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Article

Changes in the Relationship between Particulate Matter and Surface Temperature in Seoul from 2002–2017

by
Minjoong J. Kim
Department of Environmental Engineering and Energy, Myongji University, 17058 Yongin, Gyunggi, Korea
Atmosphere 2019, 10(5), 238; https://doi.org/10.3390/atmos10050238
Submission received: 19 March 2019 / Revised: 9 April 2019 / Accepted: 26 April 2019 / Published: 1 May 2019
(This article belongs to the Special Issue Long Term Trends of Air Pollutants)
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Abstract

:
This study focuses on the changes over time in the relationship between surface temperature and particulate matter (PM) concentration over Seoul using long-term observational data. Correlation coefficients between the daily mean PM10 concentration and surface temperature were calculated to investigate the relationship between the two. The PM10 and temperature displayed a strong positive correlation, suggesting the increase in PM was driven by large-scale synoptic patterns accompanying such high temperatures. It was found that the correlation coefficient in 2002–2009 was significantly higher than that of 2010–2017, indicating that the relationship between PM10 concentration and temperature has weakened over time in recent decades. Correlation coefficients between daily averaged temperature and the PM10 of each year were calculated to account for the decreased correlation in the most recent decade. We found that the correlation coefficients between surface temperature and PM of each year exhibited a clear negative correlation with the longitudinal position of the Siberian High, suggesting that the position of the Siberian High might affect the strength of the relationship between PM concentration and temperature over Seoul. We also found that the eastward shift of the Siberian High reduces the standard deviation of pressure over Seoul, indicating reduction of synoptic perturbation. These results imply that the eastward shift of the Siberian High in recent decades might weaken the relationship between the PM and surface temperature over Seoul. This study suggests that the relationship between PM and meteorological variables is changing over time through changes in large climate variability.

1. Introduction

Poor air quality as a result of particulate matter (PM) in Seoul, the largest city in South Korea, is a serious issue. This is exemplified by frequent haze events, which are particularly common in winter [1,2,3]. Although emissions over Seoul have decreased over the past few decades [4,5], haze events have still occurred in recent years [3,6,7]. Fully understanding these haze events is difficult, since PM is affected not only by emissions but also by meteorological and chemical environments [8,9,10]. Although previous studies have suggested that emission changes are the primary factor in determining the PM level over East Asia and Seoul [1,11,12], PM concentration is also significantly affected by meteorological conditions, since changes in emissions tend to occur slowly [13]. Indeed, a number of recent studies have reported that synoptic weather conditions affect the frequency and longevity of pollution episodes over East Asia [14,15,16].
More specifically, Kim et al. reported that the interannual variability of surface PM, with a diameter of ≤10 µm (PM10), in South Korea was closely linked with the interannual variations of wind speed [1]. Their study also suggested that reduced regional ventilation was likely associated with more stagnant conditions in the past few years, which have caused severe pollutant episodes in South Korea. In addition, Lee et al. showed that higher air temperatures and weak lower/upper-level winds led to high PM episodes [15]. Previous studies have also reported that the strength of large climate variability, such as the East Asia monsoon, El Niño-Southern oscillation, and Arctic oscillation, has influenced PM variability over East Asia [17,18,19]. Furthermore, numerous studies have suggested that the impact of meteorological conditions on PM variability is not negligible [11,20,21], and so the mechanisms and contribution of the meteorological effect on PM should be elucidated.
As previous studies have employed long-term datasets to understand the relationship between PM variability and meteorological conditions [11,20,22], they have tended to focus on the averaged strength and contribution of meteorological conditions to variations in PM over long periods. However, as climate change shifts the synoptic system slowly over time [23], the mechanism involved in PM variations resulting from meteorological conditions may also change in line with climate change, as may the strength and contribution of meteorological conditions to such changes. Thus, evidence of a relationship change between PM variations and meteorological variables over time is reported here by using PM and surface temperature data over Seoul. Factors that potentially affect the relationship between PM and temperature are also explored by using long-term observational data and a re-analysis dataset.

2. Domain and Data

A dataset of PM10 concentrations and surface temperatures in Seoul was used to investigate the relationship between PM and meteorological variables. PM10 data were obtained from 25 air quality monitoring stations in Seoul (blue dots in Figure 1) over 16 years (December 2001 to February 2017) [24]. Measurement of PM10 used the SPM-613D beta gauge method, which utilized the fact that a portion of beta rays were absorbed and dissipated by a substance when the rays that irradiated PM collected on filter tape. PM data were reviewed comprehensively through comparison with previously reported observational data [25,26,27]. Previous studies concluded that this method was advantageous for high temporal resolutions, but it had an uncertainty of ≤10% because of the presence of moisture-containing particles.
Surface temperature, sea level pressure, surface wind, and relative humidity data were obtained from the Seoul station of the Korea Meteorological Administration [28] for the same period (red dot in Figure 1). We also employed a re-analysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-analysis Interim (ERA-Interim) to investigate synoptic patterns relating to the changing relationship between PM and temperature [29]. We focused on the boreal winter season (December, January, and February) during which haze events frequently occur, owing to large emissions from heating activities in Korea and China [3,6,7].
The Siberian High has the most significant influence on winter climate over the Eurasian continent, including East Asia [30,31], as the position and strength of the Siberian High controls the synoptic systems over this area. Figure 2 displays the multi-year (2002–2017) averaged sea level pressure (shading) and wind at 850 hPa (vector) data of the ERA-Interim re-analysis dataset in winter. The Siberian High results in a strong northwesterly wind over western China and Korea as a result of a strong pressure gradient between the Siberian High and the Aleutian Low in winter. Thus, the intensity and the longitudinal center of the Siberian High of each year were calculated to investigate the status of the Siberian High. More specifically, the strength of the Siberian High was defined as the mean sea level pressure over Mongolia and southern Siberia between 80–120 °E and 40–65 °N (black box in Figure 2) [32]. Previous studies have shown that this definition successfully represented the strength of the Siberian High using not only climate studies but also air quality studies [33,34].
The longitudinal center of the Siberian High was also calculated as the weighted mean of the longitudes of all grids within the 1026 hPa isobar over the Siberian region of 70–130 °E and 35–65 °N (red box in Figure 2), as presented in Equation (1).
SHCI = ∑ ( P i × L i ) ∑ P i
where Li is the longitude of the grid point i within the 1026 hPa isobar and the definition domain, and Pi is the sea level pressure at i. This method was measured in degrees of longitude and was similar to the longitude index of the Siberian High defined by Hou et al. [35] and the Siberian High position index proposed by Jia et al. [36]. However, the current definition was slightly different, as previous studies defined the indices as the weighted mean longitude within the 1023 hPa isobar under a much wider area. In contrast, to prevent the current index being affected by high-pressure areas in the polar or other Asian regions, the spatial domain in which the 1026 hPa isobar was considered for the longitude center of Siberian High calculation was limited. The intensity and the longitude center of the Siberian High were calculated using the ERA-Interim dataset. The spatial domains of our calculation (black and red boxes in Figure 2, respectively) included the center of the Siberian High.

3. Results and Discussion

Figure 3a shows the time series of the seasonal averaged PM10 concentrations over Seoul in the winters of 2002–2017, where an average PM10 concentration of 63.6 μg m−3 was obtained. However, the PM10 concentration decreased continuously (i.e., −2.8 μg m−3 year−1) as a result of a decrease in emissions in Korea between 2001 and 2017 [11]. Although the PM10 concentration showed a decreasing trend, it showed clear year-to-year variations. This might represent the effect of meteorological conditions on PM10 concentration, considering emission changes were relatively small compared to interannual variability.
Correlation coefficients between the daily mean PM10 concentrations and the meteorological variables (i.e., surface temperature, surface pressure, surface wind speed, and surface relative humidity) for the analysis period were calculated, using surface observation datasets over Seoul, to investigate the relationship between the meteorological conditions and the PM10 concentration (Table 1). Correlation between the PM10 value and temperature was found to be the highest (0.35) at a 99% confidence level, and this was supported by a previous study [37]. The relative humidity, surface pressure, and wind speed also showed clear correlations with the PM10 concentration (0.17, 0.25, and −0.13, respectively); however, the absolute magnitudes of the correlation coefficients were significantly lower than that of temperature. Considering that high temperatures can promote the evaporation of nitrate aerosols, which are a major pollutant in winter, the high positive correlation between temperature and the PM10 value suggests that the increase in PM is driven by large-scale synoptic patterns accompanying such high temperatures. It should be noted that sulfate concentrations were found to exhibit a relatively low contribution to the total PM10 in winter over East Asia [12]. Previous studies have reported that high surface temperatures occurred over Seoul along with high-pressure conditions over Seoul and Eastern China during high-PM10 episodes [17,37]. Therefore, surface temperature over Seoul might be a simple indicator for synoptic patterns relating to PM variations. Figure 3b shows the seasonally averaged temperatures over Seoul in winter. As the surface temperature over Seoul has decreased slightly over the past couple of decades because of the cooling of Eurasia [38], this decrease was only on a scale of −0.11 °C year−1, which was a relatively small change compared to the year-to-year variation in temperature.
Figure 4 shows a scatterplot of the daily averaged PM10 concentration against the daily averaged surface temperature of Seoul over the winter months between 2002 and 2017. The regression slope obtained over the whole period was 2.5 μg m−3 °C−1, indicating that an increase in temperature enhanced the PM10 concentration over Seoul. The red and blue diamonds indicated the data corresponding to the first and last eight years, respectively. The regression slope of the first eight years was two times higher than that of the last eight years (i.e., 3.1 and 1.5 μg m−3 °C−1, respectively), indicating that the sensitivity of PM10 to changes in temperature decreased in the last decade. Because this study only used observational data, the difference between the regression slopes in the former and latter periods included the effect from the large reduction in pollution above Seoul during the past few decades; therefore, it was decided to focus on changes in the correlation coefficients. More specifically, the correlation coefficient of the earlier period was significantly higher than that of the later period (i.e., 0.44 and 0.24, respectively), which implied that recent climate change had weakened the relationship between PM10 and surface temperature over Seoul. These results also suggested that contribution of weather systems to PM10 may have decreased in recent decades.
The correlation coefficient between daily averaged temperature and PM10 of each year (hereafter CTP) was then calculated (Figure 5) to determine the cause of this decreased correlation. More specifically, CTP ranges of 0.13–0.64 were obtained for the majority of years, satisfying significance at a 95% confidence level (red dashed line in Figure 5). However, despite the considerable variation from year to year, the correlation coefficients clearly decreased over the 16 years during which the data were recorded. More specifically, the averaged CTP of the three most recent years (2015–2017) was only 0.22, which was not significant at a 95% confidence level, whereas the CTP between 2004 and 2006 was 0.53, and this value showed significance at a 99% confidence level.
To understand the mechanism responsible for such a decrease in correlation, the regressed sea level pressure (Figure 6a) and wind field at 850 hPa (Figure 6b) were calculated against the CTP over East Asia using the ERA-Interim re-analysis dataset for 2002–2017. It was found that in the years of a high CTP, the sea level pressure decreased over Inner Mongolia and increased over western Mongolia and western China, thereby enhancing the pressure gradient over Mongolia. Regressed wind data showed that strong pressure gradients over Mongolia enhanced the northwestern wind over central and eastern China. These regressed patterns might imply that frequent ventilation over eastern China resulted in a high correlation between PM10 concentration and surface temperature over Seoul as a result of changes in the pressure system in the Siberian region. Regressed sea level pressure and wind patterns also suggested that there might be a relationship between changes in the Siberian High and the CTP.
Previous studies have suggested that the state of the Siberian High significantly affected not only the climate but also the PM10 levels over Korea and nearby regions, thereby altering wind speed and relative humidity over eastern China [39,40]. In addition, Jeong et al. reported that development of the Siberian High resulted in a strong east–west gradient over the Eurasian continent in winter [17], which generated northerly winds that enhanced the transport of aerosols over northeastern China and Korea. Development of the Siberian High was accompanied by high temperatures over Korea owing to the winter monsoon system, and Jia et al. suggested that the PM concentration was affected by not only the strength of the Siberian High but also its location over East Asia [36].
The intensity and longitudinal center of the Siberian High were compared with the CTP to examine the potential effect of the Siberian High on the decreasing trend of correlation. As shown in Figure 7a, no relationship was observed between the intensity of the Siberian High and the CTP, indicating that the strength of the Siberian High did not alter the correlation between temperature and PM10. To examine the effect of zonal shift of the Siberian High on the correlation, we also investigated the relationship between the longitudinal center of the Siberian High and the CTP (Figure 7b). Strikingly, a negative correlation of −0.55 was obtained, which was significant at a 95% confidence level. Furthermore, a trend of 0.21 °E year−1 was obtained for the longitudinal center of the Siberian High between 2002 and 2017, indicating a clear eastward shift over the analysis period. This shift might suggest that the decreasing trend of correlation between temperature and PM10 concentration over Seoul was related to the eastward shift of the Siberian High in recent decades.
To explore how the eastward shift of the Siberian High might influence the relationship between PM10 and surface temperature, the regressed sea level pressure and wind against the longitudinal center of the Siberian High were calculated using the ERA-Interim dataset for 1979–2017 (Figure 8). It was found that the eastward shift of the Siberian High induced a sea level pressure increase over Inner Mongolia and a decrease over western Mongolia. Regressed wind data showed that these pressure patterns reduced the northwestern wind over central and eastern China. These regressed pressures and winds showed an opposite pattern from that shown in Figure 6, indicating that the eastward shift of the Siberian High drove stagnant atmospheric conditions over Seoul. Previous studies have also reported that an eastward extension of the Siberian High reduced the wind speed over Eastern China and the Yellow Sea, causing stagnant atmospheric conditions over Seoul and increasing the PM concentration [36].
Stagnant conditions from the eastward shift of the Siberian High might reduce variability of synoptic patterns, giving a lower correlation between PM and surface temperature. To investigate this, the longitudinal center of the Siberian High was compared to the standard deviation of the surface pressure and temperature over Seoul for 1979–2017 (Figure 9). The correlations between the longitudinal center of the Siberian High and standard deviation (−0.41 and −0.22, which were significant at the 99% and 95% confidence levels, respectively) displayed a negative relationship. The clear negative correlation between the longitudinal center of the Siberian High and the standard deviation of the surface pressure suggested that the eastward shift of the Siberian High drove not only stagnant conditions over Korea but also a reduction of the frequency of synoptic perturbations over Korea. These results consistently suggested that the shift in the position of the Siberian High might have changed the relationship between PM10 and temperature over Seoul in recent decades.

4. Conclusion

The changing relationship between surface temperature and PM concentration over Seoul was investigated using long-term observation (2002–2017) and re-analysis datasets. PM10 concentration was found to have continuously decreased between 2002 and 2017 as a result of overall reductions in emissions in Korea during this period. The correlation coefficients between daily mean PM10 concentration and surface temperature were calculated to examine how the relationship between surface temperature and PM concentration changed over time. A strong positive correlation was obtained between PM concentration and temperature, with the correlation coefficient over the period 2002–2009 being significantly higher than that over the period 2010–2017. This change suggests that recent climate change has weakened the relationship between the PM10 value and the meteorological conditions over Seoul. The CTP was then calculated to determine the cause of this reduced correlation. Interestingly, despite the large variation from year to year, the CTP clearly decreased over the study period. In addition, sea level pressure and wind fields at 850 hPa were regressed against the CTP over East Asia to understand the mechanism responsible for this reduced correlation. It was found that in the years of high CTP values, strong pressure gradients over Mongolia enhanced the northwestern wind over central and eastern China. These results, therefore, suggest that there might be a relationship between CTP and changes in the Siberian High. CTP was then compared with the longitudinal center of the Siberian High to examine the effect of the shift of the Siberian High on the relationship between temperature and PM10. A correlation of −0.55 was obtained, showing a clear negative relationship. This negative correlation suggests that the weakened relationship between temperature and PM10 in recent decades might relate to the eastward shift of the Siberian High. To explore how the eastward shift of the Siberian High might influence the relationship between PM10 and surface temperature, the regressed sea level pressure and wind against the longitudinal center of the Siberian High were calculated for 1979–2017. These regressed pressure and wind data showed a clear opposite pattern to the regressed patterns against CTP, indicating that the eastward shift of the Siberian High drove stagnant atmospheric conditions over Seoul. Then, the longitudinal center of the Siberian High was compared with the standard deviation of the surface pressure. Correlation between the longitudinal center of the Siberian High and standard deviation was clearly negative, suggesting that the eastward shift of the Siberian High drove a reduction of the frequency of synoptic perturbations over Korea. These results showed that the shift of the Siberian High might have changed the relationship between PM10 and temperature over Seoul in recent decades. This study suggests that the relationship between PM and meteorological variables varies over time via changes in large climate variability, which might be important for investigating the long-term effects of meteorological variables on PM.
This study, based on observational data, focused on the linear relationship between PM and temperature. However, many factors affect PM10 pollution, including nonlinear factors. Modeling studies should be conducted to elucidate the relationship change between PM and meteorological variables.

Funding

This work was funded by the 2017 Research Fund of Myongji University.

Acknowledgments

This work was supported by the 2017 Research Fund of Myongji University.

Conflicts of Interest

Declare conflicts of interest or state “The authors declare no conflict of interest.”

References

  1. Kim, H.C.; Kim, S.; Kim, B.-U.; Jin, C.-S.; Hong, S.; Park, R.; Son, S.-W.; Bae, C.; Bae, M.; Song, C.-K. Recent increase of surface particulate matter concentrations in the Seoul Metropolitan Area, Korea. Sci. Rep. 2017, 7, 4710. [Google Scholar] [CrossRef] [Green Version]
  2. Chun, Y.; Lim, J.-Y. The recent characteristics of Asian dust and haze events in Seoul, Korea. Meteorol. Atmos. Phys. 2004, 87, 143–152. [Google Scholar]
  3. Kim, Y.P.; Lee, G. Trend of air quality in Seoul: Policy and science. Aerosol Air Q. Res. 2018, 18, 2141–2156. [Google Scholar] [CrossRef]
  4. Ohara, T.; Akimoto, H.; Kurokawa, J.-I.; Horii, N.; Yamaji, K.; Yan, X.; Hayasaka, T. An Asian emission inventory of anthropogenic emission sources for the period 1980–2020. Atmos. Chem. Phys. 2007, 7, 4419–4444. [Google Scholar] [CrossRef] [Green Version]
  5. Lee, D.-G.; Lee, Y.-M.; Jang, K.-W.; Yoo, C.; Kang, K.-H.; Lee, J.-H.; Jung, S.-W.; Park, J.-M.; Lee, S.-B.; Han, J.-S. Korean national emissions inventory system and 2007 air pollutant emissions. Asian J. Atmos. Environ. 2011, 5, 278–291. [Google Scholar] [CrossRef]
  6. Ryou, H.G.; Heo, J.; Kim, S.-Y. Source apportionment of PM10 and PM2.5 air pollution, and possible impacts of study characteristics in South Korea. Environ. Pollut. 2018, 240, 963–972. [Google Scholar] [CrossRef]
  7. Han, S.B.; Song, S.K.; Choi, Y.N. Meteorological and air quality analyses of a long-lasting haze episode observed in South Korea during winter season. In Proceedings of the AGU Fall Meeting Abstracts, Washington, DC, USA, 10–14 December 2018. [Google Scholar]
  8. Huang, R.-J.; Zhang, Y.; Bozzetti, C.; Ho, K.-F.; Cao, J.-J.; Han, Y.; Daellenbach, K.R.; Slowik, J.G.; Platt, S.M.; Canonaco, F. High secondary aerosol contribution to particulate pollution during haze events in China. Nature 2014, 514, 218. [Google Scholar] [CrossRef] [PubMed]
  9. Zhang, X.; Sun, J.; Wang, Y.; Li, W.; Zhang, Q.; Wang, W.; Quan, J.; Cao, G.; Wang, J.; Yang, Y. Factors contributing to haze and fog in China. Chin. Sci. Bull. 2013, 58, 1178–1187. [Google Scholar] [CrossRef]
  10. Liu, T.; Gong, S.; He, J.; Yu, M.; Wang, Q.; Li, H.; Liu, W.; Zhang, J.; Li, L.; Wang, X. Attributions of meteorological and emission factors to the 2015 winter severe haze pollution episodes in China’s Jing-Jin-Ji area. Atmos. Chem. Phys. 2017, 17, 2971–2980. [Google Scholar] [CrossRef]
  11. Kim, K.-H.; Shon, Z.-H. Long-term changes in PM10 levels in urban air in relation with air quality control efforts. Atmos. Environ. 2011, 45, 3309–3317. [Google Scholar] [CrossRef]
  12. Han, S.H.; Kim, Y.P. Long-term Trends of the Concentrations of Mass and Chemical Composition in PM 2.5 over Seoul. J. Korean Soc. Atmos. Environ. 2015, 31, 143–156. [Google Scholar] [CrossRef]
  13. Yang, Y.; Liao, H.; Lou, S. Increase in winter haze over eastern China in recent decades: Roles of variations in meteorological parameters and anthropogenic emissions. J. Geophys. Res. Atmos. 2016, 121, 13–50. [Google Scholar] [CrossRef]
  14. Lee, J.; Kim, K.-Y. Analysis of source regions and meteorological factors for the variability of spring PM10 concentrations in Seoul, Korea. Atmos. Environ. 2018, 175, 199–209. [Google Scholar] [CrossRef]
  15. Lee, S.; Ho, C.-H.; Choi, Y.-S. High-PM10 concentration episodes in Seoul, Korea: Background sources and related meteorological conditions. Atmos. Environ. 2011, 45, 7240–7247. [Google Scholar] [CrossRef]
  16. Zhang, H.; Wang, Y.; Hu, J.; Ying, Q.; Hu, X.-M. Relationships between meteorological parameters and criteria air pollutants in three megacities in China. Environ. Res. 2015, 140, 242–254. [Google Scholar] [CrossRef]
  17. Jeong, J.I.; Park, R.J. Winter monsoon variability and its impact on aerosol concentrations in East Asia. Environ. Pollut. 2017, 221, 285–292. [Google Scholar] [CrossRef] [PubMed]
  18. Jeong, J.I.; Park, R.J.; Yeh, S.-W. Dissimilar effects of two El Niño types on PM2.5 concentrations in East Asia. Environ. Pollut. 2018, 242, 1395–1403. [Google Scholar] [CrossRef]
  19. Yi, K.; Liu, J.; Wang, X.; Ma, J.; Hu, J.; Wan, Y.; Xu, J.; Yang, H.; Liu, H.; Xiang, S. A combined Arctic-tropical climate pattern controlling the inter-annual climate variability of wintertime PM2.5 over the North China Plain. Environ. Pollut. 2019, 245, 607–615. [Google Scholar] [CrossRef]
  20. Jeong, J.I.; Park, R.J. Effects of the meteorological variability on regional air quality in East Asia. Atmos. Environ. 2013, 69, 46–55. [Google Scholar] [CrossRef]
  21. Kim, H.C.; Kim, E.; Bae, C.; Cho, J.H.; Kim, B.-U.; Kim, S. Regional contributions to particulate matter concentration in the Seoul metropolitan area, South Korea: Seasonal variation and sensitivity to meteorology and emissions inventory. Atmos. Chem. Phys. 2017, 17, 10315–10332. [Google Scholar] [CrossRef]
  22. Ahmed, E.; Kim, K.-H.; Shon, Z.-H.; Song, S.-K. Long-term trend of airborne particulate matter in Seoul, Korea from 2004 to 2013. Atmos. Environ. 2015, 101, 125–133. [Google Scholar] [CrossRef]
  23. Stocker, T.F.; Qin, D.; Plattner, G.K.; Tignor, M.; Allen, S.K.; Boschung, J.; Nauels, A.; Xia, Y.; Bex, V.; Midgley, P.M. (Eds.) Climate Change 2013: The Physical Science Basis; Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2013. [Google Scholar]
  24. Air Korea. Available online: https://www.airkorea.or.kr/eng (accessed on 30 March 2019).
  25. Jung, C.-H.; Cho, Y.-S.; Hwang, S.M.; Jung, Y.G.; Ryu, J.C.; Shin, D.S. Analysis of measurement error for PM-10 mass concentration by inter-comparison study. J. Korean Soc. Atmos. Environ. 2007, 23, 689–698. [Google Scholar] [CrossRef]
  26. Jung, C.-H.; Park, J.-H.; Hwang, S.M. Analysis of Measurement Error for PM-2.5 Mass Concentration by Inter-Comparison Study. J. Environ. Impact Assess. 2010, 19, 431–441. [Google Scholar]
  27. Chang, C.T.; Tsai, C.J. A model for the relative humidity effect on the readings of the PM10 beta-gauge monitor. J. Aerosol Sci. 2003, 34, 1685–1697. [Google Scholar] [CrossRef]
  28. Korea Meteorological Administration (KMA). Available online: http://web.kma.go.kr/eng/ (accessed on 30 March 2019).
  29. Dee, D.P.; Uppala, S.M.; Simmons, A.J.; Berrisford, P.; Poli, P.; Kobayashi, S.; Andrae, U.; Balmaseda, M.A.; Balsamo, G.; Bauer, D.P. The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Q. J. Royal Meteorol. Soc. 2011, 137, 553–597. [Google Scholar] [CrossRef]
  30. Gong, D.-Y.; Ho, C.-H. The Siberian High and climate change over middle to high latitude Asia. Theor. Appl. Climatol. 2002, 72, 1–9. [Google Scholar] [CrossRef]
  31. Cohen, J.; Saito, K.; Entekhabi, D. The role of the Siberian high in Northern Hemisphere climate variability. Geophys. Res. Lett. 2001, 28, 299–302. [Google Scholar] [CrossRef]
  32. Panagiotopoulos, F.; Shahgedanova, M.; Hannachi, A.; Stephenson, D.B. Observed trends and teleconnections of the Siberian high: A recently declining center of action. J. Clim. 2005, 18, 1411–1422. [Google Scholar] [CrossRef]
  33. He, J.; Gong, S.; Liu, H.; An, X.; Yu, Y.; Zhao, S.; Wu, L.; Song, C.; Zhou, C.; Wang, J. Influences of meteorological conditions on interannual variations of particulate matter pollution during winter in the Beijing–Tianjin–Hebei area. J. Meteorol. Res. 2017, 31, 1062–1069. [Google Scholar] [CrossRef]
  34. Zhao, S.; Feng, T.; Tie, X.; Dai, W.; Zhou, J.; Long, X.; Li, G.; Cao, J. Short-term weather patterns modulate air quality in Eastern China during 2015–2016 winter. J. Geophys. Res. Atmos. 2018, 124, 986–1002. [Google Scholar] [CrossRef]
  35. Hou, Y.H.; Yang, X.Q.; Li, G.; Wang, Q. Four indexes and their change rates Siberian High. J. Nanjing Inst. Meteorol. 2008, 31, 326–330. [Google Scholar]
  36. Jia, B.; Wang, Y.; Yao, Y.; Xie, Y. A new indicator on the impact of large-scale circulation on wintertime particulate matter pollution over China. Atmos. Chem. Phys. 2015, 15, 11919–11929. [Google Scholar] [CrossRef] [Green Version]
  37. Lee, M. An analysis on the concentration characteristics of PM2.5 in Seoul, Korea from 2005 to 2012. Asia-Pac. J. Atmos. Sci. 2014, 50, 585–594. [Google Scholar] [CrossRef]
  38. Li, C.; Stevens, B.; Marotzke, J. Eurasian winter cooling in the warming hiatus of 1998–2012. Geophys. Res. Lett. 2015, 42, 8131–8139. [Google Scholar] [CrossRef]
  39. Niu, F.; Li, Z.; Li, C.; Lee, K.-H.; Wang, M. Increase of wintertime fog in China: Potential impacts of weakening of the Eastern Asian monsoon circulation and increasing aerosol loading. J. Geophys. Res. Atmo. 2010, 115, D00K20. [Google Scholar] [CrossRef]
  40. Li, Q.; Zhang, R.; Wang, Y. Interannual variation of the wintertime fog–haze days across central and eastern China and its relation with East Asian winter monsoon. Int. J. Climatol. 2016, 36, 346–354. [Google Scholar] [CrossRef]
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Figure 1. Monitoring sites of PM10 (blue dots) and temperature (red dot) in Seoul, Korea (map obtained from Google Earth).
Figure 1. Monitoring sites of PM10 (blue dots) and temperature (red dot) in Seoul, Korea (map obtained from Google Earth).
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Figure 2. Multi-year (2002–2017) averaged sea level pressure (shaded) and 850 hPa wind fields (vectors) over Siberia and East Asia in winter. The black and red rectangles indicate the domains of the intensity and longitudinal center of Siberian High calculations, respectively. Units are hPa (sea level pressure) and m s−1 (wind).
Figure 2. Multi-year (2002–2017) averaged sea level pressure (shaded) and 850 hPa wind fields (vectors) over Siberia and East Asia in winter. The black and red rectangles indicate the domains of the intensity and longitudinal center of Siberian High calculations, respectively. Units are hPa (sea level pressure) and m s−1 (wind).
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Figure 3. Seasonal averaged time series of (a) the surface PM10 (left panel) and (b) the surface temperature (right panel) over Seoul in winter. Units are μg m−3 and °C, respectively.
Figure 3. Seasonal averaged time series of (a) the surface PM10 (left panel) and (b) the surface temperature (right panel) over Seoul in winter. Units are μg m−3 and °C, respectively.
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Figure 4. Scatterplot of the daily averaged surface PM10 versus the daily averaged surface temperature over Seoul. The red and blue diamonds indicate the data for 2002–2009 and 2010–2017, respectively. The red and blue dashed lines show the regression between the PM10 and temperature in 2002–2009 and 2010–2017, respectively. Units of PM10 concentration and temperature are μg m−3 and °C, respectively.
Figure 4. Scatterplot of the daily averaged surface PM10 versus the daily averaged surface temperature over Seoul. The red and blue diamonds indicate the data for 2002–2009 and 2010–2017, respectively. The red and blue dashed lines show the regression between the PM10 and temperature in 2002–2009 and 2010–2017, respectively. Units of PM10 concentration and temperature are μg m−3 and °C, respectively.
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Figure 5. Time series of the correlation coefficient between daily averaged temperature and PM10 (CTP) during 2001–2017. The black dashed line indicates the regressed trend. The red dashed line is the 95% confidence level.
Figure 5. Time series of the correlation coefficient between daily averaged temperature and PM10 (CTP) during 2001–2017. The black dashed line indicates the regressed trend. The red dashed line is the 95% confidence level.
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Figure 6. Regressed (a) sea level pressure (left panel) and (b) wind (shading: wind speed; vector: wind; right panel) at 850 hPa against the CTP for 2002–2017. The shading and vector only are plotted at the 90% confidence level. Units are hPa and m s−1, respectively.
Figure 6. Regressed (a) sea level pressure (left panel) and (b) wind (shading: wind speed; vector: wind; right panel) at 850 hPa against the CTP for 2002–2017. The shading and vector only are plotted at the 90% confidence level. Units are hPa and m s−1, respectively.
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Figure 7. Time series of the (a) intensity (left panel) and (b) longitudinal center (right panel) of Siberian High against the CTP for 2002–2017. The red lines indicate the intensity and longitudinal center of the Siberian High, and the black line shows the CTP. The units of the intensity and longitudinal center of the Siberian High are hPa and °E, respectively.
Figure 7. Time series of the (a) intensity (left panel) and (b) longitudinal center (right panel) of Siberian High against the CTP for 2002–2017. The red lines indicate the intensity and longitudinal center of the Siberian High, and the black line shows the CTP. The units of the intensity and longitudinal center of the Siberian High are hPa and °E, respectively.
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Figure 8. Regressed (a) sea level pressure (left panel) and (b) wind (shading: wind speed; vector: wind; right panel) at 850 hPa against the longitudinal center of the Siberian High for 1979–2017. The shading and vector only are plotted at the 90% confidence level. Units are hPa °E−1 and m s−1 °E−1, respectively.
Figure 8. Regressed (a) sea level pressure (left panel) and (b) wind (shading: wind speed; vector: wind; right panel) at 850 hPa against the longitudinal center of the Siberian High for 1979–2017. The shading and vector only are plotted at the 90% confidence level. Units are hPa °E−1 and m s−1 °E−1, respectively.
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Figure 9. Time series of the standard deviation of daily averaged (a) surface pressure (left panel) and (b) temperature (right panel) against the longitudinal center of Siberian High for 1979–2017. The red lines indicate the standard deviation, and the black line shows the longitudinal center of the Siberian High. The units are hPa and °C, respectively.
Figure 9. Time series of the standard deviation of daily averaged (a) surface pressure (left panel) and (b) temperature (right panel) against the longitudinal center of Siberian High for 1979–2017. The red lines indicate the standard deviation, and the black line shows the longitudinal center of the Siberian High. The units are hPa and °C, respectively.
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Table
Table 1. Correlation coefficients between the daily mean PM10 concentration and the surface temperature, surface pressure, surface wind speed, and surface relative humidity for 2002–2017 in winter. ** denotes significance at the 99% confidence level.
Table 1. Correlation coefficients between the daily mean PM10 concentration and the surface temperature, surface pressure, surface wind speed, and surface relative humidity for 2002–2017 in winter. ** denotes significance at the 99% confidence level.
Meteorology Variables Surface TemperatureSurface Wind SpeedSurface PressureSurface Relative Humidity
PM100.35 **−0.13 **0.25 **0.17 **

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MDPI and ACS Style

Kim, M.J. Changes in the Relationship between Particulate Matter and Surface Temperature in Seoul from 2002–2017. Atmosphere 2019, 10, 238. https://doi.org/10.3390/atmos10050238

AMA Style

Kim MJ. Changes in the Relationship between Particulate Matter and Surface Temperature in Seoul from 2002–2017. Atmosphere. 2019; 10(5):238. https://doi.org/10.3390/atmos10050238

Chicago/Turabian Style

Kim, Minjoong J. 2019. "Changes in the Relationship between Particulate Matter and Surface Temperature in Seoul from 2002–2017" Atmosphere 10, no. 5: 238. https://doi.org/10.3390/atmos10050238

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