Development and Evaluation of Bankfull Hydraulic Geometry Relationships for the Physiographic Regions of the United States

Bankfull hydraulic geometry relationships are used to estimate channel dimensions for streamflow simulation models, which require channel geometry data as input parameters. Often, one nationwide curve is used across the entire United States (U.S.) (e.g., in Soil and Water Assessment Tool), even though studies have shown that the use of regional curves can improve the reliability of predictions considerably. In this study, regional regression equations predicting bankfull width, depth, and cross‐sectional area as a function of drainage area are developed for the Physiographic Divisions and Provinces of the U.S. and compared to a nationwide equation. Results show that the regional curves at division level are more reliable than the nationwide curve. Reliability of the curves depends largely on the number of observations per region and how well the sample represents the population. Regional regression equations at province level yield even better results than the division‐level models, but because of small sample sizes, the development of meaningful regression models is not possible in some provinces. Results also show that drainage area is a less reliable predictor of bankfull channel dimensions than bankfull discharge. It is likely that the regional curves can be improved using multiple regression models to incorporate additional explanatory variables.     

Two stages kinetics of municipal solid waste inoculation composting processes

IntroductionCompostingisanatureprocess,butmanyman inducedinnovativemeasureshavebeendevelopedtoacceleratecompostingefficiency .Variousspecializedseedinoculumshavebeenappliedforpracticalengineering(Biey ,2 0 0 0 ) .ThemicrobialfloraandthemassconcentrationofmicrobesinMSWcompostingprocessesunderlyinginoculationaredifferentinvariousstages(Lei,2 0 0 0 ;Akihito ,1998) .Atthesametime ,thelivingenvironmentofmicroorganismsisalsoincessantlychangingduetotheincreaseofmetabolizingproductionandconsumption…     

A performance indicator for ranked set sampling using ranking error probability matrix

Ranked set sampling (RSS) is a sampling procedure that has been shown to provide more efficient procedures than simple random sampling, in particular the Mann-Whitney-Wilcoxon (MWW) statistic and the empirical distribution function (EDF). We briefly review the work of Bohn (1992) and Stokes and Sager (1988) on the effect of imperfect ranking on the RSS-based MWW test and on the RSS-based EDF, respectively. We propose a model for a ranking error probability matrix which we hope will become a useful tool for evaluating RSS-based statistical procedures     

Energy-efficient removal of carbamazepine in solution by electrocoagulation-electrofenton using a novel P-rGO cathode

In this study,carbamazepine(CBZ) decay in solution has been studied by coupling electrocoagulation with electro-Fenton(EC-EF) with a novel P-rGO/carbon felt(CF) cathode,aiming to accelerate the in-situ generation of ·OH,instead of adding Fe²⁺ and H₂ O₂.Firstly,the fabricated P-rGO and its derived cathode were characterized by XRD,SEM,AFM,XPS and electrochemical test(EIS,CV and LSV).Secondly,it was confirmed that the performance in removal efficiency and electric ...     

Mathematical Modeling of Leachates from Ash Ponds of Thermal Power Plants

The present study describes the development of empirical models for the prediction of various trace metals i.e., Mn, Cu, Fe, Zn and Pb found in the leachates generated from the ash ponds of various thermal power plants. The dispersion phenomenon of these trace metals followed first order reaction rate kinetics. The empirical models for individual trace metals derived from the lab scale models data correlate well with the real field data with regression coefficients varying from 0.93 to 0.98. The predicted concentrations of the trace metals varied within ±3% of the observed values in the leachates generated from the ash ponds of four thermal power plants with standard deviation varying from 0.001 to 0.032. The empirical models derived from the study can be applied for prediction of trace metals in leachates generated from similar thermal power plants.     

Single and joint effects of pesticides and mercury on soil urease

The influence of two pesticides including chlorimuron-ethyl and furadan and mercury （Hg） on urease activity in 4 soils （meadow burozem and phaeozem） was investigated. The soils were exposed to various concentrations of the two pesticides and Hg individually and simultaneously. Results showed that there was a close relationship between urease activity and organic matter content in soil. Chlorimuron-ethyl and furadan could both activate urease in the 4 soils. The maximum increment of urease activity by chlorimuronethyl was up to 14%-18%. There was almost an equal increase （up to 13%-21%） in the urease activity by furadan. On the contrary, Hg markedly inhibited soil urease activity. A logarithmic equation was used to describe the relationship （P〈0.05） between the concentration of Hg and the activity of soil urease in the 4 tested soils. Semi-effect dose （ED50） values by the stress of Hg based on the inhibition of soil urease in the 4 soils were 88, 5.5, 24 and 20 mg/kg, respectively, according to the calculation of the corresponding equations. The interactive effect of chlorimuron-ethyl or furadan with metal Hg on soil urease was mainly synergic at the highest tested concentrations.     

Synthesized magnetic nanoparticles coated zeolite for the adsorption of pharmaceutical compounds from aqueous solution using batch and column studies

The contamination of fresh water with pharmaceutical and personal care products (PPCPs) has risen during the last few years. The adsorption of some PPCPs namely, Diclofenac-Na, Naproxen, Gemfibrozil and Ibuprofen from aqueous solution has been studied, magnetic nanoparticles coated zeolite (MNCZ) has been used as the adsorbent. Batch adsorption experiment was conducted to study the influences of different adsorption parameters such as contact time, solution pH and PPCPs concentrations in order to optimize the reaction conditions. The removal was favored at low pH values. Thus, as pH turns from acidic to basic conditions these compounds were less efficiently removed. The initial concentration does not appear to exert a noticeable effect on the removal efficiency of the studied PPCPs at low concentrations, but it showed less removal efficiency during high concentration of PPCPs especially for Ibuprofen. The removal of Diclofenac-Na was independent on time, while the contact time was of significant effect on the adsorption of Naproxen, Gemfibrozil and Ibuprofen even though these compounds were removed up to 95% during 10 min using MNCZ. From the isotherm adsorption study, the adsorption of PPCPs studied on MNCZ was best fitted with Freundlich isotherm equation. Pseudo-second order model providing the best fit model with the experimental data. Column adsorption study was conducted to compare the removal efficiency of MNCZ with other processes used at drinking water treatment plants (DWTPs), MNCZ showed high removal efficiency (>99%) than other used processes at DWTPs.     

Highway-rail grade crossings accident prediction using Zero Inflated Negative Binomial and Empirical Bayes method

Introduction: Recently the Federal Railroad Administration (FRA) released a new model for accident prediction at railroad grade crossings using a Zero Inflated Negative Binomial (ZINB) model with Empirical Bayes (EB) adjustments for accident history (2). This new model is adopted from the work that was conducted by the authors (3–6). The unique feature of the new FRA model is that it has a single equation for all three warning devices (crossbuck, flashing light, and gates) and uses the same variables regardless of the warning devices at the crossing. Since the New FRA model incorporates the warning device category as one of the variables in its model equation, the predicted accident frequency is higher when a crossing has crossbucks than flashing lights, and higher when it has flashing lights than gates. While this model is significantly better than the old USDOT model (7), its shortcoming is that the single equation does not accurately represent the field condition. Method: This paper presents the ZINEBS model (Zero Inflated Negative binomial with Empirical Bayes adjustment System). The ZINEBS model gives three different equations depending on the type of warning device used at the crossings (gates, flashing lights, and crossbucks). The three equations use variables, some of which are common across all warning devices, while other variables are specific to a warning device. The predicted values for the ZINEBS model show a closer agreement with the field data than the new FRA model. This observation was true for all three warning device types analyzed. Practical Applications: Based on the results of this study, the ZINEBS compliments the new FRA model and should be used when the single equation is not adequately representing the role of traffic control device types and relevant variables associated with that device type.     

降雨径流面源污染年负荷预测方法的开发——以镇江古运河为例

在对镇江古运河2009~2013年的降雨量、降雨径流污染物量监测的基础上,运用回归分析建立降雨量-径流污染方程,并采用灰色理论对未来降雨量进行预测,进而得出降雨径流面源污染年负荷值。结果表明:2014~2020年间,镇江古运河降雨径流中污染物TP、NH₃-N和SS的年负荷与降雨量同向变化,2020年降雨量达到1 381.2 mm时,污染物TP、NH₃-N和SS的年负荷可分别达到217.15 t、421.4 t、5 811.87 t,数值较大;灰色理论与回归分析结合所提出的降雨径流面源污染年负荷预测方法,能够在小样本、贫信息和波动数据序列情况下,简捷有效的对降雨径流面源污染负荷进行高精度的预测,实用与推广价值较大。     

Dynamic growth model for Scots pine (Pinus sylvestris L.) plantations in Galicia (north-western Spain)

In this study we developed a dynamic growth model for Scots pine (Pinus sylvestris L.) plantations in Galicia (north-western Spain). The data used to develop the model were obtained from a network of permanent plots, of between 10 and 55-year-old, which the Unidade de Xestión Forestal Sostible (Sustainable Forest Management Unit) of the University of Santiago de Compostela has set up in pure plantations of this species of pine in its area of distribution in Galicia. In this model, the initial stand conditions at any point in time are defined by three state variables (number of trees per hectare, stand basal area and dominant height), and are used to estimate stand volume, classified by commercial classes, for a given projection age. The model uses three transition functions expressed as algebraic difference equations of the three corresponding state variables used to project the stand state at any point in time. In addition, the model incorporates a function for predicting initial stand basal area, which can be used to establish the starting point for the simulation. This alternative should only be used when the stand is not yet established or when no inventory data are available. Once the state variables are known for a specific moment, a distribution function is used to estimate the number of trees in each diameter class, by recovering the parameters of the Weibull function, using the moments of first and second order of the distribution (arithmetic mean diameter and variance, respectively). By using a generalized height–diameter function to estimate the height of the average tree in each diameter class, combined with a taper function that uses the above predicted diameter and height, it is then possible to estimate total or merchantable stand volume.