Faculty Publications
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Item A comprehensive assessment of increased iron ore production on the environment - A case study(2011) Thimmaiah, S.A.; Rao, Y.; Murthy, C.H.S.N.There has been a significant increase in iron ore production in the Bellary- Hospet-Sandur sector of Karnataka, India due to sudden increase in the demand of iron ore from other countries as well as by local steel plants set up in the region. An attempt has been made in this paper to study the effect of increase in the iron ore production on various environmental parameters like air, water, soil and noise. For this purpose a study area of 10 Km radius was taken and the various environmental parameters were monitored before and after increase in iron ore production in the region. Study shows that there is no significant change in the air quality in terms of SPM, RPM, SO2 and NOx in the region. In many locations, there is decrease in the concentrations of these parameters. This is mainly due to improvement of roads by asphalting, effective covering of iron ore trucks by tarpaulin, awareness among mine owners about the protection of environment by following various pollution control measures, dust suppression measures on the public road using water sprinklers and stringent monitoring of the environmental protective measures by various regulatory authorities. The concentration of SO2 and NOx was found to increase due to increase in traffic by movement of tippers in the public/village road. The deployment of heavy earth moving machinery at mine site also contributed for increase of SO2 and NOx. The surface water quality parameters were found to be within the acceptable limits in the study area. There is no possibility of disturbing/altering ground water table due to mining operations as the mining is being carried out on hill top which is above the general ground level. In agricultural soil, except that of potassium and electrical conductivity, variations in other parameters are insignificant as the agricultural lands are located 2 to 3 Kms away from the active mining area. Increase in noise level is found at most of the locations of the study area. Therefore, serious attempts should be made by mine owners as well as statutory bodies to reduce the sound level at various locations for increasing the quality of life in these locations in terms of sound level. © 2011 CAFET-INNOVA technical society. All right reserved.Item Source apportionment studies on particulate matter (PM10 and PM2.5) in ambient air of urban Mangalore, India(Academic Press, 2018) Kalaiarasan, G.; Mohan Balakrishnan, R.M.; Sethunath, N.A.; Manoharan, S.Particulate matter (PM10 and PM2.5) samples were collected from six sites in urban Mangalore and the mass concentrations for PM10 and PM2.5 were measured using gravimetric technique. The measurements were found to exceed the national ambient air quality standards (NAAQS) limits, with the highest concentration of 231.5 ?g/m3 for PM10 particles at Town hall and 120.3 ?g/m3 for PM2.5 particles at KMC Attavar. The elemental analysis using inductively coupled plasma optical emission spectrophotometer (ICPOES) revealed twelve different elements (As, Ba, Cd, Cr, Cu, Fe, Mg, Mn, Mo, Ni, Sr and Zn) for PM10 particles and nine different elements (Ba, Cd, Cr, Cu, Fe, Mn, Ni, Pb, Sr and Zn) for PM2.5 particles. Similarly, ionic composition of these samples measured by ion chromatography (IC) divulged nine different ions (F?, Cl?, NO3 ?, PO4 3?, SO4 2?, Na+, K+, Mg2+ and Ca2+) for PM10 particles and ten different ions (F?, Cl?, NO3 ?, PO4 3?, SO4 2?, Na+, NH4 +, K+, Mg2+ and Ca2+) for PM2.5 particles. The source apportionment study of PM10 and PM2.5 for urban Mangalore in accordance with these six sample sites using chemical mass balance model (CMBv8.2) revealed nine and twelve predominant contributors for both PM10 and PM2.5, respectively. The highest contributor of PM10 was found to be paved road dust followed by diesel and gasoline vehicle emissions. Correspondingly, PM2.5 was found to be contributed mainly from two-wheeler vehicle emissions followed by four-wheeler and heavy vehicle emissions (diesel vehicles). The current study depicts that the PM10 and PM2.5 in ambient air of Mangalore region has 70% of its contribution from vehicular emissions (both exhaust and non-exhaust). © 2018 Elsevier LtdItem The impact of spatiotemporal patterns of land use land cover and land surface temperature on an urban cool island: a case study of Bengaluru(Springer International Publishing, 2019) Govind, N.R.; Ramesh, H.In most of the developing countries, man-made developments in the environment have led to the growing demand to contextualize the land use land cover (LULC) changes and land surface temperature (LST) variations. Due to the modification in the surface properties of the cities, a difference in energy balance between the cities and its nonurban surroundings is observed. The aim of this study is to analyze the spatial and temporal patterns of LULC and LST and its interrelationship in Bengaluru urban district, India, during the period from 1989 to 2017 using remote sensing data. Intensity analysis was performed for the interval to analyze the LULC change and identify the driving forces. The impact of LULC change on LST was assessed using hot spot analysis (Getis–Ord Gi* statistics). The results of this study show that (a) dominant LULC change experienced is the increase in urban area (approximately 40%) and the rate of land use change was faster in the time period 1989–2001 than 2001–2017; (b) the major transition witnessed is from barren and agricultural land to urban; (c) over the period of 28 years, LST patterns for different land use classes exhibit an increasing trend with an overall increase of approximately 6 °C and the mean LST of urban area increased by about 8 °C; (d) LST pattern change can be effectively analyzed using hot spot analysis; and (e) as the urban expansion occurs, the cold spots have increased, and it is mainly clustered in the urban area. It confirms the presence of an urban cool island effect in Bengaluru urban district. The findings of this work can be used as a scientific basis for the sustainable development and land use planning of the region in the future. © 2019, Springer Nature Switzerland AG.Item Occurrence and environmental risks of nonsteroidal anti-inflammatory drugs in urban wastewater in the southwest monsoon region of India(Springer, 2020) Thalla, A.K.; Vannarath, A.S.Municipal wastewater treatment plants (MWWTPs) are considered to reduce the amount of pollutants that enter water reservoirs as a result of wastewater disposal. An assessment of the occurrence and removal of pharmaceutical compounds, mainly nonsteroidal anti-inflammatory drugs (NSAIDs), in wastewater from the Kavoor MWWTP (southwest monsoon region), India, is presented in this paper. The performance of the MWWTP was monitored in the summer (May) and monsoon (September) periods. The highest inlet concentrations of diclofenac, naproxen, ibuprofen, ketoprofen, and acetylsalicylic acid in the wastewater were observed in May and were 721.37, 2132.48, 2109.875, 2747.29, and 2213.36 ?g/L, respectively. The ketoprofen content was found to be higher than that of other NSAIDs in the influent in both seasons, whereas the diclofenac content was found to be the lowest. The removal efficiency (RE) of the target NSAIDs in the Kavoor secondary treatment plant varied from 81.82–98.92% during the summer season. During the monsoon season, the influent NSAID concentration level dropped, probably because of infiltration in old sewer pipes. In addition, a 100% RE was achieved for all the target NSAIDs in the wastewater of the MWWTP. The results showed that secondary treatment plants have the potential to remove NSAID compounds from municipal sewage with consistent performance. The environmental hazards caused by the accumulation of such compounds in water reservoirs are due to open discharge. The environmental risk levels of these compounds were also studied by the environmental risk assessment (ERA) using the European Agency for Evaluation of Medicines approach. © 2020, Springer Nature Switzerland AG.Item Modelling stream flow and soil erosion response considering varied land practices in a cascading river basin(Academic Press, 2020) Venkatesh, K.; Ramesh, H.; Das, P.[No abstract available]Item Multivariate statistics and water quality index (WQI) approach for geochemical assessment of groundwater quality—a case study of Kanavi Halla Sub-Basin, Belagavi, India(Springer editorial@springerplus.com, 2020) B Patil, V.B.; Pinto, S.M.; Govindaraju, T.; Virupaksha, V.S.; Bhat, V.; Lokesh, K.N.Groundwater quality analysis has become essentially important in the present world scenario. In recent years, advanced technologies have replaced the traditional ones which are being helpful in simplifying the complex works. In this study, multivariate statistical analysis is carried out with the help of SPSS software for 45 groundwater samples of Kanavi Halla Sub-Basin (KHSB). The quality of groundwater is determined for various parameters which were analyzed and their concentration is correlated with other parameters using correlation matrix. The PCA technique is applied on water quality parameters, from which four components are extracted with 80.28% total variance. The extracted components suggest that the sources behind the higher loadings of each factor are by geological, agricultural, rainfall, domestic wastewater and industrial activities. Results of the Kaiser–Meyer–Olkin and Bartlett’s test conducted have value of 0.659 which is greater than the standard value (0.5). Based on water quality index (WQI), it was noticeably depicted that 2/3rd of the KHSB groundwater quality falls under poor to very poor condition, and hardly 26% of groundwater available is portable. Thus, this study contributes the effective use of multivariate statistics and WQI analysis for groundwater quality. It helps in understanding the hydro-geochemistry of the groundwater and also aids in minimizing the larger set of data into smaller set with effective interpretation. © 2020, Springer Nature B.V.Item Exploring the relationship between LST and land cover of Bengaluru by concentric ring approach(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2020) Govind, N.R.; Ramesh, H.The present study aims at investigating the impact of land cover features in enhancing or mitigating Land Surface Temperature (LST) in a semi-arid tropical metropolitan city of Bengaluru, India. Spatial distribution of LST and land cover types of the area were examined in the circumferential direction, and the contribution of land cover classes on LST was studied over 28 years. Urban growth and LST were modelled using Landsat and MODIS data for the years 1989, 2001, 2005 and 2017 based on the concentric ring approach. The study provides an efficient methodology for modelling and parameterisation of LST and urban growth by fitting an inverse S-curve into urban density (UD) and mean LST data. In addition, multiple linear regression models which could effectively predict the LST distribution based on land cover types were developed for both day and night time. Based on the analysis of remotely sensed data for LST, it is observed that over the years, urban core area has increased circumferentially from 5 to 10 km, and the urban growth has spread towards outskirts beyond 15 km from the city centre. As urban expansion occurs, the area under the study experiences an expansive cooling effect during day time; at night, an expansive heating effect is experienced in accordance with the growth in UD in the suburban area and outskirts. The regression models that were developed have relatively high accuracy with R2 value of more than 0.94 and could explain the relationship between LST and land cover types. The study also revealed that there exists a negative correlation between urban, vegetation, water body and LST during day time while a positive correlation is observed during night. Thus, this study could assist urban planners and policymakers in understanding the scientific basis for urban heating effect and predict LST for the future development for implementing green infrastructure. The proposed methodology could be applied to other urban areas for quantifying the distribution of LST and different land cover types and their interrelationships. © 2020, Springer Nature Switzerland AG.Item Spatial and temporal variations in river water quality of the Middle Ganga Basin using unsupervised machine learning techniques(Springer Science and Business Media Deutschland GmbH, 2020) Krishnaraj, A.; Deka, P.C.In this study, cluster analysis (CA), principal component analysis (PCA) and correlation were applied to access the river water quality status and to understand spatiotemporal patterns in the Ganga River Basin, Uttara Pradesh. The study was carried out using data collected over 12 years (2005–2017) regarding 20 water quality parameters (WQPs) covering spatially from upstream to downstream Ankinghat to Chopan, respectively (20 stations under CWC Middle Ganga Basin). The temporal variations of river water quality were established using the Spearman non-parametric correlation coefficient test (Spearman R). The highest Spearman R (?0.866) was observed for temperature with the season and a very significant p value of (0.0000). The parameters EC, pH, TDS, T, Ca, Cl, HCO3, Mg, NO2 + NO3, SiO2 and DO had a significant correlation with the season (p < 0. 05). K-means clustering algorithm grouped the stations into four different clusters in dry and wet seasons. Based on these clusters, box and whisker plots were generated to study individual clusters in different seasons. The spatial patterns of river WQ on both seasons were examined. PCA was applied to screen out the most significant water quality parameters due to spatial and seasonal variations out of a large data set. It is a data reduction process and a more conventional way of speeding up any machine learning algorithms. A reduced number of three principal components (PCs) were drawn for 20 WQPs with an explained total variance of 75.84% and 80.57% is observed in the dry and wet season, respectively. The parameters DO, EC_ Gen, P-Tot, SO4 are the most dominating parameters with PC score more than 0.8 in the dry season; similarly, TDS, K, COD, Cl, Na, SiO2 in the wet season. The different components of water quality monitoring, such as spatiotemporal patterns, scrutinize the most relevant water quality parameters and monitoring stations are well addressed in this study and could be used for the better management of the Ganga River Basin. © 2020, Springer Nature Switzerland AG.Item Effects of land use and climate change on water scarcity in rivers of the Western Ghats of India(Springer Science and Business Media Deutschland GmbH, 2021) Sharannya, T.M.; Venkatesh, K.; Mudbhatkal, A.; Muthuvel, M.; Mahesha, A.This paper assesses the long-term combined effects of land use (LU) and climate change on river hydrology and water scarcity of two rivers of the Western Ghats of India. The historical LU changes were studied for four decades (1988–2016) using the maximum likelihood algorithm and the long-term LU (2016–2075) was estimated using the Dyna-CLUE prediction model. Five General Circulation Models (GCMs) were utilized to assess the effects of climate change (CC) and the Soil and Water Assessment Tool (SWAT) model was used for hydrological modeling of the two river catchments. To characterize granular effects of LU and CC on regional hydrology, a scenario approach was adopted and three scenarios depicting near-future (2006–2040), mid-future (2041–2070), and far-future (2071–2100) based on climate were established. The present rate of LU change indicated a reduction in forest cover by 20% and an increase in urbanized areas by 9.5% between 1988 and 2016. It was estimated that forest cover in the catchments may be expected to halve compared to the present-day LU (55% in 2016 to 23% in 2075), along with large-scale conversion to agricultural lands (13.5% in 2016 to 49.5% in 2075). As a result of changes to LU and forecasted climate, it was found that rivers in the Western Ghats of India might face scarcity of fresh water in the next two decades until the year 2040. However, because of large-scale LU conversion toward the year 2050, streamflow in rivers might increase as high as 70.94% at certain times of the year. Although an increase in streamflow is perceived favorable, the streamflow changes during summer and winter may be expected to affect the cropping calendar and crop yield. The changes to streamflow were also linked to a 4.2% increase in ecologically sensitive wetlands of the Aghanashini river catchment. © 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG.Item Remote sensing and machine learning based framework for the assessment of spatio-temporal water quality in the Middle Ganga Basin(Springer Science and Business Media Deutschland GmbH, 2022) Krishnaraj, A.; Honnasiddaiah, R.Understanding the dynamics of water quality in any water body is vital for the sustainability of our water resources. Thus, investigating spatio-temporal changes of dominant water quality parameters (WQPs) in any study is indeed critical for proposing the appropriate treatment for the water bodies. Traditionally, concentrations of WQPs have been measured through intensive fieldwork. Additionally, many studies have attempted to retrieve concentrations of WQPs from satellite images using regression-based methods. However, the relationship between WQPs and satellite data is complex to be modeled accurately by using simple regression-based methods. Our study attempts to develop a machine learning model for mapping the concentrations of dominant optical and non-optical WQPs such as electrical conductivity (EC), pH, temperature (Temp), total dissolved solids (TDS), silicon dioxide (SiO2), and dissolved oxygen (DO). In this context, a remote sensing framework based on the extreme gradient boosting (XGBoost) and multi-layer perceptron (MLP) regressor with optimized hyper parameters (HPs) to quantify concentrations of different WQPs from the Landsat-8 satellite imagery is developed. We evaluated six years of satellite data stretching spatially from upstream to downstream Ankinghat to Chopan (20 stations under Central Water Commission (CWC), Middle Ganga Basin) for characterizing the trends of dominant physico-chemical WQPs across the four clusters identified in our previous study. Through the developed XGBoost and MLP regression models between measured WQPs and the reflectance of the pixels corresponding to the sampling stations, a significant coefficient of determination (R2) in the range of 0.88–0.98 for XGBoost and 0.72–0.97 for MLP were generated, with bands B1–B4 and their ratios more consistent. Indeed, these findings indicate that from a small number of in-situ measurements, we can develop reliable models to estimate the spatio-temporal variations of physico-chemical and biological WQPs. Therefore, models generated from Landsat-8 could facilitate the environmental, economic, and social management of any waterbody. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.