Graphical estimation of the dual-enzyme kinetic parameters for Cr(VI) reduction

Chromium(VI) (Cr(VI)) contamination of soil and groundwater is a major environmental concern. Bioreduction of Cr(VI) by Shewanella oneidensis MR-1 (MR-1) can be considered a feasible option to reduce the toxic and mobile Cr(VI) to the less toxic and less mobile chromium(III) (Cr(III)). The reaction rate expression for Cr(VI) reduction is nonlinear and the rate constants are evaluated by employing nonlinear optimization techniques. The outcome of the optimization techniques, in general, depends on the initial estimate of the kinetic parameters which is not always available. A graphical approach based on sound mathematical reasoning has been developed which is accurate, simpler to use, and can provide the best initial estimate for nonlinear optimization.     

AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.     

Cadmium contamination in agricultural soils of Bangladesh and management by application of organic amendments: evaluation of field assessment and pot experiments

Environmental Geochemistry and Health - In recent years, cadmium (Cd) contamination in agricultural soils and its subsequent transfer to crops is one of the high-priority environmental and public...     

Analyses on phenological and morphological variations of mangrove forests along the southwest coast of Bangladesh

Drastic changes in river discharge and salinity levels are threatening the phenology and morphology of the coastal mangrove forests of the Sundarbans of Bangladesh. We have used AVHRR GIMMS (1985–2006) and MODIS (2005–2010) satellite Normalized Difference Vegetation Index (NDVI) data to identify the temporal variation of the phenology of the mangroves. Linear interpolation and Fourier-based adjustment were applied to remove noise from the NDVI time series. Then linear regression analysis on a single area (8 km ? 8 km) and a composite of 36 areas for three NDVI statistics the annual minimum, annual average, and annual maximum were performed--over the time periods 1985–1990, 1990–2000, 2000–2006 and 2005–2010 to identify possible functional changes in NDVI time series around the Sundarbans. Furthermore, we used fourteen LANDSAT images spanning the period 1989–2010 to estimate the spatiotemporal rate of shoreline changes over the three time periods 1989–2000, 2000–2006, and 2006–2010. A decreasing trend in the annual minimum NDVI was observed in most of the areas of the Sundarbans for the period 1990–2000. During the years 2000–2006, the trends of the three NDVI statistics became significantly positive, indicating an improvement of the mangrove phenology. In the period 2005–2010, a decreasing trend in all the NDVI variables was again dominant. The coast underwent rapid erosion from 1989–2000 and 2006–2010. However, the rate substantially declined between 2000 and 2006, when accretion was dominant. The advent of the upstream Farakka barrage caused a significant reduction in the Ganges-Gorai River discharge and increased the salinity in and around the Sundarbans. Our study concludes that this may be responsible for the degradation of mangrove phenology and accelerated erosion in the earlier and recent periods. In the interim, 2000–2006, improved river discharge and salinity levels due to the Ganges water sharing agreement (1996) and dredging of the Gorai River bed (1998–1999) enhanced the mangrove phenology and helped the coast to gain land.     

Oxidative stress in the freshwater cyprinid crucian carp (Carassius carassius L.) upon chronic exposure to endosulfan

Oxidative stress and antioxidant responses of crucian carp, upon chronic exposure to endosulfan, were evaluated in vivo. The lethal concentration (LC_50–96?h) was 70 μg L^?1; on its basis, the fish were exposed to endosulfan at 20, 35, and 50 μg L^?1 and autopsy was done on days 1, 2, 3, 4, 7, 14, 21, 28, and 35. Lipid peroxidation was induced in a concentration-dependent manner, being highest at 50 μg L^?1 (3/4 LC_{50–96 h}, sub-lethal concentration-I, SL-I) on day 4 (720% versus control), followed in its extent (490%) at 30 μg L^?1 (1/2 LC_{50–96 h}, sub-lethal concentration-II, SL-II) on day 7 and lowest (260%) at 10 μg L^?1 (1/4 LC_{50–96 h}, sub-lethal concentration-III, SL-III) on day 14. Glutathione showed a concentration- and time-dependent elevation in the initial phase, with highest level on day 4 (180%) at SL-I, but showed significant reduction in all test concentrations from day 21 of post-exposure. Superoxide dismutase was decreased significantly throughout the study, with highest reduction (63%) on day 4 at SL-I; catalase increased in all test concentrations up to day 14 but showed a significant decrease from the day 28 of post-exposure. The potential role of these parameters as indicators of pesticide pollution in aquatic systems is discussed.     

Space-time stick-breaking processes for small area disease cluster estimation

We propose a space-time stick-breaking process for the disease cluster estimation. The dependencies for spatial and temporal effects are introduced by using space-time covariate dependent kernel stick-breaking processes. We compared this model with the space-time standard random effect model by checking each model’s ability in terms of cluster detection of various shapes and sizes. This comparison was made for simulated data where the true risks were known. For the simulated data, we have observed that space-time stick-breaking process performs better in detecting medium- and high-risk clusters. For the real data, county specific low birth weight incidences for the state of South Carolina for the years 1997–2007, we have illustrated how the proposed model can be used to find grouping of counties of higher incidence rate.     

Physico‐mechanical and Flammability Properties of Cyrtostachys renda Fibers Reinforced Phenolic Resin Bio-composites

Journal of Polymers and the Environment - The aim of this study is to investigate the effect of fiber length and loading on physico-mechanical and flammability properties of Cyrtostachys renda (CR)...     

Ecotoxicological risk assessment for sediments of Çavuşlu stream in Giresun,Turkey: association between garbage disposal facility and metallic accumulation

Environmental Science and Pollution Research - The objective of this paper was to stress the possible potential toxic element (PTE) accumulation in the surface sediments of the Çavu?lu...     

Fabrication of ZnO–SnO2 nanocomposite and its photocatalytic activity for enhanced degradation of Biebrich scarlet

Environmental Science and Pollution Research - The n–n-type ZnO–SnO2 nanocomposite was fabricated using malic acid following a simple one-pot co-precipitation method. The fabricated...     

Hybrid sensor for metal grade measurement of a falling stream of solid waste particles

A hybrid sensor system for accurate detection of the metal grade of a stream of falling solid waste particles is investigated and experimentally verified. The system holds an infrared and an electromagnetic unit around a central tube and counts all the particles and only the metal particles, respectively. The count ratio together with the measured average particle mass ratio (k) of non-metal and metal particles is sufficient for calculation of grade. The performance of the system is accurately verified using synthetic mixtures of sand and metal particles. Towards an application a case study is performed using municipal solid waste incineration bottom ash in size fractions 1-6mm, which presents a major challenge for nonferrous metal recovery. The particle count ratio was inherently accurate for particle feed rates up to 13 per second. The average value and spread of k for bottom ash was determined as 0.49 ± 0.07 and used to calculate grade within 2.4% from the manually analysed grade. At higher feed rates the sensors start missing particles which fall simultaneously through the central tube, but the hybrid system still counted highly repeatable. This allowed for implementation of a count correction ratio to eliminate the stationary error. In combination with averaging in measurement intervals for suppression of stochastic variations the hybrid system regained its accuracy for particle feed rates up to 143 per second. This performance and its special design, intended to render it insensitive to external interference and noise when applied in an eddy current separator, make the hybrid sensor suitable for applications such as quality control and sensor controlled separation.