Chao Peng1,2, Mi Tian 1,7, Yang Chen1, Huanbo Wang1, Leiming Zhang3, Guangming Shi1,4, Yuan Liu1, Fumo Yang 1,4,5, Chongzhi Zhai6

Research Center for Atmospheric Environment, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China
University of Chinese Academy of Sciences, Beijing 100049, China
Air Quality Research Division, Science and Technology Branch, Environment and Climate Change Canada, Toronto, Ontario, Canada
National Engineering Research Center for Flue Gas Desulfurization, College of Architecture and Environment, Sichuan University, Chengdu 610065, China
Institute of New Energy and Low-carbon Technology, Sichuan University, Chengdu 610065, China
Chongqing Academy of Environmental Science, Chongqing 401147, China
School of Urban Construction and Environmental Engineering, Chongqing University, Chongqing 400044, China

Received: February 15, 2019
Revised: May 30, 2019
Accepted: June 8, 2019

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Cite this article:

Peng, C., Tian, M., Chen, Y., Wang, H., Zhang, L., Shi, G., Liu, Y., Yang, F. and Zhai, C. (2019). Characteristics, Formation Mechanisms and Potential Transport Pathways of PM2.5 at a Rural Background Site in Chongqing, Southwest China. Aerosol Air Qual. Res. 19: 1980-1992.


  • Chemical characteristics of PM2.5 were investigated at a rural background site.
  • Photochemical reactions and transport led to high SO42– concentrations in summer.
  • The role of NO3 formation was highlighted for the dramatic increase of PM2.5.
  • Ammonium-rich condition promoted NO3 formation under low temperature.


Daily PM2.5 samples were collected at a rural background station (JinYun) located in Chongqing across four consecutive seasons from October 2014 to July 2015. The major water-soluble inorganic ions (WSIIs), organic carbon (OC) and elemental carbon (EC) were analyzed, and their chemical characteristics, transport pathways and potential source regions were investigated. The average annual PM2.5 concentration was 56.2 ± 31.0 µg m–3, of which secondary inorganic aerosol (SNA) and carbonaceous aerosols composed 41.0% and 29.4%, respectively. Higher concentrations of and contributions from SO42–, which were likely caused by the secondary transformation of SO2 into SO42–, were observed in summer than in autumn and spring. Additionally, transportation from the urban area of Chongqing (Yubei) played an important role in elevating the SO42– during this season. Although the accumulation of PM2.5 during pollution episodes in winter was also due to aqueous-phase reactions, based on the entire year, NO3 formation may have been primarily driven by homogeneous gas-phase reactions. Furthermore, the aerosol environment was ammonium-rich, and NH4+ formation promoted the production of NO3 at lower temperatures. The carbonaceous component, which consisted of 81.0–84.6% OC, exhibited higher concentrations in winter than in the other seasons; 50.0–77.2% of the total OC, in turn, was contributed by primary organic carbon (POC). Potential source contribution function (PSCF) analysis suggests that the site was mainly affected by regional pollution originating in the southwestern and northern areas of Chongqing.

Keywords: PM2.5; Rural background site; Chemical transformation; Potential transport pathways.


Atmospheric fine particles (PM2.5, particulate matter with an aerodynamic diameter ≤ 2.5 µm) negatively impact human health, air quality, and atmospheric visibility (Anderson et al., 2012; Cao et al., 2012; Deng et al., 2014; Deng et al., 2016a; Fu et al., 2016). PM2.5 levels across China have increased sharply in the past several decadesdue to rapid economic development, industrialization and urban expansion (Tie and Cao, 2009; Ma et al., 2016; Li et al., 2017). Although annual average PM2.5 concentration is declining recently due to the implementation of several measures, PM2.5 pollution was still serious in some megacities such as Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta, and Sichuan Basin (Tao et al., 2017). Chongqing was one of the most important industrial cities in Sichuan Basin and had severe aerosol pollution (Tian et al., 2017a; Wang et al., 2018b). For example, the annual mean concentration of PM2.5 was 70.9 ± 41.4 µg m–3 during 2014–2015 at Chongqing, which was about 2 times the secondary grade limit specified in the Chinese National Ambient Air Quality Standards (NAAQS) (Wang et al., 2018b). Different from other cities, Chongqing is a municipality with a population of 8.23 million and encircled by high mountains. Additionally, Chongqing is characterized by high relative humidity, low wind speeds, and extremely high temperature (Chen and Xie, 2013; Chen et al., 2017a). The characteristics, formation mechanisms and sources of PM2.5 have been investigated in this region, but mostly focused on urban areas (Yang et al., 2011; Tao et al., 2017; Tian et al., 2017b; Wang et al., 2017; Huang et al., 2018; Wang et al., 2018a, b). Results from these studies indicated that PM2.5 pollution was largely caused by local sources in this region. However, few studies have investigated PM2.5 pollution in rural areas in this region. Field study of PM2.5 at a rural background station can help to understand the impacts of anthropogenic activities on regional air quality, as has been done in the other regions of China (Zhang et al., 2014; Feng et al., 2015; Zhang et al., 2017; Zong et al., 2018).

In this study, PM2.5 filters were sampled in four consecutive seasons from October 2014 to July 2015 at a rural background station in Chongqing and analyzed for the mass concentrations and chemical compositions. The characteristics and possible formation mechanisms of PM2.5 and its major chemical components in different seasons were investigated. Transport pathways and potential source regions of PM2.5 were identified through cluster analysis and potential source contribution function (PSCF) based on backward trajectories. The results of this study have implications for better understanding the impacts of anthropogenic activities on regional pollution of PM2.5 in Chongqing.


Site Description

PM2.5 samples were collected on the roof of a six-floor building near the top of JinYun mountain (JY) (106°22′E, 29°49′N, 800 m a.s.l.), 35 km away from the central downtown area of urban Chongqing and 5 km away from the nearest town (Beibei). Lush vegetation dominates within a few kilometers of the site. This site is mainly influenced by regional transported pollutants and can be considered as a background site in this region (Fig. S1). 

Sample Collection

Daily (22 h, from 12:00 a.m. to 10:00 a.m. next day) integrated ambient PM2.5 samples were collected from 20 October to 15 November (representative of autumn) in 2014, 6 January to 5 February (winter), 31 March to 29 April (spring), and 2 to 30 July (summer) in 2015. Two low-volume aerosol samplers (frmOMNI Ambient Air Sampler; BGI, USA), both operated at a flow rate of 5 L min–1, were employed to collect PM2.5 samples synchronously. One sampler was loaded with 47-mm quartz-fiber filter (Whatman, UK) for water-soluble inorganic ions (WSIIs) and carbonaceous components analysis. The other one was equipped with 47-mm Teflon filter (Whatman, UK) for trace elements and PM2.5 mass analysis. A total of 93 samples were collected during the entire campaign with at least 15 samples in each season.

Blank filters were also prepared every 10 samples for quality control. Before sampling, all quartz-fiber filters were preheated at 450°C in a muffle furnace for 4 h to remove potential contaminants such as some organic compounds. All the sampled and blank filters were stored in clean filter boxes at approximately –18°C before analysis to avoid cross contamination and evaporation of volatile components.

During the sampling period, gaseous pollutants were continuously measured by a set of online gas analyzers, including a pulsed UV fluorescence analyzer (Model 43i SO2 Analyzer; Thermo Scientific) for SO2 and a chemiluminescence analyzer (Model 42i NO-NO2-NOx Analyzer; Thermo Scientific) for NO/NO2/NOx. Hourly meteorological parameters, including ambient temperature (T), wind speed (WS) and direction, relative humidity (RH), and pressure (P), were obtained from an automatic weather station (Lufft WS501, Germany).

Mass and Chemical Analysis

Before and after sample collection, all the Teflon filters were equilibrated for 24 h in a temperature (20–23°C) and relative humidity (45–50%) controlled chamber and weighed for at least 3 times using an electronic microbalance with ± 0.001 mg sensitivity (ME5-F; Sartorius, Germany). The differences among replicate weights should be less than 15 µg for blank filters and 20 µg for samples.

Elemental carbon (EC) and organic carbon (OC) amounts in the collected samples were measured by using a DRI OC/EC Analyzer (Atmoslytic Inc., USA). The methodology for OC/EC analysis was based on the thermal-optical reflectance (TOR) method following the Interagency Monitoring of Protected Visual Environments (IMPROVE-A) protocol (Chow et al., 2007, 2011).

Five cations (Na+, NH4+, K+, Mg2+ and Ca2+) and four anions (F, Cl, SO42 and NO3) in the PM2.5 samples were measured using an ion chromatograph analyzer (DX-600; Dionex, USA). Cations were detected using CSRS-4 suppressor with CS12A analytical column and 20 mM methanesulfonic acid (MSA) at a flow rate of 1.0 mL min–1. Anions were determined with an ASRS-4 suppressor with an AS11-HC analytical column and 30 mM KOH at a flow rate of 1.0 mL min–1. The QA/QC procedures for sample pretreatment, instrument analysis, and data processing were described in Wang et al. (2018b).

The trace elements, including Al, Si, Ca, Fe, and Ti, were quantified on a Teflon filter using X-ray fluorescence (ED-XRF) analyzer (Epsilon 5; PANalytical, Netherlands); the concentration of fine soil (FS) was calculated by summing Al2O3, SiO2, CaO, FeO, Fe2O3, and TiO2 (Huang et al., 2014); the QA/QC procedures of the XRF analysis have been described in Cao et al. (2012). 

Backward Trajectory

Forty-eight hour air mass backward trajectories, starting at 04:00, 10:00, 16:00 and 22:00 UTC every day at the height of 100 m a.g.l. (meter above ground level), were generated for the sampling period to explore the origins and transport pathways using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model developed by NOAA with the 0.5° × 0.5° meteorological data (Stein et al., 2015; Squizzato and Masiol, 2015). A total of 368 backward trajectories were produced, which were clustered into five main paths based on the total spatial variance, as detailed in previous studies (Markou and Kassomenos, 2010; Cabello et al., 2016).

Potential Source Contribution Function (PSCF)

PSCF represents a conditional probability for a pollutant of interest having concentrations above a given value and passing through a grid cell during transport processes arriving at the receptor site. Grid cells with high PSCF values are identified as potential source locations (Hopke et al., 1995; Polissar et al., 1999).

The PSCF normalized value can be defined as follows:


where nij is the total number of endpoints falling into the grid cell (i,j) and mij is the number of endpoints having a pollutant concentration above the given threshold value in the same cell. In order to minimize the PSCF uncertainties with small nij values, a weighting function (wij) was defined as follows, where nave is considered as the average number of endpoints in each grid cell (Polissar et al., 1999; Zhang et al., 2017; Wang et al., 2018b).


In this study, PSCF values of daily PM2.5 and other pollutants were evaluated over trajectory covered area, which was in the range of 20–40°E longitude and 90–117°N latitude. The area was gridded at a resolution of 0.5° × 0.5°. The threshold value for each pollutant was set as its mean observed value.


PM2.5 Mass Concentrations

The temporal variations of meteorological parameters and concentrations of gaseous pollutants and PM2.5 observed at JY during the sampling period are given in Fig. 1. Daily PM2.5 mass concentrations ranged from 11.3 to 162.5 µg m3, with an annual average of 56.2 ± 31.0 µg m3, which was 1.6 times of the Chinese NAAQS (35 µg m3). PM2.5 concentrations were significantly lower at rural background site (JY) than at urban area of Chongqing (YB) (Table S1), likely due to strong influence of anthropogenic sources in YB (Chen et al., 2017b; Wang et al., 2018b). However, the annual average of PM2.5 at JY site was higher than those observed at the other rural sites in China (except the rural sites of Zhejiang and Bohai Sea), even at some urban sites (e.g., Deyang and Zunyi) (Table S1), which is not only due to the source emissions but also due to the regional transported contribution (e.g., from Yubei area) (Zhang and Cao, 2015; Liu et al., 2018). Furthermore, the climate of Chongqing is characterized by stagnant weather with weak wind and relatively low boundary layer height, leading to favorable atmospheric conditions for accumulation and formation of aerosols (Liao et al., 2018; Wang et al., 2018b).

Fig. 1. Temporal variations of meteorological parameters, gaseous pollutants and PM2.5 during the sampling period at JY. The long-lasting heavy pollution period is highlighted by shaded area.Fig. 1. Temporal variations of meteorological parameters, gaseous pollutants and PM2.5 during the sampling period at JY. The long-lasting heavy pollution period is highlighted by shaded area.

As shown in Fig. S2, there was an apparent seasonal variation in PM2.5 at JY. The highest seasonal average concentration of PM2.5 was seen in winter (80.4 ± 39.5 µg m3), significantly higher than those in the other three seasons (p < 0.05). This is consistent with the typical ones observed in urban areas of China (He et al., 2001; Wang et al., 2015). The highest PM2.5 mass in winter was caused not only by the meteorological conditions with calm winds and shallow planetary boundary layer height, but also by the strong source emissions and atmospheric processes (Wang et al., 2017; Liao et al., 2018) (Fig. 1). Different from the typical seasonal pattern observed in the urban areas of China, i.e., lowest in summer (He et al., 2001; Wang et al., 2015; Wang et al., 2018b; Qiao et al., 2019), the seasonal average concentration of PM2.5 was higher in summer (52.1 ± 9.4 µg m3) than in spring (43.9 ± 18.0 µg m3) and autumn (39.0 ± 17.5 µg m3) at JY, though it was not significant (p > 0.05) (Fig. S2, Table 1). The sampling site (JY) was a forest site and surrounded by lush vegetation. Previous researches have demonstrated that the emissions of monoterpene and isoprene, the major precursors of secondary organic carbon, were stronger in summer than in other seasons at forest sites (Wang et al., 2007; Shen et al., 2015). Therefore, the slightly higher PM2.5 concentration in summer might be related to the high biogenic emissions at JY (e.g., emission from vegetation), consistent with the previous research at forest site (Liu et al., 2017). In addition, regional transport might be another reason for the slightly higher PM2.5 concentration in summer. It is worth mentioning that the contribution of SO42 was highest in summer (Fig. 2(a)). Previous research indicated that the transformation of SO2 to SO42 would occur within transporting air masses and can be an important source of SO42 aerosols in summer (Wasiuta et al., 2015; Liu et al., 2018). As shown in Fig. 2(b), the prevailing wind at JY in summer was from southeast direction, where the urban area of Chongqing (YB) is located (Fig. S1). In addition, high concentration and contribution of SO42 in summer at YB were also observed, similar to the seasonal pattern at JY (Wang et al., 2018b). These results suggested the impact of the aerosol pollution in YB on JY. In addition, the influence of the pollution at YB on JY could be partly justified by the correlation between these two sites. The concentration of SO42 and SO2 in JY showed higher correlation coefficients with that in YB in summer (R2 = 0.883, 0.749), and the slopes of the linear regression were closer to 1 (0.96, 0.91) in summer (Figs. 2(c) and 2(d)). These results indicated that the regional transport of SO42 and SO2 from urban area (YB) could be an important source of SO42 aerosols at JY site in summer. Besides, these results also suggested the significant impact of YB on the relatively high PM2.5 concentration at JY site in summer.

Table 1. Concentrations (mean ± standard deviation) of PM2.5, its major chemical components, and gaseous pollutants at JY.

Fig. 2. (a) Seasonal contributions of chemical components to PM2.5 at JY and Yubei (YB), (b) seasonal direction of wind at JY, and seasonal correlations (c) between concentration of SO42– at JY and that at YB, and (d) between concentration of SO2 at JY and that at YB.Fig. 2. (a) Seasonal contributions of chemical components to PM2.5 at JY and Yubei (YB), (b) seasonal direction of wind at JY, and seasonal correlations (c) between concentration of SO42 at JY and that at YB, and (d) between concentration of SO2 at JY and that at YB.

Carbonaceous Components

The annual mean concentrations of OC and EC were 10.5 ± 7.8 and 2.2 ± 1.6 µgC m–3 at JY, accounting for 18.3% and 3.9% of PM2.5, respectively (Table 1). The highest seasonal average of OC and EC were both exhibited in winter with values of 17.1 ± 10.0 and 3.5 ± 2.2 µgC m–3, respectively, which were more than 3 times of the lowest seasonal average value found in summer. Stagnant weather conditions with calm winds and shallow planetary boundary layer height were the major causes of the highest OC and EC concentrations in winter (Fig. 1) (Liao et al., 2018). Besides, stronger source emissions in winter, such as biomass burning, may also contribute to the relatively high OC and EC concentrations in winter, noting that biomass burning events occurred frequently in Sichuan Basin (Tao et al., 2013; Chen and Xie, 2014). Actually, biomass burning activities have been observed in the wintertime domestic heating season at this background rural site. K+ is usually regarded as a tracer of biomass burning (Tao et al., 2016). As shown in Figs. 3 and S2, higher K+ concentration and its stronger correlation with OC (R2 = 0.90) and EC (R2 = 0.91) in winter than other seasons have been observed, suggesting that biomass burning might contribute to the elevated OC and EC concentration in winter.

Fig. 3. Correlations (a) between OC and EC, (b) between K+ and OC or (c) EC.Fig. 3. Correlations (a) between OC and EC, (b) between K+ and OC or (c) EC.

Fig. 4. Correlation between nitrogen oxidation ratio (NOR) and NH4+ concentration.
Fig. 4. Correlation between nitrogen oxidation ratio (NOR) and NH4+ concentration.

The OC/EC ratio was much larger than 2.0, indicating the occurrence of secondary organic carbon (SOC) formation (Table 1). The EC tracer method was generally used to estimate SOC, which can be defined as (Castro et al., 1999):


where POC represented the estimated primary OC and (OC/EC)min was the minimum ratio in each season. The highest concentration of SOC was observed in winter, which was similar to that of OC (Fig. S2 and Table 1). Relatively high SOC mass fraction (SOC/OC) occurred in summer, which might be due to low contribution of primary organic compounds from coal combustion and strong biogenic oxidation in summer at the rural background area with dense vegetation (Hallquist et al., 2009; Zhang et al., 2017). 

Water-soluble Inorganic Ions (WSIIs)

The annual average of total WSII concentration was 24.8 ± 14.4 µg m3, accounting for 44.1 ± 10.2% of PM2.5. As shown in Table 1, the annual mean of SO42, NO3 and NH4+ were 12.2 ± 6.4, 5.6 ± 4.9 and 5.5 ± 3.6 µg m3, respectively, the sum of which accounted for 93.0% of WSIIs. This indicated that the secondary inorganic ions (SNA, including SO42, NO3 and NH4+) were predominant components in WSIIs at JY. The annual average contribution of SNA to PM2.5 was 41.0% in JY, slightly higher than the typical value observed in urban Chongqing (37.4%; Wang et al., 2018b). This result might be related to the aging process for aerosols during transportation from source regions to the background area (Liu et al., 2018; Wang et al., 2018b). The PM2.5 pollution in JY was mainly from regional or long-range transport. During the transportation, particles would undergo aging process and consequently resulted in the increase of the proportion of secondary component such as SNA.

The seasonal variations of the total SNA were the same as those of PM2.5, i.e., winter (30.9 ± 17.3 µg m3) > summer (20.0 ± 8.5 µg m3) > spring (19.5 ± 11.7 µg m3) > autumn (19.3 ± 10.1 µg m3). The seasonal trend of SO42 and NH4+ concentrations were both highest in winter and lowest in autumn. However, NO3 concentration had a different trend with the lowest in summer. Besides, the scale of the seasonal variations was much larger for NO3 (up to 4.5 times) than for SO42 and NH4+ (up to 1.5 times). One main reason for the above-mentioned differences was the dissociation of NH4NO3 in summer under high temperature condition, as was found in urban areas (Wang et al., 2018b).

Sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR) have been used to investigate the conversion efficiencies of SO2 to SO42 and NO2 to NO3, respectively. They are calculated as (Lin, 2002):


where [SO42], [SO2], [NO3] and [NO2] are molar concentrations.

The seasonal variations of SO2, NO2, O3, SOR and NOR during the sampling campaign are illustrated in Fig. S3. When SOR was higher than 0.1, the secondary transformation of SO2 to SO42 would have occurred (Ohta and Okita, 1990). As shown in Fig. S3, the seasonal average of SOR was higher in summer than in other three seasons, which might be due to the following reasons: 1) Oxidation of SO2 could be enhanced under high O3 concentration and strong solar irradiation condition (Meng et al., 2016), which was supported by the significant correlation between SOR and O3 in summer (R = 0.74, p < 0.01) (Fig. S4); 2) ammonium-rich condition at JY was favorable for the formation of secondary sulfate (Fig. 6), especially during summer, when SOR was strongly related to NH4+ concentration (R = 0.93, p < 0.01) (Fig. S4) (Zhang et al., 2011; Wang et al., 2016); 3) transportation of aged SO42 aerosol from urban area (YB) might lead to high SOR value at JY in summer (Fig. 2) (Liu et al., 2018).

The lowest seasonal average of NOR in summer is largely caused by the higher temperature as mentioned above (Deng et al., 2016b). NOR showed high values in winter and positively correlated with NH4+ (R2 = 0.80) (Figs. S3 and 4), implying the association between NH4+ and NO3 under low temperature condition (Squizzato et al., 2012). The formation of NO3 may be dominated by heterogeneous process under ammonium-poor condition and by homogeneous gas-phase reaction under ammonium-rich condition (Pathak et al., 2009). The molar ratio of [NH4+]/[SO42] > 1.5 was generally regarded as ammonium-rich condition (Huang et al., 2011). A threshold value of 1.5 was used to calculate the excess NH4+ concentration in this study. The concentration of excess NH4+ as a function of NO3 showed significant positive correlations (R2 = 0.84) (Fig. 5), indicating evident gas-phase reaction between NH3 and HNO3 (Wang et al., 2018b). Thus, reducing NH3 emissions may reduce NO3 formation.

Fig. 5. (a) Molar ratio of NO3–/SO42– as a function of NH4+/SO42–, and (b) NO3– molar concentration as a function of NH4+excess.Fig. 5. (a) Molar ratio of NO3/SO42– as a function of NH4+/SO42, and (b) NO3 molar concentration as a function of NH4+excess.

Strong correlation (R2 = 0.95) was found between anion equivalents (AE) and cation equivalents (CE) (Fig. S5) with a slope of the linear regression being 1.06, indicating neutralized aerosols at JY. NH4+ is the most abundant cation and mainly associates with SO42 and NO3 (Zhang et al., 2008; Zhou et al., 2016). When NH4+ exists as (NH4)2SO4 and NH4NO3, the concentration of NH4+ can be calculated according to:


When NH4+ is in the form of NH4HSO4 and NH4NO3, it is calculated as (Lai et al., 2007):


The slope of the linear regression was 0.96 between the measured and the Eq. (7)-calculated NH4+ concentrations, and was only 0.65 if using the Eq. (8)-calculated values (Fig. 6(a)). In addition, the equivalent ratio of NH4+ to the sum of SO42 and NO3 was close to 1 (Fig. 6(b)), implying that most of sulfate and nitrate were neutralized by ammonium in the form of (NH4)2SO4 and NH4NO3 (Huang et al., 2011).

Fig. 6. Correlation (a) between the measured and calculated NH4+ concentrations, and (b) between NH4+ equivalents and NO3– + SO42– equivalents.Fig. 6. Correlation (a) between the measured and calculated NH4+ concentrations, and (b) between NH4+ equivalents and NO3 + SO42– equivalents.

Identification of Potential Source Regions

Forty-eight hour air mass backward trajectories were classified into five clusters as shown in Fig. S6. PSCF results for PM2.5 and its major components are illustrated in Fig. 7. Air masses in Clusters 1 and 2, occurring in all the seasons, were all originated from inside Chongqing with Cluster 1 (42.4% of the total trajectories) being short-distance trajectories from the north direction and Cluster 2 (30.4%) being relatively long distance from the northeast direction. Among the five clusters, Cluster 1 had the highest SO42 and the second highest PM2.5 concentrations while Cluster 2 had the highest EC (Table S2). There are some important industries located in the northern part of Chongqing, such as the thermal power plants in Hechuan, which might be important sources of SO2 and PM2.5. PSCF distributions also suggested that the northern area of Chongqing played an important role in occurrence of high concentration of PM2.5 (Fig. 7). Moderate PSCF values of secondary ions (SO42, NO3 and NH4+) and OC were observed in the northeast areas, which might be related to nearby industries, such as Changshou chemical industrial zone (Wang et al., 2018b).

Fig. 7. PSCF distribution for PM2.5, OC, EC, SO42–, NO3–, and NH4+.Fig. 7. PSCF distribution for PM2.5, OC, EC, SO42, NO3, and NH4+.

Air masses in Cluster 3 (5.4% of the total trajectories), occurring mainly in the cold season, were long-distance trajectories originated from the southwest direction, passing over some districts with heavy industries such as Neijiang and Zigong in Sichuan, and Yongchuan and Dazu in Chongqing. Relatively high levels of PM2.5 and its major components were associated with this cluster. PSCF distribution also illustrated the southwestern area of Chongqing as an important potential source region for PM2.5 and its major components (Fig. 7). Air masses in Cluster 4 (18.2% of the total trajectories) were short-distance trajectories originated from the southeast direction. Air masses in this cluster might carry contaminated air masses from urban Chongqing and consequently caused relatively high PM2.5 pollution. Air masses in Cluster 5 (3.5% of the total trajectories), mainly occurring in spring and summer, were long-distance trajectories originated from the ocean with clean air masses. Therefore, concentrations of PM2.5 and its major components were much lower in Cluster 5 than other clusters (Table S2). The above analysis suggested that PM2.5 pollution at JY was affected occasionally by regional sources.

Analysis of Pollution Episode

The pollution period (PP) was defined as daily PM2.5 concentration above 75 µg m–3 while other days were referred to as clean period (CP). The temporal variations of T, RH, O3, NOR, SOR and PM2.5 in winter are shown in Fig. 8. There was a long-lasting pollution period during 11–26 January 2015, as highlighted by the shaded area in Fig. 8. During this period, the concentrations of PM2.5 and its major chemical components were all enhanced by more than 2 times compared to the clean periods (Fig. 9(a)). Among the detected components, the largest contributor to PM2.5 in PP was SNA (39.3%), followed by OC (22.1%). Interestingly, the NO3 concentration was enhanced by 3.6 times, much more than other major components, e.g., SO42 (2.14) and OC (2.85). That results in the increased contribution of NO3 to PM2.5 and decreased one for SO42 to PM2.5 during the pollution period. Thus, NO3 played a more important role for the accumulation of PM2.5 than other components.

Fig. 8. Temporal variations of T, RH, O3, NOR, SOR and PM2.5 in winter. Long-lasting pollution episode is highlighted by the shaded area.Fig. 8. Temporal variations of T, RH, O3, NOR, SOR and PM2.5 in winter. Long-lasting pollution episode is highlighted by the shaded area.

Fig. 9. The (a) mass concentrations and (b) CO-scaled concentrations of various pollutants, as well as the (c) percentage contributions of major chemical components to PM2.5 and values of SOR and NOR during clean periods (CP) and long-lasting pollution period (PP) in winter at JY. CE: 6–10 January, 27 January–3 February and 5 February 2015; PP: 11–26 January 2015.Fig. 9. The (a) mass concentrations and (b) CO-scaled concentrations of various pollutants, as well as the (c) percentage contributions of major chemical components to PM2.5 and values of SOR and NOR during clean periods (CP) and long-lasting pollution period (PP) in winter at JY. CE: 6–10 January, 27 January–3 February and 5 February 2015; PP: 11–26 January 2015.

The accumulation of PM2.5 and its major chemical components are influenced by both meteorological conditions and atmospheric chemical processes (Zheng et al., 2015), while that of CO, an inactive tracer with long lifetime, is mainly affected by meteorological conditions (Hu et al., 2013; Quan et al., 2015; Wang et al., 2018b). Thus, the CO-scaled concentrations of various pollutants could reveal the impacts of chemical processes on pollutants’ accumulations. The ratios of PM2.5/CO, NO3/CO and SO42/CO were 2.66, 3.94 and 2.35 times higher during the pollution period than the clean period, while the ratios of NO2/CO and SO2/CO were only 1.51 and 1.89 times higher, respectively. These results were consistent with the higher NOR (0.24) and SOR (0.31) during pollution episode than those during clean period (0.11 and 0.26 for NOR and SOR, respectively). This difference between pollution and clean episode was not likely due to the transportation of aerosols which would increase the SOR and NOR values, because according to the cluster results for air mass backward trajectory analysis (Fig. S7), the air masses at JY site during pollution period were mainly from nearby areas while those during clean episode were mostly from regional or long-range transport. Thus, the higher SOR and NOR values during pollution episode than clean episode was possibly resulted from the stronger chemical conversions from gas-phase (NO2 and SO2) to particulate-phase (NO3 and SO42) during the pollution period.

In order to explore the formation mechanisms of secondary aerosols during the pollution period, the correlations of SOR and NOR against RH (at 5% interval), O3 concentration (at 15 µg m3 interval) and T (at 2°C interval) are illustrated in Fig. S8. Both NOR and SOR obviously increased with increasing RH, but not with O3 concentration and temperature. This suggested that SO42 and NO3 were mainly formed via aqueous phase processes. Previous studies have also reported the predominant role heterogeneous reactions played for SO42 formation (Quan et al., 2015; Xu et al., 2017; Wang et al., 2018b). Moreover, as shown in Fig. S9, SO42 and NO3 in PM2.5 were completely neutralized by NH4+, indicating neutralized aerosols at JY during the long-lasting pollution period, which could enhance SO42 formation via aqueous phase oxidation (Wang et al., 2016). For NO3, elevated RH would increase the water content in airborne particles and consequently enhance the uptake of gas-phase HNO3 and NH3 into existing particles (Tian et al., 2017a; Wang et al., 2018b). When O3 concentration was lower than 70 µg m3, NOR and SOR all exhibited obviously decreasing trends with increasing O3 level. In addition, high PM2.5 concentrations, RH, NOR and SOR were observed under low O3 concentrations (Fig. 8). These results indicated that photochemical reactions were weak and aqueous-phase reactions were dominant for the increased secondary aerosols during the pollution period (Zheng et al., 2015).


The chemical characteristics, formation mechanisms, transport pathways and potential source regions of PM2.5 were investigated during four consecutive seasons at a rural background site (JY) in Chongqing, Southwest China. The mean annual concentration of the PM2.5 was 56.2 ± 31.0 µg m3—1.6 times the NAAQS limit. Seasonally, the concentration was 1.6–2.0 times higher in winter (80.4 ± 39.5 µg m3) than in the other seasons (52.1 ± 9.4 µg m3, 39.0 ± 17.5 µg m3 and 43.9 ± 18.0 µg m3 for summer, autumn and spring, respectively). SO42, NO3, NH4+, OC and EC accounted for 22.2%, 9.5%, 9.4%, 18.3% and 3.9%, respectively, of the average annual PM2.5. Compared to autumn and spring, summer exhibited higher concentrations of SO42, perhaps due to the active homogeneous gas-phase formation (SOR = 0.32) in this season; furthermore, these concentrations were augmented by emissions transported from the urban area of Chongqing. The PM2.5 and SNA increased by more than 2.4 times during the pollution period compared to the clean period, and an abundance of ammonium was identified as the condition that promoted NO3 formation at lower temperatures. The findings in the present study demonstrate the effects of anthropogenic activities, which are mainly local, on the air quality at this rural background site. Hence, sensitivity studies with chemical transport models should be combined with available measurements to provide quantitative measures for emission controls on the dominant chemical components (e.g., NOx and NH3) in order to effectively reduce fine aerosol pollution in this region. 


This work was supported by the National Natural Science Foundation of China (No. 41405027, 41375123, 41773148, and 41403089), the Strategic Priority Research Program of the Chinese Academy of Sciences (No. KJZD-EW-TZ-G06), and Chongqing Science and Technology Commission (No. cstc2014yykfC20003 and cstckjcxljrc13). We are grateful to Zhichao Wang and Yingtao Gong for sample collection.

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