Xiaoyong Liu1,2,4, Xiaole Pan This email address is being protected from spambots. You need JavaScript enabled to view it.1, Zifa Wang1,2,5, Hong He1,3, Dawei Wang2, Hang Liu2,4, Yu Tian2,4, Weiling Xiang2, Jie Li2


1 Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
2 State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
3 Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
4 University of Chinese Academy of Sciences, Beijing 100049, China
5 College of Earth Sciences, University of Chinese Academy of Sciences, Beijing, 100049, China


Received: March 29, 2020
Revised: August 16, 2020
Accepted: August 16, 2020

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.4209/aaqr.2020.03.0124  

Cite this article:

Liu, X., Pan, X., Wang, Z., He, H., Wang, D., Liu, H., Tian, Y., Xiang, W. and Li, J. (2020). Chemical Characteristics and Potential Sources of PM2.5 in Shahe City during Severe Haze Pollution Episodes in the Winter. Aerosol Air Qual. Res. 20: 2741–2753. https://doi.org/10.4209/aaqr.2020.03.0124


  • SO2 oxidation pathways changed in different RH.
  • Photochemical reactions were responsible for nitrate formation.
  • Primary aerosols emissions were enhanced during the haze.
  • Local emission was the largest potential source area for regional haze.


In recent years, North China has suffered from severe air pollution. Hence, this study performed a comprehensive field experiment during Dec. 2017 in Shahe (114.5°N, 36.85°E), a typical industrial city in this region that is characterized by intensive NOx emission from the local glassmaking industry. During the study period, the mass concentration of the PM2.5 (fine particulate matter) averaged 121.6 ± 91.8 µg m–3, whereas the mass concentrations of the nitrate and sulfate in the PM2.5 averaged 21.4 ± 16.3 and 15.9 ± 20.9 µg m–3, respectively. The high sulfate mass concentration primarily resulted from the oxidation of SO2, which was mainly due to gas-phase and heterogeneous reactions during low relative humidity (RH; < 40%) and enhanced aqueous reactions during high RH (> 40%). In addition, because the nitrogen oxidation ratio (NOR) increased as the RH decreased during the day, the nitrate was largely generated through photochemical reactions. The mass concentrations of the optical organic carbon (OC), elemental carbon (EC), and water-soluble organic compounds (WSOCs) equaled 50.4 ± 31.1, 5.8 ± 4.4, and 12.8 ± 10.1 µgC m–3, respectively, and applying the EC tracer method revealed that primary emissions contributed approximately 72% of the total OC. Furthermore, intense industrial activities were detected in a nearby area to the northeast, which potential source contribution function (PSCF) analysis identified as the main potential source area for PM2.5 during haze.

Keywords: Air pollution; Chemical composition; North China Plain; Potential source analysis.


In recent decades, China has experienced rapid economic development. However, its success involves vast fossil fuel consumption, which resulted in degraded air quality, including air pollution and water contamination. Regional haze pollution occurs frequently in China and has attracted enormous attention from the public, government, and scientists (Zhang et al., 2015). Long-lasting and large-scale regional haze events imposed a great threat to public health and sustainable economic development. According to recent research, the origin of air pollution in China has gradually shifted from typical coal combustion to a combination of pollution from fossil fuel combustion, vehicle emission, and industrial discharge. Among the different pollutants of PM2.5, PM10, SO2, NO2, O3, and CO, PM2.5 is of particular interest because PM2.5 could cause the serious respiratory problem of humans (Lu et al., 2016; Kelley et al., 2020). Airborne particles could also suppress the development of the planetary boundary layer, worsening the regional pollution. The formation of haze is unclear due to the complicated chemistry and feedback mechanisms.

Due to the coupling effects of strong pollutant emissions, unfavorable meteorological conditions and unique topography, the Beijing-Tianjin-Hebei (BTH) region became one of the most seriously polluted regions in China (Cai et al., 2017; Huang et al., 2017; Deng et al., 2018; Huang et al., 2018). Recently, many studies about mass concentration, chemical components, sources of PM2.5 in BTH have been reported (Wang et al., 2014; Sun et al., 2019; Xie et al., 2019; Liu et al., 2020a). However, those studies were mainly performed in megacities and large cities of BTH, such as Beijing, Tianjin, Shijiazhuang. There are not enough studies related to haze pollution in industrial cities, especially areas characterized as distinct industry in the Hebei Province, limiting our knowledge about haze formation in BTH. The Ministry of Environmental Protection of the People’s Republic of China has formulated the scheme of air pollution prevention and control in the BTH region and surrounding areas in 2017 (Feng et al., 2018). Thus, it is urgently necessary to conduct studies on typical industrial cities in Hebei Province, which is an important part of this scheme.

Shahe is a typical industrial city located in the southern Hebei Province, facing serious haze pollution. Shahe is famous for glassmaking and accounts for ~20% of total national glass production. The mean mass concentration of PM2.5 in Shahe was 91 µg m–3 in 2017 (http://hbmis.aticloud.cn), which is approximately 1.57 times that of Beijing. However, very few studies have been pursued to understand the characteristics and formation mechanisms of pollution episodes in Shahe. In this work, a comprehensive field campaign was performed in Shahe. Hourly continuous concentrations of pollutants, including PM2.5, the chemical components, as well as the gaseous pollutants were measured. Based on the observed data, we investigated the characteristics and formation of the haze and studied the impact of pollutant transport in Shahe. Our findings will help to expand the knowledge of haze formation in BTH.


Description of the Field Campaign

The measurements were conducted from Dec. 14 to Dec. 30, 2017, on the eighth floor of a building (114.52°N, 36.88°E), approximately 28 m above the ground. This site is located north of downtown Shahe city. As shown in Fig. S1(a), NOx emissions from industrial areas in Shahe and its surrounding cities were intensive. Cities around Shahe, e.g., Xingtai (Xu et al., 2019) and Handan (Meng et al., 2016), are severely polluted as well. A large number of glass factories are located in the northeast of the monitoring site, approximately 5 km away (Fig. S1(c)).


Mass concentrations of PM2.5 and water-soluble components (e.g., sulfate, nitrate, and water-soluble organic compounds [WSOCs]) were measured using a continuous dichotomous aerosol chemical speciation analyzer (ACSA-12; Kimoto Electric Co., Ltd.) at a time resolution of 1 h and flow rate of 16.7 L min–1 (Pan et al., 2018). The mass concentrations of PM2.5, SO42– and NO3 were detected using the β-ray absorption method, BaSO4-based method and ultraviolet absorption photometric method, respectively. The mass concentrations of WSOCs were detected using the ultraviolet absorption photometric method as well. The mass concentrations of organic carbon (OC) and elemental carbon (EC) were measured by a particulate carbon analyzer (APC-710; Kimoto Electric Co., Ltd.) at a time resolution of 1 h and a flow rate of 16.7 L min–1. The OC and EC samples were collected on the banded Teflon filter membrane by the APC-710. Then, the samples were analyzed by near-infrared and ultraviolet light. The mass concentrations of OC and EC were calculated based on the attenuation of transmitted light and reflected light. Mass concentrations of CO, CO2, and volatile organic compounds (VOCs) were measured by using CA-752 and VOC-770 systems (Kimoto Electric Co., Ltd.) respectively based on non-dispersive infrared methods. CH4 was measured by a flame ionization detector (FID) using HA-771 (Kimoto Electric Co., Ltd.). All instruments were calibrated for flow before measurement. ACSA-12 was calibrated by a standard solution. ASCA-12 and APC-710 passed blank membrane experiments at a flow rate of 16.7 L min–1. CA-752, VOC-770, and HA-771 were calibrated by corresponding standard reference gas. Routine maintenance and regular calibration on the instruments were performed in the field campaign. More information about data quality assurance/quality control measures of the above instrument has been illustrated in previous studies (Yang et al., 2018, 2019; Ye et al., 2019).

PM10 mass concentration and mixing ratios of SO2 and NO2 were collected from a national air monitoring station which was in the same building that the sampling site located (on the sixth floor and approximately 21 m above the ground). Meteorological parameters, including wind direction (WD), wind speed (WS), temperature (T), atmospheric pressure (P), and relative humidity (RH), were measured by the automatic meteorological station. A micro-pulse LIDAR (AGHJ-I; Wuxi CAS Photonics Co., Ltd.) was installed at the monitoring site. The LIDAR had two receiving channels, with a laser wavelength of 532 nm. After range and overlap correction, the LIDAR signals were processed automatically. The LIDAR had a 20 MHz analog-to-digital conversion rate and a 7.5 m vertical resolution. The extinction coefficient of aerosols was retrieved based on the Fernald method (Fernald, 1984).

Backward Trajectory and PSCF Analysis

Backward Trajectory

The HYSPLIT model developed by the U.S. National Oceanic and Atmospheric Administration Air Resources Laboratory (NOAA ARL) was usually employed to identify the potential source regions and transport pathways of air masses (Yang et al., 2017b). 24-h back trajectories, starting at the arrival level of 100 m from the monitoring site (114.52°N, 36.88°E) in Shahe were calculated during the study period. The Final (FNL) Operational Global Analysis data were obtained from the National Centers for Environmental Prediction’s Global Data Assimilation System (GDAS) wind field reanalysis (http://www.arl.noaa.gov/). The backward trajectory model was run every hour of the day.

Potential Pollution Source Model

The potential source contribution function (PSCF) is widely used to identify the probable locations of emission sources (Ashbaugh et al., 1985; Wang et al., 2009; Peng et al., 2019). The domain being researched is divided into small equal grid cells (ij). The PSCF value for each cell can be defined as:


where nij represents the total number of trajectory endpoints fall in the grid cell (i, j), and mij represents the number of trajectory endpoints at the grid cell (i, j) with pollutant concentrations higher than the threshold criterion. In this study, the pollutant is PM2.5 and its mass concentration is the threshold criterion. The study domain is in the range of 103–120°E, 31–51°N, with a spatial resolution of 0.2° × 0.2°. When nij is 3 times smaller than each grid average number of trajectory endpoints (nave), an arbitrary weight function W(nij) is multiplied into the PSCF value to reduce the uncertainty in cells (Dimitriou et al., 2015; Liu et al., 2016). The weighted potential source contribution function (WPSCF) is defined below.


Data Analysis

Sulfur and Nitrogen Oxidation Ratio

To evaluate the sulfur and nitrogen oxidation, sulfur oxidation ratio (SOR) and the nitrogen oxidation ratio (NOR) are employed (Yang et al., 2015; Liu et al., 2016), which are defined as:


where n refers to the molar concentration.

Estimated Mass Concentration of Secondary Organic Carbon

An EC tracer method was employed to estimate the mass concentration of secondary organic carbon (SOC) and primary organic carbon (POC) from the following equation (Cao et al., 2007; Zhao et al., 2013; Zheng et al., 2019):


where (OC/EC)pri is the OC/EC ratio in primarily emitted combustion aerosols and b represents non-combustion POC. The (OC/EC)pri and b are often determined by regressing the OC and EC concentrations on a fixed percentile of the lowest OC/EC ratio (usually 5–20%; Yao et al., 2016, 2020).


F-factor was established to understand the roles carbonaceous gases and particles had played in haze formation (Yang et al., 2019). F-factor is defined as:


where S and G refer to the sum of calculated results for solid phase and gaseous phase respectively. Si includes OC and EC. Gi includes CO2, CO, CH4, and VOCs. As in the previous study (Yang et al., 2019), the concentrations of each carbonaceous species were converted to the same unit (µgC m–3). Wu et al. (2016) reported that all the VOC species could be represented by an equalized molecular formula of C3.51H6.78O0.24N0.02Cl0.31 (molecular weight of 62.38). We adopted 62.38 (Yang et al., 2019, also adopted this value) as the molecular weight of VOCs.


Overview of the Haze Pollution Period

General Description

The temporal variations in the mass concentrations of the PM2.5 chemical composition, the meteorological parameters and the aerosol extinction coefficient are displayed in Fig. 1. The mean mass concentrations of PM2.5 over the whole study were 121.6 ± 91.8 µg m–3, which was almost 1.6 times the second grade of Chinese National Ambient Air Quality Standards (75 µg m–3, an average of 24 h). The average mass concentrations of NO3 and SO42– were 21.4 ± 16.3 µg m–3 and 15.9 ± 20.9 µg m–3, respectively. The mean mass concentrations of CO2 were 968.0 µg m–3. The average concentrations of VOCs observed in this study (195.9 ppbC) were much higher than that of Beijing (151.3 ppbC) (Yang et al., 2019), suggesting that there were stronger VOC emissions in Shahe. Besides, the dominant wind was northern, with a frequency of 74.5%. The mean wind speed was 1.6 m s–1, which was not favorable for the diffusion of pollutants. It has been reported that relative humidity plays an important role in haze formation (Pathak et al., 2009; Liu et al., 2013; Cheng et al., 2016). A positive correlation between RH and PM2.5 concentrations (r = 0.7, p < 0.01) was found in this study. When the mass concentration of PM2.5 was above 75 µg m–3 continuously, the period was considered to be a haze episode. Combining the profiles of the aerosol extinction coefficient (Fig. 1(h)) and the PM2.5 variations (Fig. 1(a)), three different pollution episodes (gray shadows in Fig. 1) were selected for the further study: EP-I (11 a.m. on Dec. 14 to 5 a.m. on Dec. 16, 2017), EP-II (3 a.m. on Dec. 21 to 2 a.m. on Dec. 24, 2017) and EP-III (9 a.m. on Dec. 25 to 4 p.m. on Dec. 30, 2017). EP-I was a short-term pollution episode that occurred for approximately two days. In EP-I, RH was continuous at a high level with a mean value of 79.4 ± 14.8%, and the mass concentrations of PM2.5 increased from 93 to 382 µg m–3 in 37 hours. In EP-II, the average RH was 38 ± 9%, and the PM2.5 mass concentrations varied less than in EP-I and EP-III (the standard deviation of PM2.5 concentration in EP-I, II, III was 72.3, 44.1, and 99.5 µg m–3 respectively). EP-III, a long pollution episode, lasted approximately five days. During EP-III, RH and PM2.5 mass concentrations increased slowly but steadily in general. There were clean periods (CPs) with a mean PM2.5 mass concentration of 17.5 µg m–3 (blue shadows in Fig. 1).

Fig. 1. Time series of observed (a) particulate matter; (b) NO2, SO2, sulfate, and nitrate; (c) CO and CO2; (d) VOCs and CH4; (e) OC, EC, and WSOCs; (f, g) meteorological parameters; and (h) vertical profiles of aerosol extinction coefficient in Shahe from Dec. 14 to Dec. 30, 2017. The red horizontal dashed line in (a) represents the second grade of the Chinese National Ambient Air Quality Standards for PM2.5.

Chemical Composition of PM2.5

As shown in Fig. 2(a), secondary inorganic aerosols (NO3 and SO42–) accounted for 30.2 ± 10.1% of the PM2.5 on an average for the whole observation period. During the haze episodes (when PM2.5 mass concentrations were more than 75 µg m–3), the average mass fractional contribution of NO3 and SO42– to PM2.5 was 18.1 ± 4.2% and 11.8 ± 6.9%, respectively. In Beijing due to the vehicles increasing and the coal use controlling, the mass ratio of NO3/SO42– in winter decreased from 2010 at 1.2 (Zhao et al., 2013) to 2015 at 1.43 (Zhang et al., 2018). In this study, NO3/SO42– was on an average of 2.0 ± 1.3 (Fig. 2(b)). We inferred that the high value of NO3/SO42– observed in Shahe was due to intense NOx emission from industrial sources rather than from vehicles. As depicted in Fig. 2(c), the average values of SOR and NOR during the whole study were 0.19 ± 0.19 and 0.17 ± 0.09, respectively. Previous studies reported that when SOR and NOR were greater than 0.1, SO42– and NO3 mainly came from the secondary transformation of SO2 and NO2 respectively, rather than primary source emissions (Yao et al., 2002; Fu et al., 2008; Zhang et al., 2011). This suggested SO42– and NO3 mainly came from the secondary transformation in Shahe city. As listed in Table 1, SOR value varied greatly in North China in winter. The SOR value in Shahe city (0.19) was lower than observed in Beijing (0.20 in 2014 and 0.27 in 2015) but much higher than that of Handan (approximately 30 km away from Shahe) with 0.10 and Shijiazhuang (the capital city of Hebei Province) with 0.15. This indicated that the secondary formation of sulfate in Shahe was more intensive than its surrounding cities. The NOR value in Shahe (0.17) was similar to that of Handan (0.20) and Beijing (0.20 in 2014 and 0.17 in 2015) and higher than that of Shijiazhuang (0.14). The SOR value varied more than NOR value in North China, which indicated the secondary formation of sulfate was more easily affected by some factors, e.g., weather conditions, anthropogenic emissions. Although the oxidation ratios of sulfur and nitrogen were similar in Shahe, the mass concentration of NO2 was approximately 1.9 times that of SO2, which resulted in a much higher mass concentration of NO3 than that of SO42–. EC is a primary pollutant that comes from biomass and fossil fuel combustion sources, and OC is a mixture of several particulate organic compounds containing polycyclic aromatic hydrocarbons and other hazardous components (Zhao et al., 2013; Boreddy et al., 2018). The average concentrations of OC, EC and WSOCs observed in this study were 50.4 ± 31.1, 5.8 ± 4.4 and 12.8 ± 10.1 µgC m–3 respectively, which were all much higher than that of Beijing in the winter of 2016 (Yang et al., 2019). This suggested more intensive anthropogenic emissions of OC and EC in Shahe than in Beijing.

 Fig. 2. Time series of (a) the mass fraction of secondary inorganic aerosols, (b) SO2/NO2 and NO3–/SO42–, (c) SOR and NOR, and (d) OC/EC and WSOCs/OC from Dec. 14 to Dec. 30, 2017, in Shahe.
Fig. 2. Time series of (a) the mass fraction of secondary inorganic aerosols, (b) SO2/NO2 and NO3/SO42–, (c) SOR and NOR, and (d) OC/EC and WSOCs/OC from Dec. 14 to Dec. 30, 2017, in Shahe.

Table 1. Comparisons of SOR and NOR in the winter of North China.

Investigation of Characteristics and Formation of Haze

Oxidation of Sulfur and Nitrogen in Haze Episodes

The mass concentrations of pollutants varied greatly in different episodes. As listed in Table S1, the mass concentration of PM2.5 in the haze episode was approximately 10 times that in the clean period. The NOR and SOR in the haze episode were approximately 4 and 2 times that in the clean period, respectively, which suggested that the secondary transformations of SO2 and NO2 were more intense. In EP-II, with the lowest RH among the three haze episodes, the mean mass concentration of NO3 was much higher than SO42– and the mean NOR was also much higher than SOR. This suggested that the formation of nitrate was more intensive than sulfate in low RH. The mean RH and wind speed were 19.9 ± 4.5% and 2.9 ± 1.4 m s–1 in CPs, respectively, which suggested that lower RH and stronger wind were effective in reducing pollution.

Severe secondary oxidation of SO2 was found in the haze episodes. When RH was less than 20% (Fig. 3(a)), a high sulfate mass fraction in PM2.5 was found in this study. As shown in Fig. S2, when RH was 0~20%, a high ratio of SO42–/PM2.5 was mainly found under the weak northeastern wind (< 2 m s–1). This suggested that the local sulfate from the northeastern sampling site caused the high mass fraction of SO42–. When RH was under 40%, a negative correlation between SOR and RH was found (r = –0.15, p < 0.01) in this study. Oxidation pathways of SO2 include gas-phase reactions with OH radicals or stabilized Criegee intermediates, heterogeneous-phase reactions on the surface of particles, and aqueous-phase reactions with dissolved O3, NO2, H2O2, and organic peroxides (Liu et al., 2020b). In Shahe, the gas-phase reactions and heterogeneous reactions were probably the main oxidation pathways of SO2 in low RH. When RH was above 40% (Fig. 3(a)), SOR increased sharply in this study, which was also found in previous studies (Liu et al., 2019; Liu et al., 2020b). When RH was above 40%, we found the correlation between SOR and RH was strong positive (r = 0.87, p < 0.01) in this study. This suggested that SO2 oxidation related to aqueous was enhanced. In high RH, SO42– mainly came from the aqueous oxidation of SO2 due to aerosol water content and surface area increase (Zhang et al., 2015). When RH was 40–60% and above 60% (Fig. 3(a)), the mean SOR value was 0.14 and 0.50, respectively. This indicated that certain potential mechanisms can promote SOR in high RH. When in high RH (> 60–70%), the trapped SO2 by aerosols can be oxidized by NO2 to form sulfate (Cheng et al., 2016; Wang et al., 2016). During 11 a.m. to 8 p.m. LST, RH was under 40% mostly and showed an opposite diurnal trend as SOR (Fig. S3). Thus, we deduced the gas-phase reactions and heterogeneous reactions dominate in this period. During 0 a.m. to 11 a.m. LST (Fig. S2), aqueous oxidation of SO2 dominated.

Fig. 3. (a) SOR and SO42–/PM2.5 under different RH levels. (b) SOR and RH in clean periods and three pollution episodes. (c) NOR and NO3–/PM2.5 under different RH. Median (central horizontal bar within the boxes), 25th and 75th percentiles (lower and upper bars of the boxes), and minimum and maximum (lower and upper whiskers).
Fig. 3. (a) SOR and SO42–/PM2.5 under different RH levels. (b) SOR and RH in clean periods and three pollution episodes. (c) NOR and NO3/PM2.5 under different RH. Median (central horizontal bar within the boxes), 25th and 75th percentiles (lower and upper bars of the boxes), and minimum and maximum (lower and upper whiskers).

As shown in Fig. 3(c), NOR increased with RH (r = 0.86, P < 0.01). The average NOR was 0.13 at RH of 20–40%, which suggested the secondary transformation of NO2 in low RH was less important. Generally, the aqueous reactions will be enhanced with increasing RH. During the daytime in Shahe, RH decreased continuously due to the heating of the sun, but NOR increased (Fig. S3). This suggested that aqueous reaction was not the main mechanism of nitrate formation during the daytime. Instead, nitrate formation is controlled by photochemical production (Ye et al., 2019). At night when photochemical production was poor, nitrate was mainly produced by the hydrolysis of N2O5 and gas-particle partitioning (Yang et al., 2017a; Zhang et al., 2018).

Formation and Sources of the Carbonaceous Component

As shown in Fig. 4(a), a strong relationship between OC and EC was found in the four study periods, indicating that OC and EC had similar sources. The lower PM2.5 concentrations were, the stronger the relationship between OC and EC. The ratio of OC to EC is an important indicator that reflects the source type and transformation characteristics (Ji et al., 2019). It has been reported that the OC/EC ratios from vehicle emissions, residential coal combustion, and biomass burning are 1.1–5.0, 8.5–12, and 4.3–80, respectively (Cao et al., 2007; Sandradewi et al., 2008; Ji et al., 2019). In this study, the average OC/EC value was 8.5–9.3 in the four periods, indicating that carbon aerosols mainly came from coal combustion and biomass burning. In this study, to estimate the concentration of SOC, 40 samples with the lowest OC/EC ratios were used to calculate (OC/EC)pri and b (Fig. 4(a)). The mean concentration of the estimated SOC was 15.5 ± 10.7 µgC m–3. The average SOC/OC value was 27.6% in this study, indicating that OC mainly came from the primary emission in Shahe. The SOC/OC was 10.2% in the clean period and increased to 27% in haze episodes, suggesting enhanced secondary formation processes. Sources of WSOCs are complex, which can be directly from combustion, industrial, and natural sources (primary) and/or formed through secondary processes such as homogeneous gas-phase and/or heterogeneous aerosol-phase oxidation (secondary) (Tang et al., 2016). As listed in Table 2, in this study WSOC/OC value in Shahe was much lower than other cities of North China. We inferred that the intensive emissions from industry caused the low value of WSOCs/OC. Additionally, the correlation between WSOCs and SOC was weak (Fig. S4), which indicated more influences from primary sources. WSOCs correlated better with SOC in the day (r2 = 0.16) than that at night (r2 = 0.04) because reactive photochemical reactions were stronger at day. Low ratios of SOC/OC and WSOCs/OC also suggested that the observation site was not far from the emission sources.

Fig. 4. (a) Scatter plot of OC and EC concentrations in the PM2.5 at Shahe. (b) Values of WSOCs/OC, SOC/OC, and OC/EC in clean and haze episodes. Fig. 4. (a) Scatter plot of OC and EC concentrations in the PM2.5 at Shahe. (b) Values of WSOCs/OC, SOC/OC, and OC/EC in clean and haze episodes.

Table 2. Typical values of WSOCs/OC in the winter of North China.

Conversion of Carbonaceous Gases to Particles

In this study, the F-factor (mode: 0.3; Fig. S5(a)) was much higher than observed in the winter of Beijing (mode: 0.17) (Yang et al., 2019). This indicated primary emissions of particulate carbon in Shahe was more intensive than in Beijing. In the day, with the temperature raised (Fig. S5(b)), semi-volatile species were more likely to exist as gases, which led to a decrease of F-factor. An obvious positive correlation between PM2.5 concentrations and F-factor was found in this study (Fig. 5(a)). The higher F-factor in more serious haze indicated that (1) more carbonaceous particles were emitted than carbonaceous gases (Yang et al., 2019), and (2) the conversion of carbonaceous gases to particles was more intensive. When RH was 0–60% (Fig. 5(b)), the F-factor increased with the raising of RH and SOC/OC ratio increased, indicating that chemical processes that strengthened the conversion of carbonaceous gases to particles were active. When RH was above 60% (Fig. 5(b)), SOC/OC ratio and F-factor had an opposite variation trend, which suggested that the higher F-factor could be ascribed to greater emissions of primary aerosols, rather than gas-particle conversion. When RH was 40–80%, the F-factor increased slowly, which was not similar to that observed in the winter of Beijing (the F-factor increased sharply) (Yang et al., 2019), indicating the different haze formation mechanisms between Beijing and Shahe.

 Fig. 5. (a) F-factor variation with different PM2.5 concentrations. (b) Box plots of SOC/OC and F-factor under different RH. Median (central horizontal bar within the boxes), 25th and 75th percentiles (lower and upper bars of the boxes), and minimum and maximum (lower and upper whiskers).Fig. 5. (a) F-factor variation with different PM2.5 concentrations. (b) Box plots of SOC/OC and F-factor under different RH. Median (central horizontal bar within the boxes), 25th and 75th percentiles (lower and upper bars of the boxes), and minimum and maximum (lower and upper whiskers).

Sources of Air Pollutants

Local Sources

As shown in Figs. 6(a) and 6(c), the mass concentrations of PM2.5 and NO2 were enhanced when the northeasterly wind was experienced at the site, which suggested there were strong sources of pollution in the northeast of the observation site where dozens of glass factories gathered. As for SO2 (Fig. 6(b)), the high mass concentrations were influenced mainly by the west wind, which might be caused by coal combustion from residential areas. VOCs, NO3 and SO42– displayed similar distribution characteristics (Figs. 6(d), 6(e) and 6(f)). The high concentrations mainly appeared under the northeast wind, which indicated the observation site could be influenced by pollutant emissions from industries. The polar plots of OC (Fig. 6(g)) and EC (Fig. 6(h)) showed concentration hotspots under the weak northerly wind, which are different than the polar plot features of NO3 and SO42–. This indicated that the sources of OC and EC were different from NO3 and SO42–. High mass concentrations of PM2.5, SO2 and NO2 appeared at weak winds with speeds of 0–3 m s–1.

Fig. 6. Dependence of mass concentrations of (a) PM2.5, (b) SO2, (c) NO2, (d) VOCs, (e) NO3–, (f) SO42–, (g) OC, and (h) EC on the wind speed and direction from Dec. 14 to Dec. 30, 2017, in Shahe. Fig. 6. Dependence of mass concentrations of (a) PM2.5, (b) SO2, (c) NO2, (d) VOCs, (e) NO3, (f) SO42–, (g) OC, and (h) EC on the wind speed and direction from Dec. 14 to Dec. 30, 2017, in Shahe. 

Pollution Transmission

As shown in Fig. 7(a), all trajectories in the study period were divided into three groups, C1, C2 and C3, which accounted for 46.1%, 19.9% and 34.1%, respectively. Cluster C1, a short-distance transport, originated in Shijiazhuang and passed through Xingtai city. Cluster C2 started from southern Inner Mongolia and passed through Shanxi Province. When air masses came from directions C1 and C2, the mass concentrations of pollutants were relatively high (Fig. 7(b)). Cluster C3, which had a long transport across Inner Mongolia and northern Shanxi Province before arriving in Shahe, corresponded to clean air observed in Shahe. There was a negative correlation between the PM2.5 concentration and the air mass transport distance (Table 3), with the minimum value in C1 (r = –0.4, p < 0.01). In C1 and C2, WSOCs/OC was negatively correlated with air mass transport distance. These results suggest that Shahe city may be influenced by pollutant transmission originating in the short distance.

Fig. 7. (a) Analytical results of back trajectory clusters and (b) mass concentrations of pollutants under different clusters during observation periods.Fig. 7. (a) Analytical results of back trajectory clusters and (b) mass concentrations of pollutants under different clusters during observation periods.

Table 3. Spearman correlation coefficients of PM2.5, WSOCs/OC, and NO3–/SO42– with transport distance (km), RH, air temperature (°C), wind speed (m s–1), and pressure (Pa) under different back trajectory clusters.

The PSCF model was employed to identify the potential source areas for PM2.5 in Shahe. When the PM2.5 concentration was greater than 35 µg m–3 (Fig. 8(a)), the cells with high WPSCF values were mainly near Shahe and the surrounding area. With the increase in PM2.5 concentrations (Fig. 8), the cells with high WPSCF values visibly decreased, indicating the reduction of strong potential source areas. In all cases, Shahe was a strong potential source area for its PM2.5.

Fig. 8. Results of PSCF analysis for PM2.5 under different concentration thresholds from Dec. 14 to Dec. 30, 2017, in Shahe: (a) PM2.5 concentration ≥ 35 µg m–3, (b) PM2.5 concentration ≥ 75 µg m–3, (c) PM2.5 concentration ≥ 150 µg m–3, and (d) PM2.5 concentration ≥ 250 µg m–3.Fig. 8. Results of PSCF analysis for PM2.5 under different concentration thresholds from Dec. 14 to Dec. 30, 2017, in Shahe: (a) PM2.5 concentration ≥ 35 µg m–3, (b) PM2.5 concentration ≥ 75 µg m–3, (c) PM2.5 concentration ≥ 150 µg m–3, and (d) PM2.5 concentration ≥ 250 µg m–3.


This study investigated the evolution of haze in Shahe from Dec. 14 till Dec. 30, 2017, to identify the pollution characteristics and sources. Comprehensive measurements of the aerosols and the relevant gaseous pollutants, as well as the meteorological conditions, were conducted at the urban atmospheric environment monitoring station in Shahe. The mean mass concentration of the PM2.5 was 121.6 ± 91.8 µg m–3, and the maximum concentration was 510.0 µg m–3. The average concentrations of the nitrate and sulfate components were 21.4 ± 16.3 and 15.9 ± 20.9 µg m–3, respectively, accounting for 17.9% and 12.3% of the PM2.5.

NO2 and SO2 oxidation, with average oxidation ratios of 0.17 ± 0.09 and 0.19 ± 0.19, respectively, and discovered that the pathway of the latter depended on the RH. When the RH was low (< 40%), SO2 oxidation was mainly driven by gas-phase and heterogeneous reactions. When the RH was high (> 40%), however, enhanced aqueous reactions became dominant. Additionally, the nitrate largely arose from photochemical reactions during the day but exhibited a different formation mechanism at night.

The OC, EC, and WSOC mass concentrations were 50.4 ± 31.1, 5.8 ± 4.4, and 12.8 ± 10.1 µgC m–3, respectively, and the SOC mass concentration was estimated using the EC tracer method, which revealed that primary emissions contributed approximately 72% of the total OC. Secondary OC formation potentially increased the gas-to-particle conversion of carbonaceous species when the RH was 60% or less, but larger primary aerosol emissions played a greater role in determining the F-factor when the RH exceeded 60%.

Back trajectories demonstrated the influence of pollutants arriving in Shahe from a nearby area, which potential source contribution function (PSCF) analysis identified as the main potential source area for PM2.5 during haze. However, the cities surrounding Shahe also contributed to the high PM2.5 concentrations. Thus, measures such as reducing gaseous precursor emissions, promoting clean energy use, and collaborating on a regional scale are necessary to improve the air quality.


This work was sponsored by the National Natural Science Foundation of China (Grants 41675128, 41877314, and 41605104) and the National Key Research and Development Project (Grant 2017YFC0209903). The authors acknowledge the Institute of Atmospheric Physics for providing the research platform and the NOAA Air Resource Laboratory for providing the HYSPLIT trajectory model.


  1. Ashbaugh, L.L., Malm, W.C. and Sadeh, W.Z. (1985). A residence time probability analysis of sulfur concentrations at Grand-Canyon-National-Park. Atmos. Environ. 19: 1263–1270. https://doi.org/10.1016/0004-6981(85)90256-2

  2. Boreddy, S.K.R., Haque, M.M. and Kawamura, K. (2018). Long-Term (2001-2012) trends of carbonaceous aerosols from a remote island in the western North Pacific: an outflow region of Asian pollutants. Atmos. Chem. Phys. 18: 1291–1306. https://doi.org/10.5194/acp-18-1291-2018

  3. Cai, W., Li, K., Liao, H., Wang, H. and Wu, L. (2017). Weather conditions conducive to Beijing severe haze more frequent under climate change. Nat. Clim. Change 7: 257–262. https://doi.org/10.1038/nclimate3249

  4. Cao, J.J., Lee, S.C., Chow, J.C., Watson, J.G., Ho, K.F., Zhang, R.J., Jin, Z.D., Shen, Z.X., Chen, G.C., Kang, Y.M., Zou, S.C., Zhang, L.Z., Qi, S.H., Dai, M.H., Cheng, Y. and Hu, K. (2007). Spatial and seasonal distributions of carbonaceous aerosols over China. J. Geophys. Res. 112: 22S11. https://doi.org/10.1029/2006JD008205

  5. Cheng, Y., Zheng, G., Wei, C., Mu, Q., Zheng, B., Wang, Z., Gao, M., Zhang, Q., He, K., Carmichael, G., Poschl, U. and Su, H. (2016). Reactive nitrogen chemistry in aerosol water as a source of sulfate during haze events in China. Sci. Adv. 2: e1601530. https://doi.org/10.1126/sciadv.1601530

  6. Deng, J., Zhang, Y., Qiu, Y., Zhang, H., Du, W., Xu, L., Hong, Y., Chen, Y. and Chen, J. (2018). Source apportionment of PM2.5 at the Lin'an regional background site in China with three receptor models. Atmos. Res. 202: 23–32. https://doi.org/10.1016/j.atmosres.2017.11.017

  7. Dimitriou, K., Remoundaki, E., Mantas, E. and Kassomenos, P. (2015). Spatial distribution of source areas of PM2.5 by Concentration Weighted Trajectory (CWT) model applied in PM2.5 concentration and composition data. Atmos. Environ. 116: 138–145. https://doi.org/10.1016/j.atmosenv.2015.06.021

  8. Ding, X., Qi, J. and Meng, X. (2019). Characteristics and sources of organic carbon in coastal and marine atmospheric particulates over East China. Atmos. Res. 228: 281–291. https://doi.org/10.1016/j.atmosres.2019.06.015

  9. Feng, J.L., Yu, H., Mi, K., Su, X.F., Li, Y., Li, Q.L. and Sun, J.H. (2018). One year study of PM2.5 in Xinxiang city, North China: Seasonal characteristics, climate impact and source. Ecotoxicol. Environ. Saf. 154: 75–83. https://doi.org/10.1016/j.ecoenv.2018.01.048

  10. Fernald, F.G. (1984). Analysis of atmospheric lidar observations - some comments. Appl. Opt. 23: 652–653. https://doi.org/10.1364/AO.23.000652

  11. Fu, Q., Zhuang, G., Wang, J., Xu, C., Huang, K., Li, J., Hou, B., Lu, T. and Streets, D.G. (2008). Mechanism of formation of the heaviest pollution episode ever recorded in the Yangtze River Delta, China. Atmos. Environ. 42: 2023–2036. https://doi.org/10.1016/j.atmosenv.2007.12.002

  12. Huang, X., Liu, Z., Liu, J., Hu, B., Wen, T., Tang, G., Zhang, J., Wu, F., Ji, D., Wang, L. and Wang, Y. (2017). Chemical characterization and source identification of PM2.5 at Multiple sites in the Beijing-Tianjin-Hebei region, China. Atmos. Chem. Phys. 17: 12941–12962. https://doi.org/10.5194/acp-17-12941-2017

  13. Huang, X.F., Zou, B.B., He, L.Y., Hu, M., Prevot, A.S.H. and Zhang, Y.H. (2018). Exploration of PM2.5 sources on the regional scale in the Pearl River Delta based on ME-2 modeling. Atmos. Chem. Phys. 18: 11563–11580. https://doi.org/10.5194/acp-18-11563-2018

  14. Ji, D., Gao, M., Maenhaut, W., He, J., Wu, C., Cheng, L., Gao, W., Sun, Y., Sun, J., Xin, J., Wang, L. and Wang, Y. (2019). The carbonaceous aerosol levels still remain a challenge in the Beijing-Tianjin-Hebei region of China: Insights from continuous high temporal resolution measurements in multiple cities. Environ. Int. 126: 171–183. https://doi.org/10.1016/j.envint.2019.02.034

  15. Kelley, M.C., Brown, M.M., Fedler, C.B. and Ardon-Dryer, K. (2020). Long-term measurements of PM2.5 concentrations in Lubbock, Texas. Aerosol Air Qual. Res. 20: 1306–1318. https://doi.org/10.4209/aaqr.2019.09.0469

  16. Liu, B., Sun, X., Zhang, J., Bi, X., Li, Y., Li, L., Dong, H., Xiao, Z., Zhang, Y. and Feng, Y. (2020a). Characterization and spatial source apportionments of ambient PM10 and PM2.5 during the heating period in Tian'jin, China. Aerosol Air Qual. Res. 20: 1–13. https://doi.org/10.4209/aaqr.2019.06.0281

  17. Liu, P., Zhang, C., Mu, Y., Liu, C., Xue, C., Ye, C., Liu, J., Zhang, Y. and Zhang, H. (2016). The possible contribution of the periodic emissions from farmers' activities in the North China Plain to atmospheric water-soluble ions in Beijing. Atmos. Chem. Phys. 16: 10097–10109. https://doi.org/10.5194/acp-16-10097-2016

  18. Liu, P., Ye, C., Xue, C., Zhang, C., Mu, Y. and Sun, X. (2020b). Formation mechanisms of atmospheric nitrate and sulfate during the winter haze pollution periods in Beijing: Gas-phase, heterogeneous and aqueous-phase chemistry. Atmos. Chem. Phys. 20: 4153–4165. https://doi.org/10.5194/acp-20-4153-2020

  19. Liu, X.G., Li, J., Qu, Y., Han, T., Hou, L., Gu, J., Chen, C., Yang, Y., Liu, X., Yang, T., Zhang, Y., Tian, H. and Hu, M. (2013). Formation and evolution mechanism of regional haze: A case study in the megacity Beijing, China. Atmos. Chem. Phys. 13: 4501–4514. https://doi.org/10.5194/acp-13-4501-2013

  20. Liu, Z., Hu, B., Ji, D., Cheng, M., Gao, W., Shi, S., Xie, Y., Yang, S., Gao, M., Fu, H., Chen, J. and Wang, Y. (2019). Characteristics of fine particle explosive growth events in Beijing, China: Seasonal variation, chemical evolution pattern and formation mechanism. Sci. Total Environ. 687: 1073–1086. https://doi.org/10.1016/j.scitotenv.2019.06.068

  21. Lu, H.Y., Lin, S.L., Mwangi, J.K., Wang, L.C. and Lin, H.Y. (2016). Characteristics and source apportionment of atmospheric PM2.5 at a coastal city in southern Taiwan. Aerosol Air Qual. Res. 16: 1022–1034. https://doi.org/10.4209/aaqr.2016.01.0008

  22. Luo, Y., Zhou, X., Zhang, J., Xue, L., Chen, T., Zheng, P., Sun, J., Yan, X., Han, G. and Wang, W. (2020). Characteristics of airborne water-soluble organic carbon (WSOC) at a background site of the North China Plain. Atmos. Res. 231: 104668. https://doi.org/10.1016/j.atmosres.2019.104668

  23. Meng, C.C., Wang, L.T., Zhang, F.F., Wei, Z., Ma, S.M., Ma, X. and Yang, J. (2016). Characteristics of concentrations and water-soluble inorganic ions in PM2.5 in Handan City, Hebei province, China. Atmos. Res. 171: 133–146. https://doi.org/10.1016/j.atmosres.2015.12.013

  24. Pan, X. Uno, I., Wang, Z., Yamamoto, S., Hara, Y. and Wang, Z. (2018). Seasonal variabilities in chemical compounds and acidity of aerosol particles at urban site in the west Pacific. Environ. Pollut. 237: 868–877. https://doi.org/10.1016/j.envpol.2017.11.089

  25. Pathak, R.K., Wu, W.S. and Wang, T. (2009). Summertime PM2.5 ionic species in four major cities of China: nitrate formation in an ammonia-deficient atmosphere. Atmos. Chem. Phys. 9: 1711–1722. https://doi.org/10.5194/acp-9-1711-2009

  26. 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. https://doi.org/10.4209/aaqr.2019.01.0010

  27. Sandradewi, J., Prevot, A.S.H., Weingartner, E., Schmidhauser, R., Gysel, M. and Baltensperger, U. (2008). A Study of wood burning and traffic aerosols in an alpine valley using a multi-wavelength Aethalometer. Atmos. Environ. 42: 101–112. https://doi.org/10.1016/j.atmosenv.2007.09.034

  28. Sun, W., Wang, D., Yao, L., Fu, H., Fu, Q., Wang, H., Li, Q., Wang, L., Yang, X., Xian, A., Wang, G., Xiao, H. and Chen, J. (2019). Chemistry-triggered events of PM2.5 explosive growth during late autumn and winter in Shanghai, China. Environ. Pollut. 254: 112864. https://doi.org/10.1016/j.envpol.2019.07.032

  29. Tang, X., Zhang, X., Wang, Z. and Ci, Z. (2016). Water-Soluble organic carbon (WSOC) and its temperature-resolved carbon fractions in atmospheric aerosols in Beijing. Atmos. Res. 181: 200–210. https://doi.org/10.1016/j.atmosres.2016.06.019

  30. Wang, Y.Q., Zhang, X.Y. and Draxler, R.R. (2009). Trajstat: GIS-based software that uses various trajectory statistical analysis methods to identify potential sources from long-term air pollution measurement data. Environ. Modell. Software 24: 938–939. https://doi.org/10.1016/j.envsoft.2009.01.004

  31. Wang, Z., Li, J., Wang, Z., Yang, W., Tang, X., Ge, B., Yan, P., Zhu, L., Chen, X., Chen, H., Wand, W., Li, J., Liu, B., Wang, X., Wand, W., Zhao, Y., Lu, N. and Su, D. (2014). Modeling study of regional severe hazes over mid-eastern China in January 2013 and its implications on pollution prevention and control. Sci. China-Earth Sci. 57: 3–13. https://doi.org/10.1007/s11430-013-4793-0

  32. Wen, J., Shi, G., Tian, Y., Chen, G., Liu, J., Huang-Fu, Y., Ivey, C.E. and Feng, Y. (2018). Source contributions to water-soluble organic carbon and water-insoluble organic carbon in PM2.5 during Spring Festival, heating and non-heating seasons. Ecotoxicol. Environ. Saf. 164: 172–180. https://doi.org/10.1016/j.ecoenv.2018.08.002

  33. Wu, R., Li, J., Hao, Y., Li, Y., Zeng, L. and Xie, S. (2016). Evolution process and sources of ambient volatile organic compounds during a severe haze event in Beijing, China. Sci. Total Environ. 560: 62–72. https://doi.org/10.1016/j.scitotenv.2016.04.030

  34. Xie, Y., Liu, Z., Wen, T., Huang, X., Liu, J., Tang, G., Yang, Y., Li, X., Shen, R., Hu, B. and Wang, Y. (2019). Characteristics of chemical composition and seasonal variations of PM2.5 in Shijiazhuang, China: Impact of primary emissions and secondary formation. Sci. Total Environ. 677: 215–229. https://doi.org/10.1016/j.scitotenv.2019.04.300

  35. Xu, H., Xiao, Z., Chen, K., Tang, M., Zheng, N., Li, P., Yang, N., Yang, W. and Deng, X. (2019). Spatial and temporal distribution, chemical characteristics, and sources of ambient particulate matter in the Beijing-Tianjin-Hebei region. Sci. Total Environ. 658: 280–293. https://doi.org/10.1016/j.scitotenv.2018.12.164

  36. Yang, S., Ma, Y.L., Duan, F.K., He, K.B., Wang, L.T., Wei, Z., Zhu, L.D., Ma, T., Li, H. and Ye, S.Q. (2018). Characteristics and formation of typical winter haze in Handan, one of the most polluted cities in China. Sci. Total Environ. 613: 1367–1375. https://doi.org/10.1016/j.scitotenv.2017.08.033

  37. Yang, S., Duan, F., Ma, Y., He, K., Zhu, L., Ma, T., Ye, S., Li, H., Huang, T. and Kimoto, T. (2019). Haze formation indicator based on observation of critical carbonaceous species in the atmosphere. Environ. Pollut. 244: 84–92. https://doi.org/10.1016/j.envpol.2018.10.006

  38. Yang, T., Sun, Y.L., Zhang, W., Wang, Z.F., Liu, X.G., Fu, P.Q. and Wang, X.Q. (2017a). Evolutionary processes and sources of high-nitrate haze episodes over Beijing, Spring. J. Environ. Sci. 54: 142–151. https://doi.org/10.1016/j.jes.2016.04.024

  39. Yang, W., Wang, G. and Bi, C. (2017b). Analysis of long-range transport effects on PM2.5 during a short severe haze in Beijing, China. Aerosol Air Qual. Res. 17: 1610–1622. https://doi.org/10.4209/aaqr.2016.06.0220

  40. Yang, Y.R., Liu, X.G., Qu, Y., An, J.L., Jiang, R., Zhang, Y.H., Sun, Y.L., Wu, Z.J., Zhang, F., Xu, W.Q. and Ma, Q.X. (2015). Characteristics and formation mechanism of continuous hazes in China: A case study during the autumn of 2014 in the North China Plain. Atmos. Chem. Phys. 15: 8165–8178. https://doi.org/10.5194/acp-15-8165-2015

  41. Yao, L., Yang, L., Chen, J., Wang, X., Xue, L., Li, W., Sui, X., Wen, L., Chi, J., Zhu, Y., Zhanga, J., Xu, C., Zhu, T. and Wang, W. (2016). Characteristics of carbonaceous aerosols: Impact of biomass burning and secondary formation in summertime in a rural area of the North China Plain. Sci. Total Environ. 557: 520–530. https://doi.org/10.1016/j.scitotenv.2016.03.111

  42. Yao, L., Huo, J., Wang, D., Fu, Q., Sun, W., Li, Q. and Chen, J. (2020). Online measurement of carbonaceous aerosols in suburban Shanghai during winter over a three-year period: Temporal variations, meteorological effects, and sources. Atmos. Environ. 226: 11. https://doi.org/10.1016/j.atmosenv.2020.117408

  43. Yao, X., Chan, C.K., Fang, M., Cadle, S., Chan, T., Mulawa, P., He, K. and Ye, B. (2002). The water-soluble ionic composition of PM2.5 in Shanghai and Beijing, China. Atmos. Environ. 36: 4223–4234. https://doi.org/10.1016/S1352-2310(02)00342-4

  44. Ye, S., Ma, T., Duan, F., Li, H., He, K., Xia, J., Yang, S., Zhu, L., Ma, Y., Huang, T. and Kimoto, T. (2019). Characteristics and formation mechanisms of winter haze in Changzhou, a highly polluted industrial city in the Yangtze River Delta, China. Environ. Pollut. 253: 377–383. https://doi.org/10.1016/j.envpol.2019.07.011

  45. Zhang, R., Wang, G., Guo, S., Zarnora, M.L., Ying, Q., Lin, Y., Wang, W., Hu, M. and Wang, Y. (2015). Formation of urban fine particulate matter. Chem. Rev. 115: 3803–3855. https://doi.org/10.1021/acs.chemrev.5b00067

  46. Zhang, R., Sun, X.S., Shi, A.J., Huang, Y.H., Yan, J., Nie, T., Yan, X. and Li, X. (2018). Secondary inorganic aerosols formation during haze episodes at an urban site in Beijing, China. Atmos. Environ. 177: 275–282. https://doi.org/10.1016/j.atmosenv.2017.12.031

  47. Zhang, T., Cao, J.J., Tie, X.X., Shen, Z.X., Liu, S.X., Ding, H., Han, Y.M., Wang, G.H., Ho, K.F., Qiang, J. and Li, W.T. (2011). Water-soluble ions in atmospheric aerosols measured in Xi'an, China: Seasonal variations and sources. Atmos. Res. 102: 110–119. https://doi.org/10.1016/j.atmosres.2011.06.014

  48. Zhao, P.S., Dong, F., He, D., Zhao, X.J., Zhang, X.L., Zhang, W.Z., Yao, Q. and Liu, H.Y. (2013). Characteristics of concentrations and chemical compositions for PM2.5 in the region of Beijing, Tianjin, and Hebei, China. Atmos. Chem. Phys. 13: 4631–4644. https://doi.org/10.5194/acp-13-4631-2013

  49. Zheng, N., Song, S., Jin, X., Jia, H., Wang, Y., Ji, Y., Guo, L. and Li, P. (2019). Assessment of carbonaceous aerosols at Mount Tai, North China: Secondary formation and regional source analysis. Aerosol Air Qual. Res. 19: 1708–1720. https://doi.org/10.4209/aaqr.2019.06.0316

Aerosol Air Qual. Res. 20 :2741 -2753 . https://doi.org/10.4209/aaqr.2020.03.0124  

Don't forget to share this article 


Subscribe to our Newsletter 

Aerosol and Air Quality Research has published over 2,000 peer-reviewed articles. Enter your email address to receive latest updates and research articles to your inbox every second week.

Latest coronavirus research from Aerosol and Air Quality Research

2018 Impact Factor: 2.735

5-Year Impact Factor: 2.827

SCImago Journal & Country Rank

Aerosol and Air Quality Research (AAQR) is an independently-run non-profit journal, promotes submissions of high-quality research, and strives to be one of the leading aerosol and air quality open-access journals in the world.