A Methodology for Clustering Lakes in Alberta on the basis of Water Quality Parameters

In this study, a methodology for clustering 18 lakes in Alberta, Canada using the data of 19 water quality parameters for a period of 11 years (1988–2002) is presented. The methods consist of (i) principal component analysis (PCA) to determine the dominant water quality parameters, (ii) cluster analysis techniques to develop the characteristics of the clusters, and (iii) pattern‐match lakes to determine the appropriate cluster for each of the lakes. The PCA revealed that three principal components (PCs) were able to explain ～88% of the variability and the dominant water quality parameters were total dissolved solids, total phosphorus, and chlorophyll‐a. We obtained five clusters for the period 1994–1997 by using the dominant parameters with water quality deteriorating as the cluster number increased from 1 to 5. Upon matching cluster patterns with the entire dataset, it was observed that some of the lakes belonged to the same cluster all the time (e.g., cluster 1 for lakes Elkwater, Gregg, and Jarvis; cluster 3 for Sturgeon; cluster 4 for Moonshine; and cluster 5 for Saskatoon), while others changed with time. This methodology could be applied in other regions of the world to identify the most suitable source waters and prioritize their management. It could be helpful to analyze the natural controlling processes, pollution types, impact of seasonal changes and overall quality of source waters. This methodology could be used for monitoring water bodies in a cost effective and efficient way by sampling only less number of dominant parameters instead of using a large set of parameters.     

Forecasting of Air Quality Index in Delhi Using Neural Network Based on Principal Component Analysis

Forecasting of the air quality index (AQI) is one of the topics of air quality research today as it is useful to assess the effects of air pollutants on human health in urban areas. It has been learned in the last decade that airborne pollution has been a serious and will be a major problem in Delhi in the next few years. The air quality index is a number, based on the comprehensive effect of concentrations of major air pollutants, used by Government agencies to characterize the quality of the air at different locations, which is also used for local and regional air quality management in many metro cities of the world. Thus, the main objective of the present study is to forecast the daily AQI through a neural network based on principal component analysis (PCA). The AQI of criteria air pollutants has been forecasted using the previous day’s AQI and meteorological variables, which have been found to be nearly same for weekends and weekdays. The principal components of a neural network based on PCA (PCA-neural network) have been computed using a correlation matrix of input data. The evaluation of the PCA-neural network model has been made by comparing its results with the results of the neural network and observed values during 2000–2006 in four different seasons through statistical parameters, which reveal that the PCA-neural network is performing better than the neural network in all of the four seasons.     

洞庭湖支流枯水期表层水和沉积物中氯苯甲醚类化合物分布特征与生态风险

环境中的氯苯甲醚类化合物(chloroanisoles,CAs)主要来自于氯酚类化合物及其他结构相似的氯代烃.在我国洞庭湖血吸虫疫区,五氯酚作为灭螺剂施放对周围环境造成了CAs污染.运用气相色谱质谱联用方法测定洞庭湖支流(澧水、藕池河和沱江)表层水和沉积物中的CAs,并分析其分布特征与生态风险.结果显示:(1)水体是CAs的主要赋存介质.三支流表层水中CAs体现出显著差异性,污染水平由大到小为澧水藕池河沱江,三者总CAs浓度分别为18.94、8.83和4.14 ng/L;在3支流中沉积物中的总CAs无明显差异,分别为2.86、3.61和4.07 ng/g.(2)澧水表层水和沉积物中高氯取代CAs(三氯苯甲醚、四氯苯甲醚和五氯苯甲醚)为主要污染物(分别占73.75%和68.89%);藕池河表层水中低氯取代CAs(一氯苯甲醚和二氯苯甲醚)占比相对较高(48.59%),而沉积物中以高氯取代CAs为主(93.38%);沱江表层水和沉积物中占比较高的CAs为一氯苯甲醚,分别占28.26%和75.56%.(3)空间分布上,从上游到下游澧水表层水中CAs浓度呈现波动下降的趋势,而沉积物中呈上升趋势;藕池河表层水中CAs呈现波动下降的趋势,沉积物中无明显变化趋势;沱江表层水和沉积物中一氯苯甲醚呈波动上升趋势,其他CAs有波动但无明显趋势.(4)研究区水中的五氯苯甲醚浓度远低于报道的鱼类和无脊椎动物的五氯苯甲醚急性半数致死浓度,目前不会对水生生物造成太大影响,但其对人类健康和环境的潜在风险不可忽视.     

Non‐point source pollution estimation using a modified approach

Non‐point source (NPS) pollution from agricultural land is increasing exponentially in many countries of the world, including India. A modified approach based on the conservation of mass and reaction kinetics has been derived to estimate the inflow of non‐point source pollutants from a river reach. Two water quality variables, namely, nitrate (NO₃) and ortho‐phosphate (o‐PO₄), which are main contributors as non‐point source pollution, were monitored at four locations of River Kali, western Uttar Pradesh, India, and used for calibration and validation of the model. Extensive water quality sampling was done with a total of 576 field data sets collected during the period from March 1999 to February 2000. Remote sensing and geographical information system (GIS) techniques were used to obtain land use/land cover of the region, digital elevation model (DEM), delineation of basin area contributing to non‐point source pollution at each sampling location and drainage map. The results obtained from a modified approach were compared with the existing mass‐balance equations and distributed modelling, and the performances of different equations were evaluated using error estimation viz. standard error, normal mean error, mean multiplicative error and correlation statistics. The developed model for the River Kali minimizes error estimates and improves correlation between observed and computed NPS loads. Copyright © 2007 John Wiley & Sons, Ltd.     

Spatial character of the gaseous and particulate state compound correlation of urban atmospheric pollution in winter and summer

The spatial/temporal variation information of atmospheric dynamic-chemical processes at observation site points of the "canopy" boundary of Beijing urban building ensemble and over urban area "surface", as well as the seasonal correlation structure of the gaseous and particulate states of urban atmospheric pollution (UAP) and its seasonal conversion feature at observation points are investigated, using the comprehensive observation data of the Beijing City Air Pollution Observation Experiment (BECAPEX) in winter and summer 2003 with a "point-surface" combined research approach. By using "one dimension spatial empirical orthogonal function (EOF)" principal component analysis (PCA) mode, the seasonal change of gaseous and particulate states of atmospheric aerosols and the association feature of pollutant species under the background of the complicated structure of urban boundary layer (UBL) are analyzed. The comprehensive analyses of the principal components of particle concentrations,gaseous pollutant species, and meteorological conditions reveal the seasonal changes of the complex constituent and structure features of the gaseous and particulate states of UAP to further trace the impact feature of urban aerosol pollution surface sources and the seasonal difference of the component structure of UAP. Research results suggest that in the temporal evolution of the gaseous and particulate states of winter/summer UAP, NOx, CO, and SO2 showed an "in-phase" evolution feature, however, O3 showed an "inverse-phase" relation with other species,all possessing distinctive dependent feature. On the whole, summer concentrations of gaseous pollutants CO, SO2, and NOx were obviously lower than winter ones, especially, the reduction in CO concentration was most distinctive, and ones in SO2 and NOx were next. However, the summer O3 concentration was more than twice winter one. Winter/summer differences in PM10and PM2.5 particle concentrations were relatively not obvious, which indicates that responses of PM10 and PM2.5 particle concentrations to the difference of winter/summer heating period emission sources are far less distinctive than those of NOx, SO2, and CO. The correlation feature of winter/summer gaseous and particulate states depicts that both PM10 and PM2.5 particles were significantly correlated with NOx, and their correlations with NOx are more significant than those with other pollutants. Through PCA, it is found that there was a distinctive difference in the principal component combination structure of winter/summer PM10 and PM2.5 particles: SO2 and NOx dominated in the principal component of winter PM10 and PM2.5 particles; while CO and NOx played the major role in the principal component of summer PM10 and PM2.5 particles. For winter/summer PM10 and PM2.5 particles, there might exist the gaseous and particulate states correlation structures of different "combinations" of such dependent pollutant species. Research results also uncover that the interaction processes of gaseous and particulate states were also related with the vertical structure of UBL, that is to say, the low value layer of UBL O3 concentration was associated with the collocation of atmospheric vertical structures of the low level inversion,inverse humidity, and small wind, which depicts summer boundary layer atmospheric character, i.e.the compound impact of the dependent factor "combination" of wind, temperature, and humidity elements and their collocation structure on the variations of different gaseous pollutant concentrations. Such a depth structure of the extremely low value of O3 concentration in the UBL accords with its "inverse-phase" relation with other gaseous pollutant species. The PCA of meteorological factors associated with PM10 and PM2.5 concentrations also reveals the sensitivity of PM10 and PM2.5 concentration to the combinatory feature of local meteorological conditions.     

Comparison of selected biomarkers in flounder (Platichthys flesus L.) from the Douro (Portugal) and Vistula (Poland) River estuaries

Sixty female flounder (Platichthys flesus) were collected in Autumn 2011, 15 from each of the following sampling sites: at the mouths of the Douro and Vistula Rivers, and at nearby open sea locations. The aim of the study was to assess several biomarkers in the two geographically distant regions. Hepatic EROD, GST, SOD, GPx, POx, LP; muscular AChE, BChE, LP; and branchial Na⁺/K⁺-ATPase were analysed. Moreover, BTI, PY, and three gross morphometric indices were calculated. The results were analysed with t-test, ANOVA, and PCA. Many differences were found between the open sea sites and the river mouths, mainly in Portugal, and between the two rivers. Salinity and pollution seem to be the main factors that affected the biomarkers. Effects of chronic pollution were observed at the river mouths, and an indication of a possible temporary exposure to pollutants was found at the open ocean site in Portugal.     

The Flood in the Odra River 1997 – Impact of Suspended Solids on Water Quality

The flood in the Odra river in 1997 has led to considerable additional pollution of the Stettin Lagoon and the Baltic Sea with contaminated suspended solids. For some priority substances, the pollutant entries via suspended solids during the flood period are estimated to be approximately 1/3 of the usual annual load. Among these priority pollutants there are total organic carbon (TOC), nitrogen, and the heavy metals Cu, Pb and Zn. For the concentrations of the priority pollutants in suspended solids accumulation factors from 2 to 4 in the comparison with normal conditions were observed. On the basis of the analysis of sediments sampled after the flood, main sources of the pollutants should be evaluated. As reference area with an industrial background as well as a typical pollutant pattern the region around Glogow/Legnica is proposed.     

鄱阳湖西侧周边农村水塘夏季表层沉积物氮、磷、有机质分布特征及评价

水塘作为农村重要的水生态系统,其环境状况与人们生产生活和健康密切相关。为了解鄱阳湖西侧周边农村水塘沉积物的有机质和营养盐赋存状况,于2018—2019年对鄱阳湖流域西侧附近4个县的23个水塘进行沉积物营养盐的分析,同时通过相关分析和差异性分析对其来源进行解析,并采用综合污染指数评价法和主成分分析法对水塘沉积物的污染程度进行评价。结果表明：水塘沉积物污染物含量较高,其有机质、有机碳（TOC）、总氮（TN）和总磷（TP）含量分别为5.81%±2.16%、2.46%±1.02%、（6.48±2.35） mg/g和（1.89±0.80） mg/g。3种形态的无机氮中铵态氮含量最高,其次为硝态氮和亚硝态氮。沉积物TP受到洗涤废水的影响较大,清淤和活化显著减轻了沉积物TN和TOC污染。综合污染指数评价结果表明重度污染和中度污染的水塘占比分别为95.65%和4.35%,其平均评价结果为重度污染,表明水塘沉积物污染严重。而主成分分析结果表明P18水塘污染最重,而P6水塘污染最轻。综合污染指数与主成分分析总得分的相关系数为0.92,表明两者的评价结果较为一致。本研究通过分析鄱阳湖西侧农村水塘的沉积物营养...     

Geoaccumulation and distribution of heavy metals in the urban river sediment

Current study presents the application of chemometric techniques to comprehend the interrelations among sediment variables whilst identifying the possible pollution source at Langat River,Malaysia.Surface sediment samples（0-10 cm）were collected at 22 sampling stations and analyzed for total metals（~（48）Cd,~（29）Cu,~（30）Zn,~（82）Pb）,pH,redox potential（Eh）,salinity,electrical conductivity（EC）,loss on ignition（LOI）and cation exchange capacity（CEC）.The principal component analysis（PCA）scrutinized the origin of environmental pollution by various anthropogenic and natural activities：four principal components were obtained with 86.34%（5 cm）and88.34%（10 cm）.Standard,forward and backward stepwise discriminant analysis effectively discriminate 2variables（84.06%）indicating high variation of heavy metals accumulation at both depth.The cluster analysis accounted for high input of Zn and Pb at LA8,LA 10,LA 11 and LA 12 that mergers three（5 cm）and four（10cm）into clusters.This is consistent with the contamination factor（C_1）that shows high Cd（LA 1）and Pb（LA 7,LA 8,LA 10,LA 11 and LA 12）contaminations at 5cm.These indicate that Pb and Zn are the most bioavailable metals in the sediment with significant positive linear relationship at both sediment depths.Therefore,this approach is a good indication of environmental pollution status that transfers new findings on the assessment of heavy metals by interpreting large complex datasets and predicting the fate of heavy metals in the sediment.     

Using principal component analysis in the investigation of groundwater hydrochemistry of Upper Jezireh Basin,Syria

Principal component analysis (PCA) was applied to hydrochemical and isotopic data of 34 groundwater samples. This allowed the reduction of 20 variables to four significant PCs that explain 81.9% of the total variance; F1 (47.1%) explains the groundwater mineralization, whereas F2 (17%) shows isotopic enrichment and nitrate pollution. Based on an iso-factor scores map of F1, three water zones were delineated: Zone A (F1 < ?1), with fresh groundwater from the unconfined aquifer; Zone B (1 > F1 > ?1), with moderate mineralization from the confined–unconfined aquifer boundary; and Zone C (F1 > 1), with the most mineralized hot water from the confined aquifer. The iso-factor scores map of F2 delineates positive values representing samples from the unconfined aquifer, with freshwater and nitrate contamination associated with stable isotope enrichment, whereas negative values represent samples from the confined aquifer. The results clearly demonstrate the usefulness of PCA in groundwater hydrochemistry investigations.     

辽宁省大伙房水库及入库河流水质空间特征与河库水质关系

入库河流与水库存在空间上的连续性,河流污染物输入是水库水质恶化的主要原因,对大伙房水库及其入库支流61个采样点的水质状况进行调查,并运用聚类分析和主成分分析对大伙房水库及入库支流的水质空间特性和主要污染物进行分析.聚类分析显示,按照水质相似性将大伙房水库及入库支流水质可分为上游区、下游区和库区3个典型空间区域.分别对3个区域进行主成分分析,结果显示:入库支流上游区和下游区水质主要影响因素为氨氮、总氮和化学需氧量,库区影响水质的主要因素为温度、p H值、浊度、溶解氧、电导率、氨氮和总氮.对上游、下游和库区水质均有显著影响的因子为氨氮和总氮,上游区、下游区和库区氨氮浓度均值分别为0.06、0.10和0.19 mg/L,总氮浓度均值分别为0.13、0.16和0.26 mg/L.入库河流下游区对水库水质影响较大,受社河和浑河污染物输入的影响,大伙房水库水质在空间上呈现社河入库区水质优于浑河入库区水质.并且库区氨氮和总氮浓度均与距岸边距离呈负相关,溶解氧和p H值均与距入库口距离呈负相关,表明入库河流污染物输入和环库区面源污染均对大伙房水库水质产生一定影响.     

太湖流域上游平原河网区水质空间差异与季节变化特征

在太湖流域上游的宜溧—洮滆水系主要河道设置67个监测点,分别于2014年1月(冬季)、4月(春季)、8月(夏季)、11月(秋季)进行水质监测,采用多元统计方法分析了水质的空间差异性和季节性变化,并利用水质标识指数法对水环境质量进行评价.结果表明,宜溧—洮滆水系污染程度较严重,总氮(TN)、总磷(TP)和高锰酸盐指数(CODMn)浓度年均值分别为4.93、0.26和7.63 mg/L;单因素多元方差分析和聚类分析显示污染物浓度具有显著时空差异性,时间上冬、春季污染程度较高而夏、秋季较低,空间上无锡和常州氮、磷污染较为严重,宜兴和溧阳市有机污染程度较高;水质标识评价结果显示流域内水质基本为IV类或V类,其中TN、TP及CODMn是关键污染指标.     

Chemometrics data analysis of marine water quality and source identification in Southern Hong Kong

Various chemometric methods were used to analyze data sets of marine water quality for 19 parameters measured at 16 different sites of southern Hong Kong from 2000 to 2004 (18,240 observations), to determine temporal and spatial variations in marine water quality and identify pollution sources. Hierarchical cluster analysis (CA) grouped the 12 months into three periods (January-April, May-August and September-December) and the 16 sampling sites into two groups (A and B) based on similarities in marine water-quality characteristics. Discriminant analysis (DA) was important in data reduction because it used only eight parameters (TEMP, TURB, Si, NO(3)(-)-N, NH(4)(+)-N, NO(2)(-)-N, DO, and Chl-a) to correctly assign about 86% of the cases, and five parameters (SD, NH(4)(+)-N, TP, NO(2)(-)-N, and BOD(5)) to correctly assign >81.15% of the cases. In addition, principal component analysis (PCA) identified four latent pollution sources for groups A and B: organic/eutrophication pollution, natural pollution, mineral pollution, and nutrient/fecal pollution. Furthermore, during the second and third periods, all sites received more organic/eutrophication pollution and natural pollution than in the first period. SM5, SM6, SM17, SM10, SM11, SM12, and SM13 (second period) were affected by organic and eutrophication pollution, whereas SM3 (third period) and SM9 (second period) were influenced by natural pollution. However, differences between mineral pollution and nutrient/fecal pollution were not significant among the three periods. SM17 and SM10 were affected by mineral pollution, whereas SM4 and SM9 were highly polluted by nitrogenous nutrient/fecal pollution.     

云南星云湖水质变化及其人文因素驱动力分析

星云湖目前存在水污染加重、富营养化进程加快、水体功能受损等问题.以星云湖为研究对象,根据星云湖2005-2015年的水质数据、社会经济统计数据和遥感影像图,运用目视解译、叠加分析、污染足迹模型及主成分分析法,分析了星云湖流域近10年以来水质变化趋势、入湖河流污染物污染足迹及其人文因素驱动力.结果表明:(1)水质数据趋势表明,从月变化看,3月份水质最好,9月份水质最差;从年变化看,2005-2015年间,2008年水质状况最好,2014年的水质状况最差,从2008-2014年水质持续变差,到2015年好转.(2)2015年有机物、氮和磷的污染足迹分别为583.26、705.88和494.11 km~2.污染足迹前4位的入湖河流依次为:大街河东西大河东河渔村河东西大河西河,占星云湖流域总污染足迹的66.21%.污染程度大的大街河、东西大河和渔村河周边土地利用类型为水田、旱地和村庄.(3)星云湖水质影响因素第1主成分(总人口、播种面积、农村人口、化肥使用量、农膜使用量、大牲畜存栏量)与农村生活和农业面源污染有关;第2主成分(人均GDP、第一产业产值、第二产业产值、第三产业产值)与社会经济发展有关.因此,星云湖流域水质变化的人文因素驱动力为农村生活和农业面源污染类和社会经济发展类,其中第1主成分的贡献率是84.389%,农村生活和农业面源污染是水质变化的主要驱动力.     

Time series analysis and forecasting for air pollution in small urban area: an SARIMA and factor analysis approach

Despite the existing public and government measures for monitoring and control of air quality in Bulgaria, in many regions, including typical and most numerous small towns, air quality is not satisfactory. In this paper, factor analysis and Box–Jenkins methodology are applied to examine concentrations of primary air pollutants such as NO, NO₂, NO_x, PM10, SO₂ and ground level O₃ in the town of Blagoevgrad, Bulgaria within a 1 year period from 1st September 2011 to 31st August 2012, based on hourly measurements. By using factor analysis with PCA and Promax rotation, a high multicollinearity between the six pollutants has been detected. The pollutants were grouped in three factors and the degree of contribution of the factors to the overall pollution was determined. This was interpreted as the presence of common sources of pollution. The main part of the study involves the performance of time series analysis and the development of univariate stochastic seasonal autoregressive integrated moving average (ARIMA) models with recording on a hourly basis as seasonality. The study also incorporates the Yeo–Johnson power transformation for variance stabilizing of the data and model selection by using Bayersian information criterion. The obtained SARIMA models demonstrated very good fitting performance with regard to the observed air pollutants and short-term predictions for 72 h ahead, in particular in the case of ozone and particulate matter PM10. The presented statistical approaches allow the building of non-complex models, effective for short-term air pollution forecasting and useful for advance warning purposes in urban areas.     

Monitoring Mutagenic Pollution of Small Rivers

The importance of sampling frequency in the course of monitoring of mutagenic pollution of small rivers is shown, the Kotorosl River (a tributary of the Volga River) being taken as an example. In working out a sampling program, due consideration should be given for seasonal variations of the river hydrological regime and input of pollutants. Incorrect choice of sampling frequency may lead to one of the principal reasons for improper assessment of toxicogenetic situation. A water sampling program has been proposed for the Kotorosl River studies based on monitoring.__________Translated from Vodnye Resursy, Vol. 32, No. 3, 2005, pp. 347–351.Original Russian Text Copyright © 2005 by Fomicheva, Prokhorova.     

Assessment of Anthropogenic Impact on River Basins in Southern Kuzbass Mining Areas

The total pollution caused by spreading and fallout of atmospheric aerosols in river catchment areas of a coal-mining region is estimated. The contribution of the emission produced by a coal mine to the total fallout in the territory is assessed. Different types of water pollution in the coal-mining region are analyzed, the principal pollutants are identified, and their contribution to river water quality is assessed.     

Modeling spatially correlated spectral accelerations at multiple periods using principal component analysis and geostatistics

Regional seismic risk assessments and quantification of portfolio losses often require simulation of spatially distributed ground motions at multiple intensity measures. For a given earthquake, distributed ground motions are characterized by spatial correlation and correlation between different intensity measures, known as cross‐correlation. This study proposes a new spatial cross‐correlation model for within‐event spectral acceleration residuals that uses a combination of principal component analysis (PCA) and geostatistics. Records from 45 earthquakes are used to investigate earthquake‐to‐earthquake trends in application of PCA to spectral acceleration residuals. Based on the findings, PCA is used to determine coefficients that linearly transform cross‐correlated residuals to independent principal components. Nested semivariogram models are then fit to empirical semivariograms to quantify the spatial correlation of principal components. The resultant PCA spatial cross‐correlation model is shown to be accurate and computationally efficient. A step‐by‐step procedure and an example are presented to illustrate the use of the predictive model for rapid simulation of spatially cross‐correlated spectral accelerations at multiple periods.     

An Assessment on Metal Sources by Multivariate Analysis and Speciation of Metals in Soil Samples Using the BCR Sequential Extraction Procedure

The concentrations of Cr, Mn, Fe, Ni, Cu, Zn, and Pb metals in soil samples (N = 21) were determined by flame atomic absorption spectrometry. The modified Community Bureau of Reference (BCR) sequential extraction procedure (three‐step) was used in order to evaluate mobility, availability, and persistence of heavy metals in soil samples taken from an agricultural area in Erciyes University Campus. The operationally defined fractions isolated using the BCR procedure were: acid extractable, reducible, and oxidizable. The mobility sequence based on the sum of the BCR sequential extraction stages was: Mn (70.2%) > Pb (62.9%) > Ni (26.7%) > Cr (15.4%) > Zn (14.4%) > Cu (12.9%) > Fe (1.24%). Multivariate statistical analysis was used to define the possible origin of heavy metals in soils. Correlation analysis, principal component analysis (PCA), and cluster analysis (CA) were applied to the data matrix to evaluate the analytical results and to identify the possible pollution sources of heavy metals. PCA results revealed that the sampling area was mainly influenced from three sources, namely natural, airborne emissions from domestic heating and traffic.