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Exposure to PM10 while commuting to work

While the potential for a large impact on public health of pollution has been established and well-documented, it remains current practice to derive inhabitants’ exposure from the measurements of very sparse, fixed monitoring stations even for large cities. This approach does not fully capture spatio-temporal patterns of air pollutants, and causes the information on an individual’s environmental exposure to be poorly aligned with more detailed information on their personal health. In this work, we have created an ArcGIS model for Personal Exposure (PALM-GIS) to Particulate Matter for the inhabitants of the Dublin City and applied it to the assessment of the personal exposure to PM10 of commuters in Dublin. 

A number of air dispersion models are integrated in ArcGIS to calculate the total concentrations at specific locations in Dublin. GIS air quality model

The PALM-GIS exposure to PM10 model

Validation of the model

A number of significant examples were selected to be modelled, and used as validation tests for the model. The following examples are chosen:

  1. Bus: southbound route to Trinity College with bus GIS air quality model
  2. Bicycle: westbound route to Trinity College with bicycle GIS air quality model
  3. Walk: northbound route from Trinity College GIS air quality model

Performance of the model

The measured and modelled data are averaged over 5 minute periods, in order to minimise the influence of noise between the two averaged datasets compared.

GIS air quality model


The pollution  dose is calculated for the measured and modelled data. The calculation is performed for each presented test case, and the results are presented in the table below.

GIS air quality model

Identification of the pollutant uptake by the respiratory system can determine how the resulting exposure contributes to the dose. It is clear from the results that the PALM-GIS model performs well in predicting the inhaled dose for the individuals while commuting to work using three different transport modes.


Department of Civil, Structural and Environmental Engineering

Francesco Pilla, Brian Broderick



Last updated 14 January 2019 (Email).