Analytical solution of two-dimensional advection–dispersion equation with spatio-temporal coefficients for point sources in an infinite medium using Green’s function method

In the present study analytical solutions of a two-dimensional advection–dispersion equation (ADE) with spatially and temporally dependent longitudinal and lateral components of the dispersion coefficient and velocity are obtained using Green’s Function Method (GFM). These solutions describe solute transport in infinite horizontal groundwater flow, assimilating the spatio-temporal dependence of transport properties, dependence of dispersion coefficient on velocity, and the particulate heterogeneity of the aquifer. The solution is obtained in the general form of temporal dependence and the source term, from which solutions for instantaneous and continuous point sources are derived. The spatial dependence of groundwater velocity is considered non-homogeneous linear, whereas the dispersion coefficient is considered proportional to the square of spatial dependence of velocity. An asymptotically increasing temporal function is considered to illustrate the proposed solutions. The solutions are validated with the existing solutions derived from the proposed solutions in three special cases. The effect of spatially/temporally dependent heterogeneity on the solute transport is also demonstrated. To use the GFM, the ADE with spatio-temporally dependent coefficients is reduced to a dispersion equation with constant coefficients in terms of new position variables introduced through properly developed coordinate transformation equations. Also, a new time variable is introduced through a known transformation.     

MSW stabilization in an anaerobic bioreactor landfill and evaluation of in-situ leachate treatment potential with the help of quadric model

Journal of Material Cycles and Waste Management - Recirculation of the leachate using bioreactor technology for in-situ treatment of leachate is an efficient method for reducing the contaminants...     

Effect of fabricated V-rib roughness experimentally investigated in a rectangular channel of solar air heater: a comprehensive review

Environmental Science and Pollution Research - Solar power is one of the main sources of renewable energy which helps for protecting the environmental and reducing pollution. Technologies and...     

Pulling force prediction using neural networks

Purpose. In ergonomics and human factors investigations, pulling force (PF) estimation has usually been achieved using various types of biomechanical models, and independent approximation of PF was done with the help of upper extremity joints. Recently, multiple regression methods have gained importance for task-relevant inputs in predicting PF. Artificial neural networks (ANNs) also play a vital role in fitting the data; however, their use in work-related biomechanics and ergonomics is inadequate. Therefore, the current research aimed to accomplish comparative investigation of ANN and regression models by assessing their capacity to predict PF values. Methods. Multipositional PF data were acquired from 200 subjects at three different handle heights and body locations. ANN and regression models were formed using a random sample of three subsets (75% training, 15% selection, 10% validation) for proving the outcomes. Results. The comparison of ANN and regression models shows that the predictions of ANN models had a profoundly explained variance and lower root mean square difference values for the PF data at three handle heights. Conclusions. These outcomes advise that ANNs offer a precise and robust substitute for regression methods, and should be considered a useful method in biomechanics and ergonomics task assessments.     

Linking global drivers with local and regional change: a social-ecological system approach in Chilika Lagoon,Bay of Bengal

Global scale drivers such as international markets for shrimp can trigger large changes at local and regional scales. But there is also a poorly appreciated reverse process, operating from the bottom up, with potential for triggering changes at higher scales. Thus, effects of drivers can be seen as a two-way process in which global drivers and local and regional drivers can potentially impact each other. Here, we argue that not only can global drivers impact the sustainability of local and regional social-ecological systems, but sustainability at higher scales can also be impacted by changes at the scale of local and regional social-ecological systems. Using Chilika, a large lagoon on the Bay of Bengal, Odisha State in India as a case, we show that traditional small-scale capture fisheries supporting 150 fisher villages with some 400,000 people were marginalized by aquaculture development for tiger prawn and by state-driven hydrological interventions, with impacts on the ecology of the lagoon. These changes, in turn, contribute to global poverty and food insecurity, making it difficult for India to meet international targets such as millennium development goals. The marginalized fisherfolk become part of environmental justice and other social movements. With large parts of the lagoon converted into a virtual monoculture for the production of tiger prawn, changes in Chilika (a Ramsar site) contribute to wetland habitat loss at the global scale, and biodiversity losses, possibly including IUCN red-listed species.     

Rapid estimation of compost enzymatic activity by spectral analysis method combined with machine learning

The aim of this study was to investigate the feasibility of using visible near-infrared (VisNIR) diffuse reflectance spectroscopy (DRS) as an easy, inexpensive, and rapid method to predict compost enzymatic activity, which traditionally measured by fluorescein diacetate hydrolysis (FDA-HR) assay. Compost samples representative of five different compost facilities were scanned by DRS, and the raw reflectance spectra were preprocessed using seven spectral transformations for predicting compost FDA-HR with six multivariate algorithms. Although principal component analysis for all spectral pretreatments satisfactorily identified the clusters by compost types, it could not separate different FDA contents. Furthermore, the artificial neural network multilayer perceptron (residual prediction deviation = 3.2, validation r² = 0.91 and RMSE = 13.38 μg g^?1 h^?1) outperformed other multivariate models to capture the highly non-linear relationships between compost enzymatic activity and VisNIR reflectance spectra after Savitzky–Golay first derivative pretreatment. This work demonstrates the efficiency of VisNIR DRS for predicting compost enzymatic as well as microbial activity.     

Experimental investigation of solar still with opaque north triangular face

Developing a compact, simple, and productive solar still is a main challenge. This paper describes a simple modification of the solar still that significantly enhances its productivity. A laboratory scale of modified single effect double slope glass solar still with the opaque triangular north wall has been developed and tested for enhancement of productivity in sunny days of the summer season in the month of April. The experiments on still have been carried out under three levels of water depths from 1 to 3 cm and compared it with the performance of conventional still of double slope single effect solar still of the same size. It is clear from the observations that opaque type still gave maximum distillate of 1793 ml at 1 cm depth of water while conventional still could produce only 1519 ml for the same depth of water. Also, for 2 and 3 cm depth of water, the opaque type still found to be more productive than conventional double slope solar still. As the former produced 1532 and 1464 ml while the latter produced 1328 and 1235 ml, for 2 and 3 cm depth of water, respectively.