Migration and Accumulation Simulation Prediction of PPCPs in Urban Green Space Soil Irrigated with Recycled Water: A Review.

The presence of pharmaceuticals and personal care products (PPCPs) in reclaimed water introduces an ongoing challenge as they infiltrate green space soils during irrigation, leading to a gradual buildup that poses considerable ecological risks. The simulation and forecasting of PPCPs accumulation in soil are pivotal for proactive ecological risk management. However, the majority of research efforts have predominantly concentrated on the vertical transport mechanisms of PPCPs in the soil, neglecting a holistic perspective that integrates both vertical and lateral transport phenomena, alongside accumulation dynamics. To address this gap, this study introduces a comprehensive conceptual model that encapsulates the dual processes of vertical and lateral transport, coupled with accumulation of PPCPs in the soil environment. Grounded in the distinctive properties of green space soils, we delve into the determinants governing the vertical and lateral migration of PPCPs. Furthermore, we consolidate existing simulation methodologies for contaminant transport, aiming to establish a comprehensive model that accurately predicts PPCPs accumulation in green space soils. This insight is critical for deducing the emission threshold of reclaimed water necessary for the protection of green space soils, informing the formulation of rational irrigation strategies, and anticipating future environmental risks. It provides a critical theoretical basis for more informed decision-making in the realm of urban water reuse and pollution control.

Spatiotemporal distribution characteristics of potentially toxic elements in agricultural soils across China and associated health risks and driving mechanism.

Comprehensive analysis of spatiotemporal distribution characteristics of potentially toxic elements (PTEs) in soils of China, and associated driving mechanism and health risks is crucial for soil pollution prevention and risk control. In this study, a total of 8 PTEs in agricultural soils of 236 city case data from 31 provinces of China were collected from literatures published between 2000 and 2022. The pollution level, dominant drivers and probabilistic health risks of PTEs were analyzed using geo-accumulation index (Igeo), geo-detector model, and Monte Carlo simulation, respectively. The results indicated a notably accumulation of Cd and Hg, with Igeo of 1.13 and 0.63, respectively. Cd, Hg and Pb showed strong spatial heterogeneity, whereas As, Cr, Cu, Ni, and Zn has no significant differentiation. PM10 was the dominant driver for accumulation of Cd (0.248), Cu (0.141), Pb (0.108), and Zn (0.232), and PM2.5 has a significant effect on accumulation of Hg (0.245); however, soil parent material was primary driver for accumulation of As (0.066), Cr (0.113), and Ni (0.149). PM10 ∩ wind speed accounted for 72.6 % of Cd accumulation, and mining industry ∩ soil parent materials for 54.7 % of As accumulation. Approximately 38.53 %, 23.90 %, and 12.08 % of the hazard index values exceeded 1 for the minors aged 3- < 6, 6- < 12, and 12- < 18 years, respectively; whereas approximately 0.22 %, 0.21 %, 0.20 %, 0.54 % and 0.42 % of the carcinogenic risk values were greater than 1E-04 for individuals aged 3- < 6, 6- < 12, 12- < 18, 18- < 44, and 44- < 70 years, respectively. As and Cd were considered as priority elements for soil pollution prevention and risk control in China. Moreover, the hotspots of PTE pollution and associated health risks were mainly observed in southern, southwestern and central China. The results of this study provided a scientific basis for developing strategies for pollution prevention and risk control of soil PTEs in China.

Quantitative analysis of the contribution of sources, diffusion pathways, and receptor attributes for the spatial distribution of soil heavy metals and their nested structure analysis in China.

Investigation of heavy metal pollution degree, pollution sources, and spatial distribution structure is crucial for the country's soil pollution prevention, but relevant research is lacking. In this study, As, Cd, Cr, Cu, Pb and Zn in the national scope are taken as research objects. Among them, Cd has the highest pollution level. Four sources were quantitatively allocated as soil type, mining and dressing industry, GDP, and NDVI, which accounted for 92.93, 97.81, 99.30 and 96.24 % of Cr, Cd, Zn and As contamination, respectively. In addition, according to the geographical detector, the spatial distribution of As was affected by three diffusion pathways, whose influence degree were 0.822-0.947, especially the slope. Cadmium was primarily affected by both receptor attributes and diffusion pathways, with an influence degree of 0.010-0.175, especially soil water content and slope; Cr and Pb were affected by receptor attributes, with an influence degree of 0.886-0.986 and 0.007-0.288, respectively, especially for soil water content and soil organic carbon; Cu and Zn were affected by receptor attributes, with an influence degree of 0.182-0.823 and 0.002-0.150, respectively, especially for soil texture. There are two spatial distribution structures with nested scales in east-west and north-south directions. The large spatial structure has a more significant impact on the spatial distribution of heavy metals, especially in the east-west direction. Overall, the mining and dressing industry is the main source in Hunan, Yunnan, and Liaoning, where many mines exist and mining activities are frequent. GDP was the main source in Shanghai and Zhejiang areas, where the economy is developed. NDVI was the main source in Guangdong and Anhui areas, where agriculture is relatively developed. These results provide a basis for determining remediation and prevention objectives in soil pollution remediation and prevention in the national scope.

Influencing factors identification and the nested structure analysis of heavy metals in soils in entire city and surrounding the multiple pollution sources.

Identifying the sources of pollutants and analyzing the nested structure of heavy metals is vital for the prevention and control of soil pollution. However, there is a lack of research on comparison the main sources and the nested structure at different scales. In this study, two spatial extent scales were taken as the research objects, the results showed that, (1) the point exceeding standard rate of As, Cr, Ni, and Pb is higher at the entire city scale; (2) As and Pb, while Cr, Ni, and Zn, have weaker spatial variability at the entire scale and surrounding the pollution sources, respectively; (3) the contribution of the larger structure of Cr and Ni, while Cr, Ni, and Zn, at the entire scale and surrounding the pollution sources, respectively, is bigger to the total variability. The representation of semivariogram is better when the general spatial variability is weaker and the contribution of the smaller structure is lower; (4) various factors with different influencing distance could lead to nested structure even at a small extent spatial scale. The results provide a basis for the determination of remediation and prevention objectives at different spatial scales.

Process, influencing factors, and simulation of the lateral transport of heavy metals in surface runoff in a mining area driven by rainfall: A review.

The lateral transport of heavy metals can expand the scope of original contamination, and an accurate prediction of heavy metal migration is necessary to control heavy metal transport. However, previous studies have mainly focused on the migration of soil pollutants in the runoff-soil-groundwater system, whereas research on the lateral migration of heavy metals in surface soil driven by rainfall is relatively scarce. Therefore, in this study we analyzed the horizontal migration of water-soluble heavy metals with surface runoff and non-water-soluble heavy metals with sediment particles, investigated the main factors affecting the processes of runoff and sediment transport and the main factors affecting the mobility of heavy metals in soils, summarized the existing methods for the simulation of heavy metal transportation. The construction of a lateral migration model based on the migration mechanism of soil heavy metals, the hydrological model, and the application of the lateral migration model should be the focus of future research. This study provides a theoretical basis for establishing a model of the lateral migration of soil heavy metals and is of great significance for the prevention and control of the risks related to the lateral migration of soil heavy metals.

Effectiveness of predicting the spatial distributions of target contaminants of a coking plant based on their related pollutants.

The prediction accuracy of the spatial distribution of soil pollutants at a site is relatively low. Related pollutants can be used as auxiliary variables to improve the prediction accuracy. However, little relevant research has been conducted on site soil pollution. To analyze the prediction accuracy of target pollutants combined with auxiliary pollutants, Cu, toluene, and phenanthrene were selected as the target pollutants for this study. Based on geostatistical analysis and spatial analysis, the following results were obtained. (1) The reduction in the root mean square errors (RMSEs) for Cu, toluene, and phenanthrene with multivariable cokriging was 68.4%, 81.6%, and 81.2%, respectively, which are proportional to the correlation coefficient of the relationship between the auxiliary pollutants and the target pollutants. (2) The RMSEs calculated for the multivariable cokriging were lower than those obtained by only combining one related pollutants, and two co-variables should be better. (3) The predicted results for Cu, phenanthrene, and toluene and their corresponding related pollutants are more accurate than the results obtained not using the related pollutants. (4) In the interpolation process, the RMSEs for Cu, toluene, and phenanthrene with multivariable cokriging basically increase as the neighborhood sample data increases, and then they become stable. (5) When 84, 61, and 34 sample points were removed, the RMSEs for Cu, toluene, and phenanthrene, respectively, with multivariable cokriging were close to the RMSEs of the target pollutants based on the total samples. The results are of great significance to improving the prediction accuracy of the spatial distribution of soil pollutants at coking plant sites.

An effective method for determining the optimal sampling scale based on the purposes of soil pollution investigations and the factors influencing the pollutants.

There is a lack of a systematic method for determining the optimal sampling scale based on the purposes of soil pollution investigations (purposeinvest) and the factors influencing of pollutants, which could affect the accuracy of determining pollution scope of the pollution. Therefore, in this study, both the purposeinvest and the influencing factors were considered to determine the optimal sampling scale. The conclusions were obtained through geostatistical and spatial analysis. (1) The optimal sampling scale should account for 3% of the range of the pollutants, which can identify pollution information and minimize sampling costs. (2) The optimal sampling scale should be set to 3% of the range of the main factor influencing the pollutants in the absence of prior pollution information. (3) The greater the influences of the factors on the pollutants, the closer the optimal sampling scale calculated according to the influencing factors will be to that calculated based on the purposeinvest. (4) The method of determination based on both the purposeinvest and the influencing factors was concluded to be rational and reliable based on validation and advantage analysis. These results provide a method for soil pollution investigation that can minimize costs and improve the representativeness of the sample sites.

[Spatial Distribution and Risk Assessment of Heavy Metal Pollution in Farmland Soils Surrounding a Typical Industrial Area of Henan Province].

In order to fully explore the spatial distribution of soil heavy metal contaminants in farmland soil surrounding a typical industrial area in Dakuai town, Xinxiang city, Henan Province, the concentrations of As, Cd, Cr, Cu, Ni, Pb, and Zn in the surface soil and within the soil profile were determined and assessed. The principal components were also analyzed for source apportionment to provide a theoretical basis for the control and prevention of heavy metal pollution. According to the results, the soils in the study area are severely contaminated by Cd and Cu and moderated contaminated by As due to the battery manufacture and Cu (e.g., pipe and wire) processing. The concentrations of Cd, Cu, and As in soils were (2.56±1.23) mg·kg-1, (205.58±157.49) mg·kg-1, and (15.27±4.14) mg·kg-1, respectively, which exceeds standards by 100%, 89.44%, and 3.40%, respectively. Accounting for the influence of pollution sources, terrain, runoff erosion, and prevailing wind direction, all heavy metal concentrations were higher in the south direction, lower in the north direction. The concentrations of Cd and Cu in soil profile samples decreased with depth, with highest concentrations at the surface, indicating the strong effect of industrial activities. Arsenic concentrations varied little with soil depth due to its strong migration ability in alkaline soil, again suggesting an industrial source. Among them, the soil of study area was severely contaminated by Cd and Cu (Level 5). Cd poses a severe potential ecological risk (Level 5) and Cu poses a medium risk (Level 2) in the study soils, while other heavy metals show low potential ecological risk (Level 1). The order of the risk identified was Cd > Cu > Ni > As > Pb > Cr > Zn. In addition, the combined potential ecological risk of heavy metals in the target area is severe. The principal component analysis showed that the high As, Cd, and Cu contents are mainly derived from industrial areas. Therefore, it is urgent to remediate Cd and Cu soil contamination in this area and implement precautions to limit As contamination.

Quantitative analysis of the main sources of pollutants in the soils around key areas based on the positive matrix factorization method.

Quantitative identification of the main sources of heavy metals and polycyclic aromatic hydrocarbons (PAHs) in soils around multiple types of key areas is of great significance for blocking pollution sources. However, there is a lack of more comprehensive relevant research. In this study, Beijing was taken as the research area and four main sources were identified using the positive matrix factorization (PMF) method. The concentration of Pb, PAHs, Cr, and Hg in soils was significantly affected by the presence of landuse type, road traffic, natural factor, and industrial production, respectively, and the farmland, distance to main road, Proterozoic Changcheng-Jixian parent material and cinnamon soil type, and the gross industrial production make greater contributions to these four factors respectively than other variables. Moreover, the uncertainty of the PMF indicates that this four-factor PMF solution is stable and appropriate. These results provide support for the comprehensive control of soil environmental risks.

Effectiveness of predicting spatial contaminant distributions at industrial sites using partitioned interpolation method.

Soil pollution at industrial sites is an important issue in China and in most other regions of the world. The accurate prediction of the spatial distribution of pollutants at contaminated industrial sites is a requirement for the development of most soil remediation strategies, and is commonly performed using spatial interpolation methods. However, significant and abrupt variations in the spatial distribution of pollutants decrease prediction accuracy. During this study, the use of partition interpolation methods was applied to benzo fluoranthene in four soil layers at a contaminated site to determine their ability to improve prediction accuracy in comparison to unpartitioned methods. The examined methods for partitioned interpolation included inverse distance weighting (IDW), radial basis function (RBF), and ordinary kriging (OK). The prediction results of the three methods for partitioned interpolation were compared, and the applicability of partition interpolation was determined. The prediction error associated with the partitioned interpolation methods decreased by 70% compared to unpartitioned interpolation. The prediction accuracy of IDW-based partition interpolation was higher than that of RBF- and OK-based partition interpolation techniques, and it was suitable for identification of highly polluted areas. Partition interpolation is also applicable to 12 other PAHs controlled by USEPA that can be detected, and the prediction effects could also verify this interpolation choice. In addition, the results also demonstrated that the more the maximum concentration deviated from the "norm", the greater the prediction error was caused by the smoothing effects of the interpolation models. These results suggest that the partition interpolation with IDW method can be effectively used to obtain relatively accurate spatial contaminant distribution information, and to identify highly polluted areas.

Removal of polycyclic aromatic hydrocarbons (PAHs) and the response of indigenous bacteria in highly contaminated aged soil after persulfate oxidation.

Integrated chemical-biological treatment is a promising alternative to remove PAHs from contaminated soil, wherein indigenous bacteria is the key factor for the biodegradation of residual PAHs after the application of chemical oxidation. However, systematical study on the impact of persulfate (PS) oxidation on indigenous bacteria as well as PAHs removal is still scarce. In this study, the influences of different PS dosages (1%, 3%, 6%, and 10% [w/w]), as well as various activation methods (native iron, H2O2, alkaline, ferrous iron, and heat) on PAHs removal and indigenous bacteria in highly contaminated aged soil were investigated. Apparent degradation of PAHs in the soil treated with PS oxidation was observed, and the removal efficiency of total PAHs in the soil ranged from 38.28% to 79.97%. The removal efficiency of total PAHs in the soil increased with increasing consumption of PS. However, the bacterial abundance in soil was negatively affected following oxidation for all of the treatments added with PS, with bacterial abundance in the soil decreased by 0.89-2.93 orders of magnitude compared to the untreated soil. Moreover, the number of total bacteria in the soil decreased as PS consumption increased. Different PS activation methods and PS dosages exhibited different influences on the bacterial community composition. Bacteria capable of degrading PAHs under anoxic conditions were composed predominantly by Proteobacteria and Firmicutes. The total amount of Proteobacteria and Firmicutes also decreased with increasing consumption of PS. The results of this study provide important insight for the design of PAHs contaminated soil remediation projects.

Potential of indigenous plant species for phytoremediation of metal(loid)-contaminated soil in the Baoshan mining area, China.

A field survey was conducted to investigate metal(loid) concentration in soils and native plants in the Baoshan mining area for potential application in phytoremediation. Total concentrations of arsenic (As), cadmium (Cd), lead (Pb), and zinc (Zn) in soil varied from 125 to 6656, 5.10 to 1061, 568 to 49294, and 241 to 17296 mg kg-1, respectively, showing severe contamination. Among 20 species native to this area, Pteris ensiformis accumulated 1091 mg kg-1 As in the shoot, and its translocation factor (TF) was greater than 1, suggesting potential capacity for As phytoextraction. Boehmeria nivea, Aster prorerus, and Hydrocotyle sibthorpioides showed potential for phytoextraction of Cd due to their high accumulation of Cd in shoots (490.3, 175.4, and 128.5 mg kg-1, respectively) and high TFs (92.0, 22.1, and 6.7, respectively). Eleusine indica and P. ensiformis were found to contain high concentrations of Pb (7474 mg kg-1) and Zn (1662 mg kg-1) in roots, but with low TFs for Pb (0.4) and Zn (0.2), suggesting potential capability for phytostabilization. There was a positive correlation (p < 0.01, N = 25) of TFs between the metal(loid)s, indicating a synergic interaction in the uptake of metal(loid)s by these plants. According to metal(loid) concentrations in shoots, bioconcentration factors (BFs), and TFs, as well as the botanical features such as wide occurrence, high biomass yield, and rapid growth of the plants, the five native species identified above have the potential for phytoremediation in the Baoshan mining area.

Comparison of common spatial interpolation methods for analyzing pollutant spatial distributions at contaminated sites.

Accurate prediction of the spatial distribution of pollutants in soils based on applicable interpolation methods is often the basis for soil remediation in contaminated sites. However, the applicable interpolation method has not been determined for contaminated sites due to the complex spatial distribution characteristics and stronger local spatial variability of pollutants. In this research, the prediction accuracies of three interpolation methods (including the different values of their parameters) for the spatial distribution of benzo[b]fluoranthene (BbF) in four soil layers were compared. These included inverse distance weighting (IDW), radial basis function (RBF), ordinary kriging (OK). The results indicated: (1) IDW1 is applicable for the first layer, RBF-IMQ is applicable to the second, third, and fourth layers. (2) For IDW, the prediction error is bigger with high weight where high values and low values intersect, while the prediction error is smaller where high (or low) values aggregated distribution. (3) For RBF, if the pollutant concentration trend at the predicted location is consistent with the known points in its neighborhood, the prediction accuracy is higher. (4) IDW is suitable for fitting more drastic curved surfaces, while RBF is more effective for relatively gentle curved surfaces and OK is reasonable for curved surfaces without local outliers. (5) The interpolation uncertainty is positively associated with the contaminant concentration and local spatial variability. Therefore, we suggest the selection of the applicable interpolation model must be based on the principle of the model and the spatial distribution characteristics of the pollutants.

Impact of Soil Water on the Spectral Characteristics and Accuracy of Energy-Dispersive X-ray Fluorescence Measurement.

Soil water is a major interference in the on-site analysis of soil by energy-dispersive X-ray fluorescence. Apparent consequences of this interference include lowered readings for elemental concentrations and significant changes in spectral characteristics in wet soils compared with dry soils. A rigorous interpretation on this issue remains unresolved. Thus, this study evaluated the impact of soil water on the detection of Ca, Ti, Mn, Fe, Cu, Zn, As, Rb, Sr, and Pb. Specimens were prepared from 11 certified reference soils and 3 field soils with water contents from 0 to ∼40 wt %. Results from three commercial models revealed that the readings were subjected to respective internal quantification algorithms; therefore, they could not provide a fundamental perspective of this issue. We analyzed the spectra to examine the mechanism underlying this phenomenon. The spectra of wet soils feature elevated baseline, increased Compton and Rayleigh scatter peaks, and lowered characteristic peaks of elements. Previous studies attributed the lowered characteristic peaks to the absorption of fluorescent X-rays by water and considered soil water and dry soil as separate layers in the calculation. This work argues that wet soils should be treated as mixtures. Water becomes part of the soil matrix and leads to lower attenuation capability, which could be explained by the matrix effect. Meanwhile, the mass fraction of analytes is lowered because of dilution. Results confirmed that dilution lowers the characteristic peaks, whereas the matrix effect heightens them. When estimating the elemental concentrations on a wet weight basis, the matrix effect becomes the major interference. The Compton compensation method provided satisfying results on correcting the matrix effect caused by soil water on Zn, As, Rb, Sr, and Pb.

Quantitative analysis of the factors influencing spatial distribution of soil heavy metals based on geographical detector.

With the rapid development of modern industry, heavy metals in the soil introduce the risk of serious pollution. To reduce this pollution risk, the following four research questions needed to be addressed: What are the main influencing factors of soil pollution? What is the degree of influence? Do factors operate independently or are they interconnected? Which regions have high pollution risk and should be paid more attention? The study area was in Huanjiang County, with 273 km2, and geographical detector proved to be a useful tool to solve these four problems. We found that mine activity and pH value were the primary influencing factors for total and water-soluble heavy metals. The interaction effects of mine activity and soil type, pH values, and normalized difference vegetation index (NDVI) for total heavy metals, as well as pH value and mine activity for water-soluble heavy metals, were greater than the sum effect of two factors. Zones with a high concentration of heavy metals were closer to the road and farther from the mine area, which had low NDVI, large slope, high terrain, and large pH values. Concentrations of total heavy metals were higher in calcareous soils and in dryland and forests. Zones with a higher concentration of water-soluble heavy metals were closer to the mine and river, which had lower DEM and pH values. The uncertainty of geographical detector was also analyzed on the basis of their interpolation accuracy and the stratification number of influencing factors, and we found that the existing sample numbers and the stratification number of influencing factors met the needs of geographical detector calculation. This study's conclusions are useful for soil pollution control and restoration.

Development of a model to simulate soil heavy metals lateral migration quantity based on SWAT in Huanjiang watershed, China.

Lateral transportation of soil heavy metals in rainfall events could significantly increase the scope of pollution. Therefore, it is necessary to develop a model with high accuracy to simulate the migration quantity of heavy metals. A model for heavy metal migration simulation was developed based on the SWAT (Soil and Water Assessment Tool) model. This model took into consideration the influence of soil pH value, soil particle size, runoff volume, sediment amount, concentration of water-soluble heavy metals dissolved in runoff and insoluble absorbed to the soil particles. This model was reasonable in Huanjiang watershed, Guangxi Zhuang Autonomous Region, south China, covering an area of 273 km2. The optimal drainage area threshold was determined by analyzing the effects of watershed subdivision on the simulation results to ensure the simulation accuracy. The main conclusions of this paper were: (1) watershed subdivision could affect simulation migration quantity of heavy metals; (2) the quantity of heavy metals transported by sediment accounted for 97%-99% of the total migration quantity in the study watershed. Therefore, sediment played the most important role in heavy metal migration; (3) the optimal drainage area threshold percentage to ensure high simulation accuracy was determined to be 2.01% of the total watershed; (4) with the optimal threshold percentage, this model could simulate the migration quantity of As, Pb and Cd accurately at the total watershed and sub-watershed level. The results of this paper were useful for identifying the key regions with heavy metal migration.

Comparing ordinary kriging and inverse distance weighting for soil as pollution in Beijing.

Spatial interpolation method is the basis of soil heavy metal pollution assessment and remediation. The existing evaluation index for interpolation accuracy did not combine with actual situation. The selection of interpolation methods needs to be based on specific research purposes and research object characteristics. In this paper, As pollution in soils of Beijing was taken as an example. The prediction accuracy of ordinary kriging (OK) and inverse distance weighted (IDW) were evaluated based on the cross validation results and spatial distribution characteristics of influencing factors. The results showed that, under the condition of specific spatial correlation, the cross validation results of OK and IDW for every soil point and the prediction accuracy of spatial distribution trend are similar. But the prediction accuracy of OK for the maximum and minimum is less than IDW, while the number of high pollution areas identified by OK are less than IDW. It is difficult to identify the high pollution areas fully by OK, which shows that the smoothing effect of OK is obvious. In addition, with increasing of the spatial correlation of As concentration, the cross validation error of OK and IDW decreases, and the high pollution area identified by OK is approaching the result of IDW, which can identify the high pollution areas more comprehensively. However, because the semivariogram constructed by OK interpolation method is more subjective and requires larger number of soil samples, IDW is more suitable for spatial prediction of heavy metal pollution in soils.

[Risk Assessment of Heavy Metal Contamination in Farmland Soil in Du'an Autonomous County of Guangxi Zhuang Autonomous Region, China].

For a comprehensive understanding of the pollution characteristics and ecological risk of heavy metals of farmland soil in Du'an Autonomous County of Guangxi Zhuang Autonomous Region, China, this study evaluated the cadmium (Cd), arsenic (As), nickel (Ni), zinc (Zn), chromium (Cr), antimony (Sb), copper (Cu) and lead ( Pb) pollution situation using the single factor index, the Nemerow pollution index and the Hakanson ecological risk index. The results showed that heavy-metal pollution of farmland soil in Du'an County was serious. 74.6% of the soil samples had heavy metals concentrations higher than the Grade II of National Soil Environmental Quality Standard (GB 15618-1995). The over standard rates of Cd, As, Ni, Zn, Cr, Sb, Cu, Pb were 70.6%, 42.9%, 34.9%, 19.8%, 19.6%, 2.94%, 1.59%, 0.79%, respectively. Cd and As were the main contaminants in Du'an County, the pollution was far more serious than those of the national and Guangxi Zhuang Autonomous Region. In terms of the ecological risk, heavy metals of farmland soil in Du'an County showed a "middle" ecological risk, with Cd accounting for 88% of the total ecological risk. The north-west of Jiudu Town and the zone between Bao'an Town and Dongmiao Town were two areas with high ecological risk in Du'an County. The contamination of farmland soils in Du'an County was caused by two main sources, whereas the pollution of As and Sb of farmland soils near Diaojiang River was mainly caused by the upstream mining industry.

[Effect of lead on soil quality and human health around a lead smeltery].

In order to disclose soil pollution caused by lead (Pb) smeltery and its human health risks, this study investigated Pb concentrations in farmland soil, hair and blood of residents surrounding a Pb smeltery in Henan Province, and discussed the rationality of estimation of the health protection zone from the Pb smeltery. It was found that the Pb concentrations in blood of children living in both M and Y villages exceeded the international Pb poisoning diagnostic criteria. The highest Pb concentration in blood was 491 microg x L(-1), with the percentages of mild, medium and severe Pb poisoning reaching 52.5%, 42.5% and 5.0%, respectively. Pb concentrations in hair of children living in Y village were in excess of the related standard, with the highest being 156 mg x kg(-1), and the average value 2.9 times of that in hair of adults. In terms of Pb in soil, Pb concentrations in 66.7% of the topsoils (0-20 cm) around the smeltery exceeded Grade II (350 mg x kg(-1), pH > 7.5) of the National Soil Environmental Quality Standard (GB 15618-1995), with the highest reaching up to 1687 mg x kg(-1). The severe soil pollution may have played a role in children's health issues in the villages surrounding the smeltery. Therefore, more attention should be paid to the scientific estimation of health protection zone from Pb smeltery and the remediation of heavy-metal contaminated soil in the surrounding areas.