SOURCE APPORTIONMENT OF PM2.5 IN URBAN AREAS USING MULTIPLE LINEAR REGRESSION AS AN INVERSE MODELLING TECHNIQUE

In many countries emissions of particulate matter from urban sources, such as traffic and domestic wood burning, can lead to high episodic concentrations. Though it is important for air quality management and exposure studies to understand the individual source contributions to these concentrations, the complexity of the urban environment does not always allow a clear separation of sources when using conventional monitoring techniques that measure particulate mass only. Chemical analysis of the particulates, combined with receptor modelling, is one method for determining source contributions but these do not provide direct information on emissions. Inverse modelling methods, that make use of both dispersion models and measurements, can in principle be used to determine emissions strengths and distributions. However, the urban environment is generally so complex and the number of observations so limited that most inverse modelling methods cannot be effectively applied. In this paper a straight forward inverse modelling method, using multiple linear regression, is described and applied. The method determines the optimal fit of the calculated source contributions using dispersion modelling, providing scaling factors for the individual source contributions. The method is applied to the urban area of Oslo for PM2.5 in the winter of 2004 and the results of the inverse modelling are compared to independent receptor modelling. The method shows that the modelled source contribution from suspended road dust is underestimated by a factor of 7 – 10. For domestic wood burning the method shows an overestimate of the modelled source contribution by a factor of 2 - 3. These results are confirmed using independent analysis by receptor modelling. The methodology cannot distinguish directly between model or emission error and so further assessment of the model itself, and its uncertainty, is required before concrete statements concerning emission strengths can be made

Neural network trigger algorithms for heavy quark event selection in a fixed target high energy physics experiment

Abstract The study of particles containing heavy quarks is currently a major topic in high energy physics. In this paper, neural net trigger algorithms are developed to distinghish heavy quark (signal) events from light quark (background) events in a fixed target experiment. The event tracks which are parametrized by the impact parameter D and the angle Φ of the track with respect to the beam line, vary in number and in position in the Φ - D plane. An invariant second-order moment feature set and an invariant D -sequence representation are derived to characterize the signal and background event track patterns in the Φ - D plane. A three-layer perceptron is trained to classify events as signal/background via the moments and D -sequences. A nearest neighbor classifier is also developed to serve as a benchmark for comparing the performance of the neural net triggers. Results indicate that the selected moment feature set and the D -sequence representation contain essential signal/background discriminatory information. The results also show that the neural network trigger algorithms are superior to the nearest neighbor trigger algorithms. A very high discrimination against background events and a very high efficiency for selecting signal events is obtained with the D -sequence neural net trigger algorithm

Tests of Track Segment and Vertex Finding With Neural Networks

Feed forward neural networks have been trained, using back-propagation, to find the slopes of simulated track segments in a straw chamber and to find the vertex of tracks from both simulated and real events in a more conventional drift chamber geometry. Network architectures, training, and performance are presented. 12 refs., 7 figs

SMAD transcription factors are altered in cell models of HD and regulate HTT expression

Transcriptional dysregulation is observable in multiple animal and cell models of Huntington's disease, as well as in human blood and post-mortem caudate. This contributes to HD pathogenesis, although the exact mechanism by which this occurs is unknown. We therefore utilised a dynamic model in order to determine the differential effect of growth factor stimulation on gene expression, to highlight potential alterations in kinase signalling pathways that may be in part responsible for the transcriptional dysregulation observed in HD, and which may reveal new therapeutic targets. We demonstrate that cells expressing mutant huntingtin have a dysregulated transcriptional response to epidermal growth factor stimulation, and identify the transforming growth factor-beta pathway as a novel signalling pathway of interest that may regulate the expression of the Huntingtin (HTT) gene itself. The dysregulation of HTT expression may contribute to the altered transcriptional phenotype observed in HD

Towards uncertainty mapping in air-quality modelling and assessment

The aim of this paper is to promote the use of uncertainty mapping when spatial assessments of air quality are made. A large number of air quality maps are produced for scientific and policy purposes but rarely are corresponding maps of their uncertainty included. The need for such maps and the methods to produce them are described. Several uncertainty parameters are discussed but it is recommended to use the probability density function as the basis of the uncertainty estimates. Several examples are provided discussing indicative uncertainty, ensemble methods, comparisons with observations, spatial representativeness, uncertainty in exceedances and probability of exceedance.publishe