Advances in statistical methodologies for mid-long term simulation of oil spills in the sea

RESUMEN: Con esta tesis se pretenden aportar nuevas metodologías para mejorar las herramientas disponibles actualmente para la lucha contra la contaminación marina por derrames de hidrocarburos. Por un lado, se propone una metodología para predecir de forma probabilística derrames en el medio-largo plazo. La parte más innovadora de esta metodología consiste en la simulación estadística, mediante modelos de regresión logística, de aquellas variables ambientales que afectan la evolución de un derrame en el mar. El modelado basado en regresión logística es aplicado en el golfo de México y en el Golfo de Vizcaya. En este segundo caso, los patrones ambientales obtenidos son empleados, posteriormente, para la predicción en el medio-largo plazo de derrames. Los resultados obtenidos en cada caso, demuestran el potencial de las técnicas propuestas. Por otro lado, se propone otra metodología enfocada al análisis de peligrosidad asociada a derrames profundos, basada en la extracción de patrones ambientales espacio-temporales. Esta metodología ha sido aplicada en el Mar del Norte y los resultados obtenidos comparados con metodologías tradicionales de estudio de peligrosidad, evidenciando las capacidades de la metodología propuesta, habiendo reducido enormemente los costes computacionales respecto a las técnicas tradicionales. Se ha demostrado como las metodologías propuestas en esta tesis pueden mejorar y ampliar los beneficios de las herramientas existentes para la lucha contra la contaminación marina.ABSTRACT: The aim of this thesis is the improvement of existing tools for the fight against oil spill marine pollution. On the one side, we developed a methodology for the probabilistic forecast of oil spills at the mid-long term. The core of the methodology is the statistical simulation of oil spill met-ocean forcings, using a logistic regression model. Logistic regression modeling of met-ocean patterns is applied in the Gulf of Mexico and in the Bay of Biscay. In the second case, mid-long term prediction of oil spill is achieved considering the statistically simulated met-ocean conditions. On the other hand, we proposed a methodology for deep oil spill hazard assessment, based on the selection of spatio-temporal met-ocean patterns. This methodology was applied in the North Sea, and the obtained results were compared with the ones achieved with a traditional hazard estimation technique, highlighting the benefit of the proposed method. The methodologies presented in this thesis have shown their ability and the benefits they could bring to the tools for the fight against marine pollution

Risk assessment of atmospheric emissions using machine learning

Supervised and unsupervised machine learning algorithms are used to perform statistical and logical analysis of several transport and dispersion model runs which simulate emissions from a fixed source under different atmospheric conditions. <br><br> First, a clustering algorithm is used to automatically group the results of different transport and dispersion simulations according to specific cloud characteristics. Then, a symbolic classification algorithm is employed to find complex non-linear relationships between the meteorological input conditions and each cluster of clouds. The patterns discovered are provided in the form of probabilistic measures of contamination, thus suitable for result interpretation and dissemination. <br><br> The learned patterns can be used for quick assessment of the areas at risk and of the fate of potentially hazardous contaminants released in the atmosphere

Toxic Effects of Lead Disposal in Water: An Analysis of TRI Facility Releases

Using county-level TRI data from 2003 to 2016, I find evidence that lead emissions in water adversely affect birth weights within the emitting county, especially with respect to the percentage of births considered low birth weight within that county (less than 2,500 grams). I find that a one percent increase in lead emissions per square mile increases the proportion of low birth weights by 0.27 percentage points. For a county with an average number of births in a particular year, this one percent increase in lead per square mile translates to an additional $475,000 in hospitalization costs from complications with delivery and perinatal care alone. My results show that lead emissions create a substantial negative externality even at relatively small quantities and may have more significant effects for those living in poverty

Gas Migration Testing at Abandoned Well Sites in Western Canada

Fugitive releases from natural gas wells are a persistent issue in the oil and gas sector and comprises 27% of Canada’s greenhouse gas emissions. Natural gas within this sector accounts for 44% of Canada’s methane releases and 70% of Alberta’s. Releases from wells are documented; however, knowledge gaps persist for abandoned assets. When fugitive gases are suspected, regulatory standards require gas migration testing. This thesis presents the beginnings of developing ‘best practices’ in testing recommendations to better estimate emissions from abandoned wells. Testing requires detection of stray gases utilizing a worker-safety portable handheld multi-gas monitor; however, our work shows this monitor has limited application in gas migration testing. Portable monitors are equipped with non-specific, catalytic combustion sensors that underestimate methane concentrations in the subsurface. To circumvent misleading results, we suggest reporting oxygen levels for subsurface gases or the use of more sophisticated detectors. Additionally, work enclosed addresses single-sample, or sample-to-sample, risk assessments for gas migration testing. A brief commentary on previous testing at an abandoned well site in Western Canada reveals how this approach often produces insufficient evidence of stray gases. In applying a multivariate risk assessment method, using principal component analysis and K-means clustering, we showed sample sizes \u3e20 for reporting gas compositions and \u3e10 for stable isotopes will accurately detect stray gases at an abandoned well. Best practices highlighted in each study can easily be integrated into testing recommendations that will assist Canada in reducing emissions by 45% in 2025

Methods for incorporating ecological impacts with climate uncertainty to support robust flood management decision-making

Modern and historic flood risk management involves accommodating multiple sources of sources of uncertainty and potential impacts across a broad range of interrelated sectors. Sources of uncertainty that affect planning include internal climate variability, anthropogenic changes such as land use and system performance expectations, and more recently changes in climatology that affect the resources supporting the system. Flood management systems potentially impact human settlements within and beyond the systems’ scope of planning, local weather patterns, and associated ecological systems. Federal guidelines across nations have called for greater consideration of uncertainty and impacts of water resources planning projects, but methods for meeting these needs remain poorly established. At the same time, there is increased attention to the ecological impacts of water resources systems and growing expectations that negative impacts be mitigated. The confluence of climate change and increasing demand for environmental quality presents a challenging flood management decision context. This work presents several alternative methods for incorporating ecological impacts into flood risk management and evaluation procedures alongside climate uncertainty, which are illustrated through application to a flood management system on the Iowa River. First, to integrate climate change and uncertainty information into these decision models, the dissertation presents a decision-centric trend detection test in which the threshold for accepting or rejecting a trend in observed data is determined by the expected cost of drawing a false conclusion. Next, the dissertation presents a decision model to choose a portfolio of adaptation options based on portfolios’ expected economic and monetized ecological performance under uncertain future flood hazard. The dissertation also develops a robust optimization model with an alternate treatment of ecological performance to maximize the range of future conditions over which performance is acceptable in both economic and ecological impact sectors. Lastly, the dissertation presents a method for deriving a posterior distribution of changes in climate parameters based on a combination of a prior constructed based on climate model projections and likelihood based on the historic record. The goals of this work are to develop enhanced decision support tools that accommodate the unique context of flood risk management decisions and to improve the set of methods available to characterize future flood hazard and its associated uncertainty

Ecological Outcomes of Movement Behavior in Brown Pelicans from the South Atlantic Bight

Vagile organisms are expected to display movement behaviors that respond to a wide variety of both intrinsic and extrinsic factors. Identifying drivers of movement is fundamental to understanding the ecology of species, as well as implementing effective conservation measures. Technological advancements have allowed for the collection of fine-scale positional data at rapid temporal scales, which can be a powerful tool for assessing the movement behavior of tracked species and for understanding the potential fitness implications resulting from variations in animal space use. The goal of this dissertation was to identify important drivers of movement behavior and to describe the ecological outcomes of movement decisions in Eastern brown pelicans (Pelecanus occidentalis carolinensis) from the South Atlantic Bight. A total of 86 individual pelicans were outfitted with solar-powered GPS satellite transmitters in coastal South Carolina and Georgia, USA, from 2017 – 2020. Two cohorts of pelicans tracked during the passage of three tropical cyclones demonstrated a reduction in movement correlated with anomalies in barometric pressure and wind speed relative to ambient conditions, indicating a shelter-and-wait strategy for increasing survival during these extreme weather events. By measuring the concentrations of an environmental contaminant, poly- and perfluoroalkyl substances, in the eggs of pelicans from three colonies located near Charleston, South Carolina, I demonstrated that eggs contained relatively elevated concentrations of chemicals regardless of proximity to likely point sources. GPS tracking of adults from the same colonies further suggested that variations in urban habitat use for foraging adults during the breeding season were also not reflected in egg contaminant concentrations. In contrast, the relative risk to foraging adult pelicans of encountering surface oil from a ship-based spill near Charleston Harbor was significantly influenced by location, as demonstrated through the use of an oil spill modeling toolkit combined with pelican telemetry data. Finally, the partial migration strategy of brown pelicans in the South Atlantic Bight is likely maintained by the ontogenetic migration of their primary prey, Atlantic menhaden (Brevoortia tyrannus), and aligns with the fasting endurance hypothesis of partial migration. Understanding the causes and consequences of movement in brown pelicans in the South Atlantic Bight has important implications for the ecology and conservation of this species throughout their range

Int J Environ Res Public Health

THIS PAPER HAS TWO AIMS: (1) to summarize various geographic information science methods; and (2) to provide a review of studies that have employed such methods. Though not meant to be a comprehensive review, this paper explains when certain methods are useful in epidemiological studies and also serves as an overview of the growing field of spatial epidemiology.5U38EH000186/EH/NCEH CDC HHS/United StatesU19/EH000097-05/EH/NCEH CDC HHS/United StatesUA54CA116848/CA/NCI NIH HHS/United States20617032PMC287236

Data Mining for Source Apportionment of Trace Elements in Water and Solid Matrix

Trace elements migrate among different environment bodies with the natural geochemical reactions, and impacted by human industrial, agricultural, and civil activities. High load of trace elements in water, river and lake sediment, soil and air particle lead to potential to health of human being and ecological system. To control the impact on environment, source apportionment is a meaningful, and also a challenging task. Traditional methods to make source apportionment are usually based on geochemical techniques, or univariate analysis techniques. In recently years, the methods of multivariate analysis, and the related concepts data mining, machine learning, big data, are developing fast, which provide a novel route that combing the geochemical and data mining techniques together. These methods have been proved successful to deal with the source apportionment issue. In this chapter, the data mining methods used on this topic and implementations in recent years are reviewed. The basic method includes principal component analysis, factor analysis, clustering analysis, positive matrix fractionation, decision tree, Bayesian network, artificial neural network, etc. Source apportionment of trace elements in surface water, ground water, river and lake sediment, soil, air particles, dust are discussed

Chemical Accident Hazard Assessment by Spatial Analysis of Chemical Factories and Accident Records in South Korea

This study identified the potential chemical accident occurrence in Korea by analyzing the spatial distribution of chemical factories and accidents. The number of chemical factories and accidents in 25-km2 grids were used as the attribute value for spatial analysis. First, semi-variograms were conducted to examine spatial distribution patterns and to identify spatial autocorrelation of chemical factories and accidents. Semi-variograms explained that the spatial distribution of chemical factories and accidents were spatially autocorrelated. Second, the results of the semi-variograms were used in Ordinary Kriging to estimate chemical hazard levels. The level values were extracted from the Ordinary Kriging result and their spatial similarity was examined by juxtaposing the two values with respect to their location. Six peaks were identified in both the factory hazard and accident hazard estimation result, and the peaks correlated with major cities in Korea. Third, the estimated two hazard levels were classified with geometrical interval and could be classified into four quadrants: Low Factory and Low Accident (LFLA), High Factory and Low Accident (HFLA), Low Factory and High Accident(LFHA), and High Factory and High Accident (HFHA). The 4 groups identified different chemical safety management issues in Korea; safe LFLA group, many chemical reseller factories were found in HFLA group, chemical transportation accidents were in the LFHA group, and an abundance of factories and accidents were in the HFHA group. Each quadrant represented different safety management obstacles in Korea, and studying spatial differences can support the establishment of an efficient risk management plan