流溪河水库流域非点源溶解态氮磷污染负荷估算

流溪河水库作为广州市最重要的水源地之一,其水环境质量受到广泛关注. 利用基于SCS模型的污染负荷输出,对流溪河水库流域非点源溶解态氮磷污染负荷进行估算. 结果表明:流域非点源溶解态氮污染负荷为384.31 t/a,溶解态磷污染负荷为20.68 t/a.其中氮贡献最大的为林地,占50.93%;其次为园地,占18.10%.磷贡献最大的亦为林地,占49.22%;其次为旱地,占15.85%.溶解态氮单位面积污染负荷道路最高,为50.57 kg/(hm2·a);其次是水田,为33.60 kg/(hm2·a).而溶解态磷单位面积污染负荷最大是水田,为3.43 kg/(hm2·a);其次是道路,为3.19 kg/(hm2·a). 与水库流域颗粒态氮磷污染负荷比较表明,溶解态氮大于颗粒态氮,氮以溶解态迁移为主;溶解态磷则小于颗粒态磷,但由于颗粒态磷易随泥沙受阻或沉降,溶解态磷对水库水质的影响依然是总磷关注的重点.      

GWLF模型的原理、结构及应用

GWLF(Generalized Watershed Loading Function)模型是一个半分布式、半经验式的流域负荷模型。它能够利用GIS及RS提供的空间数据,在中型尺度流域的范围内进行非点源污染负荷估算,模型比较适合于数据量少,参数相对缺乏的地区。重点介绍了GWLF模型的污染物负荷估算的原理,同时将GWLF模型应用于天津市于桥水库流域,利用沙河流域1999年水量、水质数据进行校准,初步估算出于桥水库上游流域的非点源负荷。     

密云水库上游地区农田土壤有机碳储量及变化模拟

农田土壤有机碳(SOC)库对粮食安全和全球气候变化具有重要影响,因此,开展农田土壤有机碳储量及其动态变化研究在政治经济和生态环境层面具有重要意义.采用农业生物地球化学模型--DNDC对密云水库上游地区农田土壤有机碳储量及其变化进行模拟研究,首先应用当地实测结果进行模型验证,然后根据当地气候条件、土壤性质和现行农业耕作管理特点等建立GIS区域数据库,并在数据库的支持下进行区域模拟和分析.结果表明:2006年密云水库上游地区214 920 hm~2农田土壤(0～25 cm)的总有机碳储量为7 646×10~6 kg,其中位于河北省境内的该地区63.1%的农田储存了全区68.1%的SOC;平均每公顷农田SOC储量为35 576.1 kg,低于全国平均水平;由于化肥和有机肥投入的增加,经过1 a耕种后,2006年该地区农田SOC储量增加142.5×10~6 kg,整个地区及各区县农田土壤碳收支均为正,是大气CO_2的一个汇.情景分析表明,气温升高对该地区农田SOC积累具有显著的负效应;而提高秸秆还田比例、适量施用化肥、增施有机肥、增加灌溉和采取免耕方式等措施均能有效增加土壤有机碳的积累.     

三峡库区消落带土壤重金属分布特征及潜在风险评价

探讨了三峡库区重庆段沿江左右两岸、不同淹水高程消落带土壤中重金属的分布特征,并对研究区域重金属潜在风险进行了评价.结果表明,土壤中As(>19.0mg/kg)、Cd(>0.38mg/kg)含量超标,其余重金属含量均达国家土壤环境质量2级标准.沿江两岸Cu、Zn、Cr和Hg含量存在显著差异(P<0.1),左岸土壤中各重金属平均含量均小于右岸,而右岸各重金属的变异系数较左岸大.不同淹水高程重金属含量差异明显(P<0.1).沿长江水流方向,库区土壤中Cd含量先减后增,其余重金属(Cu,Pb,Zn,Hg,As,Cr)含量逐渐降低.Pb和Cr、Cd和As含量与土壤pH值均具有显著相关性(P<0.05或P<0.01).土壤中Cu与Hg,Cu与Cr间具有显著正相关关系(P<0.05);Cu、Pb、Zn与多种重金属间具有极显著正相关关系(P<0.01).库区消落带土壤重金属潜在生态风险处于轻微状态,主要生态风险元素为Cd.     

农业耕作对三峡水库支流库湾消落带土壤氮、磷含量及流失的影响

三峡水库低水位运行时,消落带由于落干期与作物生长期重叠,常被近岸农民开垦利用,然而传统的农业种植可能会影响库区水环境.为对比农耕和弃耕的水环境效应,选择一级支流澎溪河库湾消落带为研究区域,以玉米、土豆和花生地为研究对象,选择农耕期（2018年3~9月）和退耕期（2019年3~9月）对农耕地和弃耕地土壤各氮、磷形态含量进行研究,同时构建了农耕地与弃耕地氮、磷平衡模型,比较分析农耕和弃耕状态下土壤氮、磷收支特征及其流失风险.结果表明,玉米地土壤氨氮、全磷和无机磷含量在不同种植期差异显著;农耕地的氨氮和硝态氮的含量显著高于弃耕地,全磷、无机磷和钙结合态磷的含量显著低于弃耕地;土壤氮、磷盈余量大小顺序为玉米>土豆>花生,分别是76.89、51.92和43.74 kg·hm^-2以及79.69、75.76和17.78 kg·hm^-2,整体上,3种作物用地氮、磷盈余量大于流失风险值,研究区农耕地氮、磷污染潜势呈现.综上可知,消落带农业耕作将迫使氮、磷流失风险加剧,不利于水环境保护.     

Distribution of persistent organochlorine pesticides in tissue/organ of silver carp (Hypophthalmichthys molitrix) from Guanting Reservoir, China

The concentration of organochlorine pesticides（OCPs） in tissues and organs of silver carp（Hypophthalmichthys mofitrix） from Guanting Reservoir were investigated to evaluate the pollution potential and distribution of OCPs. A total of 16 OCPs were measured and the concentrations were in the range of 1.61-69.01 ng/g wet weight（ww） for total OCPs, 0.16- 0.75 ng/g ww for HCB. 0.75 -26.80 ng/g ww for SHCH（sum of α-,β-, γ- and δ-HCH） and 0.68-35.94 ng/g ww for SDDT（ sum of p, p＇-DDE, p, p＇-DDD, o, p＇-DDT and p, p＇- DDT）. The mean concentrations of total OCPs, HCB, ∑HCH, and ∑DDT were 18.04, 0.96, 7.14 and 9.28 ng/g ww, respectively. Among the organochlorine pesticides, β-HCH and p, p＇-DDE were the most dominant compounds in tissue and organ with the average concentrations of 4.42 and 8.14 ng/g, respectively. The results obtained in this study show that the levels of 16 OCP residues found in silver carps are low and pose no threat to human health and wildlife fed upon them on the basis of existing related quality guidelines. However, recent input of lindane and DDT might still exist in the area investigated and further investigation should be carried on.     

湖库富营养化敏感分级概念及指标体系研究

依据水体富营养化的危害程度与地理位置、流动条件等因素有关的概念,在一个较小的尺度上提出了水体富营养化敏感分级的概念,将富营养化的控制级别从河流-湖泊2级扩展到多级,并提出了分级采用的指标体系的一般概念.同时与美国环境保护局的大尺度分区(分级)方法进行了比较分析,发现美国14个生态分区河流与湖库的富营养化基准存在不协调之处,认为以该统计方法分别进行河流与湖库富营养化基准制定,可能产生河流基准的控制指标严于湖泊基准的不合理情况.      

改进USLE模型及其在于桥水库非点源污染敏感区划分中的应用

根据于桥水库周边流域非点源污染的特点,在充分考虑污染源与水库的距离以及库体自净作用因子的基础上,对饮用水源地非点源敏感区划分的通用土壤流失方程（USLE）方法进行了改进。结合2009年库区周边的ETM遥感影像、数字高程模型（DEM）和土地利用类型等数据,应用改进的USLE模型对库区周边非点源污染敏感区划分进行了实证研究。研究结果表明,库区西部的敏感性明显高于东部,库周的别山镇、五百户镇、渔阳镇、穿芳峪乡和马伸桥镇的水库沿岸是非点源污染敏感度较高的区域,是非点源污染的优先控制区。     

Two-dimensional habitat modeling of Chinese sturgeon spawning sites

Since the construction of the Gezhouba Dam in the 1980s, the number of Chinese sturgeon in the Yangtze River has been rapidly declining. The Gezhouba Dam has cutoff the migration path of these sturgeon, resulting in an overall reduction of suitable sturgeon habitat. This paper describes a habitat suitability index model that is used to evaluate the impacts of the Gezhouba Dam and Three Gorges Project on Chinese sturgeon spawning sites. Based on research concerning the reproduction characteristics of Chinese sturgeon, ten major ecological factors that influence reproduction were analyzed, including: water temperature, velocity, water depth, substrate, suspended sediment concentration, and the amount of egg predatory fish. The suitability index (SI) curves based on these ten ecological factors were obtained, and a habitat suitability function was developed. A two-dimensional mathematical model was also created to simulate and predict physical habitat situation (such as hydraulic, sediment, and substrate) of the Chinese sturgeon. By coupling the habitat suitability function and a two-dimensional mathematical model, a habitat suitability index model for Chinese sturgeon was established. The habitat suitability index model was validated by comparing measured data with predictions from the model. These comparisons showed that the computed results agreed well with the measured results, and the high calculated habitat suitability index (HSI) corresponded to high measured quantity of eggs per unit (1000 m³) discharge (CPUE_d). The calculated habitat suitability index for Chinese sturgeon also showed that the habitat suitability index was better in 1999, before the impoundment of the Three Gorges Project, compared with the habitat suitability in 2003. Simulation results of different discharges from Gezhouba Dam predicted that flow discharges between 10,000 and 30,000 m³/s were most suitable for sturgeon spawning.     

Neural network modeling of dissolved oxygen in the Gru?a reservoir, Serbia

The objective of this study is to develop a feedforward neural network (FNN) model to predict the dissolved oxygen in the Gru?a Reservoir, Serbia. The neural network model was developed using experimental data which are collected during a three years. The input variables of the neural network are: water pH, water temperature, chloride, total phosphate, nitrites, nitrates, ammonia, iron, manganese and electrical conductivity. Sensitivity analysis is used to determine the influence of input variables on the dependent variable. The most effective inputs are determined as pH and temperature, while nitrates, chloride and total phosphate are found to be least effective parameters. The Levenberg-Marquardt algorithm is used to train the FNN. The optimal FNN architecture was determined. The FNN architecture having 15 hidden neurons gives the best choice. Results of FNN models have been compared with the measured data on the basis of correlation coefficient (r), mean absolute error (MAE) and mean square error (MSE). Comparing the modelled values by FNN with the experimental data indicates that neural network model provides accurate results.