a. · 90 air pollution viii the importance of urban air quality air pollution is manifested in a...

Air pollution episodes: modelling tools for

improved smog management (APPETISE)

A.J. Greig\ G, Cawle/, S, Dorling\ K, Eben\ A.J. Fiala\ A.

Karppinen\ J. Keder^, M. KolehmainerA K. Kukkonen\ B,

Libero^, J, Macourr\ M. Niranjan^, A. Nucifora^, A, Nunnari^, M.

Palus~\ E. Pelikarr\ J. RuuskanetA U. Schlink^

^ Anglia Polytechnic University, UK, * University of East Anglia, UK* Academy of Sciences of the Czech Republic, * Finnish MeteorologicalInstitute, Finland, * Czech Hydrometeorological Institute, CzechRepublic, ^ University ofKuopio, Finland, ̂ University of Catania, Italy

University of Sheffield, UK, * Centre of Environmental ResearchLeipzig-Halle Ltd, Germany

Abstract

Most ambient air quality models are deterministic models or rely upon simpleregression based statistics. Their success, however, is limited either by theirfailure to capture the non-linear behaviour of air pollutants, or our incompleteunderstanding of the physical and chemical processes involved. The APPETISEproject aims to develop and test the suitability of novel non-linear statisticalmethods to improve our ability to accurately forecast variations in air quality. Italso aims to develop methods for handling missing data, which will have genericapplications for other 'real data' situations. The work is being carried out over aperiod of 2 years by a consortium from 9 institutions from 5 different Europeancountries and is funded under the European Union Fifth Framework Programme.The project concentrates on 4 key pollutants; nitrogen oxides, particulates,ground level ozone and sulphur dioxide. Since it is likely that different methodsand models will work best under different situations an ensemble approach willbe utilised to improve the confidence held in any given prediction. The projectwill work towards the construction of a prototype air quality prediction andwarning system the performance of which will be tested against existingsystems.

Air Pollution VIII, C.A. Brebbia, H. Power & J.W.S Longhurst (Editors) © 2000 WIT Press, www.witpress.com, ISBN 1-85312-822-8

90 Air Pollution VIII

The importance of urban air quality

Air pollution is manifested in a number of globally important environmentalissues such as acid rain, the ozone hole and the enhanced greenhouse effect.Although it is often seen as a small scale problem urban air pollution is also ofglobal importance. The health of over 1.6 billion people worldwide are thoughtto be at risk from the affects of poor urban air quality [1] and approximately500,000 people die each year as a direct affect of outdoor air pollution, (another2 million die from the affects of indoor air pollution). The total health costs ofparticulate pollution alone is estimated at over 100 billion dollars [2]. Urban airpollution is now an important political issue as demonstrated by new suites oflegislation such as the European Framework Directive on Air Quality [3] and theUK National Air Quality Strategy [4]. It is also an important part of theSustainable Development Agenda, for example, the UK's SustainableDevelopment Strategy [5] includes air quality as one of its key headlineindicators of environmental sustainability. These headline indicators areintended to focus public attention and give a broad overview of progress.

Unfortunately, the apparently simple solution to air quality problems, thatis to emit fewer pollutants, is likely to be extremely difficult to achieve. It willrequire a huge change in personal lifestyles and the physical and economicrestructuring of cities, in addition to technical 'solutions' based on reducing fuelconsumption and cleaning up emissions. For some time yet it is likely that manyurban areas will have air quality which is harmful to human health. It istherefore important that we are able to predict where and when this is likely tooccur. We must be able to provide individuals the opportunity to avoid harmfulconcentrations and air quality managers with the opportunity to target theirmitigation efforts and to issue public safety warnings.

Models as management tools

The predictive capabilities of models make them essential monitoring andmanagement tools. Air quality models are routinely used to estimate compliancewith air quality standards, either under present emission and meteorologicalconditions or under probable future conditions. They also provide a mechanismthrough which the level and type of air quality management required to assurecompliance can be assessed.

Air quality management strategies, such as the UK National Air QualityStrategy and the EU Air Quality Framework Directive aim to promote long-termmanagement strategies to ensure air quality standards are not breached.However, variability in the emission of air pollutants and accompanyingvariability in meteorological conditions can, and will continue to, lead to thedanger and occurrence of excedances. In this context air quality models areessential elements of air quality warning systems.

Some exceedances of air quality standards are the result of local emissionand meteorological conditions and may be best managed at a local scale. Forexample, the key sources of nitrogen oxides are often vehicles. Levels of


Air Pollution VIII 91

nitrogen oxides should therefore respond well to traffic management strategies,and trials and full-blown systems for traffic management which take account ofair quality are already in place, for example, in Athens, Greece and Leicester,UK. Some exceedances of air quality standards are the result of pollutantstravelling, and often also chemically transforming, over distances of several tensor hundreds of kilometers. Ozone, for example, has for some time beenrecognised as a pollutant whose concentration cannot be effectively managedthrough controls at a local level only. High ozone episodes do not occur overareas which fit easily within administrative and boundaries, yet it is theseboundaries which frequently determine the scale of air quality managementstrategies. Within adjacent administrative areas very different monitoring,modelling and management strategies may tackle the same pollution episode invery different ways, but the most effective strategies require cross-bordertechnical and managerial partnerships.

A substantial number of air quality models already exist, many havingbeen developed over many years. Each tends to target a particular type ofpollution situation and operate at a specific scale. Hence there are models whichare applicable, for example, to situations where the major pollution sources areindividual industrial stacks, lines of traffic or areas of domestic chimneys andwhich operate at a street, city wide or regional scale. Some models are in thepublic domain, but many of the more sophisticated models are commercialproducts requiring considerable financial investment to obtain and expertise touse. A number of these models are deterministic physio/chemical models whichattempt to reproduce the physical and chemical dimensions of dispersion. Othersare simple regression based statistical models. Hybrid approaches have alsobeen developed to combine the most useful facets of both deterministic andstatistical modelling approaches (e.g. Jakeman [6]). The success of theseapproaches, however, is limited either by their failure to capture the non-linearbehaviour of air pollutants or our incomplete understanding of the physical andchemical processes involved. Our inability to make causal links betweenemissions, meteorology and ground level concentrations is also a limitation.

AimsofAPPETISE

The over-arching aim of the APPETISE project is to trial a variety of advancednon-linear statistically based data mining and modelling techniques as practicaltools for air quality prediction and forecasting. A number of these statisticaltechniques have already been applied to air quality modelling [7-14]. This workhas been undertaken by individual institutions and has mostly been parochial interms of the air quality problem being tackled, not generally extending beyondthe national scale of each partner institution. A number of the techniques whichwill be used within the project have been developed outside the field of airquality, but for generically similar data sets. These techniques have, forexample, already been applied to problems in speech recognition [15,16],medical risk [17] electricity demand [18] and traffic flow forecasting [19]. Theindividual success of these techniques warrants their development and



application to a considerably wider set of circumstances, along with an extensiveinter-model comparison exercise. APPETISE will also foster the developmentof models which operate at a functional scale e.g. street, city, physical regionrather than within administrative boundaries.The Project aims to produce, at the end of the 2 years, statistical tools which canbe demonstrated to perform better than existing predictive tools to key endusers. They will be demonstrated, for example, by interfacing with an existingoperational air quality system.

The pollutants and case studies

The project will concentrate on surface ozone, nitrogen oxides and particulates,all of which form part of the European Air Quality Framework Directive andhave significant health affects associated with them at high concentrations. Eachof these pollutants is fundamentally different from the others in terms of; itsrange of key source type (e.g. vehicles, industry), typical height of emission,geographic scale of emission (e.g. local, national, international), primary orsecondary pollutant status (i.e. emitted directly or formed in the atmosphere) andatmospheric lifetime (resulting from chemical and deposition processes).Together, they will therefore provide a rigorous test of the methods beingdeveloped. A number of case studies from throughout Europe, and withcontrasting air quality problems, will be used to test the new methods andmodels.

Helsinki, Finland

Air quality in the Helsinki Metropolitan Area is fairly good on average and theconcentrations of many pollutants have fallen in recent years. However, theconcentrations in the vicinity of the busiest roads and streets periodically exceednational guidelines. Under unfavourable weather conditions, nitrogen dioxideand particle concentrations may, for short periods, rise to levels which are higheven compared to major European cities.

Catania, Sicily, Italy

The urban area of Catania is one of the most densely populated in Italy. Its 2million inhabitants are exposed to high levels of pollution from both vehiclesand industrial sources. Despite the presence of systems to control the pollutantemissions, people living in the surrounding areas complain of serious healthproblems and disease. The area has been the subject of several epidemiologicalstudies and is recognised by the Italian government as an area of high industrialrisk. The most dangerous pollutant in the area is sulphur dioxide, but carbonmonoxide, nitrogen oxides and ozone also occur at high levels.



Cambridge and Norwich, UK

Historic Cambridge and Norwich are the major centres for employment for thesurrounding areas and have road networks which are unable to cope with thelarge number of vehicles which now exist. Cambridge in particular, experiencesthe added traffic demand associated with a city hosting numerous school, furtherand higher education establishments and attracting approximately 3 milliontourists per year. The resulting large influxes of vehicles are particularlyproblematic in the narrow street canyons which occupy the historic centres ofboth cities. The vehicle generated pollutants nitrogen oxides and particulates areof special concern. However, both cities are also vulnerable to elevations inozone and p articulate concentration during weather conditions which promotelong-range transport of pollutants from the European continent.

Prague, Czech Republic

Traffic related emissions also form a major part of the air pollution problems inPrague. The city already has a city air pollution warning and management planwhich provides statistical air quality monitoring data to the city administrationon-line and to the public via telephone, media and the Internet. A considerableamount of physio/chemical modelling has already been undertaken in the cityand more is being planned, providing an excellent opportunity for comparisonswith new techniques. Industrial pollution is mainly responsible for the frequentsmogs which affect the region of Ostrava in the east of the country andBohemia, along the northwest border with Germany. Air quality in this borderregion, part of the so-called 'Black Triangle', is a consequence of emissionswithin Germany and the Czech Republic but is currently managed separately bythe two countries.

Leipzig, Germany

The region Leipzig-Halle is highly industrialised, with a number of chemicalindustries and power plants. Increasing traffic has led to increases in theconcentration of, for example, nitrogen oxides, carbon monoxide, benzene andtoluene. Ozone, has also recently become a significant in the surrounding lowmountain range and even within the city of Leipzig public warnings were issuesabout raised ozone concentrations on three separate occasions in 1999. Furtherto the southeast, towards the border with the Czech Republic, lies another areawhere industry is concentrated and where industry emitted pollutantscompromise air quality.

Benefits of a European scale project

The purpose of bringing together the team of scientists involved inAPPETISE is to create a critical mass of expertise across a range of themesrelevant to this project, thereby fostering a synergy which would not beforthcoming in a series of smaller, e.g. national projects. This synergy will beknowledge and site specific. For example, the APPETISE consortium consists of



partners who have experience of; making observations of air quality, in thehealth effects of air pollutants, in deterministic modelling applied to air quality,in developing novel statistical approaches applied to air quality, in bringingnovel statistical approaches to the air quality problem from other successfulapplications and in making and disseminating air quality forecasts to thecommunity. The consortium allows a larger number of techniques andapproaches to air quality modelling to be compared and more thoroughlyevaluated than is possible with smaller projects. It also provides a scientificallycritical environment and expert feedback, which should encourage thedevelopment of novel techniques and solutions to the problems being addressed.Very importantly end users in each country are involved at all stages of theproject to help steer the generic outcomes of the models. End users are alsoinvolved in a very practical way, through partners with responsibility fordisseminating air quality forecasts operationally. A number of external advisorsto the project will also ensure that the project is best informed about the othermost significant ongoing European initiatives in the field and contribute toensuring that the key findings of the APPETISE project are widelydisseminated.

Air quality, weather and traffic data is becoming more freely available, butthere are often problems associated with its quality and comparability as well asthe format it is stored and displayed at. The project is developing a centralharmonised database to which all partners across Europe contribute their localair quality, meteorological and traffic datasets. This will permit testing in awider range of environments (situations) than may be possible, for example, at anational scale and also facilitate the development of models which work atfunctional rather than at administrative. For example, encompassing the wholeindustrial region which straddles the Germany, Czech Republic border.

The project will also benefit from 'clustering' with other ongoing Europeanprojects. In particular the Framework V HEAVEN project (HealthierEnvironment through Abatement of Vehicle Emission and Noise) which hasbeen identified as offering an excellent exploitation opportunity for theAPPETISE work. The HEAVEN project seeks to put in place an infrastructurewhich enables the integration of air quality and traffic data and develop tools forcommunication of this information to key end users.

Air quality warning systems

A particular focus of APPETISE is the investigation of extreme events orepisodes of poor air quality, a theme which is naturally of great concern toindividuals. Episodes of poor air quality impinge directly on human health,particularly on the health of the most sensitive of the population, and indirectlyon the community at large through resulting management actions to mitigate theimpacts. Management decisions need to be made against a backdrop of the bestpossible advice from modelling tools. It is this site specific tailored advicewhich APPETISE seeks to improve upon through the interaction betweenpartners.



Air quality warning systems currently in place across Europe operate onlyat a national or local scale and the modelling methodologies vary from countryto country and according to the pollutant of interest. Some of these modellingapproaches are deterministic in nature based on physio/chemical principles [20].Some are based on simple statistical e.g. regression based techniques (e.g.Bremer [21] and a few involve more advanced statistical approaches (e.g. theBelgian SMOGSTOP model). Without the opportunity for robust modelintercomparison exercises it may well be that novel and successful statisticallybased modelling techniques are not being adopted across Europe to best effect.The APPETISE model seeks to redress this issue by undertaking further modeldevelopment based on advanced statistical and artificial intelligence techniquesand rigorous model inter-comparisons with existing methodologies.

Air quality forecast information is extremely perishable. In order for an airquality warning system to be useful in a practical sense, the system must operateas efficiently as possible and the output communicated, via the relevant mediachannels, in a form which is immediately useable and understandable by endusers. Information technology can provide a key role in such a warning system.Particularly relevant to the APPETISE project is also the desirability of havingnumerous different methodologies for modelling variations in air quality, theoutput from which can be compared in an ensemble approach. Such an approachcan improve the confidence held in a given prediction or it may be known that agiven modelling scheme is more likely to be accurate under a given set ofpollutant emission and meteorological conditions. Efficient InformationTechnology and network structure are also important here. The most successfulmethodologies developed in the project will be interfaced with existing airquality warning system methodologies in a full blown operational comparisonexercise.

Plan of work

The work undertaken within APPETISE is divided into a number of functionalworkpackages and can be summarised as follows:

Workpackage 1 - Database generation, validation and dissemination, datamining and handling missing data

This workpackage underpins all other workpackages being responsible for thecreation and maintenance of the project database and, later on in the project, forthe collation of the model results arising from workpackages 2 and 3, therebyaiding model inter-comparison exercises. The database will be available to allpartners via the internet, and at the end of the project it will be made available toother researchers. It is an unfortunate characteristic of real data sets that missingor spurious values are all too frequent. These missing and 'bad' data may greatlyaffect the outcome of and make it less reliable. In addition to harmonising andstandardising the data to a standard format, workpackage 1 is developing andtesting the ability of statistical and artificial intelligence techniques to fill in



these data gaps based on assumptions which can be shown to be robust. Theoutput from this work is especially significant for Workpackage 4 (workingtowards a prototype air quality warning system) since past concentration dataare often used as a first approximation of future concentrations.

Workpackage 2 - Modelling ozone, nitrogen oxide, sulphur dioxide andparticulate concentrations at a point

This workpackage focuses on data mining, model development and inter-comparison activities for each of the pollutants described above, for timeseriesconcentration data at a point. The work on nitrogen oxides and particulatesinvolves the development of neural network based models while workconcentrating on ozone and sulphur dioxide embraces a range of methodologies,including Fuzzy and time series embedding techniques. The selection oftechniques to test in a given workpackage reflects the experience of partners inthe initial work which they have already completed in this area. The work onnitrogen oxides and ozone has natural synergy since nitrogen oxide is animportant control on ground level ozone.

Workpackage 3 - Exploratory studies on spatio-temporal models andinterpolation models for ozone and nitrogen dioxide

Research within this workpackage concentrates on developing and evaluatingdata driven techniques to model variations in pollution concentrations jointly inspace and time. The workpackage includes an exploratory part and twosubsequent parallel activities that are specific to the pollutants ozone, nitrogenoxides and particulates. The starting point of this work draws from very recentdevelopments in areas such as speech processing, kriging in geostatistics andBayesian analysis of neural networks to develop algorithms suitable formodelling the joint spatio-temporal variation of pollutants. The evaluation phasewill use specific pollutants as examples of air quality problems where the scaleof the spatial and temporal concentration variations are very different, therebytesting the scale dependence of the modelling methods employed.

Workpackage 4 - Working towards a prototype air quality warning system

This workpackage seeks to utilise the most successful methods highlighted inother workpackages, integrating data handling procedures and predictionalgorithms into a prototype operational software tool. This tool will include theautomatic input of monitoring data, data quality checking and missing datatreatment. Its outputs will include the prediction of the pollutant concentration atselected points, space-time analysis of concentration fields and the comparisonof predicted air pollution levels with the corresponding air qualitystandards/alert limits. These will be presented graphically as predictedconcentration time series and fields (graphs, maps dynamic animation) and aswarnings to the public.



The APPETISE portfolio of research is aimed at ensuring that Europe hasat its disposal tools which help inform the ongoing debate regarding air qualitymanagement throughout the region. Combined with the best availableinformation from a health, economic, public/business acceptability andconfidence point of view these tools aim to improve the effectiveness of specificair quality management strategies. The European collaboration which occurswithin the Project fosters a maximum exchange of data, ideas and experiences,making the most successful air quality techniques widely available.

References

[1] Elsom, D. Smog Alert, Managing Urban Air Quality, Earthscan: London, p. 2,1996.

[2] United Nations Development Programme Human Development Report 1998Oxford University Press: Oxford, 1998.

[3] European Commission European Framework Directive on Air Quality.Directive 96/62/EC on ambient air quality assessment and management: OJ L296, 21.11.1996; Bull. 9-1996, point 1.3.110, 1996.http://europa.eu.int/abc/doc/off/bull/en/9710/pl02178.htm

[4] Department of Environment, Transport and the Regions. The Air QualityStrategy for England, Scotland, Wales and Northern Ireland. DETR: London2000.

[5] Department of Environment, Transport and the Regions Quality of LifeCounts: Indicators for a Strategy for Sustainable Development for the UK.DETR:London,1999.http://www.environment.detr.gov.uk/sustainable/index.htm

[6] Jakeman, A.J., Simpson R.W., Taylor J.A. Modelling distributions of pollutantconcentrations III The hybrid deterministic-statistical distribution approach.Atmospheric Environment 22, pp. 163-174, 1998.

[7] Gardner, M.W. & Dorling, S.R. Neural network modelling and prediction ofhourly NO% and NO] concentrations in urban air in London. AtmosphericEnvironment 33(5), pp. 709-719, 1999.

[8] Gardner, M.W. & Dorling, S.R. Statistical Surface Ozone Models: Animproved methodology to account for non-linear behaviour. AtmosphericEnvironmental), pp. 21-34, 1999.

[9] Molina, C. & Greig, A.J. A non-parametric approach for finding the embeddeddimension of a temporal and multivarate air pollution time series. Proc. of the2nd International Conference on Urban Air Quality, Madrid, March 1999.

[10] Kolehmainen, M., Martikainen, H., Hiltunen, T., & Ruuskanen, J., Forecastingair quality parameters using hybrid neural network modelling. Proc. of the 2ndInternational Conference on Urban Air Quality, Madrid, March 1999.

[11] Arena, P., Baglio, S., Fortuna, L., Nunnari, G. & Branciforte, M. NeuralNetworks to predict ozone pollution in industrial areas, European Symposiumon Intelligent Techniques, pp. 249-253. , Bari, March 1997.


98 /Wr TWWoM K///

[12] Nunnari G., Nucifora A. & Randieri C. The application of neural techniquesto the modelling of time-series of atmospheric pollution data. EcologicalModelling, 111, pp. 187-205, 1998.

[13] Schlink, U. & Herbarth, O. On the forecasting of 80% time series. AirPollution Emissions Inventory. Eds. H. Power & J.M. Baldasano, Comp.Mech. Publications, Southampton, UK, pp. 205-238, 1998.

[14] Schlink, U., Herbarth, O. & Tetzlaff, G. A component time series model forSC>2 data: forecasting, interpretation and modification. AtmosphericEnvironment 31(9), pp. 1285-1295. 1997.

[15] Cawley, G.C. & Noakes, P.O. The use of vector quantization in neural speechsynthesis. In Proc. IJCNN-94, 3, pp. 2227-2230, Nagoya, Japan. 1994.

[16] Wu, L-Z., Niranjan, M. & Fallside, F. Fully vector Quantised Neural Networkbased Code Excited Nonlinear Predictive Coding of Speech, IEEETransactions on Speech and Audio Processing, 2(4), pp. 482-489. 1994.

[17] Lovell, D.R., Rosario, B., Niranjan, M., Prager, R.W., Dalton, K.J. Derom, R.& Chalmers, J. The QAMC Project: A case study of neural networks formedical risk prediction, Australian Journal of Intelligent Informationf mcfMmg Syjfgmj, 5(1), pp. 24-28. 1998.

[18] Pelikan E., Eben K. & Petrak L. One-Day Prediction of Electric LoadReflecting Future RCS Schedule. Journal of Forecasting, 15, pp. 427-436.1996.

[19] Pelikan E. & Novak M. Traffic Flow Forecasting with Neural Networks, Proc.of Int. Conf. on Complex and Intelligent Systems and Interfaces, pp. 233-235,Nimes, France, May 1998.

[20] Bremer, P. Assessment of two methods to predict 5% concentrations in theHelsinki area. Finnish Meteorological Institute, Publications on Air Quality15. Helsinki 42pp. 1993.


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