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Article

Compromised Improvement of Poor Visibility Due to PM Chemical Composition Changes in South Korea

by
Jaein I. Jeong
,
Jisu Seo
and
Rokjin J. Park
*
School of Earth and Environmental Sciences, Seoul National University, Seoul 08826, Korea
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(21), 5310; https://doi.org/10.3390/rs14215310
Submission received: 19 September 2022 / Revised: 14 October 2022 / Accepted: 19 October 2022 / Published: 24 October 2022
(This article belongs to the Special Issue Recent Advances in Air Quality Modeling, Forecasting and Data Assimilation)
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Abstract

:
Fine particulate matter (PM) significantly affects visibility, a sensitive indicator of air pollution. Despite a continuous decrease in the PM concentrations in South Korea, the public generally believes that PM air pollution has worsened over the past years. To explain this disparity, we analyzed the characteristics of recent visibility changes using observations of visibility and PM component data observed in Seoul, South Korea, from 2012 to 2018. A significant negative correlation (R = −0.96) existed between visibility and concentrations of PM, with an aerodynamic diameter ≤ 2.5 μm (PM2.5); a high PM2.5 concentration was the most important contributor to poor visibility. Annual mean PM2.5 concentrations in Seoul decreased by −5.1% yr−1 during 2012–2018, whereas annual mean visibility improved by 2.1% yr−1. We found that a lower improvement in visibility was associated with changes in the PM component. Among the PM components affecting poor visibility, contributions of ammonium nitrate (NH4NO3) significantly increased during 2012–2018 (from 48% in 2012 to 59% in 2018). Increases in NO3− aerosol concentrations were owing to SOx emission reduction and the resulting decreases in SO42− aerosol concentrations, which led to an increase in NH3 available for additional NH4NO3 production in the atmosphere. Despite decreased PM concentrations in Seoul, the change of PM components has compromised visibility improvement; thus, NO3− concentrations need to be reduced.

1. Introduction

In 2013, the World Health Organization (WHO) declared particulate matter (PM) as a first-class carcinogen. In the past, PM was known to cause respiratory or eye diseases; however, recently, it has been a significant cause of myocardial infarction, pneumonia, and bronchitis [1,2]. In South Korea, high PM concentrations have been one of the most serious public concerns [3,4,5,6]. Consequently, PM forecasting and warning systems were implemented in early 2014 in South Korea. Since March 2018, the government has issued stringent PM air quality standards in South Korea (i.e., daily mean PM2.5 should be less than 35 µg m−3). Strict PM2.5 regulation has resulted in frequent warnings, which has also contributed to raising public awareness of the issues associated with PM. In particular, public awareness of air quality degradation is closely related to the poor visibility caused by large amounts of PM in the atmosphere [7,8,9].
Visibility degradation refers to decreased visible distance due to light scattering and absorption by particles or gases in the atmosphere [10,11,12,13]. Therefore, a visibility change is significantly related to factors including solar radiation, clouds, precipitation, and atmospheric chemical concentration and compositions (including gases and particles) [14,15,16,17]. In recent years, due to the strengthening of emission-reduction policies, concentrations of PM with an aerodynamic diameter ≤ 10 µm (PM10) and PM2.5 in South Korea have continuously decreased over the past years and, as a result, visibility has also improved [18,19,20,21]. Nevertheless, anxiety regarding air pollution continues to persist, indicating that the improvement in visibility may not be as apparent as the degree of the change in PM concentrations.
Several studies were conducted in South Korea on the long-term trends, spatial and temporal characteristics, and degradation factors related to visibility changes to understand the effects of PM’s chemical and optical properties on visibility degradation [22,23,24]. They mainly relied on visibility observations with a traditional method, which measured a maximum distance with the naked eye and was naturally prone to considerable uncertainty. As the visibility measured by maximum visible distances increases, the associated uncertainty of the observation significantly increases because the naked eye is insensitive to changes in visibility over long distances [25].
Since 2017, visibility has been measured based on the light intensity changes, using the transmitter to overcome the problem mentioned above [25,26,27]. However, the traditional eye and recent instrument-measurement methods show large discrepancies in visibility observations, depending on the environmental conditions [25]. Such discrepancies limit the analysis of continuous visibility trends in South Korea. This study used PM components and a light-extinction-coefficient equation to reconstruct visibility data, which were consistent with visibility observations from the two methods. We use this reconstructed dataset to analyze the visibility trends from 2012 to 2018 and to investigate the cause of the recent gap between visibility and PM2.5 concentration changes in South Korea. Moreover, these reconstructed visibility datasets not only suggest how to utilize both eye and instrumental observations in the long-term analysis of visibility, but also suggest directions for changes in the chemical environment in South Korea.

2. Materials and Methods

2.1. Observations

Our analysis used observations of meteorological variables, PM10, PM2.5, and PM2.5 chemical components, and NO2 concentrations in Seoul, South Korea (Table 1). Meteorological observations were conducted by the Korea Meteorological Administration (KMA; https://www.kma.go.kr/eng/ (accessed on 13 October 2022)) during 2012–2018, including observations of hourly precipitation and relative humidity (RH), which were measured by the Automated Synoptic Observing System (ASOS). Visibility observations with the naked eye were conducted every hour during the daytime and every 3 h at nighttime (1800–0300 LST). Eye observation is determined based on the clarity and color of the target after recognizing the distance to the surrounding buildings or terrain in advance. As of 1 January 2017, the KMA changed the visibility observation method from eye observation to instrument observation and measured visibility every hour. The instrument observation was produced by PWD22 (Vaisala Inc., Vantaa, Finland) and VPF-730 (Biral Ltd., Portishead, UK) and, basically, the observation techniques of the two instruments are similar. Hourly PM10 and PM2.5 concentrations, PM2.5 chemical components (SO42−, NO3−, NH4+, Ca2+, Fe2+, organic carbon (OC), and elemental carbon (EC)), and NO2 were observed by the National Institute of Environmental Research (NIER; https://nier.go.kr/NIER/eng/index.do (accessed on 13 October 2022)). The PM2.5 and PM10 concentrations were measured with BAM 1020 (MetOne Ins., Grants Pass, OR, USA) using the β-ray absorption method, and the concentrations of ions, carbon, and metal components in PM2.5 were measured with MARGA (Monitor for Aerosols and Gases in Ambient Air, Applikon Analytical, NED), Semi-continuous OCEC analyzer (Sunset Lab., Portland, OR, USA), and Xact-620 (XRF, Cooper Co., San Ramon, CA, USA), respectively. Although the NIER data were quality-controlled, the sum of PM2.5 components was 19% lower than the PM2.5 concentration due to unidentified substances, such as ions, crustal elements, and trace metals, as shown in previous studies [28,29,30]. However, the sum of PM2.5 components and PM2.5 showed a high correlation coefficient (R = 0.96), which improves the reliability of the PM2.5 component data we used. We also used domestic SOx, NOx, and NH3 emissions for 2012–2018 from the National Air Emission Inventory and Research Center (https://www.air.go.kr/eng/main.do (accessed on 13 October 2022)).

2.2. Visibility Data Reconstruction

Visibility observations may differ depending on the measurement methods. In particular, extreme meteorological conditions (e.g., cloud, precipitation, or fog) significantly affect visual observation [9,31,32], which creates a discrepancy from instrument observations [25,33]. Therefore, it is crucial to calibrate visibility observations by removing spurious signals. Precipitation is a typical meteorological phenomenon that causes poor visibility [15]. For example, in Seoul, the annual mean visibility when precipitation occurred during 2012–2018 was 5.0 km lower than that on a clear day. Another cause of visibility deterioration is yellow dust events [34,35,36], which decreases annual mean visibility by 0.05 km in Seoul compared to those with no yellow dust. The small effect of yellow dust on annual mean visibility compared to precipitation is because relatively large soil particles have low scattering efficiency and occur in fewer cases. Fog (i.e., visibility < 1 km and RH ~100%) is another crucial factor for poor visibility. Excluding fog may remove some polluted PM days together, but in a very humid environment, the scattering cross section of hygroscopic particles is more than five times higher than that of dry particles [37]. This can be a degradation factor in visibility analysis. Although these natural factors play essential roles in visibility changes, they were not the main focus of this study. Therefore, we excluded visibility observations in the presence of extreme meteorological conditions such as precipitation, fog, and yellow dust for the analysis described below to investigate the effects of PM and its composition on visibility changes.
According to the two methods, there is a systematic difference between the visibility data sets, which is a significant limitation for continuous visibility analysis. We use empirical equations that reflect light extinction by gases and aerosols to create a consistent visibility dataset. Table 2 shows the light extinction equations used in the visibility calculations developed by Interagency Monitoring of Protected Visual Environments (IMPROVE) and NIER [38,39,40], based on the observed PM component and NO2 concentrations and the ambient RH. Visibility calculation using the light-extinction coefficient was performed in previous studies [41,42,43]. IMPROVE_1994 is the light-extinction coefficient equation initially proposed by IMPROVE, based on U.S. observation data. The light-extinction coefficient is calculated by dividing the major aerosol components that cause reduced visibility, such as ammonium sulfate (NHSO), ammonium nitrate (NHNO), organic carbon (OMC), elemental carbon (EC), fine-mode soil particles (FS), and coarse-mode particles (CM; PM10–PM2.5). In addition, as water vapor in the atmosphere is absorbed by deliquescent particles, the water growth function (f(RH)) is considered. While IMPROVE_1994 applied f(RH) uniformly, regardless of particle size, IMPROVE_2007 minimized the error about f(RH) by subdividing it into fS(RH) and fL(RH) according to particle size. NIER calculated the light-extinction coefficient for each aerosol component determined through multivariate linear regression analysis based on data observed in South Korea.
We first calculated hourly light extinctions in Seoul for 2017–2018, using equations in Table 2 with PM component-concentrations and humidity data. We then used the following Koschmieder formula to convert light extinctions (Mm−1) into reconstructed visibility (km) [44].
Visibility = 3.912 light-extinction   coefficient
We evaluate the reconstructed visibility by comparing it with the instrument visibility for the same period. The correlation coefficients (R) between the instrument visibility and the reconstructed visibility using IMPROVE_1994, IMPROVE_2007, and NIER equations, were 0.26, 0.21, and 0.82, respectively. The reconstructed visibility based on the NIER equation shows the best agreement with the observation and is used for our analysis below. However, the NIER equation limits the maximum visibility up to 25.5 km, beyond which the maximum visibility observed with the naked eye was available. In particular, good visibility (>20 km) observed with the naked eye accounts for approximately 30% of the total hourly visibility observations. As mentioned above, to analyze poor visibility, which is closely related to air quality degradation, we excluded good visibility data (>17 km) from our analysis, which was not the main focus of the study. After filtering as described above, the correlation coefficient between the reconstructed visibility and instrument observation increased to 0.86. Then, the hourly reconstructed visibility data were averaged annually and used for analysis. Note that we examined all visibility data without limiting distance; however, there was no significant difference in our conclusions.

3. Results and Discussion

3.1. Annual Visibility Trend

The change in PM2.5 concentrations was an important factor in the visibility change in Seoul. Figure 1 shows the annual mean surface PM2.5 concentration and the reconstructed visibility (hereafter visibility) during 2012–2018. The PM2.5 concentration shows a clear decreasing trend, influenced not only by the emission-reduction policy in South Korea, but also by the recent clear decreasing trend of PM2.5 concentration in China [45,46,47]. Changes in PM2.5 concentration in China are known to significantly impact air quality in South Korea through long-range transport [6,48]. The annual mean visibility showed the opposite trend to the PM2.5 concentrations (R = −0.96). Interestingly, the range of change between the two differs: the annual mean PM2.5 concentrations decreased by −5.1% yr−1 during 2012–2018, while the annual mean visibility increased by only 2.1% yr−1. To understand why the increase in visibility was less than the decrease in the PM2.5 concentration, we additionally analyzed the concentrations of PM2.5 components (SO42−, NO3−, NH4+, OC, and EC). Figure 2 shows the annual mean variations of PM2.5 and PM2.5 component concentrations. Except for OC, the concentrations of PM2.5 components exhibited a decreasing trend similar to that of PM2.5 during 2012–2018.
Table 3 shows the annual trends for PM2.5 and the components during 2012–2018. First, the decreasing trend of SO42− was −1.0 µg m−3 yr−1, which made the highest contribution to the decreasing trend of PM2.5, which was −2.3 µg m−3 yr−1. NO3− and NH4+ also contributed, but were statistically insignificant (p-value > 0.10). Previous studies have suggested the importance of carbonaceous aerosols (OC and EC) on visibility changes [13,41,49,50], but in Seoul, from 2012 to 2018, their contributions to PM2.5 concentrations were relatively low compared to secondary inorganic aerosols (see Figure 2). The changes of OC and BC concentrations were small (Table 3), and their trends were statistically insignificant, especially for OC. Except for the OC (for which the trend was weak), the annual variability of PM2.5 components showed a high correlation coefficient of >0.73 with that of PM2.5. In particular, NH4+ had the highest correlation coefficient of 0.96 with PM2.5. In addition, PM2.5 components showed a high negative correlation with visibility (−0.72 or higher), indicating that the annual change in aerosol concentration played an important role in visibility change.

3.2. Poor Visibility Analysis

For the general public, poor visibility is considered an indicator of air quality degradation; therefore, it is necessary to assess the recent changes in poor visibility. In particular, the annual mean visibility in Seoul has been improving in recent years (see Figure 1); however, since the public is sensitive to poor visibility, an increase in poor visibility can be perceived by the public as air quality degradation. Here, we analyzed the frequency of poor visibility in Seoul during 2012–2018.
The definition of poor visibility is arbitrary. For example, Fu et al. [51] and Sun et al. [52] used low visibility (<5 km) as poor visibility, while Luo et al. [53] used a much smaller distance (<3 km). Poor visibility is defined as the lowest 20th percentile of daily visibility in Seoul for 2012–2018, which are values lower than 6.1 km. Using this criterion, annual and diurnal (rush hour) frequencies of poor visibility were calculated. Note that to analyze the sensitivity of our results to the poor visibility criterion, we changed the poor visibility criterion to the lowest 5th (<3.6 km) and 10th (<4.7 km) percentile values, but there was no significant difference in our conclusions.

3.2.1. Annual Poor Visibility

Despite the decreasing trend of the annual mean PM2.5 concentration observed in Seoul during the analysis period, public concern regarding air pollution is most likely due to the public perception that air pollution has been worsening from poor visibility. Therefore, to investigate the trend of poor visibility, we examined the proportion of poor visibility in the total hourly visibility data. Figure 3 shows the annual mean poor visibility proportion in Seoul during 2012–2018. As mentioned above, we applied the criterion of poor visibility to cases <6.1 km. Since 2013, the poor visibility proportion has decreased, which is consistent with the increase in annual mean visibility in Seoul (Figure 1). The correlation coefficient between the poor visibility proportion and annual mean visibility was −0.94, which shows that poor visibility has played an important role in the annual mean visibility. In particular, the trend of poor visibility since 2013 was −3.3% points (p) yr−1, implying that the proportion of poor visibility has been rapidly decreasing in recent years.

3.2.2. Poor Visibility in Rush Hour

When people are actively engaged in outdoor activities during commuting hours, they may be exposed to PM more than during other times due to traffic congestion in metropolitan cities such as Seoul. Therefore, we indirectly examined the changes in concerns of people caused by high concentrations of PM through poor visibility changes during rush hour periods. Figure 4 shows the poor visibility during two rush hour periods (morning rush hour: 0700–0900 LST; and evening rush hour: 1700–1900 LST). The 7–year mean poor visibility proportion in Seoul was 7.8%p higher in the morning rush hour (24.2%) than in the evening rush hour (16.4%); this was generally due to the lower planetary boundary layer and higher RH in the morning rush hour than in the evening rush hour [54,55]. As mentioned above, we set the criterion of poor visibility as the lowest 20th percentile value of hourly visibility data. Therefore, the high poor visibility proportion during the morning rush hour means that the morning rush hour significantly contributed to the change in the poor visibility proportion in Seoul. In recent years, poor visibility has decreased in both morning and evening rush hours; however, the change has been more significant in the morning rush hour (−1.8%p yr−1) than in the evening rush hour (−1.4%p yr−1).

3.3. Factors Affecting Poor Visibility

To better understand the change in poor visibility, we additionally analyzed the change in concentrations of PM2.5 components during poor visibility conditions. Figure 5 shows the annual mean variations of PM2.5 and the concentrations of the components during poor visibility. When all cases of observed visibility <17 km were considered, the NO3− concentration in Seoul showed the same negative trend as PM2.5 (see Figure 2). However, when only considering the poor visibility of <6.1 km, the NO3− concentration showed a different trend, unlike the negative trend of the PM2.5 concentration. In particular, since 2015, the NO3− concentration has shown a steadily increasing trend, which is opposite to that of PM2.5 with a steadily decreasing trend.
Table 4 shows the annual trends in PM2.5 and its components from 2012 to 2018 during poor visibility conditions. The decreasing trend of SO42− was −1.6 µg m−3 yr−1, contributing the most to the decreasing trend of PM2.5 of −2.6 µg m−3 yr−1 and was statistically significant. In addition, SO42− (R = 0.95) and NH4+ (R = 0.83) showed high correlation coefficients with PM2.5, indicating that they were major components in the variability of the PM2.5 concentration under poor visibility conditions. However, during poor visibility conditions, the correlations with visibility were higher with NO3− (R = −0.60) and NH4+ (R = −0.77) than with SO42− (R = −0.38), even with the low statistical significance of the annual concentration trend (p-value > 0.10). In other words, this result shows that although SO42− made a higher contribution than NO3− to the trend in the PM2.5 concentration, the contribution of the NO3− concentration had a stronger influence on the poor visibility trend. It also shows that NH4NO3 played a major role in visibility changes; this is described in more detail in Section 3.4. The correlation between carbonaceous aerosols (OC and EC) and visibility was relatively insignificant (R = −0.03 and R = 0.01, respectively), highlighting the importance of secondary inorganic aerosols (SO42−, NO3−, and NH4+) for poor visibility conditions. The mean cation to anion equivalent ratio observed in Seoul from 2012 to 2018 is 1.01, which indicates that the inorganic salts were fully neutralized in the forms of (NH4)2SO4 and NH4NO3.
The optical properties of aerosols are sensitively affected by the composition and size of hygroscopic particles, which are closely controlled by atmospheric humidity [37]. From 2012 to 2018, RH showed an increasing trend under poor visibility conditions (0.8% yr−1). An increase in RH may have contributed to suppressing the improvement in visibility, as higher RH and a more humid environment are more favorable for the hygroscopic growth of particles and subsequent decrease in visibility. However, the change in RH and visibility showed a weak correlation (R = 0.28), and the trend of RH also showed low statistical significance (p-value > 0.10).

3.4. Variations of SO42− and NO3− Concentrations

3.4.1. Budget Changes of Light-Extinction Coefficient

We derive the reconstructed visibility data by calculating the light-extinction coefficient based on the PM components (Table 2). Here, we analyze the light-extinction coefficient for each PM component to examine the cause of the recent improvement in visibility in Seoul that is lower than the improvement in PM2.5 concentrations. Figure 6 shows the annual trend of the light-extinction coefficient for PM components from 2012 to 2018 during poor visibility conditions. Since visibility has an inverse correlation with the extinction coefficient (Equation (1)), a decrease in the light-extinction coefficient means an improvement in visibility. According to the trend of light-extinction coefficient under poor visibility conditions, (NH4)2SO4 significantly contributed to the improvement of visibility (−13.6 Mm−1 yr−1), but as the concentration of NO3− increased, NH4NO3 contributed to the deterioration of visibility (9.4 Mm−1 yr−1). In poor visibility conditions, both PM2.5 and visibility are improved due to a significant decrease in SO42− concentration, but an increase in NO3− concentration suppresses the visibility improvement. Furthermore, NH4NO3 has higher mass extinction efficiency (light-extinction coefficient per unit mass) than (NH4)2SO4, resulting in larger visibility degradation with the same mass concentration (see Table 2).
We quantitatively analyzed the effects of PM component changes on visibility. Figure 7 shows the proportion of the light-extinction coefficient for 2 years (i.e., 2012 and 2018), representing the relative influence of each PM2.5 component on reduced visibility. NH4NO3 contributed the most to poor visibility among the PM components, followed by (NH4)2SO4, OC, and EC. In recent years, visibility has improved, and the poor visibility proportion has decreased (see Figure 1 and Figure 3). However, the effect of NH4NO3 on poor visibility has been increasing. In 2012, the proportion of NH4NO3 contributing to poor visibility was 48%; however, this increased to 59% in 2018. In contrast, (NH4)2SO4 decreased from 33% to 22% over the same period. The reason for this was the decrease in SOx emissions, and the excess NH3 due to the decrease in the (NH4)2SO4 concentration contributed to the increase in the NH4NO3 concentration (Figure 5).

3.4.2. Effects of Changes in SOx and NOx Emissions on Visibility

To analyze the changes in SO42− and NO3− concentrations in Seoul over two years, 2012 and 2018, we examined the changes in emissions of the precursor, such as SOx and NOx, which significantly contribute to the concentrations of both species. Although SO42− and NO3− are secondary aerosols produced by complex reactions of thermodynamic equilibrium in the atmosphere [56], different trends in their precursor emissions are critical factors in determining the respective aerosol concentrations. Here, in comparing emissions, we considered the Seoul Metropolitan area, including Seoul, Incheon, and Gyeonggi-do, which may affect the generation of secondary aerosols in Seoul.
In 2018, the SO42− concentration had decreased by 36.7% compared to 2012, while the NO3− concentration had increased by 27.0% (Table 5). The distinct difference in the concentration change between the two components was highly correlated with the change in SOx and NOx emissions. During the two periods, SOx emissions decreased by 39.8%, similar to the 36.7% decrease in the SO42− concentration. In general, SO42− and NO3− exist in the forms of (NH4)2SO4 and NH4NO3 in the atmosphere [57]. (NH4)2SO4 has a stoichiometric ratio of SO42− and NH4+ of 1:2, and NH4NO3 is composed of NO3− and NH4+ at a ratio of 1:1. Theoretically, the NO3− molar concentration can be doubled by the reduced SO42− molar concentration. Moreover, NO3− is optically 47% more effective than SO42− in reducing visibility (see Table 2). Due to the decreased SO42− concentration, all the available NH3 contributed to the increase in the NO3− concentration. Therefore, the NO3− concentration increase was due to the decrease in SOx emissions, and in addition to the 17.6% increase in NOx emission in 2018 compared to 2012, the 3.4% increase in NH3 emission also contributed to the increase in NO3− concentration. As such, different changes between PM components have played an important role in recent visibility changes. That is, in 2018 compared to 2012, although the increase in the NO3− concentration was smaller than the decrease in the SO42− concentration, the change in the light-extinction coefficient during the same period showed that the increase in NH4NO3 was larger than the decrease in (NH4)2SO4 (Table 6). This result means that the increase in NH4NO3 concentration is an important contribution to the recent slowness in visibility improvement in Seoul despite the decrease in PM2.5 concentration.
Moreover, the increase in NOx emissions in the Seoul metropolitan area (which is in an NH3-rich condition) [58,59,60] will play an important role, not only in suppressing the decrease in PM2.5 concentration, but also in increasing the NO3− proportion in the PM2.5 component. Therefore, detailed emission-reduction policies are required to reduce the PM2.5 concentration and improve visibility.
Local sources of precursor emissions strongly influence the generation of secondary inorganic aerosols. In addition, long-range transport from China is also an important part of secondary inorganic aerosols because the Korean Peninsula is located downstream of China, the largest source of pollution in East Asia [48]. Although the concentration trend of secondary inorganic aerosols in Seoul is closely related to changes in emissions in the Seoul metropolitan area, as seen above, it is not easy to evaluate the effects of emissions more accurately only with observational information. Therefore, a sensitivity experiment using a three-dimensional atmospheric chemical-transport model is required, but it is far from the scope of this study.

4. Conclusions

In recent years, the annual mean PM2.5 concentration in Seoul has been decreasing; however, people tend to perceive that the air quality is deteriorating. We investigated the cause of the discrepancy by focusing on the changes in visibility that are closely related to air quality degradation and especially evaluated the effects of changes in the PM2.5 components on poor visibility. Contrary to concerns, the annual mean PM2.5 concentration and visibility steadily improved during 2012–2018 in Seoul, which shows that PM2.5 plays an important role in improving visibility. However, while the PM2.5 concentrations decreased by −5.1% yr−1 from 2012–2018, visibility only increased by 2.1% yr−1.
To understand why the increase in visibility was less than the decrease in the PM2.5 concentrations, we investigated the trend of poor visibility, which is the lowest 20% value for hourly visibility. The trend of the poor visibility proportion in Seoul is −1.4%p yr−1, which has been steadily improving in recent years. In particular, the decrease in poor visibility during the morning rush hour (−1.8%p yr−1) was noticeable compared to that in the evening rush hour (−1.4%p yr−1). Next, we calculated the light-extinction coefficient for each PM2.5 component to determine the importance of each component that contributed to poor visibility. We found that NH4NO3 contributed the most to poor visibility and that its impact gradually increased (i.e., from 48% in 2012 to 59% in 2018).
In contrast, the contribution of (NH4)2SO4 decreased from 33% to 22% over the same period. In other words, the importance of NO3− has been increasing in Seoul recently, in terms of both the PM2.5 concentration and poor visibility; however, the influence of SO42− has been decreasing. This is evident in changes in the emissions of SOx, i.e., a precursor of SO42−. During the two periods (2012 and 2018), the SOx emissions in the Seoul Metropolitan area decreased by 39.8%, which is consistent with the decrease in the SO42− concentration during the same period. Moreover, the decrease in the SO42− concentration contributed significantly to the increase in the NO3− concentration and the available NH3 concentration, which is noteworthy in terms of the recent changes in air quality in Seoul.
A decrease in the SO42− concentration with a reduction of SOx emissions is favorable to the reduction of PM2.5 concentrations. However, in the Seoul Metropolitan area, which is in an NH3-rich condition, the available NH3 contributed significantly to the NH4NO3 concentration by increasing the NOx emission. In addition, NO3− can be generated as a double of SO42− reduction, and optically, NO3− is 47% more effective than SO42− in reducing visibility. Therefore, as the PM component recently shifted from SO42− to NO3−, the visibility improvement in Seoul appeared slower than the decrease in PM2.5 concentrations. Reducing both NOx and NH3 emissions is required to improve PM2.5 and poor visibility in Seoul. To achieve this, it is necessary to comprehensively understand the chemical reaction involved in each component and implement effective emission-reduction policies.
Visibility and PM2.5 concentrations have recently been improving in Seoul. Nevertheless, another cause of public concern regarding air quality may be increased public interest in air quality degradation. As the WHO designated PM as a class 1 carcinogen, public concerns regarding health have been increasing. Furthermore, PM forecasts can be easily accessed through mass media, such as television or the internet, which has increased public interest and concern regarding air quality.

Author Contributions

Conceptualization, J.I.J., J.S. and R.J.P.; methodology, J.I.J. and J.S.; software, J.I.J. and J.S.; validation, J.I.J. and J.S.; formal analysis, J.S.; investigation, J.S.; writing—original draft preparation, J.I.J.; writing—review and editing, J.I.J., J.S. and R.J.P.; visualization, J.I.J. and J.S.; All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant, funded by the Korea government (MSIT) (NRF-2018R1A5A1024958), and by the FRIEND (Fine Particle Research Initiative in East Asia Considering National Differences) Project through the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (2020M3G1A1114617).

Data Availability Statement

Not applicable.

Acknowledgments

We thank the anonymous reviewers for their constructive and meaningful comments.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Annual mean visibility (blue line) and surface concentrations of PM2.5 (black line) in Seoul, South Korea, during 2012–2018.
Figure 1. Annual mean visibility (blue line) and surface concentrations of PM2.5 (black line) in Seoul, South Korea, during 2012–2018.
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Figure 2. Annual mean concentrations of PM2.5, SO42−, NO3−, NH4+, OC, and EC concentrations in Seoul, South Korea, during 2012–2018.
Figure 2. Annual mean concentrations of PM2.5, SO42−, NO3−, NH4+, OC, and EC concentrations in Seoul, South Korea, during 2012–2018.
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Figure 3. Annual mean poor visibility proportion in Seoul, South Korea, during 2012–2018. Note that the trends of 2012–2018 (red dotted line) and 2013–2018 (blue dotted line) are separated. Note that %p stands for % points.
Figure 3. Annual mean poor visibility proportion in Seoul, South Korea, during 2012–2018. Note that the trends of 2012–2018 (red dotted line) and 2013–2018 (blue dotted line) are separated. Note that %p stands for % points.
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Figure 4. Annual mean poor visibility proportion during the morning (0700–0900 LST; blue solid line) and evening (1700–1900 LST; red solid line) rush hour periods in Seoul, South Korea, during 2012–2018. Note that the trends of the morning (blue dotted line) and evening (red dotted line) are separated and %p stands for % points.
Figure 4. Annual mean poor visibility proportion during the morning (0700–0900 LST; blue solid line) and evening (1700–1900 LST; red solid line) rush hour periods in Seoul, South Korea, during 2012–2018. Note that the trends of the morning (blue dotted line) and evening (red dotted line) are separated and %p stands for % points.
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Figure 5. Annual mean concentrations of PM2.5, SO42−, NO3−, NH4+, OC, and EC in Seoul, South Korea, during poor visibility from 2012 to 2018.
Figure 5. Annual mean concentrations of PM2.5, SO42−, NO3−, NH4+, OC, and EC in Seoul, South Korea, during poor visibility from 2012 to 2018.
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Figure 6. Annual mean light-extinction coefficient (M m−1) of (NH4)2SO4, NH4NO3, OC, and EC, CM, and FS in Seoul, South Korea, during poor visibility from 2012 to 2018. Note that CM = PM10 − PM2.5, and FS = 1.40[Ca] + 1.43[Fe].
Figure 6. Annual mean light-extinction coefficient (M m−1) of (NH4)2SO4, NH4NO3, OC, and EC, CM, and FS in Seoul, South Korea, during poor visibility from 2012 to 2018. Note that CM = PM10 − PM2.5, and FS = 1.40[Ca] + 1.43[Fe].
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Figure 7. Proportion of light-extinction coefficient budget for (NH4)2SO4, NH4NO3, OC, EC, PM10 − PM2.5 (CM), and 1.40[Ca] + 1.43[Fe] (FS) in Seoul, South Korea, during poor visibility in 2012 and 2018.
Figure 7. Proportion of light-extinction coefficient budget for (NH4)2SO4, NH4NO3, OC, EC, PM10 − PM2.5 (CM), and 1.40[Ca] + 1.43[Fe] (FS) in Seoul, South Korea, during poor visibility in 2012 and 2018.
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Table
Table 1. Aerosol, gas, weather, and emission data used in this study.
Table 1. Aerosol, gas, weather, and emission data used in this study.
SourcesVariablesPeriod
National Institute of Environmental ResearchPM2.5, PM10, SO42−, NO3−, NH4+, Ca2+, Fe2+, OC, EC, and NO22012–2018
Korea Meteorological
Administration		Visibility, precipitation, relative humidity, and yellow dust events2012–2018
National Air Emission Inventory and Research CenterSOx, NOx, and NH3 emissions2012–2018
Table
Table 2. Light-extinction coefficient equations of IMPROVE and NIER.
Table 2. Light-extinction coefficient equations of IMPROVE and NIER.
InstituteThe Composite Equation for the Light-Extinction Coefficient
IMPROVE_19943f(RH)([NHSO 1] + [NHNO 2]) + 4[OMC 3] + 10[EC] + 1[FS 4] + 0.6[CM 5] + babs,NO2 + bRay
IMPROVE_20072.2fS(RH)[Small Ammonium Sulfate] + 4.8fL(RH)[Large Ammonium Sulfate] + 2.4fS(RH)[Small Ammonium Nitrate] + 5.1fL(RH)[Large Ammonium Nitrate] + 2.8[Small Organic Mass] + 6.1[Large Organic Mass] + 10[Elemental Carbon] + 1[Fine Soil] + 1.7fSS(RH)[Sea Salt] + 0.6[Coarse Mass] + Rayleigh Scattering (Site Specific) + 0.33[NO2 (ppb)]
NIER0.91(3[NHSO](1-RH/100)−0.7) + 1.34(3[NHNO](1 − RH/100)−0.7) + 1.06(4[OMC](1 − RH/100)−0.4) + 0.98(10[EC]) + 2[FS] + 0.6[CM] + 153.53
1 NHSO = 1.375[SO42−], 2 NHNO = 1.29[NO3−], 3 OMC = 1.41[OC], 4 FS = 1.40[Ca] + 1.43[Fe], and 5 CM = [PM10] − [PM2.5].
Table
Table 3. Annual trends of concentrations of PM2.5, SO42−, NO3−, NH4+, OC, and EC concentrations in Seoul, South Korea, during 2012–2018, and correlation coefficients (R) between each variable and visibility trends.
Table 3. Annual trends of concentrations of PM2.5, SO42−, NO3−, NH4+, OC, and EC concentrations in Seoul, South Korea, during 2012–2018, and correlation coefficients (R) between each variable and visibility trends.
VariablesPM2.5SO42−NO3−NH4+OCEC
Trend (µg yr−1) 1−2.3 ± 5.2 ** 2−1.0 ± 1.2 ***−0.3 ± 2.8−0.3 ± 0.8 *0.0 ± 0.4−0.1 ± 0.1 ***
R with PM2.5−0.830.730.960.600.92
R with visibility−0.96−0.76−0.72−0.92−0.76−0.82
1 Include the standard deviation along with the trend. 2 p-value: *** p < 0.05, ** p < 0.10, * p < 0.20.
Table
Table 4. Annual trends of concentrations of PM2.5, SO42−, NO3−, NH4+, OC, and EC in Seoul, South Korea, from 2012 to 2018 during poor visibility conditions, and correlation coefficients (R) between each variable and visibility trends.
Table 4. Annual trends of concentrations of PM2.5, SO42−, NO3−, NH4+, OC, and EC in Seoul, South Korea, from 2012 to 2018 during poor visibility conditions, and correlation coefficients (R) between each variable and visibility trends.
VariablesPM2.5SO42−NO3−NH4+OCEC
Trend (µg yr−1) 1−2.6 ± 4.3 *** 2−1.6 ± 1.6 ***0.2 ± 3.4−0.3 ± 0.8 *0.0 ± 0.7−0.2 ± 0.2 ***
R with PM2.5−0.950.120.830.230.82
R with visibility−0.55−0.38−0.60−0.77−0.030.01
1 Include the standard deviation along with the trend. 2 p-value: *** p < 0.05, * p < 0.20.
Table
Table 5. Changes (%) in SO42−, NO3−, and NH4+ concentrations and their precursor emissions in 2012 and 2018.
Table 5. Changes (%) in SO42−, NO3−, and NH4+ concentrations and their precursor emissions in 2012 and 2018.
EmissionSOxNOxNH3
−39.817.63.4
ConcentrationSO42−NO3−NH4+
−36.727.0−1.7
Table
Table 6. Changes in (NH4)2SO4 and NH4NO3 concentrations (µg m−3) and light-extinction coefficients (Mm−1) in 2012 and 2018.
Table 6. Changes in (NH4)2SO4 and NH4NO3 concentrations (µg m−3) and light-extinction coefficients (Mm−1) in 2012 and 2018.
(NH4)2SO4NH4NO3
Concentration−8.76.2
Light-extinction coefficient−5091
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Jeong, J.I.; Seo, J.; Park, R.J. Compromised Improvement of Poor Visibility Due to PM Chemical Composition Changes in South Korea. Remote Sens. 2022, 14, 5310. https://doi.org/10.3390/rs14215310

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Jeong JI, Seo J, Park RJ. Compromised Improvement of Poor Visibility Due to PM Chemical Composition Changes in South Korea. Remote Sensing. 2022; 14(21):5310. https://doi.org/10.3390/rs14215310

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Jeong, Jaein I., Jisu Seo, and Rokjin J. Park. 2022. "Compromised Improvement of Poor Visibility Due to PM Chemical Composition Changes in South Korea" Remote Sensing 14, no. 21: 5310. https://doi.org/10.3390/rs14215310

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Jeong, J. I., Seo, J., & Park, R. J. (2022). Compromised Improvement of Poor Visibility Due to PM Chemical Composition Changes in South Korea. Remote Sensing, 14(21), 5310. https://doi.org/10.3390/rs14215310

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