Heavy metals in soils along unpaved roads in south west Cameroon: Contamination levels and health risks

Pollution and probabilistic human health risk assessment of potentially toxic elements in the soil-water-plant system in the Bolkar mining district, Niğde, south-central Turkey

Globally, potentially toxic elements (PTEs) are regarded as an important group of pollutants for the wider environment because of their intrinsic toxicity and probable accumulation in the soil-water-plant system. In this regard, this study assessed the pollution levels and probable human health risks of PTEs in the soil-water-plant system in the Bolkar mining district of the Niğde Province in south-central Turkey. Pollution assessment using contamination factor, enrichment factor, index of geoaccumulation, and soil pollution index reveals moderate to extremely high pollution of PTEs in the soil, exposing the soils to extreme toxicity levels. The areas that fall under the toxic to extremely toxic categories are in proximity to the ore slags and agricultural lands towards the central and southern domains of the study area. The water hazard index (WHI) values indicate that 100% of the samples collected in both winter and fall seasons are of extreme toxicity (WHI > 15). Arsenic is the dominant contaminant among the PTEs in the soil and water samples. The bioconcentration factor values of the PTEs in most of the fruit plants are > 1, indicating very high levels of element transfer from the soil and water to the plants. The probabilistic human health risk assessment involved exposure to arsenic in groundwater (a major pathway to humans) since it is the only carcinogenic element in this study. The estimated daily intake of arsenic-contaminated water exceeds the safe limit of 5 × 10^-8 mg/kg/day. About 33.3% and 55.6% of the groundwater samples have higher hazard quotient and carcinogenic risk values of arsenic in the winter and fall seasons, respectively. This implies that the people are more exposed to the carcinogenic effects of drinking arsenic-contaminated water.

Retrieving soil heavy metals concentrations based on GaoFen-5 hyperspectral satellite image at an opencast coal mine, Inner Mongolia, China

Soil heavy metals pollution has been becoming one of the severely environmental issues globally. Previous studies reported laboratory-measured spectra could be used to infer soil heavy metals concentrations to some extent. However, using field-obtained spectra to estimate soil heavy metals concentrations is still a great challenge due to the low precision and weak efficiency at large scales. The present study collected 110 topsoil samples from an Opencast Coal Mine of Ordos, Inner Mongolia, China. Then, the spectra and soil heavy metals concentrations of samples were measured under laboratory conditions. The direct standardization (DS) algorithm was introduced to calibrate the Gaofen-5 (GF-5) hyperspectral image based on the measured spectra of samples. The spectral reflectance of the GF-5 hyperspectral image was reconstructed using continuous wavelet transform (CWT) at different scales. The characteristic bands of GF-5 for estimating heavy metals concentrations were selected by the Boruta algorithm. Finally, the random forest (RF), the extreme learning machine (ELM), the support vector machine (SVM), and the back-propagation neural network (BPNN) algorithms were used to predict the heavy metals concentrations. Some findings were achieved. First, CWT can effectively eliminate the noise of satellite hyperspectral data. The characteristic bands of Zn (480-677, 827-1029, 1241-1334, 1435-1797, and 1949-2500 nm), Ni (514-630, 835-985, 1258-1325, 1460-1578, and 1949-2319 nm), and Cu (822-831; 1029-1300, 1486-1595, and 1730-2294 nm) can be effectively retrieved via the Boruta algorithm. Second, the estimation accuracy was significantly improved by using the DS algorithm. For zinc (Zn), nickel (Ni), and copper (Cu), the determination coefficients of the validation dataset (R_v²) were 0.77 (RF), 0.62 (RF), and 0.56 (ELM), respectively. Third, the distribution trends of heavy metals were almost consistent with the results of actual ground measurements. This paper revealed that the GF-5 can be one of the reliable satellite hyperspectral imagery for mapping soil heavy metals.

An integrated approach to the bioavailability, ecological, and health risk assessment of potentially toxic elements in soils within a barite mining area, SE Nigeria

Monitoring and assessment of soil quality are important in mining areas. In this study, indexical, spatiotemporal, and chemometric models were developed to monitor and assess the pollution level and health risk of potentially toxic elements (PTEs) within the Iyamitet-Okurumutet mine province, SE Nigeria. Surface soils were sampled within the mine area and analyzed for pH, cation exchange capacity, organic matter, and PTEs (Pb, Zn, Cd, Mn, Fe, Ba) following standard techniques. It was revealed that the soils are slightly acidic and the enrichment of PTEs except for Cd (4.08 mg/kg^-1) was within recommended standards. Contamination factor, enrichment factor, and pollution index suggest that the soils are moderately polluted. Geospatial maps and ecological risk indices revealed that higher ecological risk imprints seem to increase towards the south-eastern parts of the area. Chemometric analysis revealed that PTE enrichment in the soil is majorly influenced by anthropogenic activities. Further, bioavailability/bioaccessibility risk assessment index (BRAI) and health risk assessment models were developed to quantify the bioavailable/human bioaccessible portion of elements in soils and the associated health risks. The BRAI ranged from high (3 ≥ 5) to very high (> 5) risk of human bioaccessibility; hence, greater amount of PTEs will be bioaccessible for absorption into the human gastrointestinal system than they would for plants uptake. The hazard index and lifetime cancer risk (LCR) revealed that most of the samples present high chronic cancer risks from dermal contact and ingestion for children and adults. The LCR values ranged between 1.0E-6 and 1.0E - 04, with the children population showing greater vulnerability to cancer risks.

Retrieving zinc concentrations in topsoil with reflectance spectroscopy at Opencast Coal Mine sites

Heavy metals contaminations in mining areas aroused wide concerns globally. Efficient evaluation of its pollution status is a basis for further soil reclamation. Visible and near-infrared reflectance (Vis-NIR) spectroscopy has been diffusely used for retrieving heavy metals concentrations. However, the reliability and feasibility of calibrated models were still doubtful. The present study estimated zinc (Zn) concentrations via the random forest (RF) and partial least squares regression (PLSR) using ground in-situ Zn concentrations as well as soil spectral reflectance at an Opencast Coal Mine of Ordos, China in February 2020. The coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and the ratio of performance to deviation (RPD) were selected to assess the robustness of the methods in estimating Zn contents. Moreover, the characteristic bands were chosen by Pearson correlation analysis and Boruta Algorithm. Finally, the comparison between RF and PLSR combined with eight spectral reflectance transformation methods was conducted for four concentration groups to determine the optimal model. The results indicated that: (1) Zn contents represented a skewed distribution (coefficient of variation (CV) = 33%); (2) the spectral reflectance tended to decrease with the increase of Zn contents during 580-1850 nm based on Savitzky-Golay smoothing (SG); (3) the continuous wavelet transform (CWT) demonstrated higher effectiveness than other spectral reflectance transformation methods in enhancing spectral responses, the R2 between Zn contents and the soil spectral reflectance achieved the highest (R2 = 0.71) by using CWT; (4) the RF combined with CWT exhibited the best performance than other methods in the current study (R2 = 0.97, RPD = 3.39, RMSE = 1.05 mg kg-1, MAE = 0.79 mg kg-1). The current study supplied a scientific scheme and theoretical support for predicting heavy metals concentrations via the Vis-NIR spectral method in possible contaminated areas such as coal mines and metallic mineral deposit areas.

Comprehensive evaluation of soil quality in a desert steppe influenced by industrial activities in northern China

Desert steppe soil security issues have been the focus of attention. Therefore, to understand the impact of industrial activities on the soil quality of desert grasslands, this experiment investigated the Gaoshawo Industrial Concentration Zone in Yanchi County. Based on the distance and direction from the industrial park, sample plots were established at intervals of 1-2 km. A total of 82 surface soil samples (0-20 cm) representing different pollution sources were collected. The samples were analysed for pH, total nitrogen, total phosphorus, available phosphorus, available potassium, organic matter, copper (Cu), cadmium (Cd), chromium (Cr), lead (Pb), and zinc (Zn). The desert steppe soil quality was analysed based on the integrated fertility index (IFI) and the Nemerow pollution index (PN), followed by the calculation of the comprehensive soil quality index (SQI), which considers the most suitable soil quality indicators through a geostatistical model. The results showed that the IFI was 0.393, indicating that the soil fertility was relatively poor. Excluding the available potassium, the nugget coefficients of the fertility indicators were less than 25% and showed strong spatial autocorrelation. The average values of Cu, Cd, Cr, Pb and Zn were 21.64 ± 3.26, 0.18 ± 0.02, 44.99 ± 21.23, 87.18 ± 25.84, and 86.63 ± 24.98 mg·kg-1, respectively; the nugget coefficients of Cr, Pb and Zn were 30.79-47.35%. Pb was the main element causing heavy metal pollution in the study area. Higher PN values were concentrated north of the highway in the study area, resulting in lower soil quality in the northern region and a trend of decreasing soil quality from south to north. The results of this research showed that the average SQI was 0.351 and the soil quality was extremely low. Thus, industrial activities and transportation activities in the Gaoshawo Industrial Zone significantly impact the desert steppe soil quality index.

Effect of gut microbiota on in vitro bioaccessibility of heavy metals and human health risk assessment from ingestion of contaminated soils

To identify the role of gut microbiota in human health risk assessment, the bioaccessibility of heavy metals in 14 soil samples were determined in simulated gastrointestinal fluids. Compared to the small intestinal phase, the bioaccessibility values of the colon phase varied, either increased by 3.5-fold for As, by 2.2-fold for Cr, and by 1.6-fold for Ni, or reduced by 4.4-fold for Cu, respectively. The colon incubation with adult gut microbiota yielded higher bioaccessibility value of As (1.3 times) and Fe (3.4 times) than that of the child in most soil samples. Colon bioaccessibility was about 60% greater of Cd for the adult and 30% higher of Cr for the child. Congruent data on the bioaccessibility of Cu and Ni was observed. In addition, correlation analysis indicated that in vitro bioaccessibility was primarily related to total concentrations of heavy metals in soils, followed by soil pH and active Fe/Mn oxide. Significantly, risk assessment calculated based on colon bioaccessibility indicated that the target hazard quotient (THQ > 1) of As was presented in 3 soil samples for the adult (1.05-3.35) and in 9 soil samples for the child (1.06-26.93). The hazard index (HI) of the child was 4.00 on average, greater than that of the adult (0.62), primarily due to the contribution of As and Cd. It suggested non-carcinogenic risks are likely to occur in children through typical hand-to-mouth behavior. The adjustment of colon bioaccessibility will result in more accurate risk assessment of human exposure to heavy metals from oral ingestion of contaminated soils.

Spatial and Temporal Heterogeneity of Metal Contaminants in Soils Along Two Major Roads

The concentrations of zinc, copper, lead and cadmium owing to vehicles were analyzed in roadside soils during all the four seasons from Faisalabad-Gojra road (M-4) and Gojra-Jhang road (GJR) in the Punjab, Pakistan. Results indicated that all the polluted sites along the roadside had comparatively higher metal concentrations as compared to control, nevertheless, spatio-temporal variations in metal concentrations were apparent in different soil samples. The spatial differences in metal concentrations were strongly associated with vehicle density at the Gojra Interchange site and Forest Park site along M-4 and GJR roads, respectively. Among seasons, metal ions exhibited the following order: summer > autumn > spring > winter, indicating that high temperature might have released more metal ions. The metals were recorded in contaminated soil in the order of Zn > Cu > Pb > Cd along both roads. This investigation emphasizes the need for proper environmental management particularly the improvement of roads and proper checking of vehicles running on these regards.

Influence of soil properties on the bioaccessibility of Cr and Ni in geologic serpentine and anthropogenically contaminated non-serpentine soils in Taiwan

Serpentine minerals with high levels of geologic chromium (Cr) and nickel (Ni) and non-serpentine farmlands polluted by irrigation water causing high anthropogenic Cr and Ni levels are both found in Taiwan. Elevated levels of Cr and Ni in these soils are a concern due to their potential to promote cancer mortality in humans. Bioaccessibility is a crucial factor determining the actual health risk via oral ingestion when children are exposed to metal-contaminated soils. Furthermore, the bioaccessibility of metals varies with the source, soil properties, and fractionation of metals in the soil. Therefore in this study, soil pH, total organic carbon (TOC), texture, and the total concentrations, fractionation, and bioaccessibility of Cr and Ni were analyzed and correlated for soils collected from serpentine mineral-containing deposits and contaminated non-serpentine farmlands. The low bioaccessibility and low mobility of Cr and Ni in serpentine soils suggested that incidental ingesting of soils posed a low health risk; however, the higher bioaccessibility and mobility of Ni in non-serpentine soils contaminated by electroplating wastewater could lead to potential risks for humans. Additionally, a significant difference in the bioaccessibility of Ni was observed between serpentine and non-serpentine soils, but this was not shown for Cr. Accordingly, a correlation analysis showed that Cr bioaccessibility was positively correlated with TOC, with no distinction between serpentine and non-serpentine soils. In contrast, TOC and the fractions of the sequential extraction procedure were significantly correlated with Ni bioaccessibility both in anthropogenically contaminated non-serpentine soils and in natural serpentine soils.

Public health challenges as a result of contaminated water sources in Kumba, Cameroon

Kumba, the largest city in the Southwest Region of Cameroon, is characterized by the prevalence of waterborne diseases due to ingestion of contaminated water. Sixty-four water samples were collected from different sources including pipe-borne (PW), surface/stream (SW) and groundwater (HDW) sources as well as the catchment area (CW) in Kumba metropolis. These water samples were analyzed for physicochemical and microbiological parameters and the results compared with international standards. The results of physiochemical parameters showed that the water samples were mildly acidic, not saline and soft. The levels of some trace elements (Al, Fe, As, Cd, Co, Cu, Fe, Mn, Pb) in some water samples were higher than permissible limits. Water Quality Index, Contamination Index (C_d) and Trace Element Toxicity Index were used to evaluate the water samples. Results showed that most of the water sources are poor and unsafe for consumption due to high concentrations of Al, Fe, Mn and Pb. Microbiological parameters revealed that 74% of the water samples are in the class of high risk to grossly polluted. Pollution associated with the catchment area was probably the main factor controlling the quality of pipe-borne water, while that of the surface and groundwater may be attributed to geogenic and anthropogenic sources including unlined pit latrines. Water sources, especially those ingested by humans in Kumba, should be properly managed including regular treatment so as to protect the health of humans and improve the quality of life.