American Journal of Water Resources

ISSN (Print): 2333-4797

ISSN (Online): 2333-4819

Editor-in-Chief: Apply for this position

Website: http://www.sciepub.com/journal/AJWR

   

Article

Prediction of Missing Hydro-Meteorological Data Series Using Artificial Neural Networks (ANN) for Upper Tana River Basin, Kenya

1Egerton University Department of Agricultural Engineering, Kenya

2Jomo Kenyatta University of Agriculture and Technology, SWEED, Kenya


American Journal of Water Resources. 2016, 4(2), 35-43
doi: 10.12691/ajwr-4-2-2
Copyright © 2016 Science and Education Publishing

Cite this paper:
Raphael M. Wambua, Benedict M. Mutua, James M. Raude. Prediction of Missing Hydro-Meteorological Data Series Using Artificial Neural Networks (ANN) for Upper Tana River Basin, Kenya. American Journal of Water Resources. 2016; 4(2):35-43. doi: 10.12691/ajwr-4-2-2.

Correspondence to: Raphael  M. Wambua, Egerton University Department of Agricultural Engineering, Kenya. Email: wambuarm@gmail.com

Abstract

Accurate prediction of missing hydro-meteorological data is crucial in planning, design, development and management of water resources systems. In the present research, prediction of such data using Artificial Neural Networks (ANN) based on temporal and spatial auto-correlation has been conducted for upper Tana River basin in Kenya. Different ANN models were formulated using a combination of numerous data delays in the ANN input layer. The findings show that the best models comprise of a feed-forward neural network trained on Levenberg-Marquardt algorithm with single hidden layer. Additionally, the best ANN architecture model for predicting missing stream flow data was at gauge station 4CC03 with correlation coefficient and MSE of 0732 and 0.242 respectively during validation. Temporal auto-correlation of the observed and the predicted stream flow values were evaluated using a correlation coefficient R that resulted to highest value of 0.756 at gauge station 4AB05. The best ANN model for prediction of missing precipitation data was at station 9037112 with R value of 0.970. In both cases the best performance was at epochs 9 and 20 respectively. The spatial auto-correlation show that the best ANN architecture model for prediction of missing stream flow data was at gauge station 4CC03 with R value of 0.723, while the one for precipitation was at station 9037096 with R value of 0.712 during the validation. The results indicate that the spatial auto-correlation of hydro-meteorological data using ANN is better than the temporal auto-correlation in the data prediction in upper Tana River basin.

Keywords

References

[1]  Dreps, C., James, A. L., Sun G and Boggs, J. (2014). Water balance of the two piedmont headwater catchments; implications for regional hydrologic landscape classification, J. of American water resources association, 50(4): 1063-1079.
 
[2]  Botai, C. M., Botai, J. O., Muchuru, S. and Ngwana, I. (2015). Hydro-meteorological research in South Africa, A review, Water journal, 7: 1580-1594.
 
[3]  Ismail, M. I. S, Okamoto, Y. and Okada, A. (2013). Neural network modeling for prediction of weld bead geometry in laser microwelding , Advances in optical technologies, 2013: 1-7.
 
[4]  Lee, H. and Kang, K. (2015). Interpolation of missing precipitation data using Kernel estimations for hydrologic modeling, Advances in Meteorology, 2015: 1-12.
 
[5]  Jang, D., Park, H. and Choi, J. T. (2015a). Create a missing precipitation data base on spatial interpolation methods not covered by a region climate change scenario, Advanced Science and Technology L, 99: 109-112.
 
Show More References
[6]  Jang, D., Park, H. and Choi, J. T. (2015b). Applicability of the kriging method in data missing area using region climate change, E-proceedings of the 36th IAHR world congress, 28th June -3rd July, 2015, the Hague, the Netherlands.
 
[7]  Getahun, Y. S. and Gebre, S. L. (2015). Flood hazard assessment and mapping of flood inundation area of the Awash River Basin in Ethiopia using GIS and HEC-GEORAS/HEC-RAS Model, J. of Civil and Environmental Engineering, 5(4): 1-12.
 
[8]  Rivero, C. R., Pucheta, J.,Laboret, S., Partino, D. and Sauchelli, V. (2015). Forecasting short time series with missing data by means of energy associated to series, Applied mathematics, 2015(6): 1611-1619.
 
[9]  Belayneh, A and Adamowski, J. (2013). Drought forecasting using new machine learning methods, Journal of Water and Land development, 18(9): 3-12.
 
[10]  Ghumman, A. R., Ghazaw, Y. M., Sohail, A. R. and Watanabe, K. (2011). Runoff forecasting by artificial neural networks and conventional model, Alexandria Engineering Journal, 50(4): 345-350.
 
[11]  Mustafa, M. R., Isa, M. H., Rezaur, R. B. (2012). Artificial neural networks modeling in water resources engineering; infrastructure, and applications, Int. J. of Civil, Environmental, Structural, Construction and Architecture Engineering, 6(2) 128-136.
 
[12]  Elsafi, S. H. (2014). Artificial neural networks (ANNs) for flood forecasting at Dongola station in the River Nile Sudan, Alexandria Engineering Journals, 53(3): 655-662.
 
[13]  Valipour M. K (2014). Analysis of potential evapotranspiration using limited weather data, Appl Water Sci..
 
[14]  Valipour, M. (2015). Calibration of mass transfer-based models to predict reference crop evapotranspiration, Appl Water Sci..
 
[15]  Khoshravesh, M., Sefidkouhi M. A. G. and Valipour M. (2015). Estimation of reference evapotranspiration using multivariate fractional polynomial, Bayesian regression and robust regression models in three arid environments, Appl Water Sci..
 
[16]  Maier, A. R., Jain, A., Dandy, G. C. and Sudheer, K. P. (2010). Methods used for development of neural networks for the prediction of water resource variables in river systems: current status and future directions, Journal of Environmental modeling, 25(8): 891-909.
 
[17]  Luk K. C., Ball, J. C. and Sharma, A. (2000). A study of optimal model lag and spatial inputs to artificial neural network for rainfall forecasting, Journal of Hydrology, 227: 56-65.
 
[18]  Morid, S. Smakhtin, V. and Bagherzadeh, K. (2007). Drought forecasting using artificial neural networks and time series of drought indices International Journal of climatology, 27 (15): 2103-2111.
 
[19]  ran, H. D., Muttil, N. and Perera, B. J C. (2009). Investigation of artificial neural network models for stream flow forecasting, 19th international congress on modeling and simulation, Perth, Australia, 12th-16th December 2011.
 
[20]  Zali, M. A,, Retnam, A., Juahir, H. and Zain, S. M. (2011). Sensitivity analysis for water quality index (WQI) prediction for Kinta River, Malaysia, World Applied Sciences Journal, 14: 60-65.
 
[21]  Barua, S. (2010). Drought assessment and forecasting using a non-linear aggregated drought index, PhD thesis, Victoria University, Australia.
 
[22]  Mishra, A. K. and Desai, V. R. (2006). Drought forecasting using feed-forward recursive models, Journal of Ecological Modeling, 198: 127-138.
 
[23]  Campolo, M., Andreussi, P., Soldati, A. (1999). River flow forecasting with a neural network model, Water resources research, 35(34): 1191-1198.
 
[24]  Anmala, J., Zhang, B., Govindaraju, R. S. (2000). Comparison of neural networks and empirical approaches for predicting water shed runoff, Journal of water resources, planning and management, 126(3): 156-166.
 
[25]  Bodri, L., Cermak, V. (2000). Prediction of extreme precipitation using neural networks, application to summer and flood occurrence in Maravia, Advances in engineering software, 31: 311-321.
 
[26]  Grimes, D. I. F., Coppola, E., Verdecchia, M., Visconti, G. (2003). A neural network approach to real time rainfall estimation for Africa using satellite data, Journal of Hydrometeorology, 4: 1119-1133.
 
[27]  Lliunga, M., Stephenson, D. (2005). Infilling stream flow data using feed forward back-propagation (BP) artificial neural networks, applications of standard BP and pseudo Mac Laurin power series BP techniques, Water journal, 31(2): 171-176.
 
[28]  De Silva, R. P., Daya-wansa, N. D. K., Ratnasiri, M. D. (2007). A comparison of methods used in estimating missing rainfall data, Journal of agricultural sciences, 3(2): 101-108.
 
[29]  Sacchi, R., Ozturk, M. C., Principe, J. C.,Carneiro, A. A. F. M., Silva, I. N. D. (2007). Water inflow forecasting using the echo state network, a Brazilian case study, IEEN proceedings of international joint conference on neural network, 12-17th August 2007, Orlando FL, USA.
 
[30]  Starrett, S. K., Heir, T., Su, Y. (2010). Filling in missing peak flow data using artificial neural networks, Journal of Engineering and Applied Sciences, 5(1) 49-55.
 
[31]  Valipour, M. (2016). Optimization of neural networks for precipitation analysis in a humid region to detect drought and wet year alarms, Meteorological Applications Journal, 23(2016): 91-100.
 
[32]  NEMA. (2004). Kenya state of environment report: Chapter 7, fresh water, coastal and marine resources, Nairobi, Government printer.
 
[33]  World Resources Institute (WRI). (2011). Kenya GIS data –world resources institute, www.wri.org/resources/data-sets/kenya-gis-data.
 
[34]  GoK. (2012). Tana River Delta strategic environmental assessment scoping report.
 
Show Less References

Article

Assessment of Water Quality Index of River Salandi at Hadagada Dam and Its Down Stream upto Akhandalmani, Bhadrak, Odisha, India

1Department of Chemistry, A.B College, Basudevpur, Bhadrak, Odisha, India

2Department of Chemistry, VSSUT, Burla, Odisha, India

3Department of Chemistry, Bhadrak Autonomous College, Bhadrak, Odisha, India


American Journal of Water Resources. 2016, 4(2), 44-53
doi: 10.12691/ajwr-4-2-3
Copyright © 2016 Science and Education Publishing

Cite this paper:
Pratap Kumar Panda, Rahas Bihari Panda, Prasant Kumar Dash. Assessment of Water Quality Index of River Salandi at Hadagada Dam and Its Down Stream upto Akhandalmani, Bhadrak, Odisha, India. American Journal of Water Resources. 2016; 4(2):44-53. doi: 10.12691/ajwr-4-2-3.

Correspondence to: Pratap  Kumar Panda, Department of Chemistry, A.B College, Basudevpur, Bhadrak, Odisha, India. Email: pandapratap100@gmail.com

Abstract

The river Salandi, originated from well-known bio-sphere of Similipal forest and joints with the river Baitarani near Akhandalmani, the Tinitar Ghat after passing through Hadagada dam, Agarpada and Bhadrak town. During the course of journey, it receives forest run off, untreated and semi treated mining wastes, agricultural wastes, industrial wastes of Ferro Alloys Corporation (FACOR) and urban wastes. In the present study, physico-chemical & bacteriological parameters of water were analyzed by collecting water samples from nine different stations in summer (April and May), rainy (August), post rainy (October) and in winter (December) during the year 2015 by applying standard procedures. The mean values of twelve important parameters were calculated for the entire year and were computed to water quality index (WQI) of river Salandi by using the weighted arithmetic index method. The water quality index (WQI) reveals that the quality of water is different at different monitoring stations i.e. quality is good at Hadagada dam, Satabhauni and Dhusuri, poor at Akhandalmani & very poor at Bidyadharapur, very poor and unfit for drinking purpose at Agarpada, Randia (FACOR) and Baudpur and belongs to class ‘C’ river water at Rajghat.

Keywords

References

[1]  Panda P K, Panda R B & Dash P K: Pollution load of river Salandi in Boula Nuasahi mining belt, urban area at Bhadrak & it’s down streams in Odisha:IJIEASR,4 (12),15-23 (2015).
 
[2]  Bhadra A K, Sahu B& Rout S P:A study of water quality index (WQI) of the river Brahmani, Odisha(India) to assess its potability:Int J of Current Engg & Tec,Vol-4 (06), (2014).
 
[3]  Hujare M S: Seasonal variation of physico chemical parameters in perennial tank of Talsande, Maharastra, Ecotoxicol. Environ. Monit, 18(3), 233-242 (2008).
 
[4]  Pradhan U K, Shirodkar P V & sahu B K: Physico chemical evaluation of its seasonal changes using Chemo metric techniques, Current Science, 96(9), 1203-1209 (2009).
 
[5]  Tyagi S, Sharma B,Sing P & Dobhai R:Water quality assessment in terms of Water Quality Index, Science & Education Publishing, AJWR, Vol-I(3), 34-38 (2013).
 
Show More References
[6]  Reza R & Sing G:Heavy metal contamination & its indexing approach for river water,Int J Environ. Sci Tech, 7(4), 785-792 (2010).
 
[7]  Ammann A A, Michalke B & Schramel P: Specification of heavy metals in environmental water by ion chromatography coupled to ICP-MS, Anal. Bioanal.Chem, 372 (3), 448-452 (2002).
 
[8]  Bird G, Brewer P, Macklin M, Balteanu D, Driga B, Serban M & Zaharia S : The solid state partitioning of contaminant metals and as in river channel sediments of mining affected Tisa drainage basin, North western Romania and eastern Hungary, Appl Geo Chem,18(10),1583-1595 (2003).
 
[9]  Hatje V, Bidone E D & Maddock J L: Estimation of natural & anthropogenic components of heavy metal fluxes in fresh water Sinos river, Rio Griande do Sul State, South Brazil, Environ. Tech., 19(5), 483-487 (1998).
 
[10]  Kraft C,Tumpling W & Zachmann D W :The effect of mining in Northern Romania on the heavy metal distribution in sediments of river Szamos & Tisza, Hungary, Acta Hiroshima Hydrobiol, 34, 257-264 (2006).
 
[11]  Pandey J, Subhashish K & Pandey R: Metal contamination of Ganga river as influenced by atmospheric deposition, Bull Environ Contam Toxicol, 83 (2), 204-209 (2009).
 
[12]  Wong C S C, Li X D, Zhang G, Qi S H & Peng S Z : Atmospheric deposition of heavy metals in the Pearl river delta, China, Atmos Environ, 37 (6),767-776 (2003).
 
[13]  Wu Y F, Liu C Q & Tu C L: Atmospheric deposition of metal in TSP of Guiyang, PR China, Bull Environ Contamination Toxicol, 80 (5), 465-468 (2008).
 
[14]  Rim-Rukeh A, Lkhifa O G, & Okokoyo A P: Effect of agricultural activities on the water quality of orogodo river, Agbor Nigeria, J Appl Sci Res, 2 (5), 256-259(2006).
 
[15]  Padmanav B & Belagaali S L: Comparative study on water quality index of four lakes in the Mysore city, IJEP, 25, 941-942 (2005).
 
[16]  Swarnalatha P, Rao K N, Kumar P V R & Harikrishna M :Water quality assessment by using an index at village level- A case study, Poll Res, 26,619-622 (2007).
 
[17]  Begum A & Harikrishna M: Study on quality of water on some stream of Cauvery River, E J Chemistry, 5, 377-384 (2008).
 
[18]  Gray N F: Water Technology. An introduction for environmental scientists & Engineers, 2nd edition, Elsevier India Pvt. Ltd., New Delhi (2005).
 
[19]  Kalavathy S, Sharma T R & Kumar P S: Water quality index of River Cauvery in Tiruchirappali, Tamilnadu, ARCH, ENVIRON. Sci 5, 55-61 (2005).
 
[20]  Martin P & Haniffa M A: Water quality profile in South India river Tamiraparani, IJEP, 23,286-292 (2003).
 
[21]  Serpil S: An agricultural pollutants-chemical fertilizers, Int J Env Sci & Dev, 3 (1), (2012).
 
[22]  Panda R B et al, Occurrence of Fluoride in ground water of Patripal Panchayat in Balasore district, Odisha, India, Journal of Environment, 01 (02), 33-39 (2012).
 
[23]  Oberoi J & Gupta K C : Occurrence of fluoride in ground water of various villages of district Ambala, Haryana, Poll res, 29 (3), 435440 (2010).
 
[24]  American Public Health Association (APHA), AWWA, Standard methods of the examination of water & waste water, Washington DC, 7th edition (1995).
 
[25]  BIS IS – 10500, Indian Standard for Drinking Water Bureau of Indian Standards (IS- 10500 – 91), New Delhi (1991).
 
[26]  Cude C: Oregon Water Quality Index: A tool for evaluating water quality management effectiveness, Journal of American Water Resources Association, 37, 125-137 (2001).
 
[27]  Dash M C: Ecology, Chemistry & Management of Environmental Pollution, Ist Edition, Mac Millan India Limited, New Delhi,(2004) 82-85.
 
[28]  Environmental Impact Assessment & Environmental Management plan of Boula Chromite mines, FACOR Ltd., Source, state Pollution Control Board, Odisha (1994).
 
[29]  Kaur H, Environmental Chemistry, 5th revised Edition, Pragati Prakashan, India, 223-224 (2010).
 
[30]  Karim AA & Panda R B Assessment of water quality of Subarnarekha river in Balasore region, Odisha, India, Current World Environment, 9 (2),437-446, (2015).
 
[31]  Satya Prakash’s Modern Inorganic Chemistry by R D Madan, 2nd Edition, S Chand & Co., India (2006) 1077-1088.
 
[32]  WHO guidelines for drinking water quality, Health criteria and other supporting informations, Geneva, Switzerland, Vol-2 (1984).
 
[33]  Waste Water Concept & Design Approach by Karia G L & Christian R A, 1st Edition, Prentice Hall of India, Ltd., New Delhi (2006).
 
[34]  Panigrahi S & Patra A K: Water Quality Analysis of river Mahanadi in Cuttack City, Odisha, India, Ind J I Sci,2 (2),27-33 (2013).
 
[35]  Samantray P, Mishra B K, Panda C R & Rout S P : Assessment of water quality index in Mahanadi & Atharabanki rivers & Taladanda canal in Paradeep Area, India, J Hum Ecol, 26 (3), 153-161 (2009).
 
[36]  Kar D, Sur P, Mandal S K, Saha T & Kole R K: Assessment of heavy metal pollution in surface water, Int J Environ.Sci Tech, 5 (1), 119-124 (2008).
 
[37]  Panda R B, Sinha B K & Sahu B S :Water Quality Index of the river Brahmani at Rourkela Industrial Complex of Orissa, J.Eco.Toxico.Env. Monitoring 1 (3), 169-175 (1991).
 
[38]  Chauhan A & Singh S: Evaluation of Ganga waters for drinking purposes by water quality index at Rishikesh, Utarakhand, India, Report Opinion 2 (9), 53-61 (2010).
 
[39]  Choudhury R M, Muntasir S Y & Hossain M M: Water quality index of water bodies along Faridpur-Barisal road in Bangladesh, Glob.Engg.Tech.Rev, 2 (3),1-8 (2012).
 
[40]  Rao C S, Rao B S, Hariharan AVLNSH & Bharathi N M: Determination of water quality index of some areas in Guntur district, AndhraPradesh, Int J Appl.Bio.Pharma.Tech. 1 (1), 79-86 (2010).
 
[41]  Balan I N, shivakumar M & Kumar P D M: An assessment of ground water quality using water quality index in Chennai, Tamil Nadu, India, Chronicles Young Scient. 3(2), 146-150 (2012).
 
[42]  Rown R M, Mc Cleiland N J, Deiniger R A & O ConnorM F A: Water quality index-Crossing the physical barrier (Jenkis.S.H,ed) Proceedings in international conference on water pollution research, Jerusalem 6, 787-797 (1972).
 
[43]  Akoteyon I S, Omotayo A O, Soladoye O & Olaoye H O: Determination of water quality index and stability of urban river for Municipal water supply in Lagos, Nigeria: Euro.J Scientific Res., 54(2), 263-271(2011).
 
Show Less References

Article

Fluoride Contents of Community Drinking Water: Biological and Public Health Implications

1Department of Integrated Science/Biology Education, Federal College of Education (Technical) Omoku, Nigeria

2Medical Director, Sick Bay, Federal College of Education (Technical) Omoku, Nigeria


American Journal of Water Resources. 2016, 4(3), 54-57
doi: 10.12691/ajwr-4-3-1
Copyright © 2016 Science and Education Publishing

Cite this paper:
Dirisu C.G, Mafiana M. O, Okwodu N.E, Isaac A.U.. Fluoride Contents of Community Drinking Water: Biological and Public Health Implications. American Journal of Water Resources. 2016; 4(3):54-57. doi: 10.12691/ajwr-4-3-1.

Correspondence to: Dirisu  C.G, Department of Integrated Science/Biology Education, Federal College of Education (Technical) Omoku, Nigeria. Email: chimeziedirisu@yahoo.com

Abstract

Drinking water supplies in Omoku, Nigeria was analyzed for its fluoride content investigated. For the analysis, twenty-four different water samples were systematically collected from stream, well water, public tap and private borehole in defined locations in Omoku. Level of fluoride was determined using standard method-Ion-Selective Electrode method. The mean values obtained were compared directly with the limit recommended by the World Health organization (WHO) of 1.0mg/L. Private borehole and public tap water had overall mean fluoride levels of 0.94±0.07 mg/L and 0.86±0.30 mg/L respectively, while stream had 0.95±0.09 mg/L and well water had the lowest level of fluoride (0.48±0.03 mg/L). Fluoride content of private borehole, public tap water and stream were approximately within the specified minimum limit of World health organization, while that of well water was below stipulated limit. ANOVA statistics indicated that there was no significant difference in the mean Fluoride levels of the water samples (p>0.05). Low levels of fluoride are associated with dental caries and hence such water should be fluoridated. Biological implications of low and high fluoride levels of water are highlighted in order to create awareness on the need to protect the general public from either dental caries or fluorosis of the teeth and/or skeleton respectively.

Keywords

References

[1]  ATSDR [Agency for Toxic Substances and Disease Registry Toxicological Profile For Fluorides, Hydrogen Fluoride, And Fluorine, U.S. Department Of Health And Human Services, 2003.
 
[2]  WHO [World Health Organization]. Chapter 6.5 Fluorides. WHO Regional office for Europe, Copenhagen, Denmark, 2004.
 
[3]  Fawell, J., K. Bailey, J. Chilton, E. Dahi, L. Fewtrell and Y. Magara, Fluoride in Drinking Water, 2006 Available at <http://www.who.int/water_sanitation_health/publications/fluoride_drinking_water/en/.
 
[4]  Murray, J. J. Appropriate Use of Fluorides for Human Health, W.H.O, Geneva, 2003.
 
[5]  W.H.O. Guidelines for Drinking-water Quality, 4th Ed. ISBN 9789241548151. Page 168, 175, 370-73, 2011.
 
Show More References
[6]  Wikipedia. Water Fluoridation. Retrieved from “https://en.wikipedia.org/w/index.php?title=Water_fluoridation&oldid=718372669” accessed 5/17/2016.
 
[7]  Iheozor-Ejiofor, Z; Worthington, HV; Walsh, T; O'Malley, L; Clarkson, JE; Macey, R; Alam, R; Tugwell, P; Welch, V; Glenny, AM (2015). “Water fluoridation for the prevention of dental caries.” The Cochrane Database of systematic Reviews 6: CD010856.
 
[8]  USEPA Fluoride in Drinking Water. Scientific Review of EPA's Standards http://www.nap.edu/catalog/11571/fluoride-in-drinking-water-a-scientific-review-of-epas-standards, 2002
 
[9]  Harwood, J.E. The use of an ion-selective electrode for routine fluoride analyses on water samples, Water Research 3(4), 273-280 Available at http://www.sciencedirect.com/science/article/pii/0043135469900244-accessed 1969.
 
[10]  IPCS [International Programme on Chemical Safety], Fluorides. Environmental Health Criteria 227, World Health Organization, Geneva. p. 38.2002
 
[11]  Green Facts. Fluoride. Available at http://www.greenfacts.org/en/fluoride. Accessed 5/17/ 2016.
 
[12]  Hassan, S. A., EL-Awamry, Z. K., Omer T. M. (2004). Rate of Consumption and recommendations of Fluoride intake in Egypt from drinking water and the effect on the health of children and adult; Annals of Agriculture 49(1). 191-207, 2004.
 
[13]  Facazio M. J., Tipton, D., Shapiro, S. D., and Geiger, L. H., The Chemical Quality of Self-supplied Domestic Well Water in the United States. 2006.
 
[14]  FAN [Fluoride Action Network]. Fluoride & Health. Available at http://fluoridealert.org/issues/health/ accessed 2016-07-21.
 
[15]  Azbar, N. and Türkman, A. Defluoridation in drinking water. Water Science and Technology, 42(1-2), 403-407, 2000.
 
[16]  NAP [National Academic Press]. Fluoride in Drinking Water. Scientific Review of EPA's Standards http://www.nap.edu/catalog/11571/fluoride-in-drinking-water-a-scientific-review-of-epas-standards.
 
[17]  Li Li, Y., Liang, C., Slemenda, C.W., Ji, R., Sun, S., Cao, J., Emsley, C; Ma, F., Wu, Y., Ying, P., Zhang, Y., Gao, S., Zhang, W., Katz, B., Niu, S., Cao, S. and Johnston, C. Effect of long-term exposure to fluoride in drinking water on risks of bone fractures. J. of Bone and Mineral Res, 16(5):932-939, 2001.
 
[18]  Wongdem, J.G., Aderinokun, G.A., Sridhar, M.K. and Selkur, S. Prevalence and distribution pattern of enamel fluorosis in Langtang town, Nigeria. African Journal of Medicine and Medical Science, 29, 243-246, 2000.
 
[19]  US Environmental Protection Agency. 2013. Basic information about fluoride in drinking water: Review of fluoride drinking water standard. Available at http://www2.epa.gov/dwsixyearreview/review-fluoride-drinking-water-regulation [accessed 5/20/2016].
 
[20]  U.S. Dept of Health and Human services, Proposed National Toxicity Programme Evaluation on Fluoride Exposure and Potential for Developmental Neurobehavioral Effects, 2015. Office of Health Assessment and Translation (OHAT).
 
[21]  Standard Organization of Nigeria [SON] 2007. Nigerian Standard for Drinking Water. NIS 554. ICS 13.060.20. SON. Abuja.
 
Show Less References