Skip to main content
Download PDF
Download ePub

Impacts of COVID-19 lockdown on PM2.5-bound polycyclic aromatic hydrocarbons in Hohhot, Northern China: characteristics, sources, and source-specific health risks

Environmental Health volume 24, Article number: 5 (2025) Cite this article

Abstract

Quantifying the impacts of reduction strategies on PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) is essential for reducing the health risks of PM2.5. The COVID-19 lockdown provided an opportunity to reveal the quantitative relationship between lockdown measures and the health risks of PAHs. In this study, the characteristics, sources, and health risks of PAHs were investigated during the COVID-19 lockdown in Hohhot. The source-specific health risks of PAHs were assessed using a combination of incremental lifetime cancer risk models (ILCR) and positive matrix factorization (PMF). Compared with the pre-LD period (pre-LD, 87.41 ± 5.98 ng·m-3), the total concentration of ∑PAHs during the lockdown period (LD, 32.52 ± 2.31 ng·m-3) decreased by 62.8% in Hohhot. Coal combustion (51.5%), gasoline emissions (21.9%), diesel emissions (12.9%), industrial emissions (9.3%), and biomass burning (4.7%) were the predominant sources of PAHs in Hohhot. Except for male children, the ILCR of all groups exceeded the threshold for high health risks (1 × 10− 4). Dermal contact is the predominant exposure pathway for carcinogenic risk. Compared with the pre-LD period, the ILCR values decreased by 62.5–62.7% during the LD period. The PMF-ILCR results indicated that industrial emissions (29.1%), coal combustion (28.4%), and diesel emissions (18.5%) were the main sources of ∑ILCR. A Monte Carlo simulation revealed that the cumulative carcinogenic risks at the 95th percentile of the six groups were 1.5–6.3 times the threshold of high health risk (1 × 10− 4). These results emphasize that regulating industrial emissions and coal combustion is effective in reducing carcinogenic risks in industrial cities with large coal consumption.

Peer Review reports

Introduction

Polycyclic aromatic hydrocarbons (PAHs) are the main toxic components in PM2.5 [1, 2], which are mainly from coal combustion, industrial activities, vehicle emissions, and biomass burning [3,4,5]. PAHs can be classified into low-molecular-weight (LMW) (2–3 rings), middle-molecular-weight (MMW) (4 rings), and high-molecular-weight (HMW) (5–6 rings) PAHs [6]. Long-term exposure to PAHs causes persistent and irreversible adverse effects on human health [7,8,9]. Approximately 500 PAHs and related compounds have been detected in the air [10], and 16 have been identified as priority pollutants by the United States Environmental Protection Agency for their carcinogenicity, teratogenicity, and mutagenicity [11].

Extensive studies have been conducted on the characteristics, sources, and health risks of PAHs in Beijing-Tianjin-Hebei [12, 13], Yangtze River Delta [14], Taiyuan [6], Chongqing [15], Yuncheng [16], Xi’an [17], Xinjiang [18], Kuala Lumpur [19], Chiang Mai [20], and Portugal [21]. Few of these studies apportioned the sources of health risks. The lack of a link between sources and health risks makes it difficult to formulate effective strategies for reducing the health risks posed by PAHs [22]. The source-specific health risks of PAHs have been conducted in Huanggang [23], Tehran [24, 25], Anshan [26], Ningbo [27], and Ningxia [28] by the combination of positive matrix factorization (PMF) and incremental lifetime cancer risk (ILCR) model. The PMF-ILCR model provides a method to explore the relationship between sources and health risks of PAHs.

Since the outbreak of COVID-19 pandemic, over 170 countries have implemented government-mandated lockdown restrictions to curb its spread [29]. The lockdown measures provided an opportunity to reveal the impact of passive emission reductions on air pollutants and important information to help develop strategies to improve air quality [30,31,32,33]. However, there are few studies on PAHs during the COVID-19 epidemic. Additionally, most studies focused on the characteristics, sources, and health risks of PAHs during the lockdown (LD) period [34,35,36,37]. To our knowledge, there are no studies on the impact of lockdowns measures on source-specific health risks. The results of such studies can provide an important basis for formulating more precise strategies to control health risks. The source-specific health risks of PAHs were assessed during the COVID-19 lockdown in Hohhot to reveal the response of health risks to the control strategies. The objectives of this study were to (1) estimate the impact of the COVID-19 lockdown on the concentration of PAHs, (2) quantify the source contributions of PAHs, (3) evaluate the health risks of PAHs, and (4) quantify the source contribution of health risks posed by PAHs.

Materials and methods

Study area and sampling

Hohhot has a continental monsoon climate. It is short and hot in summer and dry and cold in winter. There are 6 months of coal-fired heating during winter. Hohhot is surrounded by the Daqing Mountain and Manhan Mountain. The semi-encircling terrain and frequent temperature inversions in winter lead to high atmospheric pollution levels. The sampling site was mainly surrounded by residential areas. Sixty-two 23 h PM2.5 samples were collected on quartz filters (Pallflex Tissuquartz™, ϕ90 mm, USA) using medium volume air samplers (Model 2050, Qingdao Laoshan Applied Technology Research Institute, China) with a flow rate of 100 L/min from December 26, 2019 to February 28, 2020. The samples were stored at − 18 °C until analysis. Before sampling, the quartz filters were baked in a Muffle furnace at 500℃ for 4 h to remove background interference [14, 19, 38]. The concentrations of atmospheric pollutants and meteorological variables were simultaneously observed at the same site (Text S1).

Chemical analysis

The concentrations of PAHs were determined according to the Environmental Protection Standards of the People’s Republic of China (HJ646-2013). The filters were cut into small pieces and extracted with 40 mL ether/n-hexane mixture (1:9 V/V) using a Soxhlet extractor for 16 h. The extraction was filtered through anhydrous sodium sulfate. The filtrate was concentrated to less than 5.0 mL using an automated parallel concentrator (ATUO EVA, Reeko, China). 5–10 mL of n-hexane was added to convert the solvent and concentrated to less than 1.0 mL. Then, 10.0 µL of 400 µg/mL internal standards (naphthalene-d8, acenaphthene-d10, phenanthrene-d10, and perylene-d12) were added to check the recovery rates. At last, the solution volume was fixed to 1.0 mL.

The concentrations of 16 PAHs were determined using gas chromatography/mass spectrometry (7890 A-5975 C, Agilent, USA) with an EI ion source at 230 °C. 1 µL of samples were injected with splitless mode at 280 °C. The PAHs were separated by an HP-5 MS UI column (30 m×250 μm×0.25 μm) with a flow rate of 1 mL/min. The selected ion monitoring (SIM) mode and internal standard method were used for quantification. Field blanks, replicates, and recovery rates were performed once per 10 samples. The method detection limits (MDLs), relative standard deviation (RSD) of replicates, and recovery rates were provided in Table S1. The MDLs were in the range of 0.4–0.9 ng·m− 3. All the concentrations of the field blanks were lower than those of the MDLs. The standard deviations of the replicates were lower than 10% (Table S1). The recoveries of the 16 PAHs ranged from 77.4 to 108%.

Positive matrix factorization (PMF)

The PMF 5.0 model was conducted to analyze the sources of PAHs in this study [28]. The principles of the PMF model were described in Text S2. The source apportionment was conducted only for the whole sampling period. The sources of PAHs during pre-LD and LD periods were not apportioned due to the limitation of data volume, which did not meet the requirements for PMF modeling. Solutions with 3–6 factors were tested to obtain an optimal solution. According to the Q values, signal-to-noise ratio, and physical interpretation of the sources, a 5-factor solution was selected (Fig. S1). Bootstrap (BS), displacement factor (DISP), and BS-DISP analyses were conducted to assess the PMF errors and rotational ambiguity (Table S2). The results of the BS, DISP, and BS-DISP analyses indicated that the model results were robust [39, 40].

Incremental lifetime cancer risk (ILCR)

The ILCR of the 16 PAHs through ingestion, inhalation, and dermal contact pathways were calculated according to Text S3. The ILCR model parameters were listed in Table S3. Monte Carlo simulation (MCS) was used to calculate the probability distribution of ILCR [41, 42]. The exposure parameters were transformed into statistical parameters using uniform or lognormal distributions [43, 44]. Sensitivity analysis was used to determine the effect of changes in the exposure parameters on the ILCR [45]. The sum of the sensitivity contributions of all the exposure parameters to the ILCR was adjusted to 100% [46]. Non-sensitive parameters were deleted from the simulation. MCS was performed for 10,000 iterations to obtain the reliability of modeling.

Results and discussion

Concentrations of PAHs

The variations in PAHs are shown in Fig. 1 and Table S4. The daily mean concentrations of PM2.5 in the whole sampling period (WP, December 26, 2019 to February 28, 2020), pre-lockdown period (pre-LD, December 26, 2019 to January 24, 2020, http://wjw.huhhot.gov.cn/zwdt/gzdt/202001/t20200126_616767.html, last access: 2 September 2024), and LD period (January 25, 2020 to February 25, 2020, https://www.gov.cn/xinwen/2020-02/26/content_5483388.htm, last access: 2 September 2024) were 81.0 ± 57.5, 99.0 ± 64.7, and 66.0 ± 47.5 µg·m-3, respectively. The concentrations of 47.7% of samples were higher than the daily mean secondary limit (75 µg·m-3) of Chinese National Ambient Air Quality Standards (CNAAQS). The daily mean concentrations of ∑16PAHs during the WP, pre-LD, and LD periods were 57.86 ± 52.72, 87.41 ± 53.20, and 35.02 ± 38.04 ng·m-3, respectively (Table S4). High levels of PM2.5 and PAHs mainly occurred during the pre-LD period under unfavorable meteorological conditions (low wind speed, high relative humidity, low temperature, and prevailing southeast wind) (Fig. 2). Compared with those in the pre-LD period, the concentrations of PM2.5 and PAHs decreased by 33.7% and 62.8%, respectively, during the LD period. This was mainly caused by the decrease in emission intensity (COVID-19 lockdown measures and higher temperatures led to lower heating intensity) and improvement in meteorological conditions (lower relative humidity and higher wind speed) (Fig. 2) [30]. The concentration of ∑16PAHs during WP period in Hohhot was significantly higher than Southern European cities [47], New York state [48], Kuala Lumpur [19], Islamabad [38], Karaj [49], and Croatia [50], whereas lower than Indian cities Durgapur [51] and Janshepur [52], compared with those of ∑16PAHs in other countries and regions (Table S5). The results indicate that developing countries are experiencing rapid economic growth and, therefore, face more serious levels of PAH pollution. The concentrations of ∑16PAHs during WP period in Hohhot were considerably higher than those in Ningxia [28], Chongqing [15], Huanggang [22], and Shanghai [53]. By contrast, the concentrations were similar to those in Xi’an [17], Yuncheng [54], and Harbin [22]. It was concluded that winter heating in the northern cities of China causes high levels of PAH pollution. The concentrations of BaP in Hohhot during the WP, pre-LD, and LD periods were 2.30 ± 2.70, 3.59 ± 3.36, and 1.10 ± 1.18 ng·m-3, respectively. The daily mean concentration of BaP (3.59 ± 3.36 ng·m-3) during the pre-LD period was approximately 1.44 times the daily mean secondary limit (2.5 ng·m-3) of CNAAQS. The mean concentration of BaP in Hohhot was lower than that in Karaj (3.89 ± 1.28–4.09 ± 3.01 ng·m-3) [49], Pakistan (3.22 ± 0.94 ng·m-3) [38], Beijing (6.32 ± 10.26 ng·m-3) [55], and Xi ‘an (4.09 ± 2.2–6.88 ± 3.0 ng·m-3) [17], whereas higher than that in America (1.5 ng·m-3) [56], Molina (0.13 ± 0.19 ng·m-3) [57], and Huanggang (0.80 ± 0.40 ng·m-3) [22]. The concentration of BaP in Hohhot was still much higher than the expected value, especially during the pre-LD period. Therefore, further clean energy promotion, industrial boilers upgrade, industrial regulation strengthens, and transport structure improvement are needed in Hohhot.

Fig. 1
figure 1

Concentration, BaPeq, and proportion of 16 individual PAHs

Full size image
Fig. 2
figure 2

Variation of PAHs, atmospheric pollutants, and meteorological parameters in Hohhot

Full size image

Positive matrix factorization

The PMF source contributions and profiles for PAHs are shown in Fig. 3 and Fig. S2, respectively. Factor 1 was characterized by high levels of BaP, Ant, and Ace. BaP is a typical tracer of blast-furnace iron making [28]. Ant and Ace are commonly found in cement factories and the coking industry [58], meaning Factor 1 was identified as an industrial source. Factor 2 had high loadings for both DBA and InP. DBA and InP are typical indicators of gasoline combustion in vehicles [19, 38, 59], therefore, Factor 2 was identified as gasoline emissions. Factor 3 was characterized by BkF and BbF. BkF and BbF are markers of diesel-powered emissions [23], meaning Factor 3 was identified as diesel emissions. In Factor 4, Ace, Ant, NaP, Flu, Phe, and Acy had the highest loadings. NaP, ACE, and Acy are emitted from corn and wheat straw burning [54]. Ace, Phe, NaP, and Ant are the typical tracers of wood burning [17, 54, 60]. In addition, NaP and Ace may be emitted from fireworks during the Spring Festival [61]. Thus, Factor 4 was identified as biomass burning. Factor 5 was highly loaded with Fla, Pyr, BaA, and Chr and moderately loaded with Acy, NaP, Phe, InP, and BghiP. MMW PAHs, such as Fla, Pyr, BaA, and Chr are important tracers of fossil fuel combustion [23, 62]. Phe is a typical marker of coal combustion [63, 64]. NaP, Acy, and Ace are indicators of coke oven emissions [51]. Additionally, InP and BghiP have been associated with coal-fired power plant emissions [28]. Factor 5 was identified as coal combustion.

Fig. 3
figure 3

Source contribution of PAHs in Hohhot

Full size image

As shown in Fig. 3, coal combustion, diesel emissions, gasoline emissions, industrial emissions, and biomass burning contributed 51.1%, 21.9%, 12.9%, 9.3%, and 4.7% to the total PAHs, respectively. Coal combustion was the main source of PAHs in Hohhot, which was similar to that in Yuncheng (45.1%) [54] and Harbin (heating period) (61%) [65]. However, vehicular emission was the main source of PAHs in Huanggang (56.8%) [23], Chongqing (49.4%) [66], and Harbin (non-heating period) (59%) [65]. This could be attributed to the fact that northern cities in China consume large amounts of coal for winter heating, which emits high levels of PAHs. In the future, the emission of coal combustion can be effectively reduced through the promotion of clean fuels in the residential sector, upgrading on industrial boilers, and phasing out outdated industrial capacities.

Health risk assessment

The daily mean concentration of BaPeq during the WP in Hohhot was 6.14 ± 5.94 ng·m-3 (0.13–23.11 ng·m-3). It was much higher than the global mean concentration of BaPeq (0.07 ± 0.14 ng·m-3) [21]. BaP, InP, and BbF were the main contributors to BaPeq, accounting for 37.5%, 20.5%, and 17.5%, respectively (Table S6). The concentration of seven carcinogenic PAHs (∑7CPAHs—Chr, BaA, BkF, BbF, BaP, InP, and DBA) accounted for 70.4% of the concentration of ∑16PAHs. However, their BaPeq accounted for 99.1% of the total BaPeq. It can be concluded that the 7CPAHs are the most important contributors to the toxicity of PAHs. As a PAH with strong carcinogenicities [67], BaP only accounted for 2.8% of ∑16PAHs; however, the contribution of BaP to the total BaPeq was 37.5%. Compared with the pre-LD period, the BaPeq values of Pyr, BaA, Fla, InP, BaP, DBA, and Chr decreased by more than 50% during the LD period. These PAHs are typical tracers for vehicular emissions [1, 68, 69]. The decrease in BaPeq in Hohhot during the COVID-19 lockdown may be associated with the reduction of vehicular emissions and the improved meteorological conditions (from 1.38 m/s in pre-LD to 1.78 m/s in LD). Compared with the pre-LD period, the daily mean BaPeq during the LD period decreased by 62.6% (Table S6). This was because of improvements in meteorological conditions (Fig. 2) and COVID-19 lockdown measures during the LD period.

The ILCR of the PAHs are presented in Fig. 4 and Table S7. The total ILCR (∑ILCR) of children, adolescents, and adults ranged from 9.97 × 10− 5 to 1.03 × 10− 4, 1.11 × 10− 4 to 1.14 × 10− 4, and 3.81 × 10− 4 to 4.14 × 10− 4, respectively. Except for male children, the ILCR of all groups exceeded the threshold for high health risks (1 × 10− 4). During the sampling period, the ∑ILCR of the six groups were in the order of female adults (4.14 × 10− 4) > male adults (3.81 × 10− 4) > female adolescents (1.14 × 10− 4) > male adolescents (1.11 × 10− 4) > female children (1.03 × 10− 4) > male children (9.97 × 10− 5). The ∑ILCR values of females were higher than those of males in all three age groups. This is consistent with the results obtained in Urumqi [26], Yuncheng [54], India [51], and Taiyuan [26]. This may be related to women’s lower body weight, larger exposed skin area, higher frequency of cooking [60], and women’s lungs having a higher susceptibility to PAHs [70]. The ∑ILCR values of the three age groups were in the following order: adults > adolescents > children. The BW and SA of the children were lower than those of the adolescents; however, the ∑ILCR of the children was similar to that of the adolescents. This may be related to children’s higher IRing, frequent hand–mouth activity, and high sensitivity to pollutants [71]. Owing to the longer ED, adults have higher exposure doses of pollutants, leading to higher ∑ ILCR. ILCRing and ILCRderm were considerably higher (10− 4–10− 6) than ILCRinh (10–10–10− 9). ILCRinh can be ignored. It can be attributed to the absorption efficiency of PAHs by different pathways. The absorption of PAHs through the ingestion pathway was more efficient than that of dermal contact and inhalation [72]. The dermal absorption by skin lipids on head, hands, and arms was comparable to the inhalation pathway. Even, it would be greater than the inhalation if an entire body was counted [73]. Furthermore, clothes can sorb PAHs and may facilitate dermal intake of PAHs [74]. Thus, the ingestion and dermal contact pose higher health risks than the inhalation pathway. Similar results were reported for Mount Tai [75], Shanghai [76], Huanggang [23], and Hohhot [77]. The government should continuously prevent and suppress dust, regulate industrial emissions, and enhance the awareness of personal protection to reduce ILCRderm. The ILCRing values of children (2.80 × 10− 5) and adults (3.02 × 10− 5) were approximately twice that of adolescents (1.67 × 10− 5). The ILCRing of female children was close to that of male adults and much higher than that of both male and female adolescents. This can be attributed to the high frequency of hand–mouth activity, which can cause children to ingest more pollutants [78]. In addition, localized particle deposition rates in the oral cavity were similar in children and adults, whereas those in the larynx, pharynx, trachea, and bronchi were much higher in children than in adults [79], which may be another reason for the higher ILCRing in children. The ILCR via the dermal contact pathway followed the order adults > adolescents > children, which may be related to the longer ED and larger SA of adults. The SA of adolescents and children were the same in the calculation; the longer ED of adolescents resulted in a slightly larger ILCRderm for adolescents than for children. Compared with the pre-LD period, ∑ILCR of the LD period decreased from 1.84 × 10− 3 to 6.91 × 10− 4, a reduction of 62.5%. The ILCR of the 16 individual PAHs in the six groups is shown in Fig. S3. The ILCR of 7CPAHs for male children, female children, male adolescents, female adolescents, male adults, and female adults were 2.68 × 10− 5, 2.76 × 10− 5, 2.38 × 10− 5, 2.46 × 10− 5, 7.03 × 10− 5, and 7.65 × 10− 5, respectively. The contribution of 7CPAHs to ∑ILCR was as high as 94.5%, indicating that the potential carcinogenic risks of PAHs in Hohhot should not be ignored.

Fig. 4
figure 4

ILCRs for the three exposure routes using the USEPA standard model

Full size image

The MCS was conducted to assess the probability distribution and sensitivity of the relative parameters for the potential carcinogenic risk of PAHs in Hohhot. The results indicated that the cumulative probability distributions of ∑ILCR in adolescents and children were similar (Fig. 5). The ∑ILCR of adults was significantly higher than that of children and adolescents in the same percentile. Approximately 48.6%, 53.3%, 65.8%, and 70.4% of ∑ILCR exceeded 1.0 × 10− 4 for male children, female children, male adolescents, and female adolescents, respectively, whereas for both male and female adults it was 100%. Most previous studies have chosen the 95th percentile as the reference health risk [66, 80, 81]. The 95th percentiles of ILCR for male children, female children, male adolescents, female adolescents, male adults, and female adults were 1.42 × 10− 4, 1.47 × 10− 4, 1.58 × 10− 4, 1.63 × 10− 4, 5.51 × 10− 4, and 5.99 × 10− 4, respectively, which were significantly higher than the results calculated by the ILCR formulas. The 50th percentiles of the ILCR values for male children, female children, male adolescents, female adolescents, male adults, and female adults were 9.91 × 10− 5, 1.02 × 10− 4, 1.09 × 10− 4, 1.14 × 10− 4, 3.77 × 10− 4, and 4.10 × 10− 4, respectively. The 50th percentile values of the ILCR from MCS were nearly equal to those of the ILCR calculation. The results suggested that PAHs in Hohhot posed non-negligible carcinogenic risks to all groups except male children.

Fig. 5
figure 5

Results of Monte Carlo simulation

Full size image

Exposure parameters are key factors for assessing the uncertainty of health risks [82]. In this study, MCS was conducted to analyze the sensitivity of exposure parameters. The results showed that ED, AT, CSFderm, SA, and ABS were the critical parameters for health risk assessment, which contributed 17.7–21.0%, 17.5–20.3%, 14.3–17.7%, 14.2–17.4%, and 13.7–16.9% to the total variance (Fig. S4). This suggested that these parameters mainly influenced the uncertainty in the health risk assessment. The localization of the sensitivity parameters (ED, SA, CSFderm, etc.) should be further studied to assess the health risks accurately.

Source apportionment of health risk

In this study, the sources and carcinogenic risk of each PAH were estimated using the PMF and ILCR models, respectively. By the combination of PMF and ILCR model (PMF-ILCR) results, the source contribution of ILCR of PAHs in Hohhot was calculated to make health-risk mitigation strategies more targeted. The results indicated that coal combustion, diesel emissions, gasoline emissions, industrial emissions, and biomass burning to ILCR were 3.47 × 104, 2.27 × 104, 2.05 × 104, 3.56 × 104, and 8.84 × 105 ng·m-3, respectively (Fig. 6 and Table S8). Industrial emissions had the highest contribution (29.1%) to ∑ILCR, whereas they contributed less (9.3%) to PAH concentration. BaP and Ant contributed 38.4% and 16.5% to ILCR of industrial emissions, respectively (Table S9). Coal combustion had the highest contribution (51.1%) to PAH concentration, whereas it only contributed 28.4% to ∑ILCR. This is consistent with the results in Anyang [83]. Diesel emissions had a similar contribution to ∑ILCR (18.6%) and PAHs (22.0%), which was much different from those in Ningxia [28] and Zhengzhou [83]. In conclusion, the source contributions of PAHs and ∑ILCR were quite different. Similar results have been observed in Tehran [25]. This could be attributed to the toxicity of the predominant PAHs from other sources.

Fig. 6
figure 6

Contributions of ILCR and PMF-ILCR from PAHs pollution sources in Hohhot

Full size image

Conclusions

The main objective of this study was to assess the impact of the COVID-19 lockdown on the characteristics, sources, and source-specific health risks of PAHs in Hohhot. Owing to the COVID-19 lockdown measures and the improvement in meteorological conditions, the concentrations of 16 PAHs decreased significantly from the pre-LD to LD period. The BaPeq of 7CPAHs accounted for 99.1% of the total BaPeq, among which BaP, InP, and BbF were the most toxic. The ILCR results indicated that the carcinogenic risks of PAHs in male children were close to the threshold of high carcinogenic risks, whereas those in the other five groups were at high carcinogenic risk levels. Compared with the pre-LD period, ∑ILCR of LD period decreased 62.5%. The PMF and PMF-ILCR results suggested that coal combustion (51.1%) and diesel emissions (22.0%) were the main sources of PAHs in Hohhot, whereas industrial emissions (29.1%), coal combustion (28.4%), and diesel emissions (18.6%) were the main sources of ILCR. This study provides a powerful approach for regulatory strategies to mitigate PAH pollution and the corresponding health risks in cities with coal-fired heating.

However, this study has some limitations. First, most of the exposure parameters of the ILCR were based on EPA, and only a few were localized. The MCS results suggested that health risk was sensitive to these exposure parameters. Second, the EF was adjusted to the winter heating period (180 days) in this study; however, using a high concentration of PAHs for 2 months to represent the entire heating period may have caused an overestimation of the ILCR. Future studies should focus on optimizing these aspects to improve the accuracy of health risk assessment.

Data availability

No datasets were generated or analysed during the current study.

References

  1. Li Y, Song N, Yu Y, Yang Z, Shen Z. Characteristics of PAHs in street dust of Beijing and the annual wash-off load using an improved load calculation method. Sci Total Environ. 2017;581–582:328–36.

    Article  Google Scholar 

  2. Sarigiannis DΑ, Karakitsios SP, Zikopoulos D, Nikolaki S, Kermenidou M. Lung cancer risk from PAHs emitted from biomass combustion. Environ Res. 2015;137:147–56.

    Article  CAS  Google Scholar 

  3. Chao S, Liu J, Chen Y, Cao H, Zhang A. Implications of seasonal control of PM2.5-bound PAHs: an integrated approach for source apportionment, source region identification and health risk assessment. Environ Pollut. 2019;247:685–95.

    Article  CAS  Google Scholar 

  4. Landis MS, Studabaker WB, Patrick Pancras J, Graney JR, Puckett K, White EM, Edgerton ES. Source apportionment of an epiphytic lichen biomonitor to elucidate the sources and spatial distribution of polycyclic aromatic hydrocarbons in the Athabasca Oil Sands Region, Alberta, Canada. Sci Total Environ. 2019;654:1241–57.

    Article  CAS  Google Scholar 

  5. Pongpiachan S, Hattayanone M, Suttinun O, Khumsup C, Kittikoon I, Hirunyatrakul P, Cao J. Assessing human exposure to PM10-bound polycyclic aromatic hydrocarbons during fireworks displays. Atmospheric Pollution Res. 2017;8(5):816–27.

    Article  Google Scholar 

  6. Zhang M, Xie J, Wang Z, Zhao L, Zhang H, Li M. Determination and source identification of priority polycyclic aromatic hydrocarbons in PM2.5 in Taiyuan, China. Atmos Res. 2016;178–179:401–14.

    Article  Google Scholar 

  7. Feng S, Huang F, Zhang Y, Feng Y, Zhang Y, Cao Y, Wang X. The pathophysiological and molecular mechanisms of atmospheric PM2.5 affecting cardiovascular health: a review. Ecotoxicol Environ Saf. 2023;249:114444.

    Article  CAS  Google Scholar 

  8. Kakavandi B, Rafiemanesh H, Giannakis S, Beheshtaeen F, Samoili S, Hashemi M, Abdi F. Establishing the relationship between Polycyclic Aromatic hydrocarbons (PAHs) exposure and male infertility: a systematic review. Ecotoxicol Environ Saf. 2023;250:114485.

    Article  CAS  Google Scholar 

  9. Pongpiachan S, Hattayanone M, Choochuay C, Mekmok R, Wuttijak N, Ketratanakul A. Enhanced PM10 bounded PAHs from shipping emissions. Atmos Environ. 2015;108:13–9.

    Article  CAS  Google Scholar 

  10. WHO. Selected non-heterocyclic polycyclic aromatic hydrocarbons. In. Geneva: World Health Organization; 1998.

  11. EPA US: Priority Pollutants (accessed 05, 10.11). In. 2011. http://water.epa.gov/scitech/methods/cwa/pollutants.cfm

  12. Li Y, Bai X, Ren Y, Gao R, Ji Y, Wang Y, Li H. PAHs and nitro-PAHs in urban Beijing from 2017 to 2018: characteristics, sources, transformation mechanism and risk assessment. J Hazard Mater. 2022;436:129143.

    Article  CAS  Google Scholar 

  13. Zhou A, Cao H, Liu J, Chen Y, Zhou X, Gao Y, Zhang B, Bi S. Urban and suburban variations in emission sources and quality-adjusted life year loss of PM2.5-bound PAHs in Beijing, China. J Clean Prod. 2022;359:132093.

    Article  CAS  Google Scholar 

  14. Hong Y, Xu X, Liao D, Ji X, Hong Z, Chen Y, Xu L, Li M, Wang H, Zhang H, et al. Air pollution increases human health risks of PM2.5-bound PAHs and nitro-PAHs in the Yangtze River Delta, China. Sci Total Environ. 2021;770:145402.

    Article  CAS  Google Scholar 

  15. Liang B, Su Z, Tian M, Yang F, Gao M, Chen Y, Zhang L, Xiang L. Sources and potential health risks of PM2.5-Bound PAHs in a megacity of Southwest China: importance of studying from a Health Risk Perspective. Polycycl Aromat Compd. 2020;42(3):815–32.

    Article  Google Scholar 

  16. Gu Y, Xu H, Feng R, Zhang B, Gao M, Sun J, Shen Z, Qu L, Ho SSH, Cao J. Insight into personal exposure characteristics and health effects of PM2.5 and PM0.25-bound PAHs and their derivatives with different heating ways in the Fenwei Plain, China. Environ Pollut. 2023;338:122699.

    Article  CAS  Google Scholar 

  17. Wang L, Dong S, Liu M, Tao W, Xiao B, Zhang S, Zhang P, Li X. Polycyclic aromatic hydrocarbons in atmospheric PM2.5 and PM10 in the semi-arid city of Xi’an, Northwest China: seasonal variations, sources, health risks, and relationships with meteorological factors. Atmos Res. 2019;229:60–73.

    Article  CAS  Google Scholar 

  18. Wang W, Ding X, Turap Y, Tursun Y, Abulizi A, Wang X, Shao L, Talifu D, An J, Zhang X, et al. Distribution, sources, risks, and vitro DNA oxidative damage of PM2.5-bound atmospheric polycyclic aromatic hydrocarbons in Urumqi, NW China. Sci Total Environ. 2020;739:139518.

    Article  CAS  Google Scholar 

  19. Sulong NA, Latif MT, Sahani M, Khan MF, Fadzil MF, Tahir NM, Mohamad N, Sakai N, Fujii Y, Othman M, et al. Distribution, sources and potential health risks of polycyclic aromatic hydrocarbons (PAHs) in PM2.5 collected during different monsoon seasons and haze episode in Kuala Lumpur. Chemosphere. 2019;219:1–14.

    Article  CAS  Google Scholar 

  20. Yabueng N, Wiriya W, Chantara S. Influence of zero-burning policy and climate phenomena on ambient PM2.5 patterns and PAHs inhalation cancer risk during episodes of smoke haze in Northern Thailand. Atmos Environ. 2020;232:117485.

    Article  CAS  Google Scholar 

  21. Matos J, Silveira C, Cerqueira M. Particle-bound polycyclic aromatic hydrocarbons in a rural background atmosphere of southwestern Europe. Sci Total Environ. 2021;787:147666.

    Article  CAS  Google Scholar 

  22. Ma L, Li B, Liu Y, Sun X, Fu D, Sun S, Thapa S, Geng J, Qi H, Zhang A, et al. Characterization, sources and risk assessment of PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) and nitrated PAHs (NPAHs) in Harbin, a cold city in Northern China. J Clean Prod. 2020;264:121673.

    Article  CAS  Google Scholar 

  23. Xu A, Mao Y, Su Y, Shi M, Li X, Chen Z, Hu T, Liu W, Cheng C, Xing X, et al. Characterization, sources and risk assessment of PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) in Huanggang City, central China. Atmos Environ. 2021;252:118296.

    Article  CAS  Google Scholar 

  24. Ali-Taleshi MS, Riyahi Bakhtiari A, Moeinaddini M, Squizzato S, Feiznia S, Cesari D. Single-site source apportionment modeling of PM2.5-bound PAHs in the Tehran metropolitan area, Iran: implications for source-specific multi-pathway cancer risk assessment. Urban Clim. 2021;39:100928.

    Article  Google Scholar 

  25. Taghvaee S, Sowlat MH, Hassanvand MS, Yunesian M, Naddafi K, Sioutas C. Source-specific lung cancer risk assessment of ambient PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) in central Tehran. Environ Int. 2018;120:321–32.

    Article  CAS  Google Scholar 

  26. Wang S, Ji Y, Zhao J, Lin Y, Lin Z. 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. 2020;713:136428.

    Article  CAS  Google Scholar 

  27. Famiyeh L, Xu H, Chen K, Tang Y-T, Ji D, Xiao H, Tong L, Jia C, Guo Q, He J. Breathing in danger: unveiling the link between human exposure to outdoor PM2.5-bound polycyclic aromatic hydrocarbons and lung cancer risk in an urban residential area of China. Sci Total Environ. 2024;907:167762.

    Article  CAS  Google Scholar 

  28. Bi S, Cao H, Zhang B, Dong H, Gao Y, Zhou X, Jiang Y, Jiang W. PM2.5-bound PAHs near a typical industrial park: determining health risks associated with specific industrial sources. Atmos Environ. 2023;302:119715.

    Article  CAS  Google Scholar 

  29. Sekiya T, Miyazaki K, Eskes H, Bowman K, Sudo K, Kanaya Y, Takigawa M. The worldwide COVID-19 lockdown impacts on global secondary inorganic aerosols and radiative budget. Sci Adv. 2023;9(30):eadh2688.

    Article  CAS  Google Scholar 

  30. Zhou H, Liu T, Sun B, Tian Y, Zhou X, Hao F, Chun X, Wan Z, Liu P, Wang J, et al. Chemical characteristics and sources of PM2.5 in Hohhot, a semi-arid city in northern China: insight from the COVID-19 lockdown. Atmos Chem Phys. 2022;22(18):12153–66.

    Article  CAS  Google Scholar 

  31. Rosa AH, Stubbings WA, Akinrinade OE, Jeunon Gontijo ES, Harrad S. Neural network for evaluation of the impact of the UK COVID-19 national lockdown on atmospheric concentrations of PAHs and PBDEs. Environ Pollut. 2024;341:122794.

    Article  CAS  Google Scholar 

  32. Pongpiachan S, Hirisajja J, Aekakkararungroj A, Gupta P, Rungsiyanon S, Choochuay C, Deelaman W, Poshyachinda S. Impacts of Meteorological parameters on COVID-19 Transmission trends in the Central Part of Thailand. Aerosol Sci Eng. 2024;8(3):370–83.

    Article  Google Scholar 

  33. Pongpiachan S, Chetiyanukornkul T, Manassanitwong W. Relationship between COVID-19-Infected number and PM2.5 level in Ambient Air of Bangkok, Thailand. Aerosol Sci Eng. 2021;5(3):383–92.

    Article  Google Scholar 

  34. Kurwadkar S, Kumar Sankar T, Kumar A, Ambade B, Gautam S, Sagar Gautam A, Biswas JK, Abdus Salam M. Emissions of black carbon and polycyclic aromatic hydrocarbons: potential implications of cultural practices during the Covid-19 pandemic. Gondwana Res. 2023;114:4–14.

    Article  CAS  Google Scholar 

  35. Guzmán MA, Fernández AJ, Boente C, Márquez G, de la Sánchez AM, Lorenzo E. Study of PM2.5-bound polycyclic aromatic hydrocarbons and anhydro-sugars in ambient air near two Spanish oil refineries: Covid-19 effects. Atmospheric Pollution Res. 2023;14(3):101694.

    Article  Google Scholar 

  36. Ambade B, Sankar TK, Kumar A, Gautam AS, Gautam S. COVID-19 lockdowns reduce the black carbon and polycyclic aromatic hydrocarbons of the Asian atmosphere: source apportionment and health hazard evaluation. Environ Dev Sustain. 2021;23(8):12252–71.

    Article  Google Scholar 

  37. Tsiodra I, Grivas G, Bougiatioti A, Tavernaraki K, Parinos C, Paraskevopoulou D, Papoutsidaki K, Tsagkaraki M, Kozonaki F-A, Oikonomou K, et al. Source apportionment of particle-bound polycyclic aromatic hydrocarbons (PAHs), oxygenated PAHs (OPAHs), and their associated long-term health risks in a major European city. Sci Total Environ. 2024;951:175416.

    Article  CAS  Google Scholar 

  38. Mehmood T, Zhu T, Ahmad I, Li X. Ambient PM2.5 and PM10 bound PAHs in Islamabad, Pakistan: concentration, source and health risk assessment. Chemosphere. 2020;257:127187.

    Article  CAS  Google Scholar 

  39. Norris G, Duvall R, Brown S, Bai S. EPA Positive Matrix Factorization (PMF) 5.0 Fundamentals and User Guide. 2014.

  40. Paatero P, Eberly S, Brown SG, Norris GA. Methods for estimating uncertainty in factor analytic solutions. Atmospheric Meas Technigues. 2014;7(3):781–97.

    Article  Google Scholar 

  41. Huang Y, Wang J, Fu N, Zhang S, Du W, Chen Y, Wang Z, Qi M, Wang W, Zhong Q, et al. Inhalation exposure to size-segregated fine particles and particulate PAHs for the population burning biomass fuels in the Eastern Tibetan Plateau area. Ecotoxicol Environ Saf. 2021;211:111959.

    Article  CAS  Google Scholar 

  42. Ma W-L, Zhu F-J, Liu L-Y, Jia H-L, Yang M, Li Y-F. PAHs in Chinese atmosphere part II: Health risk assessment. Ecotoxicol Environ Saf. 2020;200:110774.

    Article  CAS  Google Scholar 

  43. Liao C-M, Chio C-P, Chen W-Y, Ju Y-R, Li W-H, Cheng Y-H, Liao VH-C, Chen S-C, Ling M-P. Lung cancer risk in relation to traffic-related nano/ultrafine particle-bound PAHs exposure: a preliminary probabilistic assessment. J Hazard Mater. 2011;190(1):150–8.

    Article  CAS  Google Scholar 

  44. Qin N, Tuerxunbieke A, Wang Q, Chen X, Hou R, Xu X, Liu Y, Xu D, Tao S, Duan X. Key factors for improving the carcinogenic risk Assessment of PAH Inhalation exposure by Monte Carlo Simulation. 2021, 18(21):11106.

  45. Zhang H, Zhang F, Song J, Tan ML, Kung H-t, Johnson VC. Pollutant source, ecological and human health risks assessment of heavy metals in soils from coal mining areas in Xinjiang, China. Environ Res. 2021;202:111702.

    Article  CAS  Google Scholar 

  46. Hu Y, Bai Z, Zhang L, Wang X, Zhang L, Yu Q, Zhu T. Health risk assessment for traffic policemen exposed to polycyclic aromatic hydrocarbons (PAHs) in Tianjin, China. Sci Total Environ. 2007;382(2–):240–50.

    Article  CAS  Google Scholar 

  47. Alves CA, Vicente AM, Custódio D, Cerqueira M, Nunes T, Pio C, Lucarelli F, Calzolai G, Nava S, Diapouli E, et al. Polycyclic aromatic hydrocarbons and their derivatives (nitro-PAHs, oxygenated PAHs, and azaarenes) in PM2.5 from southern European cities. Sci Total Environ. 2017;595:494–504.

    Article  CAS  Google Scholar 

  48. Hopke PK, Liu W, Han Y-j, Yi S-M, Holsen TM, Cybart S, Milligan M. Measured summertime concentrations of particulate components, Hg0, and speciated polycyclic aromatic hydrocarbons at rural sites in New York State. Environ Pollut. 2003;123(3):413–25.

    Article  CAS  Google Scholar 

  49. Azimi-Yancheshmeh R, Moeinaddini M, Feiznia S, Riyahi-Bakhtiari A, Savabieasfahani M, van Hullebusch ED, Asgari Lajayer B. Seasonal and spatial variations in atmospheric PM2.5-bound PAHs in Karaj city, Iran: sources, distributions, and health risks. Sustainable Cities Soc. 2021;72:103020.

    Article  Google Scholar 

  50. Jakovljević I, Štrukil ZS, Godec R, Davila S, Pehnec G. Influence of lockdown caused by the COVID-19 pandemic on air pollution and carcinogenic content of particulate matter observed in Croatia. Air Qual Atmos Health. 2021;14(4):467–72.

    Article  Google Scholar 

  51. Gope M, Masto RE, Basu A, Bhattacharyya D, Saha R, Hoque RR, Khillare PS, Balachandran S. Elucidating the distribution and sources of street dust bound PAHs in Durgapur, India: a probabilistic health risk assessment study by Monte-Carlo simulation. Environ Pollut. 2020;267:115669.

    Article  CAS  Google Scholar 

  52. Kumar A, Ambade B, Sankar TK, Sethi SS, Kurwadkar S. Source identification and health risk assessment of atmospheric PM2.5-bound polycyclic aromatic hydrocarbons in Jamshedpur, India. Sustainable Cities Soc. 2020;52:101801.

    Article  Google Scholar 

  53. Feng J, Fan F, Feng Y, Hu M, Chen J, Shen Y, Fu Q, Wang S. Effects of COVID-19 Control measures on the concentration and composition of PM2.5-Bound polycyclic aromatic hydrocarbons in Shanghai. Atmosphere. 2023;14(1):95.

    Article  CAS  Google Scholar 

  54. Sun Y, Chen J, Qin W, Yu Q, Xin K, Ai J, Huang H, Liu X. Gas-PM2.5 partitioning, health risks, and sources of atmospheric PAHs in a northern China city: impact of domestic heating. Environ Pollut. 2022;313:120156.

    Article  CAS  Google Scholar 

  55. Chen Y, Liu J, Cao H, Zhang A, Zhou A, Zhou X. Determining priority sources of PM2.5-bound polycyclic aromatic hydrocarbons by their contribution to disability adjusted life years. Atmos Environ. 2021;248:118202.

    Article  CAS  Google Scholar 

  56. Liu B, Xue Z, Zhu X, Jia C. Long-term trends (1990–2014), health risks, and sources of atmospheric polycyclic aromatic hydrocarbons (PAHs) in the U.S. Environ Pollut. 2017;220:1171–9.

    Article  CAS  Google Scholar 

  57. Pozo K, Córtes S, Gómez V, Guida Y, Torres M, Oliveira de Carvalho G, Přibylová P, Klánová J, Jorquera H. Human exposure to polycyclic aromatic hydrocarbons in the atmosphere of an agricultural area of central Chile and inhalation cancer risk assessments. Atmospheric Pollution Res. 2023;14(3):101695.

    Article  CAS  Google Scholar 

  58. Yang H-H, Lee W-J, Chen S-J, Lai S-O. PAH emission from various industrial stacks. J Hazard Mater. 1998;60(2):159–74.

    Article  CAS  Google Scholar 

  59. Li Q, Jiang N, Yu X, Dong Z, Duan S, Zhang L, Zhang R. Sources and spatial distribution of PM2.5-bound polycyclic aromatic hydrocarbons in Zhengzhou in 2016. Atmos Res. 2019;216:65–75.

    Article  CAS  Google Scholar 

  60. Li L, Cheng Y, Dai Q, Liu B, Wu J, Bi X, Choe T-H, Feng Y. Chemical characterization and health risk assessment of VOCs and PM2.5-bound PAHs emitted from typical Chinese residential cooking. Atmos Environ. 2022;291:119392.

    Article  CAS  Google Scholar 

  61. Lai IC, Chang Y-C, Lee C-L, Chiou G-Y, Huang H-C. Source identification and characterization of atmospheric polycyclic aromatic hydrocarbons along the southwestern coastal area of Taiwan– with a GMDH approach. J Environ Manage. 2013;115:60–8.

    Article  CAS  Google Scholar 

  62. Moon HG, Bae S, Chae Y, Kim Y-J, Kim H-M, Song M, Bae M-S, Lee C-H, Ha T, Seo J-S, et al. Assessment of potential ecological risk for polycyclic aromatic hydrocarbons in urban soils with high level of atmospheric particulate matter concentration. Ecotoxicol Environ Saf. 2024;272:116014.

    Article  CAS  Google Scholar 

  63. B LWA ASD, C AML, D BX WT, A SZ APZ. Polycyclic aromatic hydrocarbons in atmospheric PM2.5 and PM10 in the semi-arid city of Xi’an, Northwest China: seasonal variations, sources, health risks, and relationships with meteorological factors. Atmos Res. 2019;229:60–73.

    Article  Google Scholar 

  64. Sofowote UM, McCarry BE, Marvin CH. Source apportionment of PAH in Hamilton Harbour suspended sediments: comparison of two factor analysis methods. Environ Sci Technol. 2008;42(16):6007–14.

    Article  CAS  Google Scholar 

  65. Ma W-L, Li Y-F, Qi H, Sun D-Z, Liu L-Y, Wang D-G. Seasonal variations of sources of polycyclic aromatic hydrocarbons (PAHs) to a northeastern urban city, China. Chemosphere. 2010;79(4):441–7.

    Article  CAS  Google Scholar 

  66. Hao Q, Lu X, Yu B, Yang Y, Lei K, Pan H, Gao Y, Liu P, Wang Z. Sources and probabilistic ecological-health risks of heavy metals in road dust from urban areas in a typical industrial city. Urban Clim. 2023;52:101730.

    Article  Google Scholar 

  67. Khairy MA, Lohmann R. Source apportionment and risk assessment of polycyclic aromatic hydrocarbons in the atmospheric environment of Alexandria, Egypt. Chemosphere. 2013;91(7):895–903.

    Article  CAS  Google Scholar 

  68. Wang J, Li X, Jiang N, Zhang W, Zhang R, Tang X. Long term observations of PM2.5-associated PAHs: comparisons between normal and episode days. Atmos Environ. 2015;104:228–36.

    Article  CAS  Google Scholar 

  69. Soltani N, Keshavarzi B, Moore F, Tavakol T, Lahijanzadeh AR, Jaafarzadeh N, Kermani M. Ecological and human health hazards of heavy metals and polycyclic aromatic hydrocarbons (PAHs) in road dust of Isfahan metropolis, Iran. Sci Total Environ. 2015;505:712–23.

    Article  CAS  Google Scholar 

  70. Uppstad H, Osnes GH, Cole KJ, Phillips DH, Haugen A, Mollerup S. Sex differences in susceptibility to PAHs is an intrinsic property of human lung adenocarcinoma cells. Lung Cancer. 2011;71(3):264–70.

    Article  CAS  Google Scholar 

  71. Martí-Cid R, Llobet JM, Castell V, Domingo JL. Evolution of the dietary exposure to polycyclic aromatic hydrocarbons in Catalonia, Spain. Food Chem Toxicol. 2008;46(9):3163–71.

    Article  Google Scholar 

  72. Lao J-Y, Xie S-Y, Wu C-C, Bao L-J, Tao S, Zeng EY. Importance of Dermal Absorption of Polycyclic Aromatic Hydrocarbons Derived from Barbecue fumes. Environ Sci Technol. 2018;52(15):8330–8.

    Article  CAS  Google Scholar 

  73. Gong M, Zhang Y, Weschler CJ. Measurement of phthalates in skin wipes: estimating exposure from dermal absorption. Environ Sci Technol. 2014;48(13):7428–35.

    Article  CAS  Google Scholar 

  74. Morrison GC, Weschler CJ, Bekö G, Koch HM, Salthammer T, Schripp T, Toftum J, Clausen G. Role of clothing in both accelerating and impeding dermal absorption of airborne SVOCs. J Expo Sci Environ Epidemiol. 2016;26(1):113–8.

    Article  CAS  Google Scholar 

  75. Zhen Z, Yin Y, Chen K, Zhen X, Zhang X, Jiang H, Wang H, Kuang X, Cui Y, Dai M, et al. Concentration and atmospheric transport of PM2.5-bound polycyclic aromatic hydrocarbons at Mount Tai, China. Sci Total Environ. 2021;786:147513.

    Article  CAS  Google Scholar 

  76. Liu Y, Yu Y, Liu M, Lu M, Ge R, Li S, Liu X, Dong W, Qadeer A. Characterization and source identification of PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) in different seasons from Shanghai, China. Sci Total Environ. 2018;644:725–35.

    Article  CAS  Google Scholar 

  77. Sun Y, Su Y, Xie F, Li L, Zhou X, Lyu C. Geochemical characteristics and health risks of pahs in PM2.5: a case study of Hohhot, Baotou, and Bayannur, China. Eco-Environmental Knowl 2024:1–17.

  78. Jiang Y, Hu X, Yves UJ, Zhan H, Wu Y. Status, source and health risk assessment of polycyclic aromatic hydrocarbons in street dust of an industrial city, NW China. Ecotoxicol Environ Saf. 2014;106:11–8.

    Article  CAS  Google Scholar 

  79. Liu Z, Li A, Xu X, Gao R. Computational Fluid Dynamics Simulation of Airflow Patterns and Particle Deposition Characteristics in Children Upper respiratory tracts. Eng Appl Comput Fluid Mech. 2012;6(4):556–71.

    Google Scholar 

  80. Azari A, Abtahi M, Dobaradaran S, Saeedi R, Reza Yari A, Hossein Vaziri M, Ali Razavinasab S, Malakoutian M, Yaghmaeain K, Jaafarzadeh N. Polycyclic aromatic hydrocarbons in high-consumption soft drinks and non-alcoholic beers in Iran: monitoring, Monte Carlo simulations and human health risk assessment. Microchem J. 2023;191:108791.

    Article  CAS  Google Scholar 

  81. Rajasekhar B, Nambi IM, Govindarajan SK. Human health risk assessment of ground water contaminated with petroleum PAHs using Monte Carlo simulations: a case study of an Indian metropolitan city. J Environ Manage. 2018;205:183–91.

    Article  CAS  Google Scholar 

  82. Durmusoglu E, Taspinar F, Karademir A. Health risk assessment of BTEX emissions in the landfill environment. J Hazard Mater. 2010;176(1):870–7.

    Article  CAS  Google Scholar 

  83. Dong Z, Dong Z, Zhang R, Li X. Seasonal characterization, sources, and source-specific risks of PM2.5 bound PAHs at different types of urban sites in central China. Atmospheric Pollution Res. 2023;14(2):101666.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (42167015) and the Natural Science Foundation of Inner Mongolia Autonomous Region (2024MS04009).

Author information

Author notes

  1. Kailin Ji and Bing Sun contributed equally to this work.

Authors and Affiliations

  1. College of Geographical Sciences, Inner Mongolia Normal University, Hohhot, 010022, China

    Kailin Ji, Haijun Zhou, Xi Chun & Zhiqiang Wan

  2. Provincial Key Laboratory of Mongolian Plateau’s Climate System at Universities of Inner Mongolia Autonomous Region, Inner Mongolia Normal University, Hohhot, 010022, China

    Haijun Zhou, Xi Chun & Zhiqiang Wan

  3. Inner Mongolia Repair Engineering Laboratory of Wetland Eco-environment System, Inner Mongolia Normal University, Hohhot, 010022, China

    Haijun Zhou, Xi Chun & Zhiqiang Wan

  4. Hohhot Environmental Monitoring Branch Station of Inner Mongolia, Hohhot, 010030, China

    Bing Sun, Wenjing Sun, Xiaotao Fu, Ying Sun, Huifang Ren & Yangchao Lv

Authors
  1. Kailin Ji
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Bing Sun
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Haijun Zhou
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Wenjing Sun
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Xiaotao Fu
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Ying Sun
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Huifang Ren
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Yangchao Lv
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Xi Chun
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Zhiqiang Wan
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

K.J.: Data curation, Formal analysis, Validation, Visualization, Methodology, Writing - original draft. B. S.: Data curation, Formal analysis, Validation, Methodology. H. Z.: Conceptualization, Project administration, Investigation, Supervision, Validation, Visualization, Writing - review & editing. W. S.: Supervision, Methodology, Writing - review & editing. X. F., Y. S., H. R., and Y.L.: Writing - review & editing. X. C. and Z. w.: Supervision, Methodology, Writing - review & editing.

Corresponding author

Correspondence to Haijun Zhou.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ji, K., Sun, B., Zhou, H. et al. Impacts of COVID-19 lockdown on PM2.5-bound polycyclic aromatic hydrocarbons in Hohhot, Northern China: characteristics, sources, and source-specific health risks. Environ Health 24, 5 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12940-025-01161-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12940-025-01161-y

Keywords

Environmental Health

ISSN: 1476-069X