Skip to main content

Advertisement

SpringerLink
Log in

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

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Aal-shamkhi, A. D. S., Mojaddadi, H., Pradhan, B., & Abdullahi, S. (2017). Extraction and modeling of urban sprawl development in Karbala City using VHR satellite imagery. In Spatial Modeling and Assessment of Urban Form (pp. 281–296): Springer.

  • Abdula, R. A. (2016). Stratigraphy and lithology of Naokelekan Formation in Iraqi Kurdistan-review. The International Journal of Engineering and Science (IJES), 5(8), 45–52.

    Google Scholar 

  • Abdulkareem, J. H., Sulaiman, W. N. A., Pradhan, B., & Jamil, N. R. (2018a). Long-term hydrologic impact assessment of non-point source pollution measured through land use/land cover (LULC) changes in a tropical complex catchment. Earth Systems and Environment, 2(1), 67–84. https://doi.org/10.1007/s41748-018-0042-1.

    Article  Google Scholar 

  • Abdulkareem, J. H., Pradhan, B., Sulaiman, W. N. A., & Jamil, N. R. (2018b). Quantification of runoff as influenced by morphometric characteristics in a rural complex catchment. Earth Systems and Environment, 2(1), 145–162. https://doi.org/10.1007/s41748-018-0043-0.

    Article  Google Scholar 

  • Abdullahi, S., Pradhan, B., & Mojaddadi, H. (2017). City compactness: assessing the influence of the growth of residential land use. Journal of Urban Technology. 1–26.

  • AL-Dulaimi, G. A., & Younes, M. K. (2017). Assessment of potable water quality in Baghdad City, Iraq. Air, Soil and Water Research, https://doi.org/10.1177/1178622117733441.

    Article  Google Scholar 

  • Althuwaynee, O. F., Pradhan, B., & Lee, S. (2012). Application of an evidential belief function model in landslide susceptibility mapping. Computers & Geosciences, 44, 120–135.

    Article  Google Scholar 

  • Althuwaynee, O. F., Pradhan, B., Park, H. J., & Lee, J. H. (2014). A novel ensemble bivariate statistical evidential belief function with knowledge-based analytical hierarchy process and multivariate statistical logistic regression for landslide susceptibility mapping. Catena, 114, 21–36.

    Article  Google Scholar 

  • Alwathaf, Y., & El Mansouri, B. (2011). Assessment of aquifer vulnerability based on GIS and ARCGIS methods: a case study of the Sana’a Basin (Yemen). Journal of Water Resource and Protection, 3(12), 845–855.

    Article  Google Scholar 

  • Ayalew, L., & Yamagishi, H. (2005). The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan. Geomorphology, 65, 15–31.

    Article  Google Scholar 

  • Bai, S., Wang, J., Zhang, Z., & Cheng, C. (2012). Combined landslide susceptibility mapping after Wenchuan earthquake at the Zhouqu segment in the Bailongjiang Basin, China. Catena, 99, 18–25.

    Article  Google Scholar 

  • Benediktsson, J. O. N. A., Swain, P. H., & Ersoy, O. K. (1990). Neural network approaches versus statistical methods in classification of multisource remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 28, 540–552.

    Article  Google Scholar 

  • Bone, J., Head, M., Jones, D. T., Barraclough, D., Archer, M., Scheib, C., … Voulvoulis, N. (2010). From chemical risk assessment to environmental quality management: the challenge for soil protection. In: ACS Publications.

  • Boy Roura, M. (2013). Nitrate groundwater pollution and aquifer vulnerability: the case of the Osona region.

  • Bui, D. T., Bui, Q.-T., Nguyen, Q.-P., Pradhan, B., Nampak, H., & Trinh, P. T. (2017a). A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area. Agricultural and Forest Meteorology, 233, 32–44.

    Article  Google Scholar 

  • Bui, D. T., Tuan, T. A., Hoang, N. D., Thanh, N. Q., Nguyen, D. B., Van Liem, N., & Pradhan, B. (2017b). Spatial prediction of rainfall-induced landslides for the Lao Cai area (Vietnam) using a hybrid intelligent approach of least squares support vector machines inference model and artificial bee colony optimization. Landslides, 14(2), 447–458.

    Article  Google Scholar 

  • Cantor, K. P. (1997). Drinking water and cancer. Cancer Causes & Control, 8(3), 292–308.

    Article  CAS  Google Scholar 

  • Capri, E., Civita, M., Corniello, A., Cusimano, G., De Maio, M., Ducci, D., et al. (2009). Assessment of nitrate contamination risk: the Italian experience. Journal of Geochemical Exploration, 102(2), 71–86.

    Article  CAS  Google Scholar 

  • Chen, W., Xie, X., Wang, J., Pradhan, B., Hong, H., Bui, D. T., Duan, Z., & Ma, J. (2017). A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility. Catena, 151, 147–160.

    Article  Google Scholar 

  • DeSimone, L. A., & Howes, B. L. (1998). Nitrogen transport and transformations in a shallow aquifer receiving wastewater discharge: a mass balance approach. Water Resources Research, 34(2), 271–285.

    Article  CAS  Google Scholar 

  • Ettazarini, S. (2007). Groundwater potentiality index: a strategically conceived tool for water research in fractured aquifers. Environmental Geology, 52(3), 477–487.

    Article  CAS  Google Scholar 

  • Ghiglieri, G., Barbieri, G., Vernier, A., Carletti, A., Demurtas, N., Pinna, R., & Pittalis, D. (2009). Potential risks of nitrate pollution in aquifers from agricultural practices in the Nurra region, northwestern Sardinia, Italy. Journal of Hydrology, 379(3–4), 339–350.

    Article  CAS  Google Scholar 

  • Golkarian, A., Naghibi, S. A., Kalantar, B., & Pradhan, B. (2018). Groundwater potential mapping using C5.0, random forest, and multivariate adaptive regression spline models in GIS. Envrionmental Monitoring & Assessment, 190, 149. https://doi.org/10.1007/s10661-018-6507-8.

    Article  CAS  Google Scholar 

  • Green, C. T., Fisher, L. H., & Bekins, B. A. (2008). Nitrogen fluxes through unsaturated zones in five agricultural settings across the United States. Journal of Environmental Quality, 37(3), 1073–1085.

    Article  CAS  Google Scholar 

  • Gross, E. L. (2008). Ground water susceptibility to elevated nitrate concentrations in South Middleton Township, Cumberland County, Pennsylvania. Shippensburg University of Pennsylvania.

  • Gumma, M. K., & Pavelic, P. (2013). Mapping of groundwater potential zones across Ghana using remote sensing, geographic information systems, and spatial modeling. Environmental Monitoring and Assessment, 185(4), 3561–3579.

    Article  Google Scholar 

  • Gupta, M., & Srivastava, P. K. (2010). Integrating GIS and remote sensing for identification of groundwater potential zones in the hilly terrain of Pavagarh, Gujarat, India. Water International, 35(2), 233–245.

    Article  Google Scholar 

  • Into, H. D. I. G. (2011). Nitrate in Drinking Water.

  • Kordestani, M. D., Naghibi, S. A., Hashemi, H., Ahmadi, K., Kalantar, B., & Pradhan, B. (2018). Groundwater potential mapping using a novel data-mining ensemble model. Hydrogeology Journal. https://doi.org/10.1007/s10040-018-1848-5.

  • Kowal, A., & Polik, A. (1987). Nitrates in groundwater. In Environmental Technology (pp. 604–609): Springer.

  • Lake, I. R., Lovett, A. A., Hiscock, K. M., Betson, M., Foley, A., Sünnenberg, G., et al. (2003). Evaluating factors influencing groundwater vulnerability to nitrate pollution: developing the potential of GIS. Journal of Environmental Management, 68(3), 315–328.

    Article  Google Scholar 

  • Lee, S., & Sambath, T. (2006). Landslide susceptibility mapping in the Damrei Romel area, Cambodia using frequency ratio and logistic regression models. Environmental Geology, 50(6), 847–855.

    Article  Google Scholar 

  • Liao, L., Green, C. T., Bekins, B. A., & Böhlke, J. (2012). Factors controlling nitrate fluxes in groundwater in agricultural areas. Water Resources Research, 48(6).

  • Mezaal, M. R., Pradhan, B., Shafri, H., Mojaddadi, H., & Yusoff, Z. (2017). Optimized hierarchical rule-based classification for differentiating shallow and deep-seated landslide using high-resolution LiDAR data. Paper presented at the Global Civil Engineering Conference.

  • Min, J. H., Yun, S. T., Kim, K., Kim, H. S., & Kim, D. J. (2003). Geologic controls on the chemical behaviour of nitrate in riverside alluvial aquifers, Korea. Hydrological Processes, 17(6), 1197–1211.

    Article  Google Scholar 

  • Mishra, N., Khare, D., Gupta, K., & Shukla, R. (2014). Impact of land use change on groundwater—a review. Adv Water Resour Protect, 2, 28–41.

    Google Scholar 

  • Mojaddadi, H., Habibnejad, M., Solaimani, K., Ahmadi, M., & Hadian-Amri, M. (2009). An investigation of efficiency of outlet runoff assessment. Journal of Applied Sciences, 9(1), 105–112.

    Article  Google Scholar 

  • Mojaddadi, H., Pradhan, B., Nampak, H., Ahmad, N., & Ghazali, A. H. B. (2017). Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. Geomatics, Natural Hazards and Risk, 1–23.

  • Nampak, H., Pradhan, B., & Manap, M. A. (2014). Application of GIS based data driven evidential belief function model to predict groundwater potential zonation. Journal of Hydrology, 513, 283–300.

    Article  Google Scholar 

  • Neshat, A., & Pradhan, B. (2015). An integrated DRASTIC model using frequency ratio and two new hybrid methods for groundwater vulnerability assessment. Natural Hazards, 76(1), 543–563.

    Article  Google Scholar 

  • Neshat, A., Pradhan, B., Pirasteh, S., & Shafri, H. Z. M. (2014). Estimating groundwater vulnerability to pollution using a modified DRASTIC model in the Kerman agricultural area, Iran. Environmental Earth Sciences, 71(7), 3119–3131.

    Article  CAS  Google Scholar 

  • Neshat, A., Pradhan, B., & Javadi, S. (2015). Risk assessment of groundwater pollution using Monte Carlo approach in an agricultural region: an example from Kerman Plain, Iran. Computers, Environment and Urban Systems, 50, 66–73.

    Article  Google Scholar 

  • Oh, H. J., & Pradhan, B. (2011). Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area. Computers & Geosciences, 37(9), 1264–1276.

    Article  Google Scholar 

  • Padovani, L., & Trevisan, M. (2002). I nitrati di origine agricola nelle acque sotterranee: un indice parametrico per l'individuazione di aree vulnerabili: Pitagora.

  • Pradhan, B. (2009). Groundwater potential zonation for basaltic watersheds using satellite remote sensing data and GIS techniques. Open Geosciences, 1(1), 120–129.

    Article  Google Scholar 

  • Pradhan, B. (2013). A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS. Computers & Geosciences, 51, 350–365.

    Article  Google Scholar 

  • Pradhan, B., & Lee, S. (2010). Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling. Environmental Modelling & Software, 25(6), 747–759.

    Article  Google Scholar 

  • Raaz Maheshwari, B. R., Yadav, A. S. R. K., & Sharma, S. (2012). Nitrate ion contaminated groundwater: its health hazards, preventive & denitrification measures. Bull Env Pharmacol Life Scien Volume, 1, 26–33.

  • Re, V., Sacchi, E., Kammoun, S., Tringali, C., Trabelsi, R., Zouari, K., & Daniele, S. (2017). Integrated socio-hydrogeological approach to tackle nitrate contamination in groundwater resources. The case of Grombalia Basin (Tunisia). Science of the Total Environment, 593, 664–676.

    Article  Google Scholar 

  • Rizeei, H. M., Pradhan, B., & Saharkhiz, M. A. (2018a). Surface runoff prediction regarding LULC and climate dynamics using coupled LTM, optimized ARIMA, and GIS-based SCS-CN models in tropical region. Arabian Journal of Geosciences, 11(3), 53.

    Article  Google Scholar 

  • Rizeei, H. M., Shafri, H. Z., Mohamoud, M. A., Pradhan, B., & Kalantar, B. (2018b). Oil palm counting and age estimation from WorldView-3 imagery and LiDAR data using an integrated OBIA height model and regression analysis. Journal of Sensors, 2018.

  • Sacco, D., Zavattaro, L., & Grignani, C. (2006). Regional-scale predictions of agricultural n losses in an area with a high livestock density. Italian Journal of Agronomy, 1(4), 689–704.

    Article  Google Scholar 

  • Sacco, D., Offi, M., De Maio, M., & Grignani, C. (2007). Groundwater nitrate contamination risk assessment: a comparison of parametric systems and simulation modelling. American Journal of Environmental Sciences, 3, 117–125.

    Article  CAS  Google Scholar 

  • Sener, E., Davraz, A., & Ozcelik, M. (2005). An integration of GIS and remote sensing in groundwater investigations: a case study in Burdur, Turkey. Hydrogeology Journal, 13(5–6), 826–834.

    Article  Google Scholar 

  • Shaban, A., Khawlie, M., & Abdallah, C. (2006). Use of remote sensing and GIS to determine recharge potential zones: the case of Occidental Lebanon. Hydrogeology Journal, 14(4), 433–443.

    Article  Google Scholar 

  • Shahid, S., Nath, S., & Roy, J. (2000). Groundwater potential modelling in a soft rock area using a GIS. International Journal of Remote Sensing, 21(9), 1919–1924.

    Article  Google Scholar 

  • Shahid, S., Nath, S. K., & Maksud Kamal, A. (2002). GIS integration of remote sensing and topographic data using fuzzy logic for ground water assessment in Midnapur District, India. Geocarto International, 17(3), 69–74.

    Article  Google Scholar 

  • Spalding, R. F., & Exner, M. E. (1993). Occurrence of nitrate in groundwater—a review. Journal of Environmental Quality, 22(3), 392–402.

    Article  CAS  Google Scholar 

  • Süzen, M. L., & Doyuran, V. (2004). A comparison of the GIS based landslide susceptibility assessment methods: multivariate versus bivariate. Environmental Geology, 45, 665–679.

    Article  Google Scholar 

  • Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2013). Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. Journal of Hydrology, 504, 69–79.

    Article  Google Scholar 

  • Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2014). Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. Journal of Hydrology, 512, 332–343.

    Article  Google Scholar 

  • Tehrany, M. S., Pradhan, B., Mansor, S., & Ahmad, N. (2015). Flood susceptibility assessment using GIS-based support vector machine model with different kernel types. Catena, 125, 91–101.

    Article  Google Scholar 

  • Umar, Z., Pradhan, B., Ahmad, A., Jebur, M. N., & Tehrany, M. S. (2014). Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena, 118, 124–135.

    Article  Google Scholar 

  • Vendrusculo, L., Magalhaes, P., & Vieira, S. (2002). Strategies for soil sampling and multi-layers maps generation through geostatic tools: development of a computational system. Paper presented at the World Congress of Computers in Agriculture and Natural Resources, Proceedings of the 2002 Conference.

  • Vikas, C., Kushwaha, R., Ahmad, W., Prasannakumar, V., Dhanya, P., & Reghunath, R. (2015). Hydrochemical appraisal and geochemical evolution of groundwater with special reference to nitrate contamination in aquifers of a semi-arid terrain of NW India. Water Quality, Exposure and Health, 7(3), 347–361.

    Article  CAS  Google Scholar 

  • Wick, K., Heumesser, C., & Schmid, E. (2009). Agriculture and nitrate contamination in Austrian groundwater: an empirical analysis. Rollen der Landwirtschaft in benachteiligten Regionen, 19.

  • Zhao, Y., De Maio, M., & Suozzi, E. (2013). Assessment of groundwater potential risk by agricultural activities, in North Italy. International Journal of Environmental Science and Development, 4(3), 286.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and IT, University of Technology Sydney, Sydney, NSW, 2007, Australia

    Hossein Mojaddadi Rizeei & Biswajeet Pradhan

  2. Department of Civil Engineering, Faculty of Engineering, University Putra Malaysia, Seri Kembangan, Selangor, Malaysia

    Omer Saud Azeez & Hayder Hassan Khamees

Authors
  1. Hossein Mojaddadi Rizeei
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Omer Saud Azeez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Biswajeet Pradhan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hayder Hassan Khamees
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Biswajeet Pradhan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rizeei, H.M., Azeez, O.S., Pradhan, B. et al. Assessment of groundwater nitrate contamination hazard in a semi-arid region by using integrated parametric IPNOA and data-driven logistic regression models. Environ Monit Assess 190, 633 (2018). https://doi.org/10.1007/s10661-018-7013-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-018-7013-8

Keywords

Search

Navigation