长沙市乔口镇土壤重金属地球化学基线值的厘定及应用

随着工业化和城市化等人类活动影响的加强,土壤表层重金属含量趋于上升.作为区分自然背景和人为影响的指标,土壤重金属地球化学基线为土壤的污染评价提供了重要的参考依据.本研究以湖南省长沙市乔口镇为研究区域,利用标准化方法和累积频率曲线法两种方法,计算了这一区域土壤重金属的地球化学基线值,并利用富集因子来评价当地的重金属污染状态.结果表明,除极少数样品外,这一区域绝大多数样品并未受到明显的人为污染.乔口镇地处湘江下游,其地球化学基线值可以为长沙-株洲-湘潭(简称长-株-潭)经济区的土壤重金属污染提供参考.     

重污染河流多源污染识别及环境背景值的确定——以宿州奎河氨氮为例

重污染河流因为受到多种人为污染的影响,确定其环境背景值难度很大。本文利用正态概率分布图和迭代标准差方法对奎河宿州段河水氨氮含量进行了分析,获取了奎河河水氨氮多源污染特征并确定了其环境背景值。结果表明,奎河河水氨氮主要来自于自然背景、农业污染、工业-生活污染,其环境背景值范围为0.52~1.08 mg/L。与区内其他河流河水分析结果的对比表明,直方图和正态概率分布图可以有效用于污染的多源识别与环境背景值确定,但利用迭代标准差方法估算环境背景值需要保证大部分样品未受到污染影响。     

承德市滦河流域土壤重金属地球化学基线厘定及其累积特征

选择承德市滦河流域为研究区,系统采集了351个表层土壤样品(0～20cm),测定了Cu、Ni、Cd、Cr、Pb、Zn、Hg、V、Ti、Mn、As和Co共12种重金属的含量,运用基于参比元素的标准化方法和累积频率曲线法确定了12种重金属元素的地球化学基线值,结合主成分分析法与地统计方法统计结果,分析了重金属的空间结构和分布特征,利用地累积指数法对不同土壤类型和土地利用方式表层土壤重金属累积程度进行了分类评价.结果表明,滦河流域表层土壤V、Ti、Cd、Pb、Mn和Co元素地球化学基线值高于河北省背景值,As、Zn、Cr、Cu、Ni和Hg元素地球化学基线值低于河北省背景值.表层土壤重金属总体累积程度由强至弱为:Cd Pb Cu Ti Mn Zn Cr Ni Co V Hg As,80%以上的土壤样品中Pb、Ti、V、As和Co元素属无累积-中度累积水平,70%以上土壤样品中Hg、Mn、Ni、Cu、As、Cd和Cr元素属无累积-中度累积水平.土壤重金属总体累积程度在棕壤、褐土和潮土中依次增大;在工矿用地、灌木林地中较高,林地、草地中相对最低.农用地表层土壤中Pb、Cd元素累积程度相对较高,分别有27. 69%和25. 38%的样品属中等以上累积水平.滦河流域表层土壤Ti、V、Co、Ni和Cr元素同源性较高,与原生高地质背景相关; Cd、Pb、Zn,Mn、Cu和As元素污染累积受成土母岩和人为因素共同作用影响,Hg元素累积则主要来源于人为活动.     

运用地球化学防治矿山环境污染

本文阐述了运用地球化学防治矿山环境污染的基本原理和一些应用实例,介绍了矿山环境污染和地球化学的基本联系;阐述了地球化学勘查方法和对尾矿、废石以及矿山酸性废水的治理的应用;探讨了美国在矿床的地球化学背景和基准值、矿床地质—环境模型应用方面的先进经验,并针对国情提出了一些其它防治矿山环境污染的地球化学方法。     

贵阳表层土壤中铬的环境地球化学基线研究

以土壤环境地球化学研究为主线,以贵州省贵阳市8046km2为研究区域,将土壤重金属污染元素铬的空间分布规律与环境地球化学机理研究相结合,建立了研究区域内表层土壤中铬元素的地球化学基线,并选用合适的判别指标判识自然作用过程与人类活动过程对土壤环境的影响.研究结果表明,贵阳市表层土壤中铬的基线值为44.0 mg·kg-1.地质累积指数分析结果显示,贵阳市46%的表层土壤未受铬的污染,47%的表层土壤在无污染到中度污染之间,6.8%的表层土壤为中度污染,只有0.2%的表层土壤介于中度污染到强污染之间.污染程度指数分析结果则显示,贵阳市69.2%的表层土壤未受到铬的污染,铬的污染程度最大为2.01,98.4%的表层土壤污染程度小于1,总污染程度小于0,即总体未受到污染.     

A methodology to estimate carbon storage and flux in forestland using existing forest and soils databases

Sequestration of carbon through expansion and management offorestland can assist in reducing greenhouse gas concentrationsin the atmosphere. Quantification of the amount of carbonpresents an ongoing challenge that calls for new approaches.These new approaches must seek to simplify the science-basedaccounting of carbon storage and flux, while adhering to generalprinciples of greenhous gas accounting. Quantifying change incarbon storage and carbon flux consists of two steps: developinga baseline of carbon storage, and measuring resulting storageand flux following a change of conditions. A methodology isproposed that accomplishes both steps, applicable to anaggregate-level analysis using the state of Iowa (U.S.A.) as a case study. The method combines existing databasesfrom the U.S. Forest Service (USFS) and U.S. Department ofAgriculture (USDA), and merges these with the methods of Birdsey(USDA, 1992, 1995; IPCC, 1997; EIIP, 1999) for partitioningcarbon stocks into storage pools. Forested ecosystems in the study area contain approximately 137.3 metric tons organiccarbon per hectare, or 114 million metric tons of carbon inaggregate. Of this total, 44.7 million tons are stored inbiomass tissue, and 69.2 million tons of carbon are contained insoils. Carbon flux due to forests in the state of Iowa isestimated to be a net annual sequestration (removal from theatmosphere) of 4.3 million metric tons of CO₂-equivalent,approximately 5% of the net annual CO₂-equivalentemissions from the state (Ney et al., 1996).     

安徽省石塘湖过去百余年湖水总磷浓度的定量重建

湖水总磷浓度对湖泊富营养化有很好的预警作用,历史湖水总磷浓度的定量重建能揭示湖泊营养演化历史.依据安徽省石塘湖沉积物钻孔的高分辨率硅藻研究结果,结合长江中下游湖泊硅藻-总磷转换函数模型,定量重建了其过去百余年来硅藻组合演替与历史湖水总磷浓度的变化过程.结果表明:石塘湖硅藻经历了从颗粒直链藻(Aulacoseira granulate)优势组合(1867-1981年)向富营养属种高山直链藻(Aulacoseira alpigae)优势组合(1981年以来)的变化.1981年以前,水体ρ(总磷)为50～60μg.L-1,湖泊一直维持在中等营养水平;1981年之后水体ρ(总磷)呈现明显升高趋势(＞100 μg·L-l),硅藻组合以高山直链藻占绝对优势,同时伴有喜好富营养环境的属种如极微小环藻(Cyclotella atomus)、梅尼小环藻(Cyclotella meneghiniana)、谷皮菱形藻(Nitzschia palea)和汉斯冠盘藻(Stephanodiscus hantzschii)等的增加,标志着湖泊富营养化的发生.20世纪80年代以来,农业及城市生活污水排放、农业化学肥料的大量使用以及过度养殖是硅藻种群转变和富营养化发生的主要根源.增温在一定程度上也影响着硅藻群落变化和富营养化的加重.根据重建的硅藻-水体总磷浓度结果,提出石塘湖治理的营养物基准水体ρ(总磷)为60 μg·L-1,为该湖整治和修复方案的制定提供可参考的目标,也为当地大量类似湖泊的治理提供重要参考.     

哈尔滨市表土重金属地球化学基线的确定及污染程度评价

在实地采样测试的基础上,分别运用地质累积指数法和污染负荷指数法对哈尔滨市表土103个样品中的Hg、Cd、Pb、As元素的单因子污染程度和多元素综合污染状态进行了评价,并在ArcGIS平台下对区域污染程度进行空间可视化。地质累积指数法表明：哈尔滨市Hg、Cd、Pb和As的污染等级主要为1级和2级;各目标元素均有一定比例的3级污染等级,以Hg最为明显,其中Hg和Cd出现了4、5级污染,所占比例不大;污染负荷指数法的综合评价结果显示,点污染负荷指数（IPL）以Ⅰ级为主,呈中度污染,区域污染负荷指数（IPLzone）值为1.478,污染程度属于Ⅰ级。     

Spatial and Temporal Trends of Physicochemical Parameters in the Water of the Reconquista River (Buenos Aires, Argentina)

The Reconquista river is one of the most polluted watercourses in Argentina. More than 3 million people and over 10,000 industries are settled on its basin. The available data show that pollution is mainly related to the discharge of domestic and industrial liquid wastes that are poured into the river almost untreated. At present no site of the river can be characterized as free of pollutants. Samples were taken monthly from 5 sites of the river; some 18–20 physicochemical parameters were determined in each sample. Analyses revealed significant differences in the degree of deterioration between sites. Two of them, close to the source of the river (Cascallares-S₁ and Paso del Rey-S₂) resulted less polluted than the two points located close to the mouth of the river (San Martin-S₄ and Bancalari-S₅). The worsening of the water quality in S₄–S₅ was attributed to the discharges of the Moron stream, a tributary that flows into the main course of the river a complex mixture of non treated waste waters. PCA was used in the ordination of samples (sites, season and physicochemical parameters). In the PCA performed using all variables, the first principal component showed positive correlation with N-NH₄ ⁺, conductivity, orthophosphate, BOD₅, COD and alkalinity, and negative correlation with DO. The second principal component was positively correlated with pH, temperature and chlorophyll a and negatively with phenols and hardness. In respect to the spatial distribution, the plot of the scores for the first two components of samples taken in each sampling station showed S₁ and S₂ values displayed farthest at the left side of the X axis with high DO. In contrast, S₄ and S₅ values stayed at the right side of this axis with high N-NH₄ ⁺, conductivity, orthophosphate, BOD₅, COD and alkalinity; data of S₃ were “intermediate". In order to identify seasonal trends in the concentration of contaminants scores of cases labeled by season were plotted. The line drawn on the ordination plane showed that summer samples tended to converge to the upper right portion of the graph where pollution variables had more importance on the first axis, and along the second axis with high correlation with pH, temperature and chlorophyll a. Winter scores were settled in the left lower part of the plot with minor contribution of pollution parameters and more importance of DO on the first axis. Samples of autumn and spring did not fit a clear cut pattern. Deceased