Multivariate statistical techniques and geostatistical methods are among the important tools used in surface water quality management. They are widely used in interpreting data, identifying the pollution sources, understanding the spatial variation of parameters, and determining the places of monitoring stations. Therefore, in this study, spatial variation of water quality and pollutants in the Anzali Wetland water (Iran) was evaluated using multivariate statistical and Kriging methods. The values of different water quality parameters measured in six stations in the wetland water were subjected to cluster analysis (CA) and principal component analysis (PCA). Cluster analysis reduced the number of stations from six to four. The results of PCA showed that industrial and agricultural pollution sources could be responsible for the Anzali Wetland water quality. Then, the spatial variation maps of the PCA scores were generated using Kriging geostatistical method in the geographical information system (GIS) to investigate the pollution sources affecting the wetland parts. These maps illustrated that a great part of the wetland body was under the effect of agricultural sources, while the industrial sources affected the outlet and central parts. Finally, a comparison between two models (multiple linear regression (MLR) and Kriging) was made to assess their ability in predicting water quality parameters in the study area. The results showed the improvement of prediction using MLR, which was by 25%-97%, compared with Kriging. The results of the present study can be effectively used in the planning and implementation of future monitoring networks in the Anzali Wetland and other similar aquatic systems.
Keywords: Anzali Wetland; Cluster analysis; GIS; Kriging; Multiple regression; Pollution sources; Principal component analysis.