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2.2.73 Early warning environmental information for the atmospheric environment: The AIRTHESS system K. Karatzas1 and K. Nikolaou2* 1

Informatics Applications and Systems Group, Dept. of Mechanical Engineering, Aristotle University of Thessaloniki, Box 483, 54124 Thessaloniki, Greece 2 Organization for the Master Plan and Environmental Protection of Thessaloniki, 105 Vas. Olgas Str., 54643 Thessaloniki, Greece * Corresponding author: E-mail: [email protected], Tel +30 2310886046, Fax: +30 2310825151

Abstract The present paper provides information on air quality (AQ) related early warning information provision, environmental information systems, and the way that such systems may support the quality of everyday life. On this basis, the paper presents the AIRTHESS system, being in operation in Thessaloniki, Greece. AIRTHESS is an informatics system for the early notification and information provision of citizens concerning air pollution problems. Computational intelligence methods are applied for the forecasting of concentration levels, while the most up-to-date technologies for internet and mobile phone information services are incorporated. In addition, historical data coming from the monitoring network of the Region of Central Macedonia are made accessible to the public via an internet-based, graphics environment. The paper reports on the design, development and functional details of the system, and draws conclusion concerning the way that such systems should be constructed and operated. Keywords: Air quality, information system, early warning.

1. INTRODUCTION Environmental quality is characterised on the basis of pollution values, that are associated with the existence of substances or energy forms, which are proven to be harmful to human beings, flora, fauna, and materials. All environmental domains like water, air, soil, etc., are of interest to man, as they are influencing health and quality of life aspects. Especially air pollution is directly associated to quality of life, due to the large percentage of people that either suffer from respiratory and cardiovascular related diseases or are sensitive to atmospheric quality degradation. Due to the fact that air pollution is a common environmental problem in urban areas, related environmental problems became part of the contemporary urban life and urban culture [1]. Human activities in cities follow patterns that are related to the consumption of energy sources, goods, and services, which are all contributors to air pollution problems. These patterns are framed on the basis of the spatial and temporal scale of human activities: early morning commuting towards working places, traffic flow in the afternoon, local activity picks at schools, shopping and entertainment centres, etc. As we are surrounded by air (the carrier of any air pollutant), we are continuously exposed to atmospheric pollutant concentrations that in many cases exceed limit values, i.e. regulated concentrations per pollutant serving as threshold marks to potential health and quality of life problems. On this basis, it was early recognized that citizens have the right to be informed about environmental problems while the latter develop, and that especially for air pollution, there should be specific pollutants which should be monitored and modelled, in order to produce air pollution information on time and in advance. The legal framework for providing air quality related information to the public was recently enriched with the “Clean Air” Directive 2008/50/EC, which states that: “Member States shall ensure that timely information about actual or predicted exceedances of alert thresholds, and any information threshold is provided to the public”. On this Proceedings of the 2nd International CEMEPE & SECOTOX Conference, Mykonos, June 21-26, 2009 ISBN 978-960-6865-09-1

Editors: A. Kungolos, K. Aravossis A. Karagiannidis, P. Samaras page 2085

basis, and taking into account related R&D experience [2,3], the project AIRTHESS was developed aiming at the provision of timely and in advance air quality information to the general public via the internet and on the basis of email and SMS oriented early warnings. 2. MATERIALS AND METHODS 2.1 2. The application area and methods Thessaloniki is the second largest city of Greece and one of the largest urban agglomerations in the Balkans, where the formation and transport of pollutants are heavily influenced by the local meteorological and topographic characteristics. Thessaloniki is located in the inner part of the Thermaikos Gulf, surrounded in the northerly and north-easterly directions by the Hortiatis mountain (1200 m height). Numerous residential suburbs circle the city and an extended industrial zone is located to the north-west of it’s outskirts. In order to develop the AQ information and forecasting system, a number Computational Intelligence (CI) algorithms were applied, with the aim to identify the basic interrelationships between parameters, and to select those that may serve as appropriate candidates for the development of an air quality forecasting service [4,5]. Then, an air quality information system was developed, including a portal and a set of visualization and notification services, making use of internet technologies, on-line mapping tools, and event-driven software services. 2.2 The environmental application domain Air pollution is being monitored in the Greater Thessaloniki Area via the (official) air quality monitoring network, operated by the Region of Central Macedonia, Environment Dept. (RCM-ED), as part of the National Air Quality Monitoring Network that operates under the umbrella of the Greek Ministry of Environment, Planning and Public Works. In addition to this network, the Municipality of Thessaloniki operates a (private) network, while the same stands for the Municipality of Echedoros (Figure 1). The monitoring network of the RCM-ED is the one producing the official yearly published air quality reports and its data are also sent to the European Environment Agency, via the EIONET network and the standard reporting procedure followed by all member states. The latter two municipal monitoring networks do not make their data public in a sufficient way: the Echedoros municipality issues a short yearly report, which is available only as a hard copy, as there is no environmental – air quality portal in operation. Such a portal is operated by the municipality of Thessaloniki, yet, the only data that are being made publicly available are the maximum hourly values of air pollutant concentrations and the station where these were observed every day. Thus, it is evident that the Greater Thessaloniki Area (GTA) was lacking of a proper air quality information system that would provide to the citizen access to all air pollution hourly data, both historical and new, for all monitoring stations, in an easy, intuitive way, with the support of appropriate graphics and comparison functions. The other important dimension that was missing from the GTA was an operational AQ forecasting service that would provide on an everyday basis with concentration estimations for basic pollutants of interest, in an automatic way. It should be mentioned that there were two AQ forecasting models already tested for Thessaloniki. The first was implemented by Moussiopoulos et al [6], which included the installation of a complete and reliable system of air pollution models for the master plan implementation and environmental protection of Thessaloniki. This system however was not intended to be used for everyday operational air quality forecasting, but for the testing and evaluation of scenarios related to the GTA master plan, and their impact to air pollution. The second system was developed in the frame of the PROMOTE project, and is in a pilot phase, providing with forecasts for the GTA on the basis of a 3-D AQ model, at a resolution of 2X2 km [7]. The third dimension missing in the GTA concerning air pollution information dissemination is an easy to use, personalized, electronic information service, that would 2086

automatically operate on the basis of incidents like high pollution values (monitored or forecasted). AIRTHESS provided with a two way electronic dissemination, one based on email and one materialized via SMS messages for mobile devices. In this way, AIRTHESS covers the information needs of the citizen with both pull and push electronic communication channels.

Figure 1. Air quality monitoring networks operated in the Greater Thessaloniki Area. The RCMED network monitoring stations are marked with the “sun” symbol; the stations belonging to the municipality of Thessaloniki network are marked with the round dots, and the stations belonging to the municipality of Echedoros are marked with the “E” symbol. 3. RESULTS AND DISCUSSION 3.1 CI methods for AQ forecasting As a firs step towards the provision of air quality information in advance, it was decided to develop a number of AQ models, that would receive input from AQ and meteorological observations, and would produce a forecast, for selected pollutants, and for the (future) period of interest. Thus, it was necessary to investigate the AQ timeseries in detail, in order to reveal hidden interdependencies and relationships, and to identify problems, missing values, etc. For this reason, it was decided to employ Computational Intelligence methods, as they are capable of in depth analysis, knowledge extraction and parameter forecasting and have been tested in the air quality field with success, where their performance was found to be similar or in some cases even better compared with that of deterministic models [8]. CI techniques such as Artificial Neural Networks (ANNs), Classification and Regression Trees (CART) and Support Vector Machines have been applied for forecasting of photochemical and particulate matter pollution in the metropolitan area of Thessaloniki [4,5,9] and Athens [10,11,12]. The results of these studies indicate that CI methods perform better compared to statistical methods, and can be potentially very accurate in forecasting parameters of interest, depending on the quantity and quality of the data. These findings, combined with the computational efficiency of CI methods, suggest that the latter methods can be an excellent tool for the creation of operational air quality forecasting modules, which may effectively support operational air quality management on a day-to-day basis. 3.2 Developing operational AQ forecasts The visualisation and dissemination of air pollution should be materialised in such a way so that it makes sense for the end user (the citizen). This means that the information provided should be simple, easily understandable, having a graphical representation that is close to other information that the citizens use in everyday life. For this reason, it was decided to make use of categorical AQ 2087

values rather than actual concentrations. Thus, a four-colour scale was applied for all pollutants addressed, as the one presented in Figure 2. This scale corresponds to the EU and national air quality limits and the air pollution episodes local limits.

Figure 2. Air quality categorization in a four-color scale: good (green), medium (blue), bad (yellow), very bad (red). Pollutants covered include (from top to bottom): PM10, NO2, SO2, CO and O3. All values (except for CO), are in •g/m3. In order to construct the AQ forecasting models for Thessaloniki, a proper computational experimental strategy had to be employed. Data used included the meteorological and air pollution hourly measurements (more than 200,000 records), supplied by RCM-ED. All computations were conducted with the aid of the Waikato Environment for Knowledge Analysis (WEKA) [13] and involved algorithms falling in the following categories: (i) Decision Tree Classifiers (Decision Stump, Logistic Model Trees, Naive Bayes Trees, Random Forest, Random Tree, REP Tree and J48 – WEKA's C4.5 implementation); (ii) Neural Networks (Multi-Layer Perceptron and RBF Network); (iii) Rule-based Classifiers (JRip, OneR, Conjunctive Rule, Decision Table, Ridor, PART and NNge); (iv) Bayesian Classifiers (Bayes Network, Naive Bayes and Naive Bayes Updateable); (v) Instance-based classifiers (IBk, IB1, KStar); (vi) Support Vector Machines (sequential minimal optimization algorithm – SMO); and (vii) Logistic Regression (Logistic, Simple Logistic). The AQ model development was divided into three stages: 1. Data pre-processing and formulation of appropriate datasets for each prediction task. 2. Evaluation of alternative classification algorithms per station and per prediction task. 3. Construction of cost-sensitive operational prediction models for the best performing algorithms of stage 2 and evaluation of these models It should be mentioned that cost-sensitive model building proved to be an effective technique to “guide” the algorithms towards the intended outcome: all models managed to outperform the corresponding non-cost-sensitive ones (comparison per algorithm-station-target pollutant triplet) achieving prediction accuracies up to 88.5% for exceedances. The algorithms that were proven to be more effective were mostly in the category of decision trees, and more specifically J48 (WEKA’s C4.5 implementation) and Logistic Model Trees (more details are available in [5]). From the operational point of view, it was interesting to note that the AIRTHESS system achieved very good performance: the Critical Success Index (or Kappa Index as it is also known) for the pilot operation and testing phase in Summer 2008 was 54%, when, in comparison to that, literature reports values between 7 and 21% in a USA application [14], and from 15 up to 52 % for Athens, Greece [15]. 3.3 The early warning notification services The development of efficient AQ forecasting models allowed for the design and construction of the proper early earning, quality of life information services, that were then integrated to the Thessaloniki air quality information and early warning system (AIRTHESS). The Thessaloniki air quality information and early warning system (AIRTHESS), was designed and developed taking into account the state of the art in Information and Communication Technologies (ICT) and the way that AQ information should be disseminated and presented, either on a daily information basis, or on the basis of alerts generated by incident forecasts. AIRTHESS makes use of Google Maps for 2088

the geographic presentation of information and Adobe Flash for the graphical presentation of air pollution time series, merged in a responsive rich web application using the Google Web Toolkit (GWT) to perform dynamic actions. In the backend, AIRTHESS is implemented using a stack of open source libraries and frameworks, mainly based on the Eclipse Equinox implementation of the OSGi Service Platform, allowing fine-grained reuse of existing code and extension of behaviour with minimal overhead. Moreover, an in-house web application framework based on Apache Velocity for rendering and Apache Torque for the database access. AIRTHESS uses the OSGi Event Service in publish-subscribe mode to handle dataflow requirements for new measurements and notifications. When new measurements arrive, an Event is generated describing their metadata (such as their station, the measurement series that have been updated, the first and last moment of the new data, etc) and asynchronously posted to the Event Service. Any OSGi services that have registered as handlers whose filters match the new event are then notified; the modeling subsystem is one such service, handling execution of prediction models. Any forecasts that are calculated emit their own events; the notification service is subscribed to these particular events and checks if any users should be notified of these new predictions. Being based on OSGi and the publish-subscribe pattern allows the various components to be very decoupled from each other; further, services can dynamically subscribe to the generated events without causing any downtime. The warning are being issues via e-mail and SMS to the (freely) subscribed users as sketched in Figure 3.

Figure 3. Basic functionalities, subsystems and notification services of the AIRTHESS system. 4. CONCLUSIONS The quality of the atmospheric environment plays an important role in quality of life. People need to be informed on time and in advance when it comes to air pollution levels that may harm their health and have a negative influence to the environment they live in. Contemporary Information and Communication Technologies, together with Computational Intelligence methods may support the development of effective, operationally successful environmental information systems and services. This has been tested and applied in the Thessaloniki area for air pollution, by incorporating an arsenal of CI methods and a proper model development strategy. On this basis, an air quality, early warning information system was developed for the Greater Thessaloniki Area, under the name AIRTHESS (www.airthess.gr). The system is easily adaptable to local needs and other geographical and environmental domains of interest, in both ICT and data infrastructure terms. Acknowledgement The authors acknowledge the support of the Organization for the Master Plan and Environmental Protection of Thessaloniki, and of the Region of Central Macedonia. 2089

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