The dynamics of dissolved inorganic nitrogen species mediated by fresh submarine groundwater discharge and their impact on phytoplankton community structure

Bunkering for change: Knowledge preparedness on the environmental aspect of ammonia as a marine fuel

Nitrogen cycling is essential to ecosystem functioning and the overall health of our planet. Ammonia, a nitrogen-containing product, as well as a nutrient, is promoted as a low-carbon fuel for the maritime sector, with spectacular production increase in plan. Similar to any other widespread fuels in the past, it is paramount to be prepared for the potential environmental impact of ammonia fuel. Here, through our preliminary calculations using literature data, we suggest that the amount of ammonia to be produced to fulfil the maritime energy need by 2050 may entail large alterations in global nitrogen cycling. Currently, the literature based on limited known cases of ammonia excess is insufficient to quantify the environmental impacts caused by the probable increase in bunkering ammonia release at global scale. With a few knowledge gaps identified, we call on the marine science community to investigate the potential environmental impact related to substantial ammonia excess, contributing new knowledge to a more environmentally sustainable future.

Spatiotemporal-aware machine learning approaches for dissolved oxygen prediction in coastal waters

Coastal waters face increasing threats from hypoxia, which can have severe consequences for marine life and fisheries. This study aims to develop a machine learning approach for hypoxia monitoring by investigating the effectiveness of four tree-based models, considering spatiotemporal effects in model prediction, and adopting the SHapley Additive exPlanations (SHAP) approach for model interpretability, using the long-term climate and marine monitoring dataset in Tolo Harbour (Zone 1) and Mirs Bay (Zone 2), Hong Kong. The LightBoost model was found to be the most effective for predicting dissolved oxygen (DO) concentrations using spatiotemporal datasets. Considering spatiotemporal effects improved the model's bottom DO prediction performance (R2 increase 0.30 in Zone1 and 0.68 in Zone 2), although the contributions from temporal and spatial factors varied depending on the complexity of physical and chemical processes. This study focused not only on error estimates but also on model interpretation. Using SHAP, we propose that hypoxia is largely influenced by hydrodynamics, but anthropogenic activities can increase the bias of systems, exacerbating chemical reactions and impacting DO levels. Additionally, the high relative importance of silicate (Zone 1:0.11 and Zone 2: 0.19) in the model suggests that terrestrial sources, particularly submarine groundwater discharge, are important factors influencing coastal hypoxia. This is the first machine learning effort to consider spatiotemporal effects in four dimensions to predict DO concentrations, and we believe it contributes to the development of a forecasting tool for alarming hypoxia, combining real-time data and machine learning models in the near future.

Capturing the influence of submarine groundwater discharge on nutrient speciation dynamics within an estuarine aquaculture ecosystem

Submarine groundwater discharge (SGD) plays a crucial role in nutrient dynamics and eutrophication status of the typical estuarine ecosystems, which are hotspots for groundwater-borne nutrient and are sensitive to aquaculture activities. To evaluate the significant role of SGD in regulating nutrient dynamics in an aquaculture estuary, a radium mass balance model combined biological feeding experiment was carried out in the present study. The results demonstrated that SGD fluxes were estimated to be 15.9 ± 9.41 cm d-1, 18.1 ± 8.51 cm d-1, and 23.0 ± 13.7 cm d-1 during July 2019, October 2019 and April 2021, and the SGD-driven dissolved inorganic/organic nutrient fluxes were 0.6-3.1-fold, 0.2-0.9-fold and 0.4-29-fold higher than those of riverine input, respectively. Seasonal variabilities of SGD rates indicated that saline SGD is dominated and is primarily modified by the oceanic forcing stimulated by tidal and wave dynamics. The contrasting conditions between bottom-up (groundwater- and river-derived nutrient fluxes) and top-down (nutrient responses in estuarine waters), showed the significance of seasonal differences in the biochemical mechanisms and aquaculture effects of modifying nitrogen dynamics. Dissimilatory nitrate reduction to ammonium and nitrification were responsible for the contrasting NOx- (NO2- and NO3-) and NH4+ conditions in July and October, respectively, and these factors jointly regulated NOx- and NH4+ in April. Dissolved organic nitrogen (DON) was the predominant component among the three seasons, except for DON degeneration in October, and it increased due to NH4+ assimilation by the phytoplankton community. These findings indicated that biochemical transformation has potential ramifications for the dynamics of SGD-driven nutrients and the management in marine aquaculture ecosystems.

Seasonal changes of dissolved organic matter chemistry and its linkage with greenhouse gas emissions in saltmarsh surface water and porewater interactions

Dissolved organic matter (DOM) is one of the largest reactive reservoirs of carbon on earth. Saltmarshes play an essential role in shaping the fate of DOM and greenhouse gas (GHG) production in surface water and groundwater interactions in coastal areas. However, the coupling mechanism between DOM and GHG production is poorly understood. In this study, DOM in both surface water and porewater were analyzed by 3D excitation-emission-matrix spectroscopy under different seasonal and tidal conditions in a saltmarsh. Protein-like DOM was likely to produce CH4, while humic-like DOM tended to produce CO2. CH4 concentration was highly enriched in porewater because increasing fresh groundwater flow introduced small-sized protein-like DOM. Based on the mass balance model, >98.5% of CH4 was oxidated to CO2 in sediment-water interface. The degradation of sediment-derived DOC (especially humic-like DOM) contributes ∼80% of the total amount of CO2 in surface water. Both hydrodynamics and chemical reactions are suggested to influence greenhouse gas (GHG) emissions. Hydrodynamics (e.g., tidal pumping) are controlling factors in short timescales (hourly/weekly) while chemical reactions become crucial in influencing DOM chemistry and related degradation rate on seasonal scales. These findings emphasize the importance of the coupling mechanism at different time scales between DOM characteristics and GHG emissions in saltmarshes.

Ecological stoichiometry influences phytoplankton alpha and beta diversity rather than the community stability in subtropical bay

Numerous studies have shown that changes in environmental factors can significantly impact and shift the structure of phytoplankton communities in marine ecosystems. However, little is known about the association between the ecological stoichiometry of seawater nutrients and phytoplankton community diversity and stability in subtropical bays. Therefore, we investigated the relationship between the phytoplankton community assemblage and seasonal variation in the Beibu Gulf, South China Sea. In this study, we found that the abundance of Bacillariophyceae in spring was relatively greater than in other seasons, whereas the abundance of Coscinodiscophyceae was relatively low in spring and winter but greatly increased in summer and autumn. Values of the alpha diversity indices gradually increased from spring to winter, revealing that seasonal variations shifted the phytoplankton community structure. The regression lines between the average variation degree and the Shannon index and Bray–Curtis dissimilarity values showed significantly positive correlations, indicating that high diversity was beneficial to maintaining community stability. In addition, the ecological stoichiometry of nutrients exhibited significantly positive associations with Shannon index and Bray–Curtis dissimilarity, demonstrating that ecological stoichiometry can significantly influence the alpha and beta diversity of phytoplankton communities. The C:N:P ratio was not statistically significantly correlated with average variation degree, suggesting that ecological stoichiometry rarely impacted the community stability. Temperature, nitrate, dissolved inorganic phosphorous, and total dissolved phosphorus were the main drivers of the phytoplankton community assemblage. The results of this study provide new perspectives about what influences phytoplankton community structure and the association between ecological stoichiometry, community diversity, and stability in response to environmental changes.

Two-decade variations of fresh submarine groundwater discharge to Tolo Harbour and their ecological significance by coupled remote sensing and radon-222 model

Although submarine groundwater discharge (SGD) comprises an insignificant proportion of the global hydrologic cycle, it contributes significantly to chemical fluxes into the coastal waters due to concentrated constituents in coastal groundwater. Large nutrient loadings derived from SGD can lead to a series of environmental and ecological problems such as algal blooms, resulting in water discoloration, severe dissolved oxygen depletion, and eventually beach closures and massive fish kills. Previous studies have demonstrated the relationship between algal blooms and SGD obtained from direct measurement with seepage meters or from geo-tracer (i.e., radon and radium) based models; these traditional methods are time-consuming, laborious and point monitoring, and can hardly achieve a high spatiotemporal resolution SGD estimation, which is vital in revealing the effects of SGD to algal blooms over a long period. Alternatively, remote sensing methods for high spatiotemporal resolution SGD localization and quantification are applicable and effective. The temperature difference or anomaly between groundwater and coastal water extracted from satellite thermal images can be used as the indicator to localize and detect SGD especially its fresh component (or fresh SGD). In this study, multi-year (2005, 2011 and 2018) radon samples in Tolo Harbour were used to train regression models between in-situ radon (Rn) activity and the temperature anomaly by Landsat satellite thermal images. The models were used to estimate two-decade variations of fresh SGD in Tolo Harbour. The synergistic analysis between the time series of fresh SGD derived from regression models and high spatiotemporal resolution ecological metrics (chlorophyll-a, algal cell counts, and E.coli) leads to the findings that the increase of the fresh SGD associated with high nutrient concentrations is witnessed 10-20 days before the observations of algal bloom events. This study makes the first attempt to demonstrate the strong relation between the SGD and algal blooms over a vicennial span, and also provides a cost effective and robust technique to estimate SGD on a bay scale.