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.     

Towards saving freshwater: halophytes as unconventional feedstuffs in livestock feed: a review

Water represents 71% of all earth area and about 97% of this water is salty water. So, only 3% of the overall world water quantity is freshwater. Human can benefit only from 1% of this water and the remaining 2% freeze at both poles of earth. Therefore, it is important to preserve the freshwater through increasing the plants consuming salty water. The future prosperity of feed resources in arid and semi-arid countries depends on economic use of alternative resources that have been marginalized for long periods of time, such as halophytic plants, which are one such potential future resource. Halophyte plants can grow in high salinity water and soil and to some extent during drought. The growth of these plants depends on the contact of the salted water with plant roots as in semi-desert saline water, mangrove swamps, marshes, and seashores. Halophyte plants need high levels of sodium chloride in the soil water for growth, and the soil water must also contain high levels of salts, as sodium hydroxide or magnesium sulfate. There are many uses for halophyte plants, including feed for animals, vegetables, drugs, sand dune stabilizers, wind shelter, soil cover, wetland cultivation, laundry detergents, and paper production. This paper will focus on the use of halophytes as a feed additive for animals. In spite of the good nutritional value of halophytes, some anti-nutritional factors as nitrates, nitrite complexes, tannins, glycosides, phenolic compounds, saponins, oxalates, and alkaloids may be present in some of them. The presence of such anti-nutritional agents makes halophytes unpalatable to animals, which tends to reduce feed intake and nutrient use. Therefore, the negative effects of these plants on animal performance are the only objection against using halophytes in animal feed diets. This review article highlights the beneficial impact of considering halophytes in animal feeding on saving freshwater and illustrates its nutritive value for livestock from different aspects.     

Groundwater Modeling in Agricultural Watershed under Different Recharge and Discharge Scenarios for Quaternary Aquifer Eastern Nile Delta,Egypt

Different scenarios of recharge and discharge were assessed for sustainable management of groundwater in Quaternary aquifer east of Nile Delta. MODFLOW was utilized to investigate the effect of land use change and damming construction in the upstream of the Nile River on the current and short-term groundwater management strategies. The interpretive transient simulation was performed between 2004 and 2016 after steady-state calibration in 2004, and transient state from 2004 to 2013 with different irrigation recharges associated with land use change in this period. Sensitivity analysis was performed for hydraulic conductivities, recharge, and conductance parameters. The predictive transient simulation was run till 2023 under three scenarios of increasing pumping rates by 15, 30, and 50% for agriculture expansion and specified head reduction of Port Said Canal by 0.2, 0.4, and 0.6 m associated with the reduction of Nile water levels after Grand Ethiopian Residence Dam, GERD operation in 2017. Results from the in- and out-flow budgets showed that groundwater aquifer is stable at the current rate of pumping till 2023. Groundwater heads decreased by 0.2 and 0.42 m in the southern section, and a slight increase in the northern part was noticed for the first and second scenarios, respectively. When additional pumping stress is applied (50% increase), groundwater head dropped by 0.66 m, and the storage is no longer able to maintain the aquifer capacity after 2020 (worst-case scenario).     

Air Quality Modeling Using the PSO-SVM-Based Approach,MLP Neural Network,and M5 Model Tree in the Metropolitan Area of Oviedo (Northern Spain)

The main aim of this study was to construct several regression models of air quality using techniques based on the statistical learning, in the metropolitan area of Oviedo, in northern Spain. In this research, a hybrid particle swarm optimization-based evolutionary support vector regression is implemented to predict the air quality from the experimental dataset (specifically, nitrogen oxides, carbon monoxide, sulfur dioxide, ozone, and dust) collected from 2013 to 2015 in the metropolitan area of Oviedo. Furthermore, a multilayer perceptron network (MLP) and the M5 model tree were also fitted to the experimental dataset for comparison purposes. Finally, the predicted results show that the hybrid proposed model is more robust than the MLP and M5 model tree prediction methods in terms of statistical estimators and testing performances.     

Plant Species Richness and Developmental Morphology Stage Influence Mycorrhizal Patagonia Plants Root Colonization

The objectives of this study were to determine the percentage of root colonization by arbuscular mycorrhizal (AM) fungi at various levels of plant species richness and developmental morphology stages in various perennial grass, and herbaceous and woody dicots species using experimental plots during 2013 and 2014. An auger was used to obtain six replicate root + soil samples at each sampling time on each of the study parameters. Roots were washed free of soil, and percentage AM was determined. The shrub Larrea divaricata was the species which showed the lowest percentage of colonization by AM at the vegetative developmental morphology stage at the monocultures and six-species-mixtures on the experimental plots. Dicots, but not grass, species showed a greater percentage colonization by AM fungi at the greatest (i.e., six-species-mixtures) than lowest (i.e., monocultures) species richness. Although at different degrees of species richness and developmental morphology stages, the perennial grasses Nassella longiglumis and N. tenuis, the herbaceous dicot Atriplex semibaccata, and the shrubs L. divaricata and Schinus fasciculatus showed a greater (p < 0.050) percentage colonization by AM fungi during the second than the first study year. Even though it was species- and sampling time-dependent, percentage colonization by AM fungi increased as species richness also increased most of the times. Our results demonstrated that the plant species differences in percentage colonization by AM fungi in the experimental plots were species richness-, developmental morphology stage-, and sampling-time dependents.     

Discrete-choice experiments valuing local environmental impacts of renewables: two approaches to a case study in Portugal

Despite the often mentioned environmental benefits associated with transition from fossil fuels to renewable energy sources, their use for electricity production has non-negligible negative environmental impacts. The most commonly mentioned in surveys concern different types of landscape impacts, impacts on the fauna and flora, and noise. These impacts differ by size and location of plants, and by source of energy, rendering the policy decision complex. In addition, there are other welfare issues to take into consideration, as positive and negative environmental impacts are not evenly distributed among population groups. This paper proposes to compare the welfare impacts of renewable energy sources controlling for the type of renewable as well as the specific environmental impact by source. To this end, two discrete-choice experiments are designed and applied to a national sample of the Portuguese population. In one case, only individual negative impacts of renewables are used, and in another case, the negative impacts interact with a specific source. Results show the robustness of discrete-choice experiments as a method to estimate the welfare change induced by the impacts of renewable energy sources. Overall, respondents are willing to pay to reduce the environmental impacts, thus making compensation for local impacts feasible. Moreover, the estimations reveal that respondents are significantly sensitive to the detrimental environmental effects of specific renewable energy sources, being willing to pay more to use these sources of energy relative to others.     

Improvement of Impact Toughness of Biodegradable Poly(butylene succinate) by Melt Blending with Sustainable Biobased Glycerol Elastomers

Poly(butylene succinate) (PBS) was melt blended with glycerol based polyesters (PGS) synthesized from pure and technical glycerol aiming to improve the impact strength of PBS. It was found that after addition of 30 wt% PGS to PBS its impact strength was significantly increased by 344% (from 31.9 to 110 J/m) and its elongation at break was maintained at 220%. Infrared spectra of the blends showed the presence of hydroxyl groups from the PGS phase suggesting that hydrogen bonding between the phases could be responsible for a good stress transfer and an efficient toughening in the PBS/PGS blends. Scanning electron microscopy imaging showed a good dispersion of PGS phase into PBS with a PGS particle size of 10 μm and less and no agglomeration. Addition of PGS to PBS was shown to be an effective strategy for improvement of PBS impact resistance without serious detrimental effects on its thermal and rheological properties.     

Exploring dynamism of cultural ecosystems services through a review of environmental education research

The field of cultural ecosystem services (CES) explores the non-material benefits that ecosystems provide to people. Human perceptions and valuations change, for many reasons and in many ways; research on CES, however, rarely accounts for this dynamism. In an almost entirely separate academic world, research on environmental education (EE) explores how EE programming affects peoples’ attitudes and values toward the natural world. In this review of 119 EE research publications, we explore whether CES (and the adjacent concept of relational values) can be dynamic. We approach this via two lines of inquiry that explore whether EE may instigate this change. First, we investigate whether the EE community measures (and tries to affect) CES-related outcomes. Second, we ask: Has EE research detected changes in CES-related outcomes? We find the EE programs measure many CES outcomes (e.g., aesthetic appreciation, social connectedness), and that in most cases studies observe increases in these outcomes after EE experiences.     

Assessment using CFD of the wind direction on the air discharges caused by natural ventilation of a poultry house

Air inside poultry houses must be removed on a regular basis to prevent excess of heat, particles and noxious gases that can imperil animals. To cope with this issue, natural ventilation could be an effective method when assisted by accurate predictions. This study investigates air discharges caused by natural ventilation of a poultry house by means of a three-dimensional computational fluid dynamics (CFD) model. It solves the governing equations of momentum, heat and mass transport, radiative transfers and animal-generated heat. Wind directions of 0°, 36° and 56° (0° corresponds to a wind blowing perpendicular to the ridgeline) were investigated; the CFD model predictions achieved a RMSE of 1.2 °C and 0.6 g[H₂O] kg^?1 [dry air] for internal temperature and absolute humidity, respectively, when air blew with an angle of 36°. Air renewal rates (ARR) were 39.5 (±?1.9), 34.9 (±?2.2) and 33.6 (±?1.7) volumes of the building per hour, when air blew at 0°, 36° and 56°, respectively. Such ARR predictions served to know how the gases contained in air would likely spread downstream from the building in order to define regions of potentially high gas concentration that could endanger neighbouring habitable facilities.