁻]/[SO₄²⁻] in aerosols can be used to analyze the contribution of mobile sources (such as vehicle exhaust) to stationary sources (such as boilers) [51]. Coal is the main source of SO₂ emissions. NO_x originates from motor vehicles and coal burning [52], [53]. An increased NO³⁻ contribution to PM₁ mass loading during polluted periods has been reported [54]. Table 5 shows the [NO₃⁻]/[SO₄²⁻] ratios of the different sites and seasons. The ratios during summer were smaller than those in the other three seasons because the high temperatures transform particulate nitrate into the gaseous state, thus reducing the [NO₃⁻]/[SO₄²⁻] ratio [55]. In winter, the [NO₃⁻] <[SO₄²⁻] in Tianjin and Shijiazhuang implies a dominance of sulfate pollution from heating and industrial coal burning. In contrast, in Beijing, [NO₃⁻]> [SO₄²⁻] and the concentration of NO_x continued to increase in Beijing in 2011 [56], which indicates that vehicles made a greater contribution to emissions than in Tianjin and Shijiazhuang.

Download:

Table 5. Seasonal [NO₃⁻]/[SO₄²⁻] ratios in three cities in the North China Plain in 2013.

https://doi.org/10.1371/journal.pone.0113831.t005

The differences among the three cities reflect both the dramatic increase in vehicle numbers and the decreased usage of coal at the different stages of urban development (Table 6). Beijing had the largest contribution from vehicles, which was threefold greater than that of Shijiazhuang. Vehicles have already become one of the main contributors to particle emissions in metropolitans as the one of the main source of NO₃⁻ [57]. For example, direct emission, road dust and secondary nitrates, which were closely related to vehicle use, contributed 27.9%-47.5% to total PM_2.5 mass concentration in Beijing [37]. Larger transportation sector in Beijing and Tianjin consumed more petroleum, thus contributed more NO₃⁻ emission. The industrial structure of the cities, which we did not investigate in depth, was the cause of the difference between coal and petroleum consumption. Coal was the main energy source, combustion of which results in release of both NO₃⁻ and SO₄²⁻. Shijiazhuang relied substantially on coal combustion, at a level almost threefold greater than that in Beijing. High coal combustion produces higher SO₂ emissions, leading to higher SO₄²⁻ concentrations. Therefore [NO₃⁻]/[SO₄²⁻] ratio here was close related to the energy use in the transport sector and the industrial sector.

Download:

Table 6. Vehicle amount and major energy consumption of 3 cities in 2012.

https://doi.org/10.1371/journal.pone.0113831.t006

Secondary ions have complex reactions. NH₄⁺ readily reacts with SO₄²⁻ to the stable form of ammonium salts [50], [61], [62]. The reaction of NH₄⁺ and NO₃⁻ is affected by the gas/aerosol distribution of the precursor gases (NH₃ and HNO₃) in terms of temperature and humidity [63]. [NH₄⁺] was linearly correlated with the micro-equivalent sum of ([SO₄²⁻]+[NO₃⁻]) in the three cities. The linear fits of the data were:

The PM_2.5 regression slopes were <1 in Beijing and Tianjin, but slightly>1 in Shijiazhuang. The PM₁₀ regression slopes of [NH₄⁺] against ([SO₄²⁻]+[NO₃⁻]) at the three cities were <1, indicating that SO₄²⁻ and NO₃⁻ in large particles were incompletely neutralized. Larger slope coefficients and higher NH₄⁺ concentrations were found in Shijiazhuang. One possible reason for this was the greater agricultural fertilizer usage (489.0 million tons in Shijiazhuang, compared to 136.7 million tons in Beijing and 244.5 million tons in Tianjin in 2012. Data from 2013 were not available at the time of our study), which was one of the primary sources of NH₄⁺. Since evaporated ammonium was not taken into account, the extent of SO₄²⁻ and NO₃⁻ neutralization might have been underestimated.

Conclusion

The concentration gradient of total water-soluble ions was in the order Shijiazhuang> Tianjin> Beijing. Spring had the highest concentrations. The total concentrations by mass for summer and winter were similar, and higher than those in autumn. The gradient of ion species across the North China region was NO₃⁻, SO₄²⁻> NH₄⁺> Cl⁻> Ca²⁺, K⁺, Na⁺> Mg²⁺, F⁻. NO₃⁻, SO₄²⁻, NH₄⁺, and Cl⁻ were the dominant ion species, contributing more than 90% and 86% of the concentration by mass of the total water-soluble ions in PM_2.5 and PM₁₀, respectively. The size of the particulate matter was an important determinant of the ion composition. Over 50% of the water-soluble ions were present in PM₁, and 73.9%-94.9% in PM_2.5. Secondary ions, including NO₃⁻, SO₄²⁻ and NH₄⁺, comprised 43.4% and 31.5% of the total average concentrations by mass. The [NO₃⁻]/[SO₄²⁻] ratio indicated that the contribution of vehicle pollution increased with economic development. The anion/cation balance varied among sites and seasons, indicating that the source might be complex. The balance of secondary ions was likely influenced by the usage of agricultural fertilizer. The pollution patterns of the whole area were somewhat significantly correlated. Further studies of organic carbon and pollution source apportionment are needed.

Acknowledgments

We thank the anonymous reviewers for their valuable comments on the earlier version of this manuscript.

Author Contributions

Conceived and designed the experiments: XD YL ET LZ CW ZW. Performed the experiments: XD. Analyzed the data: ZW XD. Contributed reagents/materials/analysis tools: XD YL ET LZ CW. Wrote the paper: ZW XD.

References

1. Rogula-Kozłowska W, Klejnowski K, Rogula-Kopiec P, Ośródka L, Krajny E, et al. (2013) Spatial and seasonal variability of the mass concentration and chemical composition of PM_2.5 in Poland. Air Qual Atmos Health 7:41–58.
- View Article
- Google Scholar
2. Xu G, Gao Y, Lin Q, Li W, Chen L (2013) Characteristics of water-soluble inorganic and organic ions in aerosols over the Southern Ocean and coastal East Antarctica during austral summer. J Geophys Res Atmos 118:13, 303–313, 318.
3. Ho KF, Lee SC, Cao JJ, Chow JC, Watson JG (2006) Seasonal variations and mass closure analysis of particulate matter in Hong Kong. Sci Total Environ 355:276–287.
- View Article
- Google Scholar
4. Contini D, Cesari D, Genga A, Siciliano M, Ielpo P, et al. (2014) Source apportionment of size-segregated atmospheric particles based on the major water-soluble components in Lecce (Italy). Sci Total Environ 472:248–261.
- View Article
- Google Scholar
5. Shon ZH, Ghosh S, Kim KH, Song SK, Jung K, et al. (2013) Analysis of water-soluble ions and their precursor gases over diurnal cycle. Atmos Res 132–133:309–321.
- View Article
- Google Scholar
6. WHO (2006) Air quality guidelines global update 2005. Copenhagen, Denmark. 496 p.
7. United States Environmental Protection Agency (2004) Air quality criteria for particulate matter (October 2004) volume. I:1–900.
- View Article
- Google Scholar
8. Cao J, Xu H, Xu Q, Chen B, Kan H (2012) Fine particulate matter constituents and cardiopulmonary mortality in a heavily polluted Chinese city. Environ Health Perspect 120:373–378.
- View Article
- Google Scholar
9. Brunekreef B, Holgate ST (2002) Air pollution and health. Lancet 360:1233–1242.
- View Article
- Google Scholar
10. Lepeule J, Laden F, Dockery D, Schwartz J (2012) Chronic exposure to fine particles and mortality: An extended follow-up of the Harvard six cities study from 1974 to 2009. Environ Health Perspect 120:965–970.
- View Article
- Google Scholar
11. Tao J, Zhang L, Ho K, Zhang R, Lin Z, et al. (2014) Impact of PM_2.5 chemical compositions on aerosol light scattering in Guangzhou – the largest megacity in South China. Atmos Res 135–136:48–58.
- View Article
- Google Scholar
12. Lopez-Villarrubia E, Iniguez C, Peral N, Garcia MD, Ballester F (2012) Characterizing mortality effects of particulate matter size fractions in the two capital cities of the Canary Islands. Environ Res 112:129–138.
- View Article
- Google Scholar
13. Zhou J, Zhang R, Cao J, Chow JC, Watson JG (2012) Carbonaceous and ionic components of atmospheric fine particles in Beijing and their impact on atmospheric visibility. Aerosol Air Qual Res 12:492–502.
- View Article
- Google Scholar
14. Hughes LS, Allen JO, Kleeman MJ, Johnson RJ, Cass GR, et al. (1999) Size and composition distribution of atmospheric particles in southern California. Environ Sci Technol 33:3506–3515.
- View Article
- Google Scholar
15. Zhang F, Cheng H-R, Wang Z-W, Lv X-P, Zhu Z-M, et al. (2014) Fine particles (PM_2.5) at a CAWNET background site in Central China: Chemical compositions, seasonal variations and regional pollution events. Atmos Environ 86:193–202.
- View Article
- Google Scholar
16. Fan J, Yue X, Jing Y, Chen Q, Wang S (2014) Online monitoring of water-soluble ionic composition of PM₁₀ during early summer over Lanzhou City. J Environ Sci 26:353–361.
- View Article
- Google Scholar
17. Satsangi A, Pachauri T, Singla V, Lakhani A (2013) Water soluble ionic species in atmospheric aerosols: Concentrations and sources at Agra in the Indo-Gangetic Plain (IGP). Aerosol Air Qual Res 13:1877–1889.
- View Article
- Google Scholar
18. Kawashima H, Kurahashi T (2011) Inorganic ion and nitrogen isotopic compositions of atmospheric aerosols at Yurihonjo, Japan: Implications for nitrogen sources. Atmos Environ 45:6309–6316.
- View Article
- Google Scholar
19. Guo Y-T, Zhang J, Wang S-G, She F, Li X (2012) Long-term characterization of major water-soluble inorganic ions in PM₁₀ in coastal site on the Japan Sea. J Atmos Chem 68:299–316.
- View Article
- Google Scholar
20. Chen J, Qiu S, Shang J, Wilfrid O, Liu X (2014) Impact of relative humidity and water soluble constituents of PM_2.5 on visibility impairment in Beijing, China. Aerosol Air Qual Res 14:260–268.
- View Article
- Google Scholar
21. Shen Z, Cao J, Zhang L, Liu L, Zhang Q, et al. (2013) Day-night differences and seasonal variations of chemical species in PM₁₀ over Xi’an, northwest China. Environ Sci Pollut Res 21:3697–3705.
- View Article
- Google Scholar
22. Zhang W-J, Sun Y-L, Zhuang G-S, Xu D-Q (2006) Characteristics and seasonal variations of PM_2.5, PM₁₀, and TSP aerosol in Beijing. Biomed Environ Sci (19):461–468.
- View Article
- Google Scholar
23. SUN Y, Pan Y-P, Lin X-R, Zhu R-H, Wang Y-S (2011) Chemical composition and mass closure of particulate matter in Beijing, Tianjin and Hebei megacities, Northern China. Chin J Environ Sci 21:2732–2740.
- View Article
- Google Scholar
24. Ni T, Li P, Han B, Bai Z, Ding X, et al. (2013) Spatial and temporal variation of chemical composition and mass closure of ambient PM₁₀ in Tianjin, China. Aerosol Air Qual Res 13:1832–1846.
- View Article
- Google Scholar
25. Li X, Wang L, Wang Y-S, Wen T, Yang Y, et al. (2012) Chemical composition and size distribution of airborne particulate matters in Beijing during the 2008 Olympics. Atmos Environ 50:278–286.
- View Article
- Google Scholar
26. He K, Yang F, Ma Y, Zhang Q, Yao X, et al. (2001) The characteristics of PM 2.5 in Beijing, China. Atmos Environ 35:4959–4970.
- View Article
- Google Scholar
27. Wang H, Zhuang Y, Wang Y, Sun Y, Yuan H, et al. (2008) Long-term monitoring and source apportionment of PM_2.5/PM₁₀ in Beijing, China. J Environ Sci 20:1323–1327.
- View Article
- Google Scholar
28. Yin L, Niu Z, Chen X, Chen J, Zhang F, et al. (2014) Characteristics of water-soluble inorganic ions in PM_2.5 and PM_2.5–10 in the coastal urban agglomeration along the Western Taiwan Strait Region, China. Environ Sci Pollut Res 21:5141–5156.
- View Article
- Google Scholar
29. Yao X, Lau APS, Fang M, Chan CK, Hu M (2003) Size distributions and formation of ionic species in atmospheric particulate pollutants in Beijing, China: 1–inorganic ions. Atmos Environ 37:2991–3000.
- View Article
- Google Scholar
30. Zhang R, Jing J, Tao J, Hsu SC, Wang G, et al. (2013) Chemical characterization and source apportionment of PM_2.5 in Beijing: seasonal perspective. Atmos Chem Phys 13:7053–7074.
- View Article
- Google Scholar
31. Gu J, Bai Z, Liu A, Wu L, Xie Y, et al. (2010) Characterization of atmospheric organic carbon and element carbon of PM_2.5 and PM₁₀ at Tianjin, China. Aerosol Air Qual Res 10:167–176.
- View Article
- Google Scholar
32. Wang S, Xing J, Jang C, Zhu Y, Fu JS, et al. (2011) Impact assessment of ammonia emissions on inorganic aerosols in east China using response surface modeling technique. Environ Sci Technol 45:9293–9300.
- View Article
- Google Scholar
33. Gray HA, Cass GR, Huntzicker JJ (1986) Characteristics of atmospheric organic and elemental carbon particle concentrations in Los Angeles. Environ Sci Technol 20:580–589.
- View Article
- Google Scholar
34. Bellouin N, Boucher O, Haywood J, Reddy MS (2005) Global estimate of aerosol direct radiative forcing from satellite measurements. Nature 438:1138–1141.
- View Article
- Google Scholar
35. Wang Y, Zhuang G, Tang A, Yuan H, Sun Y, et al. (2005) The ion chemistry and the source of PM_2.5 aerosol in Beijing. Atmos Environt 39:3771–3784.
- View Article
- Google Scholar
36. Shen J, Liu X, Zhang Y, Fangmeier A, Goulding K, et al. (2011) Atmospheric ammonia and particulate ammonium from agricultural sources in the North China Plain. Atmos Environ 45:5033–5041.
- View Article
- Google Scholar
37. Song Y, Xie S, Zhang Y, Zeng L, Salmon LG, et al. (2006) Source apportionment of PM_2.5 in Beijing using principal component analysis/absolute principal component scores and UNMIX. Sci Total Environ 372:278–286.
- View Article
- Google Scholar
38. Wang Z-S, Wu T, Shi G-L, Fu X, Ying-ze T, et al. (2012) Potential source analysis for PM₁₀ and PM_2.5 in autumn in a northern city in China. Aerosol Air Qual Res 12:39–48.
- View Article
- Google Scholar
39. Gao Q-X, Su F-Q, Ren Z-H, Zhang Z-G, Wang Y-T (2002) The dust weather of Beijing and its impact. Chin Environ Sci 22:468–471.
- View Article
- Google Scholar
40. Tao J, Zhang L, Ho K, Zhang R, Lin Z, et al. (2014) Impact of PM_2.5 chemical compositions on aerosol light scattering in Guangzhou – the largest megacity in South China. Atmos Res 135–136:48–58.
- View Article
- Google Scholar
41. Wang L, Du H, Chen J, Zhang M, Huang X, et al. (2013) Consecutive transport of anthropogenic air masses and dust storm plume: Two case events at Shanghai, China. Atmos Res 127:22–33.
- View Article
- Google Scholar
42. Karthikeyan S, Balasubramanian R (2006) Determination of water-soluble inorganic and organic species in atmospheric fine particulate matter. Microchem J 82:49–55.
- View Article
- Google Scholar
43. Khan MF, Hirano K, Masunaga S (2010) Quantifying the sources of hazardous elements of suspended particulate matter aerosol collected in Yokohama, Japan. Atmos Environ 44:2646–2657.
- View Article
- Google Scholar
44. Heo JB, Hopke PK, Yi SM (2009) Source apportionment of PM_2.5 in Seoul, Korea. Atmos Chem Phys 9:4957–4971.
- View Article
- Google Scholar
45. Tang IN, Munkelwitz HR (1993) Composition and temperature dependence of the deliquescence properties of hygroscopic aerosols. Atmos Environ 27A:467–473.
- View Article
- Google Scholar
46. Huang Z, Harrison RM, Allen AG, James JD, Tilling RM, et al. (2004) Field intercomparison of filter pack and impactor sampling for aerosol nitrate, ammonium, and sulphate at coastal and inland sites. Atmos Res 71:215–232.
- View Article
- Google Scholar
47. Balasubramanian R, Karthikeyan S, Potter J, Wurl O, Durville C (2013) Chemical characterization of aerosols in the equatorial atmosphere over the Indian Ocean. Atmos Environ 78:268–276.
- View Article
- Google Scholar
48. Kong L, Yang Y, Zhang S, Zhao X, Du H (2014) Observations of linear dependence between sulfate and nitrate in atmospheric particles. J Geophys Res Atmos 119:341–361.
- View Article
- Google Scholar
49. Pope CA III, Dockery DW (2006) Health effects of fine particulate air pollution: lines that connect. J Air Waste Manage 56:709–742.
- View Article
- Google Scholar
50. Li X, Wang L, Wang Y, Wen T, Yang Y, et al. (2012) Chemical composition and size distribution of airborne particulate matters in Beijing during the 2008 Olympics. Atmos Environ 50:278–286.
- View Article
- Google Scholar
51. Yao X, Chan CK, Fang M, Cadle S, Chan T, et al. (2002) The water-soluble ionic composition of PM_2.5 in Shanghai and Beijing, China. Atmos Environ 36:4223–4234.
- View Article
- Google Scholar
52. Liu J, Zhang X-L, Xu X-F, Xu H-H (2011) Comparison analysis of variation characteristics of SO₂, NO_x, O₃ and PM_2.5 between rural and urban areas, Beijing. Environ Sci 29:1059–1065.
- View Article
- Google Scholar
53. Mkoma SL, da Rocha GO, Regis ACD, Domingos JSS, Santos JVS, et al. (2014) Major ions in PM_2.5 and PM₁₀ released from buses: The use of diesel/biodiesel fuels under real conditions. Fuel 115:109–117.
- View Article
- Google Scholar
54. Shi Y, Chen J, Hu D, Wang L, Yang X, et al. (2014) Airborne submicron particulate (PM₁) pollution in Shanghai, China: Chemical variability, formation/dissociation of associated semi-volatile components and the impacts on visibility. Sci Total Environ 473–474:199–206.
- View Article
- Google Scholar
55. Zhang Y, Sun J, Zhang X, Shen X, Wang T, et al. (2012) Seasonal characterization of components and size distributions for submicron aerosols in Beijing. Sci China Earth Sci 56:890–900.
- View Article
- Google Scholar
56. Gao J, Zhang Y, Zhang M, Zhang J, Wang S (2014) Photochemical properties and source of pollutants during continuous pollution episodes in Beijing, October, 2011. J Environ Sci 26:44–53.
- View Article
- Google Scholar
57. Martins LD, Silva Júnior CR, Solci MC, Pinto JP, Souza DZ, et al. (2011) Particle emission from heavy-duty engine fuelled with blended diesel and biodiesel. Environ Monit Assess 184:2663–2676.
- View Article
- Google Scholar
58. Beijing Municipal Bureau of Statistics, NBS Survey Office in Beijing. (2013) Beijing statistical yearbook 2013. Beijing: China Statistics Press.
59. Tianjin Municipal Bureau of Statistics, Survey Office of the National Bureau of Statistics in Tianjin. (2013) Tianjin statistical yearbook 2013. Beijing: China Statistics Press.
60. Shijiazhuang Municipal Bureau of Statistics, Survey Office of the National Bureau of Statistics in Shijiazhuang (2013) Shijiazhuang statistical yearbook 2013. Beijing: China Statistics Press.
61. Lai S, Zou S, Cao J, Lee S, Ho K (2007) Characterizing ionic species in PM_2.5 and PM₁₀ in four Pearl River Delta cities, South China. J Environ Sci 19:939–947.
- View Article
- Google Scholar
62. Wang L, Du H, Chen J, Zhang M, Huang X, et al. (2013) Consecutive transport of anthropogenic air masses and dust storm plume: Two case events at Shanghai, China. Atmos Res 127:22–33.
- View Article
- Google Scholar
63. Han T, Liu X, Zhang Y, Gu J, Tian H, et al. (2014) Chemical characteristics of PM₁₀ during the summer in the mega-city Guangzhou, China. Atmos Res 137:25–34.
- View Article
- Google Scholar

[ref1] 1. Rogula-Kozłowska W, Klejnowski K, Rogula-Kopiec P, Ośródka L, Krajny E, et al. (2013) Spatial and seasonal variability of the mass concentration and chemical composition of PM_2.5 in Poland. Air Qual Atmos Health 7:41–58.
View Article
Google Scholar

[2] View Article

[3] Google Scholar

[ref2] 2. Xu G, Gao Y, Lin Q, Li W, Chen L (2013) Characteristics of water-soluble inorganic and organic ions in aerosols over the Southern Ocean and coastal East Antarctica during austral summer. J Geophys Res Atmos 118:13, 303–313, 318.

[ref3] 3. Ho KF, Lee SC, Cao JJ, Chow JC, Watson JG (2006) Seasonal variations and mass closure analysis of particulate matter in Hong Kong. Sci Total Environ 355:276–287.
View Article
Google Scholar

[6] View Article

[7] Google Scholar

[ref4] 4. Contini D, Cesari D, Genga A, Siciliano M, Ielpo P, et al. (2014) Source apportionment of size-segregated atmospheric particles based on the major water-soluble components in Lecce (Italy). Sci Total Environ 472:248–261.
View Article
Google Scholar

[9] View Article

[10] Google Scholar

[ref5] 5. Shon ZH, Ghosh S, Kim KH, Song SK, Jung K, et al. (2013) Analysis of water-soluble ions and their precursor gases over diurnal cycle. Atmos Res 132–133:309–321.
View Article
Google Scholar

[12] View Article

[13] Google Scholar

[ref6] 6. WHO (2006) Air quality guidelines global update 2005. Copenhagen, Denmark. 496 p.

[ref7] 7. United States Environmental Protection Agency (2004) Air quality criteria for particulate matter (October 2004) volume. I:1–900.
View Article
Google Scholar

[16] View Article

[17] Google Scholar

[ref8] 8. Cao J, Xu H, Xu Q, Chen B, Kan H (2012) Fine particulate matter constituents and cardiopulmonary mortality in a heavily polluted Chinese city. Environ Health Perspect 120:373–378.
View Article
Google Scholar

[19] View Article

[20] Google Scholar

[ref9] 9. Brunekreef B, Holgate ST (2002) Air pollution and health. Lancet 360:1233–1242.
View Article
Google Scholar

[22] View Article

[23] Google Scholar

[ref10] 10. Lepeule J, Laden F, Dockery D, Schwartz J (2012) Chronic exposure to fine particles and mortality: An extended follow-up of the Harvard six cities study from 1974 to 2009. Environ Health Perspect 120:965–970.
View Article
Google Scholar

[25] View Article

[26] Google Scholar

[ref11] 11. Tao J, Zhang L, Ho K, Zhang R, Lin Z, et al. (2014) Impact of PM_2.5 chemical compositions on aerosol light scattering in Guangzhou – the largest megacity in South China. Atmos Res 135–136:48–58.
View Article
Google Scholar

[28] View Article

[29] Google Scholar

[ref12] 12. Lopez-Villarrubia E, Iniguez C, Peral N, Garcia MD, Ballester F (2012) Characterizing mortality effects of particulate matter size fractions in the two capital cities of the Canary Islands. Environ Res 112:129–138.
View Article
Google Scholar

[31] View Article

[32] Google Scholar

[ref13] 13. Zhou J, Zhang R, Cao J, Chow JC, Watson JG (2012) Carbonaceous and ionic components of atmospheric fine particles in Beijing and their impact on atmospheric visibility. Aerosol Air Qual Res 12:492–502.
View Article
Google Scholar

[34] View Article

[35] Google Scholar

[ref14] 14. Hughes LS, Allen JO, Kleeman MJ, Johnson RJ, Cass GR, et al. (1999) Size and composition distribution of atmospheric particles in southern California. Environ Sci Technol 33:3506–3515.
View Article
Google Scholar

[37] View Article

[38] Google Scholar

[ref15] 15. Zhang F, Cheng H-R, Wang Z-W, Lv X-P, Zhu Z-M, et al. (2014) Fine particles (PM_2.5) at a CAWNET background site in Central China: Chemical compositions, seasonal variations and regional pollution events. Atmos Environ 86:193–202.
View Article
Google Scholar

[40] View Article

[41] Google Scholar

[ref16] 16. Fan J, Yue X, Jing Y, Chen Q, Wang S (2014) Online monitoring of water-soluble ionic composition of PM₁₀ during early summer over Lanzhou City. J Environ Sci 26:353–361.
View Article
Google Scholar

[43] View Article

[44] Google Scholar

[ref17] 17. Satsangi A, Pachauri T, Singla V, Lakhani A (2013) Water soluble ionic species in atmospheric aerosols: Concentrations and sources at Agra in the Indo-Gangetic Plain (IGP). Aerosol Air Qual Res 13:1877–1889.
View Article
Google Scholar

[46] View Article

[47] Google Scholar

[ref18] 18. Kawashima H, Kurahashi T (2011) Inorganic ion and nitrogen isotopic compositions of atmospheric aerosols at Yurihonjo, Japan: Implications for nitrogen sources. Atmos Environ 45:6309–6316.
View Article
Google Scholar

[49] View Article

[50] Google Scholar

[ref19] 19. Guo Y-T, Zhang J, Wang S-G, She F, Li X (2012) Long-term characterization of major water-soluble inorganic ions in PM₁₀ in coastal site on the Japan Sea. J Atmos Chem 68:299–316.
View Article
Google Scholar

[52] View Article

[53] Google Scholar

[ref20] 20. Chen J, Qiu S, Shang J, Wilfrid O, Liu X (2014) Impact of relative humidity and water soluble constituents of PM_2.5 on visibility impairment in Beijing, China. Aerosol Air Qual Res 14:260–268.
View Article
Google Scholar

[55] View Article

[56] Google Scholar

[ref21] 21. Shen Z, Cao J, Zhang L, Liu L, Zhang Q, et al. (2013) Day-night differences and seasonal variations of chemical species in PM₁₀ over Xi’an, northwest China. Environ Sci Pollut Res 21:3697–3705.
View Article
Google Scholar

[58] View Article

[59] Google Scholar

[ref22] 22. Zhang W-J, Sun Y-L, Zhuang G-S, Xu D-Q (2006) Characteristics and seasonal variations of PM_2.5, PM₁₀, and TSP aerosol in Beijing. Biomed Environ Sci (19):461–468.
View Article
Google Scholar

[61] View Article

[62] Google Scholar

[ref23] 23. SUN Y, Pan Y-P, Lin X-R, Zhu R-H, Wang Y-S (2011) Chemical composition and mass closure of particulate matter in Beijing, Tianjin and Hebei megacities, Northern China. Chin J Environ Sci 21:2732–2740.
View Article
Google Scholar

[64] View Article

[65] Google Scholar

[ref24] 24. Ni T, Li P, Han B, Bai Z, Ding X, et al. (2013) Spatial and temporal variation of chemical composition and mass closure of ambient PM₁₀ in Tianjin, China. Aerosol Air Qual Res 13:1832–1846.
View Article
Google Scholar

[67] View Article

[68] Google Scholar

[ref25] 25. Li X, Wang L, Wang Y-S, Wen T, Yang Y, et al. (2012) Chemical composition and size distribution of airborne particulate matters in Beijing during the 2008 Olympics. Atmos Environ 50:278–286.
View Article
Google Scholar

[70] View Article

[71] Google Scholar

[ref26] 26. He K, Yang F, Ma Y, Zhang Q, Yao X, et al. (2001) The characteristics of PM 2.5 in Beijing, China. Atmos Environ 35:4959–4970.
View Article
Google Scholar

[73] View Article

[74] Google Scholar

[ref27] 27. Wang H, Zhuang Y, Wang Y, Sun Y, Yuan H, et al. (2008) Long-term monitoring and source apportionment of PM_2.5/PM₁₀ in Beijing, China. J Environ Sci 20:1323–1327.
View Article
Google Scholar

[76] View Article

[77] Google Scholar

[ref28] 28. Yin L, Niu Z, Chen X, Chen J, Zhang F, et al. (2014) Characteristics of water-soluble inorganic ions in PM_2.5 and PM_2.5–10 in the coastal urban agglomeration along the Western Taiwan Strait Region, China. Environ Sci Pollut Res 21:5141–5156.
View Article
Google Scholar

[79] View Article

[80] Google Scholar

[ref29] 29. Yao X, Lau APS, Fang M, Chan CK, Hu M (2003) Size distributions and formation of ionic species in atmospheric particulate pollutants in Beijing, China: 1–inorganic ions. Atmos Environ 37:2991–3000.
View Article
Google Scholar

[82] View Article

[83] Google Scholar

[ref30] 30. Zhang R, Jing J, Tao J, Hsu SC, Wang G, et al. (2013) Chemical characterization and source apportionment of PM_2.5 in Beijing: seasonal perspective. Atmos Chem Phys 13:7053–7074.
View Article
Google Scholar

[85] View Article

[86] Google Scholar

[ref31] 31. Gu J, Bai Z, Liu A, Wu L, Xie Y, et al. (2010) Characterization of atmospheric organic carbon and element carbon of PM_2.5 and PM₁₀ at Tianjin, China. Aerosol Air Qual Res 10:167–176.
View Article
Google Scholar

[88] View Article

[89] Google Scholar

[ref32] 32. Wang S, Xing J, Jang C, Zhu Y, Fu JS, et al. (2011) Impact assessment of ammonia emissions on inorganic aerosols in east China using response surface modeling technique. Environ Sci Technol 45:9293–9300.
View Article
Google Scholar

[91] View Article

[92] Google Scholar

[ref33] 33. Gray HA, Cass GR, Huntzicker JJ (1986) Characteristics of atmospheric organic and elemental carbon particle concentrations in Los Angeles. Environ Sci Technol 20:580–589.
View Article
Google Scholar

[94] View Article

[95] Google Scholar

[ref34] 34. Bellouin N, Boucher O, Haywood J, Reddy MS (2005) Global estimate of aerosol direct radiative forcing from satellite measurements. Nature 438:1138–1141.
View Article
Google Scholar

[97] View Article

[98] Google Scholar

[ref35] 35. Wang Y, Zhuang G, Tang A, Yuan H, Sun Y, et al. (2005) The ion chemistry and the source of PM_2.5 aerosol in Beijing. Atmos Environt 39:3771–3784.
View Article
Google Scholar

[100] View Article

[101] Google Scholar

[ref36] 36. Shen J, Liu X, Zhang Y, Fangmeier A, Goulding K, et al. (2011) Atmospheric ammonia and particulate ammonium from agricultural sources in the North China Plain. Atmos Environ 45:5033–5041.
View Article
Google Scholar

[103] View Article

[104] Google Scholar

[ref37] 37. Song Y, Xie S, Zhang Y, Zeng L, Salmon LG, et al. (2006) Source apportionment of PM_2.5 in Beijing using principal component analysis/absolute principal component scores and UNMIX. Sci Total Environ 372:278–286.
View Article
Google Scholar

[106] View Article

[107] Google Scholar

[ref38] 38. Wang Z-S, Wu T, Shi G-L, Fu X, Ying-ze T, et al. (2012) Potential source analysis for PM₁₀ and PM_2.5 in autumn in a northern city in China. Aerosol Air Qual Res 12:39–48.
View Article
Google Scholar

[109] View Article

[110] Google Scholar

[ref39] 39. Gao Q-X, Su F-Q, Ren Z-H, Zhang Z-G, Wang Y-T (2002) The dust weather of Beijing and its impact. Chin Environ Sci 22:468–471.
View Article
Google Scholar

[112] View Article

[113] Google Scholar

[ref40] 40. Tao J, Zhang L, Ho K, Zhang R, Lin Z, et al. (2014) Impact of PM_2.5 chemical compositions on aerosol light scattering in Guangzhou – the largest megacity in South China. Atmos Res 135–136:48–58.
View Article
Google Scholar

[115] View Article

[116] Google Scholar

[ref41] 41. Wang L, Du H, Chen J, Zhang M, Huang X, et al. (2013) Consecutive transport of anthropogenic air masses and dust storm plume: Two case events at Shanghai, China. Atmos Res 127:22–33.
View Article
Google Scholar

[118] View Article

[119] Google Scholar

[ref42] 42. Karthikeyan S, Balasubramanian R (2006) Determination of water-soluble inorganic and organic species in atmospheric fine particulate matter. Microchem J 82:49–55.
View Article
Google Scholar

[121] View Article

[122] Google Scholar

[ref43] 43. Khan MF, Hirano K, Masunaga S (2010) Quantifying the sources of hazardous elements of suspended particulate matter aerosol collected in Yokohama, Japan. Atmos Environ 44:2646–2657.
View Article
Google Scholar

[124] View Article

[125] Google Scholar

[ref44] 44. Heo JB, Hopke PK, Yi SM (2009) Source apportionment of PM_2.5 in Seoul, Korea. Atmos Chem Phys 9:4957–4971.
View Article
Google Scholar

[127] View Article

[128] Google Scholar

[ref45] 45. Tang IN, Munkelwitz HR (1993) Composition and temperature dependence of the deliquescence properties of hygroscopic aerosols. Atmos Environ 27A:467–473.
View Article
Google Scholar

[130] View Article

[131] Google Scholar

[ref46] 46. Huang Z, Harrison RM, Allen AG, James JD, Tilling RM, et al. (2004) Field intercomparison of filter pack and impactor sampling for aerosol nitrate, ammonium, and sulphate at coastal and inland sites. Atmos Res 71:215–232.
View Article
Google Scholar

[133] View Article

[134] Google Scholar

[ref47] 47. Balasubramanian R, Karthikeyan S, Potter J, Wurl O, Durville C (2013) Chemical characterization of aerosols in the equatorial atmosphere over the Indian Ocean. Atmos Environ 78:268–276.
View Article
Google Scholar

[136] View Article

[137] Google Scholar

[ref48] 48. Kong L, Yang Y, Zhang S, Zhao X, Du H (2014) Observations of linear dependence between sulfate and nitrate in atmospheric particles. J Geophys Res Atmos 119:341–361.
View Article
Google Scholar

[139] View Article

[140] Google Scholar

[ref49] 49. Pope CA III, Dockery DW (2006) Health effects of fine particulate air pollution: lines that connect. J Air Waste Manage 56:709–742.
View Article
Google Scholar

[142] View Article

[143] Google Scholar

[ref50] 50. Li X, Wang L, Wang Y, Wen T, Yang Y, et al. (2012) Chemical composition and size distribution of airborne particulate matters in Beijing during the 2008 Olympics. Atmos Environ 50:278–286.
View Article
Google Scholar

[145] View Article

[146] Google Scholar

[ref51] 51. Yao X, Chan CK, Fang M, Cadle S, Chan T, et al. (2002) The water-soluble ionic composition of PM_2.5 in Shanghai and Beijing, China. Atmos Environ 36:4223–4234.
View Article
Google Scholar

[148] View Article

[149] Google Scholar

[ref52] 52. Liu J, Zhang X-L, Xu X-F, Xu H-H (2011) Comparison analysis of variation characteristics of SO₂, NO_x, O₃ and PM_2.5 between rural and urban areas, Beijing. Environ Sci 29:1059–1065.
View Article
Google Scholar

[151] View Article

[152] Google Scholar

[ref53] 53. Mkoma SL, da Rocha GO, Regis ACD, Domingos JSS, Santos JVS, et al. (2014) Major ions in PM_2.5 and PM₁₀ released from buses: The use of diesel/biodiesel fuels under real conditions. Fuel 115:109–117.
View Article
Google Scholar

[154] View Article

[155] Google Scholar

[ref54] 54. Shi Y, Chen J, Hu D, Wang L, Yang X, et al. (2014) Airborne submicron particulate (PM₁) pollution in Shanghai, China: Chemical variability, formation/dissociation of associated semi-volatile components and the impacts on visibility. Sci Total Environ 473–474:199–206.
View Article
Google Scholar

[157] View Article

[158] Google Scholar

[ref55] 55. Zhang Y, Sun J, Zhang X, Shen X, Wang T, et al. (2012) Seasonal characterization of components and size distributions for submicron aerosols in Beijing. Sci China Earth Sci 56:890–900.
View Article
Google Scholar

[160] View Article

[161] Google Scholar

[ref56] 56. Gao J, Zhang Y, Zhang M, Zhang J, Wang S (2014) Photochemical properties and source of pollutants during continuous pollution episodes in Beijing, October, 2011. J Environ Sci 26:44–53.
View Article
Google Scholar

[163] View Article

[164] Google Scholar

[ref57] 57. Martins LD, Silva Júnior CR, Solci MC, Pinto JP, Souza DZ, et al. (2011) Particle emission from heavy-duty engine fuelled with blended diesel and biodiesel. Environ Monit Assess 184:2663–2676.
View Article
Google Scholar

[166] View Article

[167] Google Scholar

[ref58] 58. Beijing Municipal Bureau of Statistics, NBS Survey Office in Beijing. (2013) Beijing statistical yearbook 2013. Beijing: China Statistics Press.

[ref59] 59. Tianjin Municipal Bureau of Statistics, Survey Office of the National Bureau of Statistics in Tianjin. (2013) Tianjin statistical yearbook 2013. Beijing: China Statistics Press.

[ref60] 60. Shijiazhuang Municipal Bureau of Statistics, Survey Office of the National Bureau of Statistics in Shijiazhuang (2013) Shijiazhuang statistical yearbook 2013. Beijing: China Statistics Press.

[ref61] 61. Lai S, Zou S, Cao J, Lee S, Ho K (2007) Characterizing ionic species in PM_2.5 and PM₁₀ in four Pearl River Delta cities, South China. J Environ Sci 19:939–947.
View Article
Google Scholar

[172] View Article

[173] Google Scholar

[ref62] 62. Wang L, Du H, Chen J, Zhang M, Huang X, et al. (2013) Consecutive transport of anthropogenic air masses and dust storm plume: Two case events at Shanghai, China. Atmos Res 127:22–33.
View Article
Google Scholar

[175] View Article

[176] Google Scholar

[ref63] 63. Han T, Liu X, Zhang Y, Gu J, Tian H, et al. (2014) Chemical characteristics of PM₁₀ during the summer in the mega-city Guangzhou, China. Atmos Res 137:25–34.
View Article
Google Scholar

[178] View Article

[179] Google Scholar

Figures

Abstract

Introduction

Experiment

1. Sampling

2. Analysis

Results and Discussion

1. Seasonal variation of total water-soluble ions

2. Ionic species

3. Ion enrichment in particulate matter of various sizes

4. Secondary ions

Conclusion

Acknowledgments

Author Contributions

References