Special Issue on Better Air Quality in Asia (II)

Menghui Li1,2, Liping Wu2, Xiangyan Zhang3, Xinwu Wang3, Wenyu Bai1, Jing Ming4, Chunmei Geng This email address is being protected from spambots. You need JavaScript enabled to view it.1, Wen Yang This email address is being protected from spambots. You need JavaScript enabled to view it.1

1 State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
2 School of Environmental and Municipal Engineering, Tianjin Chengjian University, Tianjin 300384, China
3 Zibo Eco-Environmental Monitoring Center, Zibo 255000, China
4 Beacon Science & Consulting, Doncaster East, VIC 3109, Australia


Received: December 29, 2019
Revised: April 29, 2020
Accepted: May 26, 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.2019.11.0591  

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Cite this article:

Li, M., Wu, L., Zhang, X., Wang, X., Bai, W., Ming, J., Geng, C. and Yang. W. (2020). Comparison of PM2.5 Chemical Compositions during Haze and Non-haze Days in a Heavy Industrial City in North China. Aerosol Air Qual. Res. 20: 1950–1960. https://doi.org/10.4209/aaqr.2019.11.0591


HIGHLIGHTS

  • Fine particles were acidic during haze days.
  • Crustal elements were the most abundant elements in PM2.5.
  • Coal combustion and motor vehicle emissions were the important sources of PM2.5.
 

ABSTRACT


This study aimed to determine the chemical composition, sources and contributing factors of airborne PM2.5 (particulate matter with an aerodynamic diameter ≤ 2.5 µm) during a haze episode in Zibo, a heavy industrial city in China. Samples of PM2.5 were collected 8–27 January 2018 and analyzed for water-soluble inorganic ions (WSIs), trace elements (TEs), organic carbon (OC) and elemental carbon (EC). The PM2.5 concentration was 76.78% higher during the haze (mean ± standard deviation [SD] = 211 ± 39 µg m–3) than before it (49 ± 38 µg m–3), and the dominant ions were NO3, SO42– and NH4+. Additionally, an elevated TE concentration was observed during the episode (exceeding the pre- and post-haze values by 54.70% and 31.98%, respectively), with crustal elements (K, Al, Ca, Si, Na, Fe and Mg), the most abundant elemental components, accounting for 88.64%. Carbonaceous species (OC and EC) contributed 15.45% of the PM2.5 on haze days and slightly more on non-haze days. The NO3/SO42– and OC/EC ratios indicated that coal combustion and motor vehicle emission were the primary sources of pollution, and back-trajectory analysis revealed that the air masses over Zibo on haze days mainly originated in adjacent areas in Shandong Province and the Beijing-Tianjin-Hebei region (BTH). The haze episode was caused by a combination of unfavorable meteorological conditions, secondary formation, the accumulation of local pollutants, and peripheral transmission.


Keywords: Chemical composition; PM2.5; Haze episode; Heavy industrial city.


INTRODUCTION


With the development of large-scale industrialization and accelerated urbanization, China has experienced rapid economic growth (Ji et al., 2014). Meanwhile, China is facing serious air pollution issues. Particulate matter pollution is one of the most urgent problems to be solved, especially in developed megalopolises, which are severely affected by emissions from industry, motor vehicle exhaust and other urban air pollution sources. Suspended particulate matter with an aerodynamic diameter ≤ 2.5 µm (PM2.5) is of great significance because of its unique physical and chemical characteristics (Taghvaee et al., 2019). Previous studies have shown that PM2.5 is a major cause of haze and can scatter and absorb sunlight, reduce atmospheric visibility and increase radiation forcing, leading to global climate change and harm to ecosystems and human health (Menon et al., 2002; Chan and Yao, 2008; Yang et al., 2011; Gens et al., 2014; Lin et al., 2014; Tan et al., 2016; Zou et al., 2017; Joharestani et al., 2019).

Many studies have been done on the chemical characteristics and source apportionment of PM2.5 worldwide (Tan et al., 2016, 2017; Turap et al., 2019). The chemical composition of PM2.5 is very complicated, and mainly includes organic substances, water-soluble inorganic salts, carbonaceous substances, trace elements, water, mineral dust, acidic substances, heavy metals and polycyclic aromatic hydrocarbons (PAHs) (Turpin and Lim, 2001; Tao et al., 2017b; Zou et al., 2017). As the chemical composition of PM2.5 is complex and diverse, it has become increasingly important to conduct an in-depth study on it and determine the key information necessary for effective emission reduction (Taghvaee et al., 2019)

Though the overall air quality has improved significantly in China, serious air pollution issues still occur frequently in heavy industrial cities in China. However, few studies have focused on the chemical composition of PM2.5 before, during and after haze episodes in such cities. With the development of industry, air pollution in this region has become quite serious (Zhang et al., 2018). However, few studies have focused on the chemical composition of PM2.5 in this area, especially during haze episodes.

Zibo is a heavy industrial city upwind of the Beijing-Tianjin-Hebei region (BTH). Serious pollution occurs in winter (Li et al., 2017), so there is an urgent need to learn more about the characteristics of PM2.5 pollution. Monitoring data show that the annual average PM2.5 concentrations in Zibo in 2015, 2016, 2017 and 2018 were 88 µg m3, 74 µg m3, 63 µg m3 and 55 µg m3, respectively, which all exceed the national annual secondary standard (35 µg m3). Zibo is often listed as one of the top ten most polluted cities in China, especially in winter (Li et al., 2017; Luo et al., 2018). Zibo is a typical heavy industrial city with petrochemical, ceramics, pharmaceutical, and building material industries (Li et al., 2017; Luo et al., 2018). It is undeniable that these industries produce a great deal of pollution.

In this study, the chemical composition of PM2.5 was studied before, during and after a haze episode, and the causes of the haze episode are discussed. PM2.5 samples and meteorological data were collected and systematically analyzed. This study increases our understanding of the chemical composition of PM2.5 during haze episodes and provides a scientific basis for the prevention and control of atmospheric pollution in such heavy industrial cities.

 
METHODS



Sample Collection

Zibo is located in the central part of Shandong Province, China. It covers an area of 5965 km2 and has a population of about 4.32 million. Zibo has a warm temperate continental monsoon climate with northwesterly, westerly and southwesterly winds dominating in winter (Luo et al., 2018). There were more than 1.237 million vehicles in Zibo in 2018 (Statistical Communiqué of Zibo on the 2018 National Economic and Social Development). There is diverse terrain with mountains in the southern, eastern and western regions and low land to the north. A central depression in the north is not conducive to the spread of air pollutants.

Air quality will be recorded as polluted when it exceeds the Grade II limit (75 µg m3) for daily average PM2.5 concentration stipulated by the Chinese National Ambient Air Quality Standard (GB 3095-2012). A haze episode is defined here as a weather phenomenon with horizontal visibility of ≤ 10 km, relative humidity (RH) of ≤ 90% (Wu et al., 2007), and a daily PM2.5 concentration of > 75 µg m3 for ≥ 5 days. There was a haze episode from 15–21 January 2018 in Zibo. Atmospheric PM2.5 samples were collected on 8–27 January 2018. The sampling site was approximately 6 m above the ground on the second rooftop of the Nanding Ambient Air Quality Monitoring Station, Zhangdian District, Zibo (36°48ʹ10.67ʺN, 118°01ʹ28.19ʺE) (Fig. 1). The sampling site was surrounded by residential and administrative agencies and located about 50 m from Xincun Road, with no other obvious sources of pollution. The sampling time was from 10:00 a.m. on the first day to 9:00 a.m. on the second day. Daily PM2.5 samples were collected with a Teflon filter (diameter = 47 mm; Whatman, USA) and a quartz filter (diameter = 47 mm; Pall, USA) at a flow rate of 16.7 L min–1 by a particulate sampler with two parallel channels (ZR-3930; Zhongrui Inc., China).

Fig. 1. Location of the sampling site in Zibo.
Fig. 1. Location of the sampling site in Zibo.

Before sampling, the quartz filters were baked in a muffle furnace at 550°C for 4 hours to remove carbonaceous pollutants. Before and after sampling, the filters were balanced under constant temperature (20 ± 1°C) and humidity (50 ± 5%) for over 24 hours and weighed using an automatic filter weighing system (AWS-1; Comde-Derenda GmbH, Germany). The difference between two consecutive measurements was not more than 5 µg. After sampling, the filters were stored at –20°C until analysis to prevent volatilization of volatile components (Xiao et al., 2014).


Chemical Analysis

One quarter of each quartz filter was extracted via ultra-sonication to analyze the concentration of water-soluble inorganic ions (WSIs). The sample was added to 10 mL of deionized water and sonicated for 20 minutes, centrifuged for 5 minutes, and then filtered through a microporous membrane (pore size = 0.45 µm; diameter = 25 mm). The concentrations of six anions (F, Cl, NO2, NO3, C2O42 and SO42) and five cations (Na+, NH4+, K+, Mg2+ and Ca2+) were analyzed by ion chromatography (Dionex ICS-1100; Thermo Fisher Scientific, USA) (Wang et al., 2005; Yang et al., 2017).

In order to analyze the concentration of elements, each Teflon filter was divided into two parts, which were separately dissolved in acid and alkali solution (Kong et al., 2010). The elements Li, Be, Na, P, K, Sc, As, Rb, Y, Mo, Cd, Sn, Sb, Cs, La, V, Cr, Mn, Co, Ni, Cu, Zn, Ce, Sm, W, Tl, Pb, Bi, Th and U were analyzed by inductively coupled plasma mass spectrometry (ICP-MS; 7500a; Agilent, USA), while the elements Zr, Al, Sr, Mg, Ti, Ca, Fe, Ba and Si were analyzed by an inductively coupled plasma optical spectrometer (ICP-OES; Agilent, USA).

The thermal/optical reflectance (TOR) carbon analysis method was used based on the Interagency Monitoring of Protected Visual Environments (IMPROVE) protocol (Chow et al., 2004; Yang et al., 2017). The Desert Research Institute (DRI) Model 2001A was used for analysis. An area of 0.5 cm2 was punched from each filter sample and analyzed for organic carbon (OC) in a helium (He) atmosphere and elemental carbon (EC) in a 2% O2/98% He atmosphere. In this process, the pyrolytic carbon was identified by reflected laser (Tao et al., 2017b; Turap et al., 2019). The method detection limits (MDLs) of OC and EC were 0.81 µgC cm–2 and 0.12 µgC cm–2, respectively.


Quality Assurance and Quality Control

All analytical processes were conducted under strict quality assurance (QA) and quality control (QC) to avoid any possible pollution. Before and after sampling, as well as during compositional analysis, the quartz filters were confirmed to be intact, and broken filters were excluded. The background was regularly monitored by blank testing, which was used to verify and correct the corresponding data (Turap et al., 2019).


RESULTS AND DISCUSSION



Mass Closure and Concentration of PM
2.5

Six conventional pollutants (O3, CO, SO2, NO2, PM10 and PM2.5) and meteorological parameters (wind direction [WD], wind speed [WS], visibility, relative humidity [RH] and temperature [T]) were recorded simultaneously at Nanding Ambient Air Quality Monitoring Station near the sampling site. Table 1 shows a summary of the mass concentrations of PM2.5 and average meteorological parameters in sampling periods. Fig. 2 shows the time series of WS, WD, visibility, T and RH, and six conventional pollutants over the sampling period. The daily concentrations of PM2.5 were 20–129 µg m3, 147–256 µg m3, and 39–124 µg m3 before, during, and after the haze episode, respectively, with means ± standard deviations (SDs) of 49 ± 38 µg m3, 211 ± 39 µg m3 and 58 ± 33 µg m3. The concentration of PM2.5 was 76.78% higher during the haze episode than before it. The highest PM2.5 value was 256 µg m3 on 19 January, which may be related to low WS (1.57 m s1), high RH (62.21%) and T (2.83°C). In contrast, the minimum value of PM2.5 was 20 µg m3 on 10 January, which may be related to high WS (1.98 m s1), low (RH 27.33%) and T (–4.4°C).

Table 1. PM2.5 mass concentrations and mean meteorological parameters before, during and after a haze episode

Fig. 2. Time series of (a) wind speed (WS), (b) temperature (T) and relative humidity (RH), and (c) visibility; online monitoring of (d) CO and SO2, (e) O3 and NO2, and (f) PM2.5 and PM10 concentration.Fig. 2. Time series of (a) wind speed (WS), (b) temperature (T) and relative humidity (RH), and (c) visibility; online monitoring of (d) CO and SO2, (e) O3 and NO2, and (f) PM2.5 and PM10 concentration.

PM2.5 concentrations were strongly affected by meteorological parameters such as wind direction, wind speed, temperature and RH. At low wind speeds, the atmosphere tends to stabilize and spread slowly and discharged contaminants easily accumulate, resulting in a higher concentration of particulate matter (Zhang et al., 2007). Lower wind speeds might inhibit the dispersion of pollutants in vertical and horizontal directions, while higher temperatures and RH promote gas-to-particle conversion and generate secondary aerosols (Wang et al., 2018). Using SPSS Statistics 22.0 software, the correlations between PM2.5 concentration and meteorological parameters were analyzed (Table 2). PM2.5 was significantly negatively correlated with wind speed and visibility, and positively correlated with RH and temperature. Similar results have been found by previous investigations (Li et al., 2017).

Table 2. Correlations between PM2.5 and meteorological parameters.

In order to ensure data quality, the method proposed by Wang et al. (2016) was used to reconstruct the PM2.5 mass from measurements of mineral dust, organic matter (OM), EC, trace elements, water-soluble inorganic ions and other chemical substances. The OM mass was calculated by multiplying the OC mass by a factor of 1.6 (Wang et al., 2016). The mass closure is shown in Fig. 3. There was a significant correlation between the measured and reconstructed PM2.5 masses (R2 = 0.96) with a slope of 0.82. This result indicates that the chemical analysis was reliable.

Fig. 3. Correlation between reconstructed and measured PM2.5 masses.
Fig. 3. Correlation between reconstructed and measured PM2.5 masses.

 
Water-soluble Inorganic Ions

As shown in Table 3, the total concentrations (mean ± SD) of water-soluble inorganic ions (TWSIs) were 24.70 ± 19.07 µg m–3, 131.97 ± 54.29 µg m–3 and 30.08 ± 17.56 µg m–3, accounting for 50.74%, 61.47% and 51.61% of the PM2.5 before, during and after the haze episode, respectively. The concentrations of water-soluble inorganic ions followed the order NO3 > SO42 > NH4+ > Cl > K+ > Na+ > Ca2+ > F > C2O42 > Mg2+ > NO2. The highest concentration during the haze episode was of NO3 (60.45 ± 27.67 µg m–3). A favorable distribution of NO3 from the gas phase to the particle phase may lead to high NO3 concentration in PM2.5 at relatively low temperatures in winter (Luo et al., 2018). The SO42 was mainly produced by the chemical reaction of gaseous precursors (such as SO2 gas, dimethyl sulfide in the ocean), which occur in the gas phase with OH radicals or in cloud drops with H2O2 or ozone (Pandis et al., 1990). The NH4+ was mainly caused by the reaction between NH3 and the acidic components of NO3 and SO42 (He et al., 2012). NH3 emissions mainly derive from human activities and natural sources (Yang et al., 2017). The NO3, SO42 and NH4+ (SNA) were the dominant ions in PM2.5. The SNA concentrations were 19.55 ± 17.59 µg m–3, 120.58 ± 52.57 µg m–3 and 26.71 ± 16.31 µg m–3, accounting for 79.5%, 91.5% and 89.0% of TWSIs before, during and after the haze episode, respectively. These three ions made high contributions to PM2.5, which is consistent with previous studies (Zhang et al., 2013; Luo et al., 2018). Moreover, a good correlation between SO42 and NO3 was found (Pearson’s correlation coefficient = 0.979, P > 0.01), consistent with an earlier study (Luo et al., 2018). Compared with the other periods, the SNA concentrations were very high during the haze episode (Fig. 4), especially on 20 January with a total concentration of 224.99 µg m–3.

Table 3. Mass concentration of major chemical components in PM2.5 (µg m–3).

Fig. 4. Time series of PM2.5 and dominant chemical components.
Fig. 4. Time series of PM2.5 and dominant chemical components.

The NO3/SO42 ratio in atmospheric particles can be used as a comparative indicator of the contributions of stationary sources (such as coal combustion) and mobile sources (such as automobile exhaust) to sulfur and nitrogen concentrations in the atmosphere (Arimoto et al., 1996; Yao et al., 2002). The ratios of NOx/SOx produced by gasoline and diesel combustion were 13:1 and 8:1, respectively (Yao et al., 2002). The ratio of NOx/SOx from burning coal was 1:2 when the coal sulfur content was 1% (Yao et al., 2002). Hence, the higher the NO3/SO42, the greater the contribution of mobile sources to PM2.5 (Yang et al., 2017). In China, reported NO3/SO42 ratios in PM2.5 include 0.77–0.87 in Zhengzhou (Jiang et al., 2018), 0.14–1.12 in Xinjiang’s Dushanzi District (Turap et al., 2019) and 1.2–1.7 in Beijing (Yang et al., 2017). The ratio of NO3/SO42 in some Chinese cities is lower than that in downtown Los Angeles and in Rubidoux in southern California (2–5) (Kim et al., 2000). During the haze episode, the NO3/SO42 ratio was 1.90, which is higher than before (1.73) and after (1.05) the haze episode. As shown in Fig. 4, the concentration of NO3 was much higher than that of SO42 during the haze episode. Due to poor visibility on heavily polluted days, vehicles travel at low speeds and might emit large amounts of NOx, resulting in high NO3 concentrations (Jiang et al., 2018).

Other characteristic ions and ion ratios can also reflect the source contribution to PM2.5. The molar ratios of Cl/Na+ in PM2.5 were 6.70, 8.20 and 4.98 before, during and after the haze episode, respectively. These values are much higher than the ratio in sea-water (1.17), indicating that coal combustion in winter is an important source of Cl (Yao et al., 2002; Zhang et al., 2013; Tan et al., 2017). K+ is used as an indicator of biomass combustion (Yamasoe et al., 2000). During the haze episode, the concentration of K+ was 2.16 µg m3, which is 3.79 times higher than before and 3.93 times higher than after the haze episode, suggesting that biomass combustion contributed to PM2.5. The concentration of Mg2+ did not change significantly and Ca2+ decreased by 44.29% during the haze episode. The major sources of Ca2+ are usually soil, road dust and construction. The decrease in Ca2+ during the haze episode was probably due to control measures, including cessation of construction and frequent road sweeping (Yang et al., 2017). The concentration of NO2 did not change much and C2O42 increased by 58.33% in the haze days. The increase of C2O42 may be due to various organic acids generated by the oxidation process of VOCs emitted by combustion, which become organic particulates through homogeneous and heterogeneous nucleation. Luo et al. (2018) showed that biomass burning and fossil fuel combustion were important sources of PM2.5 in Zibo.

The ion balance calculation is a good method for studying the acidity of aerosols. It is determined by the anion equivalents (AE) and the cation equivalents (CE) (Xu et al., 2012). If the ratio of CE to AE is ≥ 1, most of the acids are considered to be neutralized (He et al., 2011). In contrast, a ratio < 1 indicates that the aerosols are acidic. In this study, the slope of the linear regression indicated that the aerosols are acidic, especially during the haze episode (Fig. 5). The slope of the linear regression during haze days was lower than during non-haze days, indicating that the proportion of acidity increased in the atmosphere. Therefore, the emissions of SO2 and NOx should be strictly controlled to decrease the formation of SO42 and NO3 (Yang et al., 2017).

Fig. 5. Correlations between cations and anions before, during and after the haze episode.
Fig. 5. Correlations between cations and anions before, during and after the haze episode.

Sulfate and nitrate were the major aerosol components in PM2.5. In order to determine the extent of atmospheric conversion of SO2 to SO42 and of NOx to NO3, the sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR) were used (Fu et al., 2008; Lin, 2002). The higher the SOR and NOR values, the higher the oxidation degree of the gas species (Fu et al., 2008). Earlier studies reported that photochemical oxidation of SO2 or NOx in the atmosphere occurs when the SOR or NOR is > 0.10 (Ohta and Okita, 1990). As shown in Table 4, the SOR values were > 0.10 during the haze episode (mean ± SD = 0.31 ± 0.11) and after it (mean ± SD = 0.19 ± 0.08). High SOR values demonstrate that secondary formation of SO42 from SO2 occurs in the atmosphere. The values of NOR were ≥ 0.10 before the haze episode (mean ± SD = 0.13 ± 0.09), during it (mean ± SD = 0.34 ± 0.09) and afterwards (mean ± SD = 0.16 ± 0.08). High NOR values suggest that secondary formation of NO3 from NO2 occurred in the atmosphere. High NOR and SOR values indicates that there was more active gas-to-particle conversion during the haze episode (Hua et al., 2015). In addition, Sun et al. (2013) has shown that RH has a significant effect on SO42 and NO3 because higher RH can promote the formation of secondary inorganic ions in particles. In this study, the average RH during the haze episode was 61.8%, which was higher than before (33.9%) and lower than after (63.0%) the haze episode. Although the RH during the haze episode was lower than afterwards, the concentrations of SO42 and NO3 during the haze episode (0.30 µg m–3 and 0.31 µg m–3, respectively) were higher than after (0.24 µg m–3 and 0.14 µg m–3), while the SOR and NOR were highest during the haze episode. 

Table 4. SORs and NORs before, during and after a haze episode in Zibo


Trace Elements

Elemental mass accounted for a small percentage (< 20%) of the total PM2.5 mass. However, they are easily absorbed on the surfaces of PM2.5 particulates and are harmful to the human body (Chan and Yao, 2008). Thirty-nine elements in PM2.5 were investigated and the concentrations during the sampling period are given in Table 3. The concentrations of TEs were 3.47 ± 1.50 µg m3, 7.66 ± 3.67 µg m3 and 5.21 ± 3.07 µg m3 before, during and after the haze episode, respectively, making contributions to PM2.5 of 9.55%, 3.74% and 9.24%. During the haze episode, the most abundant elements were crustal elements (e.g., K, Al, Ca, Si, Na, Fe and Mg), which accounted for 88.64% of the TEs. Comparing elemental concentrations before and during the haze episode, the mean concentration of TEs increased by 57.10%, especially for crustal elements such as Na, Al, Ca, Fe and Si, with increases ranging from 43.36% to 74.56%. This increase in elements may be caused by source emissions and low wind velocities, which may cause an accumulation of atmospheric pollutants. While a large amount of particulate matter from the crust may decrease when the wind speed is low enough, crustal elements may still increase due to the build-up of dust re-suspended by road traffic (Gu et al., 2011).

Element K had the highest concentration (1.61 ± 0.47 µg m–3), which may be related to the biomass combustion. Zn (0.37 µg m–3) was the most abundant heavy metal in PM2.5, which may be related to tire wear and automotive lubricant additives (Zhang et al., 2018), followed by Pb, Mn, Cu and Cr. Ni is an indicator of petroleum combustion and is related to oil-fired power and steam boilers, while Fe and Mn are related to the steel industry (Khan et al., 2016; Luo et al., 2018; Zhang et al., 2018). There are petrochemicals, ceramics, pharmaceutical production and building materials in Zibo, so the elements detected in the samples are in line with the characteristics of Zibo’s heavy industries.

 
Carbonaceous Species

The carbonaceous fraction of ambient particulate matter is usually divided into organic carbon and elemental carbon (Park et al., 2001). The OC can be generated by primary emissions and secondary formation, while EC mainly comes from primary emissions (Tao et al., 2017a). The OC in PM2.5 varied from 15.38 µg m3 to 30.83 µg m3 (mean ± SD = 21.46 ± 4.94 µg m3), while EC varied from 8.09 µg m3 to 13.31 µg m3 (mean ± SD = 11.14 ± 1.74 µg m3) during the haze episode (Table 3). The OC accounted for 10.18% and the EC accounted for 5.41% of PM2.5 during the haze episode. Compared with before and after the haze episode, the OC concentration was 64.86% and 69.38% higher during it, while the EC concentration was 63.29% and 65.95% higher. OC and EC accounted for 23.73%, 15.45% and 17.86% of the PM2.5 before, during and after the haze episode, respectively.

The relationship between OC and EC was used to determine the source of carbon particles (Ram et al., 2008; Yang et al., 2017). If OC and EC are mainly emitted by primary sources, the correlation between the OC and EC should be high, because the relative emission rates of OC and EC are proportional (Zhang et al., 2007). Fig. 6 shows the correlation between OC and EC concentrations. The correlation between OC and EC was poor during the haze episode (R2 = 0.04), indicating that the sources of OC and EC were different. However, there was a strong correlation between OC and EC both before (R2 = 0.93) and after (R2 = 0.91) the haze episode. This difference may be interpreted as being affected by other sources in addition to local emissions (i.e., secondary OC) (Zhang et al., 2007).

Fig. 6. Correlations between OC and EC concentrations before, during and after the haze episode.
Fig. 6.
 Correlations between OC and EC concentrations before, during and after the haze episode.

The OC/EC ratio can provide source information. Watson (2002) reported OC/EC ratios of 0.3–7.6 for coal combustion, 0.7–2.4 for motor vehicle emissions and 4.1–14.5 for biomass combustion. In this study, the mean ± SD OC/EC ratios were 1.81 ± 0.34, 1.95 ± 0.39, and 1.72 ± 0.13 before, during and after the haze episode, indicating that coal combustion and vehicle exhaust were the dominant sources of pollution. An OC/EC value ≥ 2 was used to indicate the existence of significant secondary organic carbon (SOC) (Chow et al., 1996). The OC/EC values were about 2.0 during the haze episode, demonstrating that secondary organic carbon might exist. The SOC calculation method can be found in a previous study (Ram et al., 2008). As shown in Table 3, the mean ± SD concentrations of SOC were 2.22 ± 1.97 µg m–3, 7.11 ± 4.09 µg m–3 and 0.71 ± 0.62 µg m–3 before, during and after the haze episode, respectively. The ratio of SOC/OC in haze days (0.31 ± 0.15) was slightly higher than before (0.25 ± 0.15) and after (0.10 ± 0.07), which indicates that the photochemical reaction in haze days was more intense than non-haze days (Jiang et al., 2018). There was a significant correlation between SOC and PM2.5 and Pearson’s correlation coefficient was 0.815 (P < 0.01), indicating SOC is an important component in PM2.5. As a city with heavy industry, there are many petrochemical enterprises and vehicles (1.237 million in 2018), and a great deal of VOCs is released into the atmosphere. Higher concentrations of VOCs produce higher concentrations of secondary organic carbon through photochemical reactions at high temperatures (Wang et al., 2018). The mean temperature was 1.73°C during the haze episode, which is the ideal temperature for SOC formation and accumulation, because evaporation is inhibited at low temperatures (Luo et al., 2018).


Air Mass Backward Trajectory Analysis

To better understand the impact of regional transmission during the haze episode, 24-h air mass back-trajectories were derived for clusters identified from 8–27 January 2018, starting at an altitude of 500 m from the ground (Li et al., 2017). Clusters were computed with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) 4 model developed by the U.S. National Oceanic and Air Administration (NOAA) Air Resources Laboratory (ARL) (Gao et al., 2015). Cluster analysis of 24-h backward trajectories before, during and after the haze episode are shown in Fig. 7. Before the haze episode, 60% (Clusters 1,2, 3 and 4) was from the northwest and 39% (Clusters 5 and 6) was from Shandong Province and its adjacent areas. After the haze episode, the air masses were more from the Beijing-Tianjin-Hebei region (46%). During the haze episode, the air mass path was mainly derived from adjacent regions in Shandong Province (57%, Cluster 2), 36% (Cluster 1) was from the Beijing-Tianjin-Hebei region, and 7% (Cluster 3) was from Inner Mongolia and was transmitted to Zibo via Liaoning Province. Air mass backward trajectories for Cluster 2 were short, indicating that air masses moved slowly and pollutants tended to accumulate (Feng et al., 2018). PM2.5 concentrations in each air mass during the haze episode showed decreasing trends in the order of Cluster 1 (185.10 µg m–3) > Cluster 2 (179.71 µg m–3) > Cluster 3 (85.08 µg m–3). The above analysis illustrates that large amounts of pollutants from other regions were mixed in the air masses and transported to the research area, thus intensifying its PM2.5 pollution level (Jiang et al., 2018). Thus, the regional transport of pollutants has an important impact on PM2.5 (Yang et al., 2017)

Fig. 7. Cluster analysis of 24-h air mass backward trajectories before, during and after the haze episode. Fig. 7. Cluster analysis of 24-h air mass backward trajectories before, during and after the haze episode.


CONCLUSIONS


The concentration and chemical composition of PM2.5 in the heavy industrial city of Zibo were investigated in relation to a haze episode occurring 15–21 January 2018. The concentrations (mean ± SD) were 49 ± 38 µg m3, 211 ± 39 µg m3 and 58 ± 33 µg m3 before, during and after the episode, respectively, with the TWSIs, TEs and carbonaceous species (OC and EC) accounting for 50.74%, 61.47% and 51.61%; 9.55%, 3.74% and 9.24%; and 23.73%, 15.45% and 17.86%. The TWSIs primarily consisted (79.5–91.5%) of secondary ions (NO3, SO42 and NH4+), and the TEs displayed an abundance (88.64%) of crustal elements (K, Al, Ca, Si, Na, Fe and Mg) during the episode.

The CE/AE ratio indicated that the acidity increased on haze days, and the NO3/SO42 and OC/EC ratios suggested that coal combustion and vehicle exhaust emission were the largest sources of pollution. Additionally, the higher SNA and SOC values identified secondary formation as a significant contributor to the elevated PM2.5 concentration during the episode, and back-trajectory analysis traced the pollutive aerosols in Zibo on haze days to adjacent areas in Shandong Province (57%) and the Beijing-Tianjin-Hebei region (36%). Finally, unfavorable meteorological conditions were another factor that exacerbated the PM2.5 pollution during the haze episode

 
ACKNOWLEDGMENTS


 This study was supported by the National Research Program for Key Issues in Air Pollution Control (No. DQGG0107-20 and DQGG0107-19), the National Key Research and Development Program of China (2017YFC0212503) and the Special Scientific Research Business of the Central Academy of Public Welfare Research Institute of the Chinese Research Academy of Environmental Sciences (2018-YSKY-041). The authors thank the editor and reviewers for their valuable comments.


DISCLAIMER


The authors declare that they have no conflicts of interest.


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