Radioactive Isotopes of Strontium, Caesium and Plutonium in Sediments of the Northern Adriatic Sea

Sediment samples of different strata (0-3 and 12-15 cm) were collected between 1990 and 1992 during seasonal cruises in the Northern Adriatic Sea. A complete mapping of the Sr-90, Cs-137, Pu-238 and Pu-239(240) concentrations was obtained for samples covering a wide area, stretching from the Gulf of Trieste towards the Ancona shoreline. Sr-90 concentrations varied between 1.5 and 6.5 Bq kg^-1 dw, Cs-137 was in the range 0.9-38.9 Bq kg^-1 dw, Pu-239(240) in the range 0.08-1.5 Bq kg^-1 dw and Pu-238 around 0.03 Bq kg^-1 dw. Special reference was also put on the comparison between the off-shore environment and data obtained from samples collected inside the Po river delta. in spite of the major accumulation of Cs-137 inside the estuarine environment, higher concentrations of strontium and plutonium isotopes were detected in the offshore environment.     

Ecological analysis of disturbed riverbanks in the Montréal area of Québec

In revegetation trials on disturbed riverbank sites in the Montréal region (Québec, Canada), an analysis of their structure by means of their biotic and abiotic components was undertaken. Vegetation sampling and physical surveys, together with soil analyses of 62 stands, grouped in 20 sectors, were carried out in the summer of 1986. The statistical analyses of biotic and abiotic data were executed in parallel by a hierarchical agglomerative cluster analysis (arithmetic average clustering) and by an ordination in reduced space (principal coordinate analysis). The species colonizing these riverbanks are, for the most part, ruderals from the Montréal region, of which more than 50% are introduced species. The pioneering communities characterizing these artificial habitats have had very little impact on the physical conditions of their environment; therefore, the ordination on the plant communities proves to be strongly correlated to the ordination stemming from the abiotic components. The statistical analysis retains the original sectors, underlining the fact that each human intervention generally results in the creation of a particular ecological situation.     

Decision-tree and rule-induction approach to integration of remotely sensed and GIS data in mapping vegetation in disturbed or hilly environments

The integration of Landsat TM and environmental GIS data sets using artificial intelligence rule-induction and decision-tree analysis is shown to facilitate the production of vegetation maps with both floristic and structural information. This technique is particularly suited to vegetation mapping in disturbed or hilly environments that are unsuited to either conventional remote sensing methods or GIS modeling using environmental data bases.     

上海市土壤中人工放射性水平调查及评价

调查了１９８９－１９９０年上海市土壤中及黄浦江水系底泥中，人工放射性核素的含量。调查共设３２个点，结果表明，上海市土壤中的人工放射性核素含量，＾９０Ｓｒ、＾１３７Ｃｓ、总β的比活度均值分别为２、４１±０．７８、３．１４±１．４７、７１７．０±１３５．９Ｂｑ／ｋｇ，属正常本底水平。土壤中人工放射性核素的比活度在全市范围内分布没有明显差别，波动范围不大。土壤中＾１３７Ｃｓ的比活度比＾９０Ｓｒ高，这与土     

Influencing factors and prediction of ambient Peroxyacetyl nitrate concentration in Beijing, China

Peroxyacyl nitrates (PANs) are important secondary pollutants in ground-level atmosphere. Accurate prediction of atmospheric pollutant concentrations is crucial to guide effective precautions for before and during specific pollution events. In this study, four models based on the back-propagation (BP) artificial neural network (ANN) and multiple linear regression (MLR) methods were used to predict the hourly average PAN concentrations at Peking University, Beijing, in 2014. The model inputs were atmospheric pollutant data and meteorological parameters. Model 3 using a BP-ANN based on the original variables achieved the best prediction results among the four models, with a correlation coefficient (R) of 0.7089, mean bias error of ? 0.0043 ppb, mean absolute error of 0.4836?ppb, root mean squared error of 0.5320?ppb, and Willmott's index of agreement of 0.8214. Based on a comparison of the performance indices of the MLR and BP-ANN models, we concluded that the BP-ANN model was able to capture the highly non-linear relationships between PAN concentration and the conventional atmospheric pollutant and meteorological parameters, providing more accurate results than the traditional MLR models did, with a markedly higher goodness of R. The selected meteorological and atmospheric pollutant parameters described a sufficient amount of PAN variation, and thus provided satisfactory prediction results. More specifically, the BP-ANN model performed very well for capturing the variation pattern when PAN concentrations were low. The findings of this study address some of the existing knowledge gaps in this research field and provide a theoretical basis for future regional air pollution control.     

用灰色组合模型预测环保投资

针对环保投资变化的非平稳性，采用灰以ＧＭ（１，１）模型分析环保投资的趋势项并与历史环保投资比较得一系列残差，然后应用人工神经网络模型进行了正以提高精度。     

综合环境质量的B－P网络二级评价

为了探索人工神经网络应用于环境质量评价的可行性和合理性，应用人工神经网络的Ｂ－Ｐ模型，构造了大气、水质和噪声的综合环境质量的Ｂ－Ｐ网络二级评价模型。并将该模型应用于实例作效果检验。结果表明Ｂ－Ｐ网络用于环境质量评价适应性强，方法可行，结果合理。     

城市水资源社会再生能力评价模型研究

为了在城市水资源评价过程中充分考虑城市水资源的社会循环，更好地揭示城市水资源开发利用的潜力，提出了城市水资源社会再生能力概念，并分别建立了城市水资源社会绝对再生能力与相对再生能力的评价指标体系；在此基础上，建立了一种全新的城市水资源评价模型——基于人工神经网络的城市水资源社会再生能力评价模型．该模型在构造评价标准的过程中，通过“虚拟城市”构造模型训练样本；由此将评价问题转化成人工神经网络所擅长的分类问题，并赋予评价结果实际的内涵，使得评价结果更直观且更易于理解．最后，以黄河流域的主要城市为研究对象，对其绝对水资源再生能力与相对水资源再生能力分别进行评价；并与传统的灰色关联方法进行对比；评价结果与实际相吻合。     

Short-term Model of the Production of Construction Aggregates in Taiwan Based on Artificial Neural Networks

BACKGROUND: Taiwan's geography and limited stock of sandstone have caused sandstone resources to gradually decline to the point of exhaustion after long-term excavation. Moreover, the Taiwanese government has continuously increased the amount of land area near rivers that cannot be excavated to facilitate riverbed remediation and promote conservation of water resources. Accordingly, predicting and managing the annual production of construction aggregates in future construction projects, and dealing appropriately with some thorny problems, for instance, demand that excess supply, excessive excavation, unregulated excavation, and the consequent environmental damage, will significantly affect the efficient use of natural resources in a manner that accords with the national policy of Sustainable Development (SD). METHODS:. This study establishes an empirical model for forecasting the annual production of future construction aggregates using Artificial Neural Networks (ANN), based on 15 relevant socio-economic indicators, such as indicator of annual consumption of cement. A sensitivity analysis is then performed on these indicators. RESULTS AND DISCUSSION: This work applies ANN to estimate the annual production of construction aggregates; the estimates, the verification of the model and the sensitivity analysis are all acceptable. Furthermore, sensitivity analysis results indicate that the annual consumption of cement is the indicator that most strongly influences the production of construction aggregates, as well as whether construction waste can be recycled and steel structures can be used in buildings, helping to reduce the future production of construction aggregates in Taiwan. CONCLUSIONS: The elaborate prediction methodology presented in this study avoids some of the weaknesses or limitations of conventional linear statistics, linear programming or system dynamics. Additionally, the results not only provide a short-term prediction of the production of construction aggregates in Taiwan, but also provide a viable and flexible means of verifying quality certification of the production data of construction aggregates in the future by incorporating those relevant socio-economic indicators. RECOMMENDATIONS AND OUTLOOK: The continuity and quality of the database of relevant indicators used in this study should be closely scrutinized in order to ensure the SD means of exploiting resources.     

Artificial indoor streams as a method to investigate the impact of chemicals on lotic communities

The potential hazard of chemicals on aquatic communities are generally evaluated by standardised single-species bioassays. Safety assessment is based on results gained from organisms adapted to lentic systems and biological interactions in ecosystems are neglected. While lotic communities are often at first in contact with chemicals, it is astonishing that microcosms with lentic communities are mainly used as a bridge between laboratory bioassays and outdoor aquatic systems. Hence, we established five artificial indoor streams to simulate abiotic factors of small rivers. The closed-circuit system was filled with nutrients added to tap water. Washed pebbles were used as sediment. The dynamics of a simple biocoenoses consisting of aufwuchs, Lumbriculus variegatus Asellus aquaticus and Gammarus fossarum was investigated. The dynamic of aufwuchs and periphyton was determined as dry weight and chlorophyll-a, respectively and qualitatively by pigment pattern. The abundance of different developmental stages of L. variegatus was determined at the end of the experiment as well as the population dynamics of G. fossarum and A. aquaticus. Survival rates of gammarids and juveniles per female were investigated and data were used for modelling the population dynamics. The experiment was carried out to investigate the performance of the established artificial streams and the developed approaches to investigate effects of chemicals on a basic lotic community. The prime reason to establish this approach was to close a gap between complex artificial stream systems and laboratory single species tests to assess the impact of chemicals on the aquatic environment.