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Larkin A, Hystad P. Towards Personal Exposures: How Technology Is Changing Air Pollution and Health Research. Curr Environ Health Rep 2017; 4:463-471. [PMID: 28983874 PMCID: PMC5677549 DOI: 10.1007/s40572-017-0163-y] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
PURPOSE OF REVIEW We present a review of emerging technologies and how these can transform personal air pollution exposure assessment and subsequent health research. RECENT FINDINGS 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.
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Affiliation(s)
- A Larkin
- College of Public Health and Human Sciences, Oregon State University, Milam 20A, Corvallis, OR, 97331, USA
| | - P Hystad
- College of Public Health and Human Sciences, Oregon State University, Milam 20C, Corvallis, OR, 97331, USA.
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Carter E, Norris C, Dionisio KL, Balakrishnan K, Checkley W, Clark ML, Ghosh S, Jack DW, Kinney PL, Marshall JD, Naeher LP, Peel JL, Sambandam S, Schauer JJ, Smith KR, Wylie BJ, Baumgartner J. Assessing Exposure to Household Air Pollution: A Systematic Review and Pooled Analysis of Carbon Monoxide as a Surrogate Measure of Particulate Matter. ENVIRONMENTAL HEALTH PERSPECTIVES 2017; 125:076002. [PMID: 28886596 PMCID: PMC5744652 DOI: 10.1289/ehp767] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Revised: 12/19/2016] [Accepted: 12/20/2016] [Indexed: 05/08/2023]
Abstract
BACKGROUND Household air pollution from solid fuel burning is a leading contributor to disease burden globally. Fine particulate matter (PM2.5) is thought to be responsible for many of these health impacts. A co-pollutant, carbon monoxide (CO) has been widely used as a surrogate measure of PM2.5 in studies of household air pollution. OBJECTIVE The goal was to evaluate the validity of exposure to CO as a surrogate of exposure to PM2.5 in studies of household air pollution and the consistency of the PM2.5-CO relationship across different study settings and conditions. METHODS We conducted a systematic review of studies with exposure and/or cooking area PM2.5 and CO measurements and assembled 2,048 PM2.5 and CO measurements from a subset of studies (18 cooking area studies and 9 personal exposure studies) retained in the systematic review. We conducted pooled multivariate analyses of PM2.5-CO associations, evaluating fuels, urbanicity, season, study, and CO methods as covariates and effect modifiers. RESULTS We retained 61 of 70 studies for review, representing 27 countries. Reported PM2.5-CO correlations (r) were lower for personal exposure (range: 0.22-0.97; median=0.57) than for cooking areas (range: 0.10-0.96; median=0.71). In the pooled analyses of personal exposure and cooking area concentrations, the variation in ln(CO) explained 13% and 48% of the variation in ln(PM2.5), respectively. CONCLUSIONS Our results suggest that exposure to CO is not a consistently valid surrogate measure of exposure to PM2.5. Studies measuring CO exposure as a surrogate measure of PM exposure should conduct local validation studies for different stove/fuel types and seasons. https://doi.org/10.1289/EHP767.
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Affiliation(s)
- Ellison Carter
- Institute on the Environment, University of Minnesota , St. Paul, Minnesota, USA
| | - Christina Norris
- Department of Epidemiology, Biostatistics & Occupational Health, McGill University , Montreal, Quebec, Canada
| | - Kathie L Dionisio
- National Exposure Research Laboratory, U.S. Environmental Protection Agency , Research Triangle Park, North Carolina, USA
| | - Kalpana Balakrishnan
- Department Environmental Health Engineering, Sri Ramachandra University , Porur, Chennai, India
| | - William Checkley
- Division of Pulmonary and Critical Care, School of Medicine, Johns Hopkins University , Baltimore, Maryland, USA
- Program in Global Disease Epidemiology and Control, Department of International Heath, Johns Hopkins Bloomberg School of Public Health , Baltimore, Maryland, USA
| | - Maggie L Clark
- Department of Environmental and Radiological Health Sciences, Colorado State University , Fort Collins, Colorado, USA
| | - Santu Ghosh
- Department Environmental Health Engineering, Sri Ramachandra University , Porur, Chennai, India
| | - Darby W Jack
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University , New York, New York, USA
| | - Patrick L Kinney
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University , New York, New York, USA
| | - Julian D Marshall
- Department of Civil and Environmental Engineering, University of Washington , Seattle, Washington, USA
| | - Luke P Naeher
- Department of Environmental Health Science, College of Public Health, The University of Georgia , Athens, Georgia, USA
| | - Jennifer L Peel
- Department of Environmental and Radiological Health Sciences, Colorado State University , Fort Collins, Colorado, USA
| | - Sankar Sambandam
- Department Environmental Health Engineering, Sri Ramachandra University , Porur, Chennai, India
| | - James J Schauer
- Environmental Chemistry & Technology Program, University of Wisconsin-Madison , Madison, Wisconsin, USA
- Department of Civil & Environmental Engineering, University of Wisconsin-Madison , Madison, Wisconsin, USA
| | - Kirk R Smith
- Division of Environmental Health Sciences, School of Public Health, University of California, Berkeley , Berkeley, California, USA
| | - Blair J Wylie
- Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Massachusetts General Hospital and Harvard Medical School , Boston, Massachusetts, USA
| | - Jill Baumgartner
- Institute on the Environment, University of Minnesota , St. Paul, Minnesota, USA
- Department of Epidemiology, Biostatistics & Occupational Health, McGill University , Montreal, Quebec, Canada
- Institute for Health and Social Policy, McGill University , Montreal Quebec, Canada
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How Sensors Might Help Define the External Exposome. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2017; 14:ijerph14040434. [PMID: 28420222 PMCID: PMC5409635 DOI: 10.3390/ijerph14040434] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2017] [Revised: 03/14/2017] [Accepted: 03/23/2017] [Indexed: 01/23/2023]
Abstract
The advent of the exposome concept, the advancement of mobile technology, sensors, and the “internet of things” bring exciting opportunities to exposure science. Smartphone apps, wireless devices, the downsizing of monitoring technologies, along with lower costs for such equipment makes it possible for various aspects of exposure to be measured more easily and frequently. We discuss possibilities and lay out several criteria for using smart technologies for external exposome studies. Smart technologies are evolving quickly, and while they provide great promise for advancing exposure science, many are still in developmental stages and their use in epidemiology and risk studies must be carefully considered. The most useable technologies for exposure studies at this time relate to gathering exposure-factor data, such as location and activities. Development of some environmental sensors (e.g., for some air pollutants, noise, UV) is moving towards making the use of these more reliable and accessible to research studies. The possibility of accessing such an unprecedented amount of personal data also comes with various limitations and challenges, which are discussed. The advantage of improving the collection of long term exposure factor data is that this can be combined with more “traditional” measurement data to model exposures to numerous environmental factors.
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