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.     

An analysis of aquatic toxicity data: Water solubility and acute LC50 fish data

A theoretical relation has been established between the water solubility of an organic chemical and the ratio of the acute fish LC50 at two different time periods. The theory was tested by examining a data base of 24 chemicals. The finding of a positive correlation between the observed and calculated ratio of the 96 hr LC50 to the 24 hr LC50 helped to substantiate the theory.     

Fusion of SeaWiFS and TOMS satellite data with surface observations and topographic data during extreme aerosol events.

Spaceborne sensors allow near-continuous aerosol monitoring throughout the world. This paper illustrates the fusion of Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) and TOMS satellite data with surface observations and topographic data during four extreme aerosol events: (1) the April 1998 Asian dust storm that impacted the west coast of North America, (2) the May 1998 Central American forest fire smoke that impacted eastern North America, (3) the intense fall 1999 northern California fires, and (4) the massive February 2000 Sahara dust storm. During these dust and smoke events, the aerosol was visualized on true color SeaWiFS images as a distinct yellowish dye, the result of the aerosol increasing the reflectance of darker surfaces (ocean and land) and decreasing the reflectance of clouds. TOMS imagery also indicated increased aerosol absorption in the affected areas, while surface monitors measured major reductions in visual range. Fusing these data aids in the determination of the aerosol's spatial, temporal, and optical properties and provides supporting evidence for characterizing what is being visualized as dust or smoke. A 3-dimensional perspective of the events is obtained when incorporating topographic data and provides insight into the vertical properties of the aerosol plumes.     

Attempt to model laboratory-scale diffusion and retardation data

Different approaches for measuring the interaction between radionuclides and rock matrix are needed to test the compatibility of experimental retardation parameters and transport models used in assessing the safety of the underground repositories for the spent nuclear fuel. In this work, the retardation of sodium, calcium and strontium was studied on mica gneiss, unaltered, moderately altered and strongly altered tonalite using dynamic fracture column method. In-diffusion of calcium into rock cubes was determined to predict retardation in columns. In-diffusion of calcium into moderately and strongly altered tonalite was interpreted using a numerical code FTRANS. The code was able to interprete in-diffusion of weakly sorbing calcium into the saturated porous matrix. Elution curves of calcium for the moderately and strongly altered tonalite fracture columns were explained adequately using FTRANS code and parameters obtained from in-diffusion calculations. In this paper, mass distribution ratio values of sodium, calcium and strontium for intact rock are compared to values, previously obtained for crushed rock from batch and crushed rock column experiments. Kd values obtained from fracture column experiments were one order of magnitude lower than Kd values from batch experiments.     

ACCESS climate data management

Building on achievements and experience gained through the EU project DAMOCLES and international data management during the International Polar Year, ACCESS, data management was implemented using the same platform as used for DAMOCLES. A metadata-driven approach through which all datasets are properly described with discovery and use metadata was chosen in order to simplify data management and data usage. The system provides automated submission and checking of datasets, search and download as well as visualisation and transformation on user demand and metadata export. Long-term management of ACCESS climate datasets is done within the context of the Arctic Data Centre. This ensures visibility of ACCESS datasets in the context of WMO and GEOSS catalogues. Challenges with ACCESS data management have mainly been cultural with the consequence that the system has been underutilised within the duration of the project duration.     

Clinical and toxicological data in Fenthion and omethoate acute poisoning

Abstract

This study paper reports on two cases of poisoning with the organophosphorus insecticides, fenthion and omethoate. The two victims were admitted in the Intensive Care Unit (ICU) a few hours after ingestion of the two insecticides. They received appropriate treatment for organophosphorous poisoning (gastric lavage, activated charcoal, atropine and pralidoxime) and supportive care. Both patients survived. Organophosphate blood levels were determined on admission (fenthion 2.9 (ig/ml, omethoate 1.6 Hg/ml) and during the hospitalisation and proved to be considerably high. Slow elimination rate of the poison already distributed in the body was indicated for both pesticides. The patient with omethoate poisoning remained clinically well (Glasgow Coma Scale : 15) and was discharged three days later. The patient with fenthion poisoning, who had also ingested 30 mg of bromazepam and 720 mg of oxetoron, developed cholinergic crisis six hours after admission and was intubated for 24 days, with concomitant complications.     

Reporting chlorinated dibenzo-p-dioxin and dibenzofuran data in scientific publications

A literature review of recent work involving the determination of chlorinated dibenzo-p-dioxins (CDD) and dibenzofurans (CDF) in fish and incinerator samples has shown many irregularities and omissions in the reported data. Although some of these result from non-standardized nomenclature and reporting practices and do not affect the overall conclusions reached, in some cases omissions in reporting experimental procedures raise important concerns regarding the validity of data and make comparison of data from different investigations difficult. It is not always necessary or even desirable to report every experimental detail of work performed, however, the ultra-trace determination of CDDs and CDFs in complex environmental samples is still at a stage where omission of key experimental details can affect the interpretation of results.     

Spatial and temporal variability of ambient air toxics data

This paper summarizes information on the spatial and temporal variability of selected air toxics pollutants collected on a national basis primarily for a period encompassing 1990-2003. Spatial information on pollutant concentrations is characterized in terms of within-city and between-city variability. Temporal information is summarized as diurnal and seasonal variability and in multiyear trends. The information on variability is presented in the framework of a larger need for systematic documentation of information on air toxics pollutants to assess progress in air pollution control programs.     

An ensemble Kalman filter for atmospheric data assimilation: Application to wind tunnel data

In the previous work (Zheng et al., 2007, Zheng et al., 2009), a data assimilation method, based on ensemble Kalman filter, has been applied to a Monte Carlo Dispersion Model (MCDM). The results were encouraging when the method was tested by the twin experiment and a short-range field experiment. In this technical note, the measured data collected in a wind tunnel experiment have been assimilated into the Monte Carlo dispersion model. The uncertain parameters in the dispersion model, including source term, release height, turbulence intensity and wind direction have been considered. The 3D parameters, i.e. the turbulence intensity and wind direction, have been perturbed by 3D random fields. In order to find the factors which may influence the assimilation results, eight tests with different specifications were carried out. Two strategies of constructing the 3D perturbation field of wind direction were proposed, and the result shows that the two level strategy performs better than the one level strategy. It is also found that proper standard deviation and the correlation radius of the perturbation field play an important role for the data assimilation results.     

Particle concentrations in data centers

Cooling buildings with large airflow rates of outside air when temperatures are favorable is an established energy-saving measure. In data centers, this strategy is not widely used, owing to concerns that it would cause increased indoor levels of particles of outdoor origin, which could damage electronic equipment. However, environmental conditions typical of data centers and the associated potential for equipment failure are not well characterized. This study presents the first published measurements of particle concentrations in operating data centers. Indoor and outdoor particle measurements were taken at eight different sites in northern California for particulate matter 0.3–5.0 μm in diameter. One of the data centers has an energy-efficient design that employs outside air for cooling, while the rest use conventional cooling methods. Ratios of measured particle concentrations in the conventional data centers to the corresponding outside concentrations were significantly lower than those typically found in office or residential buildings. Estimates using a material-balance model match well with empirical results, indicating that the dominant particle sources and losses have been identified. Measurements taken at the more energy-efficient site show nearly an order of magnitude increase in particle concentration when ventilation rates were high. The model indicates that this increase may be even higher when including particles smaller than the monitoring-equipment size limitation. Even with the increases, the measured particle concentrations are still below concentration limits recommended in industry standards.     

Aerosol maps from GOME data

In this paper, we present a methodology to calibrate the surface reflectance seen by satellite and validate the aerosol optical properties retrieved by the GOME instrument. Data are also visualized in maps by a tool properly developed, named GOMEView. The validation procedure is based on ground measurements obtained by sunphotometers. Results show that calibration of the surface reflectance is crucial to obtain the best results, i.e. in agreement with the ground measurements. Aerosol data have also been classified on the basis of their optical properties evidencing for instance, the presence of desert aerosol over the sea along the west coast of Sahara. Cloud retrievals were also analyzed in terms of their occurrence and amount.     

Automated data analysis to rapidly derive and communicate ecological insights from satellite-tag data: A case study of reintroduced red kites

Analysis of satellite-telemetry data mostly occurs long after it has been collected, due to the time and effort needed to collate and interpret such material. Delayed reporting reduces the usefulness of such data for nature conservation where timely information about animal movements is required. To counter this problem, we present a novel approach which combines automated analysis of satellite-telemetry data with rapid communication of insights derived from such data. A relatively simple algorithm (based on radial and angular velocity calculated from fixes) allowed instantaneous detection of excursions away from settlement areas and automated calculation of home ranges on the remaining data. Automating the detection of both excursions and home-range calculations enabled us to disseminate ecological insights from satellite-tag data instantaneously through a dedicated web portal. The automated analysis, interpretation, and communication of satellite-tag and other ecological data offer clear benefits to nature conservation research and practice.     

Exploratory analysis of data sets with missing elements and outliers

The main goal of the presented paper was to develop a general strategy allowing exploration of contaminated data sets with missing elements, based on application of robust PLS for initial estimation of missing elements. Using robust distance, the outlying elements were identified. After their identification and replacing by missing elements, the expectation-maximization algorithm (which can be built in into different computational procedures, such as principal component analysis and its generalisation to the N-way data-the TUCKER3 model) was used for construction of the final model.     

Percentile estimation using variable censored data

Much progress has been made in recent years to address the estimation of summary statistics, using data that are subject to censoring of results that fall below the limit of detection (LOD) for the measuring instrument. Truncated data methods (e.g., Tobit regression) and multiple-imputation are two approaches for analyzing data results that are below the LOD. To apply these methods requires an assumption about the underlying distribution of the data. Because the log-normal distribution has been shown to fit many data sets obtained from environmental measurements, the common practice is to assume that measurements of environmental factors can be described by log-normal distributions. This article describes methods for obtaining estimates of percentiles and their associated confidence intervals when the results are log-normal and a fraction of the results are below the LOD. We present limited simulations to demonstrate the bias of the proposed estimates and the coverage probability of their associated confidence intervals. Estimation methods are used to generate summary statistics for 2,3,7,8-tetrachloro dibenzo-p-dioxin (2,3,7,8-TCDD) using data from a 2001 background exposure study in which PCDDs/PCDFs/cPCBs in human blood serum were measured in a Louisiana population. Because the congener measurements used in this study were subject to variable LODs, we also present simulation results to demonstrate the effect of variable LODs on the multiple-imputation process.