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Aromatic carbonyls have been mainly probed as photosensitizers for aqueous secondary organic aerosol (aqSOA) and light-absorbing organic aerosol (i.e., brown carbon or BrC) formation, but due to their organic nature, they can also undergo oxidation to form aqSOA and BrC. However, photochemical transformations of aromatic carbonyl photosensitizers, particularly in multicomponent systems, are understudied. This study explored aqSOA formation from the irradiation of aromatic carbonyl photosensitizers in mixed and single systems under cloud/fog conditions. Mixed systems consisting of phenolic carbonyls only (VL + ActSyr + SyrAld: vanillin [VL] + acetosyringone [ActSyr] + syringaldehyde [SyrAld]) and another composed of both nonphenolic and phenolic carbonyls (DMB + ActSyr + SyrAld: 3,4-dimethoxybenzaldehyde [DMB], a nonphenolic carbonyl, + ActSyr + SyrAld) were compared to single systems of VL (VL*) and DMB (DMB*), respectively. In mixed systems, the shorter lifetimes of VL and DMB indicate their diminished capacity to trigger the oxidation of other organic compounds (e.g., guaiacol [GUA], a noncarbonyl phenol). In contrast to the slow decay and minimal photoenhancement for DMB*, the rapid photodegradation and significant photoenhancement for VL* indicate efficient direct photosensitized oxidation (i.e., self-photosensitization). Relative to single systems, the increased oxidant availability promoted functionalization in VL + ActSyr + SyrAld and accelerated the conversion of early generation aqSOA in DMB + ActSyr + SyrAld. Moreover, the increased availability of oxidizable substrates countered by stronger oxidative capacity limited the contribution of mixed systems to aqSOA light absorption. This suggests a weaker radiative effect of BrC from mixed photosensitizer systems than BrC from single photosensitizer systems. Furthermore, more oxygenated and oxidized aqSOA was observed with increasing complexity of the reaction systems (e.g., VL* < VL + ActSyr + SyrAld < VL + ActSyr + SyrAld + GUA). This work offers new insights into aqSOA formation by emphasizing the dual role of organic photosensitizers as oxidant sources and oxidizable substrates.

The geometry of retinal layers is an important imaging feature for the diagnosis of some ophthalmic diseases. In recent years, retinal layer segmentation methods for optical coherence tomography (OCT) images have emerged one after another, and huge progress has been achieved. However, challenges due to interference factors such as noise, blurring, fundus effusion, and tissue artifacts remain in existing methods, primarily manifesting as intra-layer false positives and inter-layer boundary deviation. To solve these problems, we propose a method called Tightly combined Cross-Convolution and Transformer with Boundary regression and feature Polarization (TCCT-BP). This method uses a hybrid architecture of CNN and lightweight Transformer to improve the perception of retinal layers. In addition, a feature grouping and sampling method and the corresponding polarization loss function are designed to maximize the differentiation of the feature vectors of different retinal layers, and a boundary regression loss function is devised to constrain the retinal boundary distribution for a better fit to the ground truth. Extensive experiments on four benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance in dealing with problems of false positives and boundary distortion. The proposed method ranked first in the OCT Layer Segmentation task of GOALS challenge held by MICCAI 2022. The source code is available at https://www.github.com/tyb311/TCCT.

Stable imaging in adverse environments (e.g., total darkness) makes thermal infrared (TIR) cameras a prevalent option for night scene perception. However, the low contrast and lack of chromaticity of TIR images are detrimental to human interpretation and subsequent deployment of RGB-based vision algorithms. Therefore, it makes sense to colorize the nighttime TIR images by translating them into the corresponding daytime color images (NTIR2DC). Despite the impressive progress made in the NTIR2DC task, how to improve the translation performance of small object classes is under-explored. To address this problem, we propose a generative adversarial network incorporating feedback-based object appearance learning (FoalGAN). Specifically, an occlusion-aware mixup module and corresponding appearance consistency loss are proposed to reduce the context dependence of object translation. As a representative example of small objects in nighttime street scenes, we illustrate how to enhance the realism of traffic light by designing a traffic light appearance loss. To further improve the appearance learning of small objects, we devise a dual feedback learning strategy to selectively adjust the learning frequency of different samples. In addition, we provide pixel-level annotation for a subset of the Brno dataset, which can facilitate the research of NTIR image understanding under multiple weather conditions. Extensive experiments illustrate that the proposed FoalGAN is not only effective for appearance learning of small objects, but also outperforms other image translation methods in terms of semantic preservation and edge consistency for the NTIR2DC task. Compared with the state-of-the-art NTIR2DC approach, FoalGAN achieves at least 5.4% improvement in semantic consistency and at least 2% lead in edge consistency.

The nocturnal boundary layer (NBL) significantly influences the dispersion and fate of atmospheric species at night. Subtropical forests are crucial in carbon and water exchange between the biosphere and the atmosphere. However, the NBL characteristics and their impact on atmospheric species over these forests remain unknown. This study conducted vertical measurements of atmospheric species such as O3and volatile organic compounds (VOCs), along with meteorological variables, over a national forest reserve in Southern China. Results reveal that the NBL height ranged from 180 to 300 m in the summer and from 80 to 160 m in the winter. The vertical distribution of chemical species varied by time and season, with greater concentration gradients observed in the summer. Over 90% of VOCs above the NBL were anthropogenic, while biogenic VOCs were mainly found within the NBL. Higher O3concentration and VOC product-to-reactant ratios were observed in the residual layer, suggesting enhanced oxidation levels. This unique vertical distribution of atmospheric species at night is driven by factors, such as emission, deposition, turbulence, and atmospheric chemistry, potentially affecting ecosystem functions. Results from this study highlight the importance of incorporating NBL dynamics into atmospheric models to better understand the evolution of chemical species and their ecological effects over forests.