Spatiotemporal evaluation of EMEP4UK-WRF v4.3 atmospheric chemistry transport simulations of health-related metrics for NO<sub>2</sub>, O<sub>3</sub>, PM<sub>10</sub>, and PM<sub>2. 5</sub> for 2001–2010
This study was motivated by the use in air pollution epidemiology and health burden assessment of data simulated at 5 km × 5 km horizontal resolution by the EMEP4UK-WRF v4.3 atmospheric chemistry transport model. Thus the focus of the model–measurement comparison statistics presented here was on t...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-04-01
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| Series: | Geoscientific Model Development |
| Online Access: | http://www.geosci-model-dev.net/10/1767/2017/gmd-10-1767-2017.pdf |
| Summary: | This study was motivated by the use in air pollution epidemiology and health
burden assessment of data simulated at 5 km × 5 km horizontal
resolution by the EMEP4UK-WRF v4.3 atmospheric chemistry transport model.
Thus the focus of the model–measurement comparison statistics presented here
was on the health-relevant metrics of annual and daily means of NO<sub>2</sub>,
O<sub>3</sub>, PM<sub>2. 5</sub>, and PM<sub>10</sub> (daily maximum 8 h running mean for
O<sub>3</sub>). The comparison was temporally and spatially comprehensive, covering
a 10-year period (2 years for PM<sub>2. 5</sub>) and all non-roadside measurement
data from the UK national reference monitor network, which applies consistent
operational and QA/QC procedures for each pollutant (44, 47, 24, and 30 sites
for NO<sub>2</sub>, O<sub>3</sub>, PM<sub>2. 5</sub>, and PM<sub>10</sub>, respectively). Two
important statistics highlighted in the literature for evaluation of air
quality model output against policy (and hence health)-relevant standards –
correlation and bias – together with root mean square error, were evaluated
by site type, year, month, and day-of-week. Model–measurement statistics
were generally better than, or comparable to, values that allow for realistic
magnitudes of measurement uncertainties. Temporal correlations of daily
concentrations were good for O<sub>3</sub>, NO<sub>2</sub>, and PM<sub>2. 5</sub> at both rural
and urban background sites (median values of <i>r</i> across sites in the range
0.70–0.76 for O<sub>3</sub> and NO<sub>2</sub>, and 0.65–0.69 for PM<sub>2. 5</sub>), but
poorer for PM<sub>10</sub> (0.47–0.50). Bias differed between environments, with
generally less bias at rural
background sites (median normalized mean bias (NMB) values for daily O<sub>3</sub> and NO<sub>2</sub> of 8 and
11 %, respectively). At urban background sites there was a negative model
bias for NO<sub>2</sub> (median NMB = −29 %) and PM<sub>2. 5</sub> (−26 %)
and a positive model bias for O<sub>3</sub> (26 %). The directions of these
biases are consistent with expectations of the effects of averaging primary
emissions across the 5 km × 5 km model grid in urban areas,
compared with monitor locations that are more influenced by these emissions
(e.g. closer to traffic sources) than the grid average. The biases are also
indicative of potential underestimations of primary NO<sub><i>x</i></sub> and PM emissions
in the model, and, for PM, with known omissions in the model of some PM
components, e.g. some components of wind-blown dust. There were instances of
monthly and weekday/weekend variations in the extent of model–measurement
bias. Overall, the greater uniformity in temporal correlation than in bias is
strongly indicative that the main driver of model–measurement differences
(aside from grid versus monitor spatial representivity) was inaccuracy of
model emissions – both in annual totals and in the monthly and day-of-week
temporal factors applied in the model to the totals – rather than simulation
of atmospheric chemistry and transport processes. Since, in general for
epidemiology, capturing correlation is more important than bias, the detailed
analyses presented here support the use of data from this model framework in
air pollution epidemiology. |
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| ISSN: | 1991-959X 1991-9603 |