China National Environmental Monitoring Center

Abstract. In coastal ecosystems, microbial alkaline phosphatase (AP) production is primarily induced by low phosphate (PO4-P) availability but is additionally regulated by the dissolved inorganic nitrogen to phosphate (DIN : PO4-P) ratio and seasonal temperature variation. However, the dominant driver of alkaline phosphatase activity (APA) surges and potential synergistic effects among these factors remain unclear. Through integrated seasonal field surveys and enclosure experiments in Laizhou Bay, China, we demonstrate that PO4-P seawater concentration serves as the primary control for APA induction, with a consistent threshold of 0.05 µmol L−1. Significant positive correlation was observed between APA and the DIN : PO4-P ratio below this threshold (0.05 µmol L−1), as analyzed in the combined dataset of field and enclosure experiments (p<0.01, n=36). Notably, phytoplankton-dominated APA was evidenced in autumn. Genetic analysis confirms that AP-related gene expression increases only when PO4-P falls below the identified threshold. These findings refine the conceptual framework for AP regulation in coastal ecosystems, highlighting the hierarchical control of phosphorus limitation over stoichiometric effects.

Abstract. High-precision and efficient atmospheric environmental forecasting is essential for protecting public health and supporting environmental management. However, traditional physics-based numerical models, while mechanistically interpretable, struggle to balance computational cost and forecast accuracy. Although artificial intelligence(AI) has advanced rapidly in meteorological forecasting, most existing AI models are not optimized for atmospheric environmental prediction and rely heavily on gridded inputs, limiting their ability to integrate site observations and their operational applicability. To overcome these limitations, we develop BiXiao, a new-generation AI-based atmospheric environmental forecasting model. BiXiao features a heterogeneous architecture with non-continuous grids, coupling independent meteorological and environmental modules for synergistic use of multi-source data. The meteorological module employs a 3D Swin Transformer(Swin3D) to process structured meteorological fields, while the environmental module directly assimilates discrete station data, enabling operational urban-scale forecasts. Testing in the Beijing-Tianjin-Hebei region shows that BiXiao completes 72-hour forecasts for six major pollutants across all key cities within 30 seconds. Compared with mainstream numerical models(CAMS and WRF-Chem), BiXiao achieves substantially higher computational efficiency and forecast accuracy, particularly during heavy pollution events.

Coastal wetland vegetation exhibits pronounced spectral mixing, complex mosaic spatial patterns, and small target sizes, posing considerable challenges for fine-grained classification in high-resolution UAV imagery. At present, remote sensing classification of ground objects based on deep learning mainly relies on spectral and structural features, while the frequency domain features of ground objects are not fully considered. To address these issues, this study proposes a vegetation classification model that integrates spatial-domain and frequency-domain features. The model enhances global contextual modeling through a large-kernel convolution branch, while a frequency-domain interaction branch separates and fuses low-frequency structural information with high-frequency details. In addition, a shallow auxiliary supervision module is introduced to improve local detail learning and stabilize training. With a compact parameter scale suitable for real-world deployment, the proposed framework effectively adapts to high-resolution remote sensing scenarios. Experiments on typical coastal wetland vegetation including Reeds, Spartina alterniflora, and Suaeda salsa demonstrate that the proposed method consistently outperforms representative segmentation models such as UNet, DeepLabV3, TransUNet, SegFormer, D-LinkNet, and MCCA across multiple metrics including Accuracy, Recall, F1 Score, and mIoU. Overall, the results show that the proposed model effectively addresses the challenges of subtle spectral differences, pervasive species mixture, and intricate structural details, offering a robust and efficient solution for UAV-based wetland vegetation mapping and ecological monitoring.

As a key economic marine aquaculture species in China, sea cucumbers (Apostichopus japonicus) were widely cultured in coastal regions, where were susceptible to crude oil pollution threatening their survival and population recruitment, and the impact of crude oil on non-specific immune functions of sea cucumbers was still limited. Therefore, this study exposed sea cucumbers to water-accommodated fractions (WAFs) of Oman crude oil for 7 d to investigate the effects of crude oil on non-specific immune functions, which were composed of cellular and humoral immune responses. Results showed that WAFs exposure caused oxidative stress and lipid peroxidation, evidenced by elevated reactive oxygen species levels in coelomocytes and initially increased and then reduced malondialdehyde content in coelomic fluid of sea cucumbers. For cellular immune, results showed that WAFs exposure caused dysregulation of phagocytic activity, and a rough reduction in total cell count and an obvious increase in respiratory burst capacity in coelomocytes in a dose-dependent manner, indicating coelomocytes dysfunction and suppression of cellular immune function. Regarding humoral immune function, an obvious decrease in the lysozyme content, total nitric oxide synthase, acid phosphatase and alkaline phosphatase activities, was observed in coelomic fluid with the increase in total petroleum hydrocarbons concentrations, indicating lysosomal dysfunction and suppression of humoral immune function. These findings revealed adverse effects on both cellular and humoral immune responses, suggesting acute immunotoxicity of crude oil in sea cucumbers.