Variations in Sources, Composition, and Exposure Risks of PM2.5 in both Pre-Heating and Heating Seasons

Received: December 6, 2021
Revised: January 15, 2022
Accepted: January 16, 2022

 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.

Cite this article:

Yang, D., Li, Z., Yue, Z., Liu, J., Zhai, Z., Li, Z., Gao, M., Hu, A., Zhu, W., Ding, N., Li, Z., Guo, S., Wang, X., Wang, L., Wei, J. (2022). Variations in Sources, Composition, and Exposure Risks of PM_2.5 in both Pre-Heating and Heating Seasons. Aerosol Air Qual. Res. 22, 210333. https://doi.org/10.4209/aaqr.210333

HIGHLIGHTS

• NO₃^–/SO₄^2– decreased in the heating season.
• As and SO₄^2– enhanced in the heating season.
• Coal/biomass combustion for heating was still serious.
• Extremely high Ba was found due to glass production.
• Non-carcinogenic risks decreased in the heating season due to reduced Ba.

Keywords: PM2.5, Source apportionment, Clean heating, Health risk, Heavy metal

1 INTRODUCTION

The Beijing-Tianjin-Hebei (BTH) region, the center of the North China Plain, has suffered from severe pollution due to fine particulate matter (PM_2.5, an aerodynamic diameter less than or equal to 2.5 µm), especially in the heating season (Cheng et al., 2019; Zhai et al., 2019; Li et al., 2020a, b, 2021a, b). PM_2.5 mainly includes water-soluble ions, trace elements, organic carbon, elemental carbon, and dust, all of which have adverse effects on the climate and human health (Li et al., 2020a, b; Wang et al., 2021). To mitigate this crisis in the BTH, the Chinese government issued an “Atmospheric Pollution Prevention Plan” (Clean Air Action) in 2013, which was aimed toward markedly reducing PM_2.5 by 25% by 2017 (Chen et al., 2019). However, PM_2.5 was still at a high level in the BTH region regardless of this 20% reduction in the peak PM_2.5, which was attributed to the migration of air pollutants from other regions, especially during the heating season (Zheng et al., 2018). Airborne pollutant transport between neighboring cities also resulted in high PM_2.5 levels in Beijing, and regional integration has proven to be one of main solutions to mitigate this crisis (Chen et al., 2019). Therefore, a “2 + 26” strategy covering Beijing, Tianjin, and 26 other cities acting as an “air pollution transmission channel” in four neighboring northern provinces (Hebei, Henan, Shandong, and Shanxi) was promoted by the Ministry of Ecology and Environment to further improve the air quality in Beijing and the BTH region during heating season (Chen et al., 2019).

As a key part of “2 + 26” policy, clean heating (CH) was vigorously promoted in the winter of 2017 (Chen et al., 2019; Cheng et al., 2019). Strengthening the management of raw coal in rural areas, eliminating backward coal-fired boilers, shutting down small scale coal-related enterprises, and developing central heating for cities with more than 200,000 residents were all parts of the CH policy (http://www.mee.gov.cn/gkml/hbb/bwj/201708/t20170824_420330.htm). As a result, PM_2.5 decreased markedly, and its chemical composition was notably altered (Zhai et al., 2019). Long-term monitoring of 338 cities indicated an overall improvement in air quality along with a noticeable 33% reduction in PM_2.5 and a 59% reduction in SO₂ (Zheng et al., 2018). However, undesirable outcomes included dramatically elevated O₃, NH₃, and VOCs, accompanied with stagnant or elevated NO_x, were found in North China (Zhai et al., 2019). Nitrate formation promoted by the stagnant NO_x and enhanced NH₃ offset the present decrease in PM_2.5 due to a reduction in SO₂, and the relationship between severe pollution events and nitrate became more apparent (Xu et al., 2019). The disparity between the still high PM_2.5 and the air quality standard in the BTH region, especially during heating season, was the main obstacle to substantially improving air quality (Zhai et al., 2019).

Effective implementation of the CH policy was largely dependent on the acknowledgement of provision of PM_2.5 data compliance with policies in small cities (Carter et al., 2020). Most of the “2+26” cities are small cities; therefore, it was necessary to understand the differences in PM_2.5 sources, composition, and exposure risk between the PHS and the HS. However, current studies have mainly focused on large metropolitan areas, urban agglomerations, and rural areas (Chen et al., 2019; Zhao et al., 2020a, b).

In this study, intensive observation was carried out covering both the PHS (October 25–November 14, 2019) and the HS (November 15, 2019–January 15, 2020) in Baoding. The main purposes of this study were to (1) investigate changes in the chemical composition of PM_2.5 in the HS, (2) evaluate differences in health risk related to exposure to heavy metals between the PHS and the HS, and (3) assess changes in source contributions of PM_2.5 from the PHS to the HS.

 
2 METHODOLOGY

 
2.1 Site Description

Baoding is positioned in the center of the Beijing-Tianjin-Hebei (BTH) region (Fig. 1). Baoding covers 22,190 km² and has a permanent population of 9,390,000. PM_2.5 was collected in a typical residential area with instruments were installed on the roof of an office building (30 m above ground) at North China Electric Power University (38°88′N; 115°51′E). The site is surrounded by residential buildings within a 3.5 km radius and therefore can be categorized as a typical residential zone.

Fig. 1. Location of sampling point in Baoding City.

 
2.2 PM_2.5 Sampling

PM_2.5 samples were simultaneously collected using two medium-volume air samplers (TH-150C, Wuhan Tianhong Instrument Co., Ltd.) with a flow rate of 100 L min^–1. Two kinds of filters, including a 90 mm quartz fiber (QF) filter (Pall USA) and a 90 mm Teflon (PTEE) filter (Whatman UK) were used. The sampling lasted for approximately 23 h (10:00 AM–9:00 AM of the next day). Field blanks were used every 8 samples, and a total of 64 samples and 7 blanks were obtained.

The QF filters were pre-heated at 800°C for 3 h before sample collection. The filters were stored in a room at a constant temperature (20°C) and relative humidity (50%) before and after sampling for the purpose of weighing. The filters were weighed gravimetrically using a Sartorius ME-5F microbalance with a sensitivity of ±1 µg (Sartorius, Göttingen, Germany). Each filter was weighed at least three times before and after sampling. Deviations among replicate weights were mostly lower than 15 µg for each filter. Net mass was acquired by subtracting the pre-weight from the post-weight. The samples were stored in a freezer at –20°C prior to analysis.

 
2.3 Chemical Analysis

Each PTEE filter was cut into two equal halves and analyzed for 39 elements and 9 water-soluble ions (WSIs), respectively. The two halves underwent different digestion procedures for the analysis of 30 elements (Li, Be, Na, P, K, Sc, V, Cr, Mn, Co, Ni, Cu, Zn, As, Rb, Y, Mo, Cd, Sn, Sb, Cs, La, Ce, Sm, W, Tl, Pb, Bi, Ti, and Th) with the ICP-MS system (Agilent 7500a, USA) and 9 elements (Al, Ca, Mg, Fe, Ba, Sr, Zr, Si, and U) with the ICP-OES system (Agilent 5100, USA), respectively. For the ICP-MS, one half of the PTEE was digested using an acid mixture (aqua regia/HF) at 120°C for 2 h and then dried at 130°C. It was finally heated with HCl and held for measurement. For ICP-OES, another half was ashed at 550°C, mixed with absolute ethanol and NaOH and boiled with water. Finally, the solution was analyzed using the ICP-OES system. The same procedures were adopted for one regent blank and three filter blanks. The instrument calibration was conducted using a multi-element standard (GBW7446–7457) purchased from the Chinese Center for National Standard Material Center. The recoveries for all the elements fell within ± 10% of the certified values. Calibration was carried out with multi-element standards (GBW07446–07457). Precision for most elements were better than 5% (n = 5). Additional information about the QA/QC and analytical procedures can be found in Tao et al. (2014) and Li et al. (2021b).

One half of the QF filter was used for the WSI analysis and detailed as described in Li et al. (2021b). It was ultrasonically extracted for 20 mins at least three times. The extracts were subsequently passed through a microporous membrane with a pore size of 0.22 µm (Whatman, Middlesex, UK), put into a plastic bottle, and held at 4°C prior to analysis. The 4 anions (SO₄^2–, NO₃^–, Cl^–, and F^–) and 4 cations (K⁺, Na⁺, Ca²⁺, and Mg²⁺) were analyzed using the ICS-1000 system (Thermo Scientific, USA) and the ICP-OES system (Agilent 725, Agilent Co. USA), respectively, with the method detection limits (MDLs) were 0.005–0.013 µg mL^–1 and 0.002–0.025 µg mL^–1, respectively. An ultraviolet-visible spectrophotometer (UV-VIS, T6, Beijing General Instruments Co., Ltd.) was employed to measure the remaining NH₄⁺ and the MDL was 0.002 µg mL^–1. The recoveries for all the 9 WSIs were within 100 ± 20%.

 
2.4 Backward Trajectory Clustering

Backward trajectory clustering is a tool widely applied to evaluate the origins of pollutant-containing air masses at receptor locations (Xu et al., 2019). Three-dimensional backward trajectories were calculated using the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT-4, http://ready.arl.noaa.gov/HYSPLIT.php) at elevations of 500 m and 1500 m.

 
2.5 Health Risk Assessment

In this study, health risks posed by the inhalation of heavy metals were selected to represent total risks (Si et al., 2021). As, Cd, Cr(VI), Ni, Pb and Co were utilized to evaluate carcinogenic risks (CRs). Meanwhile, As, Cr, Ni, Pb, Co, Fe, Cu, Zn, Cd, Sb, Mn, Sr, Sn, Ba and Tl were examined in the non-cancer risk (NCR) assessment. A seventh of the total Cr was used to represent Cr(VI) (Mejido et al., 2017). NCRs were presented as hazard quotients (HQs) (Si et al., 2021). According to the regulatory purposes, the acceptable or tolerable risk was 1 × 10^–6–1 × 10^–4 for CR, and a risk lower than 1 was used for the HQ (Si et al., 2021).

In this study, we used the risk assessment model recommended by the US EPA to evaluate the risks posed by inhalation of PM_2.5-associated heavy metals (Megido et al., 2017). The exposure concentration by inhalation (EC_inh (µg m^–3)) was estimated using Eq. (1).

where EC_inh is the exposure concentration (µg m^–3); C is the 95% upper confidence limit (UCL) on the arithmetic mean, representing the “reasonable maximum exposure” in the exposure assessment; ET is the exposure time (24 h day^–1); EF is the exposure frequency (day year^–1); ED is the exposure duration (year), and AT_n is the average time (day).

The CRs posed by inhalation were further calculated following the formula, where IUR represented the inhalation unit risk ((µg m^–3) ^–1).

HQ_inh derived from inhalation of heavy metals was obtained using the following equation (Megido et al., 2017; Si et al., 2021), where the RfDo is the oral reference dose (mg kg^–1 day^–1).

All the parameters are illustrated in detail in Hu et al. (2012) and Mejido et al. (2017) and are listed in Table S1.

 
2.6 Source Apportionment of PM_2.5 by PMF

PMF was a multivariate factor analysis tool based on non-negative constraints used to obtain realistic factors (Tao et al., 2014; Si et al., 2021). Unlike principal component analysis (PCA) and chemical mass balance (CMB) models, the negative factor loadings could be eliminated, and the source spectrum was not needed in the PMF run (Lang et al., 2018). The EPA PMF 5.0 program was used for the receptor modeling in this work. The uncertainty was calculated as follows:

The half MDL was used to represent the corresponding mass content below the MDL. The geometric mean of the observed values was used to replace the missing values, and the 4 times of the geometric mean indicated their uncertainty (Zhang et al., 2018).

 
3 RESULTS AND DISCUSSION

 
3.1 PM_2.5 Concentration

The daily average PM_2.5 concentrations fluctuated from 14.6 to 232 µg m^–3, with a mean value of 110 µg m^–3 (Fig. S1). The mean PM_2.5 increased significantly from 69.1 in the PHS to 129 µg m^–3 in the HS. The higher PM_2.5 in the HS indicated strong heating effects, which was in agreement with findings suggesting this frequently occurs in north China in the HS (Zhang et al., 2018; Zhai et al., 2019). Despite the “coal banning” law, some rural areas in Hebei Province still use coal as a main energy source for heating, causing an elevation of PM_2.5 (Pang et al., 2020). Furthermore, as high as 91.1% of sampling days showed a daily PM_2.5 value of more than 75 µg m^–3 assigned as Chinese National Ambient Air Quality Standard Grade II. A previous study reported that PM_2.5 decreased by 71.2% in 2017 as compared with 2016 in Baoding City due to the “Coal to Gas” (CTG) policy in 2017 (Si et al., 2021). The increase in PM_2.5 in this study compared with that in 2017 implied that coal usage may have increased due to laxity related to the CTG policy implementation. Meanwhile, the mean PM_2.5 in Baoding in the HS was much higher than the 94.5 µg m^–3 found for Beijing, 111 µg m^–3 for Tianjin, and 106 µg m^–3 for Langfang in 2016–2017 (Pang et al., 2020).

 
3.2 Concentrations of Water-soluble Ions (WSIs)

The daily total WSIs had an average of 47.0 µg m^–3, constituting 42.6% of the PM_2.5 mass. NO₃^–, NH₄⁺, SO₄^2–, Ca²⁺, and Cl^– were the predominant ions, contributing 41.3%, 23.1%, 21.5%, 15.3%, and 8.29% to the total WSIs, respectively (Fig. 2 and Table S2). Like the results reported recently in north China, NO₃^– increased along with the CTG policy and became the highest ion in the HS (Zhai et al., 2019). 91% of the sampling days with PM_2.5 values higher than 75 µg m^–3 were closely related to high NO₃^– (Huang et al., 2021). The mass concentrations of NO₃^–, NH₄⁺, SO₄^2–, and Cl^– went up to 19.4, 10.8, 10.1, and 3.89 µg m^–3 in the HS. The total SO₄^2–, NO₃^–, and NH₄⁺ (SNN) contributed 80.4% and 32.6% to the entire WSIs and PM_2.5 for Baoding in the HS.

Fig. 2. Time series of water-soluble ion species.

The mass ratio of NO₃^–/SO₄^2– (N/S) was a distinct indicator used to identify the relative importance of the coal combustion sources and vehicular sources (Tao et al., 2014; Si et al., 2021). Fig. 3 illustrates a time series of the concentrations of SO₄^2– and NO₃^– and the N/S ratios. Both SO₄^2– and NO₃^– increased significantly along with the transition from the PHS to the HS. On average, SO₄^2– and NO₃^– (in µg m^–3) varied from 1.16 to 8.98 and from 1.05 to 29.1 in the PHS, and from 1.38 to 42.4 and from 1.73 to 43.4 in the HS. A high N/S ratio of 2.29 during the entire sampling period (ESP) in Baoding suggested the CTG impacts the emissions of SO₄^2– and NO₃^– (Pozzer et al., 2020). The N/S ratios in Baoding were much higher than 0.95 in 2016 for Shijiazhuang (Lang et al., 2018), and 1.28 for the background site in the North China Plain (Yao et al., 2016).

Fig. 3. Time series of concentrations of SO₄^2–, NO₃^–, and mass ratios of NO₃^–/SO₄^2–.

Previous studies have found more enhanced N/S ratios in the HS as compared to in the PHS in north China due to the sharp decrease in SO₄^2– due to the CTG project and coal banning law (Chen et al., 2019; Zhai et al., 2019; Li et al., 2020). It should be noted that the decreased N/S from 2.75 in the PHS to 2.08 in the HS might have been related to the revival of coal-burning for heating purposes. This suggestion was in good agreement with the fact some rural residents began to reuse coal as heating fuel driven by lower costs (Carter et al., 2020).

 
3.3 Trace Element Oxides and Soil Dust

The PM_2.5 mass was reconstructed as the total inorganic components (SNA: sulfate, nitrate, and ammonium), EC, dust, trace elements oxides (TEO), organic matter (OM). Si, Ca, Al, Fe, and Ti were used to calculate the soil dust (SD) based on [SD] = 1.89 × [Al] + 2.14 × [Si] + 1.4 × [Ca] + 1.43 × [Fe] + 1.66 × [Mg] + 1.67 × [Ti]. TEO was evaluated using TEO = 1.3 × [0.5 × (Sr + Ba + Mn + Co + Rb + Ni + V) + 1.0 × (Cu + Zn + Mo + Cd + Sn + Sb + Tl + Pb + As + Se + Ge + Cs + Ga)] (Tao et al., 2014). Because of the lack of Se, Ge and Ga, the TEO was slightly underestimated. Fig. 4 shows the TEO and SD concentrations in winter in Baoding.

Fig. 4. Mass contents of trace element oxides and soil dust for each day, the pre-heating season, and the heating season.

From the PHS to the HS, the SD fractions obviously declined from 19.8% to 7.81% due to control of fugitive dust and the low wind speed in the HS. A small variability was found for TEO, decreasing from 3.47% to 0.53%. The decreased TEO fractions were attributed to emissions control on related industries in the heating season. SD fractions in both the PHS and HS were higher than 6.3% in Chengdu (Tao et al., 2014), which suggested a strong impact of fugitive dust in North China. The decreased TEO in the HS was mainly attributed to the emission reductions in industrial processes when increases in heating-coal usage were taken into account (Zhai et al., 2019; Li et al., 2020a, b). Industrial coal combustion during the PHS was an important source of trace elements. The TEO contents during the HS and PHS were much lower than 7.2% in Shijiazhuang in 2016, which indicated the efficiency of the CTG-derived energy switch (Lang et al., 2018).

Fig. 5 shows the concentrations of metals and their rates of increase in the HP compared with the PHS. Increases in As and Cr by 65.1% and 20.4% in the HS, respectively led to an elevation in CRs. Again, Cd, Co, Pb, Rb, and Tl were enhanced by 147%, 18.7%, 152%, 77.8%, and 110%, respectively. Former studies have indicated that the above-mentioned metals are all markers of coal combustion, proving the increased coal usage in the HS (Tao et al., 2014; Cui et al., 2019; Li et al., 2021a). It should be mentioned that Ba is as high as 2240 and 326 ng m^–3 in the PHS and HS, and the reduction in the HS resulted in a decrease in TEO. Cu, Zn, and Pb were enhanced in the HS by 31.3%, 87.4%, and 152%, respectively, which indicated an increase in the mass contribution of vehicle exhaust to total PM_2.5 (Tao et al., 2014; Xu et al., 2019). Meanwhile, Al, Ca, Mg, and Fe decreased by 21.7%, 12.7%, 16.2%, and 23.7% in the HS compared with the PHS, which was attributed to the emissions control on dust and industries including iron, steel, and cement (Si et al., 2021). V and Ni reduced by 1.92% and 56.2% in the HS, respectively, indicating emission reductions from oil-burning-related industries (Cui et al., 2019). The smaller decrease in V as compared to Ni may have been a result of increased emissions from vehicles (Si et al., 2021).

Fig. 5. (a) Concentrations of heavy metals and (b) their increase rates in the heating season as compared to the non-heating season.

 
3.4 Health Risk Assessment and Concentrations of Heavy Metals

The carcinogenic risks (CRs) posed by inhalation of As, Cd, Cr(VI), Ni, Pb, and Co, and non-carcinogenic risks due to inhalation of As, Cr, Ni, Pb, Co, Fe, Cu, Zn, Cd, Sb, Mn, Sr, Sn, Ba, and Tl are shown in Fig. 6 and Tables S3–S6. Cr(VI) and As dominated in the CRs for children and adults in both the PHS and HS. The CRs to children caused by Cr(VI) were 5.32 × 10^–6 and 6.40 × 10^–6 in PHS and HS, and the corresponding values for adults were 2.13 × 10^–5 and 2.56 × 10^–5, respectively. Cr(VI) comprised 80.5% and 77.8% in the total CRs for children, and 80.7% and 77.8% for adults, in the PHS and HS, respectively. From the PHS to the HS, the total CRs increased significantly from 6.61 × 10^–6 to 8.23 × 10^–6 and 2.64 × 10^–5 to 3.29 × 10^–5 for children and adults, respectively. The significant increase in the CRs in the HS were attributed to increased use of coal for heating.

Fig. 6. Carcinogenic and non-carcinogenic risks posed by PM_2.5 associated elements via inhalation.

Compared with Baoding in recent winters, the total CRs for children in the HS in this study were higher than 7.6 × 10^–6 in 2017, and lower than 1.18 × 10^–5 in 2016 without implementation of clean heating (Si et al., 2021). The total CRs for adults in the HS showed similar trends, where the 3.29 × 10^–5 in 2019 was higher than the 3.15 × 10^–5 in 2017 and much lower than the 4.87 × 10^–5 in 2016. This was in accordance with the fact that coal usage decreased due to the strict coal banning law passed in 2017, which was not in place in 2016. However, while increased again in 2019 due to a lack of enforcement of the coal-banning policy (Zhai et al., 2019).

Unlike CRs, a reverse trend was found for non-carcinogenic risks (NCRs) represented by the total hazard quotients (HQs) (Fig. 6). The total HQs in the entire sampling period (ESP) was 3.49 ± 0.89 and exceeded the accept level of one, which should be given more attention by the local government. The total HQs decreased from 5.71 in the PHS to 2.18 in the HS, indicating the impact of an obvious reduction in Ba. It should be noted that the mass concentration of Ba was as high as 2240 in the PHS and 326 ng m^–3 in the HS, which was far higher than the 41.3 and 18.2 ng m^–3 in 2016 and 2017, respectively (Fig. 7). In addition to natural sources, glass production is a key emission source of Ba (Cui et al., 2019; Xu et al., 2019; Si et al., 2021). The extremely high Ba concentration could have been due to a local large-scale glass production factory. The backward trajectory analysis provided support for this assumption, which is discussed in detail in Section 3.6. The significantly reduced Ba in the HS by 85.5% relative to the PHS was closely related to the production stoppages/limitations and staggered peak production in the glass industry.

Fig. 7. Contributions of seven sources to the PM_2.5 mass.

 
3.5 Source Apportionment by PMF

Fig. S2 shows the source profiles for seven recognized emission sources using the PMF model. Factor 1 was mainly composed by NH₄⁺, NO₃^–, and SO₄^2–, which was attributed to secondary inorganic aerosols (SIA) (Si et al., 2021). These secondary products were formed by the oxidation of precursor gases including SO₂ and NO_x released mainly from the coal/biomass/natural gas combustion, automobile exhaust, and industrial processes, which in turn reacted with NH₃ derived from agricultural activities and livestock waste (Tao et al., 2014; Cui et al., 2019; Xu et al., 2019).

Factor 2 was represented by a high load of Cu, Zn, Ca²⁺, Mg²⁺, and specific levels of Pb and Cd. Cu and Zn have been demonstrated to be good markers of VE (Xu et al., 2019). Cu is found in brake systems, and Zn and Pb are found in automobile exhaust (Cui et al., 2019). Cd is an important element in lubricants and tires (Xu et al., 2019; Zhang et al., 2018). Ca²⁺ and Mg²⁺are often used as road dust indicators (Cui et al., 2019).

Factor 3 was identified as fugitive dust (FD) characterized by high loadings of crustal elements such as Al, Mg, Si, Ca, Ti, and Fe (Si et al., 2021). Suspended dust often comes from dust accumulated due to wind on unpaved roads, construction sites, and bare soil around cities (Tao et al., 2014).

High fractions of Cl^– and K⁺ presented in factor 4, which was attributed to biomass burning (Tao et al., 2014; Feng et al., 2018; Zhai et al., 2019). K⁺ and Cl^– have been commonly used to identify wood burning activities and agricultural residues (Feng et al., 2018).

Factor 5 possessed high loadings of a host of trace metals including Cd, Mn, Pb, Tl, Ag, Fe, Ni, V, Zn, Cu, and Cr, indicating industrial emissions. Zn, Cd, Pb, Tl, and Ag often originate from waste incineration (Cui et al., 2019; Si et al., 2021). V, Cr, and Ni are closely related to oil combustion (Xu et al., 2019). Cu, Zn, and Ag are widely used in electroplating processes (Si et al., 2021). Fe and Cr are markers for iron-steel production (Xu et al., 2019). The above-mentioned industries are clustered in Baoding and adjacent counties.

Factor 6 represents high contributions of As, SO₄^2–, and Cl^–, along with moderate loadings of Pb, Cu, Zn, Sn, Pb, Tl, and Sb was found in this factor, which were attributed to coal combustion (Tao et al., 2014; Li et al., 2021a). Zhao et al. (2020a) ascribed the high Pb and Zn in winter PM_2.5 observed in Beijing to coal combustion. Factor 7 was characterized by the highest load of Ba (85.7%), which was identified as due to glass production (Cui et al., 2019; Xu et al., 2019). Fuyao Glass Co., Ltd. is located 15 kilometers northwest of Baoding City, which supports this supposition.

Time series of source contributions for each sampling day and the averaged contributions in the PHS, HS, and ESP are shown in Fig. 7. In the case of ESP, fugitive dust (FD) and vehicle emissions (VE) were the top contributors, contributing 25.6% and 21.5% to the PM_2.5 mass, respectively. The highest fractions of FD and VE further evidenced effective emission reductions on the part of industries, as well as coal/biomass combustion. Table 1 lists the results of source contributions recently obtained using a PMF model in China. The coal combustion (CC) fraction of 12.9% obtained in this study was much lower than the previously reported 34.8% in 2014, 49% in the winter of 2015, and 22.6% in 2016 for Baoding, indicating effective prohibition of coal utilization. Also, this figure was far lower than the 43% found for the adjacent urban site of Wangdu County in the winter of 2015 and the 58% and 40.5% at a rural site in Wangdu County in the winter of 2015 and 2017, respectively. Among large cities, Beijing and Tianjin had declining CC fractions, indicating adherence to the strict coal banning law. A CC fraction of 12.9% in Baoding in 2019 was still higher than the 8.8% found for Beijing in the winter of 2017 and the 10% for Tianjin in 2016, illustrating a gap in the enforcement of the coal ban in large and small cities (Zhai et al., 2019). A biomass burning (BB) fraction of 7.27% for Baoding in 2019 was lower than the 12% and 8.7% of Beijing in 2001–2004 and 2012–2013 and the 10.2% in Tianjin in the winter of 2015, implying the impact of the biomass-burning prohibition in Baoding. However, it was higher than the 4.1% in Beijing in the winter of 2017 and the 5.3% in Tianjin from 2014–2015, indicating that biomass-burning should be further managed. After stopped/limited/staggered production and the adoption of advanced emissions control devices and techniques, the industrial sources (IS) fraction in Baoding was much lower than the 13.2% in Baoding and the 20.5% in Shijiazhuang in 2016. It should be noted that glass production had become an important source, which contributed a PM_2.5 mass of 10.1% after effective control of iron-steel production and electroplating. The FD fraction for Baoding in the ESP was higher than most reported values for Beijing, Tianjin, and Shijiazhuang, indicating that there were impacts of emission reductions on CC, BB, and IS. The high FD fraction in Baoding City suggested that control of FD must be strengthened.

Obvious variations between the PHS and HS in terms of source contributions were found in Baoding (Fig. 7). The CC contributions increased significantly from 2.42% in the PHS to 16.9% in the HS, which was mainly attributed to the coal combustion used for indoor heating. Again, the BB fractions increased from 2.92% in the PHS to 8.93% in the HS, indicating that adjacent rural residents preferred using biomass as heating fuel due to lower costs (Zhao et al., 2020a, b). VE also increased from 10.2% to 25.9%, which was a result of the citizens’ willingness to drive in cold weather. Therefore, CC, BB, and VE should be further managed. It is worth mentioning that the contribution of glass production (GP) decreased significantly from 29.6% in the PHS to 2.59% in the HS by 91.3%, demonstrating emission reductions on the part of large-size enterprises.

 
3.6 Backward Trajectory Clustering

Fig. S3 shows the origins of air masses in Baoding City in the PHS, HS and ESP. A total of four trajectories were clustered for three periods. No significant differences in air mass origins were found between the PHS and HS. In the HS, 27.4% and 24.1% of the trajectories were derived from Northwest Mongolia and Russia; 20.5% were derived from west Inner Mongolia and Shanxi Province, and 27.6% were derived from Beijing. The dust from Northwest Mongolia, Russia, and Inner Mongolia may have increased the FD fraction in Baoding. The 100% CTG penetration rate in Beijing may have been the source of NO₃^– in addition to the large amounts of SNN generated by local heating (Zhao et al., 2020a). It should be noted that 74.3% of the trajectories from the northwest flowed through the Xushui District in Baoding where a large glass-making factory is located, which caused increases in related pollutants, especially Ba.

 
4 CONCLUSIONS

A field observation was conducted in Baoding in 2019, covering the pre-heating season (PHS) and the heating season (HS) in order to evaluate the impacts of “clean heating.”

1) On average, PM_2.5 concentrations increased from 69.1 in the PHS to 129 µg m^–3 in the HS.

2) In the HS, higher increases in the rates SO₄^2– by 270% as compared to 109% of NO₃^– supported a premise of higher coal combustion (CC) for heating purposes. Also, the NO₃^–/SO₄^2– ratio decreased from 2.75 in the PHS to 2.08 in the HS, and CC markers, As, Rb, and Tl, increased by 65.1%, 77.8%, and 110%, respectively.

3) The reduced Al, Ca, Mg, and Fe, V, and Ni in the HS indicated effective control of dust and the iron-steel/cement/oil-burning industries. The total carcinogenic risks increased significantly in the HS due to the elevated As and Cr. In contrast, the HS had fewer non-carcinogenic risks due to the reduced Ba.

4) The contributions of CC and biomass burning led to significant increases of 598% and 206%, respectively, indicating they were still serious in spite of the coal/biomass banning law. It should be noted that the contribution of glass production was prominent, marked by Ba as high as 2240 and 326 ng m^–3 in the PHS and HS, respectively.

It is suggested that increasing the penetration rate of “coal to gas”, reducing the emissions from glass production, and installing the de-NOx equipment for natural gas burning enterprises should be strengthened to further improve air quality in the heating season.

 
ACKNOWLEDGEMENTS

This study was supported by the Beijing Natural Science Foundation of (8212034), the Natural Science Foundation of Hebei Province (B2020502007), the Fundamental Research Funds for the Central Universities (2020MS125), the Science and Technology Projects of Baoding (2141ZF321), and the “Three Three Three Talent Project” of Hebei Province (A202001055).