燃烧型煤排放可吸入尘中的成分分析

主要运用X 射线荧光光谱的半定量分析方法 ,测量了民用炉灶燃烧型煤排放的可吸入尘中的主要成分 ,从而得出在可吸入尘中的主要元素为Cl、S、Na、K ,并研究了其产生的原因     

显像管玻壳铅尘污染特性的研究

以石家庄市显像管玻壳厂排放的铅尘为研究对象，通过在该玻壳厂周围土壤布点采样，较系统地研究了铅尘在大气和周围土壤中的迁移和转化规律，研究表明，用高斯模式能成功地预测铅尘在大气中的迁移扩散规律，玻壳厂周围土壤表层铅含量较大值出现在当地年主导风向（SSE，N）下风向，铅在土壤中的分布主要集中在0－20cm的耕作层，在20－40cm深度范围内，铅含量呈下降趋势，最大降幅达50％。     

石灰凝胶阻化剂防灭火技术在高硫矿的应用研究

在分析现有凝胶阻化剂防灭火机理的基础上 ,笔者针对我国高硫煤矿采空区发火特点 ,研制出石灰凝胶阻化剂的配方 ,并对其防灭火机理进行了详细探讨 ,确定了喷洒该种阻化剂的工艺过程及其应注意的问题。根据现场试验的测定数据表明 ,石灰凝胶阻化剂对高硫煤矿的防灭火效果好     

北京地区15000年以来环境变迁中灾害性气候突变事件的讨论

通过高分辨率孢粉分析及烧失量、炭屑实验结果的统计分析，结合１４Ｃ、古地磁等，对北京房山东甘池１５０００ａ以来植被变化和环境变迁进行了较为深入的研究，特别强调气候变化的灾害性突变事件。初步得知约在１４１００～１４０００ａＢ．Ｐ．前后曾出现与哥得堡反转相对应的事件，在１００００ａＢ．Ｐ．左右出现类似与新仙女木事件相对应的事件，在大约５７７０ａＢ．Ｐ．和４５６０ａＢ．Ｐ．左右及２８５０～２６５０ａＢ．Ｐ．出现了大暖期的突然降温事件。     

环评中锅炉房大气污染源强的确定

在环境评价中,污染源强的确定对环境影响因素评价的分析结果有重要作用。对锅炉房污染物排放的分析表明,影响锅炉房大气污染物的主要因素有燃料的构成、发热量和燃烧方式等。确定锅炉房大气污染物的方法主要有物料衡算法、实测法和经验系数法。在这三种方法中,物料衡算法被普遍采用。在确定锅炉房大气污染物的排放量时,也可以采用物料衡算法和实测法相结合的方法。     

Leaching behaviour of different scrap materials at recovery and recycling companies: Full-, pilot- and lab-scale investigation

Scrap material recovery and recycling companies are confronted with waste water that has a highly fluctuating flow rate and composition. Common pollutants, such as COD, nutrients and suspended solids, potentially toxic metals, polyaromatic hydrocarbons and poly chlorinated biphenyls can exceed the discharge limits. An analysis of the leaching behaviour of different scrap materials and scrap yard sweepings was performed at full-scale, pilot-scale and lab-scale in order to find possible preventive solutions for this waste water problem. The results of these leaching tests (with concentrations that frequently exceeded the Flemish discharge limits) showed the importance of regular sweeping campaigns at the company, leak proof or covered storage of specific scrap materials and oil/water separation on particular leachates. The particulate versus dissolved fraction was also studied for the pollutants. For example, up to 98% of the polyaromatic hydrocarbons, poly chlorinated biphenyls and some metals were in the particulate form. This confirms the (potential) applicability of sedimentation and filtration techniques for the treatment of the majority of the leachates, and as such the rainwater run-off as a whole.     

Selenium status of children in Kashin–Beck disease endemic areas in Shaanxi,China: assessment with mercury

Environmental Geochemistry and Health - The causes of Kashin–Beck disease (KBD) in children are multifactorial, and particular consideration has been given to childhood selenium (Se)...     

同步脱氮除磷系统中两种颜色好氧颗粒污泥的微生物群落特征

为研究同步脱氮除磷系统中出现的WG（白色好氧颗粒污泥）外形特点及微生物群落特征,探究其成因,利用SEM（扫描电子显微镜）表征了系统中的WG与YG（黄色好氧颗粒污泥）的微观形态,并采用Illumina HiSeq 2500高通量测序平台对两种好氧颗粒污泥中细菌与真菌的群落组成进行研究.结果表明:WG结构疏松外形不规则,颗粒表面分布大量杆菌;而YG饱满紧实轮廓清晰,颗粒表面分布大量球菌.WG与YG的细菌群落组成相似,但真菌组成差异较大.与YG相比,WG具有更高的细菌和真菌多样性.变形菌门（Proteobacteria）和拟杆菌门（Bacteroidetes）为WG和YG中的细菌优势门,其在WG中的相对丰度分别为68.85%和26.61%,在YG中的相对丰度分别为82.52%和12.30%.Candidatus competibacter、Candidatus accumulibacter和Chiayiivirga为WG中的优势属,相对丰度分别为22.13%、8.95%和7.37%;Candidatus competibacter、Chiayiivirga和Xanthomonas为YG中的优势属,相对丰度分别为47.94%、6.95%和7.06%.子囊菌门（Ascomycota）和Rozellomycota分别为WG与YG中真菌优势门,其在两个样品中的相对丰度分别为50.10%和81.77%.在属水平,WG中存在大量青霉属（Penicillium）和假丝酵母属（Candida）等丝状真菌,为WG的形成提供了框架.研究显示,当YG破碎成为小菌胶团后,附着在真菌框架上,造成了WG的快速形成,同时WG中Candidatus competibacter的相对丰度较低,使其外形疏松、透光性较好,呈现出白色.      

Predicting vehicle fuel consumption patterns using floating vehicle data

The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to fully describe the relationship between fuel consumption and the impact factors, massive amounts of floating vehicle data were used. The fuel consumption pattern and congestion pattern based on large samples of historical floating vehicle data were explored, drivers'' information and vehicles'' parameters from different group classification were probed, and the average velocity and average fuel consumption in the temporal dimension and spatial dimension were analyzed respectively. The fuel consumption forecasting model was established by using a Back Propagation Neural Network. Part of the sample set was used to train the forecasting model and the remaining part of the sample set was used as input to the forecasting model.