• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.543-545
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• 2019

The AQI index has been developed and used to guide the action of particulate matter. Information on the AQI index can be easily provided to the general public, and various services are provided based on the AQI index. As services are provided, accurate AQI index prediction is needed. In this paper, we design the classification model using the circular neural network to predict the AQI index of particulate matter. For the evaluation of the designed model, compare the AQI index of the actual particulate matter with the predicted value.

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.4
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• pp.518-528
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• 2006

The air quality index (AQI) is an index for reporting daily or hourly air quality to the general public. The AQI focuses on health effects that can happen within a few hours or days after breathing polluted air. Many countries have their own AQI reporting systems, and the HAEI (hourly air environment index) method is now being used in Korea. In this study, in order to compare the AQI results from different methods, we applied three methods. i.e. US AQI, Canadian AQI, and Korean HAEI, to the same air quality data-base. The data-base was constructed from 10 monitoring sites in Gyeong-buk province for the last four years since 2000. Based on the results, a critical evaluation of the Korean HAEI method was made, and a number of suggestions and recommendations were presented to improve the AQI reporting system in Korea.

• Asian Journal of Atmospheric Environment
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• v.9 no.2
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• pp.101-113
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• 2015

Air quality index (AQI) or air pollution index (API) is commonly used to report the level of severity of air pollution to public. A number of methods were developed in the past by various researchers/environmental agencies for determination of AQI or API but there is no universally accepted method exists, which is appropriate for all situations. Different method uses different aggregation function in calculating AQI or API and also considers different types and numbers of pollutants. The intended uses of AQI or API are to identify the poor air quality zones and public reporting for severity of exposure of poor air quality. Most of the AQI or API indices can be broadly classify as single pollutant index or multi-pollutant index with different aggregation method. Every indexing method has its own characteristic strengths and weaknesses that affect its suitability for particular applications. This paper attempt to present a review of all the major air quality indices developed worldwide.

• International Journal of High-Rise Buildings
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• v.6 no.1
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• pp.101-111
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• 2017

The objective of this study is to determine the effectiveness of a modified air-side economizer in improving indoor air quality (IAQ). An air-side economizer, which uses all outdoor air for cooling, affects the building's IAQ depending on the outside air quality and can significantly affect the occupants' health, leading to respiratory and heart disease. The Air Quality Index (AQI), developed by the US Environmental Protection Agency (US EPA), measures air contaminants that adversely affect human beings: PM10, PM2.5, SO2, NO2, O3, and CO. In this study, AQI is applied as a control for the operation of an air-side economizer. The simulation is analyzed, comparing the results between the differential enthalpy economizer and AQI-modified economizer. The results confirm that an AQI-modified economizer has a positive effect on IAQ. Compared to the operating differential enthalpy economizer, energy increase in an operating AQI-modified economizer is 0.65% in Shanghai and 0.8% in Seoul.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.540-542
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• 2019

With many announcements on air pollution and human effects from particulate matters, particulate matter forecasts are attracting a lot of public attention. As a result, various efforts have been made to increase the accuracy of particulate matter forecasting by using statistical modeling and machine learning technique. In this paper, the particulate matter AQI index prediction is performed using the multilayer perceptron neural network for particulate matter prediction. For this purpose, a prediction model is designed by using the meteorological factors and particulate matter concentration values commonly used in a number of studies, and the accuracy of the particulate matter AQI prediction is compared.

• Advances in environmental research
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• v.4 no.2
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• pp.83-104
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• 2015

Air Quality Index (AQI) is a pointer to broadcast short term air quality. This paper presents one day ahead AQI forecasting on seasonal basis for three major cities in Maharashtra State, India by using Artificial Neural Networks (ANN) and Genetic Programming (GP). The meteorological observations & previous AQI from 2005-2008 are used to predict next day's AQI. It was observed that GP captures the phenomenon better than ANN and could also follow the peak values better than ANN. The overall performance of GP seems better as compared to ANN. Stochastic nature of the input parameters and the possibility of auto-correlation might have introduced time lag and subsequent errors in predictions. Spectral Analysis (SA) was used for characterization of the error introduced. Correlational dependency (serial dependency) was calculated for all 24 models prepared on seasonal basis. Particular lags (k) in all the models were removed by differencing the series, that is converting each i'th element of the series into its difference from the (i-k)"th element. New time series is generated for all seasonal models in synchronization with the original time line & evaluated using ANN and GP. The statistical analysis and comparison of GP and ANN models has been done. We have proposed a promising approach of use of GP coupled with SA for real time prediction of seasonal multicity AQI.

• Journal of the Korean Institute of Landscape Architecture
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• v.49 no.5
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• pp.1-11
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• 2021

The purpose of this study is to find out the main factors affecting air quality in urban physical space factors, and provide clues for environmental improvement. Nine monitoring stations in China's industrial city, Changchun, collected AQI concentration data from January 1, 2018 to December 31, 2019. This paper analyzes the types and distribution characteristics of urban physical facilities within a radius of 300m with the detection station as the center. The monitoring station is divided into three groups, and the difference in floating dust concentration among the three groups in different seasons is analyzed. The results show that AQI concentration is the highest in spring and winter, followed by summer, and the lowest in autumn. The place with the highest concentrations of AQI in spring are F (93.00), D (91.10), I (89.20), in summer are D (69.05), A (67.89), B (84.44), in autumn are I (62.80), G (60.84), D (53.27), D (53.27), in winter are I (95.82), H (95.60), f (94.04). Through SPSS analysis, it shows that the air index in a space with a diameter of 600 meters is related to forest land, grassland, bare land, water space, tree height, building area (average value), and building volume (average value). According to the statistical analysis results of spring and winter with the most serious pollution, forest land area (43,637m², 15.44%) and water surface area (18,736m², 6.63%) accounted for the majority, and group 1 (A, B, C) with the least average building area (448m², 0.17%) and average building volume (10,201m²) had the lowest pollution concentration. On the contrary, group 2 (D, E, F) had the highest AQI concentration, with less or no woodland (1,917m², 0.68%) and water surface area (0m², 0%), and the highest average building area (1,056m², 0.37%) and average building volume (17,470m³). It is confirmed that the characteristics of the area with the highest AQI concentration are that the more the site ratio of tree height above 12m, the smaller the site ratio of bare land, and the lower the pollution degree. On the contrary, the larger the area of bare land, the higher the pollution degree. By analyzing the characteristics of nine monitoring stations in Changchun, it can be seen that the air quality brought by the physical characteristics of urban space is closely related to the above factors.

• Environmental Engineering Research
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• v.23 no.1
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• pp.21-27
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• 2018

Air pollution index (API) is used in Mainland China and includes only $SO_2$, $NO_2$ and $PM_{10}$. In 2016, air quality index (AQI) replaced API. AQI contains three more air pollutants (CO, $O_3$ and $PM_{2.5}$). Both the indices emphasize on the effect of a single pollutant, whereas the contributions of all other pollutants are ignored. Therefore, in the present work, a novel air quality index (NAQI), which emphasizes on all air pollutants, has been introduced for the first time. The results showed that there were 19 d (5.2%) in API, 28 d (7.7%) in AQI and 183 d (50.1%) in NAQI when the indices were more than 100. In API, $PM_{10}$ and $SO_2$ were regarded as the primary pollutants, whereas all five air pollutants in AQI were regarded as primary. Furthermore, four air pollutants (other than the CO) in NAQI were regarded as primary pollutants. $PM_{10}$, as being the primary pollutant, contributed greatly in these air quality indices, and accounted for 51.2% (API), 37.0% (AQI) and 52.6% (NAQI). The results also showed that particulate matter pollution was significantly high in Luzhou, where stricter pollution control measures should be implemented.

• Communications for Statistical Applications and Methods
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• v.30 no.2
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• pp.119-133
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• 2023

With the rapid growth of the economy and fossil fuel consumption, the concentration of air pollutants has increased significantly and the air pollution problem is no longer limited to small areas. We conduct statistical analysis with the actual data related to air quality that covers the entire of South Korea using R and Python. Some factors such as SO₂, CO, O₃, NO₂, PM₁₀, precipitation, wind speed, wind direction, vapor pressure, local pressure, sea level pressure, temperature, humidity, and others are used as covariates. The main goal of this paper is to predict air quality index (AQI) spatio-temporal data. The observations of spatio-temporal big datasets like AQI data are correlated both spatially and temporally, and computation of the prediction or forecasting with dependence structure is often infeasible. As such, the likelihood function based on the spatio-temporal model may be complicated and some special modelings are useful for statistically reliable predictions. In this paper, we propose several methods for this big spatio-temporal AQI data. First, random effects with spatio-temporal basis functions model, a classical statistical analysis, is proposed. Next, neural networks model, a deep learning method based on artificial neural networks, is applied. Finally, random forest model, a machine learning method that is closer to computational science, will be introduced. Then we compare the forecasting performance of each other in terms of predictive diagnostics. As a result of the analysis, all three methods predicted the normal level of PM_2.5 well, but the performance seems to be poor at the extreme value.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.8-14
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• 2020

The human impact of particulate matter are revealed and demand for improved forecast accuracy is increasing. Recently, efforts is made to improve the accuracy of PM10 predictions by using machine learning, but prediction performance is decreasing due to the particulate matter data with a large rate of low concentration occurrence. In this paper, separation prediction model by concentration is proposed to improve the accuracy of PM10 particulate matter forecast. The low and high concentration prediction model was designed using the weather and air pollution factors in Cheonan, and the performance comparison with the prediction models was performed. As a result of experiments with RMSE, MAPE, correlation coefficient, and AQI accuracy, it was confirmed that the predictive performance was improved, and that 20.62% of the AQI high-concentration prediction performance was improved.