A double-arc visual analytics framework for exploring multidimensional air pollution–impact relationships

Air pollution is an acute environmental challenge to the sustainability and even human health, but traditional methods of visualization do not always succeed in clearly displaying the multidimensional relationship between the measured multidimensional pollutant and the resulting effects thereof. This paper proposes a Double-Arc Visualization (DAV) framework helping to combine the pollutant concentration variables and the environmental or health impact indicators into a dual-arc framework. The proposed scheme reduces visual clutter while preserving structural relationships through arc-based parallel coordinates expanded into a dual-polar display with curved connector encoding and optimized variable ordering. The framework facilitates co-examination of essential air pollutants (PM₂.₅, PM₁₀, NO₂, SO₂, and CO and O₃) with the impact indicators of the Air Quality Index (AQI). Real-world air quality data demonstrate that classical evaluation shows relative linearity in rendering scalability, rendering performance with large volumes of data, and 22.4% reduction in visual clutter compared with traditional visualization methods. Another case study confirms in another instance that patterns of visual outcome of DAV is in accord with statistical analysis ( R = 0.89, p = 0.001). These findings make DAV viable and a sound visual analytics model to explore multidimensional environmental data and make decisions.