ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-22-15685-2022Development and application of a multi-scale modeling framework for urban high-resolution NO2 pollution mappingDevelopment and application of a multi-scale modeling frameworkLvZhaofengLuoZhenyuDengFanyuanWangXiaotongZhaoJunchaoXuLuchengHeTingkunZhangYingzhiLiuHuanliu_env@tsinghua.edu.cnhttps://orcid.org/0000-0002-2217-0591HeKebinState Key Joint Laboratory of ESPC, School of Environment, Tsinghua
University, Beijing 100084, ChinaCollege of Ecology and Environment, Chengdu University of Technology,
Chengdu 610059, China
Vehicle emissions have become a major source of air pollution in
urban areas, especially for near-road environments, where the pollution
characteristics are difficult to capture by a single-scale air quality
model due to the complex composition of the underlying surface. Here we
developed a hybrid model CMAQ-RLINE_URBAN to quantitatively
analyze the effects of vehicle emissions on urban roadside NO2
concentrations at a high spatial resolution of 50 m × 50 m. To
estimate the influence of various street canyons on the dispersion of air
pollutants, a machine-learning-based street canyon flow (MLSCF) scheme was
established based on computational fluid dynamics and two machine learning
methods. The results indicated that compared with the Community Multi-scale Air Quality (CMAQ) model, the hybrid
model improved the underestimation of NO2 concentration at near-road
sites with the mean bias (MB) changing from -10 to 6.3 µg m-3. The
MLSCF scheme obviously increased upwind concentrations within deep street
canyons due to changes in the wind environment caused by the vortex. In
summer, the relative contribution of vehicles to NO2 concentrations in
Beijing urban areas was 39 % on average, similar to results from the CMAQ-ISAM (Integrated Source Apportionment Method) model, but it increased significantly with the decreased distance to the road
centerline, especially on urban freeways, where it reached 75 %.
Introduction
The accelerated urbanization leads to severe air pollution in China. As one
of the indicators of air pollution, nitrogen dioxide (NO2) has an
adverse impact on human health and promotes the generation of ozone and
particulate matter (Pandey et al., 2005; Khaniabadi et al., 2017). During
the last decade, benefiting from the implementations of several air
pollution control strategies by the Chinese government, the air quality has
improved (Jin et al., 2016; Zheng et al., 2018), and the vertical column
densities of NO2 displayed a decreasing trend after 2013 (Shah et
al., 2020; Cui et al., 2021). However, the economic development and nitrogen
oxide (NOx) emissions are not decoupled in China (Luo et
al., 2022a). In some megacities of China, such as Chengdu, the daily
averaged NO2 concentration could reach 200 µg m-3 (Zhu et al., 2019), far exceeding the 24 h average air quality
guideline of 80 µg m-3 suggested by the Ministry of Environmental
Protection of China.
The improvement in PM2.5 in China was mainly due to the emission
reduction and control measures of industrial and domestic sources
(Q. Zhang et al., 2019), which also relieved the NO2
pollution, but the reduction potential of these sources has been gradually
declining. Meanwhile, as the population of vehicles is growing rapidly,
vehicle emissions have become a major source of NO2 pollution,
especially in urban areas (Nguyen et al., 2018). Due to the low release
height of vehicle emissions, combined with the negative dispersion condition
caused by nearby buildings, air pollutants will be significantly accumulated
near the street. According to roadside observations, within the distance of
about 100–200 m near roads, the concentrations of CO, NO2, ultrafine
particulate matter (UFP), PM2.5, PM10, and other pollutants will
increase with the decreased distance to the road centerline, especially for
the pollution levels of NO2 and UFP, which increase exponentially.
Therefore, the gradient of concentration around the road changes
dramatically (Nayeb Yazdi et al., 2015; Hagler et al., 2012). Moreover,
the dispersion of air pollutants in the near-road environment is
significantly affected by geometric characteristics of the street canyon.
For example, in a standard street canyon, when the external wind direction
at the roof level is perpendicular to the street axis, a clockwise vortex
will be generated inside, resulting in the accumulation of pollutant
concentrations at the upwind grid receptors in the canyon (Oke, 1988;
Manning et al., 2000). Consequently, how to quantitatively identify urban
vehicle-induced air pollution around roads affected by complex underlying
surface conditions has become an urgent scientific issue.
Regionally scaled air quality models, represented by chemical transport models
(CTMs) including the Community Multi-scale Air Quality (CMAQ) model
(Byun and Schere, 2006), the Comprehensive Air quality Model with
extensions (CAMx), and the Weather Research and Forecasting/Chemistry model
(WRF-Chem) (Grell et al., 2005) have been used extensively
in assessments of the impacts of vehicle emissions on the regional
atmospheric environment, focusing on the source apportionment (Luo et
al., 2022b; Vara-Vela et al., 2016; Kheirbek et al., 2016; Lv et al., 2020)
and evaluation of control measures (Zhang et al., 2020; Yu et al., 2019;
Cheng et al., 2019; Ke et al., 2017). However, the spatial resolution of
CTMs is generally larger than 1 km × 1 km, so the significant
impacts of vehicle emissions on near-source air quality cannot be predicted
by CTMs due to the grid homogenization of vehicle emissions.
To avoid the aforementioned disadvantages, the locally scaled numerical models
based on Gaussian diffusion theory or computational fluid dynamics (CFD) are
adopted by numerous researchers for studies at a finer spatial resolution
(Y. Zhang et al., 2021; Patterson and Harley, 2019; Soulhac et al., 2012),
including the Research LINE-source Dispersion Model (RLINE)
(Snyder et al., 2013), the Operational Street Pollution Model
(OSPM), AERMOD (Cimorelli et al., 2005), and
RapidAir® (Masey et al., 2018). However, the
large uncertainties in predictions from Gaussian dispersion models come from
the provided meteorological conditions and background concentrations. The
natural logarithm function is usually used to characterize the vertical
profile of wind speed in both the inertial and rough sublayers, neglecting
the influence of urban complex underlying surface compositions on the wind
field (Cimorelli et al., 2005; Masey et al., 2018; Snyder et al., 2013).
Nevertheless, in standard and deep street canyons, the changes in vertical
wind profile cannot be described by the logarithmic form; otherwise the
actual wind speed will be greatly overestimated (Soulhac et al.,
2008). Although the OSPM has performed a large number of comparisons with
field observations in shallow or standard street canyons, the validation of
model performance in deep street canyons with a large aspect ratio was still
inadequate (Kakosimos et al., 2010). Moreover, OSPM
overestimated the bottom wind speed in a deep street canyon by about 10
times compared with the predictions from CFD, resulting in greatly
underestimated pollutant concentrations (Murena et al., 2009).
Comparatively speaking, the CFD model can accurately simulate the airflow and
pollutant concentration in complex street canyons, but the simulation domain
of the CFD model is much smaller than the urban scale, and the influence of the
long-term meteorological boundary conditions cannot be considered.
Considering the respective strengths and limitations of regional models and
local models, several studies have been carried out on the coupling of air
quality models applicable to different scales (Ketzel et al., 2012;
Stocker et al., 2012; Lefebvre et al., 2013; Jensen et al., 2017; Kim et
al., 2018; Mallet et al., 2018; Hood et al., 2018; Benavides et al., 2019;
Kamińska, 2019; Mu et al., 2022). Although these models performed
accurately in near-road simulations, the influence of street canyons is still
hard to consider. In some hybrid models (Stocker et al., 2012;
Jensen et al., 2017; Mallet et al., 2018), OSPM was still applied to
calculate concentration levels within the street, where the application of
the logarithmic wind profile probably overestimated the bottom wind speed in a
deep street canyon as mentioned above. Other models simply assumed that in
street canyons, wind direction followed the street direction, and wind speed
was uniform, which was not sufficient to resolve the concentration gradient
within street canyons (Kim et al., 2018; Benavides et al., 2019). Berchet
et al. (2017) proposed a cost-effective method for simulating
city-scale pollution taking advantage of high-resolution accurate CFD, while
the primary NOx was predicted due to the lack of a chemical module.
Therefore, it is essential to build an integrated model to predict long-term
and near-road air pollution suitable for the urban complex underlying
surface environment.
The objective of the present work is to investigate the street-level
NO2 concentrations and quantify the contribution of vehicle emissions
considering the influence of the refined wind flow in the complex urban
environment. To this end, a hybrid model CMAQ-RLINE_URBAN was
developed by offline-coupling the local RLINE model with the regional CMAQ
model and some localized urban thermodynamic parameter schemes.
Specifically, in order to predict the effects of urban street canyons on the
diffusion of pollutants, we developed a machine-learning-based street canyon flow (MLSCF) parameterization scheme to estimate the wind environment in a
cost-effective way, which was based on integrating two machine learning
methods using big wind profile data from 1600 CFD simulations. To evaluate
the performance of CMAQ-RLINE_URBAN, simulations under
several scenarios were conducted in Beijing urban areas from 1 to 31 August to
2019 and validated through comparison with observations from
monitoring sites. Furthermore, spatial distribution characteristics of
NO2 concentrations in the near-road environment were also analyzed in
this study.
Materials and methodsHybrid model framework
Here, we established the MLSCF scheme based on the R language and modified the
code of the RLINE model to add other parameterization schemes with the FORTRAN
language. Finally, a multiscale air quality hybrid model was developed to
achieve high-resolution NO2 pollution mapping in urban areas. The
framework of CMAQ-RLINE_URBAN is shown in Fig. 1. The
hybrid model was established based on the RLINE model, with offline coupling with the
gridded meteorological field provided by the WRF model and the pollutant
background concentrations from non-vehicle sources provided by the CMAQ model
with the Integrated Source Apportionment Method (ISAM), considering the
thermodynamic effects caused by the complex underlying surface compositions
of the city. Finally, in our hybrid model, an NO2 pollution map with a high
temporal (1 h) and spatial resolution (50 m × 50 m) can be
obtained.
The framework of multiscale hybrid model CMAQ-RLINE_URBAN.
RLINE is a Gaussian line source dispersion model developed by Snyder et al. (2013) to predict pollutant concentrations in near-road
environments. In the RLINE model, the mobile source is regarded as a
finite line source from which the concentration is found by approximating
the line as a series of point sources and integrating the contributions of
point sources using an efficient numerical integration scheme. The number of
points needed for convergence to the proper solution is a function of
distance from the source line to the receptor, and each point source is
simulated using a Gaussian plume formulation. The RLINE model performs
generally comparable results when evaluated with other line source models
for on-road traffic emissions dispersion (Snyder et al., 2013; Heist et
al., 2013; Chang et al., 2015), and it has been successfully used in many
studies to evaluate the impacts from traffic emissions on air quality
(Zhai et al., 2016; Valencia et al., 2018; Benavides et al., 2019;
Filigrana et al., 2020; X. Zhang et al., 2021).
The simulation for local meteorological conditions in
CMAQ-RLINE_URBAN included three steps: estimation for areas
above the top of the urban canopy layer (UCL), inside UCL, and inside the
street canyon. (1) In this study, the configuration of the WRF model referred to
our previous study (Lv et al., 2020). The height of the midpoint in
the bottom layer to the ground was set as 22.5 m, which was close to the
average height of buildings near street canyons, similar to the settings in
the previous study (Benavides et al., 2019).
Therefore, the meteorological field simulated by the WRF model was used as
the wind field and atmospheric stability at the top of UCL. During the
hybrid model running, the meteorological conditions over buildings near each
road were obtained separately from the WRF model according to the road location.
(2) Then, the surface roughness length (z0) of each road was estimated
based on the surrounding building geometry and used to recalculate the
localized meteorological parameters (e.g. Monin–Obukhov length) within UCL
according to the algorithm proposed by Benavides et al. (2019) (z0 scheme). The atmospheric
turbulence intensity in urban areas around sunset in the afternoon was
obviously enhanced considering the influence of the urban heat island effect
based on methods in the AERMOD model (Cimorelli et al.,
2005) (UHI scheme). The UHI scheme would affect the turbulent intensity
based on the evaluation of the upward surface heat flux and the urban
boundary layer height due to convective effects, and then the mixing height,
convective velocity scale, surface friction velocity, and Monin–Obhukov
length were all recalculated (details in the Supplement Sect. S1).
(3) Finally, the wind field within UCL was calculated according to different
types of road environments: open terrain and street canyon. The logarithmic
wind profile based on Monin–Obhukov similarity theory (MOST) (Foken,
2006) in the original RLINE model was still used when the grid receptor was
located in the open terrain (MOST scheme), while the MLSCF parameterization
scheme was used for grid receptors within the street canyon to
quantitatively characterize the influence of the street canyon geometry and
the external wind environment at the top of the roof. The detailed
introduction for street canyon geometry and the MLSCF scheme is described
in Sect. 2.2.
The real-time vehicle emission inventory used in both regional and local air
quality models was based on a street-level on-road vehicle emission (SLOVE)
model developed in our previous study (Lv et al., 2020), which
was based on the real-time traffic condition data from the map provider AMap (available at https://www.amap.com/, last access: 9 December 2022). The daily
averaged NOx emission from on-road vehicles in Beijing in 2019 was
estimated to be 136.0 Mg, of which emissions from heavy-duty vehicles and
heavy-duty trucks accounted for 31 % and 34 %, respectively. In our
simulation, the concentrations of NO, NO2, and O3 excluding
contributions from vehicle emissions were used as background concentrations
at the roof level, avoiding the double counting in the coupling process.
These background concentrations were simulated by the CMAQ-ISAM model, in which
the emissions were divided into local mobile and other four emission groups
to trace their contributions separately, so the influence of non-local
vehicle emissions was considered, and details were presented in our previous
study (Lv et al., 2020). The spatial resolution of the
innermost domain in both the WRF and the CMAQ model was 1.33 km × 1.33 km.
In addition, the influence of atmospheric turbulence and building geometry
on the vertical mixing of background concentration was considered (vertical
mixing scheme). The ratios of wind speed at surface and roof levels were
used as a proxy to calculate the contribution of background concentration
over street canyons to the near-ground level
(Benavides et al., 2019). In this scheme, the
surface wind was from the MLSCF scheme when the grid receptor is located within
the street canyon, and otherwise the logarithmic wind profile was used to
calculate the wind speed at the specified height, and details were shown in
the Supplement Sect. S2. Finally, combined with the vehicle-induced
primary NOx concentration calculated by the RLINE kernel, the high
spatial-resolution NO2 map could be simulated considering the
photochemical process of NOx. In this study, a simplified two-reaction
scheme, including the photolysis of NO2 and the oxidation of NO, was
incorporated into the model to characterize the photochemical process of
NOx (details in the Supplement Sect. S3), which has been
successfully applied in the SIRANE dispersion model (Soulhac et
al., 2017).
Development for MLSCF schemeThe database of street canyon geometry
We first established a database of street canyon geometry for 15 398 roads
in urban areas of Beijing based on the three-dimensional building data
obtained from our previous study (Lv et al., 2020) using
a geographic information system (GIS). Three typical parameters to represent
street canyon geometry were investigated: height ratio
(Hl/Hr) (Hl is the building height on the left side, while
Hr is the building height on the right side), aspect ratio (H/W) (H
is set to be the average height, and W is the width of the street canyon), and the canyon length-to-height ratio (L/H) (L is set to be the length of
the street canyon). In this study, the extremely special geometry of canyons
was not considered, and the typical street canyons were selected according to the
following conditions: (1) the proportion of actual street canyon length (the
length of road which the buildings are near) was greater than 0.5; (2) H/W
was greater than 0.2; (3) Hl/Hr was between 0.3 and 3.3. Finally,
the total number of typical street canyons was 1889, with a total length
of 787 km. The spatial distributions of canyon geometry are shown in Fig. S1 in the Supplement. In urban areas of Beijing, street canyons were generally
wide, with an average width of 50.3 m, and buildings on both sides were
relatively low with a mean of 23.6 m. Most street canyons were obviously
located in areas within the 4th ring road. The shallow (H/W≤0.5)
canyons and long canyons (L/H>7) dominated, accounting
for 54 % and 84 % of the total number of street canyons.
Description of CFD cases
Here, to predict airflow in street canyons comprehensively, CFD simulations
were conducted under combinations of different values of controlling factors
based on ANSYS FLUENT (v19.2). The controlling factors included the
aforementioned three typical parameters to represent canyon geometry, the
background wind speed at the height of H (V(H)), and the angle between wind
direction and street axis (α) to describe the external wind
environment. The selected values of each factor were listed in Table 1, and a total of 1600 (i.e., 5×4×4×5×4)
simulations were implemented.
Values of controlling factors used in the simulations.
In this study, the computational domain of three-dimensional (3D) full-scale
CFD simulations is shown in Fig. 2. The average building height H of the
street canyon was always set to 21 m in different simulations, which was
similar to the mean street canyon height in Beijing. Other actual sizes of
street canyons (e.g., street canyon width W) were calculated according to the
ratio of each specific simulation. Distances between urban canopy layer
(UCL) boundaries and the domain top, domain inlet, and domain outlet were set
as 5H, 5H, and 20H, respectively.
Computational domain (a) and grid arrangement (b) in all CFD test
cases.
The turbulence closure schemes for CFD include the Reynolds–Averaged
Navier–Stokes (RANS) and the large-eddy simulation (LES), the choice of
which depends on the computational cost, the accuracy required, and the
purpose of application. The RANS resolves the mean time-averaged properties
with all the turbulence motions to be modeled, while LES adopts a spatial
filtering operation and consequently resolves large-scale eddies directly
and parameterizes small-scale eddies (Zhong et al., 2016).
Compared with the LES, the RANS is more easily established and
computationally faster (Xie and Castro, 2006). However, the LES can
provide a better prediction of airflow than the RANS when
handling complex geometries (Dejoan et al., 2010; Santiago et al., 2010).
In this study, considering the huge computational burden of a large number
of simulations and the relatively simple geometry of street canyons in our
modeling, the RANS was selected to characterize the airflow.
Following the CFD guideline (Tominaga et al., 2008; Franke et al., 2011),
zero normal gradient conditions or pressure outlet conditions were applied
at the domain outlet, and symmetry boundary conditions were adopted at the
domain top and two lateral domain boundaries. For near-wall treatment,
no-slip wall boundary conditions with standard wall functions were used
(FLUENT, 2006). All governing equations for the flow and
turbulent quantities were discretized by the finite-volume method with the
second-order upwind scheme. The SIMPLE scheme was used for the pressure and
velocity coupling. The residual for continuity equation, velocity
components, turbulent kinetic energy, and its dissipation rate were all
below 10-5. Meanwhile, the CFD simulation would also stop when the
iteration steps exceeded 10 000, due to the large computing cost of so many
simulations. In summary, the average iteration steps of a total of 1600 cases
were 4443. About 54.6 % of cases met the convergence criteria, and the
median residual values of the continuity equation, velocity in the x axis, velocity
in the y axis, velocity in the z axis, k, and ε were 1.0×10-5, 8.5×10-7, 8.5×10-7, 4.1×10-7, 3.4×10-6, and 5.4×10-6,
respectively, indicating the overall model performance was acceptable. The
selected turbulence model and grid arrangement are discussed in Sect. 2.2.3.
At the domain inlet, the power-law velocity profile (Brown et al.,
2001), vertical profiles of turbulent kinetic energy kin, and its
dissipation rate εin at the domain inlet (Lien and Yee,
2004; K. Zhang et al., 2019), were described below:
1U0z=UrefzHrefα,2kinz=Iin×U0z2,3εinz=Cμ3/4kin3/2κz.
Here, U0z stood for the stream-wise velocity at the
height z. Uref represented the reference speed. The reference height
Href was 21 m. The power-law exponent of α= 0.22 denoted
underlying surface roughness above medium-dense urban area
(Kikumoto et al., 2017). Turbulence intensity Iin was 0.1, the von Kármán constant κ was 0.41, and Cμ was 0.09.
The CFD validation
In this study, the stream-wise and vertical velocity predicted by CFD within
street canyons was compared with wind tunnel data in previous research.
For buildings of the cube array model, wind tunnel data from Brown et al. (2001) was used to evaluate the reliability of CFD results by measuring
vertical profiles of velocity. In this experiment, the street canyon was
perpendicular to the wind direction at the roof level. For long-street
models, we predicted horizontal profiles of velocity along the street
centerline at the height of z=0.11H or vertical profiles at some points
and then validated CFD simulations using wind tunnel data from Hang et al. (2010). In this validation case, the wind direction at the roof
level was parallel to the axis of street canyons. The description and
validation results are shown in Figs. S2–S3 and Table S1 in the
Supplement, respectively.
We identified the influence of different minimum sizes of hexahedral cells
near wall surfaces (fine: 0.1 m; medium: 0.2 m; coarse: 0.5 m) and
turbulence models (standard k-ε model and renormalization group (RNG) k-ε
model) on the predicted velocity, to evaluate the grid independence and
turbulence model accuracy (Fig. S3 in the Supplement). The results
indicated that the predictions from the standard k-ε model
could match the variations in observed velocity within the street
canyon well; these performances were much better than that of the RNG model.
In addition, different grid resolutions used in simulations would not
obviously affect the predicted results. We finally adopted the standard
k-ε model to characterize turbulence, and the minimum size of
hexahedral cells near wall surfaces was 0.5 m; an expansion ratio of 1.1
was applied to save the computing cost, and the average mesh number of the total of 80 street canyon models is 1 367 965.
Moreover, the averaged wind speed from CFD in street canyons with different
aspect ratios and external wind direction was compared with predictions from
other empirical methods used in the SIRANE model (Soulhac et
al., 2012) and the MUNICH model (Kim et al., 2018).
Similar predictions using different methods also proved the reliability of the CFD simulation in this study (Fig. S4 in the Supplement).
Machine learning
Data-driven methods, such as machine learning and deep learning, are now successful operational geoscientific processing schemes and have co-evolved
with data availability over the past decade (Reichstein et al.,
2019). Specifically, these models have been used as computationally efficient
emulators of explicit mechanism models, to explore uncertainties
(Aleksankina et al., 2019) and sensitivities or replace complex
gas phase chemistry schemes (Keller and Evans, 2019; Conibear et al.,
2021). In addition, meta-models (Fang et al., 2005) such as neural
networks and Gaussian process (Beddows et al., 2017) are also
used to produce a quick to run model surrogate and show reliable
performance. The random forest (RF) model algorithm is an ensemble learning
method that generates many decision trees and aggregates their results and has been developed to solve the high variance errors typical of a
single decision tree (Breiman, 2001). Multivariate adaptive regression
splines (MARS) are a nonparametric and nonlinear regression method, which can
be regarded as an extension of the multivariate linear model (Friedman,
1991). RF and MARS are common machine learning methods which run efficiently
on large data sets and are relatively robust to outliers and noise.
Furthermore, they never require the specification of the underlying data model
and the complex parameter tuning, and they can still provide efficient
alternatives and generally show a high accuracy in applications for predicting air pollutant concentrations (Hu et al., 2017; Chen et al., 2018;
Kamińska, 2019; Geng et al., 2020).
Here, based on the database including 42 880 samples obtained from 1600 CFD
simulations, RF and MARS were both used to simulate the wind vector
along the x axis (Vx) and the y axis (Vy) at different heights within the
street canyon, respectively. The Vx and Vy were the average of all
velocities along the x or y axis over the same horizontal profile at a specific
height within the street canyons. The input predictor variables included
H/W, L/W, Hl/Hr, the grid receptor relative height (z/H), and the
background wind vector at the height of H along the x axis
(Vbgx=V(H)×sinα) and the y axis (Vbgy=V(H)×cosα). We finally combined the advantages of these two machine learning
models and developed the MLSCF scheme to predict wind environment in street
canyons and incorporated into the hybrid model, which is discussed in Sect. 3.1.
In the RF model, the number of predictors randomly sampled at each split node in
the decision tree (mtry) and the number of trees to grow (NumTrees)
are two important hyperparameters that determine the performance of the
model. Similarly, in the MARS model, the two important hyperparameters are the
total number of terms (nprune) and the maximum number of interactions
(degree). By comparing the mean squared error (MSE) for testing datasets
across models with candidate parameter combinations, we set mtry and
NumTrees as 6 and 200 in RF, respectively, and nprune and degree as 23
and 3 in MARS, respectively. Additionally, the 10-fold cross-validation (CV)
repeated 10 times was considered to evaluate the prediction performance of
our models. The total dataset was randomly divided into 10 subsets, where 9
subsets was used to train the
model and another was applied for validation. The
fitted coefficients of MARS are shown in Tables S2–S3 in the Supplement.
In order to identify the sensitivity and response relationship between
prediction variables and results in the RF model, we used the MSE for out-of-bag
(OOB) estimates to evaluate the relative importance of each feature to Vx and Vy, by randomly replacing the value of a single prediction variable one
by one (Liaw and Wiener, 2002). Higher values of increase in MSE indicated that
the predictor was more important. In addition, partial dependence plots
(PDPs) were applied to establish the response relationship between the change
in a single predictive variable and the predicted results, considering the
average influence of other variables (Greenwell, 2017).
Configuration of CMAQ-RLINE_URBAN
The near-ground NO2 concentrations were simulated from 1 to
31 August 2019 when the average of daily high temperatures was higher than 30 ∘C and sunlight duration was longer than 13 h, leading to strong
photochemical reactions. The simulation domain for the hybrid model covered
the core urban areas within and surrounding the 5th ring road, shown in
Fig. 3. The receptors included both grid receptors and monitor receptors.
The grid receptors were set at a spatial resolution of 50 m × 50 m,
and the height above the ground was 1.5 m, which was equivalent to the
height of human breathing. We used data from 10 observation stations
(monitor receptors) located in the normal urban environment and 5 near-road
monitoring sites for validation (Beijing Ecological Environment Monitoring
Center, available at http://zx.bjmemc.com.cn/, last access: 9 December 2022) (DSH, NSH, QM, XZM, and YDM)
in the simulation domain (Fig. 3), which were 10 and 3 m above
the ground, respectively. The QM and XZM sites were located in shallow street
canyons, and details of the morphometry of near-road measurement sites
are shown in Table S4 in the Supplement.
In general, compared to the RLINE model, CMAQ-RLINE_URBAN has
the following improvements:
The gridded meteorological parameters provided by the WRF model were used.
Gridded non-vehicle-related concentrations provided by the CMAQ-ISAM model were
used as background concentrations.
A simple NOx photochemical scheme was incorporated to simulate NO2
concentrations.
Thermodynamic effects caused by the special underlying surface structures of
the city were considered, including UHI effects, the influence of local
buildings on turbulence intensity, and vertical mixing of background
concentrations.
A newly developed MLSCF scheme was applied to predict the wind environment in
street canyons.
In our simulation, the model configurations in the base scenario
CMAQ-RLINE_URBAN included all (a)–(e) schemes, and the other
two control scenarios were set to investigate the sensitivity of urban
schemes to predictions, where all input data were set to be the same. The
scenario CMAQ-RLINE only including (a)–(c) schemes was set to analyze the
impacts of urban thermodynamic schemes, and the scenario
CMAQ-RLINE_URBAN_nc including (a)–(d) schemes
was set to identify the impacts of the MLSCF scheme. Although the wind
environment for each road at the top of the canyon was provided by the WRF
model in all scenarios, the calculation of wind profiles within the street
canyon was different. It was estimated based on the MOST theory in the
CMAQ-RLINE and CMAQ-RLINE_URBAN_nc rather than
that from MLSCF in the CMAQ-RLINE_URBAN.
ResultsFitting results of machine learning
In this study, the 10-fold cross-validation (CV) repeated 10 times was
considered to evaluate the prediction performances of RF and MARS models. As
shown in Figs. 4 and S5, both models performed with acceptable robustness
in CV, indicating that neither the RF nor the MARS model overfitted the data. In
general, the performances of both models in predicting Vy was better
than that for Vx of which the absolute value was relatively small,
especially for the MARS model. Since Vx was responsible for the formation
of the vortex within street canyons and affected by multiple factors, it was
more difficult to simulate. The averages of mean absolute error (MAE),
root mean square error (RMSE), and correlation coefficient (R) in the CV of
the RF model were 0.04, 0.02 m s-1, and 0.99, respectively, for Vx and 0.05, 0.03 m s-1, and 0.99, respectively, for Vy. Although the average of the relative
error (RE) was a little high (42.5 % and 43 %), particularly when the
predicted wind speed was low, the median RE was relatively low with 9.8 %
and 2.7 %, respectively, indicating an acceptable performance. Compared
with the advanced nonlinear RF algorithm, the MARS model did not perform very
well, especially when the absolute value of Vx was greater than 1 m s-1
and Vy was less than 3 m s-1. However, when the predicted wind speed by
machine learning methods was compared with observations from wind tunnel
experiments, we found that the performance of the MARS model was obviously
better than that of RF model in one of the validation cases (see Fig. 5). The
decision tree model like RF failed to respond to the parts beyond the range
of prediction variables (Vbgy= 17 m s-1≫ 5 m s-1), while the more reasonable predictions can be obtained by the MARS
model, which essentially used a piecewise linear function. Therefore, the MLSCF
scheme was established based on a method to combine the advantages of each
model. The RF model was used when the input value was within the range of
predictors shown in Table 1; otherwise the predictions from the MARS model
were used.
Cross-validations of machine learning models for Vx(a, c) and Vy(b, d): (a–b) RF model; (c–d) MARS model.
Performances of machine learning on the velocity profile in wind
tunnel experiments. The street canyon was perpendicular (a) or parallel (b)
to the wind direction at the roof level in different experiments. The
detailed description of each experiment was introduced in Sect. 2.2.3.
In addition, the importance of each predictor variable in the RF model was
investigated to explain their impacts on predictions. As shown in Fig. 6,
the background wind speeds on the x and y axes played vital roles in predictions
of Vx and Vy, respectively, followed by the relative height
(z/H). Among the geometric parameters of the street canyon, the impact of
L/W was the lowest. Since Vx was the main driving force for the formation
of vortices in street canyons, it was more affected by the geometry of
street canyons, especially Hl/Hr, compared to Vy. This feature
importance ranking was basically consistent with the conclusion in a
previous study (Fu et al., 2017). Figure S6 in the Supplement
shows the PDPs of each predictor variable in the RF model for Vx and
Vy. As z/H grew, Vx and Vy showed linear and logarithmic
increase patterns, respectively. Moreover, the resistant effect of windward
buildings on wind speed enhanced with increasing Hl/Hr,
resulting in a significant decrease in Vx particularly when
Hl/Hr was lower than 1.25. The relationship between predictors and
results in the model was consistent with the actual mechanism, indicating
our model could provide an accurate description of the wind field in the
street canyon.
Variable importance ranking in the RF model for (a)Vx and (b)Vy.
Impacts of MLSCF on simulations in street canyons
We compared the differences between monthly mean wind profile in different
street canyons including QM (shallow canyon: H/W=0.22), XZM (shallow
canyon: H/W=0.35), SZJ (standard canyon: H/W=1), and JTDL (deep canyon:
H/W=1.93), calculated by the default logarithmic function based on MOST in
the original RLINE model (Foken, 2006) and the MLSCF scheme developed
in this study. As shown in Fig. 7a–d, the wind profile estimated by
MOST showed a logarithmic change at the height above displacement height
(dh) with a decrease to 0 at dh and remained constant below
dh (the dh is calculated by multiplying surface roughness
length (z0) times a factor which is recommended to be set as 5).
Compared with the MOST, the simulated wind speeds near the ground and at the
top of canyons were generally lower based on the MLSCF scheme in shallow and
standard street canyons. In the deep street canyon, the significant
reduction in ventilation volume led to the mean wind speed simulated by the
MLSCF scheme being much lower than that of MOST at all heights. Although the
aspect ratios of the street canyon located in QM and XZM were similar, their
orientations were quite different, resulting in significant differences
under prevailing external winds in different directions. Since prevailing northerly and southerly winds were observed in Beijing during the
study period, the resistance effect of the buildings on both sides of the
east–west street canyon located in QM was more obvious.
Influence of MLSCF on wind environment in the street canyon. Monthly
averaged vertical profile of wind speed from the MOST and MLSCF methods in
different street canyons: (a) QM (H/W= 0.22); (b) XZM (H/W= 0.35); (c) SZJ
(H/W= 1); (b) JTDL (H/W= 1.93). The gray shading represents the standard
deviation in results of all hours. Hourly wind direction from the WRF model (at
roof level) and the MLSCF method (at ground level) in different street canyons:
(e) QM (H/W= 0.22); (f) SZJ (H/W= 1). As the gray and green shading shown,
the background wind over the street canyon provided by the WRF model was divided
into four main directions: east, west, south, and north.
We also investigated the impacts of the MLSCF hourly wind direction at
the bottom (z=3 m) of different street canyons by comparing the roof-level
predictions from the WRF model (see Fig. 7e–f). In a shallow street
canyon like QM, the simulated wind direction at the bottom was consistent
with the background on the whole, with R reaching 0.8. When the
background wind direction was less than 180∘, the averaged wind
direction at the bottom simulated by MLSCF was 91.8∘, which was
basically consistent with the angle between the street and the south
direction (84.5∘). When the background wind direction was greater
than 180∘, the average wind direction predicted by MLSCF
(257.4∘) was similar to that in the opposite direction of the
street (264.5∘), which was in line with the theory proposed by
Soulhac et al. (2008) that the average wind direction in street
canyons was assumed to be consistent with the (opposite) orientation of the
street. While in the deep street canyon of SZJ, when the external wind
perpendicularly blew to the street, the wind direction at the bottom was
completely opposite to that at the top due to the formation of vortex, with R reaching -0.97. In conclusion, compared with the traditional MOST
method, the newly developed MLSCF scheme could simulate the influence
of the external wind environment and geometry on the wind field well inside the
street canyon.
As shown in Fig. 8, the impacts of the MLSCF scheme on simulated NO2
concentration were identified by the differences between the
CMAQ-RLINE_URBAN and CMAQ-RLINE_URBAN_nc scenarios during a clean day (24 August). When the
atmosphere was stable at night, in street canyons with a large aspect ratio,
the wind direction at the bottom changed to the opposite of that at the top.
Combined with the decreased wind speed affected by the MLSCF scheme, the
NO2 concentrations at upwind grid receptors increased by up to 80 µg m-3. Meanwhile, the changes in wind direction would also decrease the
concentrations at downwind grid receptors by up to 20 µg m-3. For
example, in the SZJ standard canyon, the background wind direction over the
street was 79∘ (easterly), and the wind direction at the bottom
changed to 291∘ affected by the MLSCF scheme (westerly).
Therefore, the upwind NO2 concentrations increased, and the location of
peak NO2 concentration shifted to the windward direction. Since the changes in
NO2 concentrations were also influenced by the local on-road emissions,
the increase was only up to 2.1 µg m-3 in SZJ street, where the
traffic flow and vehicle emissions were low at night. However, a little
influence was observed during the day in the convective boundary layer.
During this period, although the wind direction at the bottom did not
change obviously due to the parallel background wind in SZJ street, the
increased surface wind speed was beneficial for the dispersion, resulting in
the decreased concentration in grid receptors within both sides of the
street canyon. In summary, the MLSCF scheme enabled the characterization of
the concentration distribution in street canyons.
Performance of near-road simulations from different models
The performances in predicting NO2 concentrations at all monitor
receptors from different models were first compared, including
the CMAQ-RLINE_URBAN, CMAQ-RLINE, and CMAQ models. The mean bias
(MB), RMSE, normalized mean bias (NMB), normalized mean gross error (NMGE),
the fraction of predictions within a factor of 2 (FAC2), the index of
agreement (IOA), and R between simulations and observations were all selected
as statistical indicators for the evaluation (Table 2). In general, the
performance of CMAQ-RLINE_URBAN was the best at all urban
sites. Compared to the CMAQ model, the averaged MB and NMB at urban sites in
the hybrid model decreased from 8 to 1.3 µg m-3 and 27 % to 4 %, respectively.
MB: mean bias; RMSE: root mean squared error; NMB: normalized mean bias; NMGE: normalized mean gross error; FAC2: fraction of predictions within a
factor of 2; IOA: index of agreement; R: correlation coefficient.
Diurnal variations in observed and predicted hourly averaged NO2
concentrations at near-road sites from different models were mainly compared
and shown in Fig. 9. The comparison of hourly and daily averaged
concentrations is shown in Fig. 10. Overall, CMAQ-RLINE_URBAN performed best with the smallest deviations. By comparing the
performances of the CMAQ and CMAQ-RLINE scenarios, we found the direct
coupling between the CMAQ and RLINE models could reproduce the high NO2
concentrations at near-road sites in the daytime and significantly improve the
underestimation of near-source concentrations due to grid dilution of
emissions in the CMAQ model. The averaged MB and NMB at all sites changed from
-10 to 25.6 µg m-3 and from -20 % to 51 %,
respectively. However, a significant overestimation was found in CMAQ-RLINE at night (00:00–06:00; all times in this paper are given in local time) and around sunset in the afternoon
(16:00–23:00), of which the peak could exceed the observed concentrations by
more than 1-fold. This overestimation was reduced in the
CMAQ-RLINE_URBAN, where the urban thermodynamic schemes were
implemented. The averaged MB and NMB decreased to 6.3 µg m-3 and
12 %, respectively, for the following reasons: (1) the increased
surface roughness length slightly enhanced local turbulence intensity near
roads; (2) the UHI scheme enhanced the intensity of atmospheric turbulence
in urban areas before and after sunset in the afternoon; (3) the effect of
turbulence intensity on the local vertical mixing of background
concentrations was considered, significantly reducing the mixing ratio of
concentrations over UCL and near the ground at nights in the stable boundary
layer (Fig. S7 in the Supplement), which was probably the main driving
force of decreased predictions in the hybrid model
(Benavides et al., 2019). However, CMAQ-RLINE_URBAN slightly overestimated the nighttime
NO2 concentration of all observation stations except the DSH, which was
probably caused by overestimations of background concentrations from
CMAQ-ISAM and vehicle emissions.
Diurnal variations in observed and predicted hourly averaged
NO2 concentrations from different models at near-road monitoring sites:
(a) DSH; (b) NSH; (c) QM; (d) XZM; (e) YDM.
Observed and predicted hourly (a–c) or daily averaged (d–f) NO2
concentrations from different models at near-road sites: (a, d) the CMAQ model;
(b, e) the CMAQ-RLINE model; (c, f) the CMAQ-RLINE_URBAN model.
The accuracy of model performances at each traffic site showed a small difference affected by the variations in the traffic flow and emissions of
nearby roads as well as the geometry of surrounding buildings and street
canyons. At the DSH and NSH sites, which were adjacent to ring roads as the main
urban freight corridors with a high traffic flow including a large
proportion of trucks, the high NOx emissions led to the highest
roadside NO2 observations among all sites. The CMAQ model would
significantly underestimate the high NO2 concentration at sites nearby
ring roads, with MB and NMB lower than -15 µg m-3 and -28 %
(Table S5 in the Supplement), respectively, which was improved using
CMAQ-RLINE_URBAN. However, the hybrid model produced a minor
overestimation at the NSH site, since the monitor was actually positioned in
the road centerline but assumed to be located downwind in the model,
resulting in a relatively large systematic error (Snyder
et al., 2013). In total, CMAQ-RLINE_URBAN performed best
among all models, especially improving the estimation of NO2
concentrations near roads by the original regional model.
Additionally, Fig. S8 in the Supplement shows the comparison between
simulated and observed roadside hourly and daily maximum 8 h average
O3 concentrations by different models, and their diurnal variations are
shown in Fig. S9. Generally, the hybrid model significantly improved the
overestimation of daytime O3 concentrations by the CMAQ model when
considering the titration effect of high NO concentration near roads on
O3. In the hybrid model, the peak time was delayed to about 15:00,
which was closer to the observation, but still 1–2 h earlier than the
actual time, which may be related to the uncertainty in the NO2 photolysis
rate.
Spatial distribution characteristics of simulated concentrations
We investigated the differences between the spatial distribution of the
monthly averaged NO2 concentration simulated by the CMAQ and
CMAQ-RLINE_URBAN models, as shown in Fig. 11. Since the
urban thermodynamic schemes were considered in the hybrid model, the
overestimation of most urban environmental grid receptors by the CMAQ model was
relieved. Within the 4th ring road and its surrounding areas, the mean
concentration of NO2 from CMAQ-RLINE_URBAN was 30.1 µg m-3, lower than that from the CMAQ model (39.5 µg m-3).
The overall spatial distribution characteristics of NO2 predictions
from both models showed that the concentrations in south regions were high
due to the pollution transport from Hebei province (An et al., 2019).
However, near-road hotspots for the NO2 pollution were identified in
the hybrid model where the spatial resolution of results increased to 50 m × 50 m. The NO2 concentrations nearby ring roads with high
traffic flow and emissions were up to 120 µg m-3, much higher than
the maximum prediction from the CMAQ model (52.4 µg m-3). In addition,
the simulated near-road concentrations from the hybrid model during traffic
peak hours (18:00–19:00) were significantly higher than those at noon
(12:00–13:00), while there were few changes in results from the CMAQ model
(Fig. S10 in the Supplement).
The NO2 concentrations estimated by CMAQ-RLINE_URBAN at
all grid receptor followed a two-mode Gaussian distribution (Fig. S11 in the Supplement), which was similar to Zhang's results
(Y. Zhang et al., 2021). The NO2
concentrations as a result of vehicle emissions were further calculated by
the differences between the total and background concentrations. In general,
the vehicle-induced NO2 concentrations in urban areas were 11.8 µg m-3, accounting for 39 % of the total concentrations, which was
similar to the predicted contribution from the CMAQ-ISAM model (42.5 %).
Monthly averaged NO2 concentrations attributed to all
emission sources or vehicles with a distance from the receptor to its nearest
road centerline. (a) NO2 attributed to all emission sources near all
roads. (b) NO2 attributed to all emission sources near different road
types. (c) Relative contribution of vehicles to NO2 near different road
types. The shaded area in (a) represents the standard deviation in the results of
all receptors.
Figure 12 shows the changes in NO2 concentrations simulated by the
hybrid model with distance from the grid receptors to its nearest road
centerline. The concentrations at grid receptors within 200 m from the road were
significantly affected by vehicle emissions. Within 50 m around the road, as
the distance from grid receptors to the road centerline gradually increased,
the NO2 concentrations decreased exponentially. The total NO2
concentrations decreased from 53.1 to 30 µg m-3,
and the vehicle-induced concentrations also dropped from 34.7 to 12.6 µg m-3. The concentrations near roads with
different types were highly dependent on the emission intensity. The
NO2 concentration was highest in the center of the urban freeway,
which was 76 µg m-3 and about 1.9 times higher than that on local
roads. The relative contribution of vehicle emissions to NO2
concentration reached up to 75.3 % on urban freeways as well as 71.9 %
and 65.5 % on artery roads and freeways but only 51.1 % on local roads.
It was worth noting that although the NO2 concentrations at grid
receptors far from the road on highways were slightly higher than those on other road types, the contribution of vehicle emissions was the lowest. This was because the NOx emission intensity of freeways was as high as that on
artery roads, but the density and height of buildings around freeways were
usually low, resulting in a high vertical flux of background concentrations
from the top of UCL to the ground. In conclusion, the results from the
hybrid model accurately reflected not only the impacts of local on-road
emissions but also the pollution characteristics affected by non-vehicle
sources at the regional scale.
Conclusion and discussions
In this study, we developed a hybrid model CMAQ-RLINE_URBAN
to quantitatively analyze the effects of vehicle emissions on urban roadside
NO2 concentrations at a high spatial resolution of 50 m × 50 m. The main conclusions of this study are as follows.
The developed MLSCF scheme revealed that, affected by the geometry of
buildings on both sides of the road, the wind environment in the street canyon was sometimes quite different from that in the environmental background. In
deep street canyons, the wind speed at the bottom decreased obviously due to
the resistant effect of buildings, and the directions of horizontal flow at
the bottom and top of the canyon were completely opposite due to the formation
of a vortex. The application of the MLSCF scheme in the hybrid model led to
increased NO2 concentrations at upwind grid receptors within deep street
canyons due to changes in the wind environment. However, the influence of
the turbulence induced by street canyon effects on the mixing of air
pollution was not considered, which we will make an effort to do in the future.
The comparison between observations and predictions showed that the hybrid
model significantly improved the underestimation of near-source
concentrations due to grid dilution of emissions in the CMAQ model. The
implementation of the urban thermodynamic schemes in the hybrid model also
relieved the overestimation in nighttime NO2 concentrations from CMAQ directly coupled with the RLINE model. The predictions from the CMAQ-RLINE_URBAN model could accurately reflect not only the
impact of local road emissions but also the pollution characteristics of
non-vehicle sources at a regional level. It revealed that in summer, the
average contribution of vehicle emission to NO2 concentrations in urban
areas of Beijing was 11.8 µg m-3, and the relative contribution
accounted for approximately 39 %. Moreover, the vehicle-induced NO2
pollution increased significantly with the decreased distance to the road
centerline, especially reaching 76 µg m-3 (75 %) on urban
freeways.
On the basis of this study, the following perspectives are proposed for
future research. (1) At present, the execution time during 1 h running
CMAQ-RLINE_URBAN over the urban domain was about 3.9 h on
average, which reached 4.8 h at night due to the difficulty of
convergence under conditions of high atmospheric stability. Therefore,
considering the running cost, the grid resolution of the area in Beijing of the 5th
ring road and its surroundings can reach 50 m × 50 m. We will make
efforts to develop a parallel computing method to reduce the computing time,
in order to improve the grid resolution of a relatively large-scale
simulation. (2) In our study, a simplified two-reaction scheme was
incorporated into the model to characterize the photochemical process of
NOx, since it performed similar predictions and less computational time
compared with those of the complicated CB05 gas phase chemical mechanism
(Kim et al., 2018). However, another study
pointed out that the impact of nonlinear O3–NOx–VOC chemistry on
NO2 concentrations in the deep canyon was non-negligible
(Zhong et al., 2017). The influence of different chemistry
schemes on near-road simulation will be investigated in the future. (3) It was suggested that the
long-term site observation of wind environment and pollutant concentrations
in various street canyons should be compared with modeling
results, especially in deep street canyons with a large aspect ratio. The
navigation monitoring technology would be applied in the model verification,
which can carry out large-scale observation of concentrations along streets.
(4) Here, we considered the dynamic impact of idealized building structures
on the wind environment in street canyons. However, there are many other
influencing factors, such as building layout and arrangement, roof shape,
green vegetation, and thermodynamic effects, which we suggest should be
considered in future studies. (5) In this study, we mainly focused on the
NO2 concentrations. In fact, the concentration of particulate matter,
especially UFP, will also have an obvious peak near the road centerline. In
the future, the process of physical and chemical changes in particulate
matter near the vehicle exhaust outlet should be further investigated. (6) The high-resolution NO2 concentration map will be beneficial for the
estimation of human health risks induced by air pollution at the street
level in future research.
Code availability
The RF and MARS models for MLSCF are both available on GitHub
(https://github.com/claus0224/MLSCF-RF-MARS, last access: 12 December 2022; 10.5281/zenodo.7418097, fanyuandeng and claus0224, 2022), and other codes are available
from the corresponding author on reasonable request.
Data availability
Data are available upon request from the corresponding author Huan Liu
(liu_env@tsinghua.edu.cn).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-22-15685-2022-supplement.
Author contributions
ZLv and ZLu contributed equally. ZLv and ZLu designed the research
and wrote the paper. HL, YZ, and KH provided guidance on the
research and revised the paper. ZLv, ZLu, and FD provided multiple
analytical perspectives on this research. XW, JZ, and LX helped collect
and clean the data. TH helped with language modifications.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Special issue statement
This article is part of the special issue “Air quality research at street level (ACP/GMD inter-journal SI)”. It is not associated with a conference.
Acknowledgements
We would like to acknowledge Jian Hang from Sun Yat-sen
University for support for CFD simulations and Jaime Benavides from the Barcelona Supercomputing Center for the application of urban thermodynamic
schemes.
Financial support
This research has been supported by the National Key Research and Development Program of China (grant no. 2022YFC3704200), the National Natural Science Foundation of China (grant nos. 6542061130213 and 41822505), and the Tsinghua-Toyota General Research Center. Huan Liu is supported by the Royal Society of the United Kingdom through a Newton Advanced Fellowship (grant no. NAF\R1\201166).
Review statement
This paper was edited by Karine Sartelet and reviewed by four anonymous referees.
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