镇江地区土壤中重金属Cu和Pb污染状况与空间分布

为了锯镇江地区土壤中重金属Cu和Ph的污染状况与空间分布，对镇江地区表层土壤中的Cu和Pb进行了采样监测。结果表明，镇江地区表层土壤中cu的含量为19．2～273mg／kg，Pb的含量为20．6～3846mg／kg。与全省土壤背景值相比，均有一定程度的富集。对cu和Pb的相互关系进行分析可得出，镇江地区土壤在一定程度上受到农业面源的污染。     

输卤管道卤水漏失对地下水的影响分析

通过卤水对土壤的淋滤试验，模拟卤水管道发生漏失事故时，盐分在土壤和地下水中的迁移转化规律，为卤水漏失对地下水的影响提供定量依据。对正在开发之中的叶县盐田的环境管理具有一定的指导意义．     

富蕴县耕地土壤环境质量现状

通过在富蕴县建立测土配方施肥示范点，开展了耕地土壤环境质量的调查，结果表明：在富蕴县不同地貌部位0 cm~20 cm耕层土壤中，土壤常量元素的分布与微量元素相比差异较大；在山区土壤中，常量元素与微量元素的含量较高，而在戈壁沙漠地带，土壤中常、微量元素的含量均表现为最低水平；土壤pH值为7.5~8.5，适合作物生长；土壤耕层盐分含量相对较低，仅表现为轻盐化土壤类型，占总耕地面积的12.52%。     

镇江地区土壤样品中重金属Cd含量与空间分布

通过对镇江地区土壤样品中 Cd 监测结果统计分析表明，全市69个样品中 Cd 质量比范围为0．06 mg/kg ～1．37 mg/kg，均值为0．23 mg/kg，与全国背景值相比，有一定程度富集；样品中 Cd 质量比成偏态分布，相对标准偏差较大。选用单项污染指数法对 Cd污染程度评价表明，镇江地区83％的土壤样品未受到 Cd 污染，14％为轻度污染，3％为中度污染。结合镇江地区的产业结构分析，电镀行业是土壤 Cd 污染的主要来源，道路运输、农药化肥在一定程度上也加重了污染。     

南京某县空气、土壤中多环芳烃的分布及来源

采用建立的采样及分析监测程序对南京某县空气、土壤中的多环芳烃进行了调查监测,探讨了多环芳烃在空气、土壤中的分布特征、相关性及可能的污染来源.     

沧州市滨海盐碱区土壤盐渍化特征

分层采集不同时期沧州市滨海盐碱区的土样,采用流动分析仪测定各土样的总含盐量及主要盐分离子,分析土壤的盐渍化特征.结果表明:该地区为硫酸盐-氯化物型盐渍化土壤,土壤中氯化钠、氯化镁和氯化钙为氯化物的主要组成部分,硫酸盐类主要为硫酸钠、硫酸镁和硫酸钙.该地区不同采样时期间、不同地区间和不同土层间土壤盐渍化状态均存在极显著差异.除碳酸根+碳酸氢根外,钾离子、钠离子、镁离子、氯离子、钙离子、硅酸根离子和硫酸根离子均为表征该地区土壤盐渍化状态的主要盐分离子.     

北京市通州污灌区土壤环境质量监测和蔬菜重金属污染状况研究

通过对北京市通州污灌区土壤现状调查与蔬菜重金属污染监测,结合土壤环境质量标准、食品卫生标准及污灌区污染历史,分析对比该区土壤和蔬菜重金属污染状况及其变化。结果表明,本次监测通州污灌区土壤中重金属平均含量均达到土壤环境质量标准(GB15618-1995)中二级标准限量。对照土壤中的重金属Cu、Pb、Cr、Cd和As均达到土壤一级标准。凉水河两岸和通惠北干渠中重金属含量均高于对照土壤,说明污灌区污水灌溉已使土壤受到一定程度的污染。与二十世纪70年代末监测结果相比,土壤中多数重金属含量处于上升趋势。污灌区蔬菜重金属含量监测结果表明,其含量水平均达到食品卫生标准,说明污灌区蔬菜尚未受到严重污染。     

城市土壤Pb污染特征及影响因素分析

以乌鲁木齐市和上海市中心城区为研究区域，按不同功能区对城市土壤采样并进行Pb含量及形态分析，结果表明：两市土壤Pb的平均值含量均超出相应土壤Pb的背景值。上海市土壤Pb的含量远远高于乌鲁木齐市；两市土壤Pb在各功能区的分布均存在明显差异，但两市土壤Pb在功能区的分布规律并不相同；两市土壤Pb的形态分布规律趋于一致，乌鲁木齐市土壤Pb活性要大于上海市。造成上述城市土壤Pb污染特征的影响因素有很多。     

乌鲁木齐城市土壤与灰尘粒径空间分布特征

结合野外调研的基础,按照四分法采集乌鲁木齐市城市土壤与城市灰尘样品共计306个,利用激光粒度仪和ArcGIS分析了城市土壤与灰尘样品的全粒径(0.02～2 000μm)组成与空间分布特征。研究结果表明:乌鲁木齐城市土壤与灰尘在小粒径范围内空间分布差异不明显,城市土壤粒径主要集中在10～50μm,城市灰尘集中于100～250μm;城市重工业对城市土壤和灰尘小粒径颗粒分布影响更大,而城市商业、居住等为主的人类其他活动行为对大粒径颗粒影响更为显著。乌鲁木齐城市土壤可能对城市灰尘PM_1、PM_(2.5)、PM_(10)粒径分布的贡献比较突出,应从城市土壤、城市灰尘、颗粒物粒径空间分布、气象等因素综合考虑乌鲁木齐市大气污染的防控。     

土壤生态环境健康监测与评价技术现状与展望

综述了国内对土壤生态环境健康的研究进展,总结了传统土壤环境监测技术的不足,介绍了土壤生态环境健康监测技术,包括植物、动物、微生物等生态监测方法,旨在通过对各项技术的比较,了解各项监测技术对土壤健康监测和评价现状。对土壤生态环境健康监测与评价技术的发展趋势进行展望,提出未来需要对土壤生态健康监测技术进行标准化和定量化;开展多生物指标联合监测;结合遥感和物联网技术扩大土壤时空监测尺度,形成完整的土壤生态环境健康监测与评价体系,为环境管理部门有效监测土壤生态环境提供依据。     

Spatial variability of soil salinity in coastal saline soil at different scales in the Yellow River Delta,China

The objectives of this study were to explore the spatial variability of soil salinity in coastal saline soil at macro, meso and micro scales in the Yellow River delta, China. Soil electrical conductivities (ECs) were measured at 0–15, 15–30, 30–45 and 45–60 cm soil depths at 49 sampling sites during November 9 to 11, 2013. Soil salinity was converted from soil ECs based on laboratory analyses. Our results indicated that at the macro scale, soil salinity was high with strong variability in each soil layer, and the content increased and the variability weakened with increasing soil depth. From east to west in the region, the farther away from the sea, the lower the soil salinity was. The degrees of soil salinization in three deeper soil layers are 1.14, 1.24 and 1.40 times higher than that in the surface soil. At the meso scale, the sequence of soil salinity in different topographies, soil texture and vegetation decreased, respectively, as follows: depression >flatland >hillock >batture; sandy loam >light loam >medium loam >heavy loam >clay; bare land >suaeda salsa >reed >cogongrass >cotton >paddy >winter wheat. At the micro scale, soil salinity changed with elevation in natural micro-topography and with anthropogenic activities in cultivated land. As the study area narrowed down to different scales, the spatial variability of soil salinity weakened gradually in cultivated land and salt wasteland except the bare land.     

The assessment of spatial distribution of soil salinity risk using neural network

Soil salinity in the Aral Sea Basin is one of the major limiting factors of sustainable crop production. Leaching of the salts before planting season is usually a prerequisite for crop establishment and predetermined water amounts are applied uniformly to fields often without discerning salinity levels. The use of predetermined water amounts for leaching perhaps partly emanate from the inability of conventional soil salinity surveys (based on collection of soil samples, laboratory analyses) to generate timely and high-resolution salinity maps. This paper has an objective to estimate the spatial distribution of soil salinity based on readily or cheaply obtainable environmental parameters (terrain indices, remote sensing data, distance to drains, and long-term groundwater observation data) using a neural network model. The farm-scale (∼15 km²) results were used to upscale soil salinity to a district area (∼300 km²). The use of environmental attributes and soil salinity relationships to upscale the spatial distribution of soil salinity from farm to district scale resulted in the estimation of essentially similar average soil salinity values (estimated 0.94 vs. 1.04 dS m⁻¹). Visual comparison of the maps suggests that the estimated map had soil salinity that was uniform in distribution. The upscaling proved to be satisfactory; depending on critical salinity threshold values, around 70–90% of locations were correctly estimated.     

Spatial variability of depth and salinity of groundwater under irrigated ustifluvents in the Middle Black Sea Region of Turkey

Information on the potential risk for soil salinity buildup can be very helpful for soil salinity management in irrigated areas. We evaluated the spatial and temporal variability of groundwater salinity (GWS) and groundwater depth (GWD), which are two of the most important indicators of soil salinity, by indicator kriging technique in a large irrigated area in northern Turkey. GWS and GWD were measured on a monthly basis from irrigation season (August 2003) to rainy season (April 2004) at 60 observation wells in the 8,187-ha irrigated area. Five indicator thresholds were used for GWS and GWD. The semivariogram for each of the thresholds for both variables was analyzed then used together with experimental data to interpolate and map the corresponding conditional cumulative distribution functions (CCDF). Risk for soil salinity buildup was greater in the irrigation season compared to that in the rainy season. The greatest risk for soil salinity buildup occurred in the eastern part of the study area, suffering from poor drainage problem due to malfunctioning drainage infrastructure, as indicated by the CCDF of GWS and GWD obtained in both seasons. It was concluded that a combination of mechanical and cultural measures should be taken in high-risk locations to avoid further salinity problems.     

Spatial assessment of soil salinity in the Harran Plain using multiple kriging techniques

The Harran Plain is located in the southeastern part of Turkey and has recently been developed for irrigation agriculture. It already faces soil salinity problems causing major yield losses. Management of the problem is hindered by the lack of information on the extent and geography of the salinization problem. A survey was carried out to delineate the spatial distribution of salt-affected areas by randomly selecting 140 locations that were sampled at two depths (0 to 30 and 30 to 60 cm) and analyzed for soil salinity variables: soil electrical conductivity (EC), soluble cations (Ca^2+, Mg²⁺, Na⁺, and K⁺), soluble anions (SO ₄ ^2? , Cl^?), exchangeable Na⁺ (me 100 g^?1) and exchangeable sodium percentage. Terrain attributes (slope, topographical wetness index) were extracted from the digital elevation model of the study area. Variogram analyses after log transformation and ordinary kriging (OK) were applied to map spatial patterns of soil salinity variables. Multivariate geostatistical methods—regression kriging (RK) and kriging with external drift (KED)—were used using elevation and soil electrical conductivity data as covariates. Performances of the three estimation methods (OK, RK, and KED) were compared using independent validation samples randomly selected from the main dataset. Soils were categorized into salinity classes using disjunctive kriging (DK) and ArcGIS, and classification accuracy was tested using the kappa statistic. Results showed that soil salinity variables all have skewed distribution and are poorly correlated with terrain indices but have strong correlations among each other. Up to 65 % improvement was obtained in the estimations of soil salinity variables using hybrid methods over OK with the best estimations obtained with RK using EC_0–30 as covariate. DK–ArcGIS successfully classified soil samples into different salinity groups with overall accuracy of 75 % and kappa of 0.55 (p?<?0.001).     

Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable

Understanding the spatial soil salinity aids farmers and researchers in identifying areas in the field where special management practices are required. Apparent electrical conductivity measured by electromagnetic induction instrument in a fairly quick manner has been widely used to estimate spatial soil salinity. However, methods used for this purpose are mostly a series of interpolation algorithms. In this study, sequential Gaussian simulation (SGS) and sequential Gaussian co-simulation (SGCS) algorithms were applied for assessing the prediction accuracy and uncertainty of soil salinity with apparent electrical conductivity as auxiliary variable. Results showed that the spatial patterns of soil salinity generated by SGS and SGCS algorithms showed consistency with the measured values. The profile distribution of soil salinity was characterized by increasing with depth with medium salinization (EC_e 4–8 dS/m) as the predominant salinization class. SGCS algorithm privileged SGS algorithm with smaller root mean square error according to the generated realizations. In addition, SGCS algorithm had larger proportions of true values falling within probability intervals and narrower range of probability intervals than SGS algorithm. We concluded that SGCS algorithm had better performance in modeling local uncertainty and propagating spatial uncertainty. The inclusion of auxiliary variable contributed to prediction capability and uncertainty modeling when using densely auxiliary variable as the covariate to predict the sparse target variable.     

新疆图木舒克地区土壤盐渍化空间分布特征

土壤盐渍化已成为影响图木舒克地区农业生产与生态环境的重要因素。本文以0~150 cm深度范围内土壤和地下水为研究对象,利用实际野外调查与数据统计分析的方法,研究得出:①强盐渍化主要分布于距离河流与渠道较远的地下水浅埋深区域,在纵向上表层聚盐现象明显,盐渍土类型随着土壤层深度加大从亚氯-亚硫酸盐渍土变为亚硫酸盐渍土。②土壤易溶盐含量与地下水化学类型在平面分布上吻合程度较高、与潜水矿化度呈正的高度相关性、与潜水埋藏深度呈负的中度相关性、沿潜水径流方向各层土壤盐渍化减弱。③当地下水埋深较浅时,潜水通过毛细管将易溶盐带入土壤表层,形成表层土壤盐渍化;地下水埋藏较深时,易溶盐分会随着降水淋滤和灌溉冲洗不断降低,地表盐渍化减弱。中、轻度盐渍化区域应控制灌溉用水矿化度,重度盐渍化及盐土区域可在建立排水系统的基础上采用泡田洗盐法。     

Water Conservation Versus Soil Salinity Control

This paper tackles the increasingly significant problem of irrigation-induced soil salinity within a groundwater management model. Irrigation can result not only in heavier salt concentrations but also in the removal of salt from the soil through return flows. Given these contradictory observations, we are interested in the effects on soil salt concentration if irrigation efficiency is improved. We develop a model of salt concentration patterns in both soil and groundwater. We introduce a negative externality to the production process by assuming that soil degradation due to higher soil salinity affects total factor productivity. Within this framework, we show that in the presence of this externality, increasing irrigation efficiency can lead to higher or lower soil salt concentration, depending on the social cost of transferring salt from one reservoir to another.     

Soil water, salt, and groundwater characteristics in shelterbelts with no irrigation for several years in an extremely arid area

This paper is based on long-term monitoring data for soil water, salt content, and groundwater characteristics taken from shelterbelts where there has been no irrigation for at least 5 years. This study investigated the distribution characteristics of soil water and salt content in soils with different textures. The relationships between soil moisture, soil salinity, and groundwater level were analyzed using 3 years of monitoring data from a typical oasis located in an extremely arid area in northwest China. The results showed that (1) the variation trend in soil moisture with soil depth in the shelterbelts varied depending on soil texture. The soil moisture was lower in sandy and loamy shelterbelts and higher in clay shelterbelts. (2) Salinity was higher (about 3.0 mS cm^?1) in clay shelterbelts and lower (about 0.8 mS cm^?1) in sandy shelterbelts. (3) There was a negative correlation between soil moisture in the shelterbelts and groundwater level. Soil moisture decreased gradually as the depth of groundwater table declined. (4) There was a positive correlation between soil salinity in the shelterbelts and the depth of groundwater table. Salinity increased gradually as groundwater levels declined.     

Spatial variability of salinity and alkalinity of a field having salination risk in semi-arid climate in northern Turkey

Spatial variability of salinity and alkalinity is important for site-specific management since they are the most important factors influencing soil quality and agricultural production. The objectives of this study were to analyze spatial variability in salinity and alkalinity and some soil properties affecting salinity and alkalinity, using classical statistics and geostatistical methods, in an irrigated field with low-quality irrigation water diverted from drainage canals. A field of 5 da was divided into 10 m x 10 m grids (5 lines in the east-west direction and 10 lines in the north-south direction). The soil samples were collected from three depths (0-30, 30-60 and 60-90 cm) at each grid corner. The variation coefficients of OM and sand contents were higher than other soil properties. OM had the maximum variability, with a mean of 1.63% at 0-30 cm depth and 0.71% at 30-60 cm depth. Significant correlations occurred between ESP, EC and each of Ca, Mg, K and CaCO(3) contents of the soils (p<0.01). Experimental semivariograms were fitted to spherical and gaussian models. All geostatistical range values were greater than 36 m. The soil properties had spatial variability at small distances at 60-90 cm depth. EC was variable within short distances at 30-60 cm depth. The nugget effect of ESP increased with soil depth. Kriged contour maps revealed that soils had a salinisation and alkalisation tendency at 60-90 cm depth based on spatial variance structure of the EC and ESP values. Spatial variability in EC and ESP can depend on ground water level, quality of irrigation water, and textural differences.     

Modeling and Mapping of Soil Salinity and Alkalinity Using Remote Sensing Data and Topographic Factors: a Case Study in Iran

Environmental Modeling & Assessment - Soil salinity and alkalinity seriously threaten crop production, soil productivity, and sustainable agriculture, especially in arid and semi-arid areas,...