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Estimating background concentrations of PM2.5 for urban air quality modelling in a data poor environment

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Estimating background concentrations of PM2.5 for urban air quality modelling in a data poor environment. / Draper, Eve; Whyatt, Duncan; Taylor, Richard et al.
In: Atmospheric Environment, Vol. 314, 120107, 01.12.2023.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Draper E, Whyatt D, Taylor R, Metcalfe S. Estimating background concentrations of PM2.5 for urban air quality modelling in a data poor environment. Atmospheric Environment. 2023 Dec 1;314:120107. Epub 2023 Sept 26. doi: 10.1016/j.atmosenv.2023.120107

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Draper, Eve ; Whyatt, Duncan ; Taylor, Richard et al. / Estimating background concentrations of PM2.5 for urban air quality modelling in a data poor environment. In: Atmospheric Environment. 2023 ; Vol. 314.

Bibtex

@article{e699452be8474da8a3a3f6386362aa39,
title = "Estimating background concentrations of PM2.5 for urban air quality modelling in a data poor environment",
abstract = "Atmospheric dispersion models are widely applied to simulate pollutant concentrations such as PM2.5 for use in long- and short-term health studies. A significant proportion of PM2.5 originates outside urban areas in which many people live. It is important to reflect this {\textquoteleft}background{\textquoteright} component in the modelling process in order to provide an accurate representation of the total pollution load experienced by human populations. To be credible, model outputs must be verified against available monitoring data, which, in the case of PM2.5, may be limited to a small number of monitoring sites across a large urban area. Here we evaluate four different approaches to representing background PM2.5 in an atmospheric dispersion model (ADMS-Urban) for Nottingham, UK. A directional approach, based on multiple urban background monitoring sites located outside the study area provides the most robust estimates. Our adopted approach allows us to model both short- and long-term air quality conditions, whilst accounting for local- and regional-scale variations in the pollution burden, and will ultimately enable us to assess short- and long-term effects of air pollution on health.",
keywords = "Atmospheric Science, General Environmental Science",
author = "Eve Draper and Duncan Whyatt and Richard Taylor and Sarah Metcalfe",
year = "2023",
month = dec,
day = "1",
doi = "10.1016/j.atmosenv.2023.120107",
language = "English",
volume = "314",
journal = "Atmospheric Environment",
issn = "1352-2310",
publisher = "PERGAMON-ELSEVIER SCIENCE LTD",

}

RIS

TY - JOUR

T1 - Estimating background concentrations of PM2.5 for urban air quality modelling in a data poor environment

AU - Draper, Eve

AU - Whyatt, Duncan

AU - Taylor, Richard

AU - Metcalfe, Sarah

PY - 2023/12/1

Y1 - 2023/12/1

N2 - Atmospheric dispersion models are widely applied to simulate pollutant concentrations such as PM2.5 for use in long- and short-term health studies. A significant proportion of PM2.5 originates outside urban areas in which many people live. It is important to reflect this ‘background’ component in the modelling process in order to provide an accurate representation of the total pollution load experienced by human populations. To be credible, model outputs must be verified against available monitoring data, which, in the case of PM2.5, may be limited to a small number of monitoring sites across a large urban area. Here we evaluate four different approaches to representing background PM2.5 in an atmospheric dispersion model (ADMS-Urban) for Nottingham, UK. A directional approach, based on multiple urban background monitoring sites located outside the study area provides the most robust estimates. Our adopted approach allows us to model both short- and long-term air quality conditions, whilst accounting for local- and regional-scale variations in the pollution burden, and will ultimately enable us to assess short- and long-term effects of air pollution on health.

AB - Atmospheric dispersion models are widely applied to simulate pollutant concentrations such as PM2.5 for use in long- and short-term health studies. A significant proportion of PM2.5 originates outside urban areas in which many people live. It is important to reflect this ‘background’ component in the modelling process in order to provide an accurate representation of the total pollution load experienced by human populations. To be credible, model outputs must be verified against available monitoring data, which, in the case of PM2.5, may be limited to a small number of monitoring sites across a large urban area. Here we evaluate four different approaches to representing background PM2.5 in an atmospheric dispersion model (ADMS-Urban) for Nottingham, UK. A directional approach, based on multiple urban background monitoring sites located outside the study area provides the most robust estimates. Our adopted approach allows us to model both short- and long-term air quality conditions, whilst accounting for local- and regional-scale variations in the pollution burden, and will ultimately enable us to assess short- and long-term effects of air pollution on health.

KW - Atmospheric Science

KW - General Environmental Science

U2 - 10.1016/j.atmosenv.2023.120107

DO - 10.1016/j.atmosenv.2023.120107

M3 - Journal article

VL - 314

JO - Atmospheric Environment

JF - Atmospheric Environment

SN - 1352-2310

M1 - 120107

ER -