Next Article in Journal
Does Active Design Influence Activity, Sitting, Wellbeing and Productivity in the Workplace? A Systematic Review
Next Article in Special Issue
Environment in Children’s Health: A New Challenge for Risk Assessment
Previous Article in Journal
Assessment of a Screening Questionnaire to Identify Exposure to Lead in Pregnant Women
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 17
Issue 24
10.3390/ijerph17249225
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Urinary Mercury Levels and Predictors of Exposure among a Group of Italian Children

by
Maria Luisa Astolfi
1,*,
Matteo Vitali
2,
Elisabetta Marconi
2,
Stefano Martellucci
3,
Vincenzo Mattei
3,
Silvia Canepari
1 and
Carmela Protano
2
1
Department of Chemistry, “Sapienza” University of Rome, 00185 Rome, Italy
2
Department of Public Health and Infectious Diseases, “Sapienza” University of Rome, 00185 Rome, Italy
3
Biomedicine and Advanced Technologies Rieti Center, “Sabina Universitas”, 02100 Rieti, Italy
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(24), 9225; https://doi.org/10.3390/ijerph17249225
Submission received: 1 December 2020 / Revised: 7 December 2020 / Accepted: 8 December 2020 / Published: 10 December 2020
(This article belongs to the Special Issue Exposure Assessment to Environmental Contaminants in Children)
Browse Figures
Review Reports Versions Notes

Abstract

:
Urinary mercury (Hg) levels are suitable to assess long-term exposure to both elemental and inorganic Hg. In this study, the urinary Hg levels of 250 children (aged 6–11 years) from three areas with different anthropogenic impacts in the Rieti province, central Italy, were assessed. The Hg concentrations were in the range of 0.04–2.18 µg L−1 with a geometric mean equal to 0.18 µg L−1 [95% confidence interval (CI), 0.17–0.20 µg L−1] or 0.21 µg g−1 creatinine (95% CI, 0.19–0.23 µg g−1 creatinine), and a reference value calculated as 95th percentile of 0.53 µg L−1 (95% CI, 0.44–0.73 µg L−1) or 0.55 µg g−1 creatinine (95% CI, 0.50–0.83 µg g−1 creatinine). In all cases, urinary Hg data were below the HBM-I values (7 µg L−1 or 5 µg g−1 creatinine) established for urine, while the 95th percentile was above the German Human Biomonitoring Commission’s RV95 (0.4 µg L−1) set for children without amalgam fillings. A significant correlation (p < 0.05) was found between creatinine-corrected results and residence area, with higher urinary Hg levels in children living in the industrial area. Multiple linear regression analysis showed that creatinine was the main predictor of urinary Hg.

Graphical Abstract

1. Introduction

Mercury (Hg) is a toxic heavy metal for both environmental and human health [1,2]. It can be released into the environment from natural and anthropogenic sources [3,4,5]. Hg can be found in the environment mainly in the following three chemical forms: organic (e.g., methyl- and ethyl-Hg), inorganic (e.g., mercuric chloride), and elemental or metallic [6,7]. Exposure to all Hg chemical forms may occur via inhalation, ingestion, or dermal contact [8,9]. Exposure to inorganic and elemental Hg occurs mainly through inhalation of ambient air [10]. However, the use of consumer products containing Hg, such as hair treatment products, skin lightening creams, and soaps, can also be an additional source of inorganic Hg intake [11,12]. Instead, dietary habits and fish consumption are the main routes of methyl-Hg intake [13].
The health impact of Hg exposure in humans varies widely and depends on several factors such as the Hg chemical form, dose, duration of exposure, and the susceptibility of the exposed subject [1,12,14,15]. Organic Hg may cause auditory, visual, and sensory problems and adverse health effects on nervous and cardiovascular systems [3,16], while inorganic Hg is mainly associated with negative outcomes on central nervous system function and kidneys [4,17]. Because of their susceptibility, children, neonates, and fetuses are subpopulations at higher risk of health effects due to Hg intake [18,19,20]. Human biomonitoring (HBM) can provide useful information on the exposure and internal dose of chemicals within the general population, various subpopulations, and individuals by analyzing biomarkers in bodily fluids and tissues, such as blood, serum, urine, breast milk, hair, and nails [21,22,23,24,25,26,27]. In particular, urinary total Hg levels are suitable to assess long-term exposure to both elemental and inorganic Hg [28,29,30,31,32,33]. Besides, urinary Hg levels require creatinine adjustment to account for renal function and hydration differences [6,34]. For this reason, only urine samples with creatinine levels in the range from 0.3 to 3.0 g L−1 should be considered to determine reference values for a specific population and to perform toxicological investigations [35,36]. The German Environmental Agency defined two guidance values (HBM I = 7 µg L−1 or 5 µg g−1 creatinine and HBM II = 25 µg L−1 or 20 µg g−1 creatinine) for the interpretation of Hg urinary concentrations [37,38]. Therefore, exposure profiles and specific reference values for different groups are needed to evaluate and compare biomonitoring data on Hg exposure in different populations, subgroups, or individuals [15]. Both exposure profiles and reference values should be periodically revised to consider changes in environmental contamination levels and population exposure [39].
In this work, a biomonitoring study was conducted to obtain the Hg exposure profiles among a group of Italian school-aged children by measuring total urinary Hg levels and collecting possible influencing factors via a questionnaire [gender, age, body mass index (BMI), sport activity, fish consumption, smoking habits of cohabitant smoker(s), residence area] on its variability.
To date, given the scientific evidence on Hg toxicity in children, the most susceptible group of the population, it is essential to provide Hg exposure data during pediatric age currently lacking worldwide. Besides, to our knowledge, no data are available on this issue for the Italian school-aged population.

2. Materials and Methods

2.1. Design, Study Population, and Urine Sample Collection

A cross-sectional study was conducted in June 2018 in Rieti province (Latium, central Italy) (Figure 1). A total of 369 children aged 5–11 years were recruited from primary district schools, as described previously [40].
One spot urine sample was collected for each participant (details are reported by Antonucci et al. (2020) [40]), subdivided into aliquots, and frozen at −20 °C until analysis. Urine samples with creatinine concentration <0.3 or >3.0 g L−1 or when the amount was not sufficient were excluded. Moreover, results on participants with at least one parent not Italian were excluded from data processing, as ethnicity influences body metabolism and excretion [41]. Thus, statistical elaborations were performed on 250 samples, as summarized in Table 1.
Figure 1 reports the three studied areas, located in Rieti province, central Italy, and named Poggio Moiano, Rieti, and Cittaducale. The Rieti province is characterized by a low urbanization rate, as described previously [42]. The three studied areas were selected based on some urbanization indicators; in particular, the population density [inhabitants (IN)/km2] and the green land consumption percentage were equal to 226.4 IN/km2 and 7.2% in Rieti, 104.3 IN/km2 and 5% in Cittaducale and 92.6 IN/km2 and 4.9% in Poggio Moiano [43,44]. In addition, Cittaducale presents a large industrial center for photovoltaic cell processing, currently not working. Therefore, although Poggio Moiano and Cittaducale have similar characteristics in urbanization, the first was considered a predominantly rural site, while the second an industrial area. Rieti was considered a predominantly urban area.
Both children’s parents signed informed consent to participate in the study. For each participant, detailed information on gender, age, weight and height, residence area, health status, sport activity, fish consumption, and parents’ smoking habits and educational level were recorded in a questionnaire reported in an already published paper [40]. In particular, exposure to environmental tobacco smoke was considered when at least one smoker lived with the child. Sport activity was investigated, assessing the participation in at least one organized sport out of the school hours and if this activity was outdoor or indoor. Fish consumption was considered only if fish-based food were present in the last meals before sampling. The study protocol and the questionnaire were reviewed and approved by the local Ethical Committee (Policlinico Umberto I, “Sapienza” University of Rome; protocol code 2894). All data obtained from biological samples and questionnaires were treated anonymously and used only for scientific purposes.

2.2. Chemical Analysis

Total Hg concentrations in urine were determined by cold vapor atomic fluorescence spectrometry (CVAFS; AFS 8220 Titan, FullTech Instruments, Rome, Italy) with 5% HCl (36%; Promochem, LGC Standards GmbH, Wesel, Germany) as a carrier, and 0.05% NaBH4 (Sigma-Aldrich, St. Louis, USA) in 0.05% NaOH (98%, anhydrous pellets, RPE for analysis, ACS–ISO; Carlo Erba Reagents, Milan, Italy) as a reducing agent. All solutions were prepared using deionized water (resistivity, ≤18.3 MΩ cm) generated by an Arioso Power I RO-UP Scholar UV deionizer (Human Corporation, Songpa-Ku, Seoul, Korea). The instrumental conditions are presented in previous studies [45,46]. Standard Hg solutions for CVAFS calibration range from 0.04 to 1.5 μg L−1 were prepared by diluting adequate stock standard volumes (1002 ± 7 mg L−1; SCP Science, Baie D’Urfé, Canada) in 3% HCl. A minimum R2 value of 0.999 was considered, and the linearity range was verified by Mandel’s fitting test [47]. Urine samples were diluted 5-fold with 3% (v/v) HCl. Urinary Hg concentrations were calculated as both μg L−1 of urine and μg g−1 of creatinine to normalize individual variation due to each participating child’s differing hydration states. Urine samples that presented critical creatinine values lower than 0.3 g L−1 or higher than 3 g L−1 were excluded from the Hg analysis [35,36,48]. The method detection limit (MDL) of Hg in this study was 0.03 μg L−1. The analytical procedure was tested using certified reference material for urine (Seronorm™ Trace Elements Urine L-2 LOT 1403081) supplied by Sero AS (Billingstad, Norway). Intra- and inter-day precision calculated as the coefficient of variation percentage was 1.8 and 5.5%, respectively, and the trueness bias percentage was 6%. A standard solution (0.4 μg L−1) and blanks (3% HCl) were checked daily to control instrumental drift, cross-contamination, and memory effects.

2.3. Statistical Elaboration

Statistical analysis was performed using MedCalc version 19.5.3 (MedCalc Software Ltd., Ostend, Belgium) and SPSS version 25 for Windows (IBM Corp., Armonk, NY, USA). To describe urinary Hg concentration in samples, descriptive statistics (arithmetic and geometric means, standard deviation, minimum, maximum, and percentiles) were calculated together with their corresponding 95% confidence intervals (95% CI), as recommended by the International Federation of Clinical Chemists [49]. All data were above the MDL and not normally distributed [D’Agostino-Pearson test for normal distribution; coefficient Skewness = 4.520, p < 0.0001 and coefficient of Kurtosis = 30.27, p < 0.0001 for unadjusted (μg L−1) values; and coefficient Skewness = 2.751, p < 0.0001, and coefficient of Kurtosis = 11.76, p < 0.0001 for normalized to creatinine (μg g−1 creatinine) values]. Hence non-parametric tests, such as the Mann–Whitney, Kruskal–Wallis and Spearman correlation tests, were used to study the association between urinary Hg level and sociodemographic/fish consumption characteristics of children. A p-value less than 0.05 (two tailed) was regarded as significant. Some studied variables were coded before statistical processing in accordance with the following information obtained via questionnaire: gender (0 = male; 1 = female); age (0 = 6–8 years old; 1 = 9–11 years old); geographical location (0 = rural area; 1 = urban area; 2 = industrial area); ponderal status based on sex-specific body mass index-for-age growth charts elaborated by the Centers for Disease Control and Prevention [50] (underweight and healthy weight = 0 or overweight and obese = 1); sport activity (0 = no; 1 = indoor; 2 = outdoor); exposure to environmental tobacco smoke (0 = no; 1 = yes); university degree study of at least one parent (0 = no; 1 = yes); fish consumption before sampling (0 = no; 1 = yes).
A backward multiple linear regression was used to assess the influence of the predictor variables, including creatinine levels, as recommended by Barr et al. (2005) [51], on urinary Hg levels. The p-values used as the stay and entry criterion were 0.10 and 0.05, respectively. Linear regression coefficients were calculated with their 95% confidence intervals to indicate the uncertainty and precision of the sample statistical estimates [52,53]. Residual plots checked the influence of possible outliers and regression assumptions [54].

3. Results

Table 1 shows the characteristics of 250 children (136 boys and 114 girls) participating in the study. 45.6% of children aged 6–8 years and 54.4% were 9–11 year-old. Most children (50%) lived in the industrial area, practiced indoor sports (53.2%), and did not consume fish (82.4%). Almost a third of the children were overweight or obese. A similar proportion concerning exposure to environmental tobacco smoke (ETS) emerged in the monitoring campaign (41.6% of exposed vs. 57.2 of not exposed to ETS). A small group of children (14.8%) had at least one graduate parent.
Table 2 presents the distribution of urinary Hg levels in the study population. The Hg concentrations were in the range 0.04–2.18 µg L−1 with a geometric mean of 0.18 µg L−1 [95% confidence interval (CI), 0.17–0.20 µg L−1], which is similar to the creatinine-corrected mean of 0.21 µg g−1 creatinine (95% CI, 0.19–0.23 µg g−1 creatinine), and a value calculated as 95th percentile of 0.53 µg L−1 (95% CI, 0.44–0.73 µg L−1) or 0.55 µg g−1 creatinine (0.50–0.83 µg g−1 creatinine).
Figure 2 compares the geometric mean concentrations in this study with those from other children’s relevant biomonitoring researches. The average concentration obtained in our research was similar to that obtained in children populations without dental amalgam of Germany (0.19 μg L−1) [55], and lower than those reported by other authors [54,56,57,58,59]. However, the urinary Hg levels were about twice those found in children from Germany in 2007 [37].
Table 3 shows the influence of the studied variables on the urinary Hg levels using the non-parametric statistical tests. The Mann–Whitney or Kruskal–Wallis test was used to compare the urinary Hg level according to the qualitative variables analyzed. The Spearman correlation test was performed to study the relationship between the urinary Hg levels and the quantitative variables selected. A significant correlation (p < 0.05) between Hg level and geographical residence area of children was found only in creatinine-corrected results, with higher levels in children living in the industrial area. Creatinine concentration mean was 0.97 ± 0.42 g L−1 and showed a significantly (p = 0.0001) positive correlation with urinary Hg levels. No other selected variables influenced Hg urinary excretion.
Table 4 presents the multiple linear regression analysis results indicating the possible association between urinary Hg concentration and independent variables. Creatinine concentration was the most important predictor of exposure to the urinary Hg level and geographical location (grouped as rural and urban as control) entered in the model with a p-value = 0.089.

4. Discussion

4.1. Urinary Hg Concentration

Metal concentrations are used in health-risk assessments and to compare populations with different environmental exposures. Age is often not considered by some clinical laboratories when interpreting urinary analysis, and the concentration intervals calculated on the adult population were used even in the case of the exposure evaluation of children [60]. However, adult reference values may not be protective enough for children because children cannot be considered small adults [26,59,61]. Children are considered the most vulnerable subgroups of the population to environmental contaminants and exhibit different behavior than adults regarding excretion and accumulation of metals due to the difference in their immature detoxification mechanisms, physiology, body growth, lifestyle, and physical activity [9,15,62,63]. In addition, children exposed to low levels of trace metals, even below concentrations considered safe for the general population, showed neurobehavioral and cognitive changes [64].
We calculated urinary Hg concentration also as the 95th percentile with the 95% CI (Table 2). Although these values cannot indicate Hg exposure’s hazardous levels, they can be statistically considered to describe the Hg body burden [37].
The urinary Hg was mainly related to elemental and inorganic Hg exposure and most clearly associated with dental fillings [55,65,66], but unfortunately, we did not address this information in the present study. However, the Italian Ministry of Health in 2014 recommended avoiding the use of dental amalgam in children under six years old, pregnant or breastfeeding women, and in patients with severe kidney disease or with an allergy to the material itself [67]. The European Union (EU) with the Regulation (EU) 2017/852 has adopted a ban, starting from 1 July 2018, on the use of amalgam fillings in children under fifteen, in pregnant or breastfeeding women [68]. The action that the European Parliament and the Council approved is part of a broader implementation of the Minamata Convention’s objectives [69], aimed at limiting the use and release of Hg into the environment. The geometric mean found in our study (0.18 µg L−1) was similar to the value of 0.19 µg L−1 reported by Pesch et al. (2002) [55] in children without amalgam fillings aged 8–10 years but lower than the geometric means reported in other biomonitoring studies in children [54,56,57,58,59], as shown in Figure 1. In all cases, our urinary Hg data were below the HBM-I values (7 µg L−1 or 5 µg g−1 creatinine) established for urine by Schulz et al., (2011) [38], while the 95th percentile [0.53 µg L−1 (95% CI, 0.44–0.73 µg L−1)] (Table 2) was above the German Human Biomonitoring Commission’s RV95 (0.4 µg L−1) set for children without amalgam fillings [38,70].

4.2. Determinants of Hg Exposure

A potential source of inorganic Hg may be diet, vaccines, and the use of cosmetics (such as soaps and skin-lightening creams) that can contain Hg salts [11,71]. Some studies showed that fish consumption might also influence the urinary Hg levels in children without amalgam fillings [14,54,72,73]. A study by Sherman et al., (2013) [30] shows using naturally occurring Hg stable isotopes that urinary Hg can also derive from organic Hg in fish due to internal de-methylation. In contrast, there are no significant differences between those who consume fish or not in our study. This may be because we only considered the meals before the urine sampling and not the children’s eating habits. The urinary Hg levels are often linked to gender-related differences [56]. In general, females appear to have a greater ability to excrete Hg in the urine than males [74]. The higher concentration of Hg in females’ urine than males is also shown in our study, but the difference between urinary Hg concentrations in males (geometric mean, 0.19 µg g−1 creatinine) and females (geometric mean, 0.23 µg g−1 creatinine) is not significant. As Woods et al. (2007) [74], the urinary Hg concentration does not necessarily represent an increased risk of Hg toxicity than in males. The use of metabolic biomarkers and toxicokinetic studies may help correctly interpret urinary Hg data to assess safe Hg exposure levels [74]. Significant differences (p = 0.010) were obtained among the three geographical areas studied with higher urinary Hg levels in children living in the industrial area [0.24 µg g−1 creatinine (95% CI, 0.21–0.27 µg g−1 creatinine)]. This result was also confirmed by regression analysis: geographical location entered in the model with a p-value at the limit of significance). Unfortunately, environmental data on air Hg concentrations are not available, thus it is not possible to give conclusive explanations, but it is plausible that the industrial area presents greater airborne levels of Hg. The use of heavy metals, including Hg, is widespread in the industry, including that for the manufacture of solar photovoltaic cells [75,76,77]. Although lower or comparable to the urinary Hg concentrations of children living in other countries of the world, our results highlight the need for further environmental monitoring studies.
The most important predictor of urinary Hg exposure among the monitored children was the urinary level creatinine (Table 4). Other authors showed a positive correlation between the urinary concentrations of some substances such as other elements [54,64], Li [26], cotinine [78], trans, trans-muconic acid, and S-phenylmercapturic acid [79] and creatinine in children. To our knowledge, this is the first time that this correlation was also evidenced for urinary Hg levels, adding new evidence in the field of biomonitoring studies.

4.3. Study Limitation

The present study has some limitations. First of all, the recruitment of participants is limited to a specific region of Italy, and the sample size, although large, does not define general reference values for urinary Hg. However, information on current Hg exposure among children in the Lazio region is valuable because, to our knowledge, this is the first study on Italian children and one of the few in the world concerning the need for scientific evidence on this issue. Finally, the present study is a cross-sectional study; therefore, it does not allow for an evaluation over time.

5. Conclusions

Urinary Hg data were generally lower in Italian children than Hg levels in other countries, probably due to different eating habits and environmental factors. We would like to highlight the importance of establishing updated reference values for urinary Hg concentrations in children to detect possible toxic effects due to this metal. Although our results provide important concentration data, further studies should be conducted in other Italian geographical areas and evaluate temporal trends to better interpret biomonitoring data for risk management and assessment.

Author Contributions

Conceptualization, M.L.A. and C.P.; methodology, M.L.A.; software, M.L.A.; validation, M.L.A.; formal analysis, M.L.A.; investigation, E.M.; writing—original draft preparation, M.L.A. and C.P.; writing—review and editing, M.L.A., M.V., C.P. and S.C.; visualization, M.L.A., S.M. and V.M.; supervision, M.L.A. and M.V.; resources, S.C., M.V., S.M. and V.M.; data curation, M.L.A. and E.M.; funding acquisition, C.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was partially funded by ALCLI “Giorgio e Silvia” ONLUS, a non-profit association, grant dated 1 March 2016.

Acknowledgments

The authors wish to thank the volunteers of ALCLI “Giorgio e Silvia” ONLUS for their support.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. World Health Organization. Children’s Exposure to Mercury Compounds; World Health Organization: Geneva, Switzerland, 2010; Available online: https://apps.who.int/iris/bitstream/handle/10665/44445/9789241500456_eng.pdf?sequence=1&isAllowed=y&ua=1 (accessed on 20 October 2020).
  2. Boerleider, R.Z.; Roeleveld, N.; Scheepers, P. Human biological monitoring of mercury for exposure assessment. AIMS Environ. Sci. 2017, 4, 251–276. [Google Scholar] [CrossRef]
  3. WHO (World Health Organization). Methylmercury. Environmental Health Criteria 101; World Health Organization: Geneva, Switzerland, 1990; Available online: https://wedocs.unep.org/bitstream/handle/20.500.11822/29413/EHC101MlMcry.pdf?sequence=1&isAllowed=y (accessed on 20 October 2020).
  4. WHO (World Health Organization). Inorganic Mercury. Environmental Health Criteria 118; World Health Organization: Geneva, Switzerland, 1991; Available online: http://www.inchem.org/documents/ehc/ehc/ehc118.htm (accessed on 20 October 2020).
  5. Astolfi, M.L.; Marconi, E.; Lorini, L.; Valentino, F.; Silva, F.; Ferreira, B.S.; Canepari, S.; Majone, M. Elemental concentration and migratability in bioplastics derived from organic waste. Chemosphere 2020, 259, 127472. [Google Scholar] [CrossRef] [PubMed]
  6. ATSDR. Agency for Toxic Substances and Disease Registry. Toxicological Profile for Mercury; U.S. Department of Health and Human Services, Public Health Service: Atlanta, GA, USA, 1999. Available online: https://www.atsdr.cdc.gov/toxprofiles/tp46.pdf (accessed on 20 October 2020).
  7. Davidson, P.W.; Myers, G.J.; Weiss, B. Mercury exposure and child development outcomes. Pediatrics 2004, 113, 1023–1029. [Google Scholar] [PubMed]
  8. De Craemer, S.; Baeyens, W.; Leermakers, M. Biomonitoring of total mercury in urine: Method validation and sample stability. Biomonitoring 2018, 5, 1–13. [Google Scholar] [CrossRef]
  9. Bose-O’Reilly, S.; McCarty, K.M.; Steckling, N.; Lettmeier, B. Mercury exposure and children’s health. Curr. Probl. Pediatr. Adolesc. Health Care 2010, 40, 186–215. [Google Scholar] [CrossRef] [Green Version]
  10. Berlin, M.; Zalups, R.K.; Fowler, B.A. Mercury. In Handbook on the Toxicology of Metals, 4th ed.; Nordberg, G.F., Fowler, B.A., Nordberg, M., Friberg, L.T., Eds.; Elsevier: London, UK, 2015; Volume 2, pp. 1013–1075. [Google Scholar] [CrossRef]
  11. Al-Saleh, I.; Al-Doush, I. Mercury content in skin-lightening creams and potential hazards to the health of Saudi women. J. Toxicol. Environ. Health 1997, 51, 123–130. [Google Scholar] [CrossRef]
  12. World Health Organization. Guidance for Identifying Populations at Risk from Mercury Exposure; World Health Organization: Geneva, Switzerland, 2008; Available online: https://www.who.int/foodsafety/publications/chem/mercuryexposure.pdf?ua=1 (accessed on 20 October 2020).
  13. Counter, S.A.; Buchanan, L.H. Mercury exposure in children: A review. Toxicol. Appl. Pharmacol. 2004, 198, 209–230. [Google Scholar] [CrossRef]
  14. Basu, N.; Tutino, R.; Zhang, Z.; Cantonwine, D.E.; Goodrich, J.M.; Somers, E.C.; Rodriguez, L.; Schnaas, L.; Solano, M.; Mercado, A.; et al. Mercury levels in pregnant women, children and seafood from Mexico City. Environ. Res. 2014, 135, 63–69. [Google Scholar] [CrossRef] [Green Version]
  15. Ruggieri, F.; Majorani, C.; Domanico, F.; Alimonti, A. Mercury in children: Current state on exposure through human biomonitoring studies. Int. J. Environ. Res. Public Health 2017, 14, 519. [Google Scholar] [CrossRef] [Green Version]
  16. National Research Council. Toxicological Effects of Methylmercury; National Academy Press: Washington, DC, USA, 2000. Available online: https://www.ncbi.nlm.nih.gov/books/NBK225778/pdf/Bookshelf_NBK225778.pdf (accessed on 20 October 2020).
  17. Rice, K.M.; Walker, E.M., Jr.; Wu, M.; Gillette, C.; Blough, E.R. Environmental mercury and its toxic effects. J. Prev. Med. Public Health 2014, 47, 74–83. [Google Scholar] [CrossRef]
  18. Bose-O’Reilly, S.; Lettmeier, B.; Gothe, R.M.; Beinhoff, C.; Siebert, U.; Drasch, G. Mercury as a serious health hazard for children in gold mining areas. Environ. Res. 2008, 107, 89–97. [Google Scholar] [CrossRef] [PubMed]
  19. Solan, T.D.; Lindow, S.W. Mercury exposure in pregnancy: A review. J. Perinat. Med. 2014, 42, 725–729. [Google Scholar] [CrossRef] [PubMed]
  20. Astolfi, M.L.; Protano, C.; Schiavi, E.; Marconi, E.; Capobianco, D.; Massimi, L.; Ristorini, M.; Baldassarre, M.E.; Laforgia, N.; Vitali, M.; et al. A prophylactic multi-strain probiotic treatment to reduce the absorption of toxic elements: In-vitro study and biomonitoring of breast milk and infant stools. Environ. Int. 2019, 130, 104818. [Google Scholar] [CrossRef] [PubMed]
  21. Angerer, J.; Ewers, U.; Wilhelm, M. Human biomonitoring: State of the art. Int. J. Hyg. Environ. Health 2007, 210, 201–228. [Google Scholar] [CrossRef] [PubMed]
  22. Di Dato, C.; Gianfrilli, D.; Greco, E.; Astolfi, M.L.; Canepari, S.; Lenzi, A.; Isidori, A.M.; Giannetta, E. Profiling of selenium absorption and accumulation in healthy subjects after prolonged l-selenomethionine supplementation. J. Endocrinol. Invest. 2017, 40, 1183–1190. [Google Scholar] [CrossRef] [PubMed]
  23. Protano, C.; Astolfi, M.L.; Canepari, S.; Andreoli, R.; Mutti, A.; Valeriani, F.; Romano Spica, V.; Antonucci, A.; Mattei, V.; Martellucci, S.; et al. Exposure to individual and multiple carcinogenic metals during paediatric age: An experience from an Italian urban scenario. Ann. Ig. 2017, 29, 494–503. [Google Scholar] [CrossRef]
  24. Protano, C.; Astolfi, M.L.; Marconi, E.; Antonucci, A.; Canepari, S.; Piamonti, D.; Brunori, M.; Vitali, M. Occupational exposure assessment of major and trace elements in human scalp hair among a group of Eritrean workers. Biol. Trace Elem. Res. 2020, 197, 89–100. [Google Scholar] [CrossRef]
  25. Astolfi, M.L.; Protano, C.; Marconi, E.; Massimi, L.; Brunori, M.; Piamonti, D.; Migliara, G.; Vitali, M.; Canepari, S. A new rapid treatment of human hair for elemental determination by inductively coupled mass spectrometry. Anal. Methods 2020, 12, 1906–1918. [Google Scholar] [CrossRef]
  26. Protano, C.; Canepari, S.; Astolfi, M.L.; D’Onorio De Meo, S.; Vitali, M. Urinary reference ranges and exposure profile for lithium among an Italian paediatric population. Sci. Total Environ. 2018, 619, 58–64. [Google Scholar] [CrossRef]
  27. Astolfi, M.L.; Pietris, G.; Mazzei, C.; Marconi, E.; Canepari, S. Element levels and predictors of exposure in the hair of Ethiopian children. Int. J. Environ. Res. Public Health 2020, 17, 8652. [Google Scholar] [CrossRef]
  28. Tsuji, J.S.; Williams, P.R.; Edwards, M.R.; Allamneni, K.P.; Kelsh, M.A.; Paustenbach, D.J.; Sheehan, P.J. Evaluation of mercury in urine as an indicator of exposure to low levels of mercury vapor. Environ. Health Perspect. 2003, 111, 623–630. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  29. McKelvey, W.; Jeffery, N.; Clark, N.; Kass, D.; Parsons, P.J. Population-based inorganic mercury biomonitoring and the identification of skin care products as a source of exposure in New York City. Environ. Health Perspect. 2011, 119, 203–209. [Google Scholar] [CrossRef] [PubMed]
  30. Sherman, L.S.; Blum, J.D.; Franzblau, A.; Basu, N. New insight into biomarkers of human mercury exposure using naturally occurring mercury stable isotopes. Environ. Sci. Technol. 2013, 47, 3403–3409. [Google Scholar] [CrossRef] [PubMed]
  31. Kristensen, A.K.B.; Thomsen, J.F.; Mikkelsen, S. A review of mercury exposure among artisanal small-scale gold miners in developing countries. Int. Arch. Occup. Environ. Health 2014, 87, 579–590. [Google Scholar] [CrossRef]
  32. Park, J.D.; Zheng, W. Human Exposure and Health Effects of Inorganic and Elemental Mercury. J. Prev. Med. Public Health 2012, 45, 344–352. [Google Scholar] [CrossRef]
  33. Clarkson, T.W.; Magos, L. The toxicology of mercury and its chemical compounds. Crit. Rev. Toxicol. 2006, 36, 609–662. [Google Scholar] [CrossRef]
  34. Wilhelm, M.; Schulz, C.; Schwenk, M. Revised and new reference values for arsenic, cadmium, lead, and mercury in blood or urine of children: Basis for validation of human biomonitoring data in environmental medicine. Int. J. Hyg. Environ. Health 2006, 209, 301–305. [Google Scholar] [CrossRef]
  35. World Health Organization. Biological Monitoring of Chemical Exposure in the Workplace; World Health Organization: Geneva, Switzerland, 1996; Available online: https://apps.who.int/iris/handle/10665/41856 (accessed on 20 October 2020).
  36. Polkowska, Ż.; Kozłowska, K.; Namieśnik, J.; Przyjazny, A. Biological fluids as a source of information on the exposure of man to environmental chemical agents. Crit. Rev. Anal. Chem. 2004, 34, 105–119. [Google Scholar] [CrossRef]
  37. Schulz, C.; Angerer, J.; Ewers, U.; Kolossa-Gehring, M. The German human biomonitoring commission. Int. J. Hyg. Environ. Health 2007, 210, 373–382. [Google Scholar] [CrossRef]
  38. Schulz, C.; Wilhelm, M.; Heudorf, U.; Kolossa-Gehring, M. Update of the reference and HBM values derived by the German Human Biomonitoring Commission. Int. J. Hyg. Environ. Health 2011, 215, 26–35. [Google Scholar] [CrossRef]
  39. Wilhelm, M.; Ewers, U.; Schulz, C. Revised and new reference values for some trace elements in blood and urine for human biomonitoring in environmental medicine. Int. J. Hyg. Environ. Health 2004, 207, 69–73. [Google Scholar] [CrossRef] [PubMed]
  40. Antonucci, A.; Vitali, M.; Martellucci, S.; Mattei, V.; Protano, C. A cross-sectional study on benzene exposure in pediatric age and parental smoking habits at home. Int. J. Environ. Res. Public Health 2020, 17, 5469. [Google Scholar] [CrossRef] [PubMed]
  41. Frackiewicz, E.J.; Shiovitz, T.M.; Jhee, S.S. Ethnicity in Drug Development and Therapeutics; Cambridge University Press: Cambridge, UK, 2011. [Google Scholar]
  42. Santilli, F.; Martellucci, S.; Di Pasquale, J.; Mei, C.; Liberati, F.; Protano, C.; Vitali, M.; Sorice, M.; Mattei, V. Cancer mortality trend in Central Italy: Focus on a “Low rate of land use” Area from 1982 to 2011. Int. J. Environ. Res. Public Health 2019, 16, 628. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. AdminStat Italia. Available online: https://ugeo.urbistat.com/AdminStat/it/it/demografia/dati-sintesi (accessed on 28 November 2020).
  44. Sistema Nazionale per la Protezione dell’Ambinte (SNPA). Consumo di Suolo, Dinamiche Territoriali e Servizi Ecosistemici. SNPA 15. 2020 (In Italian). Available online: https://www.sipotra.it/wp-content/uploads/2020/07/Rapporto-su-consumo-di-suolo-dinamiche-territoriali-e-servizi-ecosistemici-Edizione-2020.pdf (accessed on 28 November 2020).
  45. Astolfi, M.L.; Protano, C.; Marconi, E.; Piamonti, D.; Massimi, L.; Brunori, M.; Vitali, M.; Canepari, S. Simple and rapid method for the determination of mercury in human hair by cold vapour generation atomic fluorescence spectrometry. Microchem. J. 2019, 150, 104186. [Google Scholar] [CrossRef]
  46. Astolfi, M.L.; Protano, C.; Marconi, E.; Massimi, L.; Piamonti, D.; Brunori, M.; Vitali, M.; Canepari, S. Biomonitoring of mercury in hair among a group of Eritreans (Africa). Int. J. Environ. Res. Public Health 2020, 17, 1911. [Google Scholar] [CrossRef] [Green Version]
  47. Mandel, J. The Statistical Analysis of Experimental Data; John Wiley & Sons: New York, NY, USA, 1964; pp. 1–410. [Google Scholar]
  48. ACGIH. American Conference of Governmental Industrial Hygienists. In Threshold Limits Value for Chemical Substances and Physical Agents & Biological Exposure Indices; American Conference of Governmental Industrial Hygienists: Cincinnati, OH, USA, 2010. [Google Scholar]
  49. Poulson, O.M.; Holst, E.; Christensen, J.M. Calculation and application of coverage intervals for biological reference values—Technical report. Pure Appl. Chem. 1997, 69, 1601–1612. [Google Scholar] [CrossRef]
  50. Kuczmarski, R.J.; Ogden, C.L.; Grummer-Strawn, L.M.; Flegal, K.M.; Guo, S.S.; Wei, R.; Mei, Z.; Curtin, L.R.; Roche, A.F.; Johnson, C.L. CDC growth charts: United states. Adv. Data 2000, 314, 1–27. [Google Scholar]
  51. Barr, D.B.; Wang, R.Y.; Needham, L.L. Biologic monitoring of exposure to environmental chemicals throughout the life stages: Requirements and issues for consideration for the National Children’s Study. Environ. Health Perspect. 2005, 113, 1083–1091. [Google Scholar] [CrossRef]
  52. Di Ciccio, T.J.; Efron, B. Bootstrap confidence intervals (with Discussion). Stat. Sci. 1996, 11, 189–228. [Google Scholar]
  53. Davison, A.C.; Hinkley, D.V. Bootstrap Methods and Their Application; Cambridge University Press: Cambridge, UK, 1997. [Google Scholar] [CrossRef]
  54. Roca, M.; Sánchez, A.; Pérez, R.; Pardo, O.; Yusa, V. Biomonitoring of 20 elements in urine of children. Levels and predictors of exposure. Chemosphere 2016, 144, 1698–1705. [Google Scholar] [CrossRef]
  55. Pesch, A.; Wilhelm, M.; Rostek, U.; Schmitz, N.; Weishoff-Houben, M.; Ranft, U.; Idel, H. Mercury concentrations in urine, scalp hair, and saliva in children from Germany. J. Expo. Sci. Environ. Epidemiol. 2002, 12, 252–258. [Google Scholar] [CrossRef] [Green Version]
  56. Batáriová, A.; Spěváčková, V.; Beneš, B.; Čejchanová, M.; Šmíd, J.; Černá, M. Blood and urine levels of Pb, Cd and Hg in the general population of the Czech Republic and proposed reference values. Int. J. Hyg. Environ. Health 2006, 209, 359–366. [Google Scholar] [CrossRef] [PubMed]
  57. Chen, G.; Chen, X.; Yan, C.; Wu, X.; Zeng, G. Surveying mercury levels in hair, blood and urine of under 7-year old children from a coastal city in China. Int. J. Environ. Res. Public Health 2014, 11, 12029–12041. [Google Scholar] [CrossRef] [PubMed]
  58. Centers for Disease Control and Prevention. Fourth National Report on Human Exposure to Environmental Chemicals, Updated Tables. February 2015. Available online: http://www.cdc.gov/biomonitoring/pdf/fourthreport_updatedtables_feb2015.pdf (accessed on 20 October 2020).
  59. Molina-Villalba, I.; Lacasaña, M.; Rodríguez-Barranco, M.; Hernández, A.F.; Gonzalez-Alzaga, B.; Aguilar-Garduño, C.; Gil, F. Biomonitoring of arsenic, cadmium, lead, manganese and mercury in urine and hair of children living near mining and industrial areas. Chemosphere 2015, 12, 83–91. [Google Scholar] [CrossRef] [PubMed]
  60. Ewers, U.; Krause, C.; Schulz, C.; Wilhelm, M. Reference values and human biological monitoring values for environmental toxins. Int. Arch. Occup. Environ. Health 1999, 72, 255–260. [Google Scholar] [CrossRef] [PubMed]
  61. Shaw, J.L.V.; Marvasti, T.B.; Colantonio, D.; Adeli, K. Paediatric reference intervals: Challenges and recent initiatives. Crit. Rev. Clin. Lab. Sci. 2013, 50, 37–50. [Google Scholar] [CrossRef]
  62. World Health Organisation (WHO). WHO’s Guidelines for Drinking-Water Quality; WHO: Geneva, Switzerland, 2011; Available online: https://apps.who.int/iris/bitstream/handle/10665/44584/9789241548151_eng.pdf?sequence=1 (accessed on 20 October 2020).
  63. Selevan, S.G.; Kimmel, C.A.; Mendola, P. Windows of susceptibility to environmental exposures in children. In Children’s Health and the Environment—A Global Perspective; Pronczuk-Garbino, J., Ed.; World Health Organization Press: Geneva, Switzerland, 2005; pp. 17–26. [Google Scholar]
  64. Callan, A.C.; Winters, M.; Barton, C.; Boyce, M.; Hinwood, A.L. Children’s exposure to metals: A community-initiated study. Arch. Environ. Contam. Toxicol. 2012, 62, 714–722. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  65. Al-Saleh, I.; Al-Sedairi, A.A. Mercury (Hg) burden in children: The impact of dental amalgam. Sci. Total Environ. 2011, 409, 3003–3015. [Google Scholar] [CrossRef]
  66. Castaño, A.; Sánchez-Rodríguez, J.E.; Cañas, A.; Esteban, M.; Navarro, C.; Rodríguez-García, A.C.; Arribas, M.; Díaz, G.; Jiménez-Guerrero, J.A. Mercury, lead and cadmium levels in the urine of 170 Spanish adults: A pilot human biomonitoring study. Int. J. Hyg. Environ. Health 2012, 215, 191–195. [Google Scholar] [CrossRef]
  67. Ministry of Health, Department of Public Health and Innovation. Raccomandazioni Cliniche in Odontostomatologia, in Italian. 2014. Available online: http://www.salute.gov.it/imgs/C_17_pubblicazioni_2128_allegato.pdf (accessed on 25 October 2020).
  68. European Parliament. Regulation (EU) 2017/852 of the European Parliament and of the Council of 17 May 2017 on mercury, and repealing Regulation (EC) No 1102/2008. Off. J. Eur. Union 2017, 137, 1–21. Available online: https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32017R0852&from=IT (accessed on 25 October 2020).
  69. Minamata Convention on Mercury. Available online: http://www.mercuryconvention.org (accessed on 20 October 2020).
  70. Apel, P.; Angerer, J.; Wilhelm, M.; Kolossa-Gehring, M. New HBM values for emerging substances, inventory of reference and HBM values in force, and working principles of the German Human Biomonitoring Commission. Int. J. Hyg. Environ. Health 2017, 220, 152–166. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  71. Dickenson, C.A.; Woodruff, T.J.; Stotland, N.E.; Dobraca, D.; Das, R. Elevated mercury levels in pregnant woman linked to skin cream from Mexico. Am. J. Obstet. Gynecol. 2013, 209, e4–e5. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  72. Levy, M.; Schwartz, S.; Dijak, M.; Weber, J.P.; Tardif, R.; Rouah, F. Childhood urine mercury excretion: Dental amalgam and fish consumption as exposure factors. Environ. Res. 2004, 94, 283–290. [Google Scholar] [CrossRef]
  73. De Burbure, C.; Buchet, J.P.; Leroyer, A.; Nisse, C.; Haguenoer, J.M.; Mutti, A.; Smerhovský, Z.; Cikrt, M.; Trzcinka-Ochocka, M.; Rezniewska, G.; et al. Renal and neurologic effects of cadmium, lead, mercury, and arsenic in chilly effects and multiple interactions at environmental exposure levels. Environ. Health Perspect. 2006, 114, 585–590. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  74. Woods, J.S.; Martin, M.D.; Leroux, B.G.; DeRouen, T.A.; Leitão, J.G.; Bernardo, M.F.; Luis, H.S.; Simmonds, P.L.; Kushleika, J.V.; Huang, Y. The contribution of dental amalgam to urinary mercury excretion in children. Environ. Health Perspect. 2007, 115, 1527–1531. [Google Scholar] [CrossRef] [Green Version]
  75. Tchounwou, P.B.; Yedjou, C.G.; Patlolla, A.K.; Sutton, D.J. Heavy metal toxicity and the environment. In Molecular, Clinical and Environmental Toxicology; Springer: Basel, Switzerland, 2012; pp. 133–164. [Google Scholar] [CrossRef] [Green Version]
  76. Kwak, J.I.; Nam, S.H.; Kim, L.; An, Y.J. Potential environmental risk of solar cells: Current knowledge and future challenges. J. Hazard. Mater. 2020, 392, 122297. [Google Scholar] [CrossRef]
  77. Bang, Y.Y.; Hong, N.J.; Sung Lee, D.; Lim, S.R. Comparative assessment of solar photovoltaic panels based on metal-derived hazardous waste, resource depletion, and toxicity potentials. Int. J. Green Energy 2018, 15, 550–557. [Google Scholar] [CrossRef]
  78. Protano, C.; Andreoli, R.; Mutti, A.; Manigrasso, M.; Avino, P.; Vitali, M. Reference intervals for urinary cotinine levels and the influence of sampling time and other predictors on its excretion among Italian schoolchildren. Int. J. Environ. Res. Public Health 2018, 15, 817. [Google Scholar] [CrossRef] [Green Version]
  79. Protano, C.; Andreoli, R.; Manini, P.; Vitali, M. Urinary trans, trans-muconic acid and S-phenylmercapturic acid are indicative of exposure to urban benzene pollution during childhood. Sci. Total Environ. 2012, 435, 115–123. [Google Scholar] [CrossRef]
Ijerph 17 09225 g001 550
Figure 1. Map of the three sampling sites in the study area (province of Rieti, central Italy).
Figure 1. Map of the three sampling sites in the study area (province of Rieti, central Italy).
Ijerph 17 09225 g001
Ijerph 17 09225 g002 550
Figure 2. Comparison of urinary mercury concentrations (as geometric mean, µg L−1) obtained in our study and other biomonitoring studies in children: Germany 2002 [55], Czech Republic 2006 [56], Germany 2007 [37], China 2014 [57], USA 2015 [58], Spain 2015 [59], Spain 2016 [54], and this study, Italy.
Figure 2. Comparison of urinary mercury concentrations (as geometric mean, µg L−1) obtained in our study and other biomonitoring studies in children: Germany 2002 [55], Czech Republic 2006 [56], Germany 2007 [37], China 2014 [57], USA 2015 [58], Spain 2015 [59], Spain 2016 [54], and this study, Italy.
Ijerph 17 09225 g002
Table
Table 1. Studied population characteristics.
Table 1. Studied population characteristics.
VariableN = 250
Gendern (%)
Male136 (54.4)
Female114 (45.6)
Age (years)9 (6–11) a
6–8 years114 (45.6)
9–11 years136 (54.4)
Height (cm)133 (100–165) a
Missing data36
Weight (kg)30 (18–90) a
Missing data21
BMI (kg m−2)17.5 (10.7–34.3) a
Missing data40
Ponderal status
Underweight and healthy weight123 (49.2)
Overweight and obese76 (30.4)
Missing data51
Geographical location
Rural (Poggio Moiano)55 (22.0)
Urban (Rieti)65 (26.0)
Industrial (Cittaducale)125 (50.0)
Missing data5
Sport activity
Outdoor68 (27.2)
Indoor133 (53.2)
No sport activity40 (16.4)
Missing data9
Exposure to environmental tobacco smoke
Yes104 (41.6)
No143 (57.2)
Missing data3
University degree of one of the parents
Yes37 (14.8)
No210 (84.0)
Missing data3
Fish consumption
Yes44 (17.6)
No206 (82.4)
Missing data0
a Values are expressed as median (minimum and maximum).
Table
Table 2. Urinary mercury (Hg) levels of children in Italy (n = 250).
Table 2. Urinary mercury (Hg) levels of children in Italy (n = 250).
VariableHg (µg L−1)Hg (µg g−1 Creatinine)
Geometric mean (95% CI)0.18 (0.17–0.20)0.21 (0.19–0.23)
Arithmetic mean (95% CI)0.24 (0.21–0.27)0.27 (0.24–0.29)
Standard deviation0.220.21
Median (95% CI)0.18 (0.17–0.20)0.22 (0.20–0.24)
Minimum0.040.04
2.5th (95% CI)0.04 (0.03–0.054)0.04 (<MDL–0.054)
5th (95% CI)0.06 (0.04–0.07)0.06 (0.04–0.07)
25th (95% CI)0.12 (0.11–0.14)0.13 (0.12–0.16)
75th (95% CI)0.28 (0.26–0.33)0.34 (0.31–0.38)
95th (95% CI)0.53 (0.44–0.73)0.55 (0.50–0.83)
97.5th (95% CI)0.73 (0.54–1.47)0.83 (0.56–1.35)
Maximum2.181.59
Table
Table 3. Association between urinary mercury (Hg) level and sociodemographic/food consumption characteristics of children.
Table 3. Association between urinary mercury (Hg) level and sociodemographic/food consumption characteristics of children.
Hg (µg L−1)Hg (µg g−1 Creatinine)
VariableGM (95% CI)p-ValueGM (95% CI)p-Value
Gender 0.474 a 0.082 a
Male0.19 (0.16–0.20) 0.19 (0.17–0.22)
Female0.19 (0.16–0.22) 0.23 (0.20–0.26)
Age (years)−0.052 (−0.179–0.076) b0.426 c−0.0842 (−0.210–0.044) b0.198 c
6–8 years0.19 (0.17–0.22)0.545 a0.22 (0.19–0.26)0.317 a
9–11 years0.18 (0.16–0.20) 0.20 (0.17–0.22)
Ponderal Status 0.656 a 0.450 a
Underweight and healthy weight0.19 (0.17–0.24) 0.21 (0.16–0.24)
Overweight and obese0.16 (0.15–0.22) 0.19 (0.15–0.25)
Geographical location 0.192 d 0.010 d
Rural (Poggio Moiano)0.18 (0.14–0.24) 0.18 (0.14–0.23)
Urban (Rieti)0.18 (0.14–0.24) 0.18 (0.14–0.23)
Industrial (Cittaducale)0.20 (0.18–0.22) 0.24 (0.21–0.27)
Sport activity 0.978 d 0.930 d
Outdoor0.18 (0.15–0.21) 0.20 (0.17–0.25)
Indoor0.18 (0.16–0.21) 0.21 (0.19–0.24)
No sport activity0.19 (0.16–0.24) 0.23 (0.19–0.28)
Exposure to environmental tobacco smoke 0.135 a 0.095 a
Yes0.19 (0.17–0.21) 0.22 (0.19–0.25)
No0.17 (0.15–0.19) 0.19 (0.17–0.22)
University degree of one of the parents 0.469 a 0.717 a
Yes0.17 (0.13–0.21) 0.20 (0.15–0.25)
No0.18 (0.16–0.20) 0.20 (0.18–0.22)
Fish consumption 0.696 a 0.615 a
Yes0.18 (0.14–0.22) 0.22 (0.18–0.28)
No0.18 (0.17–0.20) 0.21 (0.19–0.23)
Creatinine0.276 (0.156–0.389) b0.0001 c--
GM: geometric mean. The number in italic indicates a significant association among the variables considered (p-value < 0.05). a Mann–Whitney test. b Spearman correlation (95% confidence interval). c Spearman correlation test. d Kruskal–Wallis test.
Table
Table 4. Results of the multiple linear regression analysis.
Table 4. Results of the multiple linear regression analysis.
VariablesBStandard Errorβp-ValueR2Adj R2
Constant−2.1990.129 <0.0010.0700.062
Creatinine0.4340.1090.255<0.001
Geographical location0.1560.0920.1090.089
B = standardized regression coefficient; β = no standardized regression coefficient; R2 = squared multiple correlation of predictor variable; Adj R2 = adjusted squared multiple correlation of predictor variable. Variables included in the model (backward method): gender (male = 0, female = 1), age (as continuous variable), creatinine (as a continuous variable), geographical location (as rural/urban = 0, industrial = 1).
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Astolfi, M.L.; Vitali, M.; Marconi, E.; Martellucci, S.; Mattei, V.; Canepari, S.; Protano, C. Urinary Mercury Levels and Predictors of Exposure among a Group of Italian Children. Int. J. Environ. Res. Public Health 2020, 17, 9225. https://doi.org/10.3390/ijerph17249225

AMA Style

Astolfi ML, Vitali M, Marconi E, Martellucci S, Mattei V, Canepari S, Protano C. Urinary Mercury Levels and Predictors of Exposure among a Group of Italian Children. International Journal of Environmental Research and Public Health. 2020; 17(24):9225. https://doi.org/10.3390/ijerph17249225

Chicago/Turabian Style

Astolfi, Maria Luisa, Matteo Vitali, Elisabetta Marconi, Stefano Martellucci, Vincenzo Mattei, Silvia Canepari, and Carmela Protano. 2020. "Urinary Mercury Levels and Predictors of Exposure among a Group of Italian Children" International Journal of Environmental Research and Public Health 17, no. 24: 9225. https://doi.org/10.3390/ijerph17249225

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop