艾比湖湿地植物群落变化对盐分环境梯度的响应

土壤盐分是影响干旱区荒漠植物群落动态的决定因素之一。基于2012年5月和10月在艾比湿地调查的植物样方和实验得出的土壤表层盐分数据,利用聚类分析方法将39个土壤表层盐分数据划分为3个盐分梯度0.03%~0.73%（S1）、0.81%~1.73%（S2）和2.40%~3.12%（S3）,分析艾比湖湿地植物群落植物多样性变化对土壤盐分环境梯度的响应。结果表明,（1）距湖15 km和距湖5 km采样点大部分处于第1个盐分梯度,属于轻度盐渍化;鸟岛、博河、鸭子湾、奎屯河、和距湖10 km处均处于第2个盐分梯度,盐渍化程度达到中-重度;湖边采样点,在3个盐分梯度各有分布。（2）3个盐分梯度上的主要植被类型不同：S1以梭梭（Haloxylon ammodendron）、柽柳（Tamarix ramosissima）、白刺（Nitraria tangutorum Bobr）为主,并且出现的植被种类较多（37种）;S2以柽柳（Tamarix ramosissima）、盐节木（Haloenemum strobilaceum）为主,出现的植被种类相对较少（11种）;S3以盐节木（Haloenemum strobilaceum）、盐角草（Salicornia europaea）、芦苇（Phragmites australis）为主,出现的植被种类较少（7种）,随土壤盐含量的升高,植被种类减少、群落生活型结构有所改变,草本比例减少,灌木和乔木比例有所增加。（3）植物Simposon指数、Shannon-Wiener指数和Margalef指数在中盐梯度下达到最大,最大值分别为0.84、2.28、3.82。（4）Pielow 指数则是随着盐分梯度的上升呈下降趋势,在低盐梯度下达到最大值,最大值为1.26。（5）多样性指数在S3梯度下整体显著降低,即土壤盐分含量为2.40%~3.12%时对艾比湖湿地植物多样性的影响较大。     

膜下滴灌棉田不同土层盐分变化及其对棉花生长的影响

研究选择两种不同盐分含量土壤（中度盐渍化EC25：4-7 dS.m-1和轻度盐渍化EC25：1-4 dS.m-1）,通过对棉田整个生育期的土壤盐分含量测定,研究棉田不同土层盐分变化及其对棉花生长的影响。结果表明：两种不同盐分含量土壤,盐分降幅较大的在0-20、40-80 cm土层为膜下滴灌棉田盐分积聚区。整个生育期土壤盐分变化趋势相似,都表现为土壤盐分前期（现蕾期）快速下降后期有所增加;中度盐渍化和轻度盐渍化棉田的单株干物质积累量差异不显著,单位面积的干物质积累和籽棉产量差异显著,其主要原因是两种盐分土壤棉花的成活率差异较大。     

盐碱草原土壤特性及生物量空间异质性研究

土壤盐分是土壤特性中最活跃和复杂的一部分。受土壤性质、气象、地形、地下水文及长期地球化学过程等自然条件和耕作、灌溉等人类活动因子的影响,土壤水分、盐分的变异状态在一定程度上反映了土壤各层的盐渍化程度和状态。而土壤盐分与水分、土壤物理性质等有十分密切的联系,因此,运用地统计学研究采用振动深松集成技术改良盐碱化草原前后土壤水分、盐分等特性的空间变异规律,以地理信息系统ArcGIS为平台,建立土壤水分、盐分等图形数据和属性数据相结合的信息数据库,建立其空间分异模型和空间分布图,直观的表现各特性的空间变异规律。结果表明,采用振动深松集成技术后土壤盐分含量在空间上有递减,平均ρ（土壤盐）由5.60g.L-1下降到3.73 g.L-1;土壤容重、土壤硬度分别降低13%和24%,利于牧草根系生长,生物量变异结果显示,改良前后的草层高度和产量空间分布逐渐达到了均一化,牧草产量达到了2738.1 kg.hm-2,实现了植被全覆盖,盐碱化草原得到了恢复。     

生物有机肥料对温室蔬菜硝酸盐和土壤盐分累积的影响

探讨了3种有机肥料对温室苋菜、芹菜硝酸盐和土壤盐分累积的影响。结果表明：施用有机肥可显著降低蔬菜硝酸盐含量。用3种有机肥料处理栽培的芹菜NO3^-含量平均值为2133mg/kg，比用蔬菜专用肥（对照）的低1617mg/kg，其中施用大三元生物有机肥的芹菜NO3^-值最低，为1803mg/kg，比对照低1947mg/kg。施用大三元生物有机肥的土壤盐分（EC）含量也最低，0－10cm耕层的盐分为0．079S/m，比对照低0．024S/m；10－20cm耕层的盐分为0．055S／m，比对照低0．010S/m。     

中新生态城土壤质量现状及改良利用途径

对中新生态城区域内土壤的紧实度、有机质、碱解氮、土壤盐分、酸碱度、地表水、地下水进行了调查取样分析,结果表明该区土壤容重在1.3～1.7g/cm3,有机质含量大多为中等或低水平（5.5～19.1g/kg）,速效氮与土壤有机质含量相关性达显著水平（r=0.98）。盐分垂直分布上表现为果园、农田土壤盐分含量小于1.5g/kg,荒地和水库含量在5.0～6.4g/kg之间,垫土区、养殖池和盐场盐池土壤通体盐分含量高（大于10g/kg）,且存在表聚现象,超出了一般植物生长的极限。土壤酸碱度为7.81～9.05,水库、养殖池、盐场、荒地、沟塘水的矿化度均在10g/L以上,蓟运河水和农田沟渠为5000mg/L～8500mg/L,果园灌溉水的矿化度也在2000mg/L以上,地下水矿化度高,埋藏浅。并针对以上特点提出其土壤改良利用途径和绿化建设模式。     

温州红壤的酸沉降临界负荷研究

本文简要介绍了以碱总量守恒为基础建立的兼能描述钙质与非钙质土壤长期酸化过程的动态模型（ＬＴＳＡＭ）,并利用该模型讨论了温州红壤的酸沉降临界负荷。结果表明,以硫沉降即酸沉降的硫沉降非酸沉降两观点分别来确定红壤的临界负荷,其值依次为３．９１与１．８４ｇ·ｍ＾－２·ａ＾－１,后者仅是前者的４７％,差异显著应加以区别。     

不同栽培模式下耕层土壤盐分演变规律研究

通过定位试验对有机栽培、无公害栽培和常规栽培模式下土壤盐分动态进行了研究。结果表明,有机栽培和无公害栽培模式中土壤盐分差异不显著,常规与无公害栽培模式中土壤盐分差异显著(F0.05=9.04),常规与有机栽培模式下的土壤盐分差异也极显著(F0.01=9.37)。常规栽培与无公害栽培环境中,全年土壤全盐量变化表现为两个积盐期:春季和秋季,以春季积盐最明显;有机栽培环境中,从7月份即出现了积盐过程,并且土壤全盐量变化趋势与气温变化基本一致。     

酸化土壤活性铝溶出及形态变化的初步研究

用模拟酸雨酸化浙江省３种主要类型土壤，研究了酸化土壤活性铝的溶出和铝的形态变化。结果表明：酸雨对强酸性土壤（ｐＨ＜５）中活性铝的释出有一定促进作用，活性铝以Ａ１￣（３－）离子为主。当土壤ｐＨ值＞５时．活性铝以单聚羟基铝为主。酸化土壤中的活性铝主要是由土壤中固相的Ａｌ（ＯＨ）转化而来。     

武当山土壤的粘粒矿物与表面电荷性质

本文研究了湖北省武当山风景区石英云母片岩发育的７个土样的粘土矿物组成、粘粒氧化物含量。土壤电荷量及电荷零点．结果表明：（１）供试土壤粘土矿物均以２∶１型矿物占优势，在海拔４４０ｍ以下的土壤中主要是水云母和蛭石，在海拔６７０ｍ以上的土壤中主要是１．４ｎｍ过渡矿物、绿泥石和水云母；（２）土液粘粘中氧化铁和氧化铝的含量、粘粒硅铝率及氧化铁的游离度随海拔的变化而变化的趋势不甚明显，但非晶形铝铁氧化物的含量及氧化铝和铁的活化度随海拔升高而趋于增加；（３）土壤可变负电荷量、阴离子吸附量和电荷零点随海拔升高而趋于增加。     

除草剂苄嘧黄隆在土壤和水稻中的残留

应用核素示踪技术，以￣（１４）Ｃ－苄嘧黄隆研究其土壤和作物残留性，结果表明，在常用量条件下施于水稻土壤的苄嘧黄隆，经过稻季（９８ｄ）后，土壤残留量为１．５μｇ／ｋｇ，消解半衰期为２８ｄ．稻谷残留量为１１．６３μｇ／ｋｇ，低于苄嘧黄隆允许残留量（２０μｇ／ｋｇ）。苄嘧黄隆属于易降解除草剂，按常规使用对环境是安全的。     

农用污泥中铜的生态安全阈值研究

本论文采用污泥和土壤外源铜的活性差异系数法研究了农用污泥中铜的生态安全阈值。首先通过比较确定不同土壤条件下污泥中铜和水溶性铜的活性差异的系数,然后利用系数法得到相应土壤条件下污泥铜HC5值(即能够保护95%物种的浓度),并建立了土壤理化性质参数与污泥铜HC5值的量化关系和预测模型。最后与我国现行污泥农用标准值进行了比较,提出了修改建议。结果表明,土壤中来源污泥的铜活性均值约为来源水溶性盐的40%左右。土壤阳离子交换量(CEC)是影响土壤中污泥源铜的毒性的主控因子,可影响污泥铜HC5值变异的84.6%,而土壤pH值和有机碳含量(OC)分别可影响污泥铜HC5值变异的8.4%和1.8%。基于土壤pH、OC和CEC的三因子模型进行预测优于基于土壤pH和CEC的两因子模型,其相关性达到94.8%。与模型预测值相比,我国现行污泥农用标准GB4284-84与CJ/T309-2009中对铜限值的规定均存在着不同程度的保护不足或保护过度问题。     

Spatial prediction of soil contamination based on machine learning: a review

● A review of machine learning (ML) for spatial prediction of soil contamination. ● ML have achieved significant breakthroughs for soil contamination prediction. ● A structured guideline for using ML in soil contamination is proposed. ● The guideline includes variable selection, model evaluation, and interpretation. Soil pollution levels can be quantified via sampling and experimental analysis; however, sampling is performed at discrete points with long distances owing to limited funding and human resources, and is insufficient to characterize the entire study area. Spatial prediction is required to comprehensively investigate potentially contaminated areas. Consequently, machine learning models that can simulate complex nonlinear relationships between a variety of environmental conditions and soil contamination have recently become popular tools for predicting soil pollution. The characteristics, advantages, and applications of machine learning models used to predict soil pollution are reviewed in this study. Satisfactory model performance generally requires the following: 1) selection of the most appropriate model with the required structure; 2) selection of appropriate independent variables related to pollutant sources and pathways to improve model interpretability; 3) improvement of model reliability through comprehensive model evaluation; and 4) integration of geostatistics with the machine learning model. With the enrichment of environmental data and development of algorithms, machine learning will become a powerful tool for predicting the spatial distribution and identifying sources of soil contamination in the future.     

SPACSYS: Integration of a 3D root architecture component to carbon, nitrogen and water cycling—Model description

It is an ongoing challenge to develop and demonstrate management practices that increase the sustainability of agricultural systems. Soil carbon and nitrogen dynamics directly affect soil quality, crop productivity and environmental impacts. Root systems are central to the acquisition of water and nutrients by plants, but are also a major pathway for the inputs of carbon and nutrients to soil. The complexity of both biotic and abiotic interactions, combined with stochastic changes in root architecture, makes it difficult to understand below-ground dynamics on the basis of experimentation alone. The integration of dynamic models of above-ground growth, three-dimensional root system demography, and interactions between plants and the environment, into one single model is a major challenge because of the complexity of the systems.In order to understand the interaction between a plant and the environment, it is advantageous to develop a model framework to integrate submodels that simulate various plant and environmental components. The objective of this paper is to outline a mechanistic and process-based model, which is capable of simulating interactions among environmental conditions around plants, plant growth and development, nitrogen and carbon cycles, with a three-dimensional root system submodel as an interface.The model presented in this paper is a mixed dimensional, multi-layer, field scale, weather-driven and daily time-step dynamic simulation model. The current version includes a plant growth and development component, a nitrogen cycling component, a carbon cycling component, plus a soil water component that includes representation of water flow to field drains as well as downwards through the soil layers, together with a heat transfer component. The components themselves and linkage among components are designed using object-oriented techniques, which makes the model robust, understandable and reusable. The components are implemented in the C++ programming language, and inputs and outputs of all components are organised as a database in either Microsoft^® SQL Server 2000, Access 2000 or MySQL5.0. Root architecture is visualised by using the OpenGL graphics system. Preliminary validation with two separate experimental datasets shows that the model can reasonably simulate root systems, nitrogen cycling, water movement and plant growth.     

Prediction of algal blooming using EFDC model: Case study in the Daoxiang Lake

Algal blooming has become one of the key fields of study on eutrophication of water body recently. The mechanism of algal blooming is still not understood well. However, it is obvious to understand that algal blooming has close relationship with chlorophyll-a. Therefore, if the trends of chlorophyll-a concentration can be simulated accurately, it will be helpful for the prediction of algal bloom events. In this study, a model named Environmental Fluid Dynamics Code (EFDC), which was developed by U.S. Environmental Protection Agency, was described and used to simulate the eutrophication process in the Daoxiang Lake, Beijing. To run the eutrophication model for the Lake, a field sampling was conducted in March-October of 2008 with interval of 10-20 days. Meanwhile, the algal bloom assessment criteria were investigated and the indicator of chlorophyll-a concentration was selected as input for the prediction of algal bloom in the Daoxiang Lake. After model calibration and validation, traditional statistics has been done between modeled results and observed values. The modeled results show that the simulated chlorophyll-a concentration basically agrees with the observed concentration except the later period of station 2# and the average algal bloom prediction accuracy is 63.43%. It was verified that the EFDC model can be used for chlorophyll-a concentration simulation and algal blooming prediction in the Daoxiang Lake.     

土壤中铜和镍的植物毒性预测模型的种间外推验证

在基于物种敏感性分布法推导土壤金属生态阈值过程中,利用毒性预测模型对来源于不同土壤的毒理学数据进行归一化处理可消除土壤性质差异的影响,但目前建立的毒性预测模型仅限于少数物种。本研究通过比较土壤中小白菜、西红柿和大麦的铜和镍的毒性预测模型应用于其他高等植物的预测效果,以及归一化前后各物种毒性阈值的种内变异程度,考察了土壤中铜和镍的植物毒性预测模型种间外推的可行性和适用范围,解决了铜和镍土壤生态阈值导出过程中的方法学问题。土壤中镍对小白菜的毒性预测模型能较好地预测芥菜和青椒的镍毒性阈值,利用该模型对芥菜和青椒在不同土壤中的镍毒性阈值进行归一化后亦能显著降低其种内变异,其种内变异系数分别从1.18和1.25降至0.31和0.06;但将镍对小白菜、西红柿和大麦的毒性预测模型应用于莴笋和莴苣的毒性阈值预测时,在pH<6.0的酸性土壤中其预测值均小于实测值,其实测值与预测值的比值在3.2到6.8之间。对小麦、黄瓜和青椒的铜毒性阈值而言,小白菜模型预测效果优于西红柿和大麦模型。利用西红柿模型归一化黄瓜铜毒性阈值,其毒性阈值的种内变异系数从0.83降至0.14。大麦的铜毒性预测模型能较准确地预测水稻、洋葱、芥菜、包菜和萝卜的毒性阈值,且这5个物种的铜毒性阈值经大麦模型归一化后其种内变异均显著降低。本研究结果可为土壤中铜和镍的植物毒性预测模型的种间外推提供科学依据。     

除草剂苯噻草胺在土壤中的吸附

对除草剂苯噻草胺在6种不同土壤中的吸附行为进行了研究。结果表明,土壤对苯噻草胺有较强的吸附性。在试验浓度范围内,苯噻草胺的土壤吸附行为可用线性吸附模型表征。土壤有机质是影响苯噻草胺吸附行为的重要因素,其吸附系数有随土壤有机质含量增高而增大的趋势。通过在线性吸附系数（Kd)和土壤有机质含量（OM％）之间构建的回归方程：Kd=2.6120 OM% 1.0746,可以预测苯噻草胺的Kd值,其预测的可靠性通过蒙特卡洛模拟得以检验。     

Coupling soil water and shoot dynamics in three grass species: A spatial stochastic model on water competition in Neotropical savanna

Savannas are ecosystems known for their high environmental and economic value. They cover at least 20% of the global land surface and, in some cases, can act as a boundary between tropical rainforest and deserts. Water is an important determinant of savanna ecosystems.In this paper, we present a theoretical stochastic model of root competition for water, which couples, soil water availability, phenology, and root and shoot architecture applied to three Neotropical savanna grasses. Soil moisture was simulated using a daily balance, as proposed by Rodriguez-Iturbe et al. [Rodriguez-Iturbe, I., Porporato, A., Ridolfi, L., Isham, V., Cox, D.R., 1999. Probabilistic modelling of water balance at a point: the role of climate, soil and vegetation. Proc. R. Soc. London, Ser. A 455, 3789–3805.]. To simulate rainfall stochasticity, we used daily precipitation data from the airport weather station in the State of Barinas, Venezuela, for the period 1991–2007. Competition among neighbouring plants took into account the spatial distribution of the individuals. As a final step, the model allowed us to calculate the shoot dynamic of the species as a function of soil water availability.Using these data, we compared the behaviour of isolated plants, pairs and trios, and we found below-ground competition to be a fundamental component of global (shoot + root) competition. Finally, our model suggests various circumstances that allow poor competitor plants to coexist in competition for water with more successful competitors. Apparently, this is not only due to transpiration rates, but also to differences in shoot emergence and shoot growth.     

稀土元素在土壤中迁移、转化模型的建立及验证

建立了稀土在褐土土壤中迁移、转化模型，定量评价稀土在土壤中的迁移、转化过程。模型包括土壤中稀土化学热力学平衡体系、土壤水分物理形状和土壤剖面的稀土通量。在中国农业大学内进行了田间实验，对模型进行了验证，模拟值与实测值能很好的拟合。     

An interval dynamic multimedia fugacity (IDMF) model for environmental fate of PAHs and their source apportionment in a typical oilfield,China

An interval dynamic multimedia fugacity (IDMF) model with a new validation criterion of interval average logarithmic residual error (IALRE) was developed in this study. The environmental fate of polycyclic aromatic hydrocarbons (PAHs) and their source apportionment in a typical oilfield of China were simulated from 1985 to 2010. The PAH concentrations predicted by the model were in agreement with the measured concentrations, which were indicated by the IALREs calculated at 0.41, 0.63, 0.52, and 0.58 for air, water, soil, and sediment, respectively. The multimedia concentrations of Σ16 PAHs were 29.55, 39.22, 31.98, and 26.69 times greater in 2010 than those in 1985, and were higher than any other year modelled. Additionally, 87.82% of PAHs remained in the soil in 2010. PAH source emission into the soil was the major modelled source, whereas PAH degradation in the air was the major modelled loss pathway; the dominant transfer process between the adjacent compartments was atmospheric deposition from air to soil. It was demonstrated that high-temperature combustion was the major source of PAHs in the air and soil, whereas biomass and coal combustion were attributed to water and sediment compartments. The IDMF model was effective in the dynamic source apportionment of PAHs.     

盐渍土系统土壤水-地下水转化规律研究

通过野外定位观测和室内分析,探讨了苏打盐渍土微域特征(32m长的横截面)及其水分和地下水之间的转化规律。结果表明:土壤特征以及微地貌格局是制约土壤表层水分迁移的主要因素,并由此形成了大汽降水-土壤水-地下水转化的特殊模式。盐化草甸土分布在相对低洼的部位,苏打碱土分布在微坡地和高平地。苏打碱土质地为粉砂质亚粘土-粘土-粉砂-砂砾垂直结构,盐化草甸土以粉砂和砂砾结构为主。地表径流为水分迁移的关键环节,盐化草甸土成为地表水和地下水转化的通道。承接微坡地和高平地的径流,盐化草甸土在雨季含水率较多(甚至大到饱和)且迅速补给地下水,使地下水位逐渐上升。坡地土壤(苏打盐渍土)整个土层的含水率变化不显著。运用Vensim对盐渍土系统的土壤水-地下水的转化过程进行了模拟,结果与观测数据基本一致。在现代盐渍化过程研究中,以坡面系统为研究单元并考虑坡面径流,才能认识到盐渍化地区土壤水-地下水之间转化的实质。