基于GIS的石家庄市雾霾分析

为了得出雾霾气象成因机制、影响因素和时空分布特征,以石家庄市区与郊县为研究区域,将2013年9月至2016年12月石家庄市各市区、郊县的PM2.5历史监测数据中的有效数据进行了数据分析处理得到雾霾浓度数据,还有温度、降水、风速、地形和人口密度等数据,运用GIS分析的方法,模拟绘制石家庄雾霾的时空分布图、雾霾与各影响因子的专题对比图,得到雾霾形成机制的因子、雾霾的时空分布规律、雾霾季节变化特征、雾霾与地形间的关系等;运用数据分析软件OriginPro8.SR3分析雾霾浓度与风速数据、降雨数据、温度数据间的相关性。     

A unique Galactic planetary nebula with a [WN] central star

We report the discovery of the first probable Galactic [WN] central star of a planetary nebula (CSPN). The planetary nebula candidate was found during our systematic scans of the AAO/UKST Hα Survey of the Milky Way. Subsequent confirmatory spectroscopy of the nebula and central star reveals the remarkable nature of this object. The nebular spectrum shows emission lines with large expansion velocities exceeding 150 km s⁻¹, suggesting that perhaps the object is not a conventional planetary nebula. The central star itself is very red and is identified as being of the [WN] class, which makes it unique in the Galaxy. A large body of supplementary observational data supports the hypothesis that this object is indeed a planetary nebula and not a Population I Wolf–Rayet star with a ring nebula.     

北京市区春季燃烧源大气颗粒物的污染水平和影响因素

以大气中PM2.5和PM10为研究对象,于2005-03-13—25共7天的时间内,在中国地质大学(北京)测试楼顶、首钢焦化厂和首钢东门设立3个采样点进行采样监测。结果表明:PM2.5和PM10质量浓度的日变化呈现一定规律性,在不同时段PM2.5和PM10的质量浓度不尽相同,且变化较大,在特定时刻出现峰值,主要受污染源排放和气象因素的控制;PM2.5和PM10质量浓度随气温的升高而降低,这与高温有利于颗粒物扩散、低温容易形成逆温层有关;在一定的相对湿度范围内(以大气中水汽不发生重力沉降为界限),PM2.5和PM10质量浓度与相对湿度呈正相关关系;而当发生降水时,由于水滴的冲刷和附带作用,PM2.5和PM10质量浓度降低;PM2.5和PM10质量浓度与风级呈明显的负相关关系。通过北京市与国内8个省会城市的PM2.5和PM10质量浓度的对比,发现北京市PM2.5和PM10污染比较严重,PM2.5和PM10质量浓度分别超过了1996年中国制定的PM10排放标准和1997年美国EPA制定的PM2.5排放标准。     

杭州市区春节期间空气质量变化特点

分析近3年春节期间的空气质量资料，结果表明，杭州市区环境空气中PM10、SO2、NO2浓度较高，烟花爆竹燃放期间3种污染物出现高峰值，这与烟花爆竹燃放有关，PM25／PM10的比值高于年均值。     

中心回线瞬变电磁法2.5维有限单元算法

瞬变电磁法的野外资料解释基本上停留在一维水平，本文从生产实践中常用的中心回线法入手，基于电磁场本身的叠加原理，从麦克斯韦方程组出发，导出了中心回线瞬变电磁2.5维二次场（纯异常）的有限单元计算公式. 该算法采用三角形有限元网格，在尽可能拟合地下电性断面的情况下减少有限元网格的节点数和单元数；用选主元的LU分解法求解线性方程组，做到了在移动场源时只需改变右端项；在反傅氏变换中，使用新的波数选取方案，让波数m随时间t滑动. 最后给出的算例表明，该算法直接计算异常场，计算速度快、精度高.     

伊利石层间钾释放的远红外光谱研究

本文介绍了新近研究层间交换的远红外方法，并用该方法研究了金砂伊早石层间交换的性质，随着温度的升高，伊利石的远红外谱段从１０８ｃｍ＾－１向低波数方向偏移，３００Ｃ时为１０７ｃｍ＾－１，５００℃时为１０００ｃｍ＾－１，７００℃时为９８ｃｍ＾－１，同时，伊利石的释钾程度也伴随着增加，远红外谱同释放钾的能力有一清楚对比，因此可以用远红外方法预言伊利石释钾的能力，最后，利用量子化学计算的ＰＭ３方法首次从理论     

Scattering Properties of Atmospheric Aerosols over Lanzhou City and Applications Using an Integrating Nephelometer

The data, measured by a three-wavelength Integrating Nephelometer over Lanzhou City during the winters of 2001/2002 and 2002/2003 respectively, have been analyzed for investigating the scattering properties of atmospheric aerosols and exploring their relationship and the status of air pollution. The aerosol particle volume distribution is inverted with the measured spectral scattering coefficients. The results show that the daily variation of the aerosol scattering coefficients is in a tri-peak shape. The average ratio of backscattering coefficient to total scattering coefficient at 550 nm is 0.158; there exists an excellent correlation between the scattering coefficients and the concentration of PM10. The average ratio of the concentration of PM10 to the scattering coefficients is 0.37 g m-2, which is contingent on the optical parameters of aerosol particles such as the size distribution, etc.; an algorithm is developed for inverting the volume distribution of aerosol particles by using the histogram and Monte-Carlo techniques, and the test results show that the inversion is reasonable.     

Vertical structures of PM10 and PM 2.5 and their dynamical character in low atmosphere in Beijing urban areas

The vertical structures and their dynamical character of PM2.5 and PM10 over Beijing urban areas are revealed using the 1 min mean continuous mass concentration data of PM2.5 and PM10 at 8, 100, and 320 m heights of the meteorological observation tower of 325 m at Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP CAS tower hereafter) on 10―26 August, 2003, as well as the daily mean mass concentration data of PM2.5 and PM10 and the continuous data of CO and NO2 at 8, 100 (low layer), 200 (middle layer), and 320 m (high layer) heights, in combination with the same period meteorological field observation data of the meteorological tower. The vertical distributions of aerosols observed on IAP CAS tower in Beijing can be roughly divided into two patterns: gradually and rapidly decreasing patterns, I.e. The vertical distribution of aerosols in calm weather or on pollution day belongs to the gradually decreasing pattern, while one on clean day or weak cold air day belongs to the rapidly decreasing pattern. The vertical distributive characters of aerosols were closely related with the dynamical/thermal structure and turbulence character of the atmosphere boundary layer. On the clean day, the low layer PM2.5 and PM10 concentrations were close to those at 8 m height, while the concentrations rapidly decreased at the high layer, and their values were only one half of those at 8 m, especially, the concentration of PM2.5 dropped even more. On the clean day, there existed stronger turbulence below 150 m, aerosols were well mixed, but blocked by the more stronger inversion layer aloft, and meanwhile, at various heights, especially in the high layer, the horizontal wind speed was larger, resulting in the rapid decrease of aerosol concentration, I.e. Resulting in the obvious vertical difference of aerosol concentrations between the low and high layers. On the pollution day, the concentrations of PM2.5 and PM10 at the low, middle, and high layers dropped successively by, on average, about 10% for each layer in comparison with those at 8 m height. On pollution days, in company with the low wind speed, there existed two shallow inversion layers in the boundary layer, but aerosols might be, to some extent, mixed below the inversion layer, therefore, on the pollution day the concentrations of PM2.5 and PM10 dropped with height slowly; and the observational results also show that the concentrations at 320 m height were obviously high under SW and SE winds, but at other heights, the concentrations were not correlated with wind directions. The computational results of footprint analysis suggest that this was due to the fact that the 320 m height was impacted by the pollutants transfer of southerly flow from the southern peripheral heavier polluted areas, such as Baoding, and Shijiazhuang of Hebei Province, Tianjin, and Shandong Province, etc., while the low layer was only affected by Beijing's local pollution source. The computational results of power spectra and periods preliminarily reveal that under the condition of calm weather, the periods of PM10 concentration at various heights of the tower were on the order of minutes, while in cases of larger wind speed, the concentrations of PM2.5 and PM10 at 320 m height not only had the short periods of minute-order, but also the longer periods of hour order. Consistent with the conclusion previously drawn by Ding et al., that air pollutants at different heights and at different sites in Beijing had the character of "in-phase" variation, was also observed for the diurnal variation and mean diurnal variation of PM2.5 and PM10 at various heights of the tower in this experiment, again confirming the "in-phase" temporal/spatial distributive character of air pollutants in the urban canopy of Beijing. The gentle double-peak character of the mean diurnal variation of PM2.5 and PM10 was closely related with the evident/similar diurnal variation of turbulent momentum fluxes, sensible heat fluxes, and turbulent kinetic energy at various heights in the urban canopy. Besides, under the condition of calm weather, the concentration of PM2.5 and PM10 declined with height slowly, it was 90% of 8 m concentration at the low layer, a little lesser than 90% at the middle layer, and 80% at the high layer, respectively. Under the condition of weak cold air weather, the concentration remarkably dropped with height, it was 70% of 8 m concentration at the low layer, and 20%―30% at the middle and high layers, especially the concentration of PM2.5 was even lower.     

时间尺度重构EEMD-GRNN改进模型预测PM2.5的研究

合理构建PM_2.5浓度预测模型是科学、准确地预测PM_2.5浓度变化的关键。传统PM_2.5预测EEMD-GRNN模型具有较好的预测精度,但是存在过于关注研究数据本身而忽略其物理意义的不足。本研究基于南京市2014-2017年PM_2.5浓度时间序列数据,分析PM_2.5浓度多尺度变化特征及其对气象因子和大气污染因子的尺度响应,基于时间尺度重构进行EEMD-GRNN模型的改进与实证研究。南京市样本数据PM_2.5浓度变化表现为明显的天际尺度和月际尺度,从重构尺度（天际、月际）构建GRNN模型更具有现实意义;同时,PM_2.5对PM₁₀、NO₂、O₃、RH、MinT等因子存在多尺度响应效应,以其作为GRNN模型中的输入变量更具有时间序列上的解释意义。改进后的EEMD-GRNN模型具有更高的PM_2.5浓度预测精度,MAE、MAPE、RMSE和R²分别为6.17、18.41%、8.32和0.95,而传统EEMD-GRNN模型的模型有效性检验结果分别为8.37、27.56%、11.56、0.91。对于高浓度天（PM_2.5浓度大于100 μg/m³）的预测,改进模型更是全面优于传统EEMD-GRNN模型,MAPE为12.02%,相较于传统模型提高了9.03%。