Mingkai Qu

Assessing the spatial uncertainty in soil nitrogen mapping through stochastic simulations with categorical land use information

Abstract This study explores the capability of an extended sequential Gaussian simulation algorithm with incorporation of categorical land use information (SGS-CI) for simulating spatial variability of soil total nitrogen (TN) contents and assessing associated spatial uncertainty. 402 sampled data in soil TN contents in a county scale region and the categorical land use map data of the study area were used to perform sequential simulations for comparing the SGS-CI algorithm and the conventional SGS algorithm, and 135 validation samples were used to assess the improvement of SGS-CI over SGS in prediction accuracy and uncertainty reduction. Results showed that the validation data were more strongly correlated with the optimal prediction (i.e., E-type estimates) data of SGS-CI than with those of SGS, and the mean error and the root mean square error of the optimal prediction using SGS-CI were smaller than those using SGS. SGS-CI also performed slightly better than SGS in uncertainty modeling in terms of accuracy plots and goodness statistic G. In addition, because demands for soil total nitrogen by different crops are usually different in agricultural practice, we showed that SGS-CI could be used to assess spatial uncertainty of deficiency or abundance degrees of soil TN based on demands of different crops in different land use types. Therefore, SGS-CI may provide an effective method for improving prediction accuracy and reducing uncertainty in soil TN prediction.

Effect of Land Use Types on the Spatial Prediction of Soil Nitrogen

Mapping the spatial distribution of soil nutrient contents from sample data has received much attention in the recent decade. Accurately mapping soil nutrients purely based on sample data, however, is difficult due to the sparsity and high cost of samples. Land use types usually influence the contents of soil nutrients at the local level and it is desirable to integrate such information into predictive mapping. The area-and-point kriging (AAPK) method, which was proposed recently, may provide an interpolation technique for such purposes. This study mapped the soil total nitrogen (TN) distribution of Hanchuan County, China, using AAPK with sample data (consisting of 402 points) and land use information. Ordinary kriging (OK) and residual kriging (RK) were compared to evaluate the performance of AAPK. Results showed that: (1) land use types had important impacts on the spatial distribution of soil TN; (2) measured data at 135 validation locations had stronger correlation with the data predicted by AAPK than by RK and OK, and the mean error and root mean square error with AAPK were lower than with RK and OK; and (3) AAPK generated smaller error variances than RK and OK did. This suggests that AAPK represents an effective method for increasing the interpolation accuracy of soil TN. It should be pointed out that some of the land use polygons used in this study are very large and complex, which might impact the effectiveness of AAPK in improving the prediction accuracy. Segmenting them into simple smaller areas might be helpful.

Accumulation, sources and health risks of trace metals in elevated geochemical background soils used for greenhouse vegetable production in southwestern China

Greenhouse vegetable cultivation with substantive manure and fertilizer input on soils with an elevated geochemical background can accumulate trace metals in soils and plants leading to human health risks. Studies on trace metal accumulation over a land use shift duration in an elevated geochemical background scenario are lacking. Accumulation characteristics of seven trace metals in greenhouse soil and edible plants were evaluated along with an assessment of the health risk to the consumers. A total of 118 greenhouse surface soils (0-20cm) and 30 vegetables were collected from Kunming City, Yunnan Province, southwestern China, and analyzed for total Cd, Pb, Cu, Zn, As, Hg, and Cr content by ICP-MS and AFS. The trace metals were ordered Cu>Cd>Hg>Zn>Pb>As>Cr in greenhouse soils accumulation level, and the geo-accumulation index suggested the soil more severely polluted with Cd, Cu, Hg and Zn. The greenhouse and open-field soils had significant difference in Cd, Cr and Zn. The duration of shift from paddy to greenhouse land-use significantly influenced trace metal accumulation with a dramatic change during five to ten year greenhouse land-use, and continuous increase of Cd and Hg. A spatial pattern from north to south for Cd and Hg and a zonal pattern for Cu and Zn were found. An anthropogenic source primarily caused trace metal accumulation, where the principal component analysis/multiple linear regression indicated a contribution 61.2%. While the assessment showed no potential risk for children and adults, the hazard health risks index was greater than one for adolescents. The extended duration of land use as greenhouses caused the trace metal accumulation, rotation in land use should be promoted to reduce the health risks.

Spatial Distribution and Uncertainty Assessment of Potential Ecological Risks of Heavy Metals in Soil Using Sequential Gaussian Simulation

ABSTRACT The objective of this study is to assess the spatial distribution and uncertainty of the potential ecological risks of heavy metals in soil using sequential Gaussian simulation (SGS) and the Hakanson potential ecological risk index (PERI). We collected 130 soil samples in an area of 150 km2 in the High-Tech Park of Wuhan, China, and measured the concentrations of five heavy metals in soil (i.e., Cd, Cr, Cu, Pb, and Zn). We then simulated the spatial distribution of each heavy metal using SGS, and calculated Hakanson PERIs for individual metals and multiple metals based on the simulated realizations. The spatial uncertainty of the Cd PERI and its occurrence probabilities in different risk grades were further assessed. Results show that the potential ecological risks of Cr, Cu, Pb, and Zn are relatively low in the study area, but Cd indeed reaches a serious level that deserves much attention and essential treatment. The total PERI of multiple heavy metals indicates a moderate grade in most of the study area. In general, combining SGS and the Hakanson PERI appears to be an effective method for evaluating the potential ecological risks of heavy metals in soil and the priority areas for remediation.

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.

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 km2 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.

County-Scale Spatial Variability of Macronutrient Availability Ratios in Paddy Soils

Macronutrients (N, P, and K) are essential to plants but also can be harmful to the environment when their available concentrations in soil are excessive. Availability ratios (available concentration/total concentration) of macronutrients may reflect their transforming potential between fixed and available forms in soil. Understanding their spatial distributions and impact factors can be, therefore, helpful to applying specific measures to modify the availability of macronutrients for agricultural and environmental management purposes. In this study, 636 topsoil samples (0–15 cm) were collected from paddy fields in Shayang County, Central China, for measuring soil properties. Factors influencing macronutrient availability ratios were investigated, and total and available concentrations of macronutrients were mapped using geostatistical method. Spatial distribution maps of macronutrient availability ratios were further derived. Results show that (1) availability of macronutrients is controlled by multiple factors, and (2) macronutrient availability ratios are spatially varied and may not always have spatial patterns identical to those of their corresponding total and available concentrations. These results are more useful than traditional soil macronutrient average content data for guiding site-specific field management for agricultural production and environmental protection.

Assessing the local uncertainty of precipitation by using moving window geostatistical models

Abstract Precipitation is a very important input variable for numerous models in many scientific fields such as hydrology, agriculture, ecology, and environmental sciences. However, precipitation often exhibits considerable spatial variability and cannot be adequately modeled by commonly used geostatistical techniques, particularly in terms of the prediction uncertainty accuracy, which is of great significance to determine the effects on various models’ prediction uncertainty. In this paper, a moving-window copula-based geostatistical method was proposed to assess the local spatial uncertainty of precipitation. By incorporating non-stationary, non-Gaussian, and distance-weighted spatial statistics, many geostatistical techniques can be regarded as a special case of the proposed method. Especially, in this paper, the marginal distribution in each window was fitted using Legendre polynomials. Although the proposed method has the potential to improve both prediction accuracy and the prediction uncertainty accuracy, the case study showed that the prediction accuracy of the proposed method was not better than commonly used geostatistical techniques (i.e., ordinary kriging, moving window kriging, and the global copula-based method). However, the prediction uncertainty accuracy was the best of all. The moving-window copula-based geostatistical method allows the estimation of the full conditional distribution of the precipitation at any ungauged site. With the full conditional distributions, various analyses and decisions can be conducted and made. Moreover, in the sense of statistics, more accurate results could be expected.

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.

Assessing the pollution risk of soil Chromium based on loading capacity of paddy soil at a regional scale

The accumulation of a trace metal in rice grain is not only affected by the total concentration of the soil trace metal, but also by crop variety and related soil properties, such as soil pH, soil organic matter (SOM) and so on. However, these factors were seldom considered in previous studies on mapping the pollution risk of trace metals in paddy soil at a regional scale. In this study, the spatial nonstationary relationships between rice-Cr and a set of perceived soil properties (soil-Cr, soil pH and SOM) were explored using geographically weighted regression; and the relationships were then used for calculating the critical threshold (CT) of soil-Cr concentration that may ensure the concentration of rice-Cr being below the permissible limit. The concept of “loading capacity” (LC) for Cr in paddy soil was then defined as the difference between the CT and the real concentration of Cr in paddy soil, so as to map the pollution risk of soil-Cr to rice grain and assess the risk areas in Jiaxing city, China. Compared with the information of the concentration of the total soil-Cr, such results are more valuable for spatial decision making in reducing the accumulation of rice-Cr at a regional scale.