Oily wastewater from shipping waste and marine accidents have seriously polluted the marine environment and brought great harm to human production and health. With the increasing awareness of environmental protection, the treatment of marine oily wastewater has attracted extensive attention from the international community. Marine oily wastewater has various forms and complex components, so its treatment technology faces great challenges. Sources, types, supervision, and treatment of marine oily wastewater are introduced in this paper. The research progress of marine and ship’s oily wastewater treatment technologies in recent years are reviewed from the perspectives of physical treatment, chemical treatment, biological treatment, and combined treatment, respectively. Principles and characteristics of all kinds of technologies were analyzed. In addition, this paper shows that multiple processing technologies used in combination for the purpose of high efficiency, environmental protection, economy, and energy conservation are the future development trend.

In this study, a catalytic persulfate oxidation process comprising sodium persulfate (PS) and modified sewage sludge-derived carbonaceous catalysts was tested for the degradation of phenol. Sludge-based biochar was modified by high-temperature treatment combined with hydrochloric acid oxidation. The surface properties of carbonaceous catalysts before and after modification were characterized by elemental analysis, N₂ isothermal adsorption-desorption, scanning electron microscopy (SEM) and Fourier transform infrared (FTIR) spectroscopy. The effects of reaction parameters including catalyst dosage, PS/phenol molar ratio, initial pH and reaction temperature on the degradation rate of phenol were investigated. The kinetics of phenol transformation was explored and the reaction rate appeared pseudo first-order kinetics. In SS-600-HCl/PS system, 91% phenol could be efficiently degraded under certain reaction conditions ([phenol]₀ = 100 mg/L, catalyst dosage = 0.8 g/L, PS/phenol molar ratio = 3/1, pH = 7, 25 °C) in 180 min. Thus, the results showed that the modified sewage sludge-derived carbonaceous catalyst had a better ability to activate PS for phenol degradation.

Coupled climate models integrate atmospheric, oceanic, and land submodels, while the uncertainty of model parameters from different parameterization schemes or empirically derived parameters inevitably introduces systematic biases. Coupled parameter optimization (CPO) can reduce these biases to improve weather forecast and climate prediction, but must address strong nonlinearities inherent in coupled models. The analytical four-dimensional ensemble variational (A-4DEnVar) data assimilation method retains the nonlinear processing capability of the four-dimensional variational (4D-Var) data assimilation method but gets rid of the dependence on the adjoint model. In this study, a novel dynamic independent point (DIP) scheme is introduced to the improved A-4DEnVar, which reduces computational dimensionality and further explores a broader parameter space of dimensionality reduction through the outer loop. Based on the improved A-4DEnVar, a series of geographic-dependent CPO experiments with an idealized 2D coupled model are carried out. Results show that A-4DEnVar accurately captures the geographical characteristics of parameters and effectively optimizes cross-component parameters despite strong nonlinearity. Additionally, the DIP scheme presents significant advantages compared to the static independent point scheme, especially with fewer independent points. This work is offering a new perspective for parameter optimization in coupled general circulation models used for climate estimation and prediction.

Advances in data assimilation (DA) methods have greatly improved the accuracy of Earth system predictions. To fuse multi-source data and reconstruct the nonlinear evolution missing from observations, geoscientists are developing future-oriented DA methods. In this paper, we redesign a purely data-driven latent space DA framework (DeepDA) that employs a generative artificial intelligence model to capture the nonlinear evolution in sea surface temperature. Under variational constraints, DeepDA embedded with nonlinear features can effectively fuse heterogeneous data. The results show that DeepDA remains highly stable in capturing and generating nonlinear evolutions even when a large amount of observational information is missing. It can be found that when only 10% of the observation information is available, the error increase of DeepDA does not exceed 40%. Furthermore, DeepDA has been shown to be robust in the fusion of real observations and ensemble simulations. In particular, this paper provides a mechanism analysis of the nonlinear evolution generated by DeepDA from the perspective of physical patterns, which reveals the inherent explainability of our DL model in capturing multi-scale ocean signals.

Sea ice thickness (SIT) is a key variable in improving the accuracy and lead time of sea ice forecasts. However, obtaining continuous gridded Arctic spatiotemporal SIT fields remains a significant challenge. This study proposes a machine learning framework (ICE-3D), which directly produces daily gridded pan-Arctic SIT. ICE-3D establishes a spatiotemporal, multiscale, perception learning-based estimation paradigm. We design an innovative training strategy called spatiotemporal multiscale fusion window (STMFW). The strategy fuses physical covariations at different scales to improve targeted inference, especially for summer SIT. Validated against CryoSat-2 satellite altimetry and BGEP in situ measurements, ICE-3D achieves an overall correlation coefficient exceeding 0.75 for basin-scale SIT. Its summer SIT MAE is less than 0.38 m, outperforming TOPAZ4 reanalysis and showing comparable precision to PIOMAS within validated regions. Compared with static offline training, the online learning module reduces SIT overestimation bias in marginal ice zones by approximately 16%, and the model reliably captures high-frequency regional ice variations and seasonal sea ice volume (SIV) cycles. Our derived SIV results reveal a 38% loss of pan-Arctic sea ice volume from 1991 to 2020, accompanied by a stabilized melting rate over the past decade, indicating severely depleted perennial ice reserves and largely irreversible Arctic sea ice decline under the current climate regime. The ICE-3D SIT with quantified uncertainties supports multiscale Arctic climate analysis and provides critical data support for risk assessment during the peak Arctic shipping season.