Tourism-driven activities have increasingly contributed to marine microplastic (MPs) pollution, particularly in island ecosystems. This study assesses the abundance, characteristics, and spatial distribution of MPs in Gili Trawangan, Indonesia, by analyzing samples from coastal water, sediments, and fish across three zones: a seaport, recreational beach, and mangrove area. Standardized filtration, density separation, and FTIR spectroscopy were used to identify MPs types and polymers. Results show the highest MPs concentrations in coastal water at recreational beaches (19.25 particles/L), sediment at seaports (23.15 particles/kg), and fish near seaports (17.5 particles/individual), indicating elevated risks of bioaccumulation. Fragments and fibers were the dominant forms, with prevalent polymers including PS, PE, and LDPE, mostly in black, blue, and red colors. The mangrove area exhibited lower MPs levels due to its natural filtration capacity but still showed MPs presence in biota. This multi-compartment approach highlights a clear link between tourism intensity and MPs contamination. The findings provide new insights for designing localized interventions, including waste reduction strategies and regulatory measures. By integrating ecological and anthropogenic factors, this study supports the development of sustainable tourism policies to mitigate MPs pollution and protect coastal biodiversity.

Nowadays, the limitations of media in geo-environmental learning present a significant challenge, as media plays an indispensable role in fostering students’ comprehensive understanding. Employing technology capable of delivering comprehensive landscape information is crucial for effective environmental education. Utilizing Unmanned Aerial Vehicles (UAVs) and 360-degree panoramic cameras represents a judicious technological choice, which can be seamlessly integrated into virtual tours. This research aims to develop a Mobile Virtual Tour (Movie-Tour) as a medium to support learning, especially for materials regarding environmental geography. The research and development (RnD) method is used in research with the ADDIE model (Analysis, Design, Development, Implementation and Evaluation). Data collection for the development of the product involved conducting a need assessment, performing validation tests and conducting trials with students. To gather field data for creating environmental geography materials for the Movie-Tour product, we utilized an Unmanned Aerial Vehicle (UAV) and a 360-degree stereoscopic camera. The results demonstrate that Movie- Tour is an educational medium capable of delivering an immersive learning experience and comprehensive materials, allowing for the virtual visualization of real-world conditions in the field. This capability enhances students’ engagement in exploring physical geography conditions, fosters independent knowledge acquisition, and nurtures their innate curiosity. Movie-Tour stands as a potent and practical educational tool, offering an effective and secure learning experience for students, eliminating the need for direct field visits.

This study develops a spatial synthetic population (SSP)-based computational model to produce realis-tic, high-resolution flood-risk maps for the Upper Bengawan Solo Watershed. It combines Global Hu-man Settlement (spatial distribution) with local population statistics (attributes). The SSP is created for flood risk mapping in the Upper Bengawan Solo Watershed (BSH) using a 100 m grid from the Global Human Settlement Layer (GHSL) GHS-POP R2023A. Synthetic individuals are strategically placed around the pixel centre (radius ≤ 100 m), and each is assigned demographic attributes (age, gender, ed-ucation, occupation) validated against official county-level data. Social vulnerability is calculated through weighted aggregation (AHP) across four attributes; individual scores are combined with flood hazard intensity at each location to produce a risk index for each person. Validation shows that (i) the SSP aligns closely with reference statistics: gender and age are nearly identical (MAE ≈ 0.01–0.02%), with slight deviations in occupation (MAE 6.52%) and education (MAE 4.89%), (ii) the overall suita-bility of the SSP compared to GHS counts at pixel samples, and (iii) location plausibility testing using ESRI Sentinel‑2 Land Cover (10 m). Results indicate that (i) the SSP aligns well for gender, moderately for education and occupation, but shows significant misalignment in age, (ii) 91.96% of SSP points are in built-up land, suggesting high spatial accuracy. Medium- to high-risk patterns are mainly along the main river corridors and peri-urban areas, while rural non-built zones are mostly low- to medium-risk. These findings suggest that this methodology is scalable, reproducible, and suitable for data limited regions, enabling the production of detail risk maps that can guide mitigation and preparedness efforts.

Watersheds are critical ecosystems that provide essential services, but they face increasing threats from deforestation, land use changes, and climate variability. The Mata Allo Sub-Watershed, which is characterized by steep topography and high rainfall, is particularly vulnerable to erosion, landslides, and habitat loss, necessitating robust conservation strategies. This study used principal component analysis (PCA) to assess ecological sensitivity, focusing on slope, rainfall, vegetation density, and land cover. The PCA results identified land cover as the most influential positive factor in F1 (loading value: 0.588), increasing sensitivity due to human-induced land use changes, while rainfall contributed most negatively (−0.638) by potentially mitigating extreme ecological risks. These contrasting roles underscore the complexity of interactions shaping watershed sensitivity. Slope strongly influenced F2 (−0.795), explaining 26.48% of the variance and highlighting the critical role of steep slopes in exacerbating erosion risks. Vegetation density in F3 (−0.679) and rainfall in F4 (−0.724) played significant roles in stabilizing soil and mitigating ecological risks, emphasizing their importance in reducing watershed sensitivity. The “Extremely Sensitive” class covers 48.79% of the watershed, primarily in areas with steep slopes and sparse vegetation, while “High Sensitivity” areas occupy 34.93%. Projections for 2032 suggest a reduction in “Extremely Sensitive” zones to 41.00%, reflecting improvements from targeted management interventions. These findings provide a foundation for promoting sustainable watershed management, enhancing climate resilience, and supporting biodiversity conservation efforts in vulnerable regions.