Framework for assessing impacts of pile-driving noise from offshore wind farm construction on a harbour seal population

Offshore wind farm developments may impact protected marine mammal populations, requiring appropriate assessment under the EU Habitats Directive. We describe a framework developed to assess population level impacts of disturbance from piling noise on a protected harbour seal population in the vicinity of proposed wind farm developments in NE Scotland. Spatial patterns of seal distribution and received noise levels are integrated with available data on the potential impacts of noise to predict how many individuals are displaced or experience auditory injury. Expert judgement is used to link these impacts to changes in vital rates and applied to population models that compare population changes under baseline and construction scenarios over a 25 year period. We use published data and hypothetical piling scenarios to illustrate how the assessment framework has been used to support environmental assessments, explore the sensitivity of the framework to key assumptions, and discuss its potential application to other populations of marine mammals.     

Consideration of scales in offshore wind environmental impact assessments

Quality of environmental impact assessments (EIAs) has been criticized, in part due to a lack of accounting in these tools for differing spatial and temporal scales inherent in ecological data. In the United States, leases of outer continental shelf blocks for offshore wind projects and their construction and operation plans require EIAs in accordance with the National Environmental Policy Act of 1969 and the 1978 Council on Environmental Quality Regulations for Implementing the Procedural Provisions of the National Environmental Policy Act. This study evaluated consideration of spatiotemporal scales of stressors, receptors (specifically cetaceans), and effects in eight federal offshore wind energy EIAs against 26 criteria extracted from federal regulations. The criteria analysis determined that EIAs do not consistently or comprehensively address spatiotemporal scales with respect to federal requirements. Deficiencies in addressing spatiotemporal scales may result from imprecise regulations, intent to simplify encyclopedic documents, or lack of data resulting in incomplete assessments, inappropriate mitigation actions, and projects delays. Recommendations to improve compliance with federal regulations include making federal guidance binding, focusing on non-trivial impacts of species, tiering information, and incorporating outcomes of marine spatial planning.     

Environmental impacts of decommissioning: Onshore versus offshore wind farms

Increasing concerns over climate change have prompted rapid growth of renewable energy over the past few decades, particularly wind energy. However, as the installation of wind farms rises, so will the need for decommissioning and analysis of the environmental impacts associated with decommissioning. This paper investigates how Environmental Impacts Assessments (EIA) identify, estimate and manage potential impacts of decommissioning. EIAs from 12 onshore and offshore windfarms consented between 2009 and 2014 in England and Scotland were analysed and compared. Attributes of these windfarms' Environmental Statements (ES) were scored under six categories: decommissioning in EIA stages, definitions of decommissioning, amount of analysis, depth of analysis, impacts identified, and proactive planning. Onshore windfarms generally tended to investigate the impacts of decommissioning less than offshore windfarms, even those which gained consent in the same year. The investigation of the impact of decommissioning improved for windfarms consented in the latter years of the study period. Across the ESs there was a lack of analysis of potential impacts from decommissioning in their own right: not simply as a reversal of the construction process. The impacts of different end of life scenarios were not analysed in any of the ESs studied. There is evidence to suggest the presence of windfarms, especially offshore, could in some cases be environmentally beneficial for certain species. However, the ecological impact of removing offshore structures at the end of life is unknown and is currently not investigated nor predicted in EIAs. Understanding the potential implications of full or partial removal of marine structures, or alternatives to decommissioning, could ensure that appropriate mitigation is considered at an early stage by both developer and consenting authority. That being said, it is also important to update the assessment of potential impacts over the life of the project as more information on the environment is gathered and end of life plans develop.     

Accuracy of wind farm visualisations: The effect of focal length on perceived accuracy

Wind energy has been the fastest growing renewable energy technology for more than a decade. However, the visual impacts of wind farms are still one of the most controversial effects of wind energy development. Photomontage visualisations are frequently used in Visual Impact Assessments² (VIA) to give a sense of scale of the proposed development. Yet visualisations in VIAs are often perceived to underestimate the scale and magnitude of the visual impact of wind turbines. The aim of this multiphase mixed methods study is to explore the perceived accuracy of images that represent the visual impacts of both onshore and offshore wind farms. Field visits and data from a public survey are used to assess the accuracy of visualisations and to examine the effect of camera lens focal length on perception of scale of wind turbines. Results show that panoramic photomontages are perceived as the least accurate, while images taken at 75 mm focal length in full frame format are perceived as the most accurate form of representation of the scale and visual impact of wind turbines. These findings imply that the panoramic visualisation technique, which has been used for decades to predict the scale of wind turbines in VIAs, is ineffective in predicting accurately the visual impact of wind farms, and an alternative predictive technique is needed. For wind farm visualisations the use of 75 mm full frame image format is recommended in order to improve the accuracy, enable better informed decision making and avoid the loss of credibility of visualisations and VIAs.     

Differential recovery of habitat use by birds after wind farm installation: A multi-year comparison

Onshore wind farms remain one of the most widely used technologies for the production of renewable energy. These are known to affect birds through disturbance or collision. Most research focus on the impact of wind farms on raptors or other large bird species, especially those of conservation concern. However, limited information exists on the effect of wind farms on small birds. Recovery of large versus small bird populations impacted by wind farms is also largely unstudied. A reason for this is the lack of long-term datasets based on standardized, systematic assessments. We monitored birds in the vicinity of a wind farm in an upland habitat in southern Spain (Malaga province), immediately after installation and 6.5 years post-construction. During both study periods, we observed 11 raptor and 38 non-raptor species (including 30 passerines). We found differences in recovery rates between raptors and non-raptors. Raptors showed an upturn in numbers but non-raptor abundance fell significantly.Greater attention should be paid to the recovery of wildlife after initial impact assessments than at present. This study confirms that regulatory authorities and developers should consider the likely impacts of wind farms on small bird populations. Mitigation measures focused particularly on non-raptor species should be considered and implemented as a means to reduce these negative effects.     

Approaching population thresholds in presence of uncertainty: Assessing displacement of seabirds from offshore wind farms

Assessment of the displacement impacts of offshore wind farms on seabirds is impeded by a lack of evidence regarding species-specific reactions to developed sites and the potential ecological consequences faced by displaced individuals. In this study, we present a method that makes best use of the currently limited understanding of displacement impacts. The combination of a matrix table displaying the full range of potential displacement and mortality levels together with seasonal potential biological removal (PBR) assessments provides a tool that increases confidence in the conclusions of impact assessments. If unrealistic displacement levels and/or mortality rates are required to equal or approach seasonal PBRs, this gives an indication of the likeliness of adverse impacts on the assessed population. This approach is demonstrated by assessing the displacement impacts of an offshore wind farm cluster in the German North Sea on the local common guillemot (Uria aalge) population.     

International law regulation of offshore oil and gas exploitation

In the current scenario, the exploitation of oil and natural gas deposits on and offshore still represents the most significant global energy needs. For decades, offshore hydrocarbon resources have been explored and exploited among the traditional commercial uses of coastal continental shelter. However, this activity presents considerable potential threats to the marine environment. Surprisingly, a global multilateral environment does not govern the offshore gas and oil sectors, although public international and regional law rules are generally applicable. The maritime oil & gas industry is instead regulated primarily by national legislation. Besides, it is subject to a mostly independent sector, which traditionally uses its contractual solutions in a capital-intensive industry. This paper aims to give an innovative Support Vector Machine (SVM) approach to discuss the unique regulatory framework on amplitude (2012L), frequency (28 Hz), velocity (6325 m/s), line curvature (0.62), polarity (3.2), layer thickness (25 mm) in the industry when compared to other methods.     

Detection and object-based classification of offshore oil slicks using ENVISAT-ASAR images

The aim of this study is to propose and test a multi-level methodology for detection of oil slicks in ENVISAT Advanced Synthetic Aperture Radar (ASAR) imagery, which can be used to support the identification of hydrocarbon seeps. We selected Andrusov Ridge in the Central Black Sea as the test study area where extensive hydrocarbon seepages were known to occur continuously. Hydrocarbon seepage from tectonic or stratigraphic origin at the sea floor causes oily gas plumes to rise up to the sea surface and form thin oil films called oil slicks. Microwave sensors like synthetic aperture radar (SAR) are very suitable for ocean remote sensing as they measure the backscattered radiation from the surface and show the roughness of the terrain. Oil slicks dampen the sea waves creating dark patches in the SAR image. The proposed and applied methodology includes three levels: visual interpretation, image filtering and object-based oil spill detection. Level I, after data preparation with visual interpretation, includes dark spots identification and subsets/scenes creation. After this process, the procedure continues with categorization of subsets/scenes into three cases based on contrast difference of dark spots to the surroundings. In level II, by image and morphological filtering, it includes preparation of subsets/scenes for segmentation. Level III includes segmentation and feature extraction which is followed by object-based classification. The object-based classification is applied with the fuzzy membership functions defined by extracted features of ASAR subsets/scenes, where the parameters of the detection algorithms are tuned specifically for each case group. As a result, oil slicks are discriminated from look-alikes with an overall classification accuracy of 83% for oil slicks and 77% for look-alikes obtained by averaging three different cases.     

Grouping and representativeness of monitoring stations based on wind speed and wind direction data in urban areas of Japan

Data on wind speed and wind direction from 27 air pollution monitoring stations in an urban area of Japan were analyzed and studied for the future assessment of the urban heat island phenomenon. A cluster analysis clarified the regionality of the wind speed and wind direction. In the study area, land and sea breeze developed and controlled the wind direction. On the other hand, the mountain and valley wind should be studied to determine their typical behavior.     

Deterioration of Jerusalem limestone from air pollutants; field observations and laboratory simulation

Samples of Jerusalem limestones were exposed to high levels of common air pollutants (SO₂, NO) in the presence and absence of hydrocarbon, water vapor and ultra-violet light. After exposure, the outer layers were shaved off and analyzed for sulfate and nitrate. The results revealed that even after one day of exposure significant concentrations of CaSO₄ and Ca(NO₃)₂ could be detected in the external 40 m layers. Sulfate formation was found to relate very strongly to relative humidity with nearly undetectable production at humidities below 10%. Nitrate formation was found to relate to UV light and to a limited extent to the presence of hydrocarbon but was unchanged at different humidities. Surface samples were taken from different sites of the old city wall and were also analyzed for the same substances. The data showed that the concentrations of especially CaSO₄, and to a limited extend Ca(NO₃)₂, measured at various points along the city walls were higher than the expected values extrapolated from laboratory simulations. The elevated pollution content may be explained in part due to the deposition of transported ions, especially sulfate, onto the stone surface. It was concluded that although air pollution may not cause any structural damage in the foreseeable future it definitely deteriorates the fine details of the ancient monument.     

Modeling of SO<Subscript>2</Subscript> distribution in Istanbul using artificial neural networks

Urban air pollution is a growing problem in developing countries. Some compounds especially sulphur dioxide (SO₂) is considered as typical indicators of the urban air quality. Air pollution modeling and prediction have great importance in preventing the occurrence of air pollution episodes and provide sufficient time to take the necessary precautions. Recently, various stochastic image-processing algorithms such as Artificial Neural Network (ANN) are applied to environmental engineering. ANN structure employs input, hidden and output layers. Due to the complexity of the problem, as the number of input–output parameters differs, ANN model settings such as the number of neurons of these layers changes. The ability of ANN models to learn, particularly capability of handling large amounts (or sets) of data simultaneously as well as their fast response time, are invariably the characteristics desired for predictive and forecasting purposes. In this paper, ANN models have been used to predict air pollutant parameter in meteorological considerations. We have especially focused on modeling of SO₂ distribution and predicting its future concentration in Istanbul, Turkey. We have obtained data sets including meteorological variables and SO₂ concentrations from Istanbul-Florya meteorological station and Istanbul-Yenibosna air pollution station. We have preferred three-layer perceptron type of ANN which consists of 10, 22 and 1 neurons for input, hidden and output layers, respectively. All considered parameters are measured as daily mean. The input parameters are: SO₂ concentration, pressure, temperature, humidity, wind direction, wind speed, strength of sunshine, sunshine, cloudy, rainfall and output parameter is the future prediction of SO₂. To evaluate the performance of ANN model, our results are compared to classical nonlinear regression methods. The over all system finds an optimum correlation between input–output variables. Here, the correlation parameter, r is 0.999 and 0.528 for training and test data. Thus in our model, the trend of SO₂ is well estimated and seasonal effects are well represented. As a result, we conclude that ANN is one of the compromising methods in estimation of environmental complex air pollution problems.     

An assessment of persistent organic pollutants in Scottish coastal and offshore marine environments

Concentrations of persistent organic pollutants (POPs), including polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs) and polybrominated diphenyl ethers (PBDEs) were determined in sediment and biota (fish liver) from around Scotland. The concentrations were investigated using assessment criteria developed by OSPAR and ICES. Organic contaminant concentrations, PAHs, PCBs and PBDEs in sediment, and PCBs and PBDEs in fish liver, were significantly higher in the Clyde compared to all other sea areas. This is mainly due to historic industrial inputs. Highest PCB and PAH concentrations were found in the strata furthest up the Clyde estuary, with concentrations of POPs in these strata being at levels such that there is an unacceptable risk of chronic effects occurring in marine species. Furthermore, for PAHs in Clyde sediment there was a significant negative gradient going from north to south towards the open sea. PAH and PCB concentrations in sediment and biota in all other Scottish sea areas (except for PCBs in sediment from East Scotland) were unlikely to give rise to pollution effects, being below relevant assessment criteria. Although no assessment criteria are available for PBDEs, the concentrations observed in Scottish sediments were low with all congeners below the limit of detection (LoD; 0.03 μg kg(-1) dry weight) in 140 out of a total of 307 samples analysed. Where PBDEs were detected, the dominant congeners were BDE47 and BDE99. PBDEs were detected in fish livers, although concentrations were less than 150 μg kg(-1) lipid weight in all sea areas except the Clyde where concentrations ranged between 8.9 and 2202 μg kg(-1) lipid weight. Few trends were detected in contaminant concentrations in biota or sediment at any Scottish site with more than five years data. Downward trends were detected in PAHs in sediment from the Clyde, Irish Sea and Minches and Malin Sea and PCBs in fish liver from the Moray Firth. Rules were developed for the aggregation of the contaminant data across a sea area. An overall assessment for each sea area was then assigned, looking at the frequency of sites or strata within each sea area that were above or below the relevant assessment criteria. Overall the status of the various sea areas, with respect to the assessed POPs, can be considered to be acceptable in that they were below concentrations likely to result in chronic effects for all sea areas except the Clyde.     

A critique of the performance of EIA within the offshore oil and gas sector

The oil and gas sector is a key driver of the offshore economy. Yet, it is also associated with a number of unwanted environmental impacts which potentially threaten the long term economic and environmental viability of marine ecosystems. Environmental Impact Assessment (EIA) can potentially make a significant contribution to the identification and management of adverse impacts through the promotion of evidence based decision making. However, the extent to which EIA has been embraced by key stakeholders is poorly understood. On this basis, this paper provides an initial evaluation of EIA performance within the oil and gas sector. The methodology adopted for the paper consisted of the structured review of 35 Environmental Statements (ESs) along with interviews with regulators, operators, consultants and advisory bodies. The findings reveal a mixed picture of EIA performance with a significant number of ESs falling short of satisfactory quality and a tendency for the process to be driven by compliance rather than best practice.     

Physicochemical properties associated with the presence of <Emphasis Type="Italic">Burkholderia pseudomallei</Emphasis> in small ruminant farm water supplies in Peninsular Malaysia

Burkholderia pseudomallei causes melioidosis, a life-threatening infection in both humans and animals. Water is an important reservoir of the bacteria and may serve as a source of environmental contamination leading to infection. B. pseudomallei has an unusual ability to survive in water for a long period. This paper investigates physicochemical properties of water associated with the presence of B. pseudomallei in water supply in small ruminant farms in Peninsular Malaysia. Physicochemical properties of water samples taken from small ruminant farms that included temperature, pH, dissolved oxygen (DO₂), optical density (OD), and chemical oxygen demand (COD) were measured after which the samples were cultured for B. pseudomallei. Multivariable logistic regression model revealed that slightly acidic water pH and higher COD level were significantly associated with the likelihood of the B. pseudomallei presence in the water.     

On the Observations and Environmental Modeling in Xingó Hydropower Plant - Northeast Brazil: Present and Future Hydroclimatic Features

Environmental Modeling & Assessment - The São Francisco River Basin plays a critical role in the hydroelectrical operational planning of Brazil. Understanding the hydroclimatic dynamic...     

Bicriteria decision model for farm management of the Colorado potato beetle: development and validation

This paper describes the development and testing of a deterministic simulation model for pest and resistance management of the Colorado potato beetle (CPB). Historically, CPB have developed resistance to new pesticides quickly. A potato farmer's pesticide application decisions trade off current crop yields and resistance development. The simulation incorporates major aspects of beetle life processes, pesticide resistance, and potato growth in order to predict the consequences of a pesticide application strategy over a ten-year planning horizon.     

Air Quality Modeling Using the PSO-SVM-Based Approach,MLP Neural Network,and M5 Model Tree in the Metropolitan Area of Oviedo (Northern Spain)

The main aim of this study was to construct several regression models of air quality using techniques based on the statistical learning, in the metropolitan area of Oviedo, in northern Spain. In this research, a hybrid particle swarm optimization-based evolutionary support vector regression is implemented to predict the air quality from the experimental dataset (specifically, nitrogen oxides, carbon monoxide, sulfur dioxide, ozone, and dust) collected from 2013 to 2015 in the metropolitan area of Oviedo. Furthermore, a multilayer perceptron network (MLP) and the M5 model tree were also fitted to the experimental dataset for comparison purposes. Finally, the predicted results show that the hybrid proposed model is more robust than the MLP and M5 model tree prediction methods in terms of statistical estimators and testing performances.