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.

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.

Seagrasses around the world are threatened by human activities that degrade water quality and reduce light availability. In this study, light requirements were determined for four common and abundant seagrasses along the Gulf coast of peninsular Florida using a threshold detecting algorithm. Light requirements ranged from 8% to 10% of surface irradiance for Halophila engelmannii to 25–27% of surface irradiance for Halodule wrightii. Requirements for all species differed from previous reports generated at other locations. Variations were attributed to morphological and physiological differences, as well as adaptation to light histories at specific locations. In addition, seagrasses were absent from stations with significantly higher concentrations of total nitrogen, total phosphorus, chlorophyll a and color. These results confirm the need to address links between increased anthropogenic nutrient loads, eutrophication, reduced light penetration, and loss of seagrasses and the services they provide.

Tropospheric NO2 columns observed from the Ozone Monitoring Instrument (OMI) were evaluated and the seasonal characteristics were analyzed at eastern China with surface measurements. A comparison between the DP (DOMINO) and SP (Standard Product) tropospheric NO2 products from OMI different algorithms shows similar spatial and temporal variability, but DP is generally higher than SP by 13% in wintertime and lower 9% in summertime on average over East China. Larger differences occur on the significantly contaminated regions. The differences in seasonality are associated with emissions sources. In order to investigate and monitor the air pollution monitoring over east China, the relative contributions of the stratosphere–troposphere separation and air mass factors calculations to the observed difference between DP and SP tropospheric NO2 columns were compared. The seasonal difference due to stratosphere–troposphere separation is opposite in sign to the tropospheric vertical columns. Air mass factors (AMFs) of DP are smaller than SP AMFs, leading to higher DP tropospheric columns. Impacts induced by different AMFs calculation are crucial. Then, the differences of four cities in significant polluted areas were compared. The results showed apparent discrepancies between two products in local region with irregular monthly variation, however the seasonal mean columns demonstrated that basically DP is larger than SP. Overall, this study analyses the discrepancies in DP and SP, as well as the seasonal variations over East China which is an important implication for the control of nitrogen oxides.

Although numerous studies have shown that hypoxia affects cortisol and aldosterone production in vivo, the underlying molecular mechanisms regulating the steroidogenic genes of these steroid hormones are still poorly known. MicroRNAs are post-transcriptional regulators that control diverse biological processes and this study describes the identification and validation of the hypoxia-inducible microRNA, miR-10b, as a negative regulator of the CYP11B1 and CYP11B2 steroidogenic genes in H295R human adrenocortical cells. Using the human TaqMan Low Density miRNA Arrays, we determined the miRNA expression patterns in H295R cells under normoxic and hypoxic conditions, and in cells overexpressing the human HIF-1α. Computer analysis using three in silico algorithms predicted that the hypoxia-inducible miR-10b molecule targets CYP11B1 and CYP11B2 mRNAs. Gene transfection studies of luciferase constructs containing the 3′-untranslated region of CYP11B1 or CYP11B2, combined with miRNA overexpression and knockdown experiments provide compelling evidence that CYP11B1 and CYP11B2 mRNAs are likely targets of miR-10b.

People spend most of their daytime in indoor environments. Their activities influence the composition of the indoor air by emitting volatile organic compounds (VOCs). The increasing number of different VOCs became the focus of attention in recent years as the question arises from the relationship between exposure to air pollutants and diseases. The present study of flats in Leipzig (Germany) is based on measurements of 60 different VOCs and is unique in the field of indoor air quality due to its enormous size of samples (n=2 242) and questionnaire data. The main purpose of our analysis was to identify the sources and patterns that characterize airborne VOCs in occupied flats. We combined two methods, principal components analysis (PCA) and non–negative matrix factorization (NMF), to assign compounds to their origin and to understand the coinstantaneous existence of several VOCs. PCA clustering provided a source apportionment and yielded 10 principal components (PCs) with an explained variance of 72%. However, real indoor air quality is often affected by combined sources. NMF reveals characteristic compositions of VOCs in indoor environments and emphasizes that constantly recurring structures are not single sources, but rather fusions of them, so called patterns. Interpreting these sources, we realized that homes were strongly influenced by ventilation, human activities, furnishings, natural processes (such as solar radiation) or their combinations. The very large set of samples and the combination with questionnaires applied on this comprehensive assessment of VOCs allows generalizing the results to homes in middle–scale cities with minor industrial pollution. As a conclusion, single VOC–dose–response relationships are inopportune for situations when indoor sources occur in combination. Further studies are necessary to assess associated health risks.

Phosphorus (P) is widely known as a limiting nutrient of water eutrophication for inland freshwater ecosystems. Owing to the complexity of P chemistry, remote sensing detection of total phosphorus (TP) concentrations currently remains limited especially for optically complex turbid inland waters. To address this need, a new TP remote sensing algorithm is developed based on prior water optical classification and the use of support vector regression (SVR) machine. The in situ observed datasets, used in this study, were collected at specific times during 2009 ~ 2011, covering a total of 232 stations from eight cruises in Lakes Taihu, Chaohu, Dianchi, and Three Gorges reservoir of China. Three types of waters were first classified by using a recently developed NTD675 (Normalized Trough Depth of spectral reflectance at 675 nm) water classification method. Then, spectral regions sensitive specifically to each water type were explored and expressed via several band ratios and used for retrieval algorithm development. The established type-specific SVR algorithms yield relatively high predictive accuracies. Specifically, the mean absolute percentage errors (MAPE) produced with the independent validation samples were achieved at 32.7, 23.2, and 14.1 % for type 1, type 2, and type 3 waters, respectively. Such water type-specific SVR algorithms are more accurate for the classified waters than an aggregated SVR algorithm for the nonclassified water and also superior to commonly used statistical algorithms. Moreover, application of the developed algorithms with HJ1A/HSI image data demonstrates that the algorithms have a large potential for remote sensing estimation of TP concentrations in optically complex turbid inland waters.

In allocating the waste load of a river basin, the first priority is to achieve a given water quality goal for that river by utilizing several water quality management methods. Minimizing the waste load abatement cost within the river basin through appropriate, efficient water quality management is an important aspect of this process. In the past, it was common to concentrate on economic factors when constructing a waste load allocation (WLA) model. However, environmental resources (e.g., sub-basin area, population, wastewater flow, etc.) vary in each region of a river, and the fairness in the distribution of the treatment efforts among waste dischargers must be considered. The WLA model in this study was constructed as a multi-objective optimization problem and was established to achieve the economic goal of minimizing waste load abatement and to consider the inequality among waste dischargers. Two types of inequality were introduced into the WLA model. The first type is the inequality in the waste load discharge regarding the environmental resources in each region was computed with the environmental resource-based Gini coefficient. The second type of inequality is the fairness in the distribution of the treatment efforts among waste dischargers. The suitability of this WLA model was verified with its application in a heavily polluted total maximum daily load subject river in South Korea. Furthermore, Pareto-optimal solutions drawn from the multi-objective genetic algorithm were analyzed to infer the least cost solution, the least inequality solution, and the compromise solutions and to verify critical pollution sources.

Wastewater treatment works can receive toxic substances that can kill microorganisms responsible for waste degradation. Implementation of toxicity monitors in-sewer, as part of an early warning system to help prevent toxic substances entering treatment works, is, however, very rare. This work presents results from a pilot-scale study using an in-sewer early warning system based on detection of nitrous oxide (N₂O) gas emitted by nitrifying bacteria naturally present in sewer biofilm. Nitrous oxide has potential to be an indicator of nitrification inhibition as it is typically emitted when nitrifiers are under stress. The biofilm was allowed to develop over 14 days under fixed wastewater flow and level. Presence of nitrifying bacteria was verified on day 13 followed by a 90 min toxic shock on day 14 by four different known nitrification inhibitors. Pre-shock nitrification rates averaged 0.78 mg-NH₄⁺-N mg-VS⁻¹ d⁻¹and were significantly reduced post shock to <0.2 mg-NH₄⁺-N mg-VS⁻¹ d⁻¹. Nitrous oxide emissions were found to vary with influent wastewater quality, suggesting a more complex data processing algorithm is needed instead of a simple threshold emission value. The extent of nitrification inhibition differed from the recorded response for suspended growth biomass with allylthiourea resulting in a 77 and 81 % nitrification inhibition for literature suspended growth EC₅₀and EC₇₅concentrations, respectively. Results from this study suggest nitrifying biofilms in closed pipes can be used as part of an early warning system but will likely require amplification of the response to be of practical use. Further research is required to better understand the biofilm response and calibrate the early warning system for differentiating its unique baseline from true toxicity events.

The application of advanced oxidation process (AOP) in the treatment of wastewater contaminated with oil was investigated in this study. The AOP investigated is the homogeneous photo-Fenton (UV/H₂O₂/Fe⁺²) process. The reaction is influenced by the input concentration of hydrogen peroxide H₂O₂, amount of the iron catalyst Fe⁺², pH, temperature, irradiation time, and concentration of oil in the wastewater. The removal efficiency for the used system at the optimal operational parameters (H₂O₂ = 400 mg/L, Fe⁺² = 40 mg/L, pH = 3, irradiation time = 150 min, and temperature = 30 °C) for 1,000 mg/L oil load was found to be 72 %. The study examined the implementation of artificial neural network (ANN) for the prediction and simulation of oil degradation in aqueous solution by photo-Fenton process. The multilayered feed-forward networks were trained by using a backpropagation algorithm; a three-layer network with 22 neurons in the hidden layer gave optimal results. The results show that the ANN model can predict the experimental results with high correlation coefficient (R² = 0.9949). The sensitivity analysis showed that all studied variables (H₂O₂, Fe⁺², pH, irradiation time, temperature, and oil concentration) have strong effect on the oil degradation. The pH was found to be the most influential parameter with relative importance of 20.6 %.