Climatic and genetic controls of yields of switchgrass, a model bioenergy species

The U.S. Renewable Fuel Standard calls for 136 billion liters of renewable fuels production by 2022. Switchgrass (Panicum virgatum L.) has emerged as a leading candidate to be developed as a bioenergy feedstock. To reach biofuel production goals in a sustainable manner, more information is needed to characterize potential production rates of switchgrass. We used switchgrass yield data and general additive models (GAMs) to model lowland and upland switchgrass yield as nonlinear functions of climate and environmental variables. We used the GAMs and a 39-year climate dataset to assess the spatio-temporal variability in switchgrass yield due to climate variables alone. Variables associated with fertilizer application, genetics, precipitation, and management practices were the most important for explaining variability in switchgrass yield. The relationship of switchgrass yield with climate variables was different for upland than lowland cultivars. The spatio-temporal analysis showed that considerable variability in switchgrass yields can occur due to climate variables alone. The highest switchgrass yields with the lowest variability occurred primarily in the Corn Belt region, suggesting that prime cropland regions are the best suited for a constant and high switchgrass biomass yield. Given that much lignocellulosic feedstock production will likely occur in regions with less suitable climates for agriculture, interannual variability in yields should be expected and incorporated into operational planning.     

Determination of Frequency for Remeasuring Ground and Vegetation Cover Factor Needed for Soil Erosion Modeling

Determining a remeasurement frequency of variables over time is required in monitoring environmental systems. This article demonstrates methods based on regression modeling and spatio-temporal variability to determine the time interval to remeasure the ground and vegetation cover factor on permanent plots for monitoring a soil erosion system. The spatio-temporal variability methods include use of historical data to predict semivariograms, modeling average temporal variability, and temporal interpolation by two-step kriging. The results show that for the cover factor, the relative errors of the prediction increase with an increased length of time interval between remeasurements when using the regression and semivariogram models. Given precision or accuracy requirements, appropriate time intervals can be determined. However, the remeasurement frequency also varies depending on the prediction interval time. As an alternative method, the range parameter of a semivariogram model can be used to quantify average temporal variability that approximates the maximum time interval between remeasurements. This method is simpler than regression and semivariogram modeling, but it requires a long-term dataset based on permanent plots. In addition, the temporal interpolation by two-step kriging is also used to determine the time interval. This method is applicable when remeasurements in time are not sufficient. If spatial and temporal remeasurements are sufficient, it can be expanded and applied to design spatial and temporal sampling simultaneously.     

SYNTHESIS OF FLOW-DURATION CURVES FOR UNREGULATED STREAMS IN NEW HAMPSHIRE1

ABSTRACT: Flow-duration curves are concise pictures of flow variability at a point on a stream, and provide essential information for all water-resource planning. In New Hampshire, useful estimates of flow-duration curves for ungaged points on unregulated streams can be made using only information readily available from contour maps: 1) area of the basin above the point of interest; and 2) either the measured mean basin elevation or the elevations of the highest and lowest points in the basin. Measured or estimated mean basin elevation is then used in regression equations to estimate mean flow QC and the flow exceeded 95% of the time, Q₉₅. QC is assumed to occur at the 27% exceedance frequency. Q₀₂, Q₀₅, and Q₃₀ are estimated as multiples of QC. Equations are provided for calculating 95% confidence intervals for future estimates using the method. The dependence of mean flow on elevation is due to positive vertical precipitation gradients and negative vertical evapotranspiration gradients. The dependence of Q₉₅ on elevation appears to be due largely to the fact that it rains more often, that snowmelt takes longer, and that evapotranspiration is reduced at higher elevations.     

Spatiotemporal dynamics of forest-tundra communities in the polar urals

The spatiotemporal dynamics of forest-tundra communities in the 20th century have been studied in the timberline ecotone of the Polar Urals. Maps reflecting the distribution of different types of forest-tundra communities have been made, and data on the morphological and age structure of tree stands have been obtained for three time sections (the mid-1910s, 1960s, and 2000s). They show that open and closed forests have markedly expanded due to natural afforestation of the tundra and increase in the density and productivity of existing forest stands. The unidirectional pattern of plant community transition (from the tundra to closed forests) and meteorological data provide evidence that this transition has been conditioned by climate warming and increasing humidity recorded during the past 90 years.Translated from Ekologiya, No. 2, 2005, pp. 83–90.Original Russian Text Copyright © 2005 by Shiyatov, Terentev, Fomin.     

我国Natech事件分布规律及涉水Natech事件致因分析

为弥补我国自然—技术事件(Natech事件)案例研究空白,基于多种数据挖掘方法对近20 a公开报道的自然灾害诱发的技术事故案例进行收集整理,建立了Natech事件数据库.对该类事件时空分布、行业分布进行交叉分析后发现:Natech事件季节特征明显,暴雨和大风是主要肇因,建筑业和交通运输业脆弱性较强.在引入不利气象情景对...     

水环境模拟中多孔介质场空间变异性研究方法述评

阐述了研究多孔介质空间变异性的经典统计法、地质统计学方法以及新兴的分形方法.经典统计方法由于不考虑研究对象的空间相关性,已经不能对这种变异性做出客观的分析;地质统计学方法很好地克服了经典统计学的缺点,是目前应用最多的一种方法;分形的研究对象为无规则和具有自相似性的体系,这一特性决定了它研究这种变异性有着独特的优点.最后对多孔介质空间变异性研究的趋势进行了展望.     

烟气羽流时空混沌行为的耦合映象格子模型研究

为研究烟气羽流的时空混沌行为,本文从烟气羽流的N-S动量方程出发,根据浮力羽流的半经验公式,对方程中相应的项进行替代、化简,然后无量纲化,得到了以烟气羽流的动量通量m为系统状态矢量的单向耦合映象格子模型.计算了不同参数变化时,耦合周期窗口的时空行为发展,并且进行了李雅普诺夫指数λ计算.分析表明,开放系统中的烟气羽流会出现形态的多重性,包括时空混沌行为.这是由于烟气羽流系统内各种非线性作用的相互影响,也就是方程粘性项、浮力项和惯性项作用强弱对比的变化,以及系统行为发展的不可逆性所导致.     

2002—2012年京津唐PM10变化规律及差异

对2002年6月—2012年5月京津唐城市群大气可吸入颗粒物(PM10)质量浓度的长期监测数据进行分析,结合3市的地理、气候气象条件,分析了京津唐城市群大气颗粒物质量浓度的变化特征;根据3市PM10相互之间的发散系数,定量分析了3市PM10变化的差异。结果表明,2002—2012年北京市的PM10质量浓度变化范围为0.012~0.600 mg/m3,天津、唐山2市的PM10质量浓度变化范围分别为0.014~0.600、0.019~0.452 mg/m3。2008、2011年天津市PM10质量浓度年平均值达到二级标准,唐山市从2008年后PM10质量浓度年平均值达到二级标准;北京市PM10质量浓度总体变化趋势为春季秋季冬季夏季,天津、唐山市均为冬季春季秋季夏季,但不同年份的变化趋势略不同;北京-唐山、北京-天津、天津-唐山之间PM10的月度发散系数范围分别为0.402 7~0.159 2、0.406 8~0.142 9、0.323 1~0.107 8,说明空间距离最近的天津-唐山之间大气污染的相互影响较北京-天津、北京-唐山之间大。     

Factors affecting the quantitative uncertainty of the estimated short-term intake. Part I—Calculation methods

ABSTRACT

The calculation of the combined uncertainty of the international estimated short-term intake (IESTI) of ethephon residues in apples is shown as an example. The ethephon residues in apples were reported by the Joint FAO (Food and Agriculture Organization of the United Nations)/WHO (World Health Organization) Meeting on Pesticide Residues (JMPR). The apple consumption data were taken from the IESTI (international short-term intake) calculation template used by the JMPR. The IESTI was calculated with the currently used method (case 2a) and a proposed one recommended by the EFSA (European Food Safety Authority)/RIVM (Dutch National Institute for Public Health) Scientific Workshop co-sponsored by FAO and WHO. In this example, the ratio of IESTI_proposed/IESTI_current and their combined relative uncertainty are about 2.8, and 1.7, respectively. The larger IESTI and uncertainty obtained with the proposed equation are the consequence of calculation only with the large portion (LP) instead of its combination with unit mass, and the MRL instead of the highest residue (HR). The LP is the major contributor to the combined uncertainty. Both the calculated IESTI and its combined uncertainty depend on the actual food – pesticide residue combination, and should be calculated for each case.     

Deriving heart rate variability indices from cardiac monitoring—An indicator of driver sleepiness

Objective: Driver fatigue is considered to be a major contributor to road traffic crashes. Cardiac monitoring and heart rate variability (HRV) analysis is a candidate method for early and accurate detection of driver sleepiness. This study has 2 objectives: to evaluate the (1) suitability of different preprocessing strategies for detecting and removing outlier heartbeats and spectral transformation of HRV signals and their impact of driver sleepiness assessment and (2) relation between common HRV indices and subjective sleepiness reported by a large number of drivers in real driving situations, for the first time.

Methods: The study analyzed >3,500 5-min driving epochs from 76 drivers on a public motorway in Sweden. The electrocardiograph (ECG) data were recorded in 3 studies designed to evaluate the physiological differences between awake and sleepy drivers. The drivers reported their perceived level of sleepiness according to the Karolinska Sleepiness Scale (KSS) every 5?min. Two standard methods were used for identifying outlier heartbeats: (1) percentage change (PC), where outliers were defined as interbeat intervals deviating >30% from the mean of the four previous intervals and (2) standard deviation (SD), where outliers were defined as interbeat interval deviating >4 SD from the mean interval duration in the current epoch. Three standard methods were used for spectral transformation, which is needed for deriving HRV indices in the frequency domain: (1) Fourier transform; (2) autoregressive model; and (3) Lomb-Scargle periodogram. Different preprocessing strategies were compared regarding their impact on derivation of common HRV indices and their relation to KSS data distribution, using box plots and statistical tests such as analysis of variance (ANOVA) and Student’s t test.

Results: The ability of HRV indices to discriminate between alert and sleepy drivers does not differ significantly depending on which outlier detection and spectral transformation methods are used. As expected, with increasing sleepiness, the heart rate decreased, whereas heart rate variability overall increased. Furthermore, HRV parameters representing the parasympathetic branch of the autonomous nervous system increased. An unexpected finding was that parameters representing the sympathetic branch of the autonomous nervous system also increased with increasing KSS level. We hypothesize that this increment was due to stress induced by trying to avoid an incident, because the drivers were in real driving situations.

Conclusions: The association of HRV indices to KSS did not depend on the preprocessing strategy. No preprocessing method showed superiority for HRV association to driver sleepiness. This was also true for combinations of methods for frequency domain HRV indices. The results prove clear relationships between HRV indices and perceived sleepiness. Thus, HRV analysis shows promise for driver sleepiness detection.