In this study, we present a novel approach to visualizing and compressing air flow data based on sensor-derived environmental datasets. By leveraging sensor-based atmospheric observations and applying digital twin technology, we enhanced environmental impact assessments (EIAs) by transforming nonvisible elements such as wind flow into intuitive 3D web-based visualizations. Traditional EIA methods face challenges in effectively communicating nonvisible environmental factors, such as wind flow, particularly to nonexpert stakeholders. To address these challenges, we developed solutions that combine dynamic 3D visualization with optimized data compression and transmission techniques, which enable both efficient representation and real-time interaction with large-scale atmospheric datasets. By employing advanced data packaging algorithms, the solution significantly reduces the volume of air flow data—spanning vast spatial and temporal scales—without compromising visualization quality. These innovations facilitate seamless transmission over web-based platforms, enhancing accessibility and reducing user waiting times. The results demonstrate the effectiveness of this approach in transforming air flow data into intuitive visual narratives, thereby fostering a better understanding and greater engagement among diverse stakeholders. This research represents a pivotal step toward digitalizing EIA processes, promoting both environmental sustainability and public participation in development projects.