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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/24323

Title: Extending Participatory Sensing to Personal Exposure Using Microscopic Land Use Regression Models
Authors: Dekoninck, Luc
Botteldooren, Dick
Panis, Luc Int
Issue Date: 2017
Publisher: MDPI AG
Abstract: Personal exposure is sensitive to the personal features and behavior of the individual, and including interpersonal variability will improve the health and quality of life evaluations. Participatory sensing assesses the spatial and temporal variability of environmental indicators and is used to quantify this interpersonal variability. Transferring the participatory sensing information to a specific study population is a basic requirement for epidemiological studies in the near future. We propose a methodology to reduce the void between participatory sensing and health research. Instantaneous microscopic land-use regression modeling (mu LUR) is an innovative approach. Data science techniques extract the activity-specific and route-sensitive spatiotemporal variability from the data. A data workflow to prepare and apply mu LUR models to any mobile population is presented. The mu LUR technique and data workflow are illustrated with models for exposure to traffic related Black Carbon. The example mu LURs are available for three micro-environments; bicycle, in-vehicle, and indoor. Instantaneous noise assessments supply instantaneous traffic information to the mu LURs. The activity specific models are combined into an instantaneous personal exposure model for Black Carbon. An independent external validation reached a correlation of 0.65. The mu LURs can be applied to simulated behavioral patterns of individuals in epidemiological cohorts for advanced health and policy research.
Notes: [Dekoninck, Luc; Botteldooren, Dick] Univ Ghent, Informat Technol, Res Grp WAVES, B-9052 Ghent, Belgium. [Panis, Luc Int] VITO, Boeretang 200, B-2400 Mol, Belgium. [Panis, Luc Int] Hasselt Univ, Traff Res Inst, B-3500 Diepenbeek, Belgium.
URI: http://hdl.handle.net/1942/24323
DOI: 10.3390/ijerph14060586
ISI #: 000404107600034
ISSN: 1660-4601
Category: A1
Type: Journal Contribution
Validation: ecoom, 2018
Appears in Collections: Research publications

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