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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/gmd-2019-259
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-259
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: model description paper 18 Oct 2019

Submitted as: model description paper | 18 Oct 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

EXPLUME v1.0: a model for personal exposures to ambient O3 and PM2.5

Myrto Valari1, Konstandinos Markakis1, Emilie Powaga2, Bernard Collignan2, and Olivier Perrussel3 Myrto Valari et al.
  • 1LMD/IPSL, Laboratoire de Météorologie Dynamique, Sorbonne Université, Ecole Polytechnique, IPSL Research University,Ecole Normale Supérieure, CNRS, 75252 Paris, France
  • 2Université Paris-Est, Centre scientifique et Technique du Bâtiment, Direction Santé Confort, Division Physico-chimie - Sources et Transferts de polluants, France
  • 3AIRPARIF, Association de surveillance de qualité de l’air en Île-de-France, 7 rue Crillon, 75004, Paris, France

Abstract. This paper presents the first version of the regional scale personal exposure model EXPLUME. The model uses simulated gridded data of outdoor O3 and PM2.5 concentrations and several population and building-related datasets to simulate 1) space-time activity event sequences, 2) the infiltration of atmospheric contaminants indoors and 3) daily aggregated personal exposures. The model is applied over the greater Paris region at 2 km x 2 km resolution for the entire 2017 year. Annual averaged population exposures are discussed. We show that population mobility within the region, disregarding pollutant concentrations indoors, has only a small effect on average daily exposures. By contrast, considering the infiltration of PM2.5 in buildings decreases annual average exposure by 11 % (population average). Moreover, accounting for PM2.5 exposure during transportation (in-vehicle, while waiting on subway platforms, and while crossing on-road tunnels) increases average population exposure by 5 %. We show that the spatial distribution of PM2.5 and O3 exposures is similar to the concentration maps over the region, but the exposure scale is very different when accounting for indoor exposure. We model large intra-population variability in PM2.5 exposure as a function of the transportation mode, especially for the upper percentiles of the distribution. 20 % of the population using bicycles or motorcycles is exposed to annual average PM2.5 concentrations above the EU target value (25 µg/m3), compared to 0 % for people travelling by car. Finally, we develop a 2050–horizon projection of the building stock to study how changes in the buildings' characteristics to comply with the thermal regulations will affect personal exposures. We show that exposure to ozone will decrease by as much as 14 % as a result of this projection, whereas there is no significant impact on exposure to PM2.5.

Myrto Valari et al.
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Myrto Valari et al.
Model code and software

EXPLUME-v1.0.0 M. Valari and K. Markakis https://doi.org/10.5281/zenodo.3352713

Myrto Valari et al.
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Short summary
To understand how atmospheric pollution affects human health, we need to know the inhaled dose of pollutants. We develop a model that follows the individuals of a population during their daily activities and estimates pollutant concentration levels in the ambient air. We show that certain practices, such as biking in the city, expose people to PM2.5 concentration levels higher that the WHO recommendations. We also show that living in green buildings will decrease significantly exposure to ozone.
To understand how atmospheric pollution affects human health, we need to know the inhaled dose...
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