Monitoring water surface algal blooms from surveillance perspectives faces challenges such as small objects, low texture contrasts, dynamic background interferences, and limited labeled datasets. In this study, we propose GECA-YOLOv26, a lightweight model that integrates Ghost Convolution (GhostConv) and Efficient Channel Attention (ECA) modules. First, the GhostConv lightweight module is introduced in the first layer of the YOLOv26 backbone, reducing parameters from 4608 to 2704 and achieving a 41% reduction in computational cost. Second, eight ECA modules are embedded at key locations after backbone downsampling and neck feature fusion to enhance feature representation and mitigate degradation caused by model lightweighting. Finally, the MuSGD optimizer is used for training, with adaptive modifications to resolve tensor shape conflicts with the ECA modules. Experimental results indicate that the model achieves a mAP50 of 82.16%. Compared with the YOLOv26 baseline, our model improves mAP50 by 6.42%, while mAP@0.5:0.95 decreases by 0.79% and inference speed reduces from 143 FPS to 123 FPS. The model also reduces parameters and size, achieving 5.19 MB and 1864 fewer parameters. Compared with YOLOv8, YOLOv10, and YOLOv11, the proposed model improves mAP50 by 2.12%, 5.99%, and 2.79%, respectively. To evaluate the stability of the results under small-sample conditions, we conducted 3-fold and 5-fold cross-validation experiments, which demonstrated that the model performs robustly across different folds and random seeds. Ablation studies further confirm the effectiveness of each module. Heatmap analysis demonstrates that the proposed model effectively highlights small object regions, remains robust under limited-sample conditions, and reduces model complexity. This study provides a novel solution for algal bloom detection in surveillance scenarios.

Tire additives are released during tire wear and can enter roadside air with fresh or resuspended particles and, for some compounds, through volatilization. Once airborne, they may partition between gas and particle phases, undergo atmospheric transformation, and reach terrestrial surfaces through dry and wet deposition. However, how much atmospheric deposition contributes to the transfer of tire additives from roadside air to soils and plants remains unclear. This review traces major tire additives and their transformation products from their occurrence, phase partitioning, and transformation in the atmosphere, through deposition, to their subsequent fate at the soil–plant interface. Available evidence shows that these processes are strongly compound-specific. Some additives remain mainly particle-associated, whereas others have substantial gas-phase fractions, and environmental aging can further change both parent compounds and transformation products before deposition. Field studies confirm the deposition of tire and road wear particles and selected tire additives, while controlled soil and plant experiments show retention, transformation, release, and plant uptake after environmental entry. However, the contribution of atmospheric deposition to additive burdens in field soils and crops is still difficult to separate from runoff, irrigation, road splash, and other rubber-related sources. Acute toxicity of N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine quinone (6PPD-Q) is well established in highly sensitive aquatic species, whereas risks associated with atmospheric deposition in terrestrial systems and humans remain poorly characterized. Overall, atmospheric deposition represents a possible pathway by which tire additives are transferred from roadside air to terrestrial environments. Quantifying its contribution will require phase-resolved measurements, robust source attribution, and coordinated monitoring across air, deposition, soil, and plants.