Model-based combination of spatial information for stream networks

Evolutionary improvements in Geographic Information Systems (GIS) now routinely allow the management and mapping of spatial-temporal information. In response, the development of statistical models to combine information of different types and spatial support is of vital importance to environmental science. In this paper we develop a hierarchical spatial statistical model for environmental indicators of stream and river systems in the United States Mid-Atlantic Region by combining information from separate monitoring surveys, available contextual information on hydrologic units and remote sensing information. These models are used to estimate the indicators throughout the riverine system based on information from multiple sources and aggregate scales. The analysis is based on information underlying the Landscape Atlas of the mid-Atlantic region produced by the US Environmental Monitoring and Assessment Program (EMAP). We also combine information from two overlapping separate monitoring surveys, the EMAP Stream and River Survey and the Maryland Biological Streams Survey. We present a general framework for comparative distributional analysis based on the concept of a relative spatial distribution. As an application, the spatial model is used to predict spatial distributions and relative spatial distributions for a watershed.     

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。     

沈阳市区大气颗粒物元素组分及其分布

本研究采用美国安德逊五段冲击式采样器进行大气颗粒物采样，用中子活化法进行分析，获得４３种元素质量浓度，并求得各种元素的质量中值直径和几何标准差。     

Secondary metabolism — evolution and function: Products or processes?

Summary The distinguishing characteristics of secondary metabolism and its associated metabolites are outlined. Current theories relating to the function of secondary metabolism in plants and micro-organisms are enumerated and the view that consideration of the processes rather than the products may best explain the origins of secondary metabolism is discussed.     

Resampling from stochastic simulations

To model the uncertainty of an estimate of a global property, the estimation process is repeated on multiple simulated fields, with the same sampling strategy and estimation algorithm. As opposed to conventional bootstrap, this resampling scheme allows for spatially correlated data and the common situation of preferential and biased sampling. The practice of this technique is developed on a large data set where the reference sampling distributions are available. Comparison of the resampled distributions to that reference shows the probability intervals obtained by resampling to be reasonably accurate and conservative, provided the original and actual sample has been corrected for the major biases induced by preferential sampling.Andre G. Journel is a Professor of Petroleum Engineering at Stanford University with a joint appointment in the Department of Geological and Environmental Sciences. He is, also, Director of the Stanford Center for Reservoir Forecasting. Professor Journel has pioneered applications of geostatistical techniques in the mining/petroleum industry and extended his expertise to environmental applications and repository site characterization. Most notably, he developed the concept of non-parametric geostatistics and stochastic imaging with application to modeling uncertainty in reservoir/site characterization. Although the research described in this article has been supported by the United States Environmental Protection Agency under Cooperative Agreement CR819407, it has not been subjected to Agency review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred.     

珍稀濒危植物金花茶地理分布与生境调查研究

为了对珍稀濒危植物金花茶(Camellia nitidissima)进行有效保护,通过实地调查和相关资料分析,对其地理分布与生境进行了系统的调查研究。调查结果表明:(1)金花茶主要分布在广西西南部的防城、南宁、隆安、扶绥等县(市)。位于北纬21°30′～22°55′,东经107°36′～108°33′区域内。金花茶的垂直分布高度在12～450m之间,以海拔200～300m之间的范围较为常见。(2)金花茶为喜暖热植物,对热量要求较高,属热带性树种。金花茶不能忍耐阳光直射,为喜荫耐荫植物,要求的光合有效辐射一般小于photons12.8μmol?m-2?s-1。(3)金花茶群落植物组成种类多样,且多属热带性树种。组成群落主要科有橄榄科(Burseraceae)、山榄科(Sapotaceae)、大戟科(Euphorbiaceae)、桑科(Moraceae)、藤黄科(Guttiferae)、无患子科(Sapindaceae)、番荔枝科(Annonaceae)、梧桐科(Sterculiaceae)的种类。研究结果摸清了金花茶的地理分布和主要生态特性,为金花茶的引种驯化和种质资源的保存提供科学依据。     

River water quality management model using genetic algorithm

Conventional mathematical programming methods, such as linear programming, non linear programming, dynamic programming and integer programming have been used to solve the cost optimization problem for regional wastewater treatment systems. In this study, a river water quality management model was developed through the integration of a genetic algorithm (GA). This model was applied to a river system contaminated by three determined discharge sources to achieve the water quality goals and wastewater treatment cost optimization in the river basin. The genetic algorithm solution, described the treatment plant efficiency, such that the cost of wastewater treatment for the entire river basin is minimized while the water quality constraints in each reach are satisfied. This study showed that genetic algorithm can be applied for river water quality modeling studies as an alternative to the present methods.     

九寨沟主要植物群落生物量的空间分布

研究了九寨沟自然保护区主要植物群落的生物量以及生物量在九寨沟的空间分布格局 ,并用群落的总生物量量度不同群落在九寨沟陆生生态系统中的相对重要性 .在野外实地调查中 ,采取皆伐法测定了乔木群落林下层以及灌丛和草甸群落的地上部分生物量 ;对样方中乔木的胸径、树高进行了测量 ,利用经验公式估算乔木层的生物量 .研究结果表明 ,所有群落中生物量最大的为亚高山暗针叶林 ,平均为 380thm-2 左右 ,总生物量亦占了整个九寨沟生物量总量的 90 %以上 ,为九寨沟陆地生态系统中最重要的植物群落 ;高山草甸和流石滩植被生物量最小 ,平均为 1thm-2以下 .整个九寨沟的总生物量约为 8.3× 10 6t,平均生产力大于 5 .89thm-2 a-1.垂直分布上 ,生物量一般随海拔的升高而增加 ,在halt=30 0 0m以上达到最大值 ,之后则随海拔的升高而急剧下降 ,这种分布格局与土壤、气候、人类干扰等各种环境因素密切相关 .与全国平均水平相比 ,九寨沟几乎所有类型的群落的平均生物量都要高于全国同类群落的平均水平 .图 3表 4参 18     

中国珍稀格氏栲林的数量特征

运用伽玛分布，对数正态分布、韦布（Weibull)分布，正态分布等4种概率分布模型对格氏栲林的乔木层、灌木层、藤本层，草本层的物种-多度关系进行拟合分析，并用多种物种多样性指标测定格氏栲林各层次数量特征。结果表明：格氏栲林群落各层次物种-多度关系均符合伽玛分布，即伽玛分布模型应用于格氏栲林物种-多度分布研究是理想的；格氏栲林物种多样性指标介于中亚热带常绿阔叶林与南亚热带季风常绿阔叶林之间，表5参20。