Population data play a range of vital roles in integrated assessment of environmental risks to health:

• As proxies for source activity and intensity data in models of emissions or environmental concentrations (e.g. in terms of domestic waste production, traffic density, atmospheric emissions, air pollutant concentrations)
• As a basis for modelling and estimating population exposures (e.g. by intersection of population data with environmental data, by extrapolation from personal monitoring or biomonitoring data)
• As a source of information on vulnerability (e.g. related to age, sex or socio-economic status).Population density and composition are both highly variable, often over short distances.

Substantial changes in population can also occur over periods of a few years not only as a result of natural changes of birth and death but also through migration and urban development. The spatial and temporal resolution of these data is therefore often critical in determining their utility for healtjh impact assessment. While some assessments can cope with aggregated data, based for example on national decennial population counts, for many purposes the data need to be available at the resolution of 1 km or less, in order to reflect local variations in environmental conditions, and for time periods of no greater than one or two years. Additionally, for many applications data are needed not as totals for the whole population, but for specific population groups, defined in terms of age, sex, ethnic origin etc. The ability to provide data at these levels of detail (i.e. cross-stratified by area, year, and population group) is likely to be one of the main limiting factors in the utility of available population data.

Two forms of population modelling are therefore likely to be necessary in many integrated assessments:

• temporal modelling (population projections), to calculate likely changes in population numbers and structure for future years, under the various policy scenarios being assessed;
• spatial modelling, to estimate the local distribution of these population groups under the various scenarios. 

Population projections

Spatial population modelling

Reliable information on the location and distribution of the population is essential for risk assessment in order to differentiate between, and calculate, population-level exposures.  Census data provide routinely available information on population numbers and composition, generally on a decennial basis.  By their design, however, census tracts - arranged to facilitate enumeration rather than to reflect the underlying geographic distributions of population - are of limited value for exposure assessment.

Census tracts are rarely uniform in size or population numbers, and typically cover large geographic areas including unpopulated lands (e.g. rural land, parks and industrial developments).  The smallest geographic unit for the EU administrative regions, for example, is the Local Administrative Unit 2.  Outside densely built-up urban centres most LAU2 regions are around 5-10 km².  In rural areas, they may be in excess of 100km².  Where the LAU2 regions are this large, the population tends to be dispersed or clustered in small towns with inhabitants typically residing in only a small proportion of the region.

In response to these problems, several spatial disaggregation methods have emerged to estimate small-area population for various science and policy applications including risk assessment.  The methods range in complexity from simple area weighting (Goodchild and Lam1980) to dasymetric mapping techniques (Briggs et al. 2007; Flowerdew and Green 1994; Harris and Longley 2000; Mennis 2003).

Area weighting, for example, is used to construct the gridded population of the world (GPW-3) database (CIESIN 2004).  It is an approximate approach, however, which relies on the (often false) assumption that the population is evenly distributed within each census district.  To develop more reliable models of population distribution requires more sophisticated techniques incorporating additional data on exogenous variables that can be assumed to reflect lower level (i.e. within census tract) variations in population density.  These more complex dasymetric mapping or disaggregation techniques often use land cover data derived from satellite imagery to provide the necessary ancillary information for modelling.