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.

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.

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.

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.

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.

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.

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.

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.

Resampling with in situ field portable X-ray fluorescence spectrometry (FPXRF) to reduce the uncertainty in delineating the remediation area of soil heavy metals.

There must be some uncertainty in the remediation areas delineated based on limited sample points, and resampling in the high-uncertainty areas is particularly necessary. In situ field portable X-ray fluorescence spectrometry (FPXRF), a rapid and cheap analysis method for soil heavy metals, is strongly affected by many spatially non-stationary soil factors. This study first delineated the high-uncertainty area (threshold-exceeding probabilities (PTE) between 30% and 70%) of soil Pb based on the 1000 realizations produced by sequential Gaussian simulation (SGS) with 93 ICP-MS Pb concentrations measured in a peri-urban agriculture area, China. Next, in situ FPXRF was used to increase sample density in this high-uncertainty area. Then, robust geographically weighted regression (RGWR) was used to correct the in situ FPXRF Pb, and the correction accuracies of RGWR, basic GWR, and traditionally-used ordinary least squares regression (OLSR) were compared. Finally, to explore the best way to combine these corrected in situ FPXRF concentrations in delineating the remediation area, we compared the following spatial simulation methods: basic SGS, sequential Gaussian co-simulation (CoSGS) with the RGWR-corrected in situ FPXRF Pb as auxiliary soft data (CoSGS-CorFPXRF), and SGS with the RGWR-corrected in situ FPXRF Pb as part of hard data (SGS-CorFPXRF). Results showed that (i) RGWR produced higher correction accuracy (RI = 71.5%) than GWR (RI = 59.68%) and OLSR (RI = 25.58%) for the in situ FPXRF Pb; (ii) SGS-CorFPXRF produced less uncertainty (G = 0.97) than CoSGS-CorFPXRF (G = 0.95) and SGS (G = 0.91) in the spatial simulation; (iii) High-uncertainty area (30%

Exploring the spatially varying relationships between cadmium accumulations and the main influential factors in the rice-wheat rotation system in a large-scale area.

Cadmium (Cd) accumulations in crops and the effects of the related soil factors on them are critical to developing precise soil management measures for food safety. Traditionally-used non-spatial multiple linear regression (MLR) cannot adequately model the spatially varying effects of the related soil properties on Cd accumulations in crop (or soil). Moreover, the traditionally-used methods for exploring the spatial accumulation characteristics (e.g., ordinary kriging) and the effects of other factors on Cd accumulations (e.g., MLR) are sensitive to outliers. In this study, robust geostatistics, enrichment index, and bioavailability index were first used to explore the spatial accumulation characteristics of Cd in wheat grain (wheat-Cd), Cd in rice grain (rice-Cd), and soil DTPA-extractable Cd (DTPA-Cd) in Jintan County, a typical rice-wheat rotation area in China. Then, robust geographically weighted regression (RGWR), established in geographic space rather than variable space, was used to explore the spatially varying relationships between Cd accumulations and the corresponding main influential factors determined by stepwise regression. Last, the modelling accuracy of RGWR was compared with those of basic GWR and MLR. Results showed that (i) outliers affected the spatial predictions of soil total Cd, soil DTPA-Cd, wheat-Cd, and rice-Cd and robust variograms should be used; (ii) the enrichment index of wheat grain was significantly higher than that of rice grain in almost the whole study area; (iii) the areas with the high bioavailability index of soil Cd mainly located in the southeast, southwest, and centre of the study area; (iv) RGWR acquired higher modelling accuracy than GWR and MLR; (v) the spatially varying relationships between Cd accumulations and the corresponding influential factors were revealed by RGWR, which cannot be determined by MLR. The methods suggested in this study provided more precise spatial decision support for soil management measures to guarantee main agricultural product safety in large-scale areas.

Enhancing apportionment of the point and diffuse sources of soil heavy metals using robust geostatistics and robust spatial receptor model with categorical soil-type data.

Soil-type data usually contain valuable information about soil heavy metal (HM) concentrations; however, they were rarely considered in the apportionment of point or diffuse sources in previous studies. In this study, the spatial variations of the soil HM concentrations in Jintan County, China were partitioned into two portions - the soil-type effects and the corresponding residuals, using analysis of variance (ANOVA). Standardized robust kriging error (SRKE) with soil-type data as auxiliary information (SRKE-ST) was proposed to identify the high-value spatial outliers of soil HMs, and the performance of SRKE-ST was compared with that of commonly-used SRKE. Robust absolute principal component scores/robust geographically weighted regression (RAPCS/RGWR) with soil-type data as auxiliary information (RAPCS/RGWR-ST) was proposed to apportion the diffuse sources of soil HMs, and the performance of RAPCS/RGWR-ST was compared with those of RAPCS/RGWR and commonly-used absolute principal component scores/multiple linear regression (APCS/MLR). Results showed that (i) RSKE-ST effectively excluded high-value spatial outliers resulting from the effects of complex soil-type polygons on soil HM concentrations; (ii) RAPCS/RGWR-ST generated higher estimation accuracy in source contributions and less negative contributions than RAPCS/RGWR and APCS/MLR did. It is concluded that the proposed RSKE-ST and RAPCS/RGWR-ST could effectively use categorical soil-type data to enhance, respectively, the identification of high-value spatial outliers (i.e., potential point sources) and the apportionment of diffuse sources of soil HMs in large-scale areas.

Using pXRF to assess the accumulation, sources, and potential ecological risk of potentially toxic elements in soil under two greenhouse vegetable production systems in North China.

Intensive greenhouse vegetable production (GVP) has increased the pollution risk of potentially toxic elements (PTEs) in soils. This study examined the accumulation, sources, and potential ecological risk of six PTEs (Cu, Zn, As, Ni, Pb, and Cr) in soil under two GVP (solar greenhouse (SG) and round-arched plastic greenhouse (RAPG)) systems by portable X-ray fluorescence spectroscopy (pXRF) and conventional laboratory analysis. The results indicated that all PTE concentrations were lower than their corresponding thresholds in GVP soils, presenting a low potential ecological risk in both GVP soils according to risk indices (RI ≤ 40.67). As, Ni, Pb, and Cr were not significantly accumulated in both GVP soils. Although Cu and Zn accumulated in both GVP soils, their accumulation extents in SG soil were both greater than that in RAPG soil. Cu and Zn were mainly originated from anthropogenic activities based on multivariate statistical analysis, which were greatly associated with excessive manure application. Overall, pXRF can identify the accumulation difference of PTEs between the two GVP soils, which is generally consistent with conventional laboratory analysis. Hence, pXRF can be a promising alternative to conventional laboratory analysis for rapid assessment of PTEs accumulation, sources, and the potential ecological risk in the two GVP soils. Although PTEs had a low ecological risk, Cu and Zn accumulation in SG soil was increased with the planting years. Therefore, rational application of livestock manure containing high levels of Cu and Zn should inspire strategies to mitigate the environmental risk in GVP soils, especially in SG soil.

Correction of in-situ portable X-ray fluorescence (PXRF) data of soil heavy metal for enhancing spatial prediction.

Heavy metal data measured by portable X-ray fluorescence (PXRF), especially by in-situ PXRF, are usually affected by multiple soil factors, such as soil moisture (SM), soil organic matter (SOM), and soil particle size (SPZ). Thus, a correction may be needed. However, traditionally-used correction methods, such as non-spatial linear regression (LR), cannot effectively correct the spatially non-stationary influences of the related soil factors on PXRF analysis. Moreover, these correction methods are not robust to outliers. In this study, robust geographically weighted regression (RGWR) was used to correct in-situ and ex-situ PXRF data of soil Pb in a peri-urban agricultural area of Wuhan City, China. The accuracy of the corrected PXRF data by RGWR was compared with those by non-spatial and spatial but non-robust methods (i.e., LR and GWR). In addition, to find an appropriate method of using the corrected PXRF data for more accurate spatial prediction, we compared robust ordinary kriging with the corrected PXRF data as part of hard data (ROK-CPXRF) and robust ordinary cokriging with the corrected PXRF data as auxiliary soft data (RCoK-CPXRF). Results showed that (i) RGWR obtained higher correction accuracy than LR and GWR on both the in-situ and ex-situ PXRF data; (ii) the accuracy of the RGWR-corrected in-situ PXRF data was increased nearly to that of the RGWR-corrected ex-situ PXRF data; (iii) given the same amount of sample data, ROK-CPXRF obtained higher prediction accuracy than RCoK-CPXRF. It is concluded that the methods suggested in this study may largely promote the application of in-situ PXRF technique for rapid and accurate soil heavy metal investigation in large-scale areas.

[Assessment of the Availability of Soil Copper and Related Influencing Factors at a County Scale].

Understanding the spatial distribution of total copper, available copper, and the spatial non-stationary relationships between available copper and relevant environmental factors is important for the delineation of soil risk areas and the development of related control measures. This study was conducted in Zhangjiagang County of Jiangsu Province, China. The risk status for soil copper was assessed based on 357 soil samples in the study area. The effects of soil type and land-use type on the concentration of available soil copper were discussed first. Then, ordinary kriging was adopted to map the spatial distribution patterns of the total soil copper and available soil copper, and the spatial distribution map of the copper availability ratio (i.e., available copper/total copper) was also developed for the study area. The risk areas for soil copper were delineated based on the spatial distribution patterns of available soil copper and the copper availability ratio. Finally, a new spatial local regression technique, geographic weighted regression (GWR), was used to explore the local spatial regression relationships between available copper and its three main impact factors (i.e., total soil copper, soil pH, and SOM). Results showed that both soil type and land-use type had some effect on the concentration of available soil copper. The copper availability ratio had a strong spatial heterogeneity, with the higher values mainly in the northeast, southeast, and northwest of the study area and the lower values mainly in the middle and southwest of the study area. The range of the copper availability ratio is 13.56% to 29.15%. The results of the comparison of the traditional ordinary least squares regression (OLSR) and GWR showed that the GWR model had higher fitting accuracy than the OLSR model[i.e., a larger decision coefficient R2, and smaller corrected Akaike information criteria (AICc) and the sum of squares of residuals] in modeling the relationships between available copper and its three main impact factors. The GWR analysis showed that the effect of soil factors on the concentration of soil available copper was non-stationary. The GWR could effectively reveal the spatial non-stationary influence of the related soil factors on the concentration of available soil copper, and the results could explain the reasons for the accumulation of available soil copper in local areas. Potential risk areas for available soil copper were delineated based on the copper availability ratio and the concentration of available soil copper in the study area. The results should be crucial data for developing specific control measures for soil copper at a regional scale.

Spatial uncertainty assessment of the environmental risk of soil copper using auxiliary portable X-ray fluorescence spectrometry data and soil pH.

Spatial uncertainty information of the environmental risk of soil heavy metal is crucial for precise environmental management. This study first compared three geostatistical methods for spatial simulation of soil Copper (Cu) in a peri-urban agriculture area of Wuhan city, China, that are sequential Gaussian co-simulation (CoSGS) with auxiliary in-situ portable X-ray fluorescence (PXRF) data (CoSGS_in-situ), CoSGS with auxiliary ex-situ PXRF data (CoSGS_ex-situ), and sequential Gaussian simulation without auxiliary data (SGS). Then, the environmental risk of soil Cu was assessed based on the joint thresholds of soil Cu and soil pH in the Chinese soil environmental quality standards II. The geostatistical simulated realizations of soil Cu and soil pH were used to calculate the probabilities of exceeding the joint thresholds. Validation showed that CoSGS_ex-situ is slightly better than CoSGS_in-situ in the performance of both E-type estimates (i.e., mathematical expectation estimates) and uncertainty modelling of soil Cu, and SGS is the worst. The spatial uncertainty information of both soil Cu and soil pH was transferred to the environmental risk map through the corresponding geostatistical simulated realizations. The areas with higher probabilities of exceeding the joint thresholds mainly located in the northwest and southwest of the study area. It is concluded that CoSGS_ex-situ and CoSGS_in-situ were more cost-effective than the traditional SGS in the spatial simulation of soil Cu, and the simulated realizations of soil Cu and soil pH provide a solution to the spatial assessment of the probabilities of exceeding the joint thresholds.

Source apportionment of soil heavy metals using robust absolute principal component scores-robust geographically weighted regression (RAPCS-RGWR) receptor model.

The traditional source apportionment models, such as absolute principal component scores-multiple linear regression (APCS-MLR), are usually susceptible to outliers, which may be widely present in the regional geochemical dataset. Furthermore, the models are merely built on variable space instead of geographical space and thus cannot effectively capture the local spatial characteristics of each source contributions. To overcome the limitations, a new receptor model, robust absolute principal component scores-robust geographically weighted regression (RAPCS-RGWR), was proposed based on the traditional APCS-MLR model. Then, the new method was applied to the source apportionment of soil metal elements in a region of Wuhan City, China as a case study. Evaluations revealed that: (i) RAPCS-RGWR model had better performance than APCS-MLR model in the identification of the major sources of soil metal elements, and (ii) source contributions estimated by RAPCS-RGWR model were more close to the true soil metal concentrations than that estimated by APCS-MLR model. It is shown that the proposed RAPCS-RGWR model is a more effective source apportionment method than APCS-MLR (i.e., non-robust and global model) in dealing with the regional geochemical dataset.