Please use this identifier to cite or link to this item: https://doi.org/10.1002/sim.2446
DC FieldValue
dc.titleCumulative effects of air pollution on public health
dc.contributor.authorXia, Y.
dc.contributor.authorTong, H.
dc.date.accessioned2014-10-28T05:11:07Z
dc.date.available2014-10-28T05:11:07Z
dc.date.issued2006-10-30
dc.identifier.citationXia, Y., Tong, H. (2006-10-30). Cumulative effects of air pollution on public health. Statistics in Medicine 25 (20) : 3548-3559. ScholarBank@NUS Repository. https://doi.org/10.1002/sim.2446
dc.identifier.issn02776715
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105077
dc.description.abstractCumulative effect is an important way through which the pollutants affect public health. However, few existing dynamical models are well enough understood and documented to detect or quantify the cumulative effects and to answer pertinent questions posed by the World Health Organization (WHO): 'Is there a threshold below which no effects of the pollutants on health are expected to occur in all people?' and 'What averaging period (time pattern) is the most relevant from the point of view of health?'. Using a new semi-parametric time series modelling approach, which incorporates non-linearity and latent cumulative variables, we show that the cumulative effects on health due to continual exposure to environmental pollutants can be very serious even at levels below the national ambient air quality standards of America (NAAQS). The situation is especially worrying for chronic sufferers. Our study suggests that different pollutants may require different cumulative periods (on average) to impact on health but they share a similar functional form in respect of their impact. We also suggest some possible revision of the ambient air quality standards. Copyright © 2005 John Wiley & Sons, Ltd.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1002/sim.2446
dc.sourceScopus
dc.subjectAdditive models
dc.subjectAir pollution
dc.subjectPublic health
dc.subjectTime series
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1002/sim.2446
dc.description.sourcetitleStatistics in Medicine
dc.description.volume25
dc.description.issue20
dc.description.page3548-3559
dc.description.codenSMEDD
dc.identifier.isiut000241196500011
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