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Extending participatory sensing to personal exposure using microscopic land use regression models

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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.
Keywords
AIR-POLLUTION EXPOSURE, BLACK CARBON, NOISE, HEALTH, IMPACT, personal exposure, health, policy, black carbon, noise, spatiotemporal, models, air pollution, activity

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

Chicago
Dekoninck, Luc, Dick Botteldooren, and Luc Int Panis. 2017. “Extending Participatory Sensing to Personal Exposure Using Microscopic Land Use Regression Models.” International Journal of Environmental Research and Public Health 14 (6).
APA
Dekoninck, L., Botteldooren, D., & Panis, L. I. (2017). Extending participatory sensing to personal exposure using microscopic land use regression models. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, 14(6).
Vancouver
1.
Dekoninck L, Botteldooren D, Panis LI. Extending participatory sensing to personal exposure using microscopic land use regression models. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH. Basel: Mdpi Ag; 2017;14(6).
MLA
Dekoninck, Luc, Dick Botteldooren, and Luc Int Panis. “Extending Participatory Sensing to Personal Exposure Using Microscopic Land Use Regression Models.” INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 14.6 (2017): n. pag. Print.
@article{8534290,
  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.},
  articleno    = {586},
  author       = {Dekoninck, Luc and Botteldooren, Dick and Panis, Luc Int},
  issn         = {1660-4601},
  journal      = {INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH},
  language     = {eng},
  number       = {6},
  pages        = {17},
  publisher    = {Mdpi Ag},
  title        = {Extending participatory sensing to personal exposure using microscopic land use regression models},
  url          = {http://dx.doi.org/10.3390/ijerph14060586},
  volume       = {14},
  year         = {2017},
}

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