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Title:Kemijska analiza in kemometrijska karakterizacija kvalitete vode reke Mure
Authors:ID Krajnc Galunder, Bojana (Author)
ID Brodnjak-Vončina, Darinka (Mentor) More about this mentor... New window
ID Kolar, Mitja (Co-mentor)
Files:.pdf MAG_Krajnc_Galunder_Bojana_2016.pdf (2,42 MB)
MD5: EA157DC7DC64B1A23FF8956BEA41D001
 
Language:Slovenian
Work type:Master's thesis (m2)
Typology:2.09 - Master's Thesis
Organization:FKKT - Faculty of Chemistry and Chemical Engineering
Abstract:V obdobju od leta 1996 do 2009 smo opravili analizo 367 vzorcev vode in izbrali 32 parametrov, ki najbolj značilno opisujejo kakovost vode v reki Muri. Z uporabo kemometrijskih metod kot so metoda glavni osi (PCA), hierarhično razvrščanje (CA) in linearna diskriminantna analiza (LDA) smo analizirali okoljske parametre vzorcev. Z regresijo smo spremljali spremenljivke, ki imajo največji vpliv na kakovost vode v reki. S pomočjo grafičnega prikaza podatkov smo prišli do zaključka, da se pri spremenljivkah kot so vsebnost adsorbljivih organskih halogenov, nitratov in fosfatov, pojavlja trend zmanjševanja. Z metodo glavnih osi smo med množico spremenljivk poiskali tiste, ki imajo največji vpliv na kakovost vode. V našem primeru so to za odvzemno mesto Gornja Radgona vsebnost nitrata, elektroprevodnost in BPK5, ker se odvzemno mesto nahaja na kmetijsko intenzivnem območju. Odvzemno mesto Šentilj se nahaja gorvodno od Gornje Radgone, ki je nekoliko manj intenzivno kmetijsko območje, zato sklepamo, da je to glavni vzrok razlik, ugotovljenih z metodo glavnih osi. Največji vpliv imata spremenljivki nasičenost s kisikom in elektroprevodnost. Hierarhično razvrščanje (CA) smo uporabili za ugotavljanje podobnosti med spremenljivkami. Rezultat je bil nastanek dveh glavnih skupin za odvzemno mesto Gornja Radgona in dveh glavnih skupin za odvzemno mesto Šentilj. Glavni predstavniki skupin so spremenljivke, ki lahko bistveno vplivajo na kvaliteto površinske vode. Linearno diskriminantno analizo smo uporabili za razvrščanje vzorcev vode z znano pripadnostjo določeni skupini. Pri ugotavljanju podobnosti med odvzemnima mestoma ugotavljamo, da se odvzemni mesti med seboj razlikujeta, čeprav ležita geografsko blizu. Predvidevamo, da razliko povzročajo kmetijski in industrijski vplivi. Ker se odvzemni mesti nahajata v geografskem prostoru z enakim podnebjem, imajo vremenske razmere manjši vpliv.
Keywords:voda, reka, kakovost, kemijski parametri, metoda glavnih osi, hierarhično razvrščanje, linearna diskriminantna analiza.
Year of publishing:2016
Publisher:[B. Krajnc Galunder]
Source:Maribor
PID:20.500.12556/DKUM-57846 New window
UDC:543.3:627.152:628.161.1(043)
COBISS.SI-ID:19876630 New window
NUK URN:URN:SI:UM:DK:QRXPKMFU
Publication date in DKUM:13.10.2016
Views:1520
Downloads:204
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:KTFMB - FKKT
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Secondary language

Language:English
Title:Chemical analysis and chemometric interpretation of Mura river water analyses results
Abstract:In the period from 1996 to 2009 we analyzed 367 water samples and chose 32 parameters that describe the most characteristic quality of the water in the river. Using chemometric methods such as Principal component analysis (PCA), Cluster analysis (CA) and Linear discriminant analysis (LDA), we analysed the environmental parameters of water samples. We monitored variables that have the greatest impact on the quality of water in the river. With the help of graphical representation of the data, a decreasing trend is shown. Using the Principal Component Analysis method, amongst the variables, we searched for those that have the greatest effect on the quality of water. At the sampling point Gornja Radgona, in our case, these were nitrates, electrical conductivity and COD5, as the sampling point is located in an intensively farmed area. The sample site of Šentilj, located upstream of Gornja Radgona, is a slightly less intensive agricultural area, which is why one can assume that this is the main cause of the discrepancies identified using the Principal component analysis. Oxygen saturation and electrical conductivity had the biggest impact. We used cluster analysis (CA) to determine the similarity between the variables. The result was the emergence of two main groups for the sampling point Gornja Radgona and two main groups for the sampling point Šentilj. The main representatives of the group are variables that can significantly affect the quality of surface water. Linear discriminant analysis was used to classify the water samples with a known affiliation to a particular group. When determining the similarities between sampling points we find that the sampling sites differ, even though they are geographically very close to one another. We assume that the difference is brought about by agricultural and industrial influences. As the two sites are located in the same geographical area with the same climate, weather conditions have less of an impact.
Keywords:water, river, quality, chemical parameters, method of principal component analysis, cluster analysis, linear discriminant analysis.


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