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      Development of TracMyAir Smartphone Application for Modeling Exposures to Ambient PM 2.5 and Ozone

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          Abstract

          Air pollution epidemiology studies of ambient fine particulate matter (PM 2.5) and ozone (O 3) often use outdoor concentrations as exposure surrogates. Failure to account for the variability of the indoor infiltration of ambient PM 2.5 and O 3, and time indoors, can induce exposure errors. We developed an exposure model called TracMyAir, which is an iPhone application (“app”) that determines seven tiers of individual-level exposure metrics in real-time for ambient PM 2.5 and O 3 using outdoor concentrations, weather, home building characteristics, time-locations, and time-activities. We linked a mechanistic air exchange rate (AER) model, a mass-balance PM 2.5 and O 3 building infiltration model, and an inhaled ventilation model to determine outdoor concentrations (Tier 1), residential AER (Tier 2), infiltration factors (Tier 3), indoor concentrations (Tier 4), personal exposure factors (Tier 5), personal exposures (Tier 6), and inhaled doses (Tier 7). Using the application in central North Carolina, we demonstrated its ability to automatically obtain real-time input data from the nearest air monitors and weather stations, and predict the exposure metrics. A sensitivity analysis showed that the modeled exposure metrics can vary substantially with changes in seasonal indoor-outdoor temperature differences, daily home operating conditions (i.e., opening windows and operating air cleaners), and time spent outdoors. The capability of TracMyAir could help reduce uncertainty of ambient PM 2.5 and O 3 exposure metrics used in epidemiology studies.

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          Exposure measurement error in time-series studies of air pollution: concepts and consequences.

          S Zeger, Owen D Thomas, F Dominici … (2000)
          Misclassification of exposure is a well-recognized inherent limitation of epidemiologic studies of disease and the environment. For many agents of interest, exposures take place over time and in multiple locations; accurately estimating the relevant exposures for an individual participant in epidemiologic studies is often daunting, particularly within the limits set by feasibility, participant burden, and cost. Researchers have taken steps to deal with the consequences of measurement error by limiting the degree of error through a study's design, estimating the degree of error using a nested validation study, and by adjusting for measurement error in statistical analyses. In this paper, we address measurement error in observational studies of air pollution and health. Because measurement error may have substantial implications for interpreting epidemiologic studies on air pollution, particularly the time-series analyses, we developed a systematic conceptual formulation of the problem of measurement error in epidemiologic studies of air pollution and then considered the consequences within this formulation. When possible, we used available relevant data to make simple estimates of measurement error effects. This paper provides an overview of measurement errors in linear regression, distinguishing two extremes of a continuum-Berkson from classical type errors, and the univariate from the multivariate predictor case. We then propose one conceptual framework for the evaluation of measurement errors in the log-linear regression used for time-series studies of particulate air pollution and mortality and identify three main components of error. We present new simple analyses of data on exposures of particulate matter < 10 microm in aerodynamic diameter from the Particle Total Exposure Assessment Methodology Study. Finally, we summarize open questions regarding measurement error and suggest the kind of additional data necessary to address them. Images Figure 1 Figure 2 Figure 3
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            Physical activity of Canadian adults: accelerometer results from the 2007 to 2009 Canadian Health Measures Survey.

            Rachel Colley, Didier Garriguet, Ian Janssen … (2011)
            Rising obesity rates and declining fitness levels have increased interest in understanding what underlies these trends. This article presents the first directly measured data on physical activity and sedentary behaviour on a nationally representative sample of Canadians aged 20 to 79 years. Data are from the 2007 to 2009 Canadian Health Measures Survey (CHMS). Physical activity was measured using accelerometry. Data are presented as time spent in sedentary, light, moderate and vigorous intensity movement as well as steps accumulated per day. An estimated 15% of Canadian adults accumulate 150 minutes of moderate-to-vigorous physical activity (MVPA) per week; 5% accumulate 150 minutes per week as at least 30 minutes of MVPA on 5 or more days a week. Men are more active than women and MVPA declines with increasing age and adiposity. Canadian adults are sedentary for approximately 9.5 hours per day (69% of waking hours). Men accumulate an average of 9,500 steps per day and women, 8,400 steps per day. The 10,000-steps-per-day target is achieved by 35% of adults. Before the CHMS, objective measures of physical activity and sedentary behaviour were not available for a representative sample of Canadians. The findings indicate that 85% of adults are not active enough to meet Canada's new physical activity recommendation.
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              A North American Hourly Assimilation and Model Forecast Cycle: The Rapid Refresh

              Tracy Smith, Steven E. Peckham, Haidao Lin … (2016)
              Article 0 comments Cited 140 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Int J Environ Res Public Health
                Journal ID (iso-abbrev): Int J Environ Res Public Health
                Journal ID (publisher-id): ijerph
                Title: International Journal of Environmental Research and Public Health
                Publisher: MDPI
                ISSN (Print): 1661-7827
                ISSN (Electronic): 1660-4601
                Publication date (Electronic): 18 September 2019
                Publication date (Print): September 2019
                Volume: 16
                Issue: 18
                Electronic Location Identifier: 3468
                Affiliations
                [1 ]Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA; isakov.vlad@ 123456epa.gov
                [2 ]Institute for the Environment, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA; cseppan@ 123456email.unc.edu (C.S.); sarav@ 123456email.unc.edu (S.A.)
                [3 ]Office of Research and Development, ORISE/U.S. Environmental Protection Agency, Chapel Hill, NC 27514, USA; breen.miyuki@ 123456epa.gov
                [4 ]Office of Research and Development, U.S. Environmental Protection Agency, Chapel Hill, NC 27514, USA; samet.james@ 123456epa.gov (J.S.); tong.haiyan@ 123456epa.gov (H.T.)
                Author notes
                [* ]Correspondence: breen.michael@ 123456epa.gov ; Tel.: +1-919-541-9409
                Author information
                https://orcid.org/0000-0002-2078-5015
                https://orcid.org/0000-0002-7039-3925
                Article
                Publisher ID: ijerph-16-03468
                DOI: 10.3390/ijerph16183468
                PMC ID: 6766031
                PubMed ID: 31540404
                SO-VID: 692df33a-0852-4df8-a3d9-f78bcf726123
                Copyright © © 2019 by the authors.
                License:

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                Date received : 01 August 2019
                Date accepted : 15 September 2019
                Categories
                Subject: Article

                ScienceOpen disciplines: Public health
                Keywords: mobile application,exposure model,inhaled dose,particulate matter,ozone
                Data availability:
                ScienceOpen disciplines: Public health
                Keywords: mobile application, exposure model, inhaled dose, particulate matter, ozone

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