Application of geographic information to estimate small scale air pollution exposure

Paul Fischer

EUROHEIS2 Workshop Utrecht 5-3-2009

Content

• How do we build an air pollution exposure map based on geographic information ?

• Example of the application of these maps in health studies (air pollution epidemiology)

• Example of the application of these maps to describe population air pollution exposure

• Conclusions

EUROHEIS2 Workshop Utrecht 5-3-2009

What exposure ?

Common in air pollution epidemiology:

• Usually data of existing measurement site (RIVM)

• Site in city or country (regional background)

• Applied to all participants in the study

• Problem: misclassification due to ignorance of spatial variation

EUROHEIS2 Workshop Utrecht 5-3-2009

Better measures

• Internal exposure per individual (ideally)

• Derived measure: estimated or measured personal exposure (but can be very labor intensive and expensive)

• Compromise with GIS to account for:

- Spatial variability

- Individual exposures

EUROHEIS2 Workshop Utrecht 5-3-2009

Estimation with GIS

• Principle:

- produce an air pollution exposure map with a high spatial resolution

- project the participant (x-y coordinate) in this exposure map (Mapping)

- Home address, school address, day-care centre, working address, etc.

EUROHEIS2 Workshop Utrecht 5-3-2009

How do I produce an air pollution map ?•Spatial interpolation

- Uses statistical methods to model a pollution map, based on measurement sites

•Dispersion modelling- Estimates air pollution on the basis of traffic

volumes/composition, speed, emission factors, street characteristics and meteorology

• However, both methods are not useful in urban areas !

EUROHEIS2 Workshop Utrecht 5-3-2009

• Combines measured concentrations with covariate data to predict air pollution levels at unmeasured locations

• Association between dependant variable (air pollution level) and predictor variables on measured locations is analyzed by regression

• The regression model can be used to predict concentrations at locations where no measurements are available

Elements of a land use regression model showing monitoring locations for pollutant as the dependent variable and land use characteristics within buffers as the predictor variables. (Jerrett et al, J. Exp Ana Env Epi, 2005)

Land Use Regression (LUR)

EUROHEIS2 Workshop Utrecht 5-3-2009

Application in our health study

• Study in Amsterdam (SAVIAH)

- EU-funded; Huddersfield, Poznan, Prague

- Developing methodology

• Assessing the association between personal exposure to traffic related air pollution and airway symptoms

EUROHEIS2 Workshop Utrecht 5-3-2009

Application in our health study

• First step: creating the air pollution map- Chosen for NO2 map

- Good indicator for traffic related air pollution, easy to measure

- Better: PM2.5, soot, particle number

• for each child the address was projected on the map

• then, for each child the personal exposure was estimated

EUROHEIS2 Workshop Utrecht 5-3-2009

The yellow dots represent the homes of the participating children. Our aim is to estimate the NO2 level for every home address, but also for the area around the home address. In fact, we want to know the NO2 level at every point in the map.

Measurement sites. Size of dot represents level of measured NO2

This is a busy crossing and NO2 level is high

Further down is a quiet street with low NO2

level

EUROHEIS2 Workshop Utrecht 5-3-2009

Area characteristics

d

Road density Distance to Building nearest road density

d min( )

EUROHEIS2 Workshop Utrecht 5-3-2009

Nassaukade

Ring AmsterdamWiboutstraat

Hoge luchtverontreiniging

Lage luchtverontreiniging

Watergraafsmeer

EUROHEIS2 Workshop Utrecht 5-3-2009

Second application: describing exposures distributions

Why ?

• Knowing who is exposed helpful for effective health policy

Answers on questions like:

• Where are measures most necessary ?

• Are some groups higher exposed than others ?

• Environmental (in)justice ? Is exposure equally distributed ?

EUROHEIS2 Workshop Utrecht 5-3-2009

Method

• NO2 and PM10 maps for The Netherlands

• CBS for 2004: 6 digit postal code (CBS, Voorburg 2006)

• About 400000 6PC areas

• On average about 20 houses

EUROHEIS2 Workshop Utrecht 5-3-2009

• Municipality

• Address density

• Degree of urbanization

• Population

• Sex ratio

• Age distribution

• % Non-western immigrants

• Total number private homes

• Single households

• Multi-person households

• Multi-person households with children

• Multi-person households without children

• Average household size

• Housing stock

• Average house value

• Average monthly income

• % Low incomes

• % High incomes

• Social security recipients

• Self employers

EUROHEIS2 Workshop Utrecht 5-3-2009

EUROHEIS2 Workshop Utrecht 5-3-2009

• Per 6 position postal code PM10 en NO2 at centroide determined

• (also possible at address x-y coordinate)

• Assessed distribution of PM10 and NO2 for CBS characteristics

EUROHEIS2 Workshop Utrecht 5-3-2009

EUROHEIS2 Workshop Utrecht 5-3-2009

EUROHEIS2 Workshop Utrecht 5-3-2009

EUROHEIS2 Workshop Utrecht 5-3-2009

EUROHEIS2 Workshop Utrecht 5-3-2009

Conclusies and possible input for discussion:

• GEO information can be used for development of exposure maps in environmental health studies

• GIS is a practical technique to predict individual exposure in (epidemiologic) research

• GEO information can be used to provide information about exposure distributions in populations

• This information can be used for environmental policy