Structural Decomposition Analysis of Raw Material Consumption

The aim of this article is to quantify the drivers for the changes in raw material consumption (domestic material consumption expressed in the form of all materials extracted and used in the production phase) in terms of technology, which refers to the concept of sustainable production; the product structure of final demand, which refers to the concept of sustainable consumption; and the volume of final demand, which is related to economic growth. We also aim to determine to what extent the technological development and a shift in product structure of the final demand compensate for the growth in final consumption volume. Therefore, we apply structural decomposition analysis (SDA) to the change in raw material consumption (RMC) of the Czech Republic between 2000 and 2007. To present the study in a broader context, we also show other material flow indicators for the Czech Republic for 2000 and 2007. Our findings of SDA show that final demand structure has a very limited effect on the change in material flows. The rapid change in final demand volume was not compensated for crude oil, metal ores, construction materials, food crops, and timber. For the material category of non‐iron metal ores, even the change in technology contributes to an increase in material flows. The largest relative increases are reported for non‐iron metal ores (38%) and construction materials (30%). The main changes in material flows related to the Czech Republic are driven by exports and enabled by imports, the main source of these increased material flows. This emphasizes the increasing role of international trade.     

An Extreme Value Analysis of Pollutant Concentrations in Surface Soils Due to Atmospheric Deposition

Risk assessments often rely on deterministic models using long-term averages or “steady-state” values of input variables. Such models do not provide the information needed to estimate acute exposures. This study uses extreme value theory to examine the frequency and magnitude of daily pollutant concentrations in surface soils predicted at six U.S. locations. Concentrations are predicted using a deposition-leaching model and 50 years of historical precipitation data. A stochastic model also is used to generate 1000 years of precipitation data as modeling inputs for each location. The annual maximum concentrations at each site are fitted to a Gumbel type I distribution to estimate occurrence probability. For soluble pollutants, the predicted concentration varied substantially with precipitation, and the maximum daily concentrations exceeded annual averages by 4 to 8 times. Observed and synthetic precipitation data produced similar results at most study locations, though the synthetic data provided a slightly better fit to the Gumbel type I distribution. The precipitation model allows the generation of representative precipitation data that extend limited historical records. The extreme value analysis facilitates the evaluation of maximum pollutant concentrations, return periods, and other statistics that are important in evaluating acute exposures.     

格网尺度下典型旅游城市生态服务价值估算和时空分异特征——以三亚为例

以典型旅游城市三亚市为案例地，利用2006-2018年4期Landsat遥感影像数据，借助ENVI、ArcGIS平台定量识别土地利用演变特征，在1km×1km格网尺度下估算旅游地生态系统服务价值，并结合空间探索性数据分析揭示生态系统服务价值时空分异特征及其与旅游地发展的时空耦合关系。结果表明：（1）2006-2018年间，三亚市生态系统服务价值总量呈逐年下降趋势，由6.73×10⁹元降至5.76×10⁹元，累计减少9.78×10⁸元；（2）空间格局上，三亚市呈"南低北高"空间分异格局，2006-2018年增值区域连片分布于崖州区、天涯区、吉阳区南部区域，且呈逐年减少趋势，减值区域集聚于天涯区东北部、海棠区；（3）空间集聚上，生态系统服务价值截面各年份均呈显著空间正相关且相关性先降后增。高高集聚区位于天涯区北部区域，低低集聚区分布于沿海、海湾地区；（4）旅游发展与生态系统服务价值时空演化特征关联性较强。三亚市天涯区北部林地生态环境良好，生态系统服务价值略有下降但绝对数值稳定，是生态系统服务价值主要来源；旅游发展较为迅速的三亚湾、崖州湾以及海棠湾，相对增值区域较多，但绝对生态系统服务价值损失显著，严重滞后于其他区域。     

Environmental and genetic control of crassulacean acid metabolism in two crassulacean species and an F1 hybrid with differing biomass δ13C values

Abstract The growth, biomass δ¹³C values, and ability to accumulate titratable acidity at night were compared in eight environmental treatments for Cremnophila linguifolia, Sedum greggii, and their F₁ hybrid. In the phytotron, differences in treatment daylength, day/night temperature and water availability were all found to have effects on total plant dry weight, nocturnal accumulation of titratable acidity and biomass δ¹³C value of at least some of the genotypes. However, there were differences between the genotypes both in the magnitude and direction of response of the phenotypic properties to the treatment variables. The phytotron δ¹³C values ranged from -12.9 to -19.2‰ for C. linguifolia, from -22.2 to -33.4‰ for S. greggii, and from -19.2 to -24.9‰ for the hybrid. After with-holding water for 76 h both C. linguifolia and the hybrid had midday Ψ_leaf values of -0.23 MPa; however, S. greggii had a value of -1.05 MPa. In contrast to past observations of other species, the daily watered plants of C. linguifolia had less negative δ¹³C values than did the plants watered only weekly.     

基于价值评估的黄山市生境质量时空演变特征分析

评价与分析特定区域的生境质量及其时空变化对其生态安全格局构建、可持续发展意义重大。本文以典型旅游城市黄山市为研究区，综合生态系统服务价值评价模型、基于NPP与NDVI的生境质量指数评价、InVEST生境质量评价模型、空间分析等模型与方法对黄山市1987-2017年生境质量进行研究。结果表明：（1）1987-2007年，黄山市生境质量总值持续下降，2017出现回升，但生境受到的威胁仍增加，生境最优区为北部太平湖区域，生境质量低值主要分布在各县区主城区；生境质量栅格等级变化以4等级转变为3等级为主。（2）1987-2017年，黄山市105个乡镇（街道）中有84个乡镇（街道）生境质量有所下降，主要分布在东部区域；北部环太平湖区域的乡镇为生境质量"高高"型聚集区及热点区域，屯溪区、徽州区南部、歙县、休宁县主城区的各镇为生境质量"低低"型聚集区及冷点区域。（3）黄山市城市发展对生境质量影响较大，大体上距离城市中心点越近的圈层，生境质量越差；距离黄山市旅游中心点10-20、30-40、40-50 km的3个圈层形成生境质量低值点，为旅游与城区互助发展的结果；黄山市旅游与生境质量空间分布相关性较强，旅游设施、景区核密度越高，低等级生境质量占比越高。     

How Can the Eco‐efficiency of a Region be Measured and Monitored?

The concept of eco-efficiency is commonly referred to as a business link to sustainable development. In this article, ecoefficiency is examined at a regional level as an approach to promoting the competitiveness of economic activities in the Finnish Kymenlaakso region and mitigating their harmful impacts on the environment. The aim is to develop appropriate indicators for monitoring changes in the eco-efficiency of the region. A starting point is to produce indicators for the environmental and economic dimensions of regional development and use them for measuring regional eco-efficiency. The environmental impact indicators are based on a life-cycle assessment method, producing different types of environmental impact indicators: pressure indicators (e.g., emissions of CO₂), impact category indicators (e.g., CO₂ equivalents in the case of climate change), and a total impact indicator (aggregating different impact category indicator results into a single value). Environmental impact indicators based on direct material input, total material input, and total material requirement of the Kymenlaakso region are also assessed. The economic indicators used are the gross domestic product, the value added, and the output of the main economic sectors of Kymenlaakso. In the eco-efficiency assessment, the economic and environmental impact indicators are monitored in the same graph. In a few cases eco-efficiency ratios can also be calculated (the economic indicators are divided by the environmental indicators). Output (= value added + intermediate consumption) is used as an economic indicator related to the environmental impact indicators, which also cover the upstream processes of the region's activities. In the article, we also discuss the strengths and weaknesses of using the different environmental impact indicators.     

Determination of field output correction factors of radiophotoluminescence glass dosimeter and CC01 ionization chamber and validation against IAEA-AAPM TRS-483 code of practice

PurposeTo determine the field output correction factors of the radiophotoluminescence glass dosimeter (RPLGD) in parallel and perpendicular orientations with reference to CC01, the ionization chamber.MethodsThe dose to a small water volume and the sensitive volume of the RPLGD and the IBA-CC01 were determined for 6-MV, 100-cm SAD, 10-cm depth using egs_chamber user-code. The RPLGD in perpendicular and parallel orientations to the beam axis were studied. The field output correction factors of each detector for 0.5 × 0.5 to 10 × 10 cm² field sizes were determined. These field output correction factors were validated by comparing field output factors against data determined from IAEA-AAPM TRS-483 code of practice.ResultsThe field output correction factors of all detectors were within 5% for field sizes down to 0.8 × 0.8 cm². For 0.5 × 0.5 cm², the field output correction factors of CC01, RPLGD in perpendicular and parallel orientations differed from unity by 14%, 19%, and 5%, respectively. The percentage difference between field output factors determined using RPLGD and CC01 data, corrected using the field output correction factors determined in this work and measurements with CC01 data corrected using TRS-483, was less than 3% for all field sizes, except for the smallest field size of RPLGD in perpendicular orientation and the CC01.ConclusionsThe field output correction factors of RPLGD and CC01 are reported. The validation proves that RPLGD in parallel orientation combined with the field output correction factors is the most suitable for determining the field output factors for the smallest field used in this study.     

Assessment of Optimal Selected Prognostic Factors

The identification and assessment of prognostic factors is one of the major tasks in clinical research. The assessment of one single prognostic factor can be done by recently established methods for using optimal cutpoints. Here, we suggest a method to consider an optimal selected prognostic factor from a set of prognostic factors of interest. This can be viewed as a variable selection method and is the underlying decision problem at each node of various tree building algorithms. We propose to use maximally selected statistics where the selection is defined over the set of prognostic factors and over all cutpoints in each prognostic factor. We demonstrate that it is feasible to compute the approximate null distribution. We illustrate the new variable selection test with data of the German Breast Cancer Study Group and of a small study on patients with diffuse large B‐cell lymphoma. Using the null distribution for a p‐value adjusted regression trees algorithm, we adjust for the number of variables analysed at each node as well. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)