Students investigate the question, How can we use evidence from field observations and testing to explain why two beaches do not experience the same environmental conditions? Through mystery beach data dives, field-based observations, water and air quality comparisons, and claim-evidence-reasoning analysis, they study how geoscience processes, ecosystem conditions, and access to clean resources connect to environmental justice in San Diego. Over four weeks, teams work with partner context from the South Bay International Wastewater Treatment Plant and local treatment efforts to create evidence boards, interactive comparison stations, and town-hall recommendations for the Clean Water, Clear Voice Expo and Beach Evidence Briefing. The experience builds critical thinking, collaboration, communication, content knowledge, and reflection as students analyze data, revise claims in a mid-project data clinic, defend findings orally, and connect science learning to community impact.

Students will analyze and compare quantitative and qualitative data from two beach sites, including field observations, maps, graphs, and water or air quality results, to explain patterns in environmental conditions. They will construct and defend claim-evidence-reasoning explanations that connect geoscience processes, ecosystem impacts, and environmental justice to the differences between the two locations. They will collaborate to revise investigations and evidence boards, communicate findings clearly in an expo and mock town hall, and reflect on how scientific evidence can inform community decisions.

Students will create mystery-beach evidence boards with annotated photos, field notes, data tables, maps, and a clear claim-evidence-reasoning explanation comparing the two sites. Throughout the project, teams will also build student-made graph displays, testing logs, question cards, and draft recommendation statements for local leaders, using feedback from a mid-project data clinic to revise their work. By the end, each group will present an interactive comparison station at the Clean Water, Clear Voice Expo and participate in a Beach Evidence Briefing mock town hall with a rubric-scored presentation and oral defense. The final products will show scientific analysis, environmental justice findings, and realistic recommendations connected to partner agencies such as the South Bay International Wastewater Treatment Plant and regional water treatment efforts.

Kick off with the Clear Water, Dirty Water Challenge: students analyze two anonymous beach photo sets, short air and water data samples, and a map-based scavenger hunt to generate initial claims about why conditions differ. Then launch the mystery beach data dive with a shared field experience at La Jolla Shores, where teams collect observations, practice simple testing routines, and use interview-style clue sheets from partners such as the South Bay International Wastewater Treatment Plant to build questions. End the launch with a Coastline Detectives Reveal protocol that introduces the essential question, connects the two mystery sites to environmental justice, and prepares teams to create evidence boards and town-hall recommendations.

Students will share their work at a Clean Water, Clear Voice Expo where each team leads an interactive comparison station featuring its mystery-beach evidence board, annotated photos, maps, graphs, and water and air quality findings. Visitors, including families, district leaders, and community partners such as representatives connected to the South Bay International Wastewater Treatment Plant, rotate through stations and use question cards to press students on their CER claims and recommendations. The project culminates in a Beach Evidence Briefing mock town hall, where student panels present rubric-scored claims about why the two beaches experience different conditions and propose realistic actions for local leaders. This public exhibition gives students an authentic audience for communicating scientific evidence, environmental justice impacts, and revised conclusions from their mid-project data clinic.