A GIS-based system for electromagnetic risk

Journal of Location Based Services Vol. 3, No. 1, March 2009, 3–23

A GIS-based system for electromagnetic risk management in urban areas Antonio M. Rinaldi* Dipartimento di Informatica e Sistemistica, University of Napoli Federico II, Napoli 80125, Italia

Downloaded By: [Rinaldi, Antonio Maria] At: 14:52 26 April 2009

(Received 4 October 2007; final version received 21 November 2008; accepted 26 January 2009) The wide spread of communication infrastructures brings with it a natural increase of transmission signals in several areas, in particular in urban environments. In this article we introduce a data model to represent the sensible area from an electromagnetic point of view. The risk analysis has to take into account a plethora of information related to several territorial aspects in order to have suitable knowledge about phenomena. The proposed model is the basis of a distributed sensor network to measure the field values which are used to have real-time monitoring. A prototypical system has been implemented and used in an urban area of the municipality of Napoli. Keywords: computer-related health issues; electromagnetic risk management; real-time applications; risk prevention; urban and environmental planning

1. Introduction Electromagnetic pollution has a great impact from several points of view on people and it must be analysed, monitored and reduced as are other types of environmental contamination. The hype in the media together with the fact that electromagnetic fields (EMF) spread through space without a human perception, and with their rapid proliferation in the cities, only serves to amplify the citizens fears. The development of monitoring and control systems unquestionably represents one of the major challenges in the research methodologies for the measurement of so-called environmental electromagnetic pollution. The development of such systems, together with the setting up of EMF registries, represents one of the main advances for those involved in monitoring and controlling health and environmental hazards, as prescribed by the Framework Law on protection against exposure to electric, magnetic and EMF. From the public administrators’ point of view it is necessary to know if a real danger related to EMF exists and what its potential health impact is, to recognise the reasons for concern in the community (risk perception), to enforce specified actions to guard public health and to deal with identified problems (risk management). The assessment of health risks from EMF is an extremely complex process, mainly because of the multidisciplinary nature of the phenomenon.

*Email: [email protected] ISSN 1748–9725 print/ISSN 1748–9733 online ß 2009 Taylor & Francis DOI: 10.1080/17489720902776720 http://www.informaworld.com


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Several disciplines, such as biological, medical, epidemiological, physical and technological ones sometimes express different and opposed views on the same field. Qualified organisations and national and international governmental authorities (CENELEC 1995, INCRIP 1998, 2001) have enacted laws and regulations that establish the maximum emission levels and the critical area to preserve, especially following the outcome of several scientific works about the interaction between EMF and human health (Elwood 1999, Caplan et al. 2000). Subjective parameters for event classification have a high impact on the risk perception task, together with emotional responses ranging from exasperation to indifference derived from a lack of knowledge about the considered phenomena. In the case of EMF there are no pollutants that, once emitted, become independent from the source and are dispersed into the environment in an uncontrollable manner and that are so subject to meteorological factors that their temporal and spatial distribution become difficult to forecast. The EMF emitted into the outside environment by a long-distance line, by a radio station or by radio or TV transmitters can be tracked and described in a reliable and accurate way using theoretical principles (Guru and Hiziroglu 2004). From a technological point of view, measurements of the environmental EMF may suffer from a high level of uncertainty and variability due to various physical factors, of which only a few show a deterministic and observable behaviour that in principle allow determination of its results. However, be it identifiable and measurable or not, the cumulative effect of external parameters is that measurement stations give results that are very likely to vary strongly depending on the spatial location and on the integration intervals of probes and observation time slots. Monitoring systems are useful tools for risk perception and management; they allow an automation of the measure processes and a systematic observation, in the same period and for a long time, of the analysed system. Therefore it seems quite appropriate to find a measurement method and a related data acquisition, analysis and management system that would help to find those ‘hot areas’ in the environmental EMF exposure level. This can then lead to an optimal monitoring of the area of interest, giving a comprehensive and significant view of it by appropriately sampling the environmental EMF with as small a number of probes as possible. In this article we propose a complete framework for representing and analysing integrated information useful to EMF risk management. To this end we define a novel data model to analyse the interaction between urban systems and electromagnetic phenomena which takes into account different levels of information. The need for a complete view of all the components related to the analysed phenomena (EMF) allows us to have an accurate knowledge about the exposed areas and population. Moreover, we design and implement a distributed system, based on mobile devices, to carry out real-time monitoring of EMF. However, a meaningful definition of the objectives of our work and of the methods to pursue them suffers from a poor knowledge management system, due to the large amount of unanalysed data collected and made available to the user. The advances in Geographic Information Systems (GIS) allow us to have a common framework to integrate different technologies and logical data models (Lo and Yeung 2002). The elaboration of aggregated data turns analysis into information, which will allow comprehension of phenomena and lead to the critical mass of information required to support decisions. To this end, all the components of our framework are integrated in a

Journal of Location Based Services

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GIS platform. The use of an aggregative function between measured values and information about the different informative layers of territory gives us a characterisation of EMF expressed as qualitative values. Universal mapping of EMF is, of course, impossible but our entire approach assumes and requires that the fields (and therefore risks) be modelled, and that the model reliability can be tested using real-time measurements. The article is organised as follows: in Section 2 several models, techniques and systems for risk management and monitoring are presented; the proposed logical data model is defined in Section 4; in Section 4 our prototype system for EMF monitoring is drawn; experimental results are presented in Section 5 and in Section 6 conclusion and future works are discussed.


2. Related works In this section we analyse and describe several approaches and systems to demonstrate the use of models, GIS and sensors for managing environmental risk data. Starting from these experiences, spanning over more than a decade, we design our methodology and propose a novel model and an efficient technological infrastructure for managing EMF risk in urban areas.

2.1. Models and techniques in GIS infrastructures A GIS provides a powerful collection of tools for the management, visualisation and analysis of spatial data. These tools can be even more powerful when they are integrated with models and ad hoc techniques to formally describe and manage phenomena, in particular natural and environmental risks, taking into account data from the different dimension of a territory. In Cardarelli et al. (1993) the authors describe an experiment to predict water pollution risk arising from human activity in an environmentally-threatened coastal hydrological basin using remotely-sensed satellite imagery and a model which includes terrain and hydrological information stored as separate GIS data layers. A case study about the assessment of risk of malaria transmission in Belize using remote sensing and GIS is presented in Montgomery et al. (1998). The authors define risk as the presence and abundance of vector mosquitoes for malaria transmission based on environmental criteria and villages were ranked according to several factors (e.g. elevation, distance to major rivers or streams and landuse). In Nehme and Simoes (1999) a GIS-based tool for Agricultural Planning based on an expert system for Land Evaluation is presented. A Decision Support System is used as a first step for landuse planning considering two dimensions: the ecological one, which reflects the limitations and potentialities of sustainable use of natural resources, and the economical one, which expresses the development of the communities that live in the region or zone whilst exploiting it. In Harms et al. (2003) and Goddard et al. (2003) the NADSS project is presented, which is based on a layered architecture for a distributed Geospatial Decision Support System (GDSS), using algorithms for mining partial periodic association rules for drought risk management. The GeoCollaborative CrisisManagement (GCCM) project (MacEachren et al. 2005) investigates how groups utilise geospatial technologies in crisis situations and uses findings to design novel, multimodal


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(speech and gesture-based) and collaborative interfaces. In Caiti et al. (2005) a GIS-based data presentation system, developed within the framework of the EU-sponsored project SITAR (Seafloor Imaging and Toxicity: Assessment of Risks caused by buried waste), is presented. The data presentation system has the aim of integrating in a meaningful, user oriented way, the information gathered through the methodologies and techniques investigated in the project. A framework to integrate time in spatial risk models is proposed in Maille and Espinasse (2006). It is funded on integration of land cover change (vegetation and urbanisation) dynamic models and forest fire risk models in order to design an environmental decision support system. Such a system has to integrate geographical information systems with modelling and simulation software of land cover changes. GeoModeler (Vance et al. 2007) is a prototype of how one might integrate a GIS with oceanographic and decisionsupport models. Through the use of Java-based application programming interfaces and connectors, a GIS is directly linked with the Regional Ocean Modelling System (ROMS) and with the Method of Splitting Tsunami (MOST) model. Users are able to use a graphical interface to display datasets, select the data to be used in a scenario, set the weights for factors in the model and run the model. The results are returned to the GIS for displaying and spatial analysis. Muthu and Petrou (2007) propose a rule-based expert system used to create landslide warning and alert maps using as input change of landuse data from Earth observation, as well as geological, rainfall and earthquake data stored in a geographic information system. The work presented in Benmecheta and Lansari (2007) focuses on an approach that is based on physical and chemical analysis of water sampling in situ integrated with remote sensing data in GIS to give a global vision of oil pollution in the harbours of Arzew. The use of multiple data layers for risk management is presented in Nazir et al. (2006) where the authors define a conceptual framework for earthquake disaster management. Cuff et al. (2008) carry out an interesting study of sensors in an urban area in which pervasive computing employs data from different aspects of city life. A useful reference about the use of environmental models and GIS is in Goodchild et al.(1993). The use of a continuous stream of spatio-temporal data in our approach needs a more detailed explanation. In fact the opportunity to analyse phenomena through time and at different locations has profound implications for many ICT systems and human activities (Goulias and Janelle 2006). These implications arise from the work of Ha¨gerstrand (1970) where the temporal factor in spatially extended human activities has been stressed. Methods and models defined for studying and representing localised activities not only need detailed data about the movement in time and space but also further data to study cyclical behaviours, to perform analysis of phenomena and to implement proper actions. From this point of view emergent technologies as location-aware technologies (LAT) and location-based services (LBS) can have a great impact for understanding constraints on human activity participation in space and time (Miller 2005). Problems associated with representation and analysis of spatio-temporal data also arise in geographic information science. In particular, the development of LAT and LBS is inspiring a growing literature on database design for storing information on moving objects. Since digital technologies can only sample an objects location at discrete moments in time, a key problem is



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interpolating an object’s location at any arbitrary moment based on the sampled locations. Several studies in this field indicate that this problem has interesting geometric solutions (e.g. Sistla et al. 1998, Moreira et al. 1999, Pfoser and Jensen 1999, Yanagisawa et al. 2003). These considerations require that several kind of data must be integrated to have an entire vision of the analysed phenomena that vary in time and space. In general it would be useful to develop a robust framework based on measurement theory that expresses how to map continuously varying relationships in the real world to the numeric domain in such a way to preserve these relationships as fully as possible, as well as to take into account an imperfect measurement of these entities and relationships (Miller 2005). Even if these data could be collected and stored, computational platforms are still not sufficient to conduct analysis beyond coarse summaries. Motivated by advances in GIS, researchers have developed formalisms to support computational implementation of time geographic entities (i.e. spacetime path, prism and station) (Miller 1991, 1999, Kwan and Hong 1998, Hornsby and Egenhofer 2002). An interesting approach is in Goodchild (2002) which refers to measurement-based GIS: it differs from traditional coordinate-based GIS in that it provides access either to the original measurements or the functions used to infer the locations from the original data. If time geography is to be more tightly integrated into GIS, LAT,and LBS, it requires a rigorous framework that can support high resolution but imperfect measurement of the observable components used to infer its basic entities and relationships. From a technological point of view, traditional spatial databases and GIS software are static, typically representing spatial data at a given point in time: integrating time into spatial databases and GIS is an active research topic in many fields of geographic information science (Langran 2002, Peuquet 2002).

2.2. Systems The design of an EMF monitoring and control system is a multidisciplinary effort. As a minimum requirement, it uses ICT skills as well as applied physics and environmental expertise. ICTs are essential, above all, for designing the monitoring and communication infrastructures, as it is necessary to guarantee that the stored data is correctly processed and can be easily accessed as well as ensuring that equipment and software required for interconnecting the stations and managing data are reliable. In the literature, Fabbri et al. (2001) offers a classification of monitoring stations that distinguishes between fixed land and mobile field monitoring stations, both of which offer specific advantages. The fixed land stations can be the most cost-effective solution, if basic equipment is installed, or they could be equipped with more sophisticated apparatus. Greater cost-effectiveness can be derived from possible savings on the mechanical infrastructure (if a motorised means of transport is not required) and on the electrical supplies, and on interconnection costs (very often it is possible to use the fixed telecommunications networks). In addition, the fixed land station can be protected more easily against possible theft or acts of vandalism by using buildings or fencing. This could make the hypothesis of installing more


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comprehensive and complex apparatus more acceptable. On the other hand, the main advantage of mobile stations is that they can be rapidly moved to where they are required, optimising their use in relation to effective surveillance needs which often prove to be very changeable. Due to the very different national laws concerning EMF we choose to describe some significant experiences in Italy about EMF monitoring systems because the national laws on this subject are very restrictive compared with other countries. In Table 1 (Curcuruto and Longorelli 2007) a comparison between exposure limits is shown and how they are defined in the laws of different countries for High Frequencies (HF). The 6 V m1 value for the Italian law refers to a quality target defined for a time interval of 6 min in all areas and as maximum exposure limit inside buildings and in crowded areas. The Municipality of Catania set up the project Cassiopea (Catania Municipality 2000), a fixed land monitoring system of 15 indoor and outdoor stations located across the borough area, particularly near sensitive sites (schools, hospitals, parks). Information concerning the system, as well as monitoring data from July 2000 to date, can be found at the Catania Municipality website. During 2000, the ARPA (Regional Agency for Environment Protection) of Emilia–Romagna developed the Elettra project and carried out and completed the planned experiments (ARPA 2000). Table 1. Exposure limits (V m1). Country Australia Austria Belgium Bulgaria Canada Cina Eu Cenelec Eu Council France Germany Blmschv Germany Din-Vde Holland Hungary Incrip Italy Japan New Zeland Poland Russia South Africa Sweden Switzerland Turkey UK USA (FCC) USA (IEEE)

f ¼ 400 MHz

f ¼ 900 MHz

f ¼ 1800 MHz

f 4 2 GHz

27 31 13.7 6 31 10 27 27 27 27 97 30 27 27 20 (6) 31 20 6 – 27 27 27 29 27 31 31

41 47 20.58 6 47 10 41 41 41 41 97 109 41 41 20 (6) 47 20 6 20 41 41 41 41 41 47 47

58 61 29 6 61 10 58 58 58 58 97 180 58 58 20 (6) 61 20 6 – 58 58 58 58 58 61 61

61 61 30.7 6 61 10 61 61 61 61 97 193 58 61 20 (6) 61 20 6 – 61 61 60 60 61 61 61



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This system uses re-allocable stations which send their data to a hierarchical control structure. The monitoring system of Veneto (ARPAV 2000) takes into account both low and high frequencies and uses two ways to control the different types of fields. The first approach is based on simulation algorithms to show the propagation of EMF over the territory starting from the sources’ technical features and their localisation. The other one uses a local measure with ad hoc devices and stations. The literature review discussed in this section is useful to explain and support the development choices of our approach, providing a systemic view. In contrast to the previously described systems, we have defined a suitable data model to represent in a GIS all the territorial information that are considered essential for analysing geographic areas from an EM point of view. To the best of our knowledge no territorial model exists to define sensible areas from an electromagnetic point of view. A further innovation of our system is in the design of a mobile and distributed monitoring system which does not need a relocation of its stations because they move along defined paths, i.e. buses for local transport. In this way we can take under observation large defined areas at different points of time during the same period. A prototype of this system has been implemented and a case study is presented in Section 5. In the following sections we show how the proposed framework takes into account the considerations about efficient and effective measurement systems, models and techniques for representing and analysing phenomena varying in space and time; moreover the use of different sources of data, i.e. not only positioning systems, and how it allows a whole vision of the considered phenomena together with its relations with the territory and human activities.

3. The proposed data model The need for an integrated system derived from the necessity of having a public health monitoring and environmental management system. Its efficiency and effectiveness are based on a more and more point knowledge about EM phenomena and, especially, on the correlation between EMF and the features of the inhabited area. The monitoring and control of an exposed area to pollution sources therefore assumes a fundamental role. In this context the use of new technologies for finding and managing the information related to the analysed area and the nature of the EM phenomena is a vital task. As discussed in Section 2.1 GIS are useful and well-known applications for the integration of different levels of data from multiple sources and to support decision processes. Moreover, several international projects and legislative statutes (EU 2005, 2007) emphasise the need for integrated infrastructure to manage complex data especially in an environmental context. On the other hand we must define a suitable logical data model to formally describe all the aspects of our field of interest regarding the territorial features. In this section we describe the different levels of our data model and the several components used to produce a global view of the interaction between urban areas and EMF. For this purpose we define sensed areas from an electromagnetic point of


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view considering different ways to characterise the territory following some basic criteria from the literature about land and urban planning (Faludi 1973, European Environmental Agency (A) 2000, European Environmental Agency (B) 2000, Archibugi 2004, Petroncelli 2005) and relate them to EMF. In fact the territory has to be described both in relation to the spatial features (physical system), and to the human subjects (anthropised system) that live in the space, and to the activities executed in the space in order to satisfy the needs (functional system) of the subjects. Towards a better understanding of the phenomena that influence the space territorial configuration, it is useful to proceed with a systematic and logical approach. In this view we see the territory as a system with the elements which compose it and their mutual relations (i.e. it is enough to modify one of these elements to induce effects on other ones); using this approach we can decompose the whole territorial system in different subsystems. To this end we propose a suitable model where the study of the whole system refers to three fundamental and strictly correlated subsystems: Physical system: describing the physical components of our field of interest and the analysed territory. Functional system: describing all the activities characterising territorial elements. Anthropised system: in this system we take anthropic values into account. In the rest of this section we will describe those data levels and, using GIS technologies, how we will integrate information related to each level into a complete electromagnetic risk map.

3.1. Physical system The study of the physical system takes into account the morphology, the landscape, the climate, the soil and the subsoil of analysed areas. From a general point of view this analysis is used to: recognise territorial features, find the ones which are ‘sensible’ with respect to a given field of interest and prevent the risk associated with different causes. In our context of interest we identify the physical system features that in our opinion are correlated to the EMF characteristics and sources. First of all we divided the EM installations into: Low frequency (LF) installations: electrical power production plants, transformation installations, electroducts 5150 Kv; electroducts 4150 Kv. High frequency (HF) installations: AM/FM antennas, TV antennas, Radio Base Stations, Mobile Phone antennas. Then we analysed the correlation between all the types of installations and the territorial physical system. Hence we took into account hydrogeological, geotechnical, morphological and altimetrical analysis. From those features we recognised the physical critical areas at different levels of risk from an EM point of view. In particular the physical system features are useful because not only the emission but also the EMF sources themselves could be dangerous (e.g. the existence of a transmission tower or a base station in a hydrogeologic risk area).


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3.2. Functional system The functional system refers to the space organisation and systematisation, that is to the use of the land. Therefore we must take into account the different functions (inhabited, health and cultural) to which the single territorial areas are destined. This need implies a deep knowledge of the life of the communities that, in relation to their needs, transform and uses both the publics and the private spaces. Meaningful for this study is the analysis of the activities which are executed in the territory, mainly in terms of time usage. Obviously the longer the time of execution of the activity, the greater the duration of EMF exposure. In this case the use destinations of elements have a great effect. It is opportune to make a distinction between buildings and/or areas where these activities have a long time duration (educational, formative, recreational, cultural and health activities and buildings in residential areas) and areas within social-economic activities related to the use of EMF and energy in general (industrial, directional, commercial buildings and/or areas, technological areas, transport networks). Also, those features have been correlated on the basis of their spatial and temporal aspects (EMF sources, functional components, use destinations) and we obtained functional critical areas at different levels of risk.

3.3. Anthropised system The anthropised system takes into account the territorial elements closely connected to human behaviour: the person and the family, the citizen in relation to the society as a whole. Great emphasis has been given to the elements which are related to settlements. The features considered are: kinds of settlements (aggregation modality in relation to the concentration and spread of the population within the area); settlement morphology; features of building function and heritage (use destination, use suitability, typology, consistency, age, state of preservation); characteristics of fixtures and infrastructures. The population density indicator becomes fundamental because it expresses in a synthetic way the number of people exposed to the electromagnetic waves. Eventually we have defined a logical data structure to express the agreement between territorial system and EMF sources. Using spatial correlations and GIS functions in our data model we obtain a global classification of the critical areas from an EM point of view (Figure 1). We show in Figure 1 the results of the overlap of each single theme from the three subsystems, not only the physical one: an area has a critical situation if one or more different risks (anthropic, functional, physical) exist in it; the scale in the figure gives qualitative values of the risk levels (Risk 1 ¼ low – Risk 5 ¼ very high). This data was obtained from official sources like Napoli General Urban Plan, Basin Authority Risk Maps, Italian National Institute of Statistics. We describe our model using UML (OMG 2007) to use a standardised formalism for modelling the reality of interest. The data model is divided into four packages containing the information about the sub-systems previously described and additional support information about the Urban Site. The Physical System is characterised by the description of the different entities associated with the several risk typologies, i.e. hydrogeological, seismic, etc. (Risk); we also considered the risk classification of the territorial areas provided by qualified


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Figure 1. Electromagnetic zoning map. (Available in colour online).

organisations. Besides the natural critical situations, we took into account the EMF sources (EMF installation) with their typology, location and technical features (technical feature). This system gives general information about natural risks; we argue that no infrastructures should exist in an area with a high level of natural risk. The description of the Anthropised System is obtained by official data from the Italian Institute of Statistics taking into account the urban area divided in census zones with the demographic density. The information characterising the Functional System constitute a classification of areas and buildings in which human activities are performed; hence we consider the information in the General Urban Plan on one hand, and on the other hand we identify sensible buildings and areas as schools, hospitals and so on. The Urban Site package locates all the afore-mentioned subsystems; the entity Physical Location represents the description of the relevant geographical areas. In Figure 2 the data model schema is drawn.

4. The system architecture From a general point of view, monitoring systems are designed using two different approaches, which use fixed or portable probes. Fixed systems may be economically more advantageous than portable ones if equipped with the same instruments and devices, or provided with more sophisticated devices at an equivalent price.



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Figure 2. Data model.

On the other hand, the main advantage of a portable probe is the fast relocation over the inspected territory which allows optimal use in accordance with actual surveillance requirements, which themselves may change quite rapidly over time. Anyway both systems suffer from the intrinsic limit on the extension of the area under observation, because a vast area can be monitored only with a large number of probes (due to the poor mobility features of both the kinds of devices). Instead, we propose a system with a number of mobile probes that acquire data about EMF values, and send them together with probes position and time (acquisition system) to a remote server (transmission infrastructure) (De Capua et al. 2004). Even if the management of data from multiple, distributed and mobile stations increases the complexity of implemented applications, the proposed system has several useful properties such as better scalability, the chance to measure EMF values in a very wide area, easy transportation on different forms of transport. The measured values are integrated with other relevant information collected in a GIS that can be useful in the determination of the impact of EMF on human activities. The hardware components have been chosen considering the design requirements of a modular system, i.e. each component being a package with a well-defined task and all functions built in. Every package: . . . .

can be replaced as it has a well defined interface with other packages; is compact (and has a small cabinet); will have quality in its task performance; will have easy market availability (to have a real implementation).

The choice of monitoring urban areas makes it necessary to define the density of EM sources which are localised in that kind of territory. We can divide them into two main classes related to their frequency and use as described in Section 4. We argue that HF sources are more dispersed in urban areas and, on the other side, LF sources are often localised around the boundaries of urbanised areas and their variations are usually low and well-known. Moreover specific programmes of National and Regional Environmental Protection Agencies exist for monitoring and managing LF sources. For these reasons, even if monitoring of all EMF range frequencies is


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important, we choose to consider only HF sources using a probe working in a large band from 100 KHz to 3 GHz; in this way we take into account a field of applications often not monitored. Each mobile acquisition system is a Compact FieldPoint module (cFP-2020, henceforth cFP) by National Instruments equipped with a Garmin 25HVS GPS module for localisation purposes, and a programmable optical repeater PMM OR-03 connected to a PMM EP330 probe to measure EMF. Wherever possible, connections between devices have been established through optical links to minimise interference on measurement probe. Otherwise they have been arranged in the least disturbing configuration possible. This configuration is necessary not to perturb the EMF during the measurement. We set our system to turn off the GPRS during the measure step and vice versa and we use very few electrically conductive materials. In Figure 3 a logical view of our system is shown. This architecture has been entirely implemented. The different parts of our system are described in the following sections. In particular we have implemented the: . management system (FieldPoint) with a probe and a GPS module on board; . the transmission infrastructure based on the GPRS module, which sends a file formatted by the FieldPoint with the measured data (Table 2) to a listener server; . the management DB software, which acquires the data from the probe and inserts them in a geographic database. Several ad hoc GIS modules project and analyse data on a risk map based on the model described in Section 3.

p a

s Figure 3. System architecture.

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Generally speaking, the client acquires position and time through the GPS module and the EMF amplitudes value from the EMF probe, at a rate of one sample per second. This choice of sampling rate depends on several factors. First of all there is a technological limit set by the probe: the lower bound for sampling is 250 ms; on the other hand we must consider the characteristics of the measured phenomena. We should point out that as we have an interest in monitoring HF, a sample of 1 H is enough to measure the EMF variation in the HF band. The procedure used for monitoring EMF is described as a standard process in Standard CEI 211-7 2001. In an enhanced version, acquisition rate can be tuned to the speed over ground, and therefore reduced or increased if the client moves slowly or quickly. More specifically, the GPS device outputs sentences according to standard NMEA format (NMEA 1980, Hofmann-Wellenhof et al. 2001), containing data about position and UTC time, and additional information such as number of satellites in view, direction of route and signal statistics. Such information is stored in a file along with EMF values acquired by the probe. A measurement cycle goes on for a fixed amount of time after which the file is sent by the client to a remote server. An example of this array is in Table 2. It seems appropriate to review the different causes of uncertainty that affect both measurement of space coordinates and EMF amplitude. We find three different contributions: GPS is the uncertainty pertaining to the values given by the GPS module. It is intrinsic to the GPS system and the factors for its inaccuracy are: ionospheric effects, shifts in the satellite orbits, clock errors of the satellites’ clocks, multipath effect, tropospheric effects, calculation and rounding errors (Hofmann-Wellenhof et al. 2001). The rms value of these parameters is roughly 15 m for GPS modules that do not implement differential correction algorithms like ours; EMF is related to the EMF value returned by the probe. Its value is stated in the datasheet given by the manufacturer and is usually expressed as a percentage of the value read. It varies greatly with the probe used, depends on the probe calibration method applied, spanning typically from 10 to 20%. For the case at hand the expanded uncertainty has been assumed to be 15%. According to DIN ENV 13005 1998 this is a type-B evaluation of the uncertainty. A type-A approach seems not to be applicable because repeated measurements are taken with very large time intervals compared to the typical variation intervals of the process investigated. GPS,EMF expresses how well the value measured by the EM sensor represents the EMF existing at the location measured by the GPS at the time of measurement. It can be defined as the correlation between the two values, or it can be described as the degree of confidence we assign to the statement that the values in the couple (GPS, EMF) given by the GPS module and the EMF sensor are contemporary.

Table 2. Acquisition system array. DDMMYYY HHMMSS 16042007

144015

LAT

HEM1

LON

40.829820

N

14.193263

HEM2 FIX N_SAT EMF E

1

7

1.36

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We can calculate GPS,EMF through the knowledge of the autocorrelation function of the EM field r(d, t):


GPS,EMF ¼

1 rðd,tÞ rðd,tÞ

where d is the distance between the point measured by the GPS module (supposing that autocorrelation is symmetric about the origin) and the point where the EM field has been measured, and t is the time interval between the two measurements. Of course d ¼ t with being the velocity of the mobile client. Of the three uncertainties highlighted, the latter depends strongly on the client condition, i.e. whether it is moving or steady, and the software solution used in the data acquisition process. A description of the data acquisition steps is in the experimental results section. Many options are usually available to transmit data from clients of a distributed system to a remote server. They span wired network solutions to wireless cells, including wi-fi connections. However, a wired connection is clearly inapplicable for mobile clients, while the wi-fi infrastructure still has a very low penetration in Italy. GSM/GPRS communication networks seem therefore the most appropriate choice, thanks to their large availability nationwide and the very low cost of use. The only additional requirement will be to stop EMF acquisition during data transfer to avoid interference between the transmitter and the field sensor. The files sent to the server are processed by ad hoc modules to extract relevant features and relate them to territorial information organised by a suitable logical model (as described in the previous section). Based on project specifications, the most appropriate technology for data collection and analysis seems to be a geographic information system. The task requires different spatial data layers to be taken into account for a complete analysis and description of the studied system. This means that besides data about the phenomenon under study (i.e. the EMF value in a urban environment) data about the environment itself must be collected and inserted into the database. The former gives knowledge of the main characteristics of the sources of EMF, whereas the latter describes what is inside the field: buildings typology, population density, territory destination and so on.

5. The case study In this section we present details of a measurement campaign to test our acquisition system and to show the integration of the measured values in our model. The aim of our system is to measure electromagnetic risk in urban areas. Therefore for our purposes, the resolution of single measure points is not relevant because we have many uncertainty parameters which are related to the intrinsic measure errors of used tools. On the other hand the use of a suitable sample period together with the low speed of vehicle in urban areas allow us to have high number of points in a short time and in a small area. We chose the district of Fuorigrotta in the city of Napoli as the test area because in this zone we have several types of EMF sources.



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Considering the modular architecture of our system it is interesting to observe that the use of detachable probes results in the very useful feature of the system of being capable of monitoring different bands at the same time simply attaching different probes. Therefore, pretty much like those telescopes that show the same portion of universe in different emission bands, the system allows the city to be observed under different frequency bands for the sake of a complete and detailed knowledge of the environment. As a first step we give some consideration about the uncertainty in the measure. We suppose that the measurement software acquires the GPS datum (i.e. location and time of the client) and the EMF amplitude at the same time. In this case, the autocorrelation function must be evaluated at t ¼ 0 and d ¼ 0, for which values we have r(0, 0) ¼ 1. Therefore we can claim that for the case at hand uncertainty is zero, or equally that the couplet of values is at the highest degree of significance. We can also introduce a different model in which the client performs measurements at two different times. More specifically, we suppose that EMF is measured after location and time are obtained. In this scenario, GPS and EMF have the same values as before, while GPS,EMF depend on the value that r(d, ) assumes at the time (which represents the time interval between the two measurements). If the client is steady (d ¼ 0), being r(0, ) 5 1 for any value 4 0, and the uncertainty increases and the mentioned confidence decreases consequently. We used a contemporary acquisition of these parameters. Finally, if the client moves we have to introduce its velocity over ground so that GPS,EMF takes the value that results from the r(, ), which suffers from a further reduction if compared to that pertaining to a steady system. Besides, the evaluation of the uncertainty affecting is also required. The GPS datasheet states that its rms value is 0.2 m s1. It is important to emphasise that each of the three uncertainties presented affects a quantity (i.e. position measurement, EMF value and couplet significance) that by no means is related to the others. Therefore no statement about the importance of the value of each of them is to be performed with respect to the others since they basically refer to incomparable quantities. Due to the large amount of data stored and the short-time required between storage and representation, a software procedure has been developed which processes input files stored on the server and extracts and stores information of interest. The procedure also refreshes the content of the database and the resulting information provided to the user to supply an effective real-time analysis, thus establishing an accurate analytical model that helps in developing a decision-making information system. In fact, we can make a decision based on the temporal evolution of the observed phenomena and apply whatever preventive action appears useful. The analysis can be conducted on historical data by ad hoc algorithms which implement control charts (Wheeler and Chambers 1992) to show time evolution of EMF levels for a point on the map and compare them to some control or warning limits defined by the user or automatically by the system. The context of our measures is in accordance with the scenario defined in DIN ENV 13005 1998 and we can adopt the standard procedure described in it: limits are chosen as the 3, where is the average value for the observation point and is the (daily, weekly, yearly and historically) standard deviation of data obtained in that point. Besides, the term point seems inappropriate for two reasons: (1) a mobile


18

A.M. Rinaldi

Figure 4. Controlled area.

system is very unlikely to make an acquisition exactly in the same point at successive times; (2) even if the former were true, GPS will cause a variation in the indication of the measurement point. For those reasons, historical data upon which control charts are drawn can be obtained by clustering spatial data in the following manner: a reference point on the map is chosen and a circle of radius R ¼ GPS is drawn around it. Then, all measured GPS points compatible with the point in question are grouped so that control charts will refer to a region around the reference point instead of the point itself. Figure 4 shows a representation of the set of measurements collected during a short journey. In this figure, a buffer is also shown, which is centred in a generic point on the map and has a radius R ¼ GPS. The quantity P is obtained by weighting the amplitude value with a range scale called Saaty Semantic Scale (Saaty 1980), taking into account the critical areas recognised in the EMF risk map previously defined according to our data model: P¼

Emeas SSðAreai Þ, Li

Emeas being the EMF amplitude measured, SSðAreai Þ 2 f1, . . . ,9gOdd being a function which assigns a value of criticality to each area according to a scale range obtained by the Saaty Semantic Scale and i spanning over the risk range classification defined in our model. The value Li in the denominator corresponds to the exposure limits allowed by Italian laws and arranged in our risk range classification. In Table 3 the used mapping scale is shown.

19

Journal of Location Based Services Table 3. Critical values. Risk area


Risk Risk Risk Risk Risk

1 2 3 4 5

Quantitative value

Description

Exposure limit (V m1)

1 3 5 7 9

Critical More critical Strongly more critical Very strongly more critical Extremely more critical

20 20 6 6 6

Figure 5. Control chart.

Using this table we obtain a quantitative value from a qualitative one derived from the risk map described in the previous section. Since P has two degrees of freedom (one from Emeas and one from i each) it is important to highlight that it only gives preliminary information about the overall scenario, and that final decisions can be made only after comparing its values with the corresponding values in the EMF map in Figure 4. P represents the priority with which actions aimed at limiting exposure to EM emission are to be taken. This is intended as a means to differentiate areas in which EMF affects a ‘safe’ region from those where the criticality is much higher. Its assessment appears related to the evaluation of the quality of life in such a complex environment as a city. The control map in Figure 5 showing the evolution over-time of the EMF in the point under observation as is shown in Figure 4. Along with single samples, the average value of EMF exposure and the Upper Control Limit (UCL) 3 are drawn. In the figure we can see that almost all points remain around the average value and that only one exceeds the UCL. Supposing that a point was very close to the exposure limit, we could have characterised it in terms of its probability of appearing below or above the limit when indeed it is not. In fact, exceeding the limit, as well as undershooting it, may only be a manifestation of the measurement uncertainty, which needs to be appropriately characterised. For the case at hand, thanks to the evaluation of the uncertainty of the

20

A.M. Rinaldi

measurement system, it is possible to determine the confidence with which decisions about the status of the process are taken (DeCapua et al. 2001).


6. Conclusions and future works In this article we have proposed a suitable data model and a real-time system that allow the spatio-temporal mapping of exposure to environmental EMF in a urban environment. The measure of space and time variations in the EMF level is needed to evaluate quality of life, determine to what extent human activities may be affected by environmental quantities like the EMF, determine the priority to assign to each action to be taken in order to limit exposure, and eventually assess the risk that warning limits are overcome and a possible hazard situation occurs. The system uses a GPS module for space and time localisation and a probe to sample EMF levels, collecting values at a specified rate while clients travel within the monitored area. The integration with GIS technologies give us a flexible framework to model a risk scenario taking into account the proposed data model. The Saaty Semantic Scale is used to assign a quantitative value to a qualitative one of the risk in an area; these values are aggregated to the measures performed during the measurement step, and provide a global score for the risk in a given time and a given area. The uncertainty is intrinsic to the system and it is solved using the concepts of risk area and buffer. The analysis is accomplished using control charts. As a hint for some future works, by comparing time evolution of maps over days or even hours it will be possible to detect and possibly localise and identify new sources of electromagnetic radiation, denoted by a sudden higher value of measured field, and so to spot a possible hazard situation characterised by a progressive heightening of exposure level. We put in evidence that this variation in time and space can be highlighted using control charts. Control charts give us information of standard or particular situations in a given area. Moreover, given the area, the overlap of several control charts provides us with information about the variation of EMF in time.

Acknowledgements The author wishes to thank Arch Antonia Cataldo, Prof. Robert Laurini and Prof. Nello Polese for their precious advice during the preparation of this article.

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A GIS-based system for electromagnetic risk

A GIS-based system for electromagnetic risk

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