Geospatial assessment of groundwater quality in the Noyyal basin, Tamil Nadu, India using GIS and geostatistics

Kannan Balaji; N  Janani; A  Selvaperumal; J  Ramachandaran; T  Arthi; K   Arunadevi; A  Raviraj

doi:10.14719/pst.6134

Authors

Kannan Balaji Agricultural Engineering College & Research Institute, Tamil Nadu Agricultural University, Coimbatore - 641003 https://orcid.org/0000-0003-2330-0893
N Janani Agricultural Engineering College & Research Institute, Tamil Nadu Agricultural University, Coimbatore - 641003 https://orcid.org/0000-0003-0788-2504
A Selvaperumal Agricultural Engineering College & Research Institute, Tamil Nadu Agricultural University, Coimbatore - 641003 https://orcid.org/0000-0002-6126-7978
J Ramachandaran Agricultural Engineering College & Research Institute, Tamil Nadu Agricultural University, Coimbatore - 641003 https://orcid.org/0000-0002-1194-8767
T Arthi Agricultural Engineering College & Research Institute, Tamil Nadu Agricultural University, Coimbatore - 641003 https://orcid.org/0000-0001-7714-3470
K Arunadevi Agricultural Engineering College & Research Institute, Tamil Nadu Agricultural University, Coimbatore - 641003 https://orcid.org/0000-0002-8732-1442
A Raviraj Agricultural Engineering College & Research Institute, Tamil Nadu Agricultural University, Coimbatore - 641003 https://orcid.org/0000-0001-5107-987X

DOI:

https://doi.org/10.14719/pst.6134

Keywords:

chemical parameters, geostatistics, GIS, groundwater quality, spatial distribution

Abstract

Water is crucial in agriculture, domestic use and industrial development. In recent years, the demand for groundwater has significantly risen due to industrialization, urbanization, population growth and increased agricultural activities. This study focuses on the groundwater quality spatial distribution and utilizes geostatistical analysis to predict groundwater chemical parameters within the Noyyal sub-basin, employing Geographic Information System (GIS) technology. Data transformation methods were applied to reduce skewness in several chemical parameters to improve the precision of the spatial representation of groundwater chemistry. Comparing the calculated concentrations to the established permissible limits showed that calcium, bicarbonate and sodium absorption ratio concentrations were within acceptable levels. In contrast, parameters such as magnesium, sodium, potassium, chlorine, sulfate, fluoride, pH, total hardness, electrical conductivity and total dissolved solids exceeded the permissible thresholds. The study also identified the most appropriate semi-variogram model for each water quality parameter based on the Root Mean Square Error (RMSE). The Exponential model with log-transformed data was the best fit for Ca, Na, K, HCO₃, pH, HAR and EC, providing physically meaningful results. For TDS, Mg, SO4, F and SAR, the Spherical model with log-transformed data yielded the most reliable RMSE values. The Gaussian model produced satisfactory results for Cl and Na %.

Downloads

Download data is not yet available.

References

Narayana S, Rao PS, Kumar AP. Groundwater quality assessment in urban areas: A case study from Coimbatore district, Tamil Nadu. Environ Mon Assess. 2014;186(10):6815–32. http://doi.org/10.1007/s10661-014-3962-4

Dakh A, Dakak H, Douaik A, El Khadir M, Moussadek R. Evaluation of groundwater suitability for irrigation in the Skhirat region, Northwest of Morocco. Environ Mon Assess. 2015;187(1):1–15. http://doi.org/10.1007/s10661-015-4961-4

Mostafa MG, Uddin SH, Haque AB. Assessment of hydro-geochemistry and groundwater quality of Rajshahi city in Bangladesh. App Water Sci. 2017;7:4663–71. https://doi.org/10.1007/s13201-017-0629-y

Nayanaka VGD, Vitharana WAU, Mapa RB. Geostatistical analysis of soil properties to support spatial sampling in a paddy growing alfisol. Tropical Agric Res. 2010;22(1):34–44. https://doi.org/10.4038/tar.v22i1.2668

David M. Geostatistical ore reserve estimation. Amsterdam: Elsevier; 1977

Mishra S, Bharagava RN, More N, Yadav A, Zainith S, Mani S, Chowdhary P. Heavy metal contamination: an alarming threat to environment and human health. In: Sobti R, Arora N, Kothari R, editor. Environmental biotechnology: for sustainable future. Singapore:Springer. 2019. p. 103–25 https://doi.org/10.1007/978-981-10-7284-0_5

Machiwal D, Cloutier V, Güler C, Kazakis N. A review of GIS-integrated statistical techniques for groundwater quality evaluation and protection. Environ Earth Sci. 2018;77(19):681. http://doi.org/10.1007/s12665-018-7923-6

Shaikh M, Birajdar F. Advancements in remote sensing and GIS for sustainable groundwater monitoring: applications, challenges and future directions. International J Res Engin Sci Manage. 2024;7(3):16–24.

Farzaneh G, Khorasani N, Ghodousi J, Panahi M. Application of geostatistical models to identify spatial distribution of groundwater quality parameters. Environ Sci Poll Res. 2022;29(24):36512–32. http://doi.org/10.1007/s11356-022-19540-0

Javari M. Geostatistical and spatial statistical modelling of precipitation variations in Iran. J Civil Environ Engin. 2016;6(3):1–30. https://doi.org/10.4172/2165-784X.1000230

Nas B. Geostatistical approach to assessment of spatial distribution of groundwater quality. Polish J Environ Stud. 2009;18(6):1073–82.

Ogunwole JO, Obidike EO, Timm LC, Odunze AC, Gabriels DM. Assessment of spatial distribution of selected soil properties using geospatial statistical tools. Comm Soil Sci Plant Anal. 2014;45(16):2182–200. http://doi.org/10.1080/00103624.2014.949152

Varouchakis EA. Gaussian transformation methods for spatial data. Geosci. 2021;11(5):196. http://doi.org/10.3390/geosciences11050196

Koo H, Chun Y, Griffith DA. Integrating spatial data analysis functionalities in a GIS environment: Spatial analysis using ArcGIS Engine and R (SAAR). Transac GIS. 2018;22(3):721–36. http://doi.org/10.1111/tgis.12334

Selvarani AG, Elangovan K. Hydrogeochemistry analysis of groundwater in Noyyal River basin, Tamilnadu, India. Int J App Environ Sci. 2009;4(2):211–27.

Sellamuthu KM, Mayilswami C, Valliammai A, Chellamuthu S. Effect of textile and dye industrial pollution on irrigation water quality of Noyyal River Basin of Tamil Nadu. Madras Agri J. 2011;98(4-6):129–35. https://doi.org/10.29321/MAJ.10.100259

Marko K, Al-Amri NS, Elfeki AM. Geostatistical analysis using GIS for mapping groundwater quality: case study in the recharge area of Wadi Usfan, western Saudi Arabia. Arab J Geosci. 2014;7:5239–52. http://doi.org/10.1007/s12517-014-1372-1

World Health Organization (WHO). Guidelines for drinking-water quality: Fourth edition incorporating the first addendum. Geneva: World Health Organization; 2017 [cited 2025 Feb 25]. Available from: https://www.who.int/water_sanitation_health/publications/gdwq-4-inc-first-addendum/en/

Iyer SK, Sudhakar M. Groundwater quality in some coastal areas of southern India and its suitability for drinking and irrigation. Environ Monitoring and Assess. 2011;174(1-4):261–72. http://doi.org/10.1007/s10661-010-1649-1

Bureau of Indian Standards (BIS). IS 10500:2012 – Drinking Water Specification. New Delhi: Bureau of Indian Standards; 2012 [cited 2025 Feb 25]. Available from: https://cpcb.nic.in/wqm/BIS_Drinking_Water_Specification.pdf