Based on field efforts in Lake IJssel (the Netherlands) from 2000 to 2010, dynamics of 15 priority PAHs in suspended particulate matter (SPM) and in seawater were determined. This study examined time-varying changes in PAH concentration and composition, apportioned potential anthropogenic sources, and quantified their linkages to hydrological factors. The sum of individual PAHs (defined as ∑₁₅PAHs) varied widely throughout the study period in Lake IJssel, ranging from 0.76 to 9.66 mg/kg in SPM and from 0.014 to 0.136 μg/L in seawater. High molecular weight PAHs were the most abundant PAH compounds, responsible for 73%–97%. Seasonal variation in ∑₁₅PAHs level was distinguished, peaked in late winter and early spring, and reached a minimum in summer. Over site, the heavy contamination was found at the river mouth (e.g., Genemuiden). The driving factors of SPM and water temperature controlled the fate of PAHs. Principal component analysis and diagnostic ratios suggested that PAHs may have been derived from different input sources through various transport pathways, for example, coal combustion source and petroleum source. The findings are useful for filling the knowledge gap of PAH dynamics in Lake IJssel and Wadden Sea, which exerts significant influences on lake's environment. Practitioner points: HMW PAHs (4–6 rings) were the most abundant PAH compounds. Heavy contamination was found in the mouth of IJssel River. Time-varying change in PAHs level was significantly correlated with riverine input. PAHs sources were apportioned via principal component analysis and diagnostic ratios.

Based on a monthly field survey in 2011 of the Pearl River Estuary, the dynamics of polycyclic aromatic hydrocarbons (PAHs) in surface sediments (depth < 5 cm) were explored. The seasonal variations in PAH level and composition were investigated in this study, as well as their environmental behaviors, the role of particles, and source apportionment. The concentration of the sum of 16 priority PAHs (defined as ∑₁₆PAHs) ranged from 0.32 to 1.10 μg/g, while that of the sum of 62 PAHs (defined as ∑_tPAHs) varied from 0.83 to 2.75 μg/g. The levels of both the ∑₁₆PAHs and ∑_tPAHs peaked in February, although the minimum levels appeared in different months—December and August, respectively. The seasonal difference in the ∑_tPAHs was significant (flood season, 7.69 μg/g; dry season, 10.51 μg/g). The 5-ring PAH compound (e.g., perylene) was the most abundant and was responsible for 35% of the total, which implied a terrestrial input source via the Pearl River. Sediment particles were predominantly composed of clayed sand, and sediment PAHs showed a greater tendency to be adsorbed onto the large-sized particles rather than the fine fractions. Total organic carbon (TOC) could considerably facilitate the sediment PAHs. Principal component analysis revealed that vehicle emission sources, petroleum sources, and combustion sources were the major anthropogenic contamination sources. The diagnostic ratios of various individual PAHs were also explored. These findings are particularly useful for understanding the geochemistry of organic pollutants in the complex estuarine environment.

Research on phytoplankton dynamics in coastal waters has frequently been proposed, motivated by environmental factors. The present study aims to develop a vertical phytoplankton model to investigate the phytoplankton variability in a case of the Jiangsu coastal waters, driven by physical limitation. The quality of the parameter estimation largely determines the reliability of the model output. Skill assessment results reveal that the vertical phytoplankton model is able to reproduce reliable predictions of phytoplankton biomass in this case. Significant correlations are established between phytoplankton biomass and chlorophyll a. The phytoplankton biomass is significantly correlated with the variables of temperature, light attenuation coefficient, and euphotic depth. A decrease of phytoplankton biomass corresponds to deeper water, excluding the case of Yangkou station. Particular attention has been paid to the depth-averaged phytoplankton biomass. In the presence of uncertainty, the bootstrap method is used to derive a 95% confidence interval of the estimate, as well as mean value, standard deviation, and skewness. The findings of this study contribute to understanding of the coastal ecosystem and coastal management.