Spatial distribution and ecological risk assessment of soil heavy metals in a typical volcanic area: Influence of parent materials

Heavy metal enrichment characteristics and synergistic evaluation in soil-crop-human systems of agricultural land with different soil parent materials

Heavy metal (HM) pollution in agricultural areas seriously threatens food security and ecological health. In this study, based on different soil parent materials, the HMs enrichment in the soil-crop systems of two typical eastern Chinese agricultural lands was compared and analyzed. Multivariate linear stepwise regression analysis, influence index of comprehensive quality and HHRA model were used to understand the bioaccumulation and to evaluate the soil-crop-human system. The study showed that HMs exhibited different enrichment characteristics in the two soil parent material areas. Cd faced a higher risk control rate and was a priority pollutant in the soil environment. The acidification soils in the granitic parent material area led to more widespread Ni pollution in wheat grains. The HM absorption model clarifies that driving factors such as the HM content, physicochemical properties and the distance to the river can well explain the enrichment ability of HMs in wheat grains. The synergistic evaluation revealed that only 13.04% of soil and crops were at a clean level. Soil contamination is more prevalent in the metamorphic rocks area, while crop contamination is more severe in the granitic parent material area. Probabilistic health risk assessment indicated that HMs primarily impact health through the ingestion of contaminated wheat, so residents of the granitic parent material area face a slightly higher HI. This information will be crucial for understanding the translocation and accumulation of HMs within soil-crop-human health systems of agricultural land in different soil parent material areas and for developing effective pollution prevention and control programs.

Identifying interactive effects of spatial drivers in soil heavy metal pollutants using interpretable machine learning models

The accurate identification of spatial drivers is crucial for effectively managing soil heavy metals (SHM). However, understanding the complex and diverse spatial drivers of SHM and their interactive effects remains a significant challenge. In this study, we present a comprehensive analysis framework that integrates Geodetector, CatBoost, and SHapley Additive exPlanations (SHAP) techniques to identify and elucidate the interactive effects of spatial drivers in SHM within the Pearl River Delta (PRD) region of China. Our investigation incorporated fourteen environmental factors and focused on the pollution levels of three prominent heavy metals: Hg, Cd, and Zn. These findings provide several key insights: (1) The distribution of SHM is influenced by the combined effects of various individual factors and interactions within the source-flow-sink process. (2) Compared with the spatial interpretation of individual factors, the interaction between Hg and Cd exhibited enhanced spatial explanatory power. Similarly, interactions involving Zn mainly demonstrated increased spatial explanatory power, but there was one exception in which a weakening was observed. (3) Spatial heterogeneity plays a crucial role in determining the contributions of environmental factors to soil heavy metal concentrations. Although individual factors generally promote metal accumulation, their effects fluctuate when interactions are considered. (4) The SHAP interpretable method effectively addresses the limitations associated with machine-learning models by providing understandable insights into heavy metal pollution. This enables a comparison of the importance of environmental factors and elucidates their directional impacts, thereby aiding in the understanding of interaction mechanisms. The methods and findings presented in this study offer valuable insights into the spatial heterogeneity of heavy metal pollution in soil. By focusing on the effects of interactive factors, we aimed to develop more accurate strategies for managing SHM pollution.

Identification of influential parameters and conditions in heavy metals adsorption onto Cal-LDH-PC using optimization approaches of RSM and Taguchi

Adsorption process plays an important role in the remediation of heavy metals (HMs) from wastewater. A laboratory trial was conducted to investigate effective parameters for improving the bio-adsorption removal of HMs. SEM, EDX, BET, and FTIR techniques were applied to characterize the calcined layer double hydroxide (Cal-LDH), pectin (PC), and Cal-LDH-PC composite prepared from Licorice pomace. The adsorption of zinc (Zn) cadmium, nickel (Ni) and lead (Pb) onto the most efficient sorbent was investigated using RSM methodology with operational factors such as concentration, reaction time, sorbent dose, and pH. The results related to FTIR showed that Cal-LDH-PC had the highest number of functional groups. Based on the SEM results Cal-LDH had a low surface area (9.36 m2 g-1) and a small pore size (9.22 nm). After the modification process (Cal-LDH-PC), the values of surface area and pore size increased by 13-fold (120 m2 g-1) and 1.5-fold (18 nm), respectively. Cal-LDH had high adsorption performance, more cavities, stability, various functional groups, and excessive carbon and oxygen content, which make it efficient and powerful in removing HMs from wastewater. The optimal condition for achieving the removal efficiency (RE%) values of metals was determined to be 80.79 mg L-1, 100 min, 0.167 g L-1, and 9 for concentration, reaction time, sorbent dose, and pH, respectively. Maximum adsorption capacity and RE (%) were 300 mg g-1 and 99% for Zn. According to the results concentration had a major impact on RE% (except for Ni), while for Ni, adsorbent dose had the most significant impact. The present study introduced Cal-LDH-PC prepared from Licorice pomace as a capable, useful and economical sorbent for HMs removal from polluted environments. Taguchi's statistical method is distinguished as an economic method with easier interpretation, while the RSM approach is more accurate, and it can also check the interaction of parameters.

A Monte Carlo simulation-based health risk assessment of heavy metals in soils of the tropical region in southern China

The disturbance of ecological stability may take place in tropical regions due to the elevated biomass density resulting from heavy metal and other contaminant pollution. In this study, 62 valid soil samples were collected from Sanya. Source analysis of heavy metals in the area was carried out using absolute principal component-multiple linear regression receptor modelling (APCS-MLR); the comprehensive ecological risk of the study area was assessed based on pollution sources; the Monte-Carlo model was used to accurately predict the health risk of pollution sources in the study area. The results showed that: The average contents of soil heavy metals Cu, Ni and Cd in Sanya were 5.53, 6.56 and 11.66 times higher than the background values of heavy metals. The results of soil geo-accumulation index (Igeo) showed that Cr, Mo, Mn and Zn were unpolluted to moderately polluted, Cu and Ni were moderately polluted, and Cd was moderately polluted to strongly polluted. The main sources of heavy metal pollution were natural sources (57.99%), agricultural sources (38.44%) and traffic sources (3.57%). Natural and agricultural sources were jointly identified as priority control pollution sources and Cd was the priority control pollution element for soil ecological risk. Heavy metal content in Sanya did not pose a non-carcinogenic risk to the population, but there was a carcinogenic risk to children. The element Zn had a high carcinogenic risk to children, and was a priority controlling pollutant element for the risk of human health, with agricultural sources as the priority controlling pollutant source.

Heavy metal spatial distribution, source analysis, and ecological risks in the central hilly area of Hainan Island, China: results from a high-density soil survey

The presence of heavy metals in soil has gained considerable attention due to their potential risks to ecosystems and human health. In this study, a thorough soil investigation was performed in the hilly region of central Hainan, which was formerly regarded as an area with the highest ecological environmental quality. A total of 7094 soil samples were systematically collected with high density over a large area. Simultaneously, a detailed investigation was conducted on the surrounding environment of each sampling point, including environmental factors such as soil, land use and crop types. The soil samples were analysed for heavy metals, pH, organic matter, and other parameters. The soil heavy metal pollution level, ecological risk and health risk were evaluated using the geo-accumulation index and the potential ecological risk index. The findings showed that the average contents of the heavy metals As, Cd, Cr, Cu, Hg, Ni, Pb and Zn in the soil were 1.68, 0.042, 24.2, 6.49, 0.0319, 7.06, 29.6 and 49.8 mg·kg-1 respectively. Except for Hg, the mean values of the other heavy metals were either lower than or similar to the background values of Hainan. Also, only a few localised areas showed contamination by heavy metals. The primary sources of heavy metals, identified by a positive matrix factorisation model, could be categorised into four types: natural sources related to the soil formation process from acidic intrusive rocks (such as granite); natural sources primarily influenced by atmospheric deposition; anthropogenic sources associated with agricultural activities; and natural sources related to the soil formation process from middle-mafic intrusive rocks and black shales. The correlation analysis and variance analysis findings suggested that the content of heavy metals in the soil was primarily associated with the parent rock. The study area generally had low heavy metal levels and was not significantly polluted. However, agricultural activities still affected the enrichment of heavy metals. Therefore, it is imperative to remain vigilant about the ecological risks linked to soil heavy metals while continuing land development and expanding agricultural activities in the future. These findings indicate that conducting high-density soil surveys can enhance our understanding of regional soil heavy metals and enable reliable recommendations for agricultural planning. Whether in areas with low pollution risk or potential pollution risk, it is recommended that high-density soil surveys be conducted provide scientific guidance for further agricultural development.

Spatiotemporal ecological risk evaluation and source identification of heavy metals and nutrients in the water and lake surface sediment in a protected catchment area of a volcanic lake

Indonesia has numerous lakes; however, research on the spatiotemporal sediment quality and source identification in lakes remains limited. The overaccumulation of heavy metals and nutrients in lakes severely threatens aquatic ecosystems. This study aims to identify potential sources of metallic deposits (Cu, Pb, Cr, Fe, Al, and Cd) and nutrients (TN and TP) in lake-surface sediment, utilizing enrichment factors (EF), geoaccumulation indices (Igeo), potential ecological risk indices (Er), and risk indices (RI). Multivariate statistical analyses, including principal component analysis (PCA) and Pearson's correlation analysis, were conducted to pinpoint pollution sources linked to land use. Eight sampling sites for surface sediment and water were examined in both wet and dry seasons at Menjer Lake, chosen for its diverse applications in tourism, hydropower, floating net cages, and extensive agriculture in its catchment. Correlation and PCA results indicated that Pb, Fe, and Al mainly originate from tourism, while Al, Fe, TN, and TP are associated with agriculture. The highest average loading from land use was observed in agriculture (> 0.8), floating net cages (> 0.76), and tourism (> 0.68). Furthermore, the highest loading from nutrients and all metals were TP (> 0.71) and all metals (> 0.35), respectively. Ecological risk assessment revealed low to moderately polluted EFs and Igeo in the dry season. However, Menjer Lake's Er and RI for heavy metals were generally classified as unpolluted.