Assessing Mobility-Based Real-Time Air Pollution Exposure in Space and Time Using Smart Sensors and GPS Trajectories in Beijing

Using real-time data from portable air pollutant sensors and smartphone Global Positioning System trajectories collected in Beijing, China, this study demonstrates how smart technologies and individual activity-travel microenvironments affect the assessment of individual-level pollution exposure in space and time at a very fine resolution. It compares three different types of individual-level exposure estimates generated by using residence-based monitoring station assessment, mobility-based monitoring station assessment, and mobility-based real-time assessment. Further, it examines the differences in personal exposure to PM2.5 associated with different activity places and travel modes across various environmental conditions. The results show that the exposure estimates generated by monitoring station assessment and real-time sensing assessment vary substantially across different activity locations and travel modes. Individual-level daily exposure for residents living in the same community also varies significantly, and there are substantial differences in exposure levels using different approaches. These results indicate that residence- or mobility-based monitoring station assessments, which cannot account for the differences in air pollutant exposures between outdoor and indoor environments and between different travel-related microenvironments, could generate considerably biased estimates of personal pollution exposure. Key Words: indoor environment, real-time exposure to air pollution, smart technologies, travel modes, the uncertain geographic context problem.

[1]  Individual Exposure , 2020, Definitions.

[2]  J. Pearce,et al.  Socioeconomic inequalities in children’s exposure to tobacco retailing based on individual-level GPS data in Scotland , 2019, Tobacco Control.

[3]  M. Kwan,et al.  Impacts of residential energy consumption on the health burden of household air pollution: Evidence from 135 countries , 2019, Energy Policy.

[4]  Jue Wang,et al.  Uncertainties in the geographic context of health behaviors: a study of substance users’ exposure to psychosocial stress using GPS data , 2018, Int. J. Geogr. Inf. Sci..

[5]  M. Kwan,et al.  Beyond Commuting: Ignoring Individuals’ Activity-Travel Patterns May Lead to Inaccurate Assessments of Their Exposure to Traffic Congestion , 2018, International journal of environmental research and public health.

[6]  Jue Wang,et al.  An Analytical Framework for Integrating the Spatiotemporal Dynamics of Environmental Context and Individual Mobility in Exposure Assessment: A Study on the Relationship between Food Environment Exposures and Body Weight , 2018, International journal of environmental research and public health.

[7]  Mei-Po Kwan,et al.  The Neighborhood Effect Averaging Problem (NEAP): An Elusive Confounder of the Neighborhood Effect , 2018, International journal of environmental research and public health.

[8]  M. Kwan.  The Limits of the Neighborhood Effect: Contextual Uncertainties in Geographic, Environmental Health, and Social Science Research , 2018 .

[9]  Jue Wang,et al.  An Innovative Context-Based Crystal-Growth Activity Space Method for Environmental Exposure Assessment: A Study Using GIS and GPS Trajectory Data Collected in Chicago , 2018, International journal of environmental research and public health.

[10]  Bin Chen,et al.  Real-Time Estimation of Population Exposure to PM2.5 Using Mobile- and Station-Based Big Data , 2018, International journal of environmental research and public health.

[11]  Meng Liu,et al.  Health status and air pollution related socioeconomic concerns in urban China , 2018, International Journal for Equity in Health.

[12]  Pengxiang Zhao,et al.  The Uncertain Geographic Context Problem in the Analysis of the Relationships between Obesity and the Built Environment in Guangzhou , 2018, International journal of environmental research and public health.

[13]  Bin Chen,et al.  Real-Time Estimation of Population Exposure to PM 2 . 5 Using Mobile-and Station-Based Big Data , 2018 .

[14]  Carlo Ratti,et al.  End-user perspective of low-cost sensors for outdoor air pollution monitoring. , 2017, The Science of the total environment.

[15]  Roderic L. Jones,et al.  Validating novel air pollution sensors to improve exposure estimates for epidemiological analyses and citizen science , 2017, Environmental research.

[16]  Alena Bartonova,et al.  Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates? , 2017, Environment international.

[17]  G. Mitchell,et al.  Inequality in Beijing: A Spatial Multilevel Analysis of Perceived Environmental Hazard and Self-Rated Health , 2017 .

[18]  M. Kwan,et al.  Individual exposure estimates may be erroneous when spatiotemporal variability of air pollution and human mobility are ignored , 2017, Health & place.

[19]  Wichor M Bramer,et al.  Levels of ambient air pollution according to mode of transport: a systematic review. , 2017, The Lancet. Public health.

[20]  Nor Badrul Anuar,et al.  The role of big data in smart city , 2016, Int. J. Inf. Manag..

[21]  A. Lewis,et al.  Validate personal air-pollution sensors , 2016, Nature.

[22]  M. Nieuwenhuijsen.  Urban and transport planning, environmental exposures and health-new concepts, methods and tools to improve health in cities , 2016, Environmental Health.

[23]  Eun-Hye Yoo,et al.  Geospatial Estimation of Individual Exposure to Air Pollutants: Moving from Static Monitoring to Activity-Based Dynamic Exposure Assessment , 2015 .

[24]  Edmund Seto,et al.  Variability in and agreement between modeled and personal continuously measured black carbon levels using novel smartphone and sensor technologies. , 2015, Environmental science & technology.

[25]  L. Morawska,et al.  The rise of low-cost sensing for managing air pollution in cities. , 2015, Environment international.

[26]  Yongmei Lu,et al.  Examining Personal Air Pollution Exposure, Intake, and Health Danger Zone Using Time Geography and 3D Geovisualization , 2014, ISPRS Int. J. Geo Inf..

[27]  L. Shang,et al.  The next generation of low-cost personal air quality sensors for quantitative exposure monitoring , 2014 .

[28]  Jennifer Gabrys.  Programming Environments: Environmentality and Citizen Sensing in the Smart City , 2014 .

[29]  Sharon E. Edwards,et al.  Race, socioeconomic status, and air pollution exposure in North Carolina. , 2013, Environmental research.

[30]  M. Kwan.  Beyond Space (As We Knew It): Toward Temporally Integrated Geographies of Segregation, Health, and Accessibility , 2013 .

[31]  A. Peters,et al.  Long-term air pollution exposure and cardio- respiratory mortality: a review , 2013, Environmental Health.

[32]  Audrey de Nazelle,et al.  Improving estimates of air pollution exposure through ubiquitous sensing technologies. , 2013, Environmental pollution.

[33]  Gb Stewart,et al.  The use of electrochemical sensors for monitoring urban air quality in low-cost, high-density networks , 2013 .

[34]  C. Sabel,et al.  Quantifying human exposure to air pollution--moving from static monitoring to spatio-temporally resolved personal exposure assessment. , 2013, The Science of the total environment.

[35]  Yongmei Lu,et al.  Personal real-time air pollution exposure assessment methods promoted by information technological advances , 2012, Ann. GIS.

[36]  M. Kwan.  The Uncertain Geographic Context Problem , 2012 .

[37]  Jing Huang,et al.  Comparisons of personal exposure to PM2.5 and CO by different commuting modes in Beijing, China. , 2012, The Science of the total environment.

[38]  A. Murie,et al.  The New Affordable and Social Housing Provision System in China: Implications for Comparative Housing Studies , 2011 .

[39]  Luc Int Panis,et al.  Impact of time–activity patterns on personal exposure to black carbon , 2011 .

[40]  Luc Int Panis.  New Directions: Air pollution epidemiology can benefit from activity-based models , 2010 .

[41]  Eric A Whitsel,et al.  Estimating Error in Using Ambient PM2.5 Concentrations as Proxies for Personal Exposures: A Review , 2010, Epidemiology.

[42]  S. D. Vito,et al.  CO, NO2 and NOx urban pollution monitoring with on-field calibrated electronic nose by automatic bayesian regularization , 2009 .

[43]  Antonio Krüger,et al.  Applying indoor and outdoor modeling techniques to estimate individual exposure to PM2.5 from personal GPS profiles and diaries: a pilot study. , 2009, The Science of the total environment.

[44]  Bin Zou,et al.  Air pollution exposure assessment methods utilized in epidemiological studies. , 2009, Journal of environmental monitoring : JEM.

[45]  Mark J. Nieuwenhuijsen,et al.  Fine particulate matter and carbon monoxide exposure concentrations in urban street transport microenvironments , 2007 .

[46]  G. Collins.  The next generation. , 2006, Scientific American.

[47]  L. Naeher,et al.  A review of traffic-related air pollution exposure assessment studies in the developing world. , 2006, Environment international.

[48]  Altaf Arain,et al.  A review and evaluation of intraurban air pollution exposure models , 2005, Journal of Exposure Analysis and Environmental Epidemiology.

[49]  Mark J. Nieuwenhuijsen,et al.  Fine particle (PM2.5) personal exposure levels in transport microenvironments, London, UK. , 2001, The Science of the total environment.

[50]  Christian Monn,et al.  Exposure assessment of air pollutants: a review on spatial heterogeneity and indoor/outdoor/personal exposure to suspended particulate matter, nitrogen dioxide and ozone , 2001 .