In order to examine the influence of discharge from different rivers from peninsular India and urban sewage on intensity and dissemination of heterotrophic, indicator and pathogenic bacteria, a study was carried out during peak discharge period along coastal Bay of Bengal. The coastal Bay received freshwater inputs from the river Ganges while Godavari and Krishna contributed to the south. Contrasting difference in salinity, temperature, nutrients and organic matter was observed between north and south east coast of India. The highest heterotrophic, indicator and pathogenic bacterial abundance was observed in the central coastal Bay that received urban sewage from the major city. Intensity and dissemination of heterotrophic, indicator and pathogenic bacteria displayed linear relation with magnitude of discharge. The coliform load was observed up to 100km from the coast suggesting that marine waters were polluted during the monsoon season and its impact on the ecosystem needs further studies.

The Plateau Rosa Alpine station (Italy) has collected atmospheric concentrations of carbon dioxide since 1989. If the complete set of hourly data is observed, two distinct and exceptional very high concentration events are evident for February 2004. Similar and almost contemporary peaks were registered at the European high–altitude stations of Zugspitze–Schneefernerhaus and Sonnblick in the Alps, and at Mt. Cimone in the Northern Apennines. A regional meteorological model (the Weather Research and Forecast) was applied over a medium–high resolution grid to study the evolution of the meteorological fields and to identify the trajectories of the polluted air masses during the CO2 observed peaks. The results show that, during both episodes, atmospheric circulation conveyed highly polluted air from the European plains to the Alpine stations. This conclusion has been also confirmed through concentration measurements of the atmospheric trace gases in the same area.

Shark fisheries have expanded due to increased demand for shark products. As long-lived apex predators, sharks are susceptible to bioaccumulation of metals and metalloids, and biomagnification of some such as Hg, primarily through diet. This may have negative health implications for human consumers. Concentrations of Hg, As, Cd, Cu, Fe, Se and Zn were analysed in muscle, liver and fin fibres (ceratotrichia) from dusky Carcharhinus obscurus, sandbar Carcharhinus plumbeus, and white Carcharodon carcharias sharks from south-eastern Australian waters. Concentrations of analytes were generally higher in liver than in muscle and lowest in fin fibres. Muscle tissue concentrations of Hg were significantly correlated with total length, and >50% of sampled individuals had concentrations above Food Standards Australia New Zealand’s maximum limit (1mgkg−1ww). Arsenic concentrations were also of concern, particularly in fins. Results warrant further investigation to accurately assess health risks for regular consumption of shark products.

A mathematical model was developed to simulate the oxidation of total sulfite in the holding tank of ammonia-based flue gas desulfurization (FGD) system. The model could provide predictions of the oxidation ratio of total sulfite at different operating condition such as pH, concentration of total sulfur, air flow and residence time. The calculation results of an ammonia-based FGD system for 2 × 150 MW boilers by this model were compared to that of corresponding measured values. The comparison results show that the simulated oxidation ratio of total sulfite agrees well with the measured data. The difference between calculated values and measured data is below 4.2%. This model appears to be beneficial for optimizing design and operation of total sulfite oxidation of an ammonia-based FGD system.

The objectives of this work were to investigate trimethylamine (TMA) removal by thermophilic biotrickling filtration (TBTF) and to examine the microbial community developed at 56 °C. TMA removal efficiency in the TBTF system was up to 99.9%. At a bed contact time of 25.8s, the elimination capacity at 56 °C in the TBTF was 375.2g TMAm−3 h−1, which was higher than that of mesophilic biotrickling filtration. TBTF was able to quickly recover from a normal temperature shutdown period of a month. The thermophilic bacterial communities in the TBTF which were assessed by polymerase chain reaction–denaturing gradient gel electrophoresis (PCR–DGGE), play the dominant role in the thermophilic biological processes of metabolism, ammoxidation, nitrification, denitrification and carbon oxidation. These results show that TBTF is achievable and open new possibilities for applying biotrickling filtration to hot odorous gas streams from sewage sludge drying.

Assessing the environmental impact of salmon farms on benthic systems is traditionally undertaken using biotic indices derived from microscopic analyses of macrobenthic infaunal (MI) communities. In this study, we tested the applicability of using foraminiferal-specific high-throughput sequencing (HTS) metabarcoding for monitoring these habitats. Sediment samples and physico-chemical data were collected along an enrichment gradient radiating out from three Chinook salmon (Oncorhynchus tshawytscha) farms in New Zealand. HTS of environmental DNA and RNA (eDNA/eRNA) resulted in 1,875,300 sequences that clustered into 349 Operational Taxonomic Units. Strong correlations were observed among various biotic indices calculated from MI data and normalized fourth-root transformed HTS data. Correlations were stronger using eRNA compared to eDNA data. Quantile regression spline analyses identified 12 key foraminiferal taxa that have potential to be used as bioindicator species. This study demonstrates the huge potential for using this method for biomonitoring of fish-farming and other marine industrial activities.

The spatial distribution of dinoflagellate cysts was studied to understand the impact of industrial pollution on the surface sediment of Izmir Bay, Turkey. Forty two dinoflagellate cyst morphotypes belonging to 12 genera were identified and qualified at 12 sampling points. The cyst of Gymnodinium nolleri dominated the bay and had the highest abundance in most of the stations, following Spiniferites bulloideus and Lingulodinium machaerophorum. The highest cyst concentration was recorded in the inner part of the bay. Cyst concentration ranged between 384 and 9944cystg−1 dry weight of sediment in the sampling area. Sediment metal concentrations were determined. Heavy metal levels in Izmir Inner Bay were higher than the Middle and Outer Bay. L. machaerophorum, Dubridinium caperatum and Polykrikos kofoidii showed significant positive correlation with some metals (Cd, Pb, Cu, Zn) and organic carbon content. However, there was no significant correlation between dinoflagellate cyst abundance and sediment type.

This work gives an assessment of the hazard faced by Sicily coasts regarding potential offshore surface oil spill events and provides a risk assessment for Sites of Community Importance (SCI) and Special Protection Areas (SPA). A lagrangian module, coupled with a high resolution finite element three dimensional hydrodynamic model, was used to track the ensemble of a large number of surface trajectories followed by particles released over 6 selected areas located inside the Sicily Channel. The analysis was carried out under multiple scenarios of meteorological conditions. Oil evaporation, oil weathering, and shore stranding are also considered. Seasonal hazard maps for different stranding times and seasonal risk maps were then produced for the whole Sicilian coastline. The results highlight that depending on the meteo-marine conditions, particles can reach different areas of the Sicily coast, including its northern side, and illustrate how impacts can be greatly reduced through prompt implementation of mitigation strategies.

The importance of air pollution monitoring networks in urban areas is well known because of their miscellaneous applications. At the beginning of the 1990s, Berlin had more than 40 particulate matter monitoring stations, whereas, by 2013, there were only 12 stations. In this study, a new and free–of–charge methodology for the densifying of the PM10 monitoring network of Berlin is presented. It endeavors to find the non–linear relationship between the hourly PM10 concentration of the still–operating PM10 monitoring stations and the shut–down stations by using the Artificial Neural Network (ANN), and, consequently, the results of the shut–down stations were simulated and re–constructed. However, input–variables selection is a pre–requisite for any ANN simulation, and hence a new fuzzy–heuristic input selection has been developed and joined to the ANN for the simulation. The hourly PM10 concentrations of the 20 shut–down stations were simulated and re–constructed. The mean error, bias and absolute error of the simulations were 27.7%, –0.03 (μg/m3), and 7.4 (μg/m3), respectively. Then, the simulated hourly PM10 concentration data were converted to a daily scale and the performance of ANN models which were developed for the simulation of the daily PM10 data were evaluated (correlation coefficient >0.94). These appropriate results imply the ability of the developed input selection technique to make the appropriate selection of the input variables, and it can be introduced as a new input variable selection for the ANN. In addition, a dense PM10 monitoring network was developed by the combination of both the re–constructed (20 stations) and the current (12 stations) stations. This dense monitoring network was applied in order to determine a reliable mean annual PM10 concentration in the different areas in Berlin in 2012.

The Kolmogorov–Zurbenko (KZ) filter method is a tool to improve air quality management, since it determines meteorological effects on air quality concentrations and separates out those effects in order to examine underlying trends. Air quality in Portugal is exceeding the legislated particulate matter (PM10), nitrogen oxides (NO2) and ozone (O3) levels; accordingly, measures to reduce pollutant emissions have been designed. The primary objective of this work is to investigate the influence of meteorology on O3, NO2 and PM10 levels and the long-term air quality trends. Air quality and meteorological datasets were explored for the period 2002–2012 through the decomposition of time-series using the Kolmogorov–Zurbenko (KZ) filter. To determine the best meteorological predictors for the air quality data, a stepwise regression analysis of the filtered time-series was applied. The KZ filter application revealed that the short-term component has the highest contribution to the total variance of the original air quality data (≈64%–PM10; ≈52%–O3; ≈54%–NO2) followed by the seasonal component. The long-term component exhibits the influence of the emission control regulations implemented in each study region. The statistical analysis of the air quality and the meteorological data indicated that O3 has a statistically significant relationship with temperature in most of the components. The results also indicate that emission control strategies are primary regulators for NO2 and PM10 levels. Therefore, to establish an accurate strategy to improve air quality further, it will be essential to include meteorological effects. This study highlights that the KZ filter is a useful tool to support the design and implementation of adequate air quality strategies and for the investigation of the interactions between the emissions, the meteorological conditions and the air quality levels, to improve air quality management in these two urban areas.