Uranium In Urine Research Articles

In vitro analysis of urinary uranium of cancerous patients in Dhi_Qar governorate, southern Iraq

BackgroundThe simple and effective technique of fission track etch has been applied to determine trace concentration of uranium in human urine samples taken from two groups of male and female participants: cancer patients and healthy subjects are living in Dhi-Qar governorate, southern of Iraq. This governorate was the center of industrialization and the prior military activities especially during the Gulf wars in 1991 and 2003, and the abandoned weaponry is still present in these regions. The induced fission track registration was done using the CR-39 track detector. Patients with cancer were statistically significantly distinguished by significantly higher concentrations of uranium as compared with members of a control group, the mean value of uranium concentration for the cancer patients was 3.67 ± 0.16 μg/L, with the maximum recorded uranium concentration was 5.33 ± 0.25 μg/L (male, 75 years old, has prostate cancer) and the minimum concentration was 2.04 ± 0.07 μg/L (female, 7 years old, has leukemia cancer). While the mean value of uranium concentration for the healthy group was 2.80 ± 0.14 μg/L, with the maximum uranium concentration was 4.19 ± 0.23 μg/L (male, 73 years old) and the minimum concentration was 1.28 ± 0.07 μg/L (male, 7 years old). The results also showed a variation in uranium concentration according to gender, smoking status, and age. A gender comparison employed in the study showed that men had higher concentrations of uranium in them than female subjects and this may dawn from the difference in hormonal makeup of the body. While the smoking habit, it was found that persons who smokers, contained higher levels of uranium than those who does not smokers, demonstrating that smoking is the main route for uranium absorption. The finding indicated a significant of uranium excretion with age, which matches the predictions of theICRP (International Commission on Radiological Protection) uranium model. Based on these findings, it remains necessary for increasing targeted public health interventions, strictly monitoring the environment, and utilizing campaigns to avoid uranium exposure along with related complications in high-risk patient populations.

Minimum Detectable Intakes and Doses for Uranium Bioassays-Comparison between Alpha Spectrometry and ICP-MS.

Naturally occurring uranium complicates monitoring for occupational exposures. There are several retroactive methods that can be used to monitor for occupational exposures, with benefits and drawbacks to each. Analysis of uranium in urine by mass spectrometry and alpha spectrometry is compared, and methods of determining an occupational exposure are presented. The minimum detectable concentrations from each analysis and a method for intake determination based on the analytical results are compared for various solubility types and mixtures. Mass spectrometry with radiochemical separation was found to be the most sensitive analysis for detecting occupational exposures to anthropogenic mixtures based on minimum detectable doses calculated from the proposed method for intake determination.

Geographic and dietary differences of urinary uranium levels in the Strong Heart Family Study.

American Indian (AI) communities are affected by uranium exposure from abandoned mines and naturally contaminated drinking water. Few studies have evaluated geographical differences across AI communities and the role of dietary exposures. We evaluated differences in urinary uranium levels by diet and geographical area among AI participants from the Northern Plains, the Southern Plains, and the Southwest enrolled in the Strong Heart Family Study (SHFS). We used food frequency questionnaires to determine dietary sources related to urinary uranium levels for 1,682 SHFS participants in 2001-2003. We calculated adjusted geometric mean ratios (GMRs) of urinary uranium for an interquartile range (IQR) increase in self-reported food group consumption accounting for family clustering and adjusting for sociodemographic variables and other food groups. We determined the percentage of variability in urinary uranium explained by diet. Median (IQR) urinary uranium levels were 0.027 (0.012, 0.057) μg/g creatinine. Urinary uranium levels were higher in Arizona (median 0.039 μg/g) and North Dakota and South Dakota (median 0.038 μg/g) and lower in Oklahoma (median 0.019 μg/g). The adjusted percent increase (95% confidence interval) of urinary uranium levels per IQR increase in reported food intake was 20% (5%, 36%) for organ meat, 11% (1%, 23%) for cereals, and 14% (1%, 29%) for alcoholic drinks. In analyses stratified by study center, the association with organ meat was specific to North Dakota and South Dakota participants. An IQR increase in consumption of fries and chips was inversely associated with urinary uranium levels -11% (-19%, -3%). Overall, we estimated that self-reported dietary exposures explained 1.71% of variability in urine uranium levels. Our paper provides a novel assessment of self-reported food intake and urinary uranium levels in a cohort of American Indian participants. We identify foods (organ meat, cereals, and alcohol) positively associated with urinary uranium levels, find that organ meat consumption is only associated with urine uranium in North Dakota and South Dakota, and estimate that diet explains relatively little variation in total urinary uranium concentrations. Our findings contribute meaningful data toward a more comprehensive estimation of uranium exposure among Native American communities and support the need for high-quality assessments of water and dust uranium exposures in SHFS communities.

Biomarkers of metal exposure in adolescent e-cigarette users: correlations with vaping frequency and flavouring

BackgroundYouth vaping poses a significant public health concern as metals have been detected in e-cigarette aerosols and liquids. This study investigated factors associated with biomarkers of metal exposure.MethodsData were drawn...

Reduction of haemoglobin is related to metal mixtures exposure in Chinese preschoolers: Joint effect models

Heavy metal exposure is a known risk factor for hematologic disorders in children, yet the impact of co-exposure to multiple metals remains underexplored. This cross-sectional study investigates the relationship between urinary levels of 23 metals and haemoglobin (Hb) in 1460 Chinese preschoolers. The concentrations of the 23 urinary metals were quantified using an inductively coupled plasma mass spectrometer, while Hb levels were assessed through finger prick blood samples. To evaluate the co-exposure effects, we employed three approaches: Generalized linear regression model, joint effect models including Quantile g-Computation and Bayesian Kernel Machine Regression (BKMR). From the generalized linear regression and Quantile g-computation, urinary uranium, thallium, aluminium, iron and tungsten were correlated negatively with Hb, while urinary barium was correlated positively (all P < 0.05). Moreover, significant negative associations between metal mixtures exposure with Hb were identified in both Quantile g-computation [β (95% CI): -0.083 (-0.132, -0.033), P = 0.0012] and BKMR [90th percentile vs. 50th percentile β (95% CI): -0.238 (-0.368, -0.107), P < 0.001] with aluminium emerging as the primary contributor to this joint effect (weight in Quantile g-computation = 0.399, PIPs in BKMR = 0.896). These findings provide a potential explanation for environmental exposure to metals and Hb-related disease in preschoolers.

Urinary biomarkers of exposure to toxic and essential elements: A comparison of infants fed with human milk or formula.

Early-life exposure to nonessential (toxic) and essential trace elements can influence child development. Although infant formula powders and the water used to reconstitute them can contain higher concentrations of many elements compared with human milk, the influence of feeding mode on reliable biomarkers of infant exposure has rarely been demonstrated. We evaluated associations between urinary biomarkers and feeding mode (exclusively human milk, exclusively formula, or combination-fed) for four toxic (arsenic, cadmium, nickel, and uranium) and three essential elements (cobalt, molybdenum, and selenium) using general linear models. A total of 462 participants from the rural New Hampshire Birth Cohort Study were on average 6 weeks old between July 2012 and March 2019 and had urine samples, 3-day food diaries, and relevant covariate data available. In adjusted models, urinary arsenic was 5.15 (95% confidence interval = 4.04, 6.58), molybdenum was 19.02 (14.13-25.59), and selenium was 1.51 (1.35-1.68) times higher in infants fed exclusively with formula compared with infants fed exclusively with human milk. By contrast, urinary uranium was 0.59 (0.46-0.75) and cobalt was 0.78 (0.65-0.95) times lower with formula feeding than human milk feeding. Our findings suggest that infant exposure to several potentially toxic elements varies by feeding mode, as concentrations of reliable urinary biomarkers were higher with formula or human milk, depending on the element. Importantly, exposure to arsenic increased with household tap water arsenic regardless of feeding mode, suggesting that all infants could be at risk in populations with high concentrations of arsenic in drinking water.

Association between urine uranium and asthma prevalence.

Previous studies showed that urine uranium (U) is associated with asthma prevalence in adults. However, the association between them among the general population is unclear. Therefore, this study aimed to explore this unclear association. The data of the participants were collected from the 2007-2016 National Health and Nutrition Examination Survey (NHANES) performed in the United States. Continuous variables with a skewed distribution were analyzed using Ln-transformation. The association between urine U and asthma prevalence was analyzed by multiple regression analysis, and the linear association between them was evaluated by smoothed curve fitting. The subgroup analysis was performed using the hierarchical multivariate regression analysis. A total of 13,581 participants were included in our analysis. The multivariate regression analysis showed that LnU was independently and positively correlated with asthma prevalence in the general population (OR = 1.12; 95% CI: 1.04-1.20; p = 0.002). The subgroup analysis revealed that college graduate or above showed a stronger association between LnU and asthma prevalence (<9th grade: OR = 0.84; 95% CI: 0.61-1.14; 9-11th grade: OR = 1.23; 95% CI: 0.99-1.52; high school grade: OR = 1.00; 95% CI: 0.84-1.19; college: OR = 1.04; 95% CI: 0.91-1.19; ≥college graduate: OR = 1.32; 95% CI: 1.11-1.57; P for interaction = 0.0389). Our research suggested that urinary U levels are positively associated with asthma prevalence among the general population of the United States, and the association is especially strong among people with high levels of education.

Selection of an internal standard to determine mass concentration of uranium in human urine by inductively coupled plasma mass-spectrometry

Selection of an internal standard to determine mass concentration of uranium in human urine by inductively coupled plasma mass-spectrometry

Relationships between urinary uranium and cardiac geometry and left ventricular functioning in the Strong Heart Study

Relationships between urinary uranium and cardiac geometry and left ventricular functioning in the Strong Heart Study

Associations of exposure to heavy metal mixtures with kidney stone among U.S. adults: A cross-sectional study.

Assessing the effects of heavy metals (HMs) on kidney stone is often limited to analyzing individual metal exposures, with studies on the effects of exposure to mixtures of HMs being scarce. To comprehensively evaluate the relationship between exposure to mixed HMs and kidney stones, we analyzed data from the National Health and Nutrition Examination Survey (NHANES) from 2007-2016, which included 7809 adults. We used multiple statistical methods, including multiple logistic regression models, weighted quantile sum (WQS) regression, quantile g-computation (qgcomp) and bayesian kernel machine regression (BKMR), to assess the association between single HM and mixed exposure to HMs and kidney stones. Firstly, in single exposure analysis, urinary cadmium (Cd) and cobalt (Co) demonstrated a positive association with the risk of kidney stones. Secondly, various other approaches consistently revealed that mixed exposure to HMs exhibited a positive association with kidney stone risk, primarily driven by Cd, Co, and barium (Ba) in urine, with these associations being particularly notable among the elderly population. Finally, both BKMR and survey-weighted generalized linear models consistently demonstrated a significant synergistic effect between urinary Co and urinary uranium (Ur) in elevating the risk of kidney stones. Overall, this study provides new epidemiological evidence that mixed exposure to HMs is associated with an increased risk of kidney stones. Further prospectively designed studies are needed to confirm these findings.

Comparison of urine heavy metals in exclusive menthol and non-menthol cigarette users by race/ethnicity: The 2015-2016 National Health and Nutrition Examination Survey Special Sample.

The objective of this study was to investigate the differences in urine concentrations of heavy metals (uranium, cadmium, and lead) between exclusive menthol and non-menthol cigarette smokers across three racial/ethnic groups using data from the National Health and Nutrition Examination Survey (NHANES) 2015-2016 Special Sample. Data from NHANES 2015-2016 Special Sample were analyzed to assess the association between menthol smoking and heavy metal biomarkers in urine across three racial/ethnic groups (N=351), including Non-Hispanic White (NHW), Non-Hispanic Black (NHB), and Hispanic/Other (HISPO). Multivariable linear regression models were used to estimate adjusted geometric means (GMs) and ratio of GMs (menthol/non-menthol smokers) (RGMs) for urine biomarkers of heavy metals between menthol and non-menthol smokers by race/ethnicity. Among the 351 eligible participants, 34.4% (n=121) were NHW, 33.6% (n=118) were NHB, and 32.0% (n=112) were HISPO exclusive cigarette smokers. The analysis revealed significantly higher concentrations of urine uranium in NHB menthol smokers compared to NHB non-menthol smokers (RGMs=1.3; 95% CI: 1.0-1.6; p=0.04). NHW menthol smokers appeared to have higher levels of urine uranium than non-menthol smokers, but the difference was not statistically significant (9.0 vs 6.3; RGMs=1.4; 95% CI: 1.0-2.2; p=0.08). There were no significant differences in urine metals (cadmium and lead) by menthol status among NHW, NHB, or HISPO cigarette smokers (p>0.05). The research findings regarding the higher levels of urine uranium among Non-Hispanic Black (NHB) menthol cigarette smokers raise questions about the claims suggesting that additives in cigarettes do not contribute to increased toxicity.

0875 Self-Reported Sleep Indices in Veterans Exposed to Deleted Uranium

Abstract Introduction During Gulf War I, a group of veterans sustained exposure to deplete uranium (DU) through a series of “friendly fire” incidents. This resulted in both inhalational and traumatic injuries, with a subset having retained fragments and ongoing systemic exposure to DU. These veterans have undergone biennial surveillance assessments to monitor for DU-related health effects. While effects of DU on bones and kidneys have been consistently shown, less is understood about the effect of DU on sleep. In rat models, exposure to DU affected sleep architecture by increasing both sleep fragmentation and quantity of REM sleep. We therefore sought to characterize the effect of DU on self-reported sleep complaints among this unique veteran cohort. Methods Of a dynamic cohort of 81 DU-exposed veterans, 37 completed medical history, exposure, and sleep questionnaires and 24-hour urine uranium collections. Responses on the sleep questionnaires -which included subjective assessment of sleep quality and quantity as well as an Epworth Sleepiness Scale (ESS) score – were compared in veterans with elevated (&amp;gt;0.1 mcg U/g creatinine) vs low urine uranium (uU). Results The 37 veterans assessed were all male, mostly white, and had a mean age of 54 ± 2.8 years. 10 were identified as having elevated urine DU levels. The mean ESS score for the group overall was in the pathologic range (13 ± 4) and did not differ significantly between the high and low uU groups. The most frequent complaints in the group overall were of difficulty falling asleep, awakening frequently throughout the night, restless sleep, and frequent tossing and turning. Among all self-reported sleep indices, there were no significant differences among the two groups. Conclusion In this group of DU-exposed veterans, excessive daytime sleepiness and several other sleep complaints were observed. Although there were no differences in the frequency or severity of self-reported sleep indices among this group of DU-exposed veterans, the group was overall pathologically sleepy. More work is needed to further characterize the possible impact of DU exposure on sleep. Support (if any) Funded by the US Department of Veterans Affairs and supported by the University of Maryland General Clinical Research Center.

Development of an interpretable machine learning model associated with heavy metals’ exposure to identify coronary heart disease among US adults via SHAP: Findings of the US NHANES from 2003 to 2018

Limited information is available on the links between heavy metals' exposure and coronary heart disease (CHD). We aim to establish an efficient and explainable machine learning (ML) model that associates heavy metals' exposure with CHD identification. Our datasets for investigating the associations between heavy metals and CHD were sourced from the US National Health and Nutrition Examination Survey (US NHANES, 2003–2018). Five ML models were established to identify CHD by heavy metals' exposure. Further, 11 discrimination characteristics were used to test the strength of the models. The optimally performing model was selected for identification. Finally, the SHapley Additive exPlanations (SHAP) tool was used for interpreting the features to visualize the selected model's decision-making capacity. In total, 12,554 participants were eligible for this study. The best performing random forest classifier (RF) based on 13 heavy metals to identify CHD was chosen (AUC: 0.827; 95%CI: 0.777–0.877; accuracy: 95.9%). SHAP values indicated that cesium (1.62), thallium (1.17), antimony (1.63), dimethylarsonic acid (0.91), barium (0.76), arsenous acid (0.79), total arsenic (0.01) in urine, and lead (3.58) and cadmium (4.66) in blood positively contributed to the model, while cobalt (−0.15), cadmium (−2.93), and uranium (−0.13) in urine negatively contributed to the model. The RF model was efficient, accurate, and robust in identifying an association between heavy metals' exposure and CHD among US NHANES 2003–2018 participants. Cesium, thallium, antimony, dimethylarsonic acid, barium, arsenous acid, and total arsenic in urine, and lead and cadmium in blood show positive relationships with CHD, while cobalt, cadmium, and uranium in urine show negative relationships with CHD.

Differential uranium exposure and its association with hypertension and elevated blood pressure in American Indian communities in the Strong Heart Family Study

Background: Urinary uranium (U) is present at higher concentrations in American Indian (AI) participants in the Strong Heart Study compared to the general US population. Cardiovascular disease is the leading cause of death among American Indians, of which hypertension is an important risk factor. We evaluated the association between U exposure and incident hypertension and increased blood pressure (BP) among Strong Heart Family Study (SHFS) participants. Methods: We included 1453 SHFS participants with baseline visits in 1998-1999 or 2001–2003, and follow-up visits in 2001-2003 and/or 2006–2009. We estimated the association of urinary U exposure with changes in systolic BP and diastolic BP levels using linear regressions, and hypertension incidence using Poisson regression with robust variance, accounting for family clustering. Results: Median (IQR) urinary uranium levels were 0.029 (0.013, 0.059) &amp;#x3bc;g/g creatinine. At follow-up, 17.4% of participants developed hypertension. After adjustment for sociodemographic variables, eGFR, pre-diabetes status, and BMI, comparing the highest to lowest urinary uranium quartiles, the RR (95% CI) of incident hypertension was 1.38 (1.00, 1.91) and the mean difference (95% CI) in systolic BP levels between baseline and follow-up was -2.00 (-3.83, -0.16) mmHg. After adjustment for urinary cadmium and arsenic, the corresponding RR (95% CI) was 1.44 (1.04, 1.99) and the mean difference (95% CI) for systolic BP between baseline and follow-up was -1.48 (-3.32, 0.37) mmHg. The association between uranium and blood pressure levels at follow-up was non-linear, with an increased risk at higher urinary uranium levels. Conclusions: These findings suggest moderate increased risk for hypertension at urinary uranium levels greater than 0.06 &amp;#x3bc;g/g, typical of the Southwest and Great Plains, furthermore a potential association with higher systolic BP levels and higher uranium exposure levels. Further work is needed to assess the cardiovascular effects of chronic uranium exposure in US populations. Keywords: Uranium, hypertension, blood pressure

Exploring early life exposome related to low birth weight through machine learning methods

BACKGROUND AND AIM: Regarding the multifactorial etiology of low birth weight (LBW), the contribution of environmental hazards is increasing. Exposome refer to totality of a life’s exposure including multiple pollutants, lifestyle and sociodemographic factors should provide a holistic view on LBW. This study aims to explore early life exposome related to LBW and identify important factors through machine learning methods. METHODS We recruited 486 mother-infant pairs from Taiwan Birth Panel Study, with LBW defined as birth body weight below 2500 gm. Early life factors included 27 environmental pollutants measured in cord blood or maternal urine, estimated outdoor air pollutants (particulate matter 2.5 (PM2.5), nitrogen dioxide (NO2) and greenness (Normalized Difference Vegetation Index (NDVI)) and questionnaires on socioeconomic levels, health behaviors and diet during pregnancy. The association between exposome and LBW were examined by five machine learning methods including Naïve bayes, Support vector machine (SVM), Random forest, XGBoost (eXtreme gradient boosting) and Ensemble learning. Model performance was evaluated by area under Receiver operator characteristic (ROC) curve. And, features selection was used to identify important features. RESULTS SVM method and Random forest provided the best prediction based on AUC= 0.73 and 0.74, respectively. The top five important features selected from best model prediction of Random forest include: perfluorooctyl sulfonate (PFOS) in cord blood, uranium and zinc in maternal urine, maternal pre-pregnancy body mass index, maternal gestational weight gain. CONCLUSIONS Machine learning approaches could be useful tools for modeling early life exposome associated with LBW. Environmental pollutants and maternal weight status are critical for LBW risk. KEYWORDS exposome, machine learning, environmental pollutants, low birth weight

Measurements of uranium in urine samples of uranium workers and inhabitants of nearby settlements in Akmola Region, Kazakhstan

The main purpose is determine and evaluate uranium concentration in urine of the inhabitants of nearby settlement of uranium mining sites and workers of uranium processing sites. As uranium enterprises working in Stepnogorsk of Akmola Region and its exploitation could be a risk of radiation exposure for the workers as well as for the local population, monitoring of biosamples could allow information about the exposure to uranium for the workers and inhabitants. Workers and inhabitants of male genders were asked to collect 24 h-urine samples. The concentration of uranium in urine was measured in mass spectrometry (ICP-MS). Uranium concentration in urine samples from workers values ranged from 0.05 to 12.5 μgL−1 , for Akmola region inhabitants values ranged from 0.10 to 0.60 μgL−1 . The results of concentrations of uranium in the urine of inhabitants living near SMCC were comparable to values reported in other studies. The concentration of uranium in the urine of the workers had slightly elevated values, comparable to the conditional norm of 0.9 μg/L.

The Associations between Blood and Urinary Concentrations of Metal Metabolites, Obesity, Hypertension, Type 2 Diabetes, and Dyslipidemia among US Adults: NHANES 1999-2016.

Background Heavy metals are well known to be associated with cancer outcomes, but its association with obesity and cardiometabolic risk outcomes requires further study. Methods Adult data from the National Health and Examination Survey (NHANES Continuous 1999–2016, n = 12,636 to 32,012) with data for blood or urinary metals concentrations and body mass index were used. The study aim was twofold: (1) to determine the association between heavy metals and obesity and (2) to examine the influence of heavy metals on the relationship between obesity and hypertension, type 2 diabetes, and dyslipidemia. Logistic regression was used to examine the main effects and interaction effects of metals and obesity for the odds of prevalent hypertension, type 2 diabetes, and dyslipidemia. Models were adjusted for age, gender, ethnicity, smoking status, physical active status, and poverty-income ratio, with additional adjustment for creatinine in models with the urinary measures of heavy metals. High-low concentration categories were defined by grouping metal quintiles with the most similar associations with obesity. Results Blood lead had a negative linear association with obesity (odds ratio (OR) = 0.42, 95% confidence interval (CI) = 0.37–0.47). In those with obesity, high blood lead was associated with lower risk of prevalent dyslipidemia, while no association was found in those without obesity. The study observed a curvilinear relationship between urinary antimony and obesity with the moderate group having the highest odds of obesity (OR = 1.36, 1.16–1.59). However, the relationship between urinary antimony and prevalent hypertension and dyslipidemia risk was linear, positive, and independent of obesity. While not associated with prevalent obesity risk, high urinary uranium was associated with 30% (P=0.01) higher odds for prevalent type 2 diabetes. Conclusions The impact of environmental factors on obesity and health may be complex, and this study reinforces the heterogeneous relationship between various metals, obesity, and obesity-related metabolic diseases even at levels observed in the general population.

Geographic and dietary differences of urinary uranium levels in the Strong Heart Family Study

BACKGROUND AND AIM: Chronic low-dose exposure to uranium (U) through diet is not well characterized in the US. American Indian (AI) participants in the Strong Heart Family Study (SHFS) have higher urinary uranium concentrations compared to the general US population. This disproportionate exposure might be explained by groundwater contamination in rural and sub-urban regions, or through dietary exposures. The objective of this study was to evaluate differences in urinary uranium concentrations by diet and geography among American Indians from the Northern Plains, the Southern Plains, and the Southwest. METHODS: We evaluated food frequency questionnaires to determine the major dietary sources of urinary uranium concentrations for 2,170 AI participants in the SHFS, collected during phase IV (2001-2003). We assessed adjusted geometric mean ratios (GMRs) of urinary uranium for an interquartile (IQR) increase in self-reported food group consumption using generalized estimating equations. Exploratory analyses were stratified by gender and study center. RESULTS:In fully adjusted models, the percent increase (95% confidence interval) of urinary uranium per increase in reported food consumption corresponding to the IQR was 29% (14%, 47%) for organ meat, 9% (1%, 19%) for legumes, and 17% (5%, 32%) for alcohol. In analyses stratified by study center, the associations with organ meat and legumes were only observed for North/South Dakota participants. Consumption of fries and chips [percent increase -16% (-23%, -9%)] and any shellfish [-21% (-0.35%, -4%)] were inversely associated with urinary uranium. Overall, we estimated that self-reported dietary exposures explain 16.8% of variability in urine uranium concentrations. CONCLUSIONS:Organ meat, legumes, and alcohol contribute to higher uranium exposure in the SHFS population. After stratification, organ meat remained significantly associated with uranium for North and South Dakota participants. These findings warrant further investigation into the relative contribution of diet and drinking water to total uranium exposure in American Indian communities. KEYWORDS: Heavy metals, food/nutrition, environmental epidemiology, environmental disparities

Occupational exposure to potentially toxic elements in the foundry industry: an integrated environmental and biological monitoring

Industrial foundry processes release metal dust and fumes into the environment. Our study evaluated the exposure to potentially toxic elements in foundry workers. The assessed samples consisted of air particulate matter (n = 42), urine (n = 194), and blood (n = 167). Six workers had high concentrations of arsenic (As) in urine and one of them had a high cadmium (Cd) content in blood, according to Biological Exposure Index from the American Conference of Governmental Industrial Hygienists. The work task significantly influenced the concentrations of cobalt (Co), copper (Cu), iron (Fe), and manganese (Mn) in air, barium (Ba) in urine, and lead (Pb) and cesium (Cs) in blood, while the employment years affected concentrations of Mn, tin (Sn), and uranium (U) in urine and iodine (I) in blood. Arsenic, Pb, Co, and Cd in particulate matter and biological matrices presented significant covariation by working activity, supporting the occupational exposure. In this study, subjects were occupationally exposed to multiple potentially toxic elements. Carcinogenic and noncarcinogenic risks were associated with As, Co, Ni, and Mn exposure.

Quantitative Analyses of Urinary Uranium by µ-PIXE

Simple methods for the determination of elements in biological fluids have been developed. It is important to quantify the accidental incorporation of radionuclides during the decommissioning work at nuclear power plants. Herein, we proposed the simple preparations and determination methods of uranium concentrations in urine for microbeam scanning particle induced X-ray emission (µ-PIXE) analysis in a rat model. A droplet (1 µL) of mixed solution of urine treated with a five-fold amount of concentrated nitric acid was placed on polypropylene film coated with perfluoroalkoxy alkanes (PFA) and dried at room temperature. The µ-PIXE imaging analysis revealed that successful condensation with homogeneous distribution of uranium in the specimen was achieved using by PFA coating. Uranium concentrations in the urine collected from uranium-injected rats were quantified. The obtained results were consistent with those determined by inductively coupled plasma mass spectrometry.