Spatial Prediction of Heavy Metal Pollution for Soils in Peri-Urban Beijing, China Based on Fuzzy Set Theory

Fuzzy classification combined with spatial prediction was used to assess the state of soil pollution in the peri-urban Beijing area. Total concentrations of As, Cr, Cd, Hg, and Pb were determined in 220 topsoil samples (0-20 cm) collected using a grid design in a study area of 2600 km2. Heavy metal concentrations were grouped into three classes according to the optimum number of classes and fuzziness exponent using the fuzzy c-mean (FCM) algorithm. Membership values were interpolated using ordinary kriging. The polluted soils of the study area induced by the measured heavy metals were concentrated in the northwest corner and eastern part, especially the southeastern part close to the urban zone, whereas the soils free of pollution were mainly distributed in the southwestern part. The soils with potential risk of heavy metal pollution were located in isolated spots mainly in the northern part and southeastern corner of the study region. The FCM algorithm combined with geostatistical techniques, as compared to conventional single geostatistical kriging methods, could produce a prediction with a quantitative uncertainty evaluation and higher reliability. Successful prediction of soil pollution achieved with FCM algorithm in this study indicated that fuzzy set theory had great potential for use in other areas of soil science.

Soil degradation: a global problem endangering sustainable development

Soil degradation, defined as lowering and losing of soil functions, is becoming more and more serious worldwide in recent decades, and poses a threat to agricultural production and terrestrial ecosystem. It is estimated that nearly 2 billion ha of soil resources in the world have been degraded, namely approximately 22% of the total cropland, pasture, forest, and woodland. Globally, soil erosion, chemical deterioration and physical degradation are the important parts amongst various types of soil degradation. As a natural process, soil degradation can be enhanced or dampened by a variety of human activities such as inappropriate agricultural management, overgrazing, deforestation, etc. Degraded soil means less food. As a result of soil degradation, it is estimated that about 11.9-13.4% of the global agricultural supply has been lost in the past five decades. Besides, soil degradation is also associated with off-site problems of sedimentation, climate change, watershed functions, and changes in natural habitats leading to loss of genetic stock and biodiversity. Therefore, it is essential to combat soil degradation at different levels and scales worldwide, not only for food security and ecological health, but also for the guarantee of global sustainable development.

Effect of Soil Sampling Density on Detected Spatial Variability of Soil Organic Carbon in a Red Soil Region of China

Spatial variability of soil organic carbon (SOC) of different land use patterns and soil types was examined in a county-wide red soil region of South China,using six sampling densities,14,34,68,130,255,and 525 samples designed by the method of grid sampling in 6 different grid sizes,labeled as D14,D34,D68,D130,D255,and D525,respectively.The results showed that the coefficients of variation (CVs) of SOC decreased gradually from 62.8% to 47.4% with the increase in soil sampling densities.The SOC CVs in the paddy field change slightly from 30.8% to 28.7%,while those of the dry farmland and forest land decreased remarkably from 58.1% to 48.7% and from 99.3% to 64.4%,respectively.The SOC CVs of the paddy soil change slightly,while those of red soil decreased remarkably from 82.8% to 63.9%.About 604,500,and 353 (P < 0.05) samples would be needed a number of years later if the SOC change was supposedly 1.52 g kg-1,based on the CVs of SOC acquired from the present sampling densities of D14,D68,and D525,respectively.Moreover,based on the same SOC change and the present time CVs at D255,the ratio of samples needed for paddy field,dry farmland,and forest land should be 1:0.81:3.33,while the actual corresponding ratio in an equal interval grid sampling was 1:0.74:0.46.These indicated that the sampling density had important effect on the detection of SOC variability in the county-wide region,the equal interval grid sampling was not efficient enough,and the respective CV of each land use or soil type should be fully considered when determining the sampling number in the future.

Regional Simulation of Soil Organic Carbon Dynamics for Dry Farmland in East China by Coupling a 1:500000 Soil Database with the Century Model

Changes in soil organic carbon (SOC) in agricultural soils influence soil quality and greenhouse gas concentrations in the atmosphere. Dry farmland covers more than 70% of the whole cropland area in China and plays an important role in mitigating carbon dioxide (CO2) emissions. In this study, 4109 dry farmland soil polygons were extracted using spatial overlay analysis of the soil layer (1:500000) and the land use layer (1:500000) to support Century model simulations of SOC dynamics for dry farmland in Anhui Province, East China from 1980 to 2008. Considering two field-validation sites, the Century model performed relatively well in modeling SOC dynamics for dry farmland in the province. The simulated results showed that the area-weighted mean soil organic carbon density (SOCD) of dry farmland increased from 18.77 Mg C ha1 in 1980 to 23.99 Mg C ha1 in 2008 with an average sequestration rate of 0.18 Mg C ha1 year?1. Approximately 94.9% of the total dry farmland area sequestered carbon while 5.1% had carbon lost. Over the past 29 years, the net SOC gain in dry farmland soils of the province was 19.37 Tg, with an average sequestration rate of 0.67 Tg C year1. Augmentation of SOC was primarily due to increased consumption of nitrogen fertilizer and farmyard manure. Moreover, SOC dynamics were highly differentiated among dry farmland soil groups. The integration of the Century model with a fine-scale soil database approach could be conveniently utilized as a tool for the accurate simulation of SOC dynamics at the regional scale.

Modeling Carbon Dynamics in Paddy Soils in Jiangsu Province of China with Soil Databases Differing in Spatial Resolution

A number of process-based models have been developed for quantifying carbon(C)sequestration in agro-ecosystems.The DeNitrification-DeComposition(DNDC)model was used to simulate and quantify long-term(1980-2008)soil organic carbon(SOC)dynamics in the important rice-producing province,Jiangsu,China.Changes in SOC storages were estimated from two soil databases differing in spatial resolution:a county database consisting of 68 polygons and a soil patch-based database of 701 polygons for all 3.7 Mha of rice fields in Jiangsu.The simulated SOC storage with the coarse resolution county database ranged between 131.0-320.6 Tg C in 1980 and 170.3-305.1 Tg C in 2008,respectively,while that estimated with the fine resolution database was 201.6 and 216.2 Tg C in 1980 and 2008,respectively.The results modeled with the soil databases differing in spatial resolution indicated that using the soil input data with higher resolution substantially increased the accuracy of the modeled results;and when lacking detailed soil datasets,the DNDC model,parameterized with the most sensitive factor(MSF) method to cope with attribute uncertainty,could still produce acceptable results although with deviations of up to 60% for the case study reported in this paper.

Variation of Sediment Concentration and Its Drivers Under Different Soil Management Systems

In order to prevent soil erosion in southern China,a study was performed to determine the drivers of sediment concentration variation using simulated rainfall and four soil management systems under field condition.Four soil management systems,i.e.,forest and grass coverage(FG),forest coverage with disturbed soil surface(FD),contour tillage(CT) and downslope tillage(DT),were exposed to two rainfall intensities(40 and 54 mm h-1) using a portable rainfall simulator.The drivers of sediment concentration variation were determined by the variations of runoff rate and sediment concentration as well as their relationships.The effects of the four soil management systems in preventing water and soil losses were compared using runoff rates and sediment concentrations at steady state.At runoff initial stage,sediment concentration variation was mainly driven by rainfall and management.The degree of sediment concentration variation driven by flow varied with different soil management systems.Three best relationships between runoff rate and sediment concentration were identified,i.e.,reciprocal(CT),quadratic(FG and FD) and exponential(DT).At steady state,runoff rates of the four soil management systems varied slightly,whereas their sediment concentrations varied greatly.FG and CT were recommended as the best soil management systems for preventing water and soil losses.

Regional Diferences in the Efect of Climate and Soil Texture on Soil Organic Carbon

The agricultural soil carbon pool plays an important role in mitigating greenhouse gas emission and understanding the soil organic carbon-climate-soil texture relationship is of great significance for estimating cropland soil carbon pool responses to climate change.Using data from 900 soil profiles,obtained from the Second National Soil Survey of China,we investigated the soil organic carbon(SOC)depth distribution in relation to climate and soil texture under various climate regimes of the cold northeast region(NER)and the warmer Huang-Huai-Hai region(HHHR)of China.The results demonstrated that the SOC content was higher in NER than in HHHR.For both regions,the SOC content at all soil depths had significant negative relationships with mean annual temperature(MAT),but was related to mean annual precipitation(MAP)just at the surface 0–20 cm.The climate efect on SOC content was more pronounced in NER than in HHHR.Regional diferences in the efect of soil texture on SOC content were not found.However,the dominant texture factors were diferent.The efect of sand content on SOC was more pronounced than that of clay content in NER.Conversely,the efect of clay on SOC was more pronounced than sand in HHHR.Climate and soil texture jointly explained the greatest SOC variability of 49.0%(0–20 cm)and 33.5%(20–30 cm)in NER and HHHR,respectively.Moreover,regional diferences occurred in the importance of climate vs.soil texture in explaining SOC variability.In NER,the SOC content of the shallow layers(0–30 cm)was mainly determined by climate factor,specifically MAT,but the SOC content of the deeper soil layers(30–100 cm)was more afected by texture factor,specifically sand content.In HHHR,all the SOC variability in all soil layers was predominantly best explained by clay content.Therefore,when temperature was colder,the climate efect became stronger and this trend was restricted by soil depth.The regional diferences and soil depth influence underscored the importance of explicitly considering them in modeling long-term soil responses to climate change and predicting potential soil carbon sequestration.

Spatio-Temporal Variability of Soil Salinity in Alluvial Plain of the Lower Reaches of the Yellow River——A Case Study

Soil salinity and hydrologic datasets were assembled to analyze the spatio-temporal variability of salinization in Fengqiu County, Henan Province, China, in the alluvial plain of the lower reaches of the Yellow River. The saline soil and groundwater depth data of the county in 1981 were obtained to serve as a historical reference. Electrical conductivity (EC) of 293 surface soil samples taken from 2 km × 2 km grids in 2007 and 40 soil profiles acquired in 2008 was analyzed and used for comparative mapping. Ordinary kriging was applied to predict EC at unobserved locations to derive the horizontal and vertical distribution patterns and variation of soil salinity. Groundwater table data from 22 observation wells in 2008 were collected and used as input for regression kriging to predict the maximum groundwater depth of the county in 2008. Changes in the groundwater level of Fengqiu County in 27 years from 1981 to 2008 was calculated. Two quantitative criteria, the mean error or bias (ME) and the mean squared error (MSE), were computed to assess the estimation accuracy of the kriging predictions. The results demonstrated that the soil salinity in the upper soil layers decreased dramatically and the taxonomically defined saline soils were present only in a few micro-landscapes after 27 years. Presently, the soils with relatively elevated salt content were mainly distributed in depressions along the Yellow River bed. The reduction in surface soil salinity corresponded to the locations with deepened maximum groundwater depth. It could be concluded that groundwater table recession allowed water to move deeper into the soil profile, transporting salts with it, and thus played an important role in reducing soil salinity in this region. Accumulation of salts in the soil profiles at various depths below the surface indicated that secondary soil salinization would occur when the groundwater was not controlled at a safe depth.