Magnetic Influences of Cement Dust on Soil in Industrial Area and Its Environmental Implications

To monitor environmental implications of cement dust on soil in industrial area, magnetic parameters and mineral compositions were measured by the samples of the cement dust, cement factory dustfall, cement, coal ash, topsoil of green space in the factory and topsoil outside the factory. Results showed that the major magnetic minerals in the samples were magnetite, the magnetic mineral particle size of cement dust, dustfall, cement and coal ash was coarse Multi-Domian (MD) and Pseudo-Single-Domain (PSD). As topsoil of green space in the factory and topsoil outside the factory were polluted by cement dust, their magnetic properties had been obviously changed; the concentration of magnetic minerals elevated and particle size became coarser. The magnetic parameters (χ_fd,χ_ARM/χ,χ_ARM/SIRM) value can reflect the pollution level of the soil, the lower its value was, the more serious soil was polluted. X-ray diffraction results showed that the mineral compositions of the topsoil of green space in the factory and topsoil outside the factory were quartz and calcite, which were the same as cement dust. Magnetic parameters combination characteristics of topsoil can monitor the soil environmental changes in cement industry area.     

210Pbex示踪法技术原理及其在土壤侵蚀中的应用

土壤侵蚀是全球性的环境问题之一,严重威胁到资源、环境和社会经济的可持续发展。应用核素示踪技术研究土壤侵蚀已成为当前该领域研究热点,它体现出了很多的优越性,并在今后研究中有着广阔的应用前景。本文介绍了210Pbex的示踪原理,概述了210Pbex在土壤侵蚀示踪研究中的存在的关键性问题及其应用,并对其研究进行了展望。     

长岛县南五岛土壤地球化学特征及环境质量评价

以长岛县南五岛土壤为研究对象,对区内土壤元素的含量特征进行分析,并对土壤环境质量进行评价。结果表明：研究区土壤平均含量与烟台市、山东省以及全国土壤平均水平相比,除CaO,Na2O,Hg,S,Se,P,F元素含量差异较明显,其他大部分元素含量相对稳定,元素含量的高低不仅与研究区背景条件有关,也与人类活动密切相关。研究区土壤环境质量整体较好,大部分区域属于清洁区和尚清洁区,仅城区局部受到一定程度的Cd,Hg,Zn污染。     

微波消解―ICP―MS法测土壤和水系沉积物样品中的碘

介绍了用微波消解处理土壤和水系沉积物样品,用电感耦合等离子质谱仪(ICP-MS)快速高效测试碘的方法。方法的检出限为0. 012μg/g。所选的12个土壤和水系沉积物国家一级标准物质12次测定,除GBW07307a的RSD小于10%外,其余RSD值均小于5%,12次测试的平均值和标准值对数差绝对值均小于0. 05。方法加标回收率在98. 6%104. 6%之间。     

Assessment of soil erosion in a tropical mountain river basin of the southern Western Ghats,India using RUSLE and GIS

Revised Universal Soil Loss Equation(RUSLE) model coupled with transport limited sediment delivery(TLSD) function was used to predict the longtime average annual soil loss, and to identify the critical erosion-/deposition-prone areas in a tropical mountain river basin, viz., Muthirapuzha River Basin(MRB; area=271.75 km~2), in the southern Western Ghats, India. Mean gross soil erosion in MRB is 14.36 t ha~(-1) yr~(-1), whereas mean net soil erosion(i.e., gross erosion-deposition) is only 3.60 t ha~(-1) yr~(-1)(i.e., roughly 25% of the gross erosion). Majority of the basin area(~86%) experiences only slight erosion(5 t ha~(-1) yr~(-1)), and nearly 3% of the area functions as depositional environment for the eroded sediments(e.g., the terraces of stream reaches, the gentle plains as well as the foot slopes of the plateau scarps and the terrain with concordant summits). Although mean gross soil erosion rates in the natural vegetation belts are relatively higher, compared to agriculture, settlement/built-up areas and tea plantation, the sediment transport efficiency in agricultural areas and tea plantation is significantly high,reflecting the role of human activities on accelerated soil erosion. In MRB, on a mean basis, 0.42 t of soil organic carbon(SOC) content is being eroded per hectare annually, and SOC loss from the 4th order subbasins shows considerable differences, mainly due to the spatial variability in the gross soil erosion rates among the sub-basins. The quantitative results, on soil erosion and deposition, modelled using RUSLE and TLSD, are expected to be beneficial while formulating comprehensive land management strategies for reducing the extent of soil degradation in tropical mountain river basins.     

Probabilistic calibration of a coupled hydro-mechanical slope stability model with integration of multiple observations

Multiple types of responses, such as displacements, ground water level, pore water pressures, water contents, etc., are usually measured in comprehensive monitoring programmes for rainfall-induced landslide prevention. In this study, a probabilistic calibration method for coupled hydro-mechanical modelling of slope stability is presented with integration of multiple types of measurements. A numerical example of a soil slope under rainfall infiltration is illustrated to compare the effects of single and multiple types of responses on parameter estimation and model calibration. The results show that the soil parameters can be estimated with less uncertainty and total uncertainty bounds are narrower with multiple types of responses than with a single type of response. Model calibration based on multiple types of responses can compromise different responses and hence the means and standard deviations of model error are the smallest. A feasible correlation coefficient between soil modulus and permeability can be obtained from model calibration with multiple types of responses and single type of response as long as the responses include displacement data.     

Assessing soil salinity using WorldView-2 multispectral images in Timpaki,Crete, Greece

Salinization is one of the major soil degradation threats occurring worldwide. This study evaluates the feasibility of operational surface soil salinity mapping based on state-of-the-art Earth Observation (EO) products captured by sensors on-board WorldView-2 (WV2) and Landsat 8 satellites. The proposed methods are tested in Timpaki, south-central Crete,Greece, where brackish water irrigation puts soil health at risk of soil salinization. In all cases, EO products are calibrated against soil samples collected from bare soil locations. Results indicate a moderate correlation of observed EC_e values with the investigated remote sensing parameters. Regarding sensitivity to saline soil, the yellow band displays higher values. Comparison between methods used in the literature shows that those developed specifically for soil salinity, and especially index S5, perform better. The proposed ‘detection index’ and 3D PCA transformation methodology perform reasonably well in detecting areas with high EC_e values and provide a simple and effective operational alternative for saline topsoil detection and mapping.     

Soil salinity and vegetation cover change detection from multi-temporal remotely sensed imagery in Al Hassa Oasis in Saudi Arabia

Detecting soil salinity changes and its impact on vegetation cover are necessary to understand the relationships between these changes in vegetation cover. This study aims to determine the changes in soil salinity and vegetation cover in Al Hassa Oasis over the past 28 years and investigates whether the salinity change causing the change in vegetation cover. Landsat time series data of years 1985, 2000 and 2013 were used to generate Normalized Difference Vegetation Index (NDVI) and Soil Salinity Index (SI) images, which were then used in image differencing to identify vegetation and salinity change/no-change for two periods. Soil salinity during 2000–2013 exhibits much higher increase compared to 1985–2000, while the vegetation cover declined to 6.31% for the same period. Additionally, highly significant (p < 0.0001) negative relationships found between the NDVI and SI differencing images, confirmed the potential long-term linkage between the changes in soil salinity and vegetation cover.     

A comparative study of soil erosion modelling by MMF,USLE and RUSLE

The quantitative assessment of spatial soil erosion is valuable information to control the erosion. The study area in a part of Narmada river in central India is selected. The main objective is to assess and compare the results obtained from three soil erosion models using GIS platform. Variation in the rate of erosion of the three models is compared considering varying slope, soil and land use of the area. Three models selected are Morgan–Morgan–Finney (MMF), Universal Soil Loss Equation (USLE) and Revised Universal Soil Loss Equation (RUSLE). The best fit or the most reliable model for the study area is selected after validation with the observed sedimentation data. The results give –39.45%, –9.60% and 4.80% difference in the values of sedimentation by MMF, USLE and RUSLE, respectively, from the observed data. Finally, RUSLE model has been found to be most reliable for the study area.     

土壤粒径的光谱响应特性研究

以实验室制备的5个不同粒径水平的土壤样本和室内高光谱数据为基础,通过对光谱数据进行重采样、数学变换等预处理并进行单因素方差分析、相关性分析和回归分析,探讨土壤粒径的高光谱特性,建立了光谱数据预测土壤粒径的校正模型。结果表明,土壤粒径对反射光谱有显著的影响,波长越长影响越大;在全波段范围内土壤粒径和光谱数据都呈负相关关系,对原始光谱数据进行微分变换能增加其与土壤粒径的相关性;以反射率一阶微分建立的回归模型为反演土壤粒径的最佳模型,其建模决定系数■、预测决定系数■、预测相对偏差RPD分别为0.666,0.653,2.043,预测均方根误差RMSE为0.175。