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...     

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.     

Temporal variation in total phosphorus concentrations revealed from a multidecadal monitoring program on Big Platte Lake,Michigan

Effective water quality management depends on enactment of appropriately designed monitoring programs to reveal current and forecasted conditions. Because water quality conditions are influenced by numerous factors, commonly measured attributes such as total phosphorus (TP) can be highly temporally varying. For highly varying processes, monitoring programs should be long-term and periodic quantitative analyses are needed so that temporal trends can be distinguished from stochastic variation, which can yield insights into potential modifications to the program. Using generalized additive mixed modeling, we assessed temporal (yearly and monthly) trends and quantified other sources of variation (daily and subsampling) in TP concentrations from a multidecadal depth-specific monitoring program on Big Platte Lake, Michigan. Yearly TP concentrations decreased from the late 1980s to late 1990s before rebounding through the early 2000s. At depths of 2.29 to 13.72 m, TP concentrations have cycled around stationary points since the early 2000s, while at the surface and depths ≥?18.29 concentrations have continued declining. Summer and fall peaks in TP concentrations were observed at most depths, with the fall peak at deeper depths occurring 1 month earlier than shallower depths. Daily sampling variation (i.e., variation within a given month and year) was greatest at shallowest and deepest depths. Variation in subsamples collected from depth-specific water samples constituted a small fraction of total variation. Based on model results, cost-saving measures to consider for the monitoring program include reducing subsampling of depth-specific concentrations and reducing the number of sampling depths given observed consistencies across the program period.     

Assessment of the State of Lung Lichen, <Emphasis Type="Italic">Lobaria pulmonaria</Emphasis> (L.) Hoffm., in Forest Communities with Different Times Since Disturbance in the Northeast of European Russia

Quantitative parameters of cenopopulations of Lobaria pulmonaria, an endangered cyanolichen, have been studied in spruce phytocenoses of Karelia recovering after disturbance through the successional stage of aspen stands for a period ranging from 80 to 450 years. The results show that, as the time since disturbance increases, the total number of L. pulmonaria thalli and the number of colonized substrate units increase as well, with no stabilization of these parameters being observed in the series of communities studied. The total area of thalli in the phytocenoses is restored within approximately 200 years after the last disturbance. In old-growth forests (>400 years), L. pulmonaria thalli colonize a broad spectrum of tree species in different life states, including lower branches of young spruce trees. Even if they serve as temporary substrates, this can markedly strengthen the potential of the species for further expansion within the community in case of shortage in the main phorophyte (aspen). Thus, the proportion of regressive populations decreases, while that of colonizing populations increases, which is indicative of successful reproduction of the species.     

Carbon Emission from the Surface of Coarse Woody Debris in Korean Pine Forests of Southern Primorye

Carbon dioxide fluxes from the surface of coarse woody debris (CWD) have been measured in Korean pine forests of the southern Sikhote-Alin mountain range. The seasonal dynamics of oxidative conversion of CWD carbon have been evaluated, and average values of the CO₂ emission rate have been determined for CWD fragments of three tree species at different stages of decomposition. The degree of decomposition is an important factor of spatial variation in CO₂ emission rate, and temporal variation in this parameter is adequately described by an exponential function of both CWD temperature and air temperature (R² = 0.65–0.75).     

Investigating the oil curse in OECD and Non-OECD oil-exporting economies using green measures of income

Empirical studies investigating the natural resource curse theory mostly employ cross-country and panel regression techniques subject to endogeneity bias. Also, most of these studies employ GDP in its aggregate or per-capita terms as the outcome variable in their analyses. However, the use of GDP measures of income for resource curse investigations may not portray the true incomes of resource-intensive economies. Standard national accounts treat natural resource rents as a positive contribution to income without adjusting for the value of depleted natural resource stock. This treatment often leads to a positive bias in the national income computations of resource-rich economies. Unlike previous studies, we test the robustness of the curse in the predominantly used measures of national income, GDP, by investigating the theme in genuine income measures of economic output as well. Also, the paper deviates from most empirical studies in the literature by using the Arellano–Bond difference GMM method in investigating the oil curse in OECD and Non-OECD oil-exporting economies. Additionally, we employ two alternative measures of resource intensity in our explorations; the share of oil rents in GDP and per-capita oil reserves. Our results provide evidence of the curse in Non-OECD countries employing aggregate and per-capita measures of genuine income.     

Preparation and Characterization of Collagens from the Skin of Northern Snakehead (<Emphasis Type="Italic">Channa argus)</Emphasis>

Channa argus, a type of snakehead fish native to China, is a popular food fish in certain Asian countries but is a known destructive invasive species in the US. In this study, the two collagens, i.e. acid-soluble collagen (ASC) and pepsin-solubilized collagen (PSC), were obtained from C. argus skin. The yield of ASC was 28.0% and that of PSC was 16.8% on the dry bases. The collagens were identified as the collagen of type I by SDS–PAGE patterns. The Tds were approximately 27.0?°C. Similar ultraviolet spectra of both collagens were observed. Fourier Transform infrared spectra indicated PSC structure had a little change due to the loss of terminal domains by pepsin digestion. The results of XRD proved that the two collagens retained their helical structures. The results suggest that the collagens isolated from C. argus can potentially be alternative sources of vertebrate collagens for use in the food and other industries.     

Saharan dust particles in snow samples of Alps and Apennines during an exceptional event of transboundary air pollution

Southern European countries are often affected in summer by transboundary air pollution from Saharan dust. However, very few studies deal with Saharan dust pollution at high altitudes in winter. In Italy, the exceptional event occurred on February 19, 2014, colored in red the entire mountain range (Alps and Apennines) and allowed to characterize the particulate matter deposited on snow from a morphological and chemical point of view. Snow samples were collected after this event in four areas in the Alps and one in the Apennines. The particulate matter of the melted snow samples was analyzed by scanning electron microscopy with energy dispersive X-ray spectrometry (SEM-EDS) and by inductively coupled plasma mass spectrometry (ICP-MS). These analyses confirmed the presence of Saharan dust particle components in all areas with similar percentages, supported also by the positive correlations between Mg-Ca, Al-Ca, Al-Mg, and Al-K in all samples.     

Evaluation of surface water quality by using Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI) method and discriminant analysis method: a case study Coruh River Basin

In this study, the water quality of the Coruh River Basin, which is located in the Eastern Black Sea Region of Turkey, was evaluated. The water quality data measurement results obtained by the State Hydraulic Works 26th Regional Directorate from four different sites over a course of 4 years between the years 2011 and 2014 in the Coruh River Basin were used as the data. In this study, the water quality was evaluated by using the Canadian Council of Ministers of the Environmental Water Quality Index (CCME WQI) method and discriminant analysis (DA). The water quality of the Coruh River Basin was calculated as 30.4 and 71.35 by using the CCME WQI and classified as “poor,” “marginal,” and “fair”. These values show that the water of the Coruh River Basin is degraded and under threat and its overall quality is not close to natural or desired levels. The monitoring sites were divided into two groups by the cluster analysis (CA). DA is a multivariate analysis technique used to divide individuals or objects into different groups and assign them into predetermined groups. As a result of DA, calcium (Ca) and sulfate (SO₄) were determined to be significant parameters in the determination of the water quality of the Coruh River Basin. The success of DA depends on the percentage of correct classification. As a result of the analysis, 23% of the parameters in the first measurement point, 69.2% of the parameters in the second and third measurement points, and 76.9% of the parameters in the fourth measurement point were classified correctly. Since the second measurement point is the discharge point of a copper mine, it can be said that the water quality parameters measured may provide accurate results in detecting pollution at this point.