Man-Zhi Tan

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

ABSTRACT 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 km 2 . 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.

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

Abstract 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 1:500 000 Soil Database with the Century Model

Abstract 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 (CO 2 ) emissions. In this study, 4 109 dry farmland soil polygons were extracted using spatial overlay analysis of the soil layer (1:500 000) and the land use layer (1:500 000) 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 ha −1 in 1980 to 23.99 Mg C ha −1 in 2008 with an average sequestration rate of 0.18 Mg C ha −1 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 year −1 . 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.

Application of categorical information in the spatial prediction of soil organic carbon in the red soil area of China

Abstract Predicting soil organic carbon (SOC) content distribution accurately from limited soil samples has received a great deal of attention recently in an effort to support soil fertility mapping and to improve our understanding of carbon sequestration variability. Kriging methods combined with auxiliary variables are frequently used at present. However, studies using categorical information, such as soil type and land use, which are closely related to local trends in SOC spatial variation, as auxiliary variables are seldom conducted. In the present investigation, a total of 254 surficial soil samples were collected in the study area, Yujiang county in the hilly red soil region of South China, and a comparison of performance of four kriging approaches was conducted, ordinary kriging (OK), kriging combined with soil-type information (KST), land use (KLU) and combined land use–soil type information (KLUST). Results of the assessment were based on 85 validation samples. The results indicate that the best correlation between the measured and predicted values for validation location was obtained with KLUST (r = 0.854), whereas the lowest was obtained using OK (r = 0.383). Furthermore, the root mean square error (RMSE) from KLUST (3.47 g kg−1) is the lowest, whereas the one obtained using OK (6.49 g kg−1) is the highest. The correlation coefficient and RMSE from KST (r = 0.784, RMSE = 4.15 g kg−1) and KLU (r = 0.795, RMSE = 3.95 g kg−1) are the second and third most correlated, respectively. Comparing the SOC distribution maps generated by the four prediction approaches, the KLUST rendering best reflects the local change associated with soil types and land uses, whereas the map from the OK is the least representative. The results demonstrate that soil type and land use have an important impact on SOC spatial distribution, and KLUST, which reduces their influence as a local trend, is an efficient and practical prediction approach for the hilly red soil region of South China.

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

Abstract 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 Differences in the Effect of Climate and Soil Texture on Soil Organic Carbon

Abstract 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 effect on SOC content was more pronounced in NER than in HHHR. Regional differences in the effect of soil texture on SOC content were not found. However, the dominant texture factors were different. The effect of sand content on SOC was more pronounced than that of clay content in NER. Conversely, the effect 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 differences 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 affected 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 effect became stronger and this trend was restricted by soil depth. The regional differences 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.