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.

The present study was conducted on clover (Trifolium alexandrinum L. cv Wardan), to investigate the effect of ambient and elevated (ambient +10 ppb O3) ozone (O3) on plants grown in open top chambers (OTCs) germinated from gamma (γ) irradiated seeds. Dry seeds were subjected to irradiation with 0, 5, 10 and 20 krad doses of γ rays from 60Co source. Dose dependent differential responses were observed on growth and biomass, photosynthetic pigments, metabolites, antioxidative defense system of plant. Growth parameters and biomass of plants were severely affected under elevated O3 with increasing radiation doses, except, 5 krad which showed a reverse trend of response. Photosynthetic pigments and total soluble proteins were also reduced with higher dose of γ radiation and elevated O3. Reactive oxygen species formation and membrane damage increased significantly to different extents. Plants grown from seeds irradiated with low dose (5 krad) of γ irradiation depicted more induction of antioxidants (enzymatic and non–enzymatic) than higher doses suggesting their high ameliorative capability against elevated O3. Principal component analysis has also confirmed that plants grown from 5 krad γ irradiated seeds performed better against O3 depicting reduction in negative effect against elevated O3. The experimental findings evidently showed that 5 krad γ radiations altered the O3 induced stress and thus minimized the loss in biomass of the test plant.

Global in its distribution and pervading all levels of the water column, marine debris poses a serious threat to marine habitats and wildlife. For cetaceans, ingestion or entanglement in debris can cause chronic and acute injuries and increase pollutant loads, resulting in morbidity and mortality. However, knowledge of the severity of effects lags behind that for other species groups. This literature review examines the impacts of marine debris on cetaceans reported to date. It finds that ingestion of debris has been documented in 48 (56% of) cetacean species, with rates of ingestion as high as 31% in some populations. Debris-induced mortality rates of 0–22% of stranded animals were documented, suggesting that debris could be a significant conservation threat to some populations. We identify key data that need to be collected and published to improve understanding of the threat that marine debris poses to cetaceans.

We investigated the effects of salinity (5‰, 15‰, 25‰ and 35‰) on metal ion (Cu and Zn) and nanoparticle (NP) CuO and ZnO toxicity to Tigriopus japonicus. Increasing the test media volume without renewal increased the 96-h LC50 for Cu (32.75mgL−1) compared to the reported value (3.9mgL−1). There was no significant difference in acute toxicity at different salinities between acclimated and unacclimated T. japonicus (p>0.05). Increasing salinity decreased the dissolved concentrations of Cu and Zn ions due to the precipitation of the metal ions, consequently reducing the acute toxicity to T. japonicus. The effect of salinity on acute CuO and ZnO NP toxicity was similar to that on metal ion toxicity. Since the aggregation of NPs generally enhanced at higher salinities, both the dissolution and aggregation of CuO and ZnO NPs may control the effect of salinity on acute toxicity to T. japonicus.

We examined if there is truth to the preconceptions that non-resident workers (including FIFO/DIDO’s) detract from communities. We used marine debris to test this, specifically focussing on littering behaviour and evidence of awareness of local environmental programs that focus on marine debris. Littering was most common at recreational areas, then beaches and whilst boating. Twenty-five percent of respondents that admit to littering, reported no associated guilt with their actions. Younger respondents litter more frequently. Thus, non-resident workers litter at the same rate as permanent residents, visitors and tourists in this region, within this study. Few respondents are aware of the environmental programs that operate in their local region. Awareness was influenced by a respondent’s residency (non-residents are less aware), age, and level of education. To address this failure we recommend that industries, that use non-resident workers, should develop inductions that expose new workers to the environmental programs in their region.

Effects of the antimicrobial agent triclosan to natural periphyton communities (biofilms, comprising primarily microalgae and bacteria) were assessed in two independent experiments during spring and summer. For that purpose a semi-static test system was used in which periphyton was exposed to a concentration range of 5–9054nmol/L triclosan. Effects on algae were analyzed as content and composition of photosynthetic pigments. The corresponding EC50 values were 39.25 and 302.45nmol/L for the spring and summer experiment, respectively. Effects on periphytic bacteria were assessed as effects on carbon utilization patterns, using Biolog Ecoplates. No inhibition of either total carbon utilization or functional diversity was observed, indicating a pronounced triclosan tolerance of the marine bacteria. In contrast, a small stimulation of the total carbon utilization was observed at triclosan concentrations exceeding 100nmol/L.

We evaluated the biodegradability of physically (WAF) and chemically (CEWAF) dispersed oil in brackish water (salinity ∼6.5g/L), and the influence of nutrient availability (low nutrient-LN: background water vs. high nutrient-HN: addition of 100mgNO3-N/L and 10mg PO4-P/L to background water) on oil biodegradation rates at 15±0.5°C for 42days. No oil removal occurred in WAF compared with CEWAF: 24% in HN and 14% in LN within two weeks. The oil biodegradation concerned mainly alkanes as confirmed by GC/MS analyses. Higher O2 consumption (10.30mgL−1day−1) and CO2 production (3.89mg CL−1day−1) were measured in HN compared with LN (O2: 2.79mgL−1day−1, CO2:0.18mg CL−1day−1). Estimated biomass of hydrocarbon degraders and heterotrophic bacteria was at least an order of magnitude larger in HN than in LN. Combining dispersants with nutrients could enhance oil biodegradation and help improve oil spill mitigation responses.