Heavy metal concentrations in floodplain soils of the Innerste River and in leaves of wild blackberries (Rubus fruticosus L. agg.) growing within and outside the floodplain: the legacy of historical mining activities in the Harz Mountains (Germany)

Lead concentration in blood of Asian black bears in two areas of Central Japan

To measure blood lead level, we collected a total of 88 blood samples from 66 Asian black bears (Ursus thibetanus japonicus) that were live-trapped in two study areas in Japan. In both areas, extensive sika deer (Cervus nippon) and wild boar (Sus scrofa) control programs and sport hunting using lead-based ammunition have been conducted. The area in the Ashio-Nikko Mountains is located near a former copper mine and smelter. The Okutama Mountains are situated in the suburbs of Tokyo, a mega-city with a population of nearly 14 million. In both study areas, the use of leaded gasoline was permitted until 1975 for regular gasoline vehicles. Our analysis revealed that Japanese black bears had higher lead compared to American black bears (U. americanus) and brown bears (U. arctos) in North America. Blood lead concentrations were higher in the Ashio-Nikko Mountains (110.4 ± 38.3 SD μg/L) than in the Okutama Mountains (49.3 ± 46.2 SD μg/L). The blood lead concentrations were particularly high in lactating females compared to most males and females. White blood cell count was decreased with increasing blood lead concentration. The source of lead contamination remains unknown at this time. However, Japanese black bears in our study areas commonly scavenge on large mammals, and lead-based ammunition could constitute a significant portion of the contamination in both areas. In addition, former mining activities could elevate the lead concentrations of bears in the Ashio-Nikko Mountains. Atmospheric lead depositions originating from former leaded gasoline use in Japan and current air pollution from the Asian continent are also potential factors in both areas. Future identification of exposure sources is necessary.

Spatial diffusion of potentially toxic elements in soils around non-ferrous metal mines

This study focuses on the diffusion patterns of principal ore-forming elements (Pb and Zn) and associated elements (Cd, Cu, Cr, and As) in lead-zinc ore. Sampling points in upwind and downwind directions of lead-zinc ore areas at various densities (1 N/km2 - 4 N/km2) were categorized. This study analyzed the statistical relationship between the content of PTEs in the soil around lead-zinc ore and the source strength and dominant wind direction, constructed one-dimensional and two-dimensional diffusion model, and simulated the EER scope caused by PTEs. The findings indicate that: (1) concerning source strength, the content of PTEs in soils of high-density ore aggregation areas is significantly higher than in low-density ore aggregation areas. However, the impact of source strength decreases with decreasing ore grade, with a difference in Pb content of 1.71 times among principal ore-forming elements and almost consistent Cd content among associated elements. (2) Regarding the transport pathways, for most PTEs, the inverse proportion coefficients downwind are higher than upwind, approximately 1.18-3.63 times, indicating greater migration distances of PTEs downwind due to atmospheric dispersion. (3) By establishing a two-dimensional risk diffusion model, the study simulates the maximum radius of risk diffusion (r = 5.7 km), the 50% probability radius (r = 3.1 km), and the minimum radius (r = 0.8 km) based on the maximum, median, and minimum values statistically obtained from the EER. This study provides a scientific basis for implementing preventive measures for PTEs accumulation in soil within different pollution ranges. Different risk prevention and control measures should be adopted for PTEs accumulation in soil within the three ranges after cutting off pollution sources. Subsequent research should further investigate the impact and contribution of atmospheric transmission and surface runoff on the diffusion of PTEs in areas with high risk near lead-zinc ore.

Epigenetic alterations in bioaccumulators of cadmium: Lessons from mammalian kidneys and plants

Faced with unpredictable changes in global weather patterns, release and redistribution of metals through land erosion and water movements add to the increasing use of metals in industrial activities causing high levels of environmental pollution and concern to the health of all living organisms. Cadmium is released into the environment by smelting and mining, entering the food chain via contaminated soils, water, and phosphate fertilizers. Bioaccumulation of cadmium in plants represents the first major step into the human food chain and contributes to toxicity of several organs, especially the kidneys, where biomagnification of cadmium occurs over decades of exposure. Even in small amounts, cadmium brings about alterations at the molecular and cellular levels in eukaryotes through mutagenicity, molecular mimicry at metal binding sites and oxidative stress. The epigenome dictates expression of a gene's output through a number of regulatory steps involving chromatin remodeling, nucleosome unwinding, DNA accessibility, or nucleic acid modifications that ultimately impact the transcriptional and translational machinery. Several epigenetic enzymes exhibit zinc-dependence as zinc metalloenzymes and zinc finger proteins thus making them susceptible to deregulation through displacement by cadmium. In this review, we summarize the literature on cadmium-induced epigenetic mechanisms in mammalian kidneys and plants, compare similarities in the epigenetic defense between these bioaccumulators, and explore how future studies could advance our understanding of the cadmium-induced stress response and disruption to biological health.

Pollution levels and probability risk assessment of potential toxic elements in soil of Pb-Zn smelting areas

Soil pollution around Pb-Zn smelters has attracted widespread attention around the world. In this study, we compiled a database of eight potentially toxic elements (PTEs) Pb, Zn, Cd, As, Cr, Ni, Cu, and Mn in the soil of Pb-Zn smelting areas by screening the published research papers from 2000 to 2023. The pollution assessment and risk screening of eight PTEs were carried out by geo-accumulation index (Igeo), potential ecological risk index (PERI) and health risk assessment model, and Monte Carlo simulation employed to further evaluate the probabilistic health risks. The results suggested that the mean values of the eight PTEs all exceeded the corresponding values in the upper crust, and more than 60% of the study sites had serious Pb and Cd pollution (Igeo > 4), with Brazil, Belgium, China, France and Slovenia having higher levels of pollution than other regions. Besides, PTEs in smelting area caused serious ecological risk (PERI = 10912.12), in which Cd was the main contributor to PREI (86.02%). The average hazard index (HI) of the eight PTEs for adults and children was 7.19 and 9.73, respectively, and the average value of total carcinogenic risk (TCR) was 4.20 × 10-3 and 8.05 × 10-4, respectively. Pb and As are the main contributors to non-carcinogenic risk, while Cu and As are the main contributors to carcinogenic risk. The probability of non-carcinogenic risk in adults and children was 84.05% and 97.57%, while carcinogenic risk was 92.56% and 79.73%, respectively. In summary, there are high ecological and health risks of PTEs in the soil of Pb-Zn smelting areas, and Pb, Cd, As and Cu are the key elements that cause contamination and risk, which need to be paid attention to and controlled. This study is expected to provide guidance for soil remediation in Pb-Zn smelting areas.

Multipath diffusion process and spatial accumulation simulation of Cd in lead-zinc mining areas

This study combined process simulation and actual measurement to construct a multipath diffusion and spatial accumulation model of Cd in a typical lead-zinc mining area through accuracy and root mean square error(RMSE) analysis. The results indicated that (1) the diffusion of Cd was in a quadratic inverse proportional relationship with the distance from the pollution source within watershed. The average annual atmospheric Cd sedimentation in study area was 0.71 * 10-6 g and the contribution of runoff diffusion to Cd exceeded 80%. (2) With the increase in the concentration range of Cd content (k) carried by unit runoff sediment, the model accuracy and RMSE showed decreasing trends. However, when the lower and upper limits of k were 10% and 90%, the model accuracy reached 75%. (3) Two sub-watersheds with same dominant wind direction but different runoff directions were selected to verify the model accuracy, indicating that the model construction method can precisely simulate the spatial accumulation of Cd in similar mining areas. The results provide a scientific basis for the prevention of heavy metal diffusion in lead-zinc mines. Future research should focus on the migration pathways of heavy metals through vertical infiltration caused by rainfall to further optimise the model structure and accuracy.

Delineating and identifying risk zones of soil heavy metal pollution in an industrialized region using machine learning

The ability to control the risk of soil heavy metal pollution is limited by the inability to accurately depict their spatial distributions and to reasonably delineate the risk zones. To overcome this limitation and develop machine learning methods, a hybrid data-driven method supported by random forest (RF) and fuzzy c-means with the aid of inverse distance weighted interpolation was proposed to delineate and further identify risk zones of soil heavy metal pollution on the basis of 577 soil samples and 12 environmental covariates. The results indicated that, compared to multiple linear regression, RF had a better prediction performance for As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn, with the corresponding R² values of 0.86, 0.85, 0.78, 0.85, 0.84, 0.78, 0.79 and 0.76, respectively. The relative concentrations (predicted concentrations divided by risk screening values) of Cd (17.69), Cr (1.38), Hg (0.31), Pb (6.52), and Zn (8.24) were relatively high in the north central part of the study area. There were large differences in the key influencing factors and their contributions among the eight heavy metals. Overall, industrial enterprises (21.60% for As), soil pH (31.60% for Cd), and population (15.50% for Cr) were the key influencing factors for the heavy metals in soil. Four risk zones, including one high risk zone, one medium risk zone, and two low risk zones were delineated and identified based on the characteristics of the eight heavy metals and their influencing factors, and accordingly discriminated risk control strategies were developed. In the high risk zone, it will be necessary to strictly control the discharge of heavy metals from the various industrial enterprises and mines by the adoption of cleaner production practices, centralizedly treat the domestic wastes from residents, substantially reduce the irrigation of polluted river water, and positively remediate the Cd, Cr, and Ni-polluted soil.

Mapping Plant Species in a Former Industrial Site Using Airborne Hyperspectral and Time Series of Sentinel-2 Data Sets

Industrial activities induce various impacts on ecosystems that influence species richness and distribution. An effective way to assess the resulting impacts on biodiversity lies in vegetation mapping. Species classification achieved through supervised machine learning algorithms at the pixel level has shown promising results using hyperspectral images and multispectral, multitemporal images. This study aims to determine whether airborne hyperspectral images with a high spatial resolution or phenological information obtained by spaceborne multispectral time series (Sentinel-2) are suitable to discriminate species and assess biodiversity in a complex impacted context. The industrial heritage of the study site has indeed induced high spatial heterogeneity in terms of stressors and species over a reduced scale. First, vegetation indices, derivative spectra, continuum removed spectra, and components provided by three feature extraction techniques, namely, Principal Component Analysis, Minimal Noise Fraction, and Independent Component Analysis, were calculated from reflectance spectra. These features were then analyzed through Sequential Floating Feature Selection. Supervised classification was finally performed using various machine learning algorithms (Random Forest, Support Vector Machines, and Regularized Logistic Regression) considering a probability-based rejection approach. Biodiversity metrics were derived from resulted maps and analyzed considering the impacts. Average Overall Accuracy (AOA) reached up to 94% using the hyperspectral image and Regularized Logistic Regression algorithm, whereas the time series of multispectral images never exceeded 72% AOA. From all tested spectral transformations, only vegetation indices applied to the time series of multispectral images increased the performance. The results obtained with the hyperspectral image degraded to the specifications of Sentinel-2 emphasize the importance of fine spatial and spectral resolutions to achieve accurate mapping in this complex context. While no significant difference was found between impacted and reference sites through biodiversity metrics, vegetation mapping highlighted some differences in species distribution.