Changes in water clarity across the shallow continental shelf of the central Great Barrier Reef were investigated from ten years of daily river load, oceanographic and MODIS-Aqua data. Mean photic depth (i.e., the depth of 10% of surface irradiance) was related to river loads after statistical removal of wave and tidal effects. Across the ∼25,000 km2 area, photic depth was strongly related to river freshwater and phosphorus loads (R2=0.65 and 0.51, respectively). In the six wetter years, photic depth was reduced by 19.8% and below water quality guidelines for 156days, compared to 9days in the drier years. After onset of the seasonal river floods, photic depth was reduced for on average 6–8months, gradually returning to clearer baseline values. Relationships were strongest inshore and midshelf (∼12–80km from the coast), and weaker near the chronically turbid coast. The data show that reductions in river loads would measurably improve shelf water clarity, with significant ecosystem health benefits.

Land based sources of pollution have the potential to adversely impact valuable coral reef ecosystems. In Guánica Bay (Puerto Rico) sediment samples collected and analyzed in 2009 demonstrate unusually high concentrations of total chlordane, total PCBs, nickel and chromium. A variety of other contaminants (total DDT, total PAHs, As, Cu, Hg, and Zn) were also at levels which may indicate sediment toxicity. With the exception of chromium, all of these contaminants were detected in coral tissues (Porites astreoides), although it is unclear at what level these contaminants affect coral health. PCBs and chlordane are environmentally persistent and likely represent legacy pollution from historical uses in close geographic proximity to the Bay. We hypothesize that the high nickel and chromium levels are due to a combination of naturally high Ni and Cr in rock and soils in the watershed, and enhanced (human driven) erosional rates.

In risk assessment of maritime transportation, estimation of accidental oil outflow from tankers is important for assessing environmental impacts. However, there typically is limited data concerning the specific structural design and tank arrangement of ships operating in a given area. Moreover, there is uncertainty about the accident scenarios potentially emerging from ship encounters. This paper proposes a Bayesian network (BN) model for reasoning under uncertainty for the assessment of accidental cargo oil outflow in a ship–ship collision where a product tanker is struck. The BN combines a model linking impact scenarios to damage extent with a model for estimating the tank layouts based on limited information regarding the ship. The methodology for constructing the model is presented and output for two accident scenarios is shown. The discussion elaborates on the issue of model validation, both in terms of the BN and in light of the adopted uncertainty/bias-based risk perspective.

There is a paucity of data in the published literature on the ecological and economic impacts of derelict fishing traps (DFTs) in coastal ecosystems. We synthesized results from seven NOAA-funded trap fisheries studies around the United States and determined that DFT-caused losses to habitat and harvestable annual catch are pervasive, persistent, and largely preventable. Based on this synthesis, we identified key gaps to fill in order to better manage and prevent DFTs. We conclude with suggestions for developing a U.S. DFT management strategy including: (1) targeting studies to estimate mortality of fishery stocks, (2) assessing the economic impacts of DFTs on fisheries, (3) collaborating with the fishing industry to develop solutions to ghost fishing, and (4) examining the regional context and challenges resulting in DFTs to find effective policy solutions to manage, reduce, and prevent gear loss.

The influence of early diagenesis on the vertical distribution of metal forms in the sediments of Bohai Bay was discussed in this paper. The results showed that the concentrations were: Al>Fe≈Ca>Mn>Cr>Zn>Cu>Pb>Cd. In vertical distribution, the forms of Cr and Pb were stable from the top to the bottom. However, the exchangeable forms and acid-extracted forms of Cd, Cu and Zn presented an obvious declining trend. The metals would be transformed to more stable forms during the early-diagenesis process. Further analysis found that early diagenesis can change the sedimentary environment, affecting pH, oxidation–reduction potential (ORP), total dissolved solid (TDS) and the structure of organic matter (OM), all main factors influencing metal forms in the sediments of Bohai Bay.

The objective of this study was to measure α-, β- and γ-diversity of microfaunas with different ages in colonization surveys using rarefaction-modeling methods. A dataset was complied based on a microperiphyton survey in coastal waters of the Yellow Sea, near Qingdao. The analyses showed that: (1) there was a strong residual influence of sampling effort on β- and γ-diversity after rarefaction, especially for the young communities; (2) the rarefaction curves were well fitted to the Michaelis–Menten equation, and allow modeling and removing the residual influence of sampling effort on β- and γ-diversity; and (3) the estimated values of α-, β- and γ-diversity of a community based on the rarefaction-modeling method were independent of the influence of sampling effort. The results suggest that this approach may be used as a feasible tool to detect α-, β- and γ-diversity without the influence of sampling effort in microfauna colonization surveys for marine bioassessment.

Ambient air quality assessment and management plays an important role in the current European Union policy. Among others, air pollution by PM10 is being monitored. A previous analysis of PM10 aimed at identifying factors affecting air pollution in stations in the City of Brno using available observed meteorological variables. However, the studied model cannot be used for predicting the level of PM10 pollution because the included meteorological variables are not available exactly at the time when the prediction is requested. In that case, we should base the predictions on available predicted variables, namely on temperature, wind direction, wind speed, and cloud cover. A comparison of obtained predictions with the observed values of PM10 during a testing period allows us to evaluate a loss of prediction quality when the predicted covariates are used instead of the observed ones. The presented analysis based on test of symmetry and test of homogeneity of the marginal distribution of two–way contingency tables shows that the loss of prediction quality by employing the predicted meteorological variables is non–significant in the studied case. This observation suggests that the model with predicted meteorological variables can be employed in environmental management processes.

Atmospheric circulation affects local concentrations of particulate matter with an aerodynamic diameter of 10μm or less (PM10) in different ways: Via the determination of local meteorological conditions favoring or suppressing the formation and the accumulation of PM10, and through its control on short–and long–range transport of particles and precursors. The quantitative assessment of the connections between the large–scale atmospheric circulation and local PM10 is relevant not only for the understanding of observed variations in PM10 concentrations. It is even more important for estimating the potential effects of projected future changes in large–scale atmospheric circulation on PM10. In this contribution, daily atmospheric circulation types (CTs), resulting from variants of three different classification methods, and their monthly occurrence frequencies have been utilized in three different downscaling approaches for estimating monthly indices of PM10 for the period 1980–2010 at 16 locations in Bavaria (Germany). All variants of approaches have been evaluated via a leave–one–out cross validation procedure in order to attain reliable performance ratings to detect the most suitable downscaling approaches. Results indicate that the highest performance of downscaling approaches is achieved in winter when the best performing models explain on average roughly 50% of the observed PM10 variance. From this it can be concluded that classification–based approaches are generally suitable for the downscaling of PM10, particularly during winter when PM10 concentrations in Bavaria reach maximum values. As preferable settings of the downscaling approaches, the usage of rather small spatial domains and a relatively high number of classes for circulation type classification and furthermore the utilization of multiple linear regression analyses or random forest analyses for relating CTs to PM10 have been ascertained. These findings provide the basis for further enhancements of the classification–based downscaling of monthly PM10 that will be realized in successive investigations.

The spatial representativeness of rural background air quality stations was estimated using the spatial distribution of air pollutants computed by the combinations of the results of annual WRF–CHIMERE model simulations and data measured at stations of the Iberian Peninsula in 2008, 2009 and 2010 for NO2, SO2, O3 and PM10. The advantage of using validated models combined with measurements is that effects of the emission sources distribution and atmospheric pollutant processes are both taken into account and that the model bias and errors are corrected. This methodology provides a considerably realistic spatial view of air pollutant concentration distribution around the rural background stations. The criteria for delimiting the representativeness area are based on the assumptions that: (1) concentration does not differ by more than a certain percentage from the concentration at the station; and (2) the air quality in the station and in the representativeness area should have the same status regarding the legal standard. The results showed that there is a large variability in the size and shape of the representativeness area of rural background stations in Spain, also depending on the pollutant and the limit or target value. In addition, the interannual variability of the representativeness areas, station redundancy and network coverage have been analyzed. A high interannual variability of spatial representativeness areas was found, except for daily and hourly SO2, hourly O3 and annual NO2. Roughly 50% of rural background stations measured O3 overlap with other stations in at least 80% of their spatial representativeness area, denoting a high percentage of station redundancy. Concerning network coverage, there are zones that are not covered by stations, the worst coverage being for PM10. The proposed methodology seems to be useful for determining the spatial representativeness of air quality stations.

Air pollution is the leading environmental concern in Macao. The temporal variations of particulate matter (PM) and black carbon (BC) mass concentrations and the size–resolved PM2.5 chemical compositions, including trace elements and carbonaceous species, were measured in Macao during a near–road summer campaign. The average concentrations for PM10, PM2.5 and BC at a roadside measurement site were 37.8μg m−3, 31.4μg m−3 and 5.6μg m−3, respectively. Results showed that local emissions contributed at least 35% to PM2.5 mass in Macao. Higher BC concentrations were observed in the daytime than at night, consistent with the diurnal variations of traffic flow. Factor analysis classified trace elements into three categories, representing crustal sources (Mg, Si, Al, Na, Fe, Ca and K), road traffic sources (Cu and Co) and secondary inorganic particle formation (S). Crustal elements and road traffic elements were enriched in the size ranges of >1.0μm and <0.2μm, respectively. The unimodal distribution pattern with a peak at <0.2μm for organic carbon (OC) was probably due to secondary organic aerosol formation, whereas about 70% of elemental carbon (EC) was in the size range of 0.2–1.0μm. Secondary organic aerosols were found to be a strong contributor to PM in the size ranges of <0.2μm and 1.0–2.5μm.