Nonparametric functional data estimation applied to ozone data: prediction and extreme value analysis

The study of extreme values and prediction of ozone data is an important topic of research when dealing with environmental problems. Classical extreme value theory is usually used in air-pollution studies. It consists in fitting a parametric generalised extreme value (GEV) distribution to a data set of extreme values, and using the estimated distribution to compute return levels and other quantities of interest. Here, we propose to estimate these values using nonparametric functional data methods. Functional data analysis is a relatively new statistical methodology that generally deals with data consisting of curves or multi-dimensional variables. In this paper, we use this technique, jointly with nonparametric curve estimation, to provide alternatives to the usual parametric statistical tools. The nonparametric estimators are applied to real samples of maximum ozone values obtained from several monitoring stations belonging to the Automatic Urban and Rural Network (AURN) in the UK. The results show that nonparametric estimators work satisfactorily, outperforming the behaviour of classical parametric estimators. Functional data analysis is also used to predict stratospheric ozone concentrations. We show an application, using the data set of mean monthly ozone concentrations in Arosa, Switzerland, and the results are compared with those obtained by classical time series (ARIMA) analysis.     

Influence of competing inorganic cations on the ion exchange equilibrium of the monovalent organic cation metoprolol on natural sediment

The aim of this study was to systematically investigate the influence of the mono- and divalent inorganic ions Na⁺ and Ca²⁺ on the sorption behavior of the monovalent organic cation metoprolol on a natural sandy sediment at pH = 7. Isotherms for the beta-blocker metoprolol were obtained by sediment–water batch tests over a wide concentration range (1–100 000 μg L^?1). Concentrations of the competing inorganic ions were varied within freshwater relevant ranges. Data fitted well with the Freundlich sorption model and resulted in very similar Freundlich exponents (n = 0.9), indicating slightly non-linear behavior. Results show that the influence of Ca²⁺ compared to Na⁺ is more pronounced. A logarithmic correlation between the Freundlich coefficient K_Fr and the concentration or activity of the competing inorganic ions was found allowing the prediction of metoprolol sorption on the investigated sediment at different electrolyte concentrations. Additionally, the organic carbon of the sediment was completely removed for investigating the influence of organic matter on the sorption of metoprolol. The comparison between the experiments with and without organic carbon removal revealed no significant contribution of the organic carbon fraction (0.1%) to the sorption of metoprolol on the in this study investigated sediment. Results of this study will contribute to the development of predictive models for the transport of organic cations in the subsurface.     

Temporal and spatial patterns of micropollutants in urban receiving waters

Based on a monitoring program over the course of a year, we characterize the temporal and spatial distribution of selected micropollutants in an urban watershed within the city of Leipzig, Germany. Micropollutants revealed a ubiquitous presence in untreated and treated wastewater, surface water and groundwater. The loads of 4-nonylphenol in the effluents of the municipal wastewater treatment plant followed a seasonal trend, whereas the loads of all other micropollutants were highly variable and not correlated to seasons. In the surface water, load seasonality of caffeine, galaxolide and tonalide resulted from a rapid removal with increased water temperature. The loads of 4-nonylphenol and of caffeine in the colder months increased when rainfall occurred. In the groundwater, complex spatial and temporal patterns were apparent and were related to varying input, retardation and removal processes. As a consequence, an assessment of micropollutants in urban waters should consider different micropollutants' temporal and spatial variability.     

Evaluation of Saccharomyces cerevisiae strains as probiotic agent with aflatoxin B1 adsorption ability for use in poultry feedstuffs

In this study the aflatoxin B₁ (AFB₁) removal capacity, the tolerance to salivary and gastrointestinal conditions, autoaggregation and coaggregation with pathogenic bacteria of Saccharomyces cerevisiae strains isolated from broiler feces, were evaluated. Only four of twelve isolated strains were identified as Saccharomyces cerevisiae using molecular techniques. The results obtained in AFB₁ binding studies indicated that the amount of AFB₁ removed was both strain and mycotoxin-concentration dependent. Therefore, a theoretical model was applied in order to select the most efficient strain to remove AFB₁ in a wide range of mycotoxin concentration. The results indicated that S. cerevisiae 08 and S. cerevisiae 01 strains were the most efficient microorganisms in the mycotoxin removal. Viability on simulated salivary and gastrointestinal conditions was investigated and S. cerevisiae 08 strain showed the best results, achieving 98% of total survival whereas S. cerevisiae 01 reached only 75%. Autoaggregation and coaggregation assays showed S. cerevisiae 08 as the most appropriate strain, mainly because it was the unique strain able to coaggregate with the four bacterial pathogens assayed. Consequently, S. cerevisiae 08 is the best candidate for future in vivo studies useful to prevent aflatoxicosis. Further quantitative in vitro and in vivo studies are required to evaluate the real impact of yeast-binding activity on the bioavailability of AFB₁ in poultry. However, this study could be useful in selecting efficient strains in terms of AFB₁ binding and provide an important contribution to research into microorganisms with potential probiotic effects on the host.     

Analysis and modelling of the pollutant emissions from European cars regarding the driving characteristics and test cycles

Within the European research project ARTEMIS, significant works have been conducted to analyse the hot emissions of pollutant from the passenger cars regarding the driving cycles and to propose modelling approaches taking into account large but heterogeneous datasets recorded in Europe. The review and analysis of a large range of test cycles enabled first the building-up of a set of contrasted cycles specifically designed for characterizing the influence of the driving conditions. These cycles were used for the measurement of the pollutants emission rates from nine passenger cars on a chassis dynamometer.Emissions measured on 30 vehicles tested on cycles adapted to their motorization (i.e., cycles for high- or low-powered cars, inducing thus a significant difference in the dynamic) were also considered for analysing the influence of the cycles and of the kinematic parameters on the hot emission rates of the regulated pollutants (CO, HC, NO_x, CO₂, PM). An analyses of variance demonstrated the preponderance of the driving type (urban, rural road, motorway), of the vehicle category (fuel, emission standard) and emitting status (high/normal emitter) and thus the pertinence of analysing and modelling separately the corresponding emissions. It also demonstrated that Urban driving led systematically to high diesel emission rates and to high CO₂, HC and NO_x emissions from petrol cars. Congested driving implied high CO₂ (diesel and petrol) and high diesel NO_x emission. On motorway, the very high speeds generated high CO₂, while unsteady speeds induced diesel NO_x and petrol CO over-emissions. A search for pertinent kinematic parameters showed that urban diesel emissions were mostly sensitive to stops and speed parameters, while petrol emissions were rather sensitive to acceleration parameters. On the motorway, diesel NO_x and CO₂ emissions rates increased with the speed variability and occurrence of high speeds, while CO₂ and CO over-emission from petrol cars were linked to the occurrence of strong acceleration at high speeds.A modelling approach based on partial least square regression was tested, which demonstrates its ability to discriminate satisfactorily the emissions according to dynamic related parameters and in particular when considering the two-dimensionnal distribution of instantaneous speed and acceleration.Finally, a strategy was proposed to analyse the large but heterogeneous set of hot emission data collected within the ARTEMIS project. The approach consisted in analysing the similarity of the numerous cycles as regards their kinematic, grouping them into reference test patterns through an automatic clustering, and then computing reference emissions for these patterns. These principles enabled the development of a method to compute the emissions at a low spatial scale, i.e. the so-called traffic situation approach, which was implemented in the European Artemis model for estimating the cars’ pollutant emissions.     

EGG PRODUCTION AND HATCHING SUCCESS IN NORTH ADRIATIC SEA POPULATIONS OF THE COPEPOD Acartia clausi

Egg production, fecal pellet production and hatching success are reported for Acartia clausi females sampled during three cruises in February 1997, 1998 and June 1997 at 20-24 stations along 4 transects in the North Adriatic Sea. Dramatically low hatching rates were recorded during both diatom bloom events in February as opposed to much higher rates during post-bloom conditions in June, even though A. clausi productivity during the bloom was apparently high. These results are discussed in the light of recent findings on the negative impact of diatoms on copepod reproductive potential.     

Sustainable Cellular Biocomposites from Natural Fibers and Unsaturated Polyester Resin for Housing Panel Applications

Increased environmental awareness and interest in long-term sustainability of material resources has motivated considerable advancements in composite materials made from natural fibers and resins, or biocomposites. In spite of these developments the lower stiffness and strength of biocomposites has limited their applications to non-load-bearing components. This paper presents an overview of a study aimed at showing that the shortcomings of biocomposites can be overcome through hybrid material designs and efficient structural configurations to make them suitable for load bearing structural components. Hybrid blends of natural and synthetic fibers can significantly improve the characteristics of biocomposites with minimal cost and environmental impact, and hierarchical cellular designs can maximize material efficiency in structural components. Periodic and hierarchical cellular plate designs made from natural fibers and unsaturated polyester resin were evaluated experimentally and analytically. Stiffness, strength, and dimensional stability of all-biocomposite and hybrid natural–synthetic material systems were evaluated through material tests while structural performance of cellular plate designs was assessed through flexural tests on laboratory-scale samples. The experimental results were correlated with analytical models for short-fiber composites and cellular structures. The results showed that biocomposites have adequate short-term performance and that they can efficiently compete with housing panels made from conventional structural materials.     

Analysis of aerosol particles and coarse particulate matter concentrations in Chillán, Chile, 2001-2003

Daily particle samples were collected in Chillán, Chile, at six urban locations from September 1, 2001, through September 30, 2003. Aerosol samples were collected using monitors equipped with a Sierra Andersen 246-b cyclone inlet on Teflon filters. Average concentrations of coarse particulate matter (PM10) for the 2001-2003 period ranged from 43.4 microg/m3 to 81.8 microg/m3 across the six sites. Annual PM10 concentration levels exceeded the European Union concentration limits. Mean PM10 levels during the cold season (April through September) were more than twice as high as those observed in the warm season (October through March). Average contributions to PM10 from organic matter, soil dust, nitrate (NO3-), elemental carbon, ammonium (NH4+), and sulfate (SO4(2-)) were 31%, 27%, 11%, 8%, 7%, and 5%, respectively. The chemical analyses indicated that carbonaceous substances were the most abundant components of PM10 in cold months, whereas crustal material was the most abundant component of PM10 during warm months. Higher concentration levels were observed in the downtown area suggesting a clear anthropogenic origin, whereas in the rural sites the source was mainly natural, such as resuspended soil dust associated with traffic on unpaved roads and agricultural activities.