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.

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.

Economics- and policy-driven organic carbon input enhancement dominates soil organic carbon accumulation in Chinese croplands.

China's croplands have experienced drastic changes in management practices, such as fertilization, tillage, and residue treatments, since the 1980s. There is an ongoing debate about the impact of these changes on soil organic carbon (SOC) and its implications. Here we report results from an extensive study that provided direct evidence of cropland SOC sequestration in China. Based on the soil sampling locations recorded by the Second National Soil Survey of China in 1980, we collected 4,060 soil samples in 2011 from 58 counties that represent the typical cropping systems across China. Our results showed that across the country, the average SOC stock in the topsoil (0-20 cm) increased from 28.6 Mg C ha-1 in 1980 to 32.9 Mg C ha-1 in 2011, representing a net increase of 140 kg C ha-1 year-1 However, the SOC change differed among the major agricultural regions: SOC increased in all major agronomic regions except in Northeast China. The SOC sequestration was largely attributed to increased organic inputs driven by economics and policy: while higher root biomass resulting from enhanced crop productivity by chemical fertilizers predominated before 2000, higher residue inputs following the large-scale implementation of crop straw/stover return policy took over thereafter. The SOC change was negatively related to N inputs in East China, suggesting that the excessive N inputs, plus the shallowness of plow layers, may constrain the future C sequestration in Chinese croplands. Our results indicate that cropland SOC sequestration can be achieved through effectively manipulating economic and policy incentives to farmers.

Carbon pools in China's terrestrial ecosystems: New estimates based on an intensive field survey.

China's terrestrial ecosystems have functioned as important carbon sinks. However, previous estimates of carbon budgets have included large uncertainties owing to the limitations of sample size, multiple data sources, and inconsistent methodologies. In this study, we conducted an intensive field campaign involving 14,371 field plots to investigate all sectors of carbon stocks in China's forests, shrublands, grasslands, and croplands to better estimate the regional and national carbon pools and to explore the biogeographical patterns and potential drivers of these pools. The total carbon pool in these four ecosystems was 79.24 ± 2.42 Pg C, of which 82.9% was stored in soil (to a depth of 1 m), 16.5% in biomass, and 0.60% in litter. Forests, shrublands, grasslands, and croplands contained 30.83 ± 1.57 Pg C, 6.69 ± 0.32 Pg C, 25.40 ± 1.49 Pg C, and 16.32 ± 0.41 Pg C, respectively. When all terrestrial ecosystems are taken into account, the country's total carbon pool is 89.27 ± 1.05 Pg C. The carbon density of the forests, shrublands, and grasslands exhibited a strong correlation with climate: it decreased with increasing temperature but increased with increasing precipitation. Our analysis also suggests a significant sequestration potential of 1.9-3.4 Pg C in forest biomass in the next 10-20 years assuming no removals, mainly because of forest growth. Our results update the estimates of carbon pools in China's terrestrial ecosystems based on direct field measurements, and these estimates are essential to the validation and parameterization of carbon models in China and globally.

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.

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.

Sources of heavy metal pollution in agricultural soils of a rapidly industrializing area in the Yangtze Delta of China.

The rapid industrialization and urbanization in developing countries have increased pollution by heavy metals, which is a concern for human health and the environment. In this study, 230 surface soil samples (0-20cm) were collected from agricultural areas of Jiaxing, a rapidly industrializing area in the Yangtze Delta of China. Sequential Gaussian simulation (SGS) and multivariate factorial kriging analysis (FKA) were used to identify and explore the sources of heavy metal pollution for eight metals (Cu, Zn, Pb, Cr, Ni, Cd, Hg and As). Localized hot-spots of pollution were identified for Cu, Zn, Pb, Cr, Ni and Cd with area percentages of 0.48 percent, 0.58 percent, 2.84 percent, 2.41 percent, 0.74 percent, and 0.68 percent, respectively. The areas with Hg pollution covered approximately 38 percent whereas no potential pollution risk was found for As. The soil parent material and point sources of pollution had significant influences on Cr, Ni, Cu, Zn and Cd levels, except for the influence of agricultural management practices also accounted for micro-scale variations (nugget effect) for Cu and Zn pollution. Short-range (4km) diffusion processes had a significant influence on Cu levels, although they did not appear to be the dominant sources of Zn and Cd variation. The short-range diffusion pollution arising from current and historic industrial emissions and urbanization, and long-range (33km) variations in soil parent materials and/or diffusion jointly determined the current concentrations of soil Pb. The sources of Hg pollution risk may be attributed to the atmosphere deposition of industrial emission and historical use of Hg-containing pesticides.

Impacts of human activities and sampling strategies on soil heavy metal distribution in a rapidly developing region of China.

The impacts of industrial and agricultural activities on soil Cd, Hg, Pb, and Cu in Zhangjiagang City, a rapidly developing region in China, were evaluated using two sampling strategies. The soil Cu, Cd, and Pb concentrations near industrial locations were greater than those measured away from industrial locations. The converse was true for Hg. The top enrichment factor (TEF) values, calculated as the ratio of metal concentrations between the topsoil and subsoil, were greater near industrial location than away from industrial locations and were further related to the industry type. Thus, the TEF is an effective index to distinguish sources of toxic elements not only between anthropogenic and geogenic but also among different industry types. Target soil sampling near industrial locations resulted in a greater estimation in high levels of soil heavy metals. This study revealed that the soil heavy metal contamination was primarily limited to local areas near industrial locations, despite rapid development over the last 20 years. The prevention and remediation of the soil heavy metal pollution should focus on these high-risk areas in the future.

Estimating the pollution risk of cadmium in soil using a composite soil environmental quality standard.

Estimating standard-exceeding probabilities of toxic metals in soil is crucial for environmental evaluation. Because soil pH and land use types have strong effects on the bioavailability of trace metals in soil, they were taken into account by some environmental protection agencies in making composite soil environmental quality standards (SEQSs) that contain multiple metal thresholds under different pH and land use conditions. This study proposed a method for estimating the standard-exceeding probability map of soil cadmium using a composite SEQS. The spatial variability and uncertainty of soil pH and site-specific land use type were incorporated through simulated realizations by sequential Gaussian simulation. A case study was conducted using a sample data set from a 150 km(2) area in Wuhan City and the composite SEQS for cadmium, recently set by the State Environmental Protection Administration of China. The method may be useful for evaluating the pollution risks of trace metals in soil with composite SEQSs.

Distribution, sources and potential risk of HCH and DDT in soils from a typical alluvial plain of the Yangtze River Delta region, China.

Spatial distribution, sources and potential health risks of organochlorine pesticides (OCPs), including hexachlorocyclohexane (HCH) and dichlorodiphenyltrichloroethane (DDT), in surface soils (n = 544) collected from a typical alluvial plain of the Yangtze River Delta region, China, were elucidated. Concentrations of ΣHCH and ΣDDT in soils ranged from less than the limit of detection (

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.

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.

Accumulation and health risk of heavy metals in a plot-scale vegetable production system in a peri-urban vegetable farm near Nanjing, China.

Accumulation and potential health risk of cadmium (Cd), lead (Pb), copper (Cu), and zinc (Zn) in a plot-scale vegetable production peri-urban area near Nanjing city, China was investigated through element balance method, model simulation and dietary risk assessment. The heavy metals accumulated in the surface soils were due to long-term and heavy application of organic fertilizers, among which the accumulation of Cu and Zn were greater than those of Cd and Pb. The result of a mass balance model simulation indicated that intensive vegetable production would result in accumulation of Cd, Pb, Cu and Zn in soils exceeding the target values in 55, 36, 34 and 71 years, respectively. The estimated dietary intakes of Cd, Pb, Cu, and Zn were far below the tolerable limits and the hazard quotient values were below one for both children and adults. Although there is no imminent health risk from heavy metals through vegetable consumption, more attention should be paid to the long-term accumulation and risk, especially for children.

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 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.

[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.

[Quantitative prediction of soil salinity content with visible-near infrared hyper-spectra in northeast China].

Studying the spectral property of salinized soil is an important work, for it is the base of monitoring soil salinization by remote sense. To investigate the spectral property of salinized soil and the relationship between the soil salinity and the hyperspectral data, the field soil samples were collected in the region of Northeast China and then reflectance spectra were measured. The partial least squares regression (PLSR) model was established based on the statistical analysis of the soil salinity content and the reflectance of hyperspectra. The feasibility of soil salinity prediction by hyperspectra was decided by analyzed calibration model and independent validation. Models accuracy was also analyzed, which was established in the conditions of different treatment methods and different re-sampling intervals. The results showed that it was feasible to predict soil salinity content based on measured reflectance spectrum. The results also revealed that it was necessary to smooth measured hyperspectra for spectral prediction accuracy to be improved significantly after smoothing. The best model was established based on smoothed and log(l/x) transformed hyperspectra with high determination coefficients (R2) of 0.6677 and RPD = 1.61, which showed that this math transformation could eliminate noise effectively and so as to improve the prediction accuracy. The largest re-sampling interval is 8 nm that could meet the accuracy of the soil salinity prediction. Therefore, it provided scientific reference of monitoring soil salinization by remote sensing from satellite platform.

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.