Investigating Spatial Changes in Nitrate and Arsenic in Groundwater of the Jiroft Watershed

Document Type : Research

Authors

Assistant Professor of Department of Water Engineering and Science, Faculty of Agriculture, University of Jiroft, Iran

Abstract

Introduction and Goal
Awareness of spatial variations in groundwater quality parameters is an important tool for identifying the capacity of the region and land management. Jiroft is located on an arsenic belt in the country and considering the effect that arsenic has on underground water and the carcinogenic nature of this element, analysis of this element is very important. Moreover, most rural regions of Jiroft use the well water for drinking. Therefore, the analysis of groundwater in this region appears to be very important for drinking purpose.
Materials and Methods
Using geostatistical methods, the present study attempted to analyze the spatial variation of nitrate, arsenic, manganese, and all dissolved solid parameters in the groundwater of the Jiroft watershed during 2019. For this purpose, water from 36 agricultural wells and wells used for drinking in villages, as well as urban drinking water samples, were sampled in three replicates. The geostatistical methods used for zoning the above parameters include ordinary kriging (OK), simple kriging (SK), radial basis function (RBF), and inverse distance weighting (IDW) with different powers. Geostatistical methods were evaluated using the mutual evaluation technique applying the root mean square error (RMSE) and mean bias error (MBE) criteria between the actual and estimated data.
Results and Discussion
In this research, the spatial changes of nitrate, arsenic, manganese and total solids dissolved in groundwater in the Jiroft watershed were analyzed using the evaluation criteria ranking results showed that the estimation of arsenic and TDS with the spherical model of the simple kriging method had the least error and the estimation of nitrate and manganese with the radial basis function (RBF) method had the lowest error. The study showed that the spatial correlation of the quality metric of the Jiroft watershed is very high. Therefore, the error of the semivariable model of the measured data of arsenic with a partial effect of 0.00025 and a range of influence of 36.7 km was 38.4%, whereas the error of the semivariable model of nitrate with a partial effect of 0.3 and a range of influence of 28.5% was 4.9%. In the Jiroft watershed, the concentration of arsenic in the water of other areas was much higher than the permissible amount for drinking based on standard 1051 of the Iranian Institute of Standards and Industrial Research except for urban water. In addition, the concentration of nitrate in all the samples was lower than the permissible amount for drinking water, so there is no obstacle to its use.

Keywords

References

APHA. 2005. Standard methods for the examination of water and wastewater: American Public Health Association. 21th ed Washington: APHA. 541 p.
Barkhori S. Mahdavi R. Zehtabian G. Gholami H. 2018. Investigating temporal and spatial changes trend of groundwater quality indices (Case Study: Jiroft plain). Iranian Journal of Range and Desert Research. 25(2):355-365.
Broomhead DS. Lowe D. 1988. Radial basis functions, multi-variable functional interpolation and adaptive networks. Royal Signals and Radar Establishment Malvern (United Kingdom). No. RSRE-MEMO-4148. pp. 1-34.
Chitsazan M. Shacheri S. Mirzaei SY. Aboudi ST. 2018. Assessment of the distribution and source of arsenic in Karstic Aquifers ( Case study: Garu saline Karstic spring in East of Khuzestan Province, Southwest of Iran). Iran-Water Resources Research. 14(3): 216-225.
Cordoba E. Martinez AC. Ferrer EV. 2010. Water quality indicators: comparison of a probabilistic index and a general quality index. The case of the Confederacion Hidrografica del Jucar (Spain). Ecological Indicators. 10(5):1049-1054.
Dummer TJB. Yu ZM. Nauta L. Murimboh JD. Parker L. 2015. Geostatistical modelling of arsenic in drinking water wells and related toenail arsenic concentrations across Nova Scotia. Canada. Science of the Total Environment. 505:1248-1258.
Ebrahimi Maimand M. 2018. Dispersion and origin of arsenic in the underground waters of Rafsanjan plain and providing a suitable solution for its removal. M.Sc. Thesis in Environmental Geology. Shahid Bahonar University, Kerman. (In Persian).
Esfandiari F. Nasiri Khiavi A. Mostafazadeh R. 2019. Assessing the accuracy of algebraic and geostatistical techniques to determine the spatial variations of groundwater quality in Boroojen Plain. Journal of Natural Environmental Hazards (JNEH). 8(20):115-130. (In Persian).
Esfandiari F. Mostafazadeh R. Ebadi E. Saadati R. 2020. Modelling the spatial distribution and depletion of groundwater level in Tabriz Plain. Journal of  Geographical Engineering of Territory. 3(6):1-16. (In Persian).
Fathi Hafshejani E. Beygi Harchegani H. Davoudian Dehkordi A. Tabatabaee SH. 2014. Comparison of spatial interpolation methods and selecting the appropriate method for mapping of nitrate and phosphate in the Shahrekord Aquifer. Engineering Scientific Research Institute of Iran. 4(3):51-63. (In Persian).
Gong G. Mattevada S. O’Bryant SE. 2014. Comparison of the accuracy of kriging and IDW interpolations in estimating groundwater arsenic concentrations in Texas. Environmental Research. 130:59-69
Hasani pak A. 2009. Geostatistics. University of Tehran. 328 p. (In Persian).
Hounslow A. 1995. Water quality data: Analysis and interpretation. CRC Press. 416 p.
Isaaks EH. Srivastava RM. 1989. Applied Geostatistics. New York: Oxford university press. Vol.561. 582 p.
Jiang JQ. 2001. Removing arsenic from groundwater for the developing world-a review. Water Science and Technology. 44(6):89-98.
Kalantari N. Sheikhzadeh A. Mohammadi H. 2021. Investigation of groundwater quality in Gotvand Aquifer with emphasis on Nitrate concentration. Iran-Water Resources Research. 17(1):228-238. (In Persian).
Khajepour S. 2016. Investigating the amount of heavy metals in the groundwaters of the south of Rafsanjan plain with an emphasis on the possible role of the Sarcheshme copper complex, Master's Thesis in Environmental Geology, Shahid Bahonar University, Kerman. 137 p. (In Persian).
Malakootian M. Yaghmaian K. Tahergorabi M. 2011. The efficiency of nitrate removal in drinking water using iron nano-particle and determination of optimum conditions. Scientific Research Quarterly of Rise of Health Yazd Health Faculty. pp. 35-44. (In Persian).
Naderianfar M. Ghahraman B. Ansari H. Salari M. 2012. Application of different geostatistical methods in order to estimate EC and SAR values ​​in groundwater with emphasis on changes in permeability of the basin. Engineering and Irrigation Sciences (Scientific Journal of Agriculture). 35(1):21-33. (In Persian).
Naderianfar M. Nezhad Afzali K. Bayatani F. 2019. Investigation of Anomaly maps of groundwater quality parameters (Case study: Jiroft watershed). Irrigation and Water Engineering. 10(1):93-107. (In Persian).
Naseri S. Heibati B. Asadi A. Golestani FH. Dargahi A. 2013. Performance evaluation of modified pumice on removal nitrate from aqueous solution: kinetic studies and adsorption isotherm. Tolooebehdasht. 12(1):143-54.
Sall M. Vanclooster M. 2009. Assessing the well water pollution problem by nitrates in the small scale farming systems of the Niayes region, Senegal. Agricultural Water Management. 96(9): 1360-1368
Sisr L. Mihaljevič M. Ettler V. Strnad L. Šebek O. 2007. Effect of application of phosphate and organic manure-based fertilizers on arsenic transformation in soil columns. Environmental Monitoring and Assessment. 135(1-3): 465-473.
Solgi E. Goudarzi R. 2022. The Effect of Unsanitary Landfill on Groundwater Quality (Case study: Borujerd City). Iran-Water Resources Research. 18(2):98-110. (In Persian).
Tahboub AA. Abbassi BE. Ta'any RA. Saffarini GA. 2007. Spatial variability of topsoil salinity in the lower reaches of Zerka River. Central Jordan Valley. Journal of Food Agriculture and Environment. 5(3/4):368-373.
Uyan M. Cay T. 2010. Geostatistical methods for mapping groundwater nitrate concentrations. 3rd International conference on cartography and GIS. Nessebar. Bulgaria. 1520:1-7.

Files

History

  • Receive Date: 20 January 2023
  • Revise Date: 11 February 2023
  • Accept Date: 19 March 2023

Share

How to cite

Statistics