An Application of Geostatistics to Analysis of Water Quality Parameters in Rivers and Streams in Niger State, Nigeria

Isah Audu; Abdullahi Usman

doi:doi:10.11648/j.ajtas.20150405.18

| Peer-Reviewed

An Application of Geostatistics to Analysis of Water Quality Parameters in Rivers and Streams in Niger State, Nigeria

Isah Audu, Abdullahi Usman

Published in American Journal of Theoretical and Applied Statistics (Volume 4, Issue 5)

Received: 3 August 2015 Accepted: 17 August 2015 Published: 26 August 2015

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

Abstract

Assessment of surface water quality using multivariate statistical techniques does not incorporate the spatial locations of data into their defining computations. Information on spatial continuity of surface water concentrations can help in identifying the magnitude of contamination by runoff and anthropogenic pollutions. In the present study, spatial behavior of five (5) surface water quality parameters of some rivers/streams in Niger State of Nigeria was studied using R geostatistical package gstat, in conjunction with packages sp, rgdal, spatstat and maptools. The variograms and ordinary krigged spatial maps were generated for rainy and dry seasons. The characteristics of the best variable models; range; sill and nugget effects of each parameter were obtained. The variogram analysis indicated a high spatial coherence for E.co, Mg and TDS, whereas TCo and TH indicated a low spatial coherence. The nugget to sill ratios of experimental and linear fitted variogram models in all cases were less than 0.25 indicating that the rivers/streams water level has strong spatial coherence in both seasons. This result shows that linear model is the best for both seasons. Krigged spatial variability maps revealed that an average range of 48km variograms for dry season changes more rapidly than it does in rainy season with an average range of 4.3 km and R2 values of 0.80 to 0.92.

Published in	American Journal of Theoretical and Applied Statistics (Volume 4, Issue 5)
DOI	10.11648/j.ajtas.20150405.18
Page(s)	373-388
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Kriging, Predictions, Experimental Variogram, Nugget, Water Parameters

References

[1]	Agoubi, B., Kharroubi, A., & Abida, H. (2013). Hydrochemistry of groundwater and its assessment for irrigation purpose in coastal Jeffara Aquifer, southeastern Tunisia. Arabian Journal of Geosciences, 6(4), 1163–1172. doi:10.1007/s12517-011-0409-1.
[2]	Babiker, I. S., Mohamed, M. A. A., & Hiyama, T. (2007). Assessing groundwater quality using GIS. Water Resources Manage, 21, 699–715.
[3]	Berke O.I. (1999). Estimation and Prediction in the Spatial Linear Model, Water, Air, and Soil Pollution, 110, 215-237.
[4]	Bordalo, A., Nilsumranchit, W., & Chalermwat, K. (2001). Water Quality and uses of the Bangpakong River (Eastern Thailand). Water Research, 35(15), 3635–3642.
[5]	Cambardella, C. A. Moorman, T. B. Novak, J. M. Parkin, T. B. Karlen, D. L. Turco, R. F. and Konopka, A. E. (1994). Field- Scale Variability of Soil Properties in Central Iowa Soils, Soil Science Society of America Journal, Vol. 58, pp. 1501-1511.
[6]	Cheesbrough, M. (2003). Water quality analysis. District Laboratory practice in Tropical countries (2) Cambridge University Press, United Kingdom. 146-157.
[7]	Chiles, J. P., Delfiner, P. (1999). Geostatistics: modeling spatial uncertainty. John Wiley & Sons, New York.
[8]	Cressie, N. A. C. (1993). Statistics for Spatial Data, revised edition. John Wiley & Sons, New York, p. 416.
[9]	De Gruijter, J.J., Marsman, B.A. (1985). Transect sampling for reliable information on mapping units. In: Nielsen, D.R., Bouma, J. (Eds.), Soil Spatial Variability. Pudoc, Wageningen, pp. 150– 163.
[10]	Elci, A., & Polat, R. (2010). Assessment of the statistical significance of seasonal groundwater quality change in a karstic aquifer systemnear Izmir-Turkey. Environmental Monitoring and Assessment, 172(1), 445–462. doi:10.1007/s10661-010-1346-2.
[11]	Galadima, A., & Garba, Z.N. (2012). Heavy metals pollution in Nigeria: Causes and consequences. Elixir Pollution 45, 7917-7922
[12]	Heuvelink, G.B.M., Musters, P., Pebesma, E.J. (1997). Spatio-temporal kriging of soil water content. In: Baafi, E.Y., Schofield, N.A. Eds., Geostatistics Wollongong ’96. Kluwer Academic Publishers, Dordrecht, pp. 1020–1030.
[13]	Isah A. (2009). Spatio-Temporal Modeling of Nonstationary Processes, Journal of Science, Education and Technology, Vol.2 No.1 pp372-377.
[14]	Isah A., Usman, A., & Mohammed, M., N. (2013). Application of Multivariate Methods for Assessment of Variations in Rivers/Streams Water Quality in Niger State, Nigeria. American Journal of Theoretical and Applied Statistics. Vol. 2, No. 6, 2013, pp. 176-183. doi: 10.11648/j.ajtas.20130206.14
[15]	Johnston K., Hoef J.M.V., Krivoruchko K., Lucas N. (2001).Using ArcGIS Geostatistical Analyst. ESRI.380 New York Street. Redlands, CA 92373-8100, USA.
[16]	Kazi T.G., Arain M.B., Jamali M.K., Jalbani N., Afridi H.I., Sarfraz R.A., Baig J.A., Shah A.Q. (2009). Assessment of water quality of polluted lake using multivariate statistical techniques: A case study. Ecotox. Environ. Safe. 72:301–309.
[17]	Kumar, J., & Pal, A. (2012). Water quality monitoring of Ken River of Banda District, Uttar Pradesh, India. Elixir Pollution 42, 6360-6364.
[18]	Lark, R. M. (2000). Estimating variograms of soil properties by the method-of-moments and maximum likelihood. European Journal of Soil Science, 51, 717–728.
[19]	Liu, W.C., Yu, H.L., & Chung, C.E. (2011). Assessment of water quality in a subtropical Alpine Lake using multivariate statistical techniques and geostatistical mapping: a case study. International Journal of Environmental Research and Public Health, 8(4), 1126–1140.
[20]	Morenikeji W., Sanusi Y. A, and Jinadu A. M. (2000). The Role of Private Voluntary Organizations’ in Community and Settlement Development in Niger State, A Research Report Submitted to the Centre for Research and Documentation, Kano.
[21]	Nas, B., & Berktay, A. (2010). Groundwater quality mapping in urban groundwater using GIS. Environmental Monitoring and Assessment, 160(1–4), 215–227. doi:10.1007/s10661-008-0689-4.
[22]	Niger State Bureau of Statistics (2012). Facts and Figures about Niger state of Nigeria,
[23]	Obaje N. G. (2009). Geology and Mineral Resources of Nigeria. Lecture Notes in Earth Sciences. Published by Springer Dordreccht Heidelberg, New York.
[24]	Olasehinde P. I. (2010). The Groundwater of Nigeria: A Solution to Sustainable National Water Needs. Inaugural Lecture Series 17, Federal University of Technology, Minna, Nigeria.
[25]	Oluseyi, T., Olayinka, K., and Adeleke, I. (2011). Assessment of ground water pollution in the residential areas of Ewekoro and Shagamu due to cement production. African Journal of Environmental Science and Technology 5(10), 786-794.
[26]	Robertson G.P. (1987). Geostatistics in ecology: interpolating with known variance. Ecology, 68, 744-748.
[27]	Sarukkalige, R. (2012). Geostatistical Analysis of Groundwater Quality in Western Australia, IRACSTEngineering Science and Technology: An International Journal (ESTIJ), Vol. 2, No. 4, pp. 790-794.
[28]	Sha’Ato, R., Akaahan, T.J., & Oluma, H.O.A. (2010). Physico-chemical and bacteriological quality of water from shallow wells in two rural communities in Benue State, Nigeria. Pakistan Journal of Analytical and Environmental Chemistry 11(1), 73-78.
[29]	Shyu, G. S., Cheng, B. Y., Chiang, C. T., Yao, P. H., & Chang, T. K. (2011). Applying factor analysis combined with kriging and information entropy theory for mapping and evaluating the stability of groundwater quality variation in Taiwan. International Journal of Environmental Research andPublic Health, 8(4), 1084–1109. doi:10.3390/ijerph8041084.
[30]	Singh K.P., Malik A., Mohan D., and Sinha S. (2004). Multivariate statistical techniques for the evaluation of spatial and temporal variations in water quality of Gomti river (India): A case study. Water Res. 38:3980–3992.
[31]	Taany R. A., A B Tahboub and G. A. Saffarini (2009). Geostatistical analysis of spatiotemporal variability of groundwater level fluctuations in Amman–Zarqa basin, Jordan: a case study. Environ. Geol, 57, pp. 525–535.
[32]	UNEP/ WHO, (1996). Water quality Monitoring, A practical guide to the design and implementation of freshwater quality Studies and Monitoring Programmes edited by Jamie Bartram and Richard Balance. Printed in Great Britain by TJ Press (Padstow) Ltd, Padsow, Cornwell.
[33]	Vieira R.J., Hartfield L.J., Nielsen D. R. and Biggar W.J. (1982). Geostatistical Theory and Application to Variability of some Agronomical Properties, Hilgardia 51,3.
[34]	Webster, R. and Oliver, M. (2001). Geostatistics for Environmental Scientists, John Wiley & sons, Ltd.
[35]	WHO. (2011). Guidelines for drinking-water quality. vol 4, 3rd edn. Geneva: World Health Organization.

Cite This Article

Plain Text BibTeX RIS

APA Style

Isah Audu, Abdullahi Usman. (2015). An Application of Geostatistics to Analysis of Water Quality Parameters in Rivers and Streams in Niger State, Nigeria. American Journal of Theoretical and Applied Statistics, 4(5), 373-388. https://doi.org/10.11648/j.ajtas.20150405.18

Copy | Download

ACS Style

Isah Audu; Abdullahi Usman. An Application of Geostatistics to Analysis of Water Quality Parameters in Rivers and Streams in Niger State, Nigeria. Am. J. Theor. Appl. Stat. 2015, 4(5), 373-388. doi: 10.11648/j.ajtas.20150405.18

Copy | Download

AMA Style

Isah Audu, Abdullahi Usman. An Application of Geostatistics to Analysis of Water Quality Parameters in Rivers and Streams in Niger State, Nigeria. Am J Theor Appl Stat. 2015;4(5):373-388. doi: 10.11648/j.ajtas.20150405.18

Copy | Download

@article{10.11648/j.ajtas.20150405.18,
  author = {Isah Audu and Abdullahi Usman},
  title = {An Application of Geostatistics to Analysis of Water Quality Parameters in Rivers and Streams in Niger State, Nigeria},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {4},
  number = {5},
  pages = {373-388},
  doi = {10.11648/j.ajtas.20150405.18},
  url = {https://doi.org/10.11648/j.ajtas.20150405.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150405.18},
  abstract = {Assessment of surface water quality using multivariate statistical techniques does not incorporate the spatial locations of data into their defining computations. Information on spatial continuity of surface water concentrations can help in identifying the magnitude of contamination by runoff and anthropogenic pollutions. In the present study, spatial behavior of five (5) surface water quality parameters of some rivers/streams in Niger State of Nigeria was studied using R geostatistical package gstat, in conjunction with packages sp, rgdal, spatstat and maptools. The variograms and ordinary krigged spatial maps were generated for rainy and dry seasons. The characteristics of the best variable models; range; sill and nugget effects of each parameter were obtained. The variogram analysis indicated a high spatial coherence for E.co, Mg and TDS, whereas TCo and TH indicated a low spatial coherence. The nugget to sill ratios of experimental and linear fitted variogram models in all cases were less than 0.25 indicating that the rivers/streams water level has strong spatial coherence in both seasons. This result shows that linear model is the best for both seasons. Krigged spatial variability maps revealed that an average range of 48km variograms for dry season changes more rapidly than it does in rainy season with an average range of 4.3 km and R2 values of 0.80 to 0.92.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - An Application of Geostatistics to Analysis of Water Quality Parameters in Rivers and Streams in Niger State, Nigeria
AU  - Isah Audu
AU  - Abdullahi Usman
Y1  - 2015/08/26
PY  - 2015
N1  - https://doi.org/10.11648/j.ajtas.20150405.18
DO  - 10.11648/j.ajtas.20150405.18
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 373
EP  - 388
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20150405.18
AB  - Assessment of surface water quality using multivariate statistical techniques does not incorporate the spatial locations of data into their defining computations. Information on spatial continuity of surface water concentrations can help in identifying the magnitude of contamination by runoff and anthropogenic pollutions. In the present study, spatial behavior of five (5) surface water quality parameters of some rivers/streams in Niger State of Nigeria was studied using R geostatistical package gstat, in conjunction with packages sp, rgdal, spatstat and maptools. The variograms and ordinary krigged spatial maps were generated for rainy and dry seasons. The characteristics of the best variable models; range; sill and nugget effects of each parameter were obtained. The variogram analysis indicated a high spatial coherence for E.co, Mg and TDS, whereas TCo and TH indicated a low spatial coherence. The nugget to sill ratios of experimental and linear fitted variogram models in all cases were less than 0.25 indicating that the rivers/streams water level has strong spatial coherence in both seasons. This result shows that linear model is the best for both seasons. Krigged spatial variability maps revealed that an average range of 48km variograms for dry season changes more rapidly than it does in rainy season with an average range of 4.3 km and R2 values of 0.80 to 0.92.
VL  - 4
IS  - 5
ER  -

Copy | Download

Author Information

Isah Audu

Department of Mathematics & Statistics, School of Physical Sciences, Federal University of Technology, Minna, Nigeria
Abdullahi Usman

Academic Planning Unit, Vice Chancellor’s Office, Federal University of Technology, Minna, Nigeria

Download PDF

Sections

Plain Text BibTeX RIS

APA Style

Isah Audu, Abdullahi Usman. (2015). An Application of Geostatistics to Analysis of Water Quality Parameters in Rivers and Streams in Niger State, Nigeria. American Journal of Theoretical and Applied Statistics, 4(5), 373-388. https://doi.org/10.11648/j.ajtas.20150405.18

Copy | Download

ACS Style

Isah Audu; Abdullahi Usman. An Application of Geostatistics to Analysis of Water Quality Parameters in Rivers and Streams in Niger State, Nigeria. Am. J. Theor. Appl. Stat. 2015, 4(5), 373-388. doi: 10.11648/j.ajtas.20150405.18

Copy | Download

AMA Style

Isah Audu, Abdullahi Usman. An Application of Geostatistics to Analysis of Water Quality Parameters in Rivers and Streams in Niger State, Nigeria. Am J Theor Appl Stat. 2015;4(5):373-388. doi: 10.11648/j.ajtas.20150405.18

Copy | Download

@article{10.11648/j.ajtas.20150405.18,
  author = {Isah Audu and Abdullahi Usman},
  title = {An Application of Geostatistics to Analysis of Water Quality Parameters in Rivers and Streams in Niger State, Nigeria},
  journal = {American Journal of Theoretical and Applied Statistics},
  volume = {4},
  number = {5},
  pages = {373-388},
  doi = {10.11648/j.ajtas.20150405.18},
  url = {https://doi.org/10.11648/j.ajtas.20150405.18},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150405.18},
  abstract = {Assessment of surface water quality using multivariate statistical techniques does not incorporate the spatial locations of data into their defining computations. Information on spatial continuity of surface water concentrations can help in identifying the magnitude of contamination by runoff and anthropogenic pollutions. In the present study, spatial behavior of five (5) surface water quality parameters of some rivers/streams in Niger State of Nigeria was studied using R geostatistical package gstat, in conjunction with packages sp, rgdal, spatstat and maptools. The variograms and ordinary krigged spatial maps were generated for rainy and dry seasons. The characteristics of the best variable models; range; sill and nugget effects of each parameter were obtained. The variogram analysis indicated a high spatial coherence for E.co, Mg and TDS, whereas TCo and TH indicated a low spatial coherence. The nugget to sill ratios of experimental and linear fitted variogram models in all cases were less than 0.25 indicating that the rivers/streams water level has strong spatial coherence in both seasons. This result shows that linear model is the best for both seasons. Krigged spatial variability maps revealed that an average range of 48km variograms for dry season changes more rapidly than it does in rainy season with an average range of 4.3 km and R2 values of 0.80 to 0.92.},
 year = {2015}
}

Copy | Download

TY  - JOUR
T1  - An Application of Geostatistics to Analysis of Water Quality Parameters in Rivers and Streams in Niger State, Nigeria
AU  - Isah Audu
AU  - Abdullahi Usman
Y1  - 2015/08/26
PY  - 2015
N1  - https://doi.org/10.11648/j.ajtas.20150405.18
DO  - 10.11648/j.ajtas.20150405.18
T2  - American Journal of Theoretical and Applied Statistics
JF  - American Journal of Theoretical and Applied Statistics
JO  - American Journal of Theoretical and Applied Statistics
SP  - 373
EP  - 388
PB  - Science Publishing Group
SN  - 2326-9006
UR  - https://doi.org/10.11648/j.ajtas.20150405.18
AB  - Assessment of surface water quality using multivariate statistical techniques does not incorporate the spatial locations of data into their defining computations. Information on spatial continuity of surface water concentrations can help in identifying the magnitude of contamination by runoff and anthropogenic pollutions. In the present study, spatial behavior of five (5) surface water quality parameters of some rivers/streams in Niger State of Nigeria was studied using R geostatistical package gstat, in conjunction with packages sp, rgdal, spatstat and maptools. The variograms and ordinary krigged spatial maps were generated for rainy and dry seasons. The characteristics of the best variable models; range; sill and nugget effects of each parameter were obtained. The variogram analysis indicated a high spatial coherence for E.co, Mg and TDS, whereas TCo and TH indicated a low spatial coherence. The nugget to sill ratios of experimental and linear fitted variogram models in all cases were less than 0.25 indicating that the rivers/streams water level has strong spatial coherence in both seasons. This result shows that linear model is the best for both seasons. Krigged spatial variability maps revealed that an average range of 48km variograms for dry season changes more rapidly than it does in rainy season with an average range of 4.3 km and R2 values of 0.80 to 0.92.
VL  - 4
IS  - 5
ER  -

Copy | Download