by: prof. dr radovan stojanovic, university of montenegro ppd · prof. dr radovan stojanovic,...

Geographical Information

Processing for Environmental

Pollution-Related Security within

Urban Scale Environments

GEPSUS

By:

Prof. dr Radovan Stojanovic,

University of Montenegro

PPD

Science for Peace and Security (SPS)

CONTEXT

Instead introduction

Project description

Case studies

Demonstrations


Instead introduction

Disasters triggered by nature or man-caused

destroy lives and livelihoods.

They effect millions of people every year,

reach or poor.

With help of science and technology, we can

reduce human, physical and financial cost of

disasters by understanding the risks

and applying the science and technology in

preventing and mitigation.

Simple, save lives.

Videos: About us, Flooding, Nuclear, Fire

110330 CUNAMI ZA 18 I 30h.mp4

Annapolis River Flood Simulation(480p_H.264-AAC).flv

Tsunami Simulation for the Nuclear Power Plant(360p_H.264-AAC).flv

SMARTFIRE V4.1 simulation of fire development in the Rhode Island night club(480p_H.264-AAC).flv

Video/buildingEXODUS V3.0 is used to re-create the Gothenburg Disco fire(480p_H.264-AAC).flv


GEPSUS - Goals

General objective: Given above.

Specific objectives:

Integrated system for environmental pollution-related disasters management based on fusion of Geographical Information Processing, Computer Modeling and Simulation, and Creditable Decision Making.

System simulation and testing in real conditions: industrial and traffic air pollution in the Montenegrin capital of Podgorica, with emphasis on air pollution from KAP.

System evaluation, pilot work, dissemination, embedding project into Montenegrin emergency management measures and institutions.


GEPSUS-Partners

-UNIVERSITY OF MONTENEGRO

-GRAPHITECH (ITALY)

-UNIVERSITY OF MARIBOR (SLOVENIJA)

-EMESCO (ISRAEL)

-HERBEY UNIVERSITY (ISRAEL)

- MINISTRY OF DEFENCE OF MONTENEGRO

- SECTOR FOR EMERGENCY


GEPSUS - problem

Target country – Montenegro

Types of urban pollutions Industrial air pollution

Industrial plants

Industrial facilities

Traffic pollution Number of imported old vehicles increasing

55% of vehicles between 11-15 years old

Annual max concentration of hydrocarbons

exceeded by 400 to 2000% in all 21

cities of Montenegro

Fires Every day we face the Risk from the fires


GEPSUS-at glance

Imagine real incident, release of gases in Podgorica

Policeman

or

fire-fighter

redirect traffic

to safe route

using

his PDA

(iPhone)

Connected to

The Control

Centre

Control

Centre


Objects of interest

Podgorica, KAP

Pljevlja, TPP

Industrial facilities


Expressed as sources

- Each source,

different

mathematical

approaches for

modeling.


Brussels, September 22, 2009

GEPSUS- General approach Databases:

Population

Housing

Businesses

Topography

Geology

Infrastructure

Structural

Vulnerability

Ground Truth: Imagery, Aerial

Photography

Communication Links

Internet, Land Line,

Satellite

3D GIS GeoBrowser

Modeling knowledge: Chemical release

mathematical models for

dispersion and prediction

simulation models

The disaster simulation

Decision-makers

Disaster Prevention/Mitigation plan with detailed measures and reduction strategy

Disorder management knowledge: Operational and tactical know-how,

field experience

Decision-maker

knowledge: Feature extraction and space

optimizing intelligence to support

actors

GEPSUS platform


CETIEcotoxicologicalResearch Centre

Air PollutionSimulation Models

ServersGPUHPC/

REAReal Estate

Administration

User Input

HMZCGHydrometeorological

Service

Curent state &

Prognosis

Data from the Accident Area

Updated geographic

DataEm

iss.

da

ta

GEPSUSCOMPUTINGFACILITY

AutomaticWeatherStations

AutomaticTelemetricStations

TerrainSurveyData

Automatic input data:

- Meteorological

- Topography, strategic

geoinformation

- Emission inventory

User input data:

- Location Latitude, Longitude

- Emission rate

- Wind Speed, direction (autoamatic)

- Type of Emission

- Weather description (automatic)

- Chemical description

- Release parameters description

- Threat zones determination

...

Output:

- Results provided

via mobile

devices

- Provision of

Results to

Control Centres

- Providing the

Information

to Exposed Population

WeatherSimulation

Models

Weather dataReading UK

Washington USA

Population

Rescue Crew

Database

Threat zonesEvacuation plans

Montenegro, Macro flow of data


Montenegro, Emergency flow of data

Podgorica, 16-17/03/11Brussels,

September 22, 2009

Global flow

(HMZCG, CETI, EC, AZN)


Many math and ICT models… Math Models in Meteorology

Math Models in Dispersion

Math transformations in GEO-Spatial Data

Math models in Decision Making

ICT to formalize our math and knowledge and

does it usable.


Example of modeling

Calculation of

sigma_y

sigma_z

and

concentration

Input parameters:

- Stack position and high

- Speed

- Weather conditions

- Emission rate

- Reflection

- Temperature

Single or superposition

Visualisation

wind

profiles

Point sources (Gaussian model)


Example of modeling

Stability class A Stability class D

- The situation changes by stability classes of

atmosphere


Realization

Take the data

Calculate models in software like MATLAB

Output threat zones and rescue instructions to Geo-browser

Take the action Example: Using google earth export threat zones as KML format


EXAMPLES

CASE I Aluminium Plant (KAP Podgorica).

Lat.: 42.389000, Lon.: 19.218797

Scenario SC1 is performed based on

the real data at 09:00h.

Scenario SC2 is performed based on

prognosis for 12:00h.

Scenario SC3. The validation

was performed on the real

data at 12: 00h


Scenario (KAP)

Parameter SC1 (9:00) SC2 (12:00) SC3 (12:00)

Emission rate Q [g/s] 76.3 76.3 76.3

Height H [m] 37 37 37

Wind welocity [m/s] 2 3.2 2

Wind direction [deg] 320 160 180

z [m] 0 0 0

Stack x [m] 100 100 100

Stack y [m] 200 200 200

x min [m] 1000 1000 1000

y max [m] 400 400 400

W. condition [A-F] 'A' 'A' 'A'

T [C] 28.5 28 28.7 Real time of simulation execution 5.6.2011 9:00 5.6.2011 9:00 5.6.2011 12:00


Results (KAP)

A B C

Real, prediction based and observation based pollution concentrations


Results KAP (cont.)

One could observe extreme change in the direction of air pollutant spread which occurred in the period of three hours

Actual wind change was from 320o (A) at 9:00h to 180o (C) at 12h

The predicted wind direction (B) was 160o and missed for 20o

Due to the larger predicted wind speed of meteorological prognosis, one could observe larger expected perimeter in the predicted run (B)


EXAMPLES

CASE II

Thermo Electric Plant (Pljevlja). Lat.: 43.333494, Lon.: 19.327311.

Scenario SC1 is performed

based on the real data at 09:00h.

Scenario SC2 is performed

based on prognosis for 12:00h.

Scenario SC3. The validation

was performed on the real data

at 12: 00h


RESULTS (P TPP)

CASE II


EXAMPLES • In a small industrial park outside Podgorica, a 1810 lit, 1,2m-diameter, vertical tank contains liquid

benzene. During routine control, a security guard discovers that liquid is leaking out of the tank

through a 0.2m circular hole located 0.254m above the bottom of the tank. He also sees that the liquid

is flowing onto a paved area in the industrial park. The guard thinks that the tank has just been filled

that evening. The temperature on scene is 26°C, with the wind from the southwest at 11km per hour

(as measured at a height of 10 meters by a fixed meteorological tower at the site). The sky is more

than half covered by clouds and the humidity is about 75 percent. A thunderstorm is approaching

from the southwest. There is no low-level inversion. There are very few buildings in the industrial

park and a large grassy field is located to the northeast of the industrial park.

The Local Emergency Planning Committee

has requested that on-scene responders

use ERPG-2 concentrations to define

the toxic endpoints in their analysis

of benzene hazards.


Results

THREAT ZONE:

Model Run: Heavy Gas

Red : 73 meters --(1000 ppm = ERPG-3)

Orange: 248 meters --(150 ppm = ERPG-2)

Yellow: 503 meters --(50 ppm = ERPG-1)


Conclusion

Overview of the system complexity at the air-pollution emergency control

Important aspect that emerged in the course of our study is the integration of major governmental institutions which have to provide the input data to the system as well as the response by acting on the field

For the test case the Gaussian model of air pollution dispersion was adequate to show the major characteristic of the particular crisis situation

Rapid prototyping with the MATLAB

Application of Google Earth as the initial GIS system

Better process of system development

Results of the simulation models observed in its final form

Decision makers and emergency response teams


Concluding Comment

To prepare against disasters, one of the most

effective and crucial requirements is information.

This is the core of GEPSUS.


DEMONSTRATIONS

> CASE I_II, CASE III, AWARDS

Follows

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by: prof. dr radovan stojanovic, university of montenegro ppd · prof. dr radovan stojanovic,...

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