Urban energy fluxes in Helsinki and their high frequency spectral corrections

Inadvertent climate modification has led to an increase in urban temperatures compared to the surrounding rural area. The main reason for the temperature rise is the altered energy portioning of input net radiation to heat storage and sensible and latent heat fluxes in addition to the anthropogenic heat flux. The heat storage flux and anthropogenic heat flux have not yet been determined for Helsinki and they are not directly measurable. To the contrary, turbulent fluxes of sensible and latent heat in addition to net radiation can be measured, and the anthropogenic heat flux together with the heat storage flux can be solved as a residual. As a result, all inaccuracies in the determination of the energy balance components propagate to the residual term and special attention must be paid to the accurate determination of the components. One cause of error in the turbulent fluxes is the fluctuation attenuation at high frequencies which can be accounted for by high frequency spectral corrections. The aim of this study is twofold: to assess the relevance of high frequency corrections to water vapor fluxes and to assess the temporal variation of the energy fluxes. Turbulent fluxes of sensible and latent heat have been measured at SMEAR III station, Helsinki, since December 2005 using the eddy covariance technique. In addition, net radiation measurements have been ongoing since July 2007. The used calculation methods in this study consist of widely accepted eddy covariance data post processing methods in addition to Fourier and wavelet analysis. The high frequency spectral correction using the traditional transfer function method is highly dependent on relative humidity and has an 11% effect on the latent heat flux. This method is based on an assumption of spectral similarity which is shown not to be valid. A new correction method using wavelet analysis is thus initialized and it seems to account for the high frequency variation deficit. Anyhow, the resulting wavelet correction remains minimal in contrast to the traditional transfer function correction. The energy fluxes exhibit a behavior characteristic for urban environments: the energy input is channeled to sensible heat as latent heat flux is restricted by water availability. The monthly mean residual of the energy balance ranges from 30 Wm-2 in summer to -35 Wm-2 in winter meaning a heat storage to the ground during summer. Furthermore, the anthropogenic heat flux is approximated to be 50 Wm-2 during winter when residential heating is important.

Univariate, bivariate, and multivariate methods in corpus-based lexicography : A study of synonymy

In this dissertation, I present an overall methodological framework for studying linguistic alternations, focusing specifically on lexical variation in denoting a single meaning, that is, synonymy. As the practical example, I employ the synonymous set of the four most common Finnish verbs denoting THINK, namely ajatella, miettiä, pohtia and harkita ‘think, reflect, ponder, consider’. As a continuation to previous work, I describe in considerable detail the extension of statistical methods from dichotomous linguistic settings (e.g., Gries 2003; Bresnan et al. 2007) to polytomous ones, that is, concerning more than two possible alternative outcomes. The applied statistical methods are arranged into a succession of stages with increasing complexity, proceeding from univariate via bivariate to multivariate techniques in the end. As the central multivariate method, I argue for the use of polytomous logistic regression and demonstrate its practical implementation to the studied phenomenon, thus extending the work by Bresnan et al. (2007), who applied simple (binary) logistic regression to a dichotomous structural alternation in English. The results of the various statistical analyses confirm that a wide range of contextual features across different categories are indeed associated with the use and selection of the selected think lexemes; however, a substantial part of these features are not exemplified in current Finnish lexicographical descriptions. The multivariate analysis results indicate that the semantic classifications of syntactic argument types are on the average the most distinctive feature category, followed by overall semantic characterizations of the verb chains, and then syntactic argument types alone, with morphological features pertaining to the verb chain and extra-linguistic features relegated to the last position. In terms of overall performance of the multivariate analysis and modeling, the prediction accuracy seems to reach a ceiling at a Recall rate of roughly two-thirds of the sentences in the research corpus. The analysis of these results suggests a limit to what can be explained and determined within the immediate sentential context and applying the conventional descriptive and analytical apparatus based on currently available linguistic theories and models. The results also support Bresnan’s (2007) and others’ (e.g., Bod et al. 2003) probabilistic view of the relationship between linguistic usage and the underlying linguistic system, in which only a minority of linguistic choices are categorical, given the known context – represented as a feature cluster – that can be analytically grasped and identified. Instead, most contexts exhibit degrees of variation as to their outcomes, resulting in proportionate choices over longer stretches of usage in texts or speech.

Exploiting ground-based measurements of the Global Positioning System for numerical weather prediction

Data assimilation provides an initial atmospheric state, called the analysis, for Numerical Weather Prediction (NWP). This analysis consists of pressure, temperature, wind, and humidity on a three-dimensional NWP model grid. Data assimilation blends meteorological observations with the NWP model in a statistically optimal way. The objective of this thesis is to describe methodological development carried out in order to allow data assimilation of ground-based measurements of the Global Positioning System (GPS) into the High Resolution Limited Area Model (HIRLAM) NWP system. Geodetic processing produces observations of tropospheric delay. These observations can be processed either for vertical columns at each GPS receiver station, or for the individual propagation paths of the microwave signals. These alternative processing methods result in Zenith Total Delay (ZTD) and Slant Delay (SD) observations, respectively. ZTD and SD observations are of use in the analysis of atmospheric humidity. A method is introduced for estimation of the horizontal error covariance of ZTD observations. The method makes use of observation minus model background (OmB) sequences of ZTD and conventional observations. It is demonstrated that the ZTD observation error covariance is relatively large in station separations shorter than 200 km, but non-zero covariances also appear at considerably larger station separations. The relatively low density of radiosonde observing stations limits the ability of the proposed estimation method to resolve the shortest length-scales of error covariance. SD observations are shown to contain a statistically significant signal on the asymmetry of the atmospheric humidity field. However, the asymmetric component of SD is found to be nearly always smaller than the standard deviation of the SD observation error. SD observation modelling is described in detail, and other issues relating to SD data assimilation are also discussed. These include the determination of error statistics, the tuning of observation quality control and allowing the taking into account of local observation error correlation. The experiments made show that the data assimilation system is able to retrieve the asymmetric information content of hypothetical SD observations at a single receiver station. Moreover, the impact of real SD observations on humidity analysis is comparable to that of other observing systems.

Novel Mass Spectrometric Analysis Methods for Anabolic Androgenic Steroids in Sports Drug Testing

The feasibility of different modern analytical techniques for the mass spectrometric detection of anabolic androgenic steroids (AAS) in human urine was examined in order to enhance the prevalent analytics and to find reasonable strategies for effective sports drug testing. A comparative study of the sensitivity and specificity between gas chromatography (GC) combined with low (LRMS) and high resolution mass spectrometry (HRMS) in screening of AAS was carried out with four metabolites of methandienone. Measurements were done in selected ion monitoring mode with HRMS using a mass resolution of 5000. With HRMS the detection limits were considerably lower than with LRMS, enabling detection of steroids at low 0.2-0.5 ng/ml levels. However, also with HRMS, the biological background hampered the detection of some steroids. The applicability of liquid-phase microextraction (LPME) was studied with metabolites of fluoxymesterone, 4-chlorodehydromethyltestosterone, stanozolol and danazol. Factors affecting the extraction process were studied and a novel LPME method with in-fiber silylation was developed and validated for GC/MS analysis of the danazol metabolite. The method allowed precise, selective and sensitive analysis of the metabolite and enabled simultaneous filtration, extraction, enrichment and derivatization of the analyte from urine without any other steps in sample preparation. Liquid chromatographic/tandem mass spectrometric (LC/MS/MS) methods utilizing electrospray ionization (ESI), atmospheric pressure chemical ionization (APCI) and atmospheric pressure photoionization (APPI) were developed and applied for detection of oxandrolone and metabolites of stanozolol and 4-chlorodehydromethyltestosterone in urine. All methods exhibited high sensitivity and specificity. ESI showed, however, the best applicability, and a LC/ESI-MS/MS method for routine screening of nine 17-alkyl-substituted AAS was thus developed enabling fast and precise measurement of all analytes with detection limits below 2 ng/ml. The potential of chemometrics to resolve complex GC/MS data was demonstrated with samples prepared for AAS screening. Acquired full scan spectral data (m/z 40-700) were processed by the OSCAR algorithm (Optimization by Stepwise Constraints of Alternating Regression). The deconvolution process was able to dig out from a GC/MS run more than the double number of components as compared with the number of visible chromatographic peaks. Severely overlapping components, as well as components hidden in the chromatographic background could be isolated successfully. All studied techniques proved to be useful analytical tools to improve detection of AAS in urine. Superiority of different procedures is, however, compound-dependent and different techniques complement each other.

Functional genes and gene array analysis as tools for monitoring hydrocarbon biodegradation

Bioremediation, which is the exploitation of the intrinsic ability of environmental microbes to degrade and remove harmful compounds from nature, is considered to be an environmentally sustainable and cost-effective means for environmental clean-up. However, a comprehensive understanding of the biodegradation potential of microbial communities and their response to decontamination measures is required for the effective management of bioremediation processes. In this thesis, the potential to use hydrocarbon-degradative genes as indicators of aerobic hydrocarbon biodegradation was investigated. Small-scale functional gene macro- and microarrays targeting aliphatic, monoaromatic and low molecular weight polyaromatic hydrocarbon biodegradation were developed in order to simultaneously monitor the biodegradation of mixtures of hydrocarbons. The validity of the array analysis in monitoring hydrocarbon biodegradation was evaluated in microcosm studies and field-scale bioremediation processes by comparing the hybridization signal intensities to hydrocarbon mineralization, real-time polymerase chain reaction (PCR), dot blot hybridization and both chemical and microbiological monitoring data. The results obtained by real-time PCR, dot blot hybridization and gene array analysis were in good agreement with hydrocarbon biodegradation in laboratory-scale microcosms. Mineralization of several hydrocarbons could be monitored simultaneously using gene array analysis. In the field-scale bioremediation processes, the detection and enumeration of hydrocarbon-degradative genes provided important additional information for process optimization and design. In creosote-contaminated groundwater, gene array analysis demonstrated that the aerobic biodegradation potential that was present at the site, but restrained under the oxygen-limited conditions, could be successfully stimulated with aeration and nutrient infiltration. During ex situ bioremediation of diesel oil- and lubrication oil-contaminated soil, the functional gene array analysis revealed inefficient hydrocarbon biodegradation, caused by poor aeration during composting. The functional gene array specifically detected upper and lower biodegradation pathways required for complete mineralization of hydrocarbons. Bacteria representing 1 % of the microbial community could be detected without prior PCR amplification. Molecular biological monitoring methods based on functional genes provide powerful tools for the development of more efficient remediation processes. The parallel detection of several functional genes using functional gene array analysis is an especially promising tool for monitoring the biodegradation of mixtures of hydrocarbons.

Measurements of adequacy of anesthesia and level of consciousness during surgery and intensive care

The adequacy of anesthesia has been studied since the introduction of balanced general anesthesia. Commercial monitors based on electroencephalographic (EEG) signal analysis have been available for monitoring the hypnotic component of anesthesia from the beginning of the 1990s. Monitors measuring the depth of anesthesia assess the cortical function of the brain, and have gained acceptance during surgical anesthesia with most of the anesthetic agents used. However, due to frequent artifacts, they are considered unsuitable for monitoring consciousness in intensive care patients. The assessment of analgesia is one of the cornerstones of general anesthesia. Prolonged surgical stress may lead to increased morbidity and delayed postoperative recovery. However, no validated monitoring method is currently available for evaluating analgesia during general anesthesia. Awareness during anesthesia is caused by an inadequate level of hypnosis. This rare but severe complication of general anesthesia may lead to marked emotional stress and possibly posttraumatic stress disorder. In the present series of studies, the incidence of awareness and recall during outpatient anesthesia was evaluated and compared with that of in inpatient anesthesia. A total of 1500 outpatients and 2343 inpatients underwent a structured interview. Clear intraoperative recollections were rare the incidence being 0.07% in outpatients and 0.13% in inpatients. No significant differences emerged between outpatients and inpatients. However, significantly smaller doses of sevoflurane were administered to outpatients with awareness than those without recollections (p<0.05). EEG artifacts in 16 brain-dead organ donors were evaluated during organ harvest surgery in a prospective, open, nonselective study. The source of the frontotemporal biosignals in brain-dead subjects was studied, and the resistance of bispectral index (BIS) and Entropy to the signal artifacts was compared. The hypothesis was that in brain-dead subjects, most of the biosignals recorded from the forehead would consist of artifacts. The original EEG was recorded and State Entropy (SE), Response Entropy (RE), and BIS were calculated and monitored during solid organ harvest. SE differed from zero (inactive EEG) in 28%, RE in 29%, and BIS in 68% of the total recording time (p<0.0001 for all). The median values during the operation were SE 0.0, RE 0.0, and BIS 3.0. In four of the 16 organ donors, EEG was not inactive, and unphysiologically distributed, nonreactive rhythmic theta activity was present in the original EEG signal. After the results from subjects with persistent residual EEG activity were excluded, SE, RE, and BIS differed from zero in 17%, 18%, and 62% of the recorded time, respectively (p<0.0001 for all). Due to various artifacts, the highest readings in all indices were recorded without neuromuscular blockade. The main sources of artifacts were electrocauterization, electromyography (EMG), 50-Hz artifact, handling of the donor, ballistocardiography, and electrocardiography. In a prospective, randomized study of 26 patients, the ability of Surgical Stress Index (SSI) to differentiate patients with two clinically different analgesic levels during shoulder surgery was evaluated. SSI values were lower in patients with an interscalene brachial plexus block than in patients without an additional plexus block. In all patients, anesthesia was maintained with desflurane, the concentration of which was targeted to maintain SE at 50. Increased blood pressure or heart rate (HR), movement, and coughing were considered signs of intraoperative nociception and treated with alfentanil. Photoplethysmographic waveforms were collected from the contralateral arm to the operated side, and SSI was calculated offline. Two minutes after skin incision, SSI was not increased in the brachial plexus block group and was lower (38 ± 13) than in the control group (58 ± 13, p<0.005). Among the controls, one minute prior to alfentanil administration, SSI value was higher than during periods of adequate antinociception, 59 ± 11 vs. 39 ± 12 (p<0.01). The total cumulative need for alfentanil was higher in controls (2.7 ± 1.2 mg) than in the brachial plexus block group (1.6 ± 0.5 mg, p=0.008). Tetanic stimulation to the ulnar region of the hand increased SSI significantly only among patients with a brachial plexus block not covering the site of stimulation. Prognostic value of EEG-derived indices was evaluated and compared with Transcranial Doppler Ultrasonography (TCD), serum neuron-specific enolase (NSE) and S-100B after cardiac arrest. Thirty patients resuscitated from out-of-hospital arrest and treated with induced mild hypothermia for 24 h were included. Original EEG signal was recorded, and burst suppression ratio (BSR), RE, SE, and wavelet subband entropy (WSE) were calculated. Neurological outcome during the six-month period after arrest was assessed with the Glasgow-Pittsburgh Cerebral Performance Categories (CPC). Twenty patients had a CPC of 1-2, one patient had a CPC of 3, and nine patients died (CPC 5). BSR, RE, and SE differed between good (CPC 1-2) and poor (CPC 3-5) outcome groups (p=0.011, p=0.011, p=0.008, respectively) during the first 24 h after arrest. WSE was borderline higher in the good outcome group between 24 and 48 h after arrest (p=0.050). All patients with status epilepticus died, and their WSE values were lower (p=0.022). S-100B was lower in the good outcome group upon arrival at the intensive care unit (p=0.010). After hypothermia treatment, NSE and S-100B values were lower (p=0.002 for both) in the good outcome group. The pulsatile index was also lower in the good outcome group (p=0.004). In conclusion, the incidence of awareness in outpatient anesthesia did not differ from that in inpatient anesthesia. Outpatients are not at increased risk for intraoperative awareness relative to inpatients undergoing general anesthesia. SE, RE, and BIS showed non-zero values that normally indicate cortical neuronal function, but were in these subjects mostly due to artifacts after clinical brain death diagnosis. Entropy was more resistant to artifacts than BIS. During general anesthesia and surgery, SSI values were lower in patients with interscalene brachial plexus block covering the sites of nociceptive stimuli. In detecting nociceptive stimuli, SSI performed better than HR, blood pressure, or RE. BSR, RE, and SE differed between the good and poor neurological outcome groups during the first 24 h after cardiac arrest, and they may be an aid in differentiating patients with good neurological outcomes from those with poor outcomes after out-of-hospital cardiac arrest.