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Exposure to air pollutants, such as PM2.5 and ozone, has a serious adverse effect on health, with more than 4 million deaths, including early deaths. Air pollution in ports is caused by exhaust gases from various elements, including ships, and to reduce this, the International Maritime Organization (IMO) is also making efforts to reduce air pollution by regulating the sulfur content of fuel used by ships. Nevertheless, there is a lack of measures to identify and minimize the effects of air pollution. The Community Multiscale Air Quality (CMAQ) model is the most used to understand the effects of air pollution. In this paper, we propose a hybrid model combining the CMAQ model and RNN-LSTM, an artificial neural network model. Since the RNN-LSTM model has very good predictive performance, combining these two models can improve the spatial distribution prediction performance of a large area at a relatively low cost. In fact, as a result of prediction using the hybrid model, it was found that IOA improved by 0.235~0.317 and RMSE decreased by 4.82~8.50 μg/m3 compared to the case of using only CMAQ. This means that when PM2.5 is predicted using the hybrid model, the accuracy of the spatial distribution of PM2.5 can be improved. In the future, if real-time prediction is performed using the hybrid model, the accuracy of the calculation of exposure to air pollutants can be increased, which can help evaluate the impact on health. Ultimately, it is expected to help reduce the damage caused by air pollution through accurate predictions of air pollution. [ABSTRACT FROM AUTHOR]

As climates change around the world, concern regarding environmental pollutants emitted into the atmosphere is increasing. The cement industry consistently produces more than 4000 million metric tons of cement per year. However, the problem of air pollutants being emitted from the calcination process is becoming more critical because their amount increases proportionally with cement production. Each country has established regulatory standards for pollutant emission. Accordingly, the cement industry is equipped with facilities to reduce air pollutants, one of which is the NOx removal process. NOx reduction processes under combustion conditions are modified to minimize NOx generation, and the generated NOx is removed through post-treatment. In terms of NOx removal efficiency, the post-treatment process effectively changes the combustion conditions during calcination. Selective non-catalytic reduction (SNCR) and selective catalytic reduction (SCR) processes are post-treatment environmental facilities for NOx reduction. Accordingly, considering the stringent NOx emission standards in the cement industry, SNCR is essential, and SCR is selectively applied. Therefore, this paper introduces nitrogen oxide among air pollutants emitted from the South Korean cement industry and summarizes the technologies adapted to mitigate the emission of NOx by cement companies in South Korea. [ABSTRACT FROM AUTHOR]

Harmful nitrogen oxides (NOX) are produced by vehicles, factories, mines, and power plants. In fact, over one million tons of NOX are emitted into the atmosphere every year, making it the most prevalent air pollutant. Approximately 45% of the emitted NOX in Korea is associated with the transportation sector. In this paper, the application of a new TiO2 photocatalyst on the asphalt roads to remove combustion-produced NOX was studied. In an effort to overcome the known constructability, adhesion, cost, and dispersion problems associated with TiO2 photocatalysts, the liquid polyurethane (PUD) was added with TiO2 to form a mixture later known as liquid PUD-TiO2. Laboratory and field tests were conducted to determine the optimum amount of photocatalyst to be used and the performance of asphalt pavement coated with PUD-TiO2 in terms of indirect tensile strength, water susceptibility, and rutting resistance. Additionally, the performance of PUD-TiO2 under different humidity, wind speed, and temperature conditions was also evaluated. The results showed that the application of PUD-TiO2 photocatalyst on the asphalt pavements road reduces the NOX available on the surface of the road. The PUD-TiO2 also was found to have no effects on the performance of asphalt pavement. Meanwhile, under different weather conditions, the reaction between the photocatalyst and NOX is mainly affected by the humidity. [ABSTRACT FROM AUTHOR]

Air pollution is a critical problem that is of major concern worldwide. South Korea is one of the countries most affected by air pollution. Rapid urbanization and industrialization in South Korea have induced air pollution in multiple forms, such as smoke from factories and exhaust from vehicles. In this paper, we perform a comparative analysis of predictive models for fine particulate matter in Daejeon, the fifth largest city in South Korea. This study is conducted for three purposes. The first purpose is to determine the factors that may cause air pollution. Two main factors are considered: meteorological and traffic. The second purpose is to find an optimal predictive model for air pollutant concentration. We apply machine learning and deep learning models to the collected dataset to predict hourly air pollutant concentrations. The accuracy of the deep learning models is better than that of the machine learning models. The third purpose is to analyze the influence of road conditions on predicting air pollutant concentration. Experimental results demonstrate that considering wind direction and wind speed could significantly decrease the error rate of the predictive models. [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]