Geostatistical modelling of soil properties towards long-term ecological sustainability of agroecosystems

Wani, Owais Ali; Sharma, Vikas; Kumar, Shamal Shasang; Malik, Ab. Raouf; Pandey, Aastika; Devi, Khushboo; Kumar, Vipin; Gairola, Ananya; Yadav, Devideen; Valente, Donatella; Petrosillo, Irene; Babu, Subhash

doi:10.1016/j.ecolind.2024.112540

Ecological Indicators

2024

DOI: 10.1016/j.ecolind.2024.112540

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Geostatistical modelling of soil properties towards long-term ecological sustainability of agroecosystems

Owais Ali Wani,

Vikas Sharma,

Shamal Shasang Kumar

et al.

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2024

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Spatial Distribution and Trend Analysis of Groundwater Contaminants Using the ArcGIS Geostatistical Analysis (Kriging) Algorithm; The case of Gurage Zone, Ethiopia

Ayalew,

Tegenu

2024

Preprint

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The study explores the spatial distribution and trends of groundwater pollutants focusing on calcium and four other key water quality parameters in the Gurage Zone, Ethiopia, in 2024. It uses the ArcGIS geostatistical analysis tool with the Kriging algorithm to map and analyze the spatial variability of contaminants. The primary aim is to identify areas with high levels of pollutants and understand spatial patterns. It identifies contamination hotspots associated with natural processes and human activities. Twenty-seven samples were collected from various sites, and parameters like calcium, total dissolved solids, hardness, conductivity, and alkalinity were measured. The findings show that the distribution of contaminants varies significantly across different areas, with some areas exceeding safe drinking water limits. It reveals that the southern region has the highest calcium concentration, with shallow local boreholes. The deeper wells have higher dissolved solids, hardness, and conductivity. The spatial trend shows increased pollutant levels along the X and Y axes. The Kriging model effectively predicted contaminants in unsampled areas, offering a reliable technique aimed at groundwater quality monitoring. The study provides important insights for the local authorities to implement interventions for groundwater protection in the Gurage Zone.

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Spatial Distribution and Trend Analysis of Groundwater Contaminants Using the ArcGIS Geostatistical Analysis (Kriging) Algorithm; The case of Gurage Zone, Ethiopia

Ayalew,

Tegenu

2024

Preprint

View full text Add to dashboard Cite

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Geostatistical modelling of soil properties towards long-term ecological sustainability of agroecosystems

Cited by 1 publication

References 92 publications

Spatial Distribution and Trend Analysis of Groundwater Contaminants Using the ArcGIS Geostatistical Analysis (Kriging) Algorithm; The case of Gurage Zone, Ethiopia

Spatial Distribution and Trend Analysis of Groundwater Contaminants Using the ArcGIS Geostatistical Analysis (Kriging) Algorithm; The case of Gurage Zone, Ethiopia

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