Statistical framework for scale-up of dispersivity in multi-scale heterogeneous media

A hierarchical scale-up framework is formulated to study the scaling characteristics of reservoir attributes and input dispersivities at the transport modeling scale, where heterogeneity distribution exhibits both non-stationarity (trend) and sub-scale variability. The proposed method is flexible to handle heterogeneities occurring at multiple scales, without any explicit assumption regarding the multivariate distribution of the heterogeneity. This paper extends our previous work by incorporating the effects of non-stationarity into the modeling workflow. Rock property at a given location is modeled as a random variable, which is decomposed into the sum of a trend (available on the same resolution of the transport modeling scale) and a residual component (defined at a much smaller scale). First, to scale up the residual component to the transport modeling scale, the corresponding volume variance is computed; by sampling numerous sets of “conditioning data” via bootstrapping and constructing multiple realizations of the residual components at the transport modeling, uncertainty due to this scale-up process is captured. Next, to compute the input dispersivity at the transport modeling scale, a flow-based technique is adopted: multiple geostatistical realizations of the same physical size as the transport modeling scale are generated to describe the spatial heterogeneity below the modeling scale. Each realization is subjected to particle-tracking simulation. Effective longitudinal and transverse dispersivities are estimated by minimizing the difference in effluent history for each realization and that of an equivalent average medium. Probability distributions of effective dispersivities are established by aggregating results from all realizations. The results demonstrate that both large-scale non-stationarity and sub-scale variability are both contributing to anomalous non-Fickian behavior. In comparison with our previous work, which ignored large-scale non-stationarity, the non-Fickian characteristics observed in this study is dramatically more pronounced.     

Climate change projections of the North American Regional Climate Change Assessment Program (NARCCAP)

We investigate major results of the NARCCAP multiple regional climate model (RCM) experiments driven by multiple global climate models (GCMs) regarding climate change for seasonal temperature and precipitation over North America. We focus on two major questions: How do the RCM simulated climate changes differ from those of the parent GCMs and thus affect our perception of climate change over North America, and how important are the relative contributions of RCMs and GCMs to the uncertainty (variance explained) for different seasons and variables? The RCMs tend to produce stronger climate changes for precipitation: larger increases in the northern part of the domain in winter and greater decreases across a swath of the central part in summer, compared to the four GCMs driving the regional models as well as to the full set of CMIP3 GCM results. We pose some possible process-level mechanisms for the difference in intensity of change, particularly for summer. Detailed process-level studies will be necessary to establish mechanisms and credibility of these results. The GCMs explain more variance for winter temperature and the RCMs for summer temperature. The same is true for precipitation patterns. Thus, we recommend that future RCM-GCM experiments over this region include a balanced number of GCMs and RCMs.     

An integrated web-based air pollution decision support system – a prototype

To efficiently and effectively monitor and mitigate air pollution in the urban environment, it is of paramount importance to integrate into a unified whole air pollutant concentration databases coming from different sources including the ground-based stations, mobile sensors, remote sensing, atmospheric-chemical-transport models and social media for the analysis and unraveling of the complex air pollution processes in space and time. This study constructs and implements for the first time a prototype of the fully integrated air pollution decision support system (APDSS) that put together in an integrated manner all relevant multi-scale, multi-type and multi-source data for decision-making on urban air pollution. The prototype contains the main system that handles the multi-source, multi-type and multi-scale databases, queries, visualization and data mining algorithms and the integrated modules that individually and holistically capitalize on the power of the ground-based stations, ground and aerial mobile sensors, satellite-borne remote-sensing technologies, atmospheric-chemical-transport models and social media. It renders a solid scientific foundation and system development methodology for the study of the spatiotemporal air pollution profiles crucial to the mitigation of urban air pollution. Real-life applications of the prototype are employed to illustrate the functionality of the APDSS.     

Uncertainty analysis of space–time prisms based on the moment-design method

Space–time prism (STP) is an important concept for the modeling of object movements in space and time. An STP can be conceptualized as the result of the potential path of a moving object revolving around in the three-dimensional space. Though the concept has found applications in time geography, research on the analysis and propagation of uncertainty in STPs, particularly under high degree of nonlinearity, is scanty. Based on the efficiency and effectiveness of the moment-design (M-D) method, this paper proposes an approach to deal with nonlinear error propagation problems in the potential path areas (PPAs) of STPs and their intersections. Propagation of errors to the PPA and its boundary, and to the intersection of two PPAs is investigated. Performance of the proposed method is evaluated via a series of experimental studies. In comparison with the Monte Carlo method and the implicit function method, simulation results show the advantages of the M-D method in the analysis of error propagation in STPs.     

Toxicity of antifouling biocides to the intertidal harpacticoid copepod Tigriopus japonicus (Crustacea, Copepoda): effects of temperature and salinity

Intertidal harpacticoid copepods are commonly used in eco-toxicity tests worldwide. They predominately live in mid-high shore rock pools and often experience a wide range of temperature and salinity fluctuation. Most eco-toxicity tests are conducted at fixed temperature and salinity and thus the influence of these environmental factors on chemical toxicity is largely unknown. This study investigated the combined effect of temperature and salinity on the acute toxicity of the copepod Tigriopus japonicus against two common biocides, copper (Cu) and tributyltin (TBT) using a 2 × 3 × 4 factorial design (i.e. two temperatures: 25 and 35 °C; three salinities: 15.0‰, 34.5‰ and 45.0‰; three levels of the biocide plus a control). Copper sulphate and tributyltin chloride were used as the test chemicals while distilled water and acetone were utilised as solvents for Cu and TBT respectively. 96h-LC50s of Cu and TBT were 1024 and 0.149 μg l⁻¹ respectively (at 25 °C; 34.5‰) and, based on these results, nominal biocide concentrations of LC0 (i.e. control), LC30, LC50 and LC70 were employed. Analysis of Covariance using ‘concentration’ as the covariate and both ‘temperature’ and ‘salinity’ as fixed factors, showed a significant interaction between temperature and salinity effects for Cu, mortality increasing with temperature but decreasing with elevated salinity. A similar result was revealed for TBT. Both temperature and salinity are, therefore, important factors affecting the results of acute eco-toxicity tests using these marine copepods. We recommend that such eco-toxicity tests should be conducted at a range of environmentally realistic temperature/salinity regimes, as this will enhance the sensitivity of the test and improve the safety margin in line with the precautionary principle.     

Assessment of risk of PCDD/Fs and dioxin-like PCBs in marine and freshwater fish in Pearl River Delta, China

Fish consumption is known to be beneficial to human health. However since the age of industrialization, the released/disposed chemical pollutants into water systems make fish a source of various environmental toxicants to humans. In oceanic cities with heavy industrial activities, fish products contribute the greatest proportion of exposure to pollutants. In this study, risks and potential effects of dioxins to health of coastal populations in the Pearl River Delta were assessed. Concentrations of polychlorinated dibenzo-p-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), and dioxin-like polychlorinated biphenyls (PCBs) were measured in common fish species purchased at local markets. Concentrations of total dioxins in fish ranged from 0.481 to 9.05 pg TEQ/g wet weight were similar to the lesser concentrations reported for fish from other countries. The greatest concentrations of dioxins were measured in mandarin fish, a carnivorous freshwater fish. Exposure of murine primary leydig and ovarian cells to 2,3,7,8-tetrachlorinated dibenzo-p-dioxins (2,3,7,8-TCDD) reduced the synthesis of progesterone, testosterone and/or estrogen. The reductions were probably via inhibitory effects on the expression of the steroidogenic enzymes, steroidogenic acute regulatory protein (StAR) and cytochrome P450 side-chain cleavage enzyme (P450scc). Based on these reproductive parameters, the concentrations of dioxins and dioxin-like residues represent a moderate health risk due to consumption of fish.     

Predicting tsunami arrivals: Estimates and policy implications

Tsunami is one of a few kinds of natural disasters that leave people some time for escape. This escape time, which is essentially the time for the giant wave to propagate from the epicentre to a coast, has to be estimated without delay upon the occurrence of the incident. With the advancement of water wave theories, much work has been done to model the propagation of tsunamis from deep oceans to shallow water. The authors argue that while much emphasis has been put on the expansion of the high-tech early warning system and the development of complicated tsunami models, a simple-to-use yet accurate predictive model is still wanting. This paper presents a handy linear wave model, which is capable of estimating the arrival time of a tsunami with very good accuracy, as has been verified by comparison with past incidents. With the availability of such a simple model, even local communities without access to a high-tech warning system can readily estimate the time left for emergency evacuation.     

Toxicities of antifouling biocide Irgarol 1051 and its major degraded product to marine primary producers

Irgarol 1051 (2-methythiol-4-tert-butylamino-6-cyclopropylamino-s-triazine) is an algaecide commonly used in antifouling paints. It undergoes photodegradation which yields M1 (2-methylthio-4-tert-butylamino-6-amino-s-triazine) as its major and most stable degradant. Elevated levels of both Irgarol and M1 have been detected in coastal waters worldwide; however, ecotoxicity effects of M1 to various marine autotrophs such as cyanobacteria are still largely unknown. This study firstly examined and compared the 96 h toxicities of Irgarol and M1 to the cyanobacterium Chroococcus minor and two marine diatom species, Skeletonema costatum and Thalassiosira pseudonana. Our results suggested that Irgarol was consistently more toxic to all of the three species than M1 (96 h EC50 values: C. minor, 7.71 microug L(-1) Irgarol vs. > 200 microg L(-1) M1; S. costatum, 0.29 microg L(-1) Irgarol vs. 11.32 microg L(-1)M1; and T. pseudonana, 0.41 microg L(-1) Irgarol vs. 16.50 microg L(-1)M1). Secondly, we conducted a meta-analysis of currently available data on toxicities of Irgarol and M1 to both freshwater and marine primary producers based on species sensitivity distributions (SSDs). Interestingly, freshwater autotrophs are more sensitive to Irgarol than their marine counterparts. For marine autotrophs, microalgae are generally more sensitive to Irgarol than macroalgae and cyanobacteria. With very limited available data on M1 (i.e. five species), M1 might be less toxic than Irgarol; nonetheless this finding warrants further confirmation with additional data on other autotrophic species.     

基于尺度空间的分层聚类方法及其在遥感影像分类中的应用

基于尺度空间的分层聚类方法（ＳＳＨＣ）是一种以热力学非线性动力机制为理论基础的新型聚类算法,是视觉松驰化过程的模拟。与传统基于统计方法的聚类算法相比较,ＳＳＨＣ具有样本空间可服从自由分布、通过规则可获取最优聚类中心点及类别、可在聚类过程中融合后验知识等优点。本文从聚类和热力学运动机制和视觉模拟过程出发,对ＳＳＨＣ聚类算法进行综合分析,并对如何生成聚类树的过程进行详细描述邮通过融合点的部分自由能进行     

近年中国夏季降水变化特征分析

利用台站观测资料和卫星观测换算资料,分析了近年（2002-2004年）中国夏季日内降水量变化统计学特征,其结果是较为一致的。对于东部大部分地区,日内最强降水易出现在下午到傍晚,对于西部大部分地区易出现在夜晚到清晨。夏季降水以对流降水为主,降水大的地区对应潜热通量也大。由于受观测资料的限制,计算结果有待更多的观测资料来验证。