An exergy based engineering and economic analysis of sustainable building

To achieve the goal of sustainable development, the building energy system was evaluated from both the first and second law of thermodynamics point of view. The relationship between exergy destruction and sustainable development were discussed at first, followed by the description of the resource abundance model, the life cycle analysis model and the economic investment effectiveness model. By combining the forgoing models, a new sustainable index was proposed. Several green building case studies in U.S. and China were presented. The influences of building function, geographic location, climate pattern, the regional energy structure, and the technology improvement potential of renewable energy in the future were discussed. The building’s envelope, HVAC system, on-site renewable energy system life cycle analysis from energy, exergy, environmental and economic perspective were compared. It was found that climate pattern had a dramatic influence on the life cycle investment effectiveness of the building envelope. The building HVAC system energy performance was much better than its exergy performance. To further increase the exergy efficiency, renewable energy rather than fossil fuel should be used as the primary energy. A building life cycle cost and exergy consumption regression model was set up. The optimal building insulation level could be affected by either cost minimization or exergy consumption minimization approach. The exergy approach would cause better insulation than cost approach. The influence of energy price on the system selection strategy was discussed. Two photovoltaics (PV) systems—stand alone and grid tied system were compared by the life cycle assessment method. The superiority of the latter one was quite obvious. The analysis also showed that during its life span PV technology was less attractive economically because the electricity price in U.S. and China did not fully reflect the environmental burden associated with it. However if future energy price surges and PV system cost reductions were considered, the technology could be very promising for sustainable buildings in the future.

Development of risk based treatability and engineering measures for reducing exposure to lead contaminated media in the Miami inner city, Florida

A major consequence of contamination at the local level’s population as it relates to environmental health and environmental engineering is childhood lead poisoning. Environmental contamination is one of the pressing environmental concerns facing the world today. Current approaches often focus on large contaminated industrial size sites that are designated by regulatory agencies for site remediation. Prior to this study, there were no known published studies conducted at the local and smaller scale, such as neighborhoods, where often much of the contamination is present to remediate. An environmental health study of local lead-poisoning data in Liberty City, Little Haiti and eastern Little Havana in Miami-Dade County, Florida accounted for a disproportionately high number of the county’s reported childhood lead poisoning cases. An engineering system was developed and designed for a comprehensive risk management methodology that is distinctively applicable to the geographical and environmental conditions of Miami-Dade County, Florida. Furthermore, a scientific approach for interpreting environmental health concerns, while involving detailed environmental engineering control measures and methods for site remediation in contained media was developed for implementation. Test samples were obtained from residents and sites in those specific communities in Miami-Dade County, Florida (Gasana and Chamorro 2002). Currently lead does not have an Oral Assessment, Inhalation Assessment, and Oral Slope Factor; variables that are required to run a quantitative risk assessment. However, various institutional controls from federal agencies’ standards and regulation for contaminated lead in media yield adequate maximum concentration limits (MCLs). For this study an MCL of .0015 (mg/L) was used. A risk management approach concerning contaminated media involving lead demonstrates that the linkage of environmental health and environmental engineering can yield a feasible solution.

Optimization of pavement maintenance and rehabilitation programming using shuffled complex evolution algorithm

The major barrier to practical optimization of pavement preservation programming has always been that for formulations where the identity of individual projects is preserved, the solution space grows exponentially with the problem size to an extent where it can become unmanageable by the traditional analytical optimization techniques within reasonable limit. This has been attributed to the problem of combinatorial explosion that is, exponential growth of the number of combinations. The relatively large number of constraints often presents in a real-life pavement preservation programming problems and the trade-off considerations required between preventive maintenance, rehabilitation and reconstruction, present yet another factor that contributes to the solution complexity. In this research study, a new integrated multi-year optimization procedure was developed to solve network level pavement preservation programming problems, through cost-effectiveness based evolutionary programming analysis, using the Shuffled Complex Evolution (SCE) algorithm.^ A case study problem was analyzed to illustrate the robustness and consistency of the SCE technique in solving network level pavement preservation problems. The output from this program is a list of maintenance and rehabilitation treatment (M&R) strategies for each identified segment of the network in each programming year, and the impact on the overall performance of the network, in terms of the performance levels of the recommended optimal M&R strategy. ^ The results show that the SCE is very efficient and consistent in the simultaneous consideration of the trade-off between various pavement preservation strategies, while preserving the identity of the individual network segments. The flexibility of the technique is also demonstrated, in the sense that, by suitably coding the problem parameters, it can be used to solve several forms of pavement management programming problems. It is recommended that for large networks, some sort of decomposition technique should be applied to aggregate sections, which exhibit similar performance characteristics into links, such that whatever M&R alternative is recommended for a link can be applied to all the sections connected to it. In this way the problem size, and hence the solution time, can be greatly reduced to a more manageable solution space. ^ The study concludes that the robust search characteristics of SCE are well suited for solving the combinatorial problems in long-term network level pavement M&R programming and provides a rich area for future research. ^

Development of an odor management plan for industrial stationary sources in an urban environment: A case study for Broward County, Florida

Objectionable odors remain at the top of air pollution complaints in urban areas such as Broward County that is subject to increasing residential and industrial developments. The odor complaints in Broward County escalated by 150 percent for the 2001 to 2004 period although the population increased by only 6 percent. It is estimated that in 2010 the population will increase to 2.5 million. Relying solely on enforcing the local odor ordinance is evidently not sufficient to manage the escalating odor complaint trends. An alternate approach similar to odor management plans (OMPs) that are successful in managing major malodor sources such as animal farms is required. ^ This study aims to develop and determine the feasibility of implementing a comprehensive odor management plan (COMP) for the entire Broward County. Unlike existing OMPs for single sources where the receptors (i.e. the complainants) are located beyond the boundary of the source, the COMP addresses a complex model of multiple sources and receptors coexisting within the boundary of the entire county. Each receptor is potentially subjected to malodor emissions from multiple sources within the county. Also, the quantity and quality of the source/receptor variables are continuously changing. ^ The results of this study show that it is feasible to develop a COMP that adopts a systematic procedure to: (1) Generate maps of existing odor complaint areas and malodor sources, (2) Identify potential odor sources (target sources) responsible for existing odor complaints, (3) Identify possible odor control strategies for target sources, (4) Determine the criteria for implementing odor control strategies, (5) Develop an odor complaint response protocol, and (6) Conduct odor impact analyses for new sources to prevent future odor related issues. Geographic Information System (GIS) is used to identify existing complaint areas. A COMP software that incorporates existing United States Environmental Protection Agency (EPA) air dispersion software is developed to determine the target sources, predict the likelihood of new complaints, and conduct odor impact analysis. The odor response protocol requires pre-planning field investigations and conducting surveys to optimize the local agency available resources while protecting the citizen's welfare, as required by the Clean Air Act. ^

An investigation of location effect on trip production using artificial neural networks

As traffic congestion exuberates and new roadway construction is severely constrained because of limited availability of land, high cost of land acquisition, and communities' opposition to the building of major roads, new solutions have to be sought to either make roadway use more efficient or reduce travel demand. There is a general agreement that travel demand is affected by land use patterns. However, traditional aggregate four-step models, which are the prevailing modeling approach presently, assume that traffic condition will not affect people's decision on whether to make a trip or not when trip generation is estimated. Existing survey data indicate, however, that differences exist in trip rates for different geographic areas. The reasons for such differences have not been carefully studied, and the success of quantifying the influence of land use on travel demand beyond employment, households, and their characteristics has been limited to be useful to the traditional four-step models. There may be a number of reasons, such as that the representation of influence of land use on travel demand is aggregated and is not explicit and that land use variables such as density and mix and accessibility as measured by travel time and congestion have not been adequately considered. This research employs the artificial neural network technique to investigate the potential effects of land use and accessibility on trip productions. Sixty two variables that may potentially influence trip production are studied. These variables include demographic, socioeconomic, land use and accessibility variables. Different architectures of ANN models are tested. Sensitivity analysis of the models shows that land use does have an effect on trip production, so does traffic condition. The ANN models are compared with linear regression models and cross-classification models using the same data. The results show that ANN models are better than the linear regression models and cross-classification models in terms of RMSE. Future work may focus on finding a representation of traffic condition with existing network data and population data which might be available when the variables are needed to in prediction.

Experimental analysis of soil and dust stabilizers for controlling displacement of contaminated soils by wind tunnel experiments

During the remediation of burial grounds at the US Department of Energy's (DOE's) Hanford Site in Washington State, the dispersion of contaminated soil particles and dust is an issue that is faced by site workers on a daily basis. This contamination problem is even more of a concern when one takes into account the semi-arid characteristics of the region where the site is located. To mitigate this problem, workers at the site use a variety of engineered methods to minimize the dispersion of contaminated soil and dust (i.e. use of water and/or suppression agents that stabilizes the soil prior to soil excavation, segregation, and removal activities). A primary contributor to the dispersion of contaminated soil and dust is wind soil erosion. The erosion process occurs when the wind speed exceeds a certain threshold value which depends on a number of factors including wind force loading, particle size, surface soil moisture, and the geometry of the soil. Thus under these circumstances, the mobility of contaminated soil and generation and dispersion of particulate matter are significantly influenced by these parameters. This dependence of soil and dust movement on threshold shear velocity, fixative dilution and/or application rates, soil moisture content, and soil geometry were studied for Hanford's sandy soil through a series of wind tunnel experiments, laboratory experiments and theoretical analysis. In addition, the behavior of plutonium (Pu) powder contamination in the soil was studied by introducing a Pu simulant (cerium oxide). The results showed that soil dispersion and PM10 concentrations decreased with increasing soil moisture. Also, it was shown that the mobility of the soil was affected by increasing wind velocity. It was demonstrated that the use of fixative products greatly decreased the amount of soil and PM10 concentrations when exposed to varying wind conditions. In addition, it was shown that geometry of the soil sample affected the velocity profile and calculation of roughness surface coefficient when comparing round and flat soil samples. Finally, threshold shear velocities were calculated for soil with flat surface and their dependency on surface soil moisture was demonstrated. A theoretical framework was developed to explain these dependencies.

Development of safety performance functions for empirical Bayes estimation of crash reduction factors

Crash reduction factors (CRFs) are used to estimate the potential number of traffic crashes expected to be prevented from investment in safety improvement projects. The method used to develop CRFs in Florida has been based on the commonly used before-and-after approach. This approach suffers from a widely recognized problem known as regression-to-the-mean (RTM). The Empirical Bayes (EB) method has been introduced as a means to addressing the RTM problem. This method requires the information from both the treatment and reference sites in order to predict the expected number of crashes had the safety improvement projects at the treatment sites not been implemented. The information from the reference sites is estimated from a safety performance function (SPF), which is a mathematical relationship that links crashes to traffic exposure. The objective of this dissertation was to develop the SPFs for different functional classes of the Florida State Highway System. Crash data from years 2001 through 2003 along with traffic and geometric data were used in the SPF model development. SPFs for both rural and urban roadway categories were developed. The modeling data used were based on one-mile segments that contain homogeneous traffic and geometric conditions within each segment. Segments involving intersections were excluded. The scatter plots of data show that the relationships between crashes and traffic exposure are nonlinear, that crashes increase with traffic exposure in an increasing rate. Four regression models, namely, Poisson (PRM), Negative Binomial (NBRM), zero-inflated Poisson (ZIP), and zero-inflated Negative Binomial (ZINB), were fitted to the one-mile segment records for individual roadway categories. The best model was selected for each category based on a combination of the Likelihood Ratio test, the Vuong statistical test, and the Akaike's Information Criterion (AIC). The NBRM model was found to be appropriate for only one category and the ZINB model was found to be more appropriate for six other categories. The overall results show that the Negative Binomial distribution model generally provides a better fit for the data than the Poisson distribution model. In addition, the ZINB model was found to give the best fit when the count data exhibit excess zeros and over-dispersion for most of the roadway categories. While model validation shows that most data points fall within the 95% prediction intervals of the models developed, the Pearson goodness-of-fit measure does not show statistical significance. This is expected as traffic volume is only one of the many factors contributing to the overall crash experience, and that the SPFs are to be applied in conjunction with Accident Modification Factors (AMFs) to further account for the safety impacts of major geometric features before arriving at the final crash prediction. However, with improved traffic and crash data quality, the crash prediction power of SPF models may be further improved.