Sulphate contamination in groundwater and its remediation: an overview

Sharma, M. K.; Kumar, Manoj

doi:10.1007/s10661-019-8051-6

Cited by 96 publications

(30 citation statements)

References 34 publications

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“…Nitrate pollution due to the agricultural activities did not have a strong impact on the EC value, since the nitrates and potassium from agricultural pollution participate in the second factor [67]. The participation of SO 4 in the third factor can also be attributed to pollution from agriculture due to the fertilization [68,69].…”

Section: Resultsmentioning

confidence: 99%

Use of Factor Analysis (FA), Artificial Neural Networks (ANNs), and Multiple Linear Regression (MLR) for Electrical Conductivity Prediction in Aquifers in the Gallikos River Basin, Northern Greece

et al. 2021

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Due to the fact of water resource deterioration from human activities and increased demand over the last few decades, optimization of management practices and policies is required, for which more reliable data are necessary. Cost and time are always of importance; therefore, methods that can provide low-cost data in a short period of time have been developed. In this study, the ability of an artificial neural network (ANN) and a multiple linear regression (MLR) model to predict the electrical conductivity of groundwater samples in the GallikosRiver basin, northern Greece, was examined. A total of 233 samples were collected over the years 2004–2005 from 89 sampling points. Descriptive statistics, Pearson correlation matrix, and factor analysis were applied to select the inputs of the water quality parameters. Input data to the ANN and MLR were Ca, Mg, Na, and Cl. The best results regarding the ANN were provided by a model that included one hidden layer of three neurons. The mean absolute percentage error, modeling efficiency, and root mean square error were used to evaluate the performances of the methods and to compare the prediction capabilities of the ANN and MLR. We concluded that the ANN and MLR models were valid and had similar accuracy (using the same inputs) with a large number of samples, but in the case of a smaller data set, the MLR showed a better performance.

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Section: Resultsmentioning

confidence: 99%

Use of Factor Analysis (FA), Artificial Neural Networks (ANNs), and Multiple Linear Regression (MLR) for Electrical Conductivity Prediction in Aquifers in the Gallikos River Basin, Northern Greece

et al. 2021

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“…Anthropogenic activities such as fertiliser application, burning of fossil fuels, or ore smelting can increase sulphate concentrations in the near-surface groundwater (Sharma and Kumar 2020). Sulphide-bearing minerals such as pyrite and arsenopyrite are common predominant minerals associated with gold mineralisation in Ghana (Brako et al 2020).…”

Section: Major Ions' Chemistry and Correlationsmentioning

confidence: 99%

Hydrochemical peculiarities and groundwater quality assessment of the Birimian and Tarkwaian aquifer systems in Bosome Freho District and Bekwai Municipality of the Ashanti Region, Ghana

Okofo

Anderson²,

Bedu-Addo

et al. 2021

Environ Earth Sci

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The Birimian and Tarkwaian aquifer systems are the main sources of water supply for the Bosome Freho District and Bekwai Municipality inhabitants in the Ashanti region of Ghana. A hydrogeochemical assessment was carried out to ascertain the natural baseline chemistry of the groundwaters and the factors influencing groundwater chemistry in these two areas. A multivariate statistical tool consisting of principal component analysis (PCA) and hierarchical cluster analysis (HCA) together with hydrochemical graphical plots was applied on 64 groundwater samples. The Q–mode HCA results were used to explain the changes in groundwater chemistry along the flow paths where three spatial groundwater zones and water types were delineated. The first type consists of Ca–Mg–HCO3 freshwater (recharge zone), which transitions into Ca–Na–HCO3 or Na–Ca–HCO3 mixed waters (intermediate zone) and finally evolves to the third type of Na–Ca–Mg–HCO3–Cl water (discharge zone). The study also reveals that the natural process influencing water chemistry is groundwater–rock interaction from carbonate and silicate weathering/dissolution, aided by carbonic acid from precipitation and releases concentration of Na+, Ca2+, Mg2+, and HCO3− into the groundwaters significantly. The chloro-alkaline indices also reveal cation exchange as the principal natural factor that controls groundwater chemistry in the area. Inverse geochemical modelling shows the dissolution of primary minerals such as dolomite, plagioclase, halite, gypsum, and precipitation of calcite and chlorite along the groundwater flow path. Anthropogenic activities have little influence on groundwater chemistry. The quality of groundwater in the Bosome Freho District and Bekwai Municipality is suitable for irrigational use and drinking water consumption. The results obtained so far will contribute to research paucity in the study area and serve as a guide for decision-makers for improved water resources management.

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“…Comparing the results of our maize monoculture long-term field experiment with a pot experiment [24] and forest trial [25] is indeed difficult due to the very different conditions that apply to each of these respective environments. Even though these trials are so different, what they share in common is the fact that a significant part of sulfur fertilizers is leached and unavailable to plants and may even be contaminating ground waters [59].…”

Section: Sulfur Status and Movement In Topsoil And Subsoilmentioning

confidence: 99%

Evaluation of Soil S Pools under 23 Years of Maize Monoculture

et al. 2021

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Sulfur nutrition is a critical part of proper crop growth and development. In our study, biomass yields (BY) and S uptake were investigated on long-term maize monoculture on haplic luvisol soil during the 23 years of this trial, as well as changes in water extractable (Sw), adsorbed (Sads), mineral (Sav), and pseudo-total S (St) fractions. Treatments used in this study are: (1) Control (Cont); (2) ammonium sulfate (AS); (3) urea and ammonium nitrate (UAN); (4) UAN + phosphorus and potassium (UAN + PK); (5) UAN + phosphorus, magnesium, sulfur (UAN + PMgS); and (6) Fallow. Recently, the Mehlich 3 method started to be used in the Czech Republic to determine content of plant available S. Using this method, it was found that the content of S extracted by Mehlich 3 (SM3) closely correlates to Sav in both topsoil and subsoil (r = 0.958 in 1997 and 0.990 in 2019, both at p < 0.001). We also found that, on average, during the entire experiment, all treatments had increased yields over Cont (135–147%) and increased S uptake (291, 192, 180, and 246% of Cont for AS, UAN, UAN + PK, and UAN + PMgS, respectively). Examining the changes from 1997 to 2019 in topsoil (0–30 cm depth), we discovered a decrease of S content in Sw, Sads, Sav, and St fractions on all treatments to an average of 34.6%, 65.8%, 42.2%, and 78.6% of their initial values. The exception was AS treatment, which doubled its initial content in mineral fractions and maintained the same levels of St, and which we attribute to the very high dose of S on this treatment (142 kg ha−1 year−1). Using the simple balance method, AS and UAN + PMgS treatments lost 142.2 and 95.3 kg S ha−1 year−1 to other sinks, except plant uptake, from the entire soil profile (0–60 cm) during 23 years of experiment. Other treatments also show significant losses with the exception of Fallow. Given these results, it is clear that content of sulfur in soil is generally decreasing and attention should be paid mainly towards minimizing of its losses.

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Sulphate contamination in groundwater and its remediation: an overview

Cited by 96 publications

References 34 publications

Use of Factor Analysis (FA), Artificial Neural Networks (ANNs), and Multiple Linear Regression (MLR) for Electrical Conductivity Prediction in Aquifers in the Gallikos River Basin, Northern Greece

Use of Factor Analysis (FA), Artificial Neural Networks (ANNs), and Multiple Linear Regression (MLR) for Electrical Conductivity Prediction in Aquifers in the Gallikos River Basin, Northern Greece

Hydrochemical peculiarities and groundwater quality assessment of the Birimian and Tarkwaian aquifer systems in Bosome Freho District and Bekwai Municipality of the Ashanti Region, Ghana

Evaluation of Soil S Pools under 23 Years of Maize Monoculture

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