Contamination Levels and the Ecological and Human Health Risks of Potentially Toxic Elements (PTEs) in Soil of Baoshan Area, Southwest China

Prediction of potentially toxic elements in water resources using MLP-NN, RBF-NN, and ANFIS: a comprehensive review

Water resources are constantly threatened by pollution of potentially toxic elements (PTEs). In efforts to monitor and mitigate PTEs pollution in water resources, machine learning (ML) algorithms have been utilized to predict them. However, review studies have not paid attention to the suitability of input variables utilized for PTE prediction. Therefore, the present review analyzed studies that employed three ML algorithms: MLP-NN (multilayer perceptron neural network), RBF-NN (radial basis function neural network), and ANFIS (adaptive neuro-fuzzy inference system) to predict PTEs in water. A total of 139 models were analyzed to ascertain the input variables utilized, the suitability of the input variables, the trends of the ML model applications, and the comparison of their performances. The present study identified seven groups of input variables commonly used to predict PTEs in water. Group 1 comprised of physical parameters (P), chemical parameters (C), and metals (M). Group 2 contains only P and C; Group 3 contains only P and M; Group 4 contains only C and M; Group 5 contains only P; Group 6 contains only C; and Group 7 contains only M. Studies that employed the three algorithms proved that Groups 1, 2, 3, 5, and 7 parameters are suitable input variables for forecasting PTEs in water. The parameters of Groups 4 and 6 also proved to be suitable for the MLP-NN algorithm. However, their suitability with respect to the RBF-NN and ANFIS algorithms could not be ascertained. The most commonly predicted PTEs using the MLP-NN algorithm were Fe, Zn, and As. For the RBF-NN algorithm, they were NO3, Zn, and Pb, and for the ANFIS, they were NO3, Fe, and Mn. Based on correlation and determination coefficients (R, R2), the overall order of performance of the three ML algorithms was ANFIS > RBF-NN > MLP-NN, even though MLP-NN was the most commonly used algorithm.

Magnetic signature and X-ray fluorescence for mapping trace elements in soils originating from basalt and sandstone

The knowledge of the lithological context is necessary to interpret trace elements concentrations in the soil. Soil magnetic signature (χ) and soil X-ray fluorescence (XRF) are promising approaches in the study of the spatial variability of trace elements and the environmental monitoring of soil quality. This research aimed to assess the efficiency of measurements of χ and XRF sensors for spatial characterization of zinc (Zn), manganese (Mn), and copper (Cu) contents in soils of a sandstone-basalt transitional environment, using machine learning modeling. The studied area consisted of the Western Plateau of São Paulo (WPSP), with soils originating from sandstone and basalt. A total of 253 soil samples were collected at a depth of 0.0-0.2 m. The soils were characterized by particle size and chemical analysis: organic matter (OM), cation exchange capacity (CEC), ammonium oxalate-extracted iron (Feo), sodium dithionite-citrate-bicarbonate-extracted iron (Fed), and sulfuric acid-extracted iron (Fet). Hematite (Hm), goethite (Gt), kaolinite (Kt), and gibbsite (Gb) contents were obtained by X-ray diffraction (XRD). Magnetite (Mt) and maghemite (Mh) contents were obtained by soil χ, while trace elements contents were obtained by XRF and predicted by χ. Descriptive analysis, the test of means, and correlation were performed between attributes. Zn, Mn, and Cu contents were predicted using the machine learning algorithm random forest, and the spatial variability was obtained using the ordinary kriging interpolation technique. Landscape dissections influenced iron oxides, which had the highest contents in slightly dissected environments. Trace elements contents were not influenced by landscape dissections, demonstrating that lithological knowledge is necessary to characterize trace elements in soils. The prediction models developed through the machine learning algorithm random forest showed that χ can be used to characterize trace elements. The similar spatial pattern of trace elements obtained by XRF and χ measurements confirm the applicability of these sensors for mapping it under lithological and landscape transition, aiming for sustainable strategic planning of land use and occupation.

Quantitative Source Apportionment of Potentially Toxic Elements in Baoshan Soils Employing Combined Receptor Models

Arable soils are crucial for national development and food security; therefore, contamination of agricultural soils from potentially toxic elements (PTEs) is a global concern. In this study, we collected 152 soil samples for evaluation. Considering the contamination factors and using the cumulative index and geostatistical methods, we investigated the contamination levels of PTEs in Baoshan City, China. Using principal component analysis, absolute principal component score-multivariate linear regression, positive matrix factorization, and UNMIX, we analyzed the sources and quantitatively estimated their contributions. The average Cd, As, Pb, Cu, and Zn concentrations were 0.28, 31.42, 47.59, 100.46, and 12.36 mg/kg, respectively. The Cd, Cu, and Zn concentrations exceeded the corresponding background values for Yunnan Province. The combined receptor models showed that natural and agricultural sources contributed primarily to Cd and Cu and As and Pb inputs, accounting for 35.23 and 7.67% pollution, respectively. Industrial and traffic sources contributed primarily to Pb and Zn inputs (47.12%). Anthropogenic activities and natural causes accounted for 64.76 and 35.23% of soil pollution, respectively. Industrial and traffic sources contributed 47.12% to pollution from anthropogenic activities. Accordingly, the control of industrial PTE pollution emissions should be strengthened, and awareness should be raised to protect arable land around roads.

Zeolites Reduce the Transfer of Potentially Toxic Elements from Soil to Leafy Vegetables

The ability of natural zeolite amendment to reduce the uptake of potentially toxic elements (PTEs) by lettuce, spinach and parsley was evaluated using pot experiments. PTE concentrations in roots and shoots, as well as the pseudo total (PT), water soluble (WS) and bioavailable (BA) PTE fractions in the amended soils, were assessed. Although the PT PTE concentration was high, the WS fraction was very low (<0.4%), while the BA fraction varied widely (<5% for Cr, Mn and Co, <15% for Ni, Pb and Zn, >20% for Cd and Cu). PTE concentration decreased in both roots and shoots of all leafy vegetables grown on zeolite amended soils, especially at high amendment dose (10%). The uptake of PTEs mainly depended on plant species, PTE type and amendment dose. With the exception of Zn in spinach, the bioaccumulation factor for roots was higher than for shoots. Generally, lettuce displayed the highest PTE bioaccumulation capacity, followed by spinach and parsley. Except for Zn in spinach, the transfer factors were below 1 for all PTEs, all plant species and all amendment doses. Our results showed that the natural zeolites are promising candidates in the reclamation of contaminated soils due to their ability to immobilize PTEs.

Health Risks for a Rural Community in Bokkos, Plateau State, Nigeria, Exposed to Potentially Toxic Elements from an Abandoned Tin Mine

The past mining activities in Bokkos Local Government Area (LGA) were performed in an uncontrolled way and gave rise to many abandoned ponds now serving as domestic and irrigation water sources. Past research focused mainly on the environmental impact, and we show for the first time what the human health risk through consumption of contaminated food crops is in these communities. This study was designed to determine the level of Potentially Toxic Elements (PTEs) contamination in pond water, soil, and food crops and assess the health risk of inhabitants in the abandoned tin mining community in Bokkos LGA. Samples of the mining pond water, soil, and selected food crops from farms irrigated with the pond water: bitter leaf (Vernonia amygdalina), pepper (Piper nigrum), okra (Albelmoschus esculentus), maize (Zea mays), sweet potato (Ipomoea batatas), and Irish potato (Solanum tuberosum) were analyzed for each of the eight PTEs (viz. Cu, Cr, Fe, Mn, Ni, Zn, Cd, and Pb) using inductively coupled plasma optical emission spectrometry (ICP-OES). The results obtained showed that the levels of all the PTEs analyzed in the soil, pond water, and selected food crops except for Fe and Mn in soil and Cd in sweet potato were greater than their corresponding background area values (p < 0.05). Also, the mean concentrations of all the PTEs except for Cu in pond water were significantly (p < 0.05) higher than the WHO maximum permissible limit. With the exception of Fe, Ni, and Zn for pepper and okra, Cu and Fe for maize grains as well as Cu, Ni, and Zn for sweet and Irish potatoes and Fe and Cd for sweet potato, the mean concentrations of PTEs in the food crops were significantly higher than WHO maximum permissible limit. The EF values of Cd (0.39); Cu (3.59) and Ni (2.81); Cr (9.38) and Pb (17.84); and Mn (178.13) and Zn (83.22) classified the soil as minimally, moderately, significantly, and extremely highly enriched, respectively. The PI values of all the PTEs in the soil studied were all greater than 5, indicating that the soils were severely contaminated. There was evidence that food crops significantly bioaccumulated PTEs either as a result of contaminated soil and/or irrigation water. The bioaccumulation was not uniform and was dominated by transfer from the polluted irrigation water. The bitter leaf, okra, and to some extent maize had the highest transfer of PTEs, and Mn, Cu, and Zn had the highest bioaccumulation in the food crops investigated. The hazardous index (HI) for the eight PTEs through the consumption of food crops was 107 for children and 33 for adults which greatly exceeded the recommended limit of 1, thus indicating that possible health risks exist for both local children and adults. For every PTE, the values of HI for children are many-fold higher than those for adults, which is of particular concern due to the high HI values for Pb found for maize consumption, a typical staple food. The cancer risk values for Cr and Ni for all the food crops were within 10^-3-10^-1 which is several fold higher than the permissible limits (10^-6 and < 10^-4) indicating the high carcinogenic risk. It can be concluded based on the results and risk assessment provided by this study that human exposure to mining pond water and soil in farms around the mining pond through the food chain suggests the high vulnerability of the local community to PTE toxicity. Long-term preventive measures to safeguard the health of the residents need to be put in place.