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Using GIS tools to assess the urban environment influence on the particle concentration variability in
Paris
Sarah Duché, Malika Madelin
To cite this version:
Sarah Duché, Malika Madelin. Using GIS tools to assess the urban environment influence on the particle concentration variability in Paris . International Conference on Urban Climate, Jul 2015, Toulouse, France. 0019. �hal-01398378�
Using GIS tools to assess the urban environment influence
on the particle concentration variability in Paris
Sarah D
UCHÉ
& Malika M
ADELIN
University Paris Diderot, Sorbonne Paris Cité, UMR PRODIG, Paris, France
To define urban morphology parameters,
which could explain the spatial variability of
particles concentration in Parisian street.
Using GIS analysis to study what urban
mor-phology indexes affect the spatial distribution
of particle concentration at local scale.
OBJECTIVES
Particle is one of the most important megacities' air pollution problems, with public health issues.
To assess personal exposure in Paris, we made few measurements of particles concentra-tions by foot and by bike. The results show that particles concentration and size distribu-tion are spatially highly variable due to different emissions and air mass movements.
At the scale of the city, street geometry (orientation, width…), the built-up environment (density, color, materials…) and the green spaces also strongly affect the spatial
distribu-tion of pollutant concentradistribu-tions.
sara.duche@gmail.com / malika.madelin@univ-paris-diderot.fr
ICUC9, 20-24/7/2015, Toulouse, France
CONTEXT
19th june 2014 20th june 2014 22th june 2014 500 mPOLLUTION DATA
Measurements of PM10 and PM2.5 concentra-tion in Parisian streets to assess cyclists’
ex-posure to air pollution:
in June 2014, between 16 pm and 18 pm
during anticyclonic condition, wind speed ± 5 m/s, from north.
by bike
with Dustmate sensor (Turnkey Instru-ments) & GPS ( measurement period = 30 seconds)
POLLUTION BUFFERS
200 mPARTICLE VARIABILITY
PM2,5/PM10 (%) 1 - 12 13 - 17 18 -25 26 - 71 PM10 concentrations (µg/m3) 16 - 30 31 - 42 43-61 61 - 300 PM2,5 concentrations (µg/m3) 3 - 6 7 - 10 11 - 15 15 - 68The analysis of the measured data show a different spatial variation between smaller particle (PM2.5) and coarser particle (PM10). The highest levels of
PM10 are in parks (for example, Jardin des Plantes), whereas the highest concentration of PM2.5 are near traffic. PM10 PM2.5 PM2.5/PM10 100 m Google maps Google maps 50 m 20 m
100 m around measuring point, the correction coefficient between the ratio PM2.5/PM10 and the propor-tion of pedestrian road is equal at 0,52. 27% of the variance between smaller and coarser particles is ex-plained by the suspension of coars-er particles with breezes and from the soil.
URBAN DATA
BD ALTI® - IGN
(digital terrain model ~75m)
BD TOPO® - IGN
Buildings (height)
Roads Vegetation
+ digitization of terrige-nous pedestrian roads
MORPHOLOGICAL PARAMETERS
Azimut (°)
Sky view factor
(SVF) Land cover Street Orientation
vegetation
terrigeneous soil
Each point represents a mean of particle concentration mea-surements during 30 seconds. We made different size of buffer (20 m, 50 m, 100 m and 200 m) for each point to combine the PM concentra-tions average and the urban morphological parameters.
We rasterized vector layers (street orientation and land cover) to calculate the mean/proportion of SVF, street ori-entation and different types of land to each pollution data buffer with the raster calculator.
SOME RESULTS
Influence of terrigeneous road coarsers particles
Correlations between vegetation/street orientation and particle con-centration are not significant for these days.
The correlations with the buffers of 100 m are higher than other the buffers.
The results with the buffers of 200 m are not significant. 0.65 0.7 0.75 0.8 0.85 0.9 0.95 0 10 20 30 40 50
Sky View Factor
(mean in 100 m buffer)
PM2,5/PM10 (%)
R = -0,35
Low correlation between particle size and SVF
PERSPECTIVES
Automation with different days, and meteorological and emission conditions.
Integration of a large data-base, existing and with low cost and miniaturized sen-sors.
Reflexion on spatial scales.
0,2 0,9 SVF 0 100 200 300 400 0.0 0.5 1.0 1.5 2.0
Terrigeneous pedestrian road 0
200 400 600 800 PM10 concentrations (µg/m 3 )
Surface of terrigeneous road in 100 m buffer PM10 concentrations
Jardin des Tuileries
Jardin des Tuileries
V i l l e t t e platform
