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The concentration series of PM2.5 (particulate matter ≤ 2.5 μm) is nonlinear, nonstationary, and noisy, making it difficult to predict accurately. This paper presents a new PM2.5 concentration prediction method based on a hybrid model of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and bi-directional long short-term memory (BiLSTM). The new method was applied to predict the same kind of particulate pollutant PM10 and heterogeneous gas pollutant O3, proving that the prediction method has strong generalization ability. First, CEEMDAN was used to decompose PM2.5 concentrations at different frequencies. Then, the fuzzy entropy (FE) value of each decomposed wave was calculated, and the near waves were combined by K-means clustering to generate the input sequence. Finally, the combined sequences were put into the BiLSTM model with multiple hidden layers for training. We predicted the PM2.5 concentrations of Seoul Station 116 by the hour, with values of the root mean square error (RMSE), the mean absolute error (MAE), and the symmetric mean absolute percentage error (SMAPE) as low as 2.74, 1.90, and 13.59%, respectively, and an R2 value as high as 96.34%. The "CEEMDAN-FE" decomposition-merging technology proposed in this paper can effectively reduce the instability and high volatility of the original data, overcome data noise, and significantly improve the model's performance in predicting the real-time concentrations of PM2.5. [ABSTRACT FROM AUTHOR]

Both long- and short-term exposure to high concentrations of airborne particulate matter (PM) severely affect human health. Many countries now regulate PM concentrations. Early-warning systems based on PM concentration levels are urgently required to allow countermeasures to reduce harm and loss. Previous studies sought to establish accurate, efficient predictive models. Many machine-learning methods are used for air pollution forecasting. The long short-term memory and gated recurrent unit methods, typical deep-learning methods, reliably predict PM levels with some limitations. In this paper, the authors proposed novel hybrid models to combine the strength of two types of deep learning methods. Moreover, the authors compare hybrid deep-learning methods (convolutional neural network (CNN)—long short-term memory (LSTM) and CNN—gated recurrent unit (GRU)) with several stand-alone methods (LSTM, GRU) in terms of predicting PM concentrations in 39 stations in Seoul. Hourly air pollution data and meteorological data from January 2015 to December 2018 was used for these training models. The results of the experiment confirmed that the proposed prediction model could predict the PM concentrations for the next 7 days. Hybrid models outperformed single models in five areas selected randomly with the lowest root mean square error (RMSE) and mean absolute error (MAE) values for both PM10 and PM2.5. The error rate for PM10 prediction in Gangnam with RMSE is 1.688, and MAE is 1.161. For hybrid models, the CNN–GRU better-predicted PM10 for all stations selected, while the CNN–LSTM model performed better on predicting PM2.5. [ABSTRACT FROM AUTHOR]

This paper presents the development of the global to mesoscale air quality forecast and analysis system (GMAF) and its application to particulate matter under 2.5 μm (PM2.5) forecast in Korea. The GMAF combined a mesoscale model with a global data assimilation system by the grid nudging based four-dimensional data assimilation (FDDA). The grid nudging based FDDA developed for weather forecast and analysis was extended to air quality forecast and analysis for the first time as an alternative to data assimilation of surface monitoring data. The below cloud scavenging module and the secondary organic formation module of the community multiscale air quality model (CMAQ) were modified and subsequently verified by comparing with the PM speciation observation from the PM supersite. The observation data collected from the criteria air pollutant monitoring networks in Korea were used to evaluate forecast performance of GMAF for the year of 2016. The GMAF showed good performance in forecasting the daily mean PM2.5 concentrations at Seoul; the correlation coefficient between the observed and forecasted PM2.5 concentrations was 0.78; the normalized mean error was 25%; the probability of detection for the events exceeding the national PM2.5 standard was 0.81 whereas the false alarm rate was only 0.38. Both the hybrid bias correction technique and the Kalman filter bias adjustment technique were implemented into the GMAF as postprocessors. For the continuous and the categorical performance metrics examined, the Kalman filter bias adjustment technique performed better than the hybrid bias correction technique. [ABSTRACT FROM AUTHOR]

Various studies are currently underway on PM (Particulate Matter) monitoring in view of the importance of air quality in public health management. Spatial interpolation has been used to estimate PM concentrations due to that it can overcome the shortcomings of station-based PM monitoring and provide spatially continuous information. However, PM is affected by a combination of several factors, and interpolation that only considers the spatial relationship between monitoring stations is limited in ensuring accuracy. Additionally, relatively accurate results may be obtained in the case of interpolation by using external drifts, but the methods have a disadvantage in that they require additional data and preprocessing. This study proposes a modified IDW (Inverse Distance Weighting) that allows more accurate estimations of PM based on the sole use of measurements. The proposed method improves the accuracy of the PM estimation based on weight correction according to the importance of each known point. Use of the proposed method on PM10 and PM2.5 in the Seoul-Gyeonggi region in South Korea led to an improved accuracy compared with IDW, kriging, and linear triangular interpolation. In particular, the proposed method showed relatively high accuracy compared to conventional methods in the case of a relatively large PM estimation error. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the accuracy and effectiveness of real-time personal monitoring of exposure to PM concentrations using low-cost sensors, in comparison to conventional data collection method based on fixed stations. PM2.5 data were measured every 5 min using a low-cost sensor attached to a bag carried by 47 asthmatic children living in the Seoul Metropolitan area between November 2019 and March 2020, along with the real-time GPS location, temperature, and humidity. The mobile sensor data were then matched with station-based hourly PM2.5 data using the time and location. Despite some uncertainty and inaccuracy of the sensor data, similar temporal patterns were found between the two sources of PM2.5 data on an aggregate level. However, average PM2.5 concentrations via personal monitoring tended to be lower than those from the fixed stations, particularly when the subjects were indoors, during nighttime, and located farther from the fixed station. On an individual level, a substantial discrepancy is observed between the two PM2.5 data sources while staying indoors. This study provides guidance to policymakers and researchers on improving the feasibility of personal monitoring via low-cost mobile sensors as an alternative or supplement to the conventional station-based monitoring. [ABSTRACT FROM AUTHOR]

Rapid industrialization of Korea's economy has brought with it environmental pollution that threatens human health. Among various other pollutants, ambient fine particulate matter known to endanger human health often exceeds air quality standards in Seoul, South Korea's capital. The goal of this research is to find the impact of meteorological extremes and particle levels on human health. The analysis was conducted using hourly air pollutant concentrations, meteorological variables, and the daily mortality from cerebrovascular disease. Results show that the effect of fine particulate matter on mortality from cerebrovascular disease was more noticeable during meteorological extremes. The linkage between extreme weather conditions and mortality was more apparent in winter than in summer. Comprehensive studies of various causes of diseases should be continued to more accurately analyze the effects of fine particulate matter on human health and meteorological extremes, and to further minimize the public health impact of air pollution and meteorological conditions. [ABSTRACT FROM AUTHOR]

This study analyzes the relationship between various atmospheric fields and the observed PM10 concentrations in the Seoul metropolitan area, South Korea, during the winters of the 2001–2014 period to find suitable atmospheric indices for predicting high PM10 episodes in the region. The analysis shows that PM10 concentration in the metropolitan area is mainly affected by the intensity of horizontal ventilation and the 500 hPa high-pressure system over the Korean peninsula. The modified Korea particulate matter index (MKPI) is proposed based on a 10 m wind speed for surface ventilation and 500 hPa zonal wind for the intensity of a 500 hPa high-pressure system over the Korean peninsula. It is found that a positive MKPI value is closely correlated with the occurrence of high PM10 concentration episodes, and hence, can be used as a predictor for high PM10 episodes in the area. A future projection of the MKPI using two three-member general circulation model (GCM) ensembles with four shared socioeconomic pathway (SSP) scenarios in Coupled Model Intercomparison Project Phase 6 (CMIP6) shows that positive MKPI events and high PM10 episodes are expected to increase by 5.4−16.4% depending on the SSP scenarios in the 2081−2100 period from the present-day period of 1995−2014. [ABSTRACT FROM AUTHOR]

This study identified the key chemical components based on an analysis of the seasonal variations of ground level PM2.5 concentrations and its major chemical constituents (sulfate, nitrate, ammonium, organic carbon, and elemental carbon) in the Seoul Metropolitan Area (SMA), over a period of five years, ranging from 2012 to 2016. It was found that the mean PM2.5 concentration in the SMA was 33.7 μg/m3, while inorganic ions accounted for 53% of the total mass concentration. The component ratio of inorganic ions increased by up to 61%–63% as the daily mean PM2.5 concentration increased. In spring, nitrate was the dominant component of PM2.5, accounting for 17%–32% of the monthly mean PM2.5 concentrations. In order to quantify the impact of long-range transport on the SMA PM2.5, a set of sensitivity simulations with the community multiscale air-quality model was performed. Results show that the annual averaged impact of Chinese emissions on SMA PM2.5 concentrations ranged from 41% to 44% during the five years. Chinese emissions' impact on SMA nitrate ranged from 50% (winter) to 67% (spring). This result exhibits that reductions in SO2 and NOX emissions are crucial to alleviate the PM2.5 concentration. It is expected that NOX emission reduction efforts in China will help decrease PM2.5 concentrations in the SMA. [ABSTRACT FROM AUTHOR]

The evidence linking ozone and particulate matter with adverse health impacts is increasing. The goal of this study was to assess the impact of air pollution on the mortality rate from respiratory disease in Seoul, Korea, between 2008 and 2017. The analysis was conducted using a decision tree model in two ways: using 24-h average concentrations and using 1-h maximum values to compare any health impacts from the different times of exposure to pollution. Results show that in spring an elevated level of ozone is one of the most important factors, but in summer temperature has a greater impact than air pollution. Nitrogen dioxide is one of the most important factors in fall, while high levels of particles less than 2.5 μm (PM2.5) and 10 μm in size (PM10) and cooler temperatures are key factors in winter. We checked the accuracy of our results through a 10-fold cross validation method. Error rates using 24-h average and 1-h maximum concentrations were in the ranges of 24.9–42% and 27.6–42%, respectively, indicating that 24-h average concentrations are slightly more directly related with mortality rate. These results could be useful for policy makers in determining the temporal scale of predicted pollutant concentrations for an air quality warning system to help minimize the adverse impacts of air pollution. [ABSTRACT FROM AUTHOR]