Assessment of Anthropogenic Sources of Potentially Toxic Elements in Soil from Arable Land Using Multivariate Statistical Analysis and Random Forest Analysis

Machine learning for predictive mapping of exceedance probabilities for potentially toxic elements in Czech farmland

For efficient decision-making and optimal land management trajectories, information on soil properties in relation to safety guidelines should be processed from point inventories to surface predictive maps. For large-scale predictive mapping, very few practical implementations have attempted to clarify how well indicator models can be built from large covariate sets combined with spatial proxies. This paper summarizes the performance of the weighted indicator-based random forest model which was used to predict exceedance probabilities for several potentially toxic elements (PTEs) in Czech farmland. The method was implemented for data mining in the Czech high-density monitoring data which had to be firstly regressed to achieve analytical harmony, and the reliability of the regression-based harmonisation was used as the input weights for the final model. The indicator-based models were trained for each PTE (As, Be, Cd, Co, Cr, Cu, Hg, Ni, Pb, V, and Zn) with two different sets of indicators, reflecting the two-tier nature of the Czech safety guidelines, which differentiate between soil textures of topsoil. The two separate predictive outputs are combined into a single probability map using a pragmatic meta-model of linear weights derived from a soil texture map generated by a compositional spatial model. Through validation with data splitting, the accuracy of the models showed relatively high predictive power for the probability distributions, but with pronounced differences between PTEs as the root mean square error in terms of exceedance probabilities ranged from 11 % (V) to 32 % (Cd and Cr) for independent validation. In addition, models based on high-resolution auxiliary variables allowed a meaningful and quantitative identification of the most important natural and anthropogenic drivers for areas with an increased rate of non-compliance with the protection thresholds for cultivated soils. Variable importance calculations showed the dominant influence of spatially explicit covariates (represented by geographical distances to quantile-based groups of points), but still significant contributions from other predictors. Among the natural factors, lithological information came to the fore, mainly due to continuous response variables such as mineral exploration density or geophysical ancillary variables (from remotely sensed gravimetry and radiometry). Among anthropogenic factors, particulate matter in the atmosphere was identified as the most important human-related pressure, followed by several land-use effects.

Enrichment and sources of major and trace elements in the Qinghai-Tibetan Plateau: a case study of the Golog Prefecture

The Qinghai-Tibet Plateau (QTP) is a vital region for global atmospheric circulation and biodiversity. This study aims to evaluate the contents and enrichment status of 25 soil elements, namely Al, As, Ca, Cd, Co, Cr, Cu, K, Mg, Mn, Mo, N, Na, Ni, P, Pb, S, Sc, Si, Sn, Sr, Fe, Ti, V, and Zn, in the plateau region. Specifically, our analysis revealed that As exhibited significant enrichment near fault zones and intrusive rocks, while Ca was mainly enriched due to the dissolution of carbonate rocks. Additionally, Principal Component Analysis and Multiple Linear Regression (PCA-MLR) were used to examine the origins of these elements. The potential ecological risk posed by Cd and Pb was evaluated and found to be negligible. Soil element enrichment in the QTP was mainly influenced by lithology, and high spatial variability was observed in As, Ca, and S, which were mainly affected by geological processes and grazing activities. Six sources of elements in the plateau region were identified, namely geological mixed sources, grazing activities, alkaline granite, ultrabasic rocks, fault zones and intrusive rocks, as well as atmospheric deposition. Among these, geological mixed sources and grazing activities were determined to be the priority contributors. Although grazing activities on the QTP as well as atmospheric deposition at long distances caused the enrichment of elements in the area, the ecological risk was negligible. The outcomes of this work can be used as a theoretical basis for prospective investigation on the stability of high-altitude ecosystems, species diversity, and geochemical background.

Analysis of heavy metal and arsenic sources in mangrove surface sediments at Wulishan Port on Leizhou Peninsula, China, using the APCS-MLR model

Mangrove forests are sources and sinks for various pollutants. This study analyzed the current status of heavy metal and arsenic (As) pollution in mangrove surface sediments in rapidly industrializing and urbanizing port cities. Surface sediments of mangroves at Wulishan Port on the Leizhou Peninsula, China, were analyzed using inductively coupled plasma emission spectrometry (ICP-AES) and inductively coupled plasma mass spectrometry (ICP-MS) for the presence of Cr, Pb, Ni, Zn, Cd, Cu, As, and Hg. The Pollution load index, Nemerow pollution index, and Potential ecological risk index were employed to evaluate the pollutant. Multivariate statistical methods were applied for the qualitative analysis of pollutant sources, and the APCS-MLR receptor model was used for quantification. This study indicated the following results: (1) The average content of eight pollutants surpassed the local background level but did not exceed the Marine Sediment Quality standard. The pollution levels across the four sampling areas were ranked as Ⅲ > Ⅳ > Ⅰ > Ⅱ. The area Ⅱ exhibited relatively lower pollution levels with the grain size of the sediments dominated by sand, which was not conducive to pollutant adsorption and enrichment. (2) The factor analysis and cluster analyses identified three primary sources of contamination. As, Cr, Ni, and Pb originated from nearby industrial activities and their associated wastewater, suggesting that the primary source was the industrial source. Cd, Cu, and Zn stem from the cement columns utilized in oyster farming, alongside discharges from mariculture and pig farming, establishing a secondary agricultural source. Hg originated from ship exhaust burning oil and vehicle emissions in the vicinity, representing the third traffic source. (3) The APCS-MLR receptor model results demonstrated industrial, agricultural, and traffic sources contributing 47.19 %, 33.13 %, and 13.03 %, respectively, with 6.65 % attributed to unidentified sources.

Application of Geostatistical Analysis and Random Forest for Source Analysis and Human Health Risk Assessment of Potentially Toxic Elements (PTEs) in Arable Land Soil

Arable land soil is one of the most precious natural resources of Earth, it provides the fundamental material and numerous resources essential for the development of human society. To determine the pollution of potential toxic factors in the surface soil of cultivated land and its risks to human health, concentrations of five different potentially toxic elements (PTEs) were detected in 1109 soil samples collected in Xiangzhou, China, in 2019. In this study, health risk assessment was used to judge the degree of pollution in the study area, the result of Geographic Information System (GIS) was as used to research the spatial distribution characteristics of PTEs, and random forest (RF) was used to assess the natural and man-made influencing factors. We investigated the sources of PTEs through quantifying the indicators, which gave further insights. The main results are: (1) In arable land soil, the average content of PTEs is 0.14 mg/kg cadmium (Cd), 0.05 mg/kg mercury (Hg), 12.89 mg/kg arsenic (As), 29.23 mg/kg lead (Pb), and 78.58 mg/kg chromium (Cr), respectively. The content of As and Pb outpaced the background value of Hubei soil. (2) The human health risk assessment in Xiangzhou indicates that the most important exposure pathway is soil ingestion, occupied about 99% to health risks of PTEs; non-carcinogenic risk from exposure to As, Pb and Cr in soil was higher than the limit (overall potential risk index, HI > 1) for both children and adults. Moreover, carcinogenic risk postured by Cd, Cr, and As was higher than the limit (10^-4) through soil exposure for both children and adults, indicating that Cd, As, Pb and Cr in soil have significant effect on people's health through exposure. (3) We found that the increased PTEs in the arable land soil mainly originated from potential water sources, air and soil pollution sources, breeding farms, and mining areas.