987 resultados para Maximum and minimum air temperature
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The initial curing of concrete specimens for quality assurance is addressed in different ways in testing standards, which often specify requirements that are difficult to meet in practice unless very costly initial curing chambers are available. The failure to meet these requirements in many areas of the world does not appear to result in adverse consequences. This study analyzed six initial curing temperature schemes, all with cycles similar to natural conditions to avoid the simplifications inherent in constant temperature curing. Three strengths of concrete and two initial curing times (24 and 72 hours) were used in this study. The findings showed that initial curing time had no effect on 28-day strength. The 28-day strength also proved to be resilient to maximum and minimum initial curing temperatures outside the limits stated in the standards considered in this study
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Salamanca is cataloged as one of the most polluted cities in Mexico. In order to observe the behavior and clarify the influence of wind parameters on the Sulphur Dioxide (SO2) concentrations a Self-Organizing Maps (SOM) Neural Network have been implemented at three monitoring locations for the period from January 1 to December 31, 2006. The maximum and minimum daily values of SO2 concentrations measured during the year of 2006 were correlated with the wind parameters of the same period. The main advantages of the SOM Neural Network is that it allows to integrate data from different sensors and provide readily interpretation results. Especially, it is powerful mapping and classification tool, which others information in an easier way and facilitates the task of establishing an order of priority between the distinguished groups of concentrations depending on their need for further research or remediation actions in subsequent management steps. For each monitoring location, SOM classifications were evaluated with respect to pollution levels established by Health Authorities. The classification system can help to establish a better air quality monitoring methodology that is essential for assessing the effectiveness of imposed pollution controls, strategies, and facilitate the pollutants reduction.
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En este trabajo se da un ejemplo de un conjunto de n puntos situados en posición general, en el que se alcanza el mínimo número de puntos que pueden formar parte de algún k-set para todo k con 1menor que=kmenor quen/2. Se generaliza también, a puntos en posición no general, el resultado de Erdõs et al., 1973, sobre el mínimo número de puntos que pueden formar parte de algún k-set. The study of k- sets is a very relevant topic in the research area of computational geometry. The study of the maximum and minimum number of k-sets in sets of points of the plane in general position, specifically, has been developed at great length in the literature. With respect to the maximum number of k-sets, lower bounds for this maximum have been provided by Erdõs et al., Edelsbrunner and Welzl, and later by Toth. Dey also stated an upper bound for this maximum number of k-sets. With respect to the minimum number of k-set, this has been stated by Erdos el al. and, independently, by Lovasz et al. In this paper the authors give an example of a set of n points in the plane in general position (no three collinear), in which the minimum number of points that can take part in, at least, a k-set is attained for every k with 1 ≤ k < n/2. The authors also extend Erdos’s result about the minimum number of points in general position which can take part in a k-set to a set of n points not necessarily in general position. That is why this work complements the classic works we have mentioned before.
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It is known that the Amundsenisen Icefield in Southern Spitzbergen (Svalbard achipelago) is temperate with an upper layer of snow and firn. It is an accumulation area and, though ice/water mass balance is clearly subject to time evolution, observation data on the long-term elevation changes over the past 40 years (Nuth et al., 2010) allow to assume constant icefield surface. Within our study of the plausibility of a subglacial lake (Glowacki et al., 2007), here, we focus on the sensitivity of the system to the thermal effect of the firn and snow layers.
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It is known that a green wall brings some advantages to a building. It constitutes a barrier against solar radiation, thus decreasing and delaying the incoming heat flux. The aim of this study is to quantify such advantages through analytical comparison between two facades, a vegetal facade and a conventional facade. Both were highly insulated (U-value = 0.3 W/m2K) and installed facing south on the same building in the central territory of Spain. In order to compare their thermal trend, a series of sensors were used to register superficial and indoor air temperature. The work was carried out between 17th August 2012 and 1st October 2012, with a temperature range of 12°C-36°C and a maximum horizontal radiation of 1020 W/m2. Results show that the indoor temperature of the green wall module was lower than the other. Besides, comparing superficial outdoor and indoor temperatures of the two walls to outdoor air temperatures, it was noticed that, due to the shading plants, the green wall superficial temperature was 5 °C lower on the facade, while the bare wall temperature was 15 °C higher. The living wall module temperature was 1.6 °C lower than the outdoor, while the values of the conventional one were similar to the outdoor air temperature.
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La investigación de esta tesis se centra en el estudio de técnicas geoestadísticas y su contribución a una mayor caracterización del binomio factores climáticos-rendimiento de un cultivo agrícola. El inexorable vínculo entre la variabilidad climática y la producción agrícola cobra especial relevancia en estudios sobre el cambio climático o en la modelización de cultivos para dar respuesta a escenarios futuros de producción mundial. Es información especialmente valiosa en sistemas operacionales de monitoreo y predicción de rendimientos de cultivos Los cuales son actualmente uno de los pilares operacionales en los que se sustenta la agricultura y seguridad alimentaria mundial; ya que su objetivo final es el de proporcionar información imparcial y fiable para la regularización de mercados. Es en este contexto, donde se quiso dar un enfoque alternativo a estudios, que con distintos planteamientos, analizan la relación inter-anual clima vs producción. Así, se sustituyó la dimensión tiempo por la espacio, re-orientando el análisis estadístico de correlación interanual entre rendimiento y factores climáticos, por el estudio de la correlación inter-regional entre ambas variables. Se utilizó para ello una técnica estadística relativamente nueva y no muy aplicada en investigaciones similares, llamada regresión ponderada geográficamente (GWR, siglas en inglés de “Geographically weighted regression”). Se obtuvieron superficies continuas de las variables climáticas acumuladas en determinados periodos fenológicos, que fueron seleccionados por ser factores clave en el desarrollo vegetativo de un cultivo. Por ello, la primera parte de la tesis, consistió en un análisis exploratorio sobre comparación de Métodos de Interpolación Espacial (MIE). Partiendo de la hipótesis de que existe la variabilidad espacial de la relación entre factores climáticos y rendimiento, el objetivo principal de esta tesis, fue el de establecer en qué medida los MIE y otros métodos geoestadísticos de regresión local, pueden ayudar por un lado, a alcanzar un mayor entendimiento del binomio clima-rendimiento del trigo blando (Triticum aestivum L.) al incorporar en dicha relación el componente espacial; y por otro, a caracterizar la variación de los principales factores climáticos limitantes en el crecimiento del trigo blando, acumulados éstos en cuatro periodos fenológicos. Para lleva a cabo esto, una gran carga operacional en la investigación de la tesis consistió en homogeneizar y hacer los datos fenológicos, climáticos y estadísticas agrícolas comparables tanto a escala espacial como a escala temporal. Para España y los Bálticos se recolectaron y calcularon datos diarios de precipitación, temperatura máxima y mínima, evapotranspiración y radiación solar en las estaciones meteorológicas disponibles. Se dispuso de una serie temporal que coincidía con los mismos años recolectados en las estadísticas agrícolas, es decir, 14 años contados desde 2000 a 2013 (hasta 2011 en los Bálticos). Se superpuso la malla de información fenológica de cuadrícula 25 km con la ubicación de las estaciones meteorológicas con el fin de conocer los valores fenológicos en cada una de las estaciones disponibles. Hecho esto, para cada año de la serie temporal disponible se calcularon los valores climáticos diarios acumulados en cada uno de los cuatro periodos fenológicos seleccionados P1 (ciclo completo), P2 (emergencia-madurez), P3 (floración) y P4 (floraciónmadurez). Se calculó la superficie interpolada por el conjunto de métodos seleccionados en la comparación: técnicas deterministas convencionales, kriging ordinario y cokriging ordinario ponderado por la altitud. Seleccionados los métodos más eficaces, se calculó a nivel de provincias las variables climatológicas interpoladas. Y se realizaron las regresiones locales GWR para cuantificar, explorar y modelar las relaciones espaciales entre el rendimiento del trigo y las variables climáticas acumuladas en los cuatro periodos fenológicos. Al comparar la eficiencia de los MIE no destaca una técnica por encima del resto como la que proporcione el menor error en su predicción. Ahora bien, considerando los tres indicadores de calidad de los MIE estudiados se han identificado los métodos más efectivos. En el caso de la precipitación, es la técnica geoestadística cokriging la más idónea en la mayoría de los casos. De manera unánime, la interpolación determinista en función radial (spline regularizado) fue la técnica que mejor describía la superficie de precipitación acumulada en los cuatro periodos fenológicos. Los resultados son más heterogéneos para la evapotranspiración y radiación. Los métodos idóneos para estas se reparten entre el Inverse Distance Weighting (IDW), IDW ponderado por la altitud y el Ordinary Kriging (OK). También, se identificó que para la mayoría de los casos en que el error del Ordinary CoKriging (COK) era mayor que el del OK su eficacia es comparable a la del OK en términos de error y el requerimiento computacional de este último es mucho menor. Se pudo confirmar que existe la variabilidad espacial inter-regional entre factores climáticos y el rendimiento del trigo blando tanto en España como en los Bálticos. La herramienta estadística GWR fue capaz de reproducir esta variabilidad con un rendimiento lo suficientemente significativo como para considerarla una herramienta válida en futuros estudios. No obstante, se identificaron ciertas limitaciones en la misma respecto a la información que devuelve el programa a nivel local y que no permite desgranar todo el detalle sobre la ejecución del mismo. Los indicadores y periodos fenológicos que mejor pudieron reproducir la variabilidad espacial del rendimiento en España y Bálticos, arrojaron aún, una mayor credibilidad a los resultados obtenidos y a la eficacia del GWR, ya que estaban en línea con el conocimiento agronómico sobre el cultivo del trigo blando en sistemas agrícolas mediterráneos y norteuropeos. Así, en España, el indicador más robusto fue el balance climático hídrico Climatic Water Balance) acumulado éste, durante el periodo de crecimiento (entre la emergencia y madurez). Aunque se identificó la etapa clave de la floración como el periodo en el que las variables climáticas acumuladas proporcionaban un mayor poder explicativo del modelo GWR. Sin embargo, en los Bálticos, países donde el principal factor limitante en su agricultura es el bajo número de días de crecimiento efectivo, el indicador más efectivo fue la radiación acumulada a lo largo de todo el ciclo de crecimiento (entre la emergencia y madurez). Para el trigo en regadío no existe ninguna combinación que pueda explicar más allá del 30% de la variación del rendimiento en España. Poder demostrar que existe un comportamiento heterogéneo en la relación inter-regional entre el rendimiento y principales variables climáticas, podría contribuir a uno de los mayores desafíos a los que se enfrentan, a día de hoy, los sistemas operacionales de monitoreo y predicción de rendimientos de cultivos, y éste es el de poder reducir la escala espacial de predicción, de un nivel nacional a otro regional. ABSTRACT This thesis explores geostatistical techniques and their contribution to a better characterization of the relationship between climate factors and agricultural crop yields. The crucial link between climate variability and crop production plays a key role in climate change research as well as in crops modelling towards the future global production scenarios. This information is particularly important for monitoring and forecasting operational crop systems. These geostatistical techniques are currently one of the most fundamental operational systems on which global agriculture and food security rely on; with the final aim of providing neutral and reliable information for food market controls, thus avoiding financial speculation of nourishments of primary necessity. Within this context the present thesis aims to provide an alternative approach to the existing body of research examining the relationship between inter-annual climate and production. Therefore, the temporal dimension was replaced for the spatial dimension, re-orienting the statistical analysis of the inter-annual relationship between crops yields and climate factors to an inter-regional correlation between these two variables. Geographically weighted regression, which is a relatively new statistical technique and which has rarely been used in previous research on this topic was used in the current study. Continuous surface values of the climate accumulated variables in specific phenological periods were obtained. These specific periods were selected because they are key factors in the development of vegetative crop. Therefore, the first part of this thesis presents an exploratory analysis regarding the comparability of spatial interpolation methods (SIM) among diverse SIMs and alternative geostatistical methodologies. Given the premise that spatial variability of the relationship between climate factors and crop production exists, the primary aim of this thesis was to examine the extent to which the SIM and other geostatistical methods of local regression (which are integrated tools of the GIS software) are useful in relating crop production and climate variables. The usefulness of these methods was examined in two ways; on one hand the way this information could help to achieve higher production of the white wheat binomial (Triticum aestivum L.) by incorporating the spatial component in the examination of the above-mentioned relationship. On the other hand, the way it helps with the characterization of the key limiting climate factors of soft wheat growth which were analysed in four phenological periods. To achieve this aim, an important operational workload of this thesis consisted in the homogenization and obtention of comparable phenological and climate data, as well as agricultural statistics, which made heavy operational demands. For Spain and the Baltic countries, data on precipitation, maximum and minimum temperature, evapotranspiration and solar radiation from the available meteorological stations were gathered and calculated. A temporal serial approach was taken. These temporal series aligned with the years that agriculture statistics had previously gathered, these being 14 years from 2000 to 2013 (until 2011 for the Baltic countries). This temporal series was mapped with a phenological 25 km grid that had the location of the meteorological stations with the objective of obtaining the phenological values in each of the available stations. Following this procedure, the daily accumulated climate values for each of the four selected phenological periods were calculated; namely P1 (complete cycle), P2 (emergency-maturity), P3 (flowering) and P4 (flowering- maturity). The interpolated surface was then calculated using the set of selected methodologies for the comparison: deterministic conventional techniques, ordinary kriging and ordinary cokriging weighted by height. Once the most effective methods had been selected, the level of the interpolated climate variables was calculated. Local GWR regressions were calculated to quantify, examine and model the spatial relationships between soft wheat production and the accumulated variables in each of the four selected phenological periods. Results from the comparison among the SIMs revealed that no particular technique seems more favourable in terms of accuracy of prediction. However, when the three quality indicators of the compared SIMs are considered, some methodologies appeared to be more efficient than others. Regarding precipitation results, cokriging was the most accurate geostatistical technique for the majority of the cases. Deterministic interpolation in its radial function (controlled spline) was the most accurate technique for describing the accumulated precipitation surface in all phenological periods. However, results are more heterogeneous for the evapotranspiration and radiation methodologies. The most appropriate technique for these forecasts are the Inverse Distance Weighting (IDW), weighted IDW by height and the Ordinary Kriging (OK). Furthermore, it was found that for the majority of the cases where the Ordinary CoKriging (COK) error was larger than that of the OK, its efficacy was comparable to that of the OK in terms of error while the computational demands of the latter was much lower. The existing spatial inter-regional variability between climate factors and soft wheat production was confirmed for both Spain and the Baltic countries. The GWR statistic tool reproduced this variability with an outcome significative enough as to be considered a valid tool for future studies. Nevertheless, this tool also had some limitations with regards to the information delivered by the programme because it did not allow for a detailed break-down of its procedure. The indicators and phenological periods that best reproduced the spatial variability of yields in Spain and the Baltic countries made the results and the efficiency of the GWR statistical tool even more reliable, despite the fact that these were already aligned with the agricultural knowledge about soft wheat crop under mediterranean and northeuropean agricultural systems. Thus, for Spain, the most robust indicator was the Climatic Water Balance outcome accumulated throughout the growing period (between emergency and maturity). Although the flowering period was the phase that best explained the accumulated climate variables in the GWR model. For the Baltic countries where the main limiting agricultural factor is the number of days of effective growth, the most effective indicator was the accumulated radiation throughout the entire growing cycle (between emergency and maturity). For the irrigated soft wheat there was no combination capable of explaining above the 30% of variation of the production in Spain. The fact that the pattern of the inter-regional relationship between the crop production and key climate variables is heterogeneous within a country could contribute to one is one of the greatest challenges that the monitoring and forecasting operational systems for crop production face nowadays. The present findings suggest that the solution may lay in downscaling the spatial target scale from a national to a regional level.
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Esta tesis doctoral presenta el desarrollo, verificación y aplicación de un método original de regionalización estadística para generar escenarios locales de clima futuro de temperatura y precipitación diarias, que combina dos pasos. El primer paso es un método de análogos: los "n" días cuya configuración atmosférica de baja resolución es más parecida a la del día problema, se seleccionan de un banco de datos de referencia del pasado. En el segundo paso, se realiza un análisis de regresión múltiple sobre los "n" días más análogos para la temperatura, mientras que para la precipitación se utiliza la distribución de probabilidad de esos "n" días análogos para obtener la estima de precipitación. La verificación de este método se ha llevado a cabo para la España peninsular y las Islas Baleares. Los resultados muestran unas buenas prestaciones para temperatura (BIAS cerca de 0.1ºC y media de errores absolutos alrededor de 1.9ºC); y unas prestaciones aceptables para la precipitación (BIAS razonablemente bajo con una media de -18%; error medio absoluto menor que para una simulación de referencia (la persistencia); y una distribución de probabilidad simulada similar a la observada según dos test no-paramétricos de similitud). Para mostrar la aplicabilidad de la metodología desarrollada, se ha aplicado en detalle en un caso de estudio. El método se aplicó a cuatro modelos climáticos bajo diferentes escenarios futuros de emisiones de gases de efecto invernadero, para la región de Aragón, produciendo así proyecciones futuras de precipitación y temperaturas máximas y mínimas diarias. La fiabilidad de la técnica de regionalización fue evaluada de nuevo para el caso de estudio mediante un proceso de verificación. Para determinar la capacidad de los modelos climáticos para simular el clima real, sus simulaciones del pasado (la denominada salida 20C3M) se regionalizaron y luego se compararon con el clima observado (los resultados son bastante robustos para la temperatura y menos concluyentes para la precipitación). Las proyecciones futuras a escala local presentan un aumento significativo durante todo el siglo XXI de las temperaturas máximas y mínimas para todos los futuros escenarios de emisiones considerados. Las simulaciones de precipitación presentan mayores incertidumbres. Además, la aplicabilidad práctica del método se demostró también mediante su utilización para producir escenarios climáticos futuros para otros casos de estudio en los distintos sectores y regiones del mundo. Se ha prestado especial atención a una aplicación en Centroamérica, una región que ya está sufriendo importantes impactos del cambio climático y que tiene un clima muy diferente. ABSTRACT This doctoral thesis presents the development, verification and application of an original downscaling method for daily temperature and precipitation, which combines two statistical approaches. The first step is an analogue approach: the “n” days most similar to the day to be downscaled are selected. In the second step, a multiple regression analysis using the “n” most analogous days is performed for temperature, whereas for precipitation the probability distribution of the “n” analogous days is used to obtain the amount of precipitation. Verification of this method has been carried out for the Spanish Iberian Peninsula and the Balearic Islands. Results show good performance for temperature (BIAS close to 0.1ºC and Mean Absolute Errors around 1.9ºC); and an acceptable skill for precipitation (reasonably low BIAS with a mean of - 18%, Mean Absolute Error lower than for a reference simulation, i.e. persistence, and a well-simulated probability distribution according to two non-parametric tests of similarity). To show the applicability of the method, a study case has been analyzed. The method was applied to four climate models under different future emission scenarios for the region of Aragón, thus producing future projections of daily precipitation and maximum and minimum temperatures. The reliability of the downscaling technique was re-assessed for the study case by a verification process. To determine the ability of the climate models to simulate the real climate, their simulations of the past (the 20C3M output) were downscaled and then compared with the observed climate – the results are quite robust for temperature and less conclusive for the precipitation. The downscaled future projections exhibit a significant increase during the entire 21st century of the maximum and minimum temperatures for all the considered future emission scenarios. Precipitation simulations exhibit greater uncertainties. Furthermore, the practical applicability of the method was demonstrated also by using it to produce future climate scenarios for some other study cases in different sectors and regions of the world. Special attention was paid to an application of the method in Central America, a region that is already suffering from significant climate change impacts and that has a very different climate from others where the method was previously applied.
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We thank Karim Gharbi and Urmi Trivedi for their assistance with RNA sequencing, carried out in the GenePool genomics facility (University of Edinburgh). We also thank Susan Fairley and Eduardo De Paiva Alves (Centre for Genome Enabled Biology and Medicine, University of Aberdeen) for help with the initial bioinformatics analysis. We thank Aaron Mitchell for kindly providing the ALS3 mutant, Julian Naglik for the gift of TR146 cells, and Jon Richardson for technical assistance. We thank the Genomics and Bioinformatics core of the Faculty of Health Sciences for Next Generation Sequencing and Bioinformatics support, the Information and Communication Technology Office at the University of Macau for providing access to a High Performance Computer and Jacky Chan and William Pang for their expert support on the High Performance Computer. Finally, we thank Amanda Veri for generating CaLC2928. M.D.L. is supported by a Sir Henry Wellcome Postdoctoral Fellowship (Wellcome Trust 096072), R.A.F. by a Wellcome Trust-Massachusetts Institute of Technology (MIT) Postdoctoral Fellowship, L.E.C. by a Canada Research Chair in Microbial Genomics and Infectious Disease and by Canadian Institutes of Health Research Grants MOP-119520 and MOP-86452, A.J. P.B. was supported by the UK Biotechnology and Biological Sciences Research Council (BB/F00513X/1) and by the European Research Council (ERC-2009-AdG-249793-STRIFE), KHW is supported by the Science and Technology Development Fund of Macau S.A.R (FDCT) (085/2014/A2) and the Research and Development Administrative Office of the University of Macau (SRG2014-00003-FHS) and R.T.W. by the Burroughs Wellcome fund and NIH R15AO094406. Data availability RNA-sequencing data sets are available at ArrayExpress (www.ebi.ac.uk) under accession code E-MTAB-4075. ChIP-seq data sets are available at the NCBI SRA database (http://www.ncbi.nlm.nih.gov) under accession code SRP071687. The authors declare that all other data supporting the findings of this study are available within the article and its supplementary information files, or from the corresponding author upon request.
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Advanced porous materials with tailored porosity (extremely high development of microporosity together with a narrow micropore size distribution (MPSD)) are required in energy and environmental related applications. Lignocellulosic biomass derived HTC carbons are good precursors for the synthesis of activated carbons (ACs) via KOH chemical activation. However, more research is needed in order to tailor the microporosity for those specific applications. In the present work, the influence of the precursor and HTC temperature on the porous properties of the resulting ACs is analyzed, remarking that, regardless of the precursor, highly microporous ACs could be generated. The HTC temperature was found to be an extremely influential parameter affecting the porosity development and the MPSD of the ACs. Tuning of the MPSD of the ACs was achieved by modification of the HTC temperature. Promising preliminary results in gas storage (i.e. CO2 capture and high pressure CH4 storage) were obtained with these materials, showing the effectiveness of this synthesis strategy in converting a low value lignocellulosic biomass into a functional carbon material with high performance in gas storage applications.
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Hydrogen isotope values (dD) of sedimentary terrestrial leaf wax such as n-alkanes or n-acids have been used to map and understand past changes in rainfall amount in the tropics because dD of precipitation is commonly assumed as the first order controlling factor of leaf wax dD. Plant functional types and their photosynthetic pathways can also affect leaf wax dD but these biological effects are rarely taken into account in paleo studies relying on this rainfall proxy. To investigate how biological effects may influence dD values we here present a 37,000-year old record of dD and stable carbon isotopes (d13C) measured on four n-alkanes (n-C27, n-C29, n-C31, n-C33) from a marine sediment core collected off the Zambezi River mouth. Our paleo d13C records suggest that each individual n-alkanes had different C3/C4 proportional contributions. n-C29 was mostly derived from a C3 dicots (trees, shrubs and forbs) dominant vegetation throughout the entire record. In contrast, the longer chain n-C33 and n-C31 were mostly contributed by C4 grasses during the Glacial period but shifted to a mixture of C4 grasses and C3 dicots during the Holocene. Strong correlations between dD and d13C values of n-C33 (correlation coefficient R2 = 0.75, n = 58) and n-C31 (R2 = 0.48, n = 58) suggest that their dD values were strongly influenced by changes in the relative contributions of C3/C4 plant types in contrast to n-C29 (R2 = 0.07, n = 58). Within regions with variable C3/C4 input, we conclude that dD values of n-C29 are the most reliable and unbiased indicator for past changes in rainfall, and that dD and d13C values of n-C31 and n-C33 are sensitive to C3/C4 vegetation changes. Our results demonstrate that a robust interpretation of palaeohydrological data using n-alkane dD requires additional knowledge of regional vegetation changes from which nalkanes are synthesized, and that the combination of dD and d13C values of multiple n-alkanes can help to differentiate biological effects from those related to the hydrological cycle.
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Biogenic components of sediment accumulated at high rates beneath frontal zones of the Indian and Pacific oceans during the late Miocene and early Pliocene. The delta13C of bulk and foraminiferal carbonate also decreased during this time interval. Although the two observations may be causally linked, and signify a major perturbation in global biogeochemical cycling, no site beneath a frontal zone has independent records of export production and delta13C on multiple carbonate phases across the critical interval of interest. Deep Sea Drilling Project (DSDP) site 590 lies beneath the Tasman Front (TF), an eddy-generating jetstream in the southwest Pacific Ocean. To complement previous delta13C records of planktic and benthic foraminifera at this location, late Neogene records of CaCO3 mass accumulation rate (MAR), Ca/Ti, Ba/Ti, Al/Ti, and of bulk carbonate and foraminiferal delta13C were constructed at site 590. The delta13C records include bulk sediment, bulk sediment fractions (<63 µm and 5-25 µm), and the planktic foraminifera Globigerina bulloides, Globigerinoides sacculifer (with and without sac), and Orbulina universa. Using current time scales, CaCO3 MARs, Ca/Ti, Al/Ti and Ba/Ti ratios are two to three times higher in upper Miocene and lower Pliocene sediment relative to overlying and underlying units. A significant decrease also occurs in all delta13C records. All evidence indicates that enhanced export production - the 'biogenic bloom' - extended to the southwest Pacific Ocean between ca. 9 and 3.8 Ma, and this phenomenon is coupled with changes in delta13C - the 'Chron C3AR carbon shift'. However, CaCO3 MARs peak ca. 5 Ma whereas elemental ratios are highest ca. 6.5 Ma; foraminiferal delta13C starts to decrease ca. 8 Ma whereas bulk carbonate delta13C begins to drop ca. 5.6 Ma. Temporal discrepancies between the records can be explained by changes in the upwelling regime at the TF, perhaps signifying a link between changes in ocean-atmosphere circulation change and widespread primary productivity.
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Mode of access: Internet.
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An expansion of the author's thesis.
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Mode of access: Internet.