基于多层统计分析法的河流水质发展模型研究

采用多层统计分析法,以大沽排污河为例,建立了主要污染因子COD、NH3-N的随机截距发展模型、随机截距/斜率发展模型以及纳入时间变化协变量的随机截距/斜率发展模型,其中NH3-N随机截距/斜率发展模型和纳入时间变化协变量的COD随机截距/斜率发展模型能较好地反映水质沿不同水期(枯水期、平水期、丰水期)的纵向变化规律以及采样点处污染物浓度的时空变化规律。模型的成功运用将为河流的综合整治和科学管理提供科学依据。     

沱江水质现状评价与变化趋势分析

收集了沱江2000年—2008年共9年的出口水质监测数据,利用内梅罗综合指数法和Spearman秩相关系数分析了沱江水质现状与变化趋势(分丰水期、枯水期、平水期和全年)。结果表明,2008年沱江出口丰水期、枯水期、平水期和全年的内梅罗综合指数均小于0.70,表明没有超标的污染项,说明沱江出口水质状况良好。对水质现状健康风险分析表明,化学致癌物是主要的风险来源,个人总年风险超过ICRP标准。变化趋势分析显示:沱江出口在丰水期、平水期和全年水质的内梅罗指数呈不显著性上升趋势,水质呈逐年不显著性变差;枯水期水质的内梅罗指数呈不显著性下降趋势,水质呈逐年不显著性好转趋势。     

基于主成分分析的松花江流域黑龙江段水质评价

对松花江流域黑龙江控制区段13个监测断面不同水期的水质指标进行主成分分析,以鉴别主要的污染源,进而提出有针对性的水污染控制对策。结果表明,BOD5、COD、TP、TN为主要污染指标,合计贡献率为49. 529%,首要污染源为生活污水与农业废水。第二大污染指标主要为石油类、阴离子表面活性剂、氰化物,主要来源于工业废水。总污染最严重的主要是阿什河口内平水期与龙安桥平水期。龙安桥平水期等对应的河流区段受生活污水及农业废水污染较为严重,苗家平水期等对应的河流区段受工业污染较为严重。     

沈阳市辽河保护区水环境容量研究

针对北方河流季节性强、不同水期流量差别悬殊的特点,对沈阳市辽河保护区分水期的水环境容量进行计算并采用段首、段尾、断面控制3种不同的控制模式,对计算结果进行对比分析。结果表明,辽河保护区水环境容量丰水期平水期枯水期;断面控制段尾控制段首控制,在断面控制下全年的COD环境容量为57 586.42 t/a,氨氮环境容量为5 398.19 t/a。根据各支流水质与干流水质的响应系数,推求出12条支流的环境容量。计算辽河保护区水环境容量总量并分配,研究结果可为保护区水环境管理及相似流域的污染防治提供参考。     

主成分分析法在乐安河水质评价中的应用

采用主成分分析法,借助SPSS软件,对乐安河7个监测断面的DO、COD、CODMn、BOD5、NH3-N、Cu、Cr等7个水质指标进行了分析计算.从原始数据中提取占总方差的82.367%的两个因子来反映水体的污染程度,经分析识别得到了乐安河的两个主成分因子:有机物及重金属(Cu、Cr),结果与实际情况相吻合.证实主成分分析法是一种有效的水质评价方法.     

几种水质评价方法在涑水河的应用与比较研究

采用模糊综合评价法、综合水质标识指数法、改进的内梅罗污染指数法进行水质评价,并对结果进行比较,分析3种水质评价方法的优缺点及适用条件。基于涑水河长期的水质监测数据,对涑水河9个断面进行水质评价,评价结果表明:涑水河污染严重,氨氮、总氮、COD指标严重超标,总氮的污染指数最大。水质最差的断面为庙上和郭家庄,相对污染较轻的为冷口和吕庄水库断面。模糊综合评价法对污染物的单项参数都进行了评价,解决了难以量化的模糊问题,能够对水体的功能和类型进行评价;改进的内梅罗污染指数法客观,能反映水体的污染程度,因此内梅罗污染指数法对水体污染程度更适用;综合水质标识指数法能进行定性和定量评价,并对水体类型与水体是否黑臭进行合理评价。     

基于主成分分析法的水源地水质评价

将主成分分析法运用到水源地水质评价中,对工程实例中5个监测断面的包括高锰酸盐指数、化学需氧量COD、五日生化需氧量BOD5、氨氮、石油类以及大肠菌群等6项水质指标进行分析计算,从信息量的角度讨论了水源地水质各污染组分包含的信息大小及各污染组分对断面水质的影响程度。结果表明,运用主成分分析法处理可以产生新的指标,而新的指标彼此间互补相关,可以很好地反映水质的综合状况。     

综合水质标识指数法在水体水质评价中的应用

采用水质综合标识指数法对某水体2012年全年水质污染特征进行分析.结果表明,该水体各断面水质基本达标;空间分布上水质从上游到下游呈下降趋势,时间分布上丰水期水质与枯水期水质相比基本无差异;CODMn是该水体的主要特征污染物,非点源是主要污染来源.     

排污河道沉积物中重金属风险评价

以华北某市DG排污河道为研究对象,在该排污河上科学布点,采集沉积物样品,检测分析了样品中重金属镉、锌、铜、铅、铬、镍、锰的含量。采用地积累指数法和平均沉积物质量基准系数法对DG排污河的重金属污染状况进行了风险评价。结果表明,目标河道沉积物中Cd、Zn、Cu累积较严重,是主要重金属污染物,且Cd处于最高污染水平。沉积物样品中Cd、Zn、Cu的毒性比例较大,非毒性比例较小。目标河道沉积物中重金属的综合作用使得该排污河道具有潜在生物毒性。     

不同生态等级下生态需水量模型研究

本研究建立了丰、平、枯三个水期不同生态等级下的生态需水量模型。基于生态等级评价指标体系,将生态等级评价体系中评价指标分为水质水量不可调控指标、水量可调控指标和水质可调控指标,利用OringinPro9对各评价指标之间进行了相关性分析。经过解析与计算,建立了丰水期、平水期和枯水期等不同生态等级下的生态需水量模型。该模型可反映生态等级与生态需水量间的定量关系,具有较强的实用价值。     

Assessment of seasonal variations in surface water quality

Assessment of seasonal changes in surface water quality is an important aspect for evaluating temporal variations of river pollution due to natural or anthropogenic inputs of point and non-point sources. In this study, surface water quality data for 16 physical and chemical parameters collected from 22 monitoring stations in a river during the years from 1998 to 2001 were analyzed. The principal component analysis technique was employed to evaluate the seasonal correlations of water quality parameters, while the principal factor analysis technique was used to extract the parameters that are most important in assessing seasonal variations of river water quality. Analysis shows that a parameter that is most important in contributing to water quality variation for one season may not be important for another season except for DOC and electrical conductance, which were always the most important parameters in contributing to water quality variations for all four seasons.     

主成分分析用于管网水质综合评价的研究

为明确供水管网中各种水质指标的关系，并能够综合各种水质监测指标来评价管网水的总体质量，利用基于多元统计分析原理的主成分分析法，将多种水质监测指标纳入同一系统进行定量化研究。以北方A市某小区供水管网为例，对其水质监测数据进行主成分分析，初步了解了该小区管网水质的变化情况，并按照总体水质情况对管网水质监测点进行了排序。此外，还按照水质指标间相关性的大小将所选定的13项常规监测指标分成了3组，为建立相应的宏观模型奠定了基础。     

Assessment of the water quality and identification of pollution sources of Kaduna River in Niger State (Nigeria) using exploratory data analysis

Exploratory data analysis such as hierarchical cluster analysis and principal component analysis were applied to water quality dataset of the Kaduna River, obtained during 3 years (2008–2010), monthly monitoring of eight key different sampling sites for 19 parameters to extract correlations and similarities between variables and to classify river sampling sites in groups of similar quality. Hierarchical cluster analysis grouped eight sampling sites into three statistically significant clusters of similar water composition. Six varifactors were obtained after varimax rotation of initial principal components using principal component analysis. These techniques gave an insight into the sources of pollution. Anthropogenic influence (municipal, industrial wastewater and agricultural run‐off) was the major source of river water pollution.     

The environmental impact of crude oil formation water: A multivariate approach

Water bodies receiving effluents from gas flow stations were sampled for ten months (March to December). Fifteen physicochemical parameters were monitored at six locations. Results obtained were analyzed unsing principal component analysis and cluster analysis. Five principal componets accounting for 72.43% of total variance were isolated. The first principal component was a measure of sea water intrusion, the second componet was a measure of total solids, the third component was a measure of organic pollution while the fourth and fifth principal component depicted the impact of effluent discharges. Effluents from gas flow stations were not the major causes of pollution of water resources in the locations of study. Cluster analysis showed no significant variation in the physicochemical characteristics of water samples based on location. Physicochemical parameters exhibited a seasonal pattern as a result of dilution by rainfall, reduced rate of evaporation in rainy season and dissolution of gaseous products of gas flaring in rain. Metals had no significant effect on the quality of water sampled from the six locations during the period of monitoring.     

Water quality and uses of the Bangpakong River (eastern Thailand)

The Bangpakong River is the most important watershed in the Eastern part of Thailand. Water quality parameters were sampled from June 1998 through May 1999 at 11 sites along a 227 km gradient, covering the wet season (June-November) and the dry season (December-May). Surface water was collected at three different stations per site (close to the banks and in the middle of the river), and analyzed for temperature, dissolved oxygen, turbidity, suspended solids, pH. ammonia, fecal coliforms, biochemical oxygen demand and chemical oxygen demand as well as conductivity, phosphate, and heavy metals. The Scottish water quality index (WQI) was adaptated to the tropical environment. The averaged WQI was low (41%) and quality declined significantly during the dry season (ANOVA, p<0.001). Although the quality rose somewhat at middle sites, only 27% of the WQI values during wet season and 2.5% during dry season were higher than 50%, denoting poor environmental quality. Within each season, the main sources of variability were the differences between sites along the gradient (48% during the wet season, 63% during the dry season), whereas monthly variability represented less than 20% of the variability. The seasonal results show that the river is suitable only for tolerant fish and wildlife species and is of doubtful use for potable water supply during the dry season. As quality improves during the wet period, water can be used for the production of potable water, but only with advanced treatment, and for indirect and noncontact recreational activities. In the middle stretches of the river, higher water quality permits multiple uses at moderate cost.     

自来水用户用水模式的确定方法研究

针对管网建模过程中用水量变化规律的复杂性,提出了根据实际用水量数据进行聚类分析,合理地对用户进行分类的方法,并通过主成分综合模型确定了每一类用户典型的用水模式曲线.该方法借助电磁水表实时远传的水量数据,能够及时更新用户的用水模式曲线,为提高建模的精确性打下了坚实的基础.     

Using chemical and physical parameters to define the quality of Pardo River water (Botucatu-SP-Brazil)

This paper aims to study the ecological system of the Pardo River, at the source and lower-order passages, which are in the Botucatu area, S?o Paulo State, Brazil. This study was carried out to determine water quality with some chemical-physical indicators, coliforms, and chemical species of samples taken monthly, 1995/02-1996/01, from eight sampling stations sited along the Pardo River. The results in the river monitoring are discussed based on annual averages, analysis of variance, and compared to Tukey's Studentized Range--HSD, and principal component analysis (PCA) was applied to normalize data to assess association between variables. We can conclude that the variables used are very efficient for identifying and that the dry season shows the worst water quality. These were caused by organic matter, nutrients (originate) from anthropogenic sources (spatial sources) and mainly municipal wastewater, affecting the quality and hydrochemistry of the river water, which have been differentiated and assigned to polluting sources. Meanwhile, the degree of degradation of the Pardo River is low (sewage treatment carried out by the city of Pardinho is efficient), leaving the water of the river suitable for use by the population of Botucatu, after conventional treatment (Conama, Resolu??o No. 20, CONAMA, Brazilia DF, 09-23, 1986--the water of the Pardo river is classified as level 03).     

Water quality index as a simple indicator of watersheds pollution in southwestern part of Iran

The Karun River is the most important watershed in the southwestern region of Iran. Water quality parameters were sampled from October 2006 through July 2007 at three sites along a 4 km gradient, covering both the wet and the dry season. Surface water was collected at three different stations per site (close to the banks and in the middle of the river) and analysed for 14 parameters and heavy metals. The values of 1300, 196.8 and 4042.9 ppm for chemical oxygen demand, biochemical oxygen demand and chloride, respectively, were higher than the standards limits. water quality index (WQI) values were very useful for the classification of the waters monitored. The averaged WQI was low (47%), and quality declined significantly during the dry season [analysis of variance (ANOVA, P < 0.05)]. The annual WQI values of 54.60, 40.29 and 45.71 from sites 1, 2 and 3 correspond to medium, bad and bad water qualities, respectively.