Socioeconomic and ethnic inequalities in exposure to air and noise pollution in London
Transport-related air and noise pollution, exposures linked to adverse health outcomes, varies within cities potentially resulting in exposure inequalities. Relatively little is known regarding inequalities in personal exposure to air pollution or transport-related noise. Our objectives were to quan...
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| Veröffentlicht in: | Environment international 2018-06, Vol.115, p.170-179 |
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| creator | Tonne, Cathryn Milà, Carles Fecht, Daniela Alvarez, Mar Gulliver, John Smith, James Beevers, Sean Ross Anderson, H. Kelly, Frank |
| description | Transport-related air and noise pollution, exposures linked to adverse health outcomes, varies within cities potentially resulting in exposure inequalities. Relatively little is known regarding inequalities in personal exposure to air pollution or transport-related noise.
Our objectives were to quantify socioeconomic and ethnic inequalities in London in 1) air pollution exposure at residence compared to personal exposure; and 2) transport-related noise at residence from different sources.
We used individual-level data from the London Travel Demand Survey (n = 45,079) between 2006 and 2010. We modeled residential (CMAQ-urban) and personal (London Hybrid Exposure Model) particulate matter |
| doi_str_mv | 10.1016/j.envint.2018.03.023 |
| format | Article |
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Our objectives were to quantify socioeconomic and ethnic inequalities in London in 1) air pollution exposure at residence compared to personal exposure; and 2) transport-related noise at residence from different sources.
We used individual-level data from the London Travel Demand Survey (n = 45,079) between 2006 and 2010. We modeled residential (CMAQ-urban) and personal (London Hybrid Exposure Model) particulate matter <2.5 μm and nitrogen dioxide (NO2), road-traffic noise at residence (TRANEX) and identified those within 50 dB noise contours of railways and Heathrow airport. We analyzed relationships between household income, area-level income deprivation and ethnicity with air and noise pollution using quantile and logistic regression.
We observed inverse patterns in inequalities in air pollution when estimated at residence versus personal exposure with respect to household income (categorical, 8 groups). Compared to the lowest income group (<£10,000), the highest group (>£75,000) had lower residential NO2 (−1.3 (95% CI −2.1, −0.6) μg/m3 in the 95th exposure quantile) but higher personal NO2 exposure (1.9 (95% CI 1.6, 2.3) μg/m3 in the 95th quantile), which was driven largely by transport mode and duration. Inequalities in residential exposure to NO2 with respect to area-level deprivation were larger at lower exposure quantiles (e.g. estimate for NO2 5.1 (95% CI 4.6, 5.5) at quantile 0.15 versus 1.9 (95% CI 1.1, 2.6) at quantile 0.95), reflecting low-deprivation, high residential NO2 areas in the city centre. Air pollution exposure at residence consistently overestimated personal exposure; this overestimation varied with age, household income, and area-level income deprivation. Inequalities in road traffic noise were generally small. In logistic regression models, the odds of living within a 50 dB contour of aircraft noise were highest in individuals with the highest household income, white ethnicity, and with the lowest area-level income deprivation. Odds of living within a 50 dB contour of rail noise were 19% (95% CI 3, 37) higher for black compared to white individuals.
Socioeconomic inequalities in air pollution exposure were different for modeled residential versus personal exposure, which has important implications for environmental justice and confounding in epidemiology studies. Exposure misclassification was dependent on several factors related to health, a potential source of bias in epidemiological studies. Quantile regression revealed that socioeconomic and ethnic inequalities in air pollution are often not uniform across the exposure distribution.
•Differences in inequalities in air pollution when estimated at residence vs. personal•Exposure misclassification differed by age, income, and area-level income deprivation.•Inequalities in transport noise sensitive to socioeconomic indicator and source•Quantile regression revealed inequalities not uniform across exposure distribution.</description><identifier>ISSN: 0160-4120</identifier><identifier>EISSN: 1873-6750</identifier><identifier>DOI: 10.1016/j.envint.2018.03.023</identifier><identifier>PMID: 29574337</identifier><language>eng</language><publisher>Netherlands: Elsevier Ltd</publisher><subject>Air pollution ; Air Pollution - statistics & numerical data ; Aire ; Aspectes ambientals ; Contaminació ; Housing - statistics & numerical data ; Humans ; Igualtat ; Inequalities ; London - epidemiology ; Noise ; Personal exposure ; Poverty - statistics & numerical data ; Quantile regression ; Socioeconomic Factors ; Soroll ; Transport</subject><ispartof>Environment international, 2018-06, Vol.115, p.170-179</ispartof><rights>2018 Elsevier Ltd</rights><rights>Copyright © 2018 Elsevier Ltd. All rights reserved.</rights><rights>info:eu-repo/semantics/embargoedAccess © Elsevier <a href="http://dx.doi.org/10.1016/j.envint.2018.03.023">http://dx.doi.org/10.1016/j.envint.2018.03.023</a></rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c501t-82a495cf84cbb9e0a856c5006b58d95ce0ed8b735736a2bc22cf17fccba028d63</citedby><cites>FETCH-LOGICAL-c501t-82a495cf84cbb9e0a856c5006b58d95ce0ed8b735736a2bc22cf17fccba028d63</cites><orcidid>0000-0002-2520-3344 ; 0000-0002-0738-0013 ; 0000-0002-1096-866X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0160412017321256$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>230,314,776,780,881,3537,26951,27901,27902,65534</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29574337$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Tonne, Cathryn</creatorcontrib><creatorcontrib>Milà, Carles</creatorcontrib><creatorcontrib>Fecht, Daniela</creatorcontrib><creatorcontrib>Alvarez, Mar</creatorcontrib><creatorcontrib>Gulliver, John</creatorcontrib><creatorcontrib>Smith, James</creatorcontrib><creatorcontrib>Beevers, Sean</creatorcontrib><creatorcontrib>Ross Anderson, H.</creatorcontrib><creatorcontrib>Kelly, Frank</creatorcontrib><title>Socioeconomic and ethnic inequalities in exposure to air and noise pollution in London</title><title>Environment international</title><addtitle>Environ Int</addtitle><description>Transport-related air and noise pollution, exposures linked to adverse health outcomes, varies within cities potentially resulting in exposure inequalities. Relatively little is known regarding inequalities in personal exposure to air pollution or transport-related noise.
Our objectives were to quantify socioeconomic and ethnic inequalities in London in 1) air pollution exposure at residence compared to personal exposure; and 2) transport-related noise at residence from different sources.
We used individual-level data from the London Travel Demand Survey (n = 45,079) between 2006 and 2010. We modeled residential (CMAQ-urban) and personal (London Hybrid Exposure Model) particulate matter <2.5 μm and nitrogen dioxide (NO2), road-traffic noise at residence (TRANEX) and identified those within 50 dB noise contours of railways and Heathrow airport. We analyzed relationships between household income, area-level income deprivation and ethnicity with air and noise pollution using quantile and logistic regression.
We observed inverse patterns in inequalities in air pollution when estimated at residence versus personal exposure with respect to household income (categorical, 8 groups). Compared to the lowest income group (<£10,000), the highest group (>£75,000) had lower residential NO2 (−1.3 (95% CI −2.1, −0.6) μg/m3 in the 95th exposure quantile) but higher personal NO2 exposure (1.9 (95% CI 1.6, 2.3) μg/m3 in the 95th quantile), which was driven largely by transport mode and duration. Inequalities in residential exposure to NO2 with respect to area-level deprivation were larger at lower exposure quantiles (e.g. estimate for NO2 5.1 (95% CI 4.6, 5.5) at quantile 0.15 versus 1.9 (95% CI 1.1, 2.6) at quantile 0.95), reflecting low-deprivation, high residential NO2 areas in the city centre. Air pollution exposure at residence consistently overestimated personal exposure; this overestimation varied with age, household income, and area-level income deprivation. Inequalities in road traffic noise were generally small. In logistic regression models, the odds of living within a 50 dB contour of aircraft noise were highest in individuals with the highest household income, white ethnicity, and with the lowest area-level income deprivation. Odds of living within a 50 dB contour of rail noise were 19% (95% CI 3, 37) higher for black compared to white individuals.
Socioeconomic inequalities in air pollution exposure were different for modeled residential versus personal exposure, which has important implications for environmental justice and confounding in epidemiology studies. Exposure misclassification was dependent on several factors related to health, a potential source of bias in epidemiological studies. Quantile regression revealed that socioeconomic and ethnic inequalities in air pollution are often not uniform across the exposure distribution.
•Differences in inequalities in air pollution when estimated at residence vs. personal•Exposure misclassification differed by age, income, and area-level income deprivation.•Inequalities in transport noise sensitive to socioeconomic indicator and source•Quantile regression revealed inequalities not uniform across exposure distribution.</description><subject>Air pollution</subject><subject>Air Pollution - statistics & numerical data</subject><subject>Aire</subject><subject>Aspectes ambientals</subject><subject>Contaminació</subject><subject>Housing - statistics & numerical data</subject><subject>Humans</subject><subject>Igualtat</subject><subject>Inequalities</subject><subject>London - epidemiology</subject><subject>Noise</subject><subject>Personal exposure</subject><subject>Poverty - statistics & numerical data</subject><subject>Quantile regression</subject><subject>Socioeconomic Factors</subject><subject>Soroll</subject><subject>Transport</subject><issn>0160-4120</issn><issn>1873-6750</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>XX2</sourceid><recordid>eNp9kNtOAyEQhonR2Fp9A2P2BXYdYA_0xsQ0npImXni4JSw7G2m2sMJuo28vtVXvvCAwme8b4CfknEJGgZaXqwztxtghY0BFBjwDxg_IlIqKp2VVwCGZRgzSnDKYkJMQVgDAclEckwmbF1XOeTUlr09OG4faWbc2OlG2SXB4s_FoLL6PqjODwRCLBD96F0aPyeASZfw3ap0JmPSu68bBOLvFls42zp6So1Z1Ac_2-4y83N48L-7T5ePdw-J6meoC6JAKpvJ5oVuR67qeIyhRlLEDZV2IJjYQsBF1xYuKl4rVmjHd0qrVulbARFPyGaG7uTqMWnrU6LUapFPmr9guBhWTnJW5YNHJ9453IXhsZe_NWvlPSUFuo5UruYtWbqOVwGWMNmoXO60f6zU2v9JPlhG42gEYP7wx6GXQBq3GxsS3DLJx5v8bvgAZK42s</recordid><startdate>20180601</startdate><enddate>20180601</enddate><creator>Tonne, Cathryn</creator><creator>Milà, Carles</creator><creator>Fecht, Daniela</creator><creator>Alvarez, Mar</creator><creator>Gulliver, John</creator><creator>Smith, James</creator><creator>Beevers, Sean</creator><creator>Ross Anderson, H.</creator><creator>Kelly, Frank</creator><general>Elsevier Ltd</general><general>Elsevier</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>XX2</scope><orcidid>https://orcid.org/0000-0002-2520-3344</orcidid><orcidid>https://orcid.org/0000-0002-0738-0013</orcidid><orcidid>https://orcid.org/0000-0002-1096-866X</orcidid></search><sort><creationdate>20180601</creationdate><title>Socioeconomic and ethnic inequalities in exposure to air and noise pollution in London</title><author>Tonne, Cathryn ; Milà, Carles ; Fecht, Daniela ; Alvarez, Mar ; Gulliver, John ; Smith, James ; Beevers, Sean ; Ross Anderson, H. ; Kelly, Frank</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c501t-82a495cf84cbb9e0a856c5006b58d95ce0ed8b735736a2bc22cf17fccba028d63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Air pollution</topic><topic>Air Pollution - statistics & numerical data</topic><topic>Aire</topic><topic>Aspectes ambientals</topic><topic>Contaminació</topic><topic>Housing - statistics & numerical data</topic><topic>Humans</topic><topic>Igualtat</topic><topic>Inequalities</topic><topic>London - epidemiology</topic><topic>Noise</topic><topic>Personal exposure</topic><topic>Poverty - statistics & numerical data</topic><topic>Quantile regression</topic><topic>Socioeconomic Factors</topic><topic>Soroll</topic><topic>Transport</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tonne, Cathryn</creatorcontrib><creatorcontrib>Milà, Carles</creatorcontrib><creatorcontrib>Fecht, Daniela</creatorcontrib><creatorcontrib>Alvarez, Mar</creatorcontrib><creatorcontrib>Gulliver, John</creatorcontrib><creatorcontrib>Smith, James</creatorcontrib><creatorcontrib>Beevers, Sean</creatorcontrib><creatorcontrib>Ross Anderson, H.</creatorcontrib><creatorcontrib>Kelly, Frank</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Recercat</collection><jtitle>Environment international</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tonne, Cathryn</au><au>Milà, Carles</au><au>Fecht, Daniela</au><au>Alvarez, Mar</au><au>Gulliver, John</au><au>Smith, James</au><au>Beevers, Sean</au><au>Ross Anderson, H.</au><au>Kelly, Frank</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Socioeconomic and ethnic inequalities in exposure to air and noise pollution in London</atitle><jtitle>Environment international</jtitle><addtitle>Environ Int</addtitle><date>2018-06-01</date><risdate>2018</risdate><volume>115</volume><spage>170</spage><epage>179</epage><pages>170-179</pages><issn>0160-4120</issn><eissn>1873-6750</eissn><abstract>Transport-related air and noise pollution, exposures linked to adverse health outcomes, varies within cities potentially resulting in exposure inequalities. Relatively little is known regarding inequalities in personal exposure to air pollution or transport-related noise.
Our objectives were to quantify socioeconomic and ethnic inequalities in London in 1) air pollution exposure at residence compared to personal exposure; and 2) transport-related noise at residence from different sources.
We used individual-level data from the London Travel Demand Survey (n = 45,079) between 2006 and 2010. We modeled residential (CMAQ-urban) and personal (London Hybrid Exposure Model) particulate matter <2.5 μm and nitrogen dioxide (NO2), road-traffic noise at residence (TRANEX) and identified those within 50 dB noise contours of railways and Heathrow airport. We analyzed relationships between household income, area-level income deprivation and ethnicity with air and noise pollution using quantile and logistic regression.
We observed inverse patterns in inequalities in air pollution when estimated at residence versus personal exposure with respect to household income (categorical, 8 groups). Compared to the lowest income group (<£10,000), the highest group (>£75,000) had lower residential NO2 (−1.3 (95% CI −2.1, −0.6) μg/m3 in the 95th exposure quantile) but higher personal NO2 exposure (1.9 (95% CI 1.6, 2.3) μg/m3 in the 95th quantile), which was driven largely by transport mode and duration. Inequalities in residential exposure to NO2 with respect to area-level deprivation were larger at lower exposure quantiles (e.g. estimate for NO2 5.1 (95% CI 4.6, 5.5) at quantile 0.15 versus 1.9 (95% CI 1.1, 2.6) at quantile 0.95), reflecting low-deprivation, high residential NO2 areas in the city centre. Air pollution exposure at residence consistently overestimated personal exposure; this overestimation varied with age, household income, and area-level income deprivation. Inequalities in road traffic noise were generally small. In logistic regression models, the odds of living within a 50 dB contour of aircraft noise were highest in individuals with the highest household income, white ethnicity, and with the lowest area-level income deprivation. Odds of living within a 50 dB contour of rail noise were 19% (95% CI 3, 37) higher for black compared to white individuals.
Socioeconomic inequalities in air pollution exposure were different for modeled residential versus personal exposure, which has important implications for environmental justice and confounding in epidemiology studies. Exposure misclassification was dependent on several factors related to health, a potential source of bias in epidemiological studies. Quantile regression revealed that socioeconomic and ethnic inequalities in air pollution are often not uniform across the exposure distribution.
•Differences in inequalities in air pollution when estimated at residence vs. personal•Exposure misclassification differed by age, income, and area-level income deprivation.•Inequalities in transport noise sensitive to socioeconomic indicator and source•Quantile regression revealed inequalities not uniform across exposure distribution.</abstract><cop>Netherlands</cop><pub>Elsevier Ltd</pub><pmid>29574337</pmid><doi>10.1016/j.envint.2018.03.023</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-2520-3344</orcidid><orcidid>https://orcid.org/0000-0002-0738-0013</orcidid><orcidid>https://orcid.org/0000-0002-1096-866X</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Environment international, 2018-06, Vol.115, p.170-179 |
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| subjects | Air pollution Air Pollution - statistics & numerical data Aire Aspectes ambientals Contaminació Housing - statistics & numerical data Humans Igualtat Inequalities London - epidemiology Noise Personal exposure Poverty - statistics & numerical data Quantile regression Socioeconomic Factors Soroll Transport |
| title | Socioeconomic and ethnic inequalities in exposure to air and noise pollution in London |
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