The urban dispersion model EPISODE v10.0 – Part 1: An Eulerian and sub-grid-scale air quality model and its application in Nordic winter conditions
Authors: Hamer Paul D. , Walker Sam-Erik , Sousa-Santos Gabriela , Vogt Matthias , Vo-Thanh Dam , Lopez-Aparicio Susana , Schneider Philipp , Ramacher Martin O. P. , Karl Matthias 
Published: September 2020

Abstract. This paper describes the Eulerian urban dispersion model
EPISODE. EPISODE was developed to address a need for an urban air quality
model in support of policy, planning, and air quality management in the
Nordic, specifically Norwegian, setting. It can be used for the
calculation of a variety of airborne pollutant concentrations, but we focus
here on the implementation and application of the model for NO2
pollution. EPISODE consists of an Eulerian 3D grid model with embedded
sub-grid dispersion models (e.g. a Gaussian plume model) for dispersion of
pollution from line (i.e. roads) and point sources (e.g. chimney stacks).
It considers the atmospheric processes advection, diffusion, and an NO2
photochemistry represented using the photostationary steady-state
approximation for NO2. EPISODE calculates hourly air concentrations
representative of the grids and at receptor points. The latter allow EPISODE
to estimate concentrations representative of the levels experienced by the
population and to estimate their exposure. This methodological framework
makes it suitable for simulating NO2 concentrations at fine-scale
resolution (<100 m) in Nordic environments. The model can be run in
an offline nested mode using output concentrations from a global or regional
chemical transport model and forced by meteorology from an external
numerical weather prediction model; it also can be driven by
meteorological observations. We give a full description of the overall model
function and its individual components. We then present a case study
for six Norwegian cities whereby we simulate NO2 pollution for the
entire year of 2015. The model is evaluated against in situ observations for
the entire year and for specific episodes of enhanced pollution during
winter. We evaluate the model performance using the FAIRMODE DELTA Tool that
utilises traditional statistical metrics, e.g. root mean square error (RMSE), Pearson correlation
R, and bias, along with some specialised tests for air quality model
evaluation. We find that EPISODE attains the DELTA Tool model quality
objective in all of the stations we evaluate against. Further, the other
statistical evaluations show adequate model performance but that the model
scores greatly improved correlations during winter and autumn compared to
the summer. We attribute this to the use of the photostationary steady-state
scheme for NO2, which should perform best in the absence of local ozone
photochemical production. Oslo does not comply with the NO2 annual
limit set in the 2008/50/EC directive (AQD). NO2 pollution episodes
with the highest NO2 concentrations, which lead to the occurrence of
exceedances of the AQD hourly limit for NO2, occur primarily in the
winter and autumn in Oslo, so this strongly supports the use of EPISODE for
application to these wintertime events. Overall, we conclude that the
model is suitable for an assessment of annual mean NO2 concentrations and
also for the study of hourly NO2 concentrations in the Nordic winter
and autumn environment. Further, in this work we conclude that it is
suitable for a range of policy applications specific to NO2 that
include pollution episode analysis, evaluation of seasonal statistics,
policy and planning support, and air quality management. Lastly, we identify
a series of model developments specifically designed to address the
limitations of the current model assumptions. Part 2 of this two-part paper
discusses the CityChem extension to EPISODE, which includes a number of
implementations such as a more comprehensive photochemical scheme suitable
for describing more chemical species and a more diverse range of
photochemical environments, as well as a more advanced treatment of the sub-grid
dispersion.