Chemical transport models together with emission inventories are widely used
to simulate NO
Nitrogen dioxide (NO
Chemical transport models (CTMs) have been widely used to provide predictions
of gas-phase pollutants including NO
Emission inventories are necessary input to CTMs and recognized as one of
the most important sources of uncertainties. Traditional bottom-up emissions
are calculated by aggregating information from diverse sources of
information such as fuel statistics and measurements of emission factors.
The large uncertainties in energy statistics (Guan et al., 2012) and
applications of non-Chinese emission factors (Streets et al., 2003) have
been propagated into uncertainties in bottom-up inventories for China (Zhao
et al., 2011). The lack of bottom-up inventories for most recent years
introduces additional biases for model simulations because inventories
could quickly become outdated due to the rapidly changing emissions (Zhang
et al., 2007; Liu et al., 2016a). NO
The modeling of NO
Measurements obtained from the recently developed air quality monitoring
network in China (Zhang and Cao, 2015) provide the means to evaluate the
quality of NO
We used the CHIMERE regional chemical transport model in this study, which is
designed to produce daily forecasts of tropospheric trace gas and aerosol
pollutants and make long-term simulations at a range of spatial scales (Menut
et al., 2013). We use the CHIMERE model v2013b over East Asia (18 to
50
The satellite-derived NO
The bottom-up anthropogenic NO
Both inventories show comparable spatial distributions at national and
regional scales, but distinctions between urban and rural areas (see
Sect. 3.1). The strength of the MIX inventory is that it includes detailed
source-category information (e.g., power plant and transportation sector) for
emissions, which is useful for driving atmospheric models and designing
emission mitigation policies but is not included in DECSO. The advantage of
the DECSO inventory is that emissions are timely updated (as soon as the
satellite observations are available); while bottom-up inventories usually
lag behind a few years and are outdated by the time they become available. In
addition, the spatial information in DECSO is based on OMI NO
We focused on 2015 as the most recent year with available DECSO emission
estimates and in situ measurements, but we used the MIX inventory for 2010
because the year 2015 is not available yet. However, the use of the 2010 MIX
inventory without scaling is not expected to bring significant bias, as the
similarity of NO
Maps for NO
An air quality simulation using the CHIMERE model was conducted for the full
year 2015. Pollutant concentrations including NO
Annual mean surface NO
The real-time hourly NO
The requirement for the minimum number of urban stations.
Figure 3a displays the heterogeneous spatial distribution of monitoring sites
at the scale of the model grid cell. The over 1000 monitoring sites are
allocated to a total of 594 grid cells based on their geolocations. The sites
belonging to the grid cells with one, two, and three sites account for 17,
21,
and 22 % of the total, respectively (Fig. 3b). We calculated the averaged
distance between monitoring sites by averaging individual pairwise distances
for every two stations in the same grid cell. Because most monitoring sites
are urban stations and are clustered in the city areas, which are often much
smaller than the area of a grid cell (
Daily mean surface NO
In our analysis, we excluded in situ measurements from cities with
unexpected discrepancies between urban and suburban stations. Because only
large cities potentially place the monitoring sites outside urban areas
related to the rapid expansion of built-up areas, we classified stations as
urban and suburban by visually inspecting satellite imagery from Google
Earth for large cities with over four stations. We calculated the annual
mean NO
NO
We applied a correction factor proposed by Lamsal et al. (2008) to account
for the interferences of other oxidized nitrogen compounds, based on the
modeled ratio of NO
Figure 5 shows the seasonal means of the correction factors determined with
concentrations of the interfering species predicted by the CHIMERE model
driven by the DECSO inventory. Consistent with the findings in Europe
(Huijnen et al., 2010) and the US (Lamsal et al., 2008), the correction
factor (difference with the ideal value of 1.0) is largest over polluted urban
regions, where NO
Seasonally averaged correction factors for interference in
NO
Correlation coefficient, regression slope, root-mean-square error
(RMSE), and normalized mean error (NME) in 2015 of the simulated surface
NO
We compare the modeled surface NO
The normalized difference of annual mean surface NO
Grid cells are classified into five categories, i.e., mountainous, northern,
< four stations, densely located, and main sample, and the
corresponding scatter plots of corrected measurements against simulations are
shown in Fig. 7. We define a grid cell as “mountainous” where the average
elevation is higher than 1000 m and the standard deviation of elevations is
over 15 % of the mean, based on the topographic data from the 30 arcsec
global land topography “GTOPO30” archived by the US Geological Survey
(available at
Scatter plots of the simulated annual mean surface NO
Significant regional differences are found. The small slope over mountainous
regions could be related to model limitations to resolve cities in the
valleys. Furthermore, we may expect difficulties for the model in describing
NO
Correlation among the number of stations in the same model grid
cell, ratio of the simulated annual mean surface NO
The CHIMERE model accurately reproduces the spatial variability in NO
Figure 8 depicts the ratio of the simulated annual mean surface NO
We exclude grid cells in the special categories discussed above (i.e.,
mountainous, northern, < four stations, and densely located
stations) to draw conclusions on the ability of the model to reproduce the
measurements. Statistical values (correlation, slope, root-mean-square error)
for the remaining grid cells (main sample) are given along with the plots in
Fig. 7. A slope of 0.74 and 1.3 is found for the simulation with the DECSO
and the MIX inventories, respectively. As mentioned before, the majority of
stations are located in urban, populated, and polluted areas and the model
resolution of 0.25
A positive bias may indicate an overestimation of NO
Regional bottom-up inventories tend to have large positive biases in urban areas. Those inventories usually downscale local emissions from regional totals (provincial totals are used in the MEIC/MIX inventory for China) and distribute them to grid cells using spatial proxies (e.g., population density and GDP). However, the spatial proxies may not match the locations of the individual emitting sources, especially for industrial plants located far away from urban centers that tend to have a larger population density and GDP (Liu et al., 2017). Such a decoupling will result in an overestimation of emissions over urban areas, which has been proven by the comparison of proxy-based regional inventory with high-resolution urban inventories developed from the extensive use of information of individual emitting sources (Zheng et al., 2017).
In order to better compare the spatial distributions of the two inventories
and identify the sensitivity of model performance to spatial distributions of
emissions, we further evaluate the impact of the spatial distribution of
emissions on simulating NO
Monthly mean ratio of simulated surface NO
Note that due to the lack of the 2015 inventory, the use of the 2010 MIX
emissions for other species including SO
Diurnal variability in hourly average NO
Figure 9 compares the monthly mean NO
The seasonal difference is pronounced when comparing the magnitude of the
NO
Figure 10 presents the diurnal variability in hourly-averaged surface
NO
We separately evaluated the model performance for the daytime period
(08:00–19:00), when the pattern of diurnal variations simulated by the
CHIMERE model is closer to what is observed by the in situ measurements. Not
surprisingly, a larger negative slope of 0.64 is obtained for the simulation
with the DECSO inventory compared to the surface observations, while the
slope for the simulation with the MIX inventory has been reduced
significantly to a value of 1.1 (Table 2) due to the tendency of
overestimating NO
In this work we evaluated the surface NO
The model accurately reproduces the spatial variability in annual mean
NO
The performance of the model is comparable over seasons, with a slightly
better spatial correlation in winter. This is in line with previous findings
of a lower model uncertainty in winter due to the difficulties in resolving
the more active NO
Note that the validation performed in this study is focused on urban areas,
which may bring a systematic bias to the conclusive statements, as discussed
above. In the future analysis focusing on rural areas is expected to give a
more complete picture of the performance of CTMs with inventories. In
addition, an in-depth comparison of multiple models with variable chemistry
schemes (e.g., Huijnen et al., 2010) is further required to quantify the
influence of chemical mechanisms on simulated NO
Measurements from the ground-based air quality monitoring
network of MEP were obtained from
The authors declare that they have no conflict of interest.
This research was funded by the MarcoPolo project of the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement number 606953. We acknowledge Tsinghua University for providing the MIX emission inventory. We acknowledge IPSL/LMD, INERIS, and IPSL/LISA in France for providing the CHIMERE model. We thank the three anonymous reviewers for helpful comments during the discussion phase of the paper. Edited by: Jason West Reviewed by: three anonymous referees