Verification of Wind-Driven Volatilization Models

Mass-transfer processes of paramount importance, such as reaeration and volatilization, occur at the air–water interface. Particularly, volatilization is intensively studied because it can be a relevant removal process for toxic contaminants from flowing and standing surface waters. The paper provides a comparison among predictive equations available from literature for standing waters for the case of MTBE contamination. Most of the considered equations tend to overestimate the volatilization rate, while three equations offer a good fit with the observed data. Finally, these equations are applied to a larger amount of field data.     

Testing the Generality of Bird-Habitat Models

Bird-habitat models are frequently used as predictive modeling tools—for example, to predict how a species will respond to habitat modifications. We investigated the generality of the predictions from this type of model. Multivariate models were developed for Golden Eagle (Aquila chrysaetos), Raven (Corvus corax), and Buzzard (Buteo buteo) living in northwest Scotland. Data were obtained for all habitat and nest locations within an area of 2349 km². This assemblage of species is relatively static with respect to both occupancy and spatial positioning. The area was split into five geographic subregions: two on the mainland and three on the adjacent Island of Mull, which has one of United Kingdom's richest raptor fauna assemblages. Because data were collected for all nest locations and habitats, it was possible to build models that did not incorporate sampling error. A range of predictive models was developed using discriminant analysis and logistic regression. The models differed with respect to the geographical origin of the data used for model development. The predictive success of these models was then assessed by applying them to validation data. The models showed a wide range of predictive success, ranging from only 6% of nest sites correctly predicted to 100% correctly predicted. Model validation techniques were used to ensure that the models' predictions were not statistical artefacts. The variability in prediction success seemed to result from methodological and ecological processes, including the data recording scheme and interregional differences in nesting habitat. The results from this study suggest that conservation biologists must be very careful about making predictions from such studies because we may be working with systems that are inherently unpredictable.     

Source-Sink Models and the Problem of Habitat Degradation: General Models and Applications to the Yellowstone Grizzly

I used source-sink population models to explore the consequences of habitat degradation for populations living on good and degraded habitats linked by movement. In particular, I modeled the conversion of land from good habitat quality supporting positive population growth to a degraded condition in which there was population decline. I found that with high rates of movement between good and bad quality areas populations require relatively large amounts of good habitat to remain stable. However, low movement rates resulted in greater sensitivity of population growth to habitat loss. Even small amounts of habitat degradation could result in rapid changes in overall population growth rates depending upon the rates of population increase and decline in the two habitat types. I also developed and simulated an age-structured model for grizzly bears ( Ursus arctos horribilis ) existing in good and degraded habitats and fit this model to data from the Yellowstone grizzly population. I used this model to predict the ability to detect crucial amounts of habitat degradation from census data and found that when degradation is slow (e.g., 1% conversion of good to poor habitat per year), more than a decade may pass between crucial amounts of degradation—beyond which populations begin long-term decline—and its detection, even if census data were unrealistically good. Thus these simple models indicate that, at least in some circumstances, habitat degradation can have rapid and severe impacts on population dynamics and traditional monitoring programs may not be adequate to detect the consequences of degradation.     

城市循环经济发展模式

城市循环经济发展模式是在城市生态循环系统和城市基础设施系统支撑下的循环型经济系统和社会系统的有机组合和共生,主要包括城市循环型生产系统、循环型流通服务系统、循环型消费系统、循环型社会系统、城市生态循环系统以及城市基础设施系统的发展模式等内容。     

有机污染物生物降解性预测模型

生物降解性是评估污染物环境持久性的重要依据,也是化学品是否获准生产及进入市场的评价指标。采用17位生物降解领域专家评估的生物降解等级数据,通过功能树(FT)算法建立了包含15个分子结构参数的初级生物降解和最终生物降解预测模型。外部验证结果表明,模型具有较好的预测准确性,初级生物降解性加权准确度(weighted accuracy,WA):训练集WA=84.1%,验证集WA=78.9%;最终生物降解性WA:训练集WA=91.0%;验证集WA=83.6%。预测正确性对化合物的杠杆值作图,表征了生物降解性模型的应用域。     

Dispersal in Spatially Explicit Population Models

Abstract: Ruckelshaus et al. (1997) outlined a simulation model of dispersal between patches in a fragmented landscape. They showed that dispersal success—the proportion of dispersers successfully locating a patch—was particularly sensitive to errors in dispersal mortality and concluded that this limits the utility of spatially explicit population models in conservation biology. I contend that, although they explored error propagation in a simple dispersal model, they did not explore how errors are propagated in spatially explicit population models, as no consideration of population processes was included. I developed a simple simulation model to investigate the effect of varying dispersal success on predictions of patch occupancy and population viability, the conventional outputs of spatially explicit population models. The model simulates births and deaths within habitat patches and dispersal as the transfer of individuals between them. Model predictions were sensitive to changes in dispersal success across a restricted range of within-patch growth rates, which depended on the dispersal initiation mechanism, patch carrying capacities, and number of generations simulated. Predictions of persistence and patch occupancy were generally more sensitive to changes in dispersal success (1) under presaturation rather than saturation dispersal; (2) at lower patch carrying capacities; and (3) over longer time periods. The framework I present provides a means of assessing, quantitatively, the regions of parameter space for which differences in dispersal success are likely to have a large effect on population model outputs. Investigating the effect of the representation of dispersal behavior within the demographic and landscape context provides a more useful assessment of whether our lack of knowledge is likely to cause unacceptable uncertainty in the predictions of spatially explicit population models.     

环境归趋模型参数估计方法的比较

大量的研究表明,参数估计方法的可行性和有效性,决定着环境归趋模型的成功与否。在文献检索的基础上,简单介绍了环境归趋模型参数估计方法的类型和最优化原理,分析了各种方法的优点与不足,并初步探讨了环境归趋模型参数估计方法的研究趋势。     

化学品厌氧生物降解QSBR预测模型研究

收集了155种有机化学品厌氧生物降解数据,以随机抽取的109种物质作为训练集,另外46种物质作为验证集,通过结构式拆分得到各基团,分别采用多元线性回归和BP人工神经网络2种算法对有机化合物结构与生物降解性定量关系(QSBR)进行研究。结果表明,多元线性回归模型验证集正确率为78.26%,总正确率为84.52%;BP人工神经网络模型验证集正确率为82.61%,总正确率为90.32%。可见,BP人工神经网络算法相对优于多元线性回归算法。     

Models predicting the fate of pollutants in rivers

The spill of 2,4,‐D in the Rhine river was used to show the evolution from simple to sophisticated models. The first simulation was done with an analytical solution of the dispersion‐advection equation without elimination. Elimination was introduced in a second simulation. The third simulation was carried out with a numerical model. This included elimination and variable dispersion. The lack of data limited the use of very detailed models.     

化学品环境暴露评估模型研究进展

各国政府高度重视化学品生产使用引发的环境问题,研发了一系列环境暴露预测模型,包含了点源暴露到面源暴露各种排放模式,局部暴露、区域暴露及洲际暴露的各种预测尺度,筛选评估到确认水平的不同评估层级,用于评估化学品的潜在风险。本文从模型功能、机理出发,综述了化学品环境暴露评估涉及的排放估计、水/土/气、多介质以及食物链评估模型,以期为推动我国化学品环境暴露模型的研发提供参考。     

化学品环境暴露评估模型研究进展

各国政府高度重视化学品生产使用引发的环境问题,研发了一系列环境暴露预测模型,包含了点源暴露到面源暴露各种排放模式,局部暴露、区域暴露及洲际暴露的各种预测尺度,筛选评估到确认水平的不同评估层级,用于评估化学品的潜在风险。本文从模型功能、机理出发,综述了化学品环境暴露评估涉及的排放估计、水/土/气、多介质以及食物链评估模型,以期为推动我国化学品环境暴露模型的研发提供参考。     

The Application of Neutral Landscape Models in Conservation Biology

Neutral landscape models, derived from percolation theory in the field of landscape ecology, are grid-based maps in which complex habitat distributions are generated by random or fractal algorithms. This grid-based representation of landscape structure is compatible with the raster-based format of geographical information systems (GIS), which facilitates comparisons between theoretical and real landscapes. Neutral landscape models permit the identification of critical thresholds in connectivity, which can be used to predict when landscapes will become fragmented. The coupling of neutral landscape models with generalized population models, such as metapopulation theory, provides a null model for generating predictions about population dynamics in fragmented landscapes. Neutral landscape models can contribute to the following applications in conservation: (1) incorporation of complex spatial patterns in (meta)population models; (2) identification of species' perceptions of landscape structure; (3) determination of landscape connectivity; (4) evaluation of the consequences of habitat fragmentation for population subdivision; (5) identification of the domain of metapopulation dynamics; (6) prediction of the occurrence of extinction thresholds; ( 7) determination of the genetic consequences of habitat fragmentation; and (8) reserve design and ecosystem management. This generalized, spatially explicit framework bridges the gap between spatially implicit, patch-based models and spatially realistic GIS applications which are usually parameterized for a single species in a specific landscape. Development of a generalized, spatially explicit framework is essential in conservation biology because we will not be able to develop individual models for every species of management concern.