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A high resolution spatiotemporal model for in-vehicle black carbon exposure : quantifying the in-vehicle exposure reduction due to the Euro 5 particulate matter standard legislation

(2017) ATMOSPHERE. 8(11).
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Abstract
Several studies have shown that a significant amount of daily air pollution exposure is inhaled during trips. In this study, car drivers assessed their own black carbon exposure under real-life conditions (223 h of data from 2013). The spatiotemporal exposure of the car drivers is modeled using a data science approach, referred to as microscopic land-use regression (mu LUR). In-vehicle exposure is highly dynamical and is strongly related to the local traffic dynamics. An extensive set of potential covariates was used to model the in-vehicle black carbon exposure in a temporal resolution of 10 s. Traffic was retrieved directly from traffic databases and indirectly by attributing the trips through a noise map as an alternative traffic source. Modeling by generalized additive models (GAM) shows non-linear effects for meteorology and diurnal traffic patterns. A fitted diurnal pattern explains indirectly the complex diurnal variability of the exposure due to the non-linear interaction between traffic density and distance to the preceding vehicles. Comparing the strength of direct traffic attribution and indirect noise map-based traffic attribution reveals the potential of noise maps as a proxy for traffic-related air pollution exposure. An external validation, based on a dataset gathered in 2010-2011, quantifies the exposure reduction inside the vehicles at 33% (mean) and 50% (median). The EU PM Euro 5 PM emission standard (in force since 2009) explains the largest part of the discrepancy between the measurement campaign in 2013 and the validation dataset. The mu LUR methodology provides a high resolution, route-sensitive, seasonal and meteorology-sensitive personal exposure estimate for epidemiologists and policy makers.
Keywords
PARTICLE NUMBER CONCENTRATIONS, USE REGRESSION-MODELS, PERSONAL, EXPOSURE, AIR-POLLUTION, TRANSPORT MICROENVIRONMENTS, TIME-SERIES, EPIDEMIOLOGY, CALIFORNIA, BICYCLE, HEALTH, black carbon, personal exposure, in-vehicle, traffic, LUR, data science, noise map

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Citation

Please use this url to cite or link to this publication:

Chicago
Dekoninck, Luc, and Luc Int Panis. 2017. “A High Resolution Spatiotemporal Model for In-vehicle Black Carbon Exposure : Quantifying the In-vehicle Exposure Reduction Due to the Euro 5 Particulate Matter Standard Legislation.” Atmosphere 8 (11).
APA
Dekoninck, L., & Int Panis, L. (2017). A high resolution spatiotemporal model for in-vehicle black carbon exposure : quantifying the in-vehicle exposure reduction due to the Euro 5 particulate matter standard legislation. ATMOSPHERE, 8(11).
Vancouver
1.
Dekoninck L, Int Panis L. A high resolution spatiotemporal model for in-vehicle black carbon exposure : quantifying the in-vehicle exposure reduction due to the Euro 5 particulate matter standard legislation. ATMOSPHERE. 2017;8(11).
MLA
Dekoninck, Luc, and Luc Int Panis. “A High Resolution Spatiotemporal Model for In-vehicle Black Carbon Exposure : Quantifying the In-vehicle Exposure Reduction Due to the Euro 5 Particulate Matter Standard Legislation.” ATMOSPHERE 8.11 (2017): n. pag. Print.
@article{8546916,
  abstract     = {Several studies have shown that a significant amount of daily air pollution exposure is inhaled during trips. In this study, car drivers assessed their own black carbon exposure under real-life conditions (223 h of data from 2013). The spatiotemporal exposure of the car drivers is modeled using a data science approach, referred to as microscopic land-use regression (mu LUR). In-vehicle exposure is highly dynamical and is strongly related to the local traffic dynamics. An extensive set of potential covariates was used to model the in-vehicle black carbon exposure in a temporal resolution of 10 s. Traffic was retrieved directly from traffic databases and indirectly by attributing the trips through a noise map as an alternative traffic source. Modeling by generalized additive models (GAM) shows non-linear effects for meteorology and diurnal traffic patterns. A fitted diurnal pattern explains indirectly the complex diurnal variability of the exposure due to the non-linear interaction between traffic density and distance to the preceding vehicles. Comparing the strength of direct traffic attribution and indirect noise map-based traffic attribution reveals the potential of noise maps as a proxy for traffic-related air pollution exposure. An external validation, based on a dataset gathered in 2010-2011, quantifies the exposure reduction inside the vehicles at 33% (mean) and 50% (median). The EU PM Euro 5 PM emission standard (in force since 2009) explains the largest part of the discrepancy between the measurement campaign in 2013 and the validation dataset. The mu LUR methodology provides a high resolution, route-sensitive, seasonal and meteorology-sensitive personal exposure estimate for epidemiologists and policy makers.},
  articleno    = {230},
  author       = {Dekoninck, Luc and Int Panis, Luc},
  issn         = {2073-4433},
  journal      = {ATMOSPHERE},
  keywords     = {PARTICLE NUMBER CONCENTRATIONS,USE REGRESSION-MODELS,PERSONAL,EXPOSURE,AIR-POLLUTION,TRANSPORT MICROENVIRONMENTS,TIME-SERIES,EPIDEMIOLOGY,CALIFORNIA,BICYCLE,HEALTH,black carbon,personal exposure,in-vehicle,traffic,LUR,data science,noise map},
  language     = {eng},
  number       = {11},
  pages        = {20},
  title        = {A high resolution spatiotemporal model for in-vehicle black carbon exposure : quantifying the in-vehicle exposure reduction due to the Euro 5 particulate matter standard legislation},
  url          = {http://dx.doi.org/10.3390/atmos8110230},
  volume       = {8},
  year         = {2017},
}

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