A real-time simulating non-isothermal mathematical model for the passive feed direct methanol fuel cell

In order to understand the complex transport phenomena in a passive direct methanol fuel cell (DMFC), a theoretical model is essential. The analytical model provides a computationally efficient framework with a clear physical meaning. For this, a non-isothermal, analytical model for the passive DMFC has been developed in this study. The model considers the coupled heat and mass transport along with electrochemical reactions. The model is successfully validated with the experimental data. The model accurately describes the various species transport phenomena including methanol crossover and water crossover, heat transport phenomena, and efficiencies related to the passive DMFC. It suggests that the maximum real efficiency can be achieved by running the cell at low methanol feed concentration and moderate current density. The model also accurately predicts the effect of various operating and geometrical parameters on the cell performance such as methanol feed concentration, surrounding temperature, and polymer electrolyte membrane thickness. The model predictions are in accordance with the findings of the other researchers. The model is rapidly implementable and can be used in real-time simulation and control of the passive DMFC. This comprehensive model can be used for diagnostic purpose as well.     

Nanoparticle aggregation: challenges to understanding transport and reactivity in the environment

Unique forms of manufactured nanomaterials, nanoparticles, and their suspensions are rapidly being created by manipulating properties such as shape, size, structure, and chemical composition and through incorporation of surface coatings. Although these properties make nanomaterial development interesting for new applications, they also challenge the ability of colloid science to understand nanoparticle aggregation in the environment and the subsequent effects on nanomaterial transport and reactivity. This review briefly covers aggregation theory focusing on Derjaguin-Landau-Verwey-Overbeak (DLVO)-based models most commonly used to describe the thermodynamic interactions between two particles in a suspension. A discussion of the challenges to DLVO posed by the properties of nanomaterials follows, along with examples from the literature. Examples from the literature highlighting the importance ofaggregation effects on transport and reactivity and risk of nanoparticles in the environment are discussed.     

New perspectives in ecotoxicology

The task of regulating potentially harmful chemicals in the environment is presently hindered by the lack of appropriate concepts and methods for evaluating the effects of anthropogenic chemicals on ecosystems. Toxicity tests at the molecular and physiological levels have been used successfully as indicators of adverse effects on test organisms and have been extrapolated to humans to establish a basis for risk assessment. However, laboratory measurements of effects upon individuals do not translate readily into potential effects upon natural populations, in part because natural populations interact with other populations and with the physical environment. Even more difficult to assess are the deleterious impacts of anthropogenic chemicals on ecosystems, because of effects on species interactions, diversity, nutrient cycling, productivity, climatic changes, and other processes.Effects on ecosystems resulting from chemical stresses are outside the realm of classical toxicology, and an ecosystem-level perspective is essential for the consideration of such effects; but the science that deals with ecosystem-level effects,ecotoxicology, is still developing. This article synthesizes the topics discussed at a workshop on ecotoxicology held by the Ecosystems Research Center at Cornell University. Topics covered include: the regulatory framework in which ecotoxicological research must be applied; ecosystem modification of toxicant fate and transport; how ecosystem composition, structure, and function are influenced by chemicals; methods currently available for predicting the effects of chemicals at the ecosystem level; and recommendations on research needs to enhance the state of the science of ecotoxicology.     

Electrokinetic extraction of lead from kaolinites: I. Numerical modeling

Experimental results obtained to date indicate electrokinetic extraction is viable in removing organic and inorganic contaminants from fine-grained soils. However, electrochemical reactions and soil-contaminant interactions that occur simultaneously may enhance or reduce the removal efficiency of the hazardous waste site remediation process. Many sites worldwide are contaminated by lead and its compounds, resulting in lead poisoning. It is difficult to remove lead from fine-grained soil because of the existence of a great variety of lead complexes and their pH-dependent and reversible physicochemical properties. The feasibility of electrokinetic extraction of lead from kaolinites is investigated theoretically, numerically, and experimentally in this study. This is the first paper of two companion papers presenting the theoretical and numerical modeling of the transport of lead species, and electrochemical reactions and soil-contaminant interactions occurring during the electrokinetic extraction process. The comparison between simulation results and experimental results is presented in the second paper.     

An evaluation of the predictive performance of distributional models for flora and fauna in north-east New South Wales.

To use models of species distributions effectively in conservation planning, it is important to determine the predictive accuracy of such models. Extensive modelling of the distribution of vascular plant and vertebrate fauna species within north-east New South Wales has been undertaken by linking field survey data to environmental and geographical predictors using logistic regression. These models have been used in the development of a comprehensive and adequate reserve system within the region. We evaluate the predictive accuracy of models for 153 small reptile, arboreal marsupial, diurnal bird and vascular plant species for which independent evaluation data were available. The predictive performance of each model was evaluated using the relative operating characteristic curve to measure discrimination capacity. Good discrimination ability implies that a model's predictions provide an acceptable index of species occurrence. The discrimination capacity of 89% of the models was significantly better than random, with 70% of the models providing high levels of discrimination. Predictions generated by this type of modelling therefore provide a reasonably sound basis for regional conservation planning. The discrimination ability of models was highest for the less mobile biological groups, particularly the vascular plants and small reptiles. In the case of diurnal birds, poor performing models tended to be for species which occur mainly within specific habitats not well sampled by either the model development or evaluation data, highly mobile species, species that are locally nomadic or those that display very broad habitat requirements. Particular care needs to be exercised when employing models for these types of species in conservation planning.     

Dynamic modeling of chemical fate and transport in multimedia environments at watershed scale-II: trichloroethylene test case

A multimedia environmental fate model was developed to study the temporal dynamics and spatial distribution of a chemical pollutant at watershed scale. The theoretical considerations and implementation of the model were described in the accompanying paper (Part I). This paper presents the result of a test simulation on the transport of trichloroethylene (TCE) in the Connecticut River Basin. The simulation results were reported as time series of concentrations and inter-media transport fluxes in the compartments of atmosphere, plant, soil, surface water, and sediment. Predicted concentrations from the test simulation were compared with published field data or predictions by validated models. The temporal trends in TCE predictions were evaluated by comparing the simulation results with monthly TCE concentrations in various environmental compartments and monthly fluxes of inter-media transport processes. Results indicated that the simulation results were in reasonable agreement with reported data in the literature. The results also revealed that the mass transport of TCE from the atmosphere compartment to soil and surface water was a major route of TCE dispersion in the environment.     

Characterization of cation-pi interactions in aqueous solution using deuterium nuclear magnetic resonance spectroscopy

Chemical interactions of aromatic organic contaminants control their fate, transport, and toxicity in the environment. Recent molecular modeling studies have suggested that strong interactions can occur between the pi electrons of aromatic molecules and metal cations in aqueous solutions and/or on mineral surfaces, and that such interactions may be important in some environmental systems. However, spectroscopic evidence for these so-called cation-pi interactions has been extremely limited to date. In this paper, cation-pi interactions in aqueous salt solutions were characterized via 2H nuclear magnetic resonance (NMR) spin-lattice relaxation times (T1) and calculations of molecular correlation times (tau(c)) for a series of perdeuterated (d6-benzene) benzene-cation complexes. The T1 values for d6-benzene decreased with increasing concentrations of LiCl, NaCl, KCl, RbCl, CsCl, and AgNO3, with the largest effects observed in the AgNO3 and CsCl solutions. Upon normalizing tau(c) values by solution viscosity effects, an overall affinity trend of Ag+ > Cs+ > K+ > Rb+ > Na+ > Li+ was derived for the d6-benzene-cation complexes. The ability of Ag+ to complex d6-benzene was significantly reduced upon addition of NH3, which strongly coordinates Ag+ at high pH. Results with d6-benzene, d8-naphthalene, d2-dichloromethane, and d12-cyclohexane in 0.1 M methanolic salt solutions confirmed that spin-lattice relaxation rates are characterizing cation-pi interactions. The relatively strong cation-pi bonding observed between Ag+ and aromatic hydrocarbons probably results from covalent interactions between the aromatic pi electrons and the d orbitals of Ag+, in addition to the normal electrostatic interaction.     

Utility of laboratory streams for ecosystem toxicity studies

Laboratory stream microcosms have been used to study transport, fate, and effects of toxic substances in stream ecosystems. Several general concerns exist in utilizing laboratory streams in this way. We summarize some of the most important and difficult of these problems and endeavor to provide theoretical understanding, evaluation, and empirical approaches necessary for making laboratory stream ecosystem studies more useful in solving problems of toxic substance behavior in natural stream ecosystems. Well-designed laboratory streams and other microcosms are complex dynamic systems that can contribute to our understanding of the behavior of toxic substances. But such systems are far too complex and dynamic to be employed as bioassay, monitoring, or predictive tools, as individual organisms have been.     

MULTICOMPONENT GEOCHEMICAL TRANSPORT MODELING USING HYDRUS-1D AND HP11

Abstract: The transport of reactive contaminants in the subsurface is generally affected by a large number of nonlinear and often interactive physical, chemical, and biological processes. Simulating these processes requires a comprehensive reactive transport code that couples the physical processes of water flow and advective-dispersive transport with a range of biogeochemical processes. Two recently developed coupled geochemical models that are both based on the HYDRUS-1D software package for variably saturated flow and transport are summarized in this paper. One model resulted from coupling HYDRUS-1D with the UNSATCHEM module. While restricted to major ion chemistry, this program enables quantitative predictions of such problems as analyzing the effects of salinity on plant growth and the amount of water and amendments required to reclaim salt-affected soil profiles. The second model, HPI, resulted from coupling HYDRUS-1D with the PHREEQC biogeochemical code. The latter program accounts for a wide range of instantaneous or kinetic chemical and biological reactions, including complexation, cation exchange, surface complexation, precipitation dissolution and/or redox reactions. The versatility of HP1 is illustrated in this paper by means of two examples: the leaching of toxic trace elements and the transport of the explosive TNT and its degradation products.     

MODELING THE IMPACT OF MULTICOMPONENT VOCs ON GROUND WATER USING THE STEFAN‐MAXWELL EQUATION1

ABSTRACT: Computer programs that model the fate and transport of organic contaminants through porous media typically use Fick's first law to calculate vapor phase diffusion. Fick's first law, however, is limited to the case of a single, dilute species diffusing into a stagnant, high concentration, bulk vapor phase. When dealing with more than one diffusing species and at higher concentrations, the multicomponent coupling effects on vapor phase diffusion and advection of the various constituents become significant. VLEACH, a one‐dimensional finite difference model developed for the U.S. Environmental Protection Agency (USEPA), is typical of the models using Fick's first law to model vapor‐phase diffusion. The VLEACH model was modified to accommodate up to 10 components and to calculate the binary diffusion coefficients for each of the components based on molecular weight, molecular volume, temperature and pressure, and to address the coupling effects on multiple component vapor phase diffusion and its impact on ground water. The resulting model was renamed MC‐CHEMSOIL. At low vapor phase concentrations, MC‐CHEMSOIL predicts identical ground water impacts (dissolved phase loading) to those from VLEACH 2.2a. At higher vapor phase concentrations, however, the relative difference between the models exceeded 20 percent.     

Modeling pollutant release from a surface source during rainfall runoff

Though runoff from manure spread fields is recognized as an important mode of nonpoint-source pollution, there are no models that mechanistically describe transport from a field-spread manure-type source. A mechanistic, physically based model for pollutant release from a surface source, such as field-spread manure, was hypothesized, laboratory tested, and field-applied. The primary objective of this study was to demonstrate the potential applicability of a mechanistic model to pollutant release from surface sources. The laboratory investigation used stable sources and a conservative "pollutant" (KCl) so that the dynamic effects of source dissolution and chemical transformations could be ignored and transport processes isolated. The field investigation used runoff and soluble reactive phosphorus (SP) data collected from a dairy-manure-spread field in the Cannonsville watershed in the Catskills region of New York State. The model predictions corroborated well with observations of runoff and pollutant delivery in both the laboratory and the field. "Pollutant" release from surface sources was generally predicted within 11% of laboratory KCl measurements and field SP observations. Laboratory flume runoff predictions with 15 and 26% errors for 25 and 15 mm h(-1) simulated rainfall intensity experiments, respectively, represented root mean square errors of less than 0.2 mLs(-1). A 26% error was calculated for overland flow predictions in the field, which translated into approximately a 39 mLs(-1) error. Results suggest that the hypothesized model satisfactorily represents the primary mechanisms in pollutant release from surface sources.     

Predicting Particulate Matter (PM2.5) Concentrations in the Air of Shahr‐e Ray City,Iran, by Using an Artificial Neural Network

Particulate matter (PM), along with other air pollutants, pose serious hazards to human health. The Artificial Neural Network (ANN) is a branch of artificial intelligence that has an ability to make accurate predictions. In this article, the authors describe such methods and how historical data on air quality, moisture, wind velocity, and temperature in Shahr‐e Ray City, located at the southern tip of Tehran, was used to train an ANN to provide accurate predictions of PM concentrations. The availability of such predictions can offer significant assistance to those who are working to reduce air pollution.     

Seasonal leaching and biodegradation of dicamba in turfgrass

The leaching of surface-applied herbicides, such as dicamba (2methoxy-3,6-dichlorobenzoic acid), to ground water is an environmental concern. Seasonal changes in soil temperature and water content, affecting infiltration and biodegradation, may control leaching. The objectives of this study were to (i) investigate the leaching of dicamba applied to turfgrass, (ii) measure the degradation rate of dicamba in soil and thatch in the laboratory under simulated field conditions, and (iii) test the ability of the model EXPRES (containing LEACHM) to simulate the field transport and degradation processes. Four field lysimeters, packed with sandy loam soil and topped with Kentucky bluegrass (Poa pratensis L.) sod, were monitored after receiving three applications (May, September, November) of dicamba. Concentrations of dicamba greater than 1 mg L(-1) were detected in soil water. Although drying of the soil during the summer prevented deep transport, greater leaching occurred in late autumn due to increased infiltration. From the batch experiment, the degradation rate for dicamba in thatch was 5.9 to 8.4 times greater than for soil, with a calculated half-life as low as 5.5 d. Computer modeling indicated that the soil and climatic conditions would influence the effectiveness of greater degradation in thatch for reducing dicamba leaching. In general, EXPRES predictions were similar to observed concentration profiles, though peak dicamba concentrations at the 10-cm depth tended to be higher than predicted in May and November. Differences between predictions and observations are probably a result of minor inaccuracies in the water-flow simulation and the model's inability to modify degradation rates with changing climatic conditions.     

ECOLOGICAL CONSEQUENCE ASSESSMENT: EFFECTS OF BIOENGINEERED ORGANISMS1

ABSTRACT: The introduction of genetically altered microorganisms into natural ecosystems presents fundamentally new problems in risk assessment and ecological effect evaluation. Novel microorganisms, produced by any of several new methods, have the ability to survive and reproduce in the environment. Since most of these organisms are bacteria, they have the potential to interfere with natural processes, displace natural populations, infect new hosts, move between ecosystems, and cause far-reaching ecological disturbanes. This paper reviews currently available methods in ecological research that might be used in evaluating the ecological effects of releasing genetically altered microorganisms. Both structural and functional evaluations are critically reviewed. Microcosm, mesocosm, and field tests should provide valuable predictions concerning the potential ecological impact of genetically altered organisms. Ecosystem assessments will also be useful in post-release studies such as those currently used to evaluate toxic impacts. The present problem does not require the development of new testing methods but rather the creation of adequate predictive models (both conceptual and systems-based) to predict the potential for adverse effect of genetically altered organisms.     

Climate Change, Coral Reef Ecosystems, and Management Options for Marine Protected Areas

Marine protected areas (MPAs) provide place-based management of marine ecosystems through various degrees and types of protective actions. Habitats such as coral reefs are especially susceptible to degradation resulting from climate change, as evidenced by mass bleaching events over the past two decades. Marine ecosystems are being altered by direct effects of climate change including ocean warming, ocean acidification, rising sea level, changing circulation patterns, increasing severity of storms, and changing freshwater influxes. As impacts of climate change strengthen they may exacerbate effects of existing stressors and require new or modified management approaches; MPA networks are generally accepted as an improvement over individual MPAs to address multiple threats to the marine environment. While MPA networks are considered a potentially effective management approach for conserving marine biodiversity, they should be established in conjunction with other management strategies, such as fisheries regulations and reductions of nutrients and other forms of land-based pollution. Information about interactions between climate change and more “traditional” stressors is limited. MPA managers are faced with high levels of uncertainty about likely outcomes of management actions because climate change impacts have strong interactions with existing stressors, such as land-based sources of pollution, overfishing and destructive fishing practices, invasive species, and diseases. Management options include ameliorating existing stressors, protecting potentially resilient areas, developing networks of MPAs, and integrating climate change into MPA planning, management, and evaluation.     

The psychological benefits of indoor plants: A critical review of the experimental literature

People have been bringing plants into residential and other indoor settings for centuries, but little is known about their psychological effects. In the present article, we critically review the experimental literature on the psychological benefits of indoor plants. We focus on benefits gained through passive interactions with indoor plants rather than on the effects of guided interactions with plants in horticultural therapy or the indirect effect of indoor plants as air purifiers or humidifiers. The reviewed experiments addressed a variety of outcomes, including emotional states, pain perception, creativity, task-performance, and indices of autonomic arousal. Some findings recur, such as enhanced pain management with plants present, but in general the results appear to be quite mixed. Sources of this heterogeneity include diversity in experimental manipulations, settings, samples, exposure durations, and measures. After addressing some overarching theoretical issues, we close with recommendations for further research with regard to experimental design, measurement, analysis, and reporting.     

INTEGRATION OF FOREST ECOSYSTEM AND CLIMATIC MODELS WITH A HYDROLOGIC MODEL1

ABSTRACT: The effects of changes in the landscape and climate over geological time are plain to see in the present hydrological regime. More recent anthropogenic changes may also have effects on our way of life. A prerequisite to predicting such effects is that we understand the interactions between climate, landscape and the hydrological regime. A semi-distributed hydrological model (SLURP) has been developed which can be used to investigate, in a simple way, the links between landscape, climate and hydrology for watersheds of various sizes. As well as using data from the observed climate network, the model has been used with data from atmospheric models to investigate possible changes in hydrology. A critical input to such a model is knowledge of the links between landscape and climate. While direct anthropogenic effects such as changes in forested area may presently be included, the indirect effects of climate on landscape and vice versa are not yet modeled well enough to be explicitly included. The development of models describing climate-landscape relationships such as regeneration, development and breakup, water and carbon fluxes at species, ecosystem and biome level is a necessary step in understanding and predicting the effects of changes in climate on landscape and on water resources. Forest is the predominant land cover in Canada covering 453 Mha and productivity/succession models for major forest types should be included in an integrated climate-landscape-water simulation.     

Endosulfan transport: I. Integrative assessment of airborne and waterborne pathways

To reduce endosulfan (C9H6O3Cl6S; 6,7,8,9,10,10-hexachloro-1,5, 5a,6,9,9a-hexahydro-6,9-methano-2,4,3-benzodioxathiepin 3-oxide) contamination in rivers and waterways, it is important to know the relative significances of airborne transport pathways (including spray drift, vapor transport, and dust transport) and waterborne transport pathways (including overland and stream runoff). This work uses an integrated modeling approach to assess the absolute and relative contributions of these pathways to riverine endosulfan concentrations. The modeling framework involves two parts: a set of simple models for each transport pathway, and a model for the physical and chemical processes acting on endosulfan in river water. An averaging process is used to calculate the effects of transport pathways at the regional scale. The results show that spray drift, vapor transport, and runoff are all significant pathways. Dust transport is found to be insignificant. Spray drift and vapor transport both contribute low-level but nearly continuous inputs to the riverine endosulfan load during spraying season in a large cotton (Gossypium hirsutum L.)-growing area, whereas runoff provides occasional but higher inputs. These findings are supported by broad agreement between model predictions and observed typical riverine endosulfan concentrations in two rivers.     

Predicting preference for familiar,everyday objects: An experimental confrontation between two theories of aesthetic behaviour

The work reported compared the predictive ability of two theories of aesthetic behaviour, using normal, everyday objects. This involved utilizing simulated items of furniture in three experiments in which predictions deduced from the ‘collative-motivation’ model and what has been termed a ‘preference-for-prototypes’ model were made to compete empirically. For each of the five pairs of discrepant expectations formulated in the three experiments the outcomes were inconsistent with Berlyne's model, while supporting the prototypicality model. A number of theoretical implications are drawn from these findings.     

Simulating solute transport in a structured field soil: uncertainty in parameter identification and predictions

Dual-permeability models have been developed to account for the significant effects of macropore flow on contaminant transport, but their use is hampered by difficulties in estimating the additional parameters required. Therefore, our objective was to evaluate data requirements for parameter identification for predictive modeling with the dual-permeability model MACRO. Two different approaches were compared: sequential uncertainty fitting (SUFI) and generalized likelihood uncertainty estimation (GLUE). We investigated six parameters controlling macropore flow and pesticide sorption and degradation, applying MACRO to a comprehensive field data set of bromide andbentazone [3-isopropyl-1H-2,1,3-benzothiadiazin-4(3H)-one-2,2dioxide] transport in a structured soil. The GLUE analyses of parameter conditioning for different combinations of observations showed that both resident and flux concentrations were needed to obtain highly conditioned and unbiased parameters and that observations of tracer transport generally improved the conditioning of macropore flow parameters. The GLUE "behavioral" parameter sets covered wider parameter ranges than the SUFI posterior uncertainty domains. Nevertheless, estimation uncertainty ranges defined by the 5th and 95th percentiles were similar and many simulations randomly sampled from the SUFI posterior uncertainty domains had negative model efficiencies (minimum of -3.2). This is because parameter correlations are neglected in SUFI and the posterior uncertainty domains were not always determined correctly. For the same reasons, uncertainty ranges for predictions of bentazone losses through drainflow for good agricultural practice in southern Sweden were 27% larger for SUFI compared with GLUE. Although SUFI proved to be an efficient parameter estimation tool, GLUE seems better suited as a method of uncertainty estimation for predictions.