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Abstract The rapid advancement of drone technology and digital twin systems has significantly transformed environmental monitoring, particularly in the field of water quality assessment. This paper systematically reviews the current state of research on the application of drones, digital twins, and their integration for water quality monitoring and management. It highlights key themes, insights, research trends, commonly used methodologies, and future directions from existing studies, aiming to provide a foundational reference for further research to harness the promising potential of these technologies for effective, scalable solutions in water resource management, addressing both immediate and long‐term environmental challenges. The systematic review followed PRISMA guidelines, rigorously analysing hundreds of relevant papers. Key findings emphasise the effectiveness of drones in capturing real‐time, high‐resolution spatial and temporal data, as well as the value of digital twins for predictive and simulation‐based analysis. Most importantly, the review demonstrates the potential of integrating these technologies to enhance sustainable water management practices. However, it also identifies a significant research gap in fully integrating drones with digital twins for comprehensive water quality management. In response, the review outlines future research directions, including improvements in data integration techniques, predictive models, and interdisciplinary collaboration. 

Beargrass Creek is one of the largest watersheds in Louisville, Kentucky, draining over 60 square miles within Louisville and outpouring into the Ohio River. The creek consists of three branches – the South, Middle, and Muddy forks – each flowing through a variety of residential and commercial areas as well as a multitude of parklands. Despite this creek’s prominence in the city, little research has been done documenting the concentration of anthropogenic debris within its watershed. This study documents man-made debris accumulation at 15 sites located within the Louisville metropolitan area as a continuation of the work conducted by the University of Louisville Aqlan Lab in summer of 2022. The study assesses the debris build-up within the creek and establishes debris patterns in hopes of diminishing future buildups and improving conditions within this Louisville watershed. The amount of litter increased from June 2022 to July 2023, and litter amounts also increased during the summer of 2023. Middle Fork contained the highest total amount of debris, closely followed by South Fork. Plastic bottles, bags, and fragments made up over 70% of total litter, with glass (11%) and lumber (4%) being the second and third highest categories. The most common litter items were plastic bags, foam fragments, and food wrappers. Further analysis is still needed to investigate the magnitude, character, and sources of debris in Beargrass Creek across different seasons. 

In the second year of a replication in two new cities, this paper examines two years of data on the Community-Engaged Educational Ecosystem model (C-EEEM) in three regions in different Midwestern states. Cities in the deindustrialized Midwest often have higher percentages of those underrepresented in STEM, including low socio-economic status (LSES) and underrepresented minorities (URM); this makes it difficult for them to develop and retain STEM skills in the workforce, critical to rebuilding their communities in the Digital Age. 

Participation in community-based projects provides students with invaluable benefits, including gaining practical experience and developing a sense of connection and belonging within the community. Nevertheless, the projects to which students are assigned can significantly influence their overall experience in this form of learning. Rather than relying on an approach that randomly assigns students to projects and often results in a mismatch between student preference and assigned project, we propose an optimization model to allocate community-based projects to students. The students provided a ranking of their project preference and 89 percent of all students received either their first or second choices. The optimization modeling approach not only streamlines the student-to-project allocation process for project coordinators but also ensures a consistent consideration of all relevant variables. 

Researchers at UNIVERSITY developed, piloted, and examined a community-engaged STEM learning environment at a university in Indiana. This summer, the MODEL developed from this pilot was adapted and replicated at two other universities. Over 50 students (high school and college) participated in the three regions in the Midwest in a community-engaged internship experience during the summer of 2022. Students worked on project teams of 4-6 students on a community-identified project for 8 weeks. Local high school teachers managed projects and community partners served as technical mentors as students completed their paid internship, which culminated with a formal presentation and product to their community partner. The larger research effort uses mixed-methods data collection, including surveys and interviews, to examine a variety of outcomes, including dispositional changes in STEM self-efficacy and identity. Students completed surveys and reflections at multiple points throughout their internship, including a retrospective pre/post survey capturing dispositional shifts during the experience The results of the internship experience on student intern participants' educational and professional plans at the 3 sites are evaluated in this paper. Results show significant gains on items related to professional discernment (desire to work in a STEM field, use technical skills, on open-ended problems for the betterment of society) for participants at all sites. 

In our highly mobile and global economy, STEM-related employment is key to stabilizing and rebuilding our middle class. Attrition in STEM fields, however, is disproportionately high at all educational levels for women, African Americans, Latinos, and people from low-income families. Compounding these challenges, many places in the United States struggle more than others to attract, develop, and retain STEM skills in their workforce. These cities often have poverty rates double the national average, lower educational attainment, and larger percentages of those underrepresented in STEM. So, while attraction, motivation, and retention in STEM disciplines are a national imperative, their importance within these regions is particularly acute. This poster and paper present the findings from the replication of a community-engaged educational ecosystem – as a STEM learning ‘commons’ – for delivering integrated high-impact pedagogical practices as a paid internship. The initial pilot that informs this replication effort targeted challenges with which many deindustrialized cities struggle – STEM knowledge and skills, talent retention, workforce readiness, and community engagement and vitality. The multi-year study uses a convergent mixed-methods design and collects qualitative and quantitative data throughout the summer immersion. Using data from the pilot site and the first year of the two replication sites, this poster and paper will focus on quantitative findings on a few key areas – including self-efficacy in STEM and place attachment as intermediate metrics toward the goal of rebuilding Midwestern cities. 

Community-based research (CBR) is a practice that engages researchers in collaborative, change-oriented, and inclusive projects in the community. One common example of CBR is university-community collaboration in which students and researchers come up with ideas, perspectives, and knowledge at each stage of the project with the goal to address community needs. The community is mainly involved in identifying the research questions for the projects and making decisions about how the results of the research-focused projects will be implemented. This paper presents a replication of a model focused on university-community collaboration, student engagement and Science, Technology, Engineering, and Math (STEM) attraction and retention using three research-focused projects addressing community needs. The three projects are (1) empathic design project aimed at improving quality greenspaces and pedestrian streetscape experience, (2) food justice project to study the disparities in food access between local regions, and (3) analyzing water quality in a local creek. The projects provided a unique opportunity for students to directly experience and contribute to the research process. In addition, students worked closely with their academic peers and community partners who served as collaborators and mentors. The study reports on the impact of the program on student learning and tendency to stay back in the community. The program's collaborative nature and its effect on students' satisfaction while working on specific projects are also examined. Furthermore, the program helped develop and sustain university-community partnerships. The community stakeholders participating in focus groups were satisfied with the process of identifying community projects and also expressed their satisfaction with the students’ work. 

Community-based research (CBR) is a practice that engages researchers in collaborative, change-oriented, and inclusive projects in the community. One common example of CBR is university-community collaboration in which students and researchers come up with ideas, perspectives, and knowledge at each stage of the project with the goal to address community needs. The community is mainly involved in identifying the research questions for the projects and making decisions about how the results of the research-focused projects will be implemented. This paper presents a replication of a model focused on university-community collaboration, student engagement and Science, Technology, Engineering, and Math (STEM) attraction and retention using three research-focused projects addressing community needs. The three projects are (1) empathic design project aimed at improving quality greenspaces and pedestrian streetscape experience, (2) food justice project to study the disparities in food access between local regions, and (3) analyzing water quality in a local creek. The projects provided a unique opportunity for students to directly experience and contribute to the research process. In addition, students worked closely with their academic peers and community partners who served as collaborators and mentors. The study reports on the impact of the program on student learning and tendency to stay back in the community. The program's collaborative nature and its effect on students' satisfaction while working on specific projects are also examined. Furthermore, the program helped develop and sustain university-community partnerships. The community stakeholders participating in focus groups were satisfied with the process of identifying community projects and also expressed their satisfaction with the students’ work.