Hunan Entry-Exit Inspection and Quarantine Bureau

ABSTRACT In this context, two novel multi‐cyano monomers based on phenanthro‐pyrazine derivatives, dibenzo[f,h]quinoxaline‐2,3,7,10‐tetracarbonitrile (TCP) and dibenzo[a,c]phenazine‐3,11‐dicarbonitrile (TPI), were synthesized for constructing covalent triazine‐based frameworks via ZnCl 2 ‐triggered polytrimerization. The optimal molar ratios of monomers to anhydrous ZnCl 2 were first investigated, then a series of CTF‐TCPs and CTF‐TPIs were synthesized at different polymerization temperatures and characterized systematically. The resulting frameworks were amorphous and exhibited high specific surface areas (1735 m 2 g −1 for CTF‐TCP‐7 and 1400 m 2 g −1 for CTF‐TPI‐5). The CO 2 capture capacities of as‐synthesized CTF‐TCPs and CTF‐TPIs were evaluated. Owing to the presence of pyrazine and triazine rings within their highly porous structures, CTF‐TCP‐7 and CTF‐TPI‐5 demonstrated CO 2 uptake capacities of 4.10 and 3.62 mmol g −1 at 273 K. Additionally, CTF‐TCP‐3 exhibited the highest CO 2 /N 2 selectivity of 65.1 at 273 K and 1 bar as determined by the Ideal Adsorbed Solution Theory (IAST).

Accurate prediction of PM2.5 concentrations is crucial for public health and environmental management. However, effectively capturing complex spatiotemporal dependencies across multiple time scales remains a persistent challenge for existing methods, particularly in regions with sparse monitoring stations. This study proposes a Multi-Scale Dynamic Graph Neural Network (MSDGNN) for PM2.5 forecasting in station clusters. The model incorporates multi-scale temporal modeling (hourly, daily, weekly) to capture both short- and long-term dependencies. A learnable mapping matrix dynamically groups stations to strengthen spatial correlation learning. Furthermore, MSDGNN employs multi-head attention and spatiotemporal graph attention mechanisms to construct dynamic graphs, utilizing adaptive adjacency matrices and Chebyshev graph convolutions for effective feature propagation. We evaluated MSDGNN on 22 air quality monitoring stations in the Chang-Zhu-Tan region. Results demonstrated that our model reduces MAE by 6.77% and RMSE by 8.67% compared to the best baseline, validating its capability to learn complex dependencies and deliver accurate predictions under diverse spatiotemporal conditions.

ABSTRACT Flooding is a critical driver of wetland plant growth. However, studies still often consider water depth or submergence duration in isolation. This limits our understanding of how hydrological variation affects plant performance. To address this, we have introduced cumulative emergence height (CEH) as a novel integrative parameter. This is defined as the product of emergence height above the water surface and the duration of emergence and quantifies the combined influence of hydrological exposure on wetland vegetation. This study aims to evaluate the effectiveness of CEH in predicting biomass accumulation and physiological responses of wetland plants under flood conditions. We investigated four dominant wetland species ( Phragmites australis , Persicaria hydropiper , Phalaris arundinacea and Carex brevicuspis ) in the Dongting Lake floodplain under four water levels (−30, 0, 30 and 60 cm) and three time points (45, 90 and 135 days). We measured aboveground and belowground biomass, leaf area, chlorophyll a and b concentration and nitrogen and phosphorus concentration. Partial least squares path modeling (PLS‐PM) was used to explore direct and indirect relationships between CEH and plant traits. Biomass and leaf area were significantly positively correlated with CEH, while chlorophyll and nitrogen concentrations showed negative correlations. CEH promoted biomass accumulation by increasing leaf area and individual number, and by reducing nitrogen and phosphorus concentrations. Species showed contrasting strategies: P. australis and P. hydropiper maintained consistent biomass growth, while P. arundinacea and C. brevicuspis peaked and declined under prolonged submergence. CEH effectively integrates flood depth and duration into a single, ecologically meaningful parameter. It is a useful predictor of plant growth and physiological responses under dynamic flood conditions in the Dongting Lake wetland. As the study was limited to four species from a single wetland system, the findings apply primarily to similar ecological contexts.