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Han S.,Beijing Normal University | Liu J.,Beijing Normal University | Zhao Y.,Beijing Normal University | Hu L.,Beijing Normal University | And 4 more authors.
Chinese Journal of Environmental Engineering | Year: 2014

People may face healthy threats due to the excess of heavy metals ion in soil transferred by food chain and surface water system. Through soil column leaching methods, we studied the sorption and fixation effects of composite nanomaterials (SiO2-Al2 O3-Fe2O3, etc) upon Cu2+, Cd2+, Pb2+, Zn2+, Ni2+ in soil solution. Different nano-composites (0%, 4%, 6%, 10% of soil mass) were added to the soil with the heavy metals concentrations of 4 times of Grade 2 levels in Soil Quality Standard (GB15618-1995), and then, the heavy metals concentrations in soil solution at different depths and in different organs of the plants cultivated on the soil column were analyzed. Results showed that less amount of heavy metals in alkaline loamy soil moving downward, while the soil column with 4% nano-composites absorbed 63% content of Cu, 79% of Cd, 68% of Pb, 89% of Zn and 76% of Ni in soil solution, and the soil column with 6% nano-composites absorbed 82% content of Cu, 92% of Cd, 76% of Pb, 91% of Zn and 88% of Ni in soil solution. Experimental soil column with more than 6% nano-composites in soil showed no more significant ability of absorbing heavy metals. Source


Zhang S.,Anhui University of Science and Technology | Zhang S.,China Agricultural University | Zhang S.,Key Laboratory of Arable Land Conservation North China | Zhang S.,Key Laboratory of Land Quality | And 10 more authors.
Intelligent Automation and Soft Computing | Year: 2014

High-resolution soil texture maps are essential for land-use planning and other activities related to forestry, agriculture and environment protection. The objective of the article was to find suitable methods for predicting soil texture through comprehensive comparison of different prediction methods (e.g., univariate and multivariate methods) by completely taking account of its characteristics as composition data with the same auxiliary information. This article, taking elevation as auxiliary variable, predicted the soil texture using univariate [ordinary kriging (OK)] and multivariate [i.e. regression kriging (RK), simple kriging with locally varying means (SKlm), and cokriging (COK)] methods. Soil texture was transformed by symmetry log ratio (SLR) to meet the requirements of the spatial interpolation for the compositional data. The root mean squared errors (RMSE), the relative improvement (RI) values of RMSE and Aitchison's distance (DA) were utilized to assess the accuracies of different prediction methods. The mean squared deviation ratio (MSDR) was used to assess the goodness of fit of the theoretical estimate of error. The results indicated that according to RMSE and MSDR, the SKlm, COK and RK methods were more appropriate for spatial prediction of sand, clay and silt, respectively. The RI value reached to 22.68% with the SKlm method for clay, 8.24% with the RK method for sand, and 22.49% with the COK method for silt. A scatter plot of DA for the OK method versus DA for the SKlm method, the COK and RK methods showed that predictions obtained with the SKlm method were more accurate than those obtained with the OK, COK and RK methods. By comprehensively considering of values of RMSE, MSDR, RI and DA, SKlm method was more suitable for spatial prediction of soil texture. Effects of auxiliary variable on spatial prediction accuracy of soil texture changed with different prediction methods and types of soil particles. © 2013 TSI® Press. Source


Sang L.,China Agricultural University | Sang L.,Land Consolidation and Rehabilitation Center | Sang L.,Key Laboratory of Land Quality | Hao J.,China Agricultural University | And 7 more authors.
Sensor Letters | Year: 2014

The spatial pattern of cropland was paid less attention in previous researches. How to achieve a rational regional farmland spatial pattern, fully produce the ecological and economic benefits of agriculture and realizes the large-scale agricultural operation is one of the core problems to address in the farmland-use plan in China. Based on the production efficiency of cropland, this paper establishes regional farmland spatial pattern rationality evaluation system, takes spatial analysis as the technique and builds an index system from the spatial pattern of accessibility, shape and connectivity, thus evaluating farmland spatial pattern rationality. Finally, it takes Baoying County in Yangzhou City, Jiangsu Province as the study area and 1' ×1' latitude-longitude grid as the evaluation unit to evaluate the existing cropland spatial pattern, analyzes the overall and local characteristics and then points out the existing problems. The results show that farmland spatial pattern rational analysis is not only conducive to farmland protection, but also significant and valuable for preventing the increasing deterioration of land fragmentation. Copyright © 2014 American Scientific Publishers. Source


Sang L.,Land Consolidation and Rehabilitation Center | Sang L.,China Agricultural University | Yun W.,Land Consolidation and Rehabilitation Center | Yun W.,Key Laboratory of Land Quality | And 6 more authors.
Yingyong Jichu yu Gongcheng Kexue Xuebao/Journal of Basic Science and Engineering | Year: 2015

Land consolidation and rehabilitation plays an important role of guiding on the optimization of spatial distributing of farmland. This paper selected land use data of Daxing District at the scale of 1:10000 as a case study, and analyzed the problem of spatial restructuring existing in construction of well-facilitated capital farmland. The results show that(1)Area proportion of farmland in most townships in Daxing District does not reach 60%. The townships which have the biggest difference between the proportion of farmland in Daxing and 60% were mainly located in Yizhuang, Jiugong, Xihongmen, Huangcun, etc. Only Lixian township achieves this area ratio. Land remediation can be resolved through the transfer of land use types. (2)There are only 2538 patches meet the construction requirements of well-facilitated capital farmland in the all townships in Daxing District, which account for 19.30% of the total patches of farmland. (3)Occupancy of farmland infrastructure in the study region is 8.46%, which exceeds the standard value of 0.46%. In addition to the townships of Jiugong, Lixian, Yufan, Anding, Panggezhuang, Weishanzhuang, the occupancy of farmland infrastructure is less than 8%. (4)There are only 705 patches meet the construction requirements of well-facilitated capital farmland in the all townships in Daxing District. ©, 2015, The Editorial Board of Journal of Basic Science and Engineering. All right reserved. Source

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