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Abstract Surface visibility (SV), a key indicator of atmospheric transparency, is used widely in the fields of environmental monitoring, transportation, and aviation. However, the sparse distribution and limited number of SV monitoring sites make it difficult to fulfill the urgent need for spatiotemporally seamless fine-scale monitoring. Here, we developed the operational real-time SV retrieval (RT-SVR) framework for China that incorporates information from multiple data sources, including Chinese Land Data Assimilation System meteorological data, in situ observations, and other ancillary data. Seamless hourly SV data with 6.25-km spatial resolution are available in real time via the operational RT-SVR model, which was built using a two-layer stacked ensemble approach that combines multiple machine learning algorithms and a deep learning module. Sample-based cross-validation of the RT-SVR model on approximately 41.3 million data pairs revealed strong robustness and high accuracy, with a Pearson correlation coefficient (R) value of 0.95 and a root mean square error (RMSE) of 3.17 km. An additional hindcast-validation experiment, performed with continuous observations obtained over one year (approximately 20.8 million data pairs), demonstrated the powerful generalization capabilities of the RT-SVR model, albeit with slight degradation in performance (R = 0.85, RMSE = 5.28 km). The seamless hourly SV data with real-time update capability enable tracking of the generation, development, and dissipation of various low-SV events (e.g., fog, haze, and dust storms) in China. The developed framework might also prove useful for quantitative retrieval of aerosol-related parameters (e.g., PM2.5, PM10, and aerosol optical depth).

Water regime plays a determinant role in plant community development and patterns of plant zonation in wetlands. The waters located in the middle reach of the Yangtze River (China) have experienced a large decrease in wetland area from human activities for a long time, especially after the hydrological drought events in recent years. This outdoor study was conducted to clarify the morphological responses and reproductive strategy of Vallisneria spinulosa to water depth gradients (0.6 m, 1.0 m, 1.5 m) and to drawdown (down to 0.3 m). In static water, water depth of 1.0 m was the best condition for V. spinulosa to grow, manifested as high biomass of vegetative parts, tubers and sexual structures. However, V. spinulosa growing in water depth of 1.5 m was able to adapt to the decline in water depth in autumn, expressed as increased ramet number, stolon weight and belowground biomass. The low water depth of 0.6 m produced small plants with slight vegetative biomass and drawdown produced more tuber biomass. The drawdown caused a decline in vegetative ratio of V. spinulosa. Moreover, it caused an increase in clonal ratio at water levels of 1.5 m and 0.6 m, and a decline in sexual ratio at water levels of 1.5 m and 1.0 m. The results show that under the condition of relatively large fluctuations in water level, V. spinulosa will reduce the allocation of sexual biomass and increase the allocation of clonal biomass in order to cope with environmental changes so that it can proliferate better. Thus, water level changes have a great influence on the growth and reproduction of V. spinulosa.

The problem of seasonal drought has intensified in recent years. Unfortunately, dry seasons can lead to poor water quality due to nitrogen (N) accumulation, which, in turn, may result in the gradual decline or even disappearance of submerged plants. This outdoor study was conducted to determine the morphological, biomass responses and C/N metabolism of Vallisneria spinulosa to the high N content and low water level. We used two N contents (i.e., low and high) and three simulated water levels (i.e., gradual water level increase from 60 cm to 150 cm over four periods (C), low water level of 60 cm maintained for three periods followed by an abrupt increase to 150 cm in the fourth period (CE) and low water levels of 60 ± 30 cm maintained for three periods followed by an abrupt increase to 150 cm in the fourth period (E)). For the low N content, low water levels for CE and E treatments caused increased ramet number and biomass of V. spinulosa. However, for the high N content, the low water levels did not cause such big changes in V. spinulosa. High N contents demonstrated a remarkably negative influence on the biomass of each organ (included leaf, stolon, root and tuber) of V. spinulosa and flowering ramet number. For C water level, the high N content caused an increase in free-amino acid (FAA) content in stolons and leaves, and no difference in soluble carbohydrate (SC) content. For E water level, there was no significant increase in FAA content of V. spinulosa under high N treatment; furthermore, there was no significant change in SC content under high N treatment except for leaves. In conclusion, low water levels exacerbated the damage to V. spinulosa caused by high nitrogen level.

Smoke aerosols released through frequent springtime fire activity over the Indochina Peninsula (ICP) seriously affect regional air quality, climate, and human health. However, the mechanisms driving the interannual variability of these smoke aerosols are not well understood. By analyzing multi-source historical (1980–2020) smoke aerosols and meteorological reanalysis data, we explore the response of springtime smoke aerosol changes over the ICP to the interannual variability of the Indian Ocean (IO) sea-surface temperature (SST). Our findings show a positive correlation between the variability of springtime smoke aerosol loading and the preceding winter Southeast IO (SEIO) SST anomalies. Warmer SEIO SST tends to weaken the trans-equatorial flow (TEF) and the local Hadley circulation. This weakening of the TEF impedes cyclone development in the Bay of Bengal (BOB), thereby reducing southwest water vapor transport. Simultaneously, enhanced westerly winds over the northern BOB are blocked by the northwestern mountains of ICP. These winds converge and rise on the windward slopes, while descending on the leeward side with diminished humidity. Collectively, these dynamics lead to drier and hotter local meteorological conditions that favored fire-induced smoke aerosol emissions. Our findings highlight the role of the SEIO in regulating smoke aerosol variability and provide a scientific basis for developing strategies to manage smoke aerosol emissions over the ICP.