This study systematically compares the environmental and economic performance of three wastewater treatment systems: constructed wetlands (CWs), microbial fuel cells (MFCs), and their integration (CW–MFC). Lab-scale units of each system were constructed using a multi-media matrix (gravel, zeolite, and granular activated carbon), composite native wetland species (Juncus effusus, Iris sp., and Typha angustifolia), carbon-based electrodes (graphite), and standard inoculum for CW and CW–MFC. The MFC system employed carbon-based electrodes and proton-exchange membrane. The experimental design included a parallel operation of all systems treating domestic wastewater under identical hydraulic and organic loading rates. Environmental impacts were quantified across construction and operational phases using life cycle assessment (LCA) with GaBi software 9.2, employing TRACI 2021 and ReCiPe 2016 methods, while techno-economic analysis (TEA) evaluated capital and operational costs. The key results indicate that CW demonstrates the lowest global warming potential (142.26 kg CO₂-eq) due to its reliance on natural biological processes. The integrated CW–MFC system achieved enhanced pollutant removal (82.8%, 87.13%, 78.13%, and 90.3% for COD, NO₃, TN, and TP) and bioenergy generation of 2.68 kWh, balancing environmental benefits with superior treatment efficiency. In contrast, the stand-alone MFC shows higher environmental burdens, primarily due to energy-intensive material requirements and fabrication processes. TEA results highlight CW as the most cost-effective solution (USD 627/m³), with CW–MFC emerging as a competitive alternative when considering environmental benefits and operational efficiencies (USD 718/m³). This study highlights the potential of hybrid systems, such as CW–MFC, to advance sustainable wastewater treatment technologies by minimizing environmental impacts and enhancing resource recovery, supporting their broader adoption in future water management strategies. Future research should focus on optimizing materials and energy use to improve scalability and feasibility.

Learning critical concepts that are centered on the analysis, design, and maintenance of transportation infrastructure systems poses a measure of difficulty for undergraduates in engineering. Therefore, hands-on learning pedagogy should be an excellent precursor to increase understanding of these concepts, since the pedagogy incorporates real-life experience in the delivery. This paper describes how a hands-on learning pedagogy called experiment-centric pedagogy (ECP) has been used to teach these concepts to undergraduate students at a historically Black university. The research questions are as follows: (1) How well can ECP improve students’ understanding of concepts essential to the analysis and design of transportation infrastructure systems ? (2) How has the ECP facilitated the achievement of the learning objectives of these concepts? and (3) Does an ECP increase the engagement of undergraduate students in their transportation infrastructure engineering learning and lead to measurable lasting gains ? To answer these research questions, ECP was implemented and assessed when used to teach the concepts of stress and strain utilized in the analysis of bridges and other transportation infrastructure, sound used in the development and design of noise barriers, moisture content in controlling compaction of highway infrastructure systems, and degradation of infrastructure systems exposed to various environmental settings. Assessment results from 92 undergraduates reveal an increase in students’ motivation and cognitive understanding of the relevant concepts, as well as learning gains and an improved success rate compared to the traditional method of teaching.

Low Impact Development (LID) practices reduce pollution and hydrologic instability from stormwater arising from increases in impervious surfaces and land development practices. In response to increasing demands for information and tools to quickly and easily assess LID practices in comparison to conventional development practices, an easy to use web-based LID decision support and screening tool, L-THIA/LID, has been developed to evaluate the benefits of LID practices. The Long-Term Hydrologic Impact Assessment (L-THIA) web application is a decision support system based on the integration of web-based programs, geographic information system (GIS) capabilities, and databases. It provides information for decision makers on the impacts of water quantity and quality resulting from land use change. The web-based L-THIA/LID tool, which builds on L-THIA capabilities, will enable various stakeholders to quickly evaluate: (1) the impact of urban development on average annual runoff volume; and (2) the potential stormwater and pollutant reduction of proposed LID practices. Runoff quantity and water quality impacts of proposed land use change are displayed in tables, bar charts, and pie charts. The tool's aim is to enable decision makers the ability to evaluate and understand the effects of LID practices and thus support effective watershed planning to achieve desired stormwater management goals.

Over the past few decades, there has been a notable surge in interest in green stormwater infrastructure (GSI). This trend is a result of the need to effectively address issues related to runoff, pollution, and the adverse effects of urbanization and impervious surfaces on waterways. Concurrently, umanned aerial vehicles (UAVs) have gained prominence across applications, including photogrammetry, military applications, precision farming, agricultural land, forestry, environmental surveillance, remote-sensing, and infrastructure maintenance. Despite the widespread use of GSI and UAV technologies, there remains a glaring gap in research focused on the evaluation and maintenance of the GSIs using UAV-based imagery. This study aimed to develop an integrated framework to evaluate plant density and health within GSIs using UAV-based imagery. This integrated framework incorporated the UAV (commonly known as a drone), WebOpenDroneMap (WebDOM), ArcMap, PyCharm, and the Canopeo application. The UAV-based images of GSI components, encompassing trees, grass, soil, and unhealthy trees, as well as entire GSIs (e.g., bioretention and green roofs) within the Morgan State University (MSU) campus were collected, processed, and analyzed using this integrated framework. Results indicated that the framework yielded highly accurate predictions of plant density with a high R² value of 95.8% and lower estimation errors of between 3.9% and 9.7%. Plant density was observed to vary between 63.63% and 75.30% in the GSIs at the MSU campus, potentially attributable to the different types of GSI, varying facility ages, and inadequate maintenance. Normalized difference vegetation index (NDVI) maps and scales of two GSIs were also generated to evaluate plant health. The NDVI and plant density results can be used to suggest where new plants can be added and to provide proper maintenance to achieve proper functions within the GSIs. This study provides a framework for evaluating plant performance within the GSIs using the collected UAV-based imagery.