The production of a lipopeptide surfactant from the sponge-associated eubacteria Bacillus licheniformis NIOT-AMKV06 from the Andaman and Nicobar Islands was investigated. The highest production was attained with glucose and yeast extracts as the carbon and nitrogen sources (1.789mgmL−1), respectively. The surfactant was highly stable over a pH range of 5.0–10 and a temperature range of 20–70°C with high NaCl concentrations. Excellent emulsification activity was exhibited by the purified surfactant with crude oil, kerosene, and diesel. A two-fold increase in surfactant production (3.0mgmL−1) was observed using the newly formulated medium in this study. The surfactant biosynthesis gene cluster (sfp, sfpO, and srfA) from B. licheniformis NIOT-AMKV06 was heterologously expressed in Escherichia coli, and the production was increased three-fold (11.78gL−1) over the original strain. The results confirm the potential of the surfactant for use in bioremediation of hydrocarbons in a marine environment and for enhanced oil recovery. To our knowledge, this is the first report on the ability of a hydrocarbon to degrade B. licheniformis from marine sponges for the biosynthesis of a potent lipopeptide surfactant possessing characteristics of maximum stability, outstanding surfactant activity, and exceptional emulsifying capability.

Suspended particulate matter (SPM) and colloidal matter (COM) in annual dry and wet deposition samples in urban Guangzhou were for the first time collected, and their trace metals were investigated by using inductively coupled plasma mass spectrometry (ICP-MS). The deposition flux of SPM and of metal elements varied largely among the investigated seasons, and reached the maximum in spring. The correlation analysis indicated that significant correlations existed among some of the metal elements in the deposition samples. The enrichment factors (EF) of metals in COM in the deposition ranging from 79.66 to 130,000 were much higher than those of SPM ranging from 1.65 to 286.48, indicating the important role of COM. The factor analysis showed that emissions from street dust, non-ferrous metal production, and heavy fuel oil were major sources of the trace metals. Positive matrix factorization (PMF) model was used to quantitatively estimate anthropogenic source.

Acetylcholinesterase (AChE) activity measurement is widely used as a specific biomarker of neurotoxic effects. The aim of this study was to evaluate AChE activity in a host fish (the cod) and its acanthocephalan parasite Echinorhynchus gadi from the southern Baltic. AChE activity in hosts and parasites was inversely related: the highest cod AChE activity corresponded to the lowest E. gadi enzymatic activity and vice versa (“mirror effect”). This is the first report on the simultaneous application of this biomarker in cod and its acanthocephalan parasites. Results obtained for the host–parasite system are complementary and provide comprehensive information about the response of this biomarker. Analysis of the system allows for detection of a greater number of factors influencing AChE activity in the marine environment than separate analysis of the host and parasites. Thus, AChE activity measurement in a host–parasite system may be considered to be a promising tool for biomonitoring.

Future impacts from climate change and human activities may increase the likelihood of invasions of native marine species into existing habitats as a result of range shifts. To provide an understanding of the invasion of a native seagrass species (Syringodiumisoetifolium) into a tropical multi-species meadow, detailed field assessments were conducted over a six year period. After establishing in a discrete patch, the extent and standing crop of S.isoetifolium increased 800 and 7000 fold, respectively, between 1988 and 2003 (∼300–260,000 m2 and<1kgDW to 7596±555kgDW). The expansion of S.isoetifolium was confined to subtidal areas and appears primarily from clonal growth. The observed expansion of this species into a new locality was found to be clearly influenced by cumulative impacts and chronic small-scale physical disturbances. This study has immediate relevance to managing impacts which influence the spread of invasive species.

Mapping and monitoring of the seagrass Posidonia oceanica in the eastern (Croatian) part of the Adriatic Sea since 2004 indicates a significant decline in meadow density in an area impacted by inshore capture-based tuna aquaculture. The density and overall condition of P. oceanica meadows impacted by tuna farms near Fulija Islet was compared to two reference sites (Iž Island and Mrtovnjak Islet). The factors with the most significant influence on P. oceanica meadows were found to be the input of organic matter originating from the cages, as well as high epiphyte biomass caused by nutrient enrichment. Significant differences in nutrient concentrations were found between the sites impacted by tuna farms (Fulija Islet) and the control stations. Shoot density of the P. oceanica meadows decreased at the stations in close vicinity to the tuna farm, which suggests that the tuna farm activity strongly affected the surrounding meadows.

Ambient measurements of elemental species concentrations were made using an online elemental monitor at an air pollution monitoring station in Gwangju, Korea to evaluate the performance of the monitor for near–real time PM2.5 elemental monitoring and identify possible sources of the observed elements. This study also demonstrates the utility of integrating hourly elemental data with the meteorological data to better understand the sources of elements. Good agreement between the online and filter–based measurements was obtained for the elements studied, with an R2 of 0.73–0.97 and regression slopes of 0.84–2.37, suggesting the potential use of the online monitor to observe temporal variations in anthropogenic aerosol particles. Atmospheric element concentration levels studied were 2–20 times greater than their background levels when pollution plumes coming from industrial areas had impacted the site. Examination of the elements data revealed several short spikes of elements that are associated with local industrial emissions and road dusts. For the haze episodes observed over the study period, the element K was significantly influenced by biomass burning emissions with some impacts from soil dusts and industrial activities. Based on the wind direction and wind speed data, As/Se, Se/SO42−, and correlations among Se, As, Pb, SO2, and SO42−, it is suggested that the observed As and Se were significantly impacted by local anthropogenic sources and long–range transport of aerosols, rather than local coal combustion or coke emissions. Conditional probability functions were applied to identify likely local emission source locations of the elements observed at the site, indicating that the elements contributions were from the southwest and northeast directions, where two industrial areas and an express highway are located. Results from principal component analysis indicate that the observed concentrations of the element species were likely attributed to road dust/local industrial emissions, oil combustion, and metal processing.

The characteristics and sources of organic matter in water of the Lake Shihwa, which receives inputs from rural, urban, and industrial areas, were evaluated by examining the biodegradable organic carbon concentration, fluorescence spectra, and carbon and nitrogen isotope ratios, especially during rainy season and dry season. The organic matter transported from rural areas was of refractory nature, while that of industrial origin decomposed rapidly. As compared to the dry season, the organic matter in the rainy season was characterized by a reduced labile fraction. During the dry season, the autochthonous organic matter dominated in the lake, however, the contributions of allochthonous organic sources by industrial and rural areas significantly increased at rainy season. This investigation revealed that the transport of organic matter of anthropogenic origin to the Lake Shihwa was mainly influenced by heavy rainfall. Moreover, each anthropogenic source could differently influence the occurrence of organic matter in water of the Lake Shihwa.

Little attention is given to applying the artificial neural network (ANN) modeling technique to understand site–specific air pollution dispersion mechanisms, the order of importance of meteorological variables in determining concentrations as well as the important time scales that influence emission patterns. In this paper, we propose a methodology for extracting the key information from routinely–available meteorological parameters and the emission pattern of sources present throughout the year (e.g. traffic emissions) to build a reliable and physically–based ANN air pollution forecasting tool. The methodology is tested by modeling NO2 concentrations at a site near a major highway in Auckland, New Zealand. The basic model consists of an ANN model for predicting NO2 concentrations using eight predictor variables: wind speed, wind direction, solar radiation, temperature, relative humidity, as well as “hour of the day”, “day of the week” and “month of the year” representing the time variations in emissions according to their corresponding time scales. Of the three input optimization techniques explored in this study, namely a genetic algorithm, forward selection, and backward elimination, the genetic algorithm technique gave predictions resulting in the smallest mean absolute error. The nature of the internal nonlinear function of the trained genetically–optimized neural network model was then extracted based on the response of the model to perturbations to individual predictor variables through sensitivity analyses. A simplified model, based on the successive removal of the least significant meteorological predictor variables, was then developed until subsequent removal resulted in a significant decrease in model performance. The developed ANN model was found to outperform a linear regression model based on the same input parameters. The proposed approach illustrates how the ANN modeling technique can be used to identify the key meteorological variables required to adequately capture the temporal variability in air pollution concentrations for a specific scenario.

Source apportionment study of aerosols over Bay of Bengal (BOB) were investigated during Integrated Campaign on Aerosol Radiation Budget (ICARB) in the pre–monsoon (March–April 2006) and winter (December–January 2008–09) seasons. Positive matrix factorization (PMF) was applied to identify sources of ambient particulate matter using daily chemical composition data collected in the pre–monsoon (total suspended particles, TSP) and winter season (particles with a diameter < 10μm, PM10). Sea salt (SS), secondary aerosol (SA), Si–dust, fossil fuel combustion (FFC), biomass burning (BB) sources have been identified in both seasons, however their relative contributions were different. The combined contribution of Si–dust, secondary aerosol and fossil fuel combustion, constitute ~67% of particulate matter in pre–monsoon, whereas, secondary aerosols and biomass burning were the major contributors (63.2%) to particulate matter in winter. The identified sources effectively predict the measured particulate concentration in the pre–monsoon (r2=0.74) and winter season (r2=0.82). Another receptor model, principal component analysis (PCA) was done to increase the plausibility of the results obtained by PMF. PCA resulted in the identification of the sources that were comparable to the PMF outputs. PCA of TSP in the pre–monsoon season resulted in the extraction of three components (crustal dust + secondary aerosol, biomass burning, fossil fuel combustion + industrial emissions) that explained the 83% of the variance in the data. Similarly, in winter season, PCA resulted in the extraction of four components (biomass burning + secondary aerosol, industrial emission, crustal dust, sea salt) that explained the 86% of the variance of the data.

A laboratory study was performed to investigate the composition of products formed from OH–initiated oxidation of aromatic hydrocarbon 1,3,5–trimethylbenzene. The experiments were conducted by irradiating 1,3,5–trimethylbenzene/CH3ONO/NO/air mixtures in smog chamber. The chemical composition of gas and particle–phase products have been investigated with gas chromatography/mass spectrometry (GC/MS) and the aerosol laser time–of–flight mass spectrometer (ALTOFMS), respectively. Experimental results showed that 3,5–dimethyl benzaldehyde, 2,4,6–trimethylphenol, 2–methyl–4–oxo–2–pentenal and 3,5–dimethyl–2–furanone were the predominant products in both the gas and particle–phases. However, there were some differences between detected gas–phase products and those of particle–phase, for example, oxalic acid, 2–methyl–2–hydroxy–3,4–dioxo–pentanal, and 2,3,5–trimethyl–3–nitro–phenol were only existing in the particle–phase. The possible reaction mechanisms leading to these products are also proposed. Compared to offline methods such as GC–MS measurement, the ALTOFMS detection can analyze real–time the secondary organic aerosol (SOA) successfully and provide more information on the products. Thus, ALTOFMS is a useful tool to reveal the formation and transformation processes of SOA particles in smog chambers.