Abstract
Sulfate causes various health issues for human if on average daily intake of sulfate is more than 500 mg from drinking-water, air, and food. Moreover, the presence of sulfate in rainwater causes acid rains which has harmful effects on animals and plants. Food is the major source of sulfate intake; however, in areas of South-Punjab, Pakistan, the drinking-water containing high levels of sulfate may constitute the principal source of intake. The spatial behavior of sulfate in groundwater is recorded for South-Punjab province, Pakistan. The spatial dependence of the response variable (sulfate) is modeled by using various variograms models that are estimated by maximum likelihood method, restricted maximum likelihood method, ordinary least squares, and weighted least squares. The parameters of estimated variogram models are utilized in ordinary kriging, universal kriging, Bayesian kriging with constant trend, and varying trend and the above methods are used for interpolation of sulfate concentration. The K-fold cross validation is used to measure the performances of variogram models and interpolation methods. Bayesian kriging with a constant trend produces minimum root mean square prediction error than other interpolation methods. Concentration of sulfate in drinking water within the study area is increasing to the Northern part, and health risks are really high due to poor quality of water.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adhikary PP, Chandrasekharan H, Chakraborty D, Kamble K (2010) Assessment of groundwater pollution in west Delhi, India using geostatistical approach. Environ Monit Assess 167(1–4):599–615
Adhikary PP, Dash CJ, Chandrasekharan H, Rajput T, Dubey S (2012) Evaluation of groundwater quality for irrigation and drinking using GIS and geostatistics in a peri-urban area of Delhi, India. Arab J Geosci 5(6):1423–1434
Atasoy AD, Yesilnacar MI (2010) Effect of high sulfate concentration on the corrosivity: a case study from groundwater in Harran plain, Turkey. Environ Monit Assess 166(1–4):595–607
AWWA, APHA, and WEF (1998) Standard methods for the examination of water and wastewater, 20th edn. American Public Health Association, Washington, DC
Cressie N (1985) Fitting variogram models by weighted least squares. J Int Assoc Math Geol 17(5):563–586
Cressie NA, Cassie NA (1993) Statistics for spatial data, vol 900. Wiley, New York
Diggle P, Ribeiro PJ (2007) Model-based geostatistics. Springer, Berlin
Eslami H, Dastorani J, reza Javadi M, Chamheidar H (2013) Geostatistical evaluation of ground water quality distribution with GIS (case study: Mianab-Shoushtar plain). Bull Environ Pharmacol Life Sci 3(1):78–82
Gundogdu KS, Guney I (2007) Spatial analyses of groundwater levels using universal kriging. J Earth Syst Sci 116(1):49–55
Hussain I, Mubarak N, Shabbir J, Hussain T, Faisal M (2014a) Spatial interpolation of sulfate concentration in groundwater including covariates using Bayesian hierarchical models. Water Qual Expos Health. doi:10.1007/s12403-014-0154-2
Hussain I, Shakeel M, Faisal M, Soomro ZA, Hussain M, Hussain T (2014b) Distribution of total dissolved solids in drinking water by means of Bayesian kriging and gaussian spatial predictive process. Water Qual Expos Health 6(4):177–185
Isaaks EH, Srivastava RM (1989) An introduction to applied geostatistics. Oxford University Press, New York
Jiang Y, Ma W, Luo H, Huang W (2013) Analysis of spatial distribution of groundwater quality in Huaihe river basin. In: GEOINFORMATICS, 2013. IEEE 21st international conference on geoinformatics, pp 1–7
Journel AG, Huijbregts CJ (1978) Mining geostatistics. Academic, New York
Kitanidis PK (1983) Statistical estimation of polynomial generalized covariance functions and hydrologic applications. Water Resour Res 19:909–921
Lee YD, Lahiri SN (2002) Least squares variogram fitting by spatial subsampling. J R Stat Soc B (stat method) 64(4):837–854
Matheron G (1963) Principles of geostatistics. Econ Geol 58(8):1246–1266
McGrath D, Zhang C, Carton OT (2004) Geostatistical analyses and hazard assessment on soil lead in Silvermines area, Ireland. Environ Pollut 127(2):239–248
Nas B, Berktay A (2010) Groundwater quality mapping in urban groundwater using GIS. Environ Monit Assess 160(1–4):215–227
Nazari ZF, Behnaz FA, Kamran ZV (2006) Study of spatial variability of groundwater quality of balarood plain in khuzestan province. The first congress of optimized exploitation from water source of Karoon and Zayanderood Plain. Shahre kord University, Shahrekord, pp 1236–1240 (Persian version)
Omre H, Halvorsen KB (1989) The Bayesian bridge between simple and universal kriging. Math Geol 21(7):767–786
Poon K-F, Wong RW-H, Lam MH-W, Yeung H-Y, Chiu TK-T (2000) Geostatistical modelling of the spatial distribution of sewage pollution in coastal sediments. Water Res 34(1):99–108
R Development Core Team (2005) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
Rawat EKS, Mishra AK, Sehgal VK, Tripathi VK (2012) Spatial variability of ground water quality in Mathura district (Uttar Pradesh, India) with geostatistical method. Int J Remote Sens Appl 2(1):1–9
Ribeiro PJ Jr, Diggle PJ (2001) geor: a package for geostatistical analysis. R News 1(2):14–18
Selby B, Kockelman K (2011) Spatial prediction of AADT in unmeasured locations by universal kriging. In: Transportation Research Board 90th annual meeting, number 11-1665
Shahsavari AA, Khodaei K, Asadian F, Ahmadi F, Zamanzadeh SM (2012) Groundwater pesticides residue in the southwest of Iran-Shushtar plain. Environ Earth Sci 65(1):231–239
Van Beers WC, Kleijnen JP (2003) Kriging for interpolation in random simulation. J Oper Res Soc 54(3):255–262
Acknowledgments
The authors are grateful to Pakistan Council of Research in Water Resources Regional office, Lahore for providing data to meet the objectives of the study. The authors are also thankful to the Deanship of Scientific Research, King Saud University Riyadh for funding the work through the research Group project No RGP-VPP-210. Last but not least the authors are thankful to the reviewers and editor for their valuable comments.
Conflict of Interest
The authors declare that they have no conflict of interest.
Human Participants and Informed Consent
The manuscript titled “Spatial Distribution of Sulfate Concentration in Groundwater of South-Punjab, Pakistan” submitted for possible publication in Water quality exposure and health is prepared in accordance with the ethical standards of the responsible committee on human experimentation and with the latest (2008) version of Helsinki Declaration of 1975. The authors of manuscript certify that they have NO affiliations with or involvement in any organization or entity with any financial interest, or non-financial interest in the subject matter or materials discussed in this manuscript.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Mubarak, N., Hussain, I., Faisal, M. et al. Spatial Distribution of Sulfate Concentration in Groundwater of South-Punjab, Pakistan. Water Qual Expo Health 7, 503–513 (2015). https://doi.org/10.1007/s12403-015-0165-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12403-015-0165-7