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http://dx.doi.org/10.5572/KOSAE.2018.34.1.101

Long-term Trend Analysis of Key Criteria Air Pollutants over Air Quality Control Regions in South Korea using Observation Data and Air Quality Simulation  

Ju, Hyeji (Department of Environmental and Safety Engineering, Ajou University)
Kim, Hyun Cheol (Air Resources Laboratory, National Oceanic and Atmospheric Administration)
Kim, Byeong-Uk (Georgia Environmental Protection Division)
Ghim, Young Sung (Department of Environmental Science, Hankuk University of Foreign Studies)
Shin, Hye Jung (Air Quality Research Division, National Institute of Environmental Research)
Kim, Soontae (Department of Environmental and Safety Engineering, Ajou University)
Publication Information
Journal of Korean Society for Atmospheric Environment / v.34, no.1, 2018 , pp. 101-119 More about this Journal
Abstract
In this study, we analyzed long-term measurements and air quality simulation results of four criteria air pollutants ($PM_{10}$, $O_3$, $NO_2$, and $SO_2$) for 10 years, from 2006 to 2015, with emphasis on trends of annual variabilities. With the observation data, we conducted spatial interpolation using the Kriging method to estimate spatial distribution of pollutant concentrations. We also performed air quality simulations using the CMAQ model to consider the nonlinearity of the secondary air pollutants such as $O_3$ and the influence of long-range transport. In addition, these simulations are used to deduce the effect of long-term meteorological variations on trends of air quality changes because we fixed the emissions inventory while changing meteorological inputs. The nation-wide inter-annual variability of modeled $PM_{10}$ concentrations was $-0.11{\mu}g/m^3/yr$, while that of observed concentrations was $-0.84{\mu}g/m^3/yr$. For the Seoul Metropolitan Area, the inter-annual variability of observed $PM_{10}$ concentrations was $-1.64{\mu}g/m^3/yr$ that is two times rapid improvement compared to other regions. On the other hand, the inter-annual variability of observed $O_3$ concentrations is 0.62 ppb/yr which is larger than the simulated result of 0.13 ppb/yr. Magnitudes of differences between the modeled and observed inter-annual variabilities indicated that decreasing trend of $PM_{10}$ and increasing trend of $O_3$ are more influenced by emissions and oxidation states than meteorological conditions. We also found similar patterns in $NO_2$. However, $NO_2$ trends showed greater regional and seasonal differences than other pollutants. The analytic approach used in this study can be applicable to estimate changes in factors determining air quality such as emissions, weather, and surrounding conditions over a long term. Then analysis results can be used as important data for air quality management planning and evaluation of the chronic impact of air quality.
Keywords
Criteria air pollutants; Long-term trend; Kriging; Modeling; Region;
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Times Cited By KSCI : 7  (Citation Analysis)
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