Faculty Publications

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Identification and Apportionment of Pollution Sources to Groundwater Quality
(Springer Basel info@birkhauser-science.com, 2016) Gulgundi, M.S.; Shetty, A.
Characterizing groundwater quality and apportionment of pollution sources to groundwater pollution is important for managing water resources effectively. Owing to rapid industrialization and population growth in Bengaluru city, the groundwater quality is getting deteriorated. Receptor modeling by Multi-Linear Regression of the Absolute Principal Component Scores (APCS-MLR) has been used to evaluate the source apportionment of groundwater pollution in order to recognize and quantify the pollution sources. Groundwater quality data measured for pre-monsoon and post-monsoon in the year 2014, comprising 14 physico-chemical parameters from 68 sites distributed across the study area, have been used. Principal component analysis identified four factors explaining 79.2 % of the total variance. Receptor modeling using APCS-MLR provided apportionment of different sources responsible for the groundwater quality along with percentage contribution of the recognized sources to each parameter. Results revealed that most of the variables were primarily affected by rock water interactions, seepage of sewage and industrial effluent. It was also found that few parameters gained significant contribution from the unidentified sources. Finally, the model performance was evaluated based on the ratio of estimated mean to measured mean (E/M). It was found that except for Fe with (E/M) ratio as high as 7.1, the model showed moderate strength with (E/M) values ranging from 0.51 to 2.83 of all the other parameters. © 2016, Springer International Publishing Switzerland.
Groundwater quality assessment of urban Bengaluru using multivariate statistical techniques
(Springer Verlag, 2018) Gulgundi, M.S.; Shetty, A.
Groundwater quality deterioration due to anthropogenic activities has become a subject of prime concern. The objective of the study was to assess the spatial and temporal variations in groundwater quality and to identify the sources in the western half of the Bengaluru city using multivariate statistical techniques. Water quality index rating was calculated for pre and post monsoon seasons to quantify overall water quality for human consumption. The post-monsoon samples show signs of poor quality in drinking purpose compared to pre-monsoon. Cluster analysis (CA), principal component analysis (PCA) and discriminant analysis (DA) were applied to the groundwater quality data measured on 14 parameters from 67 sites distributed across the city. Hierarchical cluster analysis (CA) grouped the 67 sampling stations into two groups, cluster 1 having high pollution and cluster 2 having lesser pollution. Discriminant analysis (DA) was applied to delineate the most meaningful parameters accounting for temporal and spatial variations in groundwater quality of the study area. Temporal DA identified pH as the most important parameter, which discriminates between water quality in the pre-monsoon and post-monsoon seasons and accounts for 72% seasonal assignation of cases. Spatial DA identified Mg, Cl and NO3 as the three most important parameters discriminating between two clusters and accounting for 89% spatial assignation of cases. Principal component analysis was applied to the dataset obtained from the two clusters, which evolved three factors in each cluster, explaining 85.4 and 84% of the total variance, respectively. Varifactors obtained from principal component analysis showed that groundwater quality variation is mainly explained by dissolution of minerals from rock water interactions in the aquifer, effect of anthropogenic activities and ion exchange processes in water. © 2018, The Author(s).
Impact of Atmospheric Correction Methods Parametrization on Soil Organic Carbon Estimation Based on Hyperion Hyperspectral Data
(MDPI, 2022) Mruthyunjaya, P.; Shetty, A.; Umesh, P.; Gomez, C.
Visible Near infrared and Shortwave Infrared (VNIR/SWIR, 400–2500 nm) remote sensing data is becoming a tool for topsoil properties mapping, bringing spatial information for environmental modeling and land use management. These topsoil properties estimates are based on regression models, linking a key topsoil property to VNIR/SWIR reflectance data. Therefore, the regression model’s performances depend on the quality of both topsoil property analysis (measured on laboratory over-ground soil samples) and Bottom-of-Atmosphere (BOA) VNIR/SWIR reflectance which are retrieved from Top-Of-Atmosphere radiance using atmospheric correction (AC) methods. This paper examines the sensitivity of soil organic carbon (SOC) estimation to BOA images depending on two parameters used in AC methods: aerosol optical depth (AOD) in the FLAASH (Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes) method and water vapor (WV) in the ATCOR (ATmospheric CORrection) method. This work was based on Earth Observing-1 Hyperion Hyperspectral data acquired over a cultivated area in Australia in 2006. Hyperion radiance data were converted to BOA reflectance using seven values of AOD (from 0.2 to 1.4) and six values of WV (from 0.4 to 5 cm), in FLAASH and ATCOR, respectively. Then a Partial Least Squares regression (PLSR) model was built from each Hyperion BOA data to estimate SOC over bare soil pixels. This study demonstrated that the PLSR models were insensitive to the AOD variation used in the FLAASH method, with R2cv and RMSEcv of 0.79 and 0.4%, respectively. The PLSR models were slightly sensitive to the WV variation used in the ATCOR method, with R2cv ranging from 0.72 to 0.79 and RMSEcv ranging from 0.41 to 0.47. Regardless of the AOD values, the PLSR model based on the best parametrization of the ATCOR model provided similar SOC prediction accuracy to PLSR models using the FLAASH method. Variation in AOD using the FLAASH method did not impact the identification of bare soil pixels coverage which corresponded to 82.35% of the study area, while a variation in WV using the ATCOR method provided a variation of bare soil pixels coverage from 75.04 to 84.04%. Therefore, this work recommends (1) the use of the FLAASH AC method to provide BOA reflectance values from Earth Observing-1 Hyperion Hyperspectral data before SOC mapping or (2) a careful selection of the WV parameter when using ATCOR. © 2022 by the authors.

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