A Case Study of Severe PM2.5 Event in the Gwangju Urban Area during February 2014

Hye, Yu Geun; Lee, Bok-Jin; Park, Seungshik; Jung, Sun A; Jo, Mi Ra; Lim, Yong Jae; Kim, Soontae

doi:10.5572/kosae.2019.35.2.195

Cited by 17 publications

(5 citation statements)

References 46 publications

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“…There, it has been judged necessary to look at the two pollutants (PM 10 and PM 2.5 ) separately because their physical properties and chemical composition differ depending on their origin. Some studies have mentioned particulate matter generated in cities, and research has pointed out that weather and topographical conditions and emission sources are the main causes of it [2][3][4]9]. The interesting points are also meaningfully related to natural factors such as topography and meteorological conditions, which can have a large impact on the process of diffusion and removal of atmospheric pollutants, and thus may be related to the causes of differences in the distribution of particulate matter in different seasons or regions [10,11].…”

Section: Literature Review 21 Main Causes Of Respirable Particulate M...mentioning

confidence: 99%

“…In addition, the focus is on managing emission sources through the regulation of automobile exhaust gases and the Indoor Air Quality Control Act. However, the concentration of particulate matter in the atmosphere is affected by a number of characteristics, such as weather, land use, and topography, in addition to pollutant [2][3][4], and its distribution depends on several other factors. Since it is difficult to achieve the particulate matter concentration target only by reducing emissions within the scope of existing policies, the legislation suggests that factors such as land use, urban environment, and human activities should be considered [5].…”

Section: Introductionmentioning

confidence: 99%

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Spatial-Temporal Characteristics and Influencing Factors of Particulate Matter: Geodetector Approach

Mun

Jung

2022

Land

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In 2019, South Korea’s Framework Act on The Management of Disasters and Safety was revised to include respirable particulate matter as a social disaster. Urban air pollution, especially particulate matter pollution, has been a serious threat to socioeconomic development and public health. In order to address this problem, strong climate crisis response strategies and policies to improve urban air quality are necessary. Therefore, it is of great importance to assess the frequency of urban air pollution occurrence and its influencing factors. The objective of this study is to develop consistent methodologies for the construction of an index system and for assessing the influencing factors of urban particulate matter pollution based on population, social welfare, land use, environmental, transportation, and economic governance considerations. We applied the local indicators of spatial association and geographical detector methods, and 35 influencing factors were selected to assess their influence on urban air pollution occurrence in 229 cities and counties in South Korea. The results indicated the spatial pattern of the particulate matter concentration in these locations showed strong spatial correlation, and it was confirmed that there was a difference in distribution according to the season. As a result of the analysis of influencing factors, it was found that environment and land use characteristics were the main influencing factors for PM10 and PM2.5. The explanatory power between the two influencing factors of particulate matter was greater than that of a single influencing factor. In addition, most influencing factors resulted in both positive and negative effects on urban fine particulate matter pollution. The interaction relationship of all factors showed a strong action effect in the case of both PM10 and PM2.5, so it was confirmed that all influencing factors were interdependent. In particular, the findings proved that combining the two factors would have a more pronounced effect on particulate matter than when they were independent. We confirmed the significant results for the factors affecting particulate matter. This study offers suggestions on reducing urban air pollution occurrence that can be used to provide a basis and reference for the government to form policies on urban air pollution control in cities and counties.

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Section: Literature Review 21 Main Causes Of Respirable Particulate M...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Characteristics and Influencing Factors of Particulate Matter: Geodetector Approach

Mun

Jung

2022

Land

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“…According to several studies, the effects of fine dust from domestic emissions in South Korea vary by sources, such as thermal power plants, industrial combustion, and road/non-road pollution sources [31][32][33]. This paper investigates the correlation between the traffic volume, the number of factories, and the population density in the Seoul area as factors affecting daily maximum concentrations of PM 2.5 in the Seoul metropolitan area.…”

Section: Correlation Between Maximum Daily Concentrations Of Pm 25 and Traffic Volume The Factory Area And Population Densitymentioning

confidence: 99%

An Efficient Method for Capturing the High Peak Concentrations of PM2.5 Using Gaussian-Filtered Deep Learning

Yeo

Choi

2021

Sustainability

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This paper proposes a deep learning model that integrates a convolutional neural network with a gate circulation unit that captures patterns of high-peak PM2.5 concentrations. The purpose is to accurately predict high-peak PM2.5 concentration data that cannot be trained in general deep learning models. For the training of the proposed model, we used all available weather and air quality data for three years from 2015 to 2017 from 25 stations of the National Institute of Environmental Research (NIER) and the Korea Meteorological Administration (KMA) observatory in Seoul, South Korea. Our model trained three years of data and predicted high-peak PM2.5 concentrations for the year 2018. In addition, we propose a Gaussian filter algorithm as a preprocessing method for capturing high concentrations of PM2.5 in the Seoul area and predicting them more accurately. This model overcomes the limitations of conventional deep learning approaches that are unable to predict high peak PM2.5 concentrations. Comparing model measurements at each of the 25 monitoring sites in 2018, we found that the deep learning model with a Gaussian filter achieved an index of agreement of 0.73–0.89 and a proportion of correctness of 0.89–0.96, and compared to the conventional deep learning method (average POC = 0.85), the Gaussian filter algorithm (average POC = 0.94) improved the accuracy of high-concentration PM2.5 prediction by an average of about 9%. Applying this algorithm in the preprocessing stage could be updated to predict the risk of high PM2.5 concentrations in real time.

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“…In urban environments, where vehicle emissions are important, haze pollution that leads to increases in the PM 2.5 concentration is directly linked to increases in the concentration of secondary inorganic components as well as carbonaceous particles [15][16][17][18][19][20][21][22][23][24][25][26]. Moreover, meteorological conditions, such as low wind speed, high relative humidity (RH), high surface pressure, and shallow boundary layers, play an important role in increasing PM 2.5 pollution [26][27][28][29][30][31][32][33][34]. The characteristics of PM 2.5 pollution vary with the season, location, and weather conditions [2,12,15,24,26,27,33,34].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, meteorological conditions, such as low wind speed, high relative humidity (RH), high surface pressure, and shallow boundary layers, play an important role in increasing PM 2.5 pollution [26][27][28][29][30][31][32][33][34]. The characteristics of PM 2.5 pollution vary with the season, location, and weather conditions [2,12,15,24,26,27,33,34]. For example, during summer, when the formation of oxidants such as OH•, O 3 , and H 2 O 2 is strongly activated, the secondary formation of particulate SO 4 2− accelerates, resulting in high PM 2.5 pollution [15,26,35].…”

Section: Introductionmentioning

confidence: 99%

Chemical Composition and Light Absorption of PM2.5 Observed at Two Sites near a Busy Road during Summer and Winter

et al. 2020

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To examine the difference in the major chemical composition of fine particulate matter (PM2.5) between two roadway sites, 24 h integrated PM2.5 samples were simultaneously collected both 15 m (Buk-Ku District Office (BKO) site) and 150 m (Chonnam National University campus (CNU) site) away from busy roads during the summer and winter periods; these samples were taken to determine the concentrations of organic and elemental carbon (OC and EC), water-soluble organic carbon (WSOC), and water-soluble inorganic species. In addition, the real-time aerosol light absorption coefficients (Abs) were measured using a dual-spot seven-wavelength aethalometer at the CNU site to evaluate the influence of traffic and biomass burning (BB) emissions on the concentrations of organic aerosol particles. The hourly NO2 concentration was also observed at an air pollution monitoring network that is about 2 km away from the CNU site. During summer, 24 h PM2.5 concentrations (PM2.5 episode) which exceeded the Korean PM2.5 standard (35 μg/m3) were linked to increases in organic matter (OM) and SO42− concentrations that accounted for on average 35–41% and 26–30%, respectively, of the PM2.5 at the two sites. The increased SO42− concentration was most likely attributable to the inflow of long-range transported aerosols, rather than local production, as demonstrated by both the MODIS (Moderate Resolution Imaging Spectroradiometer) images and transport pathways of air masses reaching the sites. On the other hand, the OM, WSOC, and EC concentrations were directly attributable to traffic emissions at the sampling sites, as supported by the tight correlation between the OC and EC. A small difference between the absorption Ångström exponent (AAE) values calculated at wavelengths of 370–950 nm (AAE370–950nm) and 370–520 nm (AAE370–520nm), and the poor correlation of absorption coefficient by brown carbon (BrC) at 370 nm (AbsBrC370nm) with K+ (R2 = 0.00) also suggest a significant contribution of traffic emissions to OM. However, the wintertime PM2.5 episode was strongly related to the enhanced OM and NO3− concentrations, which contributed 26–28% and 22–23% of the PM2.5 concentration, respectively. It is interesting to note that there were two distinct OC/EC ratios in winter: a lower OC/EC (~3.0), which indicates a significant contribution of traffic emissions to the OC and EC, and a higher OC/EC (~6.5), which suggests an additional influence of BB emissions as well as traffic emissions at the sites. Strong correlations between the OC and EC (R2 = 0.72–0.83) and the enhanced AAE370–520nm values compared to the AAE370–950nm support that BB emissions were also an important contributor to the wintertime OM concentrations as well as traffic emissions at the two sites. A good correlation between the gaseous NO2 and NO3− and meteorological conditions (e.g., low wind speed and high relative humidity) suggest that the heterogeneous oxidation of NO2 on moist particles could be an important contributor to wintertime particulate NO3− formation at the sites. The OC concentrations during summer and winter were higher at the BKO site, with a higher traffic flow and a shorter distance from the roadway than at the CNU site. However, there were slight differences in the concentrations of secondary inorganic species (NO3−, SO42−, and NH4+) between the sites during summer and winter.

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A Case Study of Severe PM2.5 Event in the Gwangju Urban Area during February 2014

Cited by 17 publications

References 46 publications

Spatial-Temporal Characteristics and Influencing Factors of Particulate Matter: Geodetector Approach

Spatial-Temporal Characteristics and Influencing Factors of Particulate Matter: Geodetector Approach

An Efficient Method for Capturing the High Peak Concentrations of PM2.5 Using Gaussian-Filtered Deep Learning

Chemical Composition and Light Absorption of PM2.5 Observed at Two Sites near a Busy Road during Summer and Winter

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