Physical Modelling of Urban Roughness using Arrays of Regular Roughness Elements

The aerodynamic behaviour of large urbanagglomerations must be represented in increasingly greaterdetail, as large-scale numerical weather prediction and airpollution dispersion models are refined. The present studyprovides detailed measurements of the flow field in regulararrays of obstacles to obtain representative data on meanflow and turbulence statistics in urban-type areas. Obstacle arrays consisting of simple cubes and flat plateroughness commonly used in boundary layer simulations wereplaced in a simulated atmospheric boundary layer flow in ahydraulic flume. The scale factor was about 1:200 based onthe obstacle height (50 mm). The results show noappreciable `constant stress' region in the internalboundary layer above the buildings, since in any finite-length test array the boundary layer is always developing.However, if the RMS turbulence components are scaled by thelocal values of the shear stress, then there seems to be auniversal scaling, with _u/u_* = 2.1,_v/u_* = 1.65 and _w/u_* = 1.2. This greatly simplifies the parameterization of the first orderturbulence statistics in obstacle arrays. It was alsoobserved during the experiments that, compared to results inthe cube arrays, the turbulence kinetic energy and theReynolds stresses were almost doubled in the flat plateroughness arrays.     

Optimization of the Resources Management in Fighting Wildfires

Wildfires lead to important economic, social, and environmental losses, especially in areas of Mediterranean climate where they are of a high intensity and frequency. Over the past 30 years there has been a dramatic surge in the development and use of fire spread models. However, given the chaotic nature of environmental systems, it is very difficult to develop real-time fire-extinguishing models. This article proposes a method of optimizing the performance of wildfire fighting resources such that losses are kept to a minimum. The optimization procedure includes discrete simulation algorithms and Bayesian optimization methods for discrete and continuous problems (simulated annealing and Bayesian global optimization). Fast calculus algorithms are applied to provide optimization outcomes in short periods of time such that the predictions of the model and the real behavior of the fire, combat resources, and meteorological conditions are similar. In addition, adaptive algorithms take into account the chaotic behavior of wildfire so that the system can be updated with data corresponding to the real situation to obtain a new optimum solution. The application of this method to the Northwest Forest of Madrid (Spain) is also described. This application allowed us to check that it is a helpful tool in the decision-making process.     

Removal of NAPLs from the unsaturated zone using steam: prevention of downward migration by injecting mixtures of steam and air

Steam injection for remediation of porous media contaminated by nonaqueous phase liquids has been shown to be a potentially efficient technology. There is, however, concern that the technique may lead to downward migration of separate phase contaminant. In this work, a modification of the steam injection technology is presented, where a mixture of steam and air was injected. In two-dimensional experiments with unsaturated porous medium contaminated with nonaqueous phase liquids, it was demonstrated how injection of pure steam lead to severe downward migration. Similar experiments, where steam and air were injected simultaneously, resulted in practically no downward migration and still rapid cleanup was achieved. The processes responsible for the prevention of downward migration when injecting steam-air mixtures were analyzed using a nonisothermal multiphase flow and transport model. Hereby, three mechanisms were identified and it was demonstrated how the effectiveness of these mechanisms depended on the air-to-steam mixing ratio.     

Urban waste recycling behavior: antecedents of participation in a selective collection program

ABSTRACT/ The aim of this study is to analyze the antecedents of urban waste recycling behavior. To achieve this goal, a concrete urban waste management program was chosen. The study focuses on the Selective Collection Program (SCP) in Zaragoza, a medium-sized city in northeastern Spain. The research starts with a conceptual model in which the variables that potentially affect recycling behavior can be classified into two groups: incentives and barriers. Moreover, the sociodemographic characteristics of the individuals are included in our study. Given that the proposed model requires specification of latent variables or constructs, the analysis is based on the Structural Equation Models (SEM) methodology. The results revealed that environmental awareness, knowledge of the environmental impact of urban waste, and the positive perception of management by local government exercise a positive effect on individual recycling behavior, while perceived personal difficulties (space and time availability) and distance to and from the container have a negative effect. As regards sociodemographic variables, this study found that annual family income sustains a negative relationship with recycling behavior, while age maintains a positive one. The results obtained clearly show the important role that the public authorities play, especially municipal governments, in achieving the waste recycling objectives established in accordance with international legislation.     

An assessment methodology for successional systems. II. Statistical tests and specific examples

This paper presents a dynamic framework for environmental assessment when the system under study is undergoing successional change. Successional differences between sites for which one wishes to detect a difference because of a treatment are essentially confounding factors. We show how successional changes over the study period or resulting from differences in study site plot ages can be factored out by developing a null model of expected behavior over time. The null model for change in state with time is characterized in terms of a stochastic envelope around a nominal trajectory. Specific tests for the detection of trends associated with succession are described and illustrated on example data. It is concluded that the methods developed work particularly well for laboratory microcosm data.     

THRESHOLD TIME SERIES MODELING OF TWO ICELANDIC RIVERFLOW SYSTEMS1

ABSTRACT: This paper reports our experience in building time series models which connect the flows in two Icelandic rivers with the meteorological variables of precipitation and temperature. Two rivers with different hydrological characteristics were studied. In areas where precipitation may be either in the form of rain or snow linear models are inadequate to describe the relationship between the river and the meteorological variables. The methodology of threshold models recently developed seems to be well suited for taking into account the sharp difference in the relationship according to whether it is freezing or not. The possibility of identifying an alternative threshold variable is also explored.     

A PHOSPHORUS MANAGEMENT LP MODEL CASE STUDY1

ABSTRACT A linear programming model to assess the cost-effectiveness of appropriate point and nonpoint phosphorus control measures was constructed for Carnegie Lake, an eutrophic lake located in Mercer County, New Jersey. The resultant model was tested for present and future conditions. Feasible solutions were obtained only after significant relaxation of receiving water quality standards. The high levels of point source treatment required to meet the in-stream water quality standards and the mesotrophic loading conditions suggest that a source control solution may not be feasible.     

Ozone Episode in the Milan Metropolitan Area A Comparative Evaluation of UAM-V and CALGRID Models

Data from an ozone episode (2–5 June, 1998) in the Milan metropolitan area were used for an application of two photochemical grid models: UAM-V and CALGRID. To assure a fair comparison, the models were run on the same domain and grid size, with same source emission inputs, CALMET diagnostic meteorology, and initial and boundary conditions taken from air quality data and literature values. Hourly emissions were derived from the AutoOil-II programme inventory except for on-road mobile source emissions; a new traffic emission inventory, based on both COPERT II methodology and road classification has been developed. NO_x and O₃ concentration results were compared to local network monitoring data. Results indicate that both models predict the highest ozone values along the north-east direction and are able to reproduce the ozone daytime trend though differences can be found between the two models on ozone spatial distribution. Average normalised bias for both models is about 50%, peak daily ozone concentrations are underestimated, with simulated peak shapes broader than the observed ones and a temporal shift between the two models. Night-time concentration levels of pollutants were not successfully reproduced due to an incorrect parameterisation of vertical turbulence calling for further work.     

First application of automated land evaluation in iran

A computerized land evaluation was employed for a speedy land evaluation to locate a new industrial-urban development site in Iran under a multiple-land-use setting. A special-purpose programming language of modeling was generated to facilitate computer application. In doing so, some types of models were initially tested. Finally, an optimization model was decided on for this purpose, because it provided the best result. Four optimization models using linear programming were developed for evaluating the study area (south of Isfahan) involving agriculture, soil conservation, outdoor recreation, and industrial-urban development. Then, the models were combined to produce a multiple-use optimization model (MOM) for land-use classification and planning of the area. In applying the MOM model, a computerized map was produced to indicate the optimum capability of the area for industrial-urban development, agriculture, soil conservation, and outdoor recreation. The employed methodology provided a quick and meaningful result for the study area, which has a low biological productivity.     

Modeling Abundance Index Data from Anuran Calling Surveys

Abstract:  Evaluation of anuran populations is commonly based on calling surveys that report categorical abundance index data. I present a statistical model for abundance index data that are observations representing ordered abundance classes (e.g., none, some, many). The proposed model provides a formal treatment of detection probability, factors that affect detection, and variation in abundance. The model can be viewed as a generalization of that proposed by MacKenzie et al. (2002) for estimating site-occupancy rates in that it allows for more than two abundance classes. Because the abundance distribution is characterized by multiple abundance classes, it may be more sensitive to subtle changes in the underlying abundance that may go undetected with simple occupancy estimates under which sites are characterized merely as occupied or not. The method is most immediately applicable to surveys of anurans in which index data related to the intensity of calling activity are collected. I applied the proposed method to calling index data from the green frog (  Rana clamitans ) collected as part of the North American Amphibian Monitoring Program. The best model indicated considerable variation in detectability over time and in response to temperature. The resulting adjusted (for detectability) abundance-state distribution demonstrates the negative bias in abundance state obtained from simplistic summaries of calling index data that disregard these sources of variation in detectability.