A Thermodynamic Geography: Night-Time Satellite Imagery as a Proxy Measure of Emergy

Night-time satellite imagery enables the measurement, visualization, and mapping of energy consumption in an area. In this paper, an index of the “sum of lights” as observed by night-time satellite imagery within national boundaries is compared with the emergy of the nations. Emergy is a measure of the solar energy equivalent used, directly or indirectly, to support the processes that characterize the economic activity in a country. Emergy has renewable and non-renewable components. Our results show that the non-renewable component of national emergy use is positively correlated with night-time satellite imagery. This relationship can be used to produce emergy density maps which enable the incorporation of spatially explicit representations of emergy in geographic information systems. The region of Abruzzo (Italy) is used to demonstrate this relationship as a spatially disaggregate case.     

基于CA-MAS的水域总有机碳扩散模拟研究

当前水环境污染扩散研究一般基于普通数值模型模拟,忽略了水污染扩散微观驱动力的影响。为能更真实地反映其动态扩散过程,针对水域总有机碳(TOC)扩散机理,基于CA和MAS技术,将影响TOC扩散的自然和社会经济因素抽象为微观的水流Agent、风速Agent、径流量Agent、污水排放口Agent、人工管理Agent以及农业生产地Agent,将研究水域抽象为CA元胞空间,建立了CA-MAS水域总有机碳扩散模型,对水域总有机碳的动态演化过程进行模拟,并以武汉理工大学鉴湖水域作为实验区域,借助NetLogo仿真平台完成了模型的实现与验证。模拟结果表明,该模型基本能够反映水体总有机碳的扩散规律,可以为水环境污染控制提供参考。     

Watershed Regressions for Pesticides (warp) Models for Predicting Atrazine Concentrations in Corn Belt Streams1

Stone, Wesley W. and Robert J. Gilliom, 2012. Watershed Regressions for Pesticides (WARP) Models for Predicting Atrazine Concentrations in Corn Belt Streams. Journal of the American Water Resources Association (JAWRA) 48(5): 970‐986. DOI: 10.1111/j.1752‐1688.2012.00661.x Abstract: Watershed Regressions for Pesticides (WARP) models, previously developed for atrazine at the national scale, are improved for application to the United States (U.S.) Corn Belt region by developing region‐specific models that include watershed characteristics that are influential in predicting atrazine concentration statistics within the Corn Belt. WARP models for the Corn Belt (WARP‐CB) were developed for annual maximum moving‐average (14‐, 21‐, 30‐, 60‐, and 90‐day durations) and annual 95th‐percentile atrazine concentrations in streams of the Corn Belt region. The WARP‐CB models accounted for 53 to 62% of the variability in the various concentration statistics among the model‐development sites. Model predictions were within a factor of 5 of the observed concentration statistic for over 90% of the model‐development sites. The WARP‐CB residuals and uncertainty are lower than those of the National WARP model for the same sites. Although atrazine‐use intensity is the most important explanatory variable in the National WARP models, it is not a significant variable in the WARP‐CB models. The WARP‐CB models provide improved predictions for Corn Belt streams draining watersheds with atrazine‐use intensities of 17 kg/km² of watershed area or greater.     

Comparison of Stream Invertebrate Response Models for Bioassessment Metrics1

Waite, Ian R., Jonathan G. Kennen, Jason T. May, Larry R. Brown, Thomas F. Cuffney, Kimberly A. Jones, and James L. Orlando, 2012. Comparison of Stream Invertebrate Response Models for Bioassessment Metrics. Journal of the American Water Resources Association (JAWRA) 48(3): 570-583. DOI: 10.1111/j.1752-1688.2011.00632.x Abstract: We aggregated invertebrate data from various sources to assemble data for modeling in two ecoregions in Oregon and one in California. Our goal was to compare the performance of models developed using multiple linear regression (MLR) techniques with models developed using three relatively new techniques: classification and regression trees (CART), random forest (RF), and boosted regression trees (BRT). We used tolerance of taxa based on richness (RICHTOL) and ratio of observed to expected taxa (O/E) as response variables and land use/land cover as explanatory variables. Responses were generally linear; therefore, there was little improvement to the MLR models when compared to models using CART and RF. In general, the four modeling techniques (MLR, CART, RF, and BRT) consistently selected the same primary explanatory variables for each region. However, results from the BRT models showed significant improvement over the MLR models for each region; increases in R² from 0.09 to 0.20. The O/E metric that was derived from models specifically calibrated for Oregon consistently had lower R² values than RICHTOL for the two regions tested. Modeled O/E R² values were between 0.06 and 0.10 lower for each of the four modeling methods applied in the Willamette Valley and were between 0.19 and 0.36 points lower for the Blue Mountains. As a result, BRT models may indeed represent a good alternative to MLR for modeling species distribution relative to environmental variables.     

Modeling urban films using a dynamic multimedia fugacity model

A thin film coats impervious urban surfaces that can act as a source or sink of organic pollutants to the greater environment. We review recent developments in the understanding of film and film-associated pollutant behavior and incorporate them into an unsteady-state version of the fugacity based Multimedia Urban Model (MUM), focusing on detailed considerations of surface film dynamics. The model is used to explore the conditions under which these atmospherically-derived films act as a temporary source of chemicals to the air and/or storm water. Assuming film growth of 2.1 nm d⁻¹ (Wu et al., 2008a), PCB congeners 28 and 180 reach air-film equilibrium within hours and days, respectively. The model results suggest that the film acts as a temporary sink of chemicals from air during dry and cool weather, as a source to air in warmer weather, and as a source to storm water and soil during rain events. Using the downtown area of the City of Toronto Canada, as a case study, the model estimates that nearly 1 g d⁻¹ of ∑₅PCBs are transferred from air to film to storm water.     

Eichhornia crassipes capability to remove naphthalene from wastewater in the absence of bacteria

The aim of the current study was to investigate the potential of an aquatic plant, the water hyacinth (Eichhornia crassipes) devoid rhizospheric bacteria, to reduce naphthalene (a polyaromatic hydrocarbon) present in wastewater and wetlands.The capability of sterile water hyacinth plants to remove naphthalene from water and wastewater was studied in batch systems. Water hyacinths enhance the removal of pollutants through their consumption as nutrients and also through microbial activity of their rhizospheric bacteria.Experimental kinetics of naphthalene removal by water hyacinth coupled with natural rhizospheric bacteria was 100% after 9 d. Plants, decoupled of rhizospheric bacteria, reduced naphthalene concentration up to 45% during 7 d. Additionally, naphthalene uptake by water hyacinth revealed a biphasic behavior: a rapid first phase completed after 2.5 h, and a second, considerably slower rate, phase (2.5-225 h). In conclusion, water hyacinth devoid rhizospheric bacteria reduced significantly naphthalene concentration in water, revealing a considerable plant contribution in the biodegradation process of this pollutant.     

基于模糊评判法的陕西农村公共物品供给模式选择分析

陕西一直实行"以农补工、优先发展城市、优先发展工业"的倾斜性战略政策,使得城乡在资源分配和社会事业发展等方面存在较大的差距,农村发展处于劣势地位,尤其是农村公共物品未能得到有效供给,其供给主要是采取以政府为主导的模式。研究利用陕西农村1990-2008年的时间序列统计数据,通过构建陕西农村经济发展的9个评判指标并对其分别赋予不同的隶属度,运用模糊评判法,对陕西农村公共物品供给的经济发展阶段进行了定量评判。实证研究表明,自20世纪90年代以来,陕西农村地区整体上处于经济发展的第二个阶段,即经济发展的成长阶段。因此,陕西应主要采取以政府为主导型的农村公共物品供给模式,而私人(市场)供给只能作为农村公共物品供给的少许补充。农村公共物品供给模式应根据经济发展阶段作出选择,以期实现各个时期农村公共物品供给的最优化:考虑不同地区农民的需求意愿,改革"一刀切"的供给模式;扭转农民负担部分公共物品供给成本的局面。     

Critical Steps for Continuing Advancement of Satellite Rainfall Applications for Surface Hydrology in the Nile River Basin1

Gebremichael, Mekonnen, Emmanouil N. Anagnostou, and Menberu M. Bitew, 2010. Critical Steps for Continuing Advancement of Satellite Rainfall Applications for Surface Hydrology in the Nile River Basin. Journal of the American Water Resources Association (JAWRA) 46(2):361-366. DOI: 10.1111/j.1752-1688.2010.00428.x. Abstract: Given the increasingly higher resolution and data accessibility, satellite precipitation products could be useful for hydrological application in the Nile River Basin, which is characterized by lack of reasonably dense hydrological in situ sensors and lack of access to the existing dataset. However, in the absence of both extreme caution and research results for the Nile basin, the satellite rainfall (SR) products may not be used, or may even be used erroneously. We identify two steps that are critical to enhance the value of SR products for hydrological applications in the Nile basin. The first step is to establish representative validation sites in the Nile basin. The validation site will help to quantify the errors in the different kinds of SR products, which will be used to select the best products for the Nile basin, include the errors in decision making, and design strategies to minimize the errors. Using rainfall measurements collected from the unprecedented high-density rain gauge network over a small region within the Nile basin, we indicate that SR estimates could be subject to significant errors, and quantification of estimation errors by way of establishing validation sites is critically important in order to use the SR products. The second step is to identify the degree of hydrologic model complexity required to obtain more accurate hydrologic simulation results for the Nile basin when using SR products as input. The level of model complexity may depend on basin size and SR algorithm, and further research is needed to spell out this dependence for the Nile basin.     

An Aquifer Classification System and Geographical Information System-Based Analysis Tool for Watershed Managers in the Western U.S.1

Payne, Scott M. and William W. Woessner, 2010. An Aquifer Classification System and Geographical Information System-Based Analysis Tool for Watershed Managers in the Western U.S. Journal of the American Water Resources Association (JAWRA) 46(5):1003-1023. DOI: 10.1111/j.1752-1688.2010.00472.x Abstract: Aquifers and groundwater systems can be classified using a variety of independent methods to characterize geologic and hydraulic properties, the degree of connection with surface water, and geochemical conditions. In light of a growing global demand for water, an approach for classifying groundwater systems at the watershed scale is needed. A comprehensive classification system is proposed that combines recognized methods and new approaches. The purpose of classification is to provide groundwater professionals, policy makers, and watershed managers with a widely applicable and repeatable system that reduces sometimes cumbersome complex databases and analyzes to straightforward terminology and graphical representations. The proposed classification system uses basin geology, aquifer productivity, water quality, and the degree of groundwater/surface water connection as classification criteria. The approach is based on literature values, reference databases, and fundamental hydrologic and hydrogeologic principles. The proposed classification system treats dataset completeness as a variable and includes a tiered assessment protocol that depends on the quality and quantity of data. In addition, it assembles and catalogs groundwater information using a consistent set of nomenclature. It is designed to analyze and display results using Geographical Information System mapping tools.     

Machine learning for radioxenon event classification for the Comprehensive Nuclear-Test-Ban Treaty

A method of weapon detection for the Comprehensive nuclear-Test-Ban-Treaty (CTBT) consists of monitoring the amount of radioxenon in the atmosphere by measuring and sampling the activity concentration of ^131mXe, ¹³³Xe, ^133mXe, and ¹³⁵Xe by radionuclide monitoring. Several explosion samples were simulated based on real data since the measured data of this type is quite rare. These data sets consisted of different circumstances of a nuclear explosion, and are used as training data sets to establish an effective classification model employing state-of-the-art technologies in machine learning. A study was conducted involving classic induction algorithms in machine learning including Naïve Bayes, Neural Networks, Decision Trees, k-Nearest Neighbors, and Support Vector Machines, that revealed that they can successfully be used in this practical application. In particular, our studies show that many induction algorithms in machine learning outperform a simple linear discriminator when a signal is found in a high radioxenon background environment.