高压水射流超细粉碎云母的实验研究

利用前混合磨料射流的工作原理，进行了高压水射流超细粉碎云母的实验研究。实验结果表明：喷嘴对云母有粉碎作用，喷嘴直径可以小于云母给料粒度；压力是影响高压水射流超细粉碎效果的最重要参数，在压力为１０—５０ＭＰａ范围内，细粒度产率随压力增加呈直线上升；给料粒度越小，粉碎产品中细粒度的产率越高；水射流超细粉碎在淹没状态下比在非淹没状态下粉碎效果好；高压水射流超细粉碎云母粉，不但能够满足珠光云母粉的细度要求，而且能够保护云母的片状结构和晶面光泽度；高压水射流超细粉碎技术是一种较实用的超细粉碎云母技术。     

高压水射流超细粉碎云母的粒度特性方程式

为了弄清高压水射流超细粉碎云母的规律，本文通过假设颗粒中的校裂纹、表面裂纹和体裂纹的扩展概率相互独立，以及颗粒中发生多少个裂纹扩展的概率服从普哇松分布，推导出粒度特性方程；并认为裂纹扩展概率与水压成正比，与给料调和平均粒率成反比；然后用粒度特性方程拟合和分析了高压水射流超细粉碎云母的实验测试数据，发现高压水射流超细粉碎云母主要是云母粉中的棱裂纹扩展所致，且粒度分布由水压和给料调和平均粒度决定。     

西安城区大气PM_(2.5)中有机碳与元素碳的污染特征

为研究西安市夏、秋、冬3季大气细颗粒物中碳组分的污染变化规律,于2013年夏、秋、冬3季在西安市明城墙内采集大气中的PM2.5样品,测定了样品中的有机碳和元素碳的含量。结果显示,PM2.5中OC和EC的季节平均浓度冬季最高,秋季次之,夏季最低。OC/EC的比值在夏、秋、冬3季均超过2.0,说明采样期间夏、秋、冬3季均存在二次污染。OC/PM2.5的比值夏秋两季差异较小,冬季明显高于夏秋两季;EC/PM2.5的比值在夏、秋、冬3季的变化不大。OC和EC的相关性:夏季秋季冬季。OC与PM2.5的相关程度冬季秋季夏季,且随季节更替变化明显;EC与PM2.5的相关性:秋季夏季冬季。即随着季节变化PM2.5浓度水平整体升高、污染加重时,PM2.5中的OC所占比例明显升高,EC比较稳定,说明受人为影响的二次排放为主要成分的OC对PM2.5贡献更大。     

Unraveling the size distributions of surface properties for purple soil and yellow soil

Soils contain diverse colloidal particles whose properties are pertinent to ecological and human health, whereas few investigations systematically analyze the surface properties of these particles. The objective of this study was to elucidate the surface properties of particles within targeted size ranges(i.e. 10, 1–10, 0.5–1, 0.2–0.5 and 0.2 μm) for a purple soil(Entisol) and a yellow soil(Ultisol) using the combined determination method. The mineralogy of corresponding particle-size fractions was determined by X-ray diffraction.We found that up to 80% of the specific surface area and 85% of the surface charge of the entire soil came from colloidal-sized particles( 1 μm), and almost half of the specific surface area and surface charge came from the smallest particles( 0.2 μm). Vermiculite,illite, montmorillonite and mica dominated in the colloidal-sized particles, of which the smallest particles had the highest proportion of vermiculite and montmorillonite. For a given size fraction, the purple soil had a larger specific surface area, stronger electrostatic field, and higher surface charge than the yellow soil due to differences in mineralogy.Likewise, the differences in surface properties among the various particle-size fractions can also be ascribed to mineralogy. Our results indicated that soil surface properties were essentially determined by the colloidal-sized particles, and the 0.2 μm nanoparticles made the largest contribution to soil properties. The composition of clay minerals within the diverse particle-size fractions could fully explain the size distributions of surface properties.     

Water quality prediction of copper-molybdenum mining-beneficiation wastewater based on the PSO-SVR model

● Data acquisition and pre-processing for wastewater treatment were summarized. ● A PSO-SVR model for predicting COD_eff in wastewater was proposed. ● The COD_eff prediction performances of the three models in the paper were compared. ● The COD_eff prediction effects of different models in other studies were discussed. The mining-beneficiation wastewater treatment is highly complex and nonlinear. Various factors like influent quality, flow rate, pH and chemical dose, tend to restrict the effluent effectiveness of mining-beneficiation wastewater treatment. Chemical oxygen demand (COD) is a crucial indicator to measure the quality of mining-beneficiation wastewater. Predicting COD concentration accurately of mining-beneficiation wastewater after treatment is essential for achieving stable and compliant discharge. This reduces environmental risk and significantly improves the discharge quality of wastewater. This paper presents a novel AI algorithm PSO-SVR, to predict water quality. Hyperparameter optimization of our proposed model PSO-SVR, uses particle swarm optimization to improve support vector regression for COD prediction. The generalization capacity tested on out-of-distribution (OOD) data for our PSO-SVR model is strong, with the following performance metrics of root means square error (RMSE) is 1.51, mean absolute error (MAE) is 1.26, and the coefficient of determination (R²) is 0.85. We compare the performance of PSO-SVR model with back propagation neural network (BPNN) and radial basis function neural network (RBFNN) and shows it edges over in terms of the performance metrics of RMSE, MAE and R², and is the best model for COD prediction of mining-beneficiation wastewater. This is because of the less overfitting tendency of PSO-SVR compared with neural network architectures. Our proposed PSO-SVR model is optimum for the prediction of COD in copper-molybdenum mining-beneficiation wastewater treatment. In addition, PSO-SVR can be used to predict COD on a wide variety of wastewater through the process of transfer learning.     

Turbulent kinetic energy budgets in a model canopy: comparisons between LES and wind-tunnel experiments

A comparative study of turbulence in a wind-tunnel model canopy is performed, using Large eddy simulation (LES) and experimental data from PIV and hot-wire anemometry measurements. The model canopy is composed of thin cylindrical stalks. In the LES, these are represented using a plant-scale approach, while the scale-dependent Lagrangian dynamic model is used as subgrid-scale model. LES predictions of turbulence statistics and energy spectra are found to be in good agreement with the experimental data. Turbulent kinetic energy (TKE) budgets from the LES simulation are analyzed to provide more information absent in the measurements. Results confirm that sloshing motions at the low levels of the canopy are mainly driven by pressure fluctuations. A difference between the energy flux obtained from the energy spectrum and the SGS dissipation rate is observed, consistent with a spectral bypass mechanism in which the real spectral flux due to cascade is smaller than that implied by the energy-spectrum level, due to direct drain by the canopy.     

ESEM–EDX characterisation of airborne particles from an industrialised area of northern Greece

The aim of this study was to characterise individual airborne particles collected from the Ptolemais-Kozani region (Western Macedonia), northern Greece. Throughout a 1-year period (March 2003 to February 2004), we collected several filters that captured airborne particles at seven sampling sites distributed throughout the area. The airborne particles captured on the filters were then characterised by environmental scanning electron microscopy (ESEM) coupled with energy-dispersive X-ray analysis (EDX). The particles were categorised as geogenic, biogenic and anthropogenic. The main anthropogenic airborne particles were fly ash (released from lignite-fired power plants) and carbonaceous (soot and char) and metalliferous (mainly iron- and copper-enriched) particulates. We present here characteristic ESEM and EDX spectra for the airborne particles and underline the presence of characteristic primary and secondary sulphates.     

大气颗粒物中多环芳烃的源解析方法

综述了用于大气颗粒物中多环芳烃（PAHs)源解析的主要定性、定量方法、并对其优缺点作了总结。比值法多用于定性解析，化学质量平衡法（CMB）要求源的成分谱较全面，而多元统计法则要求输入的数据较多。由于缺乏各污染源较完整的PAHs成分谱，且PAHs易发生化学反应，所以CMB法难以广泛推广，而多元统计法对源成分谱，且PAHs易发生化学反应，所以CMB法难以广泛推广，而多元统计不对源成分谱要求低，且不需要考虑PAHs的降解，因而具有推广价值。     

山地火蔓延过程的三维模拟

本文以林火蔓延的模型为基础,运用OpenGL图形渲染的技术,考虑实时的地形坡度和风向,动态的显示实时火场边界,通过巧妙地将林火蔓延模型转变为计算机中的数据结构,结合粒子系统,在计算机上三维模拟重现了由单个火源引发的林火蔓延过程。这对于林火的辅助应急决策,有效地组织扑救,减少火灾损失,计算火灾引起的损失评估具有重要意义。     

长江南京段近岸沉积物和土壤中重金属分布特征分析

通过测定沉积物和土壤中Cd、Pb、Cr、Zn、Cu、Ni 6种重金属元素的平均含量,计算其富集因子,分析长江南京段近岸沉积物和土壤中重金属的空间分布特征,结果表明,几种重金属在沉积物中的富集次序为:CdPbCr1NiCuZn,在土壤中为:CdZnCu1CrPbNi,除Zn和Cu外,其他几种金属在沉积物中的富集程度高于土壤,同时Cd的含量超过土壤环境质量三级标准。以Cd和Pb为例分析了重金属含量与沉积物粒级之间的关系,回归分析显示,Cd、Pb的含量与颗粒物的粒级呈显著的相关性,与细颗粒物的含量有密切关系,细颗粒携带的重金属,在长江水力分选作用下到达下游,成为沉积物中重金属的主要来源。