Assessment of seasonal and polluting effects on the quality of river water by exploratory data analysis

22 Physico-chemical variables have been analyzed in water samples collected every three months for two and a half years from three sampling stations located along a section of 25 km of a river affected by man-made and seasonal influences. Exploratory analysis of experimental data have been carried out by box plots, ANOVA, display methods (principal component analysis) and unsupervised pattern recognition (cluster analysis) in an attempt to discriminate sources of variation of water quality. PCA has allowed the identification of a reduced number of “latent” factors with a hydrochemical meaning: mineral contents, man-made pollution and water temperature. Spatial (pollution from anthropogenic origin) and temporal (seasonal and climatic) sources of variation affecting quality and hydrochemistry of river water have been differentiated and assigned to polluting sources. An ANOVA of the rotated principal components has demonstrated that (i) mineral contents are seasonal and climate dependent, thus pointing to a natural origin for this polluting form and (ii) pollution by organic matter and nutrients originates from anthropogenic sources, mainly as municipal wastewater. The application of PCA and cluster analysis has achieved a meaningful classification of river water samples based on seasonal and spatial criteria.     

再生水水质综合评价的主成分分析方法

采用主成分分析方法评价再生水综合水质,利用R软件强大的统计功能,对某再生水厂供水实测数据进行了定量化评价。结果表明,主成分分析方法是一种切实可行的水质综合评价方法,值得进一步研究,并能为其他相关行业的质量评价提供参考。     

应用综合水质指数法评价沙澧河漯河段的水质

以沙澧河漯河段3个断面2006-2009年的水质监测数据为基础,应用综合水质指数法,对沙澧河漯河段水质状况进行了分析与评价.将参与评价的项目分为3类:对人体危害严重且难以消除的污染物(第一类)、处理后容易达标的污染物(第二类)、其他污染物(第三类).评价结果表明,澧河水质优良并保持相对稳定;沙河水质稍差,但由尚好逐渐向...     

Modeling the contribution of point sources and non-point sources to Thachin River water pollution

Major rivers in developing and emerging countries suffer increasingly of severe degradation of water quality. The current study uses a mathematical Material Flow Analysis (MMFA) as a complementary approach to address the degradation of river water quality due to nutrient pollution in the Thachin River Basin in Central Thailand. This paper gives an overview of the origins and flow paths of the various point- and non-point pollution sources in the Thachin River Basin (in terms of nitrogen and phosphorus) and quantifies their relative importance within the system. The key parameters influencing the main nutrient flows are determined and possible mitigation measures discussed.The results show that aquaculture (as a point source) and rice farming (as a non-point source) are the key nutrient sources in the Thachin River Basin. Other point sources such as pig farms, households and industries, which were previously cited as the most relevant pollution sources in terms of organic pollution, play less significant roles in comparison. This order of importance shifts when considering the model results for the provincial level. Crosschecks with secondary data and field studies confirm the plausibility of our simulations. Specific nutrient loads for the pollution sources are derived; these can be used for a first broad quantification of nutrient pollution in comparable river basins. Based on an identification of the sensitive model parameters, possible mitigation scenarios are determined and their potential to reduce the nutrient load evaluated.A comparison of simulated nutrient loads with measured nutrient concentrations shows that nutrient retention in the river system may be significant. Sedimentation in the slow flowing surface water network as well as nitrogen emission to the air from the warm oxygen deficient waters are certainly partly responsible, but also wetlands along the river banks could play an important role as nutrient sinks.     

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。     

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.     

天津市北塘排污河不同水期的水质状况评价

以天津市北塘排污河为研究对象,检测分析了丰水期、平水期、枯水期的14个水质指标,采用改进的主成分分析法和内梅罗环境质量指数法分别对北塘排污河三个水期的水质状况进行了评价。运用改进的主成分分析法找出了主要污染断面和主要污染物,同时对影响水质的因素进行了初步探讨。运用内梅罗指数法验证了改进的主成分分析法的结论,并得出了三个水期的相对污染程度。结果表明,在丰水期和平水期,华明桥断面污染最为严重;在枯水期,赤贯桥断面污染最为严重。丰水期的主要污染物为镍、COD、锰、锌、铜;平水期为锌、COD、锰、浊度、铜;枯水期为锰、锌、铜、COD。北塘排污河污染状况普遍表现为枯水期〉丰水期〉平水期。     

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.     

Assessment of the surface water quality in Northern Greece

The application of different multivariate statistical approaches for the interpretation of a large and complex data matrix obtained during a monitoring program of surface waters in Northern Greece is presented in this study. The dataset consists of analytical results from a 3-yr survey conducted in the major river systems (Aliakmon, Axios, Gallikos, Loudias and Strymon) as well as streams, tributaries and ditches. Twenty-seven parameters have been monitored on 25 key sampling sites on monthly basis (total of 22,350 observations). The dataset was treated using cluster analysis (CA), principal component analysis and multiple regression analysis on principal components. CA showed four different groups of similarity between the sampling sites reflecting the different physicochemical characteristics and pollution levels of the studied water systems. Six latent factors were identified as responsible for the data structure explaining 90% of the total variance of the dataset and are conditionally named organic, nutrient, physicochemical, weathering, soil-leaching and toxic-anthropogenic factors. A multivariate receptor model was also applied for source apportionment estimating the contribution of identified sources to the concentration of the physicochemical parameters. This study presents the necessity and usefulness of multivariate statistical assessment of large and complex databases in order to get better information about the quality of surface water, the design of sampling and analytical protocols and the effective pollution control/management of the surface waters.     

Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti River (India)--a case study

This case study reports different multivariate statistical techniques applied for evaluation of temporal/spatial variations and interpretation of a large complex water-quality data set obtained during monitoring of Gomti River in Northern part of India. Water quality of the Gomti River, a major tributary of the Ganga River was monitored at eight different sites selected in relatively low, moderate and high pollution regions, regularly over a period of 5 years (1994-1998) for 24 parameters. The complex data matrix (17,790 observations) was treated with different multivariate techniques such as cluster analysis, factor analysis/principal component analysis (FA/PCA) and discriminant analysis (DA). Cluster analysis (CA) showed good results rendering three different groups of similarity between the sampling sites reflecting the different water-quality parameters of the river system. FA/PCA identified six factors, which are responsible for the data structure explaining 71% of the total variance of the data set and allowed to group the selected parameters according to common features as well as to evaluate the incidence of each group on the overall variation in water quality. However, significant data reduction was not achieved, as it needed 14 parameters to explain 71% of both the temporal and spatial changes in water quality. Discriminant analysis showed the best results for data reduction and pattern recognition during both temporal and spatial analysis. Discriminant analysis showed five parameters (pH, temperature, conductivity, total alkalinity and magnesium) affording more than 88% right assignations in temporal analysis, while nine parameters (pH, temperature, alkalinity, Ca-hardness, DO, BOD, chloride, sulfate and TKN) to afford 91% right assignations in spatial analysis of three different regions in the basin. Thus, DA allowed reduction in dimensionality of the large data set, delineating a few indicator parameters responsible for large variations in water quality. This study presents necessity and usefulness of multivariate statistical techniques for evaluation and interpretation of large complex data sets with a view to get better information about the water quality and design of monitoring network for effective management of water resources.     

基于主成分分析和综合指数法的饮用水水源地水质评价

将主成分分析和综合指数评价法相结合,对绵阳市饮用水水源地的水质现状进行了评价。结果表明,2006--2010年饮用水水源地水质呈下降趋势,特别是2010年水质污染程度严重,主成分载荷值最大者为BOD5,重金属汞的单因子指数为2,无法满足II级水源地（III类地表水）标准要求,并根据水质污染现状,提出了相应的保护水源地水质的措施。     

Spatial variation and source apportionment of water pollution in Qiantang River (China) using statistical techniques

Understanding the spatial distribution and apportioning the sources of water pollution are important in the study and efficient management of water resources. In this work, we considered data for 13 water quality variables collected during the year 2004 at 46 monitoring sites along the Qiantang River (China). Fuzzy comprehensive analysis categorized the data into three major pollution zones (low, moderate, and high) based on national quality standards for surface waters, China. Most sites classified as “low pollution zones” (LP) occurred in the main river channel, whereas those classified as “moderate and high pollution zones” (MP and HP, respectively) occurred in the tributaries. Factor analysis identified two potential pollution sources that explained 67% of the total variance in LP, two potential pollution sources that explained 73% of the total variance in MP, and three potential pollution sources that explained 80% of the total variance in HP. UNMIX was used to estimate contributions from identified pollution sources to each water quality variable and each monitoring site. Most water quality variables were influenced primarily by pollution due to industrial wastewater, agricultural activities and urban runoff. In LP, non-point source pollution such as agricultural runoff and urban runoff dominated; in MP and HP, mixed source pollution dominated. The pollution in the small tributaries was more serious than that in the main channel. These results provide information for developing better pollution control strategies for the Qiantang River.     

Static-based early-damage detection using symbolic data analysis and unsupervised learning methods

A large amount of researches and studies have been recently performed by applying statistical and machine learning techniques for vibration-based damage detection. However, the global character inherent to the limited number of modal properties issued from operational modal analysis may be not appropriate for early-damage, which has generally a local character. The present paper aims at detecting this type of damage by using static SHM data and by assuming that early-damage produces dead load redistribution. To achieve this objective a data driven strategy is proposed, consisting of the combination of advanced statistical and machine learning methods such as principal component analysis, symbolic data analysis and cluster analysis. From this analysis it was observed that, under the noise levels measured on site, the proposed strategy is able to automatically detect stiffness reduction in stay cables reaching at least 1%.     

浐河水质污染特征及水质改善措施分析

针对浐河水体遭受污染问题,通过对浐河水质特征断面的10项指标进行连续监测,分析了浐河水中污染物的组成和水质特征,对污染物的携入方式进行了调查,分析评价了浐河水体污染的原因,并提出了控制浐河水质污染的对策。结果表明,浐河水体中的物理指标水温、p H、重金属指标相对正常;重金属指标中只有少量的Fe、Zn、Cr元素;污染物主要为N、P等营养物质,水体的富营养化程度较为严重;河流水体浊度总体较高且与地表径流存在较高的相关性;污染的主要原因是浐河周边的水厂、污水处理厂所排出污水的点源污染;污染改善措施采用人工湿地、管网改建等适合不同断面特征的控制方式。     

邕江水源水质特征与变化趋势

通过对南宁市两个水源点2007-2009年监测数据的统计分析,得出邕江水源的自然水质特征和污染指标表现情况.结果表明,邕江原水的自然水质特征主要表现为浊度受季候水期影响较大,水的酸碱度为中性略偏弱碱,硬度适中;粪大肠菌群指标对邕江水源水质状况影响最大,近年来邕江水源的水质总体上保持稳定,未出现劣变的趋势.     

Influence of water quality on the presence of off-flavour compounds (geosmin and 2-methylisoborneol)

Geosmin and 2-methylisoborneol are off-flavour compounds related to poor organoleptics and a decrease in consumer satisfaction with drinking water. The relationship between these off-flavour compounds and 39 parameters of water quality (chemical, physical and biological) for three different surface waters from lotic systems that supply potable water to Quebec City and Lévis (Canada) was studied using principal component analysis. The objective of this study was to show that a multidimensional approach with principal component analysis using the component matrix serves to differentiate the processes involved in the appearance of the olfactory compounds from those not involved or little involved and to extract the most representative data of these processes. Our study shows that the presence of geosmin can be explained, in the case of the water studied, by a process associated primarily with the eutrophication of water and, to a lesser degree, by an allochthonous origin. However, the presence of 2-methylisoborneol may be linked to two processes with different origins, but of equal importance: a winter origin – most likely allochthonous – involving anthropic contributions and an autochthonous bacterial origin.     

Hydrochemical characteristics and water quality assessment of surface water and groundwater in Songnen plain, Northeast China

Water quality is the critical factor that influence on human health and quantity and quality of grain production in semi-humid and semi-arid area. Songnen plain is one of the grain bases in China, as well as one of the three major distribution regions of soda saline-alkali soil in the world. To assess the water quality, surface water and groundwater were sampled and analyzed by fuzzy membership analysis and multivariate statistics. The surface water were gather into class I, IV and V, while groundwater were grouped as class I, II, III and V by fuzzy membership analysis. The water samples were grouped into four categories according to irrigation water quality assessment diagrams of USDA. Most water samples distributed in category C1-S1, C2-S2 and C3-S3. Three groups were generated from hierarchical cluster analysis. Four principal components were extracted from principal component analysis. The indicators to water quality assessment were Na, HCO₃, NO₃, Fe, Mn and EC from principal component analysis. We conclude that surface water and shallow groundwater are suitable for irrigation, the reservoir and deep groundwater in upstream are the resources for drinking. The water for drinking should remove of the naturally occurring ions of Fe and Mn. The control of sodium and salinity hazard is required for irrigation. The integrated management of surface water and groundwater for drinking and irrigation is to solve the water issues.     

Multivariate analysis for assessing the quality of stormwater from different Urban surfaces of the Patiala city,Punjab (India)

Land use modifications associated with urbanization, such as clearance of vegetation, replacement of previously pervious areas with impervious surfaces and drainage channel modifications, result in increased runoff volumes, which often create flooding hazards and increase pollutant transport. An attempt has been made in the present study to investigate stormwater quality from five different urban sub-watersheds (that differ in land use and development activities) in the city of Patiala, India. The five sub-watersheds have similar geological, topographical and climatic conditions and were chosen to minimize the effect of these characteristics on stormwater quality and quantity. Stormwater samples were collected during six storm events between April 2010 and March 2011 and analyzed for BOD₅, COD, TSS, TDS, Oil and Grease, TKN, Total P, Coliforms and Heavy Metals (Zn, Cd, Ni, Pb, Fe and Cu). Results of the investigation indicate a strong correlation between land use and development activities and the resulting stormwater quality. TSS, COD and Oil and Grease were found to be major pollutants in surface runoff generated from commercial and urbanized catchments (all exceeded the surface water quality standards developed by Central Pollution Control Board, India). The water quality of the smaller residential catchment was better as compared to other catchments. Principal component analysis was investigated to identify linkages between stormwater quality and urban surface types. It was also confirmed through regression analysis that both antecedent dry period and rainfall intensity have telling influence on stormwater quality. Results obtained can provide practical information for improved stormwater management.     

A decision support system for water quality issues in the Manzanares River (Madrid, Spain)

The Manzanares River, located in Madrid (Spain), is the main water supplier of a highly populated region, and it also receives wastewater from the same area. The effluents of eight Waste Water Treatment Plants (WWTPs) downstream of the river, which represent 90% of the flow in the middle and lower parts of the river, are the primary sources of water pollution. Although the situation has improved slightly in the last two years, the water in the river is highly polluted, making it uninhabitable for aquatic life. Water quality modelling is typically used to assess the effect of treatment improvements in water bodies. In this work, the GESCAL module of the Aquatool Decision Support System Shell was used to simulate water quality in the Manzanares River. GESCAL is appropriate for modelling in an integrated way water quality for whole water resources systems, including reservoirs and rivers. A model was built that simulates conductivity, phosphorous, carbonaceous organic matter, dissolved oxygen, organic nitrogen, ammonia, and nitrates. The period from October 2006 to September 2008 was selected for calibration due to the many treatment modifications that occurred during this time. An earlier and longer period, from October 2000 to September 2006, was used for validation. In addition, a daily model was used to analyse the robustness of the GESCAL model. Once the GESCAL model was validated, different scenarios were considered and simulated. First, different combinations of nutrient elimination among the different WWTPs were simulated, leading to the conclusion that investments have to focus on three of the proposed WWTPs. Moreover, these treatments will not be sufficient to maintain fish habitat conditions at all times. Additional measures, such as the increment of the flow in the river or oxygen injection, were simulated. Incrementing the flow of the Manzanares River has been shown to be an efficient means of increasing water quality, but this implies an increment in the risk of water scarcity situations in the Madrid water supply system.     

基于改进型GM(1,1)模型的城市水源水质指标预测

以武汉市主要供水水源的高锰酸盐指数为研究对象,应用经指数平滑改进后的GM(1,1)模型对9个水厂的水源水质进行预测.从预测精度来看,改进型GM(1,1)模型预测精度较常规GM(1,1)模型有所提高,5个改进型GM(1,1)模型精度高于常规型,3个持平,1个稍低.从预测结果来看,6个水厂供水水源水质预测发展趋势良好,3个水厂水源水质变化趋势较差.