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Extending Participatory Sensing to Personal Exposure Assessments Using Microscopic Land Use Regression Models (µLUR)
- 1. Extending Participatory Sensing to Personal Exposure Assessments using Microscopic Land Use Regression Models (µLUR) Waves, Department of Information Technolo Ghent University, BelgiumWaves, Department of Information Technology Ghent University, Belgium Luc Dekoninck Promotors: Dick Botteldooren, Luc Int Panis
- 2. Keywords… 2 2008-2010 Traffic related Quality of Life Bike model PhD basic model Indicators Predefined Longterm Spatial Multidisciplinary Instantaneous Temporal Participatory sensing Route sensitive Person centered Trips High resolution Simulated Meteo Season Background Bias Disentangle Land-Use regression Population Non-linear Models Policy scenario’s Routing / network Awareness Changing behavior
- 3. 3 Route sensitive Person centered Indicators Policy scenario’s Personal exposure to BC External validation (VITO) Predefined Longterm Spatial Trips Instantaneous Multidisciplinary Temporal Meteo Season High resolution Participatory sensingSimulated Background Routing / network Bias Disentangle Activity Micro-environment A Discussion of Exposure Science in the 21st Century: A Vision and a Strategy by Lioy and Smith Applicability to real (epi) populations Land-Use regression Non-linear Models Awareness Changing behavior Population Developing an integrated conceptual model for health and environmental impact assessment by Reis et al. Multidisciplinary Epidemiology Dose Internal/external Exposure Physical activity Toxicology Chemical loadingSize distributions Health Keywords… Action Policy Policy and Health Impact assessments Personal
- 4. Personal exposure: a combination of spatiotemporal objects 4 Activity as common object
- 5. Person to activity breakdown 5 Person centred approach Route sensitive Activity specific attributes Time series possible
- 6. Dispatch the activity… 6 “Model inside the targetted micro-environment”
- 7. Activity Specific Model 7 Main feature = activity specific temporal resolution ! 10 seconds for in-traffic models Time series Key Object !
- 8. µLUR: model participatory sensing data 8 2. Measurement campaign designed to capture as much of the variability over all driving forces 5. Instantaneous non-linear models (I used gam…) 1. Low aggregation level of the measurements Features: 3. Spatial and temporal attribution of all driving forces 4. Activity and/or micro-environment specific models Bicycle model = traffic + traffic dynamics captured through spectral noise The microscopic and micro-environment specific non-linear spatiotemporal land-use regression model (µLUR)
- 9. MEX – model the exposure… 9
- 10. Applying the MEX data workflow… (1) 10 Activities: car trips (BC + GPS) 225 hours of data 9 individuals 10 second resolution External (Land-use) data: meteo, traffic data, background concentrations, noise map, traffic dynamics, build-up area,… Dataset to explore and build a model ASF is unknown ASF to validate
- 11. In-vehicle data exploration 11 Yearly noise map Hourly traffic
- 12. In-vehicle µLUR 12 Hour of Day Noise map Wind speed Temperature Background Street Canyon
- 13. Applying the MEX data workflow… (2) 13 Person factory: external car trips (BC + GPS) BC in 5 minutes resolution ASF to test / validate Prediction by ASF External validation dataset Choose evaluation level Identical attribution Valid ?? Correction required for Euro5 legislation
- 14. Main goal: apply on real populations data • Health impact assessments • Local policy (awareness) • Large scale (population and long term) 14
- 15. Health impact application… 15 Simulated behavior of individuals in cohort External exposure ASMs are known Personal Exposure Health outcomes Biomarkers Effects ?? Evaluate variants of dose, toxi,… for different biomarkers and health outcomes
- 16. Local policy (awareness) application… 16 Feed your route Bicycle routing app Gives alternative route… ASMs are known Purpose of app… • Quantify exposure reduction of specific behavioral change • Longterm or Instantaneous… • Initiate modal shift (diff. car, bike,…) • Promote bicycle network • … Personal awareness local community support Feeds local policy support Personal behavior evaluation
- 17. Evaluate different policy scenario’s • Modal shift scenario’s • Reducing traffic demands • Fleet emisson evolution • … Policy scenario application… 17 Simulated behavior of a population of individuals ASMs are known Population distribution of selected indicators Multidisciplinary set of indicators Aggregated indicator (QoL example) Mobility Monetary assessment Cost-Benefits…
- 18. Conclusions • Transposing spatiotemporal variation from participatory sensing campaigns to any real or virtual population can be achieved with µLUR methodology. • Presented MEX data workflow reduces gap between exposure science, health research and policy support • Multidisciplinary cooperation required to extend • External exposure to internal exposure • Build ‘awareness and behavioral change’ applications • Provide population wide policy support 18