Contents lists available at ScienceDirect Environmental Research journal homepage: www.elsevier.com/locate/envres Polycyclic aromatic hydrocarbons (PAHs) levels in urine samples collected in a subarctic region of the Northwest Territories, Canada. Mylene Ratellea,∗, Cheryl Khouryb, Bryan Adlardb, Brian Lairda a School of Public Health and Health Systems, University of Waterloo, Canada b Environmental Health Science and Research Bureau, Health Canada, Canada A R T I C L E I N F O Keywords: Polycyclic aromatic hydrocarbons (PAHs) Cotinine Smoking Biomonitoring Northwest territories A B S T R A C T Traditional food consumption for Indigenous peoples is associated with improved nutrition and health but can also pose potential risks via exposure to contaminants. Polycyclic aromatic hydrocarbons (PAHs) are compounds of interest due to their widespread presence (e.g., their metabolites are detected in up to 100% of the Canadian population) and their toxicological potential. To better understand the range of exposures faced by Indigenous populations in northern Canada and to address a contaminant of emerging concern identified by the Arctic Monitoring and Assessment Programme, a multi-year biomonitoring study investigated levels of PAH exposure in subarctic First Nations communities of the Northwest Territories, Canada. Secondary data analysis of banked samples from a subset of the cross-sectional study was done. PAHs and cotinine markers in the urine samples (n = 97) of participants from two regions from the Mackenzie Valley (Dehcho and Sahtú) was completed by liquid and gas chromatography coupled with mass spectrometry. Also, participants completed a 24-hr recall food survey. When compared according to age/sex categories, the GM of several biomarkers (1-hydroxypyrene, 1- naphthol, 2-hydroxyfluorene, 2-hydroxyphenanthrene, 2-naphthol, 3-hydroxyfluorene, 3-hydroxyphenanthrene, 4-hydroxyphenanthrene, 9-hydroxyfluorene, 9-hydroxyphenanthrene) appeared higher than observed for the general Canadian population. The PAHs levels observed were, however, below clinical levels associated with adverse health outcomes. Altogether, these elevated biomarkers are metabolites of pyrene, naphthalene, fluorene and phenanthrene. Statistically significant non-parametric associations were observed between several biomarkers and i) the consumption of cooked meat in the last 24 h; and, ii) smoking status (self-reported status and adjusted on urine cotinine level). This work is the first to report PAH levels in a northern Canadian po- pulation and provides local baseline data for monitoring the effects of changes to climate and lifestyle over time. These findings will support regional and territorial decision makers in identifying environmental health prio- rities. 1. Introduction Residents of northern regions face a range of environmental health issues, including potential elevated risks resulting from contaminants in traditional, locally harvested foods. For example, metals and persistent organic pollutants have been reported in water, soil, and food webs of remote regions of the Arctic and subarctic in Canada (Chételat et al., 2015; Corsolini and Sarà, 2017; Evans et al., 2005); some of these substances are known to be toxic. Traditional food consumption in In- digenous peoples is associated with improved nutrition and health (Chan et al., 2014; Council of Canadian Academies, 2014; Receveur et al., 1997). Reliance on traditional foods is passed through genera- tions; traditional foods include a variety of locally harvested plants, land animals, migratory and non-migratory birds, as well as fish. These foods can also pose risks via exposure to contaminants such as mercury and cadmium (Brown et al., 2016; Laird et al., 2013; Larter et al., 2018). Polycyclic aromatic hydrocarbons (PAHs) are compounds of interest for their widespread presence and their toxicological potential (Lawal and Fantke, 2017). Human populations can encounter health risks from PAHs via a variety of exposure sources and pathways, including: smoking cigarettes, food cooking methods, industrial processes, fossil fuels, forest fires, and contaminated waste sites (Abdel-Shafy and Mansour, 2016; Lawal and Fantke, 2017). Consequently, there is po- tentially variation in human PAH exposures across Canada. Although a major determinant of PAH exposure is occupation (e.g. roofing, paving, https://doi.org/10.1016/j.envres.2020.109112 Received 2 October 2019; Received in revised form 14 December 2019; Accepted 2 January 2020 ∗ Corresponding author. School of Public Health and Health Systems, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, Canada, N2L 3G1. E-mail addresses: mratelle@uwaterloo.ca (M. Ratelle), cheryl.khoury@canada.ca (C. Khoury), bryan.adlard@canada.ca (B. Adlard), brian.laird@canada.ca (B. Laird). Environmental Research 182 (2020) 109112 Available online 07 January 2020 0013-9351/ © 2020 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/). T http://www.sciencedirect.com/science/journal/00139351 https://www.elsevier.com/locate/envres https://doi.org/10.1016/j.envres.2020.109112 https://doi.org/10.1016/j.envres.2020.109112 mailto:mratelle@uwaterloo.ca mailto:cheryl.khoury@canada.ca mailto:bryan.adlard@canada.ca mailto:brian.laird@canada.ca https://doi.org/10.1016/j.envres.2020.109112 http://crossmark.crossref.org/dialog/?doi=10.1016/j.envres.2020.109112&domain=pdf firefighting, metal refining and smelting) (Keir et al., 2017; Mcclean et al., 2004; McClean et al., 2007), the main sources of exposure for most of the U.S. population are tobacco smoke, wood smoke, ambient air, and char-broiled or grilled foods (ATSDR, 1995). For example, using a wood stove or fireplace, using gasoline-powered equipment and smoking cigarettes were each associated with elevated exposure of PAHs (Egeghy et al., 2005). Consumption of some food (specifically meat and fish) prepared at high temperature (grilling, frying, roasting, baking, drying, smoking) was also reported to increase PAH exposure (Cheng et al., 2019; Zelinkova and Wenzl, 2015). Although animals studies show that PAH exposure can be reprotoxic and immunotoxic, the evidence in humans are not sufficient to assess these health hazards; several PAHs have been classified by the WHO (World Health Organi- zation) as carcinogenic to humans (e.g., benzo [a]pyrene), probably carcinogenic to humans (e.g., dibenz (a,h)anthracene), or possibly carcinogenic to humans (e.g., benz(a)anthracene, chrysene, and naph- thalene) to humans (ATSDR, 1995; International Agency for Research on Cancer, 2019). A multi-year contaminant biomonitoring study was conducted to investigate levels of contaminant exposure in participating subarctic First Nations communities. Overall, this project is using a risk-benefit approach to promote the use of country foods (locally-harvested foods) in order to improve nutrition and food security, while attempting to lessen contaminant exposure among Indigenous communities in the Northwest Territories (NWT). Similar to other biomonitoring projects in northern Canada, the project included biological sampling for chemical analysis as well as dietary surveys. In addition to the toxic metals and legacy POPs that have typically been measured in these projects, par- ticipating communities expressed an interest in a variety of other con- taminants of potential concern. These included those that may have resulted from recent severe forest fires and historical gold mining ac- tivities in the territory, as well as other industrial activities. As a set of contaminants related to some of these concerns, PAHs were added to the projects’ scope. To better understand the range of PAH exposures faced by Indigenous populations in the Northwest Territories, analysis of urinary PAH metabolites of participants from two regions from the Mackenzie Valley (Dehcho and Sahtú) was completed. Furthermore, since the project included the completion of questionnaires, including lifestyle questions in complement to a 24-hr food recall survey, we investigated the association of particular cooking methods (e.g., meats on open flame, or cooking at high temperatures) and smoking status on the PAHs levels (Alomirah et al., 2011; Lawal and Fantke, 2017; Zelinkova and Wenzl, 2015). This work provides: 1) levels of PAHs in this northern Canadian region and 2) insights on potential sources of PAHs exposure at the individual level. 2. Methods 2.1. Partnerships and preparation Consultation between the Waterloo-based researchers and the Dehcho and Sahtú First Nations regarding this work has been ongoing since November 2013. This consultation process has directly shaped several aspects of the work plan, including: selection of communities, local coordinator hiring plan, public consultation meetings, con- taminant selection, and participant inclusion criteria. Most importantly, these conversations have emphasized our common perspective that the proposed work must promote country food reliance within the Mackenzie Valley of the Northwest Territories. These consultations and the community-based approach were described in a previous publica- tion (Ratelle et al., 2018a). In summary, the cross-sectional project includes human hair, urine, and blood sampling in communities, la- boratory analysis of samples for chemicals of interest (e.g. metals, persistent organic pollutants, PAHs), the administration of two dietary surveys and a health message questionnaire on knowledge awareness and perception. Within the dietary surveys, the Food Frequency Ques- tionnaire (FFQ) is used to document all the country foods consumed over the last year while the 24-h recall is used to document all food eaten by participants in the previous 24 h. 2.2. Recruitment and consent form Funds were provided to the band office of each community for the hiring a Local Research Coordinator to assist with participant recruit- ment. We integrated a random selection approach to our approach by inviting all members of each selected household to participate. Walk-ins were also welcomed. In each community details about the project were disseminated through posters placed in public spaces throughout the community, local radio interviews and media appearances. The re- searchers sampled proportional to community population size, with a minimum target of 10% of the population per community. Overall, it was aimed for participants to represent the sex and age distribution of the combined population (i.e., children, young adults, elders); recruit- ment efforts were designed to ensure this representation in the main study. The implementation details of the biomonitoring project were pre- viously published elsewhere (Ratelle et al., 2018b). These steps helped promote the representativeness and generalizability of results. How- ever, we cannot state the representativeness of the secondary data analysis of the samples as these is no solid demographic data available from these communities. All members aged 6 years and older were eligible to participate regardless of sex, family status, parity or other characteristics. Before participating in the project, each participant was guided through an information letter by a research team member and was then invited to sign an informed consent form. For children to participate, the child indicated their assent and their parent/guardian provided consent. Participants were invited to opt-in to any or all of the six project components (i.e., the three questionnaires and three biological sam- ples). Each participant received a local store gift card regardless of whether they complete all or some of the project components. Additionally, participants could choose to enter into a raffle for a grand prize organised in each community. 2.3. Biological sampling The sample collection in the Dehcho was done in fall/winter of 2016 and 2017 (between mid-November and mid-February or each year) and in the Sahtú in winter 2017 (mid-February), which correspond to the cold season. Every participant had the choice to provide hair, blood and/or urine samples. For those who agreed to provide a spot urine sample, a research team member provided the participant a 120 ml- polypropylene container to half-fill on site. The collection was in- dependent of the fasting status of the participant. The urine was then aliquoted in 10 ml tubes and stored in a −20 °C portable freezer until it reaches the main facility at University of Waterloo where it was transferred in a−80 °C freezer. The PAH biomarkers in urine at−20 °C and −80 °C were reported to be stable (Gaudreau et al., 2016). Within the consent form, participants could opt to have their sam- ples stored in a biological sample bank (i.e., biobank). Aliquots of participants’ samples will be stored for up to 10 years so that con- taminant and nutrient biomarkers not included within the original suite of analytes examined can be quantified in the future. Samples from the biobank were used in this analysis to study levels of PAH metabolites and cotinine in urine. 2.4. Chemical analysis PAH metabolites (Table 1) and cotinine were measured in urine samples collected in 2016–2017 and analysed at the Centre de Tox- icology du Québec at the National Institute of Public Health of Québec M. Ratelle, et al. Environmental Research 182 (2020) 109112 2 (INSPQ). Cotinine was quantified by liquid chromatography (cotinine), coupled with tandem mass spectrometry. The samples for the PAHs quantification were hydrolysed and the PAHs were extracted by hexane extraction, prior to being analysed by gas chromatograph coupled with a tandem mass spectrometer (Gaudreau et al., 2016). Limits of detec- tion for PAHs metabolites were between 0.004 μg/L (e.g. 4-Hydro- xychrysene, 4-Hydroxyphenanthrene) and 0.01 μg/L (e.g. 3-Hydro- xybenzo(a)pyrene). Creatinine analysis was conducted using spectrophotometry at the Université de Montréal. Collectively, these approaches allowed for comparisons to results from other biomoni- toring programs undertaken in Canada, such as the First Nations Bio- monitoring Initiative (FNBI) and the Canadian Health Measures Survey (CHMS) (Assembly of First Nations (AFN), 2013; Health Canada , 2010; 2013b; 2015, 2017). 2.5. Food questionnaire Participants were asked in detail what they had eaten over the previous 24 h using a web-based survey of eating behaviors (Hanning et al., 2009). The survey used a multi-pass technique to sequentially ask about foods consumed by meal occasion and details (e.g., methods of preparation, portion sizes). The survey included a bank of approxi- mately 900 food and beverage options. The survey was previously va- lidated for school-aged children in First Nations communities in Hanning et al. (2009). For the current project, the survey was updated to integrate all country foods eaten in the region. Also, some language in the survey was adapted since adults were also participating. The survey took less than 20 min to complete. . For all the participants of this secondary analysis (n = 97), eating particular meats (e.g., bacon, roasted pork, steak, roasted moose, caribou) on the day before the study was categorized as yes (44.3%), no (10.3%) and missing data if the participant did not complete the food questionnaire (45.4%). 2.6. Statistical analysis The normality of the sum of the biomarkers for each of the parent compounds was assessed using a Kolmogorov-Smirnov test and found to be non-normal. The data were then transformed (log10(x)), again subjected to the Kolmogorov-Smirnov test, and were still found to follow a non-normal distribution (p < 0.001). Accordingly, non- parametric statistics were used with the untransformed data. For ex- ample, Mann-Whitney tests were used to assess the effect of smoking and the consumption of grilled meats on PAH biomarker levels. Dependent variables included: the sum of biomarkers for each parent compound (μg/g). The concentration not adjusted for creatinine (ug/L), can be found in the Supplement. Independent variables included smoking status (yes/no) and consumption of grilled meat in the pre- vious day (yes/no). Biomarker results found to be less than the limit of detection were imputed to be half the limit of detection. A covariate analysis was run to test the interactions between independent variables. Finally, after recoding for the smoking status based on the cut-off co- tinine levels, a McNemar test for related samples was done to observe the difference on the smoker/non-smoker groups before and after this adjustment. Because our sample size (n = 97) was quite small for ap- plying multiple linear regression integrating the co-variables, linear regression/stepwise model was deemed inappropriate. All data analy- tical statistics were performed in IBM® SPSS Statistics (version 20), while descriptive analysis were employed Microsoft® Office Excel. 2.7. Data management plan According to the data management principles outlined in the Community Research Agreements, the results from these chemical analyses were first returned to participating individuals and commu- nities. Following the completion of the study, anonymized and ag- gregate data will be made available to each participating community. Electronic copies of the data are kept on a password-protected com- puter in a locked room at the University of Waterloo. These data are confidential and anonymized, and no names will be kept within the dataset. The research team is available to answer questions and assist participating communities should they decide to use the data for pur- poses beyond the specific objectives outlined in the Community Research Agreements. 2.8. Research and ethics licences Ethics approval was obtained by the University of Waterloo Research Ethics Committee (#20173, #20950), the Stanton Territorial Health Authority for Human Research (December 29, 2015), and the Aurora Research Institute (#15560, #15775, #15966, #15977, #16021). Health Canada ethics approval was also obtained regarding additional analysis of the biobanked samples (REB, 2016–0022). 3. Results 3.1. Participation Between January 2016 and March 2018, nine communities parti- cipated in the biomonitoring project. The details of the full biomoni- toring study are found in Ratelle et al. (2018b); 2018c. Participation rates were between 12.5% and 40.0% for each participating community (population: 35 to 797), and participants included children, adults, and elders of both sexes. A total of 87% of participants opted to provide samples for the biobank. Analysis of PAHs included samples from six communities (Fig. 1) that participated in year 1–2 (2016–2017) of the study. In these com- munities, 97 biobanked samples were analysed for PAHs. The selection chart of participants for whom urine samples were tested for PAH is found in Fig. 2. Characteristics of these participants are presented in Table 2. A total of 41% of the participants self-reported smoking in the last 24 h. The age groups were not represented equally in this analysis. For example, more men than women provided urine samples for these PAH analyses (62% vs 38%, respectively). This was true for each age group (Fig. 3). Accordingly, the PAH results detailed here may not be re- presentative of community-level PAH exposures during the winter season of 2016 and 2017. Table 1 Polycyclic aromatic hydrocarbons (PAHs) and their metabolites analysed in urine. Parent compound Biomarkers Limit of Detection (μg/L) Benzo(a)anthracene 1-Hydroxybenz(a)anthracene 0.005 3-Hydroxybenz(a)anthracene 0.005 Benzo(a)pyrene 3-Hydroxybenzo(a)pyrene 0.01 Chrysene 2-Hydroxychrysene 0.006 3-Hydroxychrysene 0.005 4-Hydroxychrysene 0.004 6-Hydroxychrysene 0.006 Fluoranthene 3-Hydroxyfluoranthene 0.008 Fluorene 2-Hydroxyfluorene 0.005 3-Hydroxyfluorene 0.003 9-Hydroxyfluorene 0.009 Naphthalene 1-Naphthol 0.03 2-Naphthol 0.05 Phenanthrene 1-Hydroxyphenanthrene 0.004 2-Hydroxyphenanthrene 0.005 3-Hydroxyphenanthrene 0.002 4-Hydroxyphenanthrene 0.004 9-Hydroxyphenanthrene 0.005 Pyrene 1-Hydroxypyrene 0.003 M. Ratelle, et al. Environmental Research 182 (2020) 109112 3 3.2. PAHs levels The detection rate (%), the geometric mean and the 95th percentile amongst participants are presented in Tables 3 and 4, expressed in μg/g creatinine. The most common biomarkers detected in samples included metabolites of fluorene, naphthalene, phenanthrene and pyrene. In contrast, biomarkers of chrysene, benzo(a)anthracene, and benzo(a) pyrene fell below the detection limit in all samples. The main con- tributor to PAH exposure is naphthalene; 2-Naphthol contributed about 65% of the sum of all the PAH metabolite measures in the samples. Interestingly, for 10 of the 11 PAH biomarkers detected in samples, levels appeared higher among participants from the Dehcho than the northernmost Sahtú region, i.e., GM for Dehcho participants being 4–68% higher than observed in the Sahtú. (Ratelle et al., 2019). Fig. 1. Sample collection sites in Northwest Territories, Canada. Fig. 2. Criteria for inclusion for the PAH analysis from samples collected in 2016–2017. Table 2 Characterization of participants of year 1 and 2 (2016–2017) included within this secondary analysis(n = 97). Parameters Results Refusal to Respond (%) Age: Mean (range) 48.6 (7–83) 2.0 Sex Male 61.9% 0 Female 38.1% Smoking status (in the last 24 h) Smoker 41.2% 0 Not smoker 58.8% Alcohol consumption (in the last 24 h) Consumed 19.6% 0 Not consumed 78.4% Meat consumption (in the last 24 h) Consumed 44.3% 45.4 Not consumed 10.3% Fig. 3. Age and sex distribution within participants. M. Ratelle, et al. Environmental Research 182 (2020) 109112 4 However, the differences between regions were not statistically sig- nificant. 3.3. PAHs and consumption of meat Associations between potential sources of PAHs and biomarker re- sults were investigated. Eating particular meats (e.g., bacon, roasted pork, steak, roasted moose, caribou) on the day before the study was associated with higher levels of particular PAHs (Table 5). The results suggest a statistically significant association between the biomarkers of fluorene and phenanthrene and meat consumption in the previous day. Cotinine levels appeared higher among those that ate meat (e.g., GM: 39 μg/g of creatinine) than those who did not eat these meats (e.g., GM: 11 μg/g of creatinine); however, this difference was not statistically significant (p = 0.10). 3.4. PAHs and smoking status The association of PAH metabolite levels with smoking was also tested, and levels were significantly different between smokers and non- smokers for each of the parent compounds (Table 6). The smokers present twice the value of the non-smokers for all the reported parent compounds. The potential role(s) of covariables on the differences reported in Table 6 were then tested according to the non-parametric associations (Mann-Whitney) between variables (i.e., smoking status and age, sex, meat consumption). Of these, smoking status was only associated with age (p = 0.006). Therefore, the differences in PAH exposure docu- mented in Table 6 may also be a function of age differences among smokers and non-smokers. Specifically, smokers (mean: 42.7 years) were on average 10 years younger than non-smoking participants (mean: 52.9 years). Notably though, age and the sum of PAHs did not show any significant association (p = 0.34). 3.5. Cotinine and smoking status The GM and 95th percentile of urine cotinine of project participants who reported smoking in the previous 24 h were within the 95% CI of the respective statistics in the CHMS (Table 7). The smoking rate of project participants (41%, n = 40) is approximately twice that of participants of the CHMS (cycle 4, 2014–2015) similar to the national smoking rate of 18% (Statistics Canada, 2014). In this project, a small number of self-reported non-smokers had cotinine levels above 2000 μg/L, well above the threshold of 50 μg/L used to separate smoking/non smoking status (Wong et al., 2012). Based on the 50 μg/L threshold, 20% of participants would have a change in their status. While 40 participants reported smoking, 53 participants had cotinine levels higher than the threshold. Some par- ticipants with very low levels of cotinine reported smoking and others with cotinine levels> 50 μg/L reported not smoking in the last 24 h. The distribution change between the two groups was different (p = 0.004), with more individuals with high cotinine levels self-re- porting as non-smokers. Although this new classification appears to affect the GM of the PAH biomarkers (μg/L), it did not change much the PAH metabolite levels in smokers once adjusted for creatinine (μg/g). Levels of cotinine and PAHs by smoking status (corrected or not) are presented in Tables 6 and 7 The difference in the smoking/non smoking classification (i.e., according to self-reported status versus cotinine le- vels) did not alter the conclusions from the analysis of the association between smoking status and PAH metabolite levels. Regardless of smoking classification method, smoking status was significantly asso- ciated with PAH metabolite levels (Table 6). 3.6. Comparison of PAHs to a nationally representative survey To contextualize these findings with those from a nationally-re- presentative study, we compared the NWT participant results to those from the fourth cycle (2014–2015) of the CMHS, after stratifying ac- cording to sex/age categories (Table 8). This analysis showed several biomarkers levels higher than the reported 95% confidence intervals of their respective CHMS results. Altogether, the GM of at least one age category was above the CHMS for ten biomarkers. These included 1- naphthol, 1-hydroxypyrene, 2-naphthol, 2-hydroxyfluorene, 2-hydro- xyphenanthrene, 3-hydroxyfluorene, 3-hydroxyphenanthrene, 4-hy- droxyphenanthrene, 9-hydroxyfluorene, and 9-hydroxyphenanthrene. Concomitant exposure to parent compounds and association between biomarkers of the same PAH were expected. Overall, these results ap- pear to document elevated exposures to each of the parent compounds regularly detected in participants’ samples. These differences appeared particularly large for six biomarkers related to exposures from: naph- thalene (1-naphthol, 2-naphthol); fluorene (2-hydroxyfluorene, 3-hy- droxyfluorene); and phenanthrene (9-hydroxyphenanthrene). Notably, biomarkers were more likely to appear different from CHMS results after adjusting for creatinine content. Biomarkers were also most likely different from CHMS results for the 20–39 age category and least likely Table 3 Geometric mean (GM) and 5th, 50th and 95th percentile of the individual PAH metabolite levels in urine. PAH Biomarker in urine a (μg/g creatinine) Detection (%) GM P05th P50th P95th Fluoranthene 3-Hydroxyfluoranthene 2.1 NA 40% of samples were below the LOD. Table 7 Concentration (GM and 95th percentile) of cotinine quantified in urine according to smoking status. Cotinine in Urine (μg/g creatinine) Smoking status based on cotinine threshold of 50 μg/L Self-reported smoking status CHMSa Detection (%) GM P95th Detection (%) GM P95th GM P95th Smokersb 100 874 3170 100 618 3160 480 3300 Non smokersc 39 1.47 24.7 53 8.04 2150 NR NR All 72 48.2 2980 73 48.2 2980 NA NA NA. Not available. a From the Fourth Report on Human Biomonitoring of Environmental Chemicals in Canada for both sexes (Health Canada (HC), 2017). The CHMS did not report data (NR) for 95th percentile for non-smokers because the data was considered as too unreliable to report. The CHMS did not report data (NR) for GM if> 40% of samples were below the LOD. b CHMS used a category of 12–79 years for smokers, while we used 15–79 years. c CHMS used a category of 3–79 years for non smokers, while we use 6–83 years M. Ratelle, et al. Environmental Research 182 (2020) 109112 6 Table 8 Age- and Sex-Stratified concentration (GM) of urinary PAH metabolite levels detected in more than 40% of samples and comparisons with CHMS values. PAH Biomarkers Categorya This study CHMSb Concentration (μg/L) 3 Concentration (μg/g) 3 Concentration (μg/L) (CI95) Concentration (μg/g) (CI95) 1-Hydroxyphenanthrene Male 0.098 0.11 0.15 (0.13–0.18) 0.12 (0.10–0.14) Female 0.10 0.15 0.16 (0.15–0.18) 0.17 (0.15–0.19) Age 6-19 0.10 0.085 0.14 (0.13–0.16) 0.12 (0.11–0.14) Age 20-39 0.16 0.14 0.17 (0.15–0.20) 0.14 (0.12–0.17) Age 40-59 0.087 0.14 0.16 (0.13–0.18) 0.14 (0.12–0.16) Age 60-79 0.090 0.11 0.16 (0.14–0.19) 0.16 (0.14–0.18) 1-Hydroxypyrene Male 0.097 0.12c 0.10 (0.086–0.12) 0.082 (0.067–0.099) Female 0.074 0.11 0.090 (0.082–0.099) 0.094 (0.083–0.11) Age 6-19 0.13c 0.11c 0.1 (0.090–0.12) 0.089 (0.078–0.10) Age 20-39 0.16c 0.15c 0.12 (0.10–0.13) 0.096 (0.083–0.11) Age 40-59 0.078 0.13c 0.095 (0.082–0.11) 0.086 (0.075–0.099) Age 60-79 0.063 0.085c 0.072 (0.064–0.081) 0.070 (0.058–0.084) 1-Naphthol Male 1.4c 1.6c 1.0 (0.83–1.2) 0.79 (0.65–0.97) Female 1.1 1.6c 0.94 (0.73–1.2) 0.97 (0.74–1.3) Age 6-19 1.1c 0.94c 0.66 (0.58–0.75) 0.57 (0.52–0.64) Age 20-39 3.6c 3.3c 1.1 (0.85–1.3) 0.84 (0.66–1.1) Age 40-59 1.3 2.1c 1.1 (0.87–1.4) 1.0 (0.78–1.3) Age 60-79 0.70 0.88 1.1 (0.79–1.4) 1.0 (0.76–1.4) 2-Hydroxyfluorene Male 0.38c 0.43c 0.31 (0.25–0.37) 0.24 (0.20–0.30) Female 0.31c 0.44c 0.25 (0.22–0.29) 0.26 (0.23–0.30) Age 6-19 0.29c 0.24 0.21 (0.18–0.23) 0.23 (0.21–0.25) Age 20-39 0.89c 0.82c 0.34 (0.28–0.40) 0.27 (0.22–0.33) Age 40-59 0.35 0.57c 0.31 (0.26–0.36) 0.28 (0.24–0.33) Age 60-79 0.20 0.25 0.23 (0.20–0.26) 0.22 (0.19–0.26) 2-Hydroxyphenanthrene Male 0.054 0.062c 0.066 (0.059–0.074) 0.052 (0.045–0.059) Female 0.055 0.079c 0.057 (0.051–0.064) 0.059 (0.054–0.064) Age 6-19 0.048 0.040 0.052 (0.047–0.057) 0.045 (0.041–0.050) Age 20-39 0.089c 0.081c 0.072 (0.061–0.084) 0.057 (0.050–0.066) Age 40-59 0.051 0.082c 0.063 (0.055–0.074) 0.058 (0.050–0.067) Age 60-79 0.046 0.058 0.060 (0.054–0.067) 0.058 (0.051–0.067) 2-Naphthol Male 5.7c 6.6c 4.5 (4.0–5.0) 3.5 (3.0–4.0) Female 6.1c 8.8c 4.7 (3.9–5.6) 4.8 (4.2–5.5) Age 6-19 8.5c 7.2c 4.3 (3.9–4.8) 3.7 (3.4–4.0) Age 20-39 11c 10c 5.6 (4.5–6.9) 4.5 (3.9–5.2) Age 40-59 5.5c 8.8c 4.9 (4.5–5.4) 4.5 (4.1–4.9) Age 60-79 3.8 4.8c 3.3 (2.7–4.1) 3.2 (2.8–3.8) 3-Hydroxyfluorene Male 0.16c 0.18c 0.12 (0.091–0.15) 0.091 (0.070–0.12) Female 0.10 0.15c 0.092 (0.077–0.11) 0.095 (0.080–0.11) Age 6-19 0.11c 0.095c 0.087 (0.078–0.098) 0.075 (0.068–0.084) Age 20-39 0.35c 0.32c 0.12 (0.099–0.15) 0.097 (0.075–0.13) Age 40-59 0.15 0.24c 0.12 (0.10–0.15) 0.11 (0.090–0.14) Age 60-79 0.066 0.084 0.075 (0.065–0.087) 0.073 (0.061–0.086) 3-Hydroxyphenanthrene Male 0.072 0.083 0.097 (0.085–0.11) 0.076 (0.065–0.088) Female 0.062 0.089 0.081 (0.071–0.093) 0.084 (0.074–0.095) Age 6-19 0.075 0.061 0.090 (0.081–0.099) 0.077 (0.071–0.85) Age 20-39 0.13c 0.12c 0.095 (0.081–0.11) 0.076 (0.064–0.091) Age 40-59 0.062 0.10c 0.088 (0.074–0.10) 0.080 (0.068–0.094) Age 60-79 0.050 0.063 0.084 (0.075–0.093) 0.081 (0.072–0.091) 4-Hydroxyphenanthrene Male 0.021 0.024c 0.024 (0.020–0.028) 0.019 (0.016–0.022) Female 0.022 0.031c 0.022 (0.019–0.026) 0.023 (0.020–0.026) Age 6-19 0.019 0.016 0.019 (0.017–0.021) 0.017 (0.015–0.018) Age 20-39 0.045c 0.041c 0.027 (0.022–0.033) 0.021 (0.018–0.026) Age 40-59 0.018 0.029c 0.023 (0.020–0.027) 0.021 (0.018–0.024) Age 60-79 0.016 0.020 0.023 (0.020–0.028) 0.023 (0.019–0.027) 9-Hydroxyfluorene Male 0.19 0.22c 0.16 (0.13–0.19) 0.12 (0.10–0.15) Female 0.22c 0.31c 0.15 (0.13–0.17) 0.15 (0.13–0.17) Age 6-19 0.12 0.098 0.11 (0.098–0.13) 0.096 (0.085–0.11) Age 20-39 0.36c 0.33c 0.19 (0.15–0.23) 0.15 (0.12–0.18) Age 40-59 0.19 0.30c 0.16 (0.13–0.19) 0.14 (0.12–0.17) Age 60-79 0.18 0.23c 0.16 (0.14–0.18) 0.15 (0.13–0.18) 9-Hydroxyphenanthrene Male 0.050 0.058c 0.046 (0.039–0.055) 0.036 (0.031–0.042) Female 0.044 0.063c 0.045 (0.039–0.051) 0.046 (0.040–0.054) Age 6-19 0.026 0.022 0.030 (0.028–0.031) 0.025 (0.024–0.027) Age 20-39 0.095c 0.087c 0.048 (0.039–0.059) 0.039 (0.031–0.049) Age 40-59 0.045 0.072c 0.050 (0.041–0.061) 0.046 (0.039–0.055) Age 60-79 0.039 0.050 0.054 (0.046–0.065) 0.053 (0.045–0.061) NR. Not reported (data too unreliable to be published). a Sample size: Male n = 60, Female n = 37, Age 6–19 n = 9, Age 20–39 n = 18, Age 40–59 n = 37, Age 60–79 n = 30, other or unknown n = 3. Note: male and female are 3–79 years. b From the Fourth Report on Human Biomonitoring of Environmental Chemicals in Canada (Health Canada (HC), 2017). c Higher than the CI95th of the GM in the CHMS. M. Ratelle, et al. Environmental Research 182 (2020) 109112 7 (CHMS). The CHMS is an on-going survey, collected in two-year in- tervals to obtain information on the general health of Canadians. This is done through the collection of questionnaire data and direct health measures, including urine and blood samples. While it is not re- presentative of the individuals living in the north, it can be used to compare results from the north to the rest of the country. It is important to remember that such comparisons provide indications of relative ex- posures and do not provide information on health risks. Regardless of whether the contaminants currently have established guidelines, results from biomonitoring studies may provide a useful point of comparison for future projects, and enable community-based researchers to monitor changes in levels over time. Biases in the dataset are possible. The recruitment process involved both participatory and random selection, and coupled with the gap on information about demographics structure in each of the community, the participants might not represent the local population. The sample size is also limiting for more in-depth statistical analyses. In addition, incomplete dietary data decreased the paired food/biomarkers data we obtained, undermining the statistical power of the associations. Although we compared our results to those of the CHMS, there are limitations in this comparison. The average age of participants in the fourth cycle of the CHMS, was approximately 26 years, whereas our participants tended to be older, with an average age of approximately 50 years. Within each age category, there were more males than fe- males in our study. In the CHMS we observe that sex and age categories show different levels (Health Canada , 2017) and age and sex might have an impact on levels of PAHs biomarkers. However, the small sample size of our study (60 males and 37 females) suggests that any comparisons be done with caution. Our sample size had an unbalanced sex/age distribution (e.g., compared to the national rates and the CHMS). When data are compared by sub-categories, the findings show that several PAHs biomarkers (10 of the 11 having at least a 50% of detection rate) are above the results reported from the CHMS. These differences between the project categories and the CHMS categories might be partially explained by the small size in the stratified groups. While the sample numbers within these sub-groups are too small to allow for rigorous statistical comparison, the majority of the categories in the study appear to show higher exposure levels of PAHs than in the general Canadian population. Elevated levels of PAHs in younger groups may be of interest because of the endocrine disrupting potential associated with PAH exposure (Kelishadi et al., 2018; Wierzba et al., 2018), and their suspected potential to alter reproductive hormones (Yang et al., 2017). Biomarkers of chrysene, benzo(a)anthracene and benzo(a)pyrene were not detected in any samples. The low detection rate of these compounds in our samples is similar to what was obtained through the Canadian Health Measures Survey (0–4%). Authors reported that con- centration of these metabolites in spiked urine samples declined be- tween 50 and 90% after 13 days in storage at −20 ֯֯C. (Motorykin et al., 2015). The findings show that smoking is the primary behavior con- tributing to levels PAHs, followed by the consumption of cooked meat. However, other factors might explain these elevated levels, which were not documented in this study. The frequent use of snowmobile was observed on site and can contribute to PAHs emitted into the en- vironment (McDaniel and Zielinska, 2015). Indoor air quality can also affect PAH exposure (i.e. naphthalene) (Health Canada , 2013a). In addition, these elevated levels might also be associated with the re- liance on oil heating systems and wood stoves (Alkurdi et al., 2004; Tissari et al., 2007) with rates of use much higher within these com- munities than other regions of Canada (Government of the Northwest Territories (GNWT), 2012; Statistics Canada, 2012). Based on our current conclusion that smoking is the main exposure determinant in our population, PAH exposure could be managed by efforts to promote healthy living and the cessation of smoking. Implementing a targeted intervention on the group having the highest rate of smoking and the highest PAH biomarker levels (e.g., males aged 20–39 years of age), might help to mitigate the risk related to PAHs exposure. In comparison to the low molecular weight PAHs (e.g. naphthalene, fluorene, phenanthrene), which are associated with mainly acute toxic effects, the high molecular weight molecules are mainly strongly car- cinogenic (Stogiannidis and Laane, 2015). The most elevated PAH parent compounds in this project are naphthalene, classified by the International Agency for Research on Cancer (IARC) in group 2B (possibly carcinogenic to humans), and fluorene, phenanthrene and pyrene, classified in Group 3 (not classifiable as to its carcinogenicity to humans) (International Agency for Research on Cancer, 2019). The wide exposure (detection of up to 100% in the samples) makes PAHs a priority to monitor but does not mean that the exposure poses a health risk. Given the nature of the study design and the toxicokinetics of PAHs, it is not possible to assess whether the observed results are re- presentative of participant's long-term exposure levels. Additional ex- posure monitoring over time and the inclusion of biomarkers of effect are necessary to fully understand the health implications of the findings reported herein. PAHs travel through a long-range transport cycle; however, local sources can be very important contributors to individual exposure (Shen et al., 2014). The Sahtù communities (n = 32) in our study are located 700 km north of the Dehcho communities (n = 65). Despite potential differences in the environment, no significant differences in the PAH levels were observed between participants within these re- gions. This may suggest that the urinary PAH levels measured in this study are not associated with specific local sources (e.g. mines, diesel generating power plants). However, further work on geospatial analysis is required to confirm the impact of local sources. The elevated metabolites measured in this study are breakdown compounds from pyrene, naphthalene, fluorene and phenanthrene. The GM of the sum of the metabolites measured in the study can be com- pared to other publications to shed light on the type of exposure sources. The current GM is similar to what was found in rural China, but is half of the level measured in an e-waste area (Lu et al., 2016). In addition, the Chinese study reported that the contribution of 1-naphthol and 2-naphthol accounted for 70% of the sum of PAH biomarkers in the rural area, while it was 89% in the urban area, suggesting a pre- dominantly petrogenic PAH exposure through gasoline burning and vehicle traffic (Lu et al., 2016). In this current project, 1-naphthol and 2-naphthol account for 79% of the sum of all biomarkers of PAHs. It should be mentioned that naphthalene is not the only parent compound generating 1-naphthol, it can also originated from the carbaryl in- secticide (Meeker et al., 2007; Wheeler et al., 2014). Biomarker levels of phenanthrene are below what was measured in workers and in the people living in industrial areas (Angerer et al., 1997; Gündel et al., 1996). Climate change is one of the biggest challenges for northern regions. While forest fires and anthropogenic activities emitting PAHs are ex- pected to increase in the future, higher environmental temperatures may also accelerate the degradation of several compounds (Friedman et al., 2014). Although forest fires have been particularly active in the Dehcho region (relative to the Sahtú) (Environment and Natural Table 9 Smoking rates (%) comparison between the current project and CHMS cycle 4 (2014–2015). Project CHMS Male 77.9 22.4 Female 32.4 17.4 Age 12–19 33.3 6.9 Age 20–39 65.2 27.3 Age 40–59 40.0 21.2 Age 60–79 19.4 12.8 M. Ratelle, et al. Environmental Research 182 (2020) 109112 8 Resources (ENR), 2018), no significant differences in PAH levels were observed between study participants in the two regions. However, the climate change impact on PAHs exposure for subarctic and Arctic re- gions remains uncertain and involves numerous variables. As such, additional investigations of PAHs exposures and sources in these re- gions may be warranted. 5. Conclusion Overall, this project increases the knowledge on exposure to PAHs in the Northwest Territories. This study provides a dataset for the characterization of PAH exposure in this region. The levels observed were generally higher than the levels seen in the general Canadian population. Due to the wide exposure (high rate of detection), it is important to further explore the differences between northern and southern exposure levels and investigate other sources of exposure which were not part of this project (e.g., snowmobile use, wood stoves). In our study, PAHs exposure was associated with smoking, and to a lesser extent cooked meat. Cotinine levels support the classification between smokers and non-smokers, and help the interpretation of PAH levels, which will be useful to investigations other sources of exposure. The research team will continue to work with community partners and government delegates to assess contaminants issues and to provide re- sults for decision makers. Ethics approval and consent to participate Participants provided a free consent. Ethics approval was obtained by the University of Waterloo Research Ethics Committee (#20173, #20950), the Stanton Territorial Health Authority for Human Research (December 29, 2015), and the Aurora Research Institute (#15560, #15775, #15966, #15977, #16021). Health Canada ethics approval was also obtained regarding additional analysis of the biobanked samples (REB, 2016–0022). Funding Funding for this work was provided by the Northern Contaminants Program (NCP), a program of Crown-Indigenous Relations and Northern Affairs Canada and covered the partnership establishment, the collect of the samples and salaries. Additional support was received from Global Water Futures (GWF), Northern Scientific Training Program (NSTP), and the University of Waterloo. Supplemental ana- lyses of biobanked samples for additional contaminants including PAHs were funded by Health Canada. Acknowledgements The authors acknowledge the funding provided by the Northern Contaminants Program (NCP), which is a program of Crown-Indigenous Relations and Northern Affairs Canada, Global Water Futures (GWF), Northern Scientific Training Program (NSTP), and the University of Waterloo. PAHs analysis were funded by Health Canada. The research team is grateful for assistance from the following organizations: The Government of Northwest Territories Department of Health and Social Services; the Dehcho Aboriginal Aquatic Resources and Ocean Management (AAROM); the Dehcho First Nations (DFN), the Sahtú Renewable Resources Board (SRRB); the Sahtú Secretariat Incorporated (SSI); the Northwest Territories Regional Contaminants Committee (NT RCC); the Sahtú Health and Social Service Authority (SHSSA); the Dehcho Health and Social Service Authority (DHSSA); the Hay River Health and Social Service Authority (HRHSSA); the Centre de Toxicologie du Québec (CTQ); the Institut National de Santé Publique du Québec (INSPQ); the Natural Sciences and Engineering Research Council of Canada (NSERC), and the University of Waterloo. This work represents an ongoing collaboration between researchers at the University of Waterloo (Brian Laird, Heidi Swanson, Mylène Ratelle, Kelly Skinner, Rhona Hanning, Shannon Majowicz, Ken D. Stark), Trent University (Chris Furgal), University of Montréal (Michèle Bouchard), the Washington State University (Amanda Boyd), the Dehcho Aboriginal Aquatic Resources and Ocean Management (George Low), and the Sahtú Renewable Resources Board (Deborah Simmons). We would like to thank all community leaders, participants and local co- ordinators in the Dehcho and Sahtú Region for making this work pos- sible. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.envres.2020.109112. References Abdel-Shafy, H.I., Mansour, M.S.M., 2016. A review on polycyclic aromatic hydro- carbons: source, environmental impact, effect on human health and remediation. Egyptian J. Petroleum 25 (1), 107–123. Agency for Toxic Substances and Disease Registery (ATSDR), 1995. 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Introduction Methods Partnerships and preparation Recruitment and consent form Biological sampling Chemical analysis Food questionnaire Statistical analysis Data management plan Research and ethics licences Results Participation PAHs levels PAHs and consumption of meat PAHs and smoking status Cotinine and smoking status Comparison of PAHs to a nationally representative survey Discussion Conclusion Ethics approval and consent to participate Funding Acknowledgements Supplementary data References