As oil transport increasing in the Texas bays, greater risks of ship collisions will become a challenge, yielding oil spill accidents as a consequence. To minimize the ecological damage and optimize rapid response, emergency managers need to be informed with how fast and where oil will spread as soon as possible after a spill. The state-of-the-art operational oil spill forecast modeling system improves the oil spill response into a new stage. However uncertainty due to predicted data inputs often elicits compromise on the reliability of the forecast result, leading to misdirection in contingency planning. Thus understanding the forecast uncertainty and reliability become significant. In this paper, Monte Carlo simulation is implemented to provide parameters to generate forecast probability maps. The oil spill forecast uncertainty is thus quantified by comparing the forecast probability map and the associated hindcast simulation. A HyosPy-based simple statistic model is developed to assess the reliability of an oil spill forecast in term of belief degree. The technologies developed in this study create a prototype for uncertainty and reliability analysis in numerical oil spill forecast modeling system, providing emergency managers to improve the capability of real time operational oil spill response and impact assessment.

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This study report the impact of heavy metals on cytopathology and DNA damage in the gills and hepatopancreas of Perna viridis collected from Ennore estuary and the Kovalam coastal waters. Principal Component Analysis (PCA) showed significant differences among all variables at the scale of plots. The ultrastructural alterations such as lack of microvilli, distorted mitochondria, electron dense particles and the presence of large mucous droplets were common in the gill and hepatopancreatic cells of mussels from Ennore estuary. However, the gill and hepatopancreatic cells of P. viridis from Kovalam revealed normal compartmentalization of cells. The percentage of tail DNA in the mussels from Ennore estuary was recorded as 12.44 and 10.14% in the gills and hepatopancreas respectively. Overall, it has been demonstrated that the Comet and cytopathological assays are useful biomarkers to assess the level of pollution and it provide reliable information on ecotoxicology and genotoxicology of coastal waters.

Air-quality forecasting is difficult because air quality time series are heterogeneous, consisting of one-dimension series data and multi-dimension panel data. Therefore, a hybrid forecasting model with both linear and nonlinear models may be appropriate to represent the complex behavior of a heterogeneous time series data set. In this paper, a new hybrid-Garch (Generalized Autoregressive Conditional Heteroskedasticity) methodology is proposed in order to integrate the individual forecasting models of the ARIMA (Autoregressive Integrated Moving Average) and SVM(Support Vector Machine). The hybrid-Garch approach for time series prediction is tested by 10-day hourly PM2.5 concentrations data including linear and non-linear, in Shenzhen, China. Empirical results from six station data sets indicate that: 1) the PM2.5 concentrations of Shenzhen experiences a regular fluctuation during the 24 h of the whole day with the peak value in working hours due to factory and vehicle emissions. 2) Spatial difference of PM2.5 concentrations is not noticeable because of the geographical and meteorological conditions. 3) The proposed hybrid model generates a more reliable and accurate forecast capability. 4) The proposed hybrid model analyses the time series data with possibly conditional heteroscedasticity characteristics and estimates the variance for the volatility of the PM2.5 concentrations.

The vertical transport of sinking marine oil snow (MOS) and oil-sediment aggregations (OSA) during the Deepwater Horizon (DwH) spill contributed appreciably to the unexpected, and exceptional accumulation of oil on the seafloor. However, the role of the dispersant Corexit in mediating oil-sedimentation is still controversial. Here we demonstrate that the formation of diatom MOS is enhanced by chemically undispersed oil, but inhibited by Corexit-dispersed oil. Nevertheless, the sedimentation rate of oil may at times be enhanced by Corexit application, because of an elevated oil content per aggregate when Corexit is used. A conceptual framework explains the seemingly contradictory effects of Corexit application on the sedimentation of oil and marine particles. The redistribution of oil has central ecological implications, and future decisions on mediating measures or damage assessment will have to take the formation of sinking, oil-laden, marine snow into account.

Microplastics in the world's oceans are a global concern due to the potential threat they pose to marine organisms. This study investigated microplastic abundance, distribution and composition in the Atlantic Ocean on a transect from the Bay of Biscay to Cape Town, South Africa. Microplastics were sampled from sub-surface waters using the underway system of the RV Polarstern. Potential microplastics were isolated from samples and FT-IR spectroscopy was used to identify polymer types. Of the particles analysed, 63% were rayon and 37% were synthetic polymers. The majority of microplastics were identified as polyesters (49%) and blends of polyamide or acrylic/polyester (43%). Overall, fibres (94%) were predominant. Average microplastic abundance in the Atlantic Ocean was 1.15±1.45particlesm−3. Of the 76 samples, 14 were from the Benguela upwelling and there was no statistically significant difference in microplastic abundance between upwelled and non-upwelled sites.

This paper presents a novel operational oil spill modelling system based on HF radar currents, implemented in a northwest European shelf sea. The system integrates Open Modal Analysis (OMA), Short Term Prediction algorithms (STPS) and an oil spill model to simulate oil spill trajectories. A set of 18 buoys was used to assess the accuracy of the system for trajectory forecast and to evaluate the benefits of HF radar data compared to the use of currents from a hydrodynamic model (HDM). The results showed that simulated trajectories using OMA currents were more accurate than those obtained using a HDM. After 48h the mean error was reduced by 40%. The forecast skill of the STPS method was valid up to 6h ahead. The analysis performed shows the benefits of HF radar data for operational oil spill modelling, which could be easily implemented in other regions with HF radar coverage.

This research was conducted to examine variations of antioxidant enzyme activity in Phragmites australis as a biomarker for metals such as As, Pb, Cu, and Cd. Samples of sediment and plants were collected from 7 stations located in Anzali wetland. Biochemical parameters including Catalase, Peroxidase and Ascorbate Peroxidase activity were analyzed in the roots, stems and leaves of P. australis. The obtained results indicated that there were significant differences among activities of antioxidant enzymes in three organs (p<0.05). Antioxidant enzyme activities in the organs for all studied stations were as the following order: stem<leaf<root. Overall, significant positive correlations were observed among concentrations of metals in sediments and activities of antioxidant enzymes in P. australis. As a result, it can be concluded from this study that antioxidant enzymes are good biomarkers reflecting metal contamination in sediments of Anzali wetland.

Eutrophication is a complex process and often associated with not only a change in overall algal biomass but also with a change in biodiversity. Common metrics of eutrophication (e.g., chlorophyll a), total nitrogen (TN) and phosphorus (TP) are not adequate for understanding biodiversity changes, especially those associated with harmful algal bloom (HAB) proliferations. Harmful algae can increase disproportionately with eutrophication, depending on which nutrients change and in what proportion. This paper challenges several classic paradigms in our understanding of eutrophication and associated biodiversity changes. The underlying message is that nutrient proportions and forms can alter biodiversity, even when nutrients are at concentrations in excess of those considered limiting. The global HAB problem is on a trajectory for more blooms, more toxins, more often, in more places. Our approach to management of HABs and eutrophication must consider the broader complexity of nutrient effects at scales ranging from physiological to ecological.

Although anthropogenic oil spills vary in size, duration and severity, their broad impacts on complex social, economic and ecological systems can be significant. Questions pertaining to the operational challenges associated with the tactical allocation of human resources, cleanup equipment and supplies to areas impacted by a large spill are particularly salient when developing mitigation strategies for extreme oiling events. The purpose of this paper is to illustrate the application of advanced oil spill modeling techniques in combination with a developed mathematical model to spatially optimize the allocation of response crews and equipment for cleaning up an offshore oil spill. The results suggest that the detailed simulations and optimization model are a good first step in allowing both communities and emergency responders to proactively plan for extreme oiling events and develop response strategies that minimize the impacts of spills.