OPTIMIZATION OF INTERMITTENT PUMPING SCHEDULES FOR AQUIFER REMEDIATION USING A GENETIC ALGORITHM1

ABSTRACT: Using a genetic algorithm (GA), optimal intermittent pumping schedules were established to simulate pump‐and‐treat remediation of a contaminated aquifer with known hydraulic limitations and a water miscible contaminant, located within the Duke Forest in Durham, North Carolina. The objectives of the optimization model were to minimize total costs, minimize health risks, and maximize the amount of contaminant removed from the aquifer. Stochastic ground water and contaminant transport models were required to provide estimates of contaminant concentrations at pumping wells. Optimization model simulations defined a tradeoff curve between the pumping cost and the amount of contaminant extracted from the aquifer. For this specific aquifer/miscible contaminant combination, the model simulations indicated that pump‐and‐treat remediation using intermittent pumping schedules for each pumping well produced significant reductions in predicted contaminant concentrations and associated health risks at a reasonable cost, after a remediation time of two years.     

A Bayesian approach to multi-source forest area estimation

In efforts such as land use change monitoring, carbon budgeting, and forecasting ecological conditions and timber supply, there is increasing demand for regional and national data layers depicting forest cover. These data layers must permit small area estimates of forest area and, most importantly, provide associated error estimates. This paper presents a model-based approach for coupling mid-resolution satellite imagery with plot-based forest inventory data to produce estimates of probability of forest and associated error at the pixel-level. The proposed Bayesian hierarchical model provides access to each pixel’s posterior predictive distribution allowing for a highly flexible analysis of pixel and multi-pixel areas of interest. The paper presents a trial using multiple dates of Landsat imagery and USDA Forest Service Forest Inventory and Analysis plot data. The results describe the spatial dependence structure within the trial site, provide pixel and multi-pixel summaries of probability of forest land use, and explore discretization schemes of the posterior predictive distributions to forest and non-forest classes. Model prediction results of a holdout set analysis suggest the proposed model provides high classification accuracy, 88%, for the trial site.

A hierarchical Bayesian non-linear spatio-temporal model for the spread of invasive species with application to the Eurasian Collared-Dove

The spread of invasive species is a long studied subject that garners much interest in the ecological research community. Historically the phenomenon has been approached using a purely deterministic mathematical framework (usually involving differential equations of some form). These methods, while scientifically meaningful, are generally highly simplified and fail to account for uncertainty in the data and process, of which our knowledge could not possibly exist without error. We propose a hierarchical Bayesian model for population spread that accommodates data sources with errors, dependence structures between population dynamics parameters, and takes into account prior scientific understanding via non-linear relationships between model parameters and space-time response variables. We model the process (i.e., the bird population in this case) as a Poisson response with spatially varying diffusion coefficients as well as a logistic population growth term using a common reaction-diffusion equation that realistically mimics the ecological process. We focus the application on the ongoing invasion of the Eurasian Collared-Dove.     

Generation of Monthly Precipitation Under Climate Change for the Upper Blue Nile River Basin,Ethiopia1

Abstract: This work develops a methodology to project the future precipitation in large river basins under limited data and climate change while preserving the historical temporal and spatial characteristics. The computationally simple and reliable conditional generation method (CGM) is presented and applied to generate reliable monthly precipitation data in the upper Blue Nile River Basin of Ethiopia where rain‐fed agriculture is prevalent. The results showed that the temporal analysis with the CGM performs better to reproduce the historical long‐term characteristics than other methods, and the spatial analysis with the CGM reproduced the historical spatial structure accurately. A 100‐year time series analysis using the outcomes of the six general circulation models showed that precipitation changes by the 2050s (2040 through 2069) can be ?7 to 28% with a mean increase of about 11%. The seasonal results showed increasing wet conditions in all seasons with changes of mean precipitation of 5, 47, and 6% for wet, dry, and mild seasons, respectively.     

Risk assessment and prioritisation of contaminated sites on the catchment scale

Contaminated sites pose a significant threat to groundwater resources worldwide. Due to limited available resources a risk-based prioritisation of the remediation efforts is essential. Existing risk assessment tools are unsuitable for this purpose, because they consider each contaminated site separately and on a local scale, which makes it difficult to compare the impact from different sites. Hence a modelling tool for risk assessment of contaminated sites on the catchment scale has been developed. The CatchRisk screening tool evaluates the risk associated with each site in terms of its ability to contaminate abstracted groundwater in the catchment. The tool considers both the local scale and the catchment scale. At the local scale, a flexible, site specific leaching model that can be adjusted to the actual data availability is used to estimate the mass flux over time from identified sites. At the catchment scale, a transport model that utilises the source flux and a groundwater model covering the catchment is used to estimate the transient impact on the supply well. The CatchRisk model was tested on a groundwater catchment for a waterworks north of Copenhagen, Denmark. Even though data scarcity limited the application of the model, the sites that most likely caused the observed contamination at the waterworks were identified. The method was found to be valuable as a basis for prioritising point sources according to their impact on groundwater quality. The tool can also be used as a framework for testing hypotheses on the origin of contamination in the catchment and for identification of unknown contaminant sources.     

Nutrient transfer functions: the site of integration between feeding behaviour and nutritional physiology

Summary. We describe and extend a graphical approach to quantitative nutrition that focuses on the interplay between behavioural and physiological components of nutritional regulation. The site of integration is the nutrient transfer function, which is the function describing the time course of nutrient transfer between serially connected nutritional compartments (e.g., from the gut to the blood). The relationship between the shape of the nutrient transfer function and the temporal patterns of feeding determines the values of two key quantitative parameters of nutrition: the rate ('power') and the efficiency of nutrient acquisition. The approach can be extended to consider, in addition to the short-term behavioural and physiological decisions made by animals, some ecological determinants and longer-term, life history consequences of such decisions. Most importantly, this category of models can provide insights into the interplay among the various nutrients in an animal's diet. We illustrate this using hypothetical examples, and also present preliminary data for the power-efficiency relationships of protein and digestible carbohydrates in locusts. Finally, we consider existing evidence for the various means available to these and other insects for regulating such relationships. Received 24 September 1997; accepted 9 December 1997.     

改善太仓城区水环境原型调水实验研究及模型建立

为达到改善太仓城区水环境的目的,于2004年4月21日进行了调水实验.调水历时14 h,期间向城区调水21.8万m3.通过对监测点采样,得到水量、水质实测数据.根据实验数据,建立了符合太仓河网的水量水质数学模型,分析了从长江引水对太仓城区水环境的改善程度.同时对不同引水方案情况下城区水环境改善效果,以及截污和清淤对水环境的影响做了分析.研究表明,引水只能短期改善水环境;要根治水环境,必须从源头抓起,进行截污.     

Apparent relationships between anthropogenic factors and climate change indicators and POPs deposition in a lacustrine system

Climate change and anthropogenic activities are expected to impact the environmental behaviors and fates of persistent organic pollutants(POPs), however, quantitative studies on these combined factors are scarce. In this study, dichlorodiphenyltrichloroethane(DDTs), polycyclic aromatic hydrocarbons(PAHs), and polychlorinated biphenyls(PCBs)were used as examples to identify how and when those factors may be related to the deposition of POPs in the sediment of Lake Chaohu, China, using generalized additive models(GAMs). Three historical trends of DDT, PAH, and PCB deposition were delineated in a dated sediment core encompassing ～100 years of historical record: a steady state or gradually increasing stage, a rapidly increasing stage, and a declining stage. The GAM results showed that aquatic total phosphorus(TP) concentrations and regional GDP(anthropogenic factors) were dominant contributors to POP accumulation rates in the lake sediment. The fitted relationships between air temperature and sedimentary DDT and PAH concentrations were linear and negative, while a positive linear relationship was found for PCBs, suggesting that Lake Chaohu may have become a net source for DDTs and PAHs, and a sink for PCBs, under a progressively warming climate.     

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.     

Developing an empirical model of phytoplankton primary production: a neural network case study

We describe the development of a neural network model for estimating primary production of phytoplankton. Data from an enriched estuary in the eastern United States, Chesapeake Bay, were used to train, validate and test the model. Two error backpropagation multilayer perceptrons were trained: a simpler one (3-5-1) and a more complex one (12-5-1). Both neural networks outperformed conventional empirical models, even though only the latter, which exploits a larger suite of predictive variables, provided truly accurate outputs. The application of this neural network model is thoroughly discussed and the results of a sensitivity analysis are also presented.