Assessment and modeling of the groundwater hydrogeochemical quality parameters via geostatistical approaches

Karami, Shawgar; Madani, Hassan; Katibeh, Homayoon; Marj, Ahmad Fatehi

doi:10.1007/s13201-018-0641-x

Cited by 46 publications

(15 citation statements)

References 16 publications

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“…Mg, Cl, P, and NO3 showed strong spatial dependency in both seasons with Cn/C ratio < 25% while SO4, and Fe exhibited moderate dependency between 25% -75% in both seasons as well. In the study conducted by Karami et al, [15] on groundwater modelling, EC and TDS indicated moderate spatial dependence while Cl, Na+, SO42-, Total hardness revealed strong spatial dependency. However, it revealed that groundwater quality are spatially dependent.…”

Section: Spatial Dependency Resultsmentioning

confidence: 90%

Spatial dependency of groundwater quality in Benin City, Edo State, Nigeria using semi-variogram models

Ezugwu¹,

Atikpo²

2021

World J. Adv. Eng. Tech. Sci.

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Water quality is a worldwide concern especially water for human consumption. Regular monitoring and tools should be developed, to ensure continuous assessment of the level of concentration of chemical constituents prevalent in water. This will guide against health dangers and risks associated with water of low quality. The research focus on using semi-variogram models to measure the degree of spatial dependency of sampled boreholes for prediction of the concentration of water quality parameters at un-sampled locations because of the cumbersome nature of assessing the whole boreholes in Benin City. One hundred and ten (110) different domestic boreholes were systematically sampled and analyzed in laboratory for determination of the concentration of some chemical water quality parameters which include Electrical Conductivity (EC), Total Dissolve Solids (TDS), Bicarbonate (HCO3), Sodium (Na), Potassium (K), Calcium (Ca), Magnesium (Mg), etc. Laboratory examination of boreholes water quality parameters were carried out in wet and dry seasons. Geographical locations of sampled boreholes were also determined. The obtained experimental results were utilized in investigating the spatial structure of the boreholes using semi-variogram models which include spherical, exponential, Gaussian etc. Exponential model was the most fitted model. The borehole water quality parameters exhibited high degree of spatial dependency in EC, TDS, HCO3, Na, K, Ca. Mg, Cl, P, and NO3 in both seasons with ratio < 25% therefore, interpolation technique can be employed to produce spatial variation quality map of boreholes in Benin City as a monitoring technique to detect contamination or changes in water quality.

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Section: Spatial Dependency Resultsmentioning

confidence: 90%

Spatial dependency of groundwater quality in Benin City, Edo State, Nigeria using semi-variogram models

Ezugwu¹,

Atikpo²

2021

World J. Adv. Eng. Tech. Sci.

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“…Because it requires the fewest assumptions and the least knowledge, it was deemed as the "work horse" of practical geostatistics and will serve in some 90% of cases [60]. Research has shown that kriging would in general be more effective than other methods of interpolation if there is spatial autocorrelation among the sampled data points [61][62][63]. In order to minimize the bias, each of the sampling approaches was performed 100 times.…”

Section: Comparing the Correlation And Rmse Between The Predicted Surface Based On Sample And Ndvi Imagementioning

confidence: 99%

Remote Sensing-Guided Spatial Sampling Strategy over Heterogeneous Surface Ground for Validation of Vegetation Indices Products with Medium and High Spatial Resolution

Zhou

Tao

et al. 2021

Remote Sensing

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Remote sensing (RS)-derived vegetation indices (VIs) with medium and high spatial resolution have emerged as a promising dataset for fine-scale ecosystem modeling and agricultural monitoring at local or global scales. Before they can be used as reliable inputs for other research, conducting in situ measurements for validation is very critical. However, the spatial heterogeneity due to the diversity of land cover and its spatial organization in the landscape increases the uncertainty of validation, so design of optimal sampling is an important basis for the reliability of the validation. In this paper, we propose an integrative stratified sampling strategy (INTEG-STRAT) based on normalized difference vegetation index (NDVI) data as prior knowledge. The basic idea is to realize a sampling optimization by determining the optimal combination of the spatial sampling method (e.g., simple random sampling (SRS), spatial system sampling (SYS), stratified sampling, generalized random tessellation stratified (GRTS), balanced acceptance sampling (BAS)) and spatial stratification scheme with an objective rule. The objective rule in this paper is to minimize the root mean square error (RMSE) of 10-fold cross validation between estimated values (sample are not included) and the corresponding values on prior knowledge. Relative precision, correlation coefficient, and RMSE are used to compare the effectiveness of the proposed sampling strategy with each sampling method without considering sampling optimization. After comparing, we find that the INTEG-STRAT requires fewer samples to become stable and has higher accuracy. At site 1, when the correlation coefficient between NDVI image and the simulated NDVI surface reached 80%, INTEG-STRAT needed only 70 sampling points while other methods require more sampling points. At the same time, INTEG-STRAT strategy has a smaller RMSE between the estimated values and the corresponding values on prior knowledge image. In general, INTEG-STRAT is an effective method in the selection of representative samples to support the validation of vegetation indices products with medium and high spatial resolution.

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“…Eighteen (18) water samples were collected from boreholes in Rumuola community for this study. The sampling points were distributed around the community with the aid of the GIS software.…”

Section: Sampling Techniquementioning

confidence: 99%

“…Kriging which is linear interpolation method, presupposes a statistical model and has standard errors that measure the level of uncertainty of the values that have been forecasted [16,17,18]. Classical kriging assumes that the variogram estimated initially is the correct variogram of the studied data but, this assumption is not always true in practice and this is why EBK was introduced [16].…”

Section: Introductionmentioning

confidence: 99%

Geostatistical Modelling of Groundwater Quality at Rumuola Community, Port Harcourt, Nigeria

Amah

Agu

2020

AJEE

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The groundwater quality of Rumuola community of Rivers State, Nigeria was investigated. This study was done to determine the pollution potential of a solid waste open dump in a borrow pit in the community. The leachate pollution index was calculated for the borrow pit at the centre of the community using weighted additive leachate pollution index. The result showed that the LPI value was 5.31 and has low pollution potential. It was discovered that the groundwater in the entire community was acidic with pH levels ranging from 3.6 to 4.2, which is below NSDWQ’s permissible range of 6.5-8.5. Nickel and arsenic also showed concentrations that were above permissible limits with nickel values averaging 0.033 mg/l which is slightly above the limit of 0.02 mg/l. Arsenic had concentrations that ranged from 0.16 to 1.57 mg/l which is above permissible limits of 0.01 mg/l. WQI was determined using weighted arithmetic water quality index analysis. As a result of the high concentrations of arsenic, the WQI values were very high with values ranging from 144 to 1367 and this shows that the water in the study area is unsuitable for drinking. In modelling the water quality index of Rumuola community, geostatistical methods were applied. Ordinary kriging, Empirical Bayesian kriging (EBK), inverse distance weighting (IDW) and cokriging interpolations methods were used to produce surface maps showing the distribution of variables using ARCGIS software. The best interpolators were: EBK for pH, TDS, Sulphate and nitrate; Ordinary kriging for Nickel and Hardness; IDW for Iron and arsenic; Cokriging for WQI.

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Assessment and modeling of the groundwater hydrogeochemical quality parameters via geostatistical approaches

Cited by 46 publications

References 16 publications

Spatial dependency of groundwater quality in Benin City, Edo State, Nigeria using semi-variogram models

Spatial dependency of groundwater quality in Benin City, Edo State, Nigeria using semi-variogram models

Remote Sensing-Guided Spatial Sampling Strategy over Heterogeneous Surface Ground for Validation of Vegetation Indices Products with Medium and High Spatial Resolution

Geostatistical Modelling of Groundwater Quality at Rumuola Community, Port Harcourt, Nigeria

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