发展中国西部的生态旅游业

生态旅游作为“保护生态”和“发展经济”双赢理念的新兴旅游类型,被西部各省(区、市)所关注并将其定为本地区发展的主导产业.通过对西部地区的生态旅游资源优缺点、市场定位、产品转化及空间布局的分析,初步勾画了西部生态旅游业的发展前景,并提出相应地发展策略.     

红外和热分析联用在化工产品研究中的应用

运用实例论述了红外光谱分析与热分析联用在化工产品的产品开发和检测方面的应用。     

Discriminate Analysis for Dust Storm Prediction in the Gobi and Steppe Regions in Mongolia

A discriminate analysis method for probability forecast of dust storms in Mongolia has been developed. The prediction method uses data recorded at 23 meteorological stations in the Gobi and steppe regions of Mongolia, including surface air pressure and geo-potential height at the 500-hPa level on grid points, and weather maps from 1975 to 1990. Weather elements such as air temperature, pressure, geo-potential height etc, which influence the formation of dust storms, are prepared as predictors. To select the most informative/important predictors (variables), we used a mean correlation matrix of variables together with the Mahalonobis distance, and correlation coefficients between dust storms and predictors with an orthogonalization for removing correlated predictors. The most informative predictors for dust storm prediction are intensities of surface cyclones and migratory anticyclones, passage of cold fronts, the horizontal gradients of the surface air pressure in the cold frontal zone, cyclonic circulations from the ground surface up to the 500-hPa level, the geo-potential height at 500-hPa level and its temporal changes. Selected predictors are used in discriminate analysis for formulating dust storm prediction equations. Sandstorm data have been classified into three classes, viz., strong, moderate and weak dust storms, depending on their intensities, durations and areas covered. Predictions of the probabilities of dust storm occurrence use the prediction equations for each class. The prediction is made from 12 hours to 36 hours. Verification of the probability forecasts of dust storms is also shown. The accuracy of forecasts is 72.2–79.9% with the data used for developing equations (dependent variables), in contrast to 67.1–72.0% with unrelated data for deriving equations (independent variables).     

地下水水质评价的多元线性回归分析模型研究

运用回归分析理论和方法,建立了一个基于多元线性回归分析法的地下水水质评价模型,并将该模型用于遵义市海龙坝地下水水质评价.结果表明,建立的模型较符合本研究区的实际情况.     

Effect of 450 MHz Microwave Modulated with 217 Hz on Human EEG in Rest

Summary This study focuses on discrimination of changes, produced by low-level microwave exposure in intensity and time variability of the human EEG at rest. The power spectral density (PSD) method and nonlinear scaling analysis of the length distribution of low variability periods (LDLVP) were selected for analysis of the EEG signal. During the study, 19 healthy volunteers were exposed to a microwave (450 MHz) of 217 Hz frequency on-off modulation. The field power density at the scalp was 0.16 mW/cm². The experimental protocol consisted of ten cycles of repetitive microwave exposure. Signals from frontal, temporal, parietal and occipital EEG channels on EEG theta, alpha and beta rhythms were analysed. Exposure to microwave causes average increase of EEG activity. LDLVP analysis discriminated significant effect in time variability for 2 subjects (11%). PSD method detected significant changes in intensity for 4 subjects (21%). The effect of low-level microwave exposure is stronger on EEG beta rhythm in temporal and parietal regions of the human brain.     

Impact of Human Activities on Carbon Dioxide (CO<Subscript>2</Subscript>) Emissions: A Statistical Analysis

Summary The balance of evidence suggests a perceptible human influence on global ecosystems. Human activities are affecting the global ecosystem, some directly and some indirectly. If researchers could clarify the extent to which specific human activities affect global ecosystems, they would be in a much better position to suggest strategies for mitigating against the worst disturbances. Sophisticated statistical analysis can help in interpreting the influence of specific human activities on global ecosystems more carefully. This study aims at identifying significant or influential human activities (i.e. factors) on CO₂ emissions using statistical analyses. The study was conducted for two cases: (i) developed countries and (ii) developing countries. In developed countries, this study identified three influential human activities for CO₂ emissions: (i) combustion of fossil fuels, (ii) population pressure on natural and terrestrial ecosystems, and (iii) land use change. In developing countries, the significant human activities causing an upsurge of CO₂ emissions are: (i) combustion of fossil fuels, (ii) terrestrial ecosystem strength and (iii) land use change. Among these factors, combustion of fossil fuels is the most influential human activity for CO₂ emissions both in developed and developing countries. Regression analysis based on the factor scores indicated that combustion of fossil fuels has significant positive influence on CO₂ emissions in both developed and developing countries. Terrestrial ecosystem strength has a significant negative influence on CO₂ emissions. Land use change and CO₂ emissions are positively related, although regression analysis showed that the influence of land use change on CO₂ emissions was still insignificant. It is anticipated, from the findings of this study, that CO₂ emissions can be reduced by reducing fossil-fuel consumption and switching to alternative energy sources, preserving exiting forests, planting trees on abandoned and degraded forest lands, or by planting trees by social/agroforestry on agricultural lands.     

Estimating the Recreational-Use Value for Hiking in Bellenden Ker National Park, Australia

The recreational-use value of hiking in the Bellenden Ker National Park, Australia has been estimated using a zonal travel cost model. Multiple destination visitors have been accounted for by converting visitors’ own ordinal ranking of the various sites visited to numerical weights, using an expected-value approach. The value of hiking and camping in this national park was found to be $AUS 250,825 per year, or $AUS 144,45 per visitor per year, which is similar to findings from other studies valuing recreational benefits. The management of the park can use these estimates when considering the introduction of a system of user pays fees. In addition, they might be important when decisions need to be made about the allocation of resources for maintenance or upgrade of tracks and facilities.     

Life cycle energy impacts of automotive liftgate inner

This paper compares the life cycle energy use of a cast-aluminum, rear liftgate inner and a conventional, stamped steel liftgate inner used in a minivan. Using the best available aggregate life cycle inventory data and a simple spreadsheet-level analysis, energy comparisons were made at both the single-vehicle and vehicle-fleet levels. Since the product manufacture and use are distributed over long periods of time that, in a fleet, are not simple linear combinations of single product life cycles. Thus, it is all the products in use over a period of time, rather than a single product, that are more appropriate for the life cycle analysis. Using a set of consistent data, analyses also examine sensitivity to the level of analysis and the assumptions to determine the most favorable materials with respect to life cycle energy benefits.As expected, life cycle energy impacts of aluminum are lower than steel at a single-vehicle level – energy savings are determined to be 1.8 GJ/vehicle. Most energy savings occur at the vehicle operation phase due to improved fuel economy from lightweighting. The energy benefits are realized only very close to the average vehicle life of 14 years. With the incremental growth of the vehicle fleet, it takes longer – about 21 years – for aluminum to achieve life cycle equivalence with steel. The number of years aluminum needs to achieve equivalence with steel was found to be quite sensitive to aluminum manufacturing energy and fuel economy. As the steel industry races to compete with other materials for automotive lightweighting, a systems approach, instead of part-to-part comparison, is more appropriate in the determination of viability of aluminum substitution from an energy perspective.     

PREDICTING FECAL COLIFORM BACTERIA LEVELS IN THE CHARLES RIVER,MASSACHUSETTS, USA1

In Massachusetts, the Charles River Watershed Association conducts a regular water quality monitoring and public notification program in the Charles River Basin during the recreational season to inform users of the river's health. This program has relied on laboratory analyses of river samples for fecal coliform bacteria levels, however, results are not available until at least 24 hours after sampling. To avoid the need for laboratory analyses, ordinary least squares (OLS) and logistic regression models were developed to predict fecal coliform bacteria concentrations and the probabilities of exceeding the Massachusetts secondary contact recreation standard for bacteria based on meteorological conditions and streamflow. The OLS models resulted in adjusted R²s ranging from 50 to 60 percent. An uncertainty analysis reveals that of the total variability of fecal coliform bacteria concentrations, 45 percent is explained by the OLS regression model, 15 percent is explained by both measurement and space sampling error, and 40 percent is explained by time sampling error. Higher accuracy in future bacteria forecasting models would likely result from reductions in laboratory measurement errors and improved sampling designs.