呼伦贝尔草原植被覆盖度估算的光谱模型

采用美国ASD公司Fieldspec3光谱仪和日本富士数码相机,于2009年7~8月在内蒙古呼伦贝尔草原区进行了植物高光谱和植被覆盖度测定,并运用回归分析方法,建立实测归一化植被指数(ASD NDVI)和植被覆盖度之间的地面光谱模型,分析MODIS/TERRA卫星的NDVI(MODIS NDVI)与ASD NDVI的关系,建立预测植被覆盖度的MODIS光谱模型,并对模型进行精度检验.结果表明所建的MODIS光谱模型是线性函数,该模型预测精度高于亚像元分解模型,标准误差为11.58%,平均预测精度达到88.75%.     

植物硫含量法监测大气污染数量模型

运用MINITAB程序研究承德市5个常年大气监测点不同季节的10种木本植物硫含量和大气SO₂之间的一元回归和多元回归模型。一元回归模型叶以珍珠梅最好,r＝0.8695（P<0.001）,枝以油松和珍珠梅、垂柳为好,相关系数0.8（P＜0.001）;树皮以刺槐为佳,r＝0.8615（P＜0.001）;复相关模型以刺槐最好,相关系数达0.987。植物叶片硫含量与大气SO2和气孔阻力的回归模型说明,以污染状态进入植物体的硫,主要通过气孔进入;家榆表现为极显著相关,复相关系数达0.990。用植物含污指数法可以综合评价大气质量,其中总污染指数可以有效地评价总的大气质量,硫复合污染指数和重金属复合污染指数可以评价不同地点SO₂和TSP污染状况,与直接用大气SO₂浓度法和TSP浓度法结果基本相同。     

应用ARPS-CMAQ模拟研究石景山污染对北京的影响

建立了中尺度气象模型ARPS与区域多尺度空气质量模型CMAQ的耦合模型系统,应用该模型系统模拟研究了2002年1月份和8月份石景山区污染物排放对北京市空气质量的影响.模拟结果显示,1、8两个月份石景山区污染对北京市PM10的月均贡献浓度分别为29.68μg·m-3和32.09μg·m-3,其中污染较重日的日均贡献浓度在50μg·m-3左右,说明石景山污染对北京市空气质量影响较大.同时,进一步对两月典型污染过程进行深入分析,研究发现,在所选两个污染过程中,石景山对北京PM10的贡献浓度与监测浓度同步升高或降低.首先,在污染过程的前期,石景山贡献浓度较低且变化不大,之后贡献浓度与监测浓度同步快速上升,该阶段贡献浓度增量对监测浓度增量的贡献较大,分别占到了同期各自监测浓度增量的52.89%和44.78%,最后,贡献浓度与监测浓度同步降低,说明,在所选污染过程中贡献浓度短时间内的迅速增加是导致北京市监测浓度骤然升高的一个重要原因.     

Seasonal density dependence, timing of mortality, and sustainable harvesting

Birth-pulse populations are often characterized with discrete-time models, that use a single function to relate the post-breeding population size to the post-breeding size of the previous year. Recently, models of seasonal density dependence have been constructed that emphasize interactions during shorter time periods also. Here, we study two very simple forms of density-dependent mortality, that lead to Ricker and Beverton-Holt type population dynamics when viewed over the whole year. We explore the consequences of harvest timing to equilibrium population sizes under such density dependence. Whether or not individual mortality compensates for the harvested quota, the timing of harvesting has a strong impact on the sustainability of a harvesting quota. Further, we show that careless discretization of a continuous mortality scheme may seriously underestimate the reduction in population size caused by hunting and overestimate the sustainable yield. We also introduce the concept of the demographic value of an individual, which reflects the expected contribution to population size over time in the presence of density dependence. Finally, we discuss the possibility of calculating demographic values as means of optimizing harvest strategies. Here, a Pareto optimal harvest strategy will minimize the loss of demographic value from the population for a given yield.     

Imputing Unmeasured Explanatory Variables in Environmental Epidemiology With Application to Health Impact Analysis of Air Pollution

This paper presents the results of a reconsideration of earlier work that finds an association between daily hospital admissions for respiratory distress and daily concentrations of sulphate (lag 1) as well as daily maximum concentrations of ozone (lags 1 and 3). These associations are found even after clustering the data by hospital of admission and accounting for the effects of temperature. We use an adaptation of their generalized estimating equation technique for clustered data, that daily data being for southern Ontario summers from 1983 to 1988. Like them, we adjust for daily maximum temperatures. However, unlike the earlier work returned to ours includes daily average humidity as a potential explanatory variable in our model. Our analysis also differs from theirs in that we cluster the data by census subdivision to reduce the risk of confounding pollutant levels with population size within regions. Moreover, we log-transform the explanatory variables and then high-pass filter the resulting data. We also deviate from the earlier analysis by taking account of measurement error incurred in using surrogate measures of the explanatory variables. To do so we use new methodology designed for our study but of potential value in other applications. That methodology requires a spatial predictive distribution for the unmeasured explanatory variables. Each day about 700 missing measurements for each of these variables can then be imputed over the geographical domain of the study. With these imputations we get a measure of imputation error through the covariance of the predictive distribution. Along with the predictive distribution we require an impact model to link-up with the predictive distribution. We describe that model and show how it uses the imputed measurements of the missing values of the explanatory variables. We also show how through that model, uncertainty about these values is reflected in our analysis and in commensurate uncertainties in the inferences made. Apart from its substantive objectives, our analysis serves to test the new methods with the earlier results serving as a foil. The reassuring qualitative agreement between our findings and the earlier results seems encouraging.     

Multicompartmental fate of persistent substances

Background, Aim and Scope Modelling of the fate of environmental chemicals can be done by relatively simple multi-media box models or using complex atmospheric transport models. It was the aim of this work to compare the results obtained for both types of models using a small set of non-ionic and non-polar or moderately polar organic chemicals, known to be distributed over long distances. Materials and Methods Predictions of multimedia exposure models of different types, namely three multimedia mass-balance box models (MBMs), two in the steady state and one in the non-steady state mode, and one non-steady state multicompartment chemistry-atmospheric transport model (MCTM), are compared for the first time. The models used are SimpleBox, Chemrange, the MPI-MBM and the MPI-MCTM. The target parameters addressed are compartmental distributions (i.e. mass fractions in the compartments), overall environmental residence time (i.e. overall persistence and eventually including other final sinks, such as loss to the deep sea) and a measure for the long-range transport potential. These are derived for atrazine, benz-[a]-pyrene, DDT, α and γ-hexachlorocyclohexane, methyl parathion and various modes of substance entry into the model world. Results and Discussion Compartmental distributions in steady state were compared. Steady state needed 2–10 years to be established in the MCTM. The highest fraction of the substances in air is predicted by the MCTM. Accordingly, the other models predict longer substance persistence in most cases. The results suggest that temperature affects the compartmental distribution more in the box models, while it is only one among many climate factors acting in the transport model. The representation of final sinks in the models, e.g. burial in the sediment, is key for model-based compartmental distribution and persistence predictions. There is a tendency of MBMs to overestimate substance sinks in air and to underestimate atmospheric transport velocity as a consequence of the neglection of the temporal and spatial variabilities of these parameters. Therefore, the long-range transport potential in air derived from MCTM simulations exceeds the one from Chemrange in most cases and least for substances which undergo slow degradation in air. Conclusions and Perspectives MBMs should be improved such as to ascertain that the significance of the atmosphere for the multicompartmental cycling is not systematically underestimated. Both types of models should be improved such as to cover degradation in air in the particle-bound state and transport via ocean currents. A detailed understanding of the deviations observed in this work and elsewhere should be gained and multimedia fate box models could then be ‘tuned in’ to match better the results of comprehensive multicompartmental transport models. ESS-Submission Editor: Prof. Dr. Michael Matthies (matthies@uos.de)     

A tool to aid emergency managers and communities in appraising private dam safety and policy

Issues concerning dam safety and equitable sharing of catchment run-off are receiving more attention throughout the world. This paper assesses these matters in the context of Australia, and the need for policy responses. Landholders often overlook the common law obligation to review/design dams to current standards because of high costs, leaving them vulnerable to litigation if their dam fails. The paper reports on an innovative spillway design/review procedure, applicable to southeast Australia, but transferable to any region worldwide. Dam safety policy models and guidelines derived from international best practice are linked to the procedure and intended to aid government decision-making. The procedure minimises costs to landholders and provides an acceptable level of safety assurance to downstream communities. Also discussed are recent surveys testing community attitudes to the procedure and implemented dam safety and water allocation policies. These further guide any government wanting to implement this'integrated engineering and community partnerships'approach to preventing potential disasters due to private dam failure and achieving sustainable and safe water storage and use.     

Nonparametric MLE incorporation of heterogeneity and model testing into premarked cohort studies

We utilize mixture models and nonparametric maximum likelihood estimation to both develop a likelihood ratio test (lrt) for a common simplifying assumption and to allow heterogeneity within premarked cohort studies. Our methods allow estimation of the entire probability model and thus one can not only estimate many parameters of interest but one can also bootstrap from the estimated model to predict many things, including the standard deviations of estimators. Simulations suggest that our lrt has the appropriate protection for Type I error and often has good power. In practice, our lrt is important for determining the appropriateness of estimators and in examining if a simple design with only one capture period could be utilized for a future similar study.     

大气污染物扩散模式的应用研究综述

应用大气污染物扩散模式可以模拟不同尺度、气象、地形条件下工业污染物在大气中的输送与扩散特征,为大气监测、城市环境规划和空气质量预报等工作提供科学依据.归纳了目前广泛应用于模拟工业污染物扩散的模式,着重介绍了近年来国内外对这些模式的主要应用研究进展,比较了各模式在应用上的优缺点,并对大气污染物扩散模式的应用研究前景进行了讨论.     

大气污染物排放源清单不确定性定量分析方法及案例研究

大气污染物排放源清单由于在数据收集过程中存在的不可避免的监测误差、随机误差、关键数据缺乏以及数据代表性不足等因素而具有不确定性,而排放源清单的不确定性指的是人们对排放清单的真实值缺乏认识和了解.介绍了目前大气排放源清单定量不确定性方法框架,并使用电厂NO_x在线监测数据,通过实际案例量化排放源清单中的不确定性.结果表明:即使对被认为具有较高准确性的火电厂点源排放清单,案例中NO_x的排放源清单来自随机误差的不确定性在±15%左右.对排放源清单的不确定性量化有助于决策者确定污染物排放削减目标的可达性和科学制定大气污染物控制策略,指导排放源清单的改进和数据收集工作.同时,对我国排放源清单开发中不确定性分析提出建议.