Sediment biofilms, mainly composed of diatoms, bacteria, and related extracellular polymeric substances (EPS), play important roles in mangrove ecosystems. In order to better understand the microbial biodiversity in marine environment, this study aims to clarify the effects of wetland, season, and tide on benthic biofilms and related sediment properties based on in situ investigation in two mangrove wetlands (Futian and Qi’ao) in the Pearl River Estuary, South China. Owing to the tide current, the northeastern Futian is more influenced by seawater than the southwestern Qi’ao. As a consequence of the salinity-driven accumulation of nutrients in sediments, Futian is more eutrophic than Qi’ao. Compared to Qi’ao, the higher nutrimental condition in Futian conserves higher abundance of eutrophic indicators of Nitzschia and Cyclotella, which results in higher values of all pigments and bound polysaccharide with varied monosaccharide composition. Compared to the seaward site, the landward site has higher abundance of benthic (pennate) diatoms but lower abundance of planktonic (centric) diatoms due to the varied settling velocity by tidal disturbance. This varied diatom composition leads to higher levels of bound polysaccharide and more monosaccharides in the landward site than in the seaward site. Compared to late spring (March), early summer (May) appears to have similar diatom abundance but more EPS fractions (except bound protein) and monosaccharides due to the higher level of light intensity and temperature. The observed lower bacterial abundance in May than in March might be due to the higher predation pressure. The baseline data on biofilm compositions in mangrove ecosystems from this study can improve the understanding of microbial biodiversity in response to the tidal, temporal, and spatial changes.

An oil spill is considered one of the most serious polluting disasters for a marine environment. When oil is spilled into a marine environment, it is dispersed into the water column as oil droplets which often interact with suspended particles to form oil-mineral-aggregate (OMA). Knowing how OMA form, settle, and are transported is critical to oil spill modelling which can determine the fate and mass balance of the spilled volumes. This review introduces oil weathering and movement, and the commonly used numerical models that oil spill specialists use to determine how a spill will evolve. We conduct in-depth reviews of the environmental factors that influence how OMA form and their settling velocity, and we review how OMA formation and transport are modelled. We point out the existing gaps in current knowledge and the challenges of studying OMA. Such challenges include having to systematically conduct laboratory experiments to investigate how the environment affects OMA formation and settling velocities, and the need for a comprehensive algorithm that can estimate an OMA settling velocity.

The marine ecosystem, human health and social economy are always severely impacted once an offshore oil spill event has occurred. Thus, the management of oil spills is of importance but is difficult due to constraints from a number of dynamic and interactive processes under uncertain conditions. An integrated decision support system is significantly helpful for offshore oil spill management, but it is yet to be developed. Therefore, this study aims at developing an integrated decision support system for supporting offshore oil spill management (DSS-OSM). The DSS-OSM was developed with the integration of a Monte Carlo simulation, artificial neural network and simulation-optimization coupling approach to provide timely and effective decision support to offshore oil spill vulnerability analysis, response technology screening and response devices/equipment allocation. In addition, the uncertainties and their interactions were also analyzed throughout the modeling of the DSS-OSM. Finally, an offshore oil spill management case study was conducted on the south coast of Newfoundland, Canada, demonstrating the feasibility of the developed DSS-OSM.

With increasing stress posed to the marine ecosystem and coastal communities, prevention and control of coastal pollution becomes urgent and important, in which the identification of pollution sources is essential. Currently, the pollutant source apportionment in coastal areas is mainly based on receptor models, such as the positive matrix factorization (PMF) model. Nevertheless, these models still lack consideration of the changes of pollutant behaviors (e.g., the degradation of pollutants) which cause the differences in pollutant compositions. Subsequently, the source apportionment via receptor models only based on the monitoring data may not be consistent with the one in pollution sources. To fill this gap, a pollutant degradation model was firstly developed in this study. Accordingly, the degradation model was inversed to estimate the pollutant concentrations at their emitting sources, based on the monitoring concentration in the coastal area. Finally, the estimated concentrations were fed to the PMF model for pollutant source apportionment, advancing the PMF model with degradation process. To demonstrate the feasibility and accuracy of the developed model, a case study of source appointment was carried out based on the polycyclic aromatic hydrocarbons (PAHs) in the sediments of the Pearl River Estuary. The results indicated the same types of emission source identified by the original and advanced PMF models, which were oil spill, biomass and coal combustion, and traffic emission. Nevertheless, the contributions of sources were significantly varied between the two models. According to the analyses based on emission inventory, the offsets of the results from the original PMF model were −55.4%, 22.7%, and 42.2% for the emission sources of oil spill, biomass and coal combustion, and traffic emission, respectively. Comparatively, the offsets for the advanced PMF model narrowed down to −27.5%, 18.4%, and −4.4%. Therefore, the advanced PMF model is able to provide satisfactory source apportionment for organic pollutants in coastal areas, and thus further provide a scientific basis for marine pollution prevention and control.