Detecting airborne dust in common RGB images is hard. Nevertheless, monitoring airborne dust can greatly contribute to climate protection, environmentally friendly construction, research, and numerous other domains. In order to develop an efficient and robust airborne dust monitoring algorithm, various challenges have to be overcome. Airborne dust may be opaque as well translucent, can vary heavily in density, and its boundaries are fuzzy. Also, dust may be hard to distinguish from other atmospheric phenomena such as fog or clouds. To cover the demand for a performant and reliable approach for monitoring airborne dust, we propose DustNet, a dust density estimation neural network. DustNet exploits attention and convolutional-based feature pyramid structures to combine features from multiple resolution and semantic levels. Furthermore, DustNet utilizes highly aggregated global information features as an adaptive kernel to enrich high-resolution features. In addition to the fusion of local and global features, we also present multiple approaches for the fusion of temporal features from consecutive images. In order to validate our approach, we compare results achieved by our DustNet with those results achieved by methods originating from the crowd-counting and the monocular depth estimation domains on an airborne dust density dataset. Our DustNet outperforms the other approaches and achieves a 2.5% higher accuracy in localizing dust and a 14.4% lower mean absolute error than the second-best approach.