Geostatistical analysis of hydrochemical variations and nitrate pollution causes of groundwater in an alluvial fan plain

  • Shiyang Yin
  • Yong XiaoEmail author
  • Xiaomin Gu
  • Qichen Hao
  • Honglu Liu
  • Zhongyong Hao
  • Geping Meng
  • Xingyao Pan
  • Qiuming Pei
Research Article - Hydrology


Geostatistics was used in a typical alluvial fan to reveal its applicability to spatial distribution analysis and controlling mechanisms of groundwater chemistry. Normal distribution test and optimal geostatistical interpolation models for various groundwater quality indicators were discussed in this study. The optimal variogram model of each indicator was determined using prediction error analysis. The influences of human activities and structural factors on the groundwater chemistry were also determined by variability intensity and the sill ratio. The results showed that nitrate content can be served as groundwater quality indicator, which was most sensitive to human activities. The nitrate concentration of both shallow and deep groundwater showed a decreasing trend from the northwest to the southeast. In addition, the spatial distribution of groundwater nitrate was associated with the land-use type and the lithological properties of aquifer. Rapid urbanization in the northwestern part intensified groundwater extraction and aggravated the pollutant input. The central area showed little increase in nitrate content in the shallow and deep groundwater, and the effect of lateral recharge from the upstream water on the deep groundwater in the central area was greater than that of the vertical recharge from shallow groundwater. The present study suggests that geostatistics is helpful for analyzing the spatial distribution and distinguishing the influences of anthropogenic and natural factors on groundwater chemistry.


Geostatistics Groundwater chemistry Human activities Nitrate pollution Spatial variation 



This research was financially supported by the Fundamental Research Funds for the Central Universities (2019MS028; 2682019CX14), the National Basic Resources Survey Program of China (2017FY100405) and China Geological Survey (DD20160238).

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.


  1. Abu-alnaeem MF, Yusoff I, Ng TF, Alias Y, Raksmey M (2018) Assessment of groundwater salinity and quality in Gaza coastal aquifer, Gaza Strip, Palestine: an integrated statistical, geostatistical and hydrogeochemical approaches study. Sci Total Environ 615:972–989. CrossRefGoogle Scholar
  2. Adhikary PP, Chandrasekharan H, Chakraborty D, Kamble K (2010) Assessment of groundwater pollution in West Delhi, India using geostatistical approach. Environ Monit Assess 167:599–615CrossRefGoogle Scholar
  3. Adhikary PP, Dash CJ, Bej R, Chandrasekharan H (2011) Indicator and probability kriging methods for delineating Cu, Fe, and Mn contamination in groundwater of Najafgarh Block, Delhi, India. Environ Monit Assess 176:663–676CrossRefGoogle Scholar
  4. Ahmad M, Chand S, Rafique HM (2016) Predicting the spatial distribution of sulfate concentration in groundwater of Jampur-Pakistan using geostatistical methods. Desalin Water Treat 57:1–10CrossRefGoogle Scholar
  5. Alhamed M, Wohnlich S (2014) Environmental impact of the abandoned coal mines on the surface water and the groundwater quality in the south of Bochum, Germany. Environ Earth Sci 72:3251–3267CrossRefGoogle Scholar
  6. Ali MH, Mustafa A-RA, El-Sheikh AA (2016) Geochemistry and spatial distribution of selected heavy metals in surface soil of Sohag, Egypt: a multivariate statistical and GIS approach. Environ Earth Sci 75:1257. CrossRefGoogle Scholar
  7. Andelov M, Kunkel R, Uhan J, Wendland F (2014) Determination of nitrogen reduction levels necessary to reach groundwater quality targets in Slovenia. J Environ Sci 26:1806–1817CrossRefGoogle Scholar
  8. Andrade AIASS, Stigter TY (2009) Multi-method assessment of nitrate and pesticide contamination in shallow alluvial groundwater as a function of hydrogeological setting and land use. Agric Water Manag 96:1751–1765CrossRefGoogle Scholar
  9. Assaf H, Saadeh M (2009) Geostatistical Assessment of Groundwater Nitrate Contamination with Reflection on DRASTIC Vulnerability Assessment: the Case of the Upper Litani Basin, Lebanon. Water Resour Manag 23:775–796CrossRefGoogle Scholar
  10. Baalousha H (2010) Assessment of a groundwater quality monitoring network using vulnerability mapping and geostatistics: a case study from Heretaunga Plains, New Zealand. Agric Water Manag 97:240–246CrossRefGoogle Scholar
  11. Bao Z, Wu W, Liu H, Chen H, Yin S (2014) Impact of long-term irrigation with sewage on heavy metals in soils, crops, and groundwater—a case study in Beijing. Pol J Environ Stud 23:309–318Google Scholar
  12. Barca E, Passarella G (2008) Spatial evaluation of the risk of groundwater quality degradation: a comparison between disjunctive kriging and geostatistical simulation. Environ Monit Assess 137:261–273CrossRefGoogle Scholar
  13. Bhat S, Motz LH, Pathak C, Kuebler L (2015) Geostatistics-based groundwater-level monitoring network design and its application to the Upper Floridan aquifer, USA. Environ Monit Assess 187:1–15CrossRefGoogle Scholar
  14. Bian J, Nie S, Wang R, Wan H, Liu C (2018) Hydrochemical characteristics and quality assessment of groundwater for irrigation use in central and eastern Songnen Plain, Northeast China. Environ Monit Assess 190:382. CrossRefGoogle Scholar
  15. Bodrud-Doza M, Bhuiyan MAH, Islam SMD-U, Quraishi SB, Muhib MI, Rakib MA, Rahman MS (2019) Delineation of trace metals contamination in groundwater using geostatistical techniques: a study on Dhaka City of Bangladesh. Groundw Sustain Dev 9:100212. CrossRefGoogle Scholar
  16. Bonton A, Rouleau A, Bouchard C, Rodriguez MJ (2010) Assessment of groundwater quality and its variations in the capture zone of a pumping well in an agricultural area. Agric Water Manag 97:824–834CrossRefGoogle Scholar
  17. Carreira PM, Marques JM, Pina A, Gomes AM, Fernandes PAG, Santos FM (2010) Groundwater assessment at Santiago Island (Cabo Verde): a multidisciplinary approach to a recurring source of water supply. Water Resour Manag 24:1139–1159CrossRefGoogle Scholar
  18. Chandan KS, Yashwant BK (2017) Optimization of groundwater level monitoring network using GIS-based geostatistical method and multi-parameter analysis: a case study in Wainganga Sub-basin, India. Chin Geogr Sci 27:201–215. CrossRefGoogle Scholar
  19. Chaudhuri S, Ale S (2014) An appraisal of groundwater quality in Seymour and Blaine aquifers in a major agro-ecological region in Texas, USA. Environ Earth Sci 71:2765–2777CrossRefGoogle Scholar
  20. Chaves e Carvalho SDP et al (2015) Predict volume of trees integrating Lidar and Geostatistics. Sci For Sci 43:627–637Google Scholar
  21. Chen A et al (2018) Temporal-spatial variations and influencing factors of nitrogen in the shallow groundwater of the nearshore vegetable field of Erhai Lake, China. Environ Sci Pollut Res 25:4858–4870. CrossRefGoogle Scholar
  22. Desbarats AJ, Logan CE, Hinton MJ, Sharpe DR (2002) On the kriging of water table elevations using collateral information from a digital elevation model. J Hydrol 255:25–38CrossRefGoogle Scholar
  23. El Alfy M, Abdalla F, Moubark K, Alharbi T (2019) Hydrochemical equilibrium and statistical approaches as effective tools for identifying groundwater evolution and pollution sources in arid areas. Geosci J 23:299–314. CrossRefGoogle Scholar
  24. Elgallal M, Fletcher L, Evans B (2016) Assessment of potential risks associated with chemicals in wastewater used for irrigation in arid and semiarid zones: a review. Agric Water Manag 177:419–431CrossRefGoogle Scholar
  25. Gu X et al (2017) Natural and anthropogenic factors affecting the shallow groundwater quality in a typical irrigation area with reclaimed water, North China Plain. Environ Monit Assess 189:514CrossRefGoogle Scholar
  26. Gu X et al (2018) Hydrogeochemical characterization and quality assessment of groundwater in a long-term reclaimed water irrigation area, North China Plain. Water 10:1209. Google Scholar
  27. Gundogdu KS, Guney I (2007) Spatial Analysis of Groundwater Levels Using Universal Kriging. J Earth Syst Sci 116:49–55CrossRefGoogle Scholar
  28. Júnez-Ferreira HE, Herrera GS, Saucedo E, Pacheco-Guerrero A (2019) Influence of available data on the geostatistical-based design of optimal spatiotemporal groundwater-level-monitoring networks. Hydrogeol J. Google Scholar
  29. Kanagaraj G, Elango L (2019) Chromium and fluoride contamination in groundwater around leather tanning industries in southern India: implications from stable isotopic ratio δ53Cr/δ52Cr, geochemical and geostatistical modelling. Chemosphere 220:943–953. CrossRefGoogle Scholar
  30. Kasper JW, Denver JM, York JK (2015) Suburban groundwater quality as influenced by turfgrass and septic sources, Delmarva Peninsula, USA. J Environ Qual 44:642. CrossRefGoogle Scholar
  31. Kim H-s, Park S-r (2016) Hydrogeochemical characteristics of groundwater highly polluted with nitrate in an agricultural area of Hongseong, Korea. Water 8:345. Google Scholar
  32. Klauberg C, Hudak AT, Bright BC, Boschetti L, Silva CA (2018) Use of ordinary kriging and Gaussian conditional simulation to interpolate airborne fire radiative energy density estimates. Int J Wildland Fire 27:228CrossRefGoogle Scholar
  33. Kumar S, Singh RP (2016) Spatial distribution of soil nutrients in a watershed of Himalayan landscape using terrain attributes and geostatistical methods. Environ Earth Sci 75:1–11CrossRefGoogle Scholar
  34. Li P, Li X, Meng X, Li M, Zhang Y (2016) Appraising groundwater quality and health risks from contamination in a semiarid region of Northwest China. Expo Health 8:1–19CrossRefGoogle Scholar
  35. Li P, Tian R, Liu R (2018) Solute geochemistry and multivariate analysis of water quality in the Guohua phosphorite mine, Guizhou Province, China. Expo Health. Google Scholar
  36. Machiwal D, Jha MK (2015) Identifying sources of groundwater contamination in a hard-rock aquifer system using multivariate statistical analyses and GIS-based geostatistical modeling techniques. J Hydrol Reg Stud 4:80–110. CrossRefGoogle Scholar
  37. Maroufpoor S, Fakherifard A, Shiri J (2019) Study of the spatial distribution of groundwater quality using soft computing and geostatistical models. Ish J Hydraul Eng 25(2):232–238. CrossRefGoogle Scholar
  38. Matheron G (1963) Principles of geostatistics. Econ Geol 58:1246–1266CrossRefGoogle Scholar
  39. Niu Y, Yin S, Liu H, Wu W, Li B (2015) Use of geostatistics to determine the spatial variation of groundwater quality: a case study in Beijing’s reclaimed water irrigation area. Pol J Environ Stud 24:611–618Google Scholar
  40. Noshadi M, Sepaskhah AR (2005) Application of geostatistics for potential evapotranspiration estimation. Iran J Sci Technol Trans B Eng 29:343–355Google Scholar
  41. Ranjbar F, Jalali M (2016) The combination of geostatistics and geochemical simulation for the site-specific management of soil salinity and sodicity. Comput Electron Agric 121:301–312CrossRefGoogle Scholar
  42. Razmkhah H, Abrishamchi A, Torkian A (2010) Evaluation of spatial and temporal variation in water quality by pattern recognition techniques: a case study on Jajrood River (Tehran, Iran). J Environ Manag 91:852–860CrossRefGoogle Scholar
  43. Saibi H, Semmar A, Mesbah M, Ehara S (2009) Variographic analysis of water table data from the Oued-Souf phreatic aquifer, northeastern part of the Algerian Sahara. Arab J Geosci 2:83–93CrossRefGoogle Scholar
  44. Samsonova VP, Meshalkina JL, Blagoveschensky YN, Yaroslavtsev AM, Stoorvogel JJ (2018) The role of positional errors while interpolating soil organic carbon contents using satellite imagery. Precis Agric 19(6):1085–1099. CrossRefGoogle Scholar
  45. Scarpelli M, Eickhoff J, Cuna E, Perlman S, Jeraj R (2018) Optimal transformations leading to normal distributions of positron emission tomography standardized uptake values. Phys Med Biol 63:35021. CrossRefGoogle Scholar
  46. Shahabi M, Jafarzadeh AA, Neyshabouri MR, Ghorbani MA, Kamran KV (2016) Spatial modeling of soil salinity using multiple linear regression, ordinary kriging and artificial neural network methods. Arch Agron Soil Sci 63:151–160CrossRefGoogle Scholar
  47. Shi Z, Wang G (2017) Evaluation of the permeability properties of the Xiaojiang fault zone using hot springs and water wells. Geophys J Int 209:1526–1533CrossRefGoogle Scholar
  48. Shlomi S, Michalak AM (2007) A geostatistical framework for incorporating transport information in estimating the distribution of a groundwater contaminant plume. Water Resour Res 50:259–268Google Scholar
  49. Theodossiou N, Latinopoulos P (2006) Evaluation and optimisation of groundwater observation networks using the Kriging methodology. Environ Model Softw 21:991–1000CrossRefGoogle Scholar
  50. Tran GT, Oliver KIC, Holden PB, Edwards NR, Sóbester A, Challenor P (2019) Multi-level emulation of complex climate model responses to boundary forcing data. Clim Dyn 52:1505–1531. CrossRefGoogle Scholar
  51. Uyan M, Cay T (2013) Spatial analyses of groundwater level differences using geostatistical modeling. Environ Ecol Stat 20:633–646CrossRefGoogle Scholar
  52. Wang S, Wu W, Liu F, Yin S, Bao Z, Liu H (2015) Spatial distribution and migration of nonylphenol in groundwater following long-term wastewater irrigation. J Contam Hydrol 177–178:85–92CrossRefGoogle Scholar
  53. WHO (2004) Guidelines for drinking water quality, 3rd edn. World Health Organization, GenevaGoogle Scholar
  54. Xiao Y, Gu X, Yin S, Shao J, Cui Y, Zhang Q, Niu Y (2016) Geostatistical interpolation model selection based on ArcGIS and spatio-temporal variability analysis of groundwater level in piedmont plains, northwest China. SpringerPlus 5:1–15CrossRefGoogle Scholar
  55. Xiao Y, Gu X, Yin S, Pan X, Shao J, Cui Y (2017) Investigation of geochemical characteristics and controlling processes of groundwater in a typical long-term reclaimed water use area. Water 9:800. CrossRefGoogle Scholar
  56. Xiao Y, Shao J, Frape S, Cui Y, Dang X, Wang S, Ji Y (2018) Groundwater origin, flow regime and geochemical evolution in arid endorheic watersheds: a case study from the Qaidam Basin, Northwest China. Hydrol Earth Syst Sci 22:4381–4400. CrossRefGoogle Scholar
  57. Zheng Z, Zhang F, Ma F, Chai X, Zhu Z, Shi J, Zhang S (2009) Spatiotemporal changes in soil salinity in a drip-irrigated field. Geoderma 149:243–248CrossRefGoogle Scholar

Copyright information

© Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2019

Authors and Affiliations

  1. 1.School of Renewable EnergyNorth China Electric Power UniversityBeijingChina
  2. 2.Faculty of Geosciences and Environmental EngineeringSouthwest Jiaotong UniversityChengduChina
  3. 3.School of Geographic ScienceNantong UniversityNantongChina
  4. 4.Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological ScienceShijiazhuangChina
  5. 5.Beijing Water Science and Technology InstituteBeijingChina
  6. 6.Beijing Daxing Water Resources BureauBeijingChina

Personalised recommendations