The use of nanovermiculite catalyst in the study of removal of the organic load and degradation of atrazine via ozone process in RPB reactor

Abstract

The main objective of this study is the degradation of a synthetic solution of atrazine by a modified vermiculite catalyzed ozonation, in a rotating packed bed (RPB) reactor. A 0.5?L RPB reactor was used to perform the experiments, using a Central Composite Design (CCD) response surface to construct the quadratic model based on the factors: pH, catalyst concentration and reactor rotation frequency. The response variable was the removal of the organic load measured in terms of Chemical Oxygen Demand (COD). After the complete quadratic model was constructed through the response surface, the COD degradation process had an optimal removal of 41% under the following conditions: pH 8.0, rotation of 1150?rpm and catalyst concentration 0.66?g L^?1.     

Transformational leadership and creativity: A meta‐analytic review and identification of an integrated model

We conduct a meta‐analytic review that yields important insights about the existing research on transformational leadership and creativity. Additionally, we propose and test an integrated model using meta‐analytic structural equation modeling (MASEM) and full information MASEM (FIMASEM) techniques to better understand the intervening mechanism through which transformational leadership acts on creativity. The results of the meta‐analysis of 127 studies show that most of the bivariate relationships among transformational leadership, employee creativity, and pre‐identified mediators are significant; further, geographic base of studies significantly moderates some of the relationships. The MASEM results indicate that several mediators intervene in the relationship between transformational leadership and creativity. Although the total effect of transformational leadership on creativity is positive, its direct effect is negative when mediators are included. Additionally, there are significant relationships among the mediators that can be theoretically supported, but have not been investigated in prior transformational leadership and creativity studies. On the basis of these findings, we provide conclusions and directions for future studies.     

成都平原蔬菜生产中灌溉水对农药渗漏的影响研究

针对成都平原蔬菜生产中大量施用农药、大量灌水的特点,首次在国内应用欧洲官方成熟的农药评估模型——PEARL模型,研究评估了成都平原当前蔬菜生产中的灌溉方式对两种不同特性农药的渗漏的影响。杀毒矾(土壤吸附力(KOM)为0 L/kg,半衰期(DT50)为80 d)在没有灌溉条件下,其渗出土体时的最大质量浓度是190μg.L-1,而有灌溉条件下其渗出质量浓度则可达523μg.L-1,是没有灌溉条件下的2.75倍。三唑磷(KOM为200 L.kg-1,DT50为60 d)在没有灌溉条件下,其渗出土体时的最大质量浓度是0.025μg.L-1,而有灌溉条件下其渗出质量浓度为0.13μg.L-1,比没有灌溉时提高了4.2倍。不管有没有灌溉三唑磷在该地区对地下水的污染风险都很小,而杀毒矾的风险则很高。因此,农药的化学特性是影响农药渗漏的最重要的因子。在蔬菜生产中应尽量选用被土壤吸附力强、半衰期短的农药,例如:三唑磷;农药在土壤中的移动载体是土壤水,不合理的灌溉会大大地加大农药淋溶,应当多使用微喷、滴灌等节水灌溉措施,减少使用漫灌等耗水多的原始灌溉方式。     

Using Modeling to Improve Monitoring of Structured Populations: Are We Collecting the Right Data?

Abstract:  Population monitoring is central to most demographic studies and conservation efforts, but it may not always be directed at the most appropriate life stage. We used stochastic simulation modeling to evaluate the effectiveness of a monitoring program for a well-studied population of Eastern Imperial Eagles ( Aquila heliaca ) in Kazakhstan. Specifically, we asked whether the most appropriate data were being collected to understand system state and population dynamics. Our models were parameterized with data collected over the course of 25 years of study of this population. We used the models to conduct simulation experiments to evaluate relationships between monitored or potentially monitored parameters and the demographic variables of interest—population size ( N ) and population growth (λ). Static analyses showed that traditional territory-based monitoring was a poor indicator of eagle population size and growth and that monitoring survivorship would provide more information about these parameters. Nevertheless, these same traditionally monitored territory-based parameters had greater power to detect long-term changes in population size than did survivorship or population structure. Regardless of the taxa considered, threats can have immediate impacts on population size and growth or longer-term impacts on population dynamics. Prudently designed monitoring programs for any species will detect the demographic effects of both types of threats.     

Spatial modeling and prediction under stationary non-geometric range anisotropy

For modeling spatial processes, we propose a rich parametric class of stationary range anisotropic covariance structures that, when applied in R², greatly increases the scope of variogram contors. Geometric anisotropy, which provides the most common generalization of isotropy within stationarity, is a special case. Our class is built from monotonic isotropic correlation functions and special cases include the Matérn and the general exponential functions. As a result, our range anisotropic correlation specification can be attached to a second order stationary spatial process model, unlike ad hoc approaches to range anisotropy in the literature. We adopt a Bayesian perspective to obtain full inference and demonstrate how to fit the resulting model using sampling-based methods. In the presence of measurement error/microscale effect, we can obtain both the usual predictive as well as the noiseless predictive distribution. We analyze a data set of scallop catches under the general exponential range anisotropic model, withholding ten sites to compare the accuracy and precision of the standard and noiseless predictive distributions.     

A new synergetic paradigm in environmental numerical modeling: Hybrid models combining deterministic and machine learning components

A new type of environmental numerical models, hybrid environmental numerical models (HEMs) based on combining deterministic modeling and machine learning components, is introduced and formulated. Conceptual and practical possibilities of developing HEM, as an optimal synergetic combination of the traditional deterministic/first principles modeling (like that used for solving PDEs on the sphere representing model dynamics of global climate models) and machine learning components (like accurate and fast neural network emulations of model physics or chemistry processes), are discussed. Examples of developed HEMs (hybrid climate models and a hybrid wind–wave ocean model) illustrate the feasibility and efficiency of the new approach for modeling extremely complex multidimensional systems.     

沈阳市烟囱分布对大气污染的影响

本文介绍采用航空遥感技术和实地调查研究相结合的方法,分析了沈阳市烟囱数量、高度、密度以及高、中、低架烟囱的分布状况和特点,同时与大气污染监测结果对照分析了沈阳市煤烟型大气污染的规律及特点,研究了沈阳市烟囱分布对大气污染的影响。最后,提出了沈阳市燃煤污染的防治途径。     

Associations of trace elements in aerosol at the south Adriatic coast

We applied statistical methods to the data of a 5-year sampling period to characterize the features of trace elements in airborne particulate matter at the South Adriatic Coast. The analysis of surface soil samples was also performed. The data were processed by cluster analysis, enrichment factor determination and receptor rose statistics. It is shown that the principal element association consists of Fe, Mn and Ti originating from surface material resuspension. Hg and Se were shown to originate from more complex natural and anthropogenic processes. A strong emission source of Cd and Se was identified in the SSE direction.Selected article from the Regional Symposium on Chemistry and Environment Krusevac, Serbia, June 2003, organized by Dr. Branimir Jovancicevic.     

A Bayesian strategy for combining predictions from empirical and process-based models

We present a strategy for using an empirical forest growth model to reduce uncertainty in predictions made with a physiological process-based forest ecosystem model. The uncertainty reduction is carried out via Bayesian melding, in which information from prior knowledge and a deterministic computer model is conditioned on a likelihood function. We used predictions from an empirical forest growth model G-HAT in place of field observations of aboveground net primary productivity (ANPP) in a deciduous temperate forest ecosystem. Using Bayesian melding, priors for the inputs of the process-based forest ecosystem PnET-II were propagated through the model, and likelihoods for the PnET-II output ANPP were calculated using the G-HAT predictions. Posterior distributions for ANPP and many PnET-II inputs obtained using the G-HAT predictions largely matched posteriors obtained using field data. Since empirical growth models are often more readily available than extensive field data sets, the method represents a potential gain in efficiency for reducing the uncertainty of process-based model predictions when reliable empirical models are available but high-quality data are not.