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Mapping urban air pollution

using GIS: a regression-

based approachDAVID J. BRIGGS , SUSAN COLLINS , PAUL

ELLIOTT , PAUL FISCHER , SIMON KINGHAM ,

ERIK LEBRET , KAREL PRYL , HANS VANREEUWIJK , KIRSTY SMALLBONE & ANDRE VAN

DER VEEN

Published online: 29 Jun 2010.

To cite this article:DAVID J. BRIGGS , SUSAN COLLINS , PAUL ELLIOTT , PAULFISCHER , SIMON KINGHAM , ERIK LEBRET , KAREL PRYL , HANS VAN REEUWIJK ,

KIRSTY SMALLBONE & ANDRE VAN DER VEEN (1997) Mapping urban air pollution

using GIS: a regression-based approach, International Journal of GeographicalInformation Science, 11:7, 699-718, DOI: 10.1080/136588197242158

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int. j. geographical information science, 1997, v o l . 11, n o . 7, 699718

Research Article

Mapping urban air pollution using GIS: a regression-based approach

DAVID J. BRIGGS1

, SUSAN COLLINS2

, PAUL ELLIOTT3

,

PAUL FISCHER5

, SIMON KINGHAM4

, ERIK LEBRET5

,KAREL PRYL

6, HANS VAN REEUWIJK

5, KIRSTY SMALLBONE

7

and ANDRE VAN DER VEEN5

1 Nene Centre for Research, Nene College Northampton, NorthamptonNN2 7AL, England, UK2

Sheeld Centre for Geographic Information and Spatial Analysis, Universityof Sheeld, Sheeld S10 2TN, England, UK3 Department of Epidemiology and Public Health, Imperial College MedicalSchool at St. Marys, London W2 1PG, England, UK4

Institute of Environmental and Policy Analysis, University of Hudderseld,Hudderseld, HD1 1RA, England, UK5 RIVM, 1 Antonievanhoeklaan, 3720 BA Bilthoven, The Netherlands6

National Institute of Hygiene, Warsaw, Poland7

Department of Geography, University of Brighton, Brighton, BN2 4AT,England, UK

(Received 8 June 1996; accepted 23 December 1996)

Abstract. As part of the EU-funded SAVIAH project, a regression-based meth-odology for mapping trac-related air pollution was developed within a GISenvironment. Mapping was carried out for NO2in Amsterdam, Hudderseld andPrague. In each centre, surveys of NO2 , as a marker for trac-related pollution,were conducted using passive diusion tubes, exposed for four 2-week periods. AGIS was also established, containing data on monitored air pollution levels, roadnetwork, trac volume, land cover, altitude and other, locally determined, fea-tures. Data from 80 of the monitoring sites were then used to construct a regression

equation, on the basis of predictor environmental variables, and the resultingequation used to map air pollution across the study area. The accuracy of themap was then assessed by comparing predicted pollution levels with monitoredlevels at a range of independent reference sites. Results showed that the mapproduced extremely good predictions of monitored pollution levels, both forindividual surveys and for the mean annual concentration, with r

2~079087

across 810 reference points, though the accuracy of predictions for individualsurvey periods was more variable. In Hudderseld and Amsterdam, further mon-itoring also showed that the pollution map provided reliable estimates of NO 2concentrations in the following year (r

2~059086 for n=20).

1. Introduction

Despite the major improvements in air quality seen in many European citiesover the last 3040 years, the problem of urban air pollution remains. Levels oftraditional pollutants, such as smoke and sulphur dioxide (SO2 ) have declined, as a

result of industrial restructuring, technological changes and pollution control, but

the rapid growth in road trac has given rise to new pollutants and new concerns.Between 1970 and 1990, for example, passenger car transport in Europe increased

by ca. 34 per cent per annum, and car ownership in East European countries is

13658816/97 $1200 1997 Taylor & Francis Ltd.

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D. Briggs et al.700

currently rising by 712 per cent per year (Stanners and Bordeau 1995). As a resultof these increases, emissions of many pollutants are growing: in the U.K., emissions

of nitrogen dioxide (NO2 ) of which 4550 per cent is derived from the transportsector rose by 120 per cent between 1970 and 1990, before declining slightly; emissions

of volatile organic compounds ( VOCs), of which ca. 40 per cent derives from

transport, rose by 73 per cent ( Department of the Environment 1995 ). For the future,these trends seem set to continue. In the absence of major shifts in policy, a further

doubling of both passenger and freight transport in Europe is anticipated by theyear 2010 ( Stanners and Bordeau 1995 ). Notwithstanding the eects of improvements

in engine design and fuel technology, this is likely to lead to at least the maintenanceof current emission levels in most West European countries. In East Europe, where

the rates of increase are higher, emissions may be expected to grow.

Against this background, there has been heightening concern about the healtheects of trac-related pollution. Several factors contribute to this concern. One isthe simple arithmetic of exposure. In Europe, for example, about 70 per cent of the

population is classied as urbanized ( UNEP 1993 ), while Flachsbart ( 1992) estimates

that between 95 and 18 million people spend a considerable part of their workingday at or near roadsides. Equally, urban areas are estimated to account for the

major proportion of emissions. The potential for human exposure to trac-related

pollution is therefore large. Secondly, there is growing evidence from epidemiologicalstudies of a relationship between air pollution and respiratory illness and mortality(e.g., Schwartz 1993, 1994) and of increased levels of respiratory symptoms in people

living close to major roads, or in areas of high trac density (e.g., Edwards et al.1994, Ishizaki et al. 1987, Weiland et al. 1994, Wjst et al. 1993). In a study of 1000adults in Oslo, NILU (1991) also found a positive association between self-reported

symptoms of cough and chronic bronchitis and modelled levels of air pollution atthe place of residence. At the same time, there has been an apparent increase in

levels of respiratory illness, particularly asthma, in vulnerable groups such as childrenand the old (Anderson et al . 1994, Burney 1988, Burney et al . 1990, Haahtela

et al. 1990 ).

In the light of these concerns, there is clearly a need for improved information

on levels of trac-related air pollution and their potential links to human health.This information is required for a wide range of purposes: to help investigate the

relationships involved, as inputs to health risk assessment, to assist in establishing

and monitoring air quality standards, and to help evaluate and compare transportpolicies and plans. For all these purposes, information is needed not only on the

temporal trends in air pollution (as, for example, provided by data from xed-sitemonitoring stations), but also on geographical variations. Maps are needed, for

example, to identify pollution `hot-spots, to dene at-risk groups, to show changes

in spatial patterns of pollution resulting from policy or other interventions, and toprovide improved estimates of exposure for epidemiological studies.

Mapping urban air pollution nevertheless faces many problems. The complexgeography of emission sources and the equal complexity of dispersion processes inan urban environment mean that levels of air pollution typically vary over extremelyshort distances, often no more than a few tens of metres (e.g., Hewitt 1991). On the

other hand, data on both emission sources and pollution levels are often sparse. As

a result, maps of urban air pollution tend to be highly generalized, and estimates ofexposure to air pollutants subject to serious misclassication.

The development of GIS techniques, however, oers considerable potential to

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Mapping urban air pollution using GIS 701

improve upon this situation. Digital data on urban road networks, for example, arenow becoming increasingly available, providing a valuable data source for pollution

modelling. The spatial analysis and overlay techniques available in GIS also pro-

vide powerful tools for pollution mapping. This paper describes and evaluates aregression-based approach to air pollution mapping, developed as part of the

EU-funded SAVIAH ( Small Area Variations in Air quality and Health) project. Thisstudy was a multi-centre project, involving collaborators in London and Hudderseld( U.K.), Bilthoven ( Netherlands), Prague ( Czech Republic) and Warsaw ( Poland ).

The aim of the study was to develop and validate methods for analysing relationships

between air pollution and health at the small area scale. A description of the overallSAVIAH study is given by Elliott et al. ( 1995) .

2. Approaches to pollution mapping

Traditionally, two general approaches to air pollution mapping can be identied:spatial interpolation and dispersion modelling ( Briggs 1992) . The former uses statist-

ical or other methods to model the pollution surface, based upon measurements at

monitoring sites. With the development of GIS and geostatistical techniques in recentyears, a wide range of spatial interpolation methods have now become available.

Burrough (1986) divides these into global methods (e.g., trend surface analysis),

which t a single surface o n the basis of the entire data set, and local methods (e.g.,moving window methods, kriging, spline interpolation) in which a series of local

estimates are made, based on the nearest data points. Recently, particular attentionhas tended to focus on kriging in its various forms (e.g., Oliver and Webster 1990,Myers 1994). Nevertheless, despite a number of studies comparing this with other

techniques (e.g., Abbasset al. 1990, Dubrule 1984, Laslett et al. 1987, von Kuilenburg

et al. 1982, Weber and Englund 1992, Knotters et al. 1995), there is no clear consensusto suggest that any one approach is universally optimal. Instead, performance of the

various methods tends to vary depending upon the character of the underlying

spatial variation being modelled, and the specic characteristics of the data concerned(e.g., sampling density, sampling distribution).

A number of these interpolation methods have found applications in pollutionmapping, albeit mainly at a relatively broad, regional scale. Linear interpolation, forexample, has been widely used to derive contour maps of pollution surfaces on the

basis of point measurements (e.g., Archibold and Crisp 1983, Muschett 1981). Kriging

in its various forms has been used to map national patterns of NO2 concentrations

(Campbell et al. 1994), acid precipitation (Venkatram 1988, Schaug et al. 1993) and

ozone concentrations ( Lefohn et al . 1988, Liu et al . 1995), and to help designcontinental-scale monitoring networks (Haas 1992). Wartenberg (1993) also reports

the use of kriging to estimate and map exposures to groundwater pollution andmicrowave radiation. Mapping of trac-related pollution in urban areas, however,

potentially faces far more severe diculties. First and foremost is the inherent

complexity of the pollution surfaces involved. Within an urban area, emissions mayderive from a large number of intersecting line sources. The distance decay of

pollution levels away from these sources is also rapid, and greatly aected by localmeteorological and topographical conditions. Marked variations in pollutant levels

can thus occur over distances of less than 100 metres in urban areas (e.g., Hewitt

1991). In contrast, the density and distribution of most monitoring networks is

generally poor. The number of stations monitoring pollutants such as NO2 , VOCs

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D. Briggs et al.702

and ne particulates on a routine basis is generally small, and the location of these

stations is often biased towards specic pollution environments. As a consequence,

the existing monitoring networks provide only a limited picture of spatial patterns

of urban air pollution, potentially biased estimates of trends, and poor indications

of human exposure. Even where purpose-designed surveys can be conducted (e.g.,

using passive samplers), constraints of time and cost severely limit the samplingdensity which is possible.

The main alternative to interpolation is the use of dispersion modelling tech-

niques. This involves constructing a dynamic model of the dispersion processes,

taking account of all the main factors which inuence the ultimate pollution concen-

tration eld. It is an approach which has been most widely used in relation to point

sources, but several models have also been built for road trac pollution: examples

include the CALINE models, developed on behalf of the US EPA (Benson 1992)

the CAR model, developed in the Netherlands (Eerens et al. 1992), the HighwaysAgency Design Manual for Roads and Bridges (DMRB) model, and the ADMS

model (which has been developed from DMRB by Cambridge Environmental

Research Consultants Ltd).

Dispersion modelling has much to commend it as a basis for air pollutionmapping in that it attempts to reect the processes of dispersion and can relatively

easily be adapted to new pollutants or areas, without the need for additional mon-

itoring. On the other hand, it also has a number of important constraints. Amongst

the most serious are the relatively severe data demands of most dispersion models:

typically, data are required not only on the distribution of the road network, butalso trac volumes and composition, trac speed, emission factors for all main

classes of vehicle, street characteristics (e.g., road width, building height or type), and

meteorological conditions (e.g., wind speed, wind direction, atmospheric stability,

mixing height). Rarely are these data available for a suciently dense network of

locations in an urban area, with the result that considerable data extrapolation often

has to occur. Line dispersion models also provide estimates of pollution concentra-

tions only within the immediate vicinity of the roadwayup to a distance of only

35 metres in the CAR model, for example, and ca. 200 metres for the CALINEmodels. This means that they do not easily provide information on variations in

background concentrations, at greater distances from major roads. In addition,

unlike spatial interpolation techniques, there is as yet little progress either in integrat-

ing dispersion models into GIS, or coupling the two t echnologies ( though the ADMS

model does have a limited interface with GIS).

Against this background, there is clearly a need to develop more practicable

techniques of air pollution mapping, which can make use of the capability oered

by GIS, and extract the maximum amount of information from the dierent data

sets which are available within urban areas. Regression-mapping oers particular

potential in this respect. This involves using least squares regression techniques to

generate predictive models of the pollution surface, based on a combination of

monitored pollution data and exogenous information. The technique is widely used

for exploratory and explanatory investigations, and is also used to help classify

remote sensing imagery. Regression methods have only rarely been used, however,

for mapping purposes. Examples include the development of a regression-based

model of road salt contamination ( Mattson and Godfrey 1994), air pollution ( Wagner

1995) and soil depth (Knotters et al. 1995).

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D. Briggs et al.704

Table1.S

ummarydescriptionofstudyareasandsurveyresults.

Concentration(m

gm3)

Survey

Studyarea

Description

No./Date

Device(no.)

Min

Max

M

ean

SD

Amsterdam

Urbanareawithbusyroads

1.June/July1993

Badges(80)

124

735

364

122

bor

deredbyhigh-rise

Tubes(20)

blo

cks;areaca.26km2;

2.Nov1993

Tubes(80)

396

721

518

64

pop

ulation155883

3.Feb/March1994

Tubes(80)

282

682

500

72

(1992)

4.May/June1994

Tubes(80)

275

708

413

108

Hudderseld

Mixedurban-ruralarea;area

1.June1993

Badges(80)

105

884

282

141

305

km2;altituderange

Tubes(80)

80

582m

OD;population

2Oct.1993

Tubes(20)

277

785

476

102

211

300

3.Feb.1994

Tubes(80)

95

515

256

95

(1991)

4.May1994

Tubes(80)

156

694

331

127

Tubes(80)

Prague

Mixtu

reofresidential,

1.June/July1993

Badges(80)

56

658

222

139

ind

ustrialandopenspace;

Tubes(20)

areaca.48km2;altitude

2.Oct1993

Tubes(80)

219

654

396

102

ran

ge172355m

OD;

3.Feb1994

Tubes(80)

200

577

334

96

pop

ulation163700(1991)

4.May1994

Tubes(80)

148

829

367

180

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Figure 1. Monitoring results: mean annual NO2 concentration, Hudderseld, UK.

Figure 2. Monitoring results: mean annual NO2 concentration, Amsterdam, TheNetherlands.

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D. Briggs et al.706

Table 2. Data sets and sources.

Centre Variable Denition Data source

Road network All roads (stored as 10 mHudderseld Aerial photography (1510k)grid)

Trac volume Mean 18 hour trac ow Motorways: automatic(vehicles/ hour) for each counts ( Department ofroad segment Transport ); A roads:

automatic counts (WestYorkshire Highways);manual counts (KirkleesHighways Services);Other roads (estimatesbased on local knowledge)

Land cover Land cover class ( 20 Aerial photography ( 1510k)classes, stored as 10 m

grid)Altitude Metres OD 50 m DTM (YorkshireWater)

NO2 concentration Mean NO2 concentration Field monitoring(by survey period, andmodelled annual mean)

Sample height Height of sampler Field measurement(metres) above groundsurface

Site exposure Mean angle to visible Field measurementhorizon at each

monitoring siteTopographical Mean dierence in GIS-GRID based on DTM

exposure altitude between eachpixel and the eightsurrounding pixels

Amsterdam Road network All access roads within City highways authoritythe study area

Road type Classication of road on City highways authoritybasis of populationserved

Distance to road Distance to nearest road GISserving >25000people

Land cover Area of built up land Planning mapsNO2 concentration Mean NO2 concentration Field monitoring

(by survey period, andmodelled annual mean)

Prague Road network All trac routes in the Department of study area Development, Prague

Municipal AuthorityTrac volume Mean daytime trac Department of

ow (vehicles/hour) Development, PragueMunicipal Authority

Land cover Land cover class ( 6 City planning mapsclasses, based onbuilding density)

Altitude Height (metres) above Topographic mapssea level

NO2 concentration Mean NO2 concentration Field monitoring(by survey period, andmodelled annual mean)

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same regression procedure in all study areas. Instead, each centre developed its ownequation, subject only to the constraint that: (a) it included terms for trac volume,land cover and topography; (b) a similar buering approach was used. In each centre,therefore, data on the relevant input variables were rst computed for a series ofbands around each monitoring site (to a distance of 300 m) using the GRID routines

in ARC/INFO. These were then entered into a multiple regression analysis using asthe dependent variable either the monitored NO2 values for a specic survey period(to provide a pollution map for that period alone) or the modelled annual meanconcentration (to provide an annual average pollution map). The equation thusgenerated was nally used to compute the predicted pollution level at all unmeasuredsites for a ne grid of points across the study area and the results mapped.

As an example, details of the approach used in Hudderseld are presented ingure 3. In brief, the method was as follows:

1. GIS development . Coverages were compiled in ARC/INFO as outlined in

table 2.2. Computation of a weighted trac volume factor ( Tvol300) for the 300 metre

buer around each monitoring site. Daytime trac volumes (vehicle km/hour) wereestimated for each 20 m zone around each sample point ( to a maximum distance o f

300 metres) using the FOCALSUM command in ARC/INFO. Results were thenentered into a multiple regression analysis (in SPSS) against the modelled meanannual NO2 concentrations and dierent combinations of band width compared.The best-t combination (as dened by the r

2value) was selected, and weights for

each band determined by examination of the slope coecients. This gave two bands,

weighted as follows: 040 m (weight=15) and 40300m (weight=1). These werethus combined into a compound trac volume factor (equation (1).

Tvol300=15 Tvol040+ Tvol40300 ( 1 )

3. Computation of a compound land cover factor (Land300 ) for the 300 m bueraround each monitoring site. The area of each land use type (hectares) within each20 m b and around each sample point ( to a maximum distance of 300 m) was calcu-lated using the FOCALSUM command in ARC/INFO. Results were then entered

into a multiple regression analysis (in SPSS) against the residuals from the previousanalysis (step 2). Dierent combinations of land cover and distance were comparedin terms of the r

2value, and the best-t non-negative combination selected. This

gave a single band ( 0300 m) comprising two land use types: high density housing(HDH0300 ) and industry (Ind0300 ). Weights were identied by examination of theslope coecients, and a compound land use factor computed in equation (2).

Land300=18HDH0300+ Ind0300 ( 2 )

4. Stepwise multiple regression analysis was rerun using the two compound

factors ( Tvol300 and Land300 ), together with altitude (variously transformed), topex,sitex and sampler height, against the modelled mean nitrogen dioxide concentrations.Only variables signicant at the 5 per cent condence level were retained. A numberof equations were derived from this procedure, all explaining generally similarproportions of variation in the monitored NO2 levels. From these, regression equa-tion (3) was chosen for further analysis, because of its marginally higher r

2value

which was 0607 and because all variables were signicant at the 005 level.

MeanNO2=1183+ (000398 Tvol300 )+ (0268Land300 )

(00355RSAlt)+ ( 6777Sampht) ( 3)

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D. Briggs et al.708

Figure3.

Th

eregressionmappingmeth

od:Hudderseld,UK.

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5. This equation was then used to construct a complete air pollution coveragefor the study area by applying the equation on a cell by cell basis to all locations in

the study area.In Prague, a broadly similar approach was used. In this case, however, air

pollution data were seen to be skewed, so the data were log-transformed prior to

analysis. In this case, also, no attempt was made to produce a compound tracvolume; instead separate trac volume factors were computed for each zone. A

single land cover factor was computed in equation (4), representing the weightedsum of the areas of each land cover type within the buer zone around the site and

equation (5) was thus derived

Land=(area*density class) ( 4)

LogMeanNO2=348+ (117 Tvol60 )+ (0125Tvol120 )

+ (0000554Land60 ) ( 000152Alt) ( 5)where: Tvol60=trac volume (1000 vehicle km hr

1) within 60m of the site

Tvol120=trac volume (1000 vehicles kmhr1

) within 60120m of the siteLand60=land cover factor within 60 m o f the site

Alt=altitude (m)This equation gave r

2=072 for the 80 sample points. Plotting of the residuals

showed one outlier, with a large negative residual. When this was removed and theregression analysis rerun, the equation (6) was obtained

LogMeanNO2=346+ (117 Tvol60 )+ (0110Tvol120 )

+ (0000569Land60 ) ( 000155Alt) ( 6)

The r2

value was again 072, but the plot of residuals showed a better distribution,

with no outliers. This equation was therefore used for subsequent analysis.In Amsterdam, the lack of data on trac volume, and the essentially at nature

of the local terrain, meant that a dierent approach was used. In this case, road

segments were classied into broad types, based upon the classication used by the

city Highways Department. Three road types were identied, as follows:

RD1 access road for residential areas with >25 000 people

RD2 access road for residential areas with >5000 and 1000 and 25 000 people ( RD1) was also included as a variable. Land cover was dened as

all built up land within 100 m of the site, based on planning maps. The variables

thus computed were then entered into a stepwise regression model, using the modelledmean NO2 concentration as the independent variable. The resulting model (with

r2=062) is given in equation ( 7) .

MeanNO2=4164+ (05832RD150 )+ (06190RD250 )

+ (00723RD1200 ) (00570RD2200 ) (00348RD3200 )

+ (00133RD350 ) (00246 Land100 )+ (00036DistRD1 ) ( 7 )

It should be noted that the approach used here diers from that in the other two

centres, in that no attempt was made to derive compound trac ow variables,

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D. Briggs et al.712

Figure 6. Predicted mean annual concentration from the regression map versus monitoredconcentration (19931994), all centres.

tested by comparing the predicted and measured values at the 8 reference and 40

`variable sites. Table 4 includes the resultingr2

values. For all three surveys, broadlysimilar regression equations are obtained. Notably, for the last period (4), the r

2for

the regression equation is relatively high (>06) and model coecients are close tothose derived for the mean annual concentration. In the second and third surveys,

a reduced number of variables enter the equation, and the r2

value is lower

(036039). All three equations, however, provide good estimates of concentrationsat the 8 reference sites, with r2

values between 069 and 075. Survey 4 also gives a

reasonably good prediction of concentrations at the 40 `variable sites (r2=052).

Surveys 2 and 3, however, are less strongly predictive of the variable sites, with r2

values of 026 and 035 respectively. In part, this may reect the distribution of thesevariable sites in these surveys, in that they were specically selected to examine

variation in background concentrations, and are thus not representative of pollution

conditions across the whole study area. Overall, however, it appears that results ofregression mapping are more variable when applied to individual survey periods.

This should not cause surprise, for the trac data used in the model refer to long-

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Table4.

Regressionequationsforindividualsurveyperiods:Hudderseld.

r2

Coresites

Variablesites

Continuoussites

Period

Equation(Surveym

eanNO2)

(n=80)

(n=40)

(n=8)

Oct1993

40720

+000487Tvol300+0250La

nd300

0387

0256

0729

Feb1994

11505

+000326Tvol300+0197La

nd300+4092Sampht

0362

0350

0743

May1994

7011+000566Tvol300+0385Lan

d300+7674Sampht-

0611

0524

0690

005

2RSalt

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D. Briggs et al.714

term mean ows, and thus do not take account of short-term variations in tracconditions; nor is any allowance made for variations in weather conditions.

As noted earlier, in both Hudderseld and Amsterdam, pollution data were alsoavailable for a subset of 20 sites in the following year (October 1994September

1995). In Hudderseld, these were measured over 21 consecutive 2-week periods; in

Amsterdam, they were measured during four two-week campaigns. Comparing t hesemeasured data with the estimated concentrations from the regression map allowed

the temporal stability of the pollution map to be estimated. Results are summarizedin gures 7 and 8. As can be seen, the correlation between the predicted value from

the map and the mean annual concentration measured in the following year is strongin both centres (r

2=059 in Hudderseld and 086 in Amsterdam). In Hudderseld,

however, the map tends to underpredict actual concentrations in the following year.

This reects the relatively hot summer and still conditions experienced in 19945,which contributed to higher than average pollution levels. Nevertheless, it is apparentthat, notwithstanding the short-term variations in air pollution levels which

undoubtedly occur, the geography of pollution is relatively stable from year to year.

The air pollution maps derived from the regression method thus have longer-termvalidity. On this evidence, the pollution maps should provide a basis for estimating

historic exposures, at least over recent years. In the case of health outcomes with a

relatively long lag period, this has considerable signicance for environmental epi-demiology. In addition, the maps clearly provide a useful framework for designingair p ollution monitoring systems, and identifying the areas for which individual

monitoring sites can be considered representative.

Figure 7. Predicted NO2 concentrations from the regression map versus observed mean

annual concentration the following year: Hudderseld, UK.

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Figure 8. Predicted NO2 concentrations from the regression map versus observed meanannual concentration the following year: Amsterdam, The Netherlands.

5. Discussion and conclusions

The results of this study clearly illustrate the complex nature of spatial variation

in urban air pollution, and conrm the marked variation in levels of trac-relatedpollution which may occur over small distances (


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D. Briggs et al.716

an eective method for mapping trac-related pollutants, such as NO2 . The mapsproduced here consistently gave good predictions of pollution levels at unsampled

points. As with any empirical approach, however, regression mapping has its limita-tions. It potentially suers, for example, from being case- and area-specic. This is

especially likely to be true when models are developed simply through a process of

statistical optimization. As the results for Amsterdam show in this study, the regres-sion equation may then be somewhat counter-intuitive, and as such may not be

valid outside the specic study area. Nevertheless, where the regression models aredeveloped from clear underpinning principles, as, here, in Prague and Hudderseld,

it may be expected that the models would be more generally valid. Recent studies,to be reported elsewhere, do in fact demonstrate that the Hudderseld regression

model can be applied successfully to other urban areas in the UK. The empirical

nature of regression mapping is also part of its strength, for, unlike formal d ispersionmodelling, it can be readily adapted to local circumstances and data availability. Itthus allows optimal use to be made of the available data. In small area studies,

where monitored data are scarce and where the need for high resolution maps is

paramount, GIS-based regression mapping thus oers a powerful tool.

Acknowledgments

The SAVIAH study was a multi-centre project, funded under the EU Third

Framework Programme. It was led by Professor Paul Elliott ( Department of

Epidemiology and Public Health, Imperial College School of Medicine at St. Marys,

London UK formerly at the London School of Hygiene and Tropical Medicine) andco-principal investigators were Professor David Briggs (Nene Centre for Research,

Nene College, Northampton, UK formerly at the University of Hudderseld), DrErik Lebret ( Environmental Epidemiology Unit, National Institute of Public Healthand Environmental Protection, Bilthoven, Netherlands), Dr Pawel Gorynski

(National Institute of Hygiene, Warsaw, Poland) and Professor Bohimir Kriz

(Department of Public Health, Charles University Prague). Other members of the

project team were: Marco Martuzzi and Chris Grundy (London School of Hygieneand Tropical Medicine, London, UK); Susan Collins, Emma Livesely and Kirsty

Smallbone (University of Hudderseld, UK); Caroline Ameling, Gerda Doornbos,Arnold Dekker, Paul Fischer, L. Gras, Hans van Reeuwijk and Andre van der Veen

(National Institute of Public Health and Environmental Protection, Bilthoven, NL);Henrik Harssema ( Wageningen Agricultural University); Bogdan Wojtyniak and

Irene Szutowicz (National Institute of Hygiene, Warsaw, Poland); Martin Bobakand Hynek Pikhart (National Institute of Public Health, Prague, CR) and Karel

Pryl (City Development Authority, Prague, CR). All members of this team madeinvaluable contributions to all parts of the project, and this paper is a product of

their joint eort and expertise. Thanks are also due to the local authorities andhealth authorities in the four study areas, Amsterdam, Hudderseld, Prague andPoznan, for their assistance in carrying out this research.

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