Zhiyong Li This email address is being protected from spambots. You need JavaScript enabled to view it.1,2, Ziyuan Yue1, Jixiang Liu1, Zhen Zhai1, Jianwei Meng3,4, Lei Wang This email address is being protected from spambots. You need JavaScript enabled to view it.3,4, Jihong Wei5, Dingyuan Yang1, Ailian Hu1, Wenjia Zhu1, Ning Ding1, Xiaoxia Lu1, Chunhao Lin1

1 Hebei Key Lab of Power Plant Flue Gas Multi-Pollutants Control, Department of Environmental Science and Engineering, North China Electric Power University, Baoding 071003, China
2 MOE Key Laboratory of Resources and Environmental Systems Optimization, College of Environmental Science and Engineering, North China Electric Power University, Beijing 102206, China
3 Hebei Research Center for Geoanalysis, Baoding 071003, China
4 Hebei Key Laboratory of Mineral Resources and Ecological Environment Monitoring, Baoding 071003, China
5 Department of Pediatrics, Affiliated Hospital of Hebei University, Baoding 071000, China


Received: April 3, 2022
Revised: May 25, 2022
Accepted: June 12, 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.


Download Citation: ||https://doi.org/10.4209/aaqr.220162  


Cite this article:

Li, Z., Yue, Z., Liu, J., Zhai, Z., Meng, J., Wang, L., Wei, J., Yang, D., Hu, A., Zhu, W., Ding, N., Lu, X., Lin, C. (2022). Sources and Health Risks of PM2.5-bound PAHs in a Small City along with the “Clean Heating” Policy. Aerosol Air Qual. Res. 22, 220162. https://doi.org/10.4209/aaqr.220162


HIGHLIGHTS

  • PM2.5, PAHs and health risks increased in the heating season (HS).
  • MMW-PAHs increased most in the HS due to enhanced coal/biomass usage.
  • Total PAHs were lower compared with other cities before Clean Heating.
  • Biomass/natural gas burning was a biggest PAH contributor in the HS.
  • Coal combustion contribution increased and vehicle exhaust decreased.
 

ABSTRACT


Levels, composition, and sources of PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) vary significantly along with the “Clean Heating” (CH) policy in Beijing-Tianjin-Hebei (BTH) region, whereas the PAH characteristics with CH in small cities still remain unclear. A field observation was conducted in Baoding City, a small city within the BTH region, in winter of 2019 covering both the pre-heating season (PHS) and the heating season (HS). From the PHS to the HS, the mean concentrations for both PM2.5 and ∑18PAHs increased from 69.1 to 125.0 µg m3 and from 8.09 to 26.2 ng m3 due to the heating activities. The far lower PAHs in this study than those of small cities before CH implementation indicated the CH effectiveness. Higher diagnostic ratios (DRs) of FA/(FA + PY), BaA/(BaA + CHR), and IP/(IP + BgP) in the HS were in agreement with the increased coal/biomass usage. Positive matrix factorization (PMF) demonstrated that biomass/natural-gas burning (BNGB) contributed most to PAHs of 36.9% in the HS, the increased natural gas (NG) usage for heating should be responsible for this contribution due to the policies of biomass-burning prohibition and “Coal to Gas”. Coal combustion (CC) shares increased by 152% in the HS despite the “Coal Banning” project. Again, the medium-molecular-weight PAHs (MMW-PAHs) increased most by 400% in the HS, evidencing the increased impacts of fossil-fuel consumptions. As an indicator for carcinogenic risk, BaP increased from 0.937 in the PHS to 1.29 ng m3 in the HS. Furthermore, the incremental lifetime cancer risks (ILCR) and BaP equivalent concentrations (BaPeq) increased in the HS. The mean ILCR values of 1.15 × 106 for adults in the HS exceeded the threshold of 1 × 106, while they were lower than 1 × 106 for children in both the PHS and the HS, and adults in the PHS due to the CH positive effects.


Keywords: PM2.5, PAHs, Source apportionment, Clean heating, Health risk assessment


1 INTRODUCTION


Polycyclic aromatic hydrocarbons (PAHs) are a class of organic compounds consisting of two or more fused aromatic rings, and some of which have carcinogenic and mutagenic effects (Chen et al., 2021; Alvarez-Ospina et al., 2021). Therefore, PAHs were defined as a group I carcinogens by the International Agency for Research on Cancer (Vega et al., 2021; Zhang et al., 2021). PAHs can enter the human body through dietary intake, respiratory inhalation, and dermal contact, and subsequently cause the damage to lung function through direct action on alveolar epithelial cells (Zhang et al., 2020a). China was one of countries with the highest PM2.5 level in the world (Kong et al., 2018). Over the past few decades, PM2.5 has been a key risk driver of the global burden disease, especially in China (Bu et al., 2021). Former studies have highlighted that the toxic components in PM2.5 such as heavy metals, black carbon and PAHs can lead to the harmful effects on human health (Zhou et al., 2021). China is the world's largest contributor of PAHs with an average annual emission of 14,000 tons (Zhang et al., 2021). PAHs-derived health risks were more severe in Beijing-Tianjin-Hebei (BTH) region within north China, which was attributed to the winter heating and the regional heavy industries including thermal power generation, iron-steel, and coking (Li et al., 2021; Yang et al., 2022).

The Chinese government's efforts to curb environmental pollution can trace back to the 1980s (Feng and Liao, 2016). However, the air pollution was still serious in northern China, which has caused notable environmental and social problems (Chen et al., 2019; Wu et al., 2021; Yang et al., 2022). To alleviate this crisis, China launched the “Clean Air Action Plan” (CAAC) in September 2013 to markedly reducing PM2.5 levels by 25% until 2017 (Kong et al., 2018; Chen et al., 2019). In consequence, the major air pollutants such as PM2.5, SO2 and CO declined significantly from 2013 to 2018 in China by 30%–50% based on the data from Chinese National Environmental Monitoring Center (CNEMC) (Zhai et al., 2019). For example, PM2.5 in the heating season of 2018 in Beijing decreased by 40.9% compared with that in 2017 (Pang et al., 2021). However, the still high PM2.5, and ever-increasing mass contributions of organic aerosols (OA) (e.g., PAHs) to PM2.5 have been the new obstacles in the air quality improvement (Zhai et al., 2019; Zhang et al., 2020b).

As a result, the State Council issued the “Three-Year Action Plan for Defending the Blue Sky” (TYAP) on July 3, 2018. In TYAP, measures were mainly taken to reduce pollutant emissions from industries and vehicles, which made the influence of civil combustion sources more predominant (Chen et al., 2019; Zhai et al., 2019; Guo et al., 2020; Li et al., 2020; Yang et al., 2022). Therefore, the “Clean Heating” (CH) project was subsequently promoted to reduce the pollutant emissions in north China in the heating season. CH derived “coal to natural gas” (CTNG) and “coal to electricity” (CTE) were aimed toward eliminating scattered coal, which could alter the PAH levels, composition, and the health risks to a great extent (Kong et al., 2018). For instance, the concentration of PAHs and OPAHs in Urumqi decreased by 74% and 82%, respectively, after the CTNG execution (Ren et al., 2017).

The CTNG penetration-rates varied significantly among different regions in north China along with their degrees of economic and social development (Meng et al., 2020). Jacobson et al. (2018) indicated that the comprehensive utilization of coal, natural gas, electricity and other energy in rural and suburban areas of undeveloped small cities was required. To our knowledge, the recent studies were mainly focused on the large cities, while few studies were conducted in small cities (Ren et al., 2017; Yang et al., 2022). Therefore, the systematic evaluation of PAH characteristics along with CH project in small cities was urgently needed (Meng et al., 2020; Yang et al., 2022). Baoding City, one of China's most polluted cities, lies in the central of the BTH region, was chosen for PAH analysis (Li et al., 2020). The main aims of this study were: 1) evaluate the PAH levels with CH policy; 2) access the variations in source contributions between the PHS and the HS; and 3) evaluate the potential health risks of PAHs via inhalation for different groups of people.

 
2 METHODOLOGY


 
2.1 Sampling Site Description

Baoding City is situated in the central area of Beijing-Tianjin-Hebei (BTH) region within the North China Plain, which is 146 km south to Beijing, 142 km north to Shijiazhuang, and 180 km east to Tianjin (Fig. 1). The samplers were installed on the roof of a teaching building (38°88′N, 115°51′E) of North China Electric Power University (approximately 20 m above ground), which was surrounded by residential buildings within a 4 km radius and thus identified as a typical residential zone.

Fig. 1. Location of sampling site in Baoding.
Fig. 1. Location of sampling site in Baoding.

 
2.2 PM2.5 Sampling

The sampling procedures were described in detail in Yang et al. (2022). Two medium-flow air samplers (TH-150C III, Wuhan Tianhong Ltd., China) was utilized to collect PM2.5 with a flow rate of 100 L min–1. Samples were collected in the winter of 2019–2020 covering the pre-heating season (PHS, 25 October–14 November, 2019) and the heating season (HS, 16 November, 2019–10 January, 2020). Two types of filters (diameter, 90 mm) including the quartz fiber (QF) filters (Pall USA) and Teflon (PTEE) filters were used in this study. Each sampling duration was 23 hours from 8:00 A.M. to the 7:00 A.M. of the next day. A total of 32 samples and 4 field blanks were obtained. QFs and PTEEs were baked and heated at 450°C and 60°C prior to sampling. PTEEs were stabilized for 48 hours at the constant temperature and humidity to obtain the PM2.5 mass by subtracting the pre-weight from the post-weight (Yang et al., 2022). Each filter was weighed at least three times by an analytical balance with sensitivity of ± 0.010 mg. Each filter was sealed by aluminum foil and stored at –20°C before analysis.

 
2.3 Analysis of PAHs

A gas chromatography coupled with mass spectrometry (GC6890/MSD5973i; Hewlett-Packard) system was utilized to analyze eighteen PAH congeners including naphthalene (NA), acenaphthylene (ACL), acenaphthene (AC), fluorine (Fl), benzo[ghi]perylene (BgP), phenanthrene (PHE), anthracene (AN), fluoranthene (FA), pyrene (PY), benzo[a]anthracene (BaA), chrysene (CHR), benzo[b]fluoranthene (BbF), benzo[k]fluoranthene (BkF), benzo[e]pyrene (BeP), benzo[a]pyrene (BaP), indeno[1,2,3-cd]pyrene (IP), dibenzo[a,h]anthracene (DBA), and coronene (COR).

We consulted TO-13A and carried out the procedures of pre-treatment and analysis in this study. The chromatographic conditions were shown as follows: 70°C held for 2 min, ramped to 260°C at 10°C min–1 and held for 8 min, and then elevated to 300°C at 5°C min–1 and held for 5 min. More analysis and operation details are consistent with Li et al. (2020, 2021). The method detection limits (MDLs) for 18 PAHs varied from 0.010 to 1.10 ng g–1 with the mean value as 0.220 ± 0.180 ng g–1. The recoveries for 18 PAHs in 5 matrix-added samples ranged from 75% to 115%. The mean recoveries for two surrogate standards including 14-deuterium substituted terphenyl and 4-bromo-2-fluorobiphenyl in 5 samples were 90 ± 15% and 95 ± 20%, respectively. The relative standard deviation (RSD) values for 6 duplicated samples were all less than 10%.

 
2.3 Source Apportionment of PAHs


2.3.1 Diagnostic ratios (DRs)

The diagnostic ratios (DRs) have been widely used for source identification and source contribution quantities (Kong et al., 2018). The concentration ratios of AN/(AN + PHE), FA/(FA + PY), BaA/(BaA + CHR), and IP/(IP + BgP) were widely used indicators in previous studies (Suman et al., 2016). In this study, we calculated these four DRs and compared them with typical DRs for emission sources taken from the literatures (Kong et al., 2018).

 
2.3.2 Positive matrix factorization (PMF) model

PMF is a multivariate factor analysis tool developed by the U.S. EPA to apportion the source contributions for PM2.5 and PAHs, which can break down response data into major contributing factors and provide profiles for each factor (Liu et al., 2018; Taghvaee et al., 2018). EPA PMF 5.0 version available at the website of www.epa.gov/air-research/positive-matrix-factorization was used in this study. The measured concentrations of eighteen PAHs were used as the model inputs. The missing values and associated uncertainties were obtained according the documented method in Yao et al. (2016) and Yang et al. (2022). The uncertainties were estimated by the Eqs. (1) and (2). 

 

A total of 20 runs were used for each PAH. The lowest Qrobust value was 3018.8, and the ratio of Qrobust/Qtrue was 0.92. More information about PMF could be available in Yao et al. (2016).

 
2.4 Health Risk Assessment

BaP equivalent concentration (BaPeq) proposed by U.S. EPA for cancer risk assessment was used to assess the risk of PAHs via inhalation (Li et al., 2020). The equivalent concentration is calculated by multiplying the PAH mass concentration by the corresponding toxic equivalent factor (TEF) (Li et al., 2021). The sum of BaPeq of individual PAH can be used to evaluate the total carcinogenicity of PAHs, which is described in Kong et al. (2018) and shown as follows:

 

The incremental lifetime cancer risk (ILCR) was a quantitative assessment for PAH exposure risks in the environment (Wang et al., 2020) and shown as follows:

 

where, CA represents BaP equivalent (BaPeq) concentrations (mg m3). The meanings and values of the other parameters were listed in Table S1. In this study, a crucial exposure pathway as inhalation was chosen to evaluate the exposure risks for PAHs (Zhang et al., 2009; Shen et al., 2013).

The exposure risks were regarded as dangerous when the ILCR was higher than 1 × 10–4, as acceptable when the ILCR was between 1 × 10–6 and 1 × 10–4, and as negligible when the ILCR was less than 1 × 10–6 (Wang et al., 2020).

Exposure to the ambient air pollutants is one of the major reasons, which caused a loss of life expectancy (LLE) (Lelieveld et al., 2020). In this study, LLE was calculated with the following formula (Lelieveld et al., 2020):

 

where, VLL is the loss of life expectancy, min; and VILCR is the lifetime cancer risk.


3 RESULTS AND DISCUSSION


 
3.1 Levels of PAHs

The volume concentrations of PM2.5 and total PAHs, and the mass contents of PAHs in PM2.5 were shown in the Fig. 2. The daily average PM2.5 concentrations increased from 69.1 ± 32.3 in the PHS to 125 ± 50.7 µg m3 in the HS. The increase of PM2.5 by 80.9% in this study was similar to the 79.1% in a rural site in north China due to the elevated coal usage for heating (Zhao et al., 2020). PM2.5 in the HS significantly exceeded 75 µg m3 in the Chinese Standard Grade II though the major air pollutants decreased markedly by 30–50% from 2013 to 2018 in China, indicating the urgency to improve air quality in the HS (Zhai et al., 2019; Li et al., 2020).

Fig. 2. PM2.5 concentrations, PAH contents, and PAH concentrations for (a) each sampling day, and (b) the pre-heating and heating Fig. 2. PM2.5 concentrations, PAH contents, and PAH concentrations for (a) each sampling day, and (b) the pre-heating and heating seasons.

The variations of PAH mass concentrations and contents were similar to the PM2.5 concentrations in the entire sampling period (ESP). Former studies reported the similar variations between PAHs and PM2.5 (Chang et al., 2019). The volume concentrations for both PAHs and PM2.5 peaked on Oct. 29, Nov. 8, Nov. 22, Dec. 9, and Jan. 3, which was attributed to high emission intensities and unfavorable meteorological conditions such as low temperature, weak wind, and relatively low boundary layer height. The mass contents of total PAHs increased from 168 ± 179 in the PHS to the 234 ± 97.3 µg g1 in the HS, and the mass concentrations of PAHs also enhanced from 8.09 ± 4.30 to 26.2 ± 8.83 ng m3.

The ∑18PAHs in the HS was 3 times that in the PHS in Baoding City, which was much lower than 15.2 times in Beijing City in 2015 (Chang et al., 2019), which indicated the effectiveness of CH policy. Meanwhile, the ∑18PAHs of 26.2 ng m3 in Baoding City was much lower than 60.4–95.5 ng m3 of ∑8PAHs in Beijing City in the HS in 2015, which further evidenced the CH policy on PAHs emission reductions in north China (Chang et al., 2019; Zhai et al., 2019).

 
3.2 Levels of Individual PAH

A total of fourteen of eighteen PAHs except for AC, ACL, AN, and Fl were detected and the concentration statistics were listed in Table S2. BeP was the most abundant PAH, and increased from 2.93 in the PHS to 7.98 ng m3 in the HS, which accounted for 31.2% of the ∑18PAHs in the entire sampling period (ESP). BaP was widely used as an indicator of the carcinogenic risks of PAHs (Chang et al., 2019). In this study, BaP was averaged at 0.937 ± 0.090 ng m3 in the PHS, which was slight lower than the recommended threshold of 1 ng m3 by the U.S. EPA (Chang et al., 2019). However, BaP exceeded 1 ng m3 in the ESP (1.04 ng m3) and in the HS (1.29 ng m3), which should be ascribed to the heating activities, which should be more concerned. Wang et al. (2020) indicated that the main sources of BaP were coal tar, black carbon, and smoke from the combustion processes of coal, petroleum, and biomass, as well as cigarette smoke, vehicle exhaust, and cooking fumes. Therefore, the BaP increase in the HS might be ascribed to the increased coal/biomass consumptions for heating in the surrounding rural areas of Baoding City (Li et al., 2020).

 
3.3 Composition of PAHs with Different Ring Numbers

PAHs were classified into three groups including low-molecular weight (LMW-PAHs; 2- and 3-ring), medium-molecular weight (MMW-PAHs; 4-ring PAHs), and high-molecular weight PAHs (HMW-PAHs; ring number > 4-ring) (Feng and Cao, 2019; Wang et al., 2021). Fig. 3 showed the composition profiles for aforementioned three PAH groups. Kong et al. (2018) suggested that the LMW-PAHs originated mainly from coal combustion, MMW-PAHs from incomplete combustion of fossil fuels at high temperatures, and HMW-PAHs from internal combustion engine emissions. Therefore, the difference in the ring distribution of PAHs indicates the impacts of different emission intensities between the PHS and the HS. LMW-PAHs possessed the lowest levels in both the PHS and the HS because they tended to enrich in gas phase, whereas HMW-PAHs hold the highest levels due to their high boiling point (Zhang et al., 2013). The highest increase of MMW-PAHs (from 2.02 in the PHS to 10.1 ng m3 in the HS) by 400% concurred with the enhanced coal/biomass usage for heating in the HS. Accordingly, the mass shares of MMW-PAHs increased from 25.0% in the PHS to 38.5% in the HS. Contrary to MMW-PAHs, HMW-PAH contributions decreased from 71.3% in the PHS to 57.6% in the HS, which might be related to the traffic limitation in the HS (Feng and Gao, 2019; Wang et al., 2020).

Fig. 3. (a)Triangle diagram of the ratio of PAHs with different ring numbers in PM2.5 and (b) average concentrations of three categories of PAHs in the pre-heating and heating seasons.Fig. 3. (a)Triangle diagram of the ratio of PAHs with different ring numbers in PM2.5 and (b) average concentrations of three categories of PAHs in the pre-heating and heating seasons.

 
3.4 Diagnostic Ratios

Diagnostic ratios (DRs) of PAHs have been widely used as a useful tool to distinguish their sources qualitatively (Kong et al., 2018). In this study, the four mass ratios of FA/(FA + PY), BaA/(BaA + CHR), AN/(AN + PHE) and IP/(IP + BgP) were calculated to qualitatively assess the emission sources of PAHs during the PHS and HS. Fig. 4 showed the four ratios for this study and the related emission sources (Kong et al., 2018). Generally, the values of FA/(FA + PY), BaA/(BaA + CHR) and IP/ (IP + BgP) fell in the range of 0.5–0.6, 0.4–0.5 and 0.5–0.6, reflecting the significant contribution of coal and biomass combustion, vehicle emission, and diesel burning. Meanwhile, higher FA/(FA + PY) and BaA/( BaA + CHR) values were found in the HS, which was in agreement with the increased impacts of coal/biomass combustion in the HS. Furthermore, IP/(IP + BgP) increased much in the HS indicated the PAHs were more affected by coal/biomass burning for heating. However, AN/(AN + PHE) values decreased from 0.168 ± 0.052 in the PHS to 0.068 ± 0.024 in the HS, which highlighted the importance of petroleum in the HS (Kong et al., 2018). Although the AN/(AN + PHE) values yielded the contradictory conclusions with those from the other three kinds of DRs, the enhanced coal and biomass consumptions in the HS should be the explanations for DR variations (Kong et al., 2018).

Fig. 4. Graphic illustration of the diagnostic ratios for the identification of PAHs sources. Fig. 4. Graphic illustration of the diagnostic ratios for the identification of PAHs sources.


3.5 Source Apportionment by PMF Model

In the entire sampling period (ESP), totally five PAH sources were recognized by PMF model and their profiles were shown in Fig. S1. Factor 1 was characterized by high loadings of CHR, PHE, FA, FL, and BbF, which could be attributed to biomass/natural-gas burning (BNGB) (Wang et al., 2020; Azimi et al., 2021). PHE, FA, FL, and BbF were widely used as the indicators for biomass burning (BB) (Wang et al., 2020). CHR often marked the PAH emissions from natural gas burning though CHR also was released from agricultural incineration of charcoal (Azimi et al., 2021). Factor 2 possessed high levels of IP, DBA, BgP, BkF, and HMW-PAHs, which might be related to the vehicular exhaust (VE) (Li et al., 2019). BkF has been demonstrated as good markers of diesel-vehicle emissions (Wang et al., 2013). At the same time, high loadings of HMW-PAHs was associated with VE (Zhou et al., 2005; Kong et al., 2018). High laodings of FA, PY, and BaA were found in Factor 3, suggesting the emissions from coal combustion (Callénet al., 2014; Jamhari et al., 2014). Factor 4 was associated with high ACL and AC and identified as the oil spill and leakages (OPL) (Liu et al., 2018). Factor 5 was featured by the high loadings of NA, ACL, AC, and FL, which was identified as the industrial sources (IS). FL was the marker for industrial boilers and thermal power plants, ACL was for the cement industry, NA was the dominant species in the steel industry, and AN was for the coking industry (Dat and Chang, 2017).

Fig. 5 listed the source contributions in each sampling day, the pre-heating season (PHS), the heating season (HS), and the entire sampling period (ESP). The source contributions varied significantly between the PHS and the HS. Vehicle exhaust (VE) was the biggest contributor and accounted for 29.4% of the total PAHs in the PHS, whereas the corresponding source and contribution were biomass/natural-gas burning (BNGB) and 36.9%. BNGB shares increased by 221% from the PHS to the HS, which should be associated with the increased usage in natural gas (NG) and biomass, especially for NG, when the biomass-burning prohibition and “Coal to NG” were taken into accounted (Chen et al., 2019; Li et al., 2021). Coal combustion (CC) contributions increased markedly from 9.89% in the PHS to the 24.9% in the HS despite the implementation of “coal-banning” policy, which was in accordance with the significant increase of MMW-PAHs in the HS (Feng and Gao, 2019). The coal utilization for heating should be further managed (Zhai et al., 2019). As the biggest contributor in the PHS, VE declined most by 54.4% due to the traffic limitation in the HS, which concurred with the decrease of mass shares of HMW-PAHs in the HS (Wang et al., 2020). Fig. S2 listed the source contributions for each PAH congener. For the most concerned BaP, the main contributors included CC, VE, and BNGB. The variations of their contributions resulted in the increase of BaP in the HS.

Fig. 5. Time series of the contributions of five PAH sources.
Fig. 5. Time series of the contributions of five PAH sources.

 
3.6 Health Risk Assessment of PAHs

Fig. 6 showed the BaP equivalent (BaPeq) concentrations and the incremental lifetime cancer risk (ILCR) for adults and children on a daily basis throughout the entire sampling period (ESP). The BaPeq concentrations in the ESP varied from 0.193 to 3.24 ng m3 with mean value of 1.69 ng m3. The mean value of 1.69 ng m3 was far lower than Anshan in 2018 (13.8 ng m3) (Wang et al., 2020), Tianjin in 2010 (9.46 ng m3) (Han et al., 2014), and Beijing from 2003 to 2006 (43.7 ng m3) (Wang et al., 2014). BaPeq values have decreased significantly compared with those in other Chinese cities in the pre-implementation period of “Clean Heating” project, indicating that relevant governance has achieved certain results. However, they were still higher than Kuala Lumpur (0.334 ng m3), Petaling Jaya (0.640 ng m3) and Bangi (0.389 ng m3) in 2010–2011, which meant that the management of PM2.5 and related PAHs in China should be strengthened (Zhai et al., 2019). Meanwhile, BaPeq values increased much from 0.748 ng m3 in the PHS to the 2.16 ng m3 in the HS due to the elevated contributions of coal combustion and biomass/natural-gas burning to the total PAHs.

Fig. 6. Time series of BaP equivalent (BaPeq) concentrations and incremental lifetime cancer risks (ILCRs) for adults and children.Fig. 6. Time series of BaP equivalent (BaPeq) concentrations and incremental lifetime cancer risks (ILCRs) for adults and children.

The incremental lifetime cancer risk (ILCR) values for both adults and children in the PHS were lower than the recommended threshold value of 1 × 106 by the U.S. EPA, while there were up to 14 of 22 days in the HS with the ILCR values higher than 1 × 106 for adults. At the same time, the mean ILCR values for adults increased from 3.28 × 107 in the PHS to the 1.15 × 106 in the HS. The mean ILCR values for children and adults in the HS were much lower than the corresponding 5.99 × 106 and 8.36 × 106 in winter of 2014–2015 in Anshan City (Wang et al., 2020), which might be attributed to the effects of “Clean Heating”. The contributions of sources to the mean ILCR values in the PHS, the HS, and the ESP were shown in Fig. 7 based on their source profiles and mass contributions to the total PAHs. In consequence, the values of loss of life expectancy (LLE) increased from 0.72 and 2.03 mins for children and adults in the PHS to 2.53 and 7.15 mins in the HS.

 Fig. 7. Source contributions to the incremental lifetime cancer risks (ILCRs).
Fig. 7. Source contributions to the incremental lifetime cancer risks (ILCRs).

 
4 CONCLUSIONS


We systematically evaluate the “Clean Heating” policy impacts on variations of source contributions, levels, health risks of PM2.5-bound PAHs between the pre-heating season (PHS) and the heating season (HS) in a small city (Baoding City) in winter of 2019–2020.

  • 18PAHs increased by 224% in the HS compared with that in the HS. As an indicator of carcinogenic risks posed by PAHs, BaP increased from 0.937 to 1.29 ng m3. Furthermore, the mean value of total 18 PAHs of 26.2 ±83 ng m3 in the HS was far lower than that of other cities before CH implementation, indicating the positive effects of CH.

  • Comparing with those in the PHS, the mass concentrations of MMW-PAHs increased most by 400% in the HS and followed by LMW-PAHs with the increase rate of 227%, which might be associated with the increased consumptions of fossil fuels for heating. However, the mass shares of HMW-PAHs decreased from 71.3% in the PHS to 57.6% though MMW-PAHs concentrations increased in the HS due to the traffic limitation. Higher values of FA/(FA + PY), BaA/(BaA + CHR), and IP/(IP + BgP) were found in the HS due to the increased usage of coal and biomass for heating.

  • Positive matrix factorization (PMF) model analysis indicated that biomass/natural-gas burning (BNGB) contributed most to the total PAHs of 36.9% due to the “Coal to Gas” policy. Coal combustion (CC) shares increased from 9.89% in the PHS to 24.9% in the HS despite the implementation of “Coal-Banning”. Instead, the shares of vehicle exhaust (VE) and industrial sources (IS) decreased much due to the traffic limitation and the implementation of ultra-low emission measures in industries.

  • Attributing to the CH effects, the incremental lifetime cancer risk (ILCR) values for adults and children in the PHS and children in the HS were lower than the accepted threshold value of 1 × 106. However, the ILCR values for adults in 14 of 22 sampling days in the HS exceeded 1 × 106. PMF showed that the BNGB and CC contributed higher to the total ILCRs in the HS than PHS.

 
ACKNOWLEDGEMENTS


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


REFERENCES


  1. Alvarez-Ospina, H., Giordano, S., Ladino, L.A., Raga, G.B., Muñoz-Salazar, J.I., Leyte-Lugo, M., Rosas, D., Carabali, G. (2021). Particle-bound polycyclic aromatic hydrocarbons (pPAHs) in Merida, Mexico. Aerosol Air Qual. Res. 21, 200245. https://doi.org/10.4209/aaqr.200245

  2. Azimi, R., Moeinaddini, M., Feiznia, S., Riyahi-Bakhtiari, A., Savabieasfahani, M., van Hullebusch, E.D., Lajayer, B.A. (2021). Seasonal and spatial variations in atmospheric PM2.5-bound PAHs in Karaj city, Iran: Sources, distributions, and health Risks. Sustain. Cities Soc. 72, 103020. https://doi.org/10.1016/j.scs.2021.103020

  3. Bu, X., Xie, Z.L., Liu, J., Wei, L.Y., Wang, X.Q., Chen, M.W., Ren, H. (2021). Global PM2.5-attributable health burden from 1990 to 2017: Estimates from the Global Burden of disease study 2017. Environ. Res. 197, 111–123. https://doi.org/10.1016/j.envres.2021.111123

  4. Callén, M.S., Iturmendi, A., López, J.M. (2014). Source apportionment of atmospheric PM2.5-bound polycyclic aromatic hydrocarbons by a PMF receptor model. Assessment of potential risk for human health. Environ. Pollut. 195, 167–177. https://doi.org/10.1016/j.envpol.2014.​08.025

  5. Chang, J.R., Shen, J.N., Tao, J., Li, N., Xu, C.Y., Li, Y.P., Liu, Z., Wang, Q. (2019). The impact of heating season factors on eight PM2.5-bound polycyclic aromatic hydrocarbon (PAH) concentrations and cancer Risk in Beijing. Sci. Total Environ. 688, 1413–1421. https://doi.org/10.1016/j.​scitotenv.2019.06.149

  6. Chen, T.W., Chen, J.C., Liu, Z.S., Chi, K.H., Chang, M.B. (2021). Characterization of PM, PAHs and gaseous pollutants emitted from sintering process and electric arc furnace. Aerosol Air Qual. Res. 21, 210140. https://doi.org/10.4209/aaqr.210140

  7. Chen, Z.Y., Chen, D.L., Wen, W., Zhuang, Y., Kwan, M.P., Chen, B., Zhao, B., Yang, L., Gao, B.B., Li, R., Xu, B. (2019). Evaluating the “2+26” regional strategy for air quality improvement during two air pollution alerts in Beijing: variations in PM2.5 concentrations, source apportionment, and the relative contribution of local emission and regional transport. Atmos. Chem. Phys. 19, 6879–6891. https://doi.org/10.5194/acp-19-6879-2019

  8. Dat, N.D., Chang, M.B. (2017). Review on Characteristics of PAHs in atmosphere, anthropogenic sources and control technologies. Sci. Total Environ. 609, 682–693. https://doi.org/10.1016/j.​scitotenv.2017.07.204

  9. Feng, L., Liao, W. (2016). Legislation, plans, and policies for prevention and control of air pollution in China: achievements, challenges, and improvements. J. Cleaner Prod. 112, 1549–1558. https://doi.org/10.1016/j.jclepro.2015.08.013

  10. Feng, Z.B., Cao, S.J. (2019). A newly developed electrostatic enhanced pleated air filters towards the improvement of energy and filtration efficiency. Sustain. Cities Soc. 546–569. https://doi.org/10.1016/j.scs.2019.101569

  11. Guo, X.P., Ren, D.F., Li, C.B. (2020). Study on clean heating based on air pollution and energy consumption. Environ. Sci. Pollut. Res. 27, 6549–6559. https://doi.org/10.1007/s11356-019-07093-8

  12. Han, J.B., Zhang, N., Niu, C., Han, B., Bai, Z.P. (2014). Personal exposure of children to particle associated polycyclic aromatic hydrocarbons in Tianjin, China. Polycyclic Aromat. Compd. 34, 320–342. https://doi.org/10.1080/10406638.2014.883416

  13. Jacobson, M.Z., Cameron, M.A., Hennessy, E.M., Petkov, I., Meyer, C.B., Gambhir, T.K., Maki, A.T., Pfleeger, K., Clonts, H., McEvoy, A.L., Miccioli, M.L., von Krauland, A.K., Fang, R.W., Delucchi, M.A. (2018). 100% clean and renewable Wind, Water, and Sunlight (WWS) all-sector energy roadmaps for 53 towns and cities in North America. Sustain. Cities Soc. 42, 22–37. https://doi.org/10.1016/j.scs.2018.06.031

  14. Jamhari, A.A., Sahani, M., Latif, M.T., Chan, K.M., Tan, H.S., Khan, M.F., Tahir, N.M. (2014). Concentration and source identification of polycyclic aromatic hydrocarbons (PAHs) in PM10 of urban, industrial and semi-urban areas in Malaysia. Atmos. Environ. 86, 16–27. https://doi.org/​10.1016/j.atmosenv.2013.12.019

  15. Kong, S.F., Qin Y., Huang Z., Liu H.B., Wang W., Zheng S.R., Yang G.W., Zheng M.M., Wu J., Qi S.H., Shen G.F., Tang L.L., Yan Y., Zhao T.L., Yu H., Liu D.T., Zhao D.T., Zhao D.T., Zhang T., Ruan J.J., Huang M.Z. (2018). Substantial reductions in ambient PAHs pollution and lives saved as a co-benefit of effective long-term PM2.5 pollution controls. Environ. Int. 114, 266–279. https://doi.org/​10.1016/j.envint.2018.03.002

  16. Lelieveld, J., Pozzer, A., Pöschl, U., Fnais, M., Haines, A., Münzel, T. (2020). Loss of life expectancy from air pollution compared to other risk factors: A worldwide perspective. Cardiovasc. Res. 116, 1910–1917. https://doi.org/10.1093/cvr/cvaa073

  17. Li, Q., Nan J., Xue, Y., Dong, Z., Duan, S.G., Zhang, L.S., Zhang, R.Q. (2019). Sources and spatial distribution of PM2.5-bound polycyclic aromatic hydrocarbons in Zhengzhou in 2016. Atmos. Res. 216, 65–75. https://doi.org/10.1016/j.atmosres.2018.09.011

  18. Li, Z.Y., Wang, Y.T., Li, Z.X., Guo, S.T., Hu, Y. (2020). Levels and sources of PM2.5-associated PAHs during and after the wheat harvest in a central rural area of the Beijing-Tianjin-Hebei (BTH) region. Aerosol Air Qual. Res. 20, 1070–1082. https://doi.org/10.4209/aaqr.2020.03.0083

  19. Li, Z.Y., Li, Z.X., Yue, Z.Y., Yang, D.Y., Wang, Y.T., Chen, L., Guo, S.T., Yao, J.S., Wang, L., Lou, X., Xu, X.L., Wei, J.Y., Deng, B.L., Wu, H. (2021). Impact of wheat harvest on levels and sources of PM2.5-associated PAHs in an urban area located at the center of Beijing-Tianjin-Hebei region. Aerosol Air Qual. Res. 21, 200625. https://doi.org/10.4209/aaqr.200625

  20. Liu, Y.K., Yu, Y.P., Liu, M., Lu, M., Ge, R.R., Li, S.W., Liu, X.R., Dong, W.B., Qadeer, A. (2018). Characterization and source identification of PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) in different seasons from Shanghai, China. Sci. Total Environ. 644, 725–735. https://doi.org/10.1016/j.scitotenv.2018.07.049

  21. Meng, W.J., Shen, H.Z., Yun, X., Chen, Y.L., Zhong, Q.R., Zhang, W.X., Yu, X.J., Xu, H.R., Ren, Y.A., Shen, G.F., Ma, J.M., Liu, J.F., Cheng, H.F., Wang, X.L., Zhu, D.Q., Tao, S. (2020). Differentiated-rate clean heating strategy with superior environmental and health benefits in northern China. Environ. Sci. Technol. 54, 13458–13466. https://doi.org/10.1021/acs.est.0c04019

  22. Pang, N.N., Gao, J., Zhu, G.H., Hui, L.R., Zhao, P.S., Xu, Z.J., Tang, W., Chai, F.H. (2021). Impact of clean air action on the PM2.5 pollution in Beijing, China: Insights gained from two heating seasons measurements. Chemosphere 263, 127991. https://doi.org/10.1016/j.chemosphere.​2020.127991

  23. Ren, Y.Q., Wang, G.H., Wu, C., Wang, J.Y., Li, J.J., Zhang, L., Han, Y.N., Liu, L., Cao, C., Cao, J., He, Q., Liu, X.C. (2017). Changes in concentration, composition and source contribution of atmospheric organic aerosols by shifting coal to natural gas in Urumqi. Atmos. Environ. 148, 306–315. https://doi.org/10.1016/j.atmosenv.2016.10.053

  24. Shen, H.Z., Huang, Y., Wang, R., Zhu, D., Li, W., Shen, G.F., Wang, B., Zhang, Y.Y., Chen, Y.C., Lu, Y., Chen, H., Li, T.C., Sun, K., Li, B.G., Liu, W.X., Liu, J.F., Tao, S. (2013). Global atmospheric emissions of polycyclic aromatic hydrocarbons from 1960 to 2008 and future predictions. Environ. Sci. Technol. 47, 6415–6424. https://doi.org/10.1021/es400857z

  25. Suman, S., Sinha, A., Tarafdar, A. (2016). Polycyclic aromatic hydrocarbons (PAHs) concentration levels, pattern, source identification and soil toxicity assessment in urban traffic soil of Dhanbad, India. Sci. Total Environ. 545–546, 353–360. https://doi.org/10.1016/j.scitotenv.​2015.12.061

  26. Taghvaee, S., Sowlat, M.H., Hassanvand, M.S., Yunesian, M., Naddafi, K., Sioutas, C. (2018). Source-specific lung cancer risk assessment of ambient PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) in central Tehran. Environ. Int. 120, 321–332. https://doi.org/10.1016/j.​envint.2018.08.003

  27. Vega, E., López-Veneroni, D., Ramírez, O., Chow, J.C., Watson, J.G. (2021). Particle-bound PAHs and chemical composition, sources and health risk of PM2.5 in a highly industrialized area. Aerosol Air Qual. Res. 21, 210047. https://doi.org/10.4209/aaqr.210047

  28. Wang, S., Ji, Y., Zhao, J., Lin, Y. and Lin, Z. (2020). Source apportionment and toxicity assessment of PM2.5-bound PAHs in a typical iron-steel industry city in northeast China by PMF-ILCR. Sci. Total Environ. 713, 136428. https://doi.org/10.1016/j.scitotenv.2019.136428

  29. Wang, W.C., Dat, N.D., Chi, K.H., Chang, M.B. (2021). Characterizaiton of PM2.5 and particulate PAHs emitted from vehicles via tunnel sampling in different time frames. Aerosol Air Qual. Res. 21, 210074. https://doi.org/10.4209/aaqr.210074

  30. Wang, X.T., Miao, Y., Zhang, Y., Li, Y.C., Wu, M.H., Yu, G. (2013). Polycyclic aromatic hydrocarbons (PAHs) in urban soils of the megacity shanghai: Occurrence, source apportionment and potential human health risk. Sci. Total Environ. 447, 80–89. https://doi.org/10.1016/j.scitotenv.​2012.12.086

  31. Wang, Y.H., Hu, L.F., Lu, G.H. (2014). Health risk analysis of atmospheric polycyclic aromatic hydrocarbons in big cities of China. Ecotoxicology 23, 584–588. https://doi.org/10.1007/​s10646-014-1179-9

  32. Wu, X.D., Shi, G.M., Xiang, X., Yang, F.M. (2021). The characteristics of PM2.5 pollution episodes during 2016-2019 in Sichuan Basin, China. Aerosol Air Qual. Res. 21, 210126. https://doi.org/​10.4209/aaqr.210126

  33. Yang, D.Y., Li, Z.Y., Yue, Z.Y., Liu, J.X., Zhai, Z., Li, Z.L., Gao, M.L., Hu, A.L., Zhu, W.J., Ding, N., Li, Z.X., Guo, S.T., Wang, X.X., Wang, L., Wei, J.H. (2022). Variations in sources, composition, and exposure risks of PM2.5 in both pre-heating and heating seasons. Aerosol Air Qual. Res. 22, 210333. https://doi.org/10.4209/aaqr.210333

  34. Yao, L., Yang, L.X., Yuan, Q., Yan, C., Dong, C., Meng, C.P., Sui, X., Yang, F., Lu, Y.L., Wang, W.X. (2016). Source apportionment of PM2.5 in a background site in the North China Plain. Sci. Total Environ. 541, 590–598. https://doi.org/10.1016/j.scitotenv.2015.09.123

  35. Zhai, S.X., Jacob, D.J., Wang, X., Shen, L., Li, K., Zhang, Y.Z., Gui, K., Zhao, T.L., Liao, H. (2019). Fine particulate matter (PM2.5) trends in China, 2013–2018: Separating contributions from anthropogenic emissions and meteorology. Atmos. Chem. Phys. 19, 11031–11041. https://doi.org/10.5194/acp-19-11031-2019

  36. Zhang, F.W., Xu, L.L., Chen, J.S., Chen, X.Q., Niu, Z.C., Lei, T., Li, C.M., Zhao, J.P. (2013). Chemical characteristics of PM2.5 during haze episodes in the urban of Fuzhou, China. Particuology 11, 264–272. https://doi.org/10.1016/j.partic.2012.07.001

  37. Zhang, L.L., Yang, L., Zhou, Q.Y., Zhang, X., Xing, W.L., Zhang, H., Toriba, A., Hayakawa, K., Tang, N. (2020a). Impact of the COVID-19 outbreak on the long-range transport of particulate PAHs in East Asia. Aerosol Air Qual. Res. 20, 2035–2046. https://doi.org/10.4209/aaqr.2020.07.0388

  38. Zhang, L., Wang, H.W., Yang, Z., Fang, B., Zeng, H., Meng, C.Y., Rong, S.Y., Wang, Q. (2021). Personal PM2.5-bound PAH exposure, oxidative stress and lung function: The associations and mediation effects in healthy young adults. Environ. Pollut. 293, 118493. https://doi.org/10.1016/j.​envpol.2021.118493

  39. Zhang, T.C., Chen, Y.F., Xu, X.H. (2020b). Health risk assessment of PM2.5-bound components in Beijing, China during 2013-2015. Aerosol Air Qual. Res. 20, 1938–1949. https://doi.org/​10.4209/aaqr.2020.03.0108

  40. Zhang, Y.X., Tao, S., Shen, H.Z., Ma, J.M. (2009). Inhalation exposure to ambient polycyclic aromatic hydrocarbons and lung cancer risk of Chinese population. Proc. Natl. Acad. Sci. 106, 21063–21067. https://doi.org/10.1073/pnas.0905756106

  41. Zhao, X.X., Zhao, X.J., Liu, P.F., Ye, C., Xue, C.Y., Zhang, C.L., Zhang, Y.Y., Liu, C.T., Liu, J.F., Chen, H., Chen, J.M., Mu, Y.J. (2020). Pollution Levels, Composition characteristics and sources of atmospheric PM2.5 in a rural area of the North China Plain during winter. J. Environ. Sci. 95, 172–182. https://doi.org/10.1016/j.jes.2020.03.053

  42. Zhou, L., Tao, Y.M., Li, H.C., Niu, Y., Li, L., Kan, H.D., Xie, J., Chen, R.J. (2021). Acute effects of fine particulate matter constituents on cardiopulmonary function in a panel of COPD patients. Sci. Total Environ. 770, 144753. https://doi.org/10.1016/j.scitotenv.2020.14475

  43. Zhou, J.B., Wang, T.G., Huang, Y.B., Mao, T., Zhong, N.N. (2005). Seasonal variation and spatial distribution of polycyclic aromatic hydrocarbons in atmospheric PM10 of Beijing, People's Republic of China. Environ. Contam. Toxicol. 74, 660–666. https://doi.org/10.1007/s00128-005-0634-y 


Share this article with your colleagues 

 

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.

7.3
2022CiteScore
 
 
77st percentile
Powered by
Scopus
 
   SCImago Journal & Country Rank

2022 Impact Factor: 4.0
5-Year Impact Factor: 3.4

Call for Papers for the special issue on: "Carbonaceous Aerosols in the Atmosphere"

Aerosol and Air Quality Research partners with Publons

CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit
CLOCKSS system has permission to ingest, preserve, and serve this Archival Unit

Aerosol and Air Quality Research (AAQR) is an independently-run non-profit journal that promotes submissions of high-quality research and strives to be one of the leading aerosol and air quality open-access journals in the world. We use cookies on this website to personalize content to improve your user experience and analyze our traffic. By using this site you agree to its use of cookies.