Abstract The National Institute of Environmental Research, the Ministry of Environment, has been forecasting the concentrations of particulate matter (PM) with a diameter ≤ 2.5 μm (PM2.5) over Seoul, Republic of Korea, in terms of four PM2.5 concentration categories (low, moderate, high, and very high) since August 31, 2013. The current model, the Community Multiscale Air Quality (CMAQ) model, is run four times a day to forecast air quality for up to two days in 6-h intervals. In 2018, the hit ratio (i.e., accuracy) of the model was 60%, with an additional increase of 10% with the involvement of a forecaster. The CMAQ was improved in this study by incorporating a recurrent neural network (RNN) algorithm for the Seoul Metropolitan Area. Input datasets to the RNN model—PM values, meteorological parameters, and back-trajectory tracks obtained from both observations and model forecasts—were sorted according to time as the RNN algorithm learns time sequence series information, unlike typical neural network algorithms. To reflect the seasonality of the meteorological parameters that influence the PM2.5 concentrations in the region, one year was divided into 36 sets of three-month periods (i.e., there are three sets for July: May–June–July, June–July–August, and July–August–September). Several indices representing the accuracy of the forecast were calculated based on the RNN model results for 2018 after training the model for the previous three years (2015–2017). The accuracy of the RNN model is 74–81% for forecast lead times up to two days, about 20% higher than the CMAQ-only forecasts and ~10% higher than the combined CMAQ-forecaster forecast. The RNN model probabilities of detection for both high and very high PM2.5 events are comparable to those of the CMAQ model; however, the RNN model notably reduces the false alarm rate. Overall, the RNN model yields higher performance than the current forecast methods. Hence, this model can be adopted as an operational forecast model in Korea.

中文翻译：

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使用递归神经网络算法为韩国首尔市区开发 PM2.5 预测模型

摘要 韩国环境部国立环境研究所一直在预测韩国首尔上空直径≤2.5μm的颗粒物（PM2.5）浓度，以四种PM2.5浓度表示自 2013 年 8 月 31 日以来的类别（低、中、高和非常高）。当前的模型，社区多尺度空气质量 (CMAQ) 模型，每天运行四次，以预测 6-2 天中最多两天的空气质量h 间隔。2018年，该模型的命中率（即准确率）为60%，在有预测员参与的情况下，进一步提高了10%。CMAQ 在本研究中通过为首尔都市区引入循环神经网络 (RNN) 算法而得到改进。RNN 模型的输入数据集——PM 值、气象参数、与从观察和模型预测中获得的反向轨迹轨迹 - 与典型的神经网络算法不同，随着 RNN 算法学习时间序列信息，根据时间进行排序。为了反映影响该地区PM2.5浓度的气象参数的季节性，将一年分为36组三个月的时间段（即7月份有3组：5-6-7月，6-7月– 8 月和 7 月 – 8 月 – 九月）。在对模型进行了前三年（2015-2017）的训练后，根据 2018 年的 RNN 模型结果计算了几个代表预测准确性的指标。对于长达两天的预测提前期，RNN 模型的准确度为 74-81%，比仅 CMAQ 的预测高约 20%，比 CMAQ 预测的组合预测高约 10%。RNN 模型检测高和极高 PM2.5 事件的概率与 CMAQ 模型的概率相当；然而，RNN 模型显着降低了误报率。总体而言，RNN 模型的性能高于当前的预测方法。因此，该模型可用作韩国的业务预测模型。