Coral reefs are degrading worldwide, but little information exists on their previous conditions for most regions of the world. Since 1989, we have been studying the Maldives, collecting data before, during and after the bleaching and mass mortality event of 1998. As early as 1999, many newly settled colonies were recorded. Recruits shifted from a dominance of massive and encrusting corals in the early stages of recolonisation towards a dominance of Acropora and Pocillopora by 2009. Coral cover, which dropped to less than 10% after the bleaching, returned to pre-bleaching values of around 50% by 2013. The 2004 tsunami had comparatively little effect. In 2014, the coral community was similar to that existing before the bleaching. According to descriptors and metrics adopted, recovery of Maldivian coral reefs took between 6 and 15years, or may even be considered unachieved, as there are species that had not come back yet.

In order to compare the effects of weathering on two types of mixed oil, simulated weathering experiments were performed. The first sample was a mixture of two fresh oils and the second sample was a mixture of one fresh oil and another oil sample that had undergone a serious weathering process. Comparative studies evaluated decay rates and changes in diagnostic ratios of some fingerprinting biomarkers. Results showed that the mixing process affected the weathering rate of some compounds in the oils and also that certain diagnostic ratios are more suitable for estimating mixing proportions. A Pr/n-C17 versus Ph/n-C18 plot can be used to identify the end-numbers of mixed oils and the (C13+C14)/(C25+C26) ratio is a useful diagnostic ratio to detect the degree of weathering. Finally, hopane, sterane, and alkylated PAH fingerprints were found to give useful insights about the sources of the mixed oils.

Following recognition of effects in the 1980s, tributyltin (TBT) has been monitored at sites in the English Channel to evaluate the prognosis for biota – spanning the introduction of restrictions on TBT use on small boats and the recent phase-out on the global fleet. We describe how persistence and impact of TBT in clams Scrobicularia plana has changed during this period in Southampton Water and Poole Harbour. TBT contamination (and loss) in water, sediment and clams reflects the abundance and type of vessel activity: half-times in sediment (up to 8y in Poole, 33y in Southampton) are longest near commercial shipping. Recovery of clam populations – slowest in TBT-contaminated deposits – provides a useful biological measure of legislative efficacy in estuaries. On rocky shores, recovery from imposex in Nucella lapillus is evident at many sites but, near ports, is prolonged by shipping impacts, including sediment legacy, for example, in the Fal.

Using the vas deferens sequence index (VDSI) and relative penis size index (RPSI) in dogwhelks (Nucella lapillus), imposex levels were assessed at 63 sites within 11 sea inlets during 2010/2011 and compared these with levels gathered since 1987. Sterile females (VDS>5.0) were found at 14 of the 63 sites and 47 sites (75%) met the EcoQO (VDSI<2.0). The absence of imposex in ‘control’ areas on the west coast is due to the lack of vessel paint applications or net dips with TBT being used as an active anti-fouling ingredient. A significant decline was observed following 2005 when comparing VDSI levels which is consistent with the decline of TBT usage. Current levels are consistent with an overall improvement towards achieving Good Environmental Status according to the requirements under the Marine Strategy Framework Directive.

We have assessed variation in brittle star distribution patterns along a contamination gradient identified by fecal steroids and aliphatic hydrocarbons in Paranaguá Bay, southern Brazil. A hierarchical design using multiple spatial scales (centimeters-kilometers) was applied. Generalized linear mixed models (GLMMs) were used to investigate the spatial and temporal variability of brittle stars. Main principal components from the contamination and environmental matrices were used to investigate the best explanatory dataset. The abundance of brittle stars was significantly lower in sites with high concentrations of fecal steroids and aliphatic hydrocarbons. The best model fitting always included components from the contamination gradients, which precludes a purely environmental driving of brittle star abundance. Variability in spatial scales lower than kilometers was probably driven by sediment characteristics. We highlighted the importance of a robust multi-scale sampling design for a better biological indication of coastal contamination.

Potential toxicity of sedimentary Cu, Zn and Pb were evaluated based on their fractionation at the inner part of Jurujuba, a small sound at the eastern margin of Guanabara Bay, Rio de Janeiro. Biogeochemical composition revealed an environment extremely enriched with anthropic organic matter and characterized as a detrital heterotrophic system. The fractionation analysis showed that Pb exhibited more affinity with the residual fraction followed by the amorphous Fe/Mn fraction. Cu and Zn were more expressive in the amorphous Fe/Mn fraction followed by the organic one and crystalline Fe/Mn fraction, respectively. According to Igeo index, sediments proved to be highly polluted by Zn and Cu and moderated polluted by Pb. Despite the actual contamination of Jurujuba sediments, the mobility of these elements seems to be limited since the most excessive concentrations were found in the less available fractions, depending on extreme physico-chemical variations to be released.

The present study aims to optimize the slow release biostimulant ball (BSB) for bioremediation of contaminated coastal sediment using response surface methodology (RSM). Different bacterial communities were evaluated using a pyrosequencing-based approach in contaminated coastal sediments. The effects of BSB size (1–5cm), distance (1–10cm) and time (1–4months) on changes in chemical oxygen demand (COD) and volatile solid (VS) reduction were determined. Maximum reductions of COD and VS, 89.7% and 78.8%, respectively, were observed at a 3cm ball size, 5.5cm distance and 4months; these values are the optimum conditions for effective treatment of contaminated coastal sediment. Most of the variance in COD and VS (0.9291 and 0.9369, respectively) was explained in our chosen models. BSB is a promising method for COD and VS reduction and enhancement of SRB diversity.

The complexity of analyzing and predicting smoke plumes that originate from forest fire events and impact populated regions of southern Ontario motivates the innovative application of analytical techniques including trajectory–based receptor modeling for spatial source apportionment of the observed near–real–time particulate matter (PM) impacts. PM2.5 was selected as an indicator of a pollutant emitted by fires that could be transported over long distances (when entrained into the transport layer above the planetary boundary layer (PBL), and subject to sink and transformation processes) and be monitored using the existing air quality monitoring network. The source term modeling technique of simplified Quantitative Transport Bias Analysis (sQTBA) was applied to several summertime forest fire events to identify the locations of sources affecting air quality in Ontario during these events. Complementary techniques that helped to understand the movement of smoke plumes included satellite remote sensing of carbon monoxide and aerosols. All of these techniques, along with meteorological analysis, jointly provide a means of identifying the forest fire events that resulted in noticeably higher pollutant levels in Ontario. Specifically, three forest fire events in July of 2011, 2012 and 2013 were analyzed, and source regions of near–real–time PM2.5 concentrations were revealed to be both within Ontario and across northern Canada from Quebec to Yukon. The sQTBA was found to successfully identify the relative importance of various source regions contributing plumes from forest fires and non–wildfire related sources that caused higher pollutant levels that were measured in Ontario. The use of near–real–time PM2.5 data in this study facilitates the identification of the exact periods with high pollution impacts across multiple receptor sites, thus improving the overall quality of the analyses. This work shows how trajectory–based receptor models can be integrated with meteorological analyses for thorough source apportionment of wildfire–related pollution events.

The understanding of aerosol deposition in the indoor environment is relevant for assessing the exposure of occupants. This study investigates the effects of microclimatic parameters on the deposition rates of aerosols emanating from the use of household spray products in indoor environment. A three–factor factorial design was used to study the effects of interactions of air temperature, relative humidity and Air Exchange Rate (AER) on the deposition rate of particulate matter (PM). The highest deposition rate of 0.3μm particles (PM0.3) was 627.8h–1 when the relative humidity, temperature and AER were 40%, 40°C, and 12 h–1, respectively while the highest deposition rate of 5.0μm particles (PM5.0) was 709.20h–1 when the relative humidity, temperature and AER were 70%, 25 °C, and 12h–1, respectively. Regression analysis showed that air temperature and air exchange rate had significant effects on the deposition of PM0.3, while relative humidity and air exchange rate had significant effects on the deposition of PM5.0 at p<0.05. The experimental values were very close to the predicted values and were not statistically different at 95% confidence level.

The statistical regression and specific computational intelligence based models are presented in this paper for the forecasting of hourly NO2 concentrations at a historical monument Taj Mahal, Agra. The model was developed for the purpose of public health oriented air quality forecasting. Last ten–year air pollution data analysis reveals that the concentration of air pollutants increased significantly. It is also observed that the pollution levels are always higher during the months of November at around Taj Mahal, Agra. Therefore, the hourly observed data during November were used in the development of air quality forecasting models for Agra, India. Firstly, multiple linear regression (MLR) was used for building an air quality–forecasting model to forecast the NO2 concentrations at Agra. Further, a novel approach, based on regression models, principal component analysis (PCA) was analyzed to find the correlations of different predictor variables between meteorology and air pollutants. Then, the significant variables were taken as the input parameters to propose the reliable physical artificial neural network (ANN)-multi layer perceptron model for forecasting of air pollution in Agra. MLR and PCA–ANN models were evaluated through statistical analysis. The correlation coefficients (R) were 0.89 and 0.91 respectively, for PCA–ANN and were 0.69 and 0.89 respectively for MLR in the training and validation periods. Similarly, the values of normalized mean square error (NMSE), index of agreement (IOA) and fractional bias (FB) were in good agreement with the observed values. It was concluded that PCA–ANN model performs better and can be used for forecasting air pollution at Taj Mahal, Agra.