Migration, accumulation, and risk assessment of potentially toxic elements in soil-plant (shrub and herbage) systems at typical polymetallic mines in Northwest China

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.

Abilities of herbaceous plant species to phytoextract Cd, Pb, and Zn from arable soils after poly-metallic mining and smelting

Potentially toxic element (PTE) contamination deteriorates agricultural land. This study explored the accumulation of excess PTEs (Cd, Pb, and Zn) in soils by shoots of herbaceous plants growing on alluvial sediments of an abandoned mining/smelting site near the Litavka River, Czech Republic, as a means of soil remediation. Determination of total Cd, Pb, and Zn, contents in soil and plant samples decomposed with HNO3 + HCl + HF, HNO3, and H2O2, respectively, were carried out by inductively coupled optical emission spectrometry. The soil Cd, Pb, and Zn contents in the studied site ranged from 40 to 65, 3183 to 3897, and 5108 to 6553 mg kg-1, respectively, indicating serious soil contamination compared to the limits allowed by the FAO/WHO and the Czech Republic. Slightly acidic soil reactions and negative correlations between the pH, C, and N supported the assumption of relative solubility, mobility, and accumulation of studied PTEs by herbaceous species. Shoot accumulation of Cd, Pb, and Zn varied in 22 of 23 species recording a Cd content above the permissible limit. The Zn content in all plants was above the WHO limit. Except for Arabidopsis halleri, with a bioaccumulation factor (BAFshoot) > 1 for Cd and Zn, Equisetum arvense recorded a comparatively higher Cd content (10.3-28 mg kg-1) than all other species. Silene vulgaris (Moench), Leucanthemum vulgare, E. arvense, Achillea millefolium, Carex sp., Dianthus deltoides, Campanula patula, Plantago lanceolata, and Rumex acetosa accumulated more Zn than many plants (> 300 mg kg-1). Although E. arvense had a BAF < 1, it accumulated > 1000 mg Zn kg-1 and supported the phytoextraction of Zn. Only 10 species accumulated Pb above the limit permissible in plants, with L. vulgare recording the highest concentration (40 mg kg-1) among all species. Therefore, the shoots of several plant species showed promising PTE accumulation abilities and deserve more detailed studies concerning their potential use for phytoremediation of Cd-, Pb-, or Zn-contaminated soils.

Integrated assessment of potentially toxic elements in soil of the Kangdian metallogenic province: A two-point machine learning approach

The accumulation of potentially toxic elements in soil poses significant risks to ecosystems and human well-being due to their inherent toxicity, widespread presence, and persistence. The Kangdian metallogenic province, famous for its iron-copper deposits, faces soil pollution challenges due to various potentially toxic elements. This study explored a comprehensive approach that combinescombines the spatial prediction by the two-point machine learning method and ecological-health risk assessment to quantitatively assess the comprehensive potential ecological risk index (PERI), the total hazard index (THI) and the total carcinogenic risk (TCR). The proportions of copper (Cu), cadmium (Cd), manganese (Mn), lead (Pb), zinc (Zn), and arsenic (As) concentrations exceeding the risk screening values (RSVs) were 15.03%, 5.1%, 3.72%, 1.24%, 1.1%, and 0.13%, respectively, across the 725 collected samples. Spatial prediction revealed elevated levels of As, Cd, Cu, Pb, Zn, mercury (Hg), and Mn near the mining sites. Potentially toxic elements exert a slight impact on soil, some regions exhibit moderate to significant ecological risk, particularly in the southwest. Children face higher non-carcinogenic and carcinogenic health risks compared to adults. Mercury poses the highest ecological risk, while chromium (Cr) poses the greatest health hazard for all populations. Oral ingestion represents the highest non-oncogenic and oncogenic risks in all age groups. Adults faced acceptable non-carcinogenic risks. Children in the southwest region confront higher health risks, both non-carcinogenic and carcinogenic, from mining activities. Urgent measures are vital to mitigate Hg and Cr contamination while promoting handwashing practices is essential to minimize health risks.

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.

A critical review on the migration and transformation processes of heavy metal contamination in lead-zinc tailings of China

The health risks of lead-zinc (Pb-Zn) tailings from heavy metal (HMs) contamination have been gaining increasing public concern. The dispersal of HMs from tailings poses a substantial threat to ecosystems. Therefore, studying the mechanisms of migration and transformation of HMs in Pb-Zn tailings has significant ecological and environmental significance. Initially, this study encapsulated the distribution and contamination status of Pb-Zn tailings in China. Subsequently, we comprehensively scrutinized the mechanisms governing the migration and transformation of HMs in the Pb-Zn tailings from a geochemical perspective. This examination reveals the intricate interplay between various biotic and abiotic constituents, including environmental factors (EFs), characteristic minerals, organic flotation reagents (OFRs), and microorganisms within Pb-Zn tailings interact through a series of physical, chemical, and biological processes, leading to the formation of complexes, chelates, and aggregates involving HMs and OFRs. These interactions ultimately influence the migration and transformation of HMs. Finally, we provide an overview of contaminant migration prediction and ecological remediation in Pb-Zn tailings. In this systematic review, we identify several forthcoming research imperatives and methodologies. Specifically, understanding the dynamic mechanisms underlying the migration and transformation of HMs is challenging. These challenges encompass an exploration of the weathering processes of characteristic minerals and their interactions with HMs, the complex interplay between HMs and OFRs in Pb-Zn tailings, the effects of microbial community succession during the storage and remediation of Pb-Zn tailings, and the importance of utilizing process-based models in predicting the fate of HMs, and the potential for microbial remediation of tailings.