In other words, long-distance cross-border transport in East Asia consists of the complicated interactions especially. Keywords: FLEXPART, Long distance transport, Transboundary transport, Air pollutants, NOx, East Asia, Source-Receptor Relationships. Spatial distribution of mean tropospheric NO2 column density (OMI in DJF, and trend of NO2 column density (molec/cm2, just above) over East Asia.
Time variations of Seoul, Beijing and Shanghai where they are representative of mega cities and highly polluted areas in East Asia. Bar graphs for Table 3. 2 of the corresponding model experiments, which are the same meteorological condition with different emission inventories. condition and emission inventories with different decay times ranging from 24 hours to 168 hours).
Radical change estimates of emissions inventories (a) doubling, (b) control, (c) halving and control test minus doubling and halving test (d) and (e)).
Introduction
According to Ding et al (2017), about 10% of emissions reduction over China's territory was estimated during the Chinese New Year (Winter). According to the Chinese government's national air pollutant control policy scenario, air pollutants are reduced in most of eastern China where they are the main sources of emissions (Zhao et al., 2013). The multi-modeling framework has been carried out by Carmichael et al., (2002), Qu et al., (2016) and Kim et al., (2017b) to better understand different approaches to modeling long-range transport. .
Previous and ongoing studies for the model simulations have mainly investigated the seasonal variability of air pollutants in East Asia (Lee et al., 2014; Kim et al., 2013b). The SRR method has examined emissions from several locations in East Asia and was investigated by Arndt et al. 2001) and the results are very different. FLEXPART is one of the representatives of the Lagrangian particle dispersion and transport model that is widely used in the model user community (Stohl et al., 2005).
It also simulates long-range and mesoscale particle transport with calculations of wet and dry deposition and radioactive decay times released from point, line, and surface sources (Stohl et al., 2005). In this experiment, we used FLEXPART version 9.02 to map source and receptor areas in East Asia (Lee et al., 2014). The sectors shown in Figure 2.1 cover the area of China (black line), Korea (red dotted line) and Japan (yellow line) (also used in Richter et al., 2005 and Lee et al., 2014).
The given set-up was designated to identify source-receptor relationships, covering emissions from either all sources or the sources in a specific area (China, Korea and Japan) (Lee et al., 2014). To reflect the current state of China, where the fastest and greatest change is in East Asia, it calculates the futuristic coefficients in sectors, regions and substances and creates emission inventory (Woo et al., 2014). Kim et al., (2017a) investigated the effects of various emission inventories on forecasts of ozone concentrations with regional-scale model experiments.
Lee et al., (2017) also used CREATE emission inventory to investigate the migrations of various air pollutants using adjunctive model to understand the 3-dimensional long-range transport mechanism. Ozone Monitoring Instrument (OMI) on board Aura satellite launched on July 14, 2004 and has been provided with global observation dataset of ozone and major atmospheric air pollutant gases (NO2 and SO2) since October 2004 (Krotkov et al., 2015). One of the benefits of air quality model simulation is to be a scientific basis for determining the impact of emission changes on concentration levels (Clappier et al., 2015).
It is often used for simulations of how emissions affect primary chemistries as well as secondary pollutants (Clappier et al., 2015).
Results
These were determined based on the rough border of the targeted countries in East Asia, as shown in Figure 3.1. Model simulation must be properly compared with the observations or measured data, mainly because there are many sources of uncertainty in the model simulation. To ensure the feasibility of model simulation, we first validate with OMI tropospheric NO2 columns, as shown in Figure 3.3 (left: FLEXPART, right: OMI tropospheric NO2 measurement).
The distributions of the OMI and FLEXPART EOF vectors generally agree between them, implying that they have sample correlations over the center of the Korean Peninsula. The EOF vector shown in Figure 3.5 shows negative variability, indicating that the two analyzes are in agreement. Consequently, Figures 3.5 and 3.6 may indicate that this model simulation is not suitable for quantitative evaluation.
Taken as a whole, the FLEXPART simulations are feasible according to the comparison of validated plume locations with OMI returns qualitatively, which means that it may be suitable for pre-SRR budget analysis. The table shown above shows the summary of abbreviations used to simplify SRRs (eg Source - China, Receptor - Korea: CK). First we got the CK notice where emissions from China, which account for most of the emissions in East Asia, affect the Korean peninsula from the prevailing wind.
Furthermore, we expected that CK could gradually decrease each year according to the trend of tropospheric NO2 shown in fig. According to the previous study, these results are consistent with the low level of contribution shown in the LTP report (45~65%, National Environmental Research Institute, 2012). Unlike Japan, China accounts for a large share of total emissions in East Asia and being located on the upwind side has significant impacts on the Korean peninsula and Japan.
We examine simulations with the same meteorological condition (on January 2018) with different emission inventories (CREATE2010 and 2015). We also find the significant difference depending on decay time of simulated NO2 as shown in Table 3.3 (figure 3.9). Simulation on DJF 2017-18 indicates the highest value of the whole periods on CK, which means that emissions reached the Korean peninsula mostly through downwind.
In addition, we are interested in how total emissions contribute to long-range transport.
Discussion
Annual temporal variations of OMI tropospheric NO2 column density (DJF) over major city in Korea and China (top) and FLEXPART NO2 column density (DJF, bottom). According to the simulation results and SRR analysis, the sudden increase in the CK value, which occurred in DJF 2018, was not due to the given meteorological conditions and seasonality, nor to the total emission amounts. During the winter season, a continental anticyclone located adjacent to China is common, and strong northerly and northwesterly winds lead to long-range transport with large dilution compared to other seasons (Lee et al., 2014).
Wind directions and speeds are relatively faster than other periods as indicated by the blue circle, but this hint is still not enough to explain the unexpected value of CK in 2018, as there do not appear to be significant differences affecting long-range transport . According to Kim et al. (2017b) found and discussed that there is a strong correlation between surface pollutant variations and wind speed. According to this study, we confirm the inverse relationship between wind speed and lower level air pollutants.
A notable fact shown in the figure is that its inverse relationship between them is evident, which is also shown by R2 = 0.864. The average 10-m wind speed over the south in 2017-18 is the slowest in DJF compared to other periods, so the SRR in DJF 2018 reaches its highest value. Except for experiments that consider this synoptic scale of meteorological conditions, the largest factor affecting SRR is variation in decay time.
In this experiment, we determine the decay time to be 72 hours longer than normal, to overcome the absence of chemical conversion (including reconversion process) and since the model is an offline model. We also extend the decay time, ranging from 24 hours to 168 hours, and analyze how the changes in NOx duration affect the SRRs. As mentioned above, CK increases logarithmically, and other SRRs also show logarithmic changes depending on the decay time.
This suggests that seasonal variations determining NO2 decay time may have a major impact on long-distance transport; In summer, photochemical reaction rates are highest (~6 hours), so NOx decay time is relatively shorter. In addition, north and northeasterly winds become dominant, causing emissions from China to be transported to northern China and accumulate. In contrast, anthropogenic NOx emissions from China do not change significantly with season (Zhang et al., 2007), meaning that the changes in the contribution of transported NO2 caused by seasonal changes imply dependence on meteorological conditions. e), (f) and (g) are differences between each year and the mean value.
Summary and Concluding Remarks
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Acknowledgement
