Soil environmental carrying capacity and its spatial high-precision accounting framework.

Human activity intensity should be controlled within the carrying capacity of soil units, which is crucial for environmental sustainability. However, the existing assessment methods for soil environmental carrying capacity (SECC) rarely consider the relationship between human activity intensity and pollutant emissions, making it difficult to provide effective early warning of human activity intensity. Moreover, there is a lack of spatial high-precision accounting methods for SECC. This study first established a spatial soil environmental capacity (SEC) model based on the pollutant thresholds corresponding to the specific protection target. Next, a spatial net-input flux model was proposed based on soil pollutants' input/output fluxes. Then, the quantitative relationship between human activity intensity and pollutant emissions was established and further incorporated into the SECC model. Finally, the spatial high-precision accounting framework of SECC was proposed. The methodology was used to assess the SECC for the copper production capacity in a typical copper smelting area in China. The results showed that (i) the average SECs for Cu, Cd, Pb, Zn, As and Cr are 427.89, 16.84, 306.41, 376.8, 71.63, and 392.7 kg hm-2, respectively; (ii) heavy metal (HM) concentrations and land-use types jointly influence the spatial distribution pattern of SEC; (iii) atmospheric deposition is the dominant HM input pathway and the high net-input fluxes are mainly located in the southeast of the study area; (iv) with the current human activity intensity for 50 years, the average SECs for Cu, Cd, Pb, Zn, As and Cr are 202.31, 1.71, 20.9, 66.15, 36.73, and 3 kg hm-2, respectively; and (v) to maintain the protection target at the acceptable risk level within 50 years, the SECC for the increased copper production capacity is 1.53 × 106 t. This study provided an effective tool for early warning of human activity intensity.

Multi-medium residues and ecological risk of herbicides in a typical agricultural watershed of the Mollisols region, Northeast China.

The widespread use of herbicides impacts non-target organisms, promotes weed resistance, posing a serious threat to the global goal of green production in agriculture. Although the herbicide residues have been widely reported in individual environmental medium, their presence across different media has received scant attention, particularly in Mollisols regions with intensive agricultural application of herbicides. A systematic investigation was conducted in this study to clarify the occurrence of herbicide residues in soil, surface water, sediments, and grains from a typical agricultural watershed in the Mollisols region of Northeast China. Concentrations of studied herbicides ranged from 0.30 to 463.49 µg/kg in soil, 0.31-29.73 µg/kg in sediments, 0.006-1.157 µg/L in water, and 0.32-2.83 µg/kg in grains. Among these, Clomazone was the most priority herbicide detected in soil, sediments, and water, and Pendimethalin in grains. Crop types significantly affected the residue levels of herbicides in grains. Clomazone posed high ecological risks in soil and water, with 86.4 % of water samples showing high risks from herbicide mixtures (RQ > 1). These findings aid in enhancing our comprehension of the pervasive occurrence and potential ecological risks of herbicides in different media within typical agricultural watersheds, providing detailed data to inform the development of targeted mitigation strategies.

Determining the net input fluxes of pollutants based on the spatial source apportionment receptor model for early warning of regional soil pollution.

Understanding the soil pollutants' net input fluxes is essential for accurate early warning of regional soil pollution. However, the traditional input-output investigation method for soil pollutants' net input fluxes is often costly, especially at the regional scale. This study first assessed the land-use effects on soil heavy metals around a typical copper smelting area in China. Next, an improved spatial source apportionment receptor model, namely robust absolute principal component scores/robust geographically weighted regression with category land-use information (RAPCS/RGWR-CLU), was proposed to apportion the net source contributions, and its performance was compared with those of RAPCS/RGWR and the traditional absolute principal component scores/multiple linear regression (APCS/MLR). Finally, the net input fluxes of soil heavy metals were determined based on RAPCS/RGWR-CLU, and its performance was compared with that of the traditional input-output investigation method. Results showed that (i) land-use effects are significant for soil As, Cu, Pb, and Zn; (ii) RAPCS/RGWR-CLU achieves higher source apportionment accuracy than RAPCS/RGWR and APCS/MLR; and (iii) the net input fluxes determined by RAPCS/RGWR-CLU have similar accuracy to those determined by the traditional input-output investigation method but with significantly lower costs. Therefore, this study provided a cost-effective solution to determine the net input fluxes of soil pollutants.

Improving assessment quality of soil natural attenuation capacity at the point and regional scales.

Soil natural attenuation capacity (NAC) is an important ecosystem service that maintains a clean environment for organisms in the soil, which in turn supports other services. However, spatially varying indicator weights were rarely considered in the traditionally-used soil NAC assessment model (e.g., ecosystem-service performance model) at the point scale. Moreover, in the spatial simulation of soil NAC, the traditionally-used geostatistical models were usually susceptible to spatial outliers and ignored valuable auxiliary information (e.g., land-use types). This study first proposed a novel soil NAC assessment method based on the ecosystem-service performance model and moving window-entropy weight method (MW-EW) (NACMW-EW). Next, NACMW-EW was used to assess soil NAC in a typical area in Guixi City, China, and further compared with the traditionally-used NACtra and NACEW. Then, robust sequential Gaussian simulation with land-use types (RSGS-LU) was established for the spatial simulation of NACMW-EW and compared with the traditionally-used SGS, SGS-LU, and RSGS. Last, soil NAC's spatial uncertainty was evaluated based on the 1000 realizations generated by RSGS-LU. The results showed that: (i) MW-EW effectively revealed the spatially varying indicator weights but EW couldn't; (ii) NACMW-EW obtained more reasonable results than NACtra and NACEW; (iii) RSGS-LU (RMSE = 0.118) generated higher spatial simulation accuracy than SGS-LU (RMSE = 0.123), RSGS (RMSE = 0.132), and SGS (RMSE = 0.135); and (iv) the relatively high (P[NACMW-EW(u) > 0.57] ≥ 0.95) and low (P[NACMW-EW(u) > 0.57] ≤ 0.05) threshold-exceeding probability areas were mainly located in the south and east of the study area, respectively. It is concluded that the proposed methods were effective tools for soil NAC assessment at the point and regional scales, and the results provided accurate spatial decision support for soil ecosystem service management.

[Research Progress and Prospect of Herbicide Residue Characteristics in Black Soil Region of China].

Food security is the top priority of a country. As an important granary in China, the northeast black land is a "ballast" to ensure national food security. However, the long-term and high-intensity application of herbicides in black land farmland has led to the accumulation and migration of herbicides in the soil, which affects soil quality, crop yield, and quality and hinders sustainable agricultural development in the black soil. To solve the problem of herbicide residues in black land farmland, it is necessary to control the application of herbicides from the source, as well as to elucidate the current situation, spatial and temporal evolution, and driving factors of herbicide residues, in order to achieve scientific prevention and control and precise policy implementation. The main contents of this study were as follows:1systematically summarize the application status and problems of herbicides in the farmland of black soil in China, suggesting that there are currently problems such as irregular application and insufficient product innovation of herbicides in the farmland of black soil; 2 comprehensively analyze the current status of herbicide residues, identify the deficiencies in recent studies on herbicide residue characteristics, spatial distribution, and pollution diagnosis in the farmland of black soil, and clarify the gaps in the research on the residue characteristics of herbicides in the farmland of black soil; and 3 propose the research prospect and key orientation for the herbicide residue diagnosis and risk management in the farmland of the black soil region of China. The results of this study can provide science and technology support for guaranteeing soil health, food security, and ecosystem security of black land farmland in China.

Effects of atmospheric deposition on heavy metals accumulation in agricultural soils: Evidence from field monitoring and Pb isotope analysis.

Atmospheric deposition is an essential pathway of heavy metals (HMs) from the atmosphere to soils, while few studies assess the effects and contributions of atmospheric deposition on HMs accumulations in agricultural soils from the field and regional scales. In this study, eleven representative field monitoring sites from industrial areas, agricultural areas, and reference site in a typical rapid industrial development region were selected to determine the effects of atmospheric deposition on soil HMs accumulation. Industrial activities significantly increased the deposited particles flux from atmospheric deposition, with annual particles fluxes in industrial areas being 1.83 and 1.90 times higher than in agricultural areas and reference site, respectively. Although the HMs deposition fluxes had decreased significantly with time by literature comparison, the deposition fluxes of Cd and Pb were still at high levels in this study area. Precipitation was the key factor affecting seasonal variations of atmospheric HMs deposition. Lead isotope analysis indicated that atmospheric Pb originated from coal combustion, and atmospheric deposition was the primary source of Pb contamination in agricultural soil adjacent to industries. This study provided insight into the effects of atmospheric deposition on agricultural soil HMs accumulations at the regional scale and an important theoretical basis for source-preventing soil HMs contamination in industrial developed and other similar areas.

Identifying the potential soil pollution areas derived from the metal mining industry in China using MaxEnt with mine reserve scales (MaxEnt_MRS).

Identifying the potential soil pollution areas derived from the metal mining industry usually requires extensive field investigation and laboratory analysis. Moreover, the previous studies mainly focused on a single or a few mining areas, and thus couldn't provide effective spatial decision support for controlling soil pollution derived from the metal mining industry at the national scale. This study first conducted a literature investigation and web crawler for the relevant information on the metal mining areas in China. Next, MaxEnt with mine reserve scales (MaxEnt_MRS) was proposed for spatially predicting the probabilities of soil pollution derived from the metal mining industry in China. Then, MaxEnt_MRS was compared with the basic MaxEnt. Last, the potential soil pollution areas were identified based on the pollution probabilities, and the relationships between the soil pollution probabilities and the main environmental factors were quantitatively assessed. The results showed that: (i) MaxEnt_MRS (AUC = 0.822) obtained a better prediction effect than the basic MaxEnt (AUC = 0.807); (ii) the areas with the soil pollution probabilities higher than 54% were mainly scattered in the eastern, south-western, and south-central parts of China; (iii) GDP (45.7%), population density (30.1%), soil types (15.5%), average annual precipitation (3.9%), and land-use types (3.1%) contributed the most to the prediction of the soil pollution probabilities; and (iv) the soil pollution probabilities in the areas with all the following conditions were higher than 54%: GDP, 7600-2612670 thousand yuan/km2; population density, 152-551 people/km2; precipitation, 924-2869 mm/year; soil types, Ferralisols or Luvisols; and land-use types, townland, mines, and industrial areas. The above-mentioned results provided effective spatial decision support for controlling soil pollution derived from the metal mining industry at the national scale.

Three-dimensional spatial prediction of Zn in the soil of a former tire manufacturing plant using machine learning and readily attainable multisource auxiliary data.

Pollutants in the soil of industrial site are often highly heterogeneously distributed, which brought a challenge to accurately predict their three-dimensional (3D) spatial distributions. Here we attempt to create effective 3D prediction models using machine learning (ML) and readily attainable multisource auxiliary data for improving the prediction accuracy of highly heterogeneous Zn in the soil of a small-size industrial site. Using raw covariates from functional area layout, stratigraphic succession, and electrical resistivity tomography, and derived covariates of the raw covariates as predictors, we created 6 individual and 2 ensemble models for Zn, based on ML algorithms such as k-nearest neighbors, random forest, and extreme gradient boosting, and the stacking approach in ensemble ML. Results showed that the overall 3D spatial patterns of Zn predicted by individual and ensemble ML models, inverse distance weighting (IDW), and ordinary Kriging (OK) were similar, but their predictive performances differed significantly. The ensemble model with raw and derived covariates had the highest accuracy in representing the complex 3D spatial patterns of Zn (R2 = 0.45, RMSE = 344.80 mg kg-1), compared to the accuracies of individual ML models (R2 = 0.27-0.44, RMSE = 396.75-348.56 mg kg-1), OK (R2 = 0.33, RMSE = 381.12 mg kg-1), and IDW interpolation (R2 = 0.25, RMSE = 402.94 mg kg-1). Besides, the prediction accuracy gains of incorporating derived covariates were higher than adopting ensemble ML instead of single ML algorithm. These results highlighted the importance of developing derived covariates whilst adopting ML in predicting the 3D distribution of highly heterogeneous pollutant in the soil of small-size industrial site.

Effects of mining on the potentially toxic elements in the surrounding soils in China: A meta-analysis.

Previous studies about the effects of mining on the potentially toxic elements (PTEs) in the surrounding soils mainly focused on single or few mining areas. However, these studies couldn't comprehensively quantify the mining-induced variations of soil PTE concentrations at the national scale. Moreover, the quantitative relationships between the effects of mining on soil PTEs and some related factors remained unclear at the national scale. This study first conducted a literature survey for soil PTE data affected by mining in China. Then, the random-effects model in the meta-analysis was used to quantify mining-induced variations of soil PTE concentrations in the surrounding areas. Last, the single meta-regression was used to explore the relationships between the effects of mining on soil PTEs and the related factors at the national scale. Results showed that: (i) mining-induced increases of soil PTE concentrations followed the order: Cd (1017%) > Hg (319%) > Pb (291%) > Zn (176%) > Cu (129%) > As (92%) > Ni (23%); (ii) mining-induced increases of soil PTE concentrations in clay (531%), non-ferrous mine (188%), paddy field (212%), and Central South China (290%) were more than those in other soil textures, mine types, land-use types, and geographical divisions, respectively; (iii) the effects of mining on soil PTEs were negatively correlated with soil pH (QM = 29.76, p < 0.01) and positively correlated with soil organic carbon (QM = 28.54, p < 0.01) and mean annual precipitation (QM = 91.75, p < 0.01); (iv) the effects of mining overall decreased with the sampling year (QM = 35.01, p < 0.01) and showed latitudinal zonality (QM = 180.39, p < 0.01). The above results provided valuable information for soil PTE mitigation in the areas affected by mining in China.

Heavy metal accumulation in the surrounding areas affected by mining in China: Spatial distribution patterns, risk assessment, and influencing factors.

Previous studies about heavy metal (HM) accumulation in the surrounding areas affected by mining mainly focused on a single or just a few mining areas. However, these studies could not provide adequate information supporting HM controls in soils at the national scale. This study first conducted a literature investigation and collected HM data in mining areas in China from 263 pieces of published literature. Then, geo-accumulation index (Igeo), ecological risk index (ER), and health risk assessment model were adopted to evaluate their HM pollution, ecological risks, and health risks, respectively. Finally, Geodetector and Pearson correlation coefficients were used to explore the relationships between the spatial distribution patterns of HMs in soils and their influencing factors. Results showed that: (i) the average concentrations of Cd, Hg, Pb, Zn, Cu, As, Ni, and Cr were 5.4, 1.2, 335.3, 496.1, 105.8, 55.0, 42.6, and 72.4 mg kg-1, respectively, in the surrounding areas affected by mining in China; Cd pollution in soils (Igeo = 2.9) was most severe; Cd (ERCd > 320) and Hg (ERHg > 320) were the main ecological risk factors; (ii) among the selected factors, mine types, clay content, soil organic carbon, and precipitation with the highest relative importance for the spatial distribution patterns of the HMs; (iii) HM accumulation were inversely proportional to soil pH, and were proportional to clay content, precipitation, and temperature; (iv) As, Cd, Hg, Pb, and Ni should be selected as the HMs to be controlled preferentially; (v) priority attention should be given to mining areas in Central South China, Southwest China, Liaoning province, and Zhejiang province; (vi) special attention should be given to mining areas of antimony, tin, tungsten, molybdenum, manganese, and lead­zinc. The above results provided crucial information for HM control in the areas affected by mining at the national scale.

Environmental capacity of heavy metals in intensive agricultural soils: Insights from geochemical baselines and source apportionment.

Soil environmental capacity (EC) of heavy metals (HMs) can be used as an index to evaluate the pollution status of HMs and to provide basic data for HM remediation. However, the commonly used soil EC for HMs usually are prone to bias due to the lack of local background values (BVs) and the consideration of the contribution from various HM sources. Here, a modified method was proposed to estimate the soil EC by integrating the establishment of local BVs and the quantitative evaluation of contributions from HM sources in an intensive agricultural area of Shouguang city, China. The local BVs of HMs were established using the relative cumulative frequency distribution method. The source-specific EC was quantified based on the local BVs and the contributions of HM sources identified by receptor model and variable importance analysis. Results showed that the average BV values of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn were 7.67, 0.10, 62.84, 21.17, 0.031, 28.38, 19.25, and 59.60 mg kg-1, respectively, in the study area. The source-specific EC of Cd, Cu, Hg, and Zn were higher than their current EC, indicating an underestimation of soil capacity of HMs by the traditional method. The EC of HMs in these soils was generally medium indicated by their comprehensive EC index (PI) (PI >0.7), suggesting a low risk level of the targeted HMs. According to indexes such as the individual metal index (Pi) and enrichment factor (EF), special attention should be paid to Cd and Zn due to their low capacity (Pi <0.7) and high accumulation (EF > 2) in some points across this area. Altogether, our findings suggested that the modified method had a better capability for evaluating and predicting the enrichment status of soil HMs, which can be helpful for formulating the targeted measures to control HM pollution in such intensive agricultural areas.

A joint standard-exceeding risk assessment of multiple pollutants based on robust geostatistics with categorical land-use type data: A case study of soil nitrogen and phosphorus.

Joint standard-exceeding risk and its spatial uncertainty of soil available nitrogen (AN) and available phosphorus (AP) under the specific constraints are essential for guiding the joint regulation of pollutants but were rarely considered by previous studies. Moreover, traditionally-used spatial simulation models are not only non-robust but also ignoring valuable categorical information (e.g., land-use types), which may hinder the acquisition of high-precision spatial simulation results. This study first established optimally robust semi-variogram estimators to identify the spatial outliers of soil AN and AP in Jintan County, China. Then, robust sequential Gaussian simulation (RSGS) with land-use types (RSGS-LU) was proposed and further compared with RSGS, SGS-LU, and SGS in the spatial simulation accuracy. Last, a joint standard-exceeding probability model under the specific constraints was proposed, and the corresponding high-risk areas were delineated for the joint regulation of soil AN and AP. Results showed that: (i) 23 and 17 spatial outliers were identified for soil AN and AP, respectively; (ii) removing outliers or combining land-use types could improve the spatial simulation accuracy of soil AN and AP; (iii) RSGS-LU generated the highest spatial simulation accuracy for both soil AN and AP; (iv) the area with the joint standard-exceeding (AP > 30 mg kg-1∪ AN > 130 mg kg-1) probability >75% accounted for 9.98% of the county's area; (iv) the area with the joint standard-exceeding (AP > 30 mg kg-1∩ AN > 130 mg kg-1) probability >75% accounted for 2.29% of the county's area. It is concluded that RSGS-LU and joint standard-exceeding probability model under the specific constraints could provide more accurate and flexible spatial decision support for the joint regulation of soil AN and AP at a regional scale. Moreover, the methods recommended in this study also provide valuable tools for the joint standard-exceeding risk assessment of other multiple soil pollutants.

Excessive phosphorus inputs dominate soil legacy phosphorus accumulation and its potential loss under intensive greenhouse vegetable production system.

Phosphorus (P) is an essential element for crop growth and it plays a critical role in agricultural production. Excessive P applications has become a serious concern in Chinese greenhouse vegetable production (GVP) systems. Nevertheless, P accumulation (legacy P) in GVP profile soils and its potential loss remain poorly documented. Hence, this study aimed to response this issue via paired collection of 136 soil samples (0-30, 30-60 and 60-90 cm depth) and 41 vegetable samples from both plastic greenhouses (PG) and solar greenhouses (SG) in Shouguang, Shandong province. Results showed that the annual input of P ranged from 772 to 2458 kg ha-1 for different vegetables through the whole growing season versus little vegetable P uptake (ranging from 47.8 to 155 kg ha-1). Results also revealed significant P accumulation in both SG and PG profile soils. Compared to arable soils (background soils), legacy P to the depth of 90 cm in PG and SG soils were 3.28 and 11.16 Mg P ha-1, respectively. The content of total P in PG and SG soils significantly increased with cultivation duration. The maximum environmental capacity of P in SG soils was 187 Mg ha-1, and the maximum number of years for safe planting was 38 yrs. After four years of cultivation, P loss would occur in these soils and the loss rate of P increased with cultivation duration. Opposite to PG soils, a potentially higher risk of P losses took place in SG soils. Our results also demonstrated that excessive P inputs driven by intensive agricultural practices dominated legacy P accumulation within the profile soils and its losses in GVP systems. Site-specific P managements, including improving P use efficiency, reducing further P surplus and reusing legacy P in soils, are urgently needed to minimize P loss. At the same time, the potential loss of subsoil P could not be neglected.

Additional sampling using in-situ portable X-ray fluorescence (PXRF) for rapid and high-precision investigation of soil heavy metals at a regional scale.

Traditional soil heavy metal (HM) investigation usually costs a lot of human and material resources. In-situ portable X-ray fluorescence spectrometry (PXRF) is a cheap and rapid HM analysis method, but its analysis accuracy is usually affected by spatially non-stationary field environment factors. In this study, residual sequential Gaussian co-simulation (RCoSGS) was first proposed to incorporate both continuous and categorical auxiliary variables for spatial simulation of soil Cu. Next, additional in-situ PXRF sampling sites (n = 300) were allocated in the subareas with high, medium, and low conditional variances in the proportions of 50%, 33.33%, and 16.67%, respectively. Then, robust geographically weighted regression (RGWR) was established to correct the spatially non-stationary effects of field environmental factors on in-situ PXRF and further compared with the traditionally-used multiple linear regression (MLR) and basic GWR in correction accuracy. Finally, RCoSGS with the RGWR-corrected in-situ PXRF as part of hard data (RCoSGS-PXRF) was established and further compared with the model with one or multiple auxiliary variables in the spatial simulation accuracy. Results showed that (i) RCoSGS effectively incorporated both SOM and land-use types and obtained higher spatial simulation accuracy (RI = 37.52%) than residual sequential Gaussian simulation with land-use types (RI = 19.44%) and sequential Gaussian co-simulation with SOM (RI = 20.92%); (ii) RGWR significantly weakened the spatially non-stationary effects of field environmental factors on in-situ PXRF, and RGWR (RI = 58.96%) and GWR (RI = 39.61%) obtained higher correction accuracy than MLR; (iii) the RGWR-corrected in-situ PXRF (RI = 66.57%) brought higher spatial simulation accuracy than both land-use types and SOM (RI = 37.52%); (iv) RCoSGS-PXRF obtained the highest spatial simulation accuracies (RI = 83.74%). Therefore, the proposed method is cost-effective for the rapid and high-precision investigation of soil HMs at a regional scale.

Spatiotemporal variation and sources of soil heavy metals along the lower reaches of Yangtze River, China.

Excessive accumulation of soil heavy metals (HMs) result in the deterioration of soil quality and reduction of agricultural productivity and safety. The accumulation status, temporal change, and sources of soil HMs were determined by large-scale field surveys in 2014 and 2019 in rapid urbanization and industrialization area along the lower reaches of the Yangtze River, China. Eighty-two surface soil samples were collected in 2014 and ninety-five surface soil samples and seven soil profiles (0-100 cm) were collected in 2019. The mean concentrations (in, mg kg-1) of As (10.17), Cd (0.33), Cr (86.38), Cu (38.22), Hg (0.11), Ni (37.67), Pb (43.95), and Zn (113.15) were greater than the corresponding background values. The concentrations of these 8 HMs significantly varied with site-specific distributions depending on nearby landscape patterns with decreasing order: agricultural soil around industrial > agricultural soil > fallow soil. Cd and Hg were found to be priority pollutants due to their greater accumulations in this study area. Combined analyses of principal component analysis and positive matrix factorization model addressed source apportionment of soil HMs. Industrial activities, parent materials, and agricultural and traffic activities were three major sources and their contributions were 35.56%, 35.20%, and 29.23%, respectively. The concentrations of soil As, Cd, Cr and Pb increased with time. This study elucidates how changes in land uses and time affect soil HMs and provides reasonable suggestions for the effective reduction of HM contamination in economically and industrially developed areas of China, and elsewhere.

Pollution Characteristics and Risk Assessment of Soil Heavy Metals in the Areas Affected by the Mining of Metal-bearing Minerals in Southwest China.

Previous studies on the impact of the mining of metal-bearing minerals on surrounding soil mainly focused on single or a few areas. However, these studies' results cannot provide effective making-support for soil pollution control in large-scale areas, especially in cross-provincial scale. This study first collected 78 literature before 2020 on soil heavy metals (As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) affected by the mining of metal-bearing minerals in Southwest China. Then, the geo-accumulation index, ecological risk, and health risk were assessed based on the extracted heavy metal data. Results showed that As, Cd, Hg, and Pb should be selected as the preferentially controlled heavy metals; Yunnan and Guizhou Provinces should be selected as the preferentially concerned areas; children should be given priority attention. The results provided more effective decision support for reducing heavy metal pollution in the areas affected by the mining of metal-bearing minerals in Southwest China.

[Meta-analysis of the Effects of Metal Mining on Soil Heavy Metal Concentrations in Southwest China].

Metal mining is one of the main contributors of soil heavy metals. Previous studies examining the impact of metal mining on surrounding soil have mainly focused on one or a few metal mining areas. However, such studies cannot effectively inform the management of heavy metal pollution in soil at an inter-provincial scale. As part of this study, literature was collected on soil heavy metals (i.e., As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) affected by metal mining in regions of Southwest China (i.e., Yunnan Province, Sichuan Province, Guizhou Province, Chongqing Municipality, and Tibet Autonomous Region); Next, the impact of metal mining on the soil concentrations of these metals was quantified through meta-analysis, and the relationships between the selected factors (i.e., different sub-regions, metal minerals, and land-use types) and soil heavy metal concentrations were explored. Finally, the literature data was tested for publication bias. The results showed that metal mining in Southwest China has significantly increased the concentrations of heavy metals in topsoil. The different metals were ranked according to their weight effect sizes (ES+) in the following order Cd > Pb > Hg > Zn > As > Cu > Ni > Cr. Metal mining in both Sichuan and Yunnan led to higher effect sizes of soil Cd (ES+Sichuan=4.16, ES+Yunnan=3.20) and Pb (ES+Sichuan=3.47, ES+Yunnan=2.54) than those of the other heavy metals, while metal mining in Guizhou led to a higher effect size of soil Hg (ES+=2.80). The effect size of metal mining on soil heavy metals was higher in cultivated soil (ES+=1.42) than in forested soil (ES+=0.50). The mining of lead-zinc and tin significantly increased the concentrations of soil Cd, Pb, and Zn, and the mining of copper significantly increased the concentrations of soil Cu, Cd, and Pb. Of the investigated soil heavy metals in Southwest China, Pb and Zn showed slight potential publication biases (P<0.05). The above results can provide more effective information for the environmental protection of soil in metal mining areas of Southwest China.

Spatially apportioning the source-oriented ecological risks of soil heavy metals using robust spatial receptor model with land-use data and robust residual kriging.

Previous ecological risk assessments were mainly concentration-oriented rather than source-oriented. Moreover, land use is usually related to source emissions but was rarely used to improve the source apportionment accuracy. In this study, the land-use effects of heavy metals (HMs) in surface (0-20 cm) and subsurface (20-40 cm) soils were first explored using ANOVA in a suburb of Changzhou City, China; next, based on robust absolute principal component scores-robust geographically weighted regression (RAPCS/RGWR), this study proposed RAPCS/RGWR with land-use type (RAPCS/RGWR-LUT) and compared its source apportionment accuracy with those of basic RAPCS/RGWR and commonly-used absolute principal component scores/multiple linear regression (APCS/MLR); then, the source-oriented ecological risks were apportioned based on RAPCS/RGWR-LUT and Hakanson potential ecological risk index method; finally, this study proposed robust residual kriging with land-use type (RRK) for spatially predicting the source-oriented ecological risks, and compared its spatial prediction accuracy with those of robust ordinary kriging (ROK) and traditionally-used ordinary kriging (OK). Results showed that: (i) by incorporating land-use effects, RAPCS/RGWR-LUT obtained higher source apportionment accuracy than RAPCS/RGWR and APCS/MLR; (ii) the two most important external input sources of the ecological risks were 'atmospheric deposition' (PERIsurface = 47.11 and PERIsubsurface = 35.27) and 'agronomic measure' (PERIsurface = 28.93 and PERIsubsurface = 20.37); (iii) the biggest ecological risk factor was soil Cd (ERsurface = 57.14 and ERsubsurface = 47.62), which was mainly contributed by 'atmospheric deposition' (ERsurface=33.14 and ERsubsurface=25.71); (iv) RRK obtained higher spatial prediction accuracy than ROK and OK; (v) the high-risk areas derived from 'atmospheric deposition' were mainly located in the southwest of the study area, and the high-risk areas derived from 'agronomic measure' were scattered in the agricultural land in the north and south of the study area. The above information provided effective spatial decision support for reducing the source-oriented input of the ecological risks of soil HMs in a large-scale area.

Spatial Distribution and Source Apportionment of Agricultural Soil Heavy Metals in a Rapidly Developing Area in East China.

We collected 682 topsoil samples (0-20cm) from agricultural lands of Luhe County in East China, and analyzed the spatial distribution patterns and potential sources of four major heavy metals. High Pb and Cr were mainly in the southeast adjacent to the Yangtze River, and Cd were characterized by an increasing trend from northwest to southeast, while high Hg mainly occurred in the areas near downtown. Spatially-continuous sources dominated the soil heavy metal concentrations. Contributions of spatially-continuous natural source (soil parent material) to Cr and Cd were 97.0% and 77.7%, respectively, whereas contributions of spatially-continuous anthropogenic source such as diffuse pollution to Pb and Hg were 75.7% and 86.7%, respectively. The distance to factories was the most influential anthropogenic factor for localized anomaly patterns of Pb, Cd, and Cr, while the intensive agricultural land uses associated with the rapid urban expansion were particularly relevant to the anomaly patterns of Hg.

Source apportionment of soil heavy metals using robust spatial receptor model with categorical land-use types and RGWR-corrected in-situ FPXRF data.

High-density samples are usually a prerequisite for obtaining high-precision source apportionment results in large-scale areas. In-situ field portable X-ray fluorescence spectrometry (FPXRF) is a fast and cheap way to increase the sample size of soil heavy metals (HMs). Moreover, categorical land-use types may be closely associated with source contributions. However, the above information has rarely been incorporated into the source apportionment. In this study, robust geographically weighted regression (RGWR) was first used to correct the spatially varying effect of the related soil factors (e.g., soil water and soil organic matter) on in-situ FPXRF in an urban-rural fringe of Wuhan City, China, and the correction accuracy of RGWR was compared with those of the traditionally non-spatial multiple linear regression (MLR) and basic GWR. Then, the effect of land-use types on HM concentrations was partitioned using analysis of variance (ANOVA). Last, based on the robust spatial receptor model (i.e., robust absolute principal component scores/RGWR [RAPCS/RGWR]), this study proposed RAPCS/RGWR with categorical land-use types and RGWR-corrected in-situ FPXRF data (RAPCS/RGWR_LU&FPXRF), and its performance was compared with those of RAPCS/RGWR with none or one kind of auxiliary data. Results showed that (i) the performances of the correction models for in-situ FPXRF data were in the order of RGWR > GWR > MLR, and the relative improvement of RGWR over MLR ranged from 52.6% to 70.71% for each HM; (ii) categorical land-use types significantly affected the concentrations of soil Zn, Cu, As, and Pb; (iii) the highest estimation accuracy for source contributions was obtained by RAPCS/RGWR_LU&FPXRF, and the lowest estimation accuracy was obtained by basic RAPCS/RGWR. It is concluded that land-use types and RGWR-corrected in-situ FPXRF data are closely associated with the source contribution, and RAPCS/RGWR_LU&FPXRF is a cost-effective source apportionment method for soil HMs in large-scale areas.