Assessment of groundwater nitrate contamination hazard in a semi-arid region by using integrated parametric IPNOA and data-driven logistic regression models

• Published in: Environmental monitoring and assessment 2018-11, Vol.190 (11), p.633-17, Article 633
• Main Authors: Rizeei, Hossein Mojaddadi, Azeez, Omer Saud, Pradhan, Biswajeet, Khamees, Hayder Hassan
• Format: Article
• Language: English
• Subjects: Agricultural industry; Agricultural management; Arid regions; Arid zones; Atmospheric Protection/Air Quality Control/Air Pollution; Climatic conditions; Contamination; Drinking water; Drinking Water - analysis; Drinking Water - chemistry; Earth and Environmental Science; Ecology; Ecosystem management; Ecosystems; Ecotoxicology; Environment; Environment models; Environmental impact; Environmental Management; Environmental monitoring; Environmental Monitoring - methods; Environmental science; Geographic Information Systems; Geographical information systems; Groundwater; Groundwater - analysis; Groundwater - chemistry; Groundwater management; Groundwater pollution; Groundwater quality; Hazard assessment; Health hazards; Image analysis; Image processing; Image resolution; Information systems; Iraq; Land cover; Land use; Logistic Models; Mathematical models; Methods; Mineral nutrients; Modelling; Monitoring/Environmental Analysis; Multivariate Analysis; Nitrates; Nitrates - analysis; Nutrient loading; Pollution; Regression analysis; Regression models; Remote sensing; Resolution; Satellite imagery; Satellites; Semi arid areas; Semiarid lands; Soil; Soil contamination; Spatial resolution; Support vector machines; Sustainability management; Urban agriculture; Water Pollutants, Chemical - analysis; Water pollution; Water Quality
• ISSN: 0167-6369; 1573-2959
• DOI: 10.1007/s10661-018-7013-8

Groundwater hazard assessments involve many activities dealing with the impacts of pollution on groundwater, such as human health studies and environment modelling. Nitrate contamination is considered a hazard to human health, environment and ecosystem. In groundwater management, the hazard should be assessed before any action can be taken, particularly for groundwater pollution and water quality. Thus, pollution due to the presence of nitrate poses considerable hazard to drinking water, and excessive nutrient loads deteriorate the ecosystem. The parametric IPNOA model is one of the well-known methods used for evaluating nitrate content. However, it cannot predict the effect of soil and land use/land cover (LULC) types on calculations relying on parametric well samples. Therefore, in this study, the parametric model was trained and integrated with the multivariate data-driven model with different levels of information to assess groundwater nitrate contamination in Saladin, Iraq. The IPNOA model was developed with 185 different well samples and contributing parameters. Then, the IPNOA model was integrated with the logistic regression (LR) model to predict the nitrate contamination levels. Geographic information system techniques were also used to assess the spatial prediction of nitrate contamination. High-resolution SPOT-5 satellite images with 5 m spatial resolution were processed by object-based image analysis and support vector machine algorithm to extract LULC. Mapping of potential areas of nitrate contamination was examined using receiver operating characteristic assessment. Results indicated that the optimised LR-IPNOA model was more accurate in determining and analysing the nitrate hazard concentration than the standalone IPNOA model. This method can be easily replicated in other areas that have similar climatic condition. Therefore, stakeholders in planning and environmental decision makers could benefit immensely from the proposed method of this research, which can be potentially used for a sustainable management of urban, industrialised and agricultural sectors.