Short term prediction of PM10-concentrations in ambient air using machine learning techniques

Galten die mit der Staubbelastung der Luft verbundenen Gefahren und Beeinträchtigungen durch nationale und europäische Umweltrichtlinien Ende der 80er und Anfang der 90er als weitgehend gebannt, so werden nach heutigem Kenntnisstand bereits im Vergleich zu früher relativ geringen Konzentrationen feiner Stäube negative gesundheitliche Auswirkungen zugeschrieben. Erneut in den Blick der Öffentlichkeit geriet die Feinstaubproblematik im Jahre 2005 als nach der Einführung des EU–Grenzwerts für PM10 dieser Grenzwert in deutschen Städten häufig überschritten wurde. Um die zuständigen Behörden und die Bevölkerung über die Luftqualität informieren und gegebenenfallsMaßnahmen zur Vermeidung von Gesundheitsschäden veranlassen zu können, ist es notwendig ein System aufzubauen, das in der Lage ist, PM10-Konzentrationen für den folgenden Tag zu prognostizieren. Hierzu sollen etablierten Prognosemethoden verschiedene innovative maschinelle Lernverfahren (Multilayer-Perzeptron, Support-Vektor-Maschine, Instanzbasiertes Lernen mit den k-nächsten-Nachbarn) gegenübergestellt und die jeweiligen, auf gleicher Datenbasis erzielten Prognoseleistungen miteinander verglichen werden. Da die größte Belastung für die menschliche Gesundheit durch Konzentrationsspitzen mit besonders hohen PM10-Werten ausgehen, liegt hier das besondere Augenmerk der Prognose. In die PM10-Prognose gehen auch meteorologische Vorhersagen ein, die mit großen Unsicherheiten behaftet sind. Durch eine Sensitivitätsanalyse wird untersucht, wie robust die Modelle gegenüber Abweichungen in der Meteorologie sind.Fine particulate matter (PM10) in ambient air once again became an important issue of clean air policy from the beginning of the 1990s when it became apparent that even relatively low concentrations of PM10 can cause serious health problems. In order to inform the competent authorities and the general public about air quality and to assist them in taking measures to avoid health problems, it is necessary to establish a system which predicts the PM10-concentration for the following day. For that purpose some established forcasting models such as the multiple linear regression are compared to innovative machine-learning techniques: the support-vector-machine, the multilayer-perceptron and the k-nearest-neighbour method. The respective performance of all models is compared, based on the same pool of data which contains measurements from four stations in Baden-Württemberg. As the principle contribution on human health problems caused by high air pollution levels, this study puts it's main focus on the correct prediction of high PM10-concentrations. The basic concept of machine-learning techniques is to extract information from data automatically, by computational and statistical methods. For the present study this means that these machine-learning algorithms automatically extract the relationships between the PM10-concentrations and influencing factors such as meteorological parameters (windspeed, precipitation, ...) or the emission situation from historical measurement data. The information derived on relationships is integrated into a model which can now be applied to predict PM10-concentrations using meteorological forecasts. The use of meteorological forecasts for PM10-modelling is associated with major uncertainties. Therefore the performances of all applied models are analysed using forecasted meteorological parameters of different quality. This sensitivity analysis shows how well the models perform under real world conditions. The implementation of machine-learning techniques always necessitates some optimization steps. As well as the fine tuning of the model parameters, the input dataset also has to be optimized. The use of all available (more or less important) influencing factors does not result in the best model performance. In fact an optimized set of factors leads to the best results. These very complex optimization steps are realised using genetic algorithms, evolutionary strategies and raster optimization to minimise the effort and computing time

MARQUIS : generation of user-tailored multilingual air quality bulletins

Air pollution has a major influence on health. It is thus not surprising that air quality (AQ) increasingly becomes a central issue in the environmental information policy worldwide. The most common way to deliver AQ information is in terms of graphics, tables, pictograms, or color scales that display either the concentrations of the pollutant substances or the corresponding AQ indices. However, all of these presentation modi lack the explanatory dimension; nor can they be easily tailored to the needs of the individual users. MARQUIS is an AQ information generation service that produces user-tailored multilingual bulletins on the major measured and forecasted air pollution substances and their relevance to human health in five European regions. It incorporates modules for the assessment of pollutant time series episodes with respect to their relevance to a given addressee, for planning of the discourse structure of the bulletins and the selection of the adequate presentation mode, and for generation proper. The positive evaluation of the bulletins produced by MARQUIS by users shows that the use of automatic text generation techniques in such a complex and sensitive application is feasible.39 page(s

On the challenge of creating and communicating air quality information : a case for environmental engineers

Building environmental information systems is a challenging task. It involves a detailed analysis of the addressee’s needs, development of air pollutant concentration assessment and interpretation techniques, which must be tailored to the addressee, as well as the development of information planning and production strategies. Environmental engineers are increasingly often in charge of setting up environmental services. They must be thus knowledgeable in a number of issues they did not face before. We present an air quality information system MARQUIS, which is usable, on the one hand, as a base service that can be extended and adapted to a new environmental information scenario, and, on the other hand, as a source of teaching and training material for environmental engineers.10 page(s

From measurement data to environmental information : MARQUIS - A Multimodal AiR QUality Information Service for the general public

This article introduces the Multimodal AiR QUality Information Service for General Public MARQUIS. The service, which is based on air quality measurements, is able to provide information on air quality in Europe in a way that is easily understandable for the general public. The goal of the project is presented below with a special focus on the role of the users. Furthermore the architecture of the system and the components are described.10 page(s