从经济学角度对比中国与美国的能源消费

分析了中关两国能源消费总量和能源结构的差异,探讨了中关两国能源消费差异的经济学内涵,认为经济指标GDP并不能完全反映人们生活水平的提高,而其带来的环境问题却很可能影响后续的经济发展。因此,在发展经济的同时,应该充分考虑能源需求给环境带来的压力。适当调整能源结构、提高能源效率和发展替代能源是未来能源消费战略的重中之重,也是经济、环境和社会协调发展的重要保障。     

基于可靠性统计分析法的制导弹药储存寿命评估

针对制导弹药价格昂贵而无法采用大规模性能试验和实弹射击方法进行可靠性研究的问题,采用可靠性统计分析法对制导弹药储存寿命进行评估。通过建立制导弹药可靠储存寿命模型,并以制导弹药火工品中的待发程控装置为研究对象,运用Bayes法对试验数据出现的＂倒挂＂现象进行了修正。依据修正后的试验数据进行计算,得到了在一定置信度下待发程控装置的储存寿命。     

2007年5-6月国内环境事件

简要统计了2007年5-6月国内发生的各种环境事件123起,包括沙尘天气10起,污染事件23起,地震15起,山体滑坡和泥石流13起,旱灾5起,以及其他自然灾害57起.对自然灾害进行了讨论.     

VBA在环境监测数据统计中的应用

VBA是Office系列软件内嵌的通用编程语言,用于定制和扩展Office的功能。将VBA应用在环境监测数据统计中,并以功能区噪声等效声级日期筛选为例,通过编制宏代码,方便快捷地实现功能区噪声数据的自动筛选。     

Does Including Soil Moisture Observations Improve Operational Streamflow Forecasts in Snow‐Dominated Watersheds?

Changing climate and growing water demand are increasing the need for robust streamflow forecasts. Historically, operational streamflow forecasts made by the Natural Resources Conservation Service have relied on precipitation and snow water equivalent observations from Snow Telemetry (SNOTEL) sites. We investigate whether also including SNOTEL soil moisture observations improve April‐July streamflow volume forecast accuracy at 0, 1, 2, and 3‐month lead times at 12 watersheds in Utah and California. We found statistically significant improvement in 0 and 3‐month lead time accuracy in 8 of 12 watersheds and 10 of 12 watersheds for 1 and 2‐month lead times. Surprisingly, these improvements were insensitive to soil moisture metrics derived from soil physical properties. Forecasts were made with volumetric water content (VWC) averaged from October 1 to the forecast date. By including VWC at the 0‐month lead time the forecasts explained 7.3% more variability and increased the streamflow volume accuracy by 8.4% on average compared to standard forecasts that already explained an average 77% of the variability. At 1 to 3‐month lead times, the inclusion of soil moisture explained 12.3‐26.3% more variability than the standard forecast on average. Our findings indicate including soil moisture observations increased statistical streamflow forecast accuracy and thus, could potentially improve water supply reliability in regions affected by changing snowpacks.     

Sensitivity and Uncertainty of Ground‐Water Discharge Estimates for Semiarid Shrublands1

Abstract: We proposed a step‐by‐step approach to quantify the sensitivity of ground‐water discharge by evapotranspiration (ET) to three categories of independent input variables. To illustrate the approach, we adopt a basic ground‐water discharge estimation model, in which the volume of ground water lost to ET was computed as the product of the ground‐water discharge rate and the associated area. The ground‐water discharge rate was assumed to equal the ET rate minus local precipitation. The objective of this study is to outline a step‐by‐step procedure to quantify the contributions from individual independent variable uncertainties to the uncertainty of total ground‐water discharge estimates; the independent variables include ET rates of individual ET units, areas associated with the ET units, and precipitation in each subbasin. The specific goal is to guide future characterization efforts by better targeting data collection for those variables most responsible for uncertainty in ground‐water discharge estimates. The influential independent variables to be included in the sensitivity analysis are first selected based on the physical characteristics and model structure. Both regression coefficients and standardized regression coefficients for the selected independent variables are calculated using the results from sampling‐based Monte Carlo simulations. Results illustrate that, while as many as 630 independent variables potentially contribute to the calculation of the total annual ground‐water discharge for the case study area, a selection of seven independent variables could be used to develop an accurate regression model, accounting for more than 96% of the total variance in ground‐water discharge. Results indicate that the variability of ET rate for moderately dense desert shrubland contributes to about 75% of the variance in the total ground‐water discharge estimates. These results point to a need to better quantify ET rates for moderately dense shrubland to reduce overall uncertainty in estimates of ground‐water discharge. While the approach proposed here uses a basic ground‐water discharge model taken from an earlier study, the procedure of quantifying uncertainty and sensitivity can be generalized to handle other types of environmental models involving large numbers of independent variables.     

2009年11-12月国内环境事件

简要统计了2009年11-12月国内发生的各种环境事件68起,包括沙尘天气2起,污染事件14起,地震40起,山体滑坡和泥石流7起,旱灾3起以及其他自然灾害2起.     

Phospholipid fatty acid biomarkers in a freshwater periphyton community exposed to uranium: discovery by non-linear statistical learning

Phospholipid fatty acids (PLFA) have been widely used to characterize environmental microbial communities, generating community profiles that can distinguish phylogenetic or functional groups within the community. The poor specificity of organism groups with fatty acid biomarkers in the classic PLFA-microorganism associations is a confounding factor in many of the statistical classification/clustering approaches traditionally used to interpret PLFA profiles. In this paper we demonstrate that non-linear statistical learning methods, such as a support vector machine (SVM), can more accurately find patterns related to uranyl nitrate exposure in a freshwater periphyton community than linear methods, such as partial least squares discriminant analysis. In addition, probabilistic models of exposure can be derived from the identified lipid biomarkers to demonstrate the potential model-based approach that could be used in remediation. The SVM probability model separates dose groups at accuracies of ∼87.0%, ∼71.4%, ∼87.5%, and 100% for the four groups; Control (non-amended system), low dose (amended at 10 μg U L⁻¹), medium dose (amended at 100 μg U L⁻¹), and high dose (500 μg U L⁻¹). The SVM model achieved an overall cross-validated classification accuracy of ∼87% in contrast to ∼59% for the best linear classifier.     

ANALYSIS AND MODELING OF LONG-TERM STREAM TEMPERATURES ON THE STEAMBOAT CREEK BASIN,OREGON: IMPLICATIONS FOR LAND USE AND FISH HABITAT1

ABSTRACT: Steamboat Creek basin is an important source of timber and provides crucial spawning and rearing habitat for anadromous steelhead trout (Oncorhynchus mykiss). Because stream temperatures are near the upper limit of tolerance for the survival of juvenile steelhead, the possible long-term effect of clear-cut logging on stream temperatures was assessed. Twenty-year (1969–1989) records of summer stream temperature and flow from four tributaries and two reaches of Steamboat Creek and Boulder Creek (a nearby unlogged watershed) were analyzed. Logging records for the Steamboat Creek basin and air temperature records also were used in the analysis. A time-series model of the components of stream temperature (seasonal cycle of solar radiation, air temperature, streamflow, an autoregressive term of order 1, and a linear trend variable) was fitted to the water-temperature data. The linear trend variable was significant in all the fitted models except Bend Creek (a tributary fed by cool ground-water discharge) and Boulder Creek. Because no trends in either climate (i.e., air temperature) or streamflow were found in the data, the trend variable was associated with the pre-1969 loss and subsequent regrowth of riparian vegetation and shading canopies.     

CONFLICTING PERSPECTWES ABOUT DETECTION LIMITS AND ABOUT THE CENSORING OF ENVIRONMENTAL DATA1

ABSTRACT: Current conventions for reporting analytical results from environmental samples brings the objectives of laboratory scientists into conflict with those of environmental scientists. The objective of chemical analyses is to provide estimates of the true composition of samples. Reported results must reflect the analytical uncertainty. Current conventions require left-censoring of those results below the Limit of Detection. The objective of statistical interpretation of environmental data is to provide estimates of the characteristics of ecosystems. Such statistical analyses are often confounded by left-censoring of analytical results. We review the different points of view and propose a compromise which recognizes these conflicting perspectives.