Roles of serum uric acid on the association between arsenic exposure and incident metabolic syndrome in an older Chinese population.

Growing evidences showed that heavy metals exposure may be associated with metabolic diseases. Nevertheless, the mechanism underlying arsenic (As) exposure and metabolic syndrome (MetS) risk has not been fully elucidated. So we aimed to prospectively investigate the role of serum uric acid (SUA) on the association between blood As exposure and incident MetS. A sample of 1045 older participants in a community in China was analyzed. We determined As at baseline and SUA concentration at follow-up in the Yiwu Elderly Cohort. MetS events were defined according to the criteria of the International Diabetes Federation (IDF). Generalized linear model with log-binominal regression model was applied to estimate the association of As with incident MetS. To investigate the role of SUA in the association between As and MetS, a mediation analysis was conducted. In the fully adjusted log-binominal model, per interquartile range increment of As, the risk of MetS increased 1.25-fold. Compared with the lowest quartile of As, the adjusted relative risk (RR) of MetS in the highest quartile was 1.42 (95% confidence interval, CI: 1.03, 2.00). Additionally, blood As was positively associated with SUA, while SUA had significant association with MetS risk. Further mediation analysis demonstrated that the association of As and MetS risk was mediated by SUA, with the proportion of 15.7%. Our study found higher As was remarkably associated with the elevated risk of MetS in the Chinese older adults population. Mediation analysis indicated that SUA might be a mediator in the association between As exposure and MetS.

Arsenic exposure and oxidative damage to lipid, DNA, and protein among general Chinese adults: A repeated-measures cross-sectional and longitudinal study.

Arsenic-related oxidative stress and resultant diseases have attracted global concern, while longitudinal studies are scarce. To assess the relationship between arsenic exposure and systemic oxidative damage, we performed two repeated measures among 5236 observations (4067 participants) in the Wuhan-Zhuhai cohort at the baseline and follow-up after 3 years. Urinary total arsenic, biomarkers of DNA oxidative damage (8-hydroxy-2'-deoxyguanosine (8-OHdG)), lipid peroxidation (8-isoprostaglandin F2alpha (8-isoPGF2α)), and protein oxidative damage (protein carbonyls (PCO)) were detected for all observations. Here we used linear mixed models to estimate the cross-sectional and longitudinal associations between arsenic exposure and oxidative damage. Exposure-response curves were constructed by utilizing the generalized additive mixed models with thin plate regressions. After adjusting for potential confounders, arsenic level was significantly and positively related to the levels of global oxidative damage and their annual increased rates in dose-response manners. In cross-sectional analyses, each 1% increase in arsenic level was associated with a 0.406% (95% confidence interval (CI): 0.379% to 0.433%), 0.360% (0.301% to 0.420%), and 0.079% (0.055% to 0.103%) increase in 8-isoPGF2α, 8-OHdG, and PCO, respectively. More importantly, arsenic was further found to be associated with increased annual change rates of 8-isoPGF2α (ß: 0.147; 95% CI: 0.130 to 0.164), 8-OHdG (0.155; 0.118 to 0.192), and PCO (0.050; 0.035 to 0.064) in the longitudinal analyses. Our study suggested that arsenic exposure was not only positively related with global oxidative damage to lipid, DNA, and protein in cross-sectional analyses, but also associated with annual increased rates of these biomarkers in dose-dependent manners.

Predicting the efficiency of arsenic immobilization in soils by biochar using machine learning.

Arsenic (As) pollution in soils is a pervasive environmental issue. Biochar immobilization offers a promising solution for addressing soil As contamination. The efficiency of biochar in immobilizing As in soils primarily hinges on the characteristics of both the soil and the biochar. However, the influence of a specific property on As immobilization varies among different studies, and the development and application of arsenic passivation materials based on biochar often rely on empirical knowledge. To enhance immobilization efficiency and reduce labor and time costs, a machine learning (ML) model was employed to predict As immobilization efficiency before biochar application. In this study, we collected a dataset comprising 182 data points on As immobilization efficiency from 17 publications to construct three ML models. The results demonstrated that the random forest (RF) model outperformed gradient boost regression tree and support vector regression models in predictive performance. Relative importance analysis and partial dependence plots based on the RF model were conducted to identify the most crucial factors influencing As immobilization. These findings highlighted the significant roles of biochar application time and biochar pH in As immobilization efficiency in soils. Furthermore, the study revealed that Fe-modified biochar exhibited a substantial improvement in As immobilization. These insights can facilitate targeted biochar property design and optimization of biochar application conditions to enhance As immobilization efficiency.

Development of a coupled model to simulate and assess arsenic contamination and impact factors in the Jinsha River Basin, China.

With the increasing severity of arsenic (As) pollution, quantifying the environmental behavior of pollutant based on numerical model has become an important approach to determine the potential impacts and finalize the precise control strategies. Taking the industrial-intensive Jinsha River Basin as typical area, a two-dimensional hydrodynamic water quality model coupled with Soil and Water Assessment Tool (SWAT) model was developed to accurately simulate the watershed-scale distribution and transport of As in the terrestrial and aquatic environment at high spatial and temporal resolution. The effects of hydro-climate change, hydropower station construction and non-point source emissions on As were quantified based on the coupled model. The result indicated that higher As concentration areas mainly centralized in urban districts and concentration slowly decreased from upstream to downstream. Due to the enhanced rainfall, the As concentration was significantly higher during the rainy season than the dry season. Hydro-climate change and the construction of hydropower station not only affected the dissolved As concentration, but also affected the adsorption and desorption of As in sediment. Furthermore, As concentration increased with the input of non-point source pollution, with the maximum increase about 30%, resulting that non-point sources contributed important pollutant impacts to waterways. The coupled model used in pollutant behavior analysis is general with high potential application to predict and mitigate water pollution.

Assessment of internal exposure risk from metals pollution of occupational and non-occupational populations around a non-ferrous metal smelting plant.

Non-ferrous metal smelting poses significant risks to public health. Specifically, the copper smelting process releases arsenic, a semi-volatile metalloid, which poses an emerging exposure risk to both workers and nearby residents. To comprehensively understand the internal exposure risks of metal(loid)s from copper smelting, we explored eighteen metal(loid)s and arsenic metabolites in the urine of both occupational and non-occupational populations using inductively coupled plasma mass spectrometry with high-performance liquid chromatography and compared their health risks. Results showed that zinc and copper (485.38 and 14.00 µg/L), and arsenic, lead, cadmium, vanadium, tin and antimony (46.80, 6.82, 2.17, 0.40, 0.44 and 0.23 µg/L, respectively) in workers (n=179) were significantly higher compared to controls (n=168), while Zinc, tin and antimony (412.10, 0.51 and 0.15 µg/L, respectively) of residents were significantly higher than controls. Additionally, workers had a higher monomethyl arsenic percentage (MMA%), showing lower arsenic methylation capacity. Source appointment analysis identified arsenic, lead, cadmium, antimony, tin and thallium as co-exposure metal(loid)s from copper smelting, positively relating to the age of workers. The hazard index (HI) of workers exceeded 1.0, while residents and control were approximately at 1.0. Besides, all three populations had accumulated cancer risks exceeding 1.0 × 10-4, and arsenite (AsIII) was the main contributor to the variation of workers and residents. Furthermore, residents living closer to the smelting plant had higher health risks. This study reveals arsenic exposure metabolites and multiple metals as emerging contaminants for copper smelting exposure populations, providing valuable insights for pollution control in non-ferrous metal smelting.

Pristine/magnesium-loaded biochar and ZVI affect rice grain arsenic speciation and cadmium accumulation through different pathways in an alkaline paddy soil.

Cadmium (Cd) and arsenic (As) co-contamination has threatened rice production and food safety. It is challenging to mitigate Cd and As contamination in rice simultaneously due to their opposite geochemical behaviors. Mg-loaded biochar with outstanding adsorption capacity for As and Cd was used for the first time to remediate Cd/As contaminated paddy soils. In addition, the effect of zero-valent iron (ZVI) on grain As speciation accumulation in alkaline paddy soils was first investigated. The effect of rice straw biochar (SC), magnesium-loaded rice straw biochar (Mg/SC), and ZVI on concentrations of Cd and As speciation in soil porewater and their accumulation in rice tissues was investigated in a pot experiment. Addition of SC, Mg/SC and ZVI to soil reduced Cd concentrations in rice grain by 46.1%, 90.3% and 100%, and inorganic As (iAs) by 35.4%, 33.1% and 29.1%, respectively, and reduced Cd concentrations in porewater by 74.3%, 96.5% and 96.2%, respectively. Reductions of 51.6% and 87.7% in porewater iAs concentrations were observed with Mg/SC and ZVI amendments, but not with SC. Dimethylarsinic acid (DMA) concentrations in porewater and grain increased by a factor of 4.9 and 3.3, respectively, with ZVI amendment. The three amendments affected grain concentrations of iAs, DMA and Cd mainly by modulating their translocation within plant and the levels of As(III), silicon, dissolved organic carbon, iron or Cd in porewater. All three amendments (SC, Mg/SC and ZVI) have the potential to simultaneously mitigate Cd and iAs accumulation in rice grain, although the pathways are different.

Association of urinary metal element with semen quality: a cross-sectional study from Eastern China.

The effect of metallic elements on semen quality remains controversial, with limited evidence on the effects of metal mixtures. We conducted a study involving 338 participants from multiple centers in Eastern China, measuring 17 urinary metals and semen quality parameters. Our analysis used various statistical models, including multivariate logistic and linear regression, Bayesian Kernel Machine Regression, and weighted quantile sum models, to examine the associations between metal levels and semen quality. Logistic regression showed that higher urinary lead was associated with increased risk of abnormal sperm concentration (OR = 1.86, p = 0.021), arsenic to higher abnormal progressive motility risk (OR = 1.49, p = 0.027), and antimony to greater abnormal total motility risk (OR = 1.37, p = 0.018). Conversely, tin was negatively correlated with the risk of abnormal progressive motility (OR = 0.76, p = 0.012) and total motility (OR = 0.74, p = 0.003), respectively. Moreover, the linear models showed an inverse association between barium and sperm count, even after adjusting for other metals (ß = - 0.32, p < 0.001). Additionally, the WQS models showed that the metal mixture may increase the risk of abnormal total motility (ßWQS = 0.55, p = 0.046). In conclusion, semen quality may be adversely affected by exposure to metals such as arsenic, barium, lead, and antimony. The combined effect of the metal mixture appears to be particularly impaired total motility.

Unraveling gene regulation mechanisms in fish: insights into multistress responses and mitigation through iron nanoparticles.

The recent trend of global warming poses a significant threat to ecosystems worldwide. This global climate change has also impacted the pollution levels in aquatic ecosystems, subsequently affecting human health. To address these issues, an experiment was conducted to investigate the mitigating effects of iron nanoparticles (Fe-NPs) on arsenic and ammonia toxicity as well as high temperature stress (As+NH3+T). Fe-NPs were biologically synthesized using fish waste and incorporated into feed formulations at 10, 15, and 20 mg kg-1 diet. A total of 12 treatments were designed in triplicate following a completely randomized design involving 540 fish. Fe-NPs at 15 mg kg-1 diet notably reduced the cortisol levels in fish exposed to multiple stressors. The gene expressions of HSP 70, DNA damage-inducible protein (DDIP), and DNA damage were upregulated by stressors (As+NH3+T) and downregulated by Fe-NPs. Apoptotic genes (Cas 3a and 3b) and detoxifying genes (CYP 450), metallothionein (MT), and inducible nitric oxide synthase (iNOS) were downregulated by Fe-NPs at 15 mg kg-1 diet in fish subjected to As+NH3+T stress. Immune-related genes such as tumor necrosis factor (TNFα), immunoglobulin (Ig), and interleukin (IL) were upregulated by Fe-NPs, indicating enhanced immunity in fish under As+NH3+T stress. Conversely, Toll-like receptor (TLR) expression was notably downregulated by Fe-NPs at 15 mg kg-1 diet in fish under As+NH3+T stress. Immunological attributes such as nitro blue tetrazolium chloride, total protein, albumin, globulin, A:G ratio, and myeloperoxidase (MPO) were improved by dietary Fe-NPs at 15 mg kg-1 diet in fish, regardless of stressors. The antioxidant genes (CAT, SOD, and GPx) were also strengthened by Fe-NPs in fish. Genes associated with growth performance, such as growth hormone regulator (GHR1 and GHRß), growth hormone (GH), and insulin-like growth factor (IGF 1X and IGF 2X), were upregulated, enhancing fish growth under stress, while SMT and MYST were downregulated by Fe-NPs in the diet. Various growth performance indicators were improved by dietary Fe-NPs at 15 mg kg-1 diet. Notably, Fe-NPs also enhanced arsenic detoxification and reduced the cumulative mortality after a bacterial infection. In conclusion, this study highlights that dietary Fe-NPs can effectively mitigate arsenic and ammonia toxicity as well as high temperature stress by modulating gene expression in fish.

Heavy metal concentrations and clinical symptoms in patients diagnosed with schizophrenia related to cigarette smoking.

In our study, blood concentrations of lead (Pb), arsenic (As), and cadmium (Cd) and urine concentrations of thallium (Tl) were measured together with related symptoms of heavy metal poisoning in cigarette smoking volunteers diagnosed with schizophrenia, in cigarette smokers not diagnosed with schizophrenia, and in the control group of non-smokers and not diagnosed with schizophrenia volunteers. Our study was performed on 171 volunteers divided into the following subgroups: patients diagnosed with schizophrenia with at least 1 year of continuous cigarette smoking experience (56 participants), cigarette smokers not diagnosed with schizophrenia with at least one year of continuous smoking experience (58), and control group (not diagnosed with schizophrenia and non-smoking volunteers) (57). Smoking durations of cigarette smokers diagnosed with schizophrenia and cigarette smokers not diagnosed with schizophrenia are not similar (p = 0.431). Blood Pb, As, and Cd concentrations and urine Tl concentrations were the highest in the subgroup of cigarette smokers not diagnosed with schizophrenia, followed by the subgroup of cigarette smokers diagnosed with schizophrenia, and the control group. Only blood Pb concentrations were significantly higher (probability value p < 0.05) in the group of cigarette smokers not diagnosed with schizophrenia (5.16 µg/dL), comparing to the group of cigarette smokers diagnosed with schizophrenia (3.83 µg/dL) and to the control group (3.43 µg/dL). Blood Cd and As concentrations and urine Tl concentrations were significantly higher (p < 0.05) in cigarette smokers not diagnosed with schizophrenia than in the control group. The results revealed a statistically significant positive correlation (p < 0.001) in the cigarette smokers in the schizophrenia diagnosed group between blood Pb, blood As, and urine Tl concentrations and the duration of cigarette smoking.

In silico analysis of the impact of toxic metals on COVID-19 complications: molecular insights.

COVID-19 can cause a range of complications, including cardiovascular, renal, and/or respiratory insufficiencies, yet little is known of its potential effects in persons exposed to toxic metals. The aim of this study was to answer this question with in silico toxicogenomic methods that can provide molecular insights into COVID-19 complications owed to exposure to arsenic, cadmium, lead, mercury, nickel, and chromium. For this purpose we relied on the Comparative Toxicogenomic Database (CTD), GeneMANIA, and ToppGene Suite portal and identified a set of five common genes (IL1B, CXCL8, IL6, IL10, TNF) for the six metals and COVID-19, all of which code for pro-inflammatory and anti-inflammatory cytokines. The list was expanded with additional 20 related genes. Physical interactions are the most common between the genes affected by the six metals (77.64 %), while the dominant interaction between the genes affected by each metal separately is co-expression (As 56.35 %, Cd 64.07 %, Pb 71.5 %, Hg 81.91 %, Ni 64.28 %, Cr 88.51 %). Biological processes, molecular functions, and pathways in which these 25 genes participate are closely related to cytokines and cytokine storm implicated in the development of COVID-19 complications. In other words, our findings confirm that exposure to toxic metals, alone or in combinations, might escalate COVID-19 severity.

Microcystis aeruginosa aggravated arsenic accumulation in silver carp during silver carp controlling algal bloom in arsenic-contaminated water.

Silver carp mediated biological control techniques are often advocated for controlling cyanobacteria blooms in eutrophic water, which are often enriched with arsenic (As). However, the transfer and fate of As during the biological control of cyanobacteria blooms by silver carp in As-rich eutrophic water remain unclear. Based on the simulated ecosystem experiment, the accumulation of As in silver carp and the transfer and fate of As in the water-algae-silver carp system during Microcystis aeruginosa blooms controlled by silver carp were investigated. Microcystis aeruginosa showed high tolerance to As(V). The accumulation of As in different tissues of silver carp was different, as follows: intestine > liver > gill > skin > muscle. After silver carp ingested As-rich Microcystis aeruginosa, As accumulation in the intestine, liver, gill, and skin of silver carp was enhanced under the action of digestion and skin contact. Compared with the system without algal, As accumulation in the intestine, liver, gill, and skin of silver carp increased by 1.1, 3.3, 3.3, and 9.6 times, respectively, after incubation for 30 days in the system with Microcystis aeruginosa, while the accumulation of As in the muscle was only slightly increased by 0.56 mg/kg. This work revealed the transfer and fate of As during algal control by silver carp, elucidated the accumulation mechanism of As in water-algae-silver carp system, enriched our understanding of As bioaccumulation and transformation in As-rich eutrophication water, and provided a scientific basis for assessing and predicting As migration and enrichment in water-algae-silver carp system.

Urine manganese, cadmium, lead, arsenic, and selenium among autism spectrum disorder children in Kuala Lumpur.

Background: The development of autism spectrum disorder (ASD) may stem from exposure to environmental pollutants such as heavy metals. The primary objective of this study is to determine the role of heavy metals of concern such as manganese (Mn), cadmium (Cd), lead (Pb), arsenic (As), and essential trace element selenium (Se) among ASD children in Kuala Lumpur, Malaysia. Method: A total of 155 preschoolers in Kuala Lumpur between the ages 3 to 6 participated in an unmatched case-control study, comprising ASD children (n = 81) recruited from an early intervention program for autism, and 74 children without autism who were recruited from public preschools. Urine samples were collected at home, delivered to the study site, and transported to the environmental lab within 24 hours. Inductively coupled plasma mass spectrometry (ICP-MS) was applied to measure the concentration of heavy metals in the samples. Data were analysed using bivariate statistical tests (Chi-square and T-test) and logistic regression models. Result: This study demonstrated that Cd, Pb, and As urine levels were significantly greater in children without autism relative to those affected with ASD (p < 0.05). No significant difference was in the levels of Se (p = 0.659) and Mn (p = 0.875) between children with ASD and the control group. The majority of children in both groups have urine As, Pb, and Cd values lower than 15.1 µg/dL, 1.0 µg/dL, and 1.0 µg/dL, respectively which are the minimal risk values for noncarcinogenic detrimental human health effect due to the heavy metal's exposure . Factors associated with having an ASD child included being a firstborn, male, and higher parental education levels (adjusted odds ratios (aOR) > 1, p < 0.05). Conclusion: Preschoolers in this study demonstrated low levels of heavy metals in their urine samples, which was relatively lower in ASD children compared to the healthy matched controls. These findings may arise from the diminished capacity to excrete heavy metals, especially among ASD children, thereby causing further accumulation of heavy metals in the body. These findings, including the factors associated with having an ASD child, may be considered by healthcare professionals involved in child development care, for early ASD detection. Further assessment of heavy metals among ASD children in the country and interventional studies to develop effective methods of addressing exposure to heavy metals will be beneficial for future reference.

Dose-response relationship between arsenic in drinking water and mortality of urinary cancers in Taiwan.

Ingested arsenic is carcinogenic to the human urinary tract, but uncertainties remain regarding the dose-response relationship. To assess dose-response relationships between arsenic ingestion and urinary cancers, we evaluated the associations between the arsenic level in drinking water and mortality of cancers of the bladder, kidney, and prostate in Taiwan. We utilized the 1971-2000 Taiwan death registry data and calculated the age-standardized mortality rates (ASMRs) using the 1976 world standard population as the reference group. We used the data from a 1974-1976 census survey of wells on the arsenic levels in drinking water conducted by the government to assess exposure levels, which had been divided into three categories: below 0.05 ppm, 0.05-0.35 ppm, and above 0.35 ppm. The data were analyzed using multiple linear regression models and geographical information system. We found no increase in ASMR for all, or any, of the urinary cancers at exposure levels of 0.05-0.35 ppm arsenic, but at exposure levels > 0.35 ppm arsenic was associated with increased ASMR in both males and females for bladder cancer, kidney cancer, and all urinary cancers combined. There was no increased ASMR associated with prostate cancer observed for either exposure category.

Exploring the Effect of Arsenic-Containing Hydrocarbon on the Bidirectional Synaptic Plasticity of the Dorsal Hippocampus.

Arsenic-containing hydrocarbons (AsHCs) are common in marine organisms. However, there is little research on their effects on the central nervous system's advanced activities, such as cognition. Bidirectional synaptic plasticity dynamically regulates cognition through the balance of long-term potentiation (LTP) and long-term depression (LTD). However, the effects of AsHCs on bidirectional synaptic plasticity and the underlying molecular mechanisms remain unexplored. This study provides the first evidence that 15 µg As L-1 AsHC 360 enhances bidirectional synaptic plasticity, occurring during the maintenance phase rather than the baseline phase. Further calcium gradient experiments hypothesize that AsHC 360 may enhance bidirectional synaptic plasticity by affecting calcium ion levels. The enhancement of bidirectional synaptic plasticity by 15 µg As L-1 AsHC 360 holds significant implications in improving cognitive function, treating neuro-psychiatric disorders, promoting neural recovery, and enhancing brain adaptability.

Exposure to a mixture of metal(loid)s and sleep quality in pregnant women during early pregnancy: A cross-sectional study.

Biological characteristics of pregnant women during early pregnancy make them susceptible to both poor sleep quality and metal/metalloid exposure. However, the effects of metal(loid) exposure on sleep quality in pregnant women remain unknown and unexplored. We aimed to examine the relationship between exposure to a mixture of metal(loid)s and pregnant women's sleep quality during early pregnancy. We recruited 493 pregnant women in the first trimester from prenatal clinics in Jinan, Shandong Province, China, and collected their spot urine samples. All urine specimens were assessed for eight metal(loid)s: arsenic (As), cadmium (Cd), iron (Fe), zinc (Zn), molybdenum (Mo), lead (Pb), selenium (Se), and mercury (Hg). We used the Pittsburgh Sleep Quality Index (PSQI) to assess sleep quality. Linear regression, logistic regression, generalized additive models (GAMs), quantile g-computation, and Bayesian kernel machine regression (BKMR) were applied to investigate the relationships between metal(loid) exposure and sleep quality. The results from single metal(loid) models, quantile g-computation models, and BKMR models consistently suggested that Fe was positively related to women's sleep quality. Moreover, in the quantile g-computation models, As was the most critical contributor to the negative effects of the metal(loid) mixture on sleep quality. In addition, we found significant As by Fe interaction for scores of PSQI and habitual sleep efficiency, Pb by Fe interaction for PSQI and sleep latency, and Hg by Fe interaction for PSQI, suggesting the interactive effects of As and Fe, Pb and Fe, Hg and Fe on sleep quality and specific sleep components. Our study provided the first-hand evidence of the effects of metal(loid) exposure on pregnant women's sleep quality. The underlying mechanisms need to be explored in the future.

Vitamin C Alleviates the Risk of Gestational Diabetes Mellitus Associated With Exposure to Metals.

Background: Exposure to heavy metals has been suggested to increase the risk of gestational diabetes mellitus (GDM) through the oxidative stress pathway. The study is aimed at examining whether vitamin C could modify the association between exposure to heavy metals and risk of GDM. Methods: We conducted a case-control study in Taiyuan, China, with 776 GDM cases and 776 controls. Data on vitamin C intake from diet and supplements were collected through questionnaires. Concentrations of metals in participants' blood were measured using inductively coupled plasma-mass spectrometry (ICP-MS). Unconditional logistic regression models were applied to estimate effect modification of vitamin C on the association between heavy metals and GDM. Results: Women with higher blood levels of mercury (Hg) (odds ratio (OR) = 2.36, 95% confidence interval (CI): 1.43, 3.92 and 2.04, 95% CI: 1.20, 3.46 for the second and third vs. the first tertile) and arsenic (As) (OR = 2.46, 95% CI: 1.37, 4.43 and 2.16, 95% CI: 1.12, 4.17 for the second and third vs. the first tertile) exposure were associated with increased risk of GDM among women without vitamin C supplement use and having dietary vitamin C intake < 85 mg/day. We found no significant association with metals among women who took vitamin C supplements and/or dietary vitamin C ≥ 85 mg/day. Significant interactions were observed between vitamin C and exposures to metals (i.e., Hg and As) on the risk of GDM (P interaction = 0.048 and 0.045, respectively). Conclusions: Our study, for the first time, suggests that vitamin C supplement use or higher dietary vitamin C intake during preconception and early pregnancy could alleviate the risk of GDM associated with exposure to As and Hg. The results warrant further investigation.

Prediction of arsenic concentration in groundwater of Chapainawabganj, Bangladesh: machine learning-based approach to spatial modeling.

Groundwater in northwestern parts of Bangladesh, mainly in the Chapainawabganj District, has been contaminated by arsenic. This research documents the geographical distribution of arsenic concentrations utilizing machine learning techniques. The study aims to enhance the accuracy of model predictions by precisely identifying occurrences of groundwater arsenic, enabling effective mitigation actions and yielding more beneficial results. The reductive dissolution of arsenic-rich iron oxides/hydroxides is identified as the primary mechanism responsible for the release of arsenic from sediment into groundwater. The study reveals that in the research region, alongside elevated arsenic concentrations, significant levels of sodium (Na), iron (Fe), manganese (Mn), and calcium (Ca) were present. Statistical analysis was employed for feature selection, identifying pH, electrical conductivity (EC), sulfate (SO4), nitrate (NO3), Fe, Mn, Na, K, Ca, Mg, bicarbonate (HCO3), phosphate (PO4), and As as features closely associated with arsenic mobilization. Subsequently, various machine learning models, including Naïve Bayes, Random Forest, Support Vector Machine, Decision Tree, and logistic regression, were employed. The models utilized normalized arsenic concentrations categorized as high concentration (HC) or low concentration (LC), along with physiochemical properties as features, to predict arsenic occurrences. Among all machine learning models, the logistic regression and support vector machine models demonstrated high performance based on accuracy and confusion matrix analysis. In this study, a spatial distribution prediction map was generated to identify arsenic-prone areas. The prediction map also displays that Baroghoria Union and Rajarampur region under Chapainawabganj municipality are high-risk areas and Maharajpur Union and Baliadanga Union are comparatively low-risk areas of the research area. This map will facilitate researchers and legislators in implementing mitigation strategies. Logistic regression (LR) and support vector machine (SVM) models will be utilized to monitor arsenic concentration values continuously.

Spatial distribution, bioaccessibility, and human health implications of potentially toxic elements in mining-impacted topsoils in Obuasi Municipality of Ghana.

Potential toxic elements emanating from extracted ores during gold processing present occupational and unintentional health hazards in communities, the general populace, and the environment. This study investigated the concentrations and potential health effects of metal content in the topsoils of Obuasi municipality, which has been mined for gold over the past century. Surface topsoil samples, sieved to 250 µm, were initially scanned for metals using x-ray fluorescence techniques, followed by confirmation via ICP-MS. In vitro bioaccessibility assays were conducted using standard methods. The geoaccumulation indices (Igeo) indicate high enrichment of As (Igeo = 6.28) and Cd (Igeo = 3.80) in the soils, especially in the eastern part of the municipality where illegal artisanal mining is prevalent. Additionally, the southern corridor, situated near a gold mine, exhibited significant levels of As and Mn. Results obtained for the total metal concentrations and contamination indices confirmed the elevation of the studied potential toxic elements in the Obuasi community. A hazard index value of 4.42 and 3.30 among children and adults, respectively, indicates that indigens, especially children, are susceptible to non-cancer health effects.

Application of CuNPs and AMF alleviates arsenic stress by encompassing reduced arsenic uptake through metabolomics and ionomics alterations in Elymus sibiricus.

Recent studies have exhibited a very promising role of copper nanoparticles (CuNPs) in mitigation of abiotic stresses in plants. Arbuscular mycorrhizae fungi (AMF) assisted plants to trigger their defense mechanism against abiotic stresses. Arsenic (As) is a non-essential and injurious heavy-metal contaminant. Current research work was designed to elucidate role of CuNPs (100, 200 and 300 mM) and a commercial inoculum of Glomus species (Clonex® Root Maximizer) either alone or in combination (CuNPs + Clonex) on physiology, growth, and stress alleviation mechanisms of E. sibiricus growing in As spiked soils (0, 50, and 100 mg Kg- 1 soil). Arsenic induced oxidative stress, enhanced biosynthesis of hydrogen peroxide, lipid peroxidation and methylglyoxal (MG) in E. sibiricus. Moreover, As-phytotoxicity reduced photosynthetic activities and growth of plants. Results showed that individual and combined treatments, CuNPs (100 mM) as well as soil inoculation of AMF significantly enhanced root growth and shoot growth by declining As content in root tissues and shoot tissues in As polluted soils. E. sibiricus plants treated with CuNPs (100 mM) and/or AMF alleviated As induced phytotoxicity through upregulating the activity of antioxidative enzymes such as catalase (CAT) and superoxide dismutase (SOD) besides the biosynthesis of non-enzymatic antioxidants including phytochelatin (PC) and glutathione (GSH). In brief, supplementation of CuNPs (100 mM) alone or in combination with AMF reduced As uptake and alleviated the As-phytotoxicity in E. sibiricus by inducing stress tolerance mechanism resulting in the improvement of the plant growth parameters.

Unveiling New Arsenic Compounds in Plants via Tailored 2D-RP-HPLC Separation with ICP and ESI MS Detection.

Arsenic (As) speciation analysis is scientifically relevant due to the pivotal role the As chemical form plays in toxicity, which, in turn, directly influences the effect it has on the environment. The objective of this study was to develop and optimize a method tailored for studying As compounds in plant samples. Different extraction procedures and HPLC methods were explored to assess their efficiency, determine mass balance, and improve the resolution of compounds in the chromatograms. Conventionally applied anion-exchange chromatography facilitated the separation of well-documented As compounds in the extracts corresponding to 19 to 82% of As present in extracts. To gain insight into compounds which remain undetectable by anion chromatography (18 to 81% of As in the extracts), but still possibly metabolically relevant, we explored an alternative chromatographic approach. The procedure of sample purification and preconcentration through solid-phase extraction, facilitating the detection of those minor As compounds, was developed. The system was further refined to achieve an online 2D-RP-HPLC system, which was employed to analyze the extracts more comprehensively with ICP and ESI MS. Using this newly developed method, As(III)-phytochelatins, along with other arseno-thio-compounds, were detected and identified in extracts derived from the tree roots of seedlings grown in the presence of As(III) and As(V), and a group of arseno lipids was detected in the roots of plants exposed to As(V).