Arsenic levels in the hair of people exposed to arsenic and awareness of its risk factors

Arsenic levels in the hair of people exposed to arsenic and awareness of its risk factors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Arsenic levels in the hair of people exposed to arsenic and awareness of its risk factors Xiangping Chen, Siyu Liu, Manman Shi, Yan Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4209156/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Arsenic is widely found in nature, and because of its carcinogenic properties, it has come to be a serious threat to human health. The effects of arsenic on the human body are influenced by a variety of factors, including the level of arsenic in external environmental pollutants and individual human factors. Therefore, the aim of this study was to assess the level of arsenic in populations living in arsenic-contaminated areas and the influencing factors. Environmental media samples (water and wheat) and biological samples (hair) were selected for arsenic analysis in both arsenic-contaminated and arsenic-safe areas. Socio-demographic information and behavioral characteristics information were obtained from questionnaires to analyze factors that cause an increase in arsenic levels in the body. In study area, 89.33% of the water samples exceeded the national standard (10 μg/L) and 2.13% of the wheat samples had arsenic concentrations above the safe limit (0.5 mg/kg). In contrast, arsenic levels in drinking water and wheat in the control area were within safe limits. A presence of 29 (29.29%) respondents with levels of arsenic in hair higher than 1 mg/kg was found in arsenic-contaminated areas. The results of the analysis showed a significant difference (P<0.05) in the level of arsenic in the hair of the inhabitants of arsenic-contaminated areas and those of arsenic-safe areas, with concentrations of 0.967 mg/kg and 0.392 mg/kg, respectively. Univariate comparative analysis of factors affecting body arsenic levels showed correlations between sex, age, years of residence, smoking, disease history, wheat-based food intake, and levels of arsenic in hair. Multiple linear regression analysis identified gender, age, and wheat-based food intake as risk factors for increased arsenic levels. The study of factors influencing the level of arsenic in the body can provide a scientific basis for the precise prevention and control of health problems resulting from environmental pollution. arsenic hair environmental exposure risk factors Figures Figure 1 Figure 2 1. Introduction Arsenic is a metalloid element that is widely found in the natural environment. However, natural and anthropogenic factors have caused arsenic to exceed the environmental load, and approximately 200 million people worldwide are exposed to the threat of arsenic (Chen and Costa, 2021). There is arsenic-rich drinking water due to geological structures in areas such as Bangladesh and China, and elevated levels of arsenic in water sources due to mining or other industrial activities in areas such as Japan and Thailand (Huang et al., 2015). China is the largest producer of arsenic trioxide and other arsenic-containing compounds, with its global share accounting for about 50% in 2005, mainly due to geological factors and anthropogenic pollution (Shi et al., 2017). Chronic exposure to arsenic in the environment can cause people serious health problems. At an early period, arsenic poisoning was assessed in the Kuitun area of Xinjiang, China, followed by Inner Mongolia and Shanxi, where the most serious arsenic poisoning occurred (Sanjrani et al., 2019). By 2012, it was estimated that there were endemic arsenic areas in 45 counties and districts in 9 provinces in China (He and Charlet, 2013). The ways in which arsenic transmits disease in China include via long-term intake of drinking water contaminated with high levels of arsenic and the breathing of air polluted by combustion of coal with high levels of arsenic (mainly in Guizhou and Shaanxi) (He et al., 2020; Wang et al., 2019). Models for ground-source arsenic contamination in China predict that approximately 19.6 million people are at risk from the consumption of arsenic-contaminated groundwater (Rodriguez-Lado et al., 2013). Arsenic in groundwater in China is mainly caused by geological and human factors. According to statistics, as of 2006, more than 5% of drinking water wells in 16 provinces in China had arsenic concentrations greater than 50mg/L (Sun et al., 2011). In coal-powered areas, the sources of exposure are mainly air contaminated by coal being burned in unventilated rooms and food being contaminated by being placed on coal stoves (Yao et al., 2021). A related report in 2003 indicated that the population of Guizhou and Shaanxi provinces in areas contaminated with arsenic due to coal combustion was contaminated to be 330,000,of which about 40,000 suffered from arsenic poisoning due to inhalation of contaminated air and consumption of contaminated food crops (Kang et al., 2011). Since the large-scale epidemic of arsenic poisoning in China, China has been carrying out treatment work to improve the environment in arsenic-contaminated areas and to control the discharge of arsenic sources. However, the carcinogenicity and persistence of arsenic has long-term effects on human health and increases the risk of disease in humans even at low levels of arsenic exposure (Rahaman et al., 2021). DALYs (disability-adjusted life years) were used to assess cancer risk for low levels of arsenic in drinking water (mean of 8.23 μg/L) and found that the mean loss of DALYs for study individuals was 3.35×10 -5 per person-year (ppy), which was 33.5 times the reference value (1.00×10 -6 ppy), with those aged 60-65 years having the highest mean loss of DALYs (Zhang et al., 2020). Normally, arsenic undergoes metabolic transformation in the human body and is released in a less toxic form (Rasheed et al., 2016). When humans are exposed to high arsenic levels for a long period of time, their intake of arsenic is greater than their excretion of arsenic, which results in the accumulation of arsenic in various parts of the body. The body parts of exposed individuals can be used as biomarkers of levels of arsenic toxicity. At present, to confirm exposure to arsenic, many studies use hair, nails, blood, and urine as primary biomarkers of arsenic in humans (Bommarito et al., 2019; Liu et al., 2017; Rehman et al., 2019). Analysis of biomarkers of arsenic levels can reveal arsenic sources. In the Cambodian report, arsenic levels in groundwater as a source of drinking water were positively correlated with arsenic concentrations in the nails (r=0.74, P <0.0001) and hair (r=0.86, P <0.0001) of exposed individuals (Gault et al., 2008). Studies on the association of water and staple foods with arsenic in humans found that arsenic had a strong and significant relationship with arsenic concentrations in urine and toenail samples, while arsenic in staple food (rice and wheat) showed a strong relationship with arsenic concentrations in hair (Rasheed et al., 2019). Moreover, the study found arsenic in rice and vegetables in arsenic endemic areas to be significantly correlated with arsenic in the hair of the population ( P ≤0.01) (Rokonuzzaman et al., 2022). Hair can be considered as a metabolic end product that provides a more permanent record of trace elements in the body (Katz, 2019). Moreover, because hair is easy to collect and preserve and because of the significant levels of arsenic accumulated in hair, hair is considered an ideal material for assessing levels of arsenic in humans. The correlation between concentrations of arsenic in groundwater and nails in the study (r=0.26, P <0.01) was lower than that between concentrations of arsenic in groundwater and human hair (r=0.52, P <0.01), an observation that could be explained by the local habit of cutting nails regularly. This suggests that levels of arsenic in hair are a better reflector of long-term arsenic exposure (Nguyen et al., 2018). In this study, we collected the hair of individuals living in arsenic-exposed and arsenic-safe areas and also obtained information on the demographic and behavioral characteristics of the study subjects. The aim was to evaluate the arsenic exposure levels of the individuals being compared and to analyze and determine important influencing factors that lead to elevated levels of arsenic exposure in humans. This aim was achieved by developing an understanding of the key factors behind levels of arsenic in the human body, analyzing the relationship between the influence of the external environment and internal factors on these arsenic levels, highlighting health problems caused by environmental arsenic pollution, and providing a scientific basis for local health intervention measures. 2. Methods 2.1 Study population This study was a cross-sectional investigation of arsenic concentrations in the hair of people living in rural areas. The study area was selected from the central Guanzhong Plain (Rural Y), China, where previous studies by our research team have found violations of environmental arsenic standards. Areas with similar demographic characteristics to the study area and where arsenic in the environment met the criteria (Rural D) were selected as control areas. A stratified whole-group sampling study was used to select different areas in rural Y and rural D regions based on the range of ambient arsenic concentrations for the population survey study. Residents aged ≥18 years old with ≥3 years of residence in the local area were selected as respondents for the questionnaire survey and the hair sample collection. Ninety-nine subjects were included in rural Y and forty-one subjects were included in the survey in rural D. Ethical approval for this study was obtained from the Medical Ethics Committee of the Department of Medicine, Xi'an Jiaotong University (approval number 2022-1643). In the study, the right to information was ensured by voluntarily signing of an informed consent form, and the privacy of the subjects was fully protected. 2.2 Assessment of arsenic exposure Residents' arsenic exposure levels were assessed through household water and food. Household water was provided via the end of the indoor pipe networks of the residents' homes, and food was provided in the form of the residents' home-grown wheat. Before collecting the drinking water samples, the tap bein used was opened to flow for 3 minutes before using pre-cleaned sampling bottles (1% nitric acid-soaked polyethylene bottles) to collect the water, recording sampling information, and sealing and storing the samples. The mature wheat was selected and collected into clean, self-sealing bags and stored in a dry environment. In the laboratory, the water samples were filtered with acid to ensure that the pH of the measured sample solution was <2 and stored in a constant temperature chamber at 4℃. At the same time, the wheat samples were washed and dried and ground to a powder passing through a 200 mesh specimen sieve. Next, 0.5 g of the sample powder was added to 5 mL of nitric acid and soaked overnight, then heated for full acid digestion, before being filtered and processed, and stored at 4℃. The concentration of arsenic in the samples was analyzed by ICP-MS within 7 days of collecting the samples. 2.3 Hair samples: collection and analysis When the hair was dry, the hair near the back of the scalp was collected with clean scissors, and about 300 hairs of about 3-5 cm in length were taken and placed in a clean polyethylene self-sealing bag. If the hair was too long, the tip part of the hair was discarded. Samples of hair that had been dyed or undergone a perm were discarded. The hair samples were sent to the laboratory and stored in a dry environment at room temperature. The hair was pretreated using cleaning method recommended by the IAIEA (water and acetone) and dried to a certain weight after removing exogenous contamination from the hair. The 20 mg of hair was weighed and placed in a 15 mL test tube, and 0.8 mL of HNO 3 solution and 0.2 mL of hydrogen peroxide were added to the test tube and the test tube was covered and sealed overnight, and then placed in an electric hot plate digestion oven for heat treating. The digestion oven was set at 90℃ and heated for 3 h. After digestion, the samples were cooled naturally, and the arsenic content was determined by ICP-MS. 2.4 Questionnaire Each respondent was required to complete a questionnaire that included age, gender and other demographic information, disease history, alcohol and cigarette consumption, and behavioral characteristics. In line with the purpose of the study, the behavioral characteristics investigated concerned the population's water intake, wheat product intake, and bathing habits. In the questionnaire on intake, participants were asked to record the source of water and the frequency of water intake and amount per intake; in the questionnaire on bathing behavior, the frequency of bathing and the duration of each session were recorded. 2.5 Statistical analysis SPSS 26.0 was used to organize and analyze the data. The mean, median, standard deviation, and percentile were used to statistically describe the distribution of arsenic concentrations in different media. The Kolmogorov-Sminov test (K-S test) was used to determine whether the distribution of arsenic substance concentrations followed a normal distribution. Parametric tests (t-test or ANOVA) were used to analyze whether there were differences in the concentration values of arsenic substances if they followed a normal distribution; non-parametric tests (rank sum test) were used to analyze whether there were differences in the concentration values of arsenic substances if they did not follow a normal distribution. Data for the count data were tested by the chi-squared test, and differences were considered statistically significant at P <0.05. The Kruskal-Wallis H test was used for the analysis of different influencing factors concerning arsenic content in the hair of the population, and Kruskal-Wallis one-way ANOVA (k samples) multiple comparisons were used for two-by-two comparisons. The correlation between arsenic content in hair and related influencing factors was analyzed by multiple linear regression, and a significant correlation was considered at P <0.01. 3. Results 3.1 Arsenic exposure level In the monitoring of arsenic levels in different environmental media, 150 water samples were tested in rural Y and 15 in rural D; 87 wheat samples were tested in rural Y and 10 in rural D. The mean values of arsenic in daily drinking water and cereals consumed by residents of rural Y were 29.979 μg/L and 0.237 mg/kg, respectively, while the mean values of arsenic in daily drinking water and cereals consumed by residents of rural D were 0.442 μg/L and 0.104 mg/kg, respectively. The arsenic content of drinking water in the rural Y samples significantly exceeded the standard limit value (10 μg/L), and the arsenic content of wheat was predominately within the standard limit value (0.5 mg/kg); the arsenic content in both types of samples in rural D was within the standard limit value. The distribution of arsenic levels in drinking water and wheat in rural Y and rural D conformed to a normal distribution, as shown in Figure 1. The results from the t-test showed (Table 1) that the differences in arsenic levels in water and wheat between rural Y and rural D were statistically significant ( P <0.001). Table 1. Arsenic concentration levels in different environmental media Sample Group Mean±SD T test t P Water Rural Y 29.979±15.752 μg/L 7.242 <0.001 Rural D 0.442±0.331 μg/L Wheat Rural Y 0.237±0.162 mg/kg 2.532 0.014 Rural D 0.104±0.073 mg/kg 3.2 Total arsenic in hair A total of 99 hair samples from rural Y and 41 hair samples from rural D were tested in this study. From the test results, the average concentration of arsenic in the hair of the population in rural Y was 0.967 ± 0.878 mg/kg, ranging from 0.165 mg/kg to 4.463 mg/kg; the mean concentration of arsenic in the hair of the rural D population was 0.392 ± 0.215 mg/kg, ranging from 0.147 mg/kg to 0.977 mg/kg. For the concentration of arsenic in human hair investigated in 11 cities in China, the average concentration of arsenic was found to be 0.23 mg/kg (Zhou et al., 2016). In rural Y, 29.29% of the population had arsenic levels in hair above 1 mg/kg, while the population in rural D had arsenic levels in hair predominately within the range of 1 mg/kg, as shown in Figure 2. Based on the K-S test for arsenic levels in hair, it was found that the distribution of arsenic levels in hair between rural Y and rural D did not conform to a normal distribution ( P <0.05). The rank sum test for arsenic in hair (Table 2) revealed statistically significant differences in the levels of arsenic in the hair of the residents between rural Y and rural D ( P <0.001). Table 2 Levels of arsenic concentration in hair Group M(P25, P75) K-S test Wilcoxon's rank sum test Z P Rural Y 0.633（0.437,1.100） P <0.001 -5.335 < 0.001 Rural D 0.354（0.245, 0.483） P =0.006 3.3 Demographics The demographic information of the participants is shown in Table 3. A total of 140 study subjects were selected for this survey; 99 study subjects were included in rural Y; 41 study subjects were included in rural D. The mean age of the survey population in rural Y was 58.86 ± 13.39 years. The study area is a rural area, so the population living there is predominantly middle-aged and elderly, and the majority of the survey respondents are female, with 73.74% of the study area being female. Most of the local residents have lived in the area for a long time since birth or settled in the area due to marriage, and there are a few cases of middle-aged and elderly people moving out of the area. The weight of the residents in rural Y was predominately in the range of 50-70 kg, which is a normal weight standard for middle-aged and elderly people. The subjects in rural Y had a minority of chronic diseases, such as hypertension and hyperglycemia themselves. The results of the chi-squared test (Table S1) reveal no statistically significant differences ( P >0.05) between the sex, age, and weight of the residents of rural Y and rural D. Table 3 Demographics and concentration of arsenic in hair Variables n（%） M Min Max H P Gender 10.066 0.002 Female 73（73.74） 0.604 0.170 3.470 Male 26（26.26） 0.956 0.360 4.460 Age 16.272 0.006 20~30 3（3.03） 0.261 0.240 0.600 30~40 8（8.08） 0.522 0.250 2.880 40~50 14（14.14） 0.494 0.170 1.200 50~60 29（29.29） 0.571 0.200 1.750 60~70 25（25.25） 0.788 0.230 4.140 70~ 20（18.18） 0.950 0.240 4.460 Residence time 8.544 0.036 ~20 14（14.14） 0.533 0.237 1.201 20~40 34（34.34） 0.574 0.165 3.467 40~60 23（23.23） 0.788 0.196 2.914 60~ 28（28.28） 1.197 0.233 4.463 Wight（kg） 6.457 0.264 30~40 2（2.02） 0.725 0.500 0.950 40~50 6（6.06） 0.825 0.240 2.910 50~60 40（40.40） 0.575 0.230 4.140 60~70 43（43.43） 0.630 0.170 3.490 70~80 6（6.06） 1.160 0.570 4.460 80~ 2（2.02） 1.670 0.460 2.880 Chronic diseases 5.887 0.015 No 82（82.83） 0.606 0.165 4.463 Yes 17（17.17） 1.061 0.344 3.338 3.4 Behavioral characteristics Information on the behavioral characteristics of the study population regarding drinking, eating, and bathing habits is presented in Table 4. In rural Y, 66.67% of the population used tap water as a drinking method, 17.17% used buckets or water purifiers as a drinking method, and 16.16% used tap water and buckets or water purifiers as a drinking method. Of these, 59.60% of the study population had an average daily water intake within the range of 1000 mL/d, and 40.4% of the study population had an average daily water intake above 1000 mL/d. In rural Y, 37.37% of the population consumed pasta products in the range of 100 g/d per day, 38.38% consumed pasta products in the range of 100-200 g/d per day, and 24.24% consumed more than 200 g/d per day. Based on the estimated average bathing time throughout the year, 47.47% of the rural Y population was controlled within the range of 0.12 to 0.14 h/d. The percentages of the surveyed population with smoking habits and drinking habits were both small in rural Y, with 12.12% having smoking habits and 8.08% having drinking habits. According to the results of the chi-squared test of the population in rural Y and rural D (Table S2), there were no statistically significant differences ( P >0.05) between the population in the two areas in terms of drinking patterns, water consumption, diet, and behavioral habits of smoking and alcohol consumption. The difference in mean bathing time between residents of rural Y and rural D was statistically significant, with c 2 being 20.561, P -value < 0.001. Table 4 Behavioral characterization and information on concentration of arsenic in hair Variables n（%） M Min Max H P Drinking water method 2.778 0.249 Tap water 66（66.67） 0.695 0.170 4.460 Barrel water/water purifier 17（17.17） 0.763 0.250 2.880 Both 16（16.16） 0.507 0.250 1.760 Drinking water intake（mL/d） 4.414 0.110 ~1000 59（59.60） 0.788 0.196 4.463 1000~2000 35（35.35） 0.566 0.165 4.139 2000~3000 5（5.05） 0.620 0.467 0.746 Dietary intake（g/d） 11.016 0.026 ~100 37（37.37） 0.599 0.196 2.914 100~200 38（38.38） 0.609 0.165 3.491 200~300 6（6.06） 0.596 0.276 2.659 300~400 10（10.10） 1.287 0.584 4.463 400 ~ 8（8.08） 1.243 0.263 3.467 Bathing time（h/d） 5.981 0.113 ~0.1 4（4.04） 0.914 0.522 1.884 0.1~0.12 8（8.08） 0.537 0.233 2.046 0.12~0.14 47（47.47） 0.746 0.237 4.463 0.14~ 40（40.40） 0.557 0.165 3.338 Smoking 3.934 0.047 Yes 12（12.12） 0.956 0.456 3.338 No 87（87.88） 0.615 0.165 4.463 Drinking 1.732 0.188 Yes 8（8.08） 0.847 0.467 3.338 No 91（91.92） 0.618 0.165 4.463 3.5 Factors associated with arsenic accumulation in hair Univariate analysis of demographic factors is shown in Table 3. Differences in arsenic content in hair by sex were statistically significant ( P <0.05). The arsenic content of the hair was 0.956 mg/kg and 0.604 mg/kg for males and females, respectively, and the arsenic content in the hair of male residents was higher than that of females. There was a significant difference ( P <0.05) in the arsenic content in hair among the different age groups. The two comparisons adjusted for Bonferroni correction revealed a statistically significant difference in arsenic levels in hair between those over 70 years of age and those between 40 and 50 years of age, with higher levels of arsenic in hair in those over 70 years of age (0.95 mg/kg>0.494 mg/kg). The differences in arsenic levels in hair were statistically significant among people with different years of residence, and the results of the two-by-two comparison showed statistically significant differences in arsenic levels in hair between people with more than 60 years of residence and people with 20 years of residence and in the range of 20 to 40 years, with a high level of arsenic in hair (1.197 mg/kg) among people with more than 60 years of residence. The presence or absence of disease in the population had a statistically significant effect on the content of arsenic in hair, and residents with chronic diseases had significantly higher levels of arsenic in their hair (1.061 mg/kg>0.606 mg/kg). There was no statistically significant difference found in arsenic content in hair among different weight groups ( P >0.05). A univariate analysis of the behavioral characteristics factors is shown in Table 4. The difference in arsenic levels in hair was statistically significant ( P <0.05) for people with different pasta intakes. The two comparisons revealed statistically significant differences in arsenic levels in hair between the population with average daily intake of pasta products in the range of 300-400 g/d and the population with average daily intake of 100 g/d or less, 100-200 g/d, or 200-300 g/d. Among them, those with an average daily intake of wheat manufactured products in the range of 300-400 g/d had high levels of arsenic levels in their hair (1.286 mg/kg). There was also a statistically significant effect on content of arsenic in hair in relation to whether or not the population smoked, with a high level of arsenic in hair (0.956 mg/kg) in the population with smoking habits. For the multiple linear regression analysis of demographic information factors, the gender and age of the population were found to be significantly associated with the level of arsenic in hair ( P <0.01). Also, the results of multiple linear regression of behavioral factors indicated that the intake of wheat-based foods by the population was also correlated with levels of arsenic in hair ( P <0.05). The multiple linear regression analysis of the influencing factors is shown in Table 5. Table 5 Hair arsenic levels and multifactorial regression analysis Variables B 95% CI t P VIF Sociodemographic factors Age 0.019 0.007~0.031 3.039 0.003 1.142 Gender 0.708 0.322~1.093 3.646 0.000 1.200 Chronic diseases 0.183 -0.250~0.616 0.840 0.403 1.111 Wight 0.003 -0.017~0.022 0.275 0.784 1.208 Behavioral characteristics factors Dietary intake 0.001 0.000~0.001 2.148 0.034 1.215 Drinking water intake 0.000 -0.001~0.000 -1.732 0.087 1.262 Bathing time -0.828 -5.984~4.327 -0.319 0.750 1.010 Smoking 0.486 -0.167~1.139 1.479 0.143 1.617 Drinking 0.037 -0.743~0.817 0.093 0.926 1.611 4. Discussion In this study, we found that the concentration of arsenic in drinking water in rural Y was higher than the level of arsenic (10 μg/L) recommended by the WHO and China's drinking water guidelines. Several studies have found that groundwater sources of arsenic in the central Guanzhong Basin are related to geological and anthropogenic factors and that groundwater is the main source of drinking water in the rural areas of the region (Guo et al., 2014; Luo et al., 2014; Ren et al., 2021). Although the level of arsenic in wheat grown in rural Y was below our food safety limit (5 mg/kg), there may also be health risks of arsenic from wheat consumption by residents. Suman et al. estimated an additional lifetime cancer risk of 1.23x10 -4 from consumption of wheat grains containing arsenic (43.64 µg/kg), which is above the safe range (10 -4 to 10 -6 ) (Suman et al., 2020). Therefore, residents of rural Y ought to be concerned about the sources of external exposure to arsenic in drinking water and wheat food. The normal concentration of As in the hair of people living in an uncontaminated environment is 0.08 to 0.25 mg/kg, while the WHO generally considers arsenic levels in hair above 1 mg/kg to be indicative of toxicity (Nguyen, et al., 2018). Compared with the content of arsenic in hair of residents in other foreign regions due to arsenic exposure in drinking water, the difference in arsenic exposure risk for residents in this study area was not significant. Ramly et al. (2023) found arsenic concentrations of up to 22.3 µg/L in household drinking water and of 0.05-4.15 µg/g in residents' hair in rural Malaysia, with 25% of the hair having arsenic concentrations higher than 1 µg/g. Eighty-four percent of the surveyed population of children living in the northern region of Argentina had arsenic levels above 1 mg/kg in their hair, with arsenic levels ranging from 0.11 to 13.11 mg/kg (Calatayud et al., 2019). The risk of arsenic exposure for residents in this study area was still low compared to the level of arsenic in the hair of people living near mining areas in China. Arsenic levels in the hair of residents near tin mines in Hunan and antimony mines in Qinglong, Guizhou, in southwest China, ranged from 0.236 to 48.4 µg/g (mean 4.21 µg/g) and 0.130 to 16.1 µg/g (mean 2.96 µg/g), respectively (Liu et al., 2011). The results from the analysis showed that the content of arsenic in the hair of the residents in rural Y was significantly higher than that of the residents living in the safe area, and the accumulation of arsenic in the hair of the residents in rural Y may be a result of arsenic exposure in the environment of the study area and of the habits of the residents. The accumulation of arsenic in the human body can cause acute or chronic poisoning. The main hazards include skin lesions, neurological damage, and damage to the lungs, liver, kidneys, and other body organs (Muzaffar et al., 2023). Comparative analysis revealed that the concentration of arsenic in the hair of men in rural Y was higher than that of women. This is supported by the findings of Skalnaya et al. (2016) on age differences in relation to trace elements in hair, in which arsenic levels in female hair were shown to decrease with age. Meanwhile, Lindberg et al. (2008) found that arsenic methylation efficiency was higher in women of reproductive age than in men, which may be because of the influence of sex hormones on said efficiency. Compared to other age groups, we found significantly higher levels of arsenic in the hair of residents over 70 years of age and significantly higher levels in the hair of residents who had lived in the area for more than 60 years, confirming that the level of arsenic in an individual’s body is closely related to the duration of an individual’s exposure to arsenic. Arsenic accumulates in the body with age, and the degree of arsenic accumulation varies with different metabolic rates, with relatively weaker metabolic levels in the elderly leading to the accumulation of more arsenic. Previous studies have found a positive correlation between age and the rate of arsenic-induced skin damage, mainly because of the lower arsenic methylation capacity of older adults (Wei et al., 2018; Yang et al., 2017). In the study, the significance of the effect of body weight on levels of arsenic in hair in the population was not found. Liu et al. (2017) found no significant effect of levels of arsenic in hair on body weight, probably because body weight was influenced by several factors. Residents of the study area with chronic disease problems also had high levels of arsenic in their hair, suggesting that the accumulation of arsenic in the body may be responsible for the development of the disease. Residents in the study area whose average daily intake of wheat manufactured products exceeded 300 g/L had significantly higher levels of arsenic in their hair than other residents who consumed fewer wheat manufactured products. This may be due to the fact that residents add large amounts of arsenic-containing drinking water when using wheat flour to make pasta and that residents in the northern region prefer to eat mainly pasta products, so the amount of pasta products consumed also has an indirect effect on the accumulation of arsenic in the hair of residents. Mondal et al.(2021) observed that natural arsenic levels in grains and the addition of arsenic-contaminated water during cooking can lead to high arsenic concentrations in food and increase potential health risks. We also found a high level of arsenic in the hair of people with smoking habits, which to some extent explains the high level of arsenic in the hair of men. Ramly et al.(2023) also confirmed that smoking was one of the factors significantly associated with elevated levels of arsenic in hair. Arsenic is one of the harmful components of cigarette smoke, and smoking reduces the methylation of arsenic and prevents the removal of arsenic from the body (Lazarevic et al., 2012). Smokers accumulate arsenic in the body because of the high frequency of hand-to-mouth contact during the smoking process. The multifactorial regression results showed that the age and sex of the population were important factors influencing arsenic levels in humans. The different lifestyle habits of different gender populations result in higher arsenic accumulation in the male population than in the female population. Older populations have a reduced ability to eliminate long-term arsenic intake due to the lower efficiency of their metabolism of arsenic. Based on community studies, gender, age, and arsenic levels in tube well water were found to be relevant explanatory variables for arsenic poisoning (Maden et al., 2011). In addition, the intake of wheat-based foods by populations has been shown to lead to the accumulation of arsenic in people’s bodies. Several studies have found arsenic exposure to occur through the transfer of arsenic in the food chain and have found dietary habits to be important influencing factors (Arslan et al., 2017; Rehman et al., 2021). Furthermore, wheat is an emerging pathway for arsenic exposure, and its intake increases the risk of cancer (Suman, et al., 2020). 5. Conclusion The average concentration of arsenic in the hair of the rural Y population was 0.967 mg/kg, and 29.29% of the levels of arsenic in hair exceeded the WHO standard (1 mg/kg); the average level of arsenic in the hair of the residents of rural D was 0.392 mg/kg. There was a statistically significant difference ( P <0.001) in the level of arsenic in hair between residents living in arsenic-contaminated areas and those living in arsenic-safe areas. The correlation analysis of arsenic levels in the hair of the residents in rural Y found each influencing factor—age, gender, and duration of residential exposure—to be unavoidable factors that influenced the levels of arsenic in people’s bodies. In addition, this analysis found excessive intake of wheat products to be a risk factor for the level of arsenic in the body, suggesting that there may be a causal relationship between the disease histories of residents and the levels of arsenic in their bodies. The effect of environmental arsenic exposure levels on the accumulation of arsenic in the human body is one aspect of the cause. Another reason is that environmental arsenic exposure leads to increased arsenic accumulation as a result of the behavior of exposed people. Therefore, it is crucial that arsenic contamination in the environment is actively managed so that exposure to arsenic is reduced. It is also important that populations living in arsenic-contaminated areas are protected from sources of arsenic exposure, such as by ensuring a supplies water and food with safe levels of arsenic. References Arslan, B., Djamgoz, M.B.A., Akun, E., 2017. ARSENIC: A Review on Exposure Pathways, Accumulation, Mobility and Transmission into the Human Food Chain[J]. Rev Environ Contam Toxicol, 243, 27-51. https://doi.org/10.1007/398_2016_18. Bommarito, P.A., Beck, R., Douillet, C., Del Razo, L.M., Garcia-Vargas, G.G., Valenzuela, O.L., Sanchez-Pena, L.C., Styblo, M., Fry, R.C., 2019. 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Assessment of arsenic species in human hair, toenail and urine and their association with water and staple food[J]. J Expo Sci Environ Epidemiol, 29(5), 624-632. https://doi.org/10.1038/s41370-018-0056-7. Rasheed, H., Slack, R., Kay, P., 2016. Human health risk assessment for arsenic: A critical review[J]. Critical Reviews in Environmental Science and Technology, 46(19-20), 1529-1583. https://doi.org/10.1080/10643389.2016.1245551. Rehman, M.U., Khan, R., Khan, A., Qamar, W., Arafah, A., Ahmad, A., Ahmad, A., Akhter, R., Rinklebe, J., Ahmad, P., 2021. Fate of arsenic in living systems: Implications for sustainable and safe food chains[J]. J Hazard Mater, 417, 126050. https://doi.org/10.1016/j.jhazmat.2021.126050. Rehman, U.u., Khan, S., Muhammad, S., 2019. Ingestion of Arsenic-Contaminated Drinking Water Leads to Health Risk and Traces in Human Biomarkers (Hair, Nails, Blood, and Urine), Pakistan[J]. Exposure and Health, 12(2), 243-254. https://doi.org/10.1007/s12403-019-00308-w. Ren, X., Li, P., He, X., Su, F., Elumalai, V., 2021. Hydrogeochemical Processes Affecting Groundwater Chemistry in the Central Part of the Guanzhong Basin, China[J]. Arch Environ Contam Toxicol, 80(1), 74-91. https://doi.org/10.1007/s00244-020-00772-5. Rodriguez-Lado, L., Sun, G., Berg, M., Zhang, Q., Xue, H., Zheng, Q., Johnson, C.A., 2013. Groundwater arsenic contamination throughout China[J]. Science, 341(6148), 866-868. https://doi.org/10.1126/science.1237484. Rokonuzzaman, M.D., Li, W.C., Wu, C., Ye, Z.H., 2022. Human health impact due to arsenic contaminated rice and vegetables consumption in naturally arsenic endemic regions[J]. Environ Pollut, 308, 119712. https://doi.org/10.1016/j.envpol.2022.119712. Sanjrani, M.A., Zhou, B., Zhao, H., Bhutto, S.A., Muneer, A.S., Xia, S.B., 2019. Arsenic Contaminated Groundwater in China and Its Treatment Options, a Review[J]. Applied Ecology and Environmental Research, 17(2), 1655-1683. https://doi.org/10.15666/aeer/1702_16551683. Shi, Y.L., Chen, W.Q., Wu, S.L., Zhu, Y.G., 2017. Anthropogenic Cycles of Arsenic in Mainland China: 1990-2010[J]. Environ Sci Technol, 51(3), 1670-1678. https://doi.org/10.1021/acs.est.6b01669. Skalnaya, M.G., Tinkov, A.A., Demidov, V.A., Serebryansky, E.P., Nikonorov, A.A., Skalny, A.V., 2016. Age-related differences in hair trace elements: a cross-sectional study in Orenburg, Russia[J]. Ann Hum Biol, 43(5), 438-444. https://doi.org/10.3109/03014460.2015.1071424. Suman, S., Sharma, P.K., Siddique, A.B., Rahman, M.A., Kumar, R., Rahman, M.M., Bose, N., Singh, S.K., Ghosh, A.K., Matthews, H., Mondal, D., 2020. Wheat is an emerging exposure route for arsenic in Bihar, India[J]. Sci Total Environ, 703, 134774. https://doi.org/10.1016/j.scitotenv.2019.134774. Sun, G., Xu, Y., Zheng, Q., Xi, S., 2011. Arsenicosis history and research progress in Mainland China[J]. Kaohsiung J Med Sci, 27(9), 377-381. https://doi.org/10.1016/j.kjms.2011.05.004. Wang, D., Luo, P., Zou, Z., Wang, Q., Yao, M., Yu, C., Wei, S., Sun, B., Zhu, K., Zeng, Q., Li, J., Liang, B., Zhang, A., 2019. Alterations of arsenic levels in arsenicosis residents and awareness of its risk factors: A population-based 20-year follow-up study in a unique coal-borne arsenicosis County in Guizhou, China[J]. Environ Int, 129, 18-27. https://doi.org/10.1016/j.envint.2019.05.005. Wei, B., Yu, J., Kong, C., Li, H., Yang, L., Xia, Y., Wu, K., 2018. A follow-up study of the development of skin lesions associated with arsenic exposure duration[J]. Environ Geochem Health, 40(6), 2729-2738. https://doi.org/10.1007/s10653-018-0136-6. Yang, L., Chai, Y., Yu, J., Wei, B., Xia, Y., Wu, K., Gao, J., Guo, Z., Cui, N., 2017. Associations of arsenic metabolites, methylation capacity, and skin lesions caused by chronic exposure to high arsenic in tube well water[J]. Environ Toxicol, 32(1), 28-36. https://doi.org/10.1002/tox.22209. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4209156","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":289644437,"identity":"4a4393fd-51c9-4b10-88c9-36d644236455","order_by":0,"name":"Xiangping Chen","email":"","orcid":"","institution":"Shaanxi University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiangping","middleName":"","lastName":"Chen","suffix":""},{"id":289644438,"identity":"863a2b1a-b6c3-424b-9b32-e4fdb729be0e","order_by":1,"name":"Siyu Liu","email":"","orcid":"","institution":"Shaanxi University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Siyu","middleName":"","lastName":"Liu","suffix":""},{"id":289644439,"identity":"de095e56-60d4-4a0c-833c-f22d76cbda24","order_by":2,"name":"Manman Shi","email":"","orcid":"","institution":"Xi’an Huyi District Hospital ofTraditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Manman","middleName":"","lastName":"Shi","suffix":""},{"id":289644441,"identity":"5177955c-3ac7-4e9e-9440-b7aa0c034a4f","order_by":3,"name":"Yan Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIie2RsQrCMBCGLyi6VLteEQXfIFAQRMFXqRTi0sFHEIS42AfwLSoujpVAp7jrZKdOCk5iB8HW2jU6CuZb7j/4Pw4SAI3mB2kWAzs1ECHga3HUSq0YQ9skkQPofK+w8Wou6bv9SUE3wnQrxkEkb9i/CzDrHoV0q1IYs3wp7EDuNxQdAdbyTIkvVYrXwwYX7eCwX8e5Qg8erRCuVqwHFyQ4neMwV0bfKK0GZ93VTJLiCn5SjMQdtHn2yBDZFNnEQJlMd75CMevu7njhr69MWjgcdMyFu45ThZJRxTJVsmTkIVQKWfFaJnJV9TQajeZveQJFaVDQ8L0QFAAAAABJRU5ErkJggg==","orcid":"","institution":"Xi'an Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2024-04-03 00:59:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4209156/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4209156/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54865610,"identity":"eeec3a64-40cf-477f-8e7e-3719acbd536f","added_by":"auto","created_at":"2024-04-17 20:46:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":51011,"visible":true,"origin":"","legend":"\u003cp\u003eNormal distribution of arsenic concentration in different media (a. water in rural Y; b. water in rural D; c. wheat in rural Y; d. wheat in rural D)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4209156/v1/ae02d4f049701d7953d048ed.png"},{"id":54866371,"identity":"52e4dd11-07ab-4a3a-943c-67adda2ad223","added_by":"auto","created_at":"2024-04-17 20:54:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":37759,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of arsenic concentration in hair (a. rural Y; b. rural D)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4209156/v1/a014447be2cd5e26f93b451e.png"},{"id":56836568,"identity":"bc483ee2-23dd-486d-b3d1-38dd547f8cd6","added_by":"auto","created_at":"2024-05-21 06:07:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":710885,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4209156/v1/0d72ff79-bd6b-451f-9d9b-7a5df95f3d68.pdf"},{"id":54865608,"identity":"d6d6f72a-ae5e-42fe-8526-48b81bc9c96a","added_by":"auto","created_at":"2024-04-17 20:46:29","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":25740,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterials3.26.docx","url":"https://assets-eu.researchsquare.com/files/rs-4209156/v1/1b032516ba3774e071cc430f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Arsenic levels in the hair of people exposed to arsenic and awareness of its risk factors","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eArsenic is a metalloid element that is widely found in the natural environment. However, natural and anthropogenic factors have caused arsenic to exceed the environmental load, and approximately 200 million people worldwide are exposed to the threat of arsenic\u0026nbsp;(Chen and Costa, 2021). There is arsenic-rich drinking water due to geological structures in areas such as Bangladesh and China, and elevated levels of arsenic in water sources due to mining or other industrial activities in areas such as Japan and Thailand\u0026nbsp;(Huang et al., 2015). China is the largest producer of arsenic trioxide and other arsenic-containing compounds, with its global share accounting for about 50% in 2005, mainly due to geological factors and anthropogenic pollution\u0026nbsp;(Shi et al., 2017). Chronic exposure to arsenic in the environment can cause people serious health problems. At an early period, arsenic poisoning was assessed in the Kuitun area of Xinjiang, China, followed by Inner Mongolia and Shanxi, where the most serious arsenic poisoning occurred\u0026nbsp;(Sanjrani et al., 2019). By 2012, it was estimated that there were endemic arsenic areas in 45 counties and districts in 9 provinces in China\u0026nbsp;(He and Charlet, 2013). The ways in which arsenic transmits disease in China include via long-term intake of drinking water contaminated with high levels of arsenic and the breathing of air polluted by combustion of coal with high levels of arsenic (mainly in Guizhou and Shaanxi)\u0026nbsp;(He et al., 2020; Wang et al., 2019). Models for ground-source arsenic contamination in China predict that approximately 19.6 million people are at risk from the consumption of arsenic-contaminated groundwater\u0026nbsp;(Rodriguez-Lado et al., 2013). Arsenic in groundwater in China is mainly caused by geological and human factors. According to statistics, as of 2006, more than 5% of drinking water wells in 16 provinces in China had arsenic concentrations greater than 50mg/L\u0026nbsp;(Sun et al., 2011). In coal-powered areas, the sources of exposure are mainly air contaminated by coal being burned in unventilated rooms and food being contaminated by being placed on coal stoves\u0026nbsp;(Yao et al., 2021). A related report in 2003 indicated that the population of Guizhou and Shaanxi provinces in areas contaminated with arsenic due to coal combustion was contaminated to be 330,000,of which about 40,000 suffered from arsenic poisoning due to inhalation of contaminated air and consumption of contaminated food crops\u0026nbsp;(Kang et al., 2011).\u003c/p\u003e\n\u003cp\u003eSince the large-scale epidemic of arsenic poisoning in China, China has been carrying out treatment work to improve the environment in arsenic-contaminated areas and to control the discharge of arsenic sources. However, the carcinogenicity and persistence of arsenic has long-term effects on human health and increases the risk of disease in humans even at low levels of arsenic exposure\u0026nbsp;(Rahaman et al., 2021). DALYs (disability-adjusted life years) were used to assess cancer risk for low levels of arsenic in drinking water (mean of 8.23\u0026nbsp;\u0026mu;g/L) and found that the mean loss of DALYs for study individuals was 3.35\u0026times;10\u003csup\u003e-5\u003c/sup\u003e per person-year (ppy), which was 33.5 times the reference value (1.00\u0026times;10\u003csup\u003e-6\u003c/sup\u003e ppy), with those aged 60-65 years having the highest mean loss of DALYs\u0026nbsp;(Zhang et al., 2020).\u003c/p\u003e\n\u003cp\u003eNormally, arsenic undergoes metabolic transformation in the human body and is released in a less toxic form\u0026nbsp;(Rasheed et al., 2016). When humans are exposed to high arsenic levels for a long period of time, their intake of arsenic is greater than their excretion of arsenic, which results in the accumulation of arsenic in various parts of the body. The body parts of exposed individuals can be used as biomarkers of levels of arsenic toxicity. At present, to confirm exposure to arsenic, many studies use hair, nails, blood, and urine as primary biomarkers of arsenic in humans\u0026nbsp;(Bommarito et al., 2019; Liu et al., 2017; Rehman et al., 2019). Analysis of biomarkers of arsenic levels can reveal arsenic sources. In the Cambodian report, arsenic levels in groundwater as a source of drinking water were positively correlated with arsenic concentrations in the nails (r=0.74, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001) and hair (r=0.86, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.0001) of exposed individuals\u0026nbsp;(Gault et al., 2008). Studies on the association of water and staple foods with arsenic in humans found that arsenic had a strong and significant relationship with arsenic concentrations in urine and toenail samples, while arsenic in staple food (rice and wheat) showed a strong relationship with arsenic concentrations in hair\u0026nbsp;(Rasheed et al., 2019). Moreover, the study found arsenic in rice and vegetables in arsenic endemic areas to be significantly correlated with arsenic in the hair of the population (\u003cem\u003eP\u003c/em\u003e\u0026le;0.01)\u0026nbsp;(Rokonuzzaman et al., 2022). Hair can be considered as a metabolic end product that provides a more permanent record of trace elements in the body\u0026nbsp;(Katz, 2019). Moreover, because hair is easy to collect and preserve and because of the significant levels of arsenic accumulated in hair, hair is considered an ideal material for assessing levels of arsenic in humans. The correlation between concentrations of arsenic in groundwater and nails in the study (r=0.26, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01) was lower than that between concentrations of arsenic in groundwater and human hair (r=0.52, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01), an observation that could be explained by the local habit of cutting nails regularly. This suggests that levels of arsenic in hair are a better reflector of long-term arsenic exposure\u0026nbsp;(Nguyen et al., 2018).\u003c/p\u003e\n\u003cp\u003eIn this study, we collected the hair of individuals living in arsenic-exposed and arsenic-safe areas and also obtained information on the demographic and behavioral characteristics of the study subjects. The aim was to evaluate the arsenic exposure levels of the individuals being compared and to analyze and determine important influencing factors that lead to elevated levels of arsenic exposure in humans. This aim was achieved by developing an understanding of the key factors behind levels of arsenic in the human body, analyzing the relationship between the influence of the external environment and internal factors on these arsenic levels, highlighting health problems caused by environmental arsenic pollution, and providing a scientific basis for local health intervention measures.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was a cross-sectional investigation of arsenic concentrations in the hair of people living in rural areas. The study area was selected from the central Guanzhong Plain (Rural Y), China, where previous studies by our research team have found violations of environmental arsenic standards. Areas with similar demographic characteristics to the study area and where arsenic in the environment met the criteria (Rural D) were selected as control areas. A stratified whole-group sampling study was used to select different areas in rural Y and rural D regions based on the range of ambient arsenic concentrations for the population survey study. Residents aged\u0026nbsp;\u0026ge;18 years old with\u0026nbsp;\u0026ge;3 years of residence in the local area were selected as respondents for the questionnaire survey and the hair sample collection. Ninety-nine subjects were included in rural Y and forty-one subjects were included in the survey in rural D.\u003c/p\u003e\n\u003cp\u003eEthical approval for this study was obtained from the Medical Ethics Committee of the Department of Medicine, Xi\u0026apos;an Jiaotong University (approval number 2022-1643). In the study, the right to information was ensured by voluntarily signing of an informed consent form, and the privacy of the subjects was fully protected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Assessment of arsenic exposure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResidents\u0026apos; arsenic exposure levels were assessed through household water and food. Household water was provided via the end of the indoor pipe networks of the residents\u0026apos; homes, and food was provided in the form of the residents\u0026apos; home-grown wheat. Before collecting the drinking water samples, the tap bein used was opened to flow for 3 minutes before using pre-cleaned sampling bottles (1% nitric acid-soaked polyethylene bottles) to collect the water, recording sampling information, and sealing and storing the samples. The mature wheat was selected and collected into clean, self-sealing bags and stored in a dry environment. In the laboratory, the water samples were filtered with acid to ensure that the pH of the measured sample solution was \u0026lt;2 and stored in a constant temperature chamber at 4℃. At the same time, the wheat samples were washed and dried and ground to a powder passing through a 200 mesh specimen sieve. Next, 0.5 g of the sample powder was added to 5 mL of nitric acid and soaked overnight, then heated for full acid digestion, before being filtered and processed, and stored at 4℃. The concentration of arsenic in the samples was analyzed by ICP-MS within 7 days of collecting the samples.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Hair samples: collection and analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen the hair was dry, the hair near the back of the scalp was collected with clean scissors, and about 300 hairs of about 3-5 cm in length were taken and placed in a clean polyethylene self-sealing bag. If the hair was too long, the tip part of the hair was discarded. Samples of hair that had been dyed or undergone a perm were discarded. The hair samples were sent to the laboratory and stored in a dry environment at room temperature. The hair was pretreated using cleaning method recommended by the IAIEA (water and acetone) and dried to a certain weight after removing exogenous contamination from the hair. The 20 mg of hair was weighed and placed in a 15 mL test tube, and 0.8 mL of HNO\u003csub\u003e3\u003c/sub\u003e solution and 0.2 mL of hydrogen peroxide were added to the test tube and the test tube was covered and sealed overnight, and then placed in an electric hot plate digestion oven for heat treating. The digestion oven was set at 90℃ and heated for 3 h. After digestion, the samples were cooled naturally, and the arsenic content was determined by ICP-MS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Questionnaire\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach respondent was required to complete a questionnaire that included age, gender and other demographic information, disease history, alcohol and cigarette consumption, and behavioral characteristics. In line with the purpose of the study, the behavioral characteristics investigated concerned the population\u0026apos;s water intake, wheat product intake, and bathing habits. In the questionnaire on intake, participants were asked to record the source of water and the frequency of water intake and amount per intake; in the questionnaire on bathing behavior, the frequency of bathing and the duration of each session were recorded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSPSS 26.0 was used to organize and analyze the data. The mean, median, standard deviation, and percentile were used to statistically describe the distribution of arsenic concentrations in different media. The Kolmogorov-Sminov test (K-S test) was used to determine whether the distribution of arsenic substance concentrations followed a normal distribution. Parametric tests (t-test or ANOVA) were used to analyze whether there were differences in the concentration values of arsenic substances if they followed a normal distribution; non-parametric tests (rank sum test) were used to analyze whether there were differences in the concentration values of arsenic substances if they did not follow a normal distribution. Data for the count data were tested by the chi-squared test, and differences were considered statistically significant at \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05. The Kruskal-Wallis H test was used for the analysis of different influencing factors concerning arsenic content in the hair of the population, and Kruskal-Wallis one-way ANOVA (k samples) multiple comparisons were used for two-by-two comparisons. The correlation between arsenic content in hair and related influencing factors was analyzed by multiple linear regression, and a significant correlation was considered at \u003cem\u003eP\u003c/em\u003e\u0026lt;0.01.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Arsenic exposure level\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the monitoring of arsenic levels in different environmental media, 150 water samples were tested in rural Y and 15 in rural D; 87 wheat samples were tested in rural Y and 10 in rural D. The mean values of arsenic in daily drinking water and cereals consumed by residents of rural Y were 29.979\u0026nbsp;\u0026mu;g/L and 0.237 mg/kg, respectively, while the mean values of arsenic in daily drinking water and cereals consumed by residents of rural D were 0.442\u0026nbsp;\u0026mu;g/L and 0.104 mg/kg, respectively. The arsenic content of drinking water in the rural Y samples significantly exceeded the standard limit value (10\u0026nbsp;\u0026mu;g/L), and the arsenic content of wheat was predominately within the standard limit value (0.5 mg/kg); the arsenic content in both types of samples in rural D was within the standard limit value. The distribution of arsenic levels in drinking water and wheat in rural Y and rural D conformed to a normal distribution, as shown in Figure 1. The results from the t-test showed (Table 1) that the differences in arsenic levels in water and wheat between rural Y and rural D were statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Arsenic concentration levels in different environmental media\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"98%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.26530612244898%\" rowspan=\"2\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" rowspan=\"2\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\" rowspan=\"2\"\u003e\n \u003cp\u003eMean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.714285714285715%\" colspan=\"2\"\u003e\n \u003cp\u003eT test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.42857142857143%\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"48.57142857142857%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.26530612244898%\" rowspan=\"2\"\u003e\n \u003cp\u003eWater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003eRural Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003e29.979\u0026plusmn;15.752\u0026nbsp;\u0026mu;g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" rowspan=\"2\"\u003e\n \u003cp\u003e7.242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\"\u003e\n \u003cp\u003eRural D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\"\u003e\n \u003cp\u003e0.442\u0026plusmn;0.331\u0026nbsp;\u0026mu;g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.26530612244898%\" rowspan=\"2\"\u003e\n \u003cp\u003eWheat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\"\u003e\n \u003cp\u003eRural Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003e0.237\u0026plusmn;0.162\u0026nbsp;mg/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.367346938775512%\" rowspan=\"2\"\u003e\n \u003cp\u003e2.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.346938775510203%\" rowspan=\"2\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36%\"\u003e\n \u003cp\u003eRural D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"64%\"\u003e\n \u003cp\u003e0.104\u0026plusmn;0.073\u0026nbsp;mg/kg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Total arsenic in hair\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 99 hair samples from rural Y and 41 hair samples from rural D were tested in this study. From the test results, the average concentration of arsenic in the hair of the population in rural Y was 0.967 \u0026plusmn; 0.878 mg/kg, ranging from 0.165 mg/kg to 4.463 mg/kg; the mean concentration of arsenic in the hair of the rural D population was 0.392 \u0026plusmn; 0.215 mg/kg, ranging from 0.147 mg/kg to 0.977 mg/kg. For the concentration of arsenic in human hair investigated in 11 cities in China, the average concentration of arsenic was found to be 0.23 mg/kg (Zhou et al., 2016). In rural Y, 29.29% of the population had arsenic levels in hair above 1 mg/kg, while the population in rural D had arsenic levels in hair predominately within the range of 1 mg/kg, as shown in Figure 2.\u003c/p\u003e\n\u003cp\u003eBased on the K-S test for arsenic levels in hair, it was found that the distribution of arsenic levels in hair between rural Y and rural D did not conform to a normal distribution (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). The rank sum test for arsenic in hair (Table 2) revealed statistically significant differences in the levels of arsenic in the hair of the residents between rural Y and rural D (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Levels of arsenic concentration in hair\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"55%\" rowspan=\"2\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.11111111111111%\" rowspan=\"2\"\u003e\n \u003cp\u003eM(P25, P75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" rowspan=\"2\"\u003e\n \u003cp\u003eK-S\u0026nbsp;test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.555555555555557%\" colspan=\"2\"\u003e\n \u003cp\u003eWilcoxon\u0026apos;s rank sum test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.945945945945944%\"\u003e\n \u003cp\u003eZ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"54.054054054054056%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"55%\"\u003e\n \u003cp\u003eRural Y\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.11111111111111%\"\u003e\n \u003cp\u003e0.633（0.437,1.100）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.444444444444445%\" rowspan=\"2\"\u003e\n \u003cp\u003e-5.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.11111111111111%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"69.23076923076923%\"\u003e\n \u003cp\u003eRural D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.27972027972028%\"\u003e\n \u003cp\u003e0.354（0.245, 0.483）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.48951048951049%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e=0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Demographics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe demographic information of the participants is shown in Table 3. A total of 140 study subjects were selected for this survey; 99 study subjects were included in\u0026nbsp;rural Y; 41 study subjects were included in\u0026nbsp;rural D. The mean age of the survey population in\u0026nbsp;rural Y\u0026nbsp;was 58.86 \u0026plusmn; 13.39 years. The study area is a rural area, so the population living there is predominantly middle-aged and elderly, and the majority of the survey respondents are female, with 73.74% of the study area being female. Most of the local residents have lived in the area for a long time since birth or settled in the area due to marriage, and there are a few cases of middle-aged and elderly people moving out of the area. The weight of the residents in\u0026nbsp;rural Y\u0026nbsp;was predominately in the range of 50-70 kg, which is a normal weight standard for middle-aged and elderly people. The subjects in\u0026nbsp;rural Y\u0026nbsp;had a minority of chronic diseases, such as hypertension and hyperglycemia themselves. The results of the chi-squared test (Table S1) reveal no statistically significant differences (\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05) between the sex, age, and weight of the residents of\u0026nbsp;rural Y\u0026nbsp;and\u0026nbsp;rural D.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Demographics and concentration of arsenic in hair\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\" colspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003en（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\" colspan=\"2\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e10.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.25531914893617%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.27659574468085%\"\u003e\n \u003cp\u003e73（73.74）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e3.470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.25531914893617%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.27659574468085%\"\u003e\n \u003cp\u003e26（26.26）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e4.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\" colspan=\"2\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e16.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.25531914893617%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003e20~30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.27659574468085%\"\u003e\n \u003cp\u003e3（3.03）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.25531914893617%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003e30~40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.27659574468085%\"\u003e\n \u003cp\u003e8（8.08）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e2.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.25531914893617%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003e40~50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.27659574468085%\"\u003e\n \u003cp\u003e14（14.14）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e1.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.25531914893617%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003e50~60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.27659574468085%\"\u003e\n \u003cp\u003e29（29.29）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.571\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e1.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.25531914893617%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003e60~70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.27659574468085%\"\u003e\n \u003cp\u003e25（25.25）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e4.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.25531914893617%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003e70~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.27659574468085%\"\u003e\n \u003cp\u003e20（18.18）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e4.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\" colspan=\"2\"\u003e\n \u003cp\u003eResidence time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e8.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.25531914893617%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003e~20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.27659574468085%\"\u003e\n \u003cp\u003e14（14.14）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e1.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.25531914893617%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003e20~40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.27659574468085%\"\u003e\n \u003cp\u003e34（34.34）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e3.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.25531914893617%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003e40~60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.27659574468085%\"\u003e\n \u003cp\u003e23（23.23）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e2.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.25531914893617%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003e60~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.27659574468085%\"\u003e\n \u003cp\u003e28（28.28）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e1.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e4.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.210526315789473%\" colspan=\"2\"\u003e\n \u003cp\u003eWight（kg）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.05263157894737%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e6.457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.25531914893617%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003e30~40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.27659574468085%\"\u003e\n \u003cp\u003e2（2.02）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.25531914893617%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003e40~50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.27659574468085%\"\u003e\n \u003cp\u003e6（6.06）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e2.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.25531914893617%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003e50~60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.27659574468085%\"\u003e\n \u003cp\u003e40（40.40）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e4.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.25531914893617%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003e60~70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.27659574468085%\"\u003e\n \u003cp\u003e43（43.43）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e3.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.25531914893617%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003e70~80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.27659574468085%\"\u003e\n \u003cp\u003e6（6.06）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e1.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e4.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.25531914893617%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003e80~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.27659574468085%\"\u003e\n \u003cp\u003e2（2.02）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e1.670\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e2.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.833333333333336%\" colspan=\"3\"\u003e\n \u003cp\u003eChronic diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.5%\"\u003e\n \u003cp\u003e5.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.25531914893617%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.27659574468085%\"\u003e\n \u003cp\u003e82（82.83）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e4.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.25531914893617%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.148936170212767%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.27659574468085%\"\u003e\n \u003cp\u003e17（17.17）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e1.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e3.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Behavioral characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformation on the behavioral characteristics of the study population regarding drinking, eating, and bathing habits is presented in Table 4. In\u0026nbsp;rural Y, 66.67% of the population used tap water as a drinking method, 17.17% used buckets or water purifiers as a drinking method, and 16.16% used tap water and buckets or water purifiers as a drinking method. Of these, 59.60% of the study population had an average daily water intake within the range of 1000 mL/d, and 40.4% of the study population had an average daily water intake above 1000 mL/d. In\u0026nbsp;rural Y, 37.37% of the population consumed pasta products in the range of 100 g/d per day, 38.38% consumed pasta products in the range of 100-200 g/d per day, and 24.24% consumed more than 200 g/d per day. Based on the estimated average bathing time throughout the year, 47.47% of the\u0026nbsp;rural Y\u0026nbsp;population was controlled within the range of 0.12 to 0.14 h/d. The percentages of the surveyed population with smoking habits and drinking habits were both small in\u0026nbsp;rural Y, with 12.12% having smoking habits and 8.08% having drinking habits. According to the results of the chi-squared test of the population in\u0026nbsp;rural Y\u0026nbsp;and\u0026nbsp;rural D\u0026nbsp;(Table S2), there were no statistically significant differences (\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05) between the population in the two areas in terms of drinking patterns, water consumption, diet, and behavioral habits of smoking and alcohol consumption. The difference in mean bathing time between residents of\u0026nbsp;rural Y\u0026nbsp;and\u0026nbsp;rural D\u0026nbsp;was statistically significant, with\u0026nbsp;\u003cem\u003ec\u003c/em\u003e\u003cem\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e being 20.561, \u003cem\u003eP\u003c/em\u003e-value \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Behavioral characterization and information on concentration of arsenic in hair\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.394366197183096%\" colspan=\"3\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\"\u003e\n \u003cp\u003en（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.915492957746478%\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e\u003cem\u003eH\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.154929577464788%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.394366197183096%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eDrinking water method\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.915492957746478%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e2.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.154929577464788%\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.640845070422535%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.753521126760564%\" colspan=\"2\"\u003e\n \u003cp\u003eTap water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\"\u003e\n \u003cp\u003e66（66.67）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e0.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.915492957746478%\"\u003e\n \u003cp\u003e0.170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\"\u003e\n \u003cp\u003e4.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.154929577464788%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.640845070422535%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.753521126760564%\" colspan=\"2\"\u003e\n \u003cp\u003eBarrel water/water purifier\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\"\u003e\n \u003cp\u003e17（17.17）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e0.763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.915492957746478%\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\"\u003e\n \u003cp\u003e2.880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.154929577464788%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.640845070422535%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.753521126760564%\" colspan=\"2\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\"\u003e\n \u003cp\u003e16（16.16）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e0.507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.915492957746478%\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\"\u003e\n \u003cp\u003e1.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.154929577464788%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.394366197183096%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eDrinking water intake（mL/d）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.915492957746478%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e4.414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.154929577464788%\"\u003e\n \u003cp\u003e0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.640845070422535%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.753521126760564%\" colspan=\"2\"\u003e\n \u003cp\u003e~1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\"\u003e\n \u003cp\u003e59（59.60）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.915492957746478%\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\"\u003e\n \u003cp\u003e4.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.154929577464788%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.640845070422535%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.753521126760564%\" colspan=\"2\"\u003e\n \u003cp\u003e1000~2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\"\u003e\n \u003cp\u003e35（35.35）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.915492957746478%\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\"\u003e\n \u003cp\u003e4.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.154929577464788%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.640845070422535%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.753521126760564%\" colspan=\"2\"\u003e\n \u003cp\u003e2000~3000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\"\u003e\n \u003cp\u003e5（5.05）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.915492957746478%\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.154929577464788%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.394366197183096%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eDietary intake（g/d）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.915492957746478%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e11.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.154929577464788%\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.640845070422535%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.753521126760564%\" colspan=\"2\"\u003e\n \u003cp\u003e~100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\"\u003e\n \u003cp\u003e37（37.37）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.915492957746478%\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\"\u003e\n \u003cp\u003e2.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.154929577464788%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.640845070422535%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.753521126760564%\" colspan=\"2\"\u003e\n \u003cp\u003e100~200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\"\u003e\n \u003cp\u003e38（38.38）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.915492957746478%\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\"\u003e\n \u003cp\u003e3.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.154929577464788%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.640845070422535%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.753521126760564%\" colspan=\"2\"\u003e\n \u003cp\u003e200~300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\"\u003e\n \u003cp\u003e6（6.06）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e0.596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.915492957746478%\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\"\u003e\n \u003cp\u003e2.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.154929577464788%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.640845070422535%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.753521126760564%\" colspan=\"2\"\u003e\n \u003cp\u003e300~400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\"\u003e\n \u003cp\u003e10（10.10）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e1.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.915492957746478%\"\u003e\n \u003cp\u003e0.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\"\u003e\n \u003cp\u003e4.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.154929577464788%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.640845070422535%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.753521126760564%\" colspan=\"2\"\u003e\n \u003cp\u003e400 ~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\"\u003e\n \u003cp\u003e8（8.08）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e1.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.915492957746478%\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\"\u003e\n \u003cp\u003e3.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.154929577464788%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.394366197183096%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eBathing time（h/d）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.915492957746478%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e5.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.154929577464788%\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.640845070422535%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.753521126760564%\" colspan=\"2\"\u003e\n \u003cp\u003e~0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\"\u003e\n \u003cp\u003e4（4.04）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.915492957746478%\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\"\u003e\n \u003cp\u003e1.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.154929577464788%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.640845070422535%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.753521126760564%\" colspan=\"2\"\u003e\n \u003cp\u003e0.1~0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\"\u003e\n \u003cp\u003e8（8.08）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.915492957746478%\"\u003e\n \u003cp\u003e0.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\"\u003e\n \u003cp\u003e2.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.154929577464788%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.640845070422535%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.753521126760564%\" colspan=\"2\"\u003e\n \u003cp\u003e0.12~0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\"\u003e\n \u003cp\u003e47（47.47）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.915492957746478%\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\"\u003e\n \u003cp\u003e4.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.154929577464788%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.640845070422535%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.753521126760564%\" colspan=\"2\"\u003e\n \u003cp\u003e0.14~\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\"\u003e\n \u003cp\u003e40（40.40）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e0.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.915492957746478%\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\"\u003e\n \u003cp\u003e3.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.154929577464788%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"32.394366197183096%\" colspan=\"3\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.95774647887324%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.683098591549296%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.915492957746478%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.859154929577464%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.035211267605634%\"\u003e\n \u003cp\u003e3.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.154929577464788%\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.938271604938271%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.336860670194003%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.98941798941799%\"\u003e\n \u003cp\u003e12（12.12）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.934744268077601%\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.876543209876543%\"\u003e\n \u003cp\u003e3.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.052910052910052%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.171075837742505%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.938271604938271%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.336860670194003%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.98941798941799%\"\u003e\n \u003cp\u003e87（87.88）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.934744268077601%\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.876543209876543%\"\u003e\n \u003cp\u003e4.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.052910052910052%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.171075837742505%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.264550264550266%\" colspan=\"4\"\u003e\n \u003cp\u003eDrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.934744268077601%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.876543209876543%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.052910052910052%\"\u003e\n \u003cp\u003e1.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.171075837742505%\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.938271604938271%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.336860670194003%\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.98941798941799%\"\u003e\n \u003cp\u003e8（8.08）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.934744268077601%\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.876543209876543%\"\u003e\n \u003cp\u003e3.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.052910052910052%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.171075837742505%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.938271604938271%\" colspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.336860670194003%\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.98941798941799%\"\u003e\n \u003cp\u003e91（91.92）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.700176366843033%\"\u003e\n \u003cp\u003e0.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.934744268077601%\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.876543209876543%\"\u003e\n \u003cp\u003e4.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.052910052910052%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.171075837742505%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Factors associated with arsenic accumulation in hair\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate analysis of demographic factors is shown in Table 3. Differences in arsenic content in hair by sex were statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). The arsenic content of the hair was 0.956 mg/kg and 0.604 mg/kg for males and females, respectively, and the arsenic content in the hair of male residents was higher than that of females. There was a significant difference (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) in the arsenic content in hair among the different age groups. The two comparisons adjusted for Bonferroni correction revealed a statistically significant difference in arsenic levels in hair between those over 70 years of age and those between 40 and 50 years of age, with higher levels of arsenic in hair in those over 70 years of age (0.95 mg/kg\u0026gt;0.494 mg/kg). The differences in arsenic levels in hair were statistically significant among people with different years of residence, and the results of the two-by-two comparison showed statistically significant differences in arsenic levels in hair between people with more than 60 years of residence and people with 20 years of residence and in the range of 20 to 40 years, with a high level of arsenic in hair (1.197 mg/kg) among people with more than 60 years of residence. The presence or absence of disease in the population had a statistically significant effect on the content of arsenic in hair, and residents with chronic diseases had significantly higher levels of arsenic in their hair (1.061 mg/kg\u0026gt;0.606 mg/kg). There was no statistically significant difference found in arsenic content in hair among different weight groups (\u003cem\u003eP\u003c/em\u003e\u0026gt;0.05).\u003c/p\u003e\n\u003cp\u003eA univariate analysis of the behavioral characteristics factors is shown in Table 4. The difference in arsenic levels in hair was statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05) for people with different pasta intakes. The two comparisons revealed statistically significant differences in arsenic levels in hair between the population with average daily intake of pasta products in the range of 300-400 g/d and the population with average daily intake of 100 g/d or less, 100-200 g/d, or 200-300 g/d. Among them, those with an average daily intake of wheat manufactured products in the range of 300-400 g/d had high levels of arsenic levels in their hair (1.286 mg/kg). There was also a statistically significant effect on content of arsenic in hair in relation to whether or not the population smoked, with a high level of arsenic in hair (0.956 mg/kg) in the population with smoking habits.\u003c/p\u003e\n\u003cp\u003eFor the multiple linear regression analysis of demographic information factors, the gender and age of the population were found to be significantly associated with the level of arsenic in hair (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01). Also, the results of multiple linear regression of behavioral factors indicated that the intake of wheat-based foods by the population was also correlated with levels of arsenic in hair (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). The multiple linear regression analysis of the influencing factors is shown in Table 5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e Hair arsenic levels and multifactorial regression analysis\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.458333333333336%\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e95% \u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003eVIF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.458333333333336%\"\u003e\n \u003cp\u003eSociodemographic factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.458333333333336%\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e0.007~0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e3.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.142\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.458333333333336%\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e0.322~1.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e3.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.458333333333336%\"\u003e\n \u003cp\u003eChronic diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e-0.250~0.616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.458333333333336%\"\u003e\n \u003cp\u003eWight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e-0.017~0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.458333333333336%\"\u003e\n \u003cp\u003eBehavioral characteristics factors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.458333333333336%\"\u003e\n \u003cp\u003eDietary intake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e0.000~0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e2.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.458333333333336%\"\u003e\n \u003cp\u003eDrinking water intake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e-0.001~0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e-1.732\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.262\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.458333333333336%\"\u003e\n \u003cp\u003eBathing time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e-0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e-5.984~4.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e-0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.458333333333336%\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e-0.167~1.139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.617\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.458333333333336%\"\u003e\n \u003cp\u003eDrinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.75%\"\u003e\n \u003cp\u003e-0.743~0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e0.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e1.611\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we found that the concentration of arsenic in drinking water in\u0026nbsp;rural Y\u0026nbsp;was higher than the level of arsenic (10 \u0026mu;g/L) recommended by the WHO and China\u0026apos;s drinking water guidelines. Several studies have found that groundwater sources of arsenic in the central Guanzhong Basin are related to geological and anthropogenic factors and that groundwater is the main source of drinking water in the rural areas of the region\u0026nbsp;(Guo et al., 2014; Luo et al., 2014; Ren et al., 2021). Although the level of arsenic in wheat grown in\u0026nbsp;rural Y\u0026nbsp;was below our food safety limit (5 mg/kg), there may also be health risks of arsenic from wheat consumption by residents. Suman et al. estimated an additional lifetime cancer risk of 1.23x10\u003csup\u003e-4\u003c/sup\u003e from consumption of wheat grains containing arsenic (43.64 \u0026micro;g/kg), which is above the safe range (10\u003csup\u003e-4\u003c/sup\u003e to 10\u003csup\u003e-6\u003c/sup\u003e)\u0026nbsp;(Suman et al., 2020). Therefore, residents of\u0026nbsp;rural Y\u0026nbsp;ought to be concerned about the sources of external exposure to arsenic in drinking water and wheat food.\u003c/p\u003e\n\u003cp\u003eThe normal concentration of As in the hair of people living in an uncontaminated environment is 0.08 to 0.25 mg/kg, while the WHO generally considers arsenic levels in hair above 1 mg/kg to be indicative of toxicity\u0026nbsp;(Nguyen, et al., 2018). Compared with the content of arsenic in hair of residents in other foreign regions due to arsenic exposure in drinking water, the difference in arsenic exposure risk for residents in this study area was not significant. Ramly et al.\u0026nbsp;(2023)\u0026nbsp;found arsenic concentrations of up to 22.3 \u0026micro;g/L in household drinking water and of 0.05-4.15 \u0026micro;g/g in residents\u0026apos; hair in rural Malaysia, with 25% of the hair having arsenic concentrations higher than 1 \u0026micro;g/g. Eighty-four percent of the surveyed population of children living in the northern region of Argentina had arsenic levels above 1 mg/kg in their hair, with arsenic levels ranging from 0.11 to 13.11 mg/kg\u0026nbsp;(Calatayud et al., 2019). The risk of arsenic exposure for residents in this study area was still low compared to the level of arsenic in the hair of people living near mining areas in China. Arsenic levels in the hair of residents near tin mines in Hunan and antimony mines in Qinglong, Guizhou, in southwest China, ranged from 0.236 to 48.4 \u0026micro;g/g (mean 4.21 \u0026micro;g/g) and 0.130 to 16.1 \u0026micro;g/g (mean 2.96 \u0026micro;g/g), respectively\u0026nbsp;(Liu et al., 2011). The results from the analysis showed that the content of arsenic in the hair of the residents in\u0026nbsp;rural Y\u0026nbsp;was significantly higher than that of the residents living in the safe area, and the accumulation of arsenic in the hair of the residents in\u0026nbsp;rural Y\u0026nbsp;may be a result of arsenic exposure in the environment of the study area and of the habits of the residents. The accumulation of arsenic in the human body can cause acute or chronic poisoning. The main hazards include skin lesions, neurological damage, and damage to the lungs, liver, kidneys, and other body organs\u0026nbsp;(Muzaffar et al., 2023).\u003c/p\u003e\n\u003cp\u003eComparative analysis revealed that the concentration of arsenic in the hair of men in\u0026nbsp;rural Y\u0026nbsp;was higher than that of women. This is supported by the findings of Skalnaya et al.\u0026nbsp;(2016)\u0026nbsp;on age differences in relation to trace elements in hair, in which arsenic levels in female hair were shown to decrease with age. Meanwhile, Lindberg et al.\u0026nbsp;(2008)\u0026nbsp;found that arsenic methylation efficiency was higher in women of reproductive age than in men, which may be because of the influence of sex hormones on said efficiency. Compared to other age groups, we found significantly higher levels of arsenic in the hair of residents over 70 years of age and significantly higher levels in the hair of residents who had lived in the area for more than 60 years, confirming that the level of arsenic in an individual\u0026rsquo;s body is closely related to the duration of an individual\u0026rsquo;s exposure to arsenic. Arsenic accumulates in the body with age, and the degree of arsenic accumulation varies with different metabolic rates, with relatively weaker metabolic levels in the elderly leading to the accumulation of more arsenic. Previous studies have found a positive correlation between age and the rate of arsenic-induced skin damage, mainly because of the lower arsenic methylation capacity of older adults\u0026nbsp;(Wei et al., 2018; Yang et al., 2017). In the study, the significance of the effect of body weight on levels of arsenic in hair in the population was not found. Liu et al.\u0026nbsp;(2017)\u0026nbsp;found no significant effect of \u0026nbsp;levels of arsenic in hair on body weight, probably because body weight was influenced by several factors. Residents of the study area with chronic disease problems also had high levels of arsenic in their hair, suggesting that the accumulation of arsenic in the body may be responsible for the development of the disease. Residents in the study area whose average daily intake of wheat manufactured products exceeded 300 g/L had significantly higher levels of arsenic in their hair than other residents who consumed fewer wheat manufactured products. This may be due to the fact that residents add large amounts of arsenic-containing drinking water when using wheat flour to make pasta and that residents in the northern region prefer to eat mainly pasta products, so the amount of pasta products consumed also has an indirect effect on the accumulation of arsenic in the hair of residents. Mondal et al.(2021)\u0026nbsp;observed that natural arsenic levels in grains and the addition of arsenic-contaminated water during cooking can lead to high arsenic concentrations in food and increase potential health risks. We also found a high level of arsenic in the hair of people with smoking habits, which to some extent explains the high level of arsenic in the hair of men. Ramly et al.(2023)\u0026nbsp;also confirmed that smoking was one of the factors significantly associated with elevated levels of arsenic in hair. Arsenic is one of the harmful components of cigarette smoke, and smoking reduces the methylation of arsenic and prevents the removal of arsenic from the body\u0026nbsp;(Lazarevic et al., 2012). Smokers accumulate arsenic in the body because of the high frequency of hand-to-mouth contact during the smoking process.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe multifactorial regression results showed that the age and sex of the population were important factors influencing arsenic levels in humans. The different lifestyle habits of different gender populations result in higher arsenic accumulation in the male population than in the female population. Older populations have a reduced ability to eliminate long-term arsenic intake due to the lower efficiency of their metabolism of arsenic. Based on community studies, gender, age, and arsenic levels in tube well water were found to be relevant explanatory variables for arsenic poisoning (Maden et al., 2011). In addition, the intake of wheat-based foods by populations has been shown to lead to the accumulation of arsenic in people\u0026rsquo;s bodies. Several studies have found arsenic exposure to occur through the transfer of arsenic in the food chain and have found dietary habits to be important influencing factors (Arslan et al., 2017; Rehman et al., 2021). Furthermore, wheat is an emerging pathway for arsenic exposure, and its intake increases the risk of cancer (Suman, et al., 2020).\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe average concentration of arsenic in the hair of the\u0026nbsp;rural Y\u0026nbsp;population was 0.967 mg/kg, and 29.29% of the levels of arsenic in hair exceeded the WHO standard (1 mg/kg); the average level of arsenic in the hair of the residents of\u0026nbsp;rural D\u0026nbsp;was 0.392 mg/kg. There was a statistically significant difference (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001) in the level of arsenic in hair between residents living in arsenic-contaminated areas and those living in arsenic-safe areas. The correlation analysis of arsenic levels in the hair of the residents in rural Y found each influencing factor\u0026mdash;age, gender, and duration of residential exposure\u0026mdash;to be unavoidable factors that influenced the levels of arsenic in people\u0026rsquo;s bodies. In addition, this analysis found excessive intake of wheat products to be a risk factor for the level of arsenic in the body, suggesting that there may be a causal relationship between the disease histories of residents and the levels of arsenic in their bodies. The effect of environmental arsenic exposure levels on the accumulation of arsenic in the human body is one aspect of the cause. Another reason is that environmental arsenic exposure leads to increased arsenic accumulation as a result of the behavior of exposed people. Therefore, it is crucial that arsenic contamination in the environment is actively managed so that exposure to arsenic is reduced. It is also important that populations living in arsenic-contaminated areas are protected from sources of arsenic exposure, such as by ensuring a supplies water and food with safe levels of arsenic.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eArslan, B., Djamgoz, M.B.A., Akun, E., 2017. ARSENIC: A Review on Exposure Pathways, Accumulation, Mobility and Transmission into the Human Food Chain[J]. Rev Environ Contam Toxicol, 243, 27-51. https://doi.org/10.1007/398_2016_18.\u003c/li\u003e\n\u003cli\u003eBommarito, P.A., Beck, R., Douillet, C., Del Razo, L.M., Garcia-Vargas, G.G., Valenzuela, O.L., Sanchez-Pena, L.C., Styblo, M., Fry, R.C., 2019. Evaluation of plasma arsenicals as potential biomarkers of exposure to inorganic arsenic[J]. J Expo Sci Environ Epidemiol, 29(5), 718-729. https://doi.org/10.1038/s41370-019-0121-x.\u003c/li\u003e\n\u003cli\u003eCalatayud, M., Farias, S.S., de Paredes, G.S., Olivera, M., Carreras, N.A., Gimenez, M.C., Devesa, V., Velez, D., 2019. Arsenic exposure of child populations in Northern Argentina[J]. Sci Total Environ, 669, 1-6. https://doi.org/10.1016/j.scitotenv.2019.02.415.\u003c/li\u003e\n\u003cli\u003eChen, Q.Y., Costa, M., 2021. Arsenic: A Global Environmental Challenge[J]. Annu Rev Pharmacol Toxicol, 61, 47-63. https://doi.org/10.1146/annurev-pharmtox-030220-013418.\u003c/li\u003e\n\u003cli\u003eGault, A.G., Rowland, H.A., Charnock, J.M., Wogelius, R.A., Gomez-Morilla, I., Vong, S., Leng, M., Samreth, S., Sampson, M.L., Polya, D.A., 2008. Arsenic in hair and nails of individuals exposed to arsenic-rich groundwaters in Kandal province, Cambodia[J]. Sci Total Environ, 393(1), 168-176. https://doi.org/10.1016/j.scitotenv.2007.12.028.\u003c/li\u003e\n\u003cli\u003eGuo, H., Wen, D., Liu, Z., Jia, Y., Guo, Q., 2014. A review of high arsenic groundwater in Mainland and Taiwan, China: Distribution, characteristics and geochemical processes[J]. Applied Geochemistry, 41, 196-217. https://doi.org/10.1016/j.apgeochem.2013.12.016.\u003c/li\u003e\n\u003cli\u003eHe, J., Charlet, L., 2013. A review of arsenic presence in China drinking water[J]. Journal of Hydrology, 492, 79-88. https://doi.org/10.1016/j.jhydrol.2013.04.007.\u003c/li\u003e\n\u003cli\u003eHe, X., Li, P., Ji, Y., Wang, Y., Su, Z., Elumalai, V., 2020. Groundwater Arsenic and Fluoride and Associated Arsenicosis and Fluorosis in China: Occurrence, Distribution and Management[J]. Exposure and Health, 12(3), 355-368. https://doi.org/10.1007/s12403-020-00347-8.\u003c/li\u003e\n\u003cli\u003eHuang, L., Wu, H., van der Kuijp, T.J., 2015. The health effects of exposure to arsenic-contaminated drinking water: a review by global geographical distribution[J]. Int J Environ Health Res, 25(4), 432-452. https://doi.org/10.1080/09603123.2014.958139.\u003c/li\u003e\n\u003cli\u003eKang, Y., Liu, G., Chou, C.L., Wong, M.H., Zheng, L., Ding, R., 2011. Arsenic in Chinese coals: distribution, modes of occurrence, and environmental effects[J]. Sci Total Environ, 412-413, 1-13. https://doi.org/10.1016/j.scitotenv.2011.10.026.\u003c/li\u003e\n\u003cli\u003eKatz, S.A., 2019. On the Use of Hair Analysis for Assessing Arsenic Intoxication[J]. Int J Environ Res Public Health, 16(6). https://doi.org/10.3390/ijerph16060977.\u003c/li\u003e\n\u003cli\u003eLazarevic, K., Nikolic, D., Stosic, L., Milutinovic, S., Videnovic, J., Bogdanovic, D., 2012. Determination of lead and arsenic in tobacco and cigarettes: an important issue of public health[J]. Cent Eur J Public Health, 20(1), 62-66. https://doi.org/10.21101/cejph.a3728.\u003c/li\u003e\n\u003cli\u003eLindberg, A.L., Ekstrom, E.C., Nermell, B., Rahman, M., Lonnerdal, B., Persson, L.A., Vahter, M., 2008. Gender and age differences in the metabolism of inorganic arsenic in a highly exposed population in Bangladesh[J]. Environ Res, 106(1), 110-120. https://doi.org/10.1016/j.envres.2007.08.011.\u003c/li\u003e\n\u003cli\u003eLiu, B., Wu, F., Li, X., Fu, Z., Deng, Q., Mo, C., Zhu, J., Zhu, Y., Liao, H., 2011. 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Environ Toxicol Pharmacol, 48, 150-156. https://doi.org/10.1016/j.etap.2016.10.010.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"arsenic, hair, environmental exposure, risk factors","lastPublishedDoi":"10.21203/rs.3.rs-4209156/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4209156/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Arsenic is widely found in nature, and because of its carcinogenic properties, it has come to be a serious threat to human health. The effects of arsenic on the human body are influenced by a variety of factors, including the level of arsenic in external environmental pollutants and individual human factors. Therefore, the aim of this study was to assess the level of arsenic in populations living in arsenic-contaminated areas and the influencing factors. Environmental media samples (water and wheat) and biological samples (hair) were selected for arsenic analysis in both arsenic-contaminated and arsenic-safe areas. Socio-demographic information and behavioral characteristics information were obtained from questionnaires to analyze factors that cause an increase in arsenic levels in the body. In study area, 89.33% of the water samples exceeded the national standard (10 μg/L) and 2.13% of the wheat samples had arsenic concentrations above the safe limit (0.5 mg/kg). In contrast, arsenic levels in drinking water and wheat in the control area were within safe limits. A presence of 29 (29.29%) respondents with levels of arsenic in hair higher than 1 mg/kg was found in arsenic-contaminated areas. The results of the analysis showed a significant difference (P\u003c0.05) in the level of arsenic in the hair of the inhabitants of arsenic-contaminated areas and those of arsenic-safe areas, with concentrations of 0.967 mg/kg and 0.392 mg/kg, respectively. Univariate comparative analysis of factors affecting body arsenic levels showed correlations between sex, age, years of residence, smoking, disease history, wheat-based food intake, and levels of arsenic in hair. Multiple linear regression analysis identified gender, age, and wheat-based food intake as risk factors for increased arsenic levels. The study of factors influencing the level of arsenic in the body can provide a scientific basis for the precise prevention and control of health problems resulting from environmental pollution.","manuscriptTitle":"Arsenic levels in the hair of people exposed to arsenic and awareness of its risk factors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-17 20:46:25","doi":"10.21203/rs.3.rs-4209156/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"112ec605-fa2b-46a6-8ce4-4e50e82aebeb","owner":[],"postedDate":"April 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-21T05:59:05+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-17 20:46:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4209156","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4209156","identity":"rs-4209156","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}