Numerous studies have reported that ambient air pollution, which has both
local and long-range sources, causes adverse impacts on the environment and
human health. Previous studies have investigated the impacts of
transboundary air pollution (TAP) in East Asia, albeit primarily through
analyses of episodic events. In addition, it is useful to better understand
the spatiotemporal variations in TAP and the resultant impact on the
environment and human health. This study aimed at assessing and
quantifying the air quality impacts in Japan and South Korea due to local emissions and TAP from sources in East Asia - one of the most polluted
regions in the world. We applied state-of-the-science atmospheric
models to simulate air quality in East Asia and then analyzed the air
quality and acid deposition impacts of both local emissions and TAP sources
in Japan and South Korea. Our results show that ∼ 30 % of
the annual average ambient PM2.5 concentrations in Japan and South
Korea in 2010 were contributed to by local emissions within each country, while
the remaining ∼ 70 % were contributed to by TAP from other
countries in the region. More detailed analyses also revealed that the local
contribution was higher in the metropolises of Japan (∼ 40 %–79 %) and South Korea (∼ 31 %–55 %) and that minimal
seasonal variations in surface PM2.5 occurred in Japan, whereas there was a
relatively large variation in South Korea in the winter. Further, among all
five studied anthropogenic emission sectors of China, the industrial sector
represented the greatest contributor to annual surface PM2.5
concentrations in Japan and South Korea, followed by the residential and
power generation sectors. Results also show that TAP's impact on acid
deposition (SO42- and NO3-) was larger than TAP's impact
on PM2.5 concentrations (accounting for over 80 % of the total
deposition), and that seasonal variations in acid deposition were similar
for both Japan and South Korea (i.e., higher in both the winter and summer).
Finally, wet deposition had a greater impact on mixed forests in Japan and
savannas in South Korea. Given these significant impacts of TAP in the
region, it is paramount that cross-national efforts should be taken to mitigate air
pollution problems across East Asia.
Introduction
Air pollution is one of the major environmental problems facing the modern
world, leading to adverse impacts on human health (Brook et al., 2004; Brunekreef and Holgate, 2002; Cook et al., 2005; Dockery
et al., 1993; Lelieveld et al., 2015; Nel, 2005; Pope III and Dockery, 2006;
Samet et al., 2000; Yim and Barrett, 2012; Yim et al., 2013, 2015, 2019a), the environment (Gu et al., 2018; Lee et al., 2005;
Rodhe et al., 2002), climate (Guo et al., 2016; Koren et al., 2012; Li et
al., 2011; Liu et al., 2018, 2019), and economic costs (Y. J. Lee et al., 2011; Yin et al., 2017). This study focuses specifically on the phenomenon
of transboundary air pollution (TAP; Yim et al., 2019b), which creates problems of assigning
attribution and thwarts the implementation of effective policies. There is a
sense of urgency given the significant implications of TAP on the
environment, on human health and on the geographic breadth of the areas
affected. Zhang et al. (2017) investigated the health impacts due to global
transboundary air pollution and international trade, estimating that
∼ 411 000 deaths worldwide have resulted from TAP, while
762 000 deaths have resulted from international trade-associated
emissions. J. Lin et al. (2014) investigated the air pollution in the United
States due to the emissions of its international trade in China, estimating
air pollution of China contributed to 3 %–10 % and 0.5 %–1.5 % of the annual
surface sulfate and ozone concentrations, respectively, in the western
United States.
The East Asian region has been suffering from air pollution for decades,
especially transboundary air pollution. The extant literature reports
significant impacts of TAP in Japan (Aikawa et al., 2010; Kaneyasu et al.,
2014; Kashima et al., 2012; Murano et al., 2000), South Korea (Han et al.,
2008; Heo et al., 2009; B.-U. Kim et al., 2017; H. C. Kim et al., 2017; Kim et al., 2012, 2009; Koo et al.,
2012; S. Lee et al., 2011, 2013; Oh et al., 2015; Vellingiri et al., 2016), or
East Asia in general and beyond (Gao et al., 2011; Gu and Yim, 2016; Hou et
al., 2018; Tong et al., 2018a, b; Koo et al., 2008; Lai et al., 2016; C.-Y. Lin et al., 2014; Luo et
al., 2018; Nawahda et al., 2012; Park et al., 2016; Yang et al., 2019; Wang et al., 2019; Zhang
et al., 2017), emphasizing TAP's origins in China. For example, Aikawa et
al. (2010) assessed transboundary sulfate
(SO42-) concentrations at various
measurement sites across the East Asian Pacific Rim, reporting that China
contributed to 50 %–70 % of the total annual
SO42- in Japan with a maximum in the
winter of 65 %–80 %. Murano et al. (2000) examined the transboundary air
pollution over two Japanese islands, Oki Island and Okinawa Island,
reporting that the high non-sea-salt sulfate concentrations observed in Oki
in certain episodic events were associated with the air mass transported
from China and South Korea under favorable weather conditions. Focusing on Fukuoka (an
upwind area of Japan), Kaneyasu et al. (2014) investigated the
impact of transboundary particulate matter with an aerodynamic diameter
<2.5µm (PM2.5), concluding that, in northern Kyushu,
contributions were greater than those of local air pollution. In terms of
China-borne TAP in South Korea, S. Lee et al. (2013, 2011) traced contributors to
Seoul's episodic high PM10 and PM2.5 events, showing that a
stagnant high-pressure system over the city led to the updraft, transport,
and subsequent descent of PM10 and PM2.5 from China to Seoul.
While TAP from China in Japan and South Korea was identified, the
spatiotemporal variations of TAP and sectoral contributions from emissions
from China have yet to be fully understood.
Wet acid deposition due to air pollution is also critically important given
the risks to ecosystems. Adverse environmental impacts of wet deposition
have been reported in Asia (Bhatti et al., 1992), and specific research has
investigated TAP's impact on wet deposition in East Asia (Arndt et al.,
1998; Ichikawa et al., 1998; Ichikawa and Fujita, 1995; Lin et al., 2008).
Within the East Asian region, Japan and South Korea are particularly
vulnerable to acid rain (Bhatti et al., 1992; Oh et al., 2015). Arndt et al. (1998) reported that the contribution of China to sulfur deposition in Japan was 2.5 times higher in winter and spring than in summer and autumn, and that both China and South Korea have been primary contributors to the sulfur deposition in southern and western Japan. Ichikawa et al. (1998) found that TAP accounted for more than 50 % of wet sulfur deposition in Japan. In their investigation of the contribution of energy consumption emissions to
wet sulfur deposition in northeast Asia, Streets et al. (1999) identified
the impact of nitrogen oxide emissions on the region's acid deposition. Lin
et al. (2008) reported that anthropogenic emissions of Japan and the Korean
peninsula had a larger contribution to wet nitrogen deposition than to wet
sulfur deposition in Japan due to the substantial transportation sources of
the two countries. This finding highlights the importance of assessing the
contribution of various sectors to acid deposition due to their distinct
emission profiles.
To mitigate air pollution in East Asia, it is critical to conduct a more
comprehensive evaluation of the contributions of both local emissions and
transboundary air pollution sources. Thus, this study assesses the
spatiotemporal variations in the contributions of local emissions and
transboundary air pollution (from China) to air quality and thus wet
deposition in Japan and South Korea. To identify which sectors are the
largest contributors to TAP and acid deposition in Japan and South Korea, we
conducted a source apportionment analysis of China's sector-specific
emissions. The method details of the source apportionment analysis are
provided in Sect. 2. Section 3 is divided into two parts: the first part
presents model evaluation results and estimates of ambient PM2.5
concentrations and source apportionment, while the second part discusses wet
deposition results and its impact on various land covers in Japan and South
Korea. A discussion in Sect. 4 concludes this study.
List of model simulations.
Simulation numberScenario1Baseline2Baseline without Japan's emissions3Baseline without South Korea's emissions4Baseline without Japan's and China's emissions (to estimate the contribution of others in South Korea)5Baseline without South Korea's and China's emissions (to estimate the contribution of others in Japan)6Baseline without China's agricultural (AGR) emissions7Baseline without China's industrial (IND) emissions8Baseline without China's power generation (PG) emissions9Baseline without China's residential and commercial (RAC) emissions10Baseline without China's ground transportation (TRA) emissions11Only include China's, Japan's, and South Korea's emissions (to compare with the baseline to assess theimpact of emissions from other countries)Materials and methods
This study applied the state-of-the-science atmospheric models (Weather
Research and Forecasting Model (WRF) and the Community Multiscale Air Quality
modeling System, CMAQ) to simulate hourly air quality over Japan and South
Korea in the year 2010. The WRF model (Skamarock et al., 2008) was applied to
simulate meteorology over the study area with one domain at a spatial
resolution of 27 km and 26 vertical layers. Figure 1a depicts the model
domain. The 6 h and 1∘×1∘
Final Operational Global Analysis (FNL) data (National Centers for
Environmental Prediction et al., 2000) were applied to drive the WRF model,
and the land-use data were updated based on Data Center for Resources and
Environmental Sciences, Chinese Academy of Sciences (RESDC) (Liu et al.,
2014).
(a) Model simulation domain (solid black line). Monitoring
stations (green dots) and major cities (red crosses) with populations ≥ 1 million in (b) Japan and (c) South Korea.
We applied CMAQv4.7.1 (Byun and Schere, 2006) to simulate air quality over
East Asia. The boundary conditions were provided by the global chemical
transport model (GEOS-Chem) (Bey et al., 2001), while the updated Carbon
Bond mechanism (CB05) was used for chemical speciation and reaction
regulation. The hourly emissions were compiled based on multiple datasets:
the HTAP-V2 dataset (Janssens-Maenhout et al., 2012) was applied for
anthropogenic emissions, the FINN 1.5 dataset (Wiedinmyer et al., 2014) was
utilized for fire emissions, and the MEGAN-MACC database (Sindelarova et
al., 2014) was applied for biogenic emissions. The speciation scheme,
temporal profiles, and vertical profiles adopted in our emission inventory
were based on Gu and Yim (2016), while plume rise heights for large industry
sectors and power plants were based on Briggs (1972). Details of the
atmospheric models were further discussed in Gu and Yim (2016).
To investigate the contributions of local emissions and transboundary air
pollution to air quality and acid deposition over Japan and South Korea, and
in particular those originating from China sectoral emissions, a total of
11 1-year simulations were conducted (see Table 1). The first simulation
was a baseline case, in which all the emissions were included. Two simulations were performed in which emissions of Japan and South Korea were
removed in turn. One simulation was conducted in which emissions of China and South Korea were removed for estimating other countries’ contributions to air pollution in Japan. A similar simulation was also conducted for South Korea in which emissions of China and Japan were removed. Another five simulations were designed to apportion the contribution of various emission sectors of China. Similar to Gu et al. (2018), the sectors were defined as (AGR) agriculture, (IND) industry, (PG)
power generation, (RAC) residential and commercial, and (TRA) ground
transportation. Emissions of each China sector were removed in turn. The
difference in model results between the baseline scenario and other
scenarios was used to attribute the contribution of emissions from the
respective country or Chinese sector. One additional simulation was
performed in which only emissions of China, Japan, and South Korea were
included. The differences between the baseline scenario and the last
scenario were used to attribute the contribution of emissions from all other
countries in the domain except China, Japan, and South Korea.
Model evaluations of PM10 across Japanese prefectures and
South Korean provinces where measurements are available. NMB refers to
normalized mean bias, RMSE refers to root mean square error, and IoA refers
to index of agreement. We note that the evaluation of Japan was based on
hourly data, while that of South Korea was based on monthly data.
Provinces withNMBRMSEmeasurementsRatio(%)(µg m-3)IoA(South Korea)Busan0.65-36.6022.050.45Daegu0.64-37.2822.840.52Daejeon0.72-30.2016.680.63Gwangju0.70-32.5718.440.63Incheon0.74-27.4118.960.63Jeju0.49-53.0729.200.51North Gyeongsang0.75-27.9418.720.53Seoul0.86-17.5214.480.72South Chungcheong0.55-45.0726.840.48South Gyeongsang0.63-38.0120.810.48South Jeolla0.84-22.3216.340.56Ulsan0.63-38.0521.010.46Average0.68-33.8220.480.55
To examine the model capacity for estimating spatiotemporally varied
distribution of PM2.5 in South Korea and Japan, we first employed
ground-level respirable suspended particulate (PM10) observation
datasets in 2010 from Japan and South Korea to compare with respirable
suspended particulates output gathered from our air quality model. Hourly
measurements from 1678 valid observation stations in Japan were collected by
the National Institute for Environmental Studies in Japan
(http://www.nies.go.jp/igreen/, last access: 7 April 2018); monthly measurements from 121 valid
observation stations in South Korea were extracted from an annual report of
air quality in 2010 (National Institute of Environmental Research,
2011). The locations of monitoring are depicted by the green dots in Fig. 1. Each measurement was compared with model outputs at the particular grid
in which the corresponding observation stations are located. To further evaluate
the CMAQ performance, we also compared our model results to
satellite-retrieved ground-level PM2.5 concentration data, which were
fused from MODIS, MISR, and SeaWiFS AOD observations in 2014 (van Donkelaar
et al., 2016). We extracted concentration values of satellite-retrieved
PM2.5 at the center of each model grid within Japan and South Korea and then
conducted grid-to-grid comparisons with annual-averaged model outputs. Model
performance was specified by a series of widely used statistical indicators,
including ratio (r), normalized mean bias (NMB), root mean square error
(RMSE), and index of agreement (IoA). The indicators are calculated as
follows:
r=∑i=1n(Mi-M‾)×(Oi-O‾)∑i=1nMi-M‾2×(Oi-O‾)212,NMB=∑i=1n(Mi-Oi)∑i=1nOi×100%,RMSE=1n∑i=1n(Mi-Oi)212,andIoA=1-∑i=1n(Mi-Oi)2∑i=1nMi-O‾+Oi-O‾2,
where M is the model prediction; M‾ is model output mean; O is the
observation measurement, and O‾ is the observation mean.
To facilitate the discussion of model performance, evaluation results for
different stations were gathered and averaged by the basic district division
in different countries (i.e., prefectures in Japan and provinces in South
Korea).
ResultsModel evaluation
We conducted a model evaluation of PM10 to assess our model performance
over the prefectures of Japan and over the provincial divisions of South
Korea where measurements are available; see Table 2. On average, the annual
mean ratio (normalized mean bias, root mean square error) for Japan and
South Korea was 1.27 (-22.44 %, 18.98 µg m-3) and 0.68
(-33.82 %, 20.48 µg m-3), respectively. Their mean indexes of agreement were 0.56 and 0.55 for Japan and South Korea, respectively.
These results show that the model tends to underestimate PM, which is
consistent with the results reported in other studies (Ikeda et al., 2014;
Koo et al., 2012). For example, Koo et al. (2012) conducted an evaluation of
CMAQ performance on PM10 over the Seoul and Incheon metropolises as
well as the north and south Gyeonggi provinces, showing results similar to
ours.
Statistical results of model evaluation using satellite-retrieval
PM2.5 over Japan and South Korea. NMB refers to normalized mean bias,
RMSE refers to root mean square error, and IoA refers to index of agreement.
Model evaluation using satellite-retrieval PM2.5 over Japan
and South Korea.
Figure 2 and Table 3 show the model evaluation using satellite-retrieval
PM2.5 over Japan and South Korea. The indexes of agreement are 0.7 and 0.8
for Japan and South Korea, respectively, while the normalized mean biases are
∼-29 % and ∼-7 %. Ikeda et al. (2014)
reported that their CMAQ model tended to underestimate PM2.5 over Japan
with a monthly normalized mean bias of -24.1 % to 66.7 %. The
underestimation may be because the model results were an average value over
a model grid, while the measurements represented the local PM level at a
specific location. Despite the underestimation, our index of agreement
results indicate that the model can reasonably capture the PM variability
over the two countries.
Model evaluation of acid deposition in Japan and South Korea. NMB
refers to the normalized mean bias, RMSE refers to root mean square error, and
IoA refers to index of agreement.
The modeled annual average surface PM2.5 (µg m-3)
over (a) Japan and (c) South Korea in 2010 and the percentage (%) of
total PM2.5 due to transboundary air pollution over (b) Japan and (d) South Korea.
SO42- and NO3- deposition simulated by CMAQ has
been compared with monthly ground-level measurements from the Acid
Deposition Monitoring Network in East Asia (EANET)
(https://monitoring.eanet.asia/document/public/index, last access: 20 August 2018). The evaluation results are
shown in Table 4. SO42- and NO3- tend to be underestimated in Japan and South Korea, which may be associated with
simulation bias of PM2.5 concentration. Normalized mean biases of
SO42- and NO3- ranged from -93.44 % to
30.20 % and -75.13 % to 181.22 % in Japan, respectively, while they ranged from -40.55 % to -11.54 % and -51.10 % to 7.75 % in South Korea. The averaged
index of agreement and ratio of SO42- and NO3
indicate that our model could basically capture the fluctuation and
magnitude of acid deposition in Japan and South Korea. A slightly better
performance in Japan was observed.
Annual and seasonal ambient PM2.5 in Japan and South Korea
Figure 3a and c show the annual average surface PM2.5 over Japan and
South Korea, respectively. The annual average surface PM2.5 concentration over Japan
was 5.91 µg m-3, while that over South Korea was 16.90 µg m-3. Higher PM2.5 concentrations occurred in metropolises: in Japan, higher PM2.5 levels occurred in Nagoya (13.48 µg m-3), Osaka (12.07 µg m-3), and Saitama (9.36 µg m-3). Higher PM2.5 levels were also observed at Okayama (14.78 µg m-3), even though its population is not as large as the
aforementioned metropolises, which may be due to its substantial industrial
emissions in the region. In South Korea, higher PM2.5 levels occurred
in Incheon (23.90 µg m-3), Goyang (27.05 µg m-3),
Seoul (30.64 µg m-3), and Suwon (30.75 µg m-3). Two additional high annual average levels of PM2.5 can be identified in
non-metropolis areas, which may also be due to their relatively high
industrial emissions.
Annual and seasonal surface PM2.5 concentration levels (µg m-3) and
source countries' contributions to PM2.5 (%) in Japan and South
Korea.
AnnualSpringSummerAutumnWinterJapanSurface PM2.5 concentration level (µg m-3) 5.916.095.885.755.93Local 29.3 %23.4 %29.0 %36.1 %32.2 %Transboundary air pollution (TAP) 70.7 %76.6 %71.0 %63.9 %67.8 %TAP from South Korea3.3 %3.7 %2.6 %4.1 %2.1 %TAP from China53.9 %61.4 %50.5 %44.0 %55.1 %TAP from others13.5 %11.5 %17.9 %15.7 %10.6 %AnnualSpringSummerAutumnWinterSouth KoreaSurface PM2.5 concentration level (µg m-3) 16.9017.6114.0217.4418.53Local 29.4 %27.3 %33.8 %33.8 %24.0 %Transboundary air pollution (TAP) 70.6 %72.7 %66.2 %66.2 %76.0 %TAP from Japan0.4 %0.4 %1.9 %0.2 %-0.4 %TAP from China54.2 %55.5 %43.8 %51.7 %62.9 %TAP from others16.0 %16.8 %20.4 %14.3 %13.5 %
In Japan, seasonal variations in surface PM2.5 did not vary
significantly, ranging from 5.75 to 6.09 µg m-3. In South Korea, however, seasonal variations were relatively
larger. The winter surface PM2.5 level was 18.53 µg m-3,
while the next highest levels occurred in spring (17.61 µg m-3)
and autumn (17.44 µg m-3). The lowest level of PM2.5
occurred in summer (14.02 µg m-3) in South Korea.
Local and transboundary contributions
Table 5 shows the contributions of emissions of different source countries
to PM2.5 in different receptor countries. On average, approximately
29 % of annual ambient PM2.5 in both Japan and South Korea was
contributed to by local emissions, while approximately 71 % was identified
as TAP. Of TAP's contribution, China was the key contributor, accounting for
approximately 54 % of the annual surface PM2.5 in both Japan and South
Korea. The results of our analysis of the contributions of PM2.5
between Japan and South Korea show that South Korea accounted for 3.3 % of the annual surface PM2.5 in Japan, whereas Japan's contribution to
PM2.5 in South Korea was marginal (0.4 %). The contribution of other
countries was non-negligible (i.e., 13.5 % in Japan and 16.0 % in South Korea).
Figure 3b and d indicate that the local contribution was relatively higher
in the metropolises of Japan (40.2 %–78.6 %) and South Korea (31.4 %–55.2 %), which is due to greater proportions of emissions being generated by local industry, transportation, and power generation. In Japan, the western areas showed a higher TAP contribution than the eastern areas, while, in South Korea, the western and northern areas showed a higher TAP contribution than other areas.
The TAP contribution varied with seasons. In Japan, the highest relative TAP
contribution occurred in spring (76.6 %), followed by summer (71.0 %)
and winter (67.8 %). The lowest relative contribution occurred in autumn
(63.9 %). In South Korea, the highest relative contribution of TAP
occurred in winter (76.0 %) and spring (72.7 %), while the lowest
occurred in summer (66.2 %) and autumn (66.2 %). Seasonal variations in TAP were most likely due to varying emissions and prevailing wind directions across seasons.
Annual and seasonal contributions of Chinese sectoral emissions to surface PM2.5
(µg m-3) in Japan and South Korea. Emission sectors include agriculture (AGR), power generation (PG), ground
transportation (TRA), industrial (IND), and residential and commercial
(RAC). Agriculture refers to agriculture and agricultural waste burning;
power generation refers to electricity generation; ground transportation
refers to road transportation, rail, pipelines, and inland waterways;
industrial refers to energy production other than electricity generation,
industrial processes, solvent production and application, and residential
and commercial refers to heating, cooling, equipment, and waste disposal or
incineration related to buildings.
As shown in Table 6, among Chinese sectors, industrial emissions were a key
contributor to annual surface PM2.5 in both Japan and South Korea,
accounting for approximately one-fifth of annual average concentrations. As
well, there was a little seasonal variance in terms of its contribution to
Japan's and South Korea's PM2.5 levels, which may be because industrial
emissions from China remain relatively constant all year long. For both
Japan and South Korea, the second- and third-most contributors to annual
surface PM2.5 were the residential and commercial (RAC) sector and the
power generation (PG) sector, respectively. Unlike the industrial sector,
seasonal variations in relative contributions for these two sectors were
apparent. Figure 4 shows that the southerly wind in Japan and South Korea during spring and summer
provided favorable conditions for pollutant transport of the Chinese RAC
sector. We observed contributions of China's RAC sector to be 12 %–22 % of the surface PM2.5 in Japan and South Korea in spring and summer. In autumn, the relative contribution of the Chinese RAC sector was minimal due to the northerly wind that was not favorable for TAP from China. In spring and winter, the northwesterly wind was favorable for transporting pollutants
from northern China, in which emissions from PG were substantial. The
remaining Chinese contribution was from the ground transportation and
agriculture sectors. When combined, both sectors accounted for 8 % and
12 % of the annual surface PM2.5 in Japan and South Korea, respectively,
with a maximum relative contribution in autumn and winter.
Effects of acid depositionAnnual and seasonal variations
Table 7 presents the annual and seasonal acid deposition in Japan and South
Korea. We estimated that outdoor air pollution resulted in 1.08 and 0.37 Tg of acid deposition annually in Japan and South Korea, respectively. The
local / TAP ratios were estimated to be 0.18 and 0.17 for Japan and South Korea, respectively, which is lower than the respective ratios for PM2.5
concentrations, highlighting TAP's larger impact on acid deposition. We note
that PM2.5 concentrations include both primary and secondary PM2.5
species, while acid deposition focuses on SO42- and
NO3-, which are secondary species. As well, local sources may
contribute disproportionately more primary PM2.5 species, i.e., black
carbon. Given that the annual SO42-/NO3- ratio values
were greater than 1 for both Japan and South Korea, sulfur emissions can be
considered as a key contributor to acid deposition.
Annual and seasonal acid deposition (SO42- and NO3-) (Tg) in Japan and South Korea,
including SO42-/NO3- and local / TAP (transboundary air
pollution) contribution ratios.
The seasonal variation in acid deposition between Japan and South Korea was
similar: higher in winter and summer and lower in autumn and spring. For
Japan, the largest TAP occurred in winter and the smallest TAP occurred in
autumn. For South Korea, the largest and smallest TAP occurred in winter and
spring, respectively. Regarding the SO42-/NO3- ratio,
the seasonal variation in Japan and South Korea suggests that SO42- deposition was more important in summer and less important in the winter.
For Japan, the value of these ratios ranged from 1.04 to 1.89; for South
Korea they ranged from 0.96 to 1.88. It should be noted that the
SO42-/NO3- ratio is particularly lower in winter than in
other seasons. Given minor local contributions, we conclude that TAP
NOx was significant in winter. Similar to the annual
SO42-/NO3- ratios, the seasonal ratios highlight the
significant sulfate deposition in the two countries.
Percentage of land coverage (%) and air pollution-induced acid
deposition (0.01 Tg) across various land cover types in Japan and South
Korea. A total of 24 land cover types provided by the U.S. Geological Survey (USGS)
were considered, including urban and built-up land; dryland cropland and
pasture; irrigated cropland and pasture; mixed dryland or irrigated cropland
and pasture; cropland or grassland mosaics; cropland or woodland mosaics; grassland;
shrubland; mixed shrubland or grassland; savanna; deciduous broadleaf forest;
deciduous needleleaf forest; evergreen broadleaf; evergreen needleleaf;
mixed forest; water bodies; herbaceous wetland; wooden wetland; barren or
sparsely vegetated; herbaceous tundra; wooded tundra; mixed tundra; bare
ground tundra; snow or ice. The land covers with no acid deposition on them
are not listed.
Percentage of gridTotal acidrepresented bydepositionland cover type(0.01 Tg)Japan Mixed forest55.28 %59.72Water bodies11.88 %12.84Savanna8.15 %8.81Irrigated cropland and pasture5.53 %5.97Cropland or woodland mosaics5.04 %5.45Shrubland4.74 %5.12Cropland or grassland mosaics2.84 %3.07Evergreen Needleleaf2.15 %2.33Dryland cropland and pasture1.54 %1.66Herbaceous wetland1.00 %1.08Deciduous broadleaf forest0.96 %1.04Urban and built-up land0.87 %0.94South Korea Savanna45.69 %17.1Mixed forest20.86 %7.81Irrigated cropland and pasture11.02 %4.12Water bodies9.06 %3.39Cropland or woodland mosaics6.36 %2.38Dryland cropland and pasture3.18 %1.19Urban and built-up land1.88 %0.7Shrubland1.04 %0.39Deciduous broadleaf forest0.92 %0.35Acid deposition over various land covers
To assess acid deposition impact over various land cover types, Table 8
shows the percentage of each land cover type in Japan and South Korea along
with its air pollution-induced acid deposition. We note that the land cover
percentage refers to the percentage of the model grids that were dominated
by each land cover type. For Japan, the land cover distribution shows that
the most prevalent land covers (> 5 %) are mixed forest, water
bodies, savanna, irrigated cropland and pasture, and cropland or woodland
mosaics. These land covers, when combined, account for ∼ 87 %
of the land in Japan. Urban and built-up land occupies only ∼ 1 % of the land. In terms of the impact of acid deposition in the
ecosystem in Japan, total deposition over mixed forest was 0.60 Tg, which
may result in direct damage to trees and soil. In urban and built-up land,
the acid deposition was estimated to be 0.01 Tg, representing
∼ 1 % of the total Japanese acid deposition.
For South Korea, the most prevalent land cover types are savanna, mixed
forest, irrigated cropland and pasture, water bodies, and cropland or woodland
mosaics. Together, they account for ∼ 93 % of the land, while
urban and built-up land account for ∼ 2 % of the land. The
acid depositions over the savanna and mixed forest were estimated to be 0.17
and 0.08 Tg, respectively. These two land covers share more than 66 % of
the total acid deposition in the country. Acid deposition on urban and
built-up land was 0.01 Tg, which is comparable to that in Japan.
Seasonal wind roses for Japan and South Korea. Each direction bin
presents the wind direction frequency.
Discussion and conclusion
This study estimated the contributions of both local sources and TAP from
Asia on surface PM2.5 in Japan and South Korea. Our findings were
consistent with those reported by other studies (Aikawa et al., 2010; Koo et
al., 2012). Among various emission sectors of China, our results show that,
particularly with favorable prevailing wind, China's industrial emissions
were the major contributor (∼ 20 %) to surface PM2.5 as
well as to acid deposition in Japan and South Korea. Our estimated wet
deposition ratios of SO42- and NO3- were still higher
than 1.00, implying the need for further control of SO2 emissions,
particularly from China's industrial sector. Previous studies have reported
a downward trend of SO42- deposition in East Asia in recent years
due to substantial SO2 emissions reductions in China (Itahashi et al., 2018; Seto et al., 2004).
In addition, wet deposition had significant impacts on mixed forests in
Japan and the savanna in South Korea. It is noted that the dominant soils in
Japan and South Korea have a low acid buffering capacity (Yagasaki et al.,
2001). Acid deposition-attributable forest diebacks have been reported in
Japan (Izuta, 1998; Nakahara et al., 2010) and South Korea (Lee et al.,
2005). High acid deposition may cause soil acidification and eutrophication,
which are particularly harmful in pH-sensitive areas such as forest and
savanna. Despite the fact that N deposition may increase soil N availability
and hence photosynthetic capacity and plant growth in an environment with a
low N availability (Bai et al., 2010; Fan et al., 2007; Xia et al., 2009),
excessive N would suppress or damage plant growth (Fang et al., 2009; Guo et
al., 2014; Lu et al., 2009; Mo et al., 2008; Xu et al., 2009; Yang et al.,
2009) and also reduce biodiversity (Bai et al., 2010; Lu et al., 2010; Xu
et al., 2006).
In our analysis, we further revealed that higher TAP contributions from Asia
occurred in spring in Japan and in winter in South Korea due to the
favorable weather conditions in the two seasons. While emissions of East
Asia are projected to decline (Wang et al., 2014; Zhao et al., 2014),
weather and climate may play a more important role under future climate change (Guo et al., 2019).
Given the fact that summer and winter monsoons were weakening (Wang and He,
2012; Wang et al., 2015; Wang and Chen, 2016; Yang et al., 2018; Zhu et al.,
2012), the frequency of favorable weather conditions for TAP from Asia is
projected to decrease, and TAP may be reduced subsequently.
In conclusion, our findings highlight the significance of transboundary air
pollution affecting Japan and South Korea as well as the impact of wet
deposition on various land covers. In this way, this study provides a
critical reference for atmospheric scientists to understand transboundary
air pollution and for policymakers to formulate effective emission control
policies, emphasizing the significance of cross-country emission control
policies.
Data availability
Air pollution measurements of Japan and South Korea are available at http://www.nies.go.jp/igreen/ (last access: 7 April 2018) provided by the National Institute for Environmental Studies in Japan (NIES, 2018) and at http://library.me.go.kr/search/DetailView.Popup.ax?cid=5506169 (last access: 12 June 2018) provided by the National Institute of Environmental Research of South Korea (NIER, 2018). Deposition measurements are available at https://monitoring.eanet.asia/document/public/index (last access: 20 August 2018) provided by the Acid Deposition Monitoring Network in East Asia (EANET, 2018). Emission data for air quality modeling were provided by openly accessible data including HTAP-V2 (Janssens-Maenhout et al., 2012), FINN 1.5 dataset (Wiedinmyer et al., 2014), and the MEGAN-MACC database (Sindelarova et al., 2014). The speciation scheme, temporal profiles, and vertical profiles adopted in our emission inventory were based on Gu and Yim (2016). Satellite-retrieved surface PM2.5 concentrations from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) are openly available at https://sedac.ciesin.columbia.edu/data/set/sdei-global-annual-gwr-pm2-5-modis-misr-seawifs-aod/data-download (last access: 16 April 2018, van Donkelaar et al., 2018). The 6 h FNL operational model global tropospheric analyses are available at https://rda.ucar.edu/datasets/ds083.2/ (last access: 28 July 2014, National Centers for Environmental Prediction, 2014). The land cover data in China are available upon request through the database at http://www.resdc.cn/data.aspx?DATAID=99 (last access: 16 April 2014, Chinese Academy of Sciences, 2018).
Author contributions
SHLY planned the research and sought funding to support this study.
SHLY conducted the analyses with technical support from YG. SHLY wrote the paper with discussions with all the co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We would like to thank
the Hong Kong Environmental Protection Department and the Hong Kong
Observatory for providing air quality and meteorological data, respectively.
We acknowledge the support of the CUHK Central High Performance Computing Cluster, on which computation was performed for this work.
Financial support
This research has been supported by the Vice-Chancellor's Discretionary Fund of The Chinese University of Hong Kong (grant no. 4930744).
Review statement
This paper was edited by Xiaohong Liu and reviewed by three anonymous referees.
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