Evaluating the effectiveness of routine water quality monitoring in Miyun reservoir based on geostatistical analysis

This study used geographic information system techniques and geostatistics methods to evaluate the effectiveness of routine water quality monitoring in the western segment of the Miyun reservoir in Beijing. Methodologies as well as the sampling design are evaluated. The single-layer evaluation and three integrated evaluation methods including principal component analysis (PCA), ordinary kriging (OK)_Mean, and Mean_Layers were used to validate the effectiveness of evaluation methods, and the effectiveness of each sampling design was validated by comparing their errors. Results indicated that, while a single-layer evaluation only shows the trophic state of water at a specific level, an integrated evaluation synthetically analyzes and evaluates the trophic state of the entire water body. Furthermore, results of the integrated analysis show that a PCA method is more accurate and can represent the trophic state of the entire water body. The OK_Mean and Mean_Layers methods are only able to represent the mean level for trophic state of the entire water body but cannot reflect local trophic state and distribution details. Although methods used in the routine monitoring of Miyun reservoir have some similarities to the OK_Mean and Mean_Layers methods, their range of errors and uncertainty are greater because of a lack of detailed spatial continuous information. The analysis on the number of sampling points shows that, within a certain range of error, minor changes of sampling points will have no obvious impact on the monitoring results. For the routine monitoring of western Miyun reservoir, using only three to five sampling points for monitoring is inadequate. According to our analysis, it is more appropriate to use at least ten sampling points for monitoring these areas.     

农业面源污染对我国农村地表水的影响与对策研究

基于2015—2021年我国农村地表水环境质量监测数据,分析了农村地表水环境质量状况特征;选取农业农村社会经济活动相关参数,与农村地表水中主要超标因子的超标比例进行了相关性分析;以2020年为基准年,对全国31个行政区,涵盖农村地表水水质状况、农业农村活动水平和污染压力、环境容量3个方面的9个指标进行了聚类分析。结果表明,我国农村地表水的变化趋势、季节特点和主要超标因子等表现出明显的农业面源污染特征;乡村人口、农业投入品使用量和经济作物种植比例等参数与主要超标指标具有较强的相关性（R＞0.9）;聚类分析将全国31个行政区划分为7种不同的农业面源污染类型。提出,应根据不同地区农业面源污染特点,因地制宜地推进标准化规模养殖、畜禽粪污资源化利用、化肥减量行动、高效低风险农药推广等农业面源污染治理措施,进一步加大农村生活污水处理设施建设,同时,完善农村环境质量监测网络,加强农业面源污染监测和评估。     

船载自动监测系统在水质监测中的应用

水质监测是开展水生态环境评价、监管的基础性工作之一。随着对水生态环境保护与管理要求的提高，人工水质监测与自动水质监测相结合的模式应用越来越普遍。以船舶为载体的水质自动监测系统开展巡测，可实现高密度样品采集、检测及信息的实时传输，在长江泸州以下干流水域的实践中取得了良好效果。系统的应用可弥补常规监测断面间距过大、人工监测频次低、固定站房式水质自动监测站近岸取样等不足，对人工监测和自动监测形成有效补充；船载水质自动监测系统能够实现定点、定深、定时监测，可以在河流污染带监测、入河排污行为的监管以及偷排行为的溯源、水污染应急动态监测等工作中发挥有效作用，既可应用于长江干流等河道较宽且水质可能存在岸别差异的河流，也可应用于滇池、太湖、丹江口等大型湖泊、水库水生态环境监管。     

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.     

Critical Sampling Points Methodology: Case Studies of Geographically Diverse Watersheds

Only with a properly designed water quality monitoring network can data be collected that can lead to accurate information extraction. One of the main components of water quality monitoring network design is the allocation of sampling locations. For this purpose, a design methodology, called critical sampling points (CSP), has been developed for the determination of the critical sampling locations in small, rural watersheds with regard to total phosphorus (TP) load pollution. It considers hydrologic, topographic, soil, vegetative, and land use factors. The objective of the monitoring network design in this methodology is to identify the stream locations which receive the greatest TP loads from the upstream portions of a watershed. The CSP methodology has been translated into a model, called water quality monitoring station analysis (WQMSA), which integrates a geographic information system (GIS) for the handling of the spatial aspect of the data, a hydrologic/water quality simulation model for TP load estimation, and fuzzy logic for improved input data representation. In addition, the methodology was purposely designed to be useful in diverse rural watersheds, independent of geographic location. Three watershed case studies in Pennsylvania, Amazonian Ecuador, and central Chile were examined. Each case study offered a different degree of data availability. It was demonstrated that the developed methodology could be successfully used in all three case studies. The case studies suggest that the CSP methodology, in form of the WQMSA model, has potential in applications world-wide.     

Developing an environmental water quality monitoring program for Haraz River in Northern Iran

Water quality management plans are an indispensable strategy for conservation and utilization of water resources in a sustainable manner. One common industrial use of water is aquaculture. The present study is an attempt to use statistical analyses in order to prepare an environmental water quality monitoring program for Haraz River, in Northern Iran. For this purpose, the analysis of a total number of 18 physicochemical parameters was performed at 15 stations during a 1-year sampling period. According to the results of the multivariate statistical methods, the optimal monitoring would be possible by only 3 stations and 12 parameters, including NH₃, EC, BOD, TSS, DO, PO₄, NO₃, TDS, temperature, turbidity, coliform, and discharge. In other words, newly designed network, with a total number of 36 measurements (3 stations × 12 parameters = 36 parameters), could achieve exactly the same performance as the former network, designed based on 234 measurements (13 stations × 18 parameters = 234 parameters). Based on the results of cluster, principal component, and factor analyses, the stations were divided into three groups of high pollution (HP), medium pollution (MP), and low pollution (LP). By clustering the stations, it would be possible to track the water quality of Haraz River, only by one station at each cluster, which facilitates rapid assessment of the water quality in the river basin. Emphasizing on three main axes of monitoring program, including measurement parameters, sampling frequency, and spatial pattern of sampling points, the water quality monitoring program was optimized for the river basin based on natural conditions of the study area, monitoring objectives, and required financial resources (a total annual cost of about US $2625, excluding the overhead costs).     

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.     

Design of sampling locations for river water quality monitoring considering seasonal variation of point and diffuse pollution loads

The design of a water quality monitoring network (WQMN) is a complicated decision-making process because each sampling involves high installation, operational, and maintenance costs. Therefore, data with the highest information content should be collected. The effect of seasonal variation in point and diffuse pollution loadings on river water quality may have a significant impact on the optimal selection of sampling locations, but this possible effect has never been addressed in the evaluation and design of monitoring networks. The present study proposes a systematic approach for siting an optimal number and location of river water quality sampling stations based on seasonal or monsoonal variations in both point and diffuse pollution loadings. The proposed approach conceptualizes water quality monitoring as a two-stage process; the first stage of which is to consider all potential water quality sampling sites, selected based on the existing guidelines or frameworks, and the locations of both point and diffuse pollution sources. The monitoring at all sampling sites thus identified should be continued for an adequate period of time to account for the effect of the monsoon season. In the second stage, the monitoring network is then designed separately for monsoon and non-monsoon periods by optimizing the number and locations of sampling sites, using a modified Sanders approach. The impacts of human interventions on the design of the sampling net are quantified geospatially by estimating diffuse pollution loads and verified with land use map. To demonstrate the proposed methodology, the Kali River basin in the western Uttar Pradesh state of India was selected as a study area. The final design suggests consequential pre- and post-monsoonal changes in the location and priority of water quality monitoring stations based on the seasonal variation of point and diffuse pollution loadings.     

上海市浅层地下水在线监测技术要点

地下水在线监测技术可以实现地下水水质的高频监测,是未来发展的重要趋势。梳理国内外地下水在线监测技术研究进展,以上海市典型水文地质特征与环境质量状况为例,探讨地下水在线监测点位布设、指标筛选、监测方式及监测井设计等技术要点。首先,优化监测点位布设,对需要开展高频监测的区域或重点风险源开展在线监测,以代表性点位反映总体地下水环境质量状况。其次,综合筛选监测指标,除常规参数外,优先选取水体中的氨氮、高锰酸盐指数等作为在线监测指标,在具有潜在有机污染的区域,选取水中有机物、水中油等作为有机污染指示性指标。应进一步加强指标之间的相关性分析,为指示性指标的确立提供依据。再次,合理确定监测方式,根据取样方式以及污染源风险等级,设置相应的微型站和小型站。最后,优化监测井设计技术方案,进一步研究不同井管材质对地下水中无机或有机污染物的长期吸附(解吸)作用。     

决策树模型在水环境监测网络中选取代表性样点的应用

地表水体中的硝酸盐污染已经成为全球关注的热点环境问题之一。现今,国内外均建立了相关的监测网络对地表水体的水质实施长期监测,但是却导致大量的监测数据累积,给后续的科学研究工作带来了不便,尤其是在庞大的监测网络中如何选取有代表性样点的研究点则成为急需解决的问题之一。以比利时弗拉芒地区地表水的长期监测物理化学指标为例,利用决策树模型评估地表水样点的硝酸盐污染来源专家分类的有效性,为点位优化提供理论依据。原有监测点位的污染源专家分类和模型输出的可匹配率为80%,优化后监测点位从原有47个点降低到30个点,提高了监测工作效率。     

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.     

Scatterscore: A Reconnaissance Method To Evaluate Changes In Water Quality

Water quality data collected in periodic monitoring programs are often difficult to evaluate, especially if the number of parameters is large, the sampling schedule varies, and values are of different orders of magnitude. The Scatterscore Water Quality Evaluation was developed to yield a quantitative score, based on all measured variables in periodic water quality reports, indicating positive, negative or random change. This new methodology calculates a reconnaissance score based on the differences between up-gradient (control) versus down-gradient (treatment) water quality data sets. All parameters measured over a period of time at two or more sampling points are compared. The relationship between the ranges of measured values and the ratio of the medians for each parameter produces a data point that falls into one of four sections on a scattergram. The number and average values of positive, negative and random change points is used to calculate a Scatterscore that indicates the magnitude and direction of overall change in water quality. The Scatterscore Water Quality Evaluation, a reconnaissance method to track general changes, has been applied to 20 sites at which coal utilization by-products (CUB) were used to control acid mine drainage (AMD).     

水质自动监测参数的相关性分析及在水环境监测中的应用

通过水质自动监测数据与常规监测数据比较，表明了自动监测与常规监测数据的一致性；通过对自动监测数据分析，发现五参数可以更直接地反映水质变化，并且五参数之间以及五参数与其他监测指标之间都存在一定的相关性；典型污染事故预警监测案例的分析，更加凸显了五参数连续监测在预警监测中的重要性。     

农业面源污染防治的监测问题分析

近年来,农业面源污染已成为许多国家和地区水环境质量改善的主要影响因素,开展农业面源污染监测将为深入打好污染防治攻坚战提供重要支撑。该文系统分析当前我国农业面源污染监测存在的主要问题,综合考虑国内外经验,提出如下建议:采取空间嵌套式的布局模式优化地表水环境监测点位,充分发挥环境监测的预测预报和风险评估功能;建立包括污染源、产排污系数和空间传输过程的农业面源污染全过程监测网络;定期开展土壤氮、磷养分含量评估和地下水硝酸盐氮测定和评估;建立完善数据整合与共享机制。     

Seasonal monitoring of heavy metals and physicochemical characteristics in a lentic ecosystem of subtropical industrial region, India

Assessment of seasonal changes in surface water quality is an important aspect for evaluating temporal variations of lentic ecosystem (lakes and reservoirs) pollution due to industrial effluent discharge. In this study, nine metals and 15 physicochemical parameters, collected from four sampling sites in a tropical lake receiving the discharge from thermal power plant, coal mine, and chloralkali industry, during the years from 2004 to 2005, were analyzed. For greater efficacy in monitoring of heavy metals, particle-induced X-ray emission has been used during present investigation. Different statistical techniques like analysis of variance, Pearson correlation, principal component analysis, and factor analysis were employed to evaluate the seasonal correlations of physicochemical parameters. Most of the metals and physicochemical parameters monitored in the present study exhibited high spatial and temporal variability. Pertaining to metal pollution, the most polluted site was Belwadah, i.e., waters and sediments had the highest concentration of all the relevant metals. The reference site was characterized by the presence of low concentrations of metals in waters and in sediments. Based on the high metal concentration recorded in lake ambient, drinking, bathing, and irrigation water should not be used by the local people at the effluent discharge points.     

Chemometric Analysis of Surface Water Quality Data: Case Study of the Gorganrud River Basin, Iran

Canonical correlation analysis (CCA), principal component analysis (PCA), and principal factor analysis (PFA) have been adopted to provide ease of understanding: interpretation of a large complex data set in the Gorganrud River monitoring networks, evaluation of the temporal and spatial variations of water quality, and finally identification of monitoring stations and parameters which are most important in assessing annual variations of water quality in the river. In accomplishing the research, 11 surface water quality data related to both of physical and chemical parameters have been collected from seven monitoring stations from 1996 to 2002. In general, our results from CCA method indicated strong relationship between physical and chemical parameters in the Gorganrud River. In addition, analyzing data through the PCA and PFA techniques revealed that all monitoring stations are important in explaining the annual variation of data set. From the point of view of the degree of importance of parameters contributing to water quality variations, further investigations by running two scenarios (rotated factor correlation coefficient value equal to 0.95 and 0.90 for the first and second scenarios, respectively) showed that the important parameters in one season may not be important for another season. For example, unlike in summer, water temperature, total suspended solids, total phosphorous, and nitrate parameters were important, electrical conductivity, and turbidity parameters had been realized as important parameters in spring through the first scenario.     

Characterization and source apportionment of water pollution in Jinjiang River, China

Characterizing water quality and identifying potential pollution sources could greatly improve our knowledge about human impacts on the river ecosystem. In this study, fuzzy comprehensive assessment (FCA), pollution index (PI), principal component analysis (PCA), and absolute principal component score–multiple linear regression (APCS–MLR) were combined to obtain a deeper understanding of temporal–spatial characterization and sources of water pollution with a case study of the Jinjiang River, China. Measurement data were obtained with 17 water quality variables from 20 sampling sites in the December 2010 (withered water period) and June 2011 (high flow period). FCA and PI were used to comprehensively estimate the water quality variables and compare temporal–spatial variations, respectively. Rotated PCA and receptor model (APCS–MLR) revealed potential pollution sources and their corresponding contributions. Application results showed that comprehensive application of various multivariate methods were effective for water quality assessment and management. In the withered water period, most sampling sites were assessed as low or moderate pollution with characteristics pollutants of permanganate index and total nitrogen (TN), whereas 90 % sites were classified as high pollution in the high flow period with higher TN and total phosphorus. Agricultural non-point sources, industrial wastewater discharge, and domestic sewage were identified as major pollution sources. Apportionment results revealed that most variables were complicatedly influenced by industrial wastewater discharge and agricultural activities in withered water period and primarily dominated by agricultural runoff in high flow period.     

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.     

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.