Progress in Earth andPlanetary Science

Ohno et al.
Progress in Earth and Planetary Science(2022) 9:61
https://doi.org/10.1186/s40645-022-00520-4

Open Access

RESEARCH ARTICLE

Quantitative evaluation of mixed biomassburning and anthropogenic aerosols
over the Indochina Peninsula using MERRA‑2
reanalysis products validated by sky radiometerand MAX‑DOAS observations
Takeru Ohno1* , Hitoshi Irie1, Masahiro Momoi1,2,3 and Arlindo M. da Silva4

Abstract
In this study, we conducted the quantitative evaluation of aerosol optical properties in the Indochina Peninsula (ICP),
which is significantly affected yearly by both biomass burning (BB) and anthropogenic aerosols, using Modern-Era
Retrospective Analysis for Research and Applications, version 2 (MERRA-2) aerosol products. To perform spatiotemporal analysis with validated aerosol data, the MERRA-2 aerosol optical depth (AOD) and absorption AOD (AAOD) data
were evaluated based on sky radiometer observations at the SKYNET Phimai site (15.18° N, 102.56° E). Furthermore,
multi-axis differential optical absorption spectroscopy was conducted, providing additional data for the comparisonof the aerosol extinction coefficient (AEC) vertical profile data. MERRA-2 AOD, AAOD, and AEC at altitudes below 1 km
were underestimated in the dry season, with relative mean biases of 0.84, 0.54, and 0.48, respectively. These underestimations are attributed to insufficient BB emissions of light-absorbing aerosols near the surface. On the basis of theseresults, we investigated the factors that determined spatiotemporal variations in AOD over ICP from 2009 to 2020. We
found that the seasonal variations in AOD were driven mainly by organic carbon (OC) and sulfate aerosols. OC AOD
was dominant during the active BB period (from January to March), whereas sulfate AOD was high all year round,accounting for more than 25% of the total AOD. Sulfate AOD in the northeast ICP (NEIC) was approximately 74% of
the total AOD in October, indicating the remarkable effect of sulfate aerosol transportation from southern China (SC).
In the period of study, AOD decreased in NEIC and south ICP (SIC) by − 4.40% and − 3.00% ­year−1, respectively, corresponding to the decrease in sulfur dioxide concentrations in SC and NEIC. Thus, OC AOD was dominant during theactive BB periods, whereas a significant amount of anthropogenic aerosols from SC contributed to the atmosphericenvironment over ICP throughout 2009–2020.
Keywords: Biomass burning, MERRA-2, Validation, AOD, Indochina Peninsula, Trend analysis*Correspondence: t.ohno.chiba@gmail.com
1
Center for Environmental Remote Sensing, Chiba University, 1‑33 Yayoicho,
Inage‑Ku, Chiba 263‑8522, Japan
Full list of author information is available at the end of the article

1 Introduction
Atmospheric aerosols affect the Earth’s radiation budget
and its climate system as they directly affect solar radiation and indirectly affect cloud characteristics. Aerosols
produce a negative radiative forcing effect with considerable uncertainties (IPCC 2021). They also decrease air
quality (Li et al. 2017b; Lee et al. 2017) and have adverse
effects on human health, such as promoting lung cancer© 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/.

Ohno et al. Progress in Earth and Planetary Science(2022) 9:61

and cardiovascular disease (Lave and Seskin 1970; Brook
et al. 2010). There are various sources of aerosols, including anthropogenic sources (e.g., fossil fuel combustion)and natural sources (e.g., dust and sea salt particles) (Liet al. 2017a; Proestakis et al. 2018). Among the known
sources, biomass burning (BB) is particularly important(van der Werf et al. 2006).
BB is the burning of living or dead vegetation. It is classified into anthropogenic (fuel and agricultural waste)
and natural (lightning-induced) sources of aerosols. BB
releases greenhouse gases, such as carbon dioxide, methane, and carbon monoxide. It also releases aerosols, suchas black carbon (BC) and organic carbon (OC). BC andOC emitted from BB constitute 27% and 62% of the total
global emissions, respectively (Wiedinmyer et al. 2011).
BC absorbs light and exerts a warming effect on the
globe. On the other hand, OC has a net cooling effect,
although brown carbon absorbs sunlight in the ultraviolet (UV) and visible wavelength ranges (Ramanathan andCarmichael 2008; Bond et al. 2013). Several studies haveestimated the direct radiative forcing of BrC, but the estimations have been weak in models ranging from + 0.1
to + 0.6 ­Wm−2 (Wang et al. 2014; Lin et al. 2014; Brown
et al. 2018). The optical properties of light-absorbing aerosols obtained from BB have not been clarified because
their light absorption and scattering intensity vary withthe type of combustion and atmospheric conditions (e.g.,ambient temperature and relative humidity) (Dubovik
et al. 2002; Vadrevu et al. 2015).
The Indochina Peninsula (ICP) is an active fire region,similar to Africa, Australia, and South America (van derWerf et al. 2017). The ICP has a dry season (October
to May) and a wet season (June to September) becauseof its monsoon climate. In the dry season, BB occurs
throughout the ICP, thereby causing seasonal variations
in emissions (Yadav et al. 2017; Wang et al. 2015). Thereis a significant positive correlation between the monthly
aerosol optical depth (AOD) and the number of monthly
fire spots from December to May (Yin et al. 2019). Kalita
et al. (2020) reported that carbonaceous AODs (OCand BC AODs) constitute 60% of the total AOD in thenorth ICP. In addition to BB, ICP is affected by anthropogenic aerosols. Analyses of direct air sampling at Phimai, Thailand, located at the central ICP, have shownthat anthropogenic aerosols were transported from East
Asia from October 2007 to March 2008 (Tsuruta et al.
2009). Besides, Kalita et al. (2020) and Hien et al. (2004)
reported similar characteristics based on the assimilation of aerosol observations as well as the back-trajectory
analysis, respectively. Therefore, ICP is affected by both
BB and anthropogenic emissions. However, the contributions of various aerosols to AOD are complex; thus,
quantitative analysis of aerosols in ICP is challenging.

Page 2 of 12

In this study, we conducted the quantitative evaluation of aerosol optical properties in ICP, which is affectedby both BB and anthropogenic aerosols, using the US
National Aeronautics and Space Administration (NASA)
Modern-Era Retrospective Analysis for Research andApplications, version 2 (MERRA-2) aerosol products.
MERRA-2 employs state-of-the-art data assimilation
techniques to determine the spatiotemporally homogeneous quality of aerosol data. To refine the evaluation for spatiotemporal analysis using MERRA-2 aerosol
products, validation analysis was performed based on
AOD and absorption AOD (AAOD) obtained using a sky
radiometer and the aerosol extinction coefficient (AEC)
obtained by multi-axis differential optical absorption
spectroscopy (MAX-DOAS) at the SKYNET/Phimai site,
Thailand. On the basis of the results, we analyzed the factors that determine spatiotemporal variations in AOD
over ICP from 2009 to 2020.

2 Data and methods
2.1 Study area

First, we validated MERRA-2 aerosol products based on
ground-based observations obtained using a sky radiometer and MAX-DOAS at the SKYNET Phimai/Thailand site (15.18° N, 102.56° E; 212 m a.s.l.). The SKYNET
Phimai site is located in central Thailand, approximately
270 km northeast of Bangkok. It is affected by the northeast and southeast monsoon during the dry (October toMay) and wet (June to September) seasons, respectively.
We focused on southern China (SC; 20° N–30° N, 110°
E–115° E) and three ICP regions for the spatiotemporalvariability analysis (Fig. 1). According to the effects ofBB and transboundary transport from China, ICP was
divided into northwest ICP (NWIC; 15° N–25° N, 91.875°
E–101.25° E), northeast ICP (NEIC; 15° N–25° N, 101.25°
E–110° E), and south ICP (SIC; 8° N–15° N, 96.875°
E–110° E).
2.2 Datasets
2.2.1 Sky radiometerSky radiometer model POM-02 (Prede Co., Ltd.,
Japan) is a ground-based remote sensing instrumentthat measures direct and diffuse solar irradiances witha field of view of approximately 1° at 11 wavelengths
over 10-min intervals. In this study, we used aerosol
optical parameters, such as AOD and single scattering
albedo (SSA), at 340, 380, 400, 500, 675, and 870 nm,
retrieved using the Sky Radiometer analysis packageof the Center for Environmental Remote Sensing (SRCEReS) (e.g., Mok et al. 2018; Irie et al. 2019), which
implements SKYRAD.pack version 5 (Hashimoto
et al. 2012). SR-CEReS yielded homogeneous quality of aerosol optical property data by employing theOhno et al. Progress in Earth and Planetary Science(2022) 9:61

Page 3 of 12

2.2.2 MAX‑DOAS

Fig. 1 Geographical locations of the SKYNET Phimai site and thefour regions. The black open plot indicates the SKYNET Phimai site
(15.18° N, 102.56° E, 212 m asl), and the red boxes indicate southern
China (20° N–30° N, 110° E–115° E), northwest Indochina Peninsula(ICP) (NWIC; 15° N–25° N, 91.875° E–101.25° E), northwest ICP (NEIC;
15° N–25° N, 101.25° E–110° E), and south Indochina Peninsula (SIC; 8°
N–15° N, 96.875° E–110° E)

cross-improved Langley method as an onsite calibration to determine the calibration constant (F0) (Nakajima et al. 2020) and the solar disk scan method for thesolid view angle (Nakajima et al. 1996; Uchiyama et al.
2018). Cloud screening was performed based on the
Khatri and Takamura method (Khatri and Takamura2009). The sky radiometer data retrieved by SR-CEReS
were validated by comparing them with the Aerosol
Robotic Network (AERONET), a multi-filter rotating
shadow-band radiometer, and Pandora observations inSeoul, South Korea, during and after the Korea–US air
quality field campaign in 2016 (Mok et al. 2018). Most
AOD and SSA values were in good agreement, showing
differences of ± 0.01 and ± 0.05, respectively, at wavelengths of 340–870 nm.
The Ångström exponent (AE), AAOD, and absorption AE (AAE) were derived from the AOD and SSA atwavelengths of 340–870 nm. To modify the analysis, weselected data where the uncertainty in AAOD was lessthan the estimated propagation of error, and the linearity of ln(AAOD) versus ln(wavelength) was as high as
expected (Irie et al. 2019). To validate MERRA-2 aerosol products at the same wavelength, the AOD obtainedusing the sky radiometer was interpolated to 550 nm
using AE based on the following equation:

τ550 = τ500

550
500

−α

,

(1)

where τ and α represent AOD and AE, respectively. Similarly, AAOD was interpolated using AAE.

The MAX-DOAS is a ground-based remote sensing
instrument that measures UV–visible spectra of scatteredsunlight at several elevation angles between the horizon
and the zenith. MAX-DOAS retrieves the concentrations of various trace gases based on the Beer–Lambertlaw. Our MAX-DOAS instrument mainly consists of an
outdoor telescope unit and an indoor spectrometer unit,
containing the Maya2000 Pro UV–visible spectrometer(Ocean Insight, Inc.) with a slit of 25 μm. Spectra at 310–
515 nm were measured at a wavelength resolution of
approximately 0.3–0.4 nm (as the full width at half maximum). Several scattered sunlight spectra were measured
using five off-axis elevation angles (2°, 3°, 4°, 6°, and 8°)
and a reference angle of 70° every 30 min (–November 7,2016) and 15 min (November 8, 2016–).
For the retrieval, we used the Japanese MAX-DOAS
profile retrieval algorithm, version 2 (JM2) (Irie et al.
2011, 2015, 2019). First, the wavelengths were calibrated
daily to correct for possible wavelength shifts and determine the wavelength resolution as a function of wavelength. Then, nonlinear least-square spectral fitting(DOAS fitting) was performed to estimate the differential slant column density (ΔSCD), which is the difference
between the SCDs of the reference and off-axis measurements, for oxygen collision complex (­ O4) and other tracegases, including ­NO2, HCHO, CHOCHO, ­H2O, ­SO2, and­O3. Our spectrometer covered the absorption bands of­O4 for both the UV and visible regions. From the determined ­O4 ΔSCD, lower-troposphere vertical profiles of
AECs at 357 and 476 nm were retrieved using the optimal estimation method (Rodgers 2000). As revealed by
the collocated sky radiometer, LIDAR, and cavity ringdown spectroscopy results, uncertainty in MAX-DOAS
AEC data for a 0–1 km layer was less than 30% (e.g., Irieet al. 2008, 2015). The AEC from MAX-DOAS was interpolated to 532 nm using the AE value obtained using thesky radiometer within ± 8 min of the MAX-DOAS analysis (Eq. 1).
2.2.3 MERRA‑2

MERRA-2 is the latest atmospheric and aerosol reanalysis product from NASA’s Global Modeling andAssimilation Office (Gelaro et al. 2017). It is based on
the Goddard Earth Observing System, version 5 (GEOS5), with the Goddard Chemistry Aerosol Radiation andTransport model (GOCART; Chin et al. 2002) aerosol
module (Randles et al. 2017; Buchard et al. 2017). The
spatial resolution of MERRA-2 model is 0.5° latitude
by 0.625° longitude with 72 layers from the surface to0.01 hPa, and data are collected at intervals, ranging fromhourly to annually. MERRA-2 AOD in the model is theOhno et al. Progress in Earth and Planetary Science(2022) 9:61

integration of the aerosol mixing ratio, extinction coefficient, and relative humidity. AOD is expressed as thesum of the AODs of five aerosols (BC, OC, dust, sea salt,
and sulfate). Emissions in the GEOS-5/GOCART model
vary among databases and temporal resolutions, depending on the aerosol type. For BB emissions, the Global Fire
Emission Dataset version 3.1 from 1997 to 2009 and the
Quick Fire Emissions Dataset version 2.4-r6 from 2009were employed in the MERRA-2 model (van der Werf
et al. 2006; Darmenov and da Silva 2015). Anthropogenic­SO2, ­SO4, and carbonaceous aerosols are covered until
2008. MERRA-2 assimilates bias- and non-bias-corrected
AODs from observations (Buchard et al. 2017). Bias-corrected AOD observations are obtained using an advanced
very-high-resolution radiometer (1980–2002) over theocean and a Moderate-Resolution Imaging Spectroradiometer (MODIS) (Terra/Aqua satellite; 2000/2002 to present) under a < 70% cloud cover. The non-bias-corrected
AOD observations include the AERONET (1999–2014)
stations and the multi-angle imaging spectroradiometer(Terra; 2000–2014) over bright surfaces (albedo > 0.15).
MERRA-2 aerosol products are available at the NASA
Goddard Earth Sciences Data and Information Services
Center, but not for AEC products.
The MERRA-2 aerosol products have been validated
at several sites by comparing them with ground-based
observations, but such comparisons are limited to BB
regions. MERRA-2 AOD results have shown a good spatial agreement with ground-based observations (Shi et al.
2019; Gueymard and Yang 2020), but the absolute values
were underestimated in southeast Asia (Che et al. 2019).
Because BB-originating plumes are primarily affected

Page 4 of 12

by light-absorbing aerosols near the surface, AAOD and
AEC are important for validating reanalysis data.
In this study, we used the hourly extinction AOD andscattering AOD (SAOD) data for total aerosols, BC, OC,
dust, sea salt, and sulfate (‘tavg1_2d_aer_Nx’). AAOD
was calculated by subtracting SAOD from the extinction
AOD. The 3-hourly AEC was obtained from the modelestimated value constrained by column-integrated aerosol extinction (i.e., AOD). Notably, AAOD and AEC wereobtained from model-estimated values, which were constrained by only AOD.
2.2.4 MODIS

The MODIS Collection 6.1 AOD product was used tosupport the trend analysis (Levy et al. 2007; Wei et al.
2019). MODIS sensors are on board the Terra and Aqua
satellites in the morning (10:30 local time) and afternoon(13:30 local time) orbits, respectively. Data were obtainedin 36 spectral bands at 0.4–14 μm with various parameters, such as aerosols, vegetation, and cloud. We useda spatial resolution of 10 km × 10 km AOD (550 nm)
at 10:30 and 13:30 local time from 2009 to 2020. In thetrend analysis, the monthly mean AOD was calculated
for each region shown in Fig. 2. To reduce cloud-induced
uncertainties, AOD at less than 20% cloud fraction wasused.
2.3 Validation approach

First, hourly MERRA-2 AOD was validated using sky
radiometer data in the dry and wet seasons. In the dry
season, light-absorbing aerosols emitted from BB cause
uncertainties in the optical properties of the atmosphere,

Fig. 2 Scatter density plot of hourly sky radiometer (x-axis) and Modern-Era Retrospective Analysis for Research and Applications version 2(MERRA-2) aerosol optical depth (AOD) (y-axis) in the a dry and b wet seasons from 2009 to 2019 at the Phimai SKYNET site. Red line: regression line;
black dashed line: 1:1 ratio. The color bars indicate the probability density derived from the kernel density estimation

Ohno et al. Progress in Earth and Planetary Science(2022) 9:61

Page 5 of 12

especially near the surface. Thus, MERRA-2 AAOD
in the dry season was validated using the sky radiometer, which is sensitive to UV wavelengths. Besides, AEC
below 1 km from the MAX-DOAS data was used to validate MERRA-2 in the season. Considering the continuous data availability, we used sky radiometer data from2009 to 2019 and MAX-DOAS data from 2014 to 2019.
To exclude uncertainties in the observation data, weused data from 9:00 to 15:00 local time, and thresholds
were set while calculating the averages. The hourly mean
AOD and AAOD were calculated when three observation
datasets existed per hour or more. Similarly, the hourlymean AEC was calculated when two observation datasets
exist per hour or more. MERRA-2 aerosol products werevalidated using the slope of the regression line (hereafter,
slope), Pearson correlation coefficient (R), mean absolute
error (MAE), root-mean-square error (RMSE), relative
mean bias (RMB), mean fractional error (MFE), mean
fractional bias (MFB) and index of agreement (IOA), as inprevious studies (Yumimoto et al. 2017; Che et al. 2019):

MAE = n1 ni=1 |Mi − Oi | ,(2a)

RMSE =

RMB =



1 n
(Mi − Oi )2 ,
i=1
n

M
,
O

(2b)

(2c)

2 n |Mi − Oi |MFE =× 100,
i=1 Mi + Oin(2d)

2 n Mi − Oi
× 100,
i=1 Mi + Oi
n

(2e)

n

(2f )

MFB =

IOA = 1 − 

n
i=1

2
i=1 (Oi − Mi )
 
 ,

Oi − Oi  + Mi − Mi  2

where n is the total number of pairs of modeled (M,
MERRA-2 AOD, AAOD, and AEC) and observed (O, skyradiometer AOD and AAOD, and MAX-DOAS AEC)
values. M and O are the mean values of {Mi } and {Oi },respectively, and MAE and RMSE the magnitudes ofbias between MERRA-2 and observation values. RMB
indicates the underestimation (RMB < 1) or overestimation (RMB > 1). MFE and MFB are indices that normalize
the bias and error for each model and each observationby the observed value, respectively. MFE ranges from
0% to + 200%, and MFB ranges from − 200 to + 200%.
To evaluate the model performance, Boylan and Russell (2006) set model performance goals and criteria forlight extinction and particulate matter. The model performance goals include MFE ≤  + 50% and MFB ≤  ± 30%,
and the model performance criteria are MFE ≤  + 75%
and MFB ≤  ± 60%. IOA indicates the model accuracy
and ranges from 0 to 1 (Willmott 1981), IOA = 1 indicating perfect model performance.
2.4 Spatiotemporal variability analysis

In addition to MERRA-2 AOD, OC and sulfate AODs
were used to quantitatively analyze the factors that contribute to the spatial and temporal variations in aerosols
in ICP regions. NEIC is exposed to anthropogenic aerosols because Hanoi, Vietnam, and China are located in
the northeast, and BB is high in the dry season. In contrast, BB is highest in NWIC, although NWIC is least
affected by transboundary air pollution from China. BB
begins earlier in SIC than in other regions (Lasko et al.
2019), and large cities, such as Bangkok in Thailand andHo Chi Minh City in Vietnam are located in SIC. In thisanalysis, monthly AODs from 2009 to 2020 were calculated using hourly data. The trend analysis was conductedusing the following equation:

 Slope
Trend %year−1 =
× 100
µ

(3)

where slope is the slope of the regression line and µ is themean value for the period used in each dataset.

3 Results and discussion
3.1 Validation of MERRA‑2 aerosol products
3.1.1 AOD

Sensitivity to wavelengths in the UV region is importantfor observing the optical properties of BrC, which hasstrong absorption in the UV region (Mok et al. 2016).
UV SSA data retrieved from sky radiometer results are
sensitive to BB plumes (Irie et al. 2019). Figure 2 shows
the hourly MERRA-2 and sky radiometer AOD data at
Phimai in dry and wet seasons. Most AOD values were
smaller than 0.5, with significant seasonal variations,
showing some AOD values larger than unity only in thedry seasons. Similar seasonal variations in AOD werereported by Sugimoto et al. (2015).
In dry seasons, MERRA-2 and sky radiometer AOD
data have a significant positive correlation (R = 0.86;
Fig. 2a), with MAE and RMSE of 0.10 and 0.15,
respectively. The MFE and MFB values were 29.75%
and − 21.19%, both of which are within the goal criteria defined by Boylan and Russell (2006), indicating that
MERRA-2 AOD data have an accuracy comparable tothat expected for the best model. The IOA value was high
at 0.90. As quantified with these statistical indices, the
MERRA-2 AOD and sky radiometer data were in good
agreement. In contrast, the slope and RMB were 0.82

Ohno et al. Progress in Earth and Planetary Science(2022) 9:61

and 0.84, respectively, indicating an underestimation of
MERRA-2 AOD.
As shown in Fig. 2b, in the wet season, MERRA-2 andsky radiometer AOD data showed a moderate positive
correlation, with an R of 0.63 but significant differencesin the slope (0.52) and RMB (0.89). These differences may
be attributed to the small number of datasets used forthe validation in the wet seasons (N = 61), as data withcloud contaminations were excluded from the analysis.
This indicates that only a few observations were assimilated by MERRA-2. The MAE and RMSE were 0.05 and
0.07, and the MFE and MFB were within the goal criteria(31.00% and − 9.46%, respectively). The IOA was high at
0.77. Despite the sparsity in the sky radiometer data, theaccuracy of MERRA-2 was acceptable based on the statistical indices.
In the dry seasons, MERRA-2 AOD was underestimated based on the statistical indices compared withdata from the sky radiometer, as discussed above. This isconsistent with results obtained at the AERONET Phimai site (Che et al. 2019). Because the aerosol sources
are complex, there are two possible reasons for theunderestimation of MERRA-2 AOD: underestimation ofanthropogenic aerosols or underestimation of BB aerosols. Although Sun et al. (2019a, b) reported the underestimation of MERRA-2 AOD over China because nitrateemissions are absent in the MERRA-2 model, the Phimaisite is in a rural area and is geographically distant fromthe source of anthropogenic aerosol emissions (Bangkok
or China). Thus, we presumed that the underestimation of AOD was more likely because BB activities are

Page 6 of 12

strong during the dry season. Buchard et al. (2016, 2017)
reported that the underestimation of OC emissions in the
MERRA-2 model contributes to discrepancies in nearsurface observations. Therefore, the underestimation of
MERRA-2 AOD was likely attributed to the lack of BB
aerosols, including OC and BC.
3.1.2 AAOD and AEC in the dry season

Figure 3A compares hourly MERRA-2 and sky radiometer AAOD data at Phimai in dry seasons. MERRA-2
and sky radiometer data are consistent, with an R of 0.75.
The slope and RMB were 0.57 and 0.54, respectively. The
MAE and RMSE were both 0.03, and the MFE and MFB
were 69.50% and − 67.05%, respectively. These values
exceed the criteria. The IOA value was low at 0.57. Therefore, MERRA-2 AAOD data at Phimai do not satisfy thecriteria.
Figure 3b shows the MERRA-2 and MAX-DOAS AECs
near the ground (below 1 km) at Phimai in the dry season. MERRA-2 data showed a moderate correlation withan R of 0.67. MERRA-2 underestimated AEC as the slope
and RMB were 0.33 and 0.48, respectively. On the basis of
the MFE, MFB, and IOA values, AEC was not estimatedas accurate as AOD (Figs. 2a and 3b). In the MERRA-2
model, BB emissions are distributed uniformly throughout the planetary boundary layer (Randles et al. 2017).
Because the MERRA-2 AEC depends on the vertical distribution of aerosols in the model, the discrepancy in the
AECs near the ground is attributed to the lack of emissions near the ground or uncertainties in the emission
injection height.

Fig. 3 Scatter density and scatter plots of hourly observations (x-axis) and MERRA-2 (y-axis) in the dry season from 2014 to 2019 at the Phimai
SKYNET site. a Hourly absorption AOD (AAOD) (550 nm) and b aerosol extinction coefficient (AEC) (532 nm) at 0–1 km. Red line: regression line;
black dashed line: 1:1 ratio. The color bar indicates the probability density derived from the kernel density estimation

Ohno et al. Progress in Earth and Planetary Science(2022) 9:61

Page 7 of 12

For the validations of both AAOD and AEC in the dry
season, MERRA-2 was significantly underestimated, but
AOD constraints may have resulted in a moderate correlation between AAOD and AEC since AAOD and AEC
were calculated using the MERRA-2 model. These results
indicate that, because of the light-absorbing BB emissions
near the ground in the dry season at Phimai, MERRA-2
may underestimate the AOD.
3.1.3 Correction of MERRA‑2 AOD at the Phimai site

MERRA-2 underestimated AOD in dry seasons, as discussed above; thus, we corrected it. Similar to the abovementioned validation results for MERRA-2 AEC at
altitudes below 1 km, the underestimation of OC emissions in the MERRA-2 model is attributed to the uncertainty in the surface aerosols (Buchard et al. 2017). BC
aerosol has a small contribution to AOD but a large
contribution to AAOD (Randles et al. 2016). Therefore,
assuming OC and BC (light-absorbing aerosol from BB)
are underestimated by MERRA-2, a correction factor
was calculated to make MERRA-2 AOD equivalent tothose obtained using the sky radiometer. We corrected
the MERRA-2 AOD assuming that the underestimationof AOD was due to the underestimation of (1) only OC
AOD or (2) both OC and BC AODs by the same amount.
The correction factors were calculated as 1.35 for case 1
and 1.30 for case 2 using the following equations:in the case 2 correction. In the dry season, the contribution of BC AAOD was high (mean, 88%). Thus, the case
2 correction improved the underestimation of AAOD
compared to the case 1 correction. The slope and RMBof SAOD were improved to almost 1:1 in both cases ofcorrection. Because the mean ratio of SAOD to AOD was
92%, correction for strong light-absorbing BC did not significantly influence the SAOD correction. The MFE and
MFB of AAOD were improved to meet the criteria in thecase 2 correction, and IOA was improved by 0.77. The
improvement in these indices in the case 2 correction
indicates that corrections for OC and BC can improve
the MERRA-2 AAOD and SAOD at Phimai in the dry
season.

τtotal1 = 1.35τOC + τBC + τdust + τseasalt + τsulfate ,(4a)τtotal2 = 1.30τOC + 1.30τBC + τdust + τseasalt + τsulfate ,(4b)Table 1 lists the statistical metrics of the sky radiometerand the MERRA-2 AAOD and SAOD without the AOD
correction, underestimation of OC AOD, and identical underestimation of OC and BC AOD at the Phimai
SKYNET site. The biases (MAE and RMSE) of AAOD
and SAOD with and without AOD correction are not significantly different. The slope and RMB of AAOD were
improved from 0.57 to 0.74 and 0.54 to 0.70, respectively,

Fig. 4 Seasonal variations in AOD (black), OC AOD (green) and sulfate
AOD (purple) in a NWIC, b NEIC and c SIC c from 2009 to 2020. Error
bars indicate standard deviations

Table 1 Statistical metrics of AAOD and scattering AOD (SAOD) without AOD correction (normal), underestimation of OC AOD (case1), and identical underestimation of OC and BC AOD (case 2) at the Phimai SKYNET site
Correction
AAOD

SAOD

Slope

R

MAE

RSME

RMB

MFE

Normal

0.57

0.751

0.025

0.030

0.54

69.49

Case 1

0.60

0.750

0.024

0.029

0.56

67.54

Case 2

0.74

0.750

0.020

0.025

0.70

53.22

Normal

0.82

0.844

0.088

0.132

0.89

27.10

Case-1

1.01

0.842

0.094

0.147

1.03

25.41

Case-2

0.99

0.843

0.092

0.144

1.01

25.35

MFB
− 67.04− 64.48− 44.80− 14.49− 2.88− 3.83

IOA
0.57
0.61
0.77
0.91
0.91
0.91

Ohno et al. Progress in Earth and Planetary Science(2022) 9:61

3.2 3.2 Seasonal variation

We analyzed factors that influence the AOD in NWIC,
NEIC, and SIC based on the validated MERRA-2 AOD
data. The major causes of seasonal variation are OC andsulfate aerosols (Kalita et al. 2020). Figure 4 shows seasonal variations in AOD, OC AOD, and sulfate AOD in
NWIC, NEIC, and SIC from 2009 to 2020. The AODs in
the regions were significantly higher in the dry season
than in the wet season. The maximum AOD value in thedry season was higher in NWIC and NEIC than in SIC.
BB occurs in the south, and the peak AOD moves north,Page 8 of 12

reaching a maximum in NWIC and NEIC (Lasko et al.
2019). This may explain the difference in AOD intensity
between the northern region and SIC. In this study, forall regions, OC AODs were dominant from February to
April and constituted more than 50% of the total AOD in
March. In contrast, the sulfate AOD accounted for morethan 25% all year round. Besides, small AOD peaks in
NEIC and SIC were observed in October, during which
AOD in NEIC was strongly influenced by sulfate aerosol,
accounting for approximately 74% of the total AOD.

Table 2 Changes in the OC and sulfate AOD ratios before and after correction in the dry season
RegionNWIC
NEICSpecies

Feb (%)Mar (%)

Apr (%)May (%)

Oct (%)

Nov (%)Dec (%)

OC

16

15

9

13

22

20

18

18

Sulfate

− 10

− 12

− 16

− 13

−6

−8

−9

−9

−7

− 10

− 15

− 14

−8

−5

−5

−6

− 12

− 14

− 15

− 13

−8

−7

−6

−7

OC
SulfateSIC

Jan (%)

OC
Sulfate

21

15

17

12

10

10

11

13

19

19

24

21

24

22

23

21

Fig. 5 HYSPLIT frequency plot of five-day backward trajectories in every 6 h at the Phimai site from 500 m in October from 2009 to 2020. Red star:
Phimai site. The red box indicates southern China

Ohno et al. Progress in Earth and Planetary Science(2022) 9:61

Fig. 6 Total and sulfate AODs and ­SO2 emissions in a Southern
China, b NWIC, c NEIC and d SIC from 2009 to 2020. Error bar:standard deviation; blue closed and open plots: total and sulfate
AOD, respectively; red plot: MODIS AOD; purple plot: ­SO2 emissions[tonnes]

In ICP, MERRA-2 AOD was underestimated compared to AERONET data (RMBs of AOD tended to beless than 1) (Cho et al. 2019). Therefore, we appliedcase 2 correction to ICP that are similarly affected
by BB. Table 2 lists the rates of change in the OC andsulfate AOD to total AOD ratios before and after theapplication of the case 2 correction in the dry season.
Rates of change were calculated as(rafter /rbefore ) − 1,
where r is the ratio of OC or sulfate AOD to the total
AOD. In the active BB period (from January to March),
the ratio of OC AOD to the total AOD after the

Page 9 of 12

correction increased by more than 9%, whereas that ofsulfate AOD to the total AOD decreased by up to 16%.
Because OC aerosol is dominant in this period (Fig. 4),
the impact of the correction may be substantial. In
contrast, from October to December (no BB and sulfate dominance), there was no significant change in thesulfate AOD (< 10%). The rate of change was lowest in
NEIC because of anthropogenic emissions. Therefore,
the effect of corrections in this period is small.
Frequency analysis of five-day backward trajectories at Phimai in October from 2009 to 2020 showed adominant trend in two wind directions (Fig. 5), indicating that the wind transitions from southwest to northeast due to monsoon (Sugimoto et al. 2008; Tsujimoto
et al. 2018). AODs in NEIC and SIC were affected by
the movement of air masses from China. The movement of (­NH4)2SO4-rich air masses from East Asia was
detected at Phimai through sampling and trajectory
analyses (Tsuruta et al. 2008, 2009). On the basis of trajectory frequency analysis, areas northeast of NEIC witha frequency of 30% or more were defined as SC. In SC,
which is a windward region with an anthropogenic aerosol source, AOD was more than 0.5 in October (Zhang
et al. 2018), which is higher than that in NEIC and SIC.
In Hanoi, a major city in NEIC located on the trajectory
pathway to Phimai, the BC aerosol increases because of
the circulation of pollutants from SC in October (Laskoet al. 2019). Therefore, AODs in NEIC and SIC increase
because of pollutant circulation from China in October,which is driven by a change in the wind direction.
3.3 Trend analysis for October

It is suitable to investigate the impact of emissions from
China in October because BB is inactive; thus, the contribution of anthropogenic aerosols is high. The total
and sulfate AOD trends are shown in Fig. 6 and Table 3.
The total and sulfate AODs decreased in all ICP regions.
The decrease was highest in NEIC, followed by SIC and
NWIC (− 4.40, − 3.00, and − 2.53% ­year−1, respectively).
MODIS AOD showed similar trends. Therefore, the
MERRA-2 dataset suggests that the decrease in AOD isconsistent and mainly caused by sulfate AOD.

Table 3 MERRA-2 AOD, sulfate AOD, MODIS AOD, and REAS ­SO2 trends (% y­ ear−1 [slope])
Region

SC
NWIC
NEIC
SIC

MERRA-2

MODIS

REAS

AOD

Sulfate AOD

AOD

SO2

− 3.10 (− 0.0160)− 3.01 (− 0.0121)− 7.32 (− 0.0308)− 6.00 (− 17.70)− 4.40 (− 0.0146)− 5.05 (− 0.0124)− 7.27 (− 0.0253)− 5.17 (− 6.63)− 2.53 (− 0.0042)− 3.00 (− 0.0058)− 3.38 (− 0.0034)− 4.48 (− 0.0048)− 0.45 (− 0.0007)− 2.72 (− 0.0066)− 0.11 (− 0.03)
2.29 (0.87)

Ohno et al. Progress in Earth and Planetary Science(2022) 9:61

Next, we evaluated factors contributing to the decreasein the total and sulfate AODs in October. From 2010 to2017, AOD in China decreased because of the environmental protection measures mandated by the Chinese
government (Sun et al. 2019b). Trend analysis was performed in SC similar to that for ICP, as ICP is affected bytransboundary transport from SC (Fig. 5). In this study,
we used monthly ­SO2 emission data from the Regional
Emission inventory in Asia (REAS) version 3.2.1 (Kurokawa and Ohara, 2020). ­SO2 is a precursor for sulfate
aerosol, which is useful for identifying the sources ofanthropogenic emissions. ­SO2 emissions in NEIC and SC
decreased by 6.00 and 5.17% ­year−1, respectively (Fig. 6).
In contrast, they increased by 2.29% ­year−1 in SIC andshowed no significant change in NWIC. S­ O2 emissions
in SC decreased by 2.5 times compared to that in NEIC(− 17.70 and − 6.63 tonnes ­year−1, respectively), suggesting a significant effect on ICP. In SIC, despite the increasein ­SO2 emissions, sulfate AOD decreased, which is attributed to the decrease in S
­ O2 emissions in SC and NEIC.
These results indicate that the decrease in anthropogenic
aerosols in SC could affect the downwind ICP.

4 Conclusions
We conducted a spatiotemporal variation analysis in
ICP using MERRA-2 aerosol datasets. The MERRA-2
datasets were validated using ground-based sky radiometers and MAX-DOAS at the SKYNET Phimai site in
the central ICP. The MERRA-2 AOD, AAOD, and AEC
at altitudes below 1 km were underestimated in the dry
season, likely because the amounts of BB aerosols (OCand BC) near the ground were insufficient. OC AOD
was dominant in the dry season, whereas sulfate AOD
was high all year round. AOD significantly increased in
October in both NEIC and SIC, corresponding to theincrease in sulfate AOD from 2009 to 2020. Trajectory
analysis revealed the movement of anthropogenic air
masses from SC in October. AOD decreased in October NEIC and SIC (− 4.40% and − 3.00% ­year−1, respectively), and S­ O2 emissions decreased in SC and NEIC(− 17.70 and − 6.63 tonnes ­year−1, respectively) but
increased in SIC (0.87 tonnes ­year−1). Therefore, theimprovement in the air quality in SC could improve theair quality in the downwind ICP in October because of
the air transportation by northeasterly winds.
Abbreviations
ICP: Indochina Peninsula; BB: Biomass burning; MAX-DOAS: Multi-axis differential optical absorption spectroscopy; MERRA-2: Modern-Era Retrospective
Analysis for Research and Applications, version 2; NWIC: Northwest Indochina
Peninsula; NEIC: Northeast Indochina Peninsula; SIC: South Indochina
Peninsula; SC: Southern China; OC: Organic carbon; BC: Black carbon; UV:
Ultraviolet; AOD: Aerosol optical depth; SSA: Single scattering albedo; AAOD:
Absorption aerosol optical depth; AE: Ångström exponent; AAE: Absorption

Page 10 of 12Ångström exponent; AEC: Aerosol extinction coefficient; ΔSCD: Differential
slant column density; SR-CEReS: Sky radiometer analysis package from centerfor environmental remote sensing; JM2: Japanese MAX-DOAS profile retrieval
algorithm, version 2; AERONET: Aerosol robotic network; MODIS: Moderateresolution imaging spectroradiometer; NASA: National Aeronautics and Space
Administration; GEOS-5: Goddard Earth Observing System, version 5; GOCART​: Goddard chemistry aerosol radiation and transport model; R: Pearson correlation coefficient; MAE: Mean absolute error; RMSE: Root-mean-square error;
RMB: Relative mean bias; MFE: Mean fractional error; MFB: Mean fractional bias;
IOA: Index of agreement; M: MERRA-2 datasets; O: Observation datasets.
Acknowledgements
This research was supported by the Environment Research and Technology Development Fund (JPMEERF20192001 and JPMEERF20215005) of theEnvironmental Restoration and Conservation Agency of Japan, JSPS KAKENHI(grant numbers JP20H04320, JP21K12227, JP22H03727, and JP22H05004, the
JAXA 3rd research announcement on the Earth Observations (grant number
19RT000351), and the Virtual Laboratory (VL) project by the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. We are grateful to
Mr. Ravi C. Govindaraju for providing us with MERRA-2 EXT data.
Author contributions
TO and HI designed the present study, performed observation and analysis,
and wrote the paper, with support from all the authors. MM and AMS gave
useful comments. All authors read and approved the final manuscript.
Funding
This research was supported by the Environment Research and Technology Development Fund (JPMEERF20192001 and JPMEERF20215005) of theEnvironmental Restoration and Conservation Agency of Japan, JSPS KAKENHI(Grant Numbers JP20H04320, JP21K12227, JP22H03727, and JP22H05004), the
JAXA 3rd research announcement on the Earth Observations (Grant Number
19RT000351), and the Virtual Laboratory (VL) project by the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan.
Availability of data and materials
The data are available upon request to the corresponding author (t.ohno.
chiba@gmail.com).

Declarations
Competing interests
The authors declare that they have no competing interests.
Author details
1
Center for Environmental Remote Sensing, Chiba University, 1‑33 Yayoicho, Inage‑Ku, Chiba 263‑8522, Japan. 2 GRASP SAS, 3 avenue Louis Neel,59260 Lezennes, Nord, France. 3 Present Address: DoerResearch Inc., 1‑20‑3
Matsusonocho, Mizuho‑Ku, Nagoya, Aichi 467‑0065, Japan. 4 NASA Goddard
Space Flight Center, Greenbelt, MD, USA.
Received: 12 July 2022 Accepted: 31 October 2022

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