Elements for a methodology to interpret hydrochemichal data
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2099.1/6434
Tipus de documentTesina
Data2008-05-09
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
The EWFD(European Water Framework Directive) mandates reaching a good status for
water bodies. So, monitoring of our water sources in order to administer, protect and plan
water use in associated areas is mandatory. In order to accomplish the EWFD objectives the
Ag`encia Catalana de l’Aigua has a large ground water quality network monitoring points all
over the territory. This network consists of a series of wells that measures a large number of
parameters which lead to a huge amount of data to be interpreted. The size of the database
poses a tremendous challenge for interpreting, and thus, there is a need to explore the possibility
of improving data representation and initial data analysis.
The aim of this thesis is to identify a series of techniques, tools or methods to ease a systematic
interpretation of generic groundwater chemical data sets, assuming that the user might
not be an expert in managing such kind of data. So, the following tasks have been performed:
target area definition and study; techniques and tools decision; data acquisition, conversion and
filtering and, finally, technique application.
As a result of these process, an appropriate data format has shown to be be mandatory
in order to correctly export the data set to the needed codes. When applied, the most easily
understood techniques have usually been graphic methods. In some cases their interpretability
has been conditioned by data quality or code lacks so both fields are intended to be improved
in future work lines. These includes taking advantage of Aquachem’s modelling techniques and
ArcGIS plotting advantages. On the other hand, the most interesting multivariate technique
has been factor analysis, which has no unique interpretations and might have to be more studied
in. So, on the whole, to validate the conclusions of this work it might be interesting to apply
this same methodology on different data sets.
TitulacióENGINYERIA GEOLÒGICA (Pla 2000)
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