Heavy Metals In Farmland Soil Research Articles

Health Risk Assessment of Heavy Metals in Soil-crop System in Different Ecological Regions of Ningxia

Heavy metals are characterized by toxicity，non-biodegradability，and accumulation in the food chain；hence，o the distribution of heavy metals in farmland soils and crop food safety have attracted much attention. A total of 1 274 soil samples and crop samples were collected from farmland in Ningxia from 2017 to 2021 and bioconcentration coefficients were used based on the analysis of the elemental contents of Zn，Cr，Ni，Cu，Pb，As，Cd，and Hg. The ecological risk index and correlation analysis were used to characterize the heavy metal content and health risk assessment of the soil-crop system in Ningxia. The results showed that the soils in the study area were alkaline and the mechanical composition was dominated by pulverized and sandy grains，with a relatively small content of clay grains. The heavy metal contents in the soils were in the following order：Zn>Cr>Ni>Cu>Pb>As>Cd> Hg. Except for that of Cu and Ni，the contents of other heavy metals in the soils of the northern region were higher than those of the central and southern regions. The ecological risk index of soil heavy metals was Hg>Cd>As>Cu>Pb>Ni>Cr>Zn and the proportion of points with low and medium ecological risk index levels was high（98.58%），indicating that the soil environment was generally good. The average content of the eight heavy metals in crops did not exceed the limit value，and the average health risk index value was <1. However， the health risk index（HRI）of As in individual crops existed at points >1 in all three sub-regions and individual points with high values of HRI（HRI>1）for Cu and Ni were observed in crops in the northern region and for Pb in the north and south of the country，which require further attention. From the bioconcentration factor，crops showed no accumulation effect （BCF<1）for heavy metals such as As，Cd，Cr，Cu，Ni，Pb，and Zn and although the average uptake of Hg by crops did not show accumulation，a total of 31 crops in the three ecological zones showed some accumulation（BCF>1），which also requires further monitoring. Analysis using the Mantel's test showed that except for As and Ni in crops，which had a weak correlation with Cu content in the soil（0.05>Mantel's P>0.01），the other heavy metals in crops did not have a significant correlation with the corresponding soil heavy metals （Mantel's P≥0.05），indicating that the heavy metal contents in crops were less affected by soil heavy metals. RDA analysis showed that the sand content had a strong positive correlation with the transfer coefficients of heavy metals such as Hg，Ni，Pb，and Cd，while the powder content showed a strong negative correlation；however，the transfer coefficients of Cu，Zn，As，and Cr were not much affected by the mechanical composition of soils，and the correlation between the soil organic matter content and the transfer coefficients of heavy metals was also small. In summary，the quality of the soil environment and crops in the study area is generally good；however，attention must still be paid to the areas with high heavy metal content or exceeding the standard value to strictly control the input of pollutants，and at the same time，establishing long-term monitoring of the quality status of soil and agricultural products in the crop production area is a necessary control initiative.

Spatial pattern, source apportionment, and source-oriented health risk quantifying of heavy metals in farmland soils of southern China.

The contamination of heavy metal has permeated many parts of China, especially in densely populated and industrialized southern China. This study focused on the degree of pollution in farmland soil heavy metals (HMs), and its spatial distribution characteristics and source apportionment. Meanwhile, we conducted an evaluation of the health risks attributed to soil HMs and analyzed the factors that impact them. We found that the distribution of five heavy metals is mainly concentrated in the east-central and southern parts of the study area. Specifically, Cd and Hg have high levels of pollution and present potential ecological risks. The pollution sources of five HMs were analyzed utilizing positive matrix factorization. The results revealed that the contribution of different sources keeps the following order: natural source (42.42%), agricultural activities (29.93%), industrial pollution source (20.49%), and atmospheric deposition pollution (7.16%). The non-carcinogenic risks to residents were acceptable, whereas the carcinogenic risks were relatively high. Children and the elderly are more vulnerable to the negative effects of Cr, As. Using structural equation modeling, we found soil property is a vital factor affecting soil contamination, with the soil organic matter and cation exchange capacity having a relatively greater impact on heavy metals pollution. Our study provides some data reference and guidance for soil ecological protection and restoration.

Prediction of soil heavy metal content around mine tailings using multiple methods combined with transformed hyperspectral reflectance data

The extensive accumulation of tailings can potentially cause heavy metal contamination in the surrounding farmland soil. Accurately predicting the spatial distribution of heavy metals in farmland soil is crucial for assessing the potential environmental hazards of tailings.This study focuses on the spatial distribution and the quantitative prediction of heavy metals (chromium (Cr), vanadium (V), and copper (Cu)) in soils surrounding mine tailings using advanced spectral data analysis and multiple prediction models. The original hyperspectral reflectance data were processed using first-order differential (FD), second-order differential (SD), reciprocal logarithmic (LR), and continuum removal (CR) transformations to highlight the positions of characteristic bands. Multiple linear regression (MLR), stepwise linear regression (SLR), partial least squares regression (PLSR), random forest (RF), and back propagation artificial neural network (BP-ANN) models were used to establish inversion models for Cr, V, and Cu based on bands with high correlation coefficients. The performance of the inversion models was evaluated using the coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and residual predictive deviation (RPD). The results indicate that the raw hyperspectral data from the measured soil exhibit a weak response to heavy metal content in the study area. However, applying FD, SD, and CR transformations significantly enhances the sensitivity of soil spectral data to heavy metal concentrations, facilitating subsequent modeling. Among these, the SD transformation is particularly beneficial for modeling the Cr and Cu elements in the soil. For the V element, the FD transformation yields data that are more suitable for modeling. Regarding the inversion models based on the measured spectral data, the BP-ANN model exhibited the best predictive performance. Specifically, when combined with SD spectral data, the BP-ANN achieved the highest predictive accuracy for Cu content (R² = 0.85, RPD = 2.12). The RF model demonstrated the next best performance, with its optimal inversion model also utilizing SD spectral data for predicting Cu content (R² = 0.76, RPD = 1.90). On the other hand, the MLR model exhibited the poorest performance and is unsuitable for predicting heavy metal content in the region using the measured spectral data. This study highlights the potential of spectral data in environmental monitoring and provides a technical reference for the inversion assessment and regulation of heavy metals in farmlands surrounding tailing sites.

Assessment and Source Analysis of Heavy Metal Pollution in Farmland Soil Around Tin Mine

Studying the status and source analysis of heavy metal pollution in farmland in typical mining and processing areas is an important prerequisite for promoting farmland soil ecological restoration and farmland protection in concentrated mining areas. In this study, the heavy metal content of farmland soil around a mining area in southwest China was detected, and the pollution status, distribution law, health risks, and sources of heavy metals were studied by using the land accumulation index method, comprehensive pollution index method, kriging interpolation method, health risk assessment method, and PMF receptor model on the sampling data. The results showed that the mean values of eight heavy metals in farmland soil except Ni exceeded the local soil background values, and the results of the ground accumulation index evaluation showed that Cd and Hg were extremely polluted； Pb and As showed medium pollution-heavy pollution； and Cr, Zn, Ni, and Cu were lightly polluted. In the health risk assessment, oral ingestion was the main exposure route posing a health risk to the human body； the main element that constituted non-carcinogenic health risks was As, and the carcinogenic risks were from As and Cd. PMF model analysis showed that the contribution rate of weathering natural sources of iron-bearing ore was 28.02%, and the main factors were Ca and Fe. The contribution rate of agricultural sources was 3.02%, and the main factors were Pb and As. The contribution rate of industrial and atmospheric deposition composite sources was 33.09%, and the main factor was Hg. The contribution rate of the parent material source was 17.27%, and the main factor was Ca. The contribution rate of mining activities such as mining and smelting was 18.60%, and the main factors were Zn and Cd.

Distribution Characteristics, Risk Assessment, and Source Analysis of Heavy Metals in Farmland Soil of a Karst Area in Southwest China

Soil environmental quality related to the residents’ life, health, and safety, has been the hotspot issues in science of ecological environment protection. Evaluating the distribution characteristics, ecological risk, and source of heavy metals in farmland is important for protecting soil resources. The agricultural area of Lianhua town, Gongcheng County, Guilin is a typical karst landform. In response to the problem of heavy metal pollution and complex sources in the soil of this area, the characteristics and sources of heavy metal pollution in the soil profiles from farmland, abandoned land, and forest were studied using the single-factor index method, the geoaccumulation index (Igeo) principal component analysis (PCA) and positive matrix factorization (PMF) model. The results showed that: (1) that the contents of cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn) in the soil profile of the study area were higher than that of the soil elements background values in Guangxi. The total and available forms contents of all heavy metal elements exhibited the characteristics of accumulation in the surface profile; (2) among the six heavy elements, the contents of Cd were in a moderately to heavily polluted state. The contents of Cd in some soil profiles exceeded the control standard for agricultural land soil pollution. The contents of Zn and Ni were from slightly to moderately polluted in areas with frequent agricultural activities; (3) according to the PCA and PMF model, there were three main sources of heavy metals in the study area. Among them, Cd, Cu, Ni, and Zn are related to agricultural activities; the elements As, Cd, Cr, and Hg are closely related to geological background; Pb and Zn are mainly affected by atmospheric sedimentation of transportation. Agricultural activities and natural geological background are the main contribution sources of heavy metals in soil. Human activities are the main factors that cause the accumulation of heavy metals in soil. This research has theoretical guidance and practical significance for the prevention and control of soil heavy metal pollution and the protection of farmland environmental quality in the region.

Analysis and Evaluation of Heavy Metal Pollution in Farmland Soil in China：A Meta-analysis

In this study, a Meta-analysis was used to investigate the pollution status of eight farmland soil heavy metal elements (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) in China. Meanwhile, their spatiotemporal changes and differences between different types of cultivated land were explored. The research data were chosen from 449 relevant literature data collected by CNKI and Web of Science from 2005 to 2021, and the Meta-analysis used a weighted method based on "sampling numbers", "study area", and "standard deviation". The results showed that the national average values of the eight heavy metal elements in Chinese farmland soil were ω(As)11.00 mg·kg-1, ω(Cd)0.350 2 mg·kg-1, ω(Cr)62.91 mg·kg-1, ω(Cu)28.87 mg·kg-1, ω(Hg)0.135 1 mg·kg-1, ω(Ni)28.91 mg·kg-1, ω(Pb)34.67 mg·kg-1,and ω(Zn)90.24 mg·kg-1. Compared with their background values, all elements except As accumulated to some extent, and Cd and Hg accumulated the most, exceeding their background values by 177.9% and 340.3%, respectively. The research results indicated that Cd and Hg were the main pollution elements in farmland soil in China, and their accumulation was mainly influenced by human activities. In terms of their temporal and spatial changes, the Yunnan-Guizhou Plateau and the eastern coast were the most concentrated areas of pollution cases, and the pollution center shifted from the middle reaches of the Yangtze River to the southwest over time. The accumulation of heavy metals in farmland soil was affected by crop planting types, and the accumulation of heavy metals in vegetable and paddy soil was significantly greater than that in other cultivated land types.

Characteristics and Identification Priority Source of Heavy Metals Pollution in Farmland Soils in the Yellow River Basin

Assessments of the soil environmental quality of farmland and pollution source apportionment are the foundation for ensuring national food security and agricultural sustainable development, as well as an important prerequisite for the pursuit to keep our lands clean. This study evaluated the characteristics of heavy metal pollution in farmland soils in the Yellow River Basin from 2000 to 2023, based on the data of heavy metal contents including As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn, using the geo-accumulation index method. Source apportionment was conducted by employing a positive matrix factorization (PMF) model. The probabilistic health risks were evaluated by coupling Monte Carlo simulation with a human health risk assessment model, and priority pollution sources and elements were identified. The results showed that:① the average content of all heavy metals in farmland soils within the study area was lower than the screening values specified in the soil environment quality risk control standard for soil contamination of agriculture land (GB 15618-2018) (pH>7.5). However, the contents of Cd, As, and Zn in the samples exceeded their screening values, with percentages of 21.69%, 5.56%, and 1.23%, respectively, with Cd having the highest rate of exceedance. ② Hg and Cd were moderately polluted, Cu and Pb were slightly polluted, and the other elements were not polluted. ③ The main sources of heavy metals in farmland soil were traffic-industrial sources, natural-agricultural sources, industrial-natural sources, and agricultural-industrial sources, with contribution rates of 37.04%, 26.69%, 21.72%, and 14.55%, respectively. ④ Heavy metals in farmland soil posed carcinogenic health risks to adults and children but did not have non-carcinogenic risks; As and Cd were priority control elements for human health risks, and industrial-natural sources and agricultural-industrial sources were priority control sources in the study area.

Source Apportionment and Risk Assessment of Heavy Metals in Agricultural Soils in a Typical Mining and Smelting Industrial Area

Source apportionment and risk assessment are critical for making effective pollution prevention and control policies. The study was carried out to assess source-specific ecological and human health risks associated with heavy metals in farmland soils in Yingtan City based on apportionment results of receptor models. Multivariate analysis and the APCS-MLR model consistently revealed that As, Ni, and Cr in agricultural soils may be mainly derived from natural sources, while the contents of Cu, Zn, Cd, and Pb have been significantly elevated by human activities. According to the outputs of the APCS-MLR model, Cu (34.3%), Zn (67.2%), Pb (75.1%), and Cd (67.9%) primarily originated from the industrial activities related to mineral mining and non-ferrous metal smelting processes. The source-specific ecological risk assessment indicated that industrial sources were the primary contributor to the total ecological risks, posing moderate to high risks in the southern mountainous regions. Natural origins played a significant role in the health risks due to the substantial amounts of As naturally occurring in the soils. The findings could guide the development of effective risk management and pollution control measures for agricultural soils.

Spatial Distribution, Source Analysis, and Health Risk Assessment of Heavy Metals in the Farmland of Tangwang Village, Huainan City, China

The impacts of heavy metal pollution in arable soil on agricultural production, environmental health, and the wellbeing of urban and rural residents cannot be overlooked. It has become a significant bottleneck in achieving comprehensive rural revitalization. To accurately grasp the characteristics of heavy metal pollution in suburban cultivated soil, Tangwang Village (a suburb of Huainan City) was subjected to scrutiny. The contents of heavy metals (Hg, Cu, Hg, As, Pb, Cr, Cd, and Zn) in the topsoil of cultivated land in this area were detected, and their spatial distribution characteristics were analyzed using inverse distance spatial interpolation. (1) After conducting a comprehensive analysis and thorough examination of the PMF model sources, it was determined that Cu, Cd, and Zn exhibit a direct correlation with agricultural practices, collectively contributing to a cumulative percentage of 21.10%. Meanwhile, Cr is derived from a combination of sources, including both natural parent materials and human activities, accounting for a total proportion of 24.45%. Notably, lead emissions from automobile exhausts constitute a significant source, while arsenic is primarily associated with dispersed factories and their respective operations, contributing to respective proportions of 36.38% and 18.07%. It is evident that agricultural practices, transportation, and industrial activities are the main reasons for heavy metal pollution in arable soil. (2) The evaluation of geological accumulation indicators reveals that the level of soil arsenic accumulation pollution is mild to moderate (1.199). On the other hand, the cumulative pollution level of Cd, Hg, Cr, and Cu was relatively low (0.462→0.186), whereas the levels of Pb and Zn were below the threshold. (3) The assessment of the ecological risk index revealed that the predominant elements posing potential ecological risks in the investigated region were Hg, As, and Cd, with average Ei values of E(Hg) = 86.81, E(As) = 80.67, and E(Cd) = 67.83, respectively. (4) The human health risk assessment revealed significant differences in the single non-carcinogenic risk values of heavy metals generated by different exposure pathways, with oral ingestion &gt; dermal contact &gt; oral nasal inhalation. Children were more susceptible to the toxic effects of heavy metals compared to adults. Both As and Cr caused an increased risk of cancer in both children and adults, which is a matter of great concern. The results of this study contribute to a more accurate description of the sources of heavy metals in farmland soil. This study indicates that the application of PMF for soil source analysis yields clear results that can be further applied. This research also has potential policy significance as it can help to improve the sustainability of ecosystems by coordinating both environmental and human activities.

Ecological Risk Assessment and Source Analysis of Heavy Metals in Farmland Soil in Yangchun City Based on APCS-MLR and Geostatistics

Yangchun City, a typical polymetallic ore distribution area in Guangdong Province (China), was selected as the research region to study the content, distribution, source, and possible impacts of heavy metals (HMs) (Arsenic: As; Cadmium: Cd; Chromium: Cr; Copper: Cu; Mercury: Hg; Nickel: Ni; Lead: Pb; and Zinc: Zn) on the farmland soil of this City. According to our findings, the spatial distribution of HMs in Yangchun City shows higher concentrations in the north and southeast and lower in the west and other regions. Metal content in some sampled sites of the agricultural land exceeded the soil pollution risk screening values, particularly As (7.5%), Cd (12%), Cu (4%), Hg (14.5%), and Pb (3%). Additionally, the average content of As, Cu, Cd, Pb, Hg, and Zn from the studied areas surpassed the soil background value of Guangdong Province for all metals. The absolute principal component score-multiple linear regression (APCS-MLR) was used to identify potential sources of HMs in the soil samples. There are three potential sources identified by the model: traffic emissions, natural sources, and agricultural activities, accounting for 28.16%, 16.68%, and 14.42%, respectively. Based on the ecological risk assessment, the potential ecological risk (Eri = 310.77), Nemero pollution index (PN = 2.27), and multiple possible effect concentration quality (mPECQs = 0.23) indicated that the extent of heavy metal pollution in the soil samples was moderate. Three sources were identified: traffic emissions, natural sources, and agricultural activities. We suggest that by combining the above results, a monitoring and early warning system focused on Cd and Hg can be established. The system could utilize geographic information systems and remote sensing technologies to achieve dynamic monitoring and prediction of pollution. Regular testing of soils and sustainable management practices are also recommended to control and remediate contamination.

Ecological Risk and Pollution Assessment of Heavy Metals in Farmland Soil Profile with Consideration of Atmosphere Deposition in Central China.

Heavy metals (HMs) in agricultural land have caused serious environmental problems, resulting in severe contamination of the food chain and posing potential health threats. This study aims to investigate the pollution levels and potential ecological risks of HMs in farmland soils in central China, taking into account atmospheric deposition. Several indices were used to assess the status of HMs and compare surface soil with deeper soil. Descriptive statistics, Pearson correlation, and UMAP clustering methods were utilized to identify the characteristics of HMs. Additionally, stepwise linear regression models were employed to quantify the contributions of different variables to the potential ecological risks of HMs. The results showed that the average content of Zn in surface soil (289.41 ± 87.72 mg/kg) was higher than in the deeper soil (207.62 ± 37.81 mg/kg), and similar differences were observed in the mean values of related Igeo (1.622 ± 0.453 in surface soil and 1.183 ± 0.259 in deeper soil) and PEI (0.965 ± 0.292 in surface soil and 0.692 ± 0.126 in deeper soil) indices. This indicates that surface soil is more heavily polluted. The UMAP results confirmed the high variability of HMs in the surface soil, while PCA results suggested the importance of pollution and ecological risk indices. The stepwise linear model revealed that different variable structures contribute differently to the risk. In conclusion, Cr and Zn were found to be the major contaminants in the local farmland soil, with higher concentrations in the surface soil. The geoaccumulation and total potential ecological risk were classified as low risk. High variability of HMs was observed in the surface soil. Therefore, HM-related pollution indices and ecological risk indices are important for assessing the contamination status of local HMs. The local potential ecological risk can be attributed to specific heavy metals, each of which can have different effects on the local ecological risk.

Distribution and Risk Assessment of Heavy Metals in Farmland soil of Lichuan, Hubei Province

Distribution and Risk Assessment of Heavy Metals in Farmland soil of Lichuan, Hubei Province

环境监测在公路两旁农田土壤重金属的分析与评价上的应用

环境监测在公路两旁农田土壤重金属的分析与评价上的应用

Ecological Risk Assessment of Crude Oil Impacted Farmland Soils: A Case Study of Ohaji/Egbema in Niger Delta, Nigeria

The study assessed the impact of crude oil on farmland soils in Ohaji Egbema. Farmlands used for this study are farms that were indirectly impacted through overland flow and acid rain. A total of 42 farmland soil samples were used for this study. The farmland soil samples were collected at a depth range of 0 - 10 cm using a hand trowel through random sampling. The samples were air dried for 72 hours before laboratory analysis for arsenic (As), nickel (Ni), chromium (Cr), cadmium (Cd) and lead (Pb). The results obtained show that heavy metals in farmland soils at different locations in Ohaji/Egbema varied in both concentration and trend. The observed trends at different locations are: Cd&gt;As&gt;Ni&gt;Cr&gt;Pb for Mmahu, Abaezi, Abacheke and Awarra while for Assa and Obitti, it is Cd&gt;Ni&gt;As&gt;Cr&gt;Pb. To establish the extent the farmland soils were polluted, contamination factor (CF), degree of contamination (Cd) and geoaccumulation index (Igeo) were applied. Results of the Cf showed that Ni, Cr and Pb had low contamination, As had moderate contamination while Cd had high contamination in the farmland soils with a range of 155.15 - 206.87 and Cd of 1242.72. The Igeo for Cd ranged from 31.13 to 41.52, an indication of extreme contamination. The associated potential ecological risks were assessed using potential contamination index (PCI), ecological risk factor (Eri) and ecological risk index (RI). It was found that Cd had PCI ranging from 155.22 - 198.51, Eri range of 4654.44 - 6206.11 and RI of 37311.56. This indicates that the farmland soils of Ohaji/Egbema are highly contaminated with Cd and exposed to severe ecological risk.

Combined passivators regulate physiological, antioxidant potential and metals accumulation in potato grown in metals contaminated soil

To solve the problem of excessive heavy metals in farmland soil, there is a dire need for research effort to screen for the soil passivator materials. This study aimed to develop a practical novel approach for improving the potato growth and remedial effectiveness of the metals by optimal combination and dosage of various passivators. Experimental treatments were comprised of various levels of passivating agents (sepiolite, quicklime and calcium magnesium phosphate) in individual and combined form. Results showed that application of passivating agents significantly enhanced growth by optimizing photosynthetic attributes, enzymatic antioxidants, and soil health. Balanced application of passivators effectively reduce the bioavailability of metals, curbing their uptake by potato plants. Sole application of all the agents results statistically similar outcomes as compared with combined form. Additionally, passivators indirectly enhance the activity of essential antioxidant enzymes. Synergistic effect of all the agents significantly improved the tuber quality by decreasing the accumulation of proline, malondialdehyde content, and bioaccumulation of Cu, Pb, Cd, and As in potato parts. In crux, combined usage of passivating agents proved to be of better growth, improvement in antioxidative defense system, and better quality of potato. By mitigating heavy metal contamination, passivators not only enhance crop quality and yield but also ensure heavy metal-free potatoes that meet stringent food safety standards.

Changes of heavy metal concentrations in farmland soils affected by non-ferrous metal smelting in China: A meta-analysis

Long-term human smelting activities have resulted in substantial heavy metals (HMs) pollution of farmland soils around smelting sites, and the safety of farmland products is critical for human health. The current study focuses on HMs in farmland soils surrounding a single smelter, therefore the impact of smelting on a national scale needs to be investigated further. This study was based on 116 papers and 1143 sets of relevant data for meta-analysis, and a hierarchical mixed-effects model was used to quantify the changes of HMs concentrations in farmland soils affected by non-ferrous metal smelting on a national scale, as well as their relationships with relevant explanatory variables in China. Results showed that: (i) non-ferrous metal smelting substantially increased farmland soils HMs concentrations (323%), with each HM concentration increasing in the following order: Cd (2753%) > Pb (562%) > Hg (455%) > Zn (228%) > Cu (158%) > As (107%) > Ni (52%); (ii) the highest increase of HMs in vegetable fields (361%), but not significant in comparison to other farmland categories, and the increase of Pb, Zn, Cu and As concentrations were significantly different in different types of smelting areas; (iii) the increase of Hg was significantly higher in the northern region than in the southern region, and the opposite increase of Cu; (iv) the soil depth from 0 to 40 cm was significantly affected by smelting, and the increase of multiple HMs were significantly positively correlated with soil pH and negatively correlated with distance; (v) the other explanatory variables (farmland category and soil organic matter) were not significantly related to the effect of smelting. The results can provide some reference for protecting and restoring farmland soils around smelting areas.

Integrated Analysis on Source-exposure Risk of Heavy Metals in Farmland Soil Based on PMF Model: A Case Study in the E-waste Dismantling Area in Zhejiang Province

In order to explore the characteristics and sources of heavy metals in farmland soil and their risks to human health and to provide an important scientific basis for farmland pollution control, 133 surface soils (0-20 cm) were collected from typical agricultural production areas in Zhejiang Province, and the contents of soil Cd, Pb, Cr, Cu, Zn, Ni, As, and Hg were determined. Various methods were applied to evaluate the degree of heavy metal pollution in farmland and its ecological risks in the study area. The method of combining Kriging interpolation and positive definite matrix factor analysis (PMF) was applied to analyze the pollution sources and quantify the contribution of each pollution source. Combined with the health risk assessment model, the risk to human health of each pollution source was evaluated from the perspective of source exposure. The results showed that the average ω(Cd), ω(Pb), ω(Cr), ω(Cu), ω(Zn), ω(Ni), ω(As), and ω(Hg) were 0.76, 65.22, 92.02, 103.92, 198.49, 36.65, 5.97, and 0.20 mg·kg-1, respectively. The average contents of Cd and Cu were higher than the risk screening values of soil contamination of agricultural land, and 85.71% and 96.24% of soil was contaminated by heavy metals. The average contents of Pb, Cr, Zn, and Ni exceeded the soil background values of the Wenhuang Plain in Zhejiang Province, and the As and Hg contents were within the limit values. The potential soil ecological risks were mainly light-moderate, accounting for 90.98%, and both high and higher risk accounted for 4.51%; Cd was the main potential ecological risk element. The main sources of heavy metal pollution in the study area were the sources of the electronic waste dismantling process (26.82%), the mixed sources of coal combustion and traffic emissions (34.50%), mixed sources of natural parent materials and agricultural inputs (25.59%), and e-waste pickling runoff and solid waste leaching sources (13.09%). The health risk of heavy metal exposure to children was significantly greater than that in adults. Mixed sources of natural parent materials and agricultural inputs contributed the most to human health risks, and Cr was the element with the greatest contribution to human health risks.

Source Apportionment of Soil Heavy Metal(Loid)s in Farmland Using Diverse Models: A Comparative Assessment in the Yellow River Delta

The rapid development of industrialization and urbanization has posed serious challenges for coastal farmland ecosystems. Source apportionment of soil heavy metals is an effective way for the detection of non-point source pollution in farmland to help support the high-quality development of coastal agriculture. To this end, 113 surface soil samples were collected in the coastal delta of China, and the contents of As, Cd, Cr, Cu, Ni, Pb, and Zn were determined. A variety of models were integrated to apportion the source of soil heavy metals, including positive matrix factorization (PMF), geographical detector (GD), eXtreme gradient boosting (XGBoost), and structural equation modeling (SEM). The result of PMF models revealed that there was collinearity between various heavy metals, and the same heavy metal may have a mixed source. The XGBoost model analysis indicated that there were significant non-linear relationships between soil heavy metals and source factors. A synergy between air quality and human activity factors was the key source of heavy metal that entered the study area, based on the results of the GD. Furthermore, the input path effect of heavy metals in the soil of the study area was quantified by SEM. The balance of evidence from the above models showed that air quality (SO2 and NO2) and factories in the study area had the greatest impacts on Cd, Cr, and Zn. Natural sources were dominant for Pb, while As, Cu, and Ni were contributed by soil parent material and factories. The above results led to the conclusion that there was a cycle path in the study area that continuously promoted the migration and accumulation of heavy metals in farmland soil; that is, the heavy metals discharged during oil exploitation and smelting entered the atmosphere and then accumulated in the farmland soil through precipitation, atmospheric deposition, and other paths. In this study, it is shown that a variety of models can be used to more comprehensively assess the sources of soil heavy metals. This approach can provide effective support for the rapid prevention and decision-making management of soil heavy metal pollution in coastal areas.

Pollution Characteristics and Source Apportionment of Heavy Metals in Farmland Soils Around the Gangue Heap of Coal Mine Based on APCS-MLR and PMF Receptor Model

To analyze the pollution characteristics and source apportionment of heavy metal pollution in soil of farmland surrounding the Gangue Heap of Coal Mine in Nanchuan, Chongqing, the Nemerow pollution index and Muller index were used. Meanwhile, to investigate the sources and contribution rate of heavy metals in the soil, absolute principal component score-multiple linear regression receptor modeling (APCS-MLR) and positive matrix factorization (PMF) were employed. The results showed higher amounts of Cd, Hg, As, Pb, Cr, Cu, Ni, and Zn in the downstream area than those in the upstream area, and only Cu, Ni, and Zn showed significantly higher amounts in the downstream area than those in upstream area (P<0.05). The comprehensive Nemerow pollution index was as follows:downstream area (1.22)>upstream area (0.95), and the degree of heavy metal pollution was:Cd>Cu>Hg, As, Pb, Cr, Ni, and Zn. The Muller pollution index showed:Cd>As>Cu=Hg>Ni>Zn=Cr>Pb. The pollution source analysis showed that Cu, Ni, and Zn were mainly affected by mining activities such as long-term accumulation of the gangue heap of coal mine, with the contribution rates of APCS-MLR being 49.8%, 94.5%, and 73.2%, respectively. Additionally, PMF contribution rates were 62.8%, 62.2%, and 63.1%, respectively. Cd, Hg, and As were mainly affected by agricultural activities and transportation activities, with APCS-MLR contribution rates of 49.8%, 94.5%, and 73.2% and PMF contribution rates of 62.8%, 62.2%, and 63.1%, respectively. Further, Pb and Cr were mainly affected by natural factors, with APCS-MLR contribution rates of 66.4% and 94.7% and PMF contribution rates of 42.7% and 47.7%, respectively. The results of source analysis were basically consistent between the APCS-MLR and PMF receptor models.

Environmental geochemical baseline determination and pollution assessment of heavy metals in farmland soil of typical coal-based cities: A case study of Suzhou City in Anhui Province, China

Soil is the foundation of agricultural green development and human survival; the study of local environmental geochemical baselines can guide soil management and ensure the safe use of soil. In this study, a total of 100 shallow farmland soil samples were collected in each township of Yongqiao District, Suzhou City, Anhui Province, China. Herein, the contents of 10 heavy metal elements including As, Hg, Cd, Pb, Cr, Cu, Mn, Ni, Zn and Fe were determined. In addition, the geochemical baseline of heavy metals was calculated based on the relative cumulative frequency curve method, and the soil pollution status was evaluated. The results show that the average contents of As, Hg, Cd, Cu, Mn and Zn exceeded the soil background values of Anhui Province by 3.82%–64.74% (1.04–1.65 times), meanwhile, the average contents of Pb and Cr were lower than the soil background values of Anhui Province. The average contents of Cd, Cr, Cu, Mn and Ni exceeded the Chinese soil background values by 1.61%–64.74% (0.98–1.65 times). The geochemical baseline values of As, Hg, Cd, Pb, Cr, Cu, Mn, Ni, Zn and Fe were 9.585 mg/kg, 0.028 mg/kg, 0.112 mg/kg, 21.59 mg/kg, 53.66 mg/kg, 19.31 mg/kg, 543.8 mg/kg, 24.93 mg/kg, 50.57 mg/kg and 2.654%, respectively. The pollution assessment results also showed that most of the farmland soil samples in the study area were non-polluting or slightly polluted based on geochemical baselines. Hg and Cu in a few samples were moderately polluted, and Cd in only one sample was moderately intensely polluted. Combined with the distribution of pollution and field investigation, it is considered that Hg comes from atmospheric deposition and agricultural non-point source pollution of industrial pollution. Cu comes from animal husbandry and agricultural pollution. Meanwhile, Cd is related to natural sources, wood processing and agricultural fertiliser application. The study demonstrated that the calculation of soil geochemical background value should take full account of the differences between the various regions, combined with the current state, particularly the pre-consideration of the distribution of elements or pollutants. Then, reasonably select the evaluation standard value so that the evaluation results can truly reflect the state of soil pollution.