Pattern recognition analysis and classification modeling of selenium-producing areas

Abstract

Established chemometric and geochemical techniques were applied to water quality data from 23 National Irrigation Water Quality Program (NIWQP) study areas in the Western United States. These techniques were applied to the NIWQP data set to identify common geochemical processes responsible for mobilization of selenium and to develop a classification model that uses major-ion concentrations to identify areas that contain elevated selenium concentrations in water that could pose a hazard to water fowl. Pattern recognition modeling of the simple-salt data computed with the SNORM geochemical program indicate three principal components that explain 95% of the total variance. A three-dimensional plot of PC 1, 2 and 3 scores shows three distinct clusters that correspond to distinct hydrochemical facies denoted as facies 1, 2 and 3. Facies 1 samples are distinguished by water samples without the CaCO 3 simple salt and elevated concentrations of NaCI, CaSO 4 , MgSO 4 and Na 2 SO 4 simple salts relative to water samples in facies 2 and 3. Water samples in facies 2 are distinguished from facies 1 by the absence of the MgSO4 simple salt and the presence of the CaCO 3 simple salt. Water samples in facies 3 are similar to samples in facies 2, with the absence of both MgSO 4 and CaSO 4 simple salts. Water samples in facies 1 have the largest selenium concentration (10 μg 1 -1 ), compared to a median concentration of 2.0 μg 1 -1 and less than 1.0 μg 1 -1 for samples in facies 2 and 3. A classification model using the soft independent modeling by class analogy (SIMCA) algorithm was constructed with data from the NIWQP study areas. The classification model was successful in identifying water samples with a selenium concentration that is hazardous to some species of water-fowl from a test data set comprised of 2,060 water samples from throughout Utah and Wyoming. Application of chemometric and geochemical techniques during data synthesis analysis of multivariate environmental databases from other national-scale environmental programs such as the NIWQP could also provide useful insights for addressing 'real world' environmental problems.