Bioavailability, transfer, toxicological effects, and contamination assessment of arsenic and mercury in soil-corn systems

Exploring the origins and cleanup of mercury contamination: a comprehensive review

Mercury is a global pollutant that poses significant risks to human health and the environment. Natural sources of mercury include volcanic eruptions, while anthropogenic sources include industrial processes, artisanal and small-scale gold mining, and fossil fuel combustion. Contamination can arise through various pathways, such as atmospheric deposition, water and soil contamination, bioaccumulation, and biomagnification in food chains. Various remediation strategies, including phytoremediation, bioremediation, chemical oxidation/reduction, and adsorption, have been developed to address mercury pollution, including physical, chemical, and biological approaches. The effectiveness of remediation techniques depends on the nature and extent of contamination and site-specific conditions. This review discusses the challenges associated with mercury pollution and remediation, including the need for effective monitoring and management strategies. Overall, this review offers a comprehensive understanding of mercury contamination and the range of remediation techniques available to mitigate its adverse impacts.

Effects of Cadmium and Lead Co-exposure on Sleep Status in Rural Areas Northwestern China

In this study, we explored how cadmium and lead co-exposure affects sleep status among residents of a polluted area and nature reserve in rural northwestern China. Cadmium and lead levels were measured using blood samples, and sleep status was evaluated using sleep questionnaires, with the main sleep indicators including sleep duration, sleep quality, bedtime, and staying up. Furthermore, cadmium-lead co-exposure levels were divided into three groups: high exposure, medium exposure, and low exposure. Subjects in the contaminated area had significantly higher exposure levels (p < 0.001) and more negative sleep indicators (p < 0.01). Significant differences were found for all four sleep indicators in the high exposure group compared to the low exposure group (p < 0.01). Moreover, the overall evaluation of sleep status with high cadmium-lead co-exposure had a negative impact. Our data suggest that cadmium-lead co-exposure has a negative effect on sleep status and may have a synergistic effect on sleep.

Association and mediation analyses among multiple metal exposure, mineralocorticoid levels, and serum ion balance in residents of northwest China

Toxic metals are vital risk factors affecting serum ion balance; however, the effect of their co-exposure on serum ions and the underlying mechanism remain unclear. We assessed the correlations of single metal and mixed metals with serum ion levels, and the mediating effects of mineralocorticoids by investigating toxic metal concentrations in the blood, as well as the levels of representative mineralocorticoids, such as deoxycorticosterone (DOC), and serum ions in 471 participants from the Dongdagou-Xinglong cohort. In the single-exposure model, sodium and chloride levels were positively correlated with arsenic, selenium, cadmium, and lead levels and negatively correlated with zinc levels, whereas potassium and iron levels and the anion gap were positively correlated with zinc levels and negatively correlated with selenium, cadmium and lead levels (all P < 0.05). Similar results were obtained in the mixed exposure models considering all metals, and the major contributions of cadmium, lead, arsenic, and selenium were highlighted. Significant dose-response relationships were detected between levels of serum DOC and toxic metals and serum ions. Mediation analysis showed that serum DOC partially mediated the relationship of metals (especially mixed metals) with serum iron and anion gap by 8.3% and 8.6%, respectively. These findings suggest that single and mixed metal exposure interferes with the homeostasis of serum mineralocorticoids, which is also related to altered serum ion levels. Furthermore, serum DOC may remarkably affect toxic metal-related serum ion disturbances, providing clues for further study of health risks associated with these toxic metals.

Arsenic (As) accumulation in different genotypes of indica rice (Oryza sativa L.) and health risk assessment based on inorganic As

To reveal differences in arsenic (As) accumulation among indica rice cultivars and assess the human health risks arising from inorganic arsenic (iAs) intake via rice consumption, a total of 320 field indica rice samples and corresponding soil samples were collected from Fujian Province in China. The results showed that available soil As (0.03 to 3.83 mg/kg) showed a statistically significant positive correlation with total soil As (0.10 to 19.45 mg/kg). The inorganic As content in brown rice was between 0.001 and 0.316 mg/kg. Among the cultivars, ten brown rice samples (3.13%) exceeded the maximum contaminant level (MCL) of iAs in food of 0.2 mg/kg in China. The estimated daily intake (EDI) and calculated individual incremental lifetime cancer risk (ILCR) ranged from 0.337 µg/day to 106.60 µg/day and from 8.18 × 10-6 to 2.59 × 10-3, respectively. Surprisingly, the average EDI and the EDIs of 258 (80.63%) brown rice samples were higher than the maximum daily intake (MDI) of 10 µg/day in drinking water as set by the National Research Council. The mean ILCR associated with iAs was 54.3 per 100,000, which exceeds the acceptable upper limit (AUL) of 10 per 100,000 set by the USEPA. Notably, the cultivars Y-Liang-You 1 and Shi-Ji 137 exhibited significantly higher mean ILCRs compared to the AUL and other cultivars, indicating that they pose more serious cancer risks to the local population. Finally, this study demonstrated that the cultivars Yi-Xiang 2292 and Quan-Zhen 10 were the optimal cultivars to mitigate risks associated with iAs to human health from rice consumption.

Ethnoracial disparities in childhood growth trajectories in Brazil: a longitudinal nationwide study of four million children

BACKGROUND

The literature contains scarce data on inequalities in growth trajectories among children born to mothers of diverse ethnoracial background in the first 5 years of life.

OBJECTIVE

We aimed to investigate child growth according to maternal ethnoracial group using a nationwide Brazilian database.

METHODS

A population-based retrospective cohort study employed linked data from the CIDACS Birth Cohort and the Brazilian Food and Nutrition Surveillance System (SISVAN). Children born at term, aged 5 years or younger who presented two or more measurements of length/height (cm) and weight (kg) were followed up between 2008 and 2017. Prevalence of stunting, underweight, wasting, and thinness were estimated. Nonlinear mixed effect models were used to estimate childhood growth trajectories, among different maternal ethnoracial groups (White, Asian descent, Black, Pardo, and Indigenous), using the raw measures of weight (kg) and height (cm) and the length/height-for-age (L/HAZ) and weight-for-age z-scores (WAZ). The analyses were also adjusted for mother's age, educational level, and marital status.

RESULTS

A total of 4,090,271 children were included in the study. Children of Indigenous mothers exhibited higher rates of stunting (26.74%) and underweight (5.90%). Wasting and thinness were more prevalent among children of Pardo, Asian, Black, and Indigenous mothers than those of White mothers. Regarding children's weight (kg) and length/height (cm), those of Indigenous, Pardo, Black, and Asian descent mothers were on average shorter and weighted less than White ones. Regarding WAZ and L/HAZ growth trajectories, a sharp decline in average z-scores was evidenced in the first weeks of life, followed by a period of recovery. Over time, z-scores for most of the subgroups analyzed trended below zero. Children of mother in greater social vulnerability showed less favorable growth.

CONCLUSION

We observed racial disparities in nutritional status and childhood growth trajectories, with children of Indigenous mothers presenting less favorable outcomes compared to their White counterparts. The strengthening of policies aimed at protecting Indigenous children should be urgently undertaken to address systematic ethnoracial health inequalities.

Ecological restoration reduces mercury in corn kernel and the distinction of mercury in corn plants in rural China - A case in Wuchuan mercury mining area

Corn is a crucial crop in China and is widely cultivated in the mercury (Hg) mining region of Guizhou. This study analyzed the Hg content in soil and corn plant samples from the Wuchuan Hg mining area (WCMA) and the surrounding non-Hg mining regions (SNMR). The findings suggest that ongoing ecological rehabilitation and environmental conservation measures in the WCMA have significantly decreased the Hg content in corn kernels. The Hg concentration in different parts of the corn plant varied, being higher in the roots, tassels, and leaves and lower in kernels and stalks. Hg stored in corn plant growing in the WCMA primarily originates from the soil (55.4%), while in the SNMR, it mainly comes from the atmosphere (74.9%). Despite counted only about 7% of the total plant mass, corn roots play a crucial role in soil Hg pollution remediation when corn is used for remediation. Household corn residues burning release about 58.5% and 66.9% of the stored Hg in corn plants growing in the WCMA and the SNMR, respectively, into the atmosphere. Our findings indicate that corn cultivation acts as a reservoir for both soil and atmospheric Hg in the SNMR, while in the WCMA, it serves as a source of atmospheric Hg.

Mercury pollution risks of agricultural soils and crops in mercury mining areas in Guizhou Province, China: effects of large mercury slag piles

The historical large mercury slag piles still contain high concentrations of mercury and their impact on the surrounding environment has rarely been reported. In this study, three different agricultural areas [the area with untreated piles (PUT), the area with treated piles (PT), and the background area with no piles (NP)] were selected to investigate mercury slag piles pollution in the Tongren mercury mining area. The mercury concentrations of agricultural soils ranged from 0.42 to 155.00 mg/kg, determined by atomic fluorescence spectrometry of 146 soil samples; and mercury concentrations in local crops (rice, maize, pepper, eggplant, tomato and bean) all exceeded the Chinese food safety limits. Soil and crop pollution trends in the three areas were consistent as PUT > PT > NP, indicating that mercury slag piles have exacerbated pollution. Mercury in the slag piles was adsorbed by multiple pathways of transport into soils with high organic matter, which made the ecological risk of agricultural soils appear extremely high. The total hazard quotients for residents from ingesting mercury in these crops were unacceptable in all areas, and children were more likely to be harmed than adults. Compared to the PT area, treatment of slag piles in the PUT area may decrease mercury concentrations in paddy fields and dry fields by 46.02% and 70.36%; further decreasing health risks for adults and children by 47.06% and 79.90%. This study provided a scientific basis for the necessity of treating large slag piles in mercury mining areas.

Would it be better for partition prediction of heavy metal concentration in soils based on the fusion of XRF and Vis-NIR data?

Heavy metal (HM) contamination in soil necessitates effective methods to diagnose suspected contaminated areas and control rehabilitation processes. The synergistic use of proximal sensors demonstrates significant potential for rapid detection via accurate surveys of soil HM pollution at large scales and high sampling densities, and necessitates the selection of appropriate data mining and modeling methods for early diagnosis of soil pollution. The aim of this study is to evaluate the performance of a subarea model based on geographically partitioned and global models based on high-precision energy dispersive X-ray fluorescence (HD-XRF) and visible near-infrared (vis-NIR) spectra using a random forest model for predicting soil Cu and Pb concentrations. A total of 166 soil samples are acquired from a contaminated plot in Baiyin, Gansu Province, China. The soil samples are subjected to HM analysis and proximal sensor scanning in a laboratory. Vis-NIR spectral data are preprocessed using the Savitzky Golay (SG) and first-order derivative with Savitzky Golay (SGFD) methods. The results show that for predicting Cu and Pb concentrations in soil, the subarea models performs better than the global models in terms of quantitative prediction, based solely on individual HD-XRF data. For the subarea and global models, the R2 values are 0.961 and 0.981, respectively; the RMSE values are 27.8 and 79.6, respectively; and the RPD values are 4.96 and 7.38, respectively. However, making use of the random forest algorithm trained with data fusion obtained from the HD-XRF and vis-NIR sensors, the global model achieves the best predictions for Cu and Pb concentrations via HD-XRF + vis-NIR (SGFD) and HD-XRF + vis-NIR (SG), respectively. The results will provide a new perspective for modeling approaches to rapidly invert HM concentrations based on proximal sensor data fusion within a large scope of the study area.

Assessment and machine learning prediction of heavy metals fate in mining farmland assisted by Positive Matrix Factorization

The accurate pollutant prediction by Machine Learning (ML) is significant to efficient environmental monitoring and risk assessment. However, application of ML in soil is under studied. In this study, a Positive Matrix Factorization (PMF) assisted prediction method was developed with Support Vector Machine (SVM) and Random Forest (RF) for heavy metals (HMs) prediction in mining farmland. Principal Component Analysis (PCA) and Redundancy Analysis (RDA) were selected to pretreat data. Experiment results illustrated Cd was the main pollutant with heavy risks in the study area and Pb was easy to migrate. The method effects of HMs total concentration predicting were PMF > Simple > PCA > PCA - PMF, and RF predicted better than SVM. Data pretreatment by RDA prior inspection improved the model results. Characteristic HMs Tessier fractions prediction received good effects with average R value as 0.86. Risk classification prediction performed good in Cd, Cu, Ni and Zn, however, Pb showed weak effect by simple model. The best classifier method for Pb was PMF - RF method with relatively good effect (Area under ROC Curve = 0.896). Overall, our study suggested the combination between PMF and ML can assist the prediction of HMs in soil. Spatial weighted attribute of HMs can be provided by PMF.

Estimation of mercury uptake and distinction of corn cultivation in China

Corn cultivation potentially plays a vital role in the global mercury (Hg) biogeochemical cycle. Nevertheless, there have been limited studies quantifying the Hg mass flow during corn cultivation. This study focuses on Hg uptake by corn plants in China, integrating data from both sample collection and prior studies, resulting in 400 datasets. The findings reveal that the Hg in corn plants is mainly incorporated in leaves (45.5 %-47.5 %) and husks (14.5 %-15.7 %). Despite a decrease in total gaseous Hg (TGM) concentrations in the atmosphere over time, annual Hg uptake by corn cultivation in China has risen from 72.0 (ranging from 47.6 to 96.3) tons (2009-2014) to 84.3 (ranging from 51.9 to 109.6) tons (2015-2020) due to the increasing in corn kernel production. Spatial analysis demonstrates regional disparities in Hg uptake, primarily influenced by corn kernel production, TGM levels, and soil Hg content. Furthermore, temporal analysis reveals a shift in the fate of Hg in corn plants, which can be attributed to variations in corn straw treatment policy or methods. From 2009 to 2014, a substantial amount of absorbed Hg by corn plants was re-released into the atmosphere (48.9 %) due to corn residues burning, whereas, between 2015 and 2020, a greater proportion of Hg ended up accumulating in the soil (51.1 %) after the imposition of the straw burning ban in China. Prior to the ban (2009-2014), corn cultivation contributed approximately 7.7 tons of Hg input to soil annually, with a range from 1.7 to 13.5. However, following the ban (2015-2020), Hg input into the soil increased by approximately 4.5 times, reaching 34.5 (ranging from 17.5 to 52.6) tons per year. These findings emphasize the significant risks associated with soil Hg pollution caused by corn cultivation due to the straw burning ban.

Assessment of mercury bioavailability in garden soils around a former nonferrous metal mining area using DGT, accumulation in vegetables, and implications for health risk

Mercury (Hg) is a toxic, non-essential element for living organisms, frequently present in high concentrations in soils from industrial areas. The total, dissolved, and labile Hg concentrations in garden soils and their accumulation in edible vegetables (onion, garlic, lettuce, and parsley) grown on contaminated soils in localities situated a former mining area were evaluated. The labile Hg fraction was estimated by diffusive gradient in thin films (DGT). The soil-to-vegetable transfer factors, as well as the health risk by exposure to Hg, were calculated based on the labile Hg concentration in soil. The total Hg concentration in soil varied widely (0.11-3.77 mg kg-1), Hg in soil solution ranged between 2.14 and 20.2 μg L-1 and labile Hg between 1.13 and 18.6 μg L-1. About 36-96% (84% on average) of the Hg concentration in soil solution was found in labile form. Multivariate analysis revealed significant correlations between the labile Hg concentration in soil and Hg accumulated in vegetables. The hazard indices showed that, although the study area is affected by legacy pollution, exposure to soil and consumption of locally grown vegetables do not pose health risks.