Mapping of Heavy Metal Contamination in Alluvial Soils of the Middle Nile Delta of Egypt

Shokr, Mohamed S.; Baroudy, Ahmed A. El; Fullen, Michael A.; El-Beshbeshy, Talaat R.; Ali, Ramadan R.; El‐Halim, A. A. Abd; Guerra, Antônio; Jorge, M. C. O.

doi:10.3846/16486897.2016.1184152

Cited by 23 publications

(9 citation statements)

References 24 publications

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“…Coir fibres (which had the lowest proportion of macro-nutrients in this study) have been previously used to construct effective biogeotextiles (Bhattacharyya et al 2011;Fullen et al 2011a;Kertész et al 2011). Coir mats have been successfully applied in many gully rehabilitation projects world-wide, although coir is less likely to add nutrients to soil systems on decomposition (Fullen et al 2011a;Chen et al 2011;Guerra et al 2015;Shirazi et al 2016;Vasarevičius et al 2016;Shokr et al 2016;Jarašiūnas, Kinderienė 2016). Therefore, other types of plant fibre with higher proportions of macro-elements may make better biogeotextiles, with higher nutrient concentrations than coir fibre.…”

Section: Total Nutrient Concentration Analysismentioning

confidence: 99%

Geochemical Characterization of Banana, Buriti Palm, Jute and Coir Fibres for Use as Biogeotextiles for Erosion Control

Egbujuo

Fullen

Guerra

et al. 2018

Journal of Environmental Engineering and Landscape Management

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Geochemical analysis of selected plant fibres used as geotextiles for erosion control was conducted to evaluate their geochemical composition and to determine the elements that could potentially be nutrients for plants or cause heavy metal contamination in soils. Analysis of the samples was performed using X-ray Diffraction and X-ray Fluorescence spectrometry. Results revealed that potassium concentrations varied from 3.63% in jute-mat (India)-50.73% in banana-leaf (São Romão, Brazil), with a mean of 27.17%. Similarly, calcium concentrations varied from 0.09% in banana-leaf (São Romão) –37.0% in banana-leaf/stem (Oleo, Brazil). Iron concentrations varied from 0.15% in banana-leaf/stem samples (Oleo) –4.47% in jute-cloth (India). Since banana-leaf/stem had the highest concentration of macro-nutrients, it is therefore proposed that banana-leaf has the highest potential for adding nutrients to the soil system when biodegraded. In addition, heavy metal analysis of the samples revealed that none of the fibres have high concentrations that may contaminate the soil upon decomposition.

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Section: Total Nutrient Concentration Analysismentioning

confidence: 99%

Geochemical Characterization of Banana, Buriti Palm, Jute and Coir Fibres for Use as Biogeotextiles for Erosion Control

Egbujuo

Fullen

Guerra

et al. 2018

Journal of Environmental Engineering and Landscape Management

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“…Today, to identify elements that are deficient in a soil, there is a requirement for quick analysis with low cost and high accuracy; satellite image data can complement analysis in situ [34,35]. Precise measurements and efficient methods should be conducted to improve soil and water quality [36]. Measurable information for soil characteristics and their spatial spreading is very important to achieve agriculture sustainability development and reduce negative environmental effects [37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Integration of Geostatistical and Sentinal-2AMultispectral Satellite Image Analysis for Predicting Soil Fertility Condition in Drylands

Shokr

Mazrou

Abdellatif

et al. 2022

IJGI

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For modelling and predicting soil indicators to be fully operational and facilitate decision-making at any spatial level, there is a requirement for precise spatially referenced soil information to be available as input data. This paper focuses on showing the capacity of Sentinal-2A(S2A) multispectral imaging to predict soil properties and provide geostatistical analysis (ordinary kriging) for mapping dry land soil fertility conditions (SOCs). Conditioned Latin hypercube sampling was used to select the representative sampling sites within the study area. To achieve the objectives of this work, 48 surface soil samples were collected from the western part of Matrouh Governorate, Egypt, and pH, soil organic matter (SOM), available nitrogen (N), phosphorus (P), and potassium (K) levels were analyzed. Multilinear regression (MLR) was used to model the relationship between image reflectance and laboratory analysis (of pH, SOM, N, P, and K in the soil), followed by mapping the predicted outputs using ordinary kriging. Model fitting was achieved by removing variables according to the confidence level (95%).Around 30% of the samples were randomly selected to verify the validity of the results. The randomly selected samples helped express the variety of the soil characteristics from the investigated area. The predicted values of pH, SOM, N, P, and K performed well, with R2 values of 0.6, 0.7, 0.55, 0.6, and 0.92 achieved for pH, SOM, N, P, and K, respectively. The results from the ArcGIS model builder indicated a descending fertility order within the study area of: 70% low fertility, 22% moderate fertility, 3% very low fertility, and 5% reference terms. This work evidence that which can be predicted from S2A images and provides a reference for soil fertility monitoring in drylands. Additionally, this model can be easily applied to environmental conditions similar to those of the studied area.

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“…One of the aspects threatening the health and quality of soil is hazardous elements and heavy metals [1][2][3]. A large number of published studies analysing the risk elements in alluvial soils in the vicinity of rivers, or even in entire flood areas, can be found [1,[4][5][6][7][8][9][10][11][12] because the alluvial soils are specific. Their level of pollution can change relatively quickly, depending on the periodicity and extent of the floods.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Concentration of Risk Elements in Alluvial Soils Determined by pXRF In Situ, in the Laboratory, and by ICP-OES

et al. 2021

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The aim of the study was to compare the concentrations of risk elements (As, Cu, Mn, Ni, Pb, Zn) in alluvial soil, which were measured by a portable X-ray fluorescence analyser (pXRF) in situ (FIELD) and in the laboratory (LABORATORY). Subsequently, regression equations were developed for individual elements through the method of construction of the regression model, which compare the results of pXRF with classical laboratory analysis (ICP-OES). The accuracy of the measurement, expressed by the coefficient of determination (R2), was as follows in the case of FIELD–ICP-OES: Pb (0.96), Zn (0.92), As (0.72), Mn (0.63), Cu (0.31) and Ni (0.01). In the case of LABORATORY–ICP-OES, the coefficients had values: Pb (0.99), Zn (0.98), Cu and Mn (0.89), As (0.88), Ni (0.81). A higher dependence of the relationship was recorded between LABORATORY–ICP-OES than between FIELD–ICP-OES. An excellent relationship was recorded for the elements Pb and Zn, both for FIELD and LABORATORY (R2 higher than 0.90). The elements Cu, Mn and As have a worse tightness in the relationship; however, the results of the model have shown its applicability for common use, e.g., in agricultural practice or in monitoring the quality of the environment. Based on our results, we can say that pXRF instruments can provide highly accurate results for the concentration of risk elements in the soil in real time for some elements and meet the principle of precision agriculture: an efficient, accurate and fast method of analysis.

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Mapping of Heavy Metal Contamination in Alluvial Soils of the Middle Nile Delta of Egypt

Cited by 23 publications

References 24 publications

Geochemical Characterization of Banana, Buriti Palm, Jute and Coir Fibres for Use as Biogeotextiles for Erosion Control

Geochemical Characterization of Banana, Buriti Palm, Jute and Coir Fibres for Use as Biogeotextiles for Erosion Control

Integration of Geostatistical and Sentinal-2AMultispectral Satellite Image Analysis for Predicting Soil Fertility Condition in Drylands

Comparison of the Concentration of Risk Elements in Alluvial Soils Determined by pXRF In Situ, in the Laboratory, and by ICP-OES

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