ISSN: 2822-0838 Online

Significantly Increased Accumulations of PAHs in Scalp Hair During Smoke-haze Period Among Female Adolescents in Chiang Mai, Thailand      

Kessaya Radarit, Wan Wiriya, Chatree Chai-Adisaksopha, Somporn Chantara, and Tippawan Prapamontol*
Published Date : January 17, 2024
Journal Issues : Number 1, January-March 2024

Abstract Limitations in reporting polycyclic aromatic hydrocarbons (PAHs) accumulated in humans arise from the impact of intense biomass burning and air pollution in upper Southeast Asia. This study analyzed PAHs in human hair samples and explored the associations between ambient PM2.5 and hair PAH concentrations. Follow-up hair collections were conducted on 111 female students, and hair samples with a length of 4 cm were analyzed. Ten different PAHs were identified in the subjects' hair samples. The most substantial pooled estimate of cumulative effects of PM2.5 exposure was observed at lag04 (4-month average). At lag04, every 10 μg/m3 increase in PM2.5 concentration was significantly associated with 0.33, 0.29, 0.35, 0.16, 0.04, 0.64, and 1.66 ng/g hair increases in Acy, Ace, Flu, Ant, Chr, BaP, and total PAHs, respectively (P < 0.05). The findings underscore the significant contribution of ambient PM2.5 exposure to the elevation of PAHs in human hair. Specifically, each centimeter of hair represents a one-month exposure to ambient PM2.5. Moreover, the accumulation of BaP, a carcinogenic PAH, significantly increased at heightened PM2.5 levels, emphasizing the importance of hair analysis in assessing PAH exposure risks and obtaining reliable and comprehensive health risk information.


Keywords: Polycyclic aromatic hydrocarbons, Hair analysis, PM2.5


Funding: This research was funded by Science Achievement Scholarship of Thailand (SAST), grant number KKU 013/2553.


Citation: Radarit, K., Wiriya, W., Chai-Adisaksopha, C., Chantara, S., and Prapamontol, T. 2024. Significantly increased accumulations of PAHs in scalp hair during smoke-haze period among female adolescents in Chiang Mai, Thailand. Natural and Life Sciences Communications. 23(1): e2024013.



Particulate matter (PM) in the ambient environment has been associated with adverse health effects, including respiratory and cardiovascular diseases. Specifically, PM2.5 particles (particulate matter with a diameter less than 2.5 µm) can travel into and deposit in the pulmonary region (Dockery and Stone, 2007; Xing et al., 2016; de Bont et al., 2022; Mainka and Żak, 2022). Due to the rough surface of PM, various chemical compounds, such as polycyclic aromatic hydrocarbons (PAHs) and heavy metals, can adhere to the particle surface. Numerous previous studies have suggested that PAHs are significant components of PM associated with adverse health impacts, including lung cancer, asthma, and chronic obstructive pulmonary disease (Kim et al., 2013; Yan et al., 2019; Rodins et al., 2020; Kongpran et al., 2021).


PAHs are ubiquitous environmental organic pollutants, generated during the incomplete combustion of fossil fuels and biomass (including coal, petrol, wood, and tobacco). Additionally, high-temperature cooking methods such as grilling, smoking, toasting, roasting, and frying can lead to the formation of PAHs in meat and other foods (Cross and Sinha, 2004; Purcaro et al., 2013; Sampaio et al., 2021; Siddique et al., 2021). Both natural and anthropogenic activities can introduce substantial amounts of PAHs into the environment, either in the gaseous state or adsorbed onto particulates (Lee, 2010; Gachanja, 2019; Patel et al., 2020).


Human exposure to PAHs can occur through inhalation, ingestion, and dermal (skin) contact, with inhalation being the primary route of exposure to PM-bound PAHs (World Health Organization, 2016). Exposure patterns vary among individuals due to differences in physical factors such as breathing rates (Yang et al., 2021a).


Globally, several hundred PAHs have been detected, and the US Environmental Protection Agency (EPA) has identified 16 species of PAHs as priority pollutants. In recent decades, PAHs have garnered increased attention in air pollution studies due to their cytotoxic, teratogenic, mutagenic, or carcinogenic characteristics (International Agency for Research on Cancer, 2015; Yan et al., 2019; Gamboa-Loira et al., 2022).


Numerous studies have explored the health risks associated with long-term exposure to atmospheric PAHs, using airborne PAH levels as an external marker of exposure (Shen et al., 2018; Lee et al., 2021; Yang et al., 2021a). Given that concentrations of PM-bound PAHs may vary depending on individual characteristics, microenvironments, and personal behaviors, biomonitoring of internal cumulative PAH exposure becomes a crucial tool. This approach provides comprehensive information for human health risk assessments associated with long-term exposure to PAHs-enriched PM2.5 (Palazzi et al., 2018).


Biomonitoring of individual PAH exposure is commonly performed by measuring parent PAHs and their metabolites (i.e., hydroxy-PAHs and nitro-PAHs) in urine or blood (Sobus et al., 2009; Gong et al., 2015; Yang et al., 2021b). However, due to the generally short half-lives of these compounds, blood or urine PAHs and metabolites indicate only recent exposure (Li et al., 2012; Gong et al., 2015).


Recently, the use of hair analysis for PAH biomonitoring has been introduced as a potential bioindicator to assess human exposure to PAHs. This is due to its non-invasive collection, easy and cost-effective sampling procedures, and high stability for storage (Appenzeller and Tsatsakis, 2012). Given that human scalp hair typically consists of high lipid content (2-4%), hair analysis is suitable for measuring lipophilic chemicals such as persistent organic pollutants (POPs) (Król et al., 2013; Gevao et al., 2022). Moreover, it can provide individually integrated information on short to long-term exposure, ranging from weeks to months or even years, depending on hair length (Kucharska et al., 2015).


PAHs have been previously detected in human hair and proposed as biomarkers for assessing PAH exposure (Toriba et al., 2003; Li et al., 2016; Lin et al., 2019). Inter-group differences in exposure levels highlighted by hair analysis were explored between two population groups exposed to different levels of environmental PAH contamination (polluted city and less polluted city) through a comparative study, confirming the suitability of hair analysis to document PAH exposure (Palazzi et al., 2018; Wang et al., 2020). However, this study represents the first prospective follow-up investigation exploring the linkages between cumulative exposure to PM2.5 in different periods and PAH accumulations, considering the length of human hair. The study also provides estimated changes in hair PAHs with increasing atmospheric PM2.5 levels.




Reagents and Materials

The mixed standard of 15 PAHs, including acenaphthylene (Acy), acenaphthene (Ace), fluorene (Flu), phenanthrene (Phe), anthracene (Ant), fluoranthene (Fla), pyrene (Pyr), benzo (a) anthracene (BaA), chrysene (Chr), benzo (b) fluoranthene (BbF), benzo (k) fluoranthene (BkF), benzo (a) pyrene (BaP), indenol (1,2,3-cd) pyrene (IcdP), dibenzo (a,h) anthracene (DahA) and benzo (g,h,i) perylene (BghiP), was sourced from Restek (Bellefonte, Pennsylvania, US). Acenaphthene-D10 and Pyrelene-D12 from Supelco, Inc. (Bellefonte, Pennsylvania, US) served as internal quantitation standards for PAH analysis. Hexanes (Hex) and dichloromethane (DCM) were purchased from J.T. Baker® (Radnor, Pennsylvania, US). Sodium chloride (99%) was purchased from RCI Labscan Ltd (Bangkok, Thailand).


Study Design

The follow-up hair collections of female students living in Chiang Mai Province, Thailand (n = 111), were conducted every four months for a year. According to the approximate 1 cm per month growth of human scalp hair  (Society of Hair Testing, 2004; Kintz et al., 2015), four cm-long of scalp hair samples were analyzed using a gas chromatography-mass spectrometer (GC-MS). The purposes were to qualitatively and quantitatively analyze parent PAHs (15 species) in human scalp hair samples and investigate their associations with the average lags of cumulative ambient PM2.5 exposure. The participants were tasked with self-administering the questionnaire, which encompassed inquiries related to age, daily traffic exposure time (in minutes), passive smoking (yes or no), frequency of home cooking (1-2 days per week, 3-4 days per week, 5-6 days per week, or daily), and the consumption of PAH-contaminated foods (none, 1-2 days per week, 3-4 days per week, 5-6 days per week, or daily).


Study Population

The study was conducted at Navamindarajudis Phayap School (NMP) and Horpra School (HP) in Chiang Mai, Thailand, both located within 20 kilometers of air quality monitoring stations, spanning from July 2020 to March 2021. Figure 1 depicts the research location maps. The study involved female school students in grades 7-9, primarily aged 12-15. Participants were selected from eight districts of Chiang Mai: Mueang Chiang Mai, Mae Rim, San Sai, Doi Saket, San Kamphaeng, Saraphi, Hang Dong, and San Pa Tong. Eligibility criteria included having more than four cm-long hair, residing in Chiang Mai for at least 12 months, and having no history of active smoking. A total of 111 participants were recruited, and hair samples were collected three times with approximately four months between collections (Visit 1: July 22August 19, 2020; Visit 2: December 26, 2020; and Visit 3: March 2330, 2021). Hair strands (about 200 from each participant) were collected by cutting from the back of the head as close to the scalp as possible, using stainless steel scissors. The samples were then wrapped in aluminum foil, sealed in labeled polyethylene zip-lock bags, and stored at 4°C until analysis.



Figure 1. Location maps of the study area: (a) the location of the research area in the maps of Thailand and Chiang Mai Province, yellow areas show the location of Chiang Mai, (b) the study location in the map of Mueang Chiang Mai District, the marks indicate participated schools (yellow circle: NMP and green circle: HP) and air quality monitoring stations (red star: 35t and blue star: 36t)


Ambient PM2.5 Data

Hourly PM2.5 data for the period from January 2020 to March 2021 were obtained from air quality monitoring stations operated by Thailand's Pollution Control Department (PCD). The stations include Chang Phueak (Station 35t) and Siphum (Station 36t), both situated in Mueang Chiang Mai, Chiang Mai, Thailand, as indicated in Figure 1.


PAH Analysis

The PAH analysis protocol was adapted from previous studies (Li et al., 2016; Lin et al., 2019). In summary, a 4 cm hair sample, capturing a four-month exposure period, was taken from the end closest to the scalp and placed in a 9-mL screw-cap test tube. The hair sample underwent three washes in 5 mL n-hexane via vortexing at 1,500 rpm for 10 minutes, followed by air-drying before being finely cut into approximately 1 mm pieces. Twenty-five mg of the hair sample was then weighed into a 9-mL screw-cap test tube. Acenaphthene-D10 and Pyrelene-D12 were spiked onto the fine hair sample as internal standards. The hair was digested by incubating it in the dark overnight at room temperature (25°C) using 2 mL alkaline solution (1.5 M NaOH). This process resulted in the formation of a brown solution. The PAHs were extracted through liquid-liquid extraction in a 2 mL Hex/DCM mixture (2:1 v/v) with agitation at 1,200 rpm for 10 mins. The mixture was centrifuged at 2,500 rpm for 5 mins, and the upper layer extraction was transferred to cleaned test tubes with screw caps. This extraction procedure was repeated three times, and the organic phases were pooled (≈ 6 mL). The extraction solution was filtered using a 0.2 μm PTFE filter to remove particulate impurities and then concentrated to approximately 200 μL through evaporation under a rotary evaporator. The following 15 PAHs were analyzed: Acy, Ace, Flu, Phe, Ant, Fla, Pyr, BaA, Chr, BbF, BkF, BaP, IcdP, DahA, and BghiP, using a gas chromatograph (Agilent 7890A, USA) coupled with a mass spectrometer (MS, Agilent 5975A, USA) and an Automatic injector (Agilent 7683B, USA) equipped with an HP-5MS capillary column (30 m x 0.25 mm x 0.25 μL). The hair samples from the three visits of each participant were analyzed in the same batch. A laboratory blank and two QC samples (pooled hair spiked with 0.8 ppb mixed standard of PAHs) were included with each batch of sample analysis. The area ratios between the analyte and the internal standard of both calibrators and samples were determined to quantify the amount of PAHs. The concentrations of PAHs in samples were calculated by subtracting the PAH concentration of the corresponding blank from the measured concentration. If the PAH level was less than the blank, the value was considered zero. The recoveries of the 15 PAHs were determined to evaluate the efficiency of the extraction method. Intraday assay recoveries ranged from 91% to 108%, and interday assay recoveries ranged from 78% to 118%. The relative standard deviations (RSD%) of the intraday precision study for all PAHs ranged from 1% to 12%, and those of the interday assay ranged from 2% to 18%.  The limits of detection (LOD) for Acy, Ace, Flu, Phe, Ant, Fla, Pyr, BaA, Chr, BbF, BkF, BaP, IcdP, DahA, and BghiP were determined as 0.27, 0.14, 0.24, 0.80, 0.23, 0.33, 0.36, 0.16, 0.31, 0.62, 0.32, 0.30, 0.37, 0.58, and 0.51 ng/g hair, respectively.


Ethical Concerns

The Human Experimentation Committee of the Research Institute for Health Sciences (RIHES) at Chiang Mai University approved the study protocol under project No. 3/63. Prior to enrollment, the study's purpose and procedures were explained to the students and their parents. Written informed consent was obtained from both parents and students participating in the study.


Statistical Analysis

Statistical analysis was conducted using SPSS software version 23.0. One-way analysis of variance (ANOVA) was employed to assess concentration differences for each PAH across three visits, and differences between mean values were further evaluated using Duncan multiple range tests (P < 0.05). To examine lag effects of airborne PM2.5 on the accumulation of PAHs in 4 cm-long hair, linear mixed models for repeated measures with subjects as a random effect were applied. Potential covariates, including age, BMI, traffic exposure time, passive smoking, home cooking, and consumption of PAH-contaminated foods, were screened to identify the best-fit model (West, 2009). Ambient PM2.5 data from stations 35t and 36t were averaged to represent the PM2.5 exposure status for individuals in Chiang Mai Province. Concentrations of PM2.5 were transformed to cumulative lag patterns of exposure over various time periods prior to hair collections, categorized into lag01 (0-1 month), lag02 (0-2 months), lag03 (0-3 months), lag04 (0-4 months), lag05 (0-5 months), and lag06 (0-6 months). Each model was executed to explore the relationships of each PAH or the sum of PAHs with average PM2.5 concentrations in each period. Results were presented as estimated changes in cumulative hair PAHs for a 10 μg/m3 increase in atmospheric PM2.5 concentration.



PAHs in hair

Out of the 15 PAHs, ten were quantitatively determined in human hair samples obtained from 111 female students, although BaA, Chr, and BaP were not detected in all samples (Table 1). The positive detections ranged from 8% to 100% in visit1, 6% to 100% in visit2, and 10% to 100% in visit3. For Acy, Ace, Flu, Ant, and BaP, significant differences were observed in visit3, the period with the highest average concentration of PM2.5 in Chiang Mai, compared to other visits (P < 0.05).


Table 1. Concentrations of PAHs in female student hair samples.



Visit1 (22 Jul19 Aug 2020)

Visit2 (2-6 Dec 2022)

Visit3 (23-30 Mar 2021)

P value3

 Median [range]

(ng/g hair)

Detection (%)

Median [range]

(ng/g hair)

Detection (%)

Median [range]

(ng/g hair)

Detection (%)

Median [range]

(ng/g hair)





4.8 [3.5-11.6]a2


4.8 [3.5-20.6]a


5.7 [4.0-85.0]b





6.7 [1.5-14.3]a


6.6 [4.6-34.9]a


7.1 [5.0-73.4]b





2.9 [1.8-12.3]a


2.9 [1.7-17.5]a


3.3 [2.5-39.4]b



3.2 [2.6-4.9]


7.2 [3.6-59.2]a


6.5 [2.9-65.8]a


6.2 [3.2-76.9]a





2.8 [2.1-10.1]a


2.8 [1.9-15.6]a


3.3 [ 2.4-18.2]b





3.1 [1.7-29.8]a


2.5 [1.4-34.7]a


2.5 [1.5-22.4]a





2.9 [1.2-24.9]a


2.2 [1.22-29.5]a


2.3 [1.2-18.7]a





0.8 [0.5-6.7]a


0.7 [0.6-5.6]a


0.7 [0.6-10.7]a





1.4 [1.0-7.0]a


1.3 [1.0-9.7]a


1.3 [1.0-66.1]a





2.0 [1.2-4.1]a


1.5 [1.1-3.7]a


2.5 [1.6-8.2]b





33.3 [17.2-161.8]a


30.0 [20.1-172.0]a


32.3 [21.9-227.4]a



Note: Definition of abbreviation: Acy = acenaphthylene, Ace = acenaphthene, Flu = fluorene, Phe = phenanthrene, Ant = anthracene, Fla = fluoranthene, Pyr = pyrene, BaA = benzo (a) anthracene, Chr = chrysene, BaP = benzo (a) pyrene

1 Not detected.

2 Different letters indicate a significant difference among PAHs in various visits (P < 0.05)

3 P value of one-way ANOVA for different means of PAHs in three visits


Participant characteristics and its associations with PAHs in hair

The baseline characteristics of participants are provided in Table 2The median age among the female students was 13, ranging between 12 and 15 years. The median BMI was 19.9 kg/m², with a range of 13.2 to 48.3 kg/. Traffic exposure time ranged from 2 to 180 minutes per day, with a median of 30 minutes per day, primarily spent traveling to school. All participants were nonsmokers, while 33% were exposed to passive smoking. Cooking activity at home was categorized as 1-2 days per week (3%), 3-4 days per week (7%), 5-6 days per week (16%), and daily (74%). Regarding dietary PAH exposure, the frequencies of consuming food with high-temperature cooking methods (e.g., grilling, smoking, toasting, roasting, and frying) were in the descending order of 1-2 days per week (37%), 3-4 days per week (26%), 5-6 days per week (15%), none (12%), and daily (10%). To assess the potential role of these variables as covariates in the study, linear mixed models for repeated measures were conducted between these characteristics and PAHs in hair. However, no significant relationships were found between hair PAHs (individual and total PAHs) and other parameters (P > 0.05) (Table 2).


Associations of PAHs concentrations in hair with ambient PM2.5 levels

The relationships between hair PAHs and PM2.5 levels from lag01 to lag06 among female students, as determined by linear mixed models, are illustrated in Figure 2. A consistent pattern emerged in the associations between the six PAH species and PM2.5, revealing that estimated changes increased from lag01 to lag04 and then began to decrease at lag05. The largest estimates of cumulative effects were observed at a 4-month average concentration of PM2.5 (lag04). For every 10 μg/m3 increase in PM2.5 concentration, a significant association was found, resulting in 0.33 (95% CI, 0.26 to 0.40), 0.29 (95% CI, 0.19 to 0.38), 0.35 (95% CI, 0.18 to 0.52), 0.16 (95% CI, 0.12 to 0.21), 0.04 (95% CI, 0.01 to 0.07), and 0.64 (95% CI, 0.23 to 1.04) ng/g hair increases in Acy, Ace, Flu, Ant, Chr, and BaP, respectively (P < 0.01). The same trend was observed for total PAHs, with an estimated change of 1.66 (95% CI, 0.02 to 0.31) ng/g hair per 10 μg/m3 increase in PM2.5 at lag04 (P < 0.05).


Table 2. Baseline characteristics of study participants (N = 111) and its associations with total PAHs in hair.


Median [range]

n (%)

Total PAHs (ng/g hair)

Estimated changes

[95% CI]

P values3

Age (year)

13 [12 15]


-2.91 [-9.10 to 3.28]1


BMI (kg/m2)

19.9 [13.2 - 48.3)


-0.38 [-1.11 to 0.035]1


Traffic exposure time (min/day)

30 [2 - 180)


-0.04 [-0.15 to 0.08]1


Passive smoking







74 [67]

0.00 (ref)





37 [33]

4.57 [-4.68 to 13.83]2


Cooking at home





1-2 days/week


3 [3]

0.00 (ref)



3-4 days/weeks


8 [7]

10.23 [-20.15 to 40.62]2



5-6 days/weeks


18 [16]

2.56 [-25.42 to 30.55]2





82 [74]

9.45 [-16.93 to 35.84]2


Consumption of PAH contaminated foods







13 [12]

0.00 (ref)



1-2 days/week


41 [37]

2.33 [-13.09 to 17.75]2



3-4 days/week


29 [26]

14.68 [-1.40 to 30.76]2



5-6 days/week


17 [15]

5.60 [-11.97 to 23.18]2





11 [10]

2.95 [-16.41 to 22.31]2


Note:   Definition of abbreviation: CI = confidence interval, ref = reference level of each exposure parameter

1 Estimated concentration changes of total PAHs (ng/g hair) by 1 unit increase in age (continuous), BMI (continuous), and traffic exposure time (continuous)

2 Estimated concentration changes of total PAHs (ng/g hair) compared to reference level of passive smoking (yes/no), cooking activities at home (ordinal), and consumption of PAH contaminated foods (ordinal)

3 P values for associations between total PAHs and participant characteristics with subjects as a random effect in linear mixed models for repeated measures (3 visits)



Figure 2. Estimated changes in hair concentrations of: (a) acenaphthylene (Acy), acenaphthene (Ace), fluorene (Flu), phenanthrene (Phe), anthracene (Ant), fluoranthene (Fla), pyrene (Pyr), benzo (a) anthracene (BaA), chrysene (Chr), benzo (a) pyrene (BaP), and; (b) sum of PAHs with an increase of 10 μg/m3 in ambient PM2.5 concentrations from lags of 1 to 6 months. The results were from the linear mixed models with subjects as a random effect and visits as repeated measurements. The middle points indicate the mean concentration changes in PAHs, and vertical error bars indicate the 95% confidence interval of mean changes.




This study aimed to investigate hair PAH concentrations and their associations with ambient PM2.5 among female adolescents in Chiang Mai, Thailand. We identified 10 PAH species, with significant differences observed during visit3, the haze period in Chiang Mai, compared to other visits, particularly for Acy, Ace, Flu, Ant, and BaP. Additionally, potential relationships were found between certain PAHs in 4 cm-long hair and PM2.5 exposure, with the strongest associations observed at lag04 (4-month average concentrations). These results indicate that inhalation exposure to PAHs significantly contributes to their accumulation in hair and analyzing 4 cm hair sections offers a historical perspective on PAH exposure over four months.


The hair sample washing procedure is imperative to eliminate external contamination of PAHs, given their elevated concentrations in the air (Srogi, 2007). It is plausible that the hair surface acts as a substrate for the absorption of airborne PAHs. Various washing solvents, including methanol (Toriba et al., 2003), n-hexane (Toriba et al., 2003; Wang et al., 2016), dichloromethane (Toriba et al., 2003), and mixture of n-hexane and dichloromethane (Li et al., 2016), underwent assessment for their efficacy in eliminating external PAHs. In earlier research conducted by Toriba et al. (2003), it was observed that the repeated application of methanol, n-hexane, and dichloromethane during washing was successful in removing almost all external PAHs. Nevertheless, the preference for n-hexane as the optimal washing solvent was substantiated by the presence of trace amounts of Phe as an impurity in dichloromethane and the concurrent extraction of PAHs from the hair's interior during the washing process with methanol.


Ten parent PAHs, including Acy, Ace, Flu, Phe, Ant, Fla, Pyr, BaA, Chr, and BaP, were quantified in female school students in Chiang Mai Province, though BaA, Chr, and BaP were not detected in all samples. PAH concentrations in hair tended to decrease with the increasing number of rings, aligning with findings from other studies (Palazzi et al., 2018; Toriba et al., 2003; Wang et al., 2020; Yamamoto et al., 2015). Our study revealed lower PAH concentrations compared to reports from China (Lin et al., 2019; Wang et al., 2016; Wang et al., 2020). This difference may be attributed to (1) lower air pollution in Chiang Mai ProvinceThailand's annual mean levels of fine particulate matter (population-weighted) were lower than China's in 2018 (World Health Organization (WHO), 2022), and (2) variations in participant characteristics, including gender and smoking status. Previous research indicated higher PAH levels in male and smoker hair. Additionally, PAH exposure sources differed, with Chinese women facing significant exposure from coal combustion for heating and cooking (Chen et al., 2017; Lam, 2005), unlike in Chiang Mai Province, where biomass burning and vehicular exhaust are major contributors to airborne PM2.5 concentrations (ChooChuay et al., 2020; Kawichai et al., 2021; Kawichai et al., 2022; Khamkaew et al., 2016).


Covariate screening was conducted in the study to develop the best-fit linear mixed models. The absence of an association between the concentrations of PAHs in hair and variables such as age, BMI, traffic exposure time, passive smoking, cooking at home, and consumption of PAH-contaminated foods suggests that these variables were not included in the models. Additionally, in this longitudinal study, each observational unit is measured at baseline and repeatedly over time. Linear mixed models for longitudinal datasets can relax the assumption that all observations are independent, allowing for relationships between observations on the dependent variable from the same participant (West, 2009).


However, several studies have reported higher concentrations of specific and total PAHs in smokers compared to non-smokers (Toriba et al., 2003; Wang et al., 2020; Yamamoto et al., 2015). Indoor air in houses with smokers has shown higher contents of BaP compared to the air in other houses (Toriba et al., 2003). Furthermore, age has been found to have a significant effect on low-molecular-weight (LMW) PAHs and total PAHs in human hair (Wang et al., 2020). Therefore, it is essential to note that exposure routes and personal factors (e.g., BMI, age, gender, and smoking) may also influence PAH accumulations in the human body and hair. Screening potential covariates is crucial to developing the best model for each experimental design. Moreover, more research is needed to draw reliable conclusions about the effect of these factors on PAH incorporation into human hair.


Recently, several studies have highlighted the use of human hair to analyze exposure levels of PAHs in the human body (Li et al., 2016; Lin et al., 2019; Toriba et al., 2003). The concentrations of PAHs in human hair can effectively indicate long-term exposure to PAHs as hair retains these compounds longer than other biological specimens, including urine, blood, and breast milk (Gong et al., 2015; Li et al., 2012). Moreover, PAH biomonitoring using hair analysis is non-invasive, involves an easy and cost-effective sampling procedure, and ensures high stability during storage. Human hair typically contains a high lipid content, making hair analysis suitable for measuring lipophilic chemicals such as PAHs (Appenzeller and Tsatsakis, 2012). Additionally, the potential of using hair PAHs as a biomarker for long-term exposure to air pollution in humans was demonstrated through a comparative study conducted in both a polluted and a less polluted city. Median concentrations of parent PAHs were 1.5 to 2.8 times higher in the hair of subjects from Baoding (a polluted city) than in subjects from Dalian (a less polluted city) (Palazzi et al., 2018). Similarly, higher PAH exposure levels were found in Nanjing (urban area) compared to Ningbo (rural area) (Wang et al., 2020). In a comparison between visits, we observed that the concentrations of Acy, Ace, Flu, Ant, and BaP in visit3 were significantly higher than in other visits (p < 0.05). Since hair PAHs in visit3 covered up to a 4-month exposure period (DecMar), coinciding with the period of the highest average concentration of PM2.5 in Chiang Mai, this suggests that inhalation exposures to PM2.5-bound PAHs significantly contribute to hair PAHs. Consequently, PAHs in hair may serve as a practical biomarker for revealing long-term exposure to atmospheric PAHs in humans.


Additionally, lag analysis of the associations between hair PAHs and PM2.5 can enhance our understanding of the incorporation of PAHs into human hair through exposure to atmospheric PM2.5. We consistently observed an inverted Ushape in the changing patterns of most PAHs (Acy, Ace, Flu, Ant, Chr, and BaP) and total PAHs with a 1-unit increase in ambient PM2.5 from lag01 to lag06, with the highest estimate of cumulative effects consistently occurring at lag04 (4-month average exposure to PM2.5) (Figure 2). These results highlight the significant contribution of ambient PM2.5 exposure to the increase in PAH residues in human hair. Specifically, the most substantial increase in the accumulation of BaP, a known human carcinogen (IARC, 2023), was evident when exposed to heightened concentrations of PM2.5.


The urinary metabolite of BaP, 3-hydroxybenzo[a]pyrene (3-OHBaP), is considered a preferable biomarker for assessing human exposure to crucial PAHs like BaP. However, its detection in human urine is challenging due to its low concentrations (Lafontaine et al., 2006; Luo et al., 2019), and the measurement of urinary 3-OHBaP is complex. It is noteworthy that assessing hair BaP provides a valuable means to evaluate exposure to PAHs, including BaP. Furthermore, concentrations of PAHs in hair are likely to reflect exposures over an extended period, while urinary PAH metabolites indicate recent exposure events (Gong et al., 2015; Li et al., 2012). As a result, determining hair BaP levels offers valuable insights into long-term exposure scenarios, complementing the information obtained from measurements of urinary PAH metabolites.


According to the significant impact of PAH levels in a 4 cm length of hair by the average exposure over a 4-month period, our findings highlight the advantage of using hair as an ideal biological matrix to indicate internal exposure levels to ambient PM2.5, with the duration of exposure determined through the length of the hair (1 cm representing monthly exposure levels). Typically, the reported growth rates for human scalp hair range from 0.6 to 1.5 cm each month (Cooper, 2015), aligning with the average growth rate of 1 cm/month recommended by the Society of Hair Testing (SoHT) (Society of Hair Testing, 2004). However, further research is needed to obtain more reliable conclusions regarding the incorporation of PAHs in each cm of hair length.


Some limitations in our study need addressing. First, measurement errors in exposure were inevitable in this type of study. The exposure measurement error may underestimate the pollution effects. Individual exposure and the levels of PAHs in ambient air were not measured in this study; the exposure levels of PM2.5-bound PAHs were assigned using data on PM2.5 concentrations from air quality monitoring stations. The individual behaviors of participants for PM2.5 prevention were also not included in the study models. Second, detailed data regarding dietary patterns were not requested, and PAH concentrations in foods were not determined. However, we did assess dietary intake by using the intake frequency of PAH-contaminated foods in further analysis.



This study identified 10 different PAHs in hair samples. We found significant associations between PAH levels in human hair and long-term exposure to PM2.5. The results of the analysis imply that the duration of exposure can be determined by the length of hair. Specifically, it is suggested that each centimeter of hair length corresponds to an approximate exposure period of one month to PM2.5. Furthermore, the accumulation of BaP, a carcinogenic PAH, appeared to increase remarkably at heightened PM2.5 concentrations. These findings underscore the importance of hair analysis in assessing PAH exposure risks and highlight its potential to provide more reliable and comprehensive information for health risk assessments.



The authors would like to thank all students who participated in the study. We gratefully acknowledge the support provided by Navamindarajudis Phayap School and Horpra School during the data and sample collections.



Kessaya Radarit was responsible for designing and conducting the research, performing statistical analysis and data visualization, and authoring the manuscript. Tippawan Prapamontol supervised the research conduction and provided review and editing for the manuscript. Wan Wiriya, Chatree Chai-Adisaksopha and Somporn Chantara provided supervision. All authors have read and given their approval for the final version of the manuscript.



The authors declare no conflict of interest.



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OPEN access freely available online

Natural and Life Sciences Communications

Chiang Mai University, Thailand.



Kessaya Radarit1,2, Wan Wiriya3, Chatree Chai-Adisaksopha4, Somporn Chantara3, and Tippawan Prapamontol2, *


1 Program in Environmental Science, Environmental Science Research Center, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand

2 Research Institute for Health Sciences, Chiang Mai University, Chiang Mai 50200, Thailand

3 Environmental Science Research Center, Faculty of Science, Chiang Mai University, Chiang Mai 50200, Thailand

4 Division of Hematology, Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand.


Corresponding author: Tippawan  Prapamontol, E-mail:


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Editor: Waraporn Boonchieng,

Chiang Mai University, Thailand


Article history:

Received: October 19, 2023;

Revised: January 6, 2024;

Accepted: January 10, 2024;

Online First: January 17, 2024