News Update on Groundwater Quality: September 2021

 News Update on Groundwater Quality: September 2021



Development of groundwater quality index

Assessing the water quality status for special use is the main objective of any water quality monitoring studies. The water quality index (WQI) is a mathematical instrument used to transform large quantities of water quality data into a single number which represents the water quality level. In fact, developing WQI in an area is a fundamental process in the planning of land use and water resources management. In this study, a simple methodology based on multivariate analysis is developed to create a groundwater quality index (GWQI), with the aim of identifying places with best quality for drinking within the Qazvin province, west central of Iran. The methodology is based on the definition of GWQI using average value of eight cation and anion parameters for 163 wells during a 3-year period. The proportion of observed concentrations to the maximum allowable concentration is calculated as normalized value of each parameter in observing wells. Final indices for each well are calculated considering weight of each parameter. In order to assess the groundwater quality of study area, the derived indices are compared with those of well-known mineral waters. Using developed indices, groundwater iso-index map for study area and the map of areas of which the indices are near to mineral waters was drawn. In the case study, the GWQI map reveals that groundwater quality in two areas is extremely near to mineral water quality. Created index map provides a comprehensive picture of easily interpretable for regional decision makers for better planning and management. [1]

Assessing groundwater quality using GIS

Assessing the quality of groundwater is important to ensure sustainable safe use of these resources. However, describing the overall water quality condition is difficult due to the spatial variability of multiple contaminants and the wide range of indicators (chemical, physical and biological) that could be measured. This contribution proposes a GIS-based groundwater quality index (GQI) which synthesizes different available water quality data (e.g., Cl−, Na+, Ca2+) by indexing them numerically relative to the World Health Organization (WHO) standards. Also, introduces an objective procedure to select the optimum parameters to compute the GQI, incorporates the aspect of temporal variation to address the degree of water use sustainability and tests the sensitivity of the proposed model. The GQI indicated that the groundwater quality in the Nasuno basin, Tochigi Prefecture, Japan, is generally high (GQI <90). It has also displayed the natural (depth to groundwater table, geomorphologic structures) and/or anthropogenic (land-use and population density) controls over the spatial variability of groundwater quality in the basin. Temporally, groundwater quality is more variable in the upper and lower parts of the basin (variation, V, 15–30%) compared to the middle part (V, <15%) probably attributed to the seasonality of precipitation and irrigation of rice. In the lower southeastern part of the Nasuno basin and the vicinity of the Naka and Houki rivers the sustainable use of groundwater is constrained by the relatively low and variable groundwater quality. The model sensitivity analysis indicated that parameters which reflect relatively lower water quality (high mean rank value) and those of significant spatial variability imply larger impacts on the GQI and must be carefully and accurately mapped. Optimum index factor technique allows the selection of the best combination of parameters dictating the variability of groundwater quality and enables an objective and fair representation of the overall groundwater quality. [2]

Application of factor analysis in the assessment of groundwater quality in a blackfoot disease area in Taiwan

Factor analysis is applied to 28 groundwater samples collected from wells in the coastal blackfoot disease area of Yun-Lin, Taiwan. Correlations among 13 hydrochemical parameters are statistically examined. A two-factor model is suggested and explains over 77.8% of the total groundwater quality variation. Factor 1 (seawater salinization) includes concentrations of EC, TDS, Cl−, SO42−, Na+, K+ and Mg2+, and Factor 2 (arsenic pollutant) includes concentrations of Alk, TOC and arsenic. Maps are drawn to show the geographical distribution of the factors. These maps delineate high salinity and arsenic concentrations. The geographical distribution of the factor scores at individual wells does not reveal the sources of the constituents, which are instead, deduced from geological and hydrological evidence. The areas of high seawater salinization and arsenic pollution correspond well to the groundwater over-pumping area. Over-pumping of the local groundwater causes land subsidence and gradual salinization by seawater. The over-pumping also introduces excess dissolved oxygen that oxidizes the immobile minerals, releases arsenic by reductive dissolution of arsenic-rich iron oxyhydroxides and increases the arsenic concentration in water. The over-extraction of groundwater is the major cause of groundwater salinization and arsenic pollution in the coastal area of Yun-Lin, Taiwan. [3]

Aquifer Characteristics and Groundwater Quality Assessment in Ikwuano Area of Southeastern Nigeria

The present work is a review on the geoelectrical characteristics, and a study of physico-chemical parameters of groundwater in parts of Ikwuano Local Government Area of Abia State, Nigeria. The aim was to collate, synthesize and analyse geoelectrical parameters from available literature together with physico-chemical parameters in order to evaluate the geophysical and geochemical character of the aquifer systems; and subsequently determine the quality of groundwater in the area. The study shows that aquifer thickness varies from 24.5m to about 201.1m, the Formation Factor ranges from 2.02 to 13.53, and the groundwater is naturally potable. Total Hardness (TH) values range from 17.86mg/l to 46.91mg/l. The concentrations of major cations and anions are far below the permissible limit for drinking and domestic purposes recommended by World Health Organisation (WHO). With respect to agricultural and irrigation purposes, using the values of Sodium Absorption Ratio (SAR), Residual Sodium Carbonate (RSC) and Electrical Conductivity (EC), the groundwater samples are excellent. [4]

Spatial Groundwater Quality Assessment by WQI and GIS in Ogbia LGA of Bayelsa State, Nigeria

An integrated approach to investigate the spatial groundwater quality in Ogbia LGA in Bayelsa State using WQI and GIS was made. Results from 10 (ten) physicochemical parameters (pH, conductivity, TDS, sulphate, nitrate, sodium, calcium, chloride, magnesium, hardness, iron) analysed on each of the 50 (fifty) groundwater samples from shallow boreholes across the area was used to compute WQI using WHO 2006 as a standard for potable water. Based on calculated WQI, boreholes were classified into excellent, proper, weak, very poor, unsuitable for drinking. Kriging method was then used to generate a digitised WQI map of Ogbia communities based on WQI classes. The map showed excellent to good water was accessible in some parts of Onuebum, Otuasega, Otuoke, Otuogila, Elebele, Emeyal and Oloibiri, whereas, very poor to unfit water occurred at some parts of Ewol, Opume, Akipli and Otuabagi. [5]

Reference
[1] Saeedi, M., Abessi, O., Sharifi, F. and Meraji, H., 2010. Development of groundwater quality index. Environmental monitoring and assessment, 163(1), pp.327-335.
[2] Babiker, I.S., Mohamed, M.A. and Hiyama, T., 2007. Assessing groundwater quality using GIS. Water Resources Management, 21(4), pp.699-715.
[3] Liu, C.W., Lin, K.H. and Kuo, Y.M., 2003. Application of factor analysis in the assessment of groundwater quality in a blackfoot disease area in Taiwan. Science of the total environment, 313(1-3), pp.77-89.

[4] Amos-Uhegbu, C., Igboekwe, M.U., Chukwu, G.U., Okengwu, K.O. and Eke, K.T., 2014. Aquifer characteristics and groundwater quality assessment in Ikwuano area of Southeastern Nigeria. Journal of Scientific Research and Reports, pp.366-383.
[5] Oyinkuro, O.A. and Rowland, E.D., 2017. Spatial groundwater quality assessment by WQI and GIS in Ogbia LGA of Bayelsa State, Nigeria. Asian Journal of Physical and Chemical Sciences, pp.1-12.

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