MODIS-Landsat fusion-based single-band algorithms for TSS and turbidity estimation in an urban-waste-dominated river reach.

Riverine ecosystem management along an urban stretch mostly depends on high-frequent (daily-scale) monitoring of water quality at finer spatial resolutions. However, with the decrease in the number of in-situ monitoring stations owing to their expensive maintenance cost, there is a need to develop the next-generation remote sensing (RS) tools as an alternate approach with better synoptic coverage of river water quality assessment. This study advocates three novel model variants to estimate the total suspended solids (TSS) concentration at daily-scale using the public-domain MODIS and Landsat satellite datasets. The MODT model variant uses the 1-day×250 m MODIS public domain datasets, and the FUST model is based on the 1-day×30 m MODIS-Landsat fusion datasets, whereas the CFUST model integrates the Frank Copula with the FUST model. These hierarchical model variants are assessed in the urban-waste-dominated lower Ganges, namely the Hooghly River and the Brahmani River, in eastern India using the measured in-situ TSS datasets at multiple monitoring stations from 2016 to 2019. The results reveal that the CFUST is the best TSS estimation model variant that performs with the average coefficient of determination of 0.88-0.93, mean absolute error of 0.17-0.19, and normal root mean square error of 0.05-0.09. Conclusively, the proposed CFUST and CFUSTU stochastic models can be used as potential tools for TSS and turbidity assessment along the dynamic river systems, respectively.

Integrated remote sensing and machine learning tools for estimating ecological flow regimes in tropical river reaches.

With the gradual declining streamflow gauging stations in many world-rivers, emphasis is given nowadays to develop remote sensing (RS)-based approaches as the next-generation hydrometry for estimating riverine ecological flow regimes (EFR). For constructing EFR based on daily-streamflow data in scantily-gauged reaches, use of RS techniques in narrow flow-width tropical rain-fed rivers is constrained with the non-availability of finer spatial satellite data at daily scale. To address these limitations, this study proposes a novel framework that integrates the enhanced spatiotemporal adaptive reflectance fusion (FUS) of the 250 m × 1-day resolution Aqua-MODIS and 30 m × 1-day resolution Landsat satellite-based remote sensing images in the near-infrared region with the machine learning algorithms. These developed frameworks are named as Artificial Neural Network-based ANNFUS, Random Forest Regression-based RFRFUS, and Support Vector Regression-based SVRFUS models, which were tested for daily-scale streamflow estimation in a typical Brahmani River Basin, India. The results reveal that by addressing the linear and nonlinear dynamism between the streamflow and satellite signals, all the developed models could simulate the streamflow very well with the Nash-Sutcliffe efficiency>0.8, Kling-Gupta efficiency>0.8, relative root mean square error (rRMSE) of 0.051-0.12, and normalized RMSE of 0.23-0.36. However, for reproducing the high, median, and low streamflow regimes, the SVRFUS model was found to be the best with the NSE>0.85 and KGE>0.8. Conclusively, the proposed approach is found to have the potential to be replicated in other world-river basins to estimate ecological flow regimes at defunct gauging stations facilitating the basin-scale aquatic environmental management.

Robust sensor placement for sustainable leakage management in water distribution networks of developing economies: A hybrid decision support framework.

Urban water distribution networks (WDNs) in developing economies often refrain from investing in sensor-based leakage management technologies due to financial constraints and other techno-managerial issues. Thus, this study proposes a generalized decision support framework based on network sensitivity analysis (NSA) and multi-criteria decision-making (MCDM) to assess the prospect of effective leakage control through robust sensor placement in existing deficient WDNs. Four sensitivity parameters are formulated for NSA to ascertain the pressure response of the potential sensor positions for diverse hydraulic and leak scenarios. Subsequently, selecting the optimal number of sensors and their relative positions within the WDN is framed as an MCDM problem that entails the simultaneous maximization of Euclidean distances among the potential sensor positions and the leak-induced pressure residuals obtained at these sensors. The proposed methodology is developed on a numerical benchmark network assuming ideal conditions, and its applicability is verified on a sensor-equipped experimental network considering realistic system uncertainties. The outcome of this study aims to provide an insightful understanding of the system behavior that governs its leak localization potential and ascertain the practical challenges of sensor-based leakage monitoring in existing WDNs. Decision-makers of resource-strained utilities can beneficially utilize the proposed framework to assess the environmental and cost trade-offs of employing sensor-based technologies for leakage management and proactive decision-making before its actual implementation.

Scaling up the Digitalization of the Public Distribution System: A COVID-19 Pandemic Impetus.

Food grain is an essential commodity that requires proper procurement, transportation, storage, and distribution with easy access to needy people. Therefore, The Indian government has established a Public Distribution System (PDS) to distribute the food grain across the country. This study emphasizes the need for digitalization of PDS and how the COVID-19 accelerates the digitalization of PDS. We proposed a three-layer conceptual framework: food grain supply chain network, digital processes automation and digital technologies. We collaborate the three-layer perspectives to ensure food security during the COVID-19 pandemic and reduce the food grain leakage in PDS. This study also supports the United Nations Sustainable Development Goals, and National Food Security Act aims to achieve the Zero Hunger goal. The proposed research will assist policymakers, institutions, and the government in implementing the digitalization of PDS for greater efficiency and transparency during a pandemic. Furthermore, this work may help to achieve the ambitious "Digital India" programme, which aims to improve India's online infrastructure and electronic services.

A review on environmental occurrence, toxicity and microbial degradation of Non-Steroidal Anti-Inflammatory Drugs (NSAIDs).

In recent years, Non-Steroidal Anti-Inflammatory Drugs (NSAIDs) have surfaced as a novel class of pollutants due to their incomplete degradation in wastewater treatment plants and their inherent ability to promote physiological predicaments in humans even at low doses. The occurrence of the most common NSAIDs (diclofenac, ibuprofen, naproxen, and ketoprofen) in river water, groundwater, finished water samples, WWTPs, and hospital wastewater effluents along with their toxicity effects were reviewed. The typical concentrations of NSAIDs in natural waters were mostly below 1 µg/L, the rivers receiving untreated wastewater discharge have often showed higher concentrations, highlighting the importance of effective wastewater treatment. The critical analysis of potential, pathways and mechanisms of microbial degradation of NSAIDs were also done. Although studies on algal and fungal strains were limited, several bacterial strains were known to degrade NSAIDs. This microbial ability is attributed to hydroxylation by cytochrome P450 because of the decrease in drug concentrations in fungal cultures of Phanerochaete sordida YK-624 on incubation with 1-aminobenzotriazole. Moreover, processes like decarboxylation, dehydrogenation, dechlorination, subsequent oxidation, demethylation, etc. also constitute the degradation pathways. A wide array of enzymes like dehydrogenase, oxidoreductase, dioxygenase, monooxygenase, decarboxylase, and many more are upregulated during the degradation process, which indicates the possibility of their involvement in microbial degradation. Specific hindrances in upscaling the process along with analytical research needs were also identified, and novel investigative approaches for future monitoring studies are proposed.

Hydraulic performance benchmarking for effective management of water distribution networks: An innovative composite index-based approach.

Hydraulic performance assessment and benchmarking of water distribution networks (WDNs) impose a major challenge to water utilities worldwide. Presently, benchmarking strategies for WDNs are not fully developed, especially for analyzing intermittent systems commonly encountered in non-developed nations. To overcome these limitations, this paper proposes an index-based benchmarking strategy for WDNs, comparing their actual hydraulic performance and expected serviceability. A robust Hydraulic Performance Index (HPI) is developed as a global metric to account for the combined impact of multiple hydraulic outputs, concerning their benchmark values. The applicability of this index is verified on a numerical benchmark network, and its usefulness is demonstrated on a real-world intermittent WDN located in Kolkata (India) by coupling the HPI-based framework with hydraulic models using the EPANET-MATLAB programmer's toolkit. A scenario-based analysis is conducted using extended-period simulation to obtain the HPI for diverse service levels and leakage conditions of the WDN models. The HPI is designed to effectively capture the localized pressure reduction during peak flow, prioritize hydraulic outputs based on regional constraints, and penalize systems with unsustainably high hydraulic output. The developed strategy is also effective in performance benchmarking of WDNs of different nations with diverse serviceability and threshold parameters on a common platform. Finally, the practical efficacy and generalizability of the HPI-based results in the context of case-specific performance management of WDNs, along with limitations, recommendations and future perspectives are elucidated upon.

Identifying Myocardial Infarction Using Hierarchical Template Matching-Based Myocardial Strain: Algorithm Development and Usability Study.

BACKGROUND: Myocardial infarction (MI; location and extent of infarction) can be determined by late enhancement cardiac magnetic resonance (CMR) imaging, which requires the injection of a potentially harmful gadolinium-based contrast agent (GBCA). Alternatively, emerging research in the area of myocardial strain has shown potential to identify MI using strain values. OBJECTIVE: This study aims to identify the location of MI by developing an applied algorithmic method of circumferential strain (CS) values, which are derived through a novel hierarchical template matching (HTM) method. METHODS: HTM-based CS H-spread from end-diastole to end-systole was used to develop an applied method. Grid-tagging magnetic resonance imaging was used to calculate strain values in the left ventricular (LV) myocardium, followed by the 16-segment American Heart Association model. The data set was used with k-fold cross-validation to estimate the percentage reduction of H-spread among infarcted and noninfarcted LV segments. A total of 43 participants (38 MI and 5 healthy) who underwent CMR imaging were retrospectively selected. Infarcted segments detected by using this method were validated by comparison with late enhancement CMR, and the diagnostic performance of the applied algorithmic method was evaluated with a receiver operating characteristic curve test. RESULTS: The H-spread of the CS was reduced in infarcted segments compared with noninfarcted segments of the LV. The reductions were 30% in basal segments, 30% in midventricular segments, and 20% in apical LV segments. The diagnostic accuracy of detection, using the reported method, was represented by area under the curve values, which were 0.85, 0.82, and 0.87 for basal, midventricular, and apical slices, respectively, demonstrating good agreement with the late-gadolinium enhancement-based detections. CONCLUSIONS: The proposed applied algorithmic method has the potential to accurately identify the location of infarcted LV segments without the administration of late-gadolinium enhancement. Such an approach adds the potential to safely identify MI, potentially reduce patient scanning time, and extend the utility of CMR in patients who are contraindicated for the use of GBCA.

Mine sludge waste recycling as bio-stimulant for applications in anaerobic wastewater treatment.

This study examined the applicability of two mine sludge wastes, mine tailing sludge (MTS) and acid mine drainage sludge (AMDS) as iron-rich bio-stimulant for enhancing organic matter degradation in anaerobic process. Batch treatment of domestic sewage having 343 ± 10 mg/L chemical oxygen demand (COD) using MTS and AMDS as additives mixed with septic tank sludge as anaerobic inoculum produced lower start-up time, higher efficiency of COD removal, enhanced biomass retention, and higher acidogenic and methanogenic activity after stabilization. Biostimulation induced by mine sludge waste additives in anaerobic system were observed to have correlation with percentage of iron content in the additives, as well as difference in surface charge between biomass and the additives. Treatment efficiency induced by the two mine sludge waste based additives were similar at 90% confidence limit, however, was found to be higher than lower iron containing additive laterite soil, while lower than higher iron containing synthetic zero valent nano iron as additives used for comparison. The study was supported by scanning electron microscope, atomic force microscope and optical microscope images of sludge granule sand surface charge measurement.

Urban air quality forecasting based on multi-dimensional collaborative Support Vector Regression (SVR): A case study of Beijing-Tianjin-Shijiazhuang.

Today, China is facing a very serious issue of Air Pollution due to its dreadful impact on the human health as well as the environment. The urban cities in China are the most affected due to their rapid industrial and economic growth. Therefore, it is of extreme importance to come up with new, better and more reliable forecasting models to accurately predict the air quality. This paper selected Beijing, Tianjin and Shijiazhuang as three cities from the Jingjinji Region for the study to come up with a new model of collaborative forecasting using Support Vector Regression (SVR) for Urban Air Quality Index (AQI) prediction in China. The present study is aimed to improve the forecasting results by minimizing the prediction error of present machine learning algorithms by taking into account multiple city multi-dimensional air quality information and weather conditions as input. The results show that there is a decrease in MAPE in case of multiple city multi-dimensional regression when there is a strong interaction and correlation of the air quality characteristic attributes with AQI. Also, the geographical location is found to play a significant role in Beijing, Tianjin and Shijiazhuang AQI prediction.

Intracerebral infarcts following clipping of intracranial aneurysms: incidence, clinical correlation and outcome.

Subarachnoid hemorrhage (SAH) is a significant health care problem. One of the major determinants of outcome following surgery of intracranial aneurysms is development of intracranial infarcts. All patients underwent clipping for aneurysms in one year in the department of neurosurgery, PGIMER, Chandigarh were studied. Data regarding age, sex, date of ictus, date of admission, any co-morbidity, clinical grades at presentation, CT findings, infarcts, intraoperative rupture, and clinical status in the postoperative period were recorded. Outcome at discharge was assessed by Glasgow outcome scale (GOS). First, 174 patients were included in the study. Radiological cerebral infarctions occurred in 69 patients (39%). The most frequent location of infarct was deep perforator infarct followed by ACA territory infarct. 69.58% of patients developed infarct on the same side of aneurysm and 20.28% of patients developed infarct on opposite side, whereas 11% developed bilateral infarcts. Infarcts that occur early after surgery may be related to surgical factors whereas the late infarcts were probably as results of delayed ischemic deficits. Anatomical distribution of infarcts also showed two different patterns, infarcts limited to one vascular territory (more commonly seen in early onset infarcts) or multiple, cortical, bilateral infarcts (more commonly seen in late onset infarct). Patients with poor H&H grade, higher Fisher's grade, intraoperative rupture and prolonged temporary clipping had more chances of developing an intracranial infarct.