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Water Quality Study of the Struma River Basin

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Water Quality Study of the Struma River Basin CEJC 2(2003)121{136 Water QualityStudy of the Struma River Basin, Bulgaria (1989 {1998) 1 2 3 P.Simeonova ,V.Simeonov ¤, G. Andreev 1 Institute ofSolid State Physics, BulgarianA cademyof Sciences, 1172So¯ a, Tzarigradsko Chaussee Blvd. 72, Bulgaria 2 Chairof Analytical Chemistry, F acultyof Chemistry, University ofSo¯ a \St. Okhridski", 1164So¯ a, J. BourchierBlvd. 1, Bulgaria 3 Institute ofOceanology, BulgarianA cademyof Sciences, 9000V arna,P.O. Box152, Bulgaria Received 8January2003; accepted 18March 2003 Abstract: Thepresent paperdeals with anestimation ofthe waterquality ofthe Struma river. Long-termtrends, seasonalpatterns anddata set structures arestudied by the use of statistical analysis. Nineteen samplingsites alongthe mainriver stream anddi¬ erent tributaries wereincluded in the study.Thesites arepart ofthe monitoringnet ofthe regionof interest. Seventeen chemical indicators ofthe surface waterhave been measuredin the period 1989{ 1998in monthly intervals. It is shownthat the waterquality is relatively stable throughout the monitoringperiod, which is indicated by alack ofstatistically signi­cant trends for many ofthe sites andby chemical variables. Several seasonalpatterns areobserved at the sampling sites andfour latent factors areidenti­ ed asresponsible for the dataset structure. c Central EuropeanScience Journals. All rights reserved. ® Keywords:Surfac ewater quality, statistical data treatment, linear regressionanalysis, time- series analysis,cluster analysis,princip al componentsanalysis 1Introduction The Struma riveris located in the southern part ofBulgaria. Itruns from north to south and has alength of 290 kmto the Greekborder. The stream length from that point to the AegeanSea isabout 110 km.The total catchments area isnearly 10,250 km 2 within ¤ E-mail:[email protected]­ a.bg Unauthenticated Download Date | 9/24/15 11:30 PM 122 P.Simeonovaet al. / CentralEuropean Journal of Chemistry 2(2003)121{136 Bulgaria and the Vitosha Mountain and the Rila,Pirin and `surrounding’ mountain ranges form it.Being a cross border riverStruma basin isof substantial importance for both countries. That iswhy the careful monitoring of the water qualityin long-term or short-term timeperiods at di®erent sampling sites isnot onlyan ecologicalbut alsoa politicalissue. Similar studies for the Yantra river[1-3] have indicated that veryuseful information can beextracted from the data collectedin the monitoring period. In Fig.1 apresentation of the Struma °ow in the Bulgarian territory isgiven along with the location of the sampling sites from the Struma rivermonitoring network con- trolled by the Ministry of Environment and Waters. As seenthe monitoring system involvesmany sites where regular testing of the water qualityis performed {on daily, weeklyor monthly basis. The industrial and agricultural activityin the region ofthe Struma riveris relatively high. The population within the basin totals some532,000 (6.47% of the population of Bulgaria) with nearly 300,000 (71%) in urban areas. The main towns of over20,000 population are:Pernik, Blagoevgrad, Kjustendil, Dupniza, Petrich and Sandanski. As far as the land use isconcerned some29,700 ha. of land isunder irrigation and the natural conditions in this region are favorable for growing vegetables,fruits, tobacco, cotton and almonds. The water in the region isused for production ofelectricityand irrigation. Electricity isgenerated atpower stations (\Kalin",\Kamenitza", \Pastra", \Rila",etc.) in someof its tributaries. There are severaluncompleted irrigation systems:\Dolna Dikanja-Kovachevzi-Rado- mir" schemesthat are intended to use water from the reservoir \Pchelin",\Ddyakovo- Dupnitza" scheme,that should use waters from the reservoir \Dyakovo".At present an irrigation system \Pirinska Bistrica" isin process of reconstruction and modernization. Due to regular monitoring asubstantial amount ofanalyticaldata isalready available, but there isstilla lackof summarizing studies taking into account allaspects of the river system and extracting allpossible information from the data sets. Of course, someof the analyticalrecords are not complete;in the data set one could detect missing data or unmeasured cases.The onlyway to reach anew levelof information concerning the water qualityis the application of multivariate statistical methods (chemometrics and environmetrics). In the caseof the Struma riverit isof substantial importance to detect trends in the concentrations of the major chemicalconstituents determined in the monitoring net as wellas to revealthe seasonal behaviour of the components and identify possible sources of pollution. The aimof the present study isto ¯nd out and explainall these multivariate statistical parameters. Unauthenticated Download Date | 9/24/15 11:30 PM P.Simeonovaet al. / CentralEuropean Journal of Chemistry 2(2003)121{136 123 Fig. 1 StrumaRiver monitoring net 2Experimental 2.1 Sampling and chemical analysis The location of the sampling sites deliveringthe data isindicated in Fig.1. Nineteen Unauthenticated sites were chosen coveringalmost completelyDownload theDate river| 9/24/15stream 11:30 PM from its spring down to the 124 P.Simeonovaet al. / CentralEuropean Journal of Chemistry 2(2003)121{136 Greekborder. The period of observation for the region of interest was 10 years(1989 { 1998). The chemicalindicators involvedwere: pH, dissolvedoxygen, BOD5 (biological oxygendemand), COD (chemicaloxygen demand), conductivity,acidity,dissolvedmat- ter, non-dissolved matter, total hardness, chloride,sulfate, ammonium, nitrate, nitrite, iron, magnesium, calcium.The chemicalanalysis performed includes standard analytical methods as routinely applied inthe control laboratories of the monitoring net. Potentiom- etry,titrimetry,gravimetry,and spectrophotometry are the standard analyticalmethods widelyused in surface water qualityanalysis especially for major indicators likethose mentioned above.The sample preparation and sample measurements are described in detail elsewhere[4]. 2.2 Statisticalanalysis For trend analysisweighted annual averagevalues from di®erent sampling sites for chem- icalobservation ofmajor parameters inthe water ofthe Struma riverwere accumulated. Trends in the substance concentrations were evaluated using least-square regression ap- proach and statistical testing of the regression coe±cient signi¯ cance after estimation of the standard error of the coe±cient value at p < 0:05 and p < 0:01 and respective estimation ofthe residuals by F -test. The correlation coe±cient r was alsocalculated as ameasure for trend signi¯cance [5]. F orany caseof chemicalcomponent determined at a givensampling sitethe predicted valuesfor substance concentration with respect to the linear regression analysiswere determined. Until recentlythe mathematical methods of time-seriesanalysis (TSA) in the en- vironmental scienceshave only been used quiterarely; the methods havemostly been applied ineconomics.The mathematical fundamentals of time-seriesanalysis are mainly described invarious books and papers dealing with statistics and econometrics[6-10]. In general,time-series analysis has the following main purposes: 1.display ofthe series; 2.preprocessing ofdata; 3.modeling and description ofthe series; 4.forecasting with suitable models; 5.control of predicted data. Usually,the ¯rst step in the time-seriesanalysis is drawing adata plot, which gives an ideaof the shape of the time-series.It mayproperly display periodicity,trends, °uc- tuations and outliers. In the next step it isconvenient to construct, if necessary,the seasonal sub seriesplots to get additional information onseasonal °uctuations. Stripping away random °uctuations are achievedby smoothing the seriesor by ¯ltering the peri- odicities.Thus, ashort-term forecast ispossible with amemory of the last valuesof the series.In this casesimple moving average,exponential smoothing, seasonal di®erencing, cumulativesum (CUSUM) technique and seasonal decomposition are applied asmodeling methods. Regressionand correlation techniques are alsoknown in TSA. In the present study the input data for alltested indicatorsUnauthenticated and for allsampling sites with long enough Download Date | 9/24/15 11:30 PM P.Simeonovaet al. / CentralEuropean Journal of Chemistry 2(2003)121{136 125 period ofmonitoring (at least 60 consecutivemonths ofobservation) were treated in such away asto obtain seasonal decomposition. Only¯ vesampling sites are considered sinceonly they ful¯ll the requirement of ex- tended period ofobservation for TSA. The seasonal e®ects were determined by the use ofthe additive model: x(t) = l(t) + m(t) + sea(t) + e(t) where l(t)isthe levelcomponent with smoothing parameter ¬ m(t)isthe trend component with smoothing parameter ­ sea(t) isthe seasonal component with smoothing parameter ® e(t)isthe error ofthe model. The constants for the levelcomponent, for the trend and seasonal component must lie within the interval between 0and 1.The constants ¬ , ­ and ® are optimized by stepwise variation and were,respectively 0, 0 and 0.5. The software packageapplied was STATISTICA 5.0. Cluster analysis(CA) and principal components analysis(PCA) were used for mul- tivariate statistical modeling of the input data [11,12].The main goalof the hierarchical agglomerativecluster analysisis to spontaneously classifythe data into groups ofsimilar- ity(clusters) searching objects inthe n-dimensional spacelocated inclosestneighborhood and to separate astable cluster from other clusters. Usually,the sampling sites are con- sidered as objects
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