Evaluating the predictability of PM₁₀ grades 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 PM₁₀ forecasts using statistical and chemistry-transport models, but the overall performance and the hit rate for the four PM₁₀ grades 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 PM₁₀ grades in Seoul, Korea was performed for the cold seasons (October–March) of 2001–2014 when the high and very high PM₁₀ grades are frequently observed. Because synoptic patterns of the meteorological fields are distinctive for each PM₁₀ grade, 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 PM₁₀ concentration 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 PM₁₀ grades 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 PM₁₀ grade, as well as for the transboundary transport of PM₁₀ from China. This evaluation of PM₁₀ predictability can be reliably used as a statistical reference and further, complement to the current air quality forecasting system.