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Breen MS, Xu Y, Christopher Frey H, Breen M, Isakov V. Microenvironment Tracker (MicroTrac) model to estimate time-location of individuals for air pollution exposure assessments: model evaluation using smartphone data. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2023; 33:407-415. [PMID: 36526873 DOI: 10.1038/s41370-022-00514-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 12/02/2022] [Accepted: 12/05/2022] [Indexed: 06/03/2023]
Abstract
BACKGROUND A critical aspect of air pollution exposure assessments is determining the time spent in various microenvironments (ME), which can have substantially different pollutant concentrations. We previously developed and evaluated a ME classification model, called Microenvironment Tracker (MicroTrac), to estimate time of day and duration spent in eight MEs (indoors and outdoors at home, work, school; inside vehicles; other locations) based on input data from global positioning system (GPS) loggers. OBJECTIVE In this study, we extended MicroTrac and evaluated the ability of using geolocation data from smartphones to determine the time spent in the MEs. METHOD We performed a panel study, and the MicroTrac estimates based on data from smartphones and GPS loggers were compared to 37 days of diary data across five participants. RESULTS The MEs were correctly classified for 98.1% and 98.3% of the time spent by the participants using smartphones and GPS loggers, respectively. SIGNIFICANCE Our study demonstrates the extended capability of using ubiquitous smartphone data with MicroTrac to help reduce time-location uncertainty in air pollution exposure models for epidemiologic and exposure field studies.
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Affiliation(s)
- Michael S Breen
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA.
| | - Yadong Xu
- Center for Public Health and Environmental Assessment, ORAU/U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
| | - H Christopher Frey
- Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC, USA
| | - Miyuki Breen
- Center for Public Health and Environment Assessment, ORISE/U.S. Environmental Protection Agency, Chapel Hill, NC, 27514, USA
| | - Vlad Isakov
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC, 27711, USA
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Lane KJ, Levy JI, Patton AP, Durant JL, Zamore W, Brugge D. Relationship between traffic-related air pollution and inflammation biomarkers using structural equation modeling. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 870:161874. [PMID: 36716891 PMCID: PMC11044987 DOI: 10.1016/j.scitotenv.2023.161874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 01/06/2023] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Evidence suggests that exposure to traffic-related air pollution (TRAP) and social stressors can increase inflammation. Given that there are many different markers of TRAP exposure, socio-economic status (SES), and inflammation, analytical approaches can leverage multiple markers to better elucidate associations. In this study, we applied structural equation modeling (SEM) to assess the association between a TRAP construct and a SES construct with an inflammation construct. METHODS This analysis was conducted as part of the Community Assessment of Freeway Exposure and Health (CAFEH; N = 408) study. Air pollution was characterized using a spatiotemporal model of particle number concentration (PNC) combined with individual participant time-activity adjustment (TAA). TAA-PNC and proximity to highways were considered for a construct of TRAP exposure. Participant demographics on education and income for an SES construct were assessed via questionnaires. Blood samples were analyzed for high sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), and tumor necrosis factor-α receptor II (TNFRII), which were considered for the construct for inflammation. We conducted SEM and compared our findings with those obtained using generalized linear models (GLM). RESULTS Using GLM, TAA-PNC was associated with multiple inflammation biomarkers. An IQR (10,000 particles/cm3) increase of TAA-PNC was associated with a 14 % increase in hsCRP in the GLM. Using SEM, the association between the TRAP construct and the inflammation construct was twice as large as the associations with any individual inflammation biomarker. SES had an inverse association with inflammation in all models. Using SEM to estimate the indirect effects of SES on inflammation through the TRAP construct strengthened confidence in the association of TRAP with inflammation. CONCLUSION Our TRAP construct resulted in stronger associations with a combined construct for inflammation than with individual biomarkers, reinforcing the value of statistical approaches that combine multiple, related exposures or outcomes. Our findings are consistent with inflammatory risk from TRAP exposure.
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Affiliation(s)
- Kevin J Lane
- Department of Environmental Health, Boston University School of Public Health, Boston, MA, United States of America.
| | - Jonathan I Levy
- Department of Environmental Health, Boston University School of Public Health, Boston, MA, United States of America.
| | | | - John L Durant
- Department of Civil and Environmental Engineering, Tufts University, Medford, MA, United States of America.
| | - Wig Zamore
- Somerville Transportation Equity Partnership, Somerville, MA, United States of America
| | - Doug Brugge
- Department of Public Health Sciences, University of Connecticut School of Medicine, Farmington, CT, United States of America.
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Kouis P, Michanikou A, Galanakis E, Michaelidou E, Dimitriou H, Perez J, Kinni P, Achilleos S, Revvas E, Stamatelatos G, Zacharatos H, Savvides C, Vasiliadou E, Kalivitis N, Chrysanthou A, Tymvios F, Papatheodorou SI, Koutrakis P, Yiallouros PK. Responses of schoolchildren with asthma to recommendations to reduce desert dust exposure: Results from the LIFE-MEDEA intervention project using wearable technology. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 860:160518. [PMID: 36573449 DOI: 10.1016/j.scitotenv.2022.160518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/14/2022] [Accepted: 11/22/2022] [Indexed: 06/17/2023]
Abstract
Current public health recommendations for desert dust storms (DDS) events focus on vulnerable population groups, such as children with asthma, and include advice to stay indoors and limit outdoor physical activity. To date, no scientific evidence exists on the efficacy of these recommendations in reducing DDS exposure. We aimed to objectively assess the behavioral responses of children with asthma to recommendations for reduction of DDS exposure. In two heavily affected by DDS Mediterranean regions (Cyprus & Crete, Greece), schoolchildren with asthma (6-11 years) were recruited from primary schools and were randomized to control (business as usual scenario) and intervention groups. All children were equipped with pedometer and GPS sensors embedded in smartwatches for objective real-time data collection from inside and outside their classroom and household settings. Interventions included the timely communication of personal DDS alerts accompanied by exposure reduction recommendations to both the parents and school-teachers of children in the intervention group. A mixed effect model was used to assess changes in daily levels of time spent, and steps performed outside classrooms and households, between non-DDS and DDS days across the study groups. The change in the time spent outside classrooms and homes, between non-DDS and DDS days, was 37.2 min (pvalue = 0.098) in the control group and -62.4 min (pvalue < 0.001) in the intervention group. The difference in the effects between the two groups was statistically significant (interaction pvalue < 0.001). The change in daily steps performed outside classrooms and homes, was -495.1 steps (pvalue = 0.350) in the control group and -1039.5 (pvalue = 0.003) in the intervention group (interaction pvalue = 0.575). The effects on both the time and steps performed outside were more profound during after-school hours. To summarize, among children with asthma, we demonstrated that timely personal DDS alerts and detailed recommendations lead to significant behavioral changes in contrast to the usual public health recommendations.
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Affiliation(s)
- Panayiotis Kouis
- Respiratory Physiology Laboratory, Medical School, University of Cyprus, Nicosia, Cyprus
| | - Antonis Michanikou
- Respiratory Physiology Laboratory, Medical School, University of Cyprus, Nicosia, Cyprus
| | | | | | - Helen Dimitriou
- Medical School, University of Crete, Heraklion, Crete, Greece
| | - Julietta Perez
- Medical School, University of Crete, Heraklion, Crete, Greece
| | - Paraskevi Kinni
- Respiratory Physiology Laboratory, Medical School, University of Cyprus, Nicosia, Cyprus
| | - Souzana Achilleos
- Department of Primary Care and Population Health, University of Nicosia Medical School, Nicosia, Cyprus; Cyprus International Institute for Environmental & Public Health, Cyprus University of Technology, Limassol, Cyprus
| | | | | | | | - Chrysanthos Savvides
- Air Quality and Strategic Planning Section, Department of Labour Inspection, Ministry of Labour and Social Insurance, Nicosia, Cyprus
| | - Emily Vasiliadou
- Air Quality and Strategic Planning Section, Department of Labour Inspection, Ministry of Labour and Social Insurance, Nicosia, Cyprus
| | - Nikos Kalivitis
- Department of Chemistry, University of Crete, Heraklion, Crete, Greece
| | | | | | - Stefania I Papatheodorou
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Petros Koutrakis
- Department of Environmental Health, Harvard TH Chan School of Public Health, Harvard University, Boston, USA
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Chatzidiakou L, Krause A, Kellaway M, Han Y, Li Y, Martin E, Kelly FJ, Zhu T, Barratt B, Jones RL. Automated classification of time-activity-location patterns for improved estimation of personal exposure to air pollution. Environ Health 2022; 21:125. [PMID: 36482402 PMCID: PMC9733291 DOI: 10.1186/s12940-022-00939-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 11/07/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Air pollution epidemiology has primarily relied on measurements from fixed outdoor air quality monitoring stations to derive population-scale exposure. Characterisation of individual time-activity-location patterns is critical for accurate estimations of personal exposure and dose because pollutant concentrations and inhalation rates vary significantly by location and activity. METHODS We developed and evaluated an automated model to classify major exposure-related microenvironments (home, work, other static, in-transit) and separated them into indoor and outdoor locations, sleeping activity and five modes of transport (walking, cycling, car, bus, metro/train) with multidisciplinary methods from the fields of movement ecology and artificial intelligence. As input parameters, we used GPS coordinates, accelerometry, and noise, collected at 1 min intervals with a validated Personal Air quality Monitor (PAM) carried by 35 volunteers for one week each. The model classifications were then evaluated against manual time-activity logs kept by participants. RESULTS Overall, the model performed reliably in classifying home, work, and other indoor microenvironments (F1-score>0.70) but only moderately well for sleeping and visits to outdoor microenvironments (F1-score=0.57 and 0.3 respectively). Random forest approaches performed very well in classifying modes of transport (F1-score>0.91). We found that the performance of the automated methods significantly surpassed those of manual logs. CONCLUSIONS Automated models for time-activity classification can markedly improve exposure metrics. Such models can be developed in many programming languages, and if well formulated can have general applicability in large-scale health studies, providing a comprehensive picture of environmental health risks during daily life with readily gathered parameters from smartphone technologies.
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Affiliation(s)
- Lia Chatzidiakou
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, CB2 1EW Cambridge, UK
| | - Anika Krause
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, CB2 1EW Cambridge, UK
- Institute for Chemistry, University of Potsdam, Karl-Liebknecht-Straße 24-25, 14476 Potsdam, Germany
| | | | - Yiqun Han
- Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, W12 0BZ London, UK
| | - Yilin Li
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, CB2 1EW Cambridge, UK
| | - Elizabeth Martin
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, CB2 1EW Cambridge, UK
| | - Frank J. Kelly
- Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, W12 0BZ London, UK
| | - Tong Zhu
- BIC-ESAT and SKL-ESPC, College of Environmental Sciences and Engineering, Center for Environment and Health, Peking University, 100871 Beijing, China
| | - Benjamin Barratt
- Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, W12 0BZ London, UK
| | - Roderic L. Jones
- Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, CB2 1EW Cambridge, UK
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Breen MS, Isakov V, Prince S, McGuinness K, Egeghy PP, Stephens B, Arunachalam S, Stout D, Walker R, Alston L, Rooney AA, Taylor KW, Buckley TJ. Integrating Personal Air Sensor and GPS to Determine Microenvironment-Specific Exposures to Volatile Organic Compounds. SENSORS 2021; 21:s21165659. [PMID: 34451101 PMCID: PMC8402344 DOI: 10.3390/s21165659] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 08/11/2021] [Accepted: 08/17/2021] [Indexed: 11/16/2022]
Abstract
Personal exposure to volatile organic compounds (VOCs) from indoor sources including consumer products is an understudied public health concern. To develop and evaluate methods for monitoring personal VOC exposures, we performed a pilot study and examined time-resolved sensor-based measurements of geocoded total VOC (TVOC) exposures across individuals and microenvironments (MEs). We integrated continuous (1 min) data from a personal TVOC sensor and a global positioning system (GPS) logger, with a GPS-based ME classification model, to determine TVOC exposures in four MEs, including indoors at home (Home-In), indoors at other buildings (Other-In), inside vehicles (In-Vehicle), and outdoors (Out), across 45 participant-days for five participants. To help identify places with large emission sources, we identified high-exposure events (HEEs; TVOC > 500 ppb) using geocoded TVOC time-course data overlaid on Google Earth maps. Across the 45 participant-days, the MEs ranked from highest to lowest median TVOC were: Home-In (165 ppb), Other-In (86 ppb), In-Vehicle (52 ppb), and Out (46 ppb). For the two participants living in single-family houses with attached garages, the median exposures for Home-In were substantially higher (209, 416 ppb) than the three participant homes without attached garages: one living in a single-family house (129 ppb), and two living in apartments (38, 60 ppb). The daily average Home-In exposures exceeded the estimated Leadership in Energy and Environmental Design (LEED) building guideline of 108 ppb for 60% of the participant-days. We identified 94 HEEs across all participant-days, and 67% of the corresponding peak levels exceeded 1000 ppb. The MEs ranked from the highest to the lowest number of HEEs were: Home-In (60), Other-In (13), In-Vehicle (12), and Out (9). For Other-In and Out, most HEEs occurred indoors at fast food restaurants and retail stores, and outdoors in parking lots, respectively. For Home-In HEEs, the median TVOC emission and removal rates were 5.4 g h-1 and 1.1 h-1, respectively. Our study demonstrates the ability to determine individual sensor-based time-resolved TVOC exposures in different MEs, in support of identifying potential sources and exposure factors that can inform exposure mitigation strategies.
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Affiliation(s)
- Michael S. Breen
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC 27711, USA;
- Correspondence:
| | - Vlad Isakov
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC 27711, USA; (V.I.); (D.S.); (R.W.); (L.A.)
| | - Steven Prince
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC 27711, USA;
| | - Kennedy McGuinness
- Institute for the Environment, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA; (K.M.); (S.A.)
| | - Peter P. Egeghy
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC 27711, USA; (P.P.E.); (T.J.B.)
| | - Brent Stephens
- Department of Civil, Architectural and Environmental Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA;
| | - Saravanan Arunachalam
- Institute for the Environment, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA; (K.M.); (S.A.)
| | - Dan Stout
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC 27711, USA; (V.I.); (D.S.); (R.W.); (L.A.)
| | - Richard Walker
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC 27711, USA; (V.I.); (D.S.); (R.W.); (L.A.)
| | - Lillian Alston
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC 27711, USA; (V.I.); (D.S.); (R.W.); (L.A.)
| | - Andrew A. Rooney
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institute of Health, Research Triangle Park, Durham, NC 27711, USA; (A.A.R.); (K.W.T.)
| | - Kyla W. Taylor
- Division of the National Toxicology Program, National Institute of Environmental Health Sciences, National Institute of Health, Research Triangle Park, Durham, NC 27711, USA; (A.A.R.); (K.W.T.)
| | - Timothy J. Buckley
- Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC 27711, USA; (P.P.E.); (T.J.B.)
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Kouis P, Michanikou A, Anagnostopoulou P, Galanakis E, Michaelidou E, Dimitriou H, Matthaiou AM, Kinni P, Achilleos S, Zacharatos H, Papatheodorou SI, Koutrakis P, Nikolopoulos GK, Yiallouros PK. Use of wearable sensors to assess compliance of asthmatic children in response to lockdown measures for the COVID-19 epidemic. Sci Rep 2021; 11:5895. [PMID: 33723342 PMCID: PMC7971022 DOI: 10.1038/s41598-021-85358-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 02/18/2021] [Indexed: 02/07/2023] Open
Abstract
Between March and April 2020, Cyprus and Greece health authorities enforced three escalated levels of public health interventions to control the COVID-19 pandemic. We quantified compliance of 108 asthmatic schoolchildren (53 from Cyprus, 55 from Greece, mean age 9.7 years) from both countries to intervention levels, using wearable sensors to continuously track personal location and physical activity. Changes in 'fraction time spent at home' and 'total steps/day' were assessed with a mixed-effects model adjusting for confounders. We observed significant mean increases in 'fraction time spent at home' in Cyprus and Greece, during each intervention level by 41.4% and 14.3% (level 1), 48.7% and 23.1% (level 2) and 45.2% and 32.0% (level 3), respectively. Physical activity in Cyprus and Greece demonstrated significant mean decreases by - 2,531 and - 1,191 (level 1), - 3,638 and - 2,337 (level 2) and - 3,644 and - 1,961 (level 3) total steps/day, respectively. Significant independent effects of weekends and age were found on 'fraction time spent at home'. Similarly, weekends, age, humidity and gender had an independent effect on physical activity. We suggest that wearable technology provides objective, continuous, real-time location and activity data making possible to inform in a timely manner public health officials on compliance to various tiers of public health interventions during a pandemic.
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Affiliation(s)
- Panayiotis Kouis
- Respiratory Physiology Laboratory, Medical School, Shacolas Educational Center of Clinical Medicine, University of Cyprus, Palaios Dromos Lefkosias-Lemesou 215/6, 2029, Aglantzia, Nicosia, Cyprus
| | - Antonis Michanikou
- Respiratory Physiology Laboratory, Medical School, Shacolas Educational Center of Clinical Medicine, University of Cyprus, Palaios Dromos Lefkosias-Lemesou 215/6, 2029, Aglantzia, Nicosia, Cyprus
| | - Pinelopi Anagnostopoulou
- Respiratory Physiology Laboratory, Medical School, Shacolas Educational Center of Clinical Medicine, University of Cyprus, Palaios Dromos Lefkosias-Lemesou 215/6, 2029, Aglantzia, Nicosia, Cyprus
- Institute of Anatomy, University of Bern, Bern, Switzerland
| | | | | | - Helen Dimitriou
- Medical School, University of Crete, Heraklion, Crete, Greece
| | - Andreas M Matthaiou
- Respiratory Physiology Laboratory, Medical School, Shacolas Educational Center of Clinical Medicine, University of Cyprus, Palaios Dromos Lefkosias-Lemesou 215/6, 2029, Aglantzia, Nicosia, Cyprus
| | - Paraskevi Kinni
- Respiratory Physiology Laboratory, Medical School, Shacolas Educational Center of Clinical Medicine, University of Cyprus, Palaios Dromos Lefkosias-Lemesou 215/6, 2029, Aglantzia, Nicosia, Cyprus
| | - Souzana Achilleos
- Cyprus International Institute for Environmental and Public Health, Cyprus University of Technology, Limassol, Cyprus
| | | | - Stefania I Papatheodorou
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Petros Koutrakis
- Department of Environmental Health, Harvard TH Chan School of Public Health, Harvard University, Boston, USA
| | | | - Panayiotis K Yiallouros
- Respiratory Physiology Laboratory, Medical School, Shacolas Educational Center of Clinical Medicine, University of Cyprus, Palaios Dromos Lefkosias-Lemesou 215/6, 2029, Aglantzia, Nicosia, Cyprus.
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Yoo EH, Pu Q, Eum Y, Jiang X. The Impact of Individual Mobility on Long-Term Exposure to Ambient PM 2.5: Assessing Effect Modification by Travel Patterns and Spatial Variability of PM 2.5. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:2194. [PMID: 33672290 PMCID: PMC7926665 DOI: 10.3390/ijerph18042194] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 02/03/2021] [Accepted: 02/12/2021] [Indexed: 11/16/2022]
Abstract
The impact of individuals' mobility on the degree of error in estimates of exposure to ambient PM2.5 concentrations is increasingly reported in the literature. However, the degree to which accounting for mobility reduces error likely varies as a function of two related factors-individuals' routine travel patterns and the local variations of air pollution fields. We investigated whether individuals' routine travel patterns moderate the impact of mobility on individual long-term exposure assessment. Here, we have used real-world time-activity data collected from 2013 participants in Erie/Niagara counties, New York, USA, matched with daily PM2.5 predictions obtained from two spatial exposure models. We further examined the role of the spatiotemporal representation of ambient PM2.5 as a second moderator in the relationship between an individual's mobility and the exposure measurement error using a random effect model. We found that the effect of mobility on the long-term exposure estimates was significant, but that this effect was modified by individuals' routine travel patterns. Further, this effect modification was pronounced when the local variations of ambient PM2.5 concentrations were captured from multiple sources of air pollution data ('a multi-sourced exposure model'). In contrast, the mobility effect and its modification were not detected when ambient PM2.5 concentration was estimated solely from sparse monitoring data ('a single-sourced exposure model'). This study showed that there was a significant association between individuals' mobility and the long-term exposure measurement error. However, the effect could be modified by individuals' routine travel patterns and the error-prone representation of spatiotemporal variability of PM2.5.
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Affiliation(s)
- Eun-hye Yoo
- Department of Geography, State University of New York at Buffalo, Buffalo, NY 14260, USA; (Q.P.); (Y.E.)
| | - Qiang Pu
- Department of Geography, State University of New York at Buffalo, Buffalo, NY 14260, USA; (Q.P.); (Y.E.)
| | - Youngseob Eum
- Department of Geography, State University of New York at Buffalo, Buffalo, NY 14260, USA; (Q.P.); (Y.E.)
| | - Xiangyu Jiang
- Georgia Environmental Protection Division, Atlanta, GA 30354, USA;
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8
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Kouis P, Papatheodorou SI, Kakkoura MG, Middleton N, Galanakis E, Michaelidi E, Achilleos S, Mihalopoulos N, Neophytou M, Stamatelatos G, Kaniklides C, Revvas E, Tymvios F, Savvides C, Koutrakis P, Yiallouros PK. The MEDEA childhood asthma study design for mitigation of desert dust health effects: implementation of novel methods for assessment of air pollution exposure and lessons learned. BMC Pediatr 2021; 21:13. [PMID: 33407248 PMCID: PMC7786906 DOI: 10.1186/s12887-020-02472-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 12/15/2020] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Desert dust events in Mediterranean countries, originating mostly from the Sahara and Arabian deserts, have been linked to climate change and are associated with significant increase in mortality and hospital admissions from respiratory causes. The MEDEA clinical intervention study in children with asthma is funded by EU LIFE+ program to evaluate the efficacy of recommendations aiming to reduce exposure to desert dust and related health effects. METHODS This paper describes the design, methods, and challenges of the MEDEA childhood asthma study, which is performed in two highly exposed regions of the Eastern Mediterranean: Cyprus and Greece-Crete. Eligible children are recruited using screening surveys performed at primary schools and are randomized to three parallel intervention groups: a) no intervention for desert dust events, b) interventions for outdoor exposure reduction, and c) interventions for both outdoor and indoor exposure reduction. At baseline visits, participants are enrolled on MEDena® Health-Hub, which communicates, alerts and provides exposure reduction recommendations in anticipation of desert dust events. MEDEA employs novel environmental epidemiology and telemedicine methods including wearable GPS, actigraphy, health parameters sensors as well as indoor and outdoor air pollution samplers to assess study participants' compliance to recommendations, air pollutant exposures in homes and schools, and disease related clinical outcomes. DISCUSSION The MEDEA study evaluates, for the first time, interventions aiming to reduce desert dust exposure and implement novel telemedicine methods in assessing clinical outcomes and personal compliance to recommendations. In Cyprus and Crete, during the first study period (February-May 2019), a total of 91 children participated in the trial while for the second study period (February-May 2020), another 120 children completed data collection. Recruitment for the third study period (February-May 2021) is underway. In this paper, we also present the unique challenges faced during the implementation of novel methodologies to reduce air pollution exposure in children. Engagement of families of asthmatic children, schools and local communities, is critical. Successful study completion will provide the knowledge for informed decision-making both at national and international level for mitigating the health effects of desert dust events in South-Eastern Europe. TRIAL REGISTRATION ClinicalTrials.gov: NCT03503812 , April 20, 2018.
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Affiliation(s)
- Panayiotis Kouis
- Respiratory Physiology Laboratory, Medical School, University of Cyprus, Nicosia, Cyprus. .,Shiakolas Educational Center of Clinical Medicine, Palaios Dromos Lefkosias-Lemesou 215/6, 2029, Aglantzia, Nicosia, Cyprus.
| | - Stefania I Papatheodorou
- Cyprus International Institute for Environmental & Public Health, Cyprus University of Technology, Limassol, Cyprus.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA
| | - Maria G Kakkoura
- Respiratory Physiology Laboratory, Medical School, University of Cyprus, Nicosia, Cyprus.,Clinical Trial Service Unit and Epidemiological Studies Unit CTSU, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Nicos Middleton
- Department of Nursing, Cyprus University of Technology, Limassol, Cyprus
| | | | | | - Souzana Achilleos
- Cyprus International Institute for Environmental & Public Health, Cyprus University of Technology, Limassol, Cyprus
| | | | - Marina Neophytou
- Department of Civil & Environmental Engineering, University of Cyprus, Nicosia, Cyprus
| | | | | | - Efstathios Revvas
- Department of Meteorology, Ministry of Agriculture, Rural Development and Environment, Nicosia, Cyprus
| | - Filippos Tymvios
- Department of Meteorology, Ministry of Agriculture, Rural Development and Environment, Nicosia, Cyprus
| | - Chrysanthos Savvides
- Department of Labor Inspection, Ministry of Labor, Welfare and Social Insurance, Nicosia, Cyprus
| | - Petros Koutrakis
- Department of Environmental Health, Harvard TH Chan School of Public Health, Boston, USA
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9
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Garber MD, McCullough LE, Mooney SJ, Kramer MR, Watkins KE, Lobelo RF, Flanders WD. At-risk-measure Sampling in Case-Control Studies with Aggregated Data. Epidemiology 2021; 32:101-110. [PMID: 33093327 PMCID: PMC7707160 DOI: 10.1097/ede.0000000000001268] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 09/23/2020] [Indexed: 11/26/2022]
Abstract
Transient exposures are difficult to measure in epidemiologic studies, especially when both the status of being at risk for an outcome and the exposure change over time and space, as when measuring built-environment risk on transportation injury. Contemporary "big data" generated by mobile sensors can improve measurement of transient exposures. Exposure information generated by these devices typically only samples the experience of the target cohort, so a case-control framework may be useful. However, for anonymity, the data may not be available by individual, precluding a case-crossover approach. We present a method called at-risk-measure sampling. Its goal is to estimate the denominator of an incidence rate ratio (exposed to unexposed measure of the at-risk experience) given an aggregated summary of the at-risk measure from a cohort. Rather than sampling individuals or locations, the method samples the measure of the at-risk experience. Specifically, the method as presented samples person-distance and person-events summarized by location. It is illustrated with data from a mobile app used to record bicycling. The method extends an established case-control sampling principle: sample the at-risk experience of a cohort study such that the sampled exposure distribution approximates that of the cohort. It is distinct from density sampling in that the sample remains in the form of the at-risk measure, which may be continuous, such as person-time or person-distance. This aspect may be both logistically and statistically efficient if such a sample is already available, for example from big-data sources like aggregated mobile-sensor data.
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Affiliation(s)
- Michael D. Garber
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA
| | - Lauren E. McCullough
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA
| | - Stephen J. Mooney
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA
- Harborview Injury Prevention & Research Center, University of Washington, Seattle, WA
| | - Michael R. Kramer
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA
| | - Kari E. Watkins
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA
| | - R.L. Felipe Lobelo
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA
| | - W. Dana Flanders
- Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA
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10
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Quinn C, Anderson GB, Magzamen S, Henry CS, Volckens J. Dynamic classification of personal microenvironments using a suite of wearable, low-cost sensors. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2020; 30:962-970. [PMID: 31937850 PMCID: PMC7358126 DOI: 10.1038/s41370-019-0198-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2019] [Revised: 09/04/2019] [Accepted: 10/29/2019] [Indexed: 05/13/2023]
Abstract
Human exposure to air pollution is associated with increased risk of morbidity and mortality. However, personal air pollution exposures can vary substantially depending on an individual's daily activity patterns and air quality within their residence and workplace. This work developed and validated an adaptive buffer size (ABS) algorithm capable of dynamically classifying an individual's time spent in predefined microenvironments using data from global positioning systems (GPS), motion sensors, temperature sensors, and light sensors. Twenty-two participants in Fort Collins, CO were recruited to carry a personal air sampler for a 48-h period. The personal sampler was retrofitted with a GPS and a pushbutton to complement the existing sensor measurements (temperature, motion, light). The pushbutton was used in conjunction with a traditional time-activity diary to note when the participant was located at "home", "work", or within an "other" microenvironment. The ABS algorithm predicted the amount of time spent in each microenvironment with a median accuracy of 99.1%, 98.9%, and 97.5% for the "home", "work", and "other" microenvironments. The ability to classify microenvironments dynamically in real time can enable the development of new sampling and measurement technologies that classify personal exposure by microenvironment.
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Affiliation(s)
- Casey Quinn
- Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO, 80523, USA
| | - G Brooke Anderson
- Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO, 80523, USA
| | - Sheryl Magzamen
- Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO, 80523, USA
| | - Charles S Henry
- Department of Chemistry, Colorado State University, Fort Collins, CO, 80523, USA
| | - John Volckens
- Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO, 80523, USA.
- Department of Mechanical Engineering, Colorado State University, Fort Collins, CO, 80523, USA.
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11
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Barkjohn KK, Norris C, Cui X, Fang L, He L, Schauer JJ, Zhang Y, Black M, Zhang J, Bergin MH. Children's microenvironmental exposure to PM 2.5 and ozone and the impact of indoor air filtration. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2020; 30:971-980. [PMID: 32963288 DOI: 10.1038/s41370-020-00266-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2020] [Revised: 09/04/2020] [Accepted: 09/11/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND In highly polluted urban areas, personal exposure to PM2.5 and O3 occur daily in various microenvironments. Identifying which microenvironments contribute most to exposure can pinpoint effective exposure reduction strategies and mitigate adverse health impacts. METHODS This work uses real-time sensors to assess the exposures of children with asthma (N = 39) in Shanghai, quantifying microenvironmental exposure to PM2.5 and O3. An air cleaner was deployed in participants' bedrooms where we hypothesized exposure could be most efficiently reduced. Monitoring occurred for two 48-h periods: one with bedroom filtration (portable air cleaner with HEPA and activated carbon filters) and the other without. RESULTS Children spent 91% of their time indoors with the majority spent in their bedroom (47%). Without filtration, the bedroom and classroom environments were the largest contributors to PM2.5 exposure. With filtration, bedroom PM2.5 exposure was reduced by 75% (45% of total exposure). Although filtration status did not impact O3, the largest contribution of O3 exposure also came from the bedroom. CONCLUSIONS Actions taken to reduce bedroom PM2.5 and O3 concentrations can most efficiently reduce total exposure. As real-time pollutant monitors become more accessible, similar analyses can be used to evaluate new interventions and optimize exposure reductions for a variety of populations.
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Affiliation(s)
- Karoline K Barkjohn
- Duke University, Civil and Environmental Engineering, 121 Hudson Hall, Box 90287, Durham, NC, 27708, USA.
| | - Christina Norris
- Duke University, Civil and Environmental Engineering, 121 Hudson Hall, Box 90287, Durham, NC, 27708, USA
| | - Xiaoxing Cui
- Duke University, Nicholas School of the Environment, 9 Circuit Dr, Durham, NC, 27710, USA
| | - Lin Fang
- Tsinghua University, School of Architecture, Beijing, 100084, China
| | - Linchen He
- Duke University, Nicholas School of the Environment, 9 Circuit Dr, Durham, NC, 27710, USA
| | - James J Schauer
- University of Wisconsin at Madison, Civil and Environmental Engineering, 1415 Engineering Dr, Madison, WI, 53706, USA
| | - Yinping Zhang
- Tsinghua University, School of Architecture, Beijing, 100084, China
| | - Marilyn Black
- Underwriters Laboratories Inc., 2211 Newmarket Parkway, Marietta, GA, 30067, USA
| | - Junfeng Zhang
- Duke University, Nicholas School of the Environment, 9 Circuit Dr, Durham, NC, 27710, USA
| | - Michael H Bergin
- Duke University, Civil and Environmental Engineering, 121 Hudson Hall, Box 90287, Durham, NC, 27708, USA
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12
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Klous G, Kretzschmar MEE, Coutinho RA, Heederik DJJ, Huss A. Prediction of human active mobility in rural areas: development and validity tests of three different approaches. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2020; 30:1023-1031. [PMID: 31772295 DOI: 10.1038/s41370-019-0194-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2019] [Revised: 09/27/2019] [Accepted: 10/15/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND/AIM Active mobility may play a relevant role in the assessment of environmental exposures (e.g. traffic-related air pollution, livestock emissions), but data about actual mobility patterns are work intensive to collect, especially in large study populations, therefore estimation methods for active mobility may be relevant for exposure assessment in different types of studies. We previously collected mobility patterns in a group of 941 participants in a rural setting in the Netherlands, using week-long GPS tracking. We had information regarding personal characteristics, self-reported data regarding weekly mobility patterns and spatial characteristics. The goal of this study was to develop versatile estimates of active mobility, test their accuracy using GPS measurements and explore the implications for exposure assessment studies. METHODS We estimated hours/week spent on active mobility based on personal characteristics (e.g. age, sex, pre-existing conditions), self-reported data (e.g. hours spent commuting per bike) or spatial predictors such as home and work address. Estimated hours/week spent on active mobility were compared with GPS measured hours/week, using linear regression and kappa statistics. RESULTS Estimated and measured hours/week spent on active mobility had low correspondence, even the best predicting estimation method based on self-reported data, resulted in a R2 of 0.09 and Cohen's kappa of 0.07. A visual check indicated that, although predicted routes to work appeared to match GPS measured tracks, only a small proportion of active mobility was captured in this way, thus resulting in a low validity of overall predicted active mobility. CONCLUSIONS We were unable to develop a method that could accurately estimate active mobility, the best performing method was based on detailed self-reported information but still resulted in low correspondence. For future studies aiming to evaluate the contribution of home-work traffic to exposure, applying spatial predictors may be appropriate. Measurements still represent the best possible tool to evaluate mobility patterns.
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Affiliation(s)
- Gijs Klous
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands.
- Institute for Risk Assessment Sciences, Division Environmental Epidemiology and Veterinary Public Health, Utrecht University, Utrecht, The Netherlands.
| | - Mirjam E E Kretzschmar
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
- National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands
| | - Roel A Coutinho
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Dick J J Heederik
- Institute for Risk Assessment Sciences, Division Environmental Epidemiology and Veterinary Public Health, Utrecht University, Utrecht, The Netherlands
| | - Anke Huss
- Institute for Risk Assessment Sciences, Division Environmental Epidemiology and Veterinary Public Health, Utrecht University, Utrecht, The Netherlands
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13
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Goddard FB, Ban R, Barr DB, Brown J, Cannon J, Colford JM, Eisenberg JNS, Ercumen A, Petach H, Freeman MC, Levy K, Luby SP, Moe C, Pickering AJ, Sarnat JA, Stewart J, Thomas E, Taniuchi M, Clasen T. Measuring Environmental Exposure to Enteric Pathogens in Low-Income Settings: Review and Recommendations of an Interdisciplinary Working Group. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:11673-11691. [PMID: 32813503 PMCID: PMC7547864 DOI: 10.1021/acs.est.0c02421] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 08/18/2020] [Accepted: 08/19/2020] [Indexed: 05/06/2023]
Abstract
Infections with enteric pathogens impose a heavy disease burden, especially among young children in low-income countries. Recent findings from randomized controlled trials of water, sanitation, and hygiene interventions have raised questions about current methods for assessing environmental exposure to enteric pathogens. Approaches for estimating sources and doses of exposure suffer from a number of shortcomings, including reliance on imperfect indicators of fecal contamination instead of actual pathogens and estimating exposure indirectly from imprecise measurements of pathogens in the environment and human interaction therewith. These shortcomings limit the potential for effective surveillance of exposures, identification of important sources and modes of transmission, and evaluation of the effectiveness of interventions. In this review, we summarize current and emerging approaches used to characterize enteric pathogen hazards in different environmental media as well as human interaction with those media (external measures of exposure), and review methods that measure human infection with enteric pathogens as a proxy for past exposure (internal measures of exposure). We draw from lessons learned in other areas of environmental health to highlight how external and internal measures of exposure can be used to more comprehensively assess exposure. We conclude by recommending strategies for advancing enteric pathogen exposure assessments.
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Affiliation(s)
- Frederick
G. B. Goddard
- Gangarosa
Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
| | - Radu Ban
- Bill and
Melinda Gates Foundation, Seattle, Washington 98109, United States
| | - Dana Boyd Barr
- Gangarosa
Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
| | - Joe Brown
- School of
Civil and Environmental Engineering, Georgia
Institute of Technology, Atlanta, Georgia 30332, United States
| | - Jennifer Cannon
- Centers
for Disease Control and Prevention Foundation, Atlanta, Georgia 30308, United States
| | - John M. Colford
- Division
of Epidemiology and Biostatistics, School of Public Health, University of California−Berkeley, Berkeley, California 94720, United States
| | - Joseph N. S. Eisenberg
- Department
of Epidemiology, University of Michigan
School of Public Health, Ann Arbor, Michigan 48109, United States
| | - Ayse Ercumen
- Department
of Forestry and Environmental Resources, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Helen Petach
- U.S. Agency
for International Development, Washington, DC 20004, United States
| | - Matthew C. Freeman
- Gangarosa
Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
| | - Karen Levy
- Department
of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington 98105, United States
| | - Stephen P. Luby
- Division
of Infectious Diseases and Geographic Medicine, Stanford University, California 94305, United States
| | - Christine Moe
- Center
for
Global Safe Water, Sanitation and Hygiene, Rollins School of Public
Health, Emory University, Atlanta, Georgia 30322, United States
| | - Amy J. Pickering
- Department
of Civil and Environmental Engineering, School of Engineering, Tufts University, Medford, Massachusetts 02155, United States
| | - Jeremy A. Sarnat
- Gangarosa
Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
| | - Jill Stewart
- Department
of Environmental Sciences and Engineering, Gillings School of Global
Public Health, University of North Carolina, Chapel Hill, North Carolina 27599, United States
| | - Evan Thomas
- Mortenson
Center in Global Engineering, University
of Colorado Boulder, Boulder, Colorado 80303, United States
| | - Mami Taniuchi
- Division
of Infectious Diseases and International Health, Department of Medicine, University of Virginia, Charlottesville, Virginia 22903, United States
| | - Thomas Clasen
- Gangarosa
Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
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14
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Environmental Health Surveillance System for a Population Using Advanced Exposure Assessment. TOXICS 2020; 8:toxics8030074. [PMID: 32962012 PMCID: PMC7560317 DOI: 10.3390/toxics8030074] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 09/12/2020] [Accepted: 09/17/2020] [Indexed: 01/14/2023]
Abstract
Human exposure to air pollution is a major public health concern. Environmental policymakers have been implementing various strategies to reduce exposure, including the 10th-day-no-driving system. To assess exposure of an entire population of a community in a highly polluted area, pollutant concentrations in microenvironments and population time–activity patterns are required. To date, population exposure to air pollutants has been assessed using air monitoring data from fixed atmospheric monitoring stations, atmospheric dispersion modeling, or spatial interpolation techniques for pollutant concentrations. This is coupled with census data, administrative registers, and data on the patterns of the time-based activities at the individual scale. Recent technologies such as sensors, the Internet of Things (IoT), communications technology, and artificial intelligence enable the accurate evaluation of air pollution exposure for a population in an environmental health context. In this study, the latest trends in published papers on the assessment of population exposure to air pollution were reviewed. Subsequently, this study proposes a methodology that will enable policymakers to develop an environmental health surveillance system that evaluates the distribution of air pollution exposure for a population within a target area and establish countermeasures based on advanced exposure assessment.
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15
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Park YM. Assessing personal exposure to traffic-related air pollution using individual travel-activity diary data and an on-road source air dispersion model. Health Place 2020; 63:102351. [DOI: 10.1016/j.healthplace.2020.102351] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2019] [Revised: 04/26/2020] [Accepted: 05/01/2020] [Indexed: 12/21/2022]
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16
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Breen M, Chang SY, Breen M, Xu Y, Isakov V, Arunachalam S, Carraway MS, Devlin R. Fine-Scale Modeling of Individual Exposures to Ambient PM 2.5, EC, NO x, CO for the Coronary Artery Disease and Environmental Exposure (CADEE) Study. ATMOSPHERE 2020; 11:1-65. [PMID: 32461808 PMCID: PMC7252567 DOI: 10.3390/atmos11010065] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Air pollution epidemiological studies often use outdoor concentrations from central-site monitors as exposure surrogates, which can induce measurement error. The goal of this study was to improve exposure assessments of ambient fine particulate matter (PM2.5), elemental carbon (EC), nitrogen oxides (NOx), and carbon monoxide (CO) for a repeated measurements study with 15 individuals with coronary artery disease in central North Carolina called the Coronary Artery Disease and Environmental Exposure (CADEE) Study. We developed a fine-scale exposure modeling approach to determine five tiers of individual-level exposure metrics for PM2.5, EC, NOx, CO using outdoor concentrations, on-road vehicle emissions, weather, home building characteristics, time-locations, and time-activities. We linked an urban-scale air quality model, residential air exchange rate model, building infiltration model, global positioning system (GPS)-based microenvironment model, and accelerometer-based inhaled ventilation model to determine residential outdoor concentrations (Cout_home, Tier 1), residential indoor concentrations (Cin_home, Tier 2), personal outdoor concentrations (Cout_personal, Tier 3), exposures (E, Tier 4), and inhaled doses (D, Tier 5). We applied the fine-scale exposure model to determine daily 24-h average PM2.5, EC, NOx, CO exposure metrics (Tiers 1-5) for 720 participant-days across the 25 months of CADEE. Daily modeled metrics showed considerable temporal and home-to-home variability of Cout_home and Cin_home (Tiers 1-2) and person-to-person variability of Cout_personal, E, and D (Tiers 3-5). Our study demonstrates the ability to apply an urban-scale air quality model with an individual-level exposure model to determine multiple tiers of exposure metrics for an epidemiological study, in support of improving health risk assessments.
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Affiliation(s)
- Michael Breen
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Shih Ying Chang
- Institute for the Environment, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
| | - Miyuki Breen
- Center for Public Health and Environmental Assessment, ORISE/U.S. Environmental Protection Agency, Chapel Hill, NC 27514, USA
| | - Yadong Xu
- ORAU/U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Vlad Isakov
- Center for Measurements and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
| | - Sarav Arunachalam
- Institute for the Environment, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
| | - Martha Sue Carraway
- Department of Medicine, Pulmonary and Critical Care Medicine, Durham VA Medical Center, Durham, NC 27705 USA
| | - Robert Devlin
- Center for Public Health and Environmental Assessment, U.S. Environmental Protection Agency, Chapel Hill, NC 27514, USA
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17
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Development of TracMyAir Smartphone Application for Modeling Exposures to Ambient PM 2.5 and Ozone. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16183468. [PMID: 31540404 PMCID: PMC6766031 DOI: 10.3390/ijerph16183468] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 09/12/2019] [Accepted: 09/15/2019] [Indexed: 01/20/2023]
Abstract
Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) and ozone (O3) often use outdoor concentrations as exposure surrogates. Failure to account for the variability of the indoor infiltration of ambient PM2.5 and O3, 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 PM2.5 and O3 using outdoor concentrations, weather, home building characteristics, time-locations, and time-activities. We linked a mechanistic air exchange rate (AER) model, a mass-balance PM2.5 and O3 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 PM2.5 and O3 exposure metrics used in epidemiology studies.
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18
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Yu X, Stuart AL, Liu Y, Ivey CE, Russell AG, Kan H, Henneman LRF, Sarnat SE, Hasan S, Sadmani A, Yang X, Yu H. On the accuracy and potential of Google Maps location history data to characterize individual mobility for air pollution health studies. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2019; 252:924-930. [PMID: 31226517 DOI: 10.1016/j.envpol.2019.05.081] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Revised: 05/15/2019] [Accepted: 05/15/2019] [Indexed: 05/18/2023]
Abstract
Appropriately characterizing spatiotemporal individual mobility is important in many research areas, including epidemiological studies focusing on air pollution. However, in many retrospective air pollution health studies, exposure to air pollution is typically estimated at the subjects' residential addresses. Individual mobility is often neglected due to lack of data, and exposure misclassification errors are expected. In this study, we demonstrate the potential of using location history data collected from smartphones by the Google Maps application for characterizing historical individual mobility and exposure. Here, one subject carried a smartphone installed with Google Maps, and a reference GPS data logger which was configured to record location every 10 s, for a period of one week. The retrieved Google Maps Location History (GMLH) data were then compared with the GPS data to evaluate their effectiveness and accuracy of the GMLH data to capture individual mobility. We also conducted an online survey (n = 284) to assess the availability of GMLH data among smartphone users in the US. We found the GMLH data reasonably captured the spatial movement of the subject during the one-week time period at up to 200 m resolution. We were able to accurately estimate the time the subject spent in different microenvironments, as well as the time the subject spent driving during the week. The estimated time-weighted daily exposures to ambient particulate matter using GMLH and the GPS data logger were also similar (error less than 1.2%). Survey results showed that GMLH data may be available for 61% of the survey sample. Considering the popularity of smartphones and the Google Maps application, detailed historical location data are expected to be available for large portion of the population, and results from this study highlight the potential of these location history data to improve exposure estimation for retrospective epidemiological studies.
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Affiliation(s)
- Xiaonan Yu
- Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA
| | - Amy L Stuart
- College of Public Health, University of South Florida, Tampa, FL, USA; Department of Civil & Environmental Engineering, University of South Florida, Tampa, FL, USA
| | - Yang Liu
- Department of Environmental Health, Emory University, Atlanta, GA, USA
| | - Cesunica E Ivey
- Department of Chemical and Environmental Engineering, University of California Riverside, Riverside, CA, USA
| | - Armistead G Russell
- School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Haidong Kan
- School of Public Health, Fudan University, Shanghai, China
| | - Lucas R F Henneman
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
| | | | - Samiul Hasan
- Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA
| | - Anwar Sadmani
- Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA
| | - Xuchao Yang
- Institute of Island & Coastal Ecosystem, Zhejiang University, Hangzhou, Zhejiang, China
| | - Haofei Yu
- Department of Civil, Environmental, and Construction Engineering, University of Central Florida, Orlando, FL, USA.
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19
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A Time-Based Objective Measure of Exposure to the Food Environment. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2019; 16:ijerph16071180. [PMID: 30986919 PMCID: PMC6480343 DOI: 10.3390/ijerph16071180] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Revised: 03/13/2019] [Accepted: 03/29/2019] [Indexed: 12/03/2022]
Abstract
Exposure to food environments has mainly been limited to counting food outlets near participants’ homes. This study considers food environment exposures in time and space using global positioning systems (GPS) records and fast food restaurants (FFRs) as the environment of interest. Data came from 412 participants (median participant age of 45) in the Seattle Obesity Study II who completed a survey, wore GPS receivers, and filled out travel logs for seven days. FFR locations were obtained from Public Health Seattle King County and geocoded. Exposure was conceptualized as contact between stressors (FFRs) and receptors (participants’ mobility records from GPS data) using four proximities: 21 m, 100 m, 500 m, and ½ mile. Measures included count of proximal FFRs, time duration in proximity to ≥1 FFR, and time duration in proximity to FFRs weighted by FFR counts. Self-reported exposures (FFR visits) were excluded from these measures. Logistic regressions tested associations between one or more reported FFR visits and the three exposure measures at the four proximities. Time spent in proximity to an FFR was associated with significantly higher odds of FFR visits at all proximities. Weighted duration also showed positive associations with FFR visits at 21-m and 100-m proximities. FFR counts were not associated with FFR visits. Duration of exposure helps measure the relationship between the food environment, mobility patterns, and health behaviors. The stronger associations between exposure and outcome found at closer proximities (<100 m) need further research.
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20
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Effects of Individual and Environmental Factors on GPS-Based Time Allocation in Urban Microenvironments Using GIS. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8102007] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Time-activity patterns are an essential part of personal exposure assessment to various environmental factors. People move through different environments during the day and they have different daily activity patterns which are significantly influenced by individual characteristics and the residential environment. In this study, time spent in different microenvironments (MEs) were assessed for 125 participants for 7 consecutive days to evaluate the impact of individual characteristics on time-activity patterns in Kaunas, Lithuania. The data were collected with personal questionnaires and diaries. The global positioning system (GPS) sensor integrated into a smartphone was used to track daily movements and to assess time-activity patterns. The study results showed that behavioral and residential greenness have a statistically significant impact on time spent indoors. These results underline the high influence of the individual characteristics and environmental factors on time spent indoors, which is an important determinant for exposure assessment and health impact assessment studies.
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21
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Quinn C, Miller-Lionberg DD, Klunder KJ, Kwon J, Noth EM, Mehaffy J, Leith D, Magzamen S, Hammond SK, Henry CS, Volckens J. Personal Exposure to PM 2.5 Black Carbon and Aerosol Oxidative Potential using an Automated Microenvironmental Aerosol Sampler (AMAS). ENVIRONMENTAL SCIENCE & TECHNOLOGY 2018; 52:11267-11275. [PMID: 30200753 PMCID: PMC6203932 DOI: 10.1021/acs.est.8b02992] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Traditional methods for measuring personal exposure to fine particulate matter (PM2.5) are cumbersome and lack spatiotemporal resolution; methods that are time-resolved are limited to a single species/component of PM. To address these limitations, we developed an automated microenvironmental aerosol sampler (AMAS), capable of resolving personal exposure by microenvironment. The AMAS is a wearable device that uses a GPS sensor algorithm in conjunction with a custom valve manifold to sample PM2.5 onto distinct filter channels to evaluate home, school, and other (e.g., outdoors, in transit, etc.) exposures. Pilot testing was conducted in Fresno, CA where 25 high-school participants ( n = 37 sampling events) wore an AMAS for 48-h periods in November 2016. Data from 20 (54%) of the 48-h samples collected by participants were deemed valid and the filters were analyzed for PM2.5 black carbon (BC) using light transmissometry and aerosol oxidative potential (OP) using the dithiothreitol (DTT) assay. The amount of inhaled PM2.5 was calculated for each microenvironment to evaluate the health risks associated with exposure. On average, the estimated amount of inhaled PM2.5 BC (μg day-1) and OP [(μM min-1) day-1] was greatest at home, owing to the proportion of time spent within that microenvironment. Validation of the AMAS demonstrated good relative precision (8.7% among collocated instruments) and a mean absolute error of 22% for BC and 33% for OP when compared to a traditional personal sampling instrument. This work demonstrates the feasibility of new technology designed to quantify personal exposure to PM2.5 species within distinct microenvironments.
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Affiliation(s)
- Casey Quinn
- Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Daniel D. Miller-Lionberg
- Department of Mechanical Engineering, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Kevin J. Klunder
- Department of Chemistry, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Jaymin Kwon
- Department of Public Health, California State University, Fresno, California 93740, United States
| | - Elizabeth M. Noth
- Environmental Health Sciences Division, School of Public Health, University of California, Berkeley, California 94720, United States
| | - John Mehaffy
- Department of Mechanical Engineering, Colorado State University, Fort Collins, Colorado 80523, United States
| | - David Leith
- Department of Mechanical Engineering, Colorado State University, Fort Collins, Colorado 80523, United States
- Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Sheryl Magzamen
- Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado 80523, United States
| | - S. Katharine Hammond
- Environmental Health Sciences Division, School of Public Health, University of California, Berkeley, California 94720, United States
| | - Charles S. Henry
- Department of Chemistry, Colorado State University, Fort Collins, Colorado 80523, United States
| | - John Volckens
- Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, Colorado 80523, United States
- Department of Mechanical Engineering, Colorado State University, Fort Collins, Colorado 80523, United States
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22
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Mavoa S, Lamb K, O'Sullivan D, Witten K, Smith M. Are disadvantaged children more likely to be excluded from analysis when applying global positioning systems inclusion criteria? BMC Res Notes 2018; 11:578. [PMID: 30103801 PMCID: PMC6090823 DOI: 10.1186/s13104-018-3681-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2017] [Accepted: 08/04/2018] [Indexed: 11/17/2022] Open
Abstract
Objective When using global positioning systems (GPS) to assess an individual’s exposure to their environment, a first step in data cleaning is to establish minimum GPS ‘inclusion criteria’ (a set of rules used to determine which GPS data are able to be included in analyses). Care is needed at this stage to avoid any data exclusion (data loss) systematically biasing results in terms of characteristics of the environment and participants. The extent of potential systematic bias in sample retention due to GPS data loss and application of GPS inclusion criteria is unknown. The aim of this study was to describe differences in sample size and socio-demographic characteristics of the retained sample when applying three different GPS inclusion criteria. The study assessed 7-day GPS data collected from children (aged 9–13 years) recruited from nine schools in Auckland, New Zealand as part of the Kids in the City study. Results Participants from ethnic minorities and those attending schools in lower socioeconomic areas were disproportionately excluded from the retained samples. This highlights potential equity implications in basing the assessment of exposure—which ultimately influences research results on the relationship between environment and health—on non-representative GPS data.
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Affiliation(s)
- Suzanne Mavoa
- Non Communicable Disease Unit, Melbourne School of Population & Global Health, The University of Melbourne, Level 5, 333 Exhibition Street, Melbourne, VIC, 3000, Australia. .,SHORE and Whariki Research Centre, School of Public Health, Massey University, Auckland, New Zealand.
| | - Karen Lamb
- Institute for Physical Activity and Nutrition (IPAN), School of Exercise and Nutrition Sciences, Deakin University, Geelong, Australia
| | - David O'Sullivan
- Department of Geography, University of California, Berkeley, 505 McCone Hall, Berkeley, 94720-4740, USA
| | - Karen Witten
- SHORE and Whariki Research Centre, School of Public Health, Massey University, Auckland, New Zealand
| | - Melody Smith
- School of Nursing, The University of Auckland, Private Bag 92019, Auckland, 1142, New Zealand
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23
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Breen M, Xu Y, Schneider A, Williams R, Devlin R. Modeling individual exposures to ambient PM 2.5 in the diabetes and the environment panel study (DEPS). THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 626:807-816. [PMID: 29396342 PMCID: PMC6147059 DOI: 10.1016/j.scitotenv.2018.01.139] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 12/20/2017] [Accepted: 01/15/2018] [Indexed: 05/22/2023]
Abstract
Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) often use outdoor concentrations as exposure surrogates, which can induce exposure error. The goal of this study was to improve ambient PM2.5 exposure assessments for a repeated measurements study with 22 diabetic individuals in central North Carolina called the Diabetes and Environment Panel Study (DEPS) by applying the Exposure Model for Individuals (EMI), which predicts five tiers of individual-level exposure metrics for ambient PM2.5 using outdoor concentrations, questionnaires, weather, and time-location information. Using EMI, we linked a mechanistic air exchange rate (AER) model to a mass-balance PM2.5 infiltration model to predict residential AER (Tier 1), infiltration factors (Finf_home, Tier 2), indoor concentrations (Cin, Tier 3), personal exposure factors (Fpex, Tier 4), and personal exposures (E, Tier 5) for ambient PM2.5. We applied EMI to predict daily PM2.5 exposure metrics (Tiers 1-5) for 174 participant-days across the 13 months of DEPS. Individual model predictions were compared to a subset of daily measurements of Fpex and E (Tiers 4-5) from the DEPS participants. Model-predicted Fpex and E corresponded well to daily measurements with a median difference of 14% and 23%; respectively. Daily model predictions for all 174 days showed considerable temporal and house-to-house variability of AER, Finf_home, and Cin (Tiers 1-3), and person-to-person variability of Fpex and E (Tiers 4-5). Our study demonstrates the capability of predicting individual-level ambient PM2.5 exposure metrics for an epidemiological study, in support of improving risk estimation.
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Affiliation(s)
- Michael Breen
- National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, United States.
| | - Yadong Xu
- National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, United States
| | - Alexandra Schneider
- Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Institute of Epidemiology II, Neuherberg, Germany
| | - Ronald Williams
- National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, United States
| | - Robert Devlin
- National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, NC 27709, United States
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24
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Asimina S, Chapizanis D, Karakitsios S, Kontoroupis P, Asimakopoulos DN, Maggos T, Sarigiannis D. Assessing and enhancing the utility of low-cost activity and location sensors for exposure studies. ENVIRONMENTAL MONITORING AND ASSESSMENT 2018; 190:155. [PMID: 29464404 DOI: 10.1007/s10661-018-6537-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Accepted: 02/08/2018] [Indexed: 06/08/2023]
Abstract
Nowadays, the advancement of mobile technology in conjunction with the introduction of the concept of exposome has provided new dynamics to the exposure studies. Since the addressing of health outcomes related to environmental stressors is crucial, the improvement of exposure assessment methodology is of paramount importance. Towards this aim, a pilot study was carried out in the two major cities of Greece (Athens, Thessaloniki), investigating the applicability of commercially available fitness monitors and the Moves App for tracking people's location and activities, as well as for predicting the type of the encountered location, using advanced modeling techniques. Within the frame of the study, 21 individuals were using the Fitbit Flex activity tracker, a temperature logger, and the application Moves App on their smartphones. For the validation of the above equipment, participants were also carrying an Actigraph (activity sensor) and a GPS device. The data collected from Fitbit Flex, the temperature logger, and the GPS (speed) were used as input parameters in an Artificial Neural Network (ANN) model for predicting the type of location. Analysis of the data showed that the Moves App tends to underestimate the daily steps counts in comparison with Fitbit Flex and Actigraph, respectively, while Moves App predicted the movement trajectory of an individual with reasonable accuracy, compared to a dedicated GPS. Finally, the encountered location was successfully predicted by the ANN in most of the cases.
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Affiliation(s)
- Stamatelopoulou Asimina
- Environmental Research Laboratory, I.N.RA.S.T.E.S., NCSR "DEMOKRITOS", Athens, Greece.
- Department of Applied Physics, Faculty of Physics, University of Athens, Athens, Greece.
| | - D Chapizanis
- Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - S Karakitsios
- Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - P Kontoroupis
- Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - D N Asimakopoulos
- Department of Applied Physics, Faculty of Physics, University of Athens, Athens, Greece
| | - T Maggos
- Environmental Research Laboratory, I.N.RA.S.T.E.S., NCSR "DEMOKRITOS", Athens, Greece
| | - D Sarigiannis
- Environmental Engineering Laboratory, Department of Chemical Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece
- Institute for Advanced Study of Pavia, Pavia, Italy
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25
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Matz CJ, Stieb DM, Egyed M, Brion O, Johnson M. Evaluation of daily time spent in transportation and traffic-influenced microenvironments by urban Canadians. AIR QUALITY, ATMOSPHERE, & HEALTH 2018; 11:209-220. [PMID: 29568337 PMCID: PMC5847121 DOI: 10.1007/s11869-017-0532-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2017] [Accepted: 11/22/2017] [Indexed: 05/06/2023]
Abstract
Exposure to traffic and traffic-related air pollution is associated with a wide array of health effects. Time spent in a vehicle, in active transportation, along roadsides, and in close proximity to traffic can substantially contribute to daily exposure to air pollutants. For this study, we evaluated daily time spent in transportation and traffic-influenced microenvironments by urban Canadians using the Canadian Human Activity Pattern Survey (CHAPS) 2 results. Approximately 4-7% of daily time was spent in on- or near-road locations, mainly associated with being in a vehicle and smaller contributions from active transportation. Indoor microenvironments can be impacted by traffic emissions, especially when located near major roadways. Over 60% of the target population reported living within one block of a roadway with moderate to heavy traffic, which was variable with income level and city, and confirmed based on elevated NO2 exposure estimated using land use regression. Furthermore, over 55% of the target population ≤ 18 years reported attending a school or daycare in close proximity to moderate to heavy traffic, and little variation was observed based on income or city. The results underline the importance of traffic emissions as a major source of exposure in Canadian urban centers, given the time spent in traffic-influenced microenvironments.
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Affiliation(s)
- Carlyn J. Matz
- Air Health Effects Assessment Division, Health Canada, 269 Laurier Ave W, PL 4903C, Ottawa, ON K1A 0K9 Canada
| | - David M. Stieb
- Population Studies Division, Health Canada, 445-757 West Hasting St., Federal Tower, Vancouver, BC V6C 1A1 Canada
| | - Marika Egyed
- Air Health Effects Assessment Division, Health Canada, 269 Laurier Ave W, PL 4903C, Ottawa, ON K1A 0K9 Canada
| | - Orly Brion
- Population Studies Division, Health Canada, 101 Tunney’s Pasture Dr., PL 0201A, Ottawa, ON K1A 0K9 Canada
| | - Markey Johnson
- Air Health Science Division, Health Canada, 269 Laurier Ave W, PL 4903C, Ottawa, ON K1A 0K9 Canada
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26
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Hazlehurst MF, Spalt EW, Curl CL, Davey ME, Vedal S, Burke GL, Kaufman JD. Integrating data from multiple time-location measurement methods for use in exposure assessment: the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air). JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2017; 27:569-574. [PMID: 28120831 DOI: 10.1038/jes.2016.84] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Accepted: 12/06/2016] [Indexed: 06/06/2023]
Abstract
Tools to assess time-location patterns related to environmental exposures have expanded from reliance on time-location diaries (TLDs) and questionnaires to use of geospatial location devices such as data-logging Global Positioning System (GPS) equipment. The Multi-Ethnic Study of Atherosclerosis and Air Pollution obtained typical time-location patterns via questionnaire for 6424 adults in six US cities. At a later time (mean 4.6 years after questionnaire), a subset (n=128) participated in high-resolution data collection for specific 2-week periods resulting in concurrent GPS and detailed TLD data, which were aggregated to estimate time spent in various microenvironments. During these 2-week periods, participants were observed to spend the most time at home indoors (mean of 78%) and a small proportion of time in-vehicle (mean of 4%). Similar overall patterns were reported by these participants on the prior questionnaire (mean home indoors: 75%; mean in-vehicle: 4%). However, individual micro-environmental time estimates measured over specific 2-week periods were not highly correlated with an individual's questionnaire report of typical behavior (Spearman's ρ of 0.43 for home indoors and 0.39 for in-vehicle). Although questionnaire data about typical time-location patterns can inform interpretation of long-term epidemiological analyses and risk assessment, they may not reliably represent an individual's short-term experience.
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Affiliation(s)
- Marnie F Hazlehurst
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
| | - Elizabeth W Spalt
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
| | - Cynthia L Curl
- Department of Community and Environmental Health, Boise State University, Boise, Idaho, USA
| | - Mark E Davey
- Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Basel, Switzerland
| | - Sverre Vedal
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
| | - Gregory L Burke
- Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Joel D Kaufman
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
- Department of Medicine, University of Washington, Seattle, Washington, USA
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
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27
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Litchfield I, van Tongeren M, Sorahan T. Radiofrequency Exposure Amongst Employees of Mobile Network Operators and Broadcasters. RADIATION PROTECTION DOSIMETRY 2017; 175:178-185. [PMID: 27738083 PMCID: PMC5927333 DOI: 10.1093/rpd/ncw283] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2016] [Revised: 09/15/2016] [Accepted: 09/19/2016] [Indexed: 06/06/2023]
Abstract
Little is known about personal exposure to radiofrequency (RF) fields amongst employees in the telecommunications industry responsible for installing and maintaining transmitters. IARC classified RF exposure as a possible carcinogen, although evidence from occupational studies was judged to be inadequate. Hence, there is a need for improved evidence of any potentially adverse health effects amongst the workforce occupationally exposed to RF radiation. In this study, results are presented from an exposure survey using data from personal monitors used by employees in the broadcasting and telecommunication industries of the UK. These data were supplemented by spot measurements using broadband survey metres and information on daily work activities provided by employee questionnaires. The sets of real-time personal data were categorised by four types of site determined by the highest powered antenna present (high, medium or low power and ground-level sites). For measurements gathered at each type of site, the root mean square and a series of box plots were produced. Results from the daily activities diaries suggested that riggers working for radio and television broadcasters were exposed to much longer periods as compared to colleagues working for mobile operators. Combining the results from the measurements and daily activity diaries clearly demonstrate that exposures were highest for riggers working for broadcasting sites. This study demonstrates that it is feasible to carry out exposure surveys within these populations that will provide reliable estimates of exposure that can be used for epidemiological studies of occupational groups exposed to RF fields.
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Affiliation(s)
- Ian Litchfield
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
| | - Martie van Tongeren
- Institute of Occupational Medicine, Research Avenue North, Riccarton, Edinburgh EH14 4 AP, UK
| | - Tom Sorahan
- Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
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28
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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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29
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Donaire-Gonzalez D, Valentín A, de Nazelle A, Ambros A, Carrasco-Turigas G, Seto E, Jerrett M, Nieuwenhuijsen MJ. Benefits of Mobile Phone Technology for Personal Environmental Monitoring. JMIR Mhealth Uhealth 2016; 4:e126. [PMID: 27833069 PMCID: PMC5122720 DOI: 10.2196/mhealth.5771] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2016] [Revised: 08/11/2016] [Accepted: 08/28/2016] [Indexed: 01/31/2023] Open
Abstract
Background Tracking individuals in environmental epidemiological studies using novel mobile phone technologies can provide valuable information on geolocation and physical activity, which will improve our understanding of environmental exposures. Objective The objective of this study was to assess the performance of one of the least expensive mobile phones on the market to track people's travel-activity pattern. Methods Adults living and working in Barcelona (72/162 bicycle commuters) carried simultaneously a mobile phone and a Global Positioning System (GPS) tracker and filled in a travel-activity diary (TAD) for 1 week (N=162). The CalFit app for mobile phones was used to log participants’ geographical location and physical activity. The geographical location data were assigned to different microenvironments (home, work or school, in transit, others) with a newly developed spatiotemporal map-matching algorithm. The tracking performance of the mobile phones was compared with that of the GPS trackers using chi-square test and Kruskal-Wallis rank sum test. The minute agreement across all microenvironments between the TAD and the algorithm was compared using the Gwet agreement coefficient (AC1). Results The mobile phone acquired locations for 905 (29.2%) more trips reported in travel diaries than the GPS tracker (P<.001) and had a median accuracy of 25 m. Subjects spent on average 57.9%, 19.9%, 9.0%, and 13.2% of time at home, work, in transit, and other places, respectively, according to the TAD and 57.5%, 18.8%, 11.6%, and 12.1%, respectively, according to the map-matching algorithm. The overall minute agreement between both methods was high (AC1 .811, 95% CI .810-.812). Conclusions The use of mobile phones running the CalFit app provides better information on which microenvironments people spend their time in than previous approaches based only on GPS trackers. The improvements of mobile phone technology in microenvironment determination are because the mobile phones are faster at identifying first locations and capable of getting location in challenging environments thanks to the combination of assisted-GPS technology and network positioning systems. Moreover, collecting location information from mobile phones, which are already carried by individuals, allows monitoring more people with a cheaper and less burdensome method than deploying GPS trackers.
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Affiliation(s)
- David Donaire-Gonzalez
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.,Pompeu Fabra University (UPF), Barcelona, Spain.,Ciber on Epidemiology and Public Health (CIBERESP), Barcelona, Spain.,Physical Activity and Sports Sciences Department, Fundació Blanquerna, Ramon Llull University, Barcelona, Spain
| | - Antònia Valentín
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.,Pompeu Fabra University (UPF), Barcelona, Spain.,Ciber on Epidemiology and Public Health (CIBERESP), Barcelona, Spain
| | - Audrey de Nazelle
- Center for Environmental Policy, Imperial College London, London, United Kingdom
| | - Albert Ambros
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.,Pompeu Fabra University (UPF), Barcelona, Spain.,Ciber on Epidemiology and Public Health (CIBERESP), Barcelona, Spain
| | - Glòria Carrasco-Turigas
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.,Pompeu Fabra University (UPF), Barcelona, Spain.,Ciber on Epidemiology and Public Health (CIBERESP), Barcelona, Spain
| | - Edmund Seto
- Department of Environmental and Occupational Health Services, University of Washington, Seattle, WA, United States
| | - Michael Jerrett
- Environmental Health Sciences, School of Public Health, University of California, Berkeley, CA, United States.,Department of Environmental Health, Fielding School of Public Health, University of California, Los Angeles, CA, United States
| | - Mark J Nieuwenhuijsen
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.,Pompeu Fabra University (UPF), Barcelona, Spain.,Ciber on Epidemiology and Public Health (CIBERESP), Barcelona, Spain
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30
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Glasgow ML, Rudra CB, Yoo EH, Demirbas M, Merriman J, Nayak P, Crabtree-Ide C, Szpiro AA, Rudra A, Wactawski-Wende J, Mu L. Using smartphones to collect time-activity data for long-term personal-level air pollution exposure assessment. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2016; 26:356-364. [PMID: 25425137 DOI: 10.1038/jes.2014.78] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2013] [Revised: 09/08/2014] [Accepted: 09/15/2014] [Indexed: 06/04/2023]
Abstract
Because of the spatiotemporal variability of people and air pollutants within cities, it is important to account for a person's movements over time when estimating personal air pollution exposure. This study aimed to examine the feasibility of using smartphones to collect personal-level time-activity data. Using Skyhook Wireless's hybrid geolocation module, we developed "Apolux" (Air, Pollution, Exposure), an Android(TM) smartphone application designed to track participants' location in 5-min intervals for 3 months. From 42 participants, we compared Apolux data with contemporaneous data from two self-reported, 24-h time-activity diaries. About three-fourths of measurements were collected within 5 min of each other (mean=74.14%), and 79% of participants reporting constantly powered-on smartphones (n=38) had a daily average data collection frequency of <10 min. Apolux's degree of temporal resolution varied across manufacturers, mobile networks, and the time of day that data collection occurred. The discrepancy between diary points and corresponding Apolux data was 342.3 m (Euclidian distance) and varied across mobile networks. This study's high compliance and feasibility for data collection demonstrates the potential for integrating smartphone-based time-activity data into long-term and large-scale air pollution exposure studies.
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Affiliation(s)
- Mark L Glasgow
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - Carole B Rudra
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - Eun-Hye Yoo
- Department of Geography, University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - Murat Demirbas
- Department of Computer Science and Engineering, School of Engineering and Applied Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - Joel Merriman
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - Pramod Nayak
- Department of Computer Science and Engineering, School of Engineering and Applied Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - Christina Crabtree-Ide
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - Adam A Szpiro
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington, USA
| | - Atri Rudra
- Department of Computer Science and Engineering, School of Engineering and Applied Sciences, University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - Jean Wactawski-Wende
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York, Buffalo, New York, USA
| | - Lina Mu
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, The State University of New York, Buffalo, New York, USA
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31
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Re-creating daily mobility histories for health research from raw GPS tracks: Validation of a kernel-based algorithm using real-life data. Health Place 2016; 40:29-33. [PMID: 27164433 DOI: 10.1016/j.healthplace.2016.04.004] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2015] [Revised: 04/13/2016] [Accepted: 04/13/2016] [Indexed: 11/21/2022]
Abstract
BACKGROUND GPS tracking is increasingly used to document daily mobility, allowing refined analysis of daily exposures and health behaviour. Validation of algorithms processing raw GPS data to identify activity locations and trips are lacking. OBJECTIVE Propose novel ways to evaluate GPS processing algorithms data while validating an existing kernel-based algorithm with real-life GPS tracks. METHODS Seven-day GPS tracking and GPS-prompted recall interviews were conducted among 234 adult participants of the RECORD GPS Study. Raw GPS data was transformed using a kernel-based algorithm. Two match and nine mismatch configurations are analysed. Algorithm detection of activity locations and trips were validated. RESULTS Some 95.8% of available GPS time was correctly classified as an activity location or a trip. The algorithm falsely identified a trip for 2.2% of the tracking time, and falsely identified an activity location 0.7% of time. Missed trips and missed activity locations counted for less than .4% of the time. CONCLUSION The tested kernel-based algorithm provides histories of activity locations and trips that are highly concordant with GPS-prompted follow-up interviews.
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Refining Time-Activity Classification of Human Subjects Using the Global Positioning System. PLoS One 2016; 11:e0148875. [PMID: 26919723 PMCID: PMC4769278 DOI: 10.1371/journal.pone.0148875] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2015] [Accepted: 01/24/2016] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Detailed spatial location information is important in accurately estimating personal exposure to air pollution. Global Position System (GPS) has been widely used in tracking personal paths and activities. Previous researchers have developed time-activity classification models based on GPS data, most of them were developed for specific regions. An adaptive model for time-location classification can be widely applied to air pollution studies that use GPS to track individual level time-activity patterns. METHODS Time-activity data were collected for seven days using GPS loggers and accelerometers from thirteen adult participants from Southern California under free living conditions. We developed an automated model based on random forests to classify major time-activity patterns (i.e. indoor, outdoor-static, outdoor-walking, and in-vehicle travel). Sensitivity analysis was conducted to examine the contribution of the accelerometer data and the supplemental spatial data (i.e. roadway and tax parcel data) to the accuracy of time-activity classification. Our model was evaluated using both leave-one-fold-out and leave-one-subject-out methods. RESULTS Maximum speeds in averaging time intervals of 7 and 5 minutes, and distance to primary highways with limited access were found to be the three most important variables in the classification model. Leave-one-fold-out cross-validation showed an overall accuracy of 99.71%. Sensitivities varied from 84.62% (outdoor walking) to 99.90% (indoor). Specificities varied from 96.33% (indoor) to 99.98% (outdoor static). The exclusion of accelerometer and ambient light sensor variables caused a slight loss in sensitivity for outdoor walking, but little loss in overall accuracy. However, leave-one-subject-out cross-validation showed considerable loss in sensitivity for outdoor static and outdoor walking conditions. CONCLUSIONS The random forests classification model can achieve high accuracy for the four major time-activity categories. The model also performed well with just GPS, road and tax parcel data. However, caution is warranted when generalizing the model developed from a small number of subjects to other populations.
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Sloan CD, Philipp TJ, Bradshaw RK, Chronister S, Barber WB, Johnston JD. Applications of GPS-tracked personal and fixed-location PM(2.5) continuous exposure monitoring. JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION (1995) 2016; 66:53-65. [PMID: 26512925 DOI: 10.1080/10962247.2015.1108942] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
UNLABELLED Continued development of personal air pollution monitors is rapidly improving government and research capabilities for data collection. In this study, we tested the feasibility of using GPS-enabled personal exposure monitors to collect personal exposure readings and short-term daily PM2.5 measures at 15 fixed locations throughout a community. The goals were to determine the accuracy of fixed-location monitoring for approximating individual exposures compared to a centralized outdoor air pollution monitor, and to test the utility of two different personal monitors, the RTI MicroPEM V3.2 and TSI SidePak AM510. For personal samples, 24-hr mean PM2.5 concentrations were 6.93 μg/m³ (stderr = 0.15) and 8.47 μg/m³ (stderr = 0.10) for the MicroPEM and SidePak, respectively. Based on time-activity patterns from participant journals, exposures were highest while participants were outdoors (MicroPEM = 7.61 µg/m³, stderr = 1.08, SidePak = 11.85 µg/m³, stderr = 0.83) or in restaurants (MicroPEM = 7.48 µg/m³, stderr = 0.39, SidePak = 24.93 µg/m³, stderr = 0.82), and lowest when participants were exercising indoors (MicroPEM = 4.78 µg/m³, stderr = 0.23, SidePak = 5.63 µg/m³, stderr = 0.08). Mean PM(2.5) at the 15 fixed locations, as measured by the SidePak, ranged from 4.71 µg/m³ (stderr = 0.23) to 12.38 µg/m³ (stderr = 0.45). By comparison, mean 24-h PM(2.5) measured at the centralized outdoor monitor ranged from 2.7 to 6.7 µg/m³ during the study period. The range of average PM(2.5) exposure levels estimated for each participant using the interpolated fixed-location data was 2.83 to 19.26 µg/m³ (mean = 8.3, stderr = 1.4). These estimated levels were compared with average exposure from personal samples. The fixed-location monitoring strategy was useful in identifying high air pollution microclimates throughout the county. For 7 of 10 subjects, the fixed-location monitoring strategy more closely approximated individuals' 24-hr breathing zone exposures than did the centralized outdoor monitor. Highlights are: Individual PM(2.5) exposure levels vary extensively by activity, location and time of day; fixed-location sampling more closely approximated individual exposures than a centralized outdoor monitor; and small, personal exposure monitors provide added utility for individuals, researchers, and public health professionals seeking to more accurately identify air pollution microclimates. IMPLICATIONS Personal air pollution monitoring technology is advancing rapidly. Currently, personal monitors are primarily used in research settings, but could they also support government networks of centralized outdoor monitors? In this study, we found differences in performance and practicality for two personal monitors in different monitoring scenarios. We also found that personal monitors used to collect outdoor area samples were effective at finding pollution microclimates, and more closely approximated actual individual exposure than a central monitor. Though more research is needed, there is strong potential that personal exposure monitors can improve existing monitoring networks.
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Affiliation(s)
- Chantel D Sloan
- a Department of Health Science , Brigham Young University , Provo , Utah , USA
| | - Tyler J Philipp
- a Department of Health Science , Brigham Young University , Provo , Utah , USA
| | - Rebecca K Bradshaw
- a Department of Health Science , Brigham Young University , Provo , Utah , USA
| | - Sara Chronister
- a Department of Health Science , Brigham Young University , Provo , Utah , USA
| | - W Bradford Barber
- a Department of Health Science , Brigham Young University , Provo , Utah , USA
| | - James D Johnston
- a Department of Health Science , Brigham Young University , Provo , Utah , USA
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Breen MS, Long TC, Schultz BD, Williams RW, Richmond-Bryant J, Breen M, Langstaff JE, Devlin RB, Schneider A, Burke JM, Batterman SA, Meng QY. Air Pollution Exposure Model for Individuals (EMI) in Health Studies: Evaluation for Ambient PM2.5 in Central North Carolina. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2015; 49:14184-14194. [PMID: 26561729 DOI: 10.1021/acs.est.5b02765] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Air pollution health studies of fine particulate matter (diameter ≤2.5 μm, PM2.5) often use outdoor concentrations as exposure surrogates. Failure to account for variability of indoor infiltration of ambient PM2.5 and time indoors can induce exposure errors. We developed and evaluated an exposure model for individuals (EMI), which predicts five tiers of individual-level exposure metrics for ambient PM2.5 using outdoor concentrations, questionnaires, weather, and time-location information. We linked a mechanistic air exchange rate (AER) model to a mass-balance PM2.5 infiltration model to predict residential AER (Tier 1), infiltration factors (Tier 2), indoor concentrations (Tier 3), personal exposure factors (Tier 4), and personal exposures (Tier 5) for ambient PM2.5. Using cross-validation, individual predictions were compared to 591 daily measurements from 31 homes (Tiers 1-3) and participants (Tiers 4-5) in central North Carolina. Median absolute differences were 39% (0.17 h(-1)) for Tier 1, 18% (0.10) for Tier 2, 20% (2.0 μg/m(3)) for Tier 3, 18% (0.10) for Tier 4, and 20% (1.8 μg/m(3)) for Tier 5. The capability of EMI could help reduce the uncertainty of ambient PM2.5 exposure metrics used in health studies.
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Affiliation(s)
- Michael S Breen
- National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, United States
| | - Thomas C Long
- National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, United States
| | - Bradley D Schultz
- U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, United States
| | - Ronald W Williams
- National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, United States
| | - Jennifer Richmond-Bryant
- National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, United States
| | - Miyuki Breen
- Biomathematics Program, Department of Mathematics, North Carolina State University , Raleigh, North Carolina 27695, United States
| | - John E Langstaff
- Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, United States
| | - Robert B Devlin
- National Health and Environmental Effects Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, United States
| | - Alexandra Schneider
- Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Institute of Epidemiology II , Neuherberg, Germany
| | - Janet M Burke
- National Exposure Research Laboratory, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, United States
| | - Stuart A Batterman
- Environmental Health Sciences, University of Michigan , Ann Arbor, Michigan 48109, United States
| | - Qing Yu Meng
- Department of Environmental Sciences, Rutgers University , New Brunswick, New Jersey 08901, United States
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Ouidir M, Giorgis-Allemand L, Lyon-Caen S, Morelli X, Cracowski C, Pontet S, Pin I, Lepeule J, Siroux V, Slama R. Estimation of exposure to atmospheric pollutants during pregnancy integrating space-time activity and indoor air levels: Does it make a difference? ENVIRONMENT INTERNATIONAL 2015; 84:161-73. [PMID: 26300245 PMCID: PMC4776347 DOI: 10.1016/j.envint.2015.07.021] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2014] [Revised: 07/28/2015] [Accepted: 07/29/2015] [Indexed: 05/19/2023]
Abstract
Studies of air pollution effects during pregnancy generally only consider exposure in the outdoor air at the home address. We aimed to compare exposure models differing in their ability to account for the spatial resolution of pollutants, space-time activity and indoor air pollution levels. We recruited 40 pregnant women in the Grenoble urban area, France, who carried a Global Positioning System (GPS) during up to 3 weeks; in a subgroup, indoor measurements of fine particles (PM2.5) were conducted at home (n=9) and personal exposure to nitrogen dioxide (NO2) was assessed using passive air samplers (n=10). Outdoor concentrations of NO2, and PM2.5 were estimated from a dispersion model with a fine spatial resolution. Women spent on average 16 h per day at home. Considering only outdoor levels, for estimates at the home address, the correlation between the estimate using the nearest background air monitoring station and the estimate from the dispersion model was high (r=0.93) for PM2.5 and moderate (r=0.67) for NO2. The model incorporating clean GPS data was less correlated with the estimate relying on raw GPS data (r=0.77) than the model ignoring space-time activity (r=0.93). PM2.5 outdoor levels were not to moderately correlated with estimates from the model incorporating indoor measurements and space-time activity (r=-0.10 to 0.47), while NO2 personal levels were not correlated with outdoor levels (r=-0.42 to 0.03). In this urban area, accounting for space-time activity little influenced exposure estimates; in a subgroup of subjects (n=9), incorporating indoor pollution levels seemed to strongly modify them.
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Affiliation(s)
- Marion Ouidir
- Inserm and Univ. Grenoble Alpes, IAB, Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Grenoble, France
| | - Lise Giorgis-Allemand
- Inserm and Univ. Grenoble Alpes, IAB, Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Grenoble, France
| | - Sarah Lyon-Caen
- Inserm and Univ. Grenoble Alpes, IAB, Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Grenoble, France
| | - Xavier Morelli
- Inserm and Univ. Grenoble Alpes, IAB, Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Grenoble, France
| | - Claire Cracowski
- CHU de Grenoble, Clinical Pharmacology Unit, Inserm CIC 1406, Grenoble, France
| | | | - Isabelle Pin
- Inserm and Univ. Grenoble Alpes, IAB, Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Grenoble, France; CHU de Grenoble, Pediatric department, Grenoble, France
| | - Johanna Lepeule
- Inserm and Univ. Grenoble Alpes, IAB, Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Grenoble, France
| | - Valérie Siroux
- Inserm and Univ. Grenoble Alpes, IAB, Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Grenoble, France
| | - Rémy Slama
- Inserm and Univ. Grenoble Alpes, IAB, Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Grenoble, France.
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Che WW, Frey HC, Lau AKH. Comparison of sources of variability in school age children exposure to ambient PM₂.₅. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2015; 49:1511-1520. [PMID: 25560832 DOI: 10.1021/es506275c] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
School age children are particularly susceptible to exposure to ambient fine particle (PM2.5). To provide insight into factors affecting variability in ambient PM2.5 exposure, distributions of daily PM2.5 exposures for school age children are estimated for four seasons in three climatic zones of the United States using a stochastic microenvironmental exposure model, based on ambient concentration, air exchange rate, penetration factor, deposition rate, census data, meteorological data, and time pattern data. Estimated daily individual exposure varies largely among seasons, regions, and individuals. The mean ratio of ambient exposure to ambient concentration (Ea/Ca) ranges from 0.46 to 0.61 among selected regions and seasons, resulting from differences in air exchange rate. The individual Ea/Ca varies by a factor of 2 to 3 over a 95% frequency range among simulated children, resulting from variability in children's time patterns. These patterns are similar among age groups, but vary with the day of the week and outdoor temperature. Variability in exposure is larger between individuals than between groups. The high end ratio of the Ea/Ca, at the 95th percentile of inter-individual variability, is 30% to 50% higher than the mean Ea/Ca ratio. Results can be used to intepret and adjust exposure errors in epidemiology and to assist in development of exposure mitigation strategies.
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Affiliation(s)
- W W Che
- Division of Environment, The Hong Kong University of Science and Technology , Clear Water Bay, Hong Kong, China
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Modeling spatial and temporal variability of residential air exchange rates for the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS). INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2014; 11:11481-504. [PMID: 25386953 PMCID: PMC4245625 DOI: 10.3390/ijerph111111481] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/04/2014] [Revised: 10/24/2014] [Accepted: 10/27/2014] [Indexed: 11/16/2022]
Abstract
Air pollution health studies often use outdoor concentrations as exposure surrogates. Failure to account for variability of residential infiltration of outdoor pollutants can induce exposure errors and lead to bias and incorrect confidence intervals in health effect estimates. The residential air exchange rate (AER), which is the rate of exchange of indoor air with outdoor air, is an important determinant for house-to-house (spatial) and temporal variations of air pollution infiltration. Our goal was to evaluate and apply mechanistic models to predict AERs for 213 homes in the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS), a cohort study of traffic-related air pollution exposures and respiratory effects in asthmatic children living near major roads in Detroit, Michigan. We used a previously developed model (LBL), which predicts AER from meteorology and questionnaire data on building characteristics related to air leakage, and an extended version of this model (LBLX) that includes natural ventilation from open windows. As a critical and novel aspect of our AER modeling approach, we performed a cross validation, which included both parameter estimation (i.e., model calibration) and model evaluation, based on daily AER measurements from a subset of 24 study homes on five consecutive days during two seasons. The measured AER varied between 0.09 and 3.48 h(-1) with a median of 0.64 h(-1). For the individual model-predicted and measured AER, the median absolute difference was 29% (0.19 h‑1) for both the LBL and LBLX models. The LBL and LBLX models predicted 59% and 61% of the variance in the AER, respectively. Daily AER predictions for all 213 homes during the three year study (2010-2012) showed considerable house-to-house variations from building leakage differences, and temporal variations from outdoor temperature and wind speed fluctuations. Using this novel approach, NEXUS will be one of the first epidemiology studies to apply calibrated and home-specific AER models, and to include the spatial and temporal variations of AER for over 200 individual homes across multiple years into an exposure assessment in support of improving risk estimates.
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Gu J, Kraus U, Schneider A, Hampel R, Pitz M, Breitner S, Wolf K, Hänninen O, Peters A, Cyrys J. Personal day-time exposure to ultrafine particles in different microenvironments. Int J Hyg Environ Health 2014; 218:188-95. [PMID: 25458919 DOI: 10.1016/j.ijheh.2014.10.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2014] [Revised: 10/08/2014] [Accepted: 10/09/2014] [Indexed: 11/28/2022]
Abstract
In order to assess the personal exposure to ultrafine particles (UFP) during individual day-time activities and to investigate the impact of different microenvironments on exposure, we measured personal exposure to particle number concentrations (PNC), a surrogate for UFP, among 112 non-smoking participants in Augsburg, Germany over a nearly two-year period from March 2007 to December 2008. We obtained 337 personal PNC measurements from 112 participants together with dairies of their activities and locations. The measurements lasted on average 5.5h and contained on average 330 observations. In addition, ambient PNC were measured at an urban background stationary monitoring site. Personal PNC were highly variable between measurements (IQR of mean: 11780-24650cm(-3)) and also within a single measurement. Outdoor personal PNC in traffic environments were about two times higher than in non-traffic environments. Higher indoor personal PNC were associated with activities like cooking, being in a bistro or exposure to passive smoking. Overall, personal and stationary PNC were weakly to moderately correlated (r<0.41). Personal PNC were much higher than stationary PNC in traffic (ratio: 1.5), when shopping (ratio: 2.4), and indoors with water vapor (ratio: 2.5). Additive mixed models were applied to predict personal PNC by participants' activities and locations. Traffic microenvironments were significant determinants for outdoor personal PNC. Being in a bistro, passive smoking, and cooking contributed significantly to an increased indoor personal PNC.
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Affiliation(s)
- Jianwei Gu
- Institute of Epidemiology II, Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; Environment Science Center, University of Augsburg, Universitätsstr. 1a, 86159 Augsburg, Germany.
| | - Ute Kraus
- Institute of Epidemiology II, Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Alexandra Schneider
- Institute of Epidemiology II, Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Regina Hampel
- Institute of Epidemiology II, Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Mike Pitz
- Bavarian Environment Agency, Bürgermeister-Ulrich-Str. 160, 86179 Augsburg, Germany
| | - Susanne Breitner
- Institute of Epidemiology II, Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Kathrin Wolf
- Institute of Epidemiology II, Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Otto Hänninen
- Department of Environmental Health, National Institute for Health and Welfare, PO Box 95, Kuopio, Finland
| | - Annette Peters
- Institute of Epidemiology II, Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
| | - Josef Cyrys
- Institute of Epidemiology II, Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany; Environment Science Center, University of Augsburg, Universitätsstr. 1a, 86159 Augsburg, Germany
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