Mitigating the effects of preferentially selected monitoring sites for environmental policy and health risk analysis
Copyright © 2016 Elsevier Ltd. All rights reserved..
The potential effects of air pollution are a major concern both in terms of the environment and in relation to human health. In order to support both environmental and health policy there is a need for accurate estimates of the exposures that populations might experience. The information for this typically comes from environmental monitoring networks but often the locations of monitoring sites are preferentially located in order to detect high levels of pollution. Using the information from such networks has the potential to seriously affect the estimates of pollution that are obtained and that might be used in health risk analyses. In this context, we explore the topic of preferential sampling within a long-standing network in the UK that monitored black smoke due to concerns about its effect on public health, the extent of which came to prominence during the famous London fog of 1952. Abatement measures led to a decline in the levels of black smoke and a subsequent reduction in the number of monitoring locations that were thought necessary to provide the information required for policy support. There is evidence of selection bias during this process with sites being kept in the most polluted areas. We assess the potential for this to affect the estimates of risk associated air pollution and show how using Bayesian spatio-temporal exposure models may be used to attempt to mitigate the effects of preferential sampling in this case.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2016 |
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| Erschienen: |
2016 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:18 |
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| Enthalten in: |
Spatial and spatio-temporal epidemiology - 18(2016) vom: 05. Aug., Seite 44-52 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Shaddick, Gavin [VerfasserIn] |
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| Links: |
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| Themen: |
Air pollution |
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| Anmerkungen: |
Date Completed 13.11.2017 Date Revised 17.03.2018 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.sste.2016.03.005 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM263160580 |
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| 500 | |a Date Revised 17.03.2018 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2016 Elsevier Ltd. All rights reserved. | ||
| 520 | |a The potential effects of air pollution are a major concern both in terms of the environment and in relation to human health. In order to support both environmental and health policy there is a need for accurate estimates of the exposures that populations might experience. The information for this typically comes from environmental monitoring networks but often the locations of monitoring sites are preferentially located in order to detect high levels of pollution. Using the information from such networks has the potential to seriously affect the estimates of pollution that are obtained and that might be used in health risk analyses. In this context, we explore the topic of preferential sampling within a long-standing network in the UK that monitored black smoke due to concerns about its effect on public health, the extent of which came to prominence during the famous London fog of 1952. Abatement measures led to a decline in the levels of black smoke and a subsequent reduction in the number of monitoring locations that were thought necessary to provide the information required for policy support. There is evidence of selection bias during this process with sites being kept in the most polluted areas. We assess the potential for this to affect the estimates of risk associated air pollution and show how using Bayesian spatio-temporal exposure models may be used to attempt to mitigate the effects of preferential sampling in this case | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Air pollution | |
| 650 | 4 | |a Bayesian hierarchical modelling | |
| 650 | 4 | |a Preferential sampling | |
| 650 | 4 | |a Spatio–temporal models | |
| 700 | 1 | |a Zidek, James V |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Yi |e verfasserin |4 aut | |
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