Photostabilizers for azadirachtin-A (a neem-based pesticide)

Photostability of azadirachtin-A (a neem based pesticide) has been studied without and with adding stabilizers such as ter. butyl-p-cresol, 8-hydroxy quinoline and ter. butyl hydroquinone as thin film on glass surface and on leaf surface under sunlight and UV light. Half-life of azadirachtin has been found to be 48 min and 3.98 days as thin film under UV light and sunlight and 2.47 days on leaf surface, respectively. 8-Hydroxy quinoline and ter. butyl hydroquinone have been found effective in controlling degradation of azadirachtin under both sunlight and UV light with half-life of 44.42 and 35.90 days under sunlight, and 55.80 and 48.50 h under UV light, respectively. Whereas ter. butyl-p-cresol has been found effective A only under sunlight. Significant decreases in antifeedant and insect growth regulatory activity against third instar larvae of Spodopterra litura has been observed with azadirachtin when exposed to sunlight and UV light. However, by the addition of above stabilizers, the biological activity of azadirachtin-A has been retained even after 24 h of irradiation under UV light and up to 30 days of exposure to sunlight.     

Thermal and spectroscopic studies on sorption of nickel(II) ion on protonated baker's yeast

Protonated form (Hy) of yeast was subjected to thermal analysis (TGA and DTG) in the temperature range 60–800 °C. Chemically bound water volatilizes around 200 °C and the matrix undergoes extensive oxidative decomposition at 450 °C, the weight loss reaching 75% at 800 °C. The sorption capacity of the matrix for nickel(II) ion increases on heat treatment from 60 to 200 °C (from 16.9 to 25.0 mg/g), but was reduced on heating to higher temperatures at an initial nickel(II) ion concentration of 1200 mg/g. The FTIR spectra of Hy and nickel(II) ion saturated yeast, indicated that biosorption occurs on the sugar and nucleic acid regions, possibly involving –COOH and –NH groups.     

Streamflow Hydrology Estimate Using Machine Learning (SHEM)

Continuity and accuracy of near real‐time streamflow gauge (streamgage) data are critical for flood forecasting, assessing imminent risk, and implementing flood mitigation activities. Without these data, decision makers and first responders are limited in their ability to effectively allocate resources, implement evacuations to save lives, and reduce property losses. The Streamflow Hydrology Estimate using Machine Learning (SHEM) is a new predictive model for providing accurate and timely proxy streamflow data for inoperative streamgages. SHEM relies on machine learning (“training”) to process and interpret large volumes (“big data”) of historic complex hydrologic information. Continually updated with real‐time streamflow data, the model constructs a virtual dataset index of correlations and groups (clusters) of relationship correlations between selected streamgages in a watershed and under differing flow conditions. Using these datasets, SHEM interpolates estimated discharge and time data for any indexed streamgage that stops transmitting data. These estimates are continuously tested, scored, and revised using multiple regression analysis processes and methodologies. The SHEM model was tested in Idaho and Washington in four diverse watersheds, and the model's estimates were then compared to the actual recorded data for the same time period. Results from all watersheds revealed a high correlation, validating both the degree of accuracy and reliability of the model.     

Statistical and Hybrid Methods Implemented in a Web Application for Predicting Reservoir Inflows during Flood Events

Reservoir management is a critical component of flood management, and information on reservoir inflows is particularly essential for reservoir managers to make real‐time decisions given that flood conditions change rapidly. This study's objective is to build real‐time data‐driven services that enable managers to rapidly estimate reservoir inflows from available data and models. We have tested the services using a case study of the Texas flooding events in the Lower Colorado River Basin in November 2014 and May 2015, which involved a sudden switch from drought to flooding. We have constructed two prediction models: a statistical model for flow prediction and a hybrid statistical and physics‐based model that estimates errors in the flow predictions from a physics‐based model. The study demonstrates that the statistical flow prediction model can be automated and provides acceptably accurate short‐term forecasts. However, for longer term prediction (2 h or more), the hybrid model fits the observations more closely than the purely statistical or physics‐based prediction models alone. Both the flow and hybrid prediction models have been published as Web services through Microsoft's Azure Machine Learning (AzureML) service and are accessible through a browser‐based Web application, enabling ease of use by both technical and nontechnical personnel.     

A holistic study on fluoride-contaminated groundwater models and its widespread effects in healthcare and irrigation

Environmental Science and Pollution Research - Groundwater is a key resource in the world. Its importance is often undermined, despite the various applications which include irrigation, drinking,...     

Deinking of recycled pulps using column flotation: energy and environmental benefits

Deinking of recycled pulps is an important operation intended to provide pulps appropriate for making paper and paperboard products from recycled wastepaper. Conventionally, deinking of recycled pulp is conducted in a flotation cell equipped with an agitator. We have investigated the applicability of a new flotation cell based on a column without an agitator present to accomplish the deinking of typical wastepaper feedstocks and also a feedstock consisting of rejects from a conventional deinking cell. Experimental results on the deinking operation indicate that it is possible to achieve deinking of a mixture of photocopier and laserprinted paper in the column flotation cell. The performance of the column, as measured by the resultant pulp’s brightness and ink particle size distributions, is comparable to that of a conventional laboratory cell which incorporates severe agitation regimens. Thus, it is found that the agitator can be eliminated by using the column flotation design which could yield significant electrical energy savings in addition to savings in capital costs and other operational and maintenance costs. A series of deinking experiments were also performed on a feedstock consisting of rejected waste obtained from a conventional cell in a pilot plant. We found that deinking of the rejected waste could yield significant usable fiber. This indicates the potential of the column flotation technique in enhancing the reuse of a waste component thus reducing the volume of deinking waste rejected into the environment.