Soil mineral nitrogen regulation by a novel porous material in structurally degraded soils.

BACKGROUND: The availability of soil nitrogen (N) decreases as the structure of agricultural soils degrades. Traditional methods focus on organic amendments that indirectly affect the porosity and N content of soil. Due to the low efficiency of such amendments, new materials, particularly highly porous materials, are needed to improve the quality of soil, which has opened new directions. RESULTS: The addition of 2 to 7 mm of porous clay ceramic (PLC) significantly increased the fresh weight of Brassica chinensis. The soil aeration porosity (>50 µm) increased by 0.69% on average in response to 1% PLC application. Soil NO3 - -N, NH4 + -N and mineral N increased by 3.3, 1.3 and 4.6 mg kg-1 on average, respectively, following a 1% PLC application rate. The initial N content of the high PLC treatments was the lowest in the incubation experiment. The parameters of soil N mineralization, i.e. potentially mineralizable N (N0 ), the first-order rate constant (k) and the mineralization composite index (N0  × k), increased obviously as the amount of PLC increased. Porosities larger than 1000 µm were significantly more positively correlated with the parameters of soil N mineralization than those <500 µm. The Pearson correlation coefficients suggested that high porosity, mineral N and N0 values had significant positive relationships with the fresh weights in double seasons. CONCLUSION: The application of PLC increased soil aeration and enhanced the availability of soil N, which yielded large vegetable harvests in clayey soils in the short term. © 2022 Society of Chemical Industry.

Integrating hyperspectral imaging with machine learning techniques for the high-resolution mapping of soil nitrogen fractions in soil profiles.

Soil nitrogen (N) plays a central role in soil quality and biogeochemical cycles. However, little is known about the distribution and spatial variability of the different fractions of soil N within entire soil profiles. This study aimed to investigate the potential of laboratory-based hyperspectral imaging (HSI) spectroscopy to retrieve and map total N (TN), available N (AvailN), ammonium N (NH4-N), nitrate N (NO3-N), and microbial biomass N (MBN) in soil profiles at a high resolution. HSI images of eleven intact soil profiles of 100 ± 5 cm depth from three typical soil types were recorded. A variety of nonlinear machine learning techniques, such as artificial neural networks (ANN), cubist regression tree (Cubist), k-nearest neighbour (KNN), support vector machine regression (SVMR) and extreme gradient boosting (XGBoost), were compared with a partial least squares regression (PLSR) to determine the most suitable model for the prediction of the various soil N fractions. Overall, the results showed that nonlinear techniques performed better than PLSR in most cases, with a high coefficient of determination (R2) and low root mean square error (RMSE). Among the models, SVMR was found to be superior to the other tested models for TN (R2P = 0.94, RMSEP = 0.17 g kg-1), AvailN (R2P = 0.94, RMSEP = 13.35 mg kg-1), NO3-N (R2P = 0.82, RMSEP = 7.31 mg kg-1), and NH4-N (R2P = 0.70, RMSEP = 1.51 mg kg-1) based on independent validation, whereas MBN (R2P = 0.63, RMSEP = 6.62 mg kg-1) was predicted best by KNN. In addition, SVMR required less computational time and was less sensitive to spectral noise. It can therefore be concluded that HSI spectroscopy combined with SVMR is suitable for the high-resolution mapping of various soil N fractions in soil profiles.

Comparison of catchment scale 3D and 2.5D modelling of soil organic carbon stocks in Jiangxi Province, PR China.

As limited resources, soils are the largest terrestrial sinks of organic carbon. In this respect, 3D modelling of soil organic carbon (SOC) offers substantial improvements in the understanding and assessment of the spatial distribution of SOC stocks. Previous three-dimensional SOC modelling approaches usually averaged each depth increment for multi-layer two-dimensional predictions. Therefore, these models are limited in their vertical resolution and thus in the interpretability of the soil as a volume as well as in the accuracy of the SOC stock predictions. So far, only few approaches used spatially modelled depth functions for SOC predictions. This study implemented and evaluated an approach that compared polynomial, logarithmic and exponential depth functions using non-linear machine learning techniques, i.e. multivariate adaptive regression splines, random forests and support vector machines to quantify SOC stocks spatially and depth-related in the context of biodiversity and ecosystem functioning research. The legacy datasets used for modelling include profile data for SOC and bulk density (BD), sampled at five depth increments (0-5, 5-10, 10-20, 20-30, 30-50 cm). The samples were taken in an experimental forest in the Chinese subtropics as part of the biodiversity and ecosystem functioning (BEF) China experiment. Here we compared the depth functions by means of the results of the different machine learning approaches obtained based on multi-layer 2D models as well as 3D models. The main findings were (i) that 3rd degree polynomials provided the best results for SOC and BD (R2 = 0.99 and R2 = 0.98; RMSE = 0.36% and 0.07 g cm-3). However, they did not adequately describe the general asymptotic trend of SOC and BD. In this respect the exponential (SOC: R2 = 0.94; RMSE = 0.56%) and logarithmic (BD: R2 = 84; RMSE = 0.21 g cm-3) functions provided more reliable estimates. (ii) random forests with the exponential function for SOC correlated better with the corresponding 2.5D predictions (R2: 0.96 to 0.75), compared to the 3rd degree polynomials (R2: 0.89 to 0.15) which support vector machines fitted best. We recommend not to use polynomial functions with sparsely sampled profiles, as they have many turning points and tend to overfit the data on a given profile. This may limit the spatial prediction capacities. Instead, less adaptive functions with a higher degree of generalisation such as exponential and logarithmic functions should be used to spatially map sparse vertical soil profile datasets. We conclude that spatial prediction of SOC using exponential depth functions, in conjunction with random forests is well suited for 3D SOC stock modelling, and provides much finer vertical resolutions compared to 2.5D approaches.

Spatio-Temporal Change and Pollution Risk of Agricultural Soil Cadmium in a Rapidly Industrializing Area in the Yangtze Delta Region of China.

The impacts of rapid industrialization on agricultural soil cadmium (Cd) accumulation and their potential risks have drawn major attention from the scientific community and decision-makers, due to the high toxicity of Cd to animals and humans. A total of 812 topsoil samples (0⁻20 cm) was collected from the southern parts of Jiangsu Province, China, in 2000 and 2015, respectively. Geostatistical ordinary kriging and conditional sequential Gaussian simulation were used to quantify the changes in spatial distributions and the potential risk of Cd pollution between the two sampling dates. Results showed that across the study area, the mean Cd concentrations increased from 0.110 mg/kg in 2000 to 0.196 mg/kg in 2015, representing an annual average increase of 5.73 µg/kg. Given a confidence level of 95%, areas with significantly-increased Cd covered approximately 12% of the study area. Areas with a potential risk of Cd pollution in 2000 only covered 0.009% of the study area, while this figure increased to 0.75% in 2015. In addition, the locally concentrating trend of soil Cd pollution risk was evident after 15 years. Although multiple factors contributed to this elevated Cd pollution risk, the primary reason can be attributed to the enhanced atmospheric deposition and industrial waste discharge resulting from rapid industrialization, and the quick increase of traffic and transportation associated with rapid urbanization.

Impacts of species richness on productivity in a large-scale subtropical forest experiment.

Biodiversity experiments have shown that species loss reduces ecosystem functioning in grassland. To test whether this result can be extrapolated to forests, the main contributors to terrestrial primary productivity, requires large-scale experiments. We manipulated tree species richness by planting more than 150,000 trees in plots with 1 to 16 species. Simulating multiple extinction scenarios, we found that richness strongly increased stand-level productivity. After 8 years, 16-species mixtures had accumulated over twice the amount of carbon found in average monocultures and similar amounts as those of two commercial monocultures. Species richness effects were strongly associated with functional and phylogenetic diversity. A shrub addition treatment reduced tree productivity, but this reduction was smaller at high shrub species richness. Our results encourage multispecies afforestation strategies to restore biodiversity and mitigate climate change.

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.

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.

Effects of Subsetting by Parent Materials on Prediction of Soil Organic Matter Content in a Hilly Area Using Vis-NIR Spectroscopy.

Assessment and monitoring of soil organic matter (SOM) quality are important for understanding SOM dynamics and developing management practices that will enhance and maintain the productivity of agricultural soils. Visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy (350-2500 nm) has received increasing attention over the recent decades as a promising technique for SOM analysis. While heterogeneity of sample sets is one critical factor that complicates the prediction of soil properties from Vis-NIR spectra, a spectral library representing the local soil diversity needs to be constructed. The study area, covering a surface of 927 km2 and located in Yujiang County of Jiangsu Province, is characterized by a hilly area with different soil parent materials (e.g., red sandstone, shale, Quaternary red clay, and river alluvium). In total, 232 topsoil (0-20 cm) samples were collected for SOM analysis and scanned with a Vis-NIR spectrometer in the laboratory. Reflectance data were related to surface SOM content by means of a partial least square regression (PLSR) method and several data pre-processing techniques, such as first and second derivatives with a smoothing filter. The performance of the PLSR model was tested under different combinations of calibration/validation sets (global and local calibrations stratified according to parent materials). The results showed that the models based on the global calibrations can only make approximate predictions for SOM content (RMSE (root mean squared error) = 4.23-4.69 g kg-1; R2 (coefficient of determination) = 0.80-0.84; RPD (ratio of standard deviation to RMSE) = 2.19-2.44; RPIQ (ratio of performance to inter-quartile distance) = 2.88-3.08). Under the local calibrations, the individual PLSR models for each parent material improved SOM predictions (RMSE = 2.55-3.49 g kg-1; R2 = 0.87-0.93; RPD = 2.67-3.12; RPIQ = 3.15-4.02). Among the four different parent materials, the largest R2 and the smallest RMSE were observed for the shale soils, which had the lowest coefficient of variation (CV) values for clay (18.95%), free iron oxides (15.93%), and pH (1.04%). This demonstrates the importance of a practical subsetting strategy for the continued improvement of SOM prediction with Vis-NIR spectroscopy.

Effects of soil data and simulation unit resolution on quantifying changes of soil organic carbon at regional scale with a biogeochemical process model.

Soil organic carbon (SOC) models were often applied to regions with high heterogeneity, but limited spatially differentiated soil information and simulation unit resolution. This study, carried out in the Tai-Lake region of China, defined the uncertainty derived from application of the DeNitrification-DeComposition (DNDC) biogeochemical model in an area with heterogeneous soil properties and different simulation units. Three different resolution soil attribute databases, a polygonal capture of mapping units at 1:50,000 (P5), a county-based database of 1:50,000 (C5) and county-based database of 1:14,000,000 (C14), were used as inputs for regional DNDC simulation. The P5 and C5 databases were combined with the 1:50,000 digital soil map, which is the most detailed soil database for the Tai-Lake region. The C14 database was combined with 1:14,000,000 digital soil map, which is a coarse database and is often used for modeling at a national or regional scale in China. The soil polygons of P5 database and county boundaries of C5 and C14 databases were used as basic simulation units. Results project that from 1982 to 2000, total SOC change in the top layer (0-30 cm) of the 2.3 M ha of paddy soil in the Tai-Lake region was +1.48 Tg C, -3.99 Tg C and -15.38 Tg C based on P5, C5 and C14 databases, respectively. With the total SOC change as modeled with P5 inputs as the baseline, which is the advantages of using detailed, polygon-based soil dataset, the relative deviation of C5 and C14 were 368% and 1126%, respectively. The comparison illustrates that DNDC simulation is strongly influenced by choice of fundamental geographic resolution as well as input soil attribute detail. The results also indicate that improving the framework of DNDC is essential in creating accurate models of the soil carbon cycle.

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.

Approximation and spatial regionalization of rainfall erosivity based on sparse data in a mountainous catchment of the Yangtze River in Central China.

In densely populated countries like China, clean water is one of the most challenging issues of prospective politics and environmental planning. Water pollution and eutrophication by excessive input of nitrogen and phosphorous from nonpoint sources is mostly linked to soil erosion from agricultural land. In order to prevent such water pollution by diffuse matter fluxes, knowledge about the extent of soil loss and the spatial distribution of hot spots of soil erosion is essential. In remote areas such as the mountainous regions of the upper and middle reaches of the Yangtze River, rainfall data are scarce. Since rainfall erosivity is one of the key factors in soil erosion modeling, e.g., expressed as R factor in the Revised Universal Soil Loss Equation model, a methodology is needed to spatially determine rainfall erosivity. Our study aims at the approximation and spatial regionalization of rainfall erosivity from sparse data in the large (3,200 km(2)) and strongly mountainous catchment of the Xiangxi River, a first order tributary to the Yangtze River close to the Three Gorges Dam. As data on rainfall were only obtainable in daily records for one climate station in the central part of the catchment and five stations in its surrounding area, we approximated rainfall erosivity as R factors using regression analysis combined with elevation bands derived from a digital elevation model. The mean annual R factor (R a) amounts for approximately 5,222 MJ mm ha(-1) h(-1) a(-1). With increasing altitudes, R a rises up to maximum 7,547 MJ mm ha(-1) h(-1) a(-1) at an altitude of 3,078 m a.s.l. At the outlet of the Xiangxi catchment erosivity is at minimum with approximate R a=1,986 MJ mm ha(-1) h(-1) a(-1). The comparison of our results with R factors from high-resolution measurements at comparable study sites close to the Xiangxi catchment shows good consistance and allows us to calculate grid-based R a as input for a spatially high-resolution and area-specific assessment of soil erosion risk.

[Study on canopy spectral characteristics of paddy polluted by heavy metals].

Because of frequent mining, heavy metals are brought into environment like soils, water and atmosphere, resulting heavy metal contamination in the agricultural region beside mines. Heavy metals contamination causes vegetation stress like destruction of chloroplast structure, chlorophyll content decrease, blunt photosynthesis, etc. Spectral responses to changes in chlorophyll content and photosynthesis make it possible that remote sensing is applied in monitoring heavy metals stress on paddy plants. Field spectroradiometer was used to acquire canopy reflectance spectra of paddy plants contaminated by heavy metals released from local mining. The present study was conducted to (1) investigate discrimination of canopy reflectance spectra of heavy metal polluted and normal paddy plants; (2) extract spectral characteristics of contaminated paddy plants and compare them. By means of correlation analysis, sensitive bands (SB) were firstly picked out from canopy spectra. Secondly, on the basis of these sensitive bands, normalized difference vegetation indices (NDVI) were established, and then red edge position (REP) was extracted from canopy spectra via curve fitting of inverted Gaussian model. As a result of correlation analysis, 460, 560, 660 and 1 100 nm were considered respectively as sensitive band for Pb, Zn, Cu and As concentration in paddy leaves. Furthermore, heavy metal concentrations (Pb, Zn, Cu and As) were significantly correlated with NDVIs (Pb, NDV(510, 810); Zn, NDVI(510, 870; Cu, NDVI(660, 870); As, NDVI(510, 810)). Heavy metals were also significantly correlated with REP, however, the inflexion termed as spectral critical value (SCV) between low and high heavy metals concentrations should be considered during applying REP in remote sensing monitoring. Moreover, NDVI and REP are much better than SB in terms of capability of expressing spectral information. Therefore, heavy metals contamination in paddy plants can be remotely monitored via ground spectroradiometer when NDVI and REP are selected as spectral characteristics.

[Dynamics of recent cultivated land in Zhejiang Province and relevant driving factors].

Through the human-computer interactive interpretation of the 2000, 2005, and 2008 remote sensing images of Zhejiang Province with the help of RS and GIS techniques, the dynamic database of cultivated land change in the province in, 2000-2008 was established, and the driving factors of the cultivated land change were analyzed by ridge regression analysis. There was a notable cultivated land change in the province in 2000-2008. In 2000-2005 and 2005-2008, the annual cultivated land change in the province arrived -1.42% and -1.46%, respectively, and most of the cultivated land was changed into residential and industrial land. Non-agricultural population rate, real estate investment, urban green area, and orchard area were thought to be the main driving factors of the cultivated land change in Zhejiang Province, and even, in the developed areas of east China.

[Simulation of methane emissions from rice fields in the Taihu Lake region, China by using different unit of soil database with the DNDC model].

Application of a biogeochemical model, DeNitrification and DeComposition or DNDC, was discussed to assess the impact of CH4 emissions on different soil database from rice fields in Taihu Lake region of China. The results showed that CH4 emissions of the polygon-based soil database of 1:50000, which contained 52034 polygons of paddy soils representing 1107 paddy soil profiles extracted from the latest national soil map (1:50000), were located within the ranges produced by the county-based soil database of 1:50000. However, total emissions of the whole area differed by about 1680 Gg CH4-C. Moreover, CH4 emissions of the polygon-based soil database of 1:50000 and the county-based soil database of 14,000,000, which was the most popular data source when DNDC model was applied in China, have a big estimation discrepancy among each county-based unit in spite of total emissions of the whole area by a difference of 180 Gg CH4-C. This indicated that the more precise soil database was necessary to better simulate CH4 emissions from rice fields in Taihu Lake region using the DNDC model.

[Estimates and affecting factors of soil organic carbon storages in Yunnan-Guizhou-Guangxi Region of China].

Soil organic carbon (SOC) plays a key role in the terrestrial eco-systems. However, there is a large variation in SOC estimates at regional and global scales. In order to improve the accuracy of SOC estimates, the SOC storage in Yunnan-Guizhou-Guangxi Region of China (include Yunnan Province, Guizhou Province and Guangxi Zhuang Municipality) was estimated using 798 soil profiles and 1:500 000 digitized soil map, and the dominant affecting factors on SOC density were also discussed employing stepwise regression and path analysis. Results showed that the SOC storages estimated in the 0-20 cm and 0-100 cm layers are 4.39 Pg and 10.91 Pg, respectively; and the corresponding SOC density are 56.2 Mg x hm(-2) and 139.8 Mg x hm(-2), respectively. The mean SOC density of Yunnan-Guizhou-Guangxi Region is higher than that of China. The environmental factors (including altitude, longitude, latitude, annual mean precipitation and annual mean temperature), soil parent materials and land use could explain 37.9% and 30.7% of the variability of SOC density to the upper 20 cm and 100 cm, respectively. The environmental factors are the dominant affecting factors of SOC density. The effect of temperature is more important than that of precipitation, and the temperature and precipitation mainly vary with altitude and latitude, respectively. Except for temperature and precipitation, there are also other factors varying with altitude, longitude and latitude significantly affect SOC density. And the effects of other factors are more important than that of precipitation.

[Estimation models of vegetation fractional coverage (VFC) based on remote sensing image at different radiometric correction levels].

The images of post atmospheric correction reflectance (PAC), top of atmosphere reflectance (TOA), and digital number (DN) of a SPOT5 HRG remote sensing image of Nanjing, China were used to derive four vegetation indices (VIs), i. e., normalized difference vegetation index (NDVI), transformed vegetation index (TVI), soil-adjusted vegetation index (SAVI), and modified soil-adjusted vegetation index (MSAVI), and 36 VI-VFC relationship models were established based on these VIs and the VFC data obtained from ground measurement. The results showed that among the models established, the cubic polynomial models based on NDVI and TVI from PAC were the best, followed by those based on SAVI and MSAVI from DN, with the accuracy being slightly higher than that of the former two models when VFC > 0.8. The accuracy of these four models was higher in middle-densely vegetated areas (VFC = 0.4-0.8) than in sparsely vegetated areas (VFC = 0-0.4). All the established models could be used in other places via the introduction of calibration models. In VI-VFC modeling, using VIs derived from different radiometric correction levels of remote sensing image could help mining valuable information from remote sensing image, and thus, improving the accuracy of VFC estimation.

Uncertainty assessment of mapping mercury contaminated soils of a rapidly industrializing city in the Yangtze River Delta of China using sequential indicator co-simulation.

Accurate characterization of heavy-metal contaminated areas and quantification of the uncertainties inherent in spatial prediction are crucial for risk assessment, soil remediation, and effective management recommendations. Topsoil samples (0-15 cm) (n=547) were collected from the Zhangjiagang suburbs of China. The sequential indicator co-simulation (SIcS) method was applied for incorporating the soft data derived from soil organic matter (SOM) to simulate Hg concentrations, map Hg contaminated areas, and evaluate the associated uncertainties. High variability of Hg concentrations was observed in the study area. Total Hg concentrations varied from 0.004 to 1.510 mg kg(-1) and the coefficient of variation (CV) accounts for 70%. Distribution patterns of Hg were identified as higher Hg concentrations occurred mainly at the southern part of the study area and relatively lower concentrations were found in north. The Hg contaminated areas, identified using the Chinese Environmental Quality Standard for Soils critical values through SIcS, were limited and distributed in the south where the SOM concentration is high, soil pH is low, and paddy soils are the dominant soil types. The spatial correlations between Hg and SOM can be preserved by co-simulation and the realizations generated by SIcS represent the possible spatial patterns of Hg concentrations without a smoothing effect. Once the Hg concentration critical limit is given, SIcS can be used to map Hg contaminated areas and quantitatively assess the uncertainties inherent in the spatial prediction by setting a given critical probability and calculating the joint probability of the obtained areas.

Source identification and spatial variability of nitrogen, phosphorus, and selected heavy metals in surface water and sediment in the riverine systems of a peri-urban interface.

This study was conducted, using an elaborate sampling activity of surface water and sediment within an industrially developed peri-urban interface with a riverine system in Wuxi, Taihu Lake area, China, to address the following objectives: (i) to identify possible sources of selected nutrients such as N and P, and heavy metals such as Cu, Zn, Pb, Cr, and Cd in surface water and sediments, and (ii) to determine the spatial variability of these elements around the source areas. The results showed that concentrations of N and P in the surface water and Cu, Zn, Cr, and Pb in most of sediments had exceeded trigger levels established by the nation, while all metal concentrations in surface water were still below the levels. The source identification of these pollutants in water and sediments in terms of their spatial distribution pattern and principal component analysis showed that: (i) Pb, N and organic carbon (OC) were closely related to the influence of urban runoff and domestic wastewater; (ii) Cu and Cr were related to the influence of industries; and (iii) P and Zn were related to the effect of both urban and industries. The results of this study showed that urbanization is the main contributor for N and P in the peri-urban interface instead of agricultural sources. The concentrations of N, P, Cu, Zn, Pb, and Cr in the sediment along the main river decreased with the distance away from the source area. The concentrations of these elements decreased to the background levels at about 4.5-5.5 km downstream of the source of origin.

[Characteristics of references between GSCC and CST for Semi-Luvisols].

In this paper, the references between Genetic Soil Classification of China (GSCC) and the Chinese Soil Taxonomy (CST) for GSCC-Semi-Luvisols were conducted, and their quantitative and spatial distribution characteristics within CST were studied, based on the 1 : 1 M Soil Database of China, which consists of 1 : 1 M digital soil map, soils profiles attribution database and soil reference system of China. Being a reference system for converting soil names in GSCC into those in CST, ST and WRB, respectively, Chinese Soil Reference System was a computerized retrieving system jointly developed by the experienced scientists of pedology and computer science. The comparison fields and laboratory investigation data of their soil profiles with diagnostic horizons and characteristics related in the target soil classification systems, and 2,540 typical soil species names corresponding in CST, ST and WRB systems were determined, respectively, which were selected from Soil Attributes Database because of their complete sets of attribute data. Finally, the system and reference database were established. "GIS linkage based soil type" method linked the records in the Soil Reference Database to the Soil Spatial Database. In this method, all records of soil profiles in Soil Reference Database as well as their soil reference name in other classification systems were allocated one by one onto corresponding soil type polygons in Soil Spatial Database on the GIS platform, according to the principles of soil type identity and similarity, parent material identity and likeness, and the location of soil profiles relative to linked target polygons. Area statistcs of all soils were conducted based on the polygons. The results showed that GSCC-Semi-Luvisols was a type of GSCC soil with a total area of 427,843.1 km2,which could be sorted to 4 CST Orders, i. e., Luvisols (51.3%), Cambosols (35.2%), Isohumosols (10.7%) and Anthrosols (2.8%), and further into CST 22 Groups and 38 Subgroups. All dark grey forest soil, superficial gleyed black soil, and leached dry red soil of GSCC subgroups could be sorted to Calcaric Hapli-Gelic Cambosols, Pachic Argi-Udic Cambosols and Typic Ferri-Ustic Luvisols of CST subgroups, respectively, and all grey cinnamon-like soil, calcareous grey cinnamonic soil and dry cinnamon soil could be sorted to Typic Ustic Cambosols. Making the reference was so complicated that there was no one to one reference relationship among other soils. The analysis of the area ratios and standard deviations of a certain GSCC soil classified by CST showed that the lower the unit for reference, the easier the reference would be. The results of this study were of high reference value to proper reference GSCC and CST, and to the application and development of CST.

Uncertainty assessment of spatial patterns of soil organic carbon density using sequential indicator simulation, a case study of Hebei province, China.

The spatial patterns of soil organic carbon (SOC) are closely related to the global climate change. In quantifying the spatial patterns of SOC density, the concept of uncertainty of the SOC density values at unsampled locations is particularly important because such uncertainty can be propagated into the subsequent global climate change modelling and has fundamental impacts on the ultimate results of the model. A total of 361 SOC density data of topsoil (0-20 cm) in Hebei province and sequential indicator simulation (SIS) were applied to perform a conditional stochastic simulation in this study to quantitatively assess the uncertainty of mapping SOC density. The results showed that a great variation exists in the SOC density data. The conditional variance of 500 realizations generated by SIS was larger in mountainous areas of the study area where the SOC density fluctuated the most, and the uncertainty was smaller on the plain area where SOC density was consistently small. Realizations generated by SIS can represent the possible spatial patterns of SOC density without smoothing effect. A set of realizations can be used to explore all possible spatial patterns of SOC density and provide a visual and quantitative measure of the spatial uncertainty of mapping SOC density. With a given threshold of SOC density, SIS can quantitatively assess both local uncertainty and spatial uncertainty of SOC density that is greater the threshold.