广州市流溪河水库监测布点优化研究

运用方差分析及模糊聚类分析,对流溪河水库监测布点进行优化研究,结果表明：水库水质良好,各监测点间及垂向采样点间均无显著性差异;4个库区监测点可分为3类,其余监测点可分为4类。综合考虑确定流溪河水库监测布点方案,应设置对照、控制、出水3类断面,共7个监测点,分别为玉溪河、吕田河、汇水区、库区3个和大坝监测点,每年6月进行垂向分层采样,其余月份进行表层采样。     

Monitoring Studies of Augusta Bay Marine Waters

The monitoring of water quality today provides a great quantity of data consisting of the values of the parameters measured as a tunction of bme or as spatial function.In the marine environment, and especially in the suspended material, increasing importance is being given to the presence of particular pollution indices. With the increase in the number of sampling points, the amount of data increases and examining the results and their consequent interpretation becomes more difficult. To overcome such difficulties, numerous chemometric techniques have been introduced in environmental chemistry, such as Principal Component Analysis (PCA).The use of the PCA in this work has been applied to the analysis of twenty three different sampling points in three seasonal sampling cruises in the same year. This led to recognition of the influence and the localisation of wastewaters in the Augusta bay after measuring the water pollution parameters.The PCA made evident the difference between some sampling sites whose data were initially thought to be similar where the presence of hot industrial water discharge or urban wastewater determines the permanent water quality.Furthermore, it has allowed a choice of more significant parameters for monitoring programs and more representative sampling site locations.     

Assessing temporal representativeness of water quality monitoring data

The effectiveness of different monitoring methods in detecting temporal changes in water quality depends on the achievable sampling intervals, and how these relate to the extent of temporal variation. However, water quality sampling frequencies are rarely adjusted to the actual variation of the monitoring area. Manual sampling, for example, is often limited by the level of funding and not by the optimal timing to take samples. Restrictions in monitoring methods therefore often determine their ability to estimate the true mean and variance values for a certain time period or season. Consequently, we estimated how different sampling intervals determine the mean and standard deviation in a specific monitoring area by using high frequency data from in situ automated monitoring stations. Raw fluorescence measurements of chlorophyll a for three automated monitoring stations were calibrated by using phycocyanin fluorescence measurements and chlorophyll a analyzed from manual water samples in a laboratory. A moving block bootstrap simulation was then used to estimate the standard errors of the mean and standard deviations for different sample sizes. Our results showed that in a temperate, meso-eutrophic lake, relatively high errors in seasonal statistics can be expected from monthly sampling. Moreover, weekly sampling yielded relatively small accuracy benefits compared to a fortnightly sampling. The presented method for temporal representation analysis can be used as a tool in sampling design by adjusting the sampling interval to suit the actual temporal variation in the monitoring area, in addition to being used for estimating the usefulness of previously collected data.     

Groundwater monitoring plans at small-scale sites--an innovative spatial and temporal methodology

An innovative methodology for improving existing groundwater monitoring plans at small-scale sites is presented. The methodology consists of three stand-alone methods: a spatial redundancy reduction method, a well-siting method for adding new sampling locations, and a sampling frequency determination method. The spatial redundancy reduction method eliminates redundant wells through an optimization process that minimizes the errors in plume delineation and the average plume concentration estimation. The well-siting method locates possible new sampling points for an inadequately delineated plume via regression analysis of plume centerline concentrations and estimation of plume dispersivity values. The sampling frequency determination method recommends the future frequency of sampling for each sampling location based on the direction, magnitude, and uncertainty of the concentration trend derived from representative historical concentration data. Although the methodology is designed for small-scale sites, it can be easily adopted for large-scale site applications. The proposed methodology is applied to a small petroleum hydrocarbon-contaminated site with a network of 12 monitoring wells to demonstrate its effectiveness and validity.     

我国土壤环境质量监测技术路线研究

土壤环境质量监测技术路线根据"十二五"主要任务目标,设计了土壤环境监测国家、省、地(市)三级网络构架和以县级采样、地(市)级分析、省级质控、国家发布的网络运行模式,明确了每年监测一类重点区、5年完成一个监测周期的全国总报告,确立了以基本农田、蔬菜和果树基地、饮用水源地、重污染企业周边、规模化养殖场周边等为重点区域的约2.5万个国控采样点;对土壤重金属和有机污染物的分析测试与评价方法进行了优化筛选,制定了质控措施,对深入开展土壤环境质量例行监测具有重要意义。     

地表水自动与常规监测评价结果一致性对比分析

选择分布在不同流域的15个与自动监测站位置一致性较好的断面,对其进行为期1年的"同时同点位"对比监测.分析数据发现:由于采样时间和位置的偏差,自动监测月均值与常规手工监测数据的可比性并不理想.采用以下4种方法评价水质:自动5项、手动5项、手动21项、自动5项+手动16项.比较第1和第2种方法,水质评价类别相同与变化一类...     

Impact of intensive horticulture practices on groundwater content of nitrates, sodium, potassium, and pesticides

A monitoring program of nitrate, nitrite, potassium, sodium, and pesticides was carried out in water samples from an intensive horticulture area in a vulnerable zone from north of Portugal. Eight collecting points were selected and water-analyzed in five sampling campaigns, during 1 year. Chemometric techniques, such as cluster analysis, principal component analysis (PCA), and discriminant analysis, were used in order to understand the impact of intensive horticulture practices on dug and drilled wells groundwater and to study variations in the hydrochemistry of groundwater. PCA performed on pesticide data matrix yielded seven significant PCs explaining 77.67% of the data variance. Although PCA rendered considerable data reduction, it could not clearly group and distinguish the sample types. However, a visible differentiation between the water samples was obtained. Cluster and discriminant analysis grouped the eight collecting points into three clusters of similar characteristics pertaining to water contamination, indicating that it is necessary to improve the use of water, fertilizers, and pesticides. Inorganic fertilizers such as potassium nitrate were suspected to be the most important factors for nitrate contamination since highly significant Pearson correlation (r = 0.691, P < 0.01) was obtained between groundwater nitrate and potassium contents. Water from dug wells is especially prone to contamination from the grower and their closer neighbor's practices. Water from drilled wells is also contaminated from distant practices.     

Characterization of spatial and temporal patterns in surface water quality: a case study of four major Lebanese rivers

In this work, four major Lebanese rivers were investigated, the Damour, Ibrahim, Kadisha, and Orontes, which are located in South, Central, and North Lebanon and Bekaa Valley, respectively. Five sampling sites were considered from upstream to downstream, and 12 sampling campaigns over four seasons were conducted during 2010–2011. Thirty-seven physicochemical parameters and five microbial tests were evaluated. A principal component analysis (PCA) was used for data evaluation. The first PCA, applied to the matrix-containing data that was acquired on all four rivers, showed that each river was distinct in terms of trophic state and pollution sources. The Ibrahim River was more likely to be polluted with industrial and human discharges, while the Kadisha River was severely polluted with anthropogenic human wastes. The Orontes and Damour rivers seemed to have the lowest rates of water pollution, especially the Orontes, which had the best water quality. PCA was also performed on individual data matrices for each river. In all cases, the results showed that the springs of each river have good water quality and are free from severe contamination. The other monitoring sites on each river were likely exposed to human activities and showed important spatial evolution. Through this work, a spatiotemporal fingerprint was obtained for each studied river, defining a “water mass reference” for each one. This model could be used as a monitoring tool for subsequent water quality surveys to highlight any temporal evolution of water quality.

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Factors dominating stratification cycle and seasonal water quality variation in a Korean estuarine reservoir

A comprehensive monitoring program was conducted during 2005-2007 to investigate seasonal variations of hydrologic stability and water quality in the Yeongsan Reservoir (YSR), located at the downstream end of the Yeongsan River, Korea. A principal component analysis (PCA) was performed to identify factors dominating the seasonal water quality variation from a large suite of measured data--11 physico-chemical parameters from 48 sampling sites. The results showed that three principal components explained approximately 62% of spatio-seasonal water quality variation, which are related to stratifications, pollutant loadings and resultant eutrophication, and the advective mixing process during the episodic rainfall-runoff events. A comparison was then made between YSR and an upstream freshwater reservoir (Damyang Reservoir, DYR) in the same river basin during an autumn season. It was found that the saline stratification and pollutant input from the upstream contributed to greater concentrations of nutrients and organic matter in YSR compared to DYR. In YSR, saline stratification in combination with thermal stratification was a dominant cause of the longer period (for two consecutive seasons) of hypoxic conditions at the reservoir bottom. The results presented here will help better understand the season- and geography-dependent characteristics of reservoir water quality in Asian Monsoon climate regions such as Korea.     

长江水环境质量监测网络运行体系初步构建

为精准、客观、全面评价长江水环境质量和支撑长江大保护,通过研究制定一系列管理制度和技术规范,以优化调整的水环境质量监测网络为基础,建立了涉及管理制度、监测技术、质量控制和数据审核等方面的水质自动监测与采测分离手工监测相结合的网络运行机制。研究自动与手工监测的总磷前处理方式,实现了自动与手工监测数据的匹配和融合,并将融合数据应用于长江水环境质量评价、预警、考核与排名,实现了长江水环境质量监测网络的业务化运行。     

Using multivariate techniques as a strategy to guide optimization projects for the surface water quality network monitoring in the Velhas river basin,Brazil

Surface water quality monitoring networks are usually deployed and rarely re-evaluated with regard to their effectiveness. In this sense, this work sought to evaluate and to guide optimization projects for the water quality monitoring network of the Velhas river basin, using multivariate statistical methods. The cluster, principal components, and factorial analyses, associated with non-parametric tests and the analysis of violation to the standards set recommended by legislation, identified the most relevant water quality parameters and monitoring sites, and evaluated the sampling frequency. Thermotolerant coliforms, total arsenic, and total phosphorus were considered the most relevant parameters for characterization of water quality in the river basin. The monitoring sites BV156, BV141, BV142, BV150, BV137, and BV153 were considered priorities for maintenance of the network. The multivariate statistical analysis showed the importance of a monthly sampling frequency, specifically the parameters considered most important.     

Assessment of anthropogenic sources of water pollution using multivariate statistical techniques: a case study of the Alqueva’s reservoir, Portugal

Multivariate statistical techniques were applied to evaluate spatial/temporal variations, and to interpret water quality data set obtained at Alqueva reservoir (south of Portugal). The water quality was monitored at nine different sites, along the water line, over a period of 18 months (from January 2006 to May 2007) using 26 water quality parameters. The cluster analysis allowed the formation of five different similarity groups between sampling sites, reflecting differences on the water quality at different locations of the Alqueva reservoir system. The PCA/FA identified six varifactors, which were responsible for 64% of total variance in water quality data set. The principal parameters, which explained the variability of quality water, were total phosphorus, oxidability, iron, parameters that at high concentrations indicate pollution from anthropogenic sources, and herbicides indicative of an intensive agricultural activity. The spatial analysis showed that the water quality was worse in the north of the reservoir.     

基于不同方法的水生态健康评估 ——以北京市典型水体为例

在北京选择密云水库、昆明湖、福海和北运河水系河流等9个典型水体,利用基于不同水生生物的多样性指数法和综合指数法开展水生态健康评价.结果表明,基于浮游植物和底栖动物的Shannon-Wiener多样性指数所指示的水生态状况分别对应轻污染～中污染和中污染～重污染等级,而结合生境、水质和水生生物指标的两项综合指数反映出的水生...     

Classification of the Caspian Sea coastal waters based on trophic index and numerical analysis

In order to characterize the trophic state of the southern coastal waters of the Caspian Sea, trophic index (TRIX) as well as numerical analysis using cluster and discriminant analysis were employed in this study. Chemical and biological parameters (NO₃, NO₂, NH₄, PT, DO, and Chla) used in this study were collected seasonally from summer 1999 to spring 2000. A new trophic index developed by modification of TRIX indicated mesotrophic to eutrophic conditions for the Caspian Sea. Numerical analysis revealed three groups of the study area and it was found that the used methods are in good agreement. Both of them predicted poor to moderate conditions in the western part of the study area and the numerical classification predicted trophic conditions in the study area. However, TRIX was found to be a more accurate and suitable method. It performs more conservatively than the numerical classification and characterized lower classes of water quality for the stations in central and eastern parts of the study area.     

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.     

淀山湖富营养化驱动力初探

通过2015—2019年在淀山湖布设5个采样点,监测总氮、总磷等12项水质指标,运用综合营养状态指数法和潜在性富营养化评价法对该水域富营养化水平进行分析,采用主成分分析法初步确定影响该水域富营养化水平的主要驱动因子。结果表明：监测期间淀山湖水质因子时空分布差异显著。综合营养状态指数法评价结果表明,淀山湖富营养化在轻—中度富营养化水平,并且季节变化规律明显;潜在性富营养化法评价结果表明,监测期间淀山湖以磷限制潜在性富营养为主。驱动因子分析表明硝酸盐氮是该水域富营养化最主要的驱动因子。     

A multi-variate methodology for analyzing pre-existing lake water quality data

Environmental agencies are given the task of monitoring water quality in rivers, lakes, and other bodies of water, for the purpose of comparing the results with regulatory standards. Monitoring follows requirements set by regulations, and data are collected in a systematic way for the intended purpose. Monitoring enables agencies to determine whether water bodies are polluted. Much effort is spent per monitoring event, resulting in hundreds of data points typically used solely for comparison with regulatory standards and then stored for little further use. This paper devises a data analysis methodology that can make use of the pre-existing datasets to extract more useful information on water quality trends, without new sample collection and analysis. In this paper, measured lake water quality data are subjected to statistical analyses including Principal Component Analysis (PCA) to deduce changes in water quality spatially and temporally over several years. It was found that the lake as a whole changed temporally by season, rather than spatially. Storm events caused the greatest shifts in water quality, though the shifts were fairly consistent across sampling stations. This methodology can be applied to similar datasets, especially with the recent emphasis by the U.S. EPA on protection of lakes as water sources. Water quality managers using these techniques may be able to lower their monitoring costs by eliminating redundant water quality parameters found in this analysis.     

Evaluation of significant sources influencing the variation of water quality of Kandla creek, Gulf of Katchchh, using PCA

To evaluate the significant sources contributing to water quality parameters, we used principal component analysis (PCA) for the interpretation of a large complex data matrix obtained from the Kandla creek environmental monitoring program. The data set consists of analytical results of a seasonal sampling survey conducted over 2 years at four stations. PCA indicates five principal components to be responsible for the data structure and explains 76% of the total variance of the data set. The study stresses the need to include new parameters in the analysis in order to make the interpretation of principal components more meaningful. The PCA could be applied as a useful tool to eliminate multi-collinearity problems and to remove the indirect effect of parameters.     

逐步回归在评价水质监测采样过程QC/QA中的运用

评价监测采样过程ＱＣ／ＱＡ的优劣的方法并不多见,本文试图通过逐步回归的方法来探讨特定水质监测对象监测采样过程ＱＣ／ＱＡ的评价问题。