土壤养分空间估测方法研究综述

土壤养分是土壤提供的植物生长发育所必需的营养元素。由于受到自然因素和人为因素的共同作用,土壤养分具有高度的空间变异性。土壤的这种特性不仅表现在区域尺度上,而且也表现在田块尺度上。在研究方法上经历了从传统统计学到地统计学,再到神经网络、地理信息技术以及高精度曲面建模等新方法的不断改进过程。文章从地统计学方法引入到土壤养分空间变异研究中为出发点,论述了国内外基于地统计学的土壤养分空间变异的研究现状,主要包括利用地统计学方法来确定合理的土壤采样数目,土壤养分空间变异的定量化研究,土壤养分空间变异的尺度效应;然后简述了神经网络、地理信息技术、高精度曲面建模等技术在土壤养分空间变异研究中的研究现状和应用。最后对比分析了各种研究方法在应用中存在的缺陷,同时指明了今后应加强作物生长的不同时期土壤养分的空间变异性、土壤在四维空间尺度上的演变机理以及环境信息获取的不确定性等方面的研究。     

An Adaptive‐Management Framework for Optimal Control of Hiking Near Golden Eagle Nests in Denali National Park

Abstract: Unintended effects of recreational activities in protected areas are of growing concern. We used an adaptive‐management framework to develop guidelines for optimally managing hiking activities to maintain desired levels of territory occupancy and reproductive success of Golden Eagles (Aquila chrysaetos) in Denali National Park (Alaska, U.S.A.). The management decision was to restrict human access (hikers) to particular nesting territories to reduce disturbance. The management objective was to minimize restrictions on hikers while maintaining reproductive performance of eagles above some specified level. We based our decision analysis on predictive models of site occupancy of eagles developed using a combination of expert opinion and data collected from 93 eagle territories over 20 years. The best predictive model showed that restricting human access to eagle territories had little effect on occupancy dynamics. However, when considering important sources of uncertainty in the models, including environmental stochasticity, imperfect detection of hares on which eagles prey, and model uncertainty, restricting access of territories to hikers improved eagle reproduction substantially. An adaptive management framework such as ours may help reduce uncertainty of the effects of hiking activities on Golden Eagles.     

Application of statistical modeling to optimize a coastal water quality monitoring program

The long-term water quality monitoring program implemented by the Massachusetts Water Resources Authority in 1992 is extensive and has provide substantial understanding of the seasonality of the waters in both Boston Harbor and Massachusetts Bay and the response to improvements in effluent quality and offshore transfer of the effluent in September 2000. The monitoring program was designed with limited knowledge of spatial and temporal variability and long-term trends within the system. This led to an extensive spatial and temporal sampling program. The data through 2003 showed high correlation within physical parameters measured (e.g., salinity, dissolved oxygen) and in biological measures such as chlorophyll fluorescence. To address the potential sampling redundancies in the measurement program, an assessment of the impact of reduced levels of monitoring on the ability to make water quality decisions was completed. The optimization was conducted by applying statistical models that took into account whether there was evidence of a seasonal pattern in the data. The optimization used model survey average readings to identify temporal fixed effects, model survey-average-corrected individual station readings to identify spatial fixed effects, corrected the individual station readings for temporal and spatial fixed effects and derived a correlation model for the corrected data, and applied the correlation model to characterize the correlation of annual average readings from reduced monitoring programs with true parameter levels. Reductions in the number of sampling stations were found less detrimental to the quality of the data for annual decision-making than reductions in the number of surveys per year, although there is less of a difference in this regard for dissolved oxygen than there is for chlorophyll. The analysis led to recommendations for a substantially lower monitoring effort with minimal loss of information. The recommendation supported an annual budget savings of approximately $183,000. Most of the savings was from fewer surveys as approximately $21,000 came from the reduction in the number of stations monitored from 21 to 7 and associated laboratory analytical costs.     

A New Approach to Plant Diversity Assessment Combining HPLC Data, Simplex Mixture Design and Discriminant Analysis

The quantitative assessment of plant diversity and its monitoring with time represent a key environmental issue for management and conservation of natural resources. Assessment of plant diversity could be based on chemical analyses of secondary metabolites (e.g. flavonoids, terpenoids), because of the substantial quantitative and qualitative between-individual variability in such compounds. At a geographical scale, the plant populations become widely dispersed, and their monitoring from numerous routine individual analyses could become restricting. To overcome such constraint, this study develops a multivariate calibration model giving the relative frequency of a particular taxon from a simple high-performance liquid chromatography (HPLC) analysis of a plant mixture. The model was built from a complete set of mixtures combining different taxons, according to an experimental design (Scheffé’s matrix). For each mixture, a reference HPLC pattern was simulated by averaging the individual HPLC profiles of the constitutive taxons. The calibration models, based on Bayesian discriminant analysis (BDA), gave statistical relationships between the contributions of each taxon in mixtures and reference HPLC patterns of these mixtures. Finally, these models were validated on new mixtures by using outside plants. This new biodiversity survey approach is illustrated on four chemical taxons (four chemotypes) of Astragalus caprinus (Fabaceae). The more differentiated the taxon, the better predicted its contributions (in mixtures) were by BDA calibration model. This new approach could be very useful for a global routine survey of plant diversity.     

大气中持久性有机污染物在气相和颗粒相中的分配特征研究进展

大气中持久性有机污染物(POPs)的气(气相)-粒(颗粒相)分配是影响POPs在大气中分布、迁移和转化的一个重要因素,研究POPs的气-粒分配特征有助于提高POPs环境归趋预测的准确性,对区域范围内的大气POPs污染防治具有重要意义.本文简要介绍了两种经典的POPs气-粒分配理论及模型,总结了有关大气中几类典型POPs在气相和颗粒相中的分配特征研究的最新进展,讨论了不同种类POPs气-粒分配的一些差异性特征和可能的影响因素,并提出了大气中新型POPs气-粒分配特征研究中亟待解决的问题.     

华南退化生态系统三种典型生态恢复模式的小气候效应研究

为了评价南亚热带典型退化生态系统典型生态恢复模式的小气候调节效应,从而为退化生态系统生态恢复方式和造林树种的选择提供参照,作者在广东鹤山森林生态系统国家野外科学观测研究站的3种典型生态恢复模式样地,自然恢复草坡、马尾松林（Pinus massoniana）、马占相思林（Acacia mangium）中安装了HOBO小气候仪,对光辐射、风速风向、降水、土壤含水量、地温、气温等小气候指标进行为期1年的自动观测,并进行了对比分析,结果表明,（1）华南退化生态系统3种典型生态恢复模式具有不同的小气候效应,在林间温度调节方面,人工林和草坡的平均林间温度相差不大,但草坡的最低、最高温度均比人工林低和高。人工林的林间温度变化较草坡小,具有更好的保温调节作用。对比2种人工林,不管是平均温度还是最高温度马占相思林都略大于针叶林,而二者最低温度相差不明显。针叶林的保温调节作用略优于阔叶的马占相思林;（2）在土壤温度方面,地表温度全年基本都表现为草坡〉马占相思林〉针叶林,草坡的地表温度的波动远大于2种人工林;全年20 cm土壤温度3─12月都表现为草坡〉马占相思林〉针叶林,1─2月相反,草坡20 cm土层的土壤温度波动相对较大,人工林的波动很小。（3）3种恢复模式中,自然恢复草坡的辐射强度明显高于2种人工林,年辐射总量分别马占相思林和针叶林的1.9和5.8倍,马占相思林的年辐射量为针叶林的3倍。人工林,特别是乡土的针叶林能给林下生物构建更为稳定、适中的辐射环境。（4）人工林的平均林间风速、最大阵风风速均少于草坡,针叶林的风速小于阔叶林,针叶林降低风速的效果好于相思林和草坡。（5）人工林的林间相对湿度均高于草坡,针叶林的林间空气湿度略大于相思林,针叶林的保湿效果更好。在退化生态系统恢复过程中?     

健康风险评估方法在中国重金属污染中的应用及暴露评估模型的研究进展

经济的快速发展导致中国环境质量日趋恶化.随着健康意识的增强,人们越来越重视污染物暴露人群的健康风险评估.与其他污染物相比,重金属污染区域广,重金属暴露人群多且集中.为了研究重金属暴露条件下人群的健康风险,USEPA 模型、统计模型、地理信息系统、可给性研究的方法已被中国不同学者应用.暴露评估模型作为污染物暴露人群健康风险评估的主要环节,国外的研究已经比较成熟,但相关研究在中国还处于空白阶段.对中国近年来在城市表层土壤（灰尘）、矿区土壤、膳食、地下水和饮用水、大气颗粒物进行重金属风险评估中应用的健康风险评估方法,进行了归纳和评述,并对欧美常用暴露评估模型：环境暴露评估模型、膳食暴露评估模型进行了介绍.中国健康风险评估工作起步晚,在评估的各环节均存在很大缺陷.随着新技术的发展以及人群对环境健康风险认识的深化,健康风险评估将成为中国热门研究领域之一.污染的环境行为、剂量一效应关系、模型、风险信息等方面,将是未来中国健康风险评估研究的重点.     

Calibrating a Basin‐Scale Groundwater Model to Remotely Sensed Estimates of Groundwater Evapotranspiration

Remotely sensed vegetation indices correspond to canopy vigor and cover and have been successfully used to estimate groundwater evapotranspiration (ET_g) over large spatial and temporal scales. However, these data do not provide information on depth to groundwater (dtgw) necessary for groundwater models (GWM) to calculate ET_g. An iterative approach is provided that calibrates GWM to ET_g derived from Landsat estimates of the Enhanced Vegetation Index (EVI). The approach is applied to different vegetation groups in Mason Valley, Nevada over an 11‐year time span. An uncertainty analysis is done to estimate the resulting mean and 90% confidence intervals in ET_g to dtgw relationships to quantify errors associated with plant physiologic complexity, species variability, and parameter smoothing to the 100 m GWM‐grid, temporal variability in soil moisture and nonuniqueness in the solution. Additionally, a first‐order second moment analysis shows ET_g to dtgw relationships are almost exclusively sensitive to estimated land surface, or maximum, ET_g despite relatively large uncertainty in extinction depths and hydraulic conductivity. The EVI method of estimating ET_g appears to bias ET_g during years with exceptionally wet spring/summer conditions. Excluding these years improves model performance significantly but highlights the need to develop a methodology that accounts not only on quantity but timing of annual precipitation on phreatophyte greenness.     

A MANGANESE OXIDATION MODEL FOR RIVERS1

ABSTRACT: The presence of manganese in natural waters (>0.05 mg/L) degrades water-supply quality. A model was devised to predict the variation of manganese concentrations in river water released from an impoundment with the distance downstream. The model is one-dimensional and was calibrated using dissolved oxygen, biochemical oxygen demand, pH, manganese, and hydraulic data collected in the Duck River, Tennessee. The results indicated that the model can predict manganese levels under various conditions. The model was then applied to the Chattahoochee River, Georgia. Discrepancies between observed and predicted may be due to inadequate pH data, precipitation of sediment particles, unsteady flow conditions in the Chattahoochee River, inaccurate rate expressions for the low pH conditions, or their combinations.     

Conceptual Models as Hypotheses in Monitoring Urban Landscapes

Many problems and challenges of ecosystem management currently are driven by the rapid pace and spatial extent of landscape change. Parks and reserves within areas of high human population density are especially challenged to meet the recreational needs of local populations and to preserve valued environmental resources. The complex problem of managing multiple objectives and multiple resources requires an enormous quantity of information, and conceptual models have been proposed as tools for organizing and interpreting this information. Academics generally prefer a bottom-up approach to model construction that emphasizes ecologic theory and process, whereas managers often use a top-down approach that takes advantage of existing information to address more pragmatic objectives. The authors propose a formal process for developing, applying, and testing conceptual models to be used in landscape monitoring that reconciles these seemingly opposing perspectives. The four-step process embraces the role of hypothesis testing in the development of models and evaluation of their utility. An example application of the process to a network of national parks in and around Washington, DC illustrates the ability of the approach to systematically identify monitoring data that would both advance ecologic theory and inform management decisions.