Accumulation characteristics, driving factors, and model prediction of cadmium in soil-highland barley system on the Tibetan Plateau

Unraveling soil salinity on potentially toxic element accumulation in coastal Phragmites australis: A novel integration of multivariate and interpretable machine-learning models

Revealing the key mechanisms influencing the behavior of potentially toxic elements (PTEs) in soil-plant systems is of great significance for environmental protection and grassland development in coastal areas. This study utilized redundancy analysis to assess the effects of soil environmental variables on the concentrations and enrichment of various PTEs in the advantageous forage species Phragmites australis. Advanced models like PLS-PM and RF-SHAP quantitatively assessed soil salinity impacts. The main findings are as follows: (1) P. australis exhibited enrichment capacity for Cd, Cr, and Cu. (2) Soil pH, exchangeable potassium (aK), and exchangeable calcium (aCa) were key determinants of PTE distribution, with Cu being highly sensitive to these variables. (3) Significant interactions between soil electronic conductivity (EC) and pH, as well as between soil EC and aCa (p < 0.01). (4) A pH value of 8.30 and an aCa concentration of 4.4 g/kg were identified as critical thresholds affecting the Cu uptake. These results provide insights into PTE migration and management strategies for coastal grasslands.

From open-field to greenhouse cultivation: characteristics, and driving factors of soil bioavailable lead and cadmium changes in Southwest China

Understanding the dynamics of bioavailable lead (Pb-ava) and cadmium (Cd-ava) in soils from open-field and greenhouse cultivation is crucial for mitigating health risks. Research on Pb-ava and Cd-ava in greenhouse soils was limited. This study analyzed soils from leafy vegetable and grape plantations in Southwest China. Results indicated that in soils cultivated in greenhouses for 1-10 years, Pb-ava first increased and then significantly declined, while Cd-ava decreased initially and then rose sharply. The interactions of Pb-ava and Cd-ava with total lead (Pb-total) and total cadmium (Cd-total), along with meteorological factors, soil texture, properties, particulate matter 10 (PM10), and fertilization, were examined using redundancy analysis (RDA), structural equation modeling (SEM), and multiple linear regression with Lindeman-Merenda-Gold (MLR-LMG). In open-field conditions, Pb-ava and Cd-ava had minimal contributions to Pb-total and Cd-total; Pb-ava primarily stemmed from nitrogen fertilizer (Nfer, 21.57 %) and soil organic matter (SOM, 19.18 %), while PM10 contributed 16.42 % to Cd-ava. In the first 1-5 years of greenhouse cultivation, Pb-total contributed 54.50 % to Pb-ava. PM10 was the primary factor reducing soil Pb-ava, while Nfer and silt were the main factors influencing its increase. For the later 6-10 years, Pb-ava originates from Nfer. Cd-total contributed 8.40 % to Cd-ava in greenhouse soils during the first 1-5 years and 21.49 % during the 6-10 years, with sand significantly affecting Cd-ava. Our research highlights the importance of managing bioavailable lead and cadmium inputs from fertilization practices and soil texture under greenhouse conditions to mitigate soil pollution risk.

Application of machine learning in soil heavy metals pollution assessment in the southeastern Tibetan plateau

The Tibetan Plateau, a globally significant ecological region, is experiencing escalating pollution from heavy metals (HMs). This study applies a machine learning approach based on the self-organizing map hyper-clustering, alongside advanced methodologies such as Positive Matrix Factorization (PMF), Incremental Spatial Autocorrelation, and Bivariate Local Indicators of Spatial Association (BiLISA), to analyze the ecological risk of soil HMs in representative watersheds of the southeastern Tibetan Plateau, focusing on spatial pattern clustering, pollutant source identification, and interaction risk assessment. The results indicated higher HMs concentrations in the middle and downstream areas. A comprehensive ecological risk assessment integrating the Improved Potential Ecological Risk Index, Enrichment Factor, Contamination Factor, and Geo-accumulation Index identified Cd, Pb, and As as the primary pollutants of concern. By combining PMF with Mantel analysis, pollution was attributed to geological background, agricultural activities, traffic emissions, and atmospheric deposition. The BiLISA method revealed significant spatial interactions among HMs, with the composite pollution of As and Cd occupying the largest proportion in High (As)-High (Cd) aggregation zones, underscoring the need for integrated management strategies. This study offers novel insights into the spatial pollution patterns and source apportionment of soil HMs, providing an advanced analytical framework for their precise control and ecological restoration.

Machine learning and structural equation modeling for revealing the influence factors and pathways of different water management regimes acting on brown rice cadmium

Excessive cadmium (Cd) in brown rice has detrimental effects on rice growth and human health. Water management is a cost-effective, eco-friendly measure to suppress Cd accumulation in rice. However, there is no acknowledged water management regime that reduces Cd accumulation in brown rice without compromising the yield. Meanwhile, the major factors affecting brown rice Cd and the pathways of water management affecting rice Cd are not clear. This study explored major factors affecting brown rice Cd using machine learning (ML) and examined the pathways of water management affecting rice Cd using a structural equation model (SEM). Three water management systems were set up, namely flooding, water-saving, and wetting irrigation. Results showed that water-saving irrigation increased dry matter and reduced Cd content and translocation. Root uptake during the grain filling stage and Cd remobilization before the grain filling stage contributed 36 % and 64 % of the Cd accumulation in brown rice, respectively. ML explained 97 % of the variance, suggesting that crop covariates were the most important (e.g., the brown rice bioconcentration factor (12 %), stem Cd (9 %), root-to-stem translocation factor (7 %)), followed by soil covariates (e.g., reducing substances 12 %) and water management (3 %). All SEM explanatory variables collectively explained 94 % of the variation, with a predictive power of 76 %. Water treatments indirectly affected soil available Fe and Mn (indirect effect coefficient = 0.909), iron plaques (indirect effect coefficient = 0.866), soil available Cd (indirect effect coefficient = -0.671), and Cd intensity of xylem sap (BICd, indirect effect coefficient = -0.664) via pH and reducing substances. BICd significantly positively affected stem Cd (path coefficient = 0.445). These findings provide insight into the agronomic and environmental effects of water management on brown rice Cd and influence pathways in soil-rice systems, suggesting that water-saving irrigation may alleviate Cd contamination in the paddy soil.

Spatial patterns and mechanism of the impact of soil salinity on potentially toxic elements in coastal areas

Soil salinization and heavy metal pollution in the Yellow River Delta region have elicited increasing concern. Therefore, revealing the underlying mechanism of the impact of soil salinity on potential toxic elements (PTEs) is crucial for environmental protection and the rational utilization of resources in this area. In this study, we employed CatBoost-SHAP and multiscale geographically weighted regression (MGWR) models to comprehensively investigate the spatial effects of soil electrical conductivity (EC1:5) on PTEs. Additionally, we employed a space-for-time substitution strategy with the aim of investigating how increasing soil salinity, represented by EC1:5, K+, Na+, Ca2+, and Mg2+, affects the bioavailability of PTEs over time. The primary findings are as follows: (1) for most PTEs, the influence of soil EC1:5 on the bioavailable forms of these elements surpassed its impact on their total concentrations. (2) The results of the MGWR model indicated that exchangeable Ca (aCa) in the soils of the eastern coastal areas markedly increased the bioavailable Cd (aCd), bioavailable Cu (aCu), and bioavailable Zn (aZn). (3) When the soil EC1:5 ranges between 2 and 6 dS/m, exchangeable Na (aNa) primarily competed for the adsorption sites of bioavailable Pb (aPb). However, as the soil EC1:5 increases to 6-10 dS/m, exchangeable Mg (aMg) and aCa became the primary competing ions, with aMg playing a more significant role than aCa. These findings provide valuable theoretical insights and practical guidance for saline-alkali soil improvement and PTEs pollution control in the Yellow River Delta region, thereby providing a foundation for sustainable environmental management and resource utilization.

Major influencing factors identification and probabilistic health risk assessment of soil potentially toxic elements pollution in coal and metal mines across China: A systematic review

Deposition of potentially toxic elements (PTEs) in soils due to different types of mining activities has been an increasingly important concern worldwide. Quantitative differences of soil PTEs contamination and related health risk among typical mines remain unclear. Herein, data from 110 coal mines and 168 metal mines across China were analyzed based on 265 published literatures to evaluate pollution characteristics, spatial distribution, and probabilistic health risks of soil PTEs. The results showed that PTE levels in soil from both mine types significantly exceeded background values. The geoaccumulation index (Igeo) revealed metal-mine soil pollution levels exceeded those of coal mines, with average Igeo values for Cd, Hg, As, Pb, Cu, and Zn being 3.02-15.60 times higher. Spearman correlation and redundancy analysis identified natural and anthropogenic factors affecting soil PTE contamination in both mine types. Mining activities posed a significant carcinogenic risk, with metal-mine soils showing a total carcinogenic risk an order of magnitude higher than in coal-mine soils. This study provides policymakers a quantitative foundation for developing differentiated strategies for sustainable remediation and risk-based management of PTEs in typical mining soils.

Hotspot mapping and risk prediction of fluoride in natural waters across the Tibetan Plateau

Intake of high fluoride concentrations through water affects up to 1 billion people worldwide, and the Tibetan Plateau (TP) is one of the most severely affected areas. Knowledge regarding the high fluoride risk areas, the driving factors, and at-risk populations on the TP remains fragmented. We collected 1581 natural water samples from the TP to model surface water and groundwater fluoride hazard maps using machine learning. The geomean concentrations of surface water and groundwater were 0.26 mg/L and 0.92 mg/L, respectively. Surface water fluoride hazard hotspots were concentrated in the north-central region; high fluoride risk areas of groundwater were mainly concentrated in the southern TP. Hazard maps showed a maximum estimate of 15% of the total population in the TP (approximately 1.47 million people) at risk, and 500,000 people considered the most reasonable estimate. Critical environment driving factors were identified, in which climate condition was taken for the vital one. Under the moderate climate change scenario (SSP2.45) for 2089-2099, the high fluoride risk change rate differed inside the TP (surface water -24%-55% and groundwater -56%-50%), and the overall risk increased in natural waters throughout the TP, particularly in the southeastern TP.

Cadmium accumulation in tropical island paddy soils: From environment and health risk assessment to model prediction

Cultivated soil quality is crucial because it directly affects food safety and human health, and rice is of primary concern because of its centrality to global food networks. However, a detailed understanding of cadmium (Cd) geochemical cycling in paddy soils is complicated by the multiple influencing factors present in many rice-growing areas that overlap with industrial centers. This study analyzed the pollution characteristics and health risks of Cd in paddy soils across Hainan Island and identified key influencing factors based on multi-source environmental data and prediction models. Approximately 27.07% of the soil samples exceeded the risk control standard screening value for Cd in China, posing an uncontaminated to moderate contamination risk. Cd concentration and exposure duration contributed the most to non-carcinogenic and carcinogenic risks to children, teens, and adults through ingestion. Among the nine prediction models tested, Extreme Gradient Boosting (XGBoost) exhibited the best performance for Cd prediction with soil properties having the highest importance, followed by climatic variables and topographic attributes. In summary, XGBoost reliably predicted the soil Cd concentrations on tropical islands. Further research should incorporate additional soil properties and environmental variables for more accurate predictions and to comprehensively identify their driving factors and corresponding contribution rates.

Spatial distribution and source identification of potentially toxic elements in Yellow River Delta soils, China: An interpretable machine-learning approach

Identifying the driving factors and quantifying the sources of potentially toxic elements (PTEs) are essential for protecting the ecological environment of the Yellow River Delta. In this study, data from 201 surface soil samples and 16 environmental variables were collected, and the random forest (RF) and Shapley additive explanations (SHAP) methods were then combined to explore the key factors affecting soil PTEs. An innovative t-distributed random neighbor embedding-RF-SHAP model was then constructed, based on the absolute principal component score and multivariate linear regression model, to quantitatively determine PTE sources. Although average PTE concentrations did not exceed the risk control values, PTE distributions exhibited significant differences. It was found that sodium, soil organic matter, and phosphorus contents were the three most important factors affecting PTEs, and human activities and natural environmental factors both influence PTE contents by altering the soil properties. The proposed model successfully determined PTE sources in the soil, outperforming the original linear regression model with a significantly lower RMSE. Source analysis revealed that the parent material was the main contributor to soil PTEs, accounting for more than half of the total PTE content. Industrial and agricultural activities also contributed to an increase in soil PTEs, with average contributions of 19.91 % and 17.44 %, respectively. Unknown sources accounted for 10.83 % of the total PTE content. Thus, the proposed model provides innovative perspectives on source parsing. These findings provide valuable scientific insights for policymakers seeking to develop effective environmental protection measures and improve the quality of saline-alkali land in the Yellow River Delta.

Dietary sources apportionment and health risk assessment for trace elements among residents of the Tethys-Himalayan tectonic domain in Tibet, China

Dietary intake of toxic elements (TEs) and essential trace elements (ETEs) can significantly impact human health. This study collected 302 samples, including 78 food, 104 drinking water, 73 cultivated topsoil, and 47 sedimentary rock from a typical area of Tethys-Himalaya tectonic domain. These samples were used to calculate the average daily dose of oral intake (ADDoral) and assess the health risks of five TEs and five ETEs. The results indicate that grain and meat are the primary dietary sources of TEs and ETEs for local residents. The intake of manganese (Mn) and copper (Cu) is mainly from local highland barley (66.90% and 60.32%, respectively), iron (Fe) is primarily from local grains (75.51%), and zinc (Zn) is mainly from local yak meat (60.03%). The ADDoral of arsenic (As), Mn, Fe and Zn were found to be higher than the maximum oral reference dose in all townships of study area, indicating non-carcinogenic health risks for local residents. Additionally, lead (Pb) and nickel (Ni) in 36.36% townships, and Cu in 81.82% townships were above the maximum oral reference dose, while As posed a carcinogenic risk throughout the study area. The concentrations of As, mercury (Hg), Pb, Mn, Cu Fe and selenium (Se) in grains were significantly correlated with those in soils. Moreover, the average concentrations of As in Proterozoic, Triassic, Jurassic and Cretaceous was 43.09, 12.41, 15.86 and 6.22 times higher than those in the South Tibet shell, respectively. The high concentrations of TEs and ETEs in the stratum can lead to their enrichment in soils, which, in turn, can result in excessive intake by local residents through the food chain and biogeochemical cycles . To avoid the occurrence of some diseases caused by dietary intake, it is necessary to consume a variety of exotic foods, such as high-selenium foods, foreign rice and flour in order to improve the dietary structure.

Distribution of cadmium and lead in soil-rice systems and their environmental driving factors at the island scale

Toxic elements, such as Cd and Pb are of primary concern for soil quality and food security owing to their high toxicity and potential for bioaccumulation. Knowledge of the spatial variability of Cd and Pb in soil-rice systems across the landscape and identification of their driving factors are prerequisites for developing appropriate management strategies to remediate or regulate these hazardous contaminants. Considering the role of rice (Oryza sativa) as a dietary staple in China, this study aimed to examine the distribution patterns and drivers of Cd and Pb in tropical soil-rice systems across Hainan Island. To achieve this goal, 229 pairs of representative paddy soil and rice samples combined with a set of environmental covariates at the island scale were systematically analyzed. Arithmetic mean values (AMs) of Cd and Pb in rice were 0.080 and 0.199 mg kg-1, and exceeded the standard limits by 27.1% and 22.7%, respectively. We found that the AMs of Cd and Pb concentrations in paddy soil were 0.294 and 43.0 mg kg-1. Additionally, Cd in 29.26% of soil samples and Pb in 11.35% of soil samples exceeded the risk screening value for toxic elements. The enrichment factor generally showed that soil Cd and Pb on Hainan Island were both moderately enriched. Results obtained from both Spearman's correlation and stepwise regression analyses suggest that the concentrations of soil Cd and Pb are significantly influenced by the soil Na and Fe concentrations. Specifically, an increment of 1 g kg-1 in soil Na caused a rise of soil Cd and Pb by 57.1 mg kg-1 and 34.4 mg kg-1, respectively, while an increase of 1 g kg-1 in soil Fe resulted in a rise by 25.0 mg kg-1 and 14.5 mg kg-1. Similarly for rice grains, an increment of 1 g kg-1 in soil Ca resulted in a rise of rice Pb by 30.8 mg kg-1, whereas an increase of 1 g kg-1 in soil Mg led to a decrease in rice Pb by 14.8 mg kg-1. However, no significant correlation between soil Se and rice Cd concentrations was found. Furthermore, the result of geographically weighted regression revealed that the impacts of soil Na, Ca, Fe, and Mg on rice Cd were more significant in the western region, whereas the effects of soil Na and Fe on rice Pb were stronger in the northeastern region. This study provides new insights for the identification of factors influencing the distribution and accumulation of Cd and Pb in tropical island agroecosystems.