Abstract. 1 Introduction

Transcription

1 PARIS: an information system for air pollution management in the urban area of Athens D. Deligiorgi,*G. Kouroupetroglou,* C. Cartalis,* C. Moutselos,^ E. Kambitsi* "Division ofapplied Physics, University ofathens, 33 Ippokratous Street, GR Athens, Greece *Department ofinformatics, University of Athens, Panepistimioupolis, GR Athens, Greece Abstract This paper presents the Pollution of Air Research Information System (PARIS) designed and developed for the efficient management and analysis of both the air pollution loads and the meteorological conditions provided by the measurement networks of Athens urban area. PARIS accepts measurements in various formats, with fast sampling rates, for long recording periods, from a number of different stations and performs multidimensional statistical treatment and analysis as well as presentation of data and results in graphs and tables for a user defined time window. The system confronts successfully with the manipulation problem of huge amount of experimental data and using modern object oriented and user interface techniques constitutes an essential tool for the exploitation of spatial, temporal and long-term variations of actual air pollution data, the determination of air quality and the study of air pollution episodes. 1 Introduction For the efficient management of air pollution in urban areas and especially for the study of complex phenomena such as the air pollution episodes, it is necessary to have available a flexible system for the processing and analysis of spatial-time distributions for both the air pollution and the meteorological data provided by the measurement networks [1]. In many cases it is very useful to handle and correlate data for long time periods (of the order of decades) [2]. In practice the management of those data involves many difficulties for two main reasons: in one hand the amount of data is very extensive, on the other hand the measurement networks don't produce data in a homogeneous format. For the measurement of the primary and secondary air pollutants and/or the meteorological parameters at the Athens basin there are three main networks operated by: the Department of Air

2 194 Urban Pollution Pollution and Noise Monitoring of the Greek Ministry of the Environment, the Greek Meteorological Service and the National Observatory of Athens [2]. The diversity of measurement types and their spatial-time distribution complicates the process to extract information hidden beneath. Time continuity of measurements is not always guaranteed. Sampling rates of the same quantity may differ from station to station. The physical quantities in the stations are coded using dedicated conventions concerning units and digit formats. Furthermore, there is a growing interest in electronic systems for the management of huge amount of scientific or experimental data within the so called Experimental Management Systems and Scientific Databases [3]. An other issue of interest is the user interface of such systems [4]. Based on advanced methods and techniques of informatics we present in this paper the design and development of the Pollution of Air Research Information System. The main objective of PARIS is to overcome the problems mentioned in the above paragraph. 2 System Overview PARIS system integrates the stages from the conversion of raw data files obtained by the networks of the monitoring stations, to the data analysis and graphic presentations. The general overview of the system is given in Figure 1. Its architecture consists essentially of four main modules: a) preprocessing, b) data-base queries, c) statistical analysis and d) presentation of data and results (graphs and tables). PARIS functionality is mainly based in terms of time windowed data exploration, subject to the nature of air pollution episodes. However, the system is also capable to efficiently accomplish long period data statistics acquired for air-quality determination. After the determination of an established episode under investigation the following parameters have to be defined: i) the type of measurement(s), ii) the location(s) of measurements and iii) the time window for the analysis. As a result PARIS creates a workbook of homogeneous data. Optionally, a graph of the extracted data can be added to the workbook. The labels and the corresponding units are automatically added in the graphs, while the user is able to further manipulate the type and the appearance of the chart. A sizable time window technique was adopted as more approriate for the survey of air-pollution episodes. Based on a workbook the system can perform: a) multidimensional analysis of variance for each type of measurement, b) air-quality analysis compared to standards, c) correlation and covariance analysis of measurements, d) statistical analysis of wind data, e) efficient presentations of data and results. The presentation can be performed either for a specific measurement quantity (for all the measurement locations) or for a specific location of interest (for all the measured quantities). PARIS has been developed for the MS Windows software platform under the MS Excel spreadsheet environment to take advantage of the strong analysis and graphics capabilities they offer. An additional positive argument towards this decision was the fact that they incorporate a high level object oriented macro language, a wealthy tool background otherwise

3 Urban Pollution 195 needed to be built from scratch. Software techniques such as custom functions, Dynamic Link Libraries (DLL) as well as Dynamic Data Exchange (DDE) were extensively used. Special attention was given for the design of the graphical user interfaces of the human-computer interaction taking into account a detailed analysis of user requirements and task analysis. Custom dialog boxes with different type items, custom menus and toolbars were designed and developed.. Type of Measurement(s) >. Station(s) ;. Time window Analysis of Variance & Graphics Pollution Episodes, Air Quality & Graphics Correlation- Covariance Wind Statistics & Graphics Compound Analysis & Presentations Figure 1: Paris Organization 3 Preprocessing Preprocessing performs data verification and conversion into a common and homogeneous format. PARIS has been designed to flexibly handle the variety of income data, converting the different measurements in a homogeneous format that simplifies further presentations and analysis. The system provides appropriate filters for each data source. These filters are responsible for the decoding of raw data files and their transforming in standard units. Decreasing or increasing the sampling rate in a given data sequence can be performed through decimation and interpolation signal processing algorithms [5]. The output of this process feeds the data base of the system with the objective of unified approach of data handling and manipulation. Data are grouped into sets of annual measurements for a specific quantity, using common spreadsheet taxonomy: each row holds measurements for one day

4 196 Urban Pollution while each column covers the hourly data. The name of the set is given by abbreviating the network, the station and the year the data were collected. These names are used during the data base query. Preprocessing stages in the PARIS system have been designed to be transparent to the user. The user determines the network which he/she wants to work on. Appropriate menus present the available quantities measured by the different stations and the year of interest is selected. Depending on the data format, the user has the capability to extract particular subset of measurements for presentation or a dedicated analysis. 4. Data analysis and presentation Since the data base contains an important amount of information, covering spatial and temporal variations of air pollution and meteorological measurements over long time periods (e.g. decades), a broad statistical data analysis may be performed for each workbook. 4.1 Mean and Variance Analysis Mean term analysis include average arithmetic, geometric, median, harmonic standard deviation as well as the calculation of maximum and minimum values. Variance analysis can be performed in daily, weekly, monthly or annually basis. In the "daily" and "weekly" options, the mean term is computed within the time window initially defined, while with the other two, the window is automatically extended to the corresponding year or years, respectively. The results are also presented in graphs automatically along with the proper axis labels, units and line types. 4.2 Air pollution episodes and Air quality The study of air pollution in a specific area, includes observations from two different perspectives: in short terms to define the notion of pollution episodes and in longer terms to allow the air quality determination. In general, a pollution episode is considered to have taken place if the hourly measurements of some air pollutants have surpassed in duration and in grade an allowable level [6]. On the other hand, air quality determination is based on annual mean values. The definition of air pollution criterion in each case, is based on the legislation of a particular country. For the E.U. members a five pollutant air quality standard has been established (smoke, 80% NO^ O%, Mb). PARIS incorporates methods for the detection of air pollution fluctuations by analyzing air pollution data [6]. Choosing the option of air episodes for a workbook, the system calculates all the required parameters depending on the type of the pollutant, according to [6]; e.g.: smoke and SC>2 episodes are calculated in 24-hour period, NC>2 and Og in hourly periods and CO in 8-hour period. Comparisons with the corresponding standards is performed within the predefined time window. A new graph file is also added to the workbook containing the course of the pollution loads in comparison with the corresponding pollution standards. An example of the plot produced by this process is presented in Figure 2. One can ask the system for an air-quality survey; e.g. for the SO2 case, the air quality standard is subject to the smoke levels for the same time

5 Urban Pollution 197 period, the annual median value, the winter median value and the 98% of 24- hours measurements criterion. Figure 3 shows an example for the NOz air quality survey in Patission station. The graphs such as Figures 2 and 3 charts are also appropriately formed, scaled and labeled without any user intervention r -2nd- LeveL Figure 2: Air pollution episode in Patission Station (NC^ - hourly values) 4.3 Correlation-Covariance The correlation - covariance are important factors in the study of the interrelations between pollutants and weather conditions at a measurement location as well as the calculation of correlations between stations pairs. In PARIS the user can derive these values, within the defined time window and between the pairs of selected data series. In the case a data sequence includes empty data cells (due missed measurements) and the pair correspondence is not continuously kept, PARIS disregards them from the calculation. The arithmetic results are added to the workbook. 4.4 Wind Statistics Wind analysis plays a significant role for the study of natural conditions occurring during the development of an air pollution episode as well as for the extraction of long term meteorological characteristics. PARIS can compute and graphically present the wind distributions for different time periods in addition to the combinational distributions for the predefined time window case. For a given workbook the user selects from a menu the wind distributions (wind speed or wind direction) for the following cases: predefined time window, whole year, or all the inserted years. If the user operates on wind direction, distributions for 8 and 16 directions are calculated. In each case, the results along with the corresponding graphs are added to the workbook. PARIS creates these charts automatically using the histogram form, but the user is able to further manipulate or change this

6 198 Urban Pollution form. As an example, at the wind direction charts the wind rose form can be used. 270 T Figure 3: Air quality survey for NC>2 (Patission - 98% mean hourly values) 4.5 Compound Analysis and Presentations In many cases we desire the concurrent representation of various physical quantities. For instance, chart all the measured pollutants for a specific location (station) for the time window of a pollution episode, or chart for the same period of interest one specific pollutant measured at different locations (Figure 4). With this procedure the user can retrieve data simultaneously from multiple sources and have them charted automatically. Pollutant or teteorological parameter/ (^ Station J) All Stations Selected Station Meteorological Parameters ^ \. Pollutants / Same Year & Time Window \ i ' All Years at the Same Time Window \ T-H, WS Same Year & Time \ Vindow /^ \ AllPoMutants Same Year & TimeWindow Figure 4: Compound retrieval (T=temperature, H=humidity, WS=wind speed)

7 Urban Pollution 199 The task starts by selecting an initial data file from the workbook. Then the user chooses either to query all the available data for the same quantity of interest, or to query for one station all the data for the various quantities. Examples of the produced graphs are shown in Figure 5 and Figure 6 for the two cases. Athinas B Patission Peireos EJ Peristeri B N.Smirni E3 Geoponiki Liosion Marousi Figure 5: Pollutant measurement at various locations for the same time window Figure 6: Measured pollutants at Patission for the same time window 5 Conclusions Pollution of Air Research Information System has been designed and developed for the efficient management of spatial and temporal variations of air pollution and meteorological data. The system actually is an essential tool for the researcher for the determination of air quality and the study of air pollution episodes by integrating the stages from the conversion of raw data files obtained by the network of the monitoring stations, to the data

8 200 Urban Pollution analysis and graphic presentations. The system handles complex processing methods required for each case and creates compound charts transparently to the user, allowing him/her to concentrate in the study of the phenomena. PARIS system has been already successfully applied for the support of air pollution management in the wide area of Athens basin. For this case the system has been supplied with data for the period from three measurement networks operated by: the Department of Air Pollution and Noise Monitoring of the Greek Ministry of the Environment, the Greek Meteorological Service and the National Observatory of Athens. This inventory provides important information for an in-depth analysis of air pollution in the urban area of Athens. It should be mentioned that PARIS can be applied in any city or region at the precondition that relevant air pollution and meteorological data exist. In addition PARIS can be upgraded - on a systematic basis - at the acquisition of new data. Acknowledgments The authors wish to thank the National Observatory of Athens, the Greek Meteorological Service and the Department of Air Pollution and Noise Monitoring of the Greek Ministry of the Environment for providing the data used in this work. This work has been partially supported by the European Union and partially by INTRACOM SA under ESF project number 93P7005. References 1. Klimont Z., Amann M., Cofala J., Gyarfas F., Klaassen G. & Scgopp W. An Emission inventory for the central European initiative 1988, Atmospheric Environment, 1994, 28, Deligiorgi D., Cartalis C, Kouroupetroglou G., Moutselos C, Naf pliotis M., Kambitsi E., Kountras A. & Skayiannis N. Recording and formatting of meteorological measurements and pollution loads in urban and industry areas, Final Report of ESF Project 93P7005, Athens, loannidis Y., Livny M., Haber E., Miller R., Tsatalos O. & Wiener O. Desktop experiment management, IEEE Data Engineering, 1993, 16, loannidis Y., Livny M., & Haber E. Graphical user interfaces for the management of scientific experiments and data, SIGMOD Record, 1992, 21, Stearns S.D. & David R.A. Signal Processing Algorithms, Prentice-Hall, Englewood Cliffs, New Jersey, Viras L., Fotopoulos A. & Froussou M. The Atmospheric pollution in Athens, Department of Air Pollution and Noise Monitoring, Greek Ministry of the Environment, Athens, 1994.