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

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Warianty tytułu
PL
Przedstawienie koncepcyjnego modelu gromadzenia danych na potrzeby zarządzania jakością wód gruntowych
Języki publikacji
EN
Abstrakty
EN
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.
PL
W przedstawionym badaniu zaproponowano model koncepcyjny, który uwydatnia niezależne i zależne zmienne ważne dla zarządzania jakością wód gruntowych. Wyjaśniono ponadto metody doboru zmiennych i gromadzenia stosownych danych. Badania prowadzono na Równinie Tajan na północy Iranu. Próby pobierano w 50 studniach. W wybranym modelu zaproponowano proces analitycznej hierarchii (AHP) do wyboru wskaźnikowych parametrów jakości wody. Zgodnie z opiniami ekspertów i charakterystyką obszaru badań wybrano dziesięć czynników stanowiących zmienne wpływające na jakość wód gruntowych (typ użytkowania ziemi, jednostki litologiczne, jednostki geologiczne, odległość studni od odpływu, odległość od terenów zamieszkanych przez ludzi, głębokość zwierciadła wód gruntowych, typ warstwy wodonośnej, przepuszczalność i liczba ludności). Wykorzystano system informacji geograficznej (AecGIS 9.3) do zarządzania zmiennymi przestrzennymi, a dane o zmiennych niezwiązanych z rozmieszczeniem przestrzennym pozyskano z literatury. Jako wyjście z modelu stworzono bazę danych, która zawiera wszystkie zebrane dane odnoszące się do zarządzania jakością wód gruntowych. Wyjście tego koncepcyjnego modelu może być użyte jako wejście do modeli ilościowych i matematycznych. Uzyskane wyniki świadczą, że najlepsze wskaźniki do analizy jakości wód gruntowych na badanym obszarze stanowiło 6 parametrów (siarczany, żelazo, azotany, przewodnictwo elektrolityczne, wapń i suma substancji rozpuszczonych). Ponad 50% studni wiercono do poziomu zwierciadła ok. 5 m. W warunkach tak małych głębokości można spodziewać się znacznej dostawy ładunku zanieczyszczeń. Spośród badanych studni 78% było usytuowanych w promieniu 5 km od terenów miejskich. Uzyskane wyniki pozwalają sądzić, że aktywność miejska może wpływać na jakość wód gruntowych.
Wydawca
Rocznik
Tom
Strony
149--160
Opis fizyczny
Bibliogr. 39 poz., rys., tab.
Twórcy
  • Islamic Azad University, Young Researchers and Elites Club, Science and Research Branch, Tehran, Iran
autor
  • University of Tehran, Faculty of New Sciences and Technologies, 16th Azar St., Enghelab Sq., 1417466191, Tehran, Iran
Bibliografia
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  • BENRABAH S., ATTOUI B., HANNOUCHE M. 2016. Characterization of groundwater quality destined for drinking water supply of Khenchela City (eastern Algeria). Journal of Water and Land Development. No. 30 p. 13–20.
  • ELBEIH S.F. 2015. An overview of integrated remote sensing and GIS for groundwater mapping in Egypt. Ain Shams Engineering Journal. Vol. 6. Iss. 1 p. 1–15.
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  • GORAI A.K., KUMAR S. 2013. Spatial distribution analysis of groundwater quality index using GIS: A case study of Ranchi Municipal Corporation (RMC) Area [online]. Geoinformatics and Geostatistics: An overview. Vol. 1. Iss. 2. [Access 11.5.2015]. Available at: http://www.scitechnol.com
  • HAMMOURI N., AL-QINNA M., SALAHAT M., ADAMOWSKI J., PRASHER S.O. 2015. Community based adaptation options for climate change impacts on water resources: The case of Jordan. Journal of Water and Land Development. No. 26 p. 3–17.
  • HOSSAIN N., BAHAUDDIN K.H. 2013. Integrated water resource management for mega city: A case study of Dhaka city, Bangladesh. Journal of Water and Land Development. No. 19 p. 39–45.
  • JAMES C.A., KERSHNER J., SAMHOURI J., O’NEILL S., PHILLIP S., LEVIN A. 2012. A methodology for evaluating and ranking water quantity indicators in support of ecosystem-based management. Environmental Management. Vol. 49. Iss. 3 p. 703–719.
  • LAR Consulting Engineers 2001. Report on surface and groundwater monitoring: Tajan Sub-project Status. Mazandaran Regional Water Company, Iran. Mazandaran, Sari.
  • LI P.-Y., QIAN H., WU J.-H. 2010. Groundwater quality assessment based on improved water quality index in Pengyang County, Ningxia, Northwest China. E-Journal of Chemistry. Vol. 7 p. S209–S216.
  • LJUBENKOV I. 2012. Water resources of the island of Korčula (Croatia): availability and agricultural requirement. Journal of Water and Land Development. No. 17 p. 11–18.
  • MAINARDI FAN F., SANTOS FLEISCHMANN A., COLLISCHONN W., AMES D.P., RIGO D. 2015. Large-scale analytical water quality model coupled with GIS for simulation of point sourced pollutant discharges. Environmental Modelling and Software. Vol. 64 p. 58–71.
  • MASHARI S., SOLAIMANI K., OMIDVAR E. 2012. Landslide susceptibility mapping using multiple regression and GIS tools in Tajan Basin, North of Iran [online]. Environment and Natural Resources Research. Vol. 2. Iss. 3. [Access: 17.7.2015]. Available at: http://www.ccsenet.org/journal/index.php/enrr/article/view/15346/13673
  • MEHRDADI N., DARYABEIGI ZAND A., MATLOUBI A. A. 2007. Natural and human-induced impacts on costal groundwater. International Journal of Environmental Research. Vol. 1. Iss. 2 p. 170–178.
  • MULLER G. 2014.Teaching conceptual modeling at multiple system levels using multiple views [online]. Procedia CIRP. Vol. 21. [Access 7.6.2015]. Available at: https://pdfs.semanticscholar.org/7278/690219f7f2b7cf5b8264cf56afa9783dec2d.pdf
  • NESHAT A., PRADHAN, B., DADRAS, M. 2014. Groundwater vulnerability assessment using an improved DRASTIC method in GIS. Resources, Conservation and Recycling. Vol. 86 p. 74–86.
  • NOURBAKHSH Z., MEHRDADI N., MOHHARAMNEJAD N., HASSANI A.H., YOUSEFI H. 2015a. Evaluating the suitability of different parameters for qualitative analysis of groundwater based on analytical hierarchy process [online]. Desalination and Water Treatment. Vol. 57. Iss. 28 p. 13175–13182. [Access 12.8.2015] Available at: http://www.tandfonline.com/doi/full/10.1080/19443994.2015.1056837#abstract
  • NOURBAKHSH Z., MEHRDADI N., MOHHARAMNEJAD N. HASSANI A.H., YOUSEFI H. 2015b. Proposing an index to evaluate the groundwater quality using “Multi-Criteria Decision Making” approach and analyzing the spatial distribution of it in the Tajan Plain. Northern Iran. Iranian Journal of Health Sciences. Vol. 3. Iss. 3 p. 37–47.
  • OSIBANJO O., MAJOLAGBE A.O. 2012. PHYSICOCHEMICAL QUALITY ASSESSMENT of groundwater-based on land use in Lagos city, Southwest, Nigeria. Chemistry Journal. Vol. 02 p. 79–86.
  • PACHRI H., MITANI Y., IKEMI H., DJAMALUDDIN I., MORITA A. 2013. Development of water management modeling by using GIS in Chirchik River Basin, Uzbekistan [online]. Procedia Earth and Planetary Science. Vol. 6. [Access: 12.6.2015]. Available at: http://www.sciencedirect.com/science/article/pii/S1878522013000246
  • RIOS F., YE M., WANG L., LEE P.Z., DAVIS H., HICKS R. 2013. ArcNLET: A GIS-based software to simulate groundwater nitrate load from septic systems to surface waterbodies. Computers and Geosciences. Vol. 52 p. 108–116.
  • SAATY T.L. 1997. A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology. Vol. 15. Iss. 3 p. 234–281.
  • SHARIATI S.H., YAZDANI CHAMZINI A., POURGHAFFARI BASHARI B. 2013. Mining method selection by using an integrated model. International Research Journal of Applied and Basic Sciences. Vol. 6. Iss. 2 p. 199–214.
  • SHEN Z.Y., CHEN L., LIAO Q., LIU R.M., HUANG Q. 2013. A comprehensive study of the effect of GIS data on hydrology and non-point source pollution modeling. Agricultural Water Management. Vol. 118. Iss. 11 p. 93–102.
  • SHIRAZI S. M., ADHAM M.J., ZARDARI N.H., ISMAIL Z., IMRAN H.M., MANGRIO M.A. 2015. Groundwater quality and hydrogeological characteristics of Malacca state in Malaysia. Journal of Water and Land Development. No. 24 p. 11–19.
  • SINGH CH.K., SHASHTRI S., MUKHERJEE S., KUMARI R., AVATAR R., SINGH A., SINGH R.P. 2011. Aplication of GWQI to assess effect of land use change on groundwater quality in lower shiwaliks of Punjab: Remote sensing and GIS based approach [online]. Water Resources Management. Vol. 25. Iss. 7 p. 1881–1895. [Access 17.7.2015]. Available at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.933.4860&rep=rep1&type= pdf
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  • STIGTER T., RIBEIRO L., CARVALHO DILL A.M.M. 2006. Application of a groundwater quality index as an assessment and communication tool in agro-environmental policies: Two Portuguese case studies. Journal of Hydrology. Vol. 327. Iss. 3 p. 578–591.
  • The Governor of Mazandaran 2011. Statistical yearbook of Mazandaran. Office of Statistics, Information and GIS. Department of the Interior, Iran. Mazandaran, Sari.
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  • TU J. 2011. Spatially varying relationships between land use and water quality across an urbanization gradient explored by geographically weighted regression. Applied Geography. Vol. 31 p. 376–392.
  • USGS 2015. Conceptual model [online]. United States Geographical Survey. [Access 22.7.2015]. Available at: http://ca.water.usgs.gov/projects/sanbern/training/models/conceptual.html
  • VICTORINE NEH A., AKO AKO A., RICHARD AYUK A., HOSONA T. 2015. DRASTIC-GIS model for assessing vulnerability to pollution of the phreatic aquiferous formations in Douala–Cameroon. Journal of African Earth Sciences. Vol. 102 p. 180–190.
  • YOUSEFI Z., NASIRAHMADI K. 2013. Assessment of the surface water quality in Tajan river basin, Iran. Life Science Journal. Vol. 10. Iss. 3 p. 775–780.
  • ZARDARI N.H., NAUBI I.B., ROSLAN N.A.B., SHIRAZI S.H.M. 2014. Multicriteria approach for selecting the most vulnerable watershed for developing a management plan. Journal of Water and Land Development. No. 23 p. 61–68.
  • ZUSHI Y., MASUNAGA S. 2011. GIS-based source identification and apportionment of diffuse water pollution: Perfluorinated compound pollution in the Tokyo Bay basin [online]. Chemosphere. Vol. 85. Iss. 8. [Access 6.8.2015]. Available at: https://www.ncbi.nlm.nih.gov/pubmed/21885084
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-402a569b-31ed-48d1-9f93-fcb3b1266e23
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