Department of Civil, Environmental and Geomatics Engineering

In this research, a multistage (i.e., three stages) planar, and a tubular passive permeateside-heated interfacial solar membrane distillation (ISMD) has been developed. The three-stage system had an system energy efficiency of 62% in producing distilled water at an average daytime irradiance of 422 W/m2 with average distillate flux of 5 kg/(m2·day), which is higher than that of the single-stage planar systems. Production rate of distilled water in each stage of the three-stage planar system per unit area of footprint was 3.3 kg/(m2·day), while the production rate per unit area of footprint of single-stage system was 1.6 kg/(m2·day). Also, a hydrophilic nanoporous (PES NF) membrane was used in our study, which has not been found in the research of conventional MD systems. No penetration of hydrophilic nanoporous membrane was found during the operation of single-stage planar systems under simulated sunlight. The membrane was able to produce distilled water for 114 days under simulated sunlight using municipal wastewater as feed water. On the other hand, hydrophobic (0.20 and 0.45 μm) PVDF membranes were penetrated by feed water (i.e., wastewater) after approximately 50 days.

Exposure to high CO2 levels in enclosed environments may result in adverse health impacts. To provide a safe breathing environment, the exhaled gases must be removed. Currently, NASA uses a multi-bed system known as the Carbon Dioxide Removal Assembly (CDRA) for CO2 removal. The process involves cyclic adsorption-desorption using zeolite-5A molecular sieves. Owing to the presence of a wet gaseous mixture and the hydrophilic nature of zeolite-5A, the removal of CO2 and water vapor must be conducted in two separate vessels, resulting in additional costs. Therefore, the objective of this study was to integrate and intensify the process utilizing amine-grafted silica. Adsorbent performance was gauged on equilibrium CO2 uptake and kinetics, activation temperature, CO2 desorption temperature, and consecutive cycling in the presence of 1 vol.% CO2 in N2 at 25 °C. Aminosilica outperformed 5A and achieved similar equilibrium CO2 uptake while exhibiting faster kinetics, and lower desorption and regeneration temperature requirements.

The potential of amine-grafted silica materials (i.e., aminosilicas) was investigated for single-stage biogas and landfill gas purification via simultaneous removal of CO2, H2S, and water vapor. Custom aminosilicas were synthesized by covalent tethering of primary amines onto commercially available mesoporous silica. Screening adsorption experiments were completed at 40°C in the presence of dry 30 vol.% CO2 in N2, and performance was measured using thermogravimetric analysis. Selected materials with equilibrium CO2 uptakes greater than 6 wt.% were chosen for additional assessments in terms of CO2 adsorption kinetics. The highest-performing aminosilica achieved fast CO2 uptake by reaching 82% of its equilibrium CO2 uptake in one minute. This material was subjected to rigorous 100-cycle testing and retained stable performance as evidenced by maintaining 99% of its initial CO2 uptake throughout cycling. The final candidate also underwent multicomponent column-breakthrough tests and achieved complete (100%) removal of all target impurities. The results suggest promising potential of aminosilicas as a viable method of biogas and landfill gas purification.

Deep injection wells are considered among the most efficient, environmentally-friendly and cost-effective techniques to dispose of wastewater. However, formation of biofilms in the casing pipe can reduce the effective diameter, which in turn, can lower the injectivity of wastewater and ultimately results in injection failure. A class 1 deep injection well located at the Solid Waste Authority of Palm Beach County was revealed to be getting clogged due to the development of a microbial community where Entamoeba dispar, a protozoan species was found to be the abundant microorganism in the biofilm. The injection well is used to discharge industrial wastewater coming from several sources at the facility which are discharged to a collection chamber, known as the wet well, before being disposed down the deep injection well pipe. Prior to design and implementation of a suitable treatment technique to inactivate the protozoan species, it is imperative to reveal the origins of the microorganism coming to the deep injection well. Therefore, the objective of the current research was to develop a technique to identify potential sources of Entamoeba dispar. In this study, samples were collected from the seven sources as well as from the wet well. Initially, a number of onsite and laboratory experiments were conducted to monitor the water quality parameters of the collected samples. In case of microbiological investigations, microscopic analysis was carried out to detect the microorganism in the wastewater specimens followed by polymerase chain reaction (PCR) and gel-electrophoresis assays. In addition, the number of DNA copies in each of the tested samples was determined using the ImageJ app. From the microscopic analysis, no samples were found to be Entamoeba dispar positive. However, PCR and gel electrophoresis tests results indicated that wet well, NEFCO effluent, class 1, REF 1 and groundwater dilution samples were positive and the calculated number of DNA copies were 6545, 6849, 16763, 6351 and 5635 in 100 mL of the wastewater specimens respectively. The PCR technique used in this study is sensitive enough to detect even 4 DNA copies of the target microorganism. All the positive samples have one thing in common, which is they all contain local groundwater from site, indicating a potential source for further investigation.

Inductive and model-tree (MT) approach-based models are developed and evaluated for forecasting mean, minimum and maximum monthly temperature in this study. The models are developed and tested using long-term historical temperature time series data derived from U.S. Historical Climatology Network at 22 sites located in the state of Florida. Inductive models developed include conceptually simple naïve models to multiple regression models utilizing lagged temperature values, sea surface temperatures (SSTs), correction factors derived using historical data. A global model using data from all the sites is also developed. The performances of the models were evaluated using observed temperature records and several error and performance measures. A composite measure combining multiple error and performance measures is developed to select the best model. MT approach-based and regression models with SSTs and correction factors along with lagged temperature values are found to be best models for forecasting temperature based on assessments of composite measures and error diagnostics.

Efficient freight mobility plays a major role in the economy, and its performance is closely related to the quality of the transportation system. Requirements for funding transportation infrastructure projects often do not specify the analytical tools planners should use to request funding. Critical Urban and Rural Freight Corridors are sections of the National Highway Freight Network providing critical connectivity of goods and must have improved system performance. This research study offers a method for identifying these corridors considering temporal and spatial inputs. For this end, a multi-criteria spatial decision support system (MC-SDSS) was developed. This framework attributes a score to highway corridors (links) based on policy eligibility and prioritization. We apply the Analytic Hierarchy Process (AHP) to structure the problem and consider different stakeholder preferences and available data. The product of this study is a tool for decisionmakers to optimize the selection of critical freight corridors and analyze alternatives. It also offers flexibility to manipulate the framework to meet various agency goals, using the State of Florida as a case study.

Automated vehicles (AVs) are becoming more common each day as car manufacturers have started to include advanced driving assistant systems (ADAS) in trendline models. The most basic level of vehicle automation includes Adaptive Cruise Control (ACC) can disrupt and change traffic flow. The current study proposes the development of controlled experiments to obtain traffic flow properties for vehicles equipped with ACC in different scenarios. As part of this dissertation, the effects of ACC on capacity are quantified at steady state conditions, meaning cruising speeds or free flow, and at bottlenecks, where speed fluctuations occur. The effects of ACC on traffic flow properties are also assessed by the construction and study of the Fundamental Diagram. Lastly, the vehicles are submitted to less predictable deceleration scenarios that involve a leading vehicle driven in ACC mode and a leading vehicle driven manually. The reaction of ACC for these cases is documented.

This research aims to develop a large-scale locally relevant flood risk screening tool, that is, one capable of generating accurate probabilistic inundation maps quickly while still detecting localized nuisance-destructive flood potential. The CASCADE 2001 routing model is integrated with GIS to compare the predicted flood response to heavy rains at the watershed, subwatershed, and municipal levels. Therefore, the objective is to evaluate the impact of scale for determining flood risk in a community. The findings indicate that a watershed-level analysis captures most flooding. However, the flood prediction improves to match existing FEMA flood maps as drill-down occurs at the subwatershed and municipal scales. The drill-down modeling solution presented in this study provides the necessary degree of local relevance for excellent detection in developed areas because of the downscaling techniques and local infrastructure. This validated model framework supports the development and prioritization of protection plans that address flood resilience in the context of watershed master planning and the Community Rating System.

Vegetation monitoring plays a significant role in improving the quality of life above the earth's surface. However, vegetation resources management is challenging due to climate change, global warming, and urban development. The research aims to identify and extract vegetation communities for Jupiter Inlet Lighthouse Outstanding Natural Area (JILONA) using developed Unmanned Aerial Systems (UAS) deployed with five bands of RedEdge Micasence Multispectral Sensor. UAS has a lot of potential for various applications as it provides high-resolution imagery at lower altitudes. In this study, spectral reflectance values for each vegetation species were collected using a spectroradiometer instrument. Those values were correlated with five band UAS Image values to understand the sensor's performance, also added with reflectance’s similarities and divergence for vegetation species. Pixel and Object-based classification methods were performed using 0.15 ft Multispectral Imagery to identify the vegetation classes.
Supervised Machine Learning Support Vector Machine (SVM) and Random Forest (RF) algorithms with topographical information were used to produce thematic vegetation maps. The Pixel-based procedure using the SVM algorithm generated an overall accuracy and kappa coefficient of above 90 percent. Both classification approaches have provided aesthetic vegetation thematic maps. According to statistical cross-validation findings and visual interpretation of vegetation communities, the pixel classification method outperformed object-based classification.

In this thesis, a methodology and framework were created to detect the seawalls accurately and efficiently in low coastal areas and was evaluated in the study area of Hallandale Beach City, Broward County, Florida. Aerial images collected from the Florida Department of Transportation (FDOT) were processed using eCognition Developer software for Multi-Resolution Segmentation and Classification of objects. Two classification approaches, pixel-based image analysis, and the object-based image analysis (OBIA) method were applied for image classification. However, Pixel based classification was discarded for having less accuracy in output. Three techniques within object-based classification-machine learning technique, knowledge-based technique and machine learning followed by knowledge-based technique were used to compare the most efficient method of classification. While performing the machine learning technique, three algorithms: Random Forest, support vector machine and decision tree were applied to test the best algorithm. Of all the approaches used, the combination of machine learning and a knowledge-based method was able to map the sea wall effectively.