Quimic de Sarria, Ramon Llull University, 08017 Barcelona, Spain
^ Waste Water Recovering Department, Peinaje del Rio Llobregat,
Carretera de SantFruitos, 08272 SantFruitos, Barcelona, Spain
Abstract
The purpose of the work is the introductory presentation of the comprehensiveSODCAT system, a low cost PC computer based system for pollution on-linemonitoring and forecasting for the industrialized area of Tarragon. Theobjective of the SODCAT system is the on-line forecasting with 6 hours ofanticipation of atmospheric pollution levels in a 14x14km area, mainly for SOz,CO, NOx and Og It relies on an automatic network of measuring cabinsdistributed around the area that collects on-line pollution data andmeteorological information. The software is formed by two separateapplications. The first one is composed by the mathematical modelsimplemented that process the data and carry out the on-line simulations. Thesecond application is a 3D Virtual Reality Scientific Visualizer designed for 3Dreal time observation of measured and calculated data projected and animatedonto the topography in a Virtual World, allowing 2D classical scientific chartingas well.
446 Air Pollution Monitoring, Simulation and Control
1. Introduction
The SODCAT system is a comprehensive PC computer system for pollution on-line monitoring and forecasting for the Tarragon coastal area, developed at theInstitut Quimic de Sarria of the Ramon Llull University under request and withthe support of the Autonomic Government of Catalonia.
The Tarragon area is one of the most industrilized areas in Spain, withseveral industrial emissions and multiple interaction of chemical species. In 1987an automatic pollution control network was designed to continuously surveil theair quality levels around the area and detect hazard episodes. Presently, theautomatic network is composed by eighteen automatic measuring cabins, aDoppler Sodar and a 50m high meteorological tower.
In 1994 started the development of the SODCAT system as acomplementary tool for atmospheric pollution control and an indispensableinstrument for efficient air quality level forecasting. It makes use of themonitored data and relies its 6 hours anticipated prognostics on a set ofmathematical models that take into account multiple aspects of the localpollution dispersion. An innovative Virtual Reality based Visualizer allows thecomprehensive analysis and understanding of the outputted simulated data in athree-dimensional environment.
2. Geographical area
The SODCAT system covers an area of 196knf, constituting a 14km sidedsquare centered at the 41°9' latitude and 1°12' longitude coordinates (Figure 1).The analysis of the orography and the land use identifies the followingcharacteristics:
Table 1: Altimetrical and land use analysis.
Max.Min.MeanStd. Dev.
OROGRAPHY
Altitude279mOm69.3m12.1 m
Slope32.2°0°2.4*1.4°
LAND USE
CropVegetationEdificationWaterOthers
50.6 %24%12.6%12.6 %0.2 %
The area is highly industrialized with multiple chemical plants, ranging from finechemicals production to refineries. The industries are grouped in three mainindustrial parks. The Northern park contains approximately ten industries, whilethe Tarragon City and Southern parks comprise around twenty chemical plantseach (Figure 1).
448 Air Pollution Monitoring, Simulation and Control
3. Pollution monitoring network
The pollution monitoring network is an instrument ensemble that collects thenecessary data to carry out the pollutants' dispersion simulation. The network isdistributed geographically around the area and is formed by eight pollutionreceptor cabins, ten emission cabins, a Doppler Sodar and a meteorologicaltower (Figure 1). The minute averaged collected information is sent via 1.200bps radio modems to the Control Center that is located at the EnvironmentalGovernment Delegation of Tarragon. The data is then processed by thesoftware specifically developed.
a) Receptor cabins
The pollutant concentrations at receptor site and some surface meteorologicalparameters are collected by the receptor cabins. The atmospheric pollutants thatare presently automatically monitored are SC , NO%, HC's, CO, Og, HbS andHC1. Paniculate matter, VOC's and lead concentrations are manually measuredThe measured meteorological parameters comprise wind speed and direction,air temperature, relative humidity, barometric pressure, solar radiation and total
rainfall.
b) Emission cabins
The pollutant emission control is carried out at selected stack release locations.Currently, ten sources of six different plants are monitored. The measuredpollutants are CO, SO2, NOx, HCT and opacity. On the other hand, somephysical parameters as speed, temperature and volumetric flow of emissiongases are also continuously monitored.
c) Doppler Sodar
A Doppler Sodar is located in the area. This instrument monitors the verticaland horizontal wind speed, thermic structure and height of the mixing layer upto 500m.
d) Meteorological tower
A 50m high tower retrieves the necessary meteorological informationconcerning the surface layer. The meteorological instruments are distributed atdifferent height levels:
- At 0.5m: radiation and rainfall meters.- At 5m, 10m, 35m and 50m: temperature, relative humidity and two-dimensional wind meters.
- At 35m and 50m: vertical wind speed meters.
A detailed Virtual Reality interactive world representing the describedmonitoring network is being developed under Superscape VRT, withdivulgative and educational purposes.
Air Pollution Monitoring, Simulation and Control 449
4. Mathematical models
The SODCAT system has a modular structure concerning the differentmathematical models that compose the scientific background. Each module hasbeen developed as a standalone application and then joined together to form acomprehensive entity.
The different modules cover the meteorological, dispersion and chemicalaspects required to model the atmospheric pollution typical episodes of the areaunder study. The current situation as well as enhancements currently underdevelopment are described in the next section.
Figure 3: Diagram of the mathematical models of the SODCAT system(Dashed line: under testing, Dotted line: under development).
4.1 Model description
a) Meteorological models
A 3D y-mesoscale meteorological model, hydrostatic with dry thermodynamicsis being currently used. Turbulence is resolved accordingly with a second orderturbulence closure, level 2.5 following the classification of Mellor &Yamada[l]. The turbulence length scale is diagnosed accordingly to Enger[2],
450 Air Pollution Monitoring, Simulation and Control
while the averaged basic equations are solved prognostically as suggested inUliasz[3].
A simplified Cloud Model is being coupled to the main meteorologicalmodule, based on Flatau et al.[4]. Phase changes and water vapor transportequations are solved prognostically with diagnostically determined mixing ratiosofhidrometeors.
b) Emission models
Main point emissions are monitored on-line by the described network, even sothe remaining local emissions are of considerable interest. An Emission Modelunder development will estimates the partial emissions due to unmonitoredchemical plants, urban traffic, domestic heating and natural factors. It employsas inputs a satellite teledetection study of land use as well as the vectorial mapof roads and urban distribution of the area. The Emission model followsEggleston et al.[5], Lubkert & Scopp[6] and Veldt & Bakkum[7].
c) The Deposition Models
A Dry Deposition Model for SO% and NO* accordingly to Matt & Meyers[8]and Padro[9] has been added to the system to take into account the main sinksfor sulfur and nitrogen oxides.
A Wet Deposition Model based on Iribarne & Cho[10] is underdevelopment and will be coupled with the meteorological Cloud Module.
d) Dispersion Models
A Lagrangian Dispersion Model, based on particle representation of gaseouspollutants, has been implemented following Uliasz[3]. The fundamental Markovsequence of the model has been enhanced with the inclusion of forced andnatural convective movements at the stack release height, accordingly toCogan[ll]. Optionally, an effective Briggs[12] stack height can be used. Theconversion between particle and pollutant concentration can be carried out bythe particle grid cell averaging method, by the Yamada & Bunker[13] GaussianKernel or the Uliasz[14] Parabolic Kernel.
An Eulerian Dispersion Model, based on Uliasz & Pielke[15] and Pepper etal.[16], is being implemented to be tested against the Lagrangian DispersionModel.
A hybrid Dispersion Model operation is intended, applying the LagrangianDispersion Model to controlled point emissions and the Eulerian DispersionModel to the unsupervised natural and anthropogenic emissions.
e) Chemistry Models
A Dry Photochemical Model is solved coupled to the Dispersion models. Itincludes nocturnal chemistry and solar angle variation during daytime fordifferent specific reaction schemes (San Jose et al.[17], Hov et al.[18] andDechaux et al.[19]). Specific high speed solving algorithms with fixed andadaptive time step have been implemented (Odman et al.[20] and Young &Boris[21]).
An Aqueous Chemistry Model is being tested as a standalone module and
Air Pollution Monitoring, Simulation and Control 451
will be joined to the model in the next future, along with the meteorologicalCloud Model. The aqueous chemistry follows Iribarne & Cho[10] and
Chameides[22].The chemistry coupling of the Lagrangian Dispersion Model is being carried
out as proposed by Chock & Winkler[23][24]
4.2 Model implementation
The system is developed for low cost Pentium platform using a high qualityoptimization compiler, obtaining an acceptable performance. The platformlimitation is partially solved implementing the source code in C++ advancedobject oriented programming, which gives high flexibility as well as totalportability to other systems.
A Scientific Three-dimensional Workspace Library has been designed and isbeing used as the main developing engine. This library allows easy andtransparent manipulation of three-dimensional staggered grids during the modelimplementation. It spatially allocate each physical variable and it automatesinterpolations, grid loops and error checkings. The computational cost of usingthe library is fully voided turning off automation during final compilation.
5. Virtual reality scientific visualizer
a) Virtual reality benefits in science
Virtual Reality (VR) is of primal interest in scientific applications and it'sbringing an encouraging new perspective to environmental computer simulateddata. Environmental data, and specially computer simulation outputs, are highlydifficult to analyze and furthermore to assimilate and comprehend for anonskilled user. Physical data is always three-dimensional and therefore needs athree-dimensional environment to be globally and comprehensively analyzed.
VR is basically a three-dimensional computer graphic environmentreproducing the physical domain under study. It establishes a real-timeinteraction between the user commands and the three-dimensional graphicsbeing displayed. The physical data is then projected in a three-dimensionalworld and the user is left to observe and interact with this virtual world.
VR is going to dramatically lower the barrier to understand the complexityand relevance of atmospheric pollution dispersion processes, specially foreducational and divulgation purposes.
b) Visualizer description
A VR Scientific Visualizer has been developed to integrate environmental 3DVR real-time scientific presentation as well as 2D classical scientific charting.The Visualizer allows 3D full immersion real-time flights and walks over theterrain, user queries about the physical data being displayed through the virtualworld interface, particle animated time evolution and 3D isosurfacesrepresenting pollutants' clouds for detailed observation.
Air Pollution Monitoring, Simulation and Control 453
The 2D graphical output of the Visualizer covers from wind field toconcentration contour lines projection. However, the Lagrangian DispersionModel outputted particle positions clearly states the need of a three-dimensionalvirtual environment to allow the analysis of the results. Although theeffectiveness and immersion of the 3D output of the Visualizer is onlyperceptible during a computer demonstration, a particle simulation is presentedin Figure 4. The Visualizer also allows various data postprocessings (Figure 5).
The Visualizer runs under Windows 3.1 and requires a 486DX at 66MHzfor a satisfactory performance, no video graphics accelerator is required.
6. Summary and conclusions
The paper has presented a general overview to the comprehensive SODCATsystem, covering the geographical domain, the automatic sensors' network, themathematical model background and the virtual reality Visualizer that composeits global entity. The system models all the necessary physical aspects for anaccurate pollution prevention and control.
Special emphasis is given to the innovative aspects of the programminglanguage platform and the inclusion of virtual reality in 3D environmental dataanalysis and understanding. During the on-line operation of the system, specificmodeling aspects for the coastal and heavily industrialized area of Tarragon aregoing to be identified and delimited. Case studies and further conclusions aresubject to future publications
The authors wish to thank the support of the Environmental Quality Serviceof the Environmental Department of the Generalitat of Catalonia.
7. Bibliography
1. Mellor,G.L. & Yamada,T. "A hierarchy of turbulence closure models forplanetary boundary layers/% J. Atmos. Sci, 1974, 31, 1791-1805.
2. Enger,L. "Simulation of dispersion in moderately complex terrain-Part A. Thefluid dynamic model." , Atmos. Environ., 1990, 24A, 2431-2446.
3. Uliasz,M "Development of the mesoscale dispersion modeling system usingPersonal Computers Part I: Models and computer implementation", ZMeteorol, 1990,40, 110-120.
4. Flatau,P.J., Tripoli,GJ., Verlinde,J. & Cotton,W.R. "The CSU-RAMS Cloudmicrophysics module: General theory and code documentation", AtmosphericScience Paper No 451, Colorado State University, 88pp.
5. Eggleston,H.S., Goripen,N., Jourmard,R., Rijkeboer,R.C., Samaras,Z. &Zierock,K.H. "CORINAIR Working group on emission factors for calculated1985 emissions from road traffic. Volume I: Methodology and emissionfactors", Report No EUR12260 EN, Luxembourg/Wien, 1989.
6. Lubkert,B. & Scopp,W. "A model to calculate natural VOC emissions fromforest in Europe". Paper 89-082, DAS A, Luxembourg/Wien, 1989.
454 Air Pollution Monitoring, Simulation and Control
7. Veldt,C. & Bakkum,A. "DG XI CORINAIR emission factors", Report No 88-355/R.22/CAP, TOO, The Netherlands, 1988.
8. Matt,D.R. & Meyers,T.P. "On the use of the inferential technique to estimatedry deposition of SO*", Atmos. Environ., 1993, 27A, 493-501.
9. Padro,J. "Seasonal contrast in modeled and observed dry deposition velocitiesof O3,SO2 and NOz over three surfaces", Atmos.Environ., 1993, 27A, 807-814.
10. IribarneJ.V. & Cho,HR "Models of cloud chemistry", 7W/wf,1989, 418,2-23.11. Cogan,J.L. "Monte Carlo simulation of buoyant dispersion", Atmos. Environ.,
1985,19, 867-878.12. Briggs,G.A. "Plume rise predictions", in Lectures on Air Pollution &
13. Yamada,T. & Bunker,S. "Development of a nested grid, second momentturbulence closure model and application to the 1982 ASCOT Brush Creek datasimulation", J. Appl Mffeor.,1988, 27, 567-578.
14. Uliasz,M. "Development of the mesoscale dispersion modeling system usingPersonal Computers Part IT. Numerical simulations", Z Meteorol, 1990, 40,285-298.
15. Uliasz,M. & Pielke,R. A. "Receptor-oriented lagrangian-eulerian model ofmesoscale air pollution dispersion", Computer Techniques in EnvironmentalStudies m, ed. Zanetti,P., pp57-68, CMP Publications, Southampton &Springer Verlag, Berlin, 1990.
16. Pepper,D.W. Kern,C.D. & Long,PE "Modeling the dispersion of atmosphericpollution using cubic splines and chapeau functions", Atmos. Environ., 1979,13, 223-237.
17. San Jose,R., Rodriguez,L., Moreno,!., Palacios,M., Sanz,M.A., Delgado,M.,"Eulerian and photochemical modeling over Madrid area in mesoscalecontext", Air Pollution II Volume 1, eds. Baldasano,J.M., Brebbia,C.A.,Power,H, Zanetti,P. pp. 209-217, CMP Publications, Southampton, 1994.
18. Hov,O, Zlatev,Z., Berkowicz,R., Eliassen,A., Prahm,L.P. "Comparison ofnumerical techniques for use in air pollution models with non-linear chemicalreactions ", Atmos. Environ., 1989, 23, 967-983.
19. DechauxJ.C., Zimmermann,V., Nollet,V. "Sensitivity analysis of therequirements of rate coefficients for the operational models of photochemicaloxidants formation in the troposphere", Atmos. Environ., 1994, 28, 195-211.
20. Odman,M.T., Kumar,N. & Russel,A G "A comparison of fast chemical kineticsolvers for air quality modeling", Atmos. Environ., 1992, 26A, 1783-1789.
21. Young,T.R. & Boris,J.P. "A numerical technique for solving ordinarydifferential equations associated with the chemical kinetics of reactive-flowproblems", J. Appl. Meteorol, 1977, 27, 562-578.
22. Chameides,W.L. "The photochemistry of a remote marine stratiform cloud", J.Geoph. Res., 1984, 89, 4739-4755.
23. Chock,D.P. & Winkler,S.L. "A particle grid air quality modeling approach, 1.The dispersion aspect", J. Geophys. Res., 1994, 99, 1019-1031.
24. Chock,D.P. & Winkler,S.L. "A particle grid air quality modeling approach, 2.Coupling with chemistry", J. Geophys. Res., 1994, 99, 1033-1041.
