Mechanism insight into the formation of H2S from thiophene pyrolysis: A theoretical study

• Possible formation pathways of H₂S were revealed in thiophene pyrolysis. • The influence of hydrogen radicals on thiophene pyrolysis was examined. • Thiophene decomposition starts with hydrogen transfer between adjacent C atoms. • The presence of hydrogen radicals significantly promotes the formation of H₂S. Pyrolysis is an efficient and economical method for the utilization of waste rubber, but the high sulfur content limits its industrial application. Currently, the migration and transformation of the element S during pyrolysis of waste rubber is far from well known. In this work, a density functional theory (DFT) method was employed to explore the possible formation pathways of H₂S and its precursors (radicals HS· and S·) during the pyrolysis of thiophene, which is an important primary pyrolytic product of rubber. In particular, the influence of reactive hydrogen radicals was carefully investigated in the thiophene pyrolysis process. The calculation results indicate that the decomposition of thiophene tends to be initiated by hydrogen transfer between adjacent carbon atoms, which needs to overcome an energy barrier of 312.4 kJ/mol. The optimal pathway to generate H₂S in thiophene pyrolysis involves initial H migration and S-C bond cleavage, with an overall energy barrier of 525.8 kJ/mol. In addition, a thiol intermediate that bears unsaturated C-C bonds is essential for thiophene pyrolysis to generate H₂S, which exists in multiple critical reaction pathways. Moreover, the presence of hydrogen radicals significantly changes the decomposition patterns and reduces the energy barriers for thiophene decomposition, thus promoting the formation of H₂S. The current work on H₂S formation from thiophene can provide some theoretical support to explore clean utilization technologies for waste rubber.     

Geospatial Method to Map Potential Risk of Stormwater Thermal Pollution

Thermal pollution is a growing concern as communities strive to protect and improve the nation's waters. The purpose of this study was to develop and analyze a geospatial model that predicts the relative risk of contribution of thermal energy to surface runoff throughout an urban watershed. A geospatial‐based solution could serve as a screening method or planning element, prior to or instead of the development of a more complex simulation of mass‐load transport of thermal energy. The study's theoretical methodology integrated the thermal potential of land cover and thermal decay due to overland travel to inlets. The resulting thermal pollution potential (TPP) index value was then assigned to individual model grid cells for comparison. Analysis of results from application to a case study watershed in Blacksburg, Virginia, indicated the computational methodology is not very sensitive to changes in spatial resolution. The thermal decay constant used in the analysis was varied, and results indicated a low sensitivity to selection of this parameter. A comparison between methods of watershed delineation also indicated use of infrastructure‐corrected watershed delineation yields better results than derivation from automated techniques. The geospatial method presented may be used to reproduce a TPP risk map to prioritize thermal pollution mitigation efforts.     

Spatial distribution of polychlorinated biphenyls in soil around a municipal solid waste incinerator

The impact of a typical municipal solid waste incinerator (MSWI) on polychlorinated biphenyl (PCB) concentrations in the surrounding soil was studied. Six stack gas samples were taken from the MSWI and 21 soil samples were collected from sampling sites between 300 and 1700 m from the MSWI stack. The total (∑PCB) concentrations of dioxin-like (dl) PCBs and indicator PCBs in the stack gas samples were between 3.41 and 34.3 ng/m3, and the corresponding toxic equivalents (TEQs) ranged from 4.45 to 66.9 pg WHO-TEQ/m3, with a mean of 28.6 pg WHO-TEQ/m3. A total of 2.43 g WHO-TEQ of PCBs per year was calculated to be released into the environment from MSWIs in China. The ∑PCB concentrations in the soil samples ranged from 28.0 to 264.4 pg/g, with mean and median values of 127.6 and 127.7 pg/g, respectively, while the TEQ values were between 0.020 and 0.18 pg WHO-TEQ/g, with mean and median values of 0.074 and 0.062 pg WHO-TEQ/g, respectively. Comparing this study with other studies performed around the world suggest that PCB emission from incinerators has a critical influence on PCB concentrations in the surrounding soil. An exponential function equation is proposed, which indicates a clear decline in ∑PCB concentrations with increasing distance from the stack. A contour map created using an ordinary kriging interpolation technique showed that a limited area (1250 m radius) from the stack was clearly influenced by PCB emission from the MSWI.     

基于北斗高精度定位的工务现场作业全过程安全监测系统研究

为了保障在铁路快速发展的同时,确保铁路工务安全和行车安全,基于工务现场作业过程的控制,提出了利用北斗高精度差分定位、大数据等技术,研究建立针对现场每个作业个体的工务作业全过程安全监测系统。结果表明:通过对现场每名作业个体的高精度、智能化管理,有效规范了现场人员的作业行为,增强对现场安全风险和隐患的预判和规避能力,保障了铁路从业人员人身安全。     

利用数学模式计算非离子氨浓度

根据我国颁布的《地面水环境质量标准非离子氨换算方法》，用Ｆｏｘｐｒｏ语言编程计算非离子氨浓度，提高监测数据处理率，减少计算误差．通过实例计算，程序运行正常，结果正确．     

机助地图叠置分析及其在自然资源研究中的应用

机助地图叠置分析包括多边形叠置分析和网格叠置分析两种方法。本文论述了多边形叠置分析的原理和方法,以及多边形叠置分析软件设计的基本思想,探讨了机助地图叠置分析在自然资源研究中的应用领域,并以实例说明了机助地图叠置分析应用于实践的方法和过程。机助地图叠置分析可应用于自然资源数据的统计计算、自然资源的分类和评价,以及动态、变化分析等方面。它对于自然资源尤其是农业自然资源的研究有着一定的意义。     

六安—蔡河地学剖面构造岩性单元的划分

叙述了大别造山带主要岩石类型及热源体周围微量元素的分布、分配特征 ,研究了它们的影响因素和地质意义 ,并根据六安—蔡河地学剖面微量元素和收集的地球物理、同位素年龄等资料 ,对该剖面构造岩性单元进行了划分。     

Statistical selection of perimeter-area models for patch mosaics in multiscale landscape analysis

This paper presents a statistical method for detecting distinct scales of pattern for mosaics of irregular patches, by means of perimeter–area relationships. Krummel et al. (1987) were the first to develop a method for detecting different scaling domains in a landscape of irregular patches, but this method requires investigator judgment and is not completely satisfying. Grossi et al. (2001) suggested a modification of Krummel's method in order to detect objectively the change points between different scaling domains. Their procedure is based on the selection of the best piecewise linear regression model using a set of statistical tests. Even though the change points were estimated, the null distributions used for testing purposes were those appropriate for known change points. The present paper investigates the effect that estimating the change points has on the underlying distribution theory. The procedure we suggest is based on the selection of the best piecewise linear regression model using a likelihood ratio (LR) test. Each segment of the piecewise linear model corresponds to a fractal domain. Breakpoints between different segments are unknown, so the piecewise linear models are non-linear. In this case, the frequency distribution of the LR statistic cannot be approximated by a chi-squared distribution. Instead, Monte Carlo simulation is used to obtain an empirical null distribution of the LR statistic. The suggested method is applied to three patch types (CORINE biotopes) located in the Val Baganza watershed of Italy.     

Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters

Two artificial neural networks (ANNs), unsupervised and supervised learning algorithms, were applied to suggest practical approaches for the analysis of ecological data. Four major aquatic insect orders (Ephemeroptera, Plecoptera, Trichoptera, and Coleoptera, i.e. EPTC), and four environmental variables (elevation, stream order, distance from the source, and water temperature) were used to implement the models. The data were collected and measured at 155 sampling sites on streams of the Adour–Garonne drainage basin (South-western France). The modelling procedure was carried out following two steps. First, a self-organizing map (SOM), an unsupervised ANN, was applied to classify sampling sites using EPTC richness. Second, a backpropagation algorithm (BP), a supervised ANN, was applied to predict EPTC richness using a set of four environmental variables. The trained SOM classified sampling sites according to a gradient of EPTC richness, and the groups obtained corresponded to geographic regions of the drainage basin and characteristics of their environmental variables. The SOM showed its convenience to analyze relationships among sampling sites, biological attributes, and environmental variables. After accounting for the relationships in data sets, the BP used to predict the EPTC richness with a set of four environmental variables showed a high accuracy (r=0.91 and r=0.61 for training and test data sets respectively). The prediction of EPTC richness is thus a valuable tool to assess disturbances in given areas: by knowing what the EPTC richness should be, we can determine the degree to which disturbances have altered it. The results suggested that methodologies successively using two different neural networks are helpful to understand ecological data through ordination first, and then to predict target variables.     

The Threat of Transformation: Quantifying the Vulnerability of Grasslands in South Africa

Abstract:  The ability to predict which areas of conservation importance are most vulnerable to transformation and to rank the relative damage that transforming land uses could cause to biodiversity are important components of an effective and realistic conservation planning process. We used the South African grassland biome as a case study to illustrate the assessment of vulnerability to land-use transformation through the construction of a "threat map." We identified the dominant transforming land uses and created suitability models based on area appropriateness for each. Land uses were scored according to their expected relative impacts on biodiversity, with a framework that included compositional, structural, and functional components. This information, once combined, resulted in a map that highlighted the areas most vulnerable to transformation in terms of the likelihood of their transformation and the impact on their biodiversity. We propose that such an analysis of the threat of transformation, in combination with species-representation approaches, will aid the integration of conservation planning theory and decision making. This approach can be applied at any scale and in any region with a robustness and accuracy dependent on data quality, resultant suitability models, and comprehension of how land uses affect an area's biodiversity.