千层塔扦插繁殖研究

通过扦插试验,采用不同浓度水平的吲哚丁酸、萘乙酸、生根粉和吲哚丁酸＋萘乙酸等药剂处理千层塔插条,研究对插条的生根量、生根率、成活率等的影响和插条萌芽状况及扦插基质对插条生根的影响,为千层塔资源快速再生提供有效的繁殖途径。     

HAZOP分析在编制操作规程上的应用

结合实例分析,介绍如何将HAZOP分析方法引入到操作规程的编写过程当中,从而提升操作规程的有效性。     

Effects of intercropping with floricultural accumulator plants on cadmium accumulation in grapevine

Environmental Science and Pollution Research - Grapevine (Vitis vinifera L.) intercropping with floricultural cadmium (Cd) accumulator plants (Helianthus annuus L., Cosmos sulphureus Cav., Cosmos...     

Decreased blood level of β2-microglobulin in the employees of a factory which produced polychlorinated biphenyls

Under the project of evaluating the health status of the employees of CHEMKO factory (East Slovakia) which produced PCBs between 1955 and 1985, the level of β₂-microglobulin (β₂-m) was measured in the serum of 242 subjects from CHEMKO (Group A) and two control groups from much less polluted areas: 1. 277 females from a small mountainous village (Group B), 2. 179 adults from the area of a large city of Ko ice (Group C). The level of thymidine kinase (TK) was measured in the groups A and B only. In addition, age-matched groups of 155 women each from all areas were evaluated. In both the whole group and the age-matched group from CHEMKO the level of β₂-m was significantly lower (P<0.001) than that in the respective control groups, while no difference was found in the level of TK. In conclusion, it is suggested that the decrease of β₂-m in CHEMKO employees might be related to the immunotoxic effects of organochlorines.     

Geochemical distribution of selected elements in flotation tailings and soils/sediments from the dam spill at the abandoned antimony mine Stolice,Serbia

Environmental Science and Pollution Research - Materials held within mine tailings pose a serious risk to the environment in cases of tailings dam failure. Collapse of the tailing dam at the...     

Petroleum hydrocarbon contamination of the Southern Black Sea Shelf, Turkey

Introduction  

In this study, total petroleum hydrocarbon (TPH) contents and some aliphatic and aromatic hydrocarbon concentrations were analysed in coastal sediments of hot points collected from along the Southern Black Sea Shelf.     

Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations

The new method for the forecasting hourly concentrations of air pollutants is presented in the paper. The method was developed for a site in urban residential area in city of Zagreb, Croatia, for four air pollutants (NO₂, O₃, CO and PM₁₀). Meteorological variables and concentrations of the respective pollutant were taken as predictors. A novel approach, based on families of univariate regression models, was employed in selecting the averaging intervals for input variables. For each variable and each averaging period between 1 and 97 h, a separate model was built. By inspecting values of the coefficient of correlation between measured and modelled concentrations, optimal averaging periods for each variable were selected. A new dataset for building the forecasting model was then calculated as temporal moving averages (running means) of former variables. A multi-layer perceptron type of neural networks is used as the forecasting model. Index of agreement, calculated for the entire dataset including the data for model building, ranged from 0.91 to 0.97 for the respective pollutants. As suggested by the analysis of the relative importance of the input variables, different agreements for different pollutants are likely due to different sources and production mechanisms of investigated pollutants. A comparison of the new method with more traditional method, which takes hourly averages of the forecast hour as input variables, showed similar or better performance. The model was developed for the purpose of public-health-oriented air quality forecasting, aiming to use a numerical weather forecast model for the prediction of the part of input data yet unknown at the forecasting time. It is to expect that longer term averages used as inputs in the proposed method will contribute to smaller input errors and the greater accuracy of the model.     

船舶区域溢油风险程度甄别的人工神经网络方法

利用误差反向传播 (BP)人工神经网络 ,建立了海域船舶溢油风险程度甄别的评价模型。该模型能够反映出研究海域各区域发生船舶溢油事件的风险等级差别 ,具有一定的客观性和实用性。