Towards Personal Exposures: How Technology Is Changing Air Pollution and Health Research
- 346 Downloads
Purpose of Review
We present a review of emerging technologies and how these can transform personal air pollution exposure assessment and subsequent health research.
Estimating personal air pollution exposures is currently split broadly into methods for modeling exposures for large populations versus measuring exposures for small populations. Air pollution sensors, smartphones, and air pollution models capitalizing on big/new data sources offer tremendous opportunity for unifying these approaches and improving long-term personal exposure prediction at scales needed for population-based research. A multi-disciplinary approach is needed to combine these technologies to not only estimate personal exposures for epidemiological research but also determine drivers of these exposures and new prevention opportunities. While available technologies can revolutionize air pollution exposure research, ethical, privacy, logistical, and data science challenges must be met before widespread implementations occur.
Available technologies and related advances in data science can improve long-term personal air pollution exposure estimates at scales needed for population-based research. This will advance our ability to evaluate the impacts of air pollution on human health and develop effective prevention strategies.
KeywordsAir pollution Sensors Smartphones Big data Exposure assessment Epidemiology
Research reported in this publication was supported by the Office of the Director, National Institutes of Health, under Award Number DP5OD019850. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We would also like to acknowledge the reviewers’ contributions to refining the messages presented in this article.
Compliance With Ethical Standards
Conflict of Interest
The authors declare that they have no conflict of interest.
Human and Animal Rights and Informed Consent
All epidemiological studies cited by the authors were in accordance with the ethics standards of Oregon State University.
Papers of particular interest, published recently, have been highlighted as • Of importance
- 1.Cohen AJ, Brauer M, Burnett R, Anderson HR, Frostad J, Estep K, et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. Lancet. 2017;389:1907–18.CrossRefPubMedPubMedCentralGoogle Scholar
- 5.• Nieuwenhuijsen MJ, Donaire-Gonzalez D, Foraster M, Martinez D, Cisneros A. Using personal sensors to assess the exposome and acute health effects. Int J Environ Res Public Health. 2014;11:7805–19. This study provides a thorough review of how personal sensors can measure multiple exposures and acute health effects.CrossRefPubMedPubMedCentralGoogle Scholar
- 8.• Turner MC, Nieuwenhuijsen M, Anderson K, Balshaw D, Cui Y, Dunton G, et al. Assessing the exposome with external measures: commentary on the state of the science and research recommendations. Annu Rev Public Health. 2017;38:215–39. This commentary highlights how the external exposome can be quantified using modeling and measurement methods.CrossRefPubMedGoogle Scholar
- 9.• Thompson JE. Crowd-sourced air quality studies: a review of the literature & portable sensors. Trends Environ Anal Chem. 2016;11:23–34. This study provides a review of the relatively low-cost air pollution sensors that are currently available for air pollution research as well as citizen science initiatives.CrossRefGoogle Scholar
- 10.Wang A, Brauer M. Review of next generation air monitors for air pollution. 2014 [cited 2017 Apr 30]; Available from: https://open.library.ubc.ca/cIRcle/collections/facultyresearchandpublications/52383/items/1.0132725
- 21.Reis S, Cowie H, Riddell K, Semple S, Steinle S, Apsley A, et al. Urban air quality citizen science. Phase 1: review of methods and projects. 2013 [cited 2017 Apr 30]. Available from: http://www.environment.scotland.gov.uk/media/68215/Urban-air-quality-citizen-science-Phase-1.pdf
- 22.Nikzad N, Verma N, Ziftci C, Bales E, Quick N, Zappi P, et al. CitiSense: improving geospatial environmental assessment of air quality using a wireless personal exposure monitoring system. Proc Conf Wirel Health New York, NY, USA. 2012 [cited 2017 Apr 30]. p. 11:1–11:8. Available from: https://doi.org/10.1145/2448096.2448107
- 25.Purcell K. Half of adult cell phone owners have apps on their phones [Internet]. Pew Res Cent Internet Sci Tech. 2011 [cited 2017 Apr 28]. Available from: http://www.pewinternet.org/2011/11/02/half-of-adult-cell-phone-owners-have-apps-on-their-phones/
- 27.Bank of America. Trends in consumer mobility report [Internet]. 2015. Available from: http://newsroom.bankofamerica.com/files/doc_library/additional/2015_BAC_Trends_in_Consumer_Mobility_Report.pdf
- 38.Zhang C, Yan J, Li C, Rui X, Liu L, Bie R. On estimating air pollution from photos using convolutional neural network. Proc. 2016 ACM Multimed. Conf. [Internet]. New York, NY, USA: ACM; 2016 [cited 2017 Sep 8]. p. 297–301. Available from: https://doi.org/10.1145/2964284.2967230
- 41.• Barrett MA, Humblet O, Hiatt RA, Adler NE. Big data and disease prevention: from quantified self to quantified communities. Big Data. 2013;1:168–75. This commentary highlights the role of big data for quantifying communities and how this approach can facilitate public health.CrossRefPubMedGoogle Scholar
- 51.Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Inf Sci Syst [Internet]. 2014 [cited 2017 Aug 4];2. Available from: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4341817/
- 58.Oscar N, Fox PA, Croucher R, Wernick R, Keune J, Hooker K. Machine learning, sentiment analysis, and tweets: an examination of Alzheimer’s disease stigma on Twitter. J Gerontol B Psychol Sci Soc Sci [Internet]. 2017 [cited 2017 Apr 30]; Available from: https://www.ncbi.nlm.nih.gov/pubmed/28329835.