Presenting a conceptual model of data collection to manage the groundwater quality

Abstract

A conceptual model was proposed in the present study, which highlighted important independent and dependent variables in order to managing the groundwater quality. Furthermore, the methods of selection of variable and collection of related data were explained. The study was carried out in the Tajan Plain, north of Iran; 50 drinking wells were considered as sampling points. In this model the Analytical Hierarchy Process (AHP) was proposed to select the indicator water quality parameters. According to expert opinions and characteristics of the study area ten factors were chosen as variables influencing the quality of groundwater (land use types, lithology units, geology units, distance of wells to the outlet, distance to the residential areas, direction toward the residential areas, depth of the groundwater table, the type of aquifer, transmissivity and population). Geographic Information System (AecGIS 9.3) was used to manage the spatial-based variables and the data of non-spatial-based variables were obtained from relevant references. A database, which contains all collected data related to groundwater quality management in the studied area, was created as the output of the model. The output of this conceptual model can be used as an input for quantitative and mathematical models. Results show that 6 parameters (sulphate, iron, nitrate, electrical conductivity, calcium, and total dissolved solids (TDS) were the best indicators for groundwater quality analysis in the area. More than 50% of the wells were drilled in the depth of groundwater table about 5 meters, in this low depth pollutants can load into the wells and also 78% of the wells are located within 5 km from the urban area; it can be concluded from this result that the intensive urban activities could affect groundwater quality.

Multivariate statistical characterization of groundwater quality in Fesdis, East of Algeria

“…The multi-usage of groundwater for drinking, agricultural and industrial purposes, fisheries and energy productions depend considerably on their quality [ISCEN et al 2008]. This quality, defined in terms of physical, chemical and biological compositions, is governed by both natural (precipitation, watershed geology, topography, climate) and anthropogenic (point and non-point sources like urban and industrial activities, other domestic activities, agricultural runoff) factors [MUS- TAPHA, ABDU 2012;NOURBAKHSH, YOUSEFI 2017].…”