Evaluation of spatial and temporal variation in water quality by pattern recognition techniques: A case study on Jajrood River (Tehran,Iran)

In this paper, principal component analysis (PCA) and hierarchical cluster analysis (CA) methods have been used to investigate the water quality of Jajrood River (Iran) and to assess and discriminate the relative magnitude of anthropogenic and “natural” influences on the quality of river water. T, EC, pH, TDS, NH₄, NO₃, NO₂, Turb., T.Hard., Ca, Mg, Na, K, Cl, SO₄, SiO₂ as physicochemical and TC, FC as biochemical variables have been analyzed in the water samples collected every month over a three-year period from 18 sampling stations along a 50 km section of Jajrood River that is under the influence of anthropogenic and natural changes. Exploratory analysis of experimental data has been carried out by means of PCA and CA in an attempt to discriminate sources of variation in water quality. PCA has allowed identification of a reduced number of mean 5 varifactors, pointing out 85% of both temporal and spatial changes. CA classified similar water quality stations and indicated Out-Meygoon as the most polluted one. Ahar, Baghgol, Rooteh, Befor Zaygan, Fasham, Roodak and Lashgarak were identified as affected by organic pollution. A Scree plot of stations in the first and second extracted components on PCA also gave us a classification of stations due to the similarity of pollution sources. CA and PCA led to similar results, though Out-Meygoon was identified as the most polluted station in both methods. Box-plots showed that PCA could approximately demonstrate temporal and spatial variations. CA gave us an overview of the problem and helped us to classify and better explain the PCA results.     

Surface water quality and its control in a river with intensive human impacts–a case study of the Xiangjiang River,China

Surface water quality and its natural and anthropogenic controls in the Xiangjiang River were investigated using multivariate statistical approaches and a comprehensive observation dataset collected from 2004 to 2008. Cluster analysis (CA) grouped the 15 different sampling stations into five clusters with similar hydrochemistry characteristics and pollution levels. Four principal components (PCs), nutrients, heavy metals, natural components, and organic components, were extracted from the entire dataset. Comparison of the different regional characteristics of these four PCs revealed a decreasing trend for heavy metals and an increasing trend for organic factor on an annual scale, and the seasonal trend was only observed for natural factor. We also conducted analysis of variance (ANOVA) in combination with principal component analysis (PCA) to quantify the relative contribution of spatial and temporal variations to each of the four PCs. The results revealed that 62% of the contributions from the spatial sites were responsible for variations in heavy metals, while 83% of the contributions from the sampling time were responsible for natural variations observed. However, no significant spatial or temporal contributions were found to be responsible for the nutrient and organic variations. Finally, some suggestions regarding water management were put forward based on the current status and future trends of surface water quality in the Xiangjiang River.     

京杭运河(苏州段)水质时空分布特征分析

本文基于数据分析工具Excel 2007和SPSS17.0,采用多元统计方法中的聚类分析与判别分析对2008年京杭运河(苏州段)水质时空分布特征进行分析,以识别水质指标的时间与空间差异性。时间分析结果将京杭运河(苏州段)划分为与丰、平、枯水期略有差异的3个时期,其中7项显著性时间差异的水质指标为pH、NH4-N、CODMn、DO、BOD5、V-phen、cn-total。空间分析结果将研究区域分为3个污染区域;空间分析判别函数的构建表明仅pH、NH4-N、CODMn、DO、BOD55项水质指标即可反映研究区域水质的差异性。本研究结果对改善省界河流水质、缓解省际纠纷、维护社会和谐稳定具有重要意义。     

Evaluation of spatial and temporal variation in Karun River water quality during five decades (1968–2014)

In the present study, the multivariate statistical technique cluster analysis (CA) is used to evaluate the spatial and temporal variations in the water quality data for the Karun River, which was gathered during 46 years of monitoring (from 1968 to 2014). The data recorded electrical conductivity (EC) in the river at six water monitoring stations along its course. The mean of EC was 972.05 ± 365.466 micromhos per centimeter (μmhos/cm) at the most upstream station and showed an increase to 1458.41 ± 675.048 μmhos/cm at the most downstream of the stations. By using hierarchical CA, the six sampling stations were grouped into three clusters of similar characteristics, which may be a result of different land uses in proximity to the stations. Furthermore, two‐way analysis of variance showed that EC had a significant correlation (p < 0.001) with the season, and the mean of the pollution depended on the level of the cluster and the season (spring, summer, autumn, and winter).     

Assessment of surface water quality of selected estuaries of Malaysia: multivariate statistical techniques

Multivariate statistical techniques such as cluster analysis (CA), factor analysis (FA) were used for the evaluation of spatial variations and the interpretation of a large complex water quality data set of two selected estuaries of Malaysia. The two locations of interest with 10 sites in each location were Kuala Juru (Juru estuary) and Bukit Tambun (Jejawi estuary). Cluster analysis showed that some sites in both locations have similar sources of pollution from point or non-point sources whereas FA yielded four factors which are responsible for water quality variations explaining more than 80% of the total variance of the data set and allowed to group the selected water quality. Correlation analysis of the data showed that some parameters have strong association with other parameters and they share a common origin source. This study illustrates the usefulness of multivariate statistical analysis for evaluation and interpretation of complex data sets to get better information about the pollution sources/factors and understanding the behavior of the parameters in water quality for effective river water quality management.     

Determination of arsenic levels in the water resources of Aksaray Province, Turkey

Arsenic levels were determined in 62 stations utilized as drinking and potable water resources by local community for Turkey's Aksaray Province (4589 km(2); 980 m above sea level). The samplings were implemented every two months for 1 year. The arsenic values were found to be ranging between 10 and 50 μg/L in 22 points and were found to be >50 μg/L in 5 stations, according to the mean value of the 6 samples. WHO and the Turkish Standards have permitted an arsenic concentration of 10 μg/L in drinking waters. The multivariate statistical technique, cluster analysis (CA), followed by principal component analysis (PCA) were applied to the data on 17 water quality parameters in 47 stations that are used for drinking and other domestic resources. Two significant sampling locations were detected based on the similarity of their water quality. The chemical correlations were observed in the two sub-sampling locations by Principal Component Analysis.     

用模糊数学对大连湾水质的评价研究

由于海域水环境系统的不确定性、随机性以及海域水质分级的模糊性，传统的海域水质评价方法如单因子评价法、综合污染指数法往往不能客观地反映海域水资源的质量状况。用模糊数学的方法对大连湾的水质环境质量进行评价，能够客观地分析出大连湾的污染状况，为合理规划、控制大连湾沿岸污染企业的污水排放提供科学依据，从而达到有计划地控制和治理海域污染，改善海域环境质量的目的。     

邛海水体富营养化的综合评价

为了研究近年来邛海水质的富营养化状况,本文以2010年邛海4个水质监测点水质监测资料为基础,采用模糊数学的方法对邛海营养状态进行综合评价。结果表明,邛海水质在青龙寺、二水厂和海河口属于贫营养,在公园属于中营养,说明近年来随着邛海及周边环境治理工程的加强,水体富营养化得到一定的遏制。     

Multivariate statistical characterization of water quality in Lake Lanier, Georgia, USA

Watershed monitoring programs depend on water quality characterization data collected for many parameters, at many times and places, and with limited resources. Our objective is to present a strategy that reduces the measured parameters, locations, and frequency without compromising the quality of the monitoring program. One year of twice-monthly (growing season) and monthly (dormant season) water quality data collected from 17 lake and 10 tributary sites are used in conjunction with multivariate statistical techniques to improve the utility of collected data by identifying key parameters and monitoring locations. Factor analysis shows that tributary water quality data consists of three components-stormwater runoff, municipal and industrial discharges, and ground water-which can be distinguished using total suspended solids, total dissolved solids, and alkalinity plus soluble reactive P, respectively. Lake water quality characterization is more ambiguous than tributary water quality characterization, but factor analysis indicates that anoxia associated with lake stratification is the largest source of lake water quality variation, followed by nutrient abundance, and finally by biomass abundance. Cluster analysis suggests that tributary and lake monitoring stations can be consolidated. Reducing the number of parameters and stations frees up resources for increased monitoring elsewhere.     

MULTIVARIATE ANALYSIS OF WATER QUALITY IN THE RICHIBUCTO DRAINAGE BASIN (NEW BRUNSWICK,CANADA)1

ABSTRACT: Specific conductivity, pH, dissolved oxygen, carbon, phosphorous, and nitrogen species were measured at 36 stations in the Richibucto River drainage basin, including the estuary, in New Brunswick, Canada, over the six‐year period 1996 through 2001. Each station was sampled between 1 and 26 times (mean = 7.5, standard deviation = 6.0) during the ice free seasons without regard to tide. There was significant variance among stations in most parameters. Principal component analysis (PCA) was used to identify the processes explaining the observed variance in water quality. Because of the high variability in specific conductance, stations were first grouped in a freshwater subset and an estuarine (brackish water) subset. For freshwater stations, most of the variance in water quality was explained by pH and total organic carbon, as well as high nutrient concentrations. These high nutrient concentrations, along with water salinity, which varies with flow and tides, are also important in determining water quality variability in brackish water. It is recommended that water quality parameters that were found to explain most of the variance by PCA be monitored more closely, as they are key elements in understanding the variability in water quality in the Richibucto drainage basin. Cluster analyses showed that high phosphorous and nitrate concentrations were mostly found in areas of peat runoff, tributaries receiving treated municipal effluent, and lentic zones upstream of culverts. Peat runoff was also shown to be acidic, whether it is runoff from a harvested area or a natural bog.     

Deriving Chesapeake Bay Water Quality Standards

Achieving and maintaining the water quality conditions necessary to protect the aquatic living resources of the Chesapeake Bay and its tidal tributaries has required a foundation of quantifiable water quality criteria. Quantitative criteria serve as a critical basis for assessing the attainment of designated uses and measuring progress toward meeting water quality goals of the Chesapeake Bay Program partnership. In 1987, the Chesapeake Bay Program partnership committed to defining the water quality conditions necessary to protect aquatic living resources. Under section 303(c) of the Clean Water Act, States and authorized tribes have the primary responsibility for adopting water quality standards into law or regulation. The Chesapeake Bay Program partnership worked with U.S. Environmental Protection Agency to develop and publish a guidance framework of ambient water quality criteria with designated uses and assessment procedures for dissolved oxygen, water clarity, and chlorophyll a for Chesapeake Bay and its tidal tributaries in 2003. This article reviews the derivation of the water quality criteria, criteria assessment protocols, designated use boundaries, and their refinements published in six addendum documents since 2003 and successfully adopted into each jurisdiction's water quality standards used in developing the Chesapeake Bay Total Maximum Daily Load.     

Assessment of water quality and identification of pollution sources of plateau lakes in Yunnan (China)

The plateau lakes of Yunnan are important both ecologically and economically in China. Nevertheless, the human impact on water quality in these lakes has become increasingly highlighted. The water quality of 10 plateau lakes was monitored regularly over the period of 2000 through 2004 for 24 parameters. Multivariate statistical techniques, including cluster analysis (CA), factor analysis (FA), and principal component analysis (PCA), were employed to better interpret information about the water quality and its pollution sources. No obvious data reduction from CA/FA was found because three principal components (PCs) needed 14 variables to explain 85.01% of the total variance. However, three latent factors accounted for pollution mainly from the following sources: agricultural activities, residential activities and anthropogenic-toxic pollution from industrial effluents, or other special activities. Box-whiskers plots were employed to visually interpret the spatiotemporal variations of water quality variables, which were highly correlated with three PCs. Three types of water quality (i.e., low-, medium-, and high-polluted lakes) were determined through CA based on the similarity of water quality variables. Our results may provide helpful information for the authorities to effectively manage the water quality and make sound policies.     

Regional and Temporal Differences in Nitrate Trends Discerned from Long‐Term Water Quality Monitoring Data

Riverine nitrate (NO₃) is a well‐documented driver of eutrophication and hypoxia in coastal areas. The development of the elevated river NO₃ concentration is linked to anthropogenic inputs from municipal, agricultural, and atmospheric sources. The intensity of these sources has varied regionally, through time, and in response to multiple causes such as economic drivers and policy responses. This study uses long‐term water quality, land use, and other ancillary data to further describe the evolution of river NO₃ concentrations at 22 monitoring stations in the United States (U.S.). The stations were selected for long‐term data availability and to represent a range of climate and land‐use conditions. We examined NO₃ at the monitoring stations, using a flow‐weighting scheme meant to account for interannual flow variability allowing greater focus on river chemical conditions. River NO₃ concentration increased strongly during 1945‐1980 at most of the stations and have remained elevated, but stopped increasing during 1981‐2008. NO₃ increased to a greater extent at monitoring stations in the Midwest U.S. and less so at those in the Eastern and Western U.S. We discuss 20th Century agricultural development in the U.S. and demonstrate that regional differences in NO₃ concentration patterns were strongly related to an agricultural index developed using principal components analysis. This unique century‐scale dataset adds to our understanding of long‐term NO₃ patterns in the U.S.     

CONTRASTING WATER QUALITY FROM PAIRED DOMESTIC/PUBLIC SUPPLY WELLS,CENTRAL HIGH PLAINS1

ABSTRACT: Closely located domestic and public supply wells were sampled using identical sampling procedures to allow comparison of water quality associated with well type. Water samples from 15 pairs of wells with similar screened intervals completed in the central High Plains regional aquifer in parts of Kansas, Oklahoma, and Texas were analyzed for more than 200 water quality constituents. No statistically significant differences were observed between the concentrations of naturally‐derived constituents (major ions, trace elements, and radon) in paired wells. However, differences in water quality between paired wells were observed for selected anthropogenic compounds (pesticides and tritium), in that some public supply wells produced water that was more recently recharged and contained constituents derived from surface activities. The presence of recently recharged water and compounds indicative of anthropogenic activities in some public supply wells was likely due to operational variations (pumping rate and pumping cycles), as demonstrated in a particle tracking simulation. Water containing surface‐derived anthropogenic compounds from near the water table was more quickly drawn to high volume public supply wells (less than five years) than domestic wells (greater than 120 years) with small pumping rates. These findings indicate that water quality samples collected from different well types in the same area are not necessarily directly comparable. Sampling domestic wells provides the best broad‐scale assessment of water quality in this aquifer setting because they are less susceptible to localized contamination from near the water table. However, sampling public supply wells better represents the quality of the used resource because of the population served.     

Mapping Three‐Dimensional Water‐Quality Data in the Chesapeake Bay Using Geostatistics1

Abstract:  Data interpretation and visualization software tools with geostatistical capabilities were adapted, customized, and tested to assist the Chesapeake Bay Program in improving its water‐quality modeling protocols. Tools were required to interpolate, map, and visualize three‐dimensional (3D) water‐quality data, with the capability to determine estimation errors. Components of the software, originally developed for ground‐water modeling, were customized for application in estuaries. Additional software components were developed for retrieval, and for pre‐ and post‐ processing of data. The Chesapeake Bay Program uses the 3D mapped data for input to the Bay water‐quality model that projects the future health of the Bay and its tidal tributary system. In determining water‐quality attainment criteria, 3D kriging estimation errors are needed as a statistical measure of uncertainty. Furthermore, given the high cost of installing and operating new monitoring stations, geostatistical techniques can assist the Chesapeake Bay Program in the identification of suitable data collection locations. Following the evaluation, selection, and development of the software components phase, 3D ordinary kriging techniques with directional semi‐variograms to account for anisotropy were successfully demonstrated for mapping 3D fixed station water‐quality data, such as dissolved oxygen and salinity. Additionally, an improved delineation tool was implemented to simulate the upper and lower pycnocline boundary surfaces allowing the segregation of the interpolated 3D data into three separate zones for a better characterization of the pycnocline layer.     

模糊数学在西昌邛海富营养化评价中的应用

本文以西昌邛海2006年4个水质监测点水质监测资料为基础,采用模糊数学的方法对邛海营养状态进行综合评价,通过评价得出:邛海海河口和二水厂处水质已成轻富营养化状态.同时分析了造成这一结果的主要原因是生活污水中排放的大量的氮磷,并提出了一些合理建议.     

海口湾人工填海前后冲淤演变数值模拟

为了解近年南海明珠项目、葫芦岛、秀英港扩建工程等人工填海工程对海口湾冲淤变化的影响,基于FVCOM海洋数值模型,对研究区人工填海前后潮流场、波浪场及冲淤变化进行了数值模拟。人工填海后,综合各条件下的冲淤情况,海口湾受潮流和波浪共同作用大部分区域处于淤积状态,年淤积量预测值为0.1～1.0 m;白沙角等局部区域处于侵蚀状态,年冲刷量预测值为0.1～0.3 m;受海口湾人工填海工程的影响,秀英港航道的水动力条件减弱,对通航条件改善有利,需加强航道的水深监测和定时的清淤工作;在南海明珠人工岛南侧波影区泥沙堆积会形成向海的舌状的突出体,其两侧海岸形成侵蚀后退带,需人工补沙等措施以保证岸线稳定。     

Valuing algal bloom in the Black Sea Coast of Bulgaria: A choice experiments approach

Increased interest in water quality in coastal and marine areas stemming from the Water Framework Directive and the Marine Strategy Framework Directive has led to important questions in relation to policies that address nutrient loadings. This paper presents the results from a choice experiment study to assess the recreational damage associated with algal blooms caused by nutrients flows into Varna Bay, Bulgaria. Varna Bay is an important beach destination on the Black Sea coast of Bulgaria. Algal bloom events have been experienced frequently in the area. A choice experiment questionnaire was developed and applied in the Varna Bay area to assess the extent to which the quantity of algal blooms and the duration of the bloom affect recreational activities. The amount of bloom was found to be important, as respondents were on average willing to pay a one off tax of 18.97 Leva (€9.73) for a program that provides beaches free from algal blooms.     

Modelling hydroenvironmental and health risk assessment parameters along the South Wales Coast

This paper highlights the increasing concerns relating to hydroenvironmetal issues and cites recent examples of the challenges now being regularly faced by hydroenvironmetal scientists and engineers. The limitations and restrictions of both physical (or laboratory) and numerical (or computer based) hydraulic models used in the planning and management of aquatic basins are discussed. General details are given of numerical models used for flow and water quality concentration predictions in estuarine waters, with particular application to the challenges occurring along the South Wales coast. A highly accurate and non-diffusive finite difference scheme that solves the transport equation for predicting water quality indicators and suspended sediment concentration distributions is also discussed. In particular, details are outlined of the extension of the water quality indicators of faecal coliforms, as required to comply with the EU Bathing Water Directive, to predict health risk assessment, in the form of predicting the risk of gastroenteritis. Three example research projects along the South Wales coast are described; the projects involve the application of two-dimensional and three-dimensional hydroenvironmetal models to predict flow patterns and water quality indicator organism distributions in the coastal receiving waters. These studies include: (i) a curvilinear finite difference approach to modelling flows in the Bristol Channel, (ii) coastal health risk predictions in Swansea Bay using combined water quality and epidemiological models, and (iii) combined sewer overflow discharges into Cardiff Bay.