Tahoora Sheikhy Narany

In recent years, groundwater quality has become a global concern due to its effect on human life and natural ecosystems. To assess the groundwater quality in the Amol–Babol Plain, a total of 308 water samples were collected during wet and dry seasons in 2009. The samples were analysed for their physico-chemical and biological constituents. Multivariate statistical analysis and geostatistical techniques were applied to assess the spatial and temporal variabilities of groundwater quality and to identify the main factors and sources of contamination. Principal component analysis (PCA) revealed that seven factors explained around 75 % of the total variance, which highlighted salinity, hardness and biological pollution as the dominant factors affecting the groundwater quality in the Plain. Two-way analysis of variance (ANOVA) was conducted on the dataset to evaluate the spatio-temporal variation. The results showed that there were no significant temporal variations between the two seasons, which explained the similarity between six component factors in dry and wet seasons based on the PCA results. There are also significant spatial differences (p > 0.05) of the parameters under study, including salinity, potassium, sulphate and dissolved oxygen in the plain. The least significant difference (LSD) test revealed that groundwater salinity in the eastern region is significantly different to the central and western side of the study area. Finally, multivariate analysis and geostatistical techniques were combined as an effective method for demonstrating the spatial structure of multivariate spatial data. It was concluded that multiple natural processes and anthropogenic activities were the main sources of groundwater salinization, hardness and microbiological contamination of the study area.

The main aim of monitoring wells is to assess the conditions of groundwater quality in the aquifer system. An inappropriate distribution of sampling wells could produce insufficient or redundant data concerning groundwater quality. An optimal selection of representative monitoring well locations can be obtained by considering the natural and anthropogenic potential of pollution sources; the hydrogeological setting; and assessment of any existing data regarding monitoring networks. The main objective of this paper was to develop a new approach to identifying areas with a high risk of nitrate pollution for the Amol-Babol Plain, Iran. The indicator kriging method was applied to identify regions with a high probability of nitrate contamination using data obtained from 147 monitoring wells. The US-EPA DRASTIC method was then used in a GIS environment to assess groundwater vulnerability to nitrate contamination, and combined with data concerning the distribution of sources to produce a risk map. In the study area, around 3% of the total area has a strong probability of exceeding the nitrate threshold and a high–moderate risk of pollution, but is not covered adequately by sampling wells. However, the number of monitoring wells could be reduced in most parts of the study area to minimize redundant data and the cost of monitoring.