Seagrass ecosystems, considered among the most efficient carbon sinks worldwide, encompass a wide variety of spatial configurations in the coastal landscape. Here we evaluated the influence of the spatial configuration of seagrass meadows at small scales (metres) on carbon storage in seagrass sediments. We intensively sampled carbon stocks and other geochemical properties (δ13C, particle size, depositional fluxes) across seagrass–sand edges in a Zostera muelleri patchy seagrass landscape. Carbon stocks were significantly higher (ca. 20%) inside seagrass patches than at seagrass–sand edges and bare sediments. Deposition was similar among all positions and most of the carbon was from allochthonous sources. Patch level attributes (e.g. edge distance) represent important determinants of the spatial heterogeneity of carbon stocks within seagrass ecosystems. Our findings indicate that carbon stocks of seagrass areas have likely been overestimated by not considering the influence of meadow landscapes, and have important relevance for the design of seagrass carbon stock assessments.

We assessed the effects of 40 ocean outfalls on adjacent macrobenthic invertebrates. Data were obtained from a review of gray and peer-review literature. Different parameters describing the outfall characteristics were compiled (length, maximum depth, treatment level, flow and organic matter mass discharged). Exposure to wave action was represented by significant wave height. The magnitude of the effect was categorized in three impact levels and classified considering different ecological indicators. A theoretical predictive model was formulated in which the lower the organic matter and the higher the energy of the system, the lower the benthic impact. The main conclusion was that the general pattern of the succession of benthic communities brought about by ocean outfalls fits the model of Pearson–Rosenberg but with some deviations i) the probability of a significant impact is much lower, ii) not all the successional stages occur and, iii) the magnitude of the changes are usually lower.

Perna viridis was used as biomonitor to assess heavy metal levels in the Chacopata-Bocaripo lagoon axis, Venezuela, during rain and drought seasons. The mussels were weighed and measured. The metal concentrations were determined by atomic absorption spectrophotometry. For rain period, the order of bioavailability was: Cu>Ni>Mn>Co>Cd>Pb, and for drought: Cu>Mn>Ni>Co>Pb>Cd. The concentrations of Ni, Co, Cd and Pb showed significant differences (P<0.05) in both periods. There was higher metal accumulation during drought season, possibly related to upwelling, since it produces an increase in primary productivity, which translates more food into organisms, making metals bioavailable for mussels. Only Cu and Mn showed significant relationships between the size and metal concentration, during drought period, it may be because of the organisms need for these essential metals in different physiological processes.

The Georgia Sea Turtle Center has a mission of conservation based rehabilitation, research, and education. Marine debris is a serious threat to marine species. In an effort to educate local students, the GSTC obtained a grant to provide educational opportunities to local third graders. Third and fourth grade classes in Glynn County, Georgia were offered a Garbage in the Water program and 964 students were reached. After programming, students showed a statistically significant (p<.0001) increase in test scores between the pre and posttests. This success led to repeat funding for additional programming for first grades as well as a formalized relationship with the Glynn County School District. As part of this relationship the Georgia Sea Turtle Center is now the official field trip location for all third grades in the district.

Pollution caused by particles with aerodynamic diameters less than 2.5μm (PM2.5) is now a major environmental problem in many Asian cities. Planting more trees has been suggested as an unconventional approach to alleviate the problem. In this study, we developed a ranking approach to evaluate the PM2.5 removal efficiency, negative impacts on air quality, and the suitability to urban environments of commonly occurring urban tree species. The results showed that the most frequently occurring tree species in global cities were not the best performers in removing PM2.5. Among the ten most frequently occurring tree species, only London plane (Platanus acerifolia (Aiton) Wild.), silver maple (Acer saccharinum L.) and honey locust (Gleditsia triacanthos L.) were ranked above average. However, there is great potential for improving the removal of PM2.5 from urban air by using species that have high PM2.5 removal efficiency, especially conifer species. Use of conifer species requires choosing the correct gender and matching trees with appropriate sites. The results from this study can assist environmental management agencies in the selection of tree species for urban greening projects focusing on PM2.5 control.

Urban air quality management plan (UAQMP) is an effective and efficient tool employed in managing acceptable urban air quality. However, the UAQM practices are specific to a country’s needs and requirements. Majority of the developed countries have full–fledged UAQMP with a regulatory management framework. However, developing countries are still working in formulating the effective and efficient UAQMPs to manage their deteriorating urban air environment. The first step in the process of formulation of UAQMP is to identify the air quality control regions based on ambient air quality status and second, initiate a time bound program involving all stakeholders to develop UAQMPs. The successful implementation of UAQMPs depends on the strength of its key components, e.g. goal/objective, monitoring network, emission inventory, air quality modeling, control strategies and public participation. This paper presents a comprehensive review on UAQMPs, being implemented worldwide at different scales e.g., national (macro), city (medium), and local (micro).

The four–dimensional data assimilation (FDDA) technique in the Weather Research and Forecasting (WRF) meteorological model has recently undergone an important update from the original version. Previous evaluation results have demonstrated that the updated FDDA approach in WRF provides more accurate wind fields aloft than the original approach, particularly during the nocturnal period when low level jets are a common feature in the Eastern United States. Due to the importance of WRF/FDDA meteorological fields in retrospective air quality applications, a modeling study with the Community Multiscale Air Quality (CMAQ) model was undertaken to ascertain if the improved wind flow fields translate into better performance for ozone. To undertake this objective, separate CMAQ model simulations were performed with meteorological inputs generated by WRF using the original and the updated FDDA approaches for a three month summer period. The evaluation effort focused on observed and modeled surface ozone from a mid–morning hour (10 local daylight time (LDT)). Comparisons of modeled results against concentrations aloft from an instrumented tall tower and from available morning vertical profile measurements were also examined. Surface concentrations near 10 LDT are desirable for evaluating the transport process since they are often representative of ozone that has been transported aloft overnight and has undergone downward entrainment in response to convective mixing the following morning. Statistical results from surface observed and modeled concentration pairs indicated modeled ozone from the CMAQ simulation using the updated FDDA meteorology displayed smaller biases and lower absolute errors at 88% and 80% of monitoring sites, respectively, in the Eastern United States. The CMAQ results with the updated FDDA generally exhibited smaller biases and lower absolute errors at monitoring sites across the northern states than in the southeastern states. The results provide evidence that the more accurate wind flows generated with the updated WRF/FDDA approach improved CMAQ model performance based on the statistical results from 10 LDT ozone concentrations.

A nitrous oxide (N2O) emission database was compiled for arable land (284 measurements from 62 studies) to establish predictive models for building a greenhouse gas emission inventory in China. Arable lands were grouped into dry land and rice paddy based on the IPCC 2006 guidelines. The results of the meta analysis show that the annual mean N2O fluxes from dry land and rice paddy were 4.69±4.62 (SD) and 5.89±3.23kg N2O–N ha–1 yr–1. Fertilizer–induced N2O emission factors were 0.68±0.41% for dry land, and 0.49±0.43% for rice paddy. The relationship between N2O flux from arable lands and various environmental variables were analyzed, and the magnitude of N2O emissions from zero mineral N addition control plots (background emission) was determined based on precipitation. Based on the above background emissions and correlation coefficients, two new predictive models were established to estimate N2O emissions from arable lands in China. Comparison showed that the precipitation–rectified background emissions could largely improve the model predictions, and the two new models had better performance than the 1996 IPCC guideline method. Therefore, it is strongly recommended that the important local environmental variables be included in the estimates when compiling a national N2O emission inventory.

Samples of surface sediment were collected off the north Shandong Peninsula for grain size and element analyses. Based on the grain size analysis, the surface sediments were dominated by silt and sand, with a small portion of clay, and were probably from the coastal erosion of the Shandong Peninsula. The spatial distribution patterns of the heavy metals were primarily controlled by the sediment types. The geo-accumulation indexes suggest that there was no Cu, Zn and Cr pollution in the study area; Pb and Cd contaminations appeared only at a few stations, while As pollution was distributed widely. The enrichment factors indicated that Cu, Zn and Cr were primarily from terrigenous materials. By contrast, Cd, Pb and As, and especially Cd and As, were probably largely provided by anthropogenic sources. Due to the dilution of coarse-grained matters, there was no contamination at some of the stations at which the influence of human activities was obvious.

A microbiological survey of the Priolo Bay (eastern coast of Sicily, Ionian Sea), a chronically polluted marine coastal area, was carried out in order to discern its intrinsic bioremediation potential. Microbiological analysis, 16S rDNA-based DGGE fingerprinting and PLFAs analysis were performed on seawater and sediment samples from six stations on two transects. Higher diversity and variability among stations was detected by DGGE in sediment than in water samples although seawater revealed higher diversity of culturable hydrocarbon-degrading bacteria. The most polluted sediment hosted higher total bacterial diversity and higher abundance and diversity of culturable HC degraders. Alkane- and PAH-degrading bacteria were isolated from all stations and assigned to Alcanivorax, Marinobacter, Thalassospira, Alteromonas and Oleibacter (first isolation from the Mediterranean area). High total microbial diversity associated to a large selection of HC degraders is believed to contribute to natural attenuation of the area, provided that new contaminant contributions are avoided.