Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
doi:10.5194/gmd-2016-183
© Author(s) 2016. This work is distributed
under the Creative Commons Attribution 3.0 License.
Model evaluation paper
15 Aug 2016
Review status
A revision of this discussion paper was accepted for the journal Geoscientific Model Development (GMD) and is expected to appear here in due course.
Spatiotemporal evaluation of EMEP4UK-WRF v4.3 atmospheric chemistry transport simulations of health-related metrics for NO2, O3, PM10 and PM2.5 for 2001–2010
C. Lin1, M. R. Heal1, M. Vieno2, I. A. MacKenzie3, B. G. Armstrong4, B. K. Butland5, A. Milojevic4, Z. Chalabi4, R. W. Atkinson5, D. S. Stevenson3, R. M. Doherty3, and P. Wilkinson4 1School of Chemistry, University of Edinburgh, Edinburgh, UK
2NERC Centre for Ecology & Hydrology, Penicuik, UK
3School of GeoSciences, University of Edinburgh, Edinburgh, UK
4Department of Social and Environmental Health Research, London School of Hygiene and Tropical Medicine, London, UK
5Population Heath Research Institute and MRC-PHE Centre for Environment and Health, St George’s, University of London, London, UK
Abstract. 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 NO2, O3, PM2.5 and PM10 (daily maximum 8-hour running mean for O3). The comparison was temporally and spatially comprehensive covering a 10-year period (2 years for PM2.5) and all measurement data from the UK national reference monitor network, which applies consistent operational and QC/QA procedures for each pollutant (60, 49, 29 and 35 sites for NO2, O3, PM2.5 and PM10, respectively). The two most important statistics highlighted in the literature for evaluation of air quality model output against policy (and hence health)-relevant standards – correlation and bias – were evaluated by site type, year, month and day-of-week. Model-measurement correlation and bias were generally better than values found in past studies that allowed for measurement uncertainties. Temporal correlations of daily concentrations were good for O3, NO2 and PM2.5 at both rural and urban background sites (median values of r across sites in the range 0.70–0.76 for O3 and NO2, and 0.65–0.69 for PM2.5), but poorer for PM10 (0.47–0.50). Bias differed between environments, with generally less bias at the background sites and least bias at rural background sites (median normalised mean bias (NMB) values for daily O3 and NO2 of 8 % and 11 %, respectively). At urban background sites there was a negative model bias for NO2 (median NMB = −29 %) and PM2.5 (−26 %) and a positive model bias for O3 (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 than the grid average. This effect was particularly pronounced for comparison against urban traffic monitors, which are deliberately located close to strong sources of NOx and PM. The biases are also indicative of potential underestimations of primary NOx and PM emissions in the model, and, for PM, with known omissions in the model of some PM components, e.g. wind-blown dust. There were instances of monthly and weekday/weekend variations in 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 vs 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.

Citation: Lin, C., Heal, M. R., Vieno, M., MacKenzie, I. A., Armstrong, B. G., Butland, B. K., Milojevic, A., Chalabi, Z., Atkinson, R. W., Stevenson, D. S., Doherty, R. M., and Wilkinson, P.: Spatiotemporal evaluation of EMEP4UK-WRF v4.3 atmospheric chemistry transport simulations of health-related metrics for NO2, O3, PM10 and PM2.5 for 2001–2010, Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-183, in review, 2016.
C. Lin et al.
C. Lin et al.

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Short summary
We evaluated simulations from the UK EMEP4UK-WRF v4.3 atmospheric chemistry transport model at 5 km x 5 km horizontal resolution for use in air pollution epidemiology and health burden assessment. Model-measurement comparison focused on daily and annual means for NO2, O3, PM10 and PM2.5. For epidemiology, capturing correlation is more important than bias, so the generally considerably better correlations than bias reported here supports use of data from this model framework for this purpose.
We evaluated simulations from the UK EMEP4UK-WRF v4.3 atmospheric chemistry transport model at...
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