Department of Civil Engineering
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Item Heavy metal remediation using chelator-enhanced washing of municipal solid waste compost based on spectroscopic characterization(Springer, 2023-04) Singhal, Anupam; Srinivas, RallapalliDue to high metal toxicity, mixed municipal solid waste (MSW) compost is difficult to use. This study detected the presence of heavy metals (Cd, Cu, Pb, Ni, and Zn) in MSW compost through mineralogical analysis using X-ray diffraction (XRD) and performed topographical imaging and elemental mapping using a scanning electron microscope and energy dispersive X-ray analysis (SEM–EDX). Ethylenediaminetetraacetic acid (EDTA), a typical chelator, is tested to remove heavy metals from Indian MSW compost (New Delhi and Mumbai). It deals with two novel aspects, viz., (i) investigating the influence of EDTA-washing conditions, molarity, dosage, MSW compost-sample size, speed, and contact time, on their metal removal efficiencies, and (ii) maximizing the percentage removal of heavy metals by determining the optimal process control process parameters. These parameters were optimized in a batch reactor utilizing Taguchi orthogonal (L25) array. The optimization showed that the removal efficiencies were 96.71%, 47.37%, and 49.94% for Cd, Pb, and Zn in Delhi samples, whereas 45.55%, 79.52%, 59.63%, 82.31%, and 88.40% for Cd, Cu, Pb, Ni, and Zn in Mumbai samples. Results indicate that the removal efficiency of heavy metals was greatly influenced by EDTA-molarity. Fourier-transform infrared spectroscopy (FTIR) confirmed the presence of hydroxyl group, which aids heavy metal chelation. The results reveal the possibility of EDTA to reduce the hazardous properties of MSW compost.Item Effect of urbanization on the urban lake water quality by using water quality index (WQI)(Elsevier, 2023-07) Srinivas, Rallapalli; Singhal, AnupamLake water serves an efficient source of drinking, irrigation, agriculture, industry, construction, domestic and recreation use for the urban and rural population of developing countries. The paper focuses on the assessment of water quality on the selected lakes which is affected by the speedy development of the city under the sprawl of urbanization and concretization by applying Water Quality Index (WQI) tool. Four lakes, namely Hebbal, Ulsoor, Allasandra and Mahadevapura are selected in the silicon city, Bengaluru for water quality assessment. A total of 10 parameters were taken into consideration, such as pH, turbidity, total alkalinity, total acidity, total phosphorus, COD, BOD, DO, nitrates and total nitrogen from 2 sampling sites depending upon the source of wastewater or sewage discharges. Water samples were collected and prepared for composite samples. These composite samples were examined for their different chemical and physical properties and the results were compared with standard permissible values. The results of WQI of Hebbal Lake (70.89–72.74), Ulsoor Lake (83.44–83.3), Allasandra Lake (54.47–51.84) and Mahadevapura Lake (159.41–155.81) showed that the lakes fall under poor, very poor and unsuitable categories. The results pointed out the anthropogenic activities and entry of untreated sewage into the lake. This confirms the urgent need for regular monitoring of lakes and setting up of certain policies for lake water management.Item Bio-chelate assisted leaching for enhanced heavy metal remediation in municipal solid waste compost(2024-06) Singhal, Anupam; Srinivas, RallapalliMunicipal solid waste compost, the circular economy's closed-loop product often contains excessive amounts of toxic heavy metals, leading to market rejection and disposal as waste material. To address this issue, the study develops a novel approach based on: (i) utilizing plant-based biodegradable chelating agent, l-glutamic acid, N,N-diacetic acid (GLDA) to remediate heavy metals from contaminated MSW compost, (ii) comparative assessment of GLDA removal efficiency at optimal conditions with conventional nonbiodegradable chelator EDTA, and (iii) enhanced pre- and post-leaching to evaluate the mobility, toxicity, and bioavailability of heavy metals. The impact of treatment variables, such as GLDA concentration, pH, and retention time, on the removal of heavy metals was investigated. The process was optimized using response surface methodology to achieve the highest removal effectiveness. The findings indicated that under optimal conditions (GLDA concentration of 150 mM, pH of 2.9, retention time for 120 min), the maximum removal efficiencies were as follows: Cd-90.32%, Cu-81.96%, Pb-91.62%, and Zn-80.34%. This process followed a pseudo-second-order kinetic equation. Following GLDA-assisted leaching, the geochemical fractions were studied and the distribution highlighted Cd, Cu, and Pb's potential remobilization in exchangeable fractions, while Zn displayed integration with the compost matrix. GLDA-assisted leaching and subsequent fractions illustrated transformation and stability. Therefore, this process could be a sustainable alternative for industrial applications (agricultural fertilizers and bioenergy) and social benefits (waste reduction, urban landscaping, and carbon sequestration) as it has controlled environmental footprints. Hence, the proposed remediation strategy, chemically assisted leaching, could be a practical option for extracting heavy metals from MSW compost, thereby boosting circular economy.Item Innovative lake pollution profiling: unveiling pollutant sources through advanced multivariate clustering techniques(Springer, 2024-07) Singhal, Anupam; Srinivas, RallapalliIn many developed and developing nations, lakes are the primary source of drinking water. In the current scenario, due to rapid mobilization in anthropogenic activities, lakes are becoming increasingly contaminated. Such practices not only destroy lake ecosystems but also jeopardize human health through water-borne diseases. This study employs advanced hierarchical clustering through multivariate analysis to establish a novel method for concurrently identifying significantly polluted lakes and critical pollutants. A systematic approach has been devised to generate rotating component matrices, dendrograms, monoplots, and biplots by combining R-mode and Q-mode analyses. This enables the identification of contaminant sources and their grouping. A case study analyzing five lakes in Bengaluru, India, has been conducted to demonstrate the effectiveness of the proposed methodology. Additionally, one pristine lake from Jammu & Kashmir, India, has been included to validate the findings from the aforementioned five lakes. The study explored correlations among various physical, chemical, and biological characteristics such as temperature, pH, dissolved oxygen, conductivity, nitrates, biological oxygen demand (BOD), fecal coliform (FC), and total coliform (TC). Critical contaminants forming clusters included conductivity, nitrates, BOD, TC, and FC. Factor analysis identified four primary components that collectively accounted for 85% of the overall variance. Following identification of pollution hotspots, the study recommends source-based pollution control and integrated watershed management, which could significantly reduce lake pollution levels. Continuous monitoring of lake water quality is essential for identifying actual contaminant sources. These findings provide practical recommendations for maximizing restoration efforts, enforcing regulations on pollutant sources, and improving water quality conditions to ensure sustainable development of lakes.Item Attention-enabled Deep Neural Network for Enhancing UAV-Captured Pavement Imagery in Poor Visibility(IEEE, 2023) Singh, Ajit Pratap; Srinivas, Rallapalli; Narang, PratikIntegrating Unmanned Aerial Vehicle (UAV) technology with Artificial Intelligence AI and Computer Vision has revolutionized asset management, particularly pavement health monitoring. However, current AI-based methods often struggle in low-visibility scenarios, limiting their effectiveness. To address this, we present a novel end-to-end deep learning pipeline that detects image degradation using an efficient Attention mechanism and performs subsequent enhancement. This algorithm can be seamlessly integrated into drones or used for post-processing of pavement imagery. Its efficiency allows for scalability, making it a valuable tool for downstream road health monitoring tasks, such as cost estimation for road repairs. Our approach achieves mean accuracies of 93.34% with a mean inference time of 0.154 sec., demonstrating its efficacy.Item Effect of varying hydrologic regime on seasonal total maximum daily loads (TDML) in an agricultural watershed(Elsevier, 2024-02) Srinivas, Rallapalli; Singh, Ajit Pratap; Goonetilleke, AshanthaRising hypoxia due to the eutrophication of riverine ecosystems is primarily caused by the transport of nutrients. The majority of existing TMDL models cannot be efficienty applied to represent nutrient concentrations in riverine ecosystems having varying flow regimes due to seasonal differences. Accurate TMDL assessment requires nutrient loads and suspended matter estimation under varying flow regimes with minimal uncertainty. Though a large database can enhance accuracy, it can be resource intensive. This study presents the design of an innovative modeling strategy to optimize the use of existing datasets to effectively represent streamflow-load dynamics while minimizing uncertainty. The study developed an approach to assess TMDLs using six different flux models and kriging techniques (i) to enhance the accuracy of nutrient load estimation under different hydrologic regimes (flow stratifications) and (ii) to derive an optimal modeling strategy and sampling scheme for minimizing uncertainty. The flux models account for uncertainty in load prediction across varying flow strata, and the deployment of multiple load calculation procedures. Further, the proposed flux approach allows the determination of load exceedance under different TMDL scenarios aimed at minimizing uncertainty to achieve reliable load predictions. The study employed a 10-year dataset (2009–2018) consisting of daily flow data (m3/sec) and weekly data (mg/L) for nitrogen (N), phosphorus (P) and total suspended solids (TSS) concentrations in three distinct agricultural sites in+ the Minnesota River Watershed. The outcomes were analyzed geospatially in a Geographic Information System (GIS) environment using the kriging interpolation technique. The study recommends (i) triple stratification of flows to obtain accurate load estimates, and (ii) an optimal sampling scheme for nitrogen and phosphorous with 30.6 % and 49.8 % datapoints from high flow strata. The study outcomes are expected to contribute to the planning of economically and technically sound combinations of best management practices (BMPs) required for achieving total maximum daily loads (TMDL) in a watershed.Item An innovative approach for predicting pandemic hotspots in complex wastewater networks using graph theory coupled with fuzzy logic(Springer, 2023-05) Srinivas, RallapalliEarly prediction of COVID-19 infected communities (potential hotspots) is essential to limit the spread of virus. Diagnostic testing has limitations in big populations because it cannot deliver information at a fast enough rate to stop the spread in its early phases. Wastewater based epidemiology (WBE) experiments showed promising results for brisk detection of ‘SARS CoV-2’ RNA in urban wastewater. However, a systematic and targeted approach to track COVID-19 virus in the complex wastewater networks at a community level is lacking. This research combines graph network (GN) theory with fuzzy logic to determine the chances of a specific community being a COVID-19 hotspot in a wastewater network. To detect 'SARS-CoV-2' RNA, GN divides wastewater network into communities and fuzzy logic-based inference system is used to identify targeted communities. For the propose of tracking, 4000 sample cases from Minnesota (USA) were tested based on various contributing factors. With a probability score of greater than 0.8, 42% of cases were likely to be designated as COVID-19 hotspots based on multiple demographic characteristics. The research enhances the conventional WBE approach through two novel aspects, viz. (1) by integrating graph theory with fuzzy logic for quick prediction of potential hotspot along with its likelihood percentage in a wastewater network, and (2) incorporating the uncertainty associated with COVID-19 contributing factors using fuzzy membership functions. The targeted approach allows for rapid testing and implementation of vaccination campaigns in potential hotspots. Consequently, governmental bodies can be well prepared to check future pandemics and variant spreading in a more planned manner.Item Wetland functional assessment and uncertainty analysis using fuzzy α-cut–based modified hydrogeomorphic approach(Springer, 2023-05) Srinivas, Rallapalli; Singh, Ajit PratapWetlands are significant ecosystems which perform several functions such as ground water recharge, flood control, carbon sequestration, and pollution reduction. Accurate evaluation of wetland functions is challenging, due to uncertainty associated with variables such as vegetation, soil, hydrology, land use, and landscape. Uncertainty is due to the factors such as the cost of evaluating quality parameters, measurement, and human errors. This study proposes an innovative framework based on modified hydrogeomorphic approach (HGMA) using fuzzy α-cut technique. HGMA has been used for wetland functional assessment and α-cut technique is used to characterize uncertainty corresponding to the input variables and wetland functions. The most uncertain variables were found to be the density of wetlands and basin count in the landscape assessment area with the scores of 4.38% and 3.614% respectively. Among the functions, the highest uncertainty is found in functional capacity index (FCI) corresponding to water storage (1.697%) and retain particulate (1.577%). The quantified uncertainty can help the practitioners to make informed decisions regarding planning best management practices for preserving and restoring the wetland functionalityItem Compounding effects of urbanization, climate change and sea-level rise on monetary projections of flood damage(Elsevier, 2023-05) Srinivas, Rallapalli; Goonetilleke, AshanthaClimate change and urbanization play critical roles in compounding future flood risk due to their adverse impacts on the rainfall regime and sea level rise. Although past studies have predicted the spatiotemporal variations in flood risk, these have appreciable limitations, viz. (i) flood risk is predicted mainly by accounting for one driver at a time (either ocean flooding or fluvial flooding); and (ii) monetization of flood damage due to future flooding had not been investigated. However, multiple drivers could lead to flooding in coastal areas. This study presents an innovative approach for investigating the cumulative effects of urbanization, changes to the rainfall regime, and sea level rise on consequential flood damage in a coastal urban area. A comprehensive flood damage and hazard prediction model was developed by integrating 1D-2D aspects of MIKE FLOOD and GIS technology to assess the flood scenarios for 2040, 2070, and 2100 by investigating three predictor variables: urbanization, rainfall regime, and sea level rise. The factorial design approach was used to construct a total of 27 future flood scenarios. Time horizons of 30 years provided for effectively capturing climate change and its influence on the hydrologic regime. The Generalized Linear Model (GLM) was applied to create a statistical model based on future scenarios for each time horizon. Results confirmed that changes to the rainfall regime significantly influence the average annual damage (AAD) caused by flooding for all time horizons. At the same time, the significance of the effects of urbanization and sea level rise was found to vary. The model predicts that by 2040, urbanization would exacerbate AAD, with a significant contribution from sea level rise. In contrast, sea level rise would provide a marginally greater and more significant contribution to AAD compared to urbanization in 2040 and 2070. Compared to the base year 2017, AAD was 78%, 197%, and 351% higher in 2040, 2070, and 2100, respectively. The proposed flood damage prediction model developed can guide modelers and decision-makers in assessing the compounding flood damage for future flood management in any geographic location.Item Simulating Landscape Hydrologic Connectivity in a Precise Manner Using Hydro-Conditioning(Springer, 2022) Srinivas, Rallapalli; Singh, Ajit PratapHigh resolution Light Detection and Ranging (LiDAR)-derived Digital Elevation Models (DEMs) have significantly enhanced hydrological modeling and agricultural planning. However, it is important to accurately simulate the landscape flow network by modifying the LiDAR derived DEM. Hydro-conditioning identifies false pools and depressions and places breach lines to ensure continuous flow through the surfaces such as bridges, culverts, and railroads. This study explores the variations in the criteria namely flow network, impeded flows, depression etc. which determine the finest locations of best management practices using watershed models. Study compares different levels of hydro-conditioned DEMs for the Plum Creek sub-watershed, Minnesota. Results indicate that both manual and automated ‘hDEMs’ facilitate field scale planning and practice siting to different degrees. Outcomes help in planning cost-effective precision agriculture activities.