Itahashi and Irie ﻿Progress in Earth and Planetary Science(2022) 9:15
https://doi.org/10.1186/s40645-022-00474-7

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open Access

Surface and aloft ­NO2 pollution
over the greater Tokyo area observed
by ground‑based and MAX‑DOAS
measurements bridged by kilometer‑scaleregional air quality modeling
Syuichi Itahashi1*

and Hitoshi Irie2

Abstract
To advance our understanding of surface and aloft nitrogen dioxide ­(NO2) pollution, this study extensively evaluated­NO2 concentrations simulated by the regional air quality modeling system with a 1.3 km horizontal grid resolution
using the Atmospheric Environmental Regional Observation System ground-based observation network and aloft
measurements by multi-axis differential optical absorption spectroscopy (MAX-DOAS) over the greater Tokyo area.
Observations are usually limited to the surface level, and gaps remain in our understanding of the behavior of air
pollutants above the near-surface layer, particularly within the planetary boundary layer (PBL). Therefore, MAX-DOAS
measurement was used, which observes scattered sunlight in the ultraviolet/visible range at several elevation angles
between the horizon and zenith to determine the aloft ­NO2 pollution averaged over 0–1 km. In total, four MAX-DOAS
measurement systems at Chiba University (35.63°N, 140.10°E) systematically covered the north, east, west, and south
directions to capture the aloft ­NO2 pollution over the greater Tokyo area. The target period was Chiba-Campaign 2015
conducted during 9–23 November 2015. The evaluations showed that the air quality modeling system can generally
capture the observed behavior of both surface and aloft ­NO2 pollution in terms of spatial and temporal coverage. The
diurnal variation, which typically showed an increase from evening to early morning without daylight and a decreaseduring the daytime, was also captured by the model. During Chiba-Campaign 2015, two cases of episodic higher N­ O2
concentration were identified: one during the nighttime and another during the daytime as different diurnal patterns.
These were related to a stagnant wind field, with the latter also connected to a lower PBL height in cloudy conditions.
Comparison of the modeled daily-averaged surface and aloft ­NO2 concentrations showed that aloft ­NO2 concentration exhibited a strong linear correlation with surface ­NO2 concentration, with the aloft (0–1 km) value scaled to
0.4–0.5-fold the surface value, irrespective of whether the day was clean or polluted. This scaling value was lower during the nighttime and higher during the daytime. Based on this synergetic analysis of surface and aloft observation
bridged by a kilometer-scale fine-resolution modeling simulation, this study contributes to fostering understanding ofaloft ­NO2 pollution.

*Correspondence: isyuichi@criepi.denken.or.jp
1
Sustainable System Research Laboratory (SSRL), Central ResearchInstitute of Electric Power Industry (CRIEPI), 1646 Abiko, Abiko, Chiba
270‑1194, Japan
Full list of author information is available at the end of the article© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.

Itahashi and Irie ﻿Progress in Earth and Planetary Science(2022) 9:15

Page 2 of 19

Keywords: Nitrogen dioxide, AEROS, MAX-DOAS, Air quality model, High-resolution modeling, Planetary boundary
layer, Chiba-Campaign 2015

1 Introduction
Nitrogen oxides (­NOx), which collectively refers tonitric oxide (NO) and nitrogen dioxide ­(NO2), play a
crucial role in tropospheric chemistry and have important implications for air quality and climate (Seinfeld and Pandis 2016). ­N Ox is mainly emitted into theatmosphere as NO from anthropogenic sources (e.g.,fossil fuel combustion) and natural sources (e.g., microbiological processes in soil and lightning) (Lin 2012;
Kurokawa et al. 2013). Since N­ O2 can affect humanrespiratory organs at high concentrations, an air quality standard (AQS) was established for N­ O2 in 1973
and revised in 1978 in Japan. To satisfy this AQS, thedaily average of hourly values of ­N O2 concentrations
must be 0.04–0.06 ppm or lower. In Japan, the AQS
for ­N O2 is mostly satisfied except at a few sites locatedclose to roadsides (Wakamatsu et al. 2013). Given thatthe placement of ground-based observation is generally
designed to protect human health, and most observation sites are located in densely populated areas, observation sites are sparse in rural and remote areas. To
obtain a homogeneous spatial distribution of air pollutants, satellite observation can serve as a useful tool andour previous studies have reported space-borne ­N O2
pollution over Asia and Japan (Irie et al. 2016; Itahashi
et al. 2014, 2019; Choi et al. 2021). Although satellite
observations have the potential to contribute to understanding spatial distribution patterns, the observed
concentration is the amount vertically integrated overthe column from the surface through the entire troposphere or sliced layers within the troposphere. To focus
on the near-surface ­N O2 behavior, additional observations are desired. This study utilizes multi-axis differential optical absorption spectroscopy (MAX-DOAS).
MAX-DOAS is able to measure vertical profiles byobserving scattered sunlight in the ultraviolet (UV)/visible range at several elevation angles between thehorizon and zenith. By combining ground-based observation with MAX-DOAS, surface and aloft N­ O2 pollution can be monitored. However, nighttime aloft ­N O2
cannot be detected because the principle of MAXDOAS relies on sunlight. To bridge spatial and temporal information, further regional air quality modelingneeds to be conducted. This study therefore aims tounderstand the behavior of surface and aloft ­N O2 andto fill in gaps in our understanding of the status of N
­ O2
pollution within the planetary boundary layer (PBL).

2 Methods
Chiba prefecture is located to the east of Tokyo andis part of the greater Tokyo area (Tokyo, Kanagawa,
Saitama, and Chiba). At Chiba University, located in thecentral part of Chiba prefecture, an intensive observation campaign (Chiba-Campaign 2015) was conductedfrom 9 to 22 November 2015. This campaign is the target of this study, through which we aim to understandthe behavior of surface and aloft ­N O2 and to fill in gaps
in our understanding of the status of N
­ O2 pollution
within the PBL. Section 2.1 presents the ground-based
observation. Section 2.2 describes the MAX-DOAS
measurement. Section 2.3 introduces the regional air
quality modeling.
2.1 Ground‑based observation

Ground-based observation of air pollutants that are
regulated by AQS (carbon monoxide (CO), sulfur
dioxide ­(SO2), ­N O2, photochemical oxidants ­(Ox), suspended particulate matter (SPM), and particulate matter with an aerodynamic diameter less than 2.5 μm­(PM2.5)) have been routinely measured by the Atmospheric Environmental Regional Observation System(AEROS) (http://​soram​ame.​taiki.​go.​jp). AEROS has twogoals: to monitor ambient air quality using ambient air
pollution monitoring stations (APMSs) and to measure
air pollution particularly due to transportation such as
automobiles using roadside air pollution monitoring
stations (RAPMSs). The locations of AEROS over Chiba
prefecture are shown in Fig. 1a. During the analyzed
period of Chiba-Campaign 2015, surface N­ O2 concentration was monitored at a total of 100 APMS sites
and 27 RAPMS sites. The chemiluminescent method
using ozone (JIS B 7953) is recommended for measuring ­N O2, and most sites used this method, although
two APMS sites and one RAPMS site used the Saltzman reagent. Chemiluminescent methods using molybdenum converters could suffer from interference from
­N Oz (defined as all reactive oxygenated nitrogen except­N Ox); however, the effect is probably small (Kondo
et al. 2008). The observation data from AEROS are validated by data quality checking, and then are distributed
as open data. Among these AEROS sites, sites locatedwithin 24 km (the maximum horizontal distance measured by MAX-DOAS, see Sect. 2.2) of Chiba University
were used for the analysis.

Itahashi and Irie ﻿Progress in Earth and Planetary Science(2022) 9:15

Page 3 of 19

Fig. 1 Locations of ground-based observations by AEROS and 4AZ-MAX-DOAS. a Map of Kanto region, Japan and locations of ground-based
observation by AEROS over Chiba prefecture. b Enlarged map around Chiba University, which is the center of 4AZ-MAX-DOAS. Circles are drawn
at 4 km radius intervals from Chiba University up to 24 km (maximum horizontal distance observed by MAX-DOAS). AEROS observation sites arecolored white (APMSs) and dark gray (RAPMSs)

2.2 MAX‑DOAS observation

Aloft observations using MAX-DOAS systems (Irieet al. 2008, 2011, 2015, 2019, 2021) have been conducted at Chiba University (35.63°N, 140.10°E, 21 ma.s.l.), Chiba, Japan. The MAX-DOAS methodology isbased on the differential optical absorption spectroscopy (DOAS) method, which quantitatively detects
narrow-band absorption by trace gases by applying theBeer-Lambert law (Platt and Stutz 2008). Following on
from the pioneering studies of Hönninger and Platt(2002) and Hönninger et al. (2004), various instruments
and algorithms have been developed worldwide for
MAX-DOAS. The MAX-DOAS system at Chiba University has participated in the Cabauw Intercomparison
Campaign of Nitrogen Dioxide measuring Instruments(CINDI) (Roscoe et al. 2010) and CINDI-2 (Kreher
et al. 2020). The MAX-DOAS system in this study uses
the Maya2000Pro spectrometer (Ocean Optics, Inc.)
with a 25 μm slit placed in a temperature-controlled
enclosure to record high-resolution spectra (full widthat half maximum: 0.3–0.4 nm; oversampling: 3–4) inthe wavelength range 310 to 515 nm. Measurement was
performed at five off-axis elevation angles (e.g., 2°, 3°,
4°, 6°, and 8°) and at a single reference elevation angle,

with 70° used instead of 90° to avoid saturation of thespectral intensity due to the effect of direct sunlight.
The elevation angle setting was considered fully in calculating the differential air mass factors for the verticalprofile retrieval (e.g., Irie et al. 2011, 2015). Thus, thechoice of either 70° or 90° was not critical. Although theoriginal observation by MAX-DOAS targets a single
direction, four different azimuth-viewing MAX-DOAS(4AZ-MAX-DOAS) instruments are used at Chiba University to cover the cardinal compass points simultaneously: north (347°E), east (118°E), west (265°E), andsouth (175°E). The location of Chiba University and the
directions covered by the 4AZ-MAX-DOAS are shownin Fig. 1b. To derive near-surface concentrations, weused our retrieval algorithm called JM2 (Japanese
MAX-DOAS profile retrieval algorithm, version 2) (Irieet al. 2008, 2011, 2015). JM2 uses the recorded high-resolution UV–visible spectra from 310 to 515 nm to perform spectral analysis by the DOAS method (Platt andStutz 2008) by spectral fitting using the nonlinear leastsquares method and subsequent vertical profile retrievals using the optimal estimation method, which allows
us to retrieve lower-tropospheric vertical profile information for eight quantities including ­N O2. The fittingItahashi and Irie ﻿Progress in Earth and Planetary Science(2022) 9:15

Page 4 of 19

windows (460–490 nm) and cross section data wereidentical to those described by Irie et al. (2011, 2015).
The minimum detectable level of the N­ O2 differential
slant column density estimated from the fitting residual
was about ­1014 molecules ­cm−2. In the retrieval, off-axis
elevation angles are limited to below 10° to minimize
the systematic error in the oxygen collision complex fitting results (Irie et al. 2015). This limitation improves
the observation of the PBL owing the loss of sensitivity to extinction at high altitudes, where clouds rather
than aerosols predominate. Clouds in the PBL were
excluded by choosing data with a relative humidity overwater (RHw) of less than 90% for the 0–1 km layer. The
RHw was estimated from the MAX-DOAS ­H2O data
retrieved using a fitting window of 495–515 nm andthe National Centers for Environmental Prediction(NCEP) pressure and temperature reanalysis data (2.5°
grid and 6-hourly). Thus, our MAX-DOAS system was
optimized to retrieve information on aerosols and tracegases in the PBL rather than in the whole troposphericcolumn. For the vertical profile retrieval, full consideration is given to the elevation angle setting when computing differential air mass factors (Irie et al. 2011,2015). There are usually about 1–2 degrees of freedomof signals for trace gas vertical profiles retrieved in thisway. With the retrieved vertical profiles, the present
study analyzed the layer at 0–1 km, which corresponds
to the lowermost layer with the highest sensitivity

owing to the longest light path in profiles retrieved by
JM2. In the horizontal direction, the viewed horizontal distance in each direction is up to 24 km. In Fig. 1b,
horizontal scales up to 24 km from Chiba University are shown at 4 km intervals as a visual guide. The
total uncertainties, including random and systematic
errors, in a single measurement were estimated as 15%
for ­N O2 (Irie et al. 2011), and the estimated systematic error was based on additional retrievals of JM2aerosol retrieval uncertainties of as large as 30% (Irieet al. 2008, 2011). Previous studies also highlighted theuse of MAX-DOAS measurements. In Europe, MAXDOAS and regional air quality model simulations havebeen compared over Paris (Shaiganfar et al. 2015) andthe Netherlands (Vlemmix et al. 2015), and four sites
over Europe with six model ensemble experiments(Blechschmidt et al. 2020). The MAX-DOAS networkin Russia and Asia (MADRAS) elucidated the longterm trends in N
­ O2 pollution status (Kanaya et al. 2014;
Choi et al. 2021), and these trends have been comparedwith model results (Kanaya et al. 2014). In this study,
to improve the fine-scale horizontal coverage by 4AZMAX-DOAS measurements (Irie et al. 2021), kilometer-scale regional air quality modeling was conducted.
2.3 Regional air quality modeling

The regional air quality modeling was based on Community Multiscale Air Quality (CMAQ) version 5.2.1

Fig. 2 NOx emission over quadruple nesting domains of air quality modeling system. The horizontal grid resolutions are 36, 12, 4, and 1.3 km

Itahashi and Irie ﻿Progress in Earth and Planetary Science(2022) 9:15

Page 5 of 19

released by the US Environmental Protection Agency(US EPA 2018). The simulation used quadruple nesting
covering all of Asia, Japan, the extended Kanto region,
and Chiba prefecture with horizontal grid resolutions of
36, 12, 4, and 1.3 km (Fig. 2). For comparison with observations, the domain with the finest resolution of 1.3 km
was used. In the vertical direction, 44 nonuniform layers from the surface to 50 hPa were set to fully represent
the stratosphere-to-troposphere transport (Mathur et al.
2017; Itahashi et al. 2020). The height of the lowermost
layer, which corresponded to modeling surface level,
was approximately 20 m. The meteorological input data
were simulated by the Weather Research and Forecasting (WRF) model version 3.6.1 (Skamarock et al. 2008)
to drive the CMAQ. The configuration of the WRF simulation was basically the same as our previous study (Itahashi 2018). For both longwave and shortwave radiation,
the rapid radiative transfer model for global climate models (RRTMG) radiation scheme was used (Iacono et al.
2008), and the Morrison double-moment scheme (Morrison et al. 2009) and Grell convective parameterization(Grell and Devenyi 2002) were used for microphysics
and cumulus parameterization. The cumulus scheme
was not applied for domains of 4 and 1.3 km horizontal
resolution. The Mellor-Yamada-Janjic scheme was usedas the PBL option (Janjić 1994). The outermost domainof 36 km horizontal grid resolution was independently
simulated with nudging for wind, temperature, and water
vapor fields to NCEP/National Center for AtmosphericResearch (NCAR) final analysis data for all vertical layers,
where the analysis data has 1° spatial and 6 h temporal
resolution (NCEP/NCAR 2021). For inner domains of 12,
4, and 1.3 km, the nudging was applied toward the Mesoscale Analysis (MANAL) by the Japan Meteorological
Agency (JMA), which has a grid spacing of 5 km spatial
and 3 h temporal variation.
The emission dataset was created using anthropogenic
emissions from Hemispheric Transport of Air Pollution(HTAP) version 2.2 (Janssens-Maenhout et al. 2015) over
Asia and the P­ M2.5 emission inventory for fiscal year 2015
(defined as April 2015 to March 2016) developed by the
Ministry of Environment (MOE 2019) over Japan, biogenic
emissions from the Model of Emissions of Gases and Aerosols from Nature (MEGAN) (Guenther et al. 2012), emissions from biomass burning in the Global Fire Emissions
Database (GFED) version 4.1 (van der Werf et al. 2017),
emissions from 16 volcanoes in Japan from observational
data obtained by the Japan Meteorological Agency (JMA2021), and emissions from 33 degassing volcanoes in Russia, India, Indonesia, Philippines, Papua New Guinea, andNorthern Mariana Islands constrained by satellite measurement (Carn et al. 2017). Because anthropogenic emissions from China have shown dramatic changes in recent

years (Irie et al. 2016; Itahashi et al. 2019), Chinese emissions from each sector were adjusted to the estimated values for 2015 (Zheng et al. 2018). The domestic emission
inventory by the Ministry of Environment was createdover Japanese MESH3 boundaries, which corresponded
to approximate 1 km. N­ Ox (including the ratio of NO and­NO2), ­SO2, ammonia (­NH3), CO, non-methane volatileorganic compounds (NMVOC; 46 species), and primary­PM2.5 (18 elements) were considered as hourly data foreach month. For some sectors (e.g., automobiles), weekdays and weekends were also taken. Ship emissions were
provided by the Sasakawa Peace Foundation (SPF), whichwere estimated based on the Automatic Identification
System (AIS) for each day of the week and hour. The ­NOxemissions used in this study are shown in Fig. 2. For the finest resolution of 1.3 km, the proportions of the main ­NOxemission sources of automobiles, stationary sources (sumof industry and power plants), ships, and others (all emissions minus automobiles, stationary, and ships) are also
shown in Fig. 2. Gas chemistry in the CMAQ was used
for the SAPRC07 mechanism (Hutzell et al. 2011) andaerosol chemistry in the CMAQ was used for the aero6
mechanism with non-volatile primary organic aerosol(Simon and Bhave 2012). The lateral boundary condition
for the outermost domain was taken from the global modelof the Model for Ozone and Related Chemical Tracers(MOZART) version 4 (Emmons et al. 2010), and the nested
domains used the outer domains simulation results for thelateral boundary condition. The default CMAQ profile wasused for the initial condition. The WRF and CMAQ simulations were conducted from 2 November 2015 in order to
set a 1 week spin-up time for removing the effect of the initial conditions.
For assessing model performance, three statistical metrics were used: correlation coefficient (R), normalized
mean bias (NMB), and normalized mean error (NME).
These are defined as follows:


N 
1 Oi − O Mi − M

R= 
(1)
2 N 2N 1 Oi − O
1 Mi − M

NMB =

N

(2)

NME =

N

(3)

1 (Mi − Oi )N
1 Oi

1 |Mi − Oi |N
1 Oi

where N is the total number of paired observations (O)and models (M), and these averages are denoted as O andM , respectively.

Itahashi and Irie ﻿Progress in Earth and Planetary Science(2022) 9:15

Page 6 of 19

3 Results and discussion
Section 3.1 presents the temporal variation of ­NO2concentration at the surface level using ground-based
observation by AEROS, and Sect. 3.2 presents the temporal variation of aloft N­ O2 concentration based on
MAX-DOAS. The model evaluations are also presented.
Section 3.3 examines the diurnal variation of N
­ O2 concentration at the surface and aloft. Section 3.4 considers
the reason for episodic high concentrations of ­NO2 basedon the meteorological field. Finally, Sect. 3.5 provides thecorrespondence between surface and aloft ­NO2 pollution
as analyzed by the well-evaluated modeling simulation.

NME ranged from 25 to 85% over China based on model
results using three different emission datasets and theirevaluation against the various surface observation categories (Liu et al. 2018). The Model Inter-Comparison
Study for Asia (MICS-Asia) Phase III reported that R was
0.02–0.70 and NMB ranged between − 44% and + 36%(Kong et al. 2020). The model intercomparison study
over Japan, J-STREAM, reported that R was 0.38–0.54
and NMB ranged between − 19.4% and + 14.7% over the
Kanto region (Yamaji et al. 2020). The results of this study
demonstrate that the model could generally capture thesurface ­NO2 concentration around Chiba University
through Chiba-Campaign 2015.
For the purpose of detailed comparison using groundbased AEROS observations, the modeling performanceat ground-based AEROS observations located along thepath of 4AZ-MAX-DOAS measurements (see Fig. 1)
was further evaluated. The temporal variations during
Chiba-Campaign 2015 at these AEROS sites are shownin Fig. 4. Hereinafter, timeseries data are shown as local
time (LT), which is defined as Universal Time Coordinate(UTC) + 9 h. In the north, east, west, and south directions, a total of 5, 3, 1, and 4 sites were located along themeasurement paths of 4AZ-MAX-DOAS, respectively.
These AEROS sites are named in order of closeness from
Chiba University in each direction (see Fig. 4). High ­NO2
concentration is defined as greater than 30 ppbv in this
discussion, and is denoted by a yellow highlight at eachsite in Fig. 4. In the north direction (green in Fig. 4), R wasaround 0.5, NMB was within ± 25%, and NME was within
50%. At the N2 site, classified as an AEROS RAPMS site,the model performance was lower than that at other
APMS sites in the north direction. Because the N1 and3.1 Temporal variation of surface ­NO2 concentration

The overall modeling performance during Chiba-Campaign 2015 was evaluated using ground-based AEROS
observation. A comparison of the surface observation
by AEROS located within 24 km (maximum horizontal
distance measured by MAX-DOAS, see Fig. 1) of Chiba
University and the model at the surface level is shown
as a scatterplot in Fig. 3. In this comparison, the corresponding grid for observational sites over the finest
modeling domain of 1.3 km was used. During ChibaCampaign 2015, surface N­ O2 concentration around
Chiba University ranged mostly within 30 ppbv (dense
circles in Fig. 3), and sometimes exhibited high concentrations of up to 70 ppbv. The histogram in Fig. 3
showed that the model undercounted the observed feature between 20 and 30 ppbv and overcounted it over 30
ppbv. Over 20,000 pairs of observed and modeled hourly­NO2 concentrations, R of 0.50 showed a moderate linear
correlation, NMB was + 9.8%, and NME was 55.9%. It
has been reported that R ranged from 0.51 to 0.92 andFig. 3 (Left) Scatter-plot of hourly surface level ­NO2 concentration during Chiba-Campaign 2015. (Right) Histogram for observations (white) andthe model (gray). Ground-based observation by AEROS and the model are shown. AEROS sites located within 24 km of Chiba University (see, Fig. 1)
are usedItahashi and Irie ﻿Progress in Earth and Planetary Science(2022) 9:15

Page 7 of 19

Fig. 4 Temporal variation in hourly ­NO2 concentration at the surface level along 4AZ-MAX-DOAS measurement paths. Ground-based observation
by AEROS and the model results are shown. AEROS sites are named in order of nearness to Chiba University in each direction. Statistical scores areshown at the top-right of each panel. Yellow highlighting denotes high ­NO2 concentration defined as greater than 30 ppbv. See also Fig. 1N2 sites are located close together, the modeling results
were taken from the same grid point in this comparison.
Compared with the modeling performance found at N1,
all statistical scores were worse, and model underestimation was identified at N2. This result indicates that evenwhen the finer horizontal resolution of 1.3 km was used
for air quality modeling, it was difficult to capture local­NO2 pollution mainly caused by automobile sources. In
the east direction (blue in Fig. 4), the comparison at theE2 and E3 sites, which are classified as AEROS APMS
sites, was better than at the E1 site, which was classified
as an AEROS RAPMS site, in terms of NMB and NME.

The model also underestimated N­ O2 concentration at
RAPMS sites, and this result in the east direction was
similar to that found in the north direction. In the west
direction (orange in Fig. 4), only a single APMS site close
to Chiba University was available because this direction
covers the Tokyo Bay area. The model performance at theW1 site was comparable to that at the N3 and E3 sites. In
the south direction (red in Fig. 4), at the S1 site, which isclassified as an AEROS RAPMS site, located near Chiba
University, the model performance was mostly comparable to that found in the other directions. In contrast, themodel generally tended to overestimate the surface ­NO2

Itahashi and Irie ﻿Progress in Earth and Planetary Science(2022) 9:15

Page 8 of 19

pollution at the other sites S2, S3, and S4 in the south
direction. At the S2, S3, and S4 sites, the model overestimation was suggested by NMB exceeding + 50%. As
indicated by the temporal variation at S2, S3, and S4, themodel overestimation was seen during 15–16, 17–18, and20–21 November 2015, when N­ O2 concentrations weregenerally high. Because of this tendency of the model foroverestimation in the south direction, model underestimation at RAPMS sites as found in the north and east
directions was not detected. Based on these detailed
comparisons of the temporal variation at AEROS sites
located along the path of 4AZ-AMAX-DOAS, it was
found that the model had some difficulties with underestimation when capturing local air pollution caused byautomobile sources at RAPMSs sites in the north andeast direction and overestimation in the south direction.
Overall, the model generally captured the temporal variation of surface ­NO2 pollution around Chiba University
along the path of 4AZ-MAX-DOAS.
From the temporal variation shown in Fig. 4, surface­NO2 concentration typically showed diurnal variation
with minima during the daytime and maxima during thenighttime. During Chiba-Campaign 2015, elevated ­NO2
concentrations were detected on 9, 15, 16, 17–18, and20–21 November 2015, as distinguished by yellow highlights in Fig. 4. For the elevated ­NO2 concentrations on 9,15, and 16 November, a brief peak was found during thenighttime, whereas 17–18 and 20–21 November exhibited continuously higher concentrations over the whole
day as the diurnal variations changed such that there
was no daytime minima. Diurnal variation and episodic
peaks of N
­ O2 concentration are respectively discussed in
Sect. 3.3 and 3.4.
3.2 Temporal variation of aloft N­ O2 concentration

The temporal variation of aloft N­ O2 concentration
obtained by comparing 4AZ-MAX-DOAS and model
results averaged over 0–1 km are shown in Fig. 5. The
horizontal measurement distance of MAX-DOAS isalso plotted. Since the horizontal distance viewed by
MAX-DOAS is dependent on aerosol pollution, it varied from 4 to 24 km, with longer distances on clean days,
such as during lower N­ O2 concentration periods on
11–13 November 2015. The modeled N­ O2 concentration
within 24 km from Chiba University from model results
is shown as the range in Fig. 5, and statistical analysis of
the comparison with MAX-DOAS observation was conducted using the data within the observed horizontal

Fig. 5 Temporal variation of ­NO2 concentration by MAX-DOAS and model averaged over 0–1 km (each direction). The MAX-DOAS measured
horizontal distance is also plotted. The range of model results showed simulated N­ O2 concentration within 4–24 km from Chiba University in eachdirection. Statistical scores are shown in the inset, and 1 h averaged MAX-DOAS observation and corresponded horizontal distance to observation
were used for this analysis

Itahashi and Irie ﻿Progress in Earth and Planetary Science(2022) 9:15

Page 9 of 19

distance to match the view by MAX-DOAS. From therange of modeled N­ O2 concentration within 4–24 km
from Chiba University, the modeled N­ O2 pollution range
within the horizontal direction of MAX-DOAS was
generally small except for pollution events. This result
implies that N­ O2 pollution around Chiba University
was generally dominated by regional-scale broad pollution over the greater Tokyo area. In the north, east, andwest directions, the range of modeled ­NO2 concentration
was small except for high concentration events on 9, 15,
16, 17–18, and 20–21 November 2015. Because MAXDOAS observation was limited to during the daytime,
the higher ­NO2 concentrations at night on 9, 15, and 16
November were not measured. However, the cases of episodic high concentration on 17–18 and 20–21 November,which were characterized by continuous high concentration, corresponded well between MAX-DOAS and aloftmodel results. The modeling performance for the north,east, and west directions was R in the range 0.36–0.56,
NMB around + 20%, and NME around 50%. These results
are comparable with the surface results. In the south
direction, the range of modeled ­NO2 concentration was
also large during episodic N­ O2 pollution events, but also
showed larger variation throughout Chiba-Campaign
2015 compared with the other directions. As found in
the surface comparison, the model also tended to overestimate aloft N­ O2 concentration in the south direction.
Although the value of NMB was larger in the south direction than in the other directions, NME was comparable
and R was better compared with the other directions.
In summary, it was validated that the model captured
aloft ­NO2 concentration through comparison with 4AZMAX-DOAS observations.

directions. As found from the analysis of temporal variations shown in Fig. 3, the model tended to overestimate
in the south direction, and this modeling overestimation
was identified during the nighttime from the analysis ofdiurnal variation at the surface level. Apart from this difficulty, the model captured the diurnal variation of ­NO2
concentration at the surface level. The evening peak was agood match between surface observation and the model,
whereas the morning peak showed a slight difference intiming. This suggests that investigation into the chemical mechanisms related to NOx and sunlight is needed infuture research. In addition, because the nighttime ­NO2
concentration is also related to NO and O­ 3 reproduction,
this reaction and related meteorological variation must
be examined to obtain the morning maxima at sunrise.
In the comparison of aloft ­N O2 concentration, all
MAX-DOAS observations are plotted as light colors,and 1 h averaged data are plotted as dark colors (rightside of Fig. 6). The hourly averages at 7 AM and 4 PM
were not well counted (1–3 times through Chiba-Campaign-2015) and hence the 1 h averaged plot is notshown for these times. All 4AZ-MAX-DOAS showeda slight decrease in concentration during the daytime
around noon, and the model reproduced these observations for all directions. As with the surface concentration in the south direction, the aloft ­N O2 concentrationin the south direction was also overestimated. However, the temporal variation consisting of a drop aroundnoon was captured. Based on this comparison, it was
concluded that the diurnal variations were well reproduced by the model at both the surface and aloft. To
our knowledge, there has been limited research on
the diurnal variation in surface and aloft N­ O2 concentrations. The findings from CINDI revealed that thediurnal variation during summertime had a U-shaped
curve with morning and evening maxima and daytime
minima (Irie et al. 2011). At Yokosuka, which is about
30 km south of the Tokyo metropolitan area, MAXDOAS and surface monitoring were compared (Kanaya
et al. 2014). The daytime decrease was also recorded
for both the MAX-DOAS and surface observations inthese studies, and this was consistent with the findings in the present study. The present study extended
comprehensive analyses for verifying the air quality
modeling performance at both the surface and aloft
using surface observation and MAX-DOAS. Considering the better modeling performance in the north andeast directions than in the south direction, one possible
reason for the difficulty in modeling may be issues inthe emission estimates. The coastline of the Tokyo Bay
area has many stationary sources (industry and power
plants) (Fig. 2), which have relatively high stack heights,
and the impact of these may be observed in the south

3.3 Diurnal variation of surface and aloft ­NO2
concentrations

Sections 3.1 and 3.2 evaluated the temporal variationsof surface and aloft N­ O2 concentrations and confirmedthat the modeling system generally captured surface andaloft ­NO2 pollution. The diurnal variations are further
analyzed here in Sect. 3.3. The surface and aloft diurnal
variations in ­NO2 concentration averaged during ChibaCampaign 2015 for the direction observed by 4AZ-MAXDOAS are presented in Fig. 6. From the comparison at
the surface level (left side of Fig. 6), the model could capture the observed diurnal variation, which consisted of amorning peak, subsequent decline during the daytime,
and then an evening peak. In detail, the observed values
showed morning maxima during 8 to 9 AM whereas themodel showed morning maxima at the sunrise time of 6
AM. In contrast, for the evening peak seen after the sunset time of 5 PM, the timing of maxima was well reproduced by the model, particularly in the north and eastItahashi and Irie ﻿Progress in Earth and Planetary Science(2022) 9:15

Page 10 of 19

Fig. 6 Diurnal variation of ­NO2 concentration at (left) surface and (right) aloft. Surface data are taken from AEROS sites located along
4AZ-MAX-DOAS directions and model results at surface level. Aloft data is taken from MAX-DOAS with light colors for all data and thick colors for 1 haveraged data, and model results averaged over 0–1 km. Dark blue shading indicates nighttime

direction from Chiba University. Because N­ O2 concentration was overestimated both at the surface and aloft,
the emission amount itself from stationary sources
might be overestimated. Moreover, chemical andphysical processes related to NOx emissions from point
sources could be related. Other issues may stem fromthe ship emissions, which dominated N­ Ox emissions
over the ocean zone. These points should be exploredItahashi and Irie ﻿Progress in Earth and Planetary Science(2022) 9:15

Page 11 of 19

in the future to improve the modeling performancearound the greater Tokyo area.

daylight duration and modeled solar radiation reaching
the surface level are plotted in Fig. 7b. These indicate
the weather conditions, and the results show that 10,
13–14, and 20 November 2015 had cloudy conditions.
Rainy conditions were clarified by analyses of precipitation as shown in Fig. 7c and relative humidity as shownin Fig. 7d. During Chiba-Campaign 2015, precipitation
events occurred on 9, 10, 14, 15, and 19 November. In
these precipitation events, relative humidity also exhibited high values of close to 100%. Wind components arepresented as wind speed in Fig. 7e and wind direction in
Fig. 7f. The wind speed was generally around 5 m/s andsometimes reached 10 m/s, and it was found that thehigh ­N O2 concentration (yellow highlight) correlated
well with weak wind speed. Throughout Chiba-Campaign 2015, the prevailing wind was in the north (0°) toeast (90°) direction. However, wind changes to the west
(270°) or northwest direction were found which corresponded to intense emission sources around the Tokyo
Bay area. Therefore, the stagnant conditions in thetransport of air mass from the dense emission sources
around the Tokyo Bay area are thought to be the reason
for increased N­ O2 concentration in terms of meteorological conditions. The highest ­N O2 concentration wasseen on 17–18 November and the longest event with
continuously high ­N O2 concentration was found during
20–21 November with different diurnal patterns; theseare further discussed in Sect. 3.5.

3.4 Episodic peaks in ­NO2 concentration

The diurnal variation presented in Sect. 3.3 was theaveraged characteristics during Chiba-Campaign
2015. As shown in the temporal variation presented
in Figs. 4 and 5, some episodic peaks in N
­ O2 concentration were detected on 9, 15, 16, 17–18, and 20–21
November 2015. Here in Sect. 3.4, the reason for theincreased ­N O2 concentration is considered based on
the meteorological field. The meteorological parameters measured by the Automated Meteorological
Data Acquisition System (AMeDAS) of the Chiba special area meteorological observatory, which is locatednear Chiba University (see Fig. 1), and its modelingperformance are shown in Fig. 7. In this figure, highconcentrations of N
­ O2 of more than 20 ppbv are also
indicated by yellow highlights based on the averaged
concentration of APMS sites located along the pathof 4AZ-MAX-DOAS for clarity (see also Fig. 4). The
meteorological field was well reproduced by the modeling system throughout the Chiba-Campaign 2015.
The air temperature (Fig. 7a), ranged from 10 to 20 °C
with clear diurnal variation of daytime maxima. During
Chiba-Campaign 2015, there were drops in temperature on 13 and 20 November 2015. The observed hourly

Fig. 7 Meteorological field measured at AMeDAS Chiba (gray lines) and model results (black lines). The results shown are for (b) wind speed, (c)wind direction, (d) temperature, (e) precipitation, and (f ) relative humidity. In (d), observation of the duration time of sunlight per day is also shownItahashi and Irie ﻿Progress in Earth and Planetary Science(2022) 9:15

3.5 Correspondence between surface and aloft N­ O2
pollution

Section 3.1 and 3.2 validated the modeling at the surface
and aloft and Sect. 3.3 evaluated the diurnal variation at
the surface and aloft by combining surface AEROS andaloft MAX-DOAS measurements. Section 3.4 examined the meteorological field causing episodic high ­NO2
concentrations, and stagnant conditions with a westerly
wind direction can be considered as a reason for the highconcentrations. Finally, here in Sect. 3.5, the relationshipbetween surface and aloft ­NO2 pollution was analyzed
based on well-evaluated modeling simulation results. The
hourly ­NO2 concentration averaged during Chiba-Campaign 2015 is plotted at each hour for surface and aloft in
Fig. 8. Throughout the day, high ­NO2 concentration was
found over the Tokyo Bay area both at the surface andaloft. As seen for the diurnal variation reported in Fig. 6,
the range of diurnal variation was larger at the surface
than aloft. At the surface, high concentrations of greaterthan 20 ppbv (shown as orange to red in Fig. 8) were distributed over eastern Tokyo and Kanagawa prefecture
and western Chiba prefecture, but these areas of highconcentration were limited to only along the coastline ofTokyo Bay area from 10–14 LT. Compared with the features of the spatial distribution pattern of the high concentration area found at the surface, a moderately highconcentration (shown as green in Fig. 8) was continuously
seen aloft through the day. In order to find the relationship between the surface and aloft N­ O2 concentrations,linear regression was performed using all grid point dataover this finest modeling domain for surface and aloft­NO2 concentrations. The results are listed in Table 1 withdomain averaged concentration (mean ± standard deviation), slope, intercept, and R. The results show clear linearity with R exceeding 0.918 through the day. The value ofslope in the linear regression was around 0.4 during thenighttime and reached around 0.55 during the daytime.
This result indicates that the aloft concentration was linearly scaled to 0.4 times the surface concentration during
the nighttime and also linearly scaled to 0.55 times thesurface level around noon (10 LT to 14 LT). The higher
fraction during the daytime was due to the well-mixed
air pollution within the PBL. This linear relationshipbetween surface and aloft ­NO2 concentrations throughthe day was a key finding of this study; and this relation
variated with a lower scaling value of around 0.4 during the nighttime and a higher value reaching 0.55 during the daytime. The same analyses listed in Table 1 but
limited to only over land area were conducted to see therelation over the land area where the N­ Ox emissions weremore intense compared with those over the ocean area
(Fig. 2). The results are listed in Table 2. The relationships
between the surface and aloft ­NO2 concentration were

Page 12 of 19

mostly similar to that for the whole modeling domain
with slightly a smaller slope, slightly higher intercept,
and a higher correlation suggested by R. The diurnal variation of the scaled value around 0.4 (minimum value of
0.348) during the nighttime and a higher value reaching 0.55 during the daytime were also similar. During
the daytime, the observed correspondence could also be
calculated directly from the ground-based observationand MAX-DOAS (Fig. 6). The scaled values calculated ateach hour were 0.227–0.425, 0.211–0.384, 0.220–0.618,
0.263–0.627 for the north, east, west, and south directions, respectively. The variation of the scaled values werelarger than the model estimated values (Tables 1 and 2).
The lower scale factors were mostly found early in themorning (7–9 LT), when the observed concentration
reached a peak, whereas the model missed this observed
peak, as discussed in Sect. 3.3. Considering this problem
with the model, we concluded that the modeled scale
factor between the surface and aloft ­NO2 generally corresponded to the observed results except early morning.
The previous discussion focused on the diurnal variation. Because episodic high concentrations were foundduring Chiba-Campaign 2015, the daily average surface
and aloft ­NO2 concentrations over the modeling domain
were further analyzed in order to investigate the temporal change in the linear relationship between surface
and aloft. The daily average was based on the averageover 0–23 LT. The spatial mappings of each day are plotted in Fig. 9, and the period average during Chiba-Campaign 2015 are also plotted at the bottom-right corner ofFig. 9. ­NO2 concentration showed day-to-day variationand the spatial distribution pattern between surface andaloft were in generally good agreement. For example, inthe case of high ­NO2 concentration on 9 November 2015as suggested by temporal variation (Figs. 4 and 5), thehigher ­NO2 concentration stretched into northern Chiba
and up to Ibaraki and Saitama prefectures at the surface
level, and this feature was also seen aloft. On 9 November
2015, a southerly wind close to 10 m/s prevailed (Fig. 7).
In another case of high ­NO2 concentration during 20–21
November 2015, higher ­NO2 concentrations were foundover a broad area of Kanagawa prefecture, eastern Tokyo,
and western Chiba prefecture on these days at the surface, and the aloft concentration also showed a similar
spatial distribution. To confirm these characterizationsof the surface and aloft N­ O2 pollution, linear regression
was performed for each day and period average in thesame way as for the diurnal variation listed in Table 1.
The result for daily and period averages are listed inTable 3. The daily averaged concentrations varied from
6 to 15 ppbv during Chiba-Campaign 2015. In spite ofthis variation in concentration, comparison of surface
and aloft found an R exceeding 0.924, which suggested

Itahashi and Irie ﻿Progress in Earth and Planetary Science(2022) 9:15

Page 13 of 19

Fig. 8 Spatial distribution of modeled hourly ­NO2 concentration at surface and aloft (averaged over 0–1 km). Results are averages over the
Chiba-Campaign 2015

Itahashi and Irie ﻿Progress in Earth and Planetary Science(2022) 9:15

Page 14 of 19

Table 1 Hourly correspondence between modeled surface andaloft (averaged over 0–1 km) ­NO2 concentrations

Table 2 Hourly correspondence between modeled surface andaloft (averaged over 0–1 km) ­NO2 concentrations over land area

LT

Surface

Aloft (0-1
km)

Slope

Intercept

R

LT

Surface

Aloft (0-1
km)

Slope

Intercept

R08.73 ± 7.00

4.37 ± 2.88

10.54 ± 6.73

5.10 ± 2.68

8.19 ± 6.72

9.29 ± 6.47

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23

8.33 ± 6.68

8.31 ± 6.91

8.72 ± 7.04

9.65 ± 7.45

10.99 ± 8.10

10.57 ± 7.44
9.50 ± 6.59

8.63 ± 6.13

7.84 ± 5.80

7.15 ± 5.42

6.72 ± 4.95

6.76 ± 4.89

7.12 ± 5.19

8.12 ± 6.04

10.04 ± 7.83

11.24 ± 8.96

11.44 ± 8.85

11.61 ± 8.66

11.47 ± 6.34

10.79 ± 7.93
9.82 ± 7.69

9.23 ± 7.60

0.398

0.897

0.966

0

4.15 ± 2.82

0.409

0.753

0.968

1

4.03 ± 2.91

0.418

0.604

0.966

2

4.00 ± 3.03

0.421

0.503

0.959

3

4.08 ± 3.09

0.415

0.459

0.945

4

4.28 ± 3.14

0.391

0.511

0.926

5

4.65 ± 3.26

0.370

0.577

0.918

6

4.67 ± 3.11

0.391

0.536

0.933

7

4.64 ± 3.08

0.447

0.394

0.957

8

4.63 ± 3.10

0.493

0.381

0.972

9

4.55 ± 3.17

0.531

0.382

0.975

10

4.39 ± 3.12

0.558

0.397

0.969

11

4.23 ± 2.92

0.567

0.417

0.962

12

4.26 ± 2.86

0.561

0.462

0.958

13

4.44 ± 2.98

0.548

0.536

0.954

14

4.91 ± 3.32

0.523

0.654

0.951

15

5.52 ± 3.87

0.469

0.809

0.950

16

5.64 ± 3.99

0.424

0.881

0.952

17

5.58 ± 3.81

0.408

0.913

0.947

18

5.49 ± 3.53

0.383

1.038

0.940

19

5.32 ± 3.28

0.371

1.063

0.942

20

5.03 ± 3.17

0.380

0.933

0.950

21

4.71 ± 3.11

0.388

0.902

0.959

22

4.53 ± 3.10

0.393

0.903

0.964

23

9.71 ± 6.41

9.36 ± 6.72

9.89 ± 6.92

11.20 ± 7.47

13.16 ± 8.22

12.64 ± 7.39

11.06 ± 6.35
9.81 ± 5.87

8.82 ± 5.60

8.03 ± 5.21

7.61 ± 4.74

7.89 ± 4.78

8.56 ± 5.15

10.08 ± 6.12

12.95 ± 8.02

14.78 ± 9.13

14.83 ± 8.90

14.74 ± 8.63

14.26 ± 8.43

13.13 ± 8.01

12.01 ± 7.73

11.23 ± 7.52

0.384

1.047

0.964

4.66 ± 2.60

0.393

0.847

0.966

4.36 ± 2.67

0.399

0.654

0.966

4.22 ± 2.75

0.393

0.547

0.957

4.26 ± 2.78

0.380

0.497

0.945

4.49 ± 2.89

0.360

0.451

0.932

4.98 ± 3.07

0.348

0.406

0.931

5.10 ± 2.90

0.370

0.419

0.945

5.15 ± 2.81

0.426

0.435

0.963

5.22 ± 2.85

0.475

0.563

0.978

5.20 ± 2.97

0.520

0.613

0.980

5.07 ± 2.93

0.547

0.676

0.974

4.91 ± 2.67

0.542

0.785

0.963

5.00 ± 2.61

0.520

0.892

0.955

5.29 ± 2.73

0.503

0.979

0.951

5.94 ± 3.09

0.478

1.115

0.949

6.78 ± 3.67

0.436

1.134

0.953

6.95 ± 3.85

0.406

0.948

0.963

6.74 ± 3.65

0.393

0.903

0.957

6.55 ± 3.34

0.368

1.115

0.952

6.24 ± 3.15

0.357

1.143

0.958

5.83 ± 3.09

0.371

0.960

0.960

5.48 ± 3.03

0.379

0.927

0.965

5.24 ± 2.98

0.383

0.936

0.968

Total grid numbers in finest modeling domain are 11,025

The total number of grid cells in the finest modeling domain of land area is 6234

clearly linear correspondence between surface and aloft­NO2 concentrations through the Chiba-Campaign 2015.
Although the intercept of the linear regression ranged
between 0.0 and 0.75, it was found that the slope ranged
within 0.4–0.5 except on 16 November 2015 when it was
0.37. This result indicates that the aloft ­NO2 concentration is linearly scaled to 0.4–0.5 times the surface N­ O2
concentration on both clean and polluted days. Similar
analyses to those shown in Tables 1 and 2, but only forthe land area were conducted for the daily averaged concentrations. The result are listed in Table 4. As shown in
Table 2, the results only focusing on the land area weresimilar with a slightly lower slope, whereas the slope was
0.406 on 16 November 2015 over the land area. We concluded that the daily-averaged aloft ­NO2 concentration
was also linearly scaled to 0.4–0.5 times the surface ­NO2
concentration during clean and polluted days over theland area in Chiba-Campaign 2015.
Finally, the vertical profile of ­NO2 concentration fromthe model simulation is shown in Fig. 10. This curtain
plot shows the vertical structure for ­NO2 concentration

during the Chiba-Campaign 2015 with modeled PBL
height at the grid point of Chiba University. The PBL
height showed variation from the near surface up to
1.0 km with a general diurnal profile featuring daytime
maxima. As discussed for temporal variations in the surface and aloft ­NO2 concentrations, high ­NO2 concentration events were found on 9, 15, 16, 17–18, and 20–21
November 2015. The vertical profiles show that high ­NO2
concentrations (red in Fig. 10) were mostly limited within
0.2 km, and also within the PBL. In the vertical direction,­NO2 concentrations were lower than 5 ppbv (light blue in
Fig. 10) above 0.5 km, even though PBL height sometimes
reached up to 1.0 km. Note the highest ­NO2 concentration on 17–18 November 2015 and the longest continuous ­NO2 concentration during 20–21 November 2015.
During these events, the PBL height was close to thenear-surface level. Taking into consideration analyses ofthe meteorological field, as shown in Fig. 7, the stagnant
air mass with weak wind speed and cloudy conditions
with a lower PBL close to the surface were possible factors causing the increased ­NO2 concentration. ThroughItahashi and Irie ﻿Progress in Earth and Planetary Science(2022) 9:15

Page 15 of 19

Fig. 9 Spatial distribution of modeled daily ­NO2 concentration at surface and aloft (averaged over 0–1 km). The right-bottom panel shows theperiod average over the Chiba-Campaign 2015

Itahashi and Irie ﻿Progress in Earth and Planetary Science(2022) 9:15

Page 16 of 19

Table 3 Daily/period averaged correspondence between
modeled surface and aloft (averaged over 0–1 km) ­NO2
concentration

Table 4 Daily/period averaged correspondence between
modeled surface and aloft (averaged over 0–1 km) ­NO2
concentration over land area

Date

Surface

Aloft (0-1
km)

Date

Surface

Aloft (0-1km)

9 November
2015

9.49 ± 9.21

4.65 ± 4.69 0.483

0.061

0.949

9 November
2015

13.16 ± 9.52

6.77 ± 4.89 0.484

0.391

0.948

10 November
2015

9.04 ± 6.87

5.15 ± 3.50 0.486

0.752

0.954

10 November
2015

10.50 ± 6.70

5.54 ± 3.17 0.449

0.822

0.950

11 November
2015

6.13 ± 6.00

3.46 ± 3.03 0.478

0.530

0.946

11 November
2015

7.94 ± 6.61

4.03 ± 3.19 0.463

0.359

0.958

12 November
2015

5.97 ± 6.31

3.15 ± 3.16 0.469

0.361

0.933

12 November
2015

7.77 ± 6.71

3.66 ± 3.04 0.432

0.305

0.954

13 November
2015

6.61 ± 6.58

3.40 ± 3.28 0.464

0.329

0.932

13 November
2015

8.69 ± 6.85

3.99 ± 3.17 0.436

0.200

0.944

14 November
2015

6.17 ± 6.26

3.28 ± 3.17 0.478

0.329

0.944

14 November
2015

8.21 ± 6.43

4.16 ± 3.17 0.458

0.409

0.928

15 November
2015

13.36 ± 8.65

6.62 ± 4.23 0.470

0.332

0.961

15 November
2015

13.59 ± 8.86

6.44 ± 4.36 0.472

0.020

0.960

16 November
2015

9.30 ± 8.51

4.17 ± 3.40 0.371

0.714

0.928

16 November
2015

11.54 ± 8.28

5.26 ± 3.63 0.406

0.573

0.926

17 November
2015

12.11 ± 10.11 6.19 ± 5.16 0.483

0.347

0.946

17 November
2015

15.76 ± 9.13

8.50 ± 4.77 0.493

0.725

0.944

18 November
2015

12.97 ± 7.66

6.54 ± 3.96 0.496

0.107

0.959

18 November
2015

12.73 ± 7.02

6.56 ± 3.62 0.499

0.210

0.965

19 November
2015

5.50 ± 5.44

2.95 ± 2.62 0.448

0.485

0.927

19 November
2015

7.20 ± 5.83

3.52 ± 2.68 0.430

0.426

0.936

20 November
2015

15.48 ± 12.15 7.34 ± 6.22 0.474− 0.001

0.926

20 November
2015

17.47 ± 11.01 7.50 ± 4.93 0.425

0.077

0.949

21 November
2015

8.97 ± 7.13

4.48 ± 3.24 0.425

0.674

0.935

21 November
2015

10.16 ± 7.20

4.54 ± 2.99 0.393

0.541

0.948

22 November
2015

7.81 ± 7.81

4.01 ± 3.90 0.461

0.409

0.924

22 November
2015

10.21 ± 8.06

4.63 ± 3.68 0.430

0.239

0.942

Period average

9.20 ± 6.74

4.67 ± 3.06 0.436

0.665

0.961

Period average

11.04 ± 6.66

5.33 ± 2.81 0.409

0.815

0.970

Slope Intercept R

The total number of grid cells in the finest modeling domain is 11025

the validated modeling performance, the vertical ­NO2
profile with its important connection to the PBL heightwas clarified. Because high ­NO2 concentration was limited to below 0.5 km, the corresponding scale factors
between the surface and aloft (0–1 km) were mainly
0.4–0.5 despite the dynamic variation of the PBL height.
In terms of the scale factor between the surface to aloft(0–1 km), the lower scale factors were observed in earlymorning (7–9 LT). The model showed a slight difference
to capture this timing, and furthermore investigation into
the chemical mechanisms related to NOx and sunlight issuggested to improve the modeling diurnal variation.

4 Conclusions
Surface and aloft ­NO2 concentrations were extensively investigated by comprehensive analyses of theground-based observation network of AEROS and aloft
MAX-DOAS measurement, and bridged by an air quality modeling system with a horizontal grid resolution of

Slope Intercept R

The total number of grid cells in the finest modeling domain of land area is 6234

Fig. 10 Curtain plot of modeled N­ O2 concentration during
Chiba-Campaign 2015. The thick pink line indicates modeledplanetary boundary layer (PBL) height

1.3 km. We presented detailed analyses on surface andaloft ­NO2 concentrations targeting the period of the
Chiba-Campaign 2015 conducted from 9 to 23 November
2015. The evaluations showed that the present air quality modeling system can generally capture the observed
behavior of both surface and aloft ­NO2 pollution. Some

Itahashi and Irie ﻿Progress in Earth and Planetary Science(2022) 9:15

Page 17 of 19

difficulties in the current modeling system were also
found. One issue is model underestimation when capturing roadside ­NO2 pollution with a horizontal grid resolution of 1.3 km, and the other is model overestimationin the south direction from Chiba University. For the latter, this is thought to be related to the emission estimation over the Tokyo Bay area, which has intense sourcesfrom industrial and power plants. From the comparisonon diurnal variation patterns, a slight difference in themorning peak of ­NO2 concentration was also found, andfurther investigation of daytime chemical mechanisms
related to NOx may help refine our understanding of this.
Based on the comparison between modeled daily-averaged surface and aloft N­ O2 concentrations, it was foundthat aloft N­ O2 concentration linearly scaled to 0.4–0.5
times the surface ­NO2 concentration during both clean
and polluted days, with higher value around noon due tothe well-mixed air mass within the PBL. This study gave
important suggestions for fostering our understanding of
the vertical profiles of ­NO2 over the greater Tokyo area.
Horizontal coverage based on ground-based measurements is sparse in terms of spatial coverage. Space-based
satellite measurement can provide a broad spatial distribution of air pollutants. Although previous sensors havebeen limited in terms of spatial resolution for the purpose of detecting urban-scale air pollution (Itahashi et al.
2014; Irie et al. 2016), the state-of-the-science sensor of
TROPOspheric Monitoring Instrument (TROPOMI)
onboard the Sentinel-5 precursor satellite launchedon 13 October 2017 can provide finer spatial resolution below 10 km (Griffin et al. 2019). Although the useof these recent satellite observations may be helpful forunderstanding the spatial distribution patterns, the values measured by the satellite are vertically integrated
concentrations. Comprehensive analysis of surface andaloft observation as conducted in this study combinedwith this kind of satellite observation with finer resolution is required for future studies. From this perspective, because cloudy and lower PBL height conditions,which lead to increased N­ O2 pollution, as found in thisstudy, are unfavorable for satellite measurements, accurate modeling is required to bridge the missing information among surface, aloft, and satellite measurements. In
addition, a clear linear relationship between surface andaloft ­NO2 concentrations was limited to the analysis of
Chiba-Campaign 2015 conducted in autumn, and analysis of other seasons is needed for further understandingof ­NO2 behavior at the surface and aloft.

monitoring stations; AQS: Air Quality Standard; CINDI: Cabauw Intercomparison Campaign of Nitrogen Dioxide Measuring Instruments; CMAQ: Community Multiscale Air Quality; DOAS: Differential optical absorption spectroscopy;
GFED: Global Fire Emissions Database; HTAP: Hemispheric Transport of Air
Pollution; JM2: Japanese MAX-DOAS profile retrieval algorithm, version 2; JMA:
Japan Meteorological Agency; LT: Local time; M: Models; MANAL: Meso-scale
Analysis; MAX-DOAS: Multi-axis differential optical absorption spectroscopy;
MEGAN: Model of Emissions of Gases and Aerosols from Nature; MOZART​: Model for Ozone and Related Chemical Tracers; NCAR​: National Center for
Atmospheric Research; NCEP: National Centers for Environmental Prediction;
NMB: Normalized mean bias; NME: Normalized mean error; NO: Nitric oxide;NO2: Nitrogen dioxide; NOx: Nitrogen oxides; O: Observations; Ox: Oxidants;
PBL: Planetary boundary layer; PM2.5: Particulate matter with aerodynamic
diameter less than to 2.5 μm; R: Correlation coefficient; RAPMSs: Roadside air pollution monitoring stations; RHw: Relative humidity over water;
RRTMG: Rapid radiative transfer model for global climate models; TROPOMI:
TROPOspheric Monitoring Instrument; UTC​: Universal Time Coordinate; UV:
Ultraviolet; WRF: Weather Research and Forecasting.

Abbreviations
4AZ-MAX-DOAS: Four different azimuth-viewing MAX-DOAS; AEROS: Atmospheric Environmental Regional Observation System; AMeDAS: AutomatedMeteorological Data Acquisition System; APMSs: Ambient air pollution

Acknowledgements
We are grateful for the use of the MANAL dataset distributed by the Japan
Meteorological Business Support Center (JMBSC). The ship emissions around
Japan were produced by the SPF (formerly the Ocean Policy Research Foundation (OPRF)) in the project to evaluate air quality improvements due to Emission Control Areas (ECA).
Authors’ contributions
SI carried out the numerical modeling simulations, conducted the evaluation
with the observation dataset, designed the analyses, and wrote the paper.
HI performed the MAX-DOAS observation and its analyses during ChibaCampaign 2015, proposed this topic, helped write the paper, and designed
the study. All authors read and approved the final manuscript.
Funding
This research was supported by the Environment Research and Technology Development Fund (Grant Numbers JPMEERF20192001 and
JPMEERF20215005) of the Environmental Restoration and Conservation Agency of Japan, JSPS KAKENHI (Grant Numbers JP19H04235 andJP20H04320), and the JAXA 2nd Research Announcement on the Earth
Observations (Grant Number 19RT000351).
Availability of data and materials
The datasets used and analyzed during the current study are available fromthe corresponding author on request.

Declarations
Competing interests
The authors declare that they have no competing interest.
Author details
1
Sustainable System Research Laboratory (SSRL), Central Research Instituteof Electric Power Industry (CRIEPI), 1646 Abiko, Abiko, Chiba 270‑1194, Japan.
2Center for Environmental Remote Sensing (CEReS), Chiba University, 1‑33
Yayoicho, Inage‑ku, Chiba 263‑8522, Japan.
Received: 11 July 2021 Accepted: 25 February 2022

References
Japan Meteorological Agency (JMA) (2021) http://​www.​data.​jma.​go.​jp/​svd/​
vois/​data/​tokyo/​volca​no.​html (in Japanese). Accessed 1 July 2021.
United States Environmental Protection Agency (US EPA) (2018) CMAQ (version 5.2.1). https://​doi.​org/​10.​5281/​zenodo.​12126​01. Accessed 1 July
2021.
Blechschmimdt AM, Arteta J, Coman A, Cuirier L, Eskes H, Foret G, Gielen C,
Hendrick F, Marecal V, Meleux F, Partmentier J, Peters E, Pinardi G, Piters
AJM, Plu M, Richter A, Segers A, Sofiev M, Valdebenito AM, Roozendael

Itahashi and Irie ﻿Progress in Earth and Planetary Science(2022) 9:15

Page 18 of 19

MV, Vira J, Vlemmix T, Burrows JP (2020) Comparison of tropospheric­NO2 columns from MAX-DOAS retrievals and regional air quality model
simulations. Atmos Chem Phys 20:2795–2823. https://​doi.​org/​10.​5194/​
acp-​20-​2795-​2020
Carn SA, Fioletov VE, McLinden CA, Li C, Krotkov NA (2017) A decade of global
volcanic ­SO2 emissions measured from space. Sci Rep 7:44095. https://​doi.​org/​10.​1038/​srep4​4095
Choi Y, Kanaya Y, Takashima H, Irie H, Park K, Chong J (2021) Long-term variation in the tropospheric nitrogen dioxide vertical column density over
Korea and Japan from the MAX-DOAS Network, 2007–2017. Remote Sens
13:1937. https://​doi.​org/​10.​3390/​rs131​01937
Grell GA, Dévényi D (2002) A generalized approach to parameterizing convection combining ensemble and data assimilation techniques. Geophys Res
Lett 29:1693. https://​doi.​org/​10.​1029/​2002G​L0153​11
Griffin D, McLinden CA, Boersma F, Bourassa A, Dammers E, Degenstein D,
Eskes H, Fehr L, Fioletov V, Hayden K, Kharol SK, Li S-M, Makar P, Martin RV,
Mihele C, Mittermeier RL, Krotkov N, Sneep M, Lamsal LN, Ter Linden M,
van Geffen J, Veefkind P, Wolde M, Zhao X (2019) High-resolution mapping of nitrogen dioxide with TROPOMI: First results and validation over
the Canadian oil sands. Geophys Res Lett 46:1049–1060. https://​doi.​org/​
10.​1029/​2018G​L0810​95
Guenther AB, Jiang X, Heald CL, Sakulyanontvittaya T, Duhl T, Emmons LK,
Wang X (2012) The Model of Emissions of Gases and Aerosols fromNature version 2.1 (MEGAN2.1). An extended and updated framework formodeling biogenic emissions. Geosci Model Dev 5:1471–1492
Hönninger G, Platt U (2002) Observations of BrO and its vertical distribution
during surface ozone depletion at Alert. Atmos Environ 36:2481–2489.
https://​doi.​org/​10.​1016/​S1352-​2310(02)​00104-8
Hönninger G, von Friedeburg C, Platt U (2004) Multi axis differential opticalabsorption spectroscopy (MAX-DOAS). Atmos Chem Phys 4:231–254.
https://​doi.​org/​10.​5194/​acp-4-​231-​2004
Iacono MJ, Delamere JS, Mlawer EJ, Shephard MW, Clough SA, Collins WD(2008) Radiative forcing by long-lived greenhouse gases: calculations
with the AER radiative transfer models. J Geophys Res 113:D13103.
https://​doi.​org/​10.​1029/​2008J​D0099​44
Hutzell WT, Luecken DJ, Appel WK, Carter WP (2011) Interpreting predictions
from the SAPRC07 mechanism based on regional and continental simulations. Atmos Environ 46:417–429
Irie H, Hoque HMS, Damiani A, Okamoto H, Fatmi AM, Khatri P, Takamura T,
Jarupongsakul T (2019) Simultaneous observations by sky radiometerand MAX-DOAS for characterization of biomass burning plumes in central Thailand in January-April 2016. Atmos Meas Tech 12:599–606. https://​doi.​org/​10.​5194/​amt-​12-​599-​2019
Irie H, Kanaya Y, Akimoto H, Iwabuchi H, Shimizu A, Aoki K (2008) First retrievalof tropospheric aerosol profiles using MAX-DOAS and comparison withlidar and sky radiometer measurements. Atmos Chem Phys 8:341–350.
https://​doi.​org/​10.​5194/​acp-8-​341-​2008
Irie H, Muto T, Itahashi S, Kurokawa J, Uno I (2016) Turnaround of troposphericnitrogen dioxide pollution trends in China, Japan, and South Korea. SOLA
12:170–174. https://​doi.​org/​10.​2151/​sola.​2016-​035
Irie H, Nakayama T, Shimizu A, Yamazaki A, Nagai T, Uchiyama A, Zaizen Y,
Kagamitani S, Matsumi Y (2015) Evaluation of MAX-DOAS aerosol retrievals by coincident observations using CRDS, lidar, and sky radiometer in
Tsukuba, Japan. Atmos Meas Tech 8:2775–2788. https://​doi.​org/​10.​5194/​
amt-8-​2775-​2015
Irie H, Takashima H, Kanaya Y, Boersma KF, Gast L, Wittrock F, Brunner D, Zhou Y,
Van Roozendael M (2011) Eight-component retrievals from ground-based
MAX-DOAS observations. Atmos Meas Tech 4:1027–1044. https://​doi.​org/​
10.​5194/​amt-4-​1027-​2011
Irie H, Yonekawa D, Damiani A, Hoque HMS, Sudo K, Itahashi S (2021) Continuous multi-component MAX-DOAS observations for the planetary
boundary layer ozone variation analysis at Chiba and Tsukuba, Japan,
from 2013 to 2019. Prog Earth Planet Sci 8:31. https://​doi.​org/​10.​1186/​
s40645-​021-​00424-9
Itahashi S (2018) Toward synchronous evaluation of source apportionments
for atmospheric concentration and deposition of sulfate aerosol over East
Asia. J Geophys Res Atmos 123:2927–2953. https://​doi.​org/​10.​1002/​2017J​
D0281​10
Itahashi S, Mathur R, Hogrefe C, Zhang Y (2020) Modeling trans-Pacifictransport on tropospheric ozone using hemispheric CMAQ during
April 2010—Part 1: model evaluation and air mass characterization forstratosphere-troposphere transport. Atmos Chem Phys 20:3373–3396.
https://​doi.​org/​10.​5194/​acp-​20-​3373-​2020
Itahashi S, Uno I, Irie H, Kurokawa J-I, Ohara T (2014) Regional modelling of
tropospheric ­NO2 vertical column density over East Asia during theperiod 2000–2010: Comparison with multisatellite observations. Atmos
Chem Phys 14:3623–3635. https://​doi.​org/​10.​5194/​acp-​14-​3623-​2014
Itahashi S, Yumimoto K, Kurokawa J, Morino Y, Nagashima T, Miyazaki K, Maki T,
Ohara T (2019) Inverse estimation of NOx emissions over China and India
2005–2016: contrasting recent trends and future perspectives. Environ
Res Lett 14:124020. https://​doi.​org/​10.​1088/​1748-​9326/​ab4d7f
Janjić ZI (1994) The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure
schemes. Month Wet Rev 122:927–945. https://​doi.​org/​10.​1175/​1520-​
0493(1994)​122%​3c0927:​TSMECM%​3e2.0.​CO;2
Janssens-Maenhout G, Crippa M, Guizzardi D, Dentener F, Muntean M, Pouliot
G, Keating T, Zhang Q, Kurokawa J, Wankmuller R, Danier van der Gon H,
Kuenen JJP, Kilmont Z, Frost G, Darras S, Koffi B, Li M (2015) HTAP_v2.2:
a mosaic of regional and global emission grid maps for 2008 and 2010
to study hemispheric transport of air pollution. Atmos Chem Phys
15:11411–11432. https://​doi.​org/​10.​5194/​acp-​15-​11411-​2015
Kanaya Y, Irie H, Takashima H, Iwabuchi H, Akimoto H, Sudo K, Gu M, ChongJ, Kim YJ, Lee H, Li A, Si F, Xu J, Xie PH, Liu WQ, Dzhola A, Postylyakov
O, Ivanov V, Grechko E, Terpugova S, Panchenko M (2014) Long-term
MAX-DOAS network observations of ­NO2 in Russia and Asia (MADRAS)
during the period 2007–2012: instrumentation, elucidation of climatology, and comparisons with OMI satellite observations and global model
simulations. Atmos Chem Phys 14:7909–7927. https://​doi.​org/​10.​5194/​
acp-​14-​7909-​2014
Kondo Y, Morino Y, Fukuda M, Kanaya Y, Miyazaki Y, Takegawa N, Tanimoto H,
McKenzie R, Johnston P, Blake DR, Murayama T, Koike M (2008) Formation and transport of oxidized reactive nitrogen, ozone, and secondaryorganic aerosol in Tokyo. J Geophys Res Atmos 113:D21310. https://​doi.​
org/​10.​1002/​2008J​D0101​34
Kong L, Tang X, Zhu J, Wang Z, Fu JS, Wang X, Itahashi S, Yamaji K, Nagashima
T, Lee H-J, Kim C-H, Lin C-Y, Chen L, Zhang M, Tao Z, Li J, Kajino M, Liao
H, Wang Z, Sudo K, Wang Y, Pan Y, Tang G, Li M, Wu Q, Ge B (2020) Carmichael GR (2020) Evaluation and uncertainty investigation of the N­ O2,
CO and ­NH3 modeling over China under the framework of MICS-Asia III.
Atmos Chem Phys 20:181–202. https://​doi.​org/​10.​5194/​acp-​20-​181-​2020
Kreher K, Van Roozendael M, Hendrick F, Apituley A, Dimitropoulou E, FrießU, Richter A, Wagner T, Lampel J, Abuhassan N, Ang L, Anguas M, Bais A,
Benavent N, Bösch T, Bognar K, Borovski A, Bruchkouski I, Cede A, Chan
KL, Donner S, Drosoglou T, Fayt C, Finkenzeller H, Garcia-Nieto D, Gielen C,
Gómez-Martín L, Hao N, Henzing B, Herman JR et al (2020) Intercomparison of ­NO2, ­O4, ­O3 and HCHO slant column measurements by MAX-DOAS
and zenith-sky UV-Visible spectrometers during CINDI-2. Atmos Meas
Tech 13:2169–2208. https://​doi.​org/​10.​5194/​amt-​13-​2169-​2020
Kurokawa J, Ohara T, Morikawa T, Hanayama S, Janssens-Maenhout G, Fukui
T, Kawashima K, Akimoto H (2013) Emissions of air pollutants and greenhouse gases over Asian regions during 2000–2008: regional Emission
inventory in ASia (REAS) version 2. Atmos Chem Phys 13:11019–11058.
https://​doi.​org/​10.​5194/​acp-​13-​11019-​2013
Lin J-T (2012) Satellite constraint for emissions of nitrogen oxides fromanthropogenic, lightning and soil sources over East China on a highresolution grid. Atmos Chem Phys 12:2881–2898. https://​doi.​org/​10.​
5194/​acp-​12-​2881-​2012
Liu F, van der A RJ, Eskes H, Ding J, Mijling B (2018) Evaluation of modeling­NO2 concentrations driven by satellite-derived and bottom-up emission
inventories using in situ measurements over China. Atmos Chem Phys
18:4171–4186. https://​doi.​org/​10.​5194/​acp-​18-​4171-​2018
Mathur R, Xing J, Gilliam R, Sarwar G, Hogrefe C, Pleim J, Pouliot G, Roselle S,
Spero TL, Wong DC, Young J (2017) Extending the Community Multiscale
Air Quality (CMAQ) modeling system to hemispheric scales: overview
of process considerations and initial applications. Atmos Chem Phys
17:12449–12474. https://​doi.​org/​10.​5194/​acp-​17-​12449-​2017
Ministry of Environment (MOE) (2019) PM2.5 emission inventory. http://​www.​env.​go.​jp/​air/​osen/​pm/​info.​html#​INVEN​TORY (in Japanese). Accessed 1
Jun 2021.
Morrison H, Thompson G, Tatarskii V (2009) Impacts of cloud microphysics onthe development of trailing stratiform precipitation in a simulated squall

Itahashi and Irie ﻿Progress in Earth and Planetary Science(2022) 9:15

line: Comparison of one- and two-moment schemes. Mon Weather Rev
137:991–1007. https://​doi.​org/​10.​1175/​2008M​WR2556.1
National Centers for Environmental Prediction (NCEP)/National Center for
Atmospheric Research (NCAR) (2021) Final (FNL) Operational Global
Analysis Data. https://​rda.​ucar.​edu/​datas​ets/​ds083.2/. Accessed 1 July
2021.
Platt U, Stutz J (2008) Differential optical absorption spectroscopy: principles and applications. Springer, Berlin. https://​doi.​org/​10.​1007/​
978-3-​540-​75776-4
Roscoe HK, Van Roozendael M, Fayt C, du Piesanie A, Abuhassan N, Adams
C, Akrami M, Cede A, Chong J, Clémer K, Friess U, Gil Ojeda M, Goutail F,
Graves R, Griesfeller A, Grossmann K, Hemerijckx G, Hendrick F, Herman
J, Hermans C, Irie H, Johnston PV, Kanaya Y, Kreher K, Leigh R, Merlaud A,
Mount GH, Navarro M, Oetjen H, Pazmino A et al (2010) Intercomparisonof slant column measurements of N
­ O2 and O­ 4 by MAX-DOAS and zenith
sky UV and visible spectrometers. Atmos Meas Tech 3:1629–1646. https://​doi.​org/​10.​5194/​amt-3-​1629-​2010
Seinfeld JH, Pandis SN (2016) Atmospheric chemistry and physics: from air
pollution to climate change, 3rd edn. Wiley, New YorkSkamarock WC, Klemp JB, Dudhia J, Gill DO, Barker DM, Duda MG, Huang X-Y,
Wang W, Powers JG (2008) A description of the advanced research WRF
version 3. NCAR Tech Note, NCAR/TN-475CSTR. https://​doi.​org/​10.​5065/​
1dfh-​6p97.
Shaiganfar R, Beirle S, Petetin H, Zhang Q, Beekmann M, Wagner T (2015)
New concept for the comparison of tropospheric ­NO2 column densities
derived from car-MAX-DOAS observations, OMI satellite observations
and the regional model CHIMERA during two MEGAPOLI campaigns in
Paris 2009/10. Atmos Meas Tech 8:2827–2852. https://​doi.​org/​10.​5194/​
amt-8-​2827-​2015
Simon H, Bhave PV (2012) Simulating the degree of oxidation in atmosphericorganic particles. Environ Sci Tech 46:331–339
van der Werf GR, Randerson JT, Giglio L, van Leeuwen TT, Chen Y, Rogers BM,
Mu M, van Marle MJE, Morton DC, Collatz GJ, Yokelson RJ, Kasibhatla PS(2017) Global fire emissions estimates during 1997–2016. Earth Syst Sci
Data 9:697–720
Vlemmix T, Eskes HJ, Piters AJM, Schaap M, Sauter FJ, Kelder H, Lvelt PF (2015)
MAX-DOAS tropospheric nitrogen dioxide column measurements
compared with the Lotos-Euros air quality model. Atmos Chem Phys
15:1313–1330. https://​doi.​org/​10.​5194/​acp-​15-​1313-​2015
Wakamatsu S, Morikawa T, Ito A (2013) Air pollution trends in Japan between
1970 and 2012 and impact of urban air pollution countermeasures. AsianJ Atmos Environ 7:177–190. https://​doi.​org/​10.​5572/​ajae.​2013.7.​4.​177
Yamaji K, Chatani S, Itahashi S, Saito M, Takigawa M, Morikawa T, Kanda I, Miya
Y, Komatsu H, Sakurai T, Morino Y, Kitayama K, Nagashima T, Shimadera
H, Uranishi K, Fujiwara Y, Hashimoto T, Sudo K, Misaki T, Hayami H (2020)
Model inter-comparison for PM2.5 components over urban areas inJapan in the J-STREAM framework. Atmosphere 11:222. https://​doi.​org/​
10.​3390/​atmos​11030​222
Zheng B, Tong D, Li M, Liu F, Hong C, Geng G, Li H, Li X, Peng L, Qi J, Yan
L, Zhang Y, Zhao H, Zheng Y, He K, Zhang Q (2018) Trends in China’s
anthropogenic emissions since 2010 as a consequence of clean air
actions. Atmos Chem Phys 18:14095–14111. https://​doi.​org/​10.​5194/​
acp-​18-​14095-​2018

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Page 19 of 19