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.

In this study we present an analysis of spatio–temporal variability of monthly averaged Vertical Tropospheric Columns (VTCs) of NO2 over Pakistan using OMI (ozone monitoring instrument) dataset from December 2004 to November 2008. The results have shown significant spatial and temporal variability of NO2 column values over the study region. Four NO2 hotspots and a high density corridor were identified within the study region. The main sources of NO2 emissions in these areas were also investigated. During the study period, an average value of NO2 was observed to be 1.102±0.081×1015 molecules/cm2, with an increasing trend of 3.29% per year. Twin cities of Islamabad/Rawalpindi, Lahore, Dera Ghazi Khan and Karachi have shown positive trends of 44.10%, 23.48%, 31.40%, and 32.32% per year respectively. Karachi has shown the highest and the lowest mean monthly average values of 11.33×1015 molecules/cm2 and 0.98×1015 molecules/cm2 respectively. Air mass trajectories for hotspot regions have been used to track possible long–range transport of NO2.

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.

Surface sediments from the coastal area of the Leizhou Peninsula in the South China Sea were collected and analyzed and the potential ecological risks in the area were assessed based on acid-volatile sulfide (AVS) model. The AVS levels are between 0.109 and 55.6μmolg−1, with the average at 4.45μmolg−1. The high AVS-concentration zones include the aquaculture areas of Liusha Bay and the densely populated areas of Zhanjiang Bay. The simultaneously extracted metals (SEM) range from 0.026μmolg−1 to 8.61μmolg–1, with the average at 0.843μmolg–1. Most of high SEM-concentration stations were located in ports or aquaculture zones. Most of the coastal surface sediments of the Leizhou Peninsula (90%) had no adverse biological effects according to the criterion proposed by USEPA (2005); while adverse effects were uncertain in some stations (8%); even in 2 stations (2%) adverse biological effects may be expected.

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.

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.

Alcohol sulfates (AS) and alcohol ethoxysulfates (AES) are all High Production Volume and ‘down-the-drain’ chemicals used globally in detergent and personal care products, resulting in low levels ultimately released to the environment via wastewater treatment plant effluents. They have a strong affinity for sorption to sediments. Almost 50% of Tenerife Island surface area is environmentally protected. Therefore, determination of concentration levels of AS/AES in marine sediments near wastewater discharge points along the coast of the Island is of interest. These data were obtained after pressurized liquid extraction and liquid chromatography–tandem mass spectrometry analysis. Short chains of AES and especially of AS dominated the homologue distribution for AES. The Principal Components Analysis was used. The results showed that the sources of AS and AES were the same and that both compounds exhibit similar behavior. Three different patterns in the distribution for homologues and ethoxymers were found.

The size distribution of water-soluble inorganic components of urban aerosols in Shanghai was studied. The size-resolved aerosol samples collected by an 8-stage cascade sampler between April and May of 2012 were analyzed by ion chromatography. The ion mass concentrations followed the sequence of SO42−>NO3−~NH4+>Ca2+>Na+ ~Cl−>K+>Mg2+>F− for each size fraction below 2.1μm, while the sequence was NO3−>SO4−2>Ca2+>NH4+>Na+>Cl−>K+>Mg2+>F− for coarse mode particles larger than 3.3μm. The size distribution in 5 fractions showed that SO42−, NO3−, and NH4+ were generally in the fine mode peaking below 1μm while Ca2+, Mg2+, Na+, and Cl− were bimodally distributed with a second peak larger than 2.1μm. Back trajectory analyses revealed that the air masses could be classified into three main groups. The total ion concentrations were comparable between the terrestrial and mixing regimes. In the terrestrial regime, fine mode sulfate and nitrate were predominantly associated with ammonium. The excessive sulfate and nitrate over the whole size range might exist in the forms of Ca(NO3)2 and CaSO4. In the maritime regime, the decrease in SO42−, NO3−, and NH4+ contributed to the improvement in air quality. Besides marine aerosols, local emissions from soil dust and coal combustion were also important sources of sea-salt type ions (i.e., Na+ and Cl−).

Continuous operational monitoring by means of remote sensing contributes significantly towards less occurrence of oil spills over European waters however, operational activities show regular occurrence of accidental and deliberate oil spills over the North Sea, particularly from offshore platform installations. Since the areas covered by oil spills are usually large and scattered over the North Sea, satellite remote sensing particularly Synthetic Aperture Radar (SAR) represents an effective tool for operational oil spill detection. This paper describes the development of a semi-automated approach for oil spill detection, optimized for near real time offshore platform sourced pollution monitoring context. Eight feature parameters are extracted from each segmented dark spot. The classification algorithm is based on artificial neural network. An initial evaluation of this methodology has been carried out on 156 TerraSAR-X images. Wind and current history information also have been analyzed for particular cases in order to evaluate their influences on spill trajectory.

The microbial community and the nirS- and nirK-encoding denitrifiers in the intertidal sediments along Laizhou Bay in China were studied using pyrosequencing and real-time quantitative PCR (qPCR), respectively. There were three primary intertidal zones: Laizhou (La), Weifang Harbor (We), and Dongying (Do). Significant differences in composition and abundances at the different taxonomic levels were observed among the three bacterial communities. The qPCR results indicated that the nirS gene abundance varied from 8.67×105 to 5.68×106copies/gwet weight (ww), whereas the nirK gene abundance varied from 1.26×105 to 1.89×106copies/gww. The canonical correlation analysis (CCA) indicated that the sand percentage was the most important factor in shaping the bacterial community followed by silt percentage, NO2−, TOC, DO, pH, and clay percentage, whereas the clay percentage, pH, NO3−, DO, NO2−, TOC, silt percentage, and sand percentage were the most important factors associated with regulating the abundance of nirS- and nirK-encoding denitrifiers.