Estimation of microplastics in sediments at the southernmost coast of India (Kanyakumari)

Environmental Science and Pollution Research - Increasing urbanization and anthropogenic activities of the last couple of decades have left significant amounts of plastic debris in both coastal and...     

Use of 2–methoxyethyl ether and nitromethane as oxygenated additives for performance improvement and emission reduction of CI engine: experimental investigation and numerical simulation

Environmental Science and Pollution Research - Diesel engines are playing a vital responsibility in the field of automobile, agriculture, construction, and power generation. In the present world,...     

Ceria-doped SnO2 nanocubes for solar light–driven photocatalytic hydrogen production

Environmental Science and Pollution Research - The photocatalytic generation of hydrogen via solar energy using metal oxide semiconductor catalysts is a clean and renewable process which has the...     

A near real-time system for continuously monitoring airborne subtilisin-type enzymes in the industrial atmosphere

We describe the development and validation of a portable system comprising an air sampler coupled to an automated flow injection analysis device. The system is able to monitor airborne concentrations of subtilisin-type enzymes in the workplace atmosphere on a continuous basis. Sampling is in two stages: using a sampling head that is designed to mimic human respiration at approx. 1 m s(-1) at a sampling rate of 600 l min(-1). In the second stage, the captured particles are deposited by impaction from the air stream onto the inner surface of a cyclone that is continuously washed with a jet of buffer solution. Deposited particles are then washed into a reservoir from which samples are taken every 5-6 min and injected automatically into a continuous flow injection analysis system. Proteolytic enzyme in the sample passes through a bioreactor maintained at about 40 degrees C. This contains a cellulose solid phase matrix on which is covalently immobilised Texas Red-labelled gelatin as substrate. The passing enzyme partially digests the substrate releasing fluorophore that is detected down stream in a flow cell coupled to a fluorimeter. The system is calibrated using enzyme standards and the intensity of the resulting peaks from the ex-air samples is converted to airborne concentrations using a mathematical model programmed into a PC. The system has a limit of detection of 4.8 ng m(-3) and a dynamic range of 5-60 ng m(-3). The within assay precision (RSD) is 6.3-9.6% over this range. The within batch precision is 20.3% at 20 ng m(-3) and the corresponding between batch value is 19.5%. The system has been run for periods up to 8 h in the laboratory and for up to 4 h at a factory site and the values obtained compared with time-averaged values obtained from a conventional Galley sampler and in-house analysis when reasonable agreement of the results was observed. The stability of the system over 21 days of continuous use with standards injected periodically was studied. Linearity was observed for all the standard plots throughout. At the end of 21 days, after a total exposure equivalent to 2395 ng ml(-1) of Savinase, the signal due to the 5.0 ng ml(-1) standard was still easily detectable.     

Addressing data challenges in riverine nutrient load modeling of an intensively managed agro-industrial watershed

Data limitations often challenge the reliability of water quality models, especially in intensively managed watersheds. While numerous studies report successful hydrological model setup and calibration, few have addressed in detail the data challenges for multisite and multivariable model calibration to an intensively managed watershed. In this study, we address some of these challenges based on our reflective experience calibrating the Soil and Water Assessment Tool (SWAT) to the Upper Sangamon River Watershed in central Illinois based on daily flow, annual crop yield, and monthly sediment, nitrate, and total phosphorus loads. We highlight some challenges in SWAT calibration processes due to data errors and inconsistencies, and insufficient precipitation and water quality observations. Following, we demonstrate the merits of additional weather and water quality observations that could help reduce input uncertainties, and we provide suggestions for selecting appropriate observations for the model calibration. After dealing with the data issues, we show that the SWAT model could be calibrated with acceptable results for the case study watershed.     

Modelling the Removal of Gaseous Pollutants and Particulate Matters from the Atmosphere of a City by Rain: Effect of Cloud Density

In this paper, an original nonlinear mathematical model for the removal of gaseous pollutants and particulate matters from the atmosphere of an industrial city by rain is proposed and analyzed. It is assumed that five interacting phases in the atmosphere of the city exist, i.e., cloud droplets phase, raindrops phase, gaseous pollutants phase, particulate matters phase, and the phase of absorbed gaseous pollutants in raindrops. It is assumed further that these phases undergo nonlinear interactions in the atmosphere, while gaseous pollutant interacts with cloud droplets as well as with raindrops but particulate matters interact only with raindrops. The gravitational settling and reversible reaction processes have also been considered appropriately in the model. By analyzing the model, it is shown that both the gaseous pollutants and particulate matters may be removed from the atmosphere under certain conditions, provided the rates of formation of cloud droplets and raindrops are sufficiently large. It is noted that under unfavorable atmospheric conditions, rain does not occur and pollutants are not removed from the atmosphere.     

Spatio-temporal classification and prediction of land use and land cover change for the Vembanad Lake system,Kerala: a machine learning approach

Environmental Science and Pollution Research - Land use and land cover (LULC) change has become a critical issue for decision planners and conservationists due to inappropriate growth and its...     

An efficient supply management in water flow network using graph spectral techniques

An efficient way of distributing water in urban cities is one of the major global challenges of our time. A Water Distribution Network (WDN) is a vital infrastructure, intended to provide fresh water to households within a city or designated boundary. Given the WDN’s complexity, effective numerical techniques are required to aid in the development of an ideal monitoring system. Flow meters and gate valves should be placed in low-connected areas of the WDN with water flow reaching several regions of the network. This study proposes a general strategy for assisting water utilities in making decisions for effective water supply. The aim of the research is to use weighted spectral clustering algorithms to outline water districts while addressing hydraulic restrictions via weighted adjacency and Laplacian matrices of the weighted network. This work aims to identify influenced nodes in the network based on Eigen centrality and effectively distribute water across those nodes. This project also looks at how to measure the network’s connection strength to avoid water leaks. The best clusters are found using Eigenvalues and Eigenvectors of weighted adjacency matrices and Laplacian matrices of the water network in the proposed graph spectral framework. In order to establish the optimum water network division approach, topological and graph metrics were used to compare multiple spectral clustering techniques. The proposed graph spectral approach is tested using a genuine water distribution network serving an urban area of Coimbatore city in India and offering a method for partitioning complex networks that employ the spectral graph partitioning algorithm.