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Hoek G, Vienneau D, de Hoogh K. Does residential address-based exposure assessment for outdoor air pollution lead to bias in epidemiological studies? Environ Health 2024; 23:75. [PMID: 39289774 PMCID: PMC11406750 DOI: 10.1186/s12940-024-01111-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 08/26/2024] [Indexed: 09/19/2024]
Abstract
BACKGROUND Epidemiological studies of long-term exposure to outdoor air pollution have consistently documented associations with morbidity and mortality. Air pollution exposure in these epidemiological studies is generally assessed at the residential address, because individual time-activity patterns are seldom known in large epidemiological studies. Ignoring time-activity patterns may result in bias in epidemiological studies. The aims of this paper are to assess the agreement between exposure assessed at the residential address and exposures estimated with time-activity integrated and the potential bias in epidemiological studies when exposure is estimated at the residential address. MAIN BODY We reviewed exposure studies that have compared residential and time-activity integrated exposures, with a focus on the correlation. We further discuss epidemiological studies that have compared health effect estimates between the residential and time-activity integrated exposure and studies that have indirectly estimated the potential bias in health effect estimates in epidemiological studies related to ignoring time-activity patterns. A large number of studies compared residential and time-activity integrated exposure, especially in Europe and North America, mostly focusing on differences in level. Eleven of these studies reported correlations, showing that the correlation between residential address-based and time-activity integrated long-term air pollution exposure was generally high to very high (R > 0.8). For individual subjects large differences were found between residential and time-activity integrated exposures. Consistent with the high correlation, five of six identified epidemiological studies found nearly identical health effects using residential and time-activity integrated exposure. Six additional studies in Europe and North America showed only small to moderate potential bias (9 to 30% potential underestimation) in estimated exposure response functions using residence-based exposures. Differences of average exposure level were generally small and in both directions. Exposure contrasts were smaller for time-activity integrated exposures in nearly all studies. The difference in exposure was not equally distributed across the population including between different socio-economic groups. CONCLUSIONS Overall, the bias in epidemiological studies related to assessing long-term exposure at the residential address only is likely small in populations comparable to those evaluated in the comparison studies. Further improvements in exposure assessment especially for large populations remain useful.
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Affiliation(s)
- Gerard Hoek
- Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands.
| | - Danielle Vienneau
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Kees de Hoogh
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
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Xu D, Lin W, Gao J, Jiang Y, Li L, Gao F. PM 2.5 Exposure and Health Risk Assessment Using Remote Sensing Data and GIS. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:6154. [PMID: 35627689 PMCID: PMC9141174 DOI: 10.3390/ijerph19106154] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 05/15/2022] [Accepted: 05/16/2022] [Indexed: 11/29/2022]
Abstract
Assessing personal exposure risk from PM2.5 air pollution poses challenges due to the limited availability of high spatial resolution data for PM2.5 and population density. This study introduced a seasonal spatial-temporal method of modeling PM2.5 distribution characteristics at a 1-km grid level based on remote sensing data and Geographic Information Systems (GIS). The high-accuracy population density data and the relative exposure risk model were used to assess the relationship between exposure to PM2.5 air pollution and public health. The results indicated that the spatial-temporal PM2.5 concentration could be simulated by MODIS images and GIS method and could provide high spatial resolution data sources for exposure risk assessment. PM2.5 air pollution risks were most serious in spring and winter, and high risks of environmental health hazards were mostly concentrated in densely populated areas in Shanghai-Hangzhou Bay, China. Policies to control the total population and pollution discharge need follow the principle of adaptation to local conditions in high-risk areas. Air quality maintenance and ecological maintenance should be carried out in low-risk areas to reduce exposure risk and improve environmental health.
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Affiliation(s)
- Dan Xu
- School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China; (D.X.); (Y.J.); (L.L.); (F.G.)
| | - Wenpeng Lin
- School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China; (D.X.); (Y.J.); (L.L.); (F.G.)
- Yangtze River Delta Urban Wetland Ecosystem National Field Observation and Research Station, Shanghai 200234, China
| | - Jun Gao
- School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China; (D.X.); (Y.J.); (L.L.); (F.G.)
- Yangtze River Delta Urban Wetland Ecosystem National Field Observation and Research Station, Shanghai 200234, China
| | - Yue Jiang
- School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China; (D.X.); (Y.J.); (L.L.); (F.G.)
| | - Lubing Li
- School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China; (D.X.); (Y.J.); (L.L.); (F.G.)
| | - Fei Gao
- School of Environmental and Geographical Sciences, Shanghai Normal University, Shanghai 200234, China; (D.X.); (Y.J.); (L.L.); (F.G.)
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Poulhès A, Proulhac L. Exposed to NO 2 in the center, NO x polluters in the periphery: Evidence from the Paris region. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 821:153476. [PMID: 35093371 DOI: 10.1016/j.scitotenv.2022.153476] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 01/22/2022] [Accepted: 01/24/2022] [Indexed: 06/14/2023]
Abstract
Air pollution is the cause of many health problems. In cities, combustion vehicles are a major contributor to emissions of key air pollutants. While many studies have focused on populations exposed to pollutants and the resulting environmental and social inequalities, few compare exposures and contributions. In this research, the population of the Household Travel Survey of the Paris region is studied by confronting two elements: the average individual exposure to NO2 during an average working day and the average traffic NOx emitted during a day by the motorized trips for each resident surveyed. The dynamic exposure to NO2 of each resident is estimated according to activities in an average working day. The results confirm an environmental inequality according to the place of residence: on average, the center residents contribute little to pollutant emissions but are highly exposed. Some categories of the population, including women and the socially disadvantaged, are the most affected by these inequalities.
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Affiliation(s)
- Alexis Poulhès
- Ecole des Ponts et Chaussées, Université Gustave Eiffel, Laboratoire Ville Mobilité Transport, 14-20 boulevard Newton, Cité Descartes, Champs-sur-Marne, 77447 Marne-la-Vallée Cedex 2, France.
| | - Laurent Proulhac
- Ecole des Ponts et Chaussées, Université Gustave Eiffel, Laboratoire Ville Mobilité Transport, 14-20 boulevard Newton, Cité Descartes, Champs-sur-Marne, 77447 Marne-la-Vallée Cedex 2, France
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Hong J, McArthur DP, Sim J, Kim CH. Did air pollution continue to affect bike share usage in Seoul during the COVID-19 pandemic? JOURNAL OF TRANSPORT & HEALTH 2022; 24:101342. [PMID: 35198380 PMCID: PMC8853829 DOI: 10.1016/j.jth.2022.101342] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 01/17/2022] [Accepted: 02/15/2022] [Indexed: 05/29/2023]
Abstract
INTRODUCTION The role of cycling has become more important in the urban transport system during the Covid-19 pandemic. As public transport passengers have tried to avoid crowded vehicles due to safety concerns, a rapid surge of cycling activities has been noted in many countries. This implies that more cyclists might be exposed to air pollution, potentially leading to health problems in cities like Seoul where the level of air pollution is high. METHODS We utilised three years of bike sharing programme (Ddareungi) data in Seoul and time series models to examine the changes in the relationship between particulate concentration (PM2.5) and total daily cycling duration before and during the pandemic. RESULTS We find that cyclists reacted less to the PM2.5 level during the pandemic, potentially due to the lack of covid-secure travel modes. Specifically, our results show significant negative associations between concentrations of PM2.5 and total daily cycling duration before the pandemic (year 2018 and 2019). However, this association became insignificant in 2020. CONCLUSIONS Building comprehensive cycling infrastructure that can reduce air pollution exposure of cyclists and improving air quality alert systems could help build a more resilient city for the future.
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Affiliation(s)
- Jinhyun Hong
- Department of Urban Studies, The University of Glasgow, Glasgow, United Kingdom
| | | | - Jaehun Sim
- Korea Rural Economic Institute, Naju-si, South Korea
| | - Chung Ho Kim
- Department of Urban Planning and Design, University of Seoul, Seoul, South Korea
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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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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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Lakerveld J, Wagtendonk A, Vaartjes I, Karssenberg D. Deep phenotyping meets big data: the Geoscience and hEalth Cohort COnsortium (GECCO) data to enable exposome studies in The Netherlands. Int J Health Geogr 2020; 19:49. [PMID: 33187515 PMCID: PMC7662022 DOI: 10.1186/s12942-020-00235-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 09/15/2020] [Indexed: 01/24/2023] Open
Abstract
Environmental exposures are increasingly investigated as possible drivers of health behaviours and disease outcomes. So-called exposome studies that aim to identify and better understand the effects of exposures on behaviours and disease risk across the life course require high-quality environmental exposure data. The Netherlands has a great variety of environmental data available, including high spatial and often temporal resolution information on urban infrastructure, physico-chemical exposures, presence and availability of community services, and others. Until recently, these environmental data were scattered and measured at varying spatial scales, impeding linkage to individual-level (cohort) data as they were not operationalised as personal exposures, that is, the exposure to a certain environmental characteristic specific for a person. Within the Geoscience and hEalth Cohort COnsortium (GECCO) and with support of the Global Geo Health Data Center (GGHDC), a platform has been set up in The Netherlands where environmental variables are centralised, operationalised as personal exposures, and used to enrich 23 cohort studies and provided to researchers upon request. We here present and detail a series of personal exposure data sets that are available within GECCO to date, covering personal exposures of all residents of The Netherlands (currently about 17 M) over the full land surface of the country, and discuss challenges and opportunities for its use now and in the near future.
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Affiliation(s)
- Jeroen Lakerveld
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, VU University Amsterdam, De Boelelaan 1089a, 1081 HV, Amsterdam, The Netherlands. .,Global Geo Health Data Center, Utrecht University, Utrecht, The Netherlands. .,Upstream Team, www.upstreamteam.nl, Amsterdam UMC, VU University Amsterdam, Amsterdam, The Netherlands.
| | - Alfred Wagtendonk
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam UMC, VU University Amsterdam, De Boelelaan 1089a, 1081 HV, Amsterdam, The Netherlands.,Upstream Team, www.upstreamteam.nl, Amsterdam UMC, VU University Amsterdam, Amsterdam, The Netherlands
| | - Ilonca Vaartjes
- Global Geo Health Data Center, Utrecht University, Utrecht, The Netherlands.,Department of Epidemiology, UMC Utrecht, Div. Julius Centrum, Huispoststraat 6.131, 3508 GA, Utrecht, The Netherlands
| | - Derek Karssenberg
- Global Geo Health Data Center, Utrecht University, Utrecht, The Netherlands.,Department of Physical Geography, Faculty of Geoscience, Utrecht University, Princetonlaan 8a, 3584 CB, Utrecht, The Netherlands
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Lu M, Schmitz O, de Hoogh K, Kai Q, Karssenberg D. Evaluation of different methods and data sources to optimise modelling of NO 2 at a global scale. ENVIRONMENT INTERNATIONAL 2020; 142:105856. [PMID: 32593835 DOI: 10.1016/j.envint.2020.105856] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 04/16/2020] [Accepted: 06/02/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND In countries where air pollution stations are unavailable or scarce, station measurements from other countries and atmospheric remote sensing could jointly provide information to estimate ambient air quality at a sufficiently fine resolution to study the relationship between air pollution exposure and health. Predicting NO2 concentration globally with sufficient spatial and temporal resolution and accuracy for health studies is, however, not a trivial task. Challenges are data deficiency, in terms of NO2 measurements and NO2 predictors, and the development of a statistical model that can typify the regional and continental differences, such as traffic regulations, energy sources, and local weather. OBJECTIVE We investigated the feasibility of mapping daytime and nighttime NO2 globally at a high spatial resolution (25 m), by including TROPOMI (TROPOspheric Monitoring Instrument) data and comparing various statistical learning techniques. METHOD We separated daytime (7:00 am - 9:59 pm) and nighttime (10:00 pm - 6:59 am) based on the local times. To study if one should build models for each country separately, national models in 4 selected countries (the US, China, Germany, Spain) were developed. We build the models for 2017 and used 3636 stations. Seven statistical learning techniques were applied and the impact of the predictors, model fitting, and predicting accuracy was compared between different techniques, national models, national and global models, and models with and without including the NO2 vertical column density retrieved from TROPOMI. RESULT AND CONCLUSION The ensemble tree-based methods obtained higher accuracy compared to the linear regression-based methods in national and global models. The global tree-based methods obtained similar accuracy to national models. Different spatial prediction patterns are observed even when the prediction accuracy is very similar. Separating between day and night can be important for more accurate air pollution exposure assessment. The TROPOMI variable is ranked as one of the most important variables in the statistical learning techniques but adding it to global models that contain other precedent remote sensing products does not improve the prediction accuracy.
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Affiliation(s)
- Meng Lu
- Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, the Netherlands.
| | - Oliver Schmitz
- Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, the Netherlands
| | - Kees de Hoogh
- Swiss Tropical and Public Health Institute, Basel, Switzerland; University of Basel, Basel, Switzerland
| | - Qin Kai
- China University of Mining and Technology, Xuzhou, China
| | - Derek Karssenberg
- Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, the Netherlands
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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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