Assessment of air pollution exposures across a population: differences between home-based workers and bike commuters
Meng Lu, Oliver Schmitz, Ivan Soenario, Marco Helbich, Ilonca Vaartjes, and Derek Karssenberg Utrecht University, the Netherlands 1
Goal Assess long-term personal air pollution exposure over entire population at city or larger scale.
Source [1]
2
Challenge: complete space-time path is unknown
? ? ?
home location
work location
routes 3
Personal expossure assessed as front door air pollution concentration NO2 (5m)
60
Exposure assesssed at front door locations
30
0
Hour Utrecht
0
5km
NO2 map author: Ivan Soenario
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Objective • Developing an agent-based modeling approach to model human activity pattern for air pollute exposure assessment. • Compare our approach with air pollution exposure assessed around front door loactions.
5
Commuter and homemaker Commuter
Home location Routes
Homemaker
Simulated work location 6
Method • Modeling air pollution exposure based on a presumed activity schedule • Monte Carlo simulation of working locations
Commuter’s calendar weekday
00:00 - 08:00 08:00 - 09:00 09:00 - 17:00 17:00 - 18:00 18:00 - 23:59
home commuting work commuting Home
weekend
08:00 - 23:00 23:00 - 8:00
One hour outside home 7
Air pollution exposure assessed at different time of the day 18:00 - 8:00 Utrecht
Utrecht
around home location
Home location
9:00 – 17:00
8:00 – 9:00 &17:00-18:00
along commuting route
Utrecht
around work location
Simulated work location
Shortest route 8
Monte Carlo simulation of working locations
9
Implementation Study area: Utrecht city, Netherlands
Bike speed: 16km/h
Utrecht
Indoor infiltration rate: 0.6 Buffer window for indoor locations: 60m x 60m
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NO2 exposure of different realizations for one pixel
1
8
17 11
Homemaker Utrecht, Netherlands
NO2 (μg/𝑚3 )
12
Commuter Utrecht, Netherlands
NO2 (μg/𝑚3 )
13
Distribution Homemaker Median： 20.64 IQR： 2.9
Commuter Median: 21.35 IQR: 1.9
IQR： interquaritile range
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Conclusion • There is a difference between air pollution exposure assessed to commuters and Home makers . • Our approach can be extended to study air pollution exposure of other mobility patterns or more complicated activity scheduals.
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Thank you!
Meng Lu
[email protected]
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reference [1] https://inhabitat.com/nasa-releases-amazing-new-images-of-earth-at-night/nasa-night-images-earth2/ [2] http://indianexpress.com/article/technology/science/european-satellite-sentinel-5p-sends-images-ofglobal-air-pollution-4967726/
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Path visited
path visited (count)
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Weekend activity One hour of 8:00 - 23:00 Utrecht
23:00 – 8:00 Utrecht
Average population over a 10 x 10 km window
Pollution around home location
Home location 19
Method Example of working locations and routes simulation
Probability that a location is visited 1
0
Home location
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Expousure of a single location
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Exposure over population
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Distribution of median with different number of realisaions Kolmogorov-Smirnovtoets test (ks test): 11 -15 realisations for median
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Exposure modeling Mapping of Environment
Modeling human trajectory Open street map
Physical environmental data
Air pollution
Land use regression model, Sentinel 5, portable sensors
Water body Green area
Remote sensing data, hydrological model simulation
Climate
Climate sensors and model simulations
urban facilities
Restaurant
Agent-based modeling Buffer-based methods
Sport center
Exposure: average environmental variable along space-time path visited 24
Phenomenon dataset
Propertyset: point
Proerty: home locations
Propertyset: raster
Property: work locations
Property: route
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Multiple linear regression model
𝑌 = 𝑋𝐵 + 𝑒
Y: 𝑆𝑡𝑎𝑡𝑖𝑜𝑛_𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑚𝑒𝑛𝑡𝑠 X: variable matrix e: error B: coefficient matrix
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commuter
NO2 (μg/s )
1
2
3
km
27
homemaker
1
NO2 (μg/s )
2
3
km
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