Evaluating the predictability of PM10grades in Seoul, Korea using a neural network model based on synoptic patterns.
As of November 2014, the Korean Ministry of Environment (KME) has been forecasting the concentration of particulate matter with diameters ≤ 10 μm (PM10) classified into four grades: low (PM10≤ 30 μg m-3), moderate (30 < PM10≤ 80 μg m-3), high (80 < PM10≤ 150 μg m-3), and very high (PM10> 150 μg m-3). The KME operational center generates PM10forecasts using statistical and chemistry-transport models, but the overall performance and the hit rate for the four PM10grades has not previously been evaluated. To provide a statistical reference for the current air quality forecasting system, we have developed a neural network model based on the synoptic patterns of several meteorological fields such as geopotential height, air temperature, relative humidity, and wind. Hindcast of the four PM10grades in Seoul, Korea was performed for the cold seasons (October-March) of 2001-2014 when the high and very high PM10grades are frequently observed. Because synoptic patterns of the meteorological fields are distinctive for each PM10grade, these fields were adopted and quantified as predictors in the form of cosine similarities to train the neural network model. Using these predictors in conjunction with the PM10concentration in Seoul from the day before prediction as an additional predictor, an overall hit rate of 69% was achieved; the hit rates for the low, moderate, high, and very high PM10grades were 33%, 83%, 45%, and 33%, respectively. Our findings also suggest that the synoptic patterns of meteorological variables are reliable predictors for the identification of the favorable conditions for each PM10grade, as well as for the transboundary transport of PM10from China. This evaluation of PM10predictability can be reliably used as a statistical reference and further, complement to the current air quality forecasting system.
School of Earth and Environmental Sciences, Seoul National University, Seoul, Republic of Korea.
Sun-Kyong
SK
Hur
School of Earth and Environmental Sciences, Seoul National University, Seoul, Republic of Korea. Electronic address: buso2000@cpl.snu.ac.kr.
Hye-Ryun
HR
Oh
School of Earth and Environmental Sciences, Seoul National University, Seoul, Republic of Korea.
Chang-Hoi
CH
Ho
Dept. of Atmospheric and Oceanic Sciences, University of California, Los Angeles, USA.
Jinwon
J
Kim
National Institute of Environmental Research, Incheon, Republic of Korea.
Chang-Keun
CK
Song
National Institute of Environmental Research, Incheon, Republic of Korea.
Lim-Seok
LS
Chang
National Institute of Environmental Research, Incheon, Republic of Korea.
Jae-Bum
JB
Lee
D000393:Air Pollutants / Q000032:analysis*; D002681:China; D004784:Environmental Monitoring / Q000379:methods* / Q000706:statistics & numerical data; D005544:Forecasting / Q000379:methods; D055904:Meteorology; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010316:Particle Size; D052638:Particulate Matter / Q000032:analysis*; D056910:Republic of Korea; D012621:Seasons; D066106:Seoul; D013696:Temperature; D014919:Wind
D000393:Air Pollutants; D052638:Particulate Matter
Neural network model; PM(10) grade; Seoul; Synoptic pattern