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.     

Catalytic degradation of toxic organic dye using an aluminum-doped Cu0.4Zn0.6-xAlxFe2O4 (x = 0.0, 0.2, 0.4, 0.6) spinel ferrite in a photo-Fenton system

Catalytic activity of spinel ferrite in breaking down toxic dye materials are promising due to their uniqueness. In this study, aluminum-doped copper zinc ferrite, Cu_0.4Zn_0.6-xAl_xFe₂O₄ (x = 0.0, 0.2, 0.4, 0.6), a catalyst for toxic dye degradation is synthesized through chemical co-precipitation route. The formation of the spinel ferrite catalyst is initially confirmed by Fourier transform infrared spectra, which shows the frequency of metal-oxygen bond vibration at 539 and 427 cm⁻¹ attributed to the tetrahedral and octahedral sites respectively. Higher intensity sharp peak of X-ray diffraction for (311) plane is the evidence for the phase purity and the formation of spinel ferrite. The crystallite size is found to decrease with the increase of Al³⁺ ion. The surface structure of the obtained particles is investigated using a scanning electron microscope. Analyses of the material's magnetic characteristics using a vibrating sample magnetometer (VSM) revealed that it is, in fact, a soft magnet, as evidenced by the loop of its hysteresis, which is narrow. The catalytic degradation of methylene blue dye under the mechanism of the photo-Fenton process is studied with the obtained spinel ferrites and the result is found to be as high as 96.5%. The process follows pseudo-second order kinetics and the Langmuir isotherm.     

Water,sanitation, hygiene and waste disposal practices as COVID-19 response strategy: insights from Bangladesh

Environment, Development and Sustainability - The COVID-19 pandemic has caused a global emergence, and the absence of a proven vaccine or medicine has led to the implementation of measures to...     

Consumption of unsafe food in the adjacent area of Hazaribag tannery campus and Buriganga River embankments of Bangladesh: heavy metal contamination

The concentrations of Cr, Cd, Pb, Hg, and As in water, poultry meat, fish, vegetables, and rice plants obtained from the area adjacent to the Hazaribag tannery campus, Dhaka, Bangladesh, were estimated and compared with permissible levels established by the WHO and FAO and levels reported previously by other authors. The metal contents were in the following order according to the concentration in contaminated irrigation water: Cr?>?Pb?>?As?>?Hg?>?Cd. Mean concentrations of Cr, Pb, Hg, and As in irrigated water were above the permissible levels, whereas the results were below the permissible levels for Cd. The metal concentrations in poultry meat, fish, rice, and vegetables were in the following orders: Pb?>?Cr?>?Cd?>?Hg?>?As, Pb?>?Cr?>?Cd?>?As?>?Hg, Pb?>?As, and Cr?>?Pb?>?Cd?>?As?>?Hg, respectively. The mean concentrations of metals in poultry meat, fish, rice, and vegetables were much higher than the permissible levels. The trends of weekly intake of heavy metals (WIMs) from poultry meat, fish, rice, and vegetables were as follows: Pb?>?Cr?>?Cd?>?Hg?>?As, Pb?>?Cr?>?Cd?>?As?>?Hg, Pb?>?As, and Cr?>?Pb?>?Cd?>?As?>?Hg, respectively. WIMs for all the metals were lower than the provisional maximum weekly intake recommended by WHO/FAO and USNAS.     

Influence of polymethyl acrylate additive on the formation of particulate matter and NOX emission of a biodiesel–diesel-fueled engine

Environmental Science and Pollution Research - The aim of this study is to investigate the effect of the polymethyl acrylate (PMA) additive on the formation of particulate matter (PM) and nitrogen...     

Assessing regulatory violations of disinfection by-products in water distribution networks using a non-compliance potential index

Inactivating pathogens is essential to eradicate waterborne diseases. However, disinfection forms undesirable disinfection by-products (DBPs) in the presence of natural organic matter. Many regulations and guidelines exist to limit DBP exposure for eliminating possible health impacts such as bladder cancer, reproductive effects, and child development effects. In this paper, an index named non-compliance potential (NCP) index is proposed to evaluate regulatory violations by DBPs. The index can serve to evaluate water quality in distribution networks using the Bayesian Belief Network (BBN). BBN is a graphical model to represent contributing variables and their probabilistic relationships. Total trihalomethanes (TTHM), haloacetic acids (HAA5), and free residual chlorine (FRC) are selected as the variables to predict the NCP index. A methodology has been proposed to implement the index using either monitored data, empirical model results (e.g., multiple linear regression), and disinfectant kinetics through EPANET simulations. The index’s usefulness is demonstrated through two case studies on municipal distribution systems using both full-scale monitoring and modeled data. The proposed approach can be implemented for data-sparse conditions, making it especially useful for smaller municipal drinking water systems.     

Fiscal spending and the environment: Theory and empirics

During economic crises, governments often increase fiscal spending to stimulate the economy. While the fiscal spending surge may be temporary, spending composition is often altered in favor of expenditures on social programs and other public goods which may persist over time. We model and measure the impact of fiscal spending patterns on the environment. The model predicts that a reallocation of government spending composition towards social and public goods reduces pollution. However, increasing total government spending without altering its composition does not reduce pollution. We empirically test these predictions for air and water pollutants showing that they are fully supported.     

Preconcentration of metal ions through chelation on a synthesized resin containing O, O donor atoms for quantitative analysis of environmental and biological samples

Nascent Amberlite XAD-4 has been used as the polymeric support for the synthesis of a stable extractor of metal ions, by incorporating phthalic acid through azo bridging. Elemental analyses and infra-red spectral and thermal studies were carried out for its characterization. The water regain value and hydrogen ion capacity were found to be 12.50 and 5.75 mmol g^?1, respectively. The optimum pH range for the maximum sorption of Ni(II), Mn(II), Cu(II), Zn(II), Cd(II), Cr(III), and Co(II) was observed at pH 5.5–8.0 with the corresponding half-loading time (t _1/2) of 9, 5, 9, 9, 3, 9, and 5 min, respectively. The preconcentration factor for Ni(II), Mn(II), Cu(II), Zn(II), Cd(II), Cr(III), and Co(II) are 190, 190, 190, 180, 180, 160, and 160, with the corresponding limit of preconcentration in the range of 5.25–6.25 μg L^?1. The detection limits, for flame atomic absorption spectrophotometry, were found to be 0.62, 0.60, 0.65, 0.75, 0.72, 0.84, and 0.85 μg L^?1, respectively. Method has been successfully applied to the analysis of water samples, multivitamin formulations, infant food substitutes, hydrogenated oil, and fishes.     

Multi‐strainer model concept for conjunctive pumping of saline and non‐saline groundwater from coastal aquifers

The current paper discusses the multi‐strainer technique developed to augment usable water by the combined use of saline and non‐saline aquifers in locations where a freshwater aquifer is underlain or overlain by a saline water aquifer. The multi‐strainer technique evaluates design criteria for the formulation of multi‐strainer schemes to supply water at an acceptable salinity limit by combined use of the saline and non‐saline aquifers. The design ratio of discharges can be maintained by adjusting the strainers’ lengths in the saline and non‐saline aquifers. The multi‐strainer scheme has been applied in the coastal aquifers of Bangladesh and found to be effective at lowering the water salinity concentrations to acceptable levels, thus increasing the availability of water for sustainable use. The multi‐strainer scheme should be designed based on the thickness of the aquifer layers to be screened, the salinity concentrations of the screened layers, and the level of salinity concentration to be maintained.