Groundwater quality assessment and optimization of monitored wells using multivariate geostatistical techniques in Amol-Babol Plain, Iran

Groundwater plays an essential role for human, animal, and plant life as well as an indispensable resource for the economy, especially in arid and semi-arid region. Appropriate monitoring strategies are required to assess the conditions of groundwater quality in the aquifer system, prevention of a potential threat to human health, and measurement of the efficiency of water protection. The main aim of this study is to assess and redesign the information-cost-effective groundwater monitoring network using geostatistical techniques in Amol-Babol Plain, Iran. The integration of multivariate statistical methods with geostatistical interpolation techniques revealed that salinity and total and faecal coliforms as time independent variables and hardness as a time dependent variable influenced the groundwater quality in the study area. The graphical geochemical analyses justified that the groundwater types vary from fresh water type in the west and south sides, to brackish-saline water type in central and eastern sides, and to saline water on the north-eastern area. Hydrogeochemical investigation revealed that evaporation/precipitation and dissolution of carbonate minerals as dominant factors, which control groundwater salinity and hardness in the study area, respectively. Since the agricultural lands cover more than 80% of the plain, the newly devised GIS-Index integration approach was proposed in order to identify the suitability of groundwater for irrigation usage and to determine suitable zones for irrigation activities based on the irrigation water quality index (IWQ) and hydrogeological factors. The index approach shows that more than 90% of the total study area has good to excellent suitability condition for irrigation purpose. Groundwater quality assessment based on the data obtained from arbitrary sampling wells might be presented redundant or shortage of information. Therefore, monitoring network wells should be optimized in information-cost-effective way, based on the current groundwater quality data and vulnerability of aquifer to contamination. DRASTIC model was applied as a vulnerability assessment method based on the physical environmental aquifer parameters for assessing potential risk zone of aquifer to contamination, which showed more than 88% of the total area was classified as low to moderate risk to pollutant. A new optimization approach was proposed for redesign monitoring network wells using optimization algorithm based on the vulnerability of aquifer to contaminations, estimation error of sampling wells, nearest distance between wells, and source of contamination in the study area. Application of mass estimation error revealed that 100 and 74 sampling wells are suitable scenarios for monitoring natural and anthropogenic contaminant, respectively. Combination of the selected scenarios in GIS showed that contaminant mass detection capacity of around 86% can be obtained from 114 sampling wells, instead of 154 initial sampling wells.

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