在评价微污染水体中均值污染指数评价方法和活性污染指数评价方法的比较

针对均值污染指数评价方法在微污染水体环境质量评价上存在的缺陷,提出新的改进方法即活性污染指数法进行评价.根据吉林省白城市月亮湖水库2001～2005年监测结果,同时利用这两种方法进行评价,结果显示活性污染指数法评价白城市水库水质更客观和真实有效.     

Spatial water quality assessment of Langat River Basin (Malaysia) using environmetric techniques

This study investigates the spatial water quality pattern of seven stations located along the main Langat River. Environmetric methods, namely, the hierarchical agglomerative cluster analysis (HACA), the discriminant analysis (DA), the principal component analysis (PCA), and the factor analysis (FA), were used to study the spatial variations of the most significant water quality variables and to determine the origin of pollution sources. Twenty-three water quality parameters were initially selected and analyzed. Three spatial clusters were formed based on HACA. These clusters are designated as downstream of Langat river, middle stream of Langat river, and upstream of Langat River regions. Forward and backward stepwise DA managed to discriminate six and seven water quality variables, respectively, from the original 23 variables. PCA and FA (varimax functionality) were used to investigate the origin of each water quality variable due to land use activities based on the three clustered regions. Seven principal components (PCs) were obtained with 81% total variation for the high-pollution source (HPS) region, while six PCs with 71% and 79% total variances were obtained for the moderate-pollution source (MPS) and low-pollution source (LPS) regions, respectively. The pollution sources for the HPS and MPS are of anthropogenic sources (industrial, municipal waste, and agricultural runoff). For the LPS region, the domestic and agricultural runoffs are the main sources of pollution. From this study, we can conclude that the application of environmetric methods can reveal meaningful information on the spatial variability of a large and complex river water quality data.     

Surface Water Quality Assessment by Environmetric Methods

This environmetric study deals with the interpretation of river water monitoring data from the basin of the Buyuk Menderes River and its tributaries in Turkey. Eleven variables were measured to estimate water quality at 17 sampling sites. Factor analysis was applied to explain the correlations between the observations in terms of underlying factors. Results revealed that, water quality was strongly affected from agricultural uses. Cluster analysis was used to classify stations with similar properties and results distinguished three groups of stations. Water quality at downstream of the river was quite different from the other part. It is recommended to involve the environmetric data treatment as a substantial procedure in assessment of water quality data.     

Water quality monitoring and assessment of an urban Mediterranean lake facilitated by remote sensing applications

Degradation of water quality is a major problem worldwide and often leads to serious environmental impacts and concerns about public health. In this study, the water quality monitoring and assessment of the Koumoundourou Lake, a brackish urban shallow lake located in the northeastern part of Elefsis Bay (Greece), were evaluated. A number of water quality parameters (pH, temperature, dissolved oxygen concentration, electrical conductivity, turbidity, nutrients, and chlorophyll-a concentration) were analyzed in water samples collected bimonthly over a 1-year period from five stations throughout the lake. Moreover, biological quality elements were analysed seasonally over the 1-year period (benthic fauna). Statistical analysis was performed in order to evaluate the water quality of the lake and distinguish sources of variation measured in the samples. Furthermore, the chemical and trophic status of the lake was evaluated according to the most widely applicable classification schemes. Satellite images of Landsat 5 Thematic Mapper were used in order for algorithms to be developed and calculate the concentration of chlorophyll-a (Chl-a). The trophic status of the lake was characterized as oligotrophic based on phosphorus and as mesotrophic–eutrophic based on Chl-a concentrations. The results of the remote sensing application indicated a relatively high coefficient of determination (R ²) among point sampling results and the remotely sensed data, which implies that the selected algorithm is reliable and could be used for the monitoring of Chl-a concentration in the particular water body when no field data are available.     

基于3S技术的桂林市南溪河污染现状调查

城区小流域常是水污染较重的区域,又是污染源密集的地方,其非点源污染的监测与评估,需要实用方法。本调查基于3S技术,开展流域的监测与评价,包括空间定位的精度控制、非点源的遥感分类解译、采用排污系数对非点源污染评估、对污染源及污水管网做GIS空间分析。结合水质监测进行污径比和水环境容量的评价。重点讨论了城区人口解译方法和污染源评估方法,探讨典型城区非点源污染监测的思路。     

流域水污染监测与溯源技术研究进展

水污染特征识别和溯源是实施水污染精准治污的关键一步,也是流域水污染防治工作的前提。该文概述了不同分析监测技术、传统溯源方法与人工智能在水污染监测与溯源中的应用进展。光谱分析由于灵敏度高、准确度好,在实现水污染快速监测中应用最为广泛。传统溯源方法在复杂多样的水污染事故中不能准确快速地确定其污染源类型,而人工智能技术由于可以解决动态环境问题中的不确定性和复杂性,能够准确、智能地识别水质特征和追踪污染源。将人工智能与传统技术相结合是流域水污染溯源的发展趋势。展望了人工智能在水污染溯源方面的应用前景,为实现流域水污染全面监管提供了新的思路。     

国家污染源监测数据管理系统构建

针对全国污染源监测数据管理和应用需求，设计了一套污染源监测数据指标集，分析了污染源排放达标评价对象及评价结果表征方法，突破了建立电子化排放标准库、数据录入全过程多层次质量控制等关键技术，并在需求分析、功能设计和技术方案选择的基础上，开发了覆盖国家、省、市3级应用的污染源监测数据管理信息软件平台，取得了很好的应用效果。     

Assessment of water quality monitoring for the optimal sensor placement in lake Yahuarcocha using pattern recognition techniques and geographical information systems

Pollution and the eutrophication process are increasing in lake Yahuarcocha and constant water quality monitoring is essential for a better understanding of the patterns occurring in this ecosystem. In this study, key sensor locations were determined using spatial and temporal analyses combined with geographical information systems (GIS) to assess the influence of weather features, anthropogenic activities, and other non-point pollution sources. A water quality monitoring network was established to obtain data on 14 physicochemical and microbiological parameters at each of seven sample sites over a period of 13 months. A spatial and temporal statistical approach using pattern recognition techniques, such as cluster analysis (CA) and discriminant analysis (DA), was employed to classify and identify the most important water quality parameters in the lake. The original monitoring network was reduced to four optimal sensor locations based on a fuzzy overlay of the interpolations of concentration variations of the most important parameters.     

The assessment and prediction of temporal variations in surface water quality—a case study

In order to optimize the processes of sampling, monitoring, and management, the initial aim of this paper was to develop a model for the definition and prediction of temporal changes of water quality. In the case of the Morava River Basin (Serbia), the patterns of temporal changes have been recognized by applying different multivariate statistical techniques. The results of the conducted cluster analysis are the indicators of the existence of the three monitoring periods: the low-water, transitional, and high-water periods, which is in accordance with changes in the water flow in the analyzed river basin. A possibility of reducing the initial data set and recognizing the main pollution sources was examined by carrying out the principal component/factor analysis. The results indicate that the natural factor has a dominant influence in temporal groups. In order to recognize the discriminatory water quality parameters, a discriminant analysis (DA) was carried out. Conducting the DA enabled a significant reduction in the data set by the extraction of two parameters (the water temperature and electrical conductivity). Furthermore, the artificial neural network technique was used for testing the possibility of predicting changes in the values of the discriminant factors in the monitoring periods. The reliability of this method for the prediction of temporal variations of both extracted parameters within all temporal clusters has been proven.     

遥感技术在水生态环境管理的应用与前景

随着遥感数据源的不断丰富,遥感技术不断提高,可以解决越来越多的水环境问题。指出了当前水生态环境管理方面的主要需求,结合目前遥感技术的发展,对国内外的水环境遥感研究进展进行综述。以湖泊富营养化监测与评估、核电站温排水遥感监测及城市黑臭水体遥感监测为案例,具体阐述遥感在水环境管理中的应用方法及成效。未来水生态环境管理发展趋势将以水污染防治为主向水污染防治和水生态修复与保护并重发展。基于此趋势,提出遥感在水生态修复的应用潜力,利于更多地方部门积极有效应用遥感技术,解决水生态环境问题。     

Assessment of surface water quality using multivariate statistical techniques: a case study of Behrimaz Stream, Turkey

Multivariate statistical techniques, such as cluster analysis (CA), principal component analysis, and factor analysis, were applied for the evaluation of temporal/spatial variations and for the interpretation of a water quality data set of the Behrimaz Stream, obtained during 1 year of monitoring of 20 parameters at four different sites. Hierarchical CA grouped 12 months into two periods (the first and second periods) and classified four monitoring sites into two groups (group A and group B), i.e., relatively less polluted (LP) and medium polluted (MP) sites, based on similarities of water quality characteristics. Factor analysis/principal component analysis, applied to the data sets of the two different groups obtained from cluster analysis, resulted in five latent factors amounting to 88.32% and 88.93% of the total variance in water quality data sets of LP and MP areas, respectively. Varifactors obtained from factor analysis indicate that the parameters responsible for water quality variations are mainly related to discharge, temperature, and soluble minerals (natural) and nutrients (nonpoint sources: agricultural activities) in relatively less polluted areas; and organic pollution (point source: domestic wastewater) and nutrients (nonpoint sources: agricultural activities and surface runoff from villages) in medium polluted areas in the basin. Thus, this study illustrates the utility of multivariate statistical techniques for analysis and interpretation of data sets and, in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective stream water quality management.     

Application of multivariate statistical techniques in the assessment of water quality in the Southwest New Territories and Kowloon, Hong Kong

The application of different multivariate statistical techniques for the interpretation of a complex data matrix obtained during 2000?C2007 from the watercourses in the Southwest New Territories and Kowloon, Hong Kong was presented in this study. The data set consisted of the analytical results of 23 parameters measured monthly at 16 different sampling sites. Hierarchical cluster analysis grouped the 12 months into two periods and the 16 sampling sites into three groups based on similarity in water quality characteristics. Discriminant analysis (DA) provided better results both temporally and spatially. DA also offered an important data reduction as it only used four parameters for temporal analysis, affording 84.2% correct assignations, and eight parameters for spatial analysis, affording 96.1% correct assignations. Principal component analysis/factor analysis identified four latent factors standing for organic pollution, industrial pollution, nonpoint pollution, and fecal pollution, respectively. KN1, KN4, KN5, and KN7 were greatly affected by organic pollution, industrial pollution, and nonpoint pollution. The main pollution sources of TN1 and TN2 were organic pollution and nonpoint pollution, respectively. Industrial pollution had high effect on TN3, TN4, TN5, and TN6.     

杭州市钱塘江干支流水质多元统计分析

运用多元统计方法分析了杭州市钱塘江干支流上26个断面的水质监测指标。利用系统聚类分析方法将断面所在河流分为3组,与钱塘江流域污染空间分布现状基本一致。对各组水质的主成分分析表明,第1组河流水质以有机污染为主,水体中氮、磷营养盐浓度较高,水体污染程度较轻,污染来源相对单一;第2组河流水体受有机物、重金属、石油类等多个污染指标的影响,水体水质较第1组差,污染来源相对复杂;第3组河流水体既有一般有机污染,也有重金属、有毒有害物质的污染,水体水质污染严重。     

A comparative chemometric study for water quality expertise of the Athenian water reservoirs

The aim of the present study is to compare the application of unsupervised and supervised pattern recognition techniques for the quality assessment and classification of the reservoirs used as the source for the domestic and industrial water supply of the city of Athens, Greece. A new optimization strategy for sampling, monitoring, and water management is proposed. During the period of October 2006 to April 2007, 89 samples were collected from the three water reservoirs (Iliki, Mornos, and Marathon), and 13 parameters (metals and metalloids) were analytically determined. Generally, all the elements were found to fluctuate at very low levels, especially for Mornos that comprises the main water reservoir of Athens. Iliki and Marathon showed relatively elevated values, compared to Mornos, but below the legislative limits. Multivariate unsupervised statistical techniques, such as factor analysis/principal components analysis, and cluster analysis and supervised ones, like discriminant analysis and classification trees, were applied to the data set, and their classification abilities were compared. All the chemometric techniques successfully revealed the critical variables and described the similarities and dissimilarities among the sampling points, emphasizing the individual characteristics in every sample and revealing the sources of elements in the region. New data from posterior samplings (November and December 2007) were used for the validation of the supervised techniques. Finally, water management strategies were proposed concerning the sampling points and representative parameters.     

湖库水质评价污染因子选择方案探讨

目前我国湖库水质评价中,评价项目不统一,造成评价结果不具可比性.为抓住水体污染的主要矛盾,同时节约监测成本,对有代表性的湖库监测数据进行分析,提出了湖库水质评价项目的选择方案.     

Environmetrics to evaluate marine environment quality

The environmetric data analysis of analytical datasets from sediment and benthic organisms samples collected from different sampling sites along the coast of Black Sea near to City of Varna, Bulgaria has given some important indications about the bioindication properties of both type of samples. Various multivariate statistical methods like cluster analysis, principal components analysis, source apportioning modeling and partial least square (PLS) modeling were used in order to classify and interpret the parameters describing the chemical content of the coastal sediments (major components, heavy metals and total organic carbon) and benthic organisms (heavy metals). It has been shown that seriously polluted coastal zones are indicated in the same way by all benthic species, although some specificity could be detected for moderate polluted regions' e.g. polychaeta accumulated preferably Co, Cr, Cu, and Pb; crustacea - As, Cd, and Ni; mollusca - Zn. The identified latent factors responsible for the dataset structure are clearly indicated and apportioned with respect to their contribution to the total mass or total concentration of the species in the samples. The linear regression and PLS models indicated that a reliable forecast about the relation between naturally occurring chemical components and polluting species accumulated in the benthic organisms is possible.     

Development of the microbial communities in lake donghu in relation to water quality

There is increasing recognition that protozoa is very useful in monitoring and evaluating water ecological healthy and quality. In order to study the relationship between structure and function of protozoan communities and water qualities, six sampling stations were set on Lake Donghu, a hypereutrophic subtropical Chinese lake. Microbial communities and protists sampling from the six stations was conducted by PFU (Polyurethane foam unit) method. Species number (S), diversity index (DI), percentage of phytomastigophra, community pollution value (CPV), community similarity and heterophy index (HI) were mensurated. The measured indicators of water quality included total phosphorus (TP), dissolved oxygen (DO), Chemical oxygen demand (COD), NH(4)(+), NO(2)(-) and NO(3)(-). Every month water samples from stations I, II, III, IV were chemically analyzed for a whole year, Among the chemically analyzed stations, station I was the most heavily polluted, station II was the next, stations III and IV had similar pollution degrees. The variable tendencies of COD, TP, NH(3), NO(2)(-), NO(3)(-), and DO during the year was approximately coincident among the six stations. Analysis from the community parameters showed that the pollution of station 0 was much more serious than others, and station V was the most slight. Of the community parameters, CPV and HI were sensitive in reflecting the variables of the water quality. Community similarity index was also sensitive in dividing water qualities and the water quality status of different stations could be correctly classified by the cluster analysis. DI could reflect the tendency of water quality gradient, species number and percentage of Phytomastigophora was not obvious in indicating the water quality gradient.     

The use of multivariate statistical methods for optimization of the surface water quality network monitoring in the Paraopeba river basin,Brazil

This study sought to evaluate and propose adjustments to the water quality monitoring network of surface freshwaters in the Paraopeba river basin (Minas Gerais, Brazil), using multivariate statistical methods. A total of 13,560 valid data were analyzed for 19 water quality parameters at 30 monitoring sites, over a period of 5 years (2008–2013). The cluster analysis grouped the monitoring sites in eight groups based on similarities of water quality characteristics. This analysis made it possible to detect the most relevant monitoring stations in the river basin. The principal components analysis associated with non-parametric tests and the analysis of violation of the standards prescribed by law, allowed for identifying the most relevant parameters which must be maintained in the network (thermotolerant coliforms, total manganese, and total phosphorus). The discharge of domestic sewage and industrial wastewater, that from mining activities and diffuse pollution from agriculture and pasture areas are the main sources of pollution responsible for the surface water quality deterioration in this basin. The BP073 monitoring site presents the most degraded water quality in the Paropeba river basin. The monitoring sites BP094 and BP092 are located geographically close and they measure similar water quality, so a possible assessment of the need to maintain only one of the two in the monitoring network is suggested. Therefore, multivariate analyses were efficient to assess the adequacy of the water quality monitoring network of the Paraopeba river basin, and it can be used in other watersheds.     

Assessment of surface water quality using a growing hierarchical self-organizing map: a case study of the Songhua River Basin,northeastern China,from 2011 to 2015

The analysis of a large number of multidimensional surface water monitoring data for extracting potential information plays an important role in water quality management. In this study, growing hierarchical self-organizing map (GHSOM) was applied to a water quality assessment of the Songhua River Basin in China using 22 water quality parameters monitored monthly from 13 monitoring sites from 2011 to 2015 (14,782 observations). The spatial and temporal features and correlation between the water quality parameters were explored, and the major contaminants were identified. The results showed that the downstream of the Second Songhua River had the worst water quality of the Songhua River Basin. The upstream and midstream of Nenjiang River and the Second Songhua River had the best. The major contaminants of the Songhua River were chemical oxygen demand (COD), ammonia nitrogen (NH₃-N), total phosphorus (TP), and fecal coliform (FC). In the Songhua River, the water pollution at downstream has been gradually eased in years. However, FC and biochemical oxygen demand (BOD₅) showed growth over time. The component planes showed that three sets of parameters had positive correlations with each other. GHSOM was found to have advantages over self-organizing maps and hierarchical clustering analysis as follows: (1) automatically generating the necessary neurons, (2) intuitively exhibiting the hierarchical inheritance relationship between the original data, and (3) depicting the boundaries of the classification much more clearly. Therefore, the application of GHSOM in water quality assessments, especially with large amounts of monitoring data, enables the extraction of more information and provides strong support for water quality management.     

Using discriminant analysis to assess polycyclic aromatic hydrocarbons contamination in Yongding New River

Yongding New River has been polluted by polycyclic aromatic hydrocarbons (PAHs) which are carcinogenic and mutagenic. In three periods (the abundant water period, mean water period, dry water period), ten sites (totally 30 samples) in Yongding New River were clustered into four categories by hierarchical cluster analysis (hierarchical CA). In the same cluster, the samples had the same approximate contamination situation. In order to eliminate the dimensional differences, the data in each sample, containing 16 kinds of PAHs, were standardized with normal standardization and maximum difference standardization. According to the results of the cubic clustering criterion, pseudo F, and pseudo t ² (PST2), the proper number of clustering for the 30 samples is 4. Before conducting hierarchical CA and K-means cluster analysis on the samples, we used principal component analysis to obtain another group data set. This data set was composed of the principal component scores which are uncorrelated variables. Hierarchical CA and K-means cluster analysis were used to classify the two data sets into four categories. With the classification results of hierarchical CA and K-means cluster analysis, discriminant analysis is applied to determine which method was better for normalization of the original data and which one was proper to cluster the samples and establish discriminant functions so that a new sample can be grouped into the right categories.