Details

Autor(en) / Beteiligte

• El Behairy, Radwa A.
• El Baroudy, Ahmed A.
• Ibrahim, Mahmoud M.
• Kheir, Ahmed M. S.
• Shokr, Mohamed S.

Titel

Modelling and Assessment of Irrigation Water Quality Index Using GIS in Semi-arid Region for Sustainable Agriculture

Ist Teil von

• Water, air, and soil pollution, 2021-09, Vol.232 (9), Article 352

Ort / Verlag

Cham: Springer International Publishing

Erscheinungsjahr

2021

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Agriculture is the largest consumer of water, particularly in arid and semi-arid regions, so identifying and managing surface water quality in these areas is critical to preserving water resources and ensuring sustainable agriculture. Irrigation water quality (IWQ) assessment integrated with geographic information system (GIS) of West Nile Delta, Egypt, was carried out using suitability indicators such as hazards of salinity, permeability hazard, specific ion toxicity, and miscellaneous impacts on sensitive crops. In ArcGIS 10.7, inverse distance-weighted algorithms and the Model Builder function were used to categorize irrigation water quality into different classes. According to the findings, 87% and 13% of the water samples from the study area were categorized as medium and high suitability for irrigation, respectively. The heavy metal pollution index (HPI), Nemerow index (NeI), ecological risks of heavy metal index (ERI), heavy metal evaluation index (HEI), pollution load index (PLI), and modified degree of contamination (mCd) for five selected metals, namely As, Co, Cu, Ni, and Zn, were calculated to assess heavy metal contamination levels in the study area. The results showed that HPI had 3.7% medium contamination and 96.3% high contamination; NeI was 7.4% moderately contaminated and 92.6% heavily contaminated; ERI has almost 7% low risk, 30% moderate risk, 41% considerable risk, and 22% very high risk; HEI had 100% low contamination; PLI was 100% polluted; and mCd has 18.5% moderately-heavily polluted, 63% heavily polluted, and 18.5% severely polluted samples. This research can help decision-makers manage water resources more effectively for sustainable agriculture.