外来有害生物风险评估技术

概述了有害生物风险分析的概念、必要性、生物学基础和一般程序 ;论述了生态气候图、农业气候相似距库、生态气候评价的分析模型、地理信息系统、专家系统、基于定性分析与定量估算相结合的数学模型等有害生物风险分析技术的原理和特点 ;认为应用网络技术 ,建立基于分布式计算的全球入侵物种风险评价数据体系 ,可有效提高风险评价的速度和准确性。     

Statistics: an essential technology in environmental research and management

Statistical methods as developed and used in decision making and scientific research are of recent origin. The logical foundations of statistics are still under discussion and some care is needed in applying the existing methodology and interpreting results. Some pitfalls in statistical data analysis are discussed and the importance of cross examination of data (or exploratory data analysis) before using specific statistical techniques are emphasized. Comments are made on the treatment of outliers, choice of stochastic models, use of multivariate techniques and the choice of software (expert systems) in statistical analysis. The need for developing new methodology with particular relevance to environmental research and policy is stressed.Dr Rao is Eberly Professor of Statistics and Director of the Penn State Center for Multivariate Analysis. He has received PhD and ScD degrees from Cambridge University, and has been awarded numerous honorary doctorates from universities around the world. He is a Fellow of Royal Society, UK; Fellow of Indian National Science Academy; Foreign Honorary Member of American Academy of Arts and Science; Life Fellow of King's College, Cambridge; and Founder Fellow of the Third World Academy of Sciences. He is Honorary Fellow and President of International Statistical Institute, Biometric Society and elected Fellow of the Institute of Mathematical Statistics. He has made outstanding contributions to virtually all important topics of theoretical and applied statistics, and many results bear his name. He has been Editor of Sankhya and theJournal of Multivariate Analysis, and serves on international advisory boards of several professional journals, includingEnvironmetrics and theJournal of Environmental Statistics. This paper is based on the keynote address to the Seventh Annual Conference on Statistics of the United States Environmental Protection Agency.     

Automated Classification of an Environmental Sensitivity Index

Environmental Sensitivity Indices (ESI) composed of many field-data are essential for monitoring and control systems. At the beginning of the last decade an ESI of the German Wadden Sea was developed for use by the relevant authorities. This ESI was derived by experts semi-manually analysing the extensive field data-set. An algorithm is presented here which emulates human expert-decisions on the classification of sensitivity classes. This will permit the necessary regular updates of ESI-determination when new field data become available using automated classifications procedures. After tuning the algorithm parameters it generates decisions identical to those of human experts in about 97% of all locations tested. In addition, the algorithm presented also enables erroneous or extremely seldom field data to be identified.     

Incorporating GIS and MCE for Suitability Assessment Modelling of Coral Reef Resources

Assessment, planning and management for coral reef ecosystems are particularly challenging tasks, especially in developing countries. In this study, a methodological approach which integrates Geographic Information Systems (GIS) and Multicriteria Evaluation (MCE) for the development of suitability assessment models for a variety of uses of coral reef resources is discussed. Such an approach is sustained by an extensive use of local expert knowledge coded in the form of automated decision trees (DTs). The usefulness of the approach and the models developed is demonstrated by their application to the participatory assessment and resources planning at “Alacranes Reef National Park” (ARNP), Yucatán, México. Overlaying of resulting suitability maps was also applied for identifying potential conflicting areas.     

General plume dispersion model (GPDM) for point source emission

Gaussian-based dispersion models are widely used to estimate local pollution levels. The accuracy of such models depends on stability classification schemes as well as plume rise equations. A general plume dispersion model (GPDM) for a point source emission, based on Gaussian plume dispersion equation, was developed. The program complex was developed using Java and Visual basic tools. It has the flexibility of using five kinds of stability classification schemes, i.e., Lapse Rate, Pasquill–Gifford (PG), Turner, σ–θ and Richardson number. It also has the option of using two types of plume rise formulations – Briggs and Holland’s. The model, applicable for both rural and urban roughness conditions, uses meteorological and emission data as its input parameters, and calculates concentrations of pollutant at the center of each cell in a predefined grid area with respect to the given source location. Its performance was tested by comparing with 4-h average field data of continuous releases of SO₂ from Dadri thermal power plant (Uttar Pradesh, India). Results showed that the Turner scheme used with Holland’s equation gives the best outcome having a degree of agreement (d) of 0.522.     

Variations in the elemental compositions of plants,and computer‐aided sampling in ecosystems∗

To obtain comparable results of multi‐element analysis of plant materials by different laboratories, a harmonized sampling procedure for terrestrial and marine ecosystems is essential. The heterogeneous distribution of chemical elements in living organisms is influenced by different biological parameters. These parameters are mainly characterized by genetic predetermination, seasonal changes, edaphic and climatic conditions, and delocalization processes of chemical substances by metabolic activities.

The biological variations of the element content in plants were divided into 5 systematic levels, which are: 1. the plant species; 2. the population; 3. the stand (within an ecosystem); 4. the individual; and 5. the plant compartment. Each of these systematic levels can be related to: 1. genetic variabilities; 2. different climatic, edaphic and anthropogenic influences; 3. microclimatic or microedaphic conditions; 4. age of plants (stage of development), exposure to environmental influences (light, wind, pollution etc.), seasonal changes; and 5. transport and deposition of substances within the different plant compartments (organs, tissues, cells, organelles).

An expert system for random and systematic sampling for multi‐element analysis of environmental materials, such as plants, soils and precipitation is presented. After statistical division of the research area, the program provides advice for contamination‐free collection of environmental samples.