“城郊乡”梯度下土壤Cu、Zn、Pb含量的空间变异特征

为揭示城市化、工业化等人为活动对土壤环境质量的影响,选择能反映上海城郊乡梯度差异的中心城区、城郊结合部和远郊,采用地统计学方法对表层土壤样品Cu、Zn、Pb的空间变异结构和分布特征进行了对比分析。结果表明:城市土壤Pb、Cu、Zn的变异系数范围为0.24~0.62,均属中等变异强度。徐汇区土壤Cu、Pb、Zn符合正态分布,闵行区土壤Cu、Pb和Zn符合对数正态分布,奉贤区土壤Zn呈正态分布,土壤Cu、Pb符合对数正态分布。由中心城区到远郊,城市土壤Cu、Pb、Zn的各项统计特征值和变异系数均有较大差异,存在明显的空间分布差异。半方差函数分析结果表明,徐汇区作为中心城区,土壤Cu、Pb、Zn符合球状模型,土壤Cu、Zn具有强烈的空间相关性,土壤Pb具有明显的空间自相关。奉贤区以农业用地为主,土壤Pb符合线性模型,土壤Cu符合高斯模型,土壤Zn符合指数模型,具有强烈的空间相关性。闵行区地处城郊结合部,土壤Cu、Pb、Zn的半方差拟合模型均为线性模型,表现为纯块金形式,以随机变异为主,空间相关性弱。采用Kriging最优内插法进行无偏估值,绘制了表层土壤重金属含量的空间分布图,中心城区、城郊结合部、郊区土壤重金属的空间分布受城市化、工业化、城市交通等因素的影响,均表现出不同的空间分布规律。     

川中丘陵县域土壤pH空间变异及影响因素分析——以四川仁寿县为例

利用2012年在仁寿县采集的555个表层土样(0~20cm),应用地统计学方法分析了该县域尺度表层土壤pH值的空间变异特征;并采用方差分析和回归分析量化主要影响因素对土壤pH值空间变异的影响程度。结果表明:研究区土壤pH值在4.02~8.14之间,平均为6.80,总体上以中性和碱性土壤为主;变异系数为14.48%,属中等程度的空间变异性。地统计分析表明,研究区土壤pH值变异函数的最佳理论模型为球状模型,具有中等程度的空间自相关性,且空间自相关范围较大。方差分析和回归分析表明,土壤类型、成土母质和土地利用方式是显著影响土壤pH值的主要因素。其中,成土母质和土地利用方式分别能独立解释76.2%和4.8%的土壤pH值空间变异。土壤类型的解释能力与分类级别有关,土类、亚类和土属可分别独立解释41.3%、57.3%和83.7%的土壤pH值空间变异,因而能反映成土过程和母质特性的土属是研究区土壤pH值空间变异的主控因素。研究结果可为川中丘陵县域尺度土壤pH空间变异分析及区域生态环境管理与建设提供有益参考。     

长三角典型地区土壤环境中Se的空间变异特征及其与人类健康的关系

农业土壤环境中硒（Se）的缺乏或过量都会通过食物链最终影响人类健康。在203个土壤采样点的基础上,利用GIS和地统计学相结合的方法,对江苏省如皋市农田土壤环境中Se的空间变异特征及其影响因素进行分析,同时,对土壤中Se的空间分布与长寿人口的空间分布进行空间叠加分析。研究结果表明,如皋市土壤环境中总Se及水溶态Se的平均含量分别为0.13 mg/kg和2.16 μg/kg,研究区处在相对低Se的土壤环境中。总Se的空间变异性不大,其空间分布比较均一;但水溶态Se的空间变异性较大,主要受成土条件及土壤母质、土壤类型及地形地貌等结构性因子的影响。水溶态Se对土壤的理化性质也比较敏感,特别是速效磷、有机质及全钾含量与水溶态硒的相关性较显著,因此在有机质及土壤养分含量较高、土壤颗粒组成较细的东部及北部地区水溶态硒的含量也较高。土壤水溶态Se的空间分异与长寿人口比率的空间分异呈极显著的正相关关系(〖WTBX〗P〖WTBZ〗<001),相关系数达〖WTBX〗r〖WTBZ〗=0325,表明人类的健康长寿现象与环境中水溶态硒的含量有很大关系,但人类长寿的本质和真正机理仍需进一步研究验证。     

The role of interactions between individual animals in the response to heat stress in the freshwater crustacean <Emphasis Type="Italic">Daphnia magna</Emphasis>

The thermal resistance of Daphnia magna Straus is a trait depending on the interaction between individuals. The group effect is expressed in D. magna as both a decrease in thermal resistance and a marked individual differentiation with respect to thermal resistance. Two types of interactions between individual D. magna have been found: (1) the release of an unidentified factor decreasing thermal resistance (without noticeably changing the phenotypic variability of this trait) into the medium and (2) a decrease in the thermal resistance of individuals poorly adapted to heat stress, with the thermal resistance of well-adapted individuals (determined by the dependence of thermal resistance on interactions between individuals) remaining the same or even slightly increasing.Translated from Ekologiya, No. 2, 2005, pp. 126–131.Original Russian Text Copyright © 2005 by Kalinnikova, Krasnoperova, T. Gainutdinov, Sadykova, M. Gainutdinov.     

HYDROCLIMATIC VARIABILITY IN THE ROCKY MOUNTAINS1

ABSTRACT: The spatial and temporal variability of hydroclimatic elements were investigated in the central and northern Rocky Mountains (Colorado, Idaho, Montana, Utah, and Wyoming) during the 1951–1985 period. The three hydroclimatic elements studied were total water-year (October 1-September 30) streamflow (ST), winter (October 1-March 31) accumulated precipitation (PR), and April 1 snowpack (SN). An analysis of 14 virgin watersheds showed wide spatial djfferences in the temporal variability of SN, PR, and ST, and these were found to be caused largely by basin exposure to moist air flows. The more stable (low variability) basins were those exposed to prevailing northerly to westerly flow, while unstable (high variability) basins were exposed to occasional southwesterly to southeasterly moist flow. Snowpack was the better indicator of ST in 11 of the 14 watersheds, explaining 37 to 87 percent of the ST variance. Analysis of the spatial variability, based on all SN and PR data from across the study area, revealed 11 discrete climatic regions. Both SN and PR exhibited coherent regions of stable and unstable temporal variability. The average variability between stable and unstable regions differed by a factor of two, and the differences were best explained by the exposure of the mountain barrier to moist air flows.     

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.     

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.     

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.     

广东省雾霾天气能见度的时空特征分析Ⅰ:季节变化

雾霾已经成为一种新的气象及环境灾害性现象,分析雾霾天气下低能见度的时空分布特征,对掌握雾霾的发生规律和减灾预报具有重要意义。文章利用1980—2003年广东省沿海地区26个地面气象观测站23年的气象观测资料,分析了广东省雾霾天气下能见度的时空分布特征。分析结果发现,1980—2003年广东地区雾霾天气下能见度的季节变化规律与雾天气时能见度的季节变化规律大体一致,即广东地区低能见度天气主要受雾天气的影响;另一方面,雾、霾天气时能见度的季节变化规律有很大不同,雾天气时能见度观测值在冬、春两季相对较高,最低值出现在3月份,最高值出现在9月份,其年变化表现为峰—谷型、稳定型和不规则波动型三种变化;霾天气时能见度的值在秋、冬两季相对较高,最低值出现在6月份,而且在5、6、7三个月中能见度值都很低;受广东沿海地形特征影响,雾天能见度的空间分布从西到东呈低—高—低—高的波列状分布,而霾天气时能见度的空间分布则没有明显的区域差异。     

地统计学在土壤重金属研究中的应用及展望

从采矿学与地质学研究中发展起来的地统计学是应用数理统计学的一个分支。与传统的统计学相比，地统计学可应用于土壤重金属研究中，能探索土壤重金属的空间分布特征及其变异规律。地统计学的基础理论与方法主要包括：区域化变量、半方差函数、克立格空间插值技术。半方差函数可以用来描述研究土壤重金属分布的空间相关性；而克立格插值可以对未采样区土壤重金属的含量进行无偏最优估计。在对地统计学理论进行简要阐述的基础上，回顾了近些年在土壤重金属研究的采样设计、空间结构分析、空间插值等方面的应用，并就其应用前景作了展望。