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In situ release of hydrogen from liquid organic hydrogen carriers―such as methanol by steam reforming, offers a feasible way to potentially solve the problem of hydrogen storage and transport. Here we report that the titanosilicalite-1 (TS-1) zeolite support efficiently accelerates the Pt catalyzed methanol steam reforming (MSR), giving the hydrogen productivity at 639.7 molH2 molPt−1 h−1 at 250 °C, which steadily outperforms the Pt nanoparticles supported on other carriers, including ZSM-5 zeolite, amorphous SiO2, and TiO2 anatase. Further studies reveal that the electronic metal-support interactions (EMSIs) between Pt and TS-1 zeolite occurs, which modifies Pt nanoparticles surface charge state to accelerate the reaction. More importantly, the interactions could stabilize the Pt nanoparticles, achieving sinter-resistant Pt/TS-1 catalyst in the continuous MSR tests. This finding indicates the important role of heteroatoms in the zeolite framework for supported metal nanoparticles, which open a new way to guide the design of highly efficient heterogeneous catalysts in the future.

In this study, a novel metaheuristic optimization algorithm, gradient-based optimizer (GBO) is proposed. The GBO, inspired by the gradient-based Newton’s method, uses two main operators: gradient search rule (GSR) and local escaping operator (LEO) and a set of vectors to explore the search space. The GSR employs the gradient-based method to enhance the exploration tendency and accelerate the convergence rate to achieve better positions in the search space. The LEO enables the proposed GBO to escape from local optima. The performance of the new algorithm was evaluated in two phases. 28 mathematical test functions were first used to evaluate various characteristics of the GBO, and then six engineering problems were optimized by the GBO. In the first phase, the GBO was compared with five existing optimization algorithms, indicating that the GBO yielded very promising results due to its enhanced capabilities of exploration, exploitation, convergence, and effective avoidance of local optima. The second phase also demonstrated the superior performance of the GBO in solving complex real-world engineering problems. Source codes of the GBO algorithm are publicly available at http://imanahmadianfar.com/codes/ .

Increasing attention has been paid to rechargeable aqueous batteries due to their high safety and low cost. However, they remain in their infancy because of the limited choice of available anode materials with high specific capacity and satisfying cycling performance. Bi metal with layered structure can act as an ideal anode material with high capacity; however, the energy storage mechanism has not well elucidated. Herein, we demonstrate that Bi metal enables affording ultra-high specific capacity (254.3 mAh g−1), superior rate capability and a capacity retention of 88.8% after 1600 cycles. Different from the previously-reported redox reaction mechanisms of Bi electrode, efficient (de)alloying of K+ is responsible for its excellent performance. An excellent aqueous Bi battery is fabricated by matching Bi anode with Co(OH)2 cathode in KOH (1 M) electrolyte. Its outstanding performance is quite adequate and competitive for electrochemical energy storage devices.

Bimetallic materials are of great interest in scientific research and applications due to their unique chemical and physical properties. Here, we report a solid strategy to prepare AuNi bimetallic nanoparticles in large spherical cages of ordered mesoporous silica with uniform size distribution (AuxNi/mSiO2). The surface chemical composition and catalytic performance of BMNPs (Bimetallic Nanoparticles) are precisely varied via confined inter-particle hetero-atom migration. The AuxNi/mSiO2 catalyst displayed greatly improved catalytic performance and excellent stability in gas-phase oxidation of benzyl alcohol. Moreover, the strategy has been successfully extended to the synthesis of PtyNi/mSiO2, which performed higher activity and selectivity compared to the monometallic Pt catalyst in CO oxidation. Keywords: Bimetallic nanocatalysts, Au/Pt-Ni, Synergistic effect, Alcohol oxidation, Selective oxidation of CO

It is still a challenge to effectively optimize operation policies for reservoir systems, due to their large-scale and stochastic natures. The development and improvement of the optimization methods for optimizing reservoir operation systems are, therefore, a worthy undertaking. Hence, the objective of this study is to develop an effective hybrid of differential evolution (DE) and particle swarm optimization (PSO) with multi-strategy (MS-DEPSO) to optimize the operating policies for reservoir systems. The proposed MS-DEPSO promotes the local and global search capabilities of the basic DE algorithm to obtain an effective optimal operating policy. Fourteen mathematical functions were applied to verify the performance of the proposed optimization method. Furthermore, a multi-reservoir hydropower system with three various monthly operation periods over 10, 15, and 20 years was used as a real case study to evaluate the efficiency of MS-DEPSO in hydropower energy generation. Finally, the optimal operating rules were obtained based on the reservoir rule curves for a single reservoir with the purpose of agricultural water supply. The results highlighted the competency of the proposed optimization model to reduce the impact of severe drought periods. It is demonstrated in this study that the proposed algorithm has a superior ability to extract the optimal operating rules for reservoir systems.

An efficient and low-cost supported Pt catalyst for hydrogenation of niroarenes was prepared with colloid Pt precursors and α-Fe2O3 as a support. The catalyst with Pt content as low as 0.2 wt% exhibits high activities, chemoselectivities and stability in the hydrogenation of nitrobenzene and a variety of niroarenes. The conversion of nitrobenzene can reach 3170 molconv h−1 molPt−1 under mild conditions (30 °C, 5 bar), which is much higher than that of commercial Pt/C catalyst and many reported catalysts under similar reaction conditions. The spatial separation of the active sites for H2 dissociation and hydrogenation should be responsible for the high chemoselectivity, which decreases the contact possibility between the reducible groups of nitroarenes and Pt nanoparticles. The unique surface properties of α-Fe2O3 play an important role in the reaction process. It provides active sites for hydrogen spillover and reactant adsorption, and ultimately completes the hydrogenation of the nitro group on the catalyst surface.

Non-stationarity due to climate change and/or variability can reduce the capabilities of drought prediction models. The objective of this study is to improve drought prediction by eliminating non-stationarity from temperature time series, a key factor in development and propagation of droughts in a changing climate. In order to relax the assumption of stationarity, an ensemble stationary-based support vector regression (ESSVR) method was developed and compared with the traditional support vector regression (SVR). Three types of drought indices in three time scales (monthly, seasonal, and semiannual), including multivariate, bivariate standardized drought indices, and univariate standardized drought indices were used as the target variables. In an application to the Red River of the North Basin (RRB), the North American Land Data Assimilation System (NLDAS) data from 1979 to 2016 were used for the training and testing of the prediction model. The Pearson correlation, root mean square error (RMSE), and Taylor diagram were used to evaluate the performances of the ESSVR. Remarkably, the distribution of identified change points varies by climate divisions. The results of the SVR and ESSVR in the RRB were compared, demonstrating the better performances of the ESSVR for most of the drought indices, particularly those with higher sensitivity to temperature. It was found that the extreme (high and low) values of hyperparameters mostly assigned by SVR cause a higher risk of overfitting for SVR. In contrast, ESSVR improves the drought prediction by removing the non-stationarity, thus providing more accurate drought predictions, especially for a warming climate.

The optimization field suffers from the metaphor-based “pseudo-novel” or “fancy” optimizers. Most of these cliche methods mimic animals' searching trends and possess a small contribution to the optimization process itself. Most of these cliche methods suffer from the locally efficient performance, biased verification methods on easy problems, and high similarity between their components' interactions. This study attempts to go beyond the traps of metaphors and introduce a novel metaphor-free population-based optimization method based on the mathematical foundations and ideas of the Runge Kutta (RK) method widely well-known in mathematics. The proposed RUNge Kutta optimizer (RUN) was developed to deal with various types of optimization problems in the future. The RUN utilizes the logic of slope variations computed by the RK method as a promising and logical searching mechanism for global optimization. This search mechanism benefits from two active exploration and exploitation phases for exploring the promising regions in the feature space and constructive movement toward the global best solution. Furthermore, an enhanced solution quality (ESQ) mechanism is employed to avoid the local optimal solutions and increase convergence speed. The RUN algorithm's efficiency was evaluated by comparing with other metaheuristic algorithms in 50 mathematical test functions and four real-world engineering problems. The RUN provided very promising and competitive results, showing superior exploration and exploitation tendencies, fast convergence rate, and local optima avoidance. In optimizing the constrained engineering problems, the metaphor-free RUN demonstrated its suitable performance as well. The authors invite the community for extensive evaluations of this deep-rooted optimizer as a promising tool for real-world optimization. The source codes, supplementary materials, and guidance for the developed method will be publicly available at different hubs at http://imanahmadianfar.com and http://aliasgharheidari.com/RUN.html .

Surface depressions play an important role in overland flow generation, infiltration, and other hydrologic processes. They undergo filling, spilling, and merging during rainfall/snowmelt events, affecting hydrologic connectivity and the size of the contributing area. However, such variability is often ignored or oversimplified in traditional hydrologic models. Consequently, they fail to simulate the threshold-controlled overland flow dynamics. The goal of this study is to improve hydrologic modeling, especially for depression-dominated areas, by capturing the variable contributing area and the threshold behavior of overland flow. To achieve this goal, a new depression-oriented variable contributing area (D-VCA) model is developed to simulate the contributing area, runoff dynamics, and their likelihood of occurrence. Specifically, the D-VCA model integrates the simulation of hydrologic processes with a surface topographic analysis procedure, which is able to (1) determine the probability distribution functions of depression storage and the corresponding contributing area and (2) examine the intrinsic changing patterns of depression storage and contributing area. The model was applied to a depression-dominated watershed in North Dakota and its performance was evaluated by comparing the simulated and observed discharges at two USGS gaging stations. The improved performance of the D-VCA model was demonstrated by comparing different modeling methods including the widely-used lumped depression approach. Modeling results demonstrated the unique capabilities of the D-VCA model in simulating depression-influenced overland flow dynamics and the associated threshold behavior. The new model also provides an improved understanding of the evolution of contributing areas and their influence on overland flow generation across different topographic landscapes.

Many developing countries face water deficit due to various reasons such as population growth, limited surface water resources, uneven spatial and temporal distributions of precipitation, industrialization, climate change, and lack of efficient management. This fact has led to a significant reduction in water resources, which has further caused water conflicts among the stakeholders. Hence, developing comprehensive water allocation policies to account for social standards, economic efficiency, and environmental sustainability is necessary. The objective of this study is to integrate the system dynamics simulation-optimization technique, and the Nash bargaining theory for optimal allocations of water resources. The proposed simulation, optimization, and conflict resolution modeling approach was applied for the joint allocation of surface water and groundwater among drinking, industrial, agricultural, and environmental sectors in the Najaf-Abad sub-basin in Iran. To obtain the Pareto front, the NSGA-II multi-objective optimization algorithm was used to maximize the water supply (i.e., minimize the water deficit) (objective function 1) and also minimize the groundwater extraction from the Najaf-Abad aquifer (objective function 2) over the entire operation period. The decision variables, the percentages of surface water and groundwater extractions, were determined for a set of optimal solutions. The optimum water supply (objective function 1) was 16.84 × 106 m3 and the optimum reduction of the water table of the aquifer (objective function 2) was 0.63 m, representing an improvement of 30% over the existing condition. This study demonstrates the ability of the proposed simulation-optimization-conflict resolution modeling approach and its applicability for water resources allocation.

A series of Sm-doped Co3O4 catalysts for the N2O direct catalytic decomposition were synthesized by the sol–gel method using citric acid as a chelating agent and urea as a combustion agent. The doping of Sm significantly altered the activity of N2O decomposition on the Co3O4 catalyst. Sm was incorporated into the Co3O4 matrix, leading to the distortion of Co3O4 lattice with the spinel configuration maintained. Sm-doping can effectively promote the oxygen vacancy generation, and the electron interaction between Co and Sm species occurred as the Co3+ + Sm2+ ↔ Co2+ + Sm3+ redox cycle. Combined with in situ Raman and in situ NAP-XPS results proposed that this redox cycle is the main reason that the Sm-doping accelerates the high-efficiency N2O decomposition.

Background To explore the relationship between sleep disturbances and falls in an elderly Chinese population. Methods Data from 1726 individuals aged 70–87 years from the Rugao Longevity and Ageing Study were used. The Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep variables. Outcomes were falls ≥1 time per year and falls ≥2 times per year. Results A total of 22.7% of the participants experienced ≥1 fall, and 9.8% experienced ≥2 falls per year. Poor sleep quality was associated with ≥1 fall (OR 1.08, 95% CI 1.05–1.12; OR 1.27, 95% CI 1.14–1.41) and ≥2 falls (OR 1.08, 95% CI 1.03–1.14; OR 1.28, 95% CI 1.10–1.48), with an increase per PSQI score and SD PSQI score, respectively. In addition, sleep quality, sleep latency, sleep efficiency, and sleep disturbance subcomponents were associated with an increased risk of ≥1 fall with ORs of 1.44 (95% CI, 1.21–1.72), 1.23 (95%CI,1.09–1.40), 1.12 (95%CI, 1.01–1.23) and 1.70 (95% CI,1.35–2.14), respectively, and were associated with an increased risk of ≥2 falls with ORs 1.54 (95%CI, 1.22–1.96), 1.21(95%CI, 1.02–1.44), 1.17 (95% CI 1.02–1.33), and 1.78 (95%CI, 1.31–2.44), respectively. Further, participants slept ≤5 h per night had an increased risk of ≥1 fall (OR 2.34; 95%CI, 1.59–3.46) and ≥2 falls (OR 2.19; 95%CI, 1.30–3.69). Conclusions Poor sleep quality and several subcomponent sleep symptoms were consistently associated with increased risk of falls ≥1 time and ≥2 times in Chinese elderly. The identification of sleep disturbances may help identify high-risk Chinese elders who may benefit from fall prevention education.

The electrochemical responses of human centrin 3 (HsCen3) binding with hemin were studied by cyclic voltammetry (CV) and differential pulse voltammetry (DPV) using glassy carbon electrodes (GCEs). In CV, the formal potential (E0') of hemin with the addition of HsCen3 shifted from -0.51 to -0.36V (versus saturated calomel electrode, SCE), indicating that a new species of hemin-HsCen3 had formed. Upon binding with HsCen3, the redox current of hemin in CV and DPV decreased significantly. Based on their titration curves, the association constant of HsCen3 with hemin was obtained with a logK of approximately 4, which was consistent with that obtained from spectroscopy. Combining UV-Vis, fluorescence emission, and electrochemical methods, His100 located on the α-helix between the two domains of HsCen3 was identified as the ligand binding residue of hemin. The protein binding-induced change in electrochemical signal was thus used to construct the diffusion coefficient (D=1.43×10-7cm2/s), the charge-transfer coefficient (α=0.49), and electron transfer standard rate constant (ks=2.54×10-2s-1) in the presence or absence of HsCen3. The electrochemical investigation of hemin bound with HsCen3 may provide useful data for understanding the biological processes of calcium-binding protein.

Here we reported the synthesis of Al sites with ultra-high dispersion on anatase by an atomic layer deposition (ALD) method (ALD-Al/TiO2), which exhibits Bronsted acidity and satisfactory activity in the dehydration of alcohols, a key step in the deoxygenation of biomass. More importantly, the ALD-Al/TiO2 catalyst has good stability, which is sintering-resistant and gives constant catalytic performances after treatments at high temperature.

Climate-driven temperature variations manipulate the snowmelt process and affect various aspects of aquatic and terrestrial life. The objective of this study is to evaluate the impacts of sub-daily temperature fluctuations around the freezing temperature on the quantity and spatial distribution of macro-scale snowmelt simulations (i.e., over macro-scale basins and monthly or annual temporal scales). A macro-scale snow accumulation and ablation model was developed to account for sub-daily temperature fluctuations. Unlike other existing macro-scale models that rely upon a single daily average temperature, the new model incorporates minimum and maximum temperatures and their occurrence timing within the day in a hybrid temperature index method (HTIM). The model was applied to the Missouri River Basin (MRB) to simulate grid-based snowmelt for water years 2011 and 2012 and compared with the monthly snowmelt data from the SNOw Data Assimilation System (SNODAS), which is a physically-based energy and mass balance snow model. Also, the HTIM was compared with a standard TIM, in which only a daily average temperature was used for snowmelt simulations. Although the HTIM provided simulations comparable to the SNODAS snowmelt data (R2 = 0.9), overall results indicated that the TIM slightly outperformed the HTIM. When minimum temperatures fluctuated around the freezing temperature, HTIM underestimated the snowmelt over the MRB. However, HTIM showed a better performance particularly in the months when maximum temperatures fluctuated around the freezing temperature. Results suggested that the role of refreezing in the generation of snowmelt was overestimated by HTIM, which led to a more significant discrepancy between the SNODAS and HTIM snowmelt values in specific months. The findings from this study highlight the influence of sub-daily temperature fluctuations on macro-scale snowmelt simulations.

Surface depressions are important topographic characteristics for surface runoff initiation, and the spatial distribution of depressions may further affect the timing and quantity of surface runoff reaching channels and outlets. However, many hydrologic models simulate the fill-spill processes for depression-dominated areas in a lumped manner and release outflows from depressions to channels or outlets directly. As a result, the progressive formation of contributing area (CA) and the dynamic runoff contribution process are not well characterized. The objective of this study is to improve depression-oriented hydrologic modeling by incorporating the influence of depression storages and their spatial distribution into the simulation of surface runoff generation and flow routing. To achieve this objective, a modified depression-oriented variable contributing area (MD-VCA) model is developed to simulate the depression-dominated catchment response during rainfall events, which employs a new depressional time-area zone scheme to deal with the spatially distributed depression storages, tracks the intrinsic changing patterns of the connected areas and depression storage, simulates the connected area-based surface runoff generation dynamics, implements a new CA-based surface runoff routing technique, and quantifies the likelihood of occurrence of outlet CA and runoff contributions using the joint probability distribution associated with depression storages and their spatial distribution. The performance of the MD-VCA model was evaluated through the application to a depression-dominated watershed in the Prairie Pothole Region of North Dakota. Simulation results demonstrated that the MD-VCA model was able to simulate the threshold-controlled overland flow dynamics under different rainfall conditions, and it effectively revealed the influences of depression storages and their spatial distribution on surface runoff generation and propagation processes.

In irrigation channel system, diverse side orifice shapes (e.g., circular, rectangular, or triangular) are widely designed for flow control and regulation. Hence, accurate estimating the discharge diverted from the main channel to the side one is an essential for water management. The main objective of this research is to estimate the discharge coefficient ( C d ) for a sharp-crested triangular side orifice under a free flow condition. Three linear data-driven models including locally weighted learning regression (LWLR), multiple linear regressions with interaction (MLRI), and multivariate linear regression (MLR) were developed for this purpose. 570 experimental datasets were used to build the predictive models. Two modeling scenarios (with and without incorporating the upstream flow Froude number) were investigated for estimating C d . The best input combinations for both scenarios were identified by applying the Gamma test approach. The performance of the models was assessed by using various graphical analysis and statistical metrics. The modeling results indicated that LWLR and MLRI had similar good performance for both modeling scenarios and provided accurate estimation of the C d values. Overall, this study demonstrated the capacities of the data-driven models in estimating the discharge coefficient of triangular side orifice under a free flow condition.

Many water needs are provided by reservoirs. Increasing water retention time in reservoirs leads to thermal stratification and, consequently, affects the water quality of reservoirs. This situation can be exacerbated under climate change. The objective of this study is to assess the climate change effect on thermal stratification of the Aidoghmoush reservoir (East Azarbaijan, Iran) for baseline (1987–2000) and climate change (2026–2039) periods. For this purpose, future temperature and precipitation were estimated by HadCM3 under the A2 emission scenario, and inflow of the reservoir in 2026–2039 was simulated by IHACRES. Results show that the average annual reservoir inflow in climate change decreases about 1% relative to the baseline. In addition, CE-QUAL-W2 was calibrated and used to simulate the thermal stratification of the reservoir. Results show that on average, the surface and bottom water temperatures of the reservoir during the climate change period respectively increase by 1.69 and 1.24 °C compared to those of the baseline. The maximum and minimum surface water temperatures under climate change are respectively 0.14–3.98 °C higher than those in the baseline. Under climate change, thermal stratification mainly occurs in shallow water of the reservoir in spring and summer, but it is extended to deeper water in autumn. In winter, however, complete mixing may occur.

Hydrologic processes in depression-dominated areas are controlled by the spatial distribution of surface depressions and their dynamic hydrologic connectivity. Existing hydrologic models often utilize lumped ways to handle depressions, and hence their spatial features are lost in this simplification process. In this study, a unique watershed-scale, semi-distributed hydrologic model accounting for dynamic hydrologic connectivity (HYDROL-DC) is developed and the functionalities of depressions in runoff generation processes and hydrologic connectivity are respectively quantified by introducing two concepts of depression impact coefficient and connected area. Unlike other models, a new modeling framework is proposed in HYDROL-DC to facilitate separate modeling for the puddle-based units, off-stream channel-based units, and on-stream channel-based units of each subbasin. HYDROL-DC was applied to the Edmore Coulee watershed in North Dakota. The modeling results showed that depressions significantly influenced hydrologic processes and their impact capacities were mainly dominated by the storages and spatial distributions of depressions. In a subbasin with depressions of similar storage, depending on the spatial distribution of fully-filled depressions, hydrologic connectivity varied within a range. With the increase in depression storage, the variation of the range exhibited a progressive and hierarchical pattern. Additionally, depressions “blocked” the pathways of runoff water from the activated topographic units to the associated outlet in a subbasin, and such a “blocking” function became significant as more depressions were fully filled.

This study investigated whether sleep duration and quality were related to life satisfaction (LS) among older Chinese adults and whether depression mediated those relationships. Cross-sectional data from the aging arm of the Rugao Longevity and Aging Study were used. Sleep duration, sleep quality, depression, LS and covariates were analyzed using logistic regressions. To assess the potential mediation of depression on the association between sleep duration and quality and LS, Aroian tests were used. Of 1756 older Chinese adults aged 70-84 years, 90.7% of the men and 83.3% of the women reported being satisfied with their lives. After adjusting for covariates, older adults who slept ≤6h per night were more likely to suffer from life dissatisfaction compared with those who slept 7-8h (OR=2.67, 95% CI 1.86-3.79), and individuals who slept poorly were almost 2 times (OR=2.91, 95% CI 2.16-3.91) more likely to have life dissatisfaction. The Aroian tests confirmed that these relationships were partially mediated by depression (p

Background C-reactive protein (CRP), an important inflammatory biomarker, has been linked to various diseases (e.g., cardiovascular disease). Here, we aimed to investigate the associations of high sensitivity C-reactive protein (hsCRP) with frailty and overnight hospital admission in an elderly Chinese population. Methods We used cross-sectional data from 1478 participants (aged 70–84 years) who were randomly recruited from 31 villages in Jiang'an township, Rugao city, China. Frailty status was defined using a modified frailty phenotype. The number of overnight hospital admissions was ascertained. Results The sample mean age was 75.3 years, and 53.0% of participants were female ( n =784). The mean level of hsCRP was 3.6mg/L. From the low (hsCRP≤1.00mg/L) to high hsCRP (hsCRP≥3.0mg/L) group, the proportion of overnight hospital admission increased linearly (8.7%, 10.6%, vs. 15.5%; P for trend=0.007). For frailty, the age- and sex-adjusted odds ratios (ORs) were 1.21 (95% CI: 0.89, 1.64) for participants in the intermediate hsCRP group and 1.49 (95% CI: 1.05, 2.09) for participants in the high hsCRP group compared with those in the low hsCRP group. For overnight hospital admission, the corresponding ORs were 1.26 (95% CI: 0.72, 2.19) and 1.94 (95% CI: 1.08, 3.48), respectively. After adjustment for other covariates, the significance of these associations remained. Conclusions Elevated levels of hsCRP were associated with increased risks of frailty and overnight hospital admission among elderly individuals in Rugao. More public health concerns about inflammation and subsequent outcomes are needed to improve the quality of life in elderly populations.

Modeling of overland flow for a depression-dominated area is a difficult task due to the spatial distribution of depressions. Particularly, the hierarchical relationships of depressions and their variable contributing areas make the hydrologic modeling more complicated. In traditional hydrologic modeling, however, digital elevation models (DEMs) that are used to represent surface topography are often edited by filling depressions before delineation, and the impacts of depressions are considered by using certain simplified and lumped approaches. Due to such flaws, these hydrologic models fail to account for the actual dynamic influences of surface depressions. The objective of this study is to improve hydrologic modeling for depression-dominated regions by developing a new modeling framework to quantify the influence of spatially distributed depressions. To achieve this objective, a new puddle-based unit (PBU) probability distributed model (PBU-PDM) is developed and coupled with the existing Soil and Water Assessment Tool (SWAT). The PBU-PDM facilitates separate simulations for non-depressional and depressional areas, and accounts for the hierarchical relationships of depressions, their filling and spilling processes, and the dynamic variations in their contributing areas. The PBU-PDM enhanced SWAT was applied to the Upper Maple River watershed in North Dakota, and calibrated and validated using the observed data. In particular, it was also compared with the original SWAT model, which demonstrated the abilities of PBU-PDM to mimic the filling-spilling overland flow dynamics and the threshold behavior. The PBU-PDM provides improved watershed-scale hydrologic modeling, especially for depression-dominated areas.

Total dissolved solids (TDS) are recognized as an essential indicator of surface water quality. The current research investigates the potential of a novel computer aid approach based on the hybridization of wavelet pre-processing with multigene genetic programming (W-MGGP) for monthly TDS prediction at the Sefid Rud River in Northern Iran. 20-year historical monthly river flow (Q) and TDS data measured at the Astaneh station were used for the model training and testing. The employed time series data were decomposed into several sub-series using three mother wavelets (i.e., Daubechies4 (db4), biorthogonal (bior6.8), and discrete meyer (dmey)) to assess appropriate combinations of the time series and their lag times, which were further used for prediction process. The W-MGGP model was compared against the wavelet-gene expression programming (W-GEP), stand-alone MGGP, and GEP models. Results were evaluated using several performance metrics including root mean square error (RMSE), correlation coefficient (R), and Nash-Sutcliffe efficiency (NSE). Modeling results indicated that W-MGGP and W-GEP provided a superior prediction capacity for the TDS in comparison with the other stand-alone artificial intelligence (AI) models. The discrete meyer method exhibited the best performance in time series data decomposition as a pre-processing approach. The proposed W-MGGP model based on the dmey mother wavelet attained the best statistical metrics (R = 0.942, RMSE = 90.383, and NSE = 0.862). The research findings demonstrated the hybridization of the wavelet pre-processing approach with MGGP predictive model for the TDS simulation.

The Horton equation has been widely used to simulate infiltration under ponding conditions and many efforts have been made to expand the applicability of this method to non-ponding conditions. However, because of the absence of appropriate drainage controls, most existing modified Horton methods cannot be used for continuous rainfall-runoff simulations with long dry time periods. The objective of this study is to develop a modified Horton infiltration model (MHI) for both event and continuous simulations with soil moisture threshold controls. Unlike other modified Horton methods, nonlinear and linear equations are used in this new method to quantify the relationships between infiltration capacity and soil water storage respectively for wet and dry soil moisture conditions (wet: gravity-driven free drainage stage; dry: capillarity-dominated non-drainage stage). Two tests of the MHI model were conducted to evaluate its performance in both event and continuous simulations. Fourteen modeling scenarios were considered in the event modeling test of MHI, which involved three soil types of different initial water contents under various rainfall conditions. The simulation results of MHI were compared with those simulated by a modified Green-Ampt model and the reference data from Mein and Larson (1971). Furthermore, MHI was applied to a field site in Grand Forks, North Dakota for continuous modeling and compared against the observed data, which demonstrated its capability in the modeling of infiltration and soil moisture variations.

This study introduces a new Macro-scale Hydrologic Processes Simulator (Macro-HyProS), tailored for cold climate regions and depression-dominated terrains. Macro-HyProS is a grid-based hydrologic model of a unique structure to deal with hydrologic complexities in depression-dominated cold climate regions. The model runs on a daily time step and incorporates a LEGO-fashion horizontal layout to account for sub-grid land use heterogeneity. On the vertical layout, each grid consists of different bands, each of which is responsible for simulating specific hydrologic processes. Macro-HyProS employs improved methodologies to account for snow accumulation and ablation, depressions, and frozen ground condition. The Red River of the North Basin (RRB) was selected to highlight the capabilities of Macro-HyProS in describing the spatiotemporal complexities associated with depression-dominated cold climate regions. Results indicated that the model provided a satisfactory performance, having the Nash–Sutcliffe efficiency coefficients of 0.56 and 0.72 in the calibration and validation periods, respectively. The modeling results accentuated the impacts of frozen ground, snowmelt, and depressions in the generation of surface runoff over the basin. On average, the RRB experienced 145 days of frozen or partially frozen ground conditions per year, which coincided with early spring rainfall and snowmelt events. Due to the widespread frozen ground conditions over the basin, up to 60% of the generated snowmelt on specific days contributed to surface runoff, whereas infiltration had a smaller portion. Also, the results revealed that the depression-dominated areas acted as “regulators” of surface runoff, particularly in early springs. Macro-HyProS provides spatiotemporal simulations of various hydrologic processes to facilitate a better understanding of macro-scale hydrologic trends.

Realistic modeling of discontinuous overland flow on irregular topographic surfaces has been proven to be a challenge. This study is aimed to develop a new modeling framework to simulate the discontinuous puddle-to-puddle (P2P) overland flow dynamics for infiltrating surfaces with various microtopographic characteristics. In the P2P model, puddles were integrated in a well-delineated, cascaded drainage system to facilitate explicit simulation of their dynamic behaviors and interactions. Overland flow and infiltration were respectively simulated by using the diffusion wave model and a modified Green–Ampt model for the DEM-derived flow drainage network that consisted of a series of puddle-based units (PBUs). The P2P model was tested by using a series of data from laboratory overland flow experiments for various microtopography, soil, and rainfall conditions. The modeling results indicated that the hierarchical relationships and microtopographic properties of puddles significantly affected their connectivity, filling–spilling dynamics, and the associated threshold flow. Surface microtopography and rainfall characteristics also exhibited strong influences on the spatio-temporal distributions of infiltration rates, runoff fluxes, and unsaturated flow. The model tests demonstrated its applicability in simulating microtopography-dominated overland flow on infiltrating surfaces.

La 0.75 Sr 0.25 MnO 3 perovskite nanofibers were successfully synthesized by electrospinning method and structurally characterized. These nanofibers display higher catalytic activities for CO oxidation and CH 4 combustion than the nanoparticles prepared by traditional hydrothermal method. The high catalytic activities could be attributed to the large surface areas, pore volumes and excellent thermal stability even under 750 °C calcination. In addition, the nanofibers provide more contents of Mn 4+ sites and adsorbed surface oxygen atoms for the dissociation of gaseous O 2 on the surface.

A kind of highly efficient and low cost supported-Pt catalyst for oxidation of toluene is obtained through controlling both the chemical state of Pt active sites and surface properties of supports. The optimized Pt/Al2O3 catalyst with Pt loading as low as 0.1 wt% could completely convert toluene (1000 ppm) to CO2 at about 180 ℃ under a space velocity of 24,000 mL g−1 h−1. It also exhibits a high stability and moisture resistance properties under reaction atmosphere. TOF value calculated with the dispersion of Pt can reach 0.0685 s-1, representing a high utilization efficiency of Pt. A series of characterizations are carried out to investigate the key factors affecting the catalytic performance of Pt/Al2O3, and disclose the concrete role of Pt and Al2O3 in the activation of molecular oxygen and toluene. In situ DRIFTS and EPR results show that Pt is the active center for O2 activation. Metallic Pt nanoparticles (Pt0) can activate the molecular O2 even at room temperature. Al2O3 offers sites for the adsorption of toluene and desorption of CO2 product. The weak and medium strength of acid-base sites favorite the adsorption and desorption process. The oxidation of toluene over Pt/Al2O3 obeys modified L-H mechanism. The synergistic effect of metallic Pt nanoparticles and suitable Al2O3 support is critical for obtaining highly efficient Pt-based catalysts with low Pt contents for toluene oxidation.

Summary Land surfaces are generally not smooth and show certain irregularity. Overland flow on such surfaces is essentially discontinuous and exhibits strong variability and complexity. Modeling of the spatio-temporal variability and heterogeneity of overland flow and the hydrotopographic effects has been proven to be a challenge. The objective of this study is to quantitatively describe the intrinsic spatio-temporal variations in hydrologic connectivity associated with overland flow generation. Firstly, a puddle-to-puddle (P2P) hydrologic connectivity concept was proposed to characterize runoff generation processes and the related spatio-temporal dynamics. Secondly, a laboratory overland flow experiment was conducted to characterize the dynamic puddle filling and spilling processes, hydrologic connectivity, and outlet discharge. Thirdly, a conceptual P2P model was applied to simulate the P2P overland flow dynamics, calculate flow discharge, and track the evolution of hydrologically connected areas. Particularly, two modified hydrologic connectivity indices, time-varying connectivity function and connectivity length, were proposed to characterize the properties and dynamic changes in hydrologic connectivity. Furthermore, the influences of surface topography, rainfall, and surface slope on hydrologic connectivity were evaluated. The proposed hydrologic connectivity indices effectively revealed the variability and the threshold behavior of overland flow generation. It was demonstrated that the dynamic P2P processes governed hydrologic connectivity, controlled surface runoff generation, and altered the flow drainage patterns. Temporal variations of rainfall intensity changed the occurrence timing of the P2P dynamics and evolution of connected areas, while spatial variations of rainfall intensity directly influenced the overall development of hydrologic connectivity of a hydrologic system. Surface slope showed considerable influences on hydrologic connectivity. The results suggested that critical slope(s) could exist, at which a sharp change in flow drainage area and hydrologic connectivity occurred.

A Windows-based integrated pesticide transport modeling system (IPTM-CS) has been developed for simulating three-phase (dissolved, adsorbed, and vapor phases) pesticide environmental fate in a canopy-soil system (or in the vadose zone alone). The modeling system integrates pre-processing of data (parameters estimation and input), model run, and post-processing of simulation results (summary tables, Excel spreadsheets, and graphs) in a user-friendly Windows interface. To facilitate parameter estimation, an extensive data supporting system that includes convenient parameter calculators and databases has also been developed and incorporated in the IPTM-CS. The data supporting system also provides links to a number of web-based databases maintained by government agencies and institutions. A time-continuous and space-discrete (TC-SD) method is employed to solve the transport problem and five different solution schemes of varying accuracy and features have been incorporated in the interfaced IPTM-CS. Finally, testing of the IPTM-CS at a field site in Chico, California is presented. Comparison of the simulated and observed diazinon concentrations is conducted for three selected depths (0.15, 0.25, and 0.5m) and the results are further evaluated by using three methods (linear regression, normalized objective function, and modeling efficiency). The quantitative evaluation indicates that the IPTM-CS yields fairly good simulations.

Few infiltration models, based on the Green–Ampt approach, in current use are suitable for simulating infiltration into heterogeneous soils of variable initial moisture distributions during unsteady rainfall. An algorithm is proposed for determining the ponding condition, simulating infiltration into a layered soil profile of arbitrary initial water distributions under unsteady rainfall, and partitioning the rainfall input into infiltration and surface runoff. Two distinct periods, pre-ponding and post-ponding, are taken into account. The model tracks the movement of the wetting front along the soil profile, checks the ponding status, and in particular, handles the shift between ponding and non-ponding conditions. The modeling also covers a fully saturated flow condition when the wetting front reaches the bottom of the soil profile (water table). Detailed procedures that deal with the variability in both soil properties and rainfall intensity are presented. For the purpose of model testing, three different cases (homogeneous soil and unsteady rainfall; layered soils and steady rainfall; and layered soils and unsteady rainfall) were discussed. Comparisons of the developed model with other infiltration models (both modified Green–Ampt infiltration model and fully numerical model) and field measurements were conducted and good agreements were achieved.

A time-continuous and space-discrete method is proposed for three-phase (dissolved, adsorbed, and vapor phases) pesticide transport modeling in the vadose zone and a generalized semidiscrete solution is derived under conditions of heterogeneous media, unsteady flow fields, and space-time-dependent physical and biochemical processes concerning pesticide environmental fate. The developed model also takes into account pesticide runoff and erosion and pesticide transport in the plant canopy zone. The model is able to deal with various pesticide application methods commonly used in practice, including over-canopy, under-canopy, or any combined foliar and soil surface spray, as well as soil-incorporated applications. The hybrid semidiscrete solution method, formulated herein, incorporates analytical and numerical methodologies into a uniform, flexible modeling framework, which makes the model suitable for either screening-level investigations as a lumped model or detailed studies as a distributed model. Furthermore, the semidiscrete pesticide transport model is applied in the Orestimba Creek Basin, California, for evaluating the vulnerability of the hydrosystem to diazinon contamination. In accordance with the observations, the simulation demonstrates that diazinon exposure levels in the creek frequently exceed criteria for aquatic life, and many peak pulses even exceed the water-quality standard for human health. In the subsurface environment, however, high concentrations of diazinon are generally limited within the shallow soil. It is also found that the magnitude and timing of pesticide application and rainfall/irrigation dominate exposure levels of diazinon in both subsurface and surface environments.