Integration of Earth Observation and in-situ spatial data for the
development of a Decision Support Tool for Technological Risk
Management
Foundation for Research and Technology – Hellas, Institute of Applied Mathematics,
Regional Analysis Division, FORTH - IACM, Vassilika Vouton, P.O. Box 1527,
GR-71110, Heraklion, Crete, Greece.
(tel. +30 810 391762, fax. +30 810 391761, e-mail: zedd2@iacm.forth.gr)
Abstract. In this study the potential of Earth Observation techniques for major
technological accidents analysis is presented and a GIS platform is proposed for
the support of technological risk management. This GIS tool is based on the
detection and space-time monitoring of the produced plumes by integrating
moderate and high resolution satellite imagery and in-situ vector data. The
Advanced Very High Resolution (AVHRR) on board the NOAA satellites has
been used for the detection of fire as well as for the detection and monitoring of
plumes caused by major technological accidents. The detection algorithms have been
presented in previous studies for past accidents in Europe. AVHRR images, were
adjusted to the broader area of Athens (Greece) in order to develop a major
technological accident scenario based on real plumes. This scenario was used to
present the functionality of the developed GIS platform for the support of decision
making during the crisis, as well as for the assessment of the accident’s impact to
natural and human environment.
Key Words: Technological Risk Management, Earth Observation, Fire and Plume detection, Decision Support System, Impact assessment.
1. Introduction
Major technological accidents at industrial sites impose considerable dangers
for the environment and public health. A number of accidents have taken place
in recent years with serious costs to human life as well as with considerable -
and in many cases irreversible - damages to the natural environment. In many
cases toxic substances or released airborne material develop into plumes which
may create high concentrations at ground level and pose dangers to the human
and natural environment. Damages may thus occur both as an immediate and
direct consequence of the accident, and subsequently during propagation and
dispersion of the resulting plume. It should be mentioned that in several cases,
while exhaustive consideration has been given to the immediate ground-level
effects in close vicinity to the installation, only limited effort is usually given to
examining the impacts of the plume in the wider geographic area during the
course of the hours or days following the accident.
Risk analysis forecasts the likelihood of accidents, estimates the consequences
of an accident which is likely to occur, works out strategies to prevent accidents
and assesses the adverse impacts in the event that an accident occurs.
Technological accidents can be characterized by a number of different events
and processes, including spillage or sudden release of materials, fire, or
explosion. The most common effect is the release of gases and liquids used and
processed in the installations concerned. Fire and explosion are also common
effects, while a combination of the above is not rare. Releases may be toxic and
can be either to land and water or to air. Airborne releases usually develop in
plumes, which can thereafter be monitored either due to their optical depth or
their temperature difference from the ambient air.
A number of experiments (Davie and Nolan 1993, Lang 1993, Bartelds et al. 1993,
Atkinson et al. 1994, Miles Cox 1994, Marliere 1996, Grant and Drysdale 1994,
Martins and Borrego 1994, Porter et at. 1996, Martins et al. 1996, Cozzani et al. 1996)
involving fires in warehouses and awareness of the hazards of plumes in a fire
situation have been oriented towards the definition of the properties and of the
amount of the plume particulates generated by different materials, including
pesticides, under varying fire conditions. The emission of toxic gases and
formation of carbon particles which can carry toxic material absorbed onto their
surface as well as obscuring vision both present hazards in a fire situation. Over
150 substances or groups of substances are commonly identified in accidents.
The substances occurring most often include ammonia, chlorine, hydrogen,
hydrogen chloride, nitrogen oxides and sulphur dioxide, all of which are bulk
chemicals.
In past studies various numerical models have been developed for the
simulation of the conditions and the process of technological accidents (Hanna
et al. 1993, Webber et al. 1993, Andronopoulos et al. 1994, Kukkonen et al.
1994, Cleaver et al. 1995). Software packages have been also developed for
technological risk management; some of these software packages are WHZAN
(Technica 1992), RISKIT (VVT 1993), EFFECTS (TNO 1991), SAVE (TNO
1992) and MAXCRED (Khan and Abbasi 1999).
In this study, the design and implementation of a technological management
support tool is presented. This tool is based on the detection and space-time
monitoring of the produced plumes by integrating moderate and high resolution
satellite imagery and vector data in a GIS platform, capable to support
technological risk management. A major technological accident scenario has
been also developed using satellite images acquired during real past events.
This scenario is used to present the functionality of the developed GIS-based
system for the support of decision making during the crisis, as well as for the
assessment of the accident’s impact to natural and human environment.
2. Fire and plume detection methodology
The methodology for the detection of fires caused by major technological
accidents with the use of AVHRR imagery has been presented in a past study
(Chrysoulakis and Cartalis 2000). The detection algorithm carries the
advantages of a multispectral analysis and provides valuable results for the
detection of fires caused by technological accidents. In practice, the
pseudochannel image of brightness temperature difference between AVHRR
channels 3 (3.55 – 3.93 µm) and 4 (10.5-11.3 µm) is created and filtered for
clouds by applying a cloud-masking algorithm which is based on the
combination of AVHRR channels 1 (0.58 – 0.68 µm) and 5 (11.5-12.5 µm). In
this filtered image, pixels with brightness values greater than an experimental
derived temperature threshold correspond to fires produced by major
technological accidents. Fire detection algorithm has been programmed as a
stand alone application for the automatic detection of fires caused by major
technological accidents with use of AVHRR imagery (Chrysoulakis and
Cartalis 2002a).
The methodology for the automatic detection and monitoring of plumes caused
by industrial accidents with the use of AVHRR imagery has been also presented
in a previous study (Chrysoulakis and Cartalis 2002b): A two-dimensional
feature space image is used in order to discriminate pixels that contain plumes
from those that may contain clouds or the underlying surface. The two-
dimensional feature space is generated by combining AVHRR channels 1, 2
(0.72-1.10 µm) and 5.
Both methodologies have been evaluated on the basis of past major
technological accidents (Thessaloniki, February 24, 1986; Lyon, June 2, 1987;
Genoa, April 13, 1991; Enschede May 13, 2000). The effectiveness and
reliability of these algorithms was found to be satisfactory in all case studies.
AVHRR images acquired over the broader area of Netherlands on May 13,
2000 (14.44 UTC and 17.20 UTC) were used for the development of a major
technological accident scenario in this study. This date refers to the massive
explosion in a firework factory in the town of Enschede. The produced fire was
detected by applying the aforementioned fire detection algorithm to the
AVHRR images, whereas, in order to simplify the scenario, AVHRR channel 2
images were used for the monitoring of the produced plumes (figure 1).
3. Design of a GIS based tool for the support of technological risk
management
The problem of monitoring the atmospheric results of major industrial accidents
has been addressed by the EC Directive 82/501/EC regarding Major Accident
Hazards of Certain Industrial Activities. This Directive, which is often known
as the Seveso Directive, has set the obligation for major industrial installations
in Member States to develop ‘Emergency Plans for the Control of Major
Technological Accidents’. Typically, emergency plans provide exhaustive
consideration of the immediate on-site effects of such accidents, but give much
more limited attention to the wider and longer-term effects generated by the
pollutant plume produced by the incident. In the Directive, the principles for
regulating industrial hazards are laid down so as to prevent major accidents,
and, should one occur, to limit the consequences for man and the environment.
In 1995, a fundamental review of this Directive was accepted by the Council of
Environment Ministers and the European Parliament. The review laid down a
demand for the establishment of safety systems management structures.
Measures to be taken included land use planning, the assessment of substances
on the basis of their toxicity/hazardousness, and evaluation of potential side
effects. The main factors that need to be covered in the course of preparing an
Emergency Plan for Major Industrial accidents range from the definition of land
use/cover and the potentially exposed population to the rate and direction of
propagation of the produced plume. An analytical description of these needs is
given in table 1. In this table the potential of Earth Observation to meet these
needs is also recorded as well as a synopsis of the satellites which can be used in
conjunction with the requirements.
Figure 2 presents the architecture of the proposed GIS based platform. Satellite
data are the main information sources in this system. The aforementioned fire
detection and plume detection and monitoring algorithms are used for the
analysis of NOAA/AVHRR imagery. These algorithms operate independently,
but the results of the first algorithm are compared with the results of the second.
If both results are positive (this means that an industrial fire and a plume have
been detected in the same area), the system will classify the corresponded pixels
as ‘potential incident pixels’. Since the AVHRR images are geometrically
corrected, the coordinations of these pixels are automatically stored in the
system.
As it can be seen in figure 2, the areas in which a technological accident is most
possible to occur (industrial installations, warehouses of toxic substances,
offshore installations, petroleum products storage sites, ports, airports etc.) have
been mapped with the use of high and very high spatial resolution satellite
imagery in combination with other sources of information (CORINE land cover
data, land use maps, ground survey maps etc.). These areas have been classified
as ‘possible areas of occurrence’.
Therefore, the system examines if the detected ‘potential incident pixels’ are
located within any possible area of occurrence. A positive result will verify the
occurrence of a technological accident and will comprise an ‘alarm’ for the
system. This alarm enables the plume monitoring modules as well as the
modules used by the system for the estimation of the population at risk. The
latter combine the high spatial resolution satellite derived information (urban
areas, road network) with in-situ derived vector data (spatial distribution of
population in the vicinity of the incident).
During the crisis phase, the GIS-based tool has the potential:
• to support (in terms of input data and information on boundary
conditions) dispersion models usually employed in Emergency Plans
(with the use of NOAA imagery);
• to describe and map the characteristics of the natural environment (e.g.
land cover, land use) and the population, in the area of concern, thereby
providing a means to assess the environmental and health impacts of the
plume (with the use of high and very high spatial resolution satellite
imagery and in-situ data);
• to provide the state of the environment in the vicinity of the accident and
to feed the aforementioned models with topographic information (based
on high and very high spatial resolution satellite imagery);
• to verify simulation models results with respect to the position and the
speed of propagation of the plume in the atmosphere (based on NOAA
imagery);
• to help identify at-risk populations and environments, which may need
special protection by providing the land cover and the spatial distribution
of population along the predictable plume propagation direction and by
calculating the number of persons expected to be straightway affected,
according to the plume speed of propagation (with the use of high and
very high spatial resolution satellite imagery for the depiction of the
urban areas and in-situ data for the monitoring of the spatial distribution
of population);
• to support the coordination among involved parties as well as the
decision making regarding the evacuation of urban areas to avoid plumes
(with the combined use of high spatial resolution satellite imagery and
in-situ data for the depiction of road network together with urban areas
and spatial distribution of population along plume propagation
direction);
• to support modelling of exposures and health risks through the improved
monitoring of the environmental and social characteristics of the area
(with the combined use of high and very high spatial resolution satellite
imagery and in-situ data);
During the post-crisis assessment phase, the GIS-based tool has the potential:
• to thematic map disaster areas (based on NOAA and high spatial
resolution satellite imagery);
• to support the assessment of the impacts to the local (anthropogenic and
natural) environment due to the produced plume (with the combined use
of NOAA and high spatial resolution satellite imagery and in-situ data);
• to help design and plan follow-up strategies to deal with the
consequences of accidents (e.g. long-term monitoring of human health or
environmental impacts, health support, environmental clean-up);
The main advantage of the proposed GIS tool is the wide area coverage with
very good spatial and temporal resolution. The main disadvantages are:
a) It has the potential to detect and monitor incidents which cause fire
and/or explosion, whereas equally common incidents such as release of
dangerous substances can not be detected with the use of Earth
Observation techniques. Release of dangerous substances is often
expected as gases and liquids are used and processed in the installations
and, in case of damage to the container/reactor, loss of containment takes
place.
b) The observation of a given area with the use of AVHRR (swath width
2400 km) does not accomplished in a continuous basis, because NOAA
are polar orbiting satellites. However, the simultaneous operation of a
system of two NOAA satellites results in increase of their temporal
resolution, especially in mid and high latitudes.
3. Application: A major technological accident scenario for the region of
Athens
Athens concentrates about half of the population of Greece. A lot of refineries,
chemical industries and warehouses are located in the broader region of Athens
and especially within the industrial zone of ‘Thriasio Pedio’, about 15 km NW
from the centre of the city. For the application of the proposed GIS tool, taking
into account that the prevailing winds in the area of concern are from N – NW
directions, a major technological accident scenario for a refinery installation at
Thriasio Pedio was generated. In the past, similar accidents took place in this
area (i.e. Petrola refinery, February 1, 1992), but for the application of the GIS
based tool in this study, real plumes depicted on AVHRR imagery were needed.
For this reason, the available AVHRR data of May 13, 2000, were used (major
technological accident in Enschede). Rectangular portions (50 x 30 km), around
the pixels corresponded to plumes, were extracted from these AVHRR images
and adjusted to the broader area of Athens. High spatial resolution satellite
imagery and in-situ vector data have been also used. More specifically, for the
development and implementation of the scenario the following data were
integrated:
a. NOAA/AVHRR images (May 13, 2000, at 14.44 UTC and 17.20 UTC).
The fire detection algorithm was applied to these images in order to
detect the pixels corresponded to the accident site in Enschede. Since
pixels corresponded to fire caused by this accident were detected,
AVHRR images were adjusted to the area of Athens by corresponding
these pixels to the position of the refinery at the Thriasio Pedio. A
transverse Mercator projection was applied (Projection System:
Hellenic Geodetic Reference System 87 - HGRS87; Reference
Ellipsoid: GRS80). Figure 3 presents the final position of the filtered
pseudochannel image of brightness temperature difference between
AVHRR channels 3 and 4, after the application of the fire detection
algorithm and the adjustment to the area of Athens. As it can be seen in
Figure 3, the pixels for which the brightness temperature difference
between AVHRR channels 3 and 4 peaks (very bright tones, indicative
for the accident site) are now located over the area of Thriasio Pedio.
Figure 4 presents the position of the plume at 14.44 UTC (AVHRR
channel 2), whereas figure 5 presents the position of the plume about
2.5 hours later (AVHRR channel 2, 17.20 UTC).
b. Landsat Thematic Mapper (TM) image (April 26, 1994). Ground
Control Points (GCP’s) were used for the geometric correction of this
image and for its projection to the HGRS87 system. The TM image was
used for the depiction of urban areas and of the main road network, as
well as for the depiction of the industrial zones in combination with
land use maps. It was also used for the estimation of the state of the
environment in the area of concern. For example, the spatial distribution
of vegetation was estimated with the use of NDVI (Normalized
Deference Vegetation Index), which is based on the combination of TM
channels 3 (0.63 –0.69 µm) and 4(0.76 – 0.90 µm).
c. CORINE Land Cover data and land use maps. These data were used in
combination with Landsat TM imagery for the depiction of areas in
which a technological accident is most possible to occur (possible areas
of occurrence).
d. 100 m contours were used for the production of a Digital Elevation
Model (DEM) for the broader area of Athens. This DEM is used to feed
the dispersion models with topographic information, as well as to
produce 3D views of the landscape with the combined use of Landsat
TM imagery.
e. Vectors of the main and secondary road networks for the broader area
of Athens. The integration of these vectors with the Landsat TM image
has the potential to support either the development of emergency plans
for the area of concern, or the decision making during the crisis
mitigation phase.
f. Vectors of the spatial distribution of population for the broader area of
Athens. These vectors were used by the GIS platform in order to
support the decision making during the crisis mitigation phase,
especially regarding the evacuation of urban areas to avoid plumes. The
system has the capability to combine these vectors with the satellite
derived information in order to estimate the population at risk.
4. Results
The possible areas of occurrence are presented in red colour in figure 6. These
areas are superimposed on a pseudocoloured composition (RGB: 3-2-1) of
Lansat/TM channels 1, 2 and 3. As it has been shown in figure 2, the system
examines if the detected ‘potential incident pixels’ are located within any
possible area of occurrence. According to the scenario used in this study, the
area represented by the detected ‘potential incident pixels’ (pixels in bright
tones in figure 3), is located within a possible area of occurrence (area within
the yellow polygon in figure 6).
Figure 7 presents the physical and artificial characteristics of the area located
around the accident’s site. Three sources of information have been used: a)
NOAA/AVHRR imagery for the detection of the exact position of the
technological accident (yellow hatched area); b) Landsat/TM imagery for the
monitoring of the area located around the accident’s site; c) vectors of the main
and secondary road network. Figure 7 can be used to inform decision making
Authorities about the accessibility of the area of interest, as well as about its
artificial characteristics, which may be used during the crisis mitigation phase
(i.e. airports).
Figure 8 presents a product of the ‘monitoring module’ of the proposed GIS
platform. The selected GIS layers in figure 8 present:
• the urban areas (Landsat/TM pseudocoloured image 3-2-1);
• the spatial distribution of vegetation, in green tones (Landsat/TM,
NDVI);
• the possible areas of occurrence, in red colour (from Landsat/TM, land
use maps and CORINE land cover data);
• the position of the accident’s site, within the yellow polygon (from
NOAA/AVHRR channels 3 and 4);
• the position of the produced plume at 14.44 UTC, within the vertically
hatched black polygon (from NOAA/AVHRR channel 2);
• the position of the produced plume at 17.20 UTC, within the horizontally
hatched black polygon (from NOAA/AVHRR channel 2);
This product may be used for the monitoring of urban and natural disaster areas
as well as for the location of areas where high ground level concentrations of
toxic substances are expected. It can be also used for the estimation of the
horizontal propagation velocity of the produced plume. For the scenario used in
this study, the analysis of this product indicated that, the mean plume
propagation velocity along the main propagation direction (NW to SE) was
about 3.5 Km/h, whereas its mean diffusion velocity in perpendicular direction
was about 0.8 Km/h. Therefore, this product has the potential to offer valuable
information to the decision making Authorities, either during the crisis
mitigation phase, or during the post crisis assessment. However, for the
estimation of the population at risk during the propagation of the toxic plume,
the spatial distribution of population was needed.
The proposed GIS tool has the capability to integrate moderate spatial
resolution satellite data with the spatial distribution of population for the area
located around the accident’s site in order to estimate the population at risk.
Figure 9 presents a product of the ‘population distribution module’ of the GIS
platform. The selected GIS layers in figure 9 present:
• The spatial distribution of population between 5 and 10 Km (blue
circles) from the accident’s site. This vector has been produced using in-
situ spatial data. Inhabited areas are presented in different colours,
according to their population density, as it is shown at the legend.
• The position of the produced plume at 14.44 UTC, within the green
polygon (from NOAA/AVHRR channel 2).
• The position of the produced plume at 17.20 UTC, within the red
polygon (from NOAA/AVHRR channel 2).
The population distribution module has been designed for the automatic
estimation of the population at risk. In terms of the scenario used in this study,
the number of potentially affected inhabitants at the area within a circle centred
at the accident’s site with 5 km radius, was found to be at the order of
magnitude of 15000 persons, whereas the number of potentially affected
inhabitants at the area between 5 and 10 Km from the accident’s site (figure 9),
was found to be at the order of magnitude of 300000 persons. Therefore, the
integration of the spatial distribution of population with moderate spatial
resolution satellite data has the potential to estimate the population at risk
during the dispersion of a toxic plume, as well as to support (in combination
with other elements of the proposed GIS platform such as the road network) the
decision making regarding the evacuation of the disaster area.
5. Conclusions
In this study the potential of Earth Observation techniques for major
technological accidents analysis was presented and a GIS platform was
designed for the support of technological risk management. This GIS based
decision support tool carries the advantages of the detection and space-time
monitoring of the produced toxic plumes, by integrating moderate and high
spatial resolution satellite imagery and vector data in order to estimate the
population at risk during an emergency, as well as to support the assessment of
impacts of these plumes to natural and human environment.
Satellite data are the main information sources in this GIS system. The areas in
which a technological accident is most possible to occur were mapped with the
combined use of high and spatial resolution satellite imagery and in-situ spatial
data. Special fire detection and plume detection and monitoring algorithms
were used for the analysis of NOAA/AVHRR imagery. A major technological
accident scenario was developed in order to present the functionality of the GIS
based tool for the support of decision making during the crisis, as well as for
the support of the assessment of the accident’s impact to natural and human
environment.
The main advantage of the proposed GIS platform is the wide area coverage
with very good spatial and temporal resolution. The main disadvantages are: a)
It is able to detect the technological accidents which cause fire and/or
explosion, whereas incidents such as release of dangerous substances can not be
detected. b) The observation of a given area with the use of AVHRR does not
accomplished in a continuous basis, because NOAA are polar orbiting
satellites.
The proposed GIS based tool can be used to support the development of
emergency plans for a given area, the decision making during the crisis
mitigation phase, as well the post crisis assessments. Therefore, it has the
potential to support the activities of involved parties, including:
• Civil Protection Authorities in charge of management and mitigation of both
natural hazards and technological accidents.
• Local or Regional Administrations responsible for the control and audit of
industrial installations such as refineries, power stations, natural gas
industries etc.
• Environmental Agencies.
Finally, it may be used to assist the activities of researchers (e.g.
epidemiologists, environmental scientists) and agencies responsible for
following up the effects of industrial accidents on the population and the
environment.
Acknowledgements
The author is grateful to Dr. P. Pastacos (Scientific Director of the Infocarta
Ltd., Science & Technology Park of Crete), for the supply of road network and
population distribution vectors as well as for his support for the GIS
integration. The author is also grateful to Assist. Prof. C. Cartalis (Director of
the Remote Sensing Laboratory, University of Athens, Dept. of Applied
Physics) for the supply of Landsat/TM and NOAA/AVHRR images used in this
study, as well as for his valuable comments and suggestions.
References
ASIMAKOPOULOS, D., 1994, Modelling the effects of obstacles on the
dispersion of denser than air gases. Journal of Hazardous Materials, 37,
327 – 352.
ATKINSON, G. T., and JAGGER, S. F., 1996, Plume seeding mechanisms in
warehouse fires. Industrial Fires II Workshop Proceedings, Cadarache,
France, 17-18 May 1994, EUR 15967 EN.
BANDINELLI, G., and CARLÁ, R., 1993, Fire Detection by Analysis of
Infrared Satellite Imagery. In Advanced Infrared Technology and
Applications, edited by G. M. GARLOMAGNO and C. CORSI (Firenze:
Instituto di Ricerca sulle Onde Elettromagnetiche (I.R.O.E.) - C.N.R.), pp.
415-428.
BARTELDS, H., SMIT, E., and MOLAG, M., 1993, Emissions from medium
scale fires of chemicals. Industrial Fires Workshop Proceedings,
Apeldoorn, The Netherlands, 11-12 March 1993, EUR 15340 EN.
DAVIE, F. M., and NOLAN, P. F., 1993, Combustion of chemical substances
and the impact on the environment of the fire products. Particle
characterisation. Industrial Fires Workshop Proceedings, Apeldoorn, The
Netherlands, 11-12 March 1993, EUR 15340 EN.
DOZIER, J., 1981, A method of Satellite Identification of Surface Temperature
fields of subpixel resolution. Remote Sensing of Environment, 11, 221 -
229.
CHRYSOULAKIS, N., and CARTALIS, C., 2000, A new approach for the
detection of industrial accidents, using NOAA/AVHRR imagery.
International Journal of Remote Sensing, 21, 1743 - 1748.
CHRYSOULAKIS, N., and CARTALIS, C., 2002a, TAD - a new Satellite
Images Analysis Software Tool for the Detection of Major Fires caused by
Technological Accidents. International Journal of Remote Sensing (in
press).
CHRYSOULAKIS, N., and CARTALIS, C., 2002b, A new algorithm for the
detection of plumes caused by industrial accidents, on the basis of
NOAA/AVHRR imagery. International Journal of Remote Sensing (in
press).
CLEAVER, R. P., COOPER, M.G., and HALFORD, A. R., 1995, Further
model development of a model for dense gas dispersion over real terrain.
Journal of Hazardous Materials, 40, 85 – 108.
COZZANI, V., AMENDOLA, A., and ZANELLI, S., 1996, Hazardous
substances formed as a consequence of industrial fires. Industrial Fires III
Workshop Proceedings, Riso, Denmark, 17-18 September 1996, EUR
17477 EN.
GRANT., G., and DRYSDALE, D., 1994, Flame spread modelling and fire
growth in warehouses. Industrial Fires II Workshop Proceedings,
Cadarache, France, 17-18 May 1994, EUR 15967 EN.
HANNA, S. R., CHANG, J., and STRIMAITIS D. G., 1993, Hazardous gas
model evaluation with field observations. Atmospheric Environment, 27A,
2265 – 2285.
KHAN, F. I. and ABBASI, S. A., 1999, MAXCRED - a new software package
for rapid risk assessment in chemical process industries. Environmental
Modelling & Software, 14, 11 - 25.
KUKKONEN, J., KULMALA, M., NIKMO, J., VESALA, T., WEBBER, D.
M., and WREN, T., 1994, the homogeneous equilibrium approximation in
models of aerosol cloud dispersion. Atmospheric Environment, 28, 2763 –
2776.
LANG, D., 1993, Development of a methodology for the prediction of the
formation of toxic species in warehouse fires. Industrial Fires Workshop
Proceedings, Apeldoorn, The Netherlands, 11-12 March 1993, EUR
15340 EN.
MARLIERE F., 1996, Acute toxicity of smoke: Study of gaseous interactions.
Industrial Fires III Workshop Proceedings, Riso, Denmark, 17-18
September 1996, EUR 17477 EN.
MARTINS, J. M., and BORREGO, C., 1994, Simulation of the atmospheric
dispersion of gases and aerosols. Industrial Fires II Workshop
Proceedings, Cadarache, France, 17-18 May 1994, EUR 15967 EN.
MARTINS, J. M., BORREGO, C., HOLDIN, A., and MEJEAN, P., 1996,
Comparison of the real scale, wind tunnel and numerical model results of
atmospheric dispersion in the vicinity of a building. Industrial Fires III
Workshop Proceedings, Riso, Denmark, 17-18 September 1996, EUR
17477 EN.
MATSON, M., and DOZIER, J., 1981, Identification of subresolution high
temperature sources using a thermal IR sensor. Photogrammetric
Engineering and Remote Sensing, 47, 1311 - 1318.
MATSON, M., SCHNEIDER, S. R., ALDRIDGE, B. and SATCHWELL, B.,
1984, Fire Detection Using the NOAA-Series Satellites. NOAA Technical
Report NESDIS 7.
MATSON, M., STEPHENS, G., and ROBINSON, J., 1987, Fire detection
using data from the NOAA - N satellites. International Journal of Remote
Sensing, 8, 961 - 970.
MILES, S. D. and COX, G., 1994, Transient computational fluid dynamics
model to predict fire product emissions from warehouses. Industrial Fires
II Workshop Proceedings, Cadarache, France, 17-18 May 1994, EUR
15967 EN.
MUIRHEAD, K., and CRACKNELL, A. P., 1984, Identification of gas flares
in the North Sea using Satellite data. International Journal of Remote
Sensing, 5, 199 - 212.
MUIRHEAD, K., and CRACKNELL, A. P., 1985, Straw burning over Great
Britain detected by AVHRR. International Journal of Remote Sensing, 6,
827 - 833.
PORTER, S. R., HALL, D. J., and CARRUTHERS, D. J., 1996, Physical and
computer modelling of dispersion of toxic smoke from warehouse fires.
Industrial Fires III Workshop Proceedings, Riso, Denmark, 17-18
September 1996, EUR 17477 EN.
TECHNICA, 1992, WHAZAN: A Software for Hazard Assessment. Technica,
London.
TNO, 1991, EFFECTS: A software for Hazard Assessment. TNO Prins
Mauritis Research Laboratory, The Netherlands.
TNO, 1992, SAVE: A software for Hazard Assessment. TNO Prins Mauritis
Research Laboratory, The Netherlands.
VVT, 1993, RISKIT: A Software for Risk Assessment. The Technical Research
Centre of Finland, Tempere, Finland.
WEBBER, D. M., JONES, S. J., MARTIN, D., 1993, A model of the motion of
a heavy gas cloud released on a uniform slope. Journal of Hazardous
Materials, 33, 101 – 122.
Captions
Table 1. Potential of Earth Observation to support the requirements of an
emergency plan and examples of satellites which can be used.
Figure 1. NOAA/AVHRR channel 2 image of May 13, 2000 (14.44 UTC)
calibrated and geometrically corrected. The country borders have been also
superimposed. The white arrow shows the position of the plume caused by the
massive explosion in a firework factory in the town of Enschede.
Figure 2. Architecture of the GIS based technological risk management support
tool.
Figure 3. Pseudochannel image of brightness temperature difference between
NOAA/AVHRR channels 3 and 4 (May 13, 2000; 14.44 UTC) adjusted to the
broader area of Athens. Bright tones are indicative for pixels with maximum
brightness temperature difference values. These pixels, which are located over
Thriasio Pedio area, correspond to the scenario accident’s site.
Figure 4. NOAA/AVHRR channel 2 image depicting the position of the plume
at 14.44 UTC, according to the technological accident scenario.
Figure 5. NOAA/AVHRR channel 2 image depicting the position of the plume
at 17.20 UTC, according to the technological accident scenario. The plume in
this case has been diffused over the city of Athens.
Figure 6. Landsat/TM pseudocoloured composition 3-2-1 (April 26, 1994). The
possible areas of technological accident occurrence have been superimposed
(areas in red colour). The position of the scenario accident’s site (derived from
AVHRR imagery) has been also superimposed (area within the yellow
polygon).
Figure 7. Landsat/TM pseudocoloured composition 3-2-1 (April 26, 1994).
Physical and artificial characteristics of the area located around the accident’s
site are presented. The road network has been sumperimposed. The position of
the accident’s site (derived from AVHRR imagery) has been also superimposed
(yellow hatched area).
Figure 8. Integration of Landsat, NOAA, CORINE and in-situ data for the
monitoring of urban areas (TM pseudocoloured composition 3-2-1); of the
spatial distribution of vegetation (in green tones); of the possible areas of
occurrence (in red colour); of the position of the accident’s site (yellow
polygon); of the position of the produced plume at 14.44 UTC (smaller black
polygon) and 17.20 UTC (greater black polygon).
Figure 9. Integration of NOAA and in-situ data for the estimation of population
at risk during the propagation of the toxic plume over the city of Athens. The
spatial distribution of population between 5 and 10 Km (blue circles) from the
accident’s site is monitored. Inhabited areas are presented in different colours
according to their population density. The positions of the plume at 14.44 UTC
(green polygon) and 17.20 UTC (red polygon) are also presented.
Requirement for an Emergency Plan
Potential of Earth Observation to support requirements Satellites
Assessment of land use High Landsat, SPOT, IRS, JERS, IKONOS Assessment of land cover High Landsat , SPOT, NOAA, IRS, JERS Depiction of urban areas and major installations High Landsat , SPOT, IRS, JERS, IKONOS Depiction of road network High Landsat, SPOT, IRS, JERS, IKONOS Detection of the fire High NOAA Detection of the plume High Landsat, SPOT, NOAA, IRS, JERS Definition of the size of the plume High Landsat, SPOT, NOAA, IRS, JERS Definition of speed and direction of propagation of the plume High NOAA Definition of the chemical composition of the plume Not possible - Definition of the opacity of the plume High Landsat, SPOT, IRS, JERS, NOAA
Co-ordination among involved parties Indirect (through the production of EO based thematic maps)
Landsat, SPOT, NOAA, IRS, JERS, IKONOS
Decision making regarding the evacuation of urban areas to avoid plumes
High
Decisions regarding long-term follow-up of the effects (e.g. monitoring, health protection)
High (e.g. by integrating above information)
Landsat, SPOT, NOAA, IRS, JERS, IKONOS