Spatiotemporal air pollution exposure assessment for a Canadian population-based lung cancer case-control study
<p<Abstract</p< <p<Background</p< <p<Few epidemiological studies of air pollution have used residential histories to develop long-term retrospective exposure estimates for multiple ambient air pollutants and vehicle and industrial emissions. We present such an exposure...
Ausführliche Beschreibung
Autor*in: |
Hystad Perry [verfasserIn] Demers Paul A [verfasserIn] Johnson Kenneth C [verfasserIn] Brook Jeff [verfasserIn] van Donkelaar Aaron [verfasserIn] Lamsal Lok [verfasserIn] Martin Randall [verfasserIn] Brauer Michael [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2012 |
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Übergeordnetes Werk: |
In: Environmental Health - BMC, 2003, 11(2012), 1, p 22 |
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Übergeordnetes Werk: |
volume:11 ; year:2012 ; number:1, p 22 |
Links: |
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DOI / URN: |
10.1186/1476-069X-11-22 |
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Katalog-ID: |
DOAJ005380189 |
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520 | |a <p<Abstract</p< <p<Background</p< <p<Few epidemiological studies of air pollution have used residential histories to develop long-term retrospective exposure estimates for multiple ambient air pollutants and vehicle and industrial emissions. We present such an exposure assessment for a Canadian population-based lung cancer case-control study of 8353 individuals using self-reported residential histories from 1975 to 1994. We also examine the implications of disregarding and/or improperly accounting for residential mobility in long-term exposure assessments.</p< <p<Methods</p< <p<National spatial surfaces of ambient air pollution were compiled from recent satellite-based estimates (for PM<sub<2.5 </sub<and NO<sub<2</sub<) and a chemical transport model (for O<sub<3</sub<). The surfaces were adjusted with historical annual air pollution monitoring data, using either spatiotemporal interpolation or linear regression. Model evaluation was conducted using an independent ten percent subset of monitoring data per year. Proximity to major roads, incorporating a temporal weighting factor based on Canadian mobile-source emission estimates, was used to estimate exposure to vehicle emissions. A comprehensive inventory of geocoded industries was used to estimate proximity to major and minor industrial emissions.</p< <p<Results</p< <p<Calibration of the national PM<sub<2.5 </sub<surface using annual spatiotemporal interpolation predicted historical PM<sub<2.5 </sub<measurement data best (R<sup<2 </sup<= 0.51), while linear regression incorporating the national surfaces, a time-trend and population density best predicted historical concentrations of NO<sub<2 </sub<(R<sup<2 </sup<= 0.38) and O<sub<3 </sub<(R<sup<2 </sup<= 0.56). Applying the models to study participants residential histories between 1975 and 1994 resulted in mean PM<sub<2.5</sub<, NO<sub<2 </sub<and O<sub<3 </sub<exposures of 11.3 μg/m<sup<3 </sup<(SD = 2.6), 17.7 ppb (4.1), and 26.4 ppb (3.4) respectively. On average, individuals lived within 300 m of a highway for 2.9 years (15% of exposure-years) and within 3 km of a major industrial emitter for 6.4 years (32% of exposure-years). Approximately 50% of individuals were classified into a different PM<sub<2.5</sub<, NO<sub<2 </sub<and O<sub<3 </sub<exposure quintile when using study entry postal codes and spatial pollution surfaces, in comparison to exposures derived from residential histories and spatiotemporal air pollution models. Recall bias was also present for self-reported residential histories prior to 1975, with cases recalling older residences more often than controls.</p< <p<Conclusions</p< <p<We demonstrate a flexible exposure assessment approach for estimating historical air pollution concentrations over large geographical areas and time-periods. In addition, we highlight the importance of including residential histories in long-term exposure assessments.</p< <p<For submission to: Environmental Health</p< | ||
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650 | 4 | |a Exposure assessment | |
650 | 4 | |a Lung cancer | |
650 | 4 | |a Residential mobility | |
650 | 4 | |a Spatiotemporal | |
653 | 0 | |a Industrial medicine. Industrial hygiene | |
653 | 0 | |a Public aspects of medicine | |
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700 | 0 | |a Brook Jeff |e verfasserin |4 aut | |
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700 | 0 | |a Lamsal Lok |e verfasserin |4 aut | |
700 | 0 | |a Martin Randall |e verfasserin |4 aut | |
700 | 0 | |a Brauer Michael |e verfasserin |4 aut | |
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10.1186/1476-069X-11-22 doi (DE-627)DOAJ005380189 (DE-599)DOAJ2426d0875dfa463582076de73529890d DE-627 ger DE-627 rakwb eng RC963-969 RA1-1270 Hystad Perry verfasserin aut Spatiotemporal air pollution exposure assessment for a Canadian population-based lung cancer case-control study 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Few epidemiological studies of air pollution have used residential histories to develop long-term retrospective exposure estimates for multiple ambient air pollutants and vehicle and industrial emissions. We present such an exposure assessment for a Canadian population-based lung cancer case-control study of 8353 individuals using self-reported residential histories from 1975 to 1994. We also examine the implications of disregarding and/or improperly accounting for residential mobility in long-term exposure assessments.</p< <p<Methods</p< <p<National spatial surfaces of ambient air pollution were compiled from recent satellite-based estimates (for PM<sub<2.5 </sub<and NO<sub<2</sub<) and a chemical transport model (for O<sub<3</sub<). The surfaces were adjusted with historical annual air pollution monitoring data, using either spatiotemporal interpolation or linear regression. Model evaluation was conducted using an independent ten percent subset of monitoring data per year. Proximity to major roads, incorporating a temporal weighting factor based on Canadian mobile-source emission estimates, was used to estimate exposure to vehicle emissions. A comprehensive inventory of geocoded industries was used to estimate proximity to major and minor industrial emissions.</p< <p<Results</p< <p<Calibration of the national PM<sub<2.5 </sub<surface using annual spatiotemporal interpolation predicted historical PM<sub<2.5 </sub<measurement data best (R<sup<2 </sup<= 0.51), while linear regression incorporating the national surfaces, a time-trend and population density best predicted historical concentrations of NO<sub<2 </sub<(R<sup<2 </sup<= 0.38) and O<sub<3 </sub<(R<sup<2 </sup<= 0.56). Applying the models to study participants residential histories between 1975 and 1994 resulted in mean PM<sub<2.5</sub<, NO<sub<2 </sub<and O<sub<3 </sub<exposures of 11.3 μg/m<sup<3 </sup<(SD = 2.6), 17.7 ppb (4.1), and 26.4 ppb (3.4) respectively. On average, individuals lived within 300 m of a highway for 2.9 years (15% of exposure-years) and within 3 km of a major industrial emitter for 6.4 years (32% of exposure-years). Approximately 50% of individuals were classified into a different PM<sub<2.5</sub<, NO<sub<2 </sub<and O<sub<3 </sub<exposure quintile when using study entry postal codes and spatial pollution surfaces, in comparison to exposures derived from residential histories and spatiotemporal air pollution models. Recall bias was also present for self-reported residential histories prior to 1975, with cases recalling older residences more often than controls.</p< <p<Conclusions</p< <p<We demonstrate a flexible exposure assessment approach for estimating historical air pollution concentrations over large geographical areas and time-periods. In addition, we highlight the importance of including residential histories in long-term exposure assessments.</p< <p<For submission to: Environmental Health</p< Air pollution Canada Exposure assessment Lung cancer Residential mobility Spatiotemporal Industrial medicine. Industrial hygiene Public aspects of medicine Demers Paul A verfasserin aut Johnson Kenneth C verfasserin aut Brook Jeff verfasserin aut van Donkelaar Aaron verfasserin aut Lamsal Lok verfasserin aut Martin Randall verfasserin aut Brauer Michael verfasserin aut In Environmental Health BMC, 2003 11(2012), 1, p 22 (DE-627)355849550 (DE-600)2092232-2 1476069X nnns volume:11 year:2012 number:1, p 22 https://doi.org/10.1186/1476-069X-11-22 kostenfrei https://doaj.org/article/2426d0875dfa463582076de73529890d kostenfrei http://www.ehjournal.net/content/11/1/22 kostenfrei https://doaj.org/toc/1476-069X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2012 1, p 22 |
spelling |
10.1186/1476-069X-11-22 doi (DE-627)DOAJ005380189 (DE-599)DOAJ2426d0875dfa463582076de73529890d DE-627 ger DE-627 rakwb eng RC963-969 RA1-1270 Hystad Perry verfasserin aut Spatiotemporal air pollution exposure assessment for a Canadian population-based lung cancer case-control study 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Few epidemiological studies of air pollution have used residential histories to develop long-term retrospective exposure estimates for multiple ambient air pollutants and vehicle and industrial emissions. We present such an exposure assessment for a Canadian population-based lung cancer case-control study of 8353 individuals using self-reported residential histories from 1975 to 1994. We also examine the implications of disregarding and/or improperly accounting for residential mobility in long-term exposure assessments.</p< <p<Methods</p< <p<National spatial surfaces of ambient air pollution were compiled from recent satellite-based estimates (for PM<sub<2.5 </sub<and NO<sub<2</sub<) and a chemical transport model (for O<sub<3</sub<). The surfaces were adjusted with historical annual air pollution monitoring data, using either spatiotemporal interpolation or linear regression. Model evaluation was conducted using an independent ten percent subset of monitoring data per year. Proximity to major roads, incorporating a temporal weighting factor based on Canadian mobile-source emission estimates, was used to estimate exposure to vehicle emissions. A comprehensive inventory of geocoded industries was used to estimate proximity to major and minor industrial emissions.</p< <p<Results</p< <p<Calibration of the national PM<sub<2.5 </sub<surface using annual spatiotemporal interpolation predicted historical PM<sub<2.5 </sub<measurement data best (R<sup<2 </sup<= 0.51), while linear regression incorporating the national surfaces, a time-trend and population density best predicted historical concentrations of NO<sub<2 </sub<(R<sup<2 </sup<= 0.38) and O<sub<3 </sub<(R<sup<2 </sup<= 0.56). Applying the models to study participants residential histories between 1975 and 1994 resulted in mean PM<sub<2.5</sub<, NO<sub<2 </sub<and O<sub<3 </sub<exposures of 11.3 μg/m<sup<3 </sup<(SD = 2.6), 17.7 ppb (4.1), and 26.4 ppb (3.4) respectively. On average, individuals lived within 300 m of a highway for 2.9 years (15% of exposure-years) and within 3 km of a major industrial emitter for 6.4 years (32% of exposure-years). Approximately 50% of individuals were classified into a different PM<sub<2.5</sub<, NO<sub<2 </sub<and O<sub<3 </sub<exposure quintile when using study entry postal codes and spatial pollution surfaces, in comparison to exposures derived from residential histories and spatiotemporal air pollution models. Recall bias was also present for self-reported residential histories prior to 1975, with cases recalling older residences more often than controls.</p< <p<Conclusions</p< <p<We demonstrate a flexible exposure assessment approach for estimating historical air pollution concentrations over large geographical areas and time-periods. In addition, we highlight the importance of including residential histories in long-term exposure assessments.</p< <p<For submission to: Environmental Health</p< Air pollution Canada Exposure assessment Lung cancer Residential mobility Spatiotemporal Industrial medicine. Industrial hygiene Public aspects of medicine Demers Paul A verfasserin aut Johnson Kenneth C verfasserin aut Brook Jeff verfasserin aut van Donkelaar Aaron verfasserin aut Lamsal Lok verfasserin aut Martin Randall verfasserin aut Brauer Michael verfasserin aut In Environmental Health BMC, 2003 11(2012), 1, p 22 (DE-627)355849550 (DE-600)2092232-2 1476069X nnns volume:11 year:2012 number:1, p 22 https://doi.org/10.1186/1476-069X-11-22 kostenfrei https://doaj.org/article/2426d0875dfa463582076de73529890d kostenfrei http://www.ehjournal.net/content/11/1/22 kostenfrei https://doaj.org/toc/1476-069X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2012 1, p 22 |
allfields_unstemmed |
10.1186/1476-069X-11-22 doi (DE-627)DOAJ005380189 (DE-599)DOAJ2426d0875dfa463582076de73529890d DE-627 ger DE-627 rakwb eng RC963-969 RA1-1270 Hystad Perry verfasserin aut Spatiotemporal air pollution exposure assessment for a Canadian population-based lung cancer case-control study 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Few epidemiological studies of air pollution have used residential histories to develop long-term retrospective exposure estimates for multiple ambient air pollutants and vehicle and industrial emissions. We present such an exposure assessment for a Canadian population-based lung cancer case-control study of 8353 individuals using self-reported residential histories from 1975 to 1994. We also examine the implications of disregarding and/or improperly accounting for residential mobility in long-term exposure assessments.</p< <p<Methods</p< <p<National spatial surfaces of ambient air pollution were compiled from recent satellite-based estimates (for PM<sub<2.5 </sub<and NO<sub<2</sub<) and a chemical transport model (for O<sub<3</sub<). The surfaces were adjusted with historical annual air pollution monitoring data, using either spatiotemporal interpolation or linear regression. Model evaluation was conducted using an independent ten percent subset of monitoring data per year. Proximity to major roads, incorporating a temporal weighting factor based on Canadian mobile-source emission estimates, was used to estimate exposure to vehicle emissions. A comprehensive inventory of geocoded industries was used to estimate proximity to major and minor industrial emissions.</p< <p<Results</p< <p<Calibration of the national PM<sub<2.5 </sub<surface using annual spatiotemporal interpolation predicted historical PM<sub<2.5 </sub<measurement data best (R<sup<2 </sup<= 0.51), while linear regression incorporating the national surfaces, a time-trend and population density best predicted historical concentrations of NO<sub<2 </sub<(R<sup<2 </sup<= 0.38) and O<sub<3 </sub<(R<sup<2 </sup<= 0.56). Applying the models to study participants residential histories between 1975 and 1994 resulted in mean PM<sub<2.5</sub<, NO<sub<2 </sub<and O<sub<3 </sub<exposures of 11.3 μg/m<sup<3 </sup<(SD = 2.6), 17.7 ppb (4.1), and 26.4 ppb (3.4) respectively. On average, individuals lived within 300 m of a highway for 2.9 years (15% of exposure-years) and within 3 km of a major industrial emitter for 6.4 years (32% of exposure-years). Approximately 50% of individuals were classified into a different PM<sub<2.5</sub<, NO<sub<2 </sub<and O<sub<3 </sub<exposure quintile when using study entry postal codes and spatial pollution surfaces, in comparison to exposures derived from residential histories and spatiotemporal air pollution models. Recall bias was also present for self-reported residential histories prior to 1975, with cases recalling older residences more often than controls.</p< <p<Conclusions</p< <p<We demonstrate a flexible exposure assessment approach for estimating historical air pollution concentrations over large geographical areas and time-periods. In addition, we highlight the importance of including residential histories in long-term exposure assessments.</p< <p<For submission to: Environmental Health</p< Air pollution Canada Exposure assessment Lung cancer Residential mobility Spatiotemporal Industrial medicine. Industrial hygiene Public aspects of medicine Demers Paul A verfasserin aut Johnson Kenneth C verfasserin aut Brook Jeff verfasserin aut van Donkelaar Aaron verfasserin aut Lamsal Lok verfasserin aut Martin Randall verfasserin aut Brauer Michael verfasserin aut In Environmental Health BMC, 2003 11(2012), 1, p 22 (DE-627)355849550 (DE-600)2092232-2 1476069X nnns volume:11 year:2012 number:1, p 22 https://doi.org/10.1186/1476-069X-11-22 kostenfrei https://doaj.org/article/2426d0875dfa463582076de73529890d kostenfrei http://www.ehjournal.net/content/11/1/22 kostenfrei https://doaj.org/toc/1476-069X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2012 1, p 22 |
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10.1186/1476-069X-11-22 doi (DE-627)DOAJ005380189 (DE-599)DOAJ2426d0875dfa463582076de73529890d DE-627 ger DE-627 rakwb eng RC963-969 RA1-1270 Hystad Perry verfasserin aut Spatiotemporal air pollution exposure assessment for a Canadian population-based lung cancer case-control study 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Few epidemiological studies of air pollution have used residential histories to develop long-term retrospective exposure estimates for multiple ambient air pollutants and vehicle and industrial emissions. We present such an exposure assessment for a Canadian population-based lung cancer case-control study of 8353 individuals using self-reported residential histories from 1975 to 1994. We also examine the implications of disregarding and/or improperly accounting for residential mobility in long-term exposure assessments.</p< <p<Methods</p< <p<National spatial surfaces of ambient air pollution were compiled from recent satellite-based estimates (for PM<sub<2.5 </sub<and NO<sub<2</sub<) and a chemical transport model (for O<sub<3</sub<). The surfaces were adjusted with historical annual air pollution monitoring data, using either spatiotemporal interpolation or linear regression. Model evaluation was conducted using an independent ten percent subset of monitoring data per year. Proximity to major roads, incorporating a temporal weighting factor based on Canadian mobile-source emission estimates, was used to estimate exposure to vehicle emissions. A comprehensive inventory of geocoded industries was used to estimate proximity to major and minor industrial emissions.</p< <p<Results</p< <p<Calibration of the national PM<sub<2.5 </sub<surface using annual spatiotemporal interpolation predicted historical PM<sub<2.5 </sub<measurement data best (R<sup<2 </sup<= 0.51), while linear regression incorporating the national surfaces, a time-trend and population density best predicted historical concentrations of NO<sub<2 </sub<(R<sup<2 </sup<= 0.38) and O<sub<3 </sub<(R<sup<2 </sup<= 0.56). Applying the models to study participants residential histories between 1975 and 1994 resulted in mean PM<sub<2.5</sub<, NO<sub<2 </sub<and O<sub<3 </sub<exposures of 11.3 μg/m<sup<3 </sup<(SD = 2.6), 17.7 ppb (4.1), and 26.4 ppb (3.4) respectively. On average, individuals lived within 300 m of a highway for 2.9 years (15% of exposure-years) and within 3 km of a major industrial emitter for 6.4 years (32% of exposure-years). Approximately 50% of individuals were classified into a different PM<sub<2.5</sub<, NO<sub<2 </sub<and O<sub<3 </sub<exposure quintile when using study entry postal codes and spatial pollution surfaces, in comparison to exposures derived from residential histories and spatiotemporal air pollution models. Recall bias was also present for self-reported residential histories prior to 1975, with cases recalling older residences more often than controls.</p< <p<Conclusions</p< <p<We demonstrate a flexible exposure assessment approach for estimating historical air pollution concentrations over large geographical areas and time-periods. In addition, we highlight the importance of including residential histories in long-term exposure assessments.</p< <p<For submission to: Environmental Health</p< Air pollution Canada Exposure assessment Lung cancer Residential mobility Spatiotemporal Industrial medicine. Industrial hygiene Public aspects of medicine Demers Paul A verfasserin aut Johnson Kenneth C verfasserin aut Brook Jeff verfasserin aut van Donkelaar Aaron verfasserin aut Lamsal Lok verfasserin aut Martin Randall verfasserin aut Brauer Michael verfasserin aut In Environmental Health BMC, 2003 11(2012), 1, p 22 (DE-627)355849550 (DE-600)2092232-2 1476069X nnns volume:11 year:2012 number:1, p 22 https://doi.org/10.1186/1476-069X-11-22 kostenfrei https://doaj.org/article/2426d0875dfa463582076de73529890d kostenfrei http://www.ehjournal.net/content/11/1/22 kostenfrei https://doaj.org/toc/1476-069X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2012 1, p 22 |
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10.1186/1476-069X-11-22 doi (DE-627)DOAJ005380189 (DE-599)DOAJ2426d0875dfa463582076de73529890d DE-627 ger DE-627 rakwb eng RC963-969 RA1-1270 Hystad Perry verfasserin aut Spatiotemporal air pollution exposure assessment for a Canadian population-based lung cancer case-control study 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<Few epidemiological studies of air pollution have used residential histories to develop long-term retrospective exposure estimates for multiple ambient air pollutants and vehicle and industrial emissions. We present such an exposure assessment for a Canadian population-based lung cancer case-control study of 8353 individuals using self-reported residential histories from 1975 to 1994. We also examine the implications of disregarding and/or improperly accounting for residential mobility in long-term exposure assessments.</p< <p<Methods</p< <p<National spatial surfaces of ambient air pollution were compiled from recent satellite-based estimates (for PM<sub<2.5 </sub<and NO<sub<2</sub<) and a chemical transport model (for O<sub<3</sub<). The surfaces were adjusted with historical annual air pollution monitoring data, using either spatiotemporal interpolation or linear regression. Model evaluation was conducted using an independent ten percent subset of monitoring data per year. Proximity to major roads, incorporating a temporal weighting factor based on Canadian mobile-source emission estimates, was used to estimate exposure to vehicle emissions. A comprehensive inventory of geocoded industries was used to estimate proximity to major and minor industrial emissions.</p< <p<Results</p< <p<Calibration of the national PM<sub<2.5 </sub<surface using annual spatiotemporal interpolation predicted historical PM<sub<2.5 </sub<measurement data best (R<sup<2 </sup<= 0.51), while linear regression incorporating the national surfaces, a time-trend and population density best predicted historical concentrations of NO<sub<2 </sub<(R<sup<2 </sup<= 0.38) and O<sub<3 </sub<(R<sup<2 </sup<= 0.56). Applying the models to study participants residential histories between 1975 and 1994 resulted in mean PM<sub<2.5</sub<, NO<sub<2 </sub<and O<sub<3 </sub<exposures of 11.3 μg/m<sup<3 </sup<(SD = 2.6), 17.7 ppb (4.1), and 26.4 ppb (3.4) respectively. On average, individuals lived within 300 m of a highway for 2.9 years (15% of exposure-years) and within 3 km of a major industrial emitter for 6.4 years (32% of exposure-years). Approximately 50% of individuals were classified into a different PM<sub<2.5</sub<, NO<sub<2 </sub<and O<sub<3 </sub<exposure quintile when using study entry postal codes and spatial pollution surfaces, in comparison to exposures derived from residential histories and spatiotemporal air pollution models. Recall bias was also present for self-reported residential histories prior to 1975, with cases recalling older residences more often than controls.</p< <p<Conclusions</p< <p<We demonstrate a flexible exposure assessment approach for estimating historical air pollution concentrations over large geographical areas and time-periods. In addition, we highlight the importance of including residential histories in long-term exposure assessments.</p< <p<For submission to: Environmental Health</p< Air pollution Canada Exposure assessment Lung cancer Residential mobility Spatiotemporal Industrial medicine. Industrial hygiene Public aspects of medicine Demers Paul A verfasserin aut Johnson Kenneth C verfasserin aut Brook Jeff verfasserin aut van Donkelaar Aaron verfasserin aut Lamsal Lok verfasserin aut Martin Randall verfasserin aut Brauer Michael verfasserin aut In Environmental Health BMC, 2003 11(2012), 1, p 22 (DE-627)355849550 (DE-600)2092232-2 1476069X nnns volume:11 year:2012 number:1, p 22 https://doi.org/10.1186/1476-069X-11-22 kostenfrei https://doaj.org/article/2426d0875dfa463582076de73529890d kostenfrei http://www.ehjournal.net/content/11/1/22 kostenfrei https://doaj.org/toc/1476-069X Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2012 1, p 22 |
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Spatiotemporal air pollution exposure assessment for a Canadian population-based lung cancer case-control study |
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<p<Abstract</p< <p<Background</p< <p<Few epidemiological studies of air pollution have used residential histories to develop long-term retrospective exposure estimates for multiple ambient air pollutants and vehicle and industrial emissions. We present such an exposure assessment for a Canadian population-based lung cancer case-control study of 8353 individuals using self-reported residential histories from 1975 to 1994. We also examine the implications of disregarding and/or improperly accounting for residential mobility in long-term exposure assessments.</p< <p<Methods</p< <p<National spatial surfaces of ambient air pollution were compiled from recent satellite-based estimates (for PM<sub<2.5 </sub<and NO<sub<2</sub<) and a chemical transport model (for O<sub<3</sub<). The surfaces were adjusted with historical annual air pollution monitoring data, using either spatiotemporal interpolation or linear regression. Model evaluation was conducted using an independent ten percent subset of monitoring data per year. Proximity to major roads, incorporating a temporal weighting factor based on Canadian mobile-source emission estimates, was used to estimate exposure to vehicle emissions. A comprehensive inventory of geocoded industries was used to estimate proximity to major and minor industrial emissions.</p< <p<Results</p< <p<Calibration of the national PM<sub<2.5 </sub<surface using annual spatiotemporal interpolation predicted historical PM<sub<2.5 </sub<measurement data best (R<sup<2 </sup<= 0.51), while linear regression incorporating the national surfaces, a time-trend and population density best predicted historical concentrations of NO<sub<2 </sub<(R<sup<2 </sup<= 0.38) and O<sub<3 </sub<(R<sup<2 </sup<= 0.56). Applying the models to study participants residential histories between 1975 and 1994 resulted in mean PM<sub<2.5</sub<, NO<sub<2 </sub<and O<sub<3 </sub<exposures of 11.3 μg/m<sup<3 </sup<(SD = 2.6), 17.7 ppb (4.1), and 26.4 ppb (3.4) respectively. On average, individuals lived within 300 m of a highway for 2.9 years (15% of exposure-years) and within 3 km of a major industrial emitter for 6.4 years (32% of exposure-years). Approximately 50% of individuals were classified into a different PM<sub<2.5</sub<, NO<sub<2 </sub<and O<sub<3 </sub<exposure quintile when using study entry postal codes and spatial pollution surfaces, in comparison to exposures derived from residential histories and spatiotemporal air pollution models. Recall bias was also present for self-reported residential histories prior to 1975, with cases recalling older residences more often than controls.</p< <p<Conclusions</p< <p<We demonstrate a flexible exposure assessment approach for estimating historical air pollution concentrations over large geographical areas and time-periods. In addition, we highlight the importance of including residential histories in long-term exposure assessments.</p< <p<For submission to: Environmental Health</p< |
abstractGer |
<p<Abstract</p< <p<Background</p< <p<Few epidemiological studies of air pollution have used residential histories to develop long-term retrospective exposure estimates for multiple ambient air pollutants and vehicle and industrial emissions. We present such an exposure assessment for a Canadian population-based lung cancer case-control study of 8353 individuals using self-reported residential histories from 1975 to 1994. We also examine the implications of disregarding and/or improperly accounting for residential mobility in long-term exposure assessments.</p< <p<Methods</p< <p<National spatial surfaces of ambient air pollution were compiled from recent satellite-based estimates (for PM<sub<2.5 </sub<and NO<sub<2</sub<) and a chemical transport model (for O<sub<3</sub<). The surfaces were adjusted with historical annual air pollution monitoring data, using either spatiotemporal interpolation or linear regression. Model evaluation was conducted using an independent ten percent subset of monitoring data per year. Proximity to major roads, incorporating a temporal weighting factor based on Canadian mobile-source emission estimates, was used to estimate exposure to vehicle emissions. A comprehensive inventory of geocoded industries was used to estimate proximity to major and minor industrial emissions.</p< <p<Results</p< <p<Calibration of the national PM<sub<2.5 </sub<surface using annual spatiotemporal interpolation predicted historical PM<sub<2.5 </sub<measurement data best (R<sup<2 </sup<= 0.51), while linear regression incorporating the national surfaces, a time-trend and population density best predicted historical concentrations of NO<sub<2 </sub<(R<sup<2 </sup<= 0.38) and O<sub<3 </sub<(R<sup<2 </sup<= 0.56). Applying the models to study participants residential histories between 1975 and 1994 resulted in mean PM<sub<2.5</sub<, NO<sub<2 </sub<and O<sub<3 </sub<exposures of 11.3 μg/m<sup<3 </sup<(SD = 2.6), 17.7 ppb (4.1), and 26.4 ppb (3.4) respectively. On average, individuals lived within 300 m of a highway for 2.9 years (15% of exposure-years) and within 3 km of a major industrial emitter for 6.4 years (32% of exposure-years). Approximately 50% of individuals were classified into a different PM<sub<2.5</sub<, NO<sub<2 </sub<and O<sub<3 </sub<exposure quintile when using study entry postal codes and spatial pollution surfaces, in comparison to exposures derived from residential histories and spatiotemporal air pollution models. Recall bias was also present for self-reported residential histories prior to 1975, with cases recalling older residences more often than controls.</p< <p<Conclusions</p< <p<We demonstrate a flexible exposure assessment approach for estimating historical air pollution concentrations over large geographical areas and time-periods. In addition, we highlight the importance of including residential histories in long-term exposure assessments.</p< <p<For submission to: Environmental Health</p< |
abstract_unstemmed |
<p<Abstract</p< <p<Background</p< <p<Few epidemiological studies of air pollution have used residential histories to develop long-term retrospective exposure estimates for multiple ambient air pollutants and vehicle and industrial emissions. We present such an exposure assessment for a Canadian population-based lung cancer case-control study of 8353 individuals using self-reported residential histories from 1975 to 1994. We also examine the implications of disregarding and/or improperly accounting for residential mobility in long-term exposure assessments.</p< <p<Methods</p< <p<National spatial surfaces of ambient air pollution were compiled from recent satellite-based estimates (for PM<sub<2.5 </sub<and NO<sub<2</sub<) and a chemical transport model (for O<sub<3</sub<). The surfaces were adjusted with historical annual air pollution monitoring data, using either spatiotemporal interpolation or linear regression. Model evaluation was conducted using an independent ten percent subset of monitoring data per year. Proximity to major roads, incorporating a temporal weighting factor based on Canadian mobile-source emission estimates, was used to estimate exposure to vehicle emissions. A comprehensive inventory of geocoded industries was used to estimate proximity to major and minor industrial emissions.</p< <p<Results</p< <p<Calibration of the national PM<sub<2.5 </sub<surface using annual spatiotemporal interpolation predicted historical PM<sub<2.5 </sub<measurement data best (R<sup<2 </sup<= 0.51), while linear regression incorporating the national surfaces, a time-trend and population density best predicted historical concentrations of NO<sub<2 </sub<(R<sup<2 </sup<= 0.38) and O<sub<3 </sub<(R<sup<2 </sup<= 0.56). Applying the models to study participants residential histories between 1975 and 1994 resulted in mean PM<sub<2.5</sub<, NO<sub<2 </sub<and O<sub<3 </sub<exposures of 11.3 μg/m<sup<3 </sup<(SD = 2.6), 17.7 ppb (4.1), and 26.4 ppb (3.4) respectively. On average, individuals lived within 300 m of a highway for 2.9 years (15% of exposure-years) and within 3 km of a major industrial emitter for 6.4 years (32% of exposure-years). Approximately 50% of individuals were classified into a different PM<sub<2.5</sub<, NO<sub<2 </sub<and O<sub<3 </sub<exposure quintile when using study entry postal codes and spatial pollution surfaces, in comparison to exposures derived from residential histories and spatiotemporal air pollution models. Recall bias was also present for self-reported residential histories prior to 1975, with cases recalling older residences more often than controls.</p< <p<Conclusions</p< <p<We demonstrate a flexible exposure assessment approach for estimating historical air pollution concentrations over large geographical areas and time-periods. In addition, we highlight the importance of including residential histories in long-term exposure assessments.</p< <p<For submission to: Environmental Health</p< |
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Approximately 50% of individuals were classified into a different PM<sub<2.5</sub<, NO<sub<2 </sub<and O<sub<3 </sub<exposure quintile when using study entry postal codes and spatial pollution surfaces, in comparison to exposures derived from residential histories and spatiotemporal air pollution models. Recall bias was also present for self-reported residential histories prior to 1975, with cases recalling older residences more often than controls.</p< <p<Conclusions</p< <p<We demonstrate a flexible exposure assessment approach for estimating historical air pollution concentrations over large geographical areas and time-periods. 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