Design of aquifer remediation systems: (1) describing hydraulic structure and NAPL architecture using tracers

Aquifer heterogeneity (structure) and NAPL distribution (architecture) are described based on tracer data. An inverse modelling approach that estimates the hydraulic structure and NAPL architecture based on a Lagrangian stochastic model where the hydraulic structure is described by one or more populations of lognormally distributed travel times and the NAPL architecture is selected from eight possible assumed distributions. Optimization of the model parameters for each tested realization is based on the minimization of the sum of the square residuals between the log of measured tracer data and model predictions for the same temporal observation. For a given NAPL architecture the error is reduced with each added population. Model selection was based on a fitness which penalized models for increasing complexity. The technique is demonstrated under a range of hydrologic and contaminant settings using data from three small field-scale tracer tests: the first implementation at an LNAPL site using a line-drive flow pattern, the second at a DNAPL site with an inverted five-spot flow pattern, and the third at the same DNAPL site using a vertical circulation flow pattern. The Lagrangian model was capable of accurately duplicating experimentally derived tracer breakthrough curves, with a correlation coefficient of 0.97 or better. Furthermore, the model estimate of the NAPL volume is similar to the estimates based on moment analysis of field data.     

The importance of spatial autocorrelation,extent and resolution in predicting forest bird occurrence

Concerns about declines in forest biodiversity underscore the need for accurate estimates of the distribution and abundance of organisms at large scales and at resolutions that are fine enough to be appropriate for management. This paper addresses three major objectives: (i) to determine whether the resolution of typical air photo-derived forest inventory is sufficient for the accurate prediction of site occupancy by forest birds. We compared prediction success of habitat models using air photo variables to models with variables derived from finer resolution, ground-sampled vegetation plots. (ii) To test whether incorporating spatial autocorrelation into habitat models via autologistic regression increases prediction success. (iii) To determine whether landscape structure is an important factor in predicting bird distribution in forest-dominated landscapes. Models were tested locally (Greater Fundy Ecosystem [GFE]) using cross-validation, and regionally using an independent data set from an area located ca. 250 km to the northwest (Riley Brook [RB]). We found significant positive spatial autocorrelation in the residuals of at least one habitat model for 76% (16/21) of species examined. In these cases, the logistic regression assumption of spatially independent errors was violated. Logistic models that ignored spatial autocorrelation tended to overestimate habitat effects. Though overall prediction success was higher for autologistic models than logistic models in the GFE, the difference was only significantly improved for one species. Further, the inclusion of spatial covariates did little to improve model performance in the geographically discrete study area. For 62% (13/21) of species examined, landscape variables were significant predictors of forest bird occurrence even after statistically controlling for stand-level variability. However, broad spatial extents explained less variation than local factors. In the GFE, 76% (16/21) of air photo and 81% (17/21) of ground plot models were accurate enough to be of practical utility (AUC > 0.7). When applied to RB, both model types performed effectively for 55% (11/20) of the species examined. We did not detect an overall difference in prediction success between air photo and ground plot models in either study area. We conclude that air photo data are as effective as fine resolution vegetation data for predicting site occupancy for the majority of species in this study. These models will be of use to forest managers who are interested in mapping species distributions under various timber harvest scenarios, and to protected areas planners attempting to optimize reserve function.     

Calibration of the forest vegetation simulator (FVS) model for the main forest species of Ontario,Canada

The forest vegetation simulator (FVS) model was calibrated for use in Ontario, Canada, to predict the growth of forest stands. Using data from permanent sample plots originating from different regions of Ontario, new models were derived for dbh growth rate, survival rate, stem height and species group density index for large trees and height and dbh growth rate for small trees. The dataset included black spruce (Picea mariana (Mill.) B.S.P.) and jack pine (Pinus banksiana Lamb.) for the boreal region, sugar maple (Acer saccharum Marsh.), white pine (Pinus strobus L.), red pine (Pinus resinosa Ait.) and yellow birch (Betula alleghaniensis Britton) for the Great Lakes-St. Lawrence region, and balsam fir (Abies balsamea (L.) Mill.) and trembling aspen (Populus tremuloides Michx.) for both regions. These new models were validated against an independent dataset that consisted of permanent sample plots located in Quebec. The new models predicted biologically consistent growth patterns whereas some of the original models from the Lake States version of FVS occasionally did not. The new models also fitted the calibration (Ontario) data better than the original FVS models. The validation against independent data from Quebec showed that the new models generally had a lower prediction error than the original FVS models.     

Impact of aquacultures on the marine ecosystem: Modelling benthic carbon loading over variable depth

In order to be used within Environmental Impact Assessment study, we have developed a three-dimensional particle tracking model for prediction of benthic carbon loading (BCL) caused by fish farms. The model is based on stochastic differential equations for particle transport consistent with the well-known semi-empirical advection/diffusion equation. It requires only easily obtainable input data in the form of measured current record, the source location and a specification of local bathymetry. The model accounts for advection by long-term residual and tidal currents, turbulent diffusion, realistic bathymetry and variations in daily (monthly or yearly) emissions from fish farm.Here, we concentrate on the changes in sedimentation pattern caused by various bathymetric shapes. Examination of idealized cases reveals where and why we can expect the worst impact on benthic communities. For future reference, these results will be included into guidelines for fish farming.     

SEIB–DGVM: A new Dynamic Global Vegetation Model using a spatially explicit individual-based approach

We report the development of a new spatially explicit individual-based Dynamic Global Vegetation Model (SEIB–DGVM), the first DGVM that can simulate the local interactions among individual trees within a spatially explicit virtual forest. In the model, a sample plot is placed at each grid box, and then the growth, competition, and decay of each individual tree within each plot is calculated by considering the environmental conditions for that tree as it relates to the trees that surround it. Based on these parameters only, the model simulated time lags between climate change and vegetation change. This time lags elongated when original biome was forest, because existing trees prevent newly establish trees from receiving enough sunlight and space to quickly replace the original vegetation. This time lags also elongated when horizontal heterogeneity of sunlight distribution was ignored, indicating the potential importance of horizontal heterogeneity for predicting transitional behavior of vegetation under changing climate. On a local scale, the model reproduced climate zone-specific patterns of succession, carbon dynamics, and water flux, although on a global scale, simulations were not always in agreement with observations. Because the SEIB–DGVM was formulated to the scale at which field biologists work, the measurements of relevant parameters and data comparisons are relatively straightforward, and the model should enable more robust modeling of terrestrial ecosystems.     

Using equation discovery to revise an Earth ecosystem model of the carbon net production

Equation discovery approaches to automated modeling from observed data usually derive equation-based models from scratch rather than from an initial model already established in the domain of use. In this paper, we present an approach that uses new or recent observational data to improve an existing equation-based model. The approach is used to reduce the error of the Earth ecosystem model of the net production of carbon in the atmosphere. We revise the initial ecosystem model in two directions. First, we calibrate the values of the constant parameters in the model on new observational data. Second, we allow the use of alternative equation structures for some of the sub-models of the initial model and use our approach to choose among them. Experiments show that both revision of values of the constant parameters and revision of the structures of sub-models can considerably reduce the error of the initial model.     

环境信息系统中关于办公自动化的探讨

在简要介绍环境信息系统概况的同时 ,重点在办公自动化系统上提出了一些体会 ,并从开发、应用等几方面说明了办公自动化系统作为一项系统工程在目前信息中的作用     

环境质量灰色评价模型及其应用

根据灰色关联分析的地质，构造目标函数，运用拉格朗日乘数法求极值，建立了一种新的环境质量评价模型．结合实例说明了该模型的具体应用．与其它方法进行了对比，结果完全一致．该模型数学推导严谨、科学合理、分辩率高、可靠性强．     

湿地可持续发展与经济损益的理论分析

文章分析了中国湿地可持续发展的基本内涵和人为干扰的限制因子，建立了湿地人为干扰破坏损益分析技术路线，提出了经济损益分析理论模型及损益度计量模型。     

公路工程施工安全事故致因模型构建

为了探究公路施工安全事故诱因及其影响关系,基于"4M"理论和事故致因"2-4"模型,识别公路工程施工安全事故致因因素并确定模型框架,运用SPSS Modeler软件对2007~2017年426起事故进行关联规则挖掘,采取路径分析与重要度分析,构建出公路工程施工安全事故致因模型。结果表明:施工单位内外部原因共同引发公路工程施工安全事故,外部原因来源于建设单位、监理单位和勘察设计单位的安全管理缺失,而施工单位内部影响因素中,人的不安全动作、物的不安全因素、不良的生产环境和自然环境是事故发生的直接原因,无效的安全监管和作业层安全素质及能力不强是间接原因,根本原因是施工程序和技术方案存在缺陷,根源原因是决策层和管理层的安全素质及能力有待提高。该模型系统展现了工程施工项目安全事故致因因素的影响关系和重要程度,为公路工程施工安全事故预防提供指导。