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EN
Aluminium slag waste is a residue from aluminium recycling activities, classified as hazardous waste so its disposal into the environment without processing can cause environmental problems, including groundwater pollution. There are 90 illegal dumping areas for aluminium slag waste spread in the Sumobito District, Jombang Regency. This study aims to evaluate the quality of shallow groundwater surrounding aluminium slag disposal in the Sumobito District for drinking water. The methods applied an integrated water quality index (WQI) and heavy metal pollution index (HPI), multivariate analysis (principal component analysis (PCA) and hierarchical clustering analysis (HCA)), and geospatial analysis for assessing groundwater quality. The field campaign conducted 40 groundwater samples of the dug wells for measuring the groundwater level and 30 of them were analysed for the chemical contents. The results showed that some locations exceeded the quality standards for total dissolved solids (TDS), electrical conductivity (EC), and Al2+. The WQI shows that 7% of dug well samples are in poor drinking water condition, 73% are in good condition, and 20% are in excellent condition. The level of heavy metal contamination based on HPI is below the standard limit, but 13.3% of the water samples are classified as high contamination. The multivariate analysis shows that anthropogenic factors and natural sources/geogenic factors contributed to shallow groundwater quality in the study area. The geospatial map shows that the distribution of poor groundwater quality is in the northern area, following the direction of groundwater flow, and is a downstream area of aluminium slag waste contaminants.
EN
The objective of this study is to reveal the spatial and temporal variations of surface water quality in this part of the River Nile with respect to heavy metals pioneerution. Seventeen parameters in total were monitored at seven sites on a monthly basis from October 2013 to September 2014. The dataset was treated using the tools of univariate and multivariate statistical analyses. Cluster analysis showed three different groups of similarity between the sampling sites reflecting the variability in physicochemical characteristics and pollution levels of the study area. Six PCs factors were identified as responsible for the data structure explaining 91 % of the total variance. These were eutrophication factor (23.2 %), physicochemical factor (20.6 %), nutrients (16.3 %) and three additional factors, affected by alkalinity and heavy metals, recorded variance less than 15 % each. Also, the heavy metals pollution index (HPI) revealed that most of the calculated values were below the critical index limit of 100. However, two higher values (124.89 and 133.11) were calculated at sites V and VI during summer due to the temperature and increased run-off in the river system.
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