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13 pages, 17472 KiB

Open AccessArticle

High-Resolution Daily PM2.5 Exposure Concentrations in South Korea Using CMAQ Data Assimilation with Surface Measurements and MAIAC AOD (2015–2021)

by Jin-Goo Kang, Ju-Yong Lee, Jeong-Beom Lee, Jun-Hyun Lim, Hui-Young Yun and Dae-Ryun Choi

Atmosphere 2024, 15(10), 1152; https://doi.org/10.3390/atmos15101152 - 26 Sep 2024

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Abstract

Particulate matter (PM) in the atmosphere poses significant risks to both human health and the environment. Specifically, PM2.5, particulate matter with a diameter less than 2.5 micrometers, has been linked to increased rates of cardiovascular and respiratory diseases. In South Korea, concerns about [...] Read more.

Particulate matter (PM) in the atmosphere poses significant risks to both human health and the environment. Specifically, PM2.5, particulate matter with a diameter less than 2.5 micrometers, has been linked to increased rates of cardiovascular and respiratory diseases. In South Korea, concerns about PM2.5 exposure have grown due to its potential for causing premature death. This study aims to estimate high-resolution exposure concentrations of PM2.5 across South Korea from 2015 to 2021. We integrated data from the Community Multiscale Air Quality (CMAQ) model with surface air quality measurements, the Weather Research Forecast (WRF) model, the Normalized Difference Vegetation Index (NDVI), and the Multi-Angle Implementation of Atmospheric Correction (MAIAC) Aerosol Optical Depth (AOD) satellite data. These data, combined with multiple regression analyses, allowed for the correction of PM2.5 estimates, particularly in suburban areas where ground measurements are sparse. The simulated PM2.5 concentration showed strong correlations with observed values R (ranging from 0.88 to 0.94). Spatial distributions of annual PM2.5 showed a significant decrease in PM2.5 concentrations from 2015 to 2021, with some fluctuation due to the COVID-19 pandemic, such as in 2020. The study produced highly accurate daily average high-resolution PM2.5 exposure concentrations. Full article

(This article belongs to the Special Issue Novel Insights into Air Pollution over East Asia)

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15 pages, 18352 KiB

Open AccessArticle

Characteristics and Source Identification for PM_2.5 Using PMF Model: Comparison of Seoul Metropolitan Area with Baengnyeong Island

by Kyoung-Chan Kim, Hui-Jun Song, Chun-Sang Lee, Yong-Jae Lim, Joon-Young Ahn, Seok-Jun Seo and Jin-Seok Han

Atmosphere 2024, 15(10), 1146; https://doi.org/10.3390/atmos15101146 - 24 Sep 2024

Cited by 1 | Viewed by 624

Abstract

To establish and implement effective policies for controlling fine particle matters (PM_2.5), which is associated with high-risk diseases, continuous research on identifying PM_2.5 sources was conducted. This study utilized the positive matrix factorization (PMF) receptor model to estimate the sources [...] Read more.

To establish and implement effective policies for controlling fine particle matters (PM_2.5), which is associated with high-risk diseases, continuous research on identifying PM_2.5 sources was conducted. This study utilized the positive matrix factorization (PMF) receptor model to estimate the sources and characteristics of PM_2.5 between Baengnyeong Island (BNI) and the Seoul Metropolitan Area (SMA). We conducted PMF modeling and backward trajectory analysis using the data on PM_2.5 and its components collected from 2020 to 2021 at the Air quality Research Centers (ARC). The PMF modeling identified nine pollution sources in both BNI and the SMA, including secondary sulfate, secondary nitrate, vehicles, biomass burning, dust, industry, sea salt particles, coal combustion, and oil combustion. Secondary particulate matter, vehicles, and biomass burning were found to be major contributors to PM_2.5 concentrations in both regions. A backward trajectory analysis indicated that air masses, passing through BNI to the SMA, showed higher concentrations and contributions of ammonium nitrate, vehicles, and biomass burning in the SMA site compared to BNI site. These findings suggest that controlling nitrogen oxides (NOx) and ammonia emissions in the SMA, as well as monitoring the intermediate products that form aerosols, such as HNO₃, are needed. Full article

(This article belongs to the Special Issue Novel Insights into Air Pollution over East Asia)

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12 pages, 1931 KiB

Open AccessArticle

Estimation of Ammonia Emission Inventory Using Life Cycle Assessment Based on Livestock Manure Flow: A Case Study of the Manure Management Sector in Korea

by Hye-Min Lee, Kyoung-Chan Kim, Min-Wook Kim, Ju-Yong Lee and Hung-Soo Joo

Atmosphere 2024, 15(8), 910; https://doi.org/10.3390/atmos15080910 - 30 Jul 2024

Viewed by 694

Abstract

Ammonia is one of the precursor gases in the formation of particulate matter (PM) that reacts with nitrogen oxides and sulfur oxides in the atmosphere. Based on the Clean Air Policy Support System (CAPSS) of Korea, the annual ammonia emissions amounted to 261,207 [...] Read more.

Ammonia is one of the precursor gases in the formation of particulate matter (PM) that reacts with nitrogen oxides and sulfur oxides in the atmosphere. Based on the Clean Air Policy Support System (CAPSS) of Korea, the annual ammonia emissions amounted to 261,207 tons in 2020 and the agricultural source (manure management sector) contributes the highest proportion of the ammonia inventory. However, the methodology for the study of ammonia emissions in Korea has some limitations regarding the representativeness of the sites selected and the reliability of the measurement method. In this study, we aimed to recalculate the ammonia emissions from the livestock industry in Korea using the UK’s estimation method, which uses the life cycle assessment of livestock manure. Three major animal types, i.e., cattle (beef cattle and dairy cows), pigs and chickens, and three major processes based on the manure flow, i.e., housing, manure storage and treatment and land application processes, were considered. The total ammonia emissions were estimated to be approximately 33% higher than the official ammonia emissions stated by the CAPSS. For the manure flow, the ammonia emissions were the highest from land application processes. The ammonia emissions from dairy cow and poultry manure were much higher than those stated by the CAPSS, while the emissions from beef cattle and pig manure showed similar levels. The methodology used in this study can offer an alternative approach to the ammonia emission estimation of the manure management sector in the agriculture industry of Korea. Korean emission factors based on the manure flow should be developed and applied in the future. Full article

(This article belongs to the Special Issue Novel Insights into Air Pollution over East Asia)

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12 pages, 10725 KiB

Open AccessArticle

Characterizing the Regional Differences in Carbon Dioxide Concentration Based on Satellite Observations in the Beijing-Tianjin-Hebei Region during 2015–2021

by Yanfang Hou, Wenliang Liu, Litao Wang, Futao Wang, Jinfeng Zhu and Shixin Wang

Atmosphere 2024, 15(7), 816; https://doi.org/10.3390/atmos15070816 - 8 Jul 2024

Viewed by 749

Abstract

The regional differences in carbon dioxide (CO₂) variations from the Orbiting Carbon Observatory-2 (OCO-2) over the Beijing-Tianjin-Hebei (Jing-Jin-Ji) region from 2015 to 2021 are analyzed in this study. This study shows an annual increase and a seasonal cycle; the CO₂ [...] Read more.

The regional differences in carbon dioxide (CO₂) variations from the Orbiting Carbon Observatory-2 (OCO-2) over the Beijing-Tianjin-Hebei (Jing-Jin-Ji) region from 2015 to 2021 are analyzed in this study. This study shows an annual increase and a seasonal cycle; the CO₂ annual growth rate was about 2.63 ppm year⁻¹, with the highest value being in spring and the lowest in summer. The spatial distribution is unbalanced, regional differences are prominent, and the CO₂ concentration is lower in the north of the Jing-Jin-Ji region (like Zhangjiakou, Chengde, and Qinhuangdao). Land-type structures and population economy distributions are the key factors affecting CO₂ concentration. By analyzing the land-type structures over Jing-Jin-Ji in 2020, we find that cropland, woodland, and grassland (CWG) are the main land cover types in Jing-Jin-Ji; the proportion of these three types is about 83.3%. The woodland areas in Zhangjiakou, Chengde, and Qinhuangdao account for about 65% of the total woodland areas in Jing-Jin-Ji; meanwhile, the grassland areas in these three regions account for 62% of the total grassland areas in Jing-Jin-Ji. CO₂ concentration variation shows a high negative correlation with CWG land areas (coefficient of determination (R²) > 0.76). The regions with lower population and GDP secondary industry (SI) density also have lower CO₂ concentration (like Zhangjiakou, Chengde, and Qinhuangdao), and the regions with higher population and GDP SI density also have higher CO₂ concentration (like the southeast of Jing-Jin-Jin). Full article

(This article belongs to the Special Issue Novel Insights into Air Pollution over East Asia)

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13 pages, 2596 KiB

Open AccessArticle

Long-Term and Seasonal Changes in Emission Sources of Atmospheric Particulate-Bound Pyrene and 1-Nitropyrene in Four Selected Cities in the Western Pacific

by Kazuichi Hayakawa

Atmosphere 2024, 15(6), 634; https://doi.org/10.3390/atmos15060634 - 24 May 2024

Viewed by 767

Abstract

Estimating the source contribution to polycyclic aromatic hydrocarbons (PAHs) and nitropolycyclic aromatic hydrocarbons (NPAHs) in the atmosphere is necessary for developing effective disease control and pollution control measures. The NPAH-PAH combination method (NP method) was used to elucidate the contributions of vehicles and [...] Read more.

Estimating the source contribution to polycyclic aromatic hydrocarbons (PAHs) and nitropolycyclic aromatic hydrocarbons (NPAHs) in the atmosphere is necessary for developing effective disease control and pollution control measures. The NPAH-PAH combination method (NP method) was used to elucidate the contributions of vehicles and coal/biomass combustion to seasonal and long-term urban atmospheric particulate matter (PM)-bound Pyr and 1-NP concentrations in Kanazawa, Kitakyushu, Shenyang and Shanghai in the Western Pacific region from 1997 to 2021. Among the four cities, Kanazawa demonstrated the lowest Pyr concentration. The contribution of vehicles to Pyr before and after 2010 was 35% and 5%, respectively. The 1-NP concentration was reduced by a factor of more than 1/10. These changes can be attributed to the emission control from vehicles. Kitakyushu revealed the second-lowest Pyr and the lowest 1-NP concentrations. Coal combustion was found to be the main contributor to Pyr, while its contribution to 1-NP increased from 9% to 19%. The large contribution of coal combustion is attributed to iron manufacturers. Shenyang demonstrated the highest atmospheric Pyr concentration with its largest seasonal change. Vehicles are the largest contributors to 1-NP. However, coal combustion, including winter coal heating, contributed 97% or more to Pyr and more than 14% to 1-NP. Shanghai revealed the second-highest Pyr and 1-NP concentrations, but the former was substantially lower than that in Shenyang. Coal combustion was the major contributor, but the contribution of vehicles to Pyr was larger before 2010, which was similar to Kanazawa. Full article

(This article belongs to the Special Issue Novel Insights into Air Pollution over East Asia)

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13 pages, 3986 KiB

Open AccessEditor’s ChoiceArticle

Characteristics of Atmospheric Pollutants in Paddy and Dry Field Regions: Analyzing the Oxidative Potential of Biomass Burning

by Myoungki Song, Minwook Kim, Sea-Ho Oh, Geun-Hye Yu, Seoyeong Choe, Hajeong Jeon, Dong-Hoon Ko, Chaehyeong Park and Min-Suk Bae

Atmosphere 2024, 15(4), 493; https://doi.org/10.3390/atmos15040493 - 17 Apr 2024

Cited by 5 | Viewed by 1116

Abstract

This study aimed to identify the characteristics of atmospheric pollutants emitted by agricultural activities and to evaluate factors that may cause harm to human health. For the research, atmospheric pollutants were measured over the course of a year in representative rice farming and [...] Read more.

This study aimed to identify the characteristics of atmospheric pollutants emitted by agricultural activities and to evaluate factors that may cause harm to human health. For the research, atmospheric pollutants were measured over the course of a year in representative rice farming and field crop farming areas in South Korea. The results confirmed that the characteristics of atmospheric pollutants in agricultural areas are influenced by the nature of agricultural activities. Specifically, when comparing rice paddies and field crop areas, during summer, the correlation between oxidative potential and levoglucosan—a marker for biomass burning—weakens due to less burning activity in the rice-growing season, leading to lower oxidative potential despite different PM_2.5 across areas. The study also finds that methyl sulfonic acid, indicating marine influence, plays a big role in keeping oxidative potential low in summer. This suggests that the main causes of PM_2.5-related health risks in the area are from biomass burning and external sources, with burning being a significant factor in increasing oxidative potential. Based on these results, it is hoped that measures can be taken in the future to reduce atmospheric pollutants in agricultural areas. Full article

(This article belongs to the Special Issue Novel Insights into Air Pollution over East Asia)

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27 pages, 4182 KiB

Open AccessArticle

Enhancing Air Quality Forecasting: A Novel Spatio-Temporal Model Integrating Graph Convolution and Multi-Head Attention Mechanism

by Yumeng Wang, Ke Liu, Yuejun He, Pengfei Wang, Yuxin Chen, Hang Xue, Caiyi Huang and Lin Li

Atmosphere 2024, 15(4), 418; https://doi.org/10.3390/atmos15040418 - 27 Mar 2024

Cited by 2 | Viewed by 1488

Abstract

Forecasting air quality plays a crucial role in preventing and controlling air pollution. It is particularly significant for improving preparedness for heavily polluted weather conditions and ensuring the health and safety of the population. In this study, a novel deep learning model for [...] Read more.

Forecasting air quality plays a crucial role in preventing and controlling air pollution. It is particularly significant for improving preparedness for heavily polluted weather conditions and ensuring the health and safety of the population. In this study, a novel deep learning model for predicting air quality spatio-temporal variations is introduced. The model, named graph long short-term memory with multi-head attention (GLSTMMA), is designed to capture the temporal patterns and spatial relationships within multivariate time series data related to air quality. The GLSTMMA model utilizes a hybrid neural network architecture to effectively learn the complex dependencies and correlations present in the data. The extraction of spatial features related to air quality involves the utilization of a graph convolutional network (GCN) to collect air quality data based on the geographical distribution of monitoring sites. The resulting graph structure is imported into a long short-term memory (LSTM) network to establish a Graph LSTM unit, facilitating the extraction of temporal dependencies in air quality. Leveraging a Graph LSTM unit, an encoder-multiple-attention decoder framework is formulated to enable a more profound and efficient exploration of spatio-temporal correlation features within air quality time series data. The research utilizes the 2019–2021 multi-source air quality dataset of Qinghai Province for experimental assessment. The results indicate that the model effectively leverages the impact of multi-source data, resulting in optimal accuracy in predicting six air pollutants. Full article

(This article belongs to the Special Issue Novel Insights into Air Pollution over East Asia)

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16 pages, 4392 KiB

Open AccessArticle

The Impact of Vertical Eddy Diffusivity Changes in the CMAQ Model on PM_2.5 Concentration Variations in Northeast Asia: Focusing on the Seoul Metropolitan Area

by Dong-Ju Kim, Tae-Hee Kim, Jin-Young Choi, Jae-bum Lee, Rhok-Ho Kim, Jung-Seok Son and Daegyun Lee

Atmosphere 2024, 15(3), 376; https://doi.org/10.3390/atmos15030376 - 19 Mar 2024

Viewed by 1313

Abstract

The vertical eddy diffusion process plays a crucial role in PM_2.5 prediction, yet accurately predicting it remains challenging. In the three-dimensional atmospheric chemistry transport model (3-D AQM) CMAQ, a parameter, Kz, is utilized, and it is known that PM_2.5 prediction tendencies [...] Read more.

The vertical eddy diffusion process plays a crucial role in PM_2.5 prediction, yet accurately predicting it remains challenging. In the three-dimensional atmospheric chemistry transport model (3-D AQM) CMAQ, a parameter, Kz, is utilized, and it is known that PM_2.5 prediction tendencies vary according to the floor value of this parameter (Kz_min). This study aims to examine prediction characteristics according to Kz_min values, targeting days exceeding the Korean air quality standards, and to derive appropriate Kz_min values for predicting PM_2.5 concentrations in the DJFM Seoul Metropolitan Area (SMA). Kz_min values of 0.01, 0.5, 1.0, and 2.0, based on the model version and land cover, were applied as single values. Initially focusing on December 4th to 12th, 2020, the prediction characteristics were examined during periods of local and inflow influence. Results showed that in both periods, as Kz_min increased, surface concentrations over land decreased while those in the upper atmosphere increased, whereas over the sea, concentrations increased in both layers due to the influence of advection and diffusion without emissions. During the inflow period, the increase in vertically diffused pollutants led to increased inflow concentrations and affected contribution assessments. Long-term evaluations from December 2020 to March 2021 indicated that the prediction performance was superior when Kz_min was set to 0.01, but it was not significant for the upwind region (China). To improve trans-boundary effects, optimal values were applied differentially by region (0.01 for Korea, 1.0 for China, and 0.01 for other regions), resulting in significantly improved prediction performance with an R of 0.78, IOA of 0.88, and NMB of 0.7%. These findings highlight the significant influence of Kz_min values on winter season PM_2.5 prediction tendencies in the SMA and underscore the need for considering differential application of optimal values by region when interpreting research and making policy decisions. Full article

(This article belongs to the Special Issue Novel Insights into Air Pollution over East Asia)

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11 pages, 1922 KiB

Open AccessArticle

A Novel Approach to Assessing Light Extinction with Decade-Long Observations of Chemical and Optical Properties in Seoul, South Korea

by Seung-Myung Park, Jong Sung Park, In-Ho Song, Jeonghwan Kim, Hyun Woong Kim, Jaeyun Lee, Jung Min Park, Jeong-ho Kim, Yongjoo Choi, Hye Jung Shin, Joon Young Ahn, Yu Woon Jang, Taehyoung Lee and Gangwoong Lee

Atmosphere 2024, 15(3), 320; https://doi.org/10.3390/atmos15030320 - 4 Mar 2024

Viewed by 1216

Abstract

We performed continuous long-term measurements of PM_2.5 mass, comprehensive chemical composition, and optical properties, including scattering and absorption coefficients, from March 2011 to December 2020 at the Metropolitan Air Quality Research Center in Seoul, South Korea. PM_2.5 peaked at 38 μg/m [...] Read more.

We performed continuous long-term measurements of PM_2.5 mass, comprehensive chemical composition, and optical properties, including scattering and absorption coefficients, from March 2011 to December 2020 at the Metropolitan Air Quality Research Center in Seoul, South Korea. PM_2.5 peaked at 38 μg/m³ in 2013 and has been declining steadily since then, reaching 22 μg/m³ in 2020. The extinction coefficients also decreased with the decline in PM_2.5, but the correlation between the two factors was not as pronounced. This deviation was mainly attributed to the rapid changes in the chemical composition of PM_2.5 over the same period. The mass contribution of sulphate to PM_2.5 decreased from 33.9 to 24.1%, but the fraction of nitrate and organic carbon increased from 23.4 and 20.0 to 34.1 and 32.2%, respectively, indicating that sulphate has been replaced by nitrate and organic carbon over the past decade. To assess the effect of changing aerosol chemical compositions on light extinction, we compared the measured extinction coefficients with those estimated via the various existing light extinction approaches, including the revised IMPROVE algorithm. We found that the simplified linear regression model provided the best fit to our data, with a slope of 1.03 and R² of 0.87, and that all non-linear methods, such as the IMPROVE algorithms, overestimated the observed long-term light extinction by 23 to 48%. This suggests that the simple linear regression scheme may be more appropriate for reflecting the varying aerosol conditions over long periods of time, especially for urban air. However, for conditions where the chemical composition does not change much, non-linear methods such as the IMPROVE scheme are likely to be more appropriate for reproducing light extinction. Full article

(This article belongs to the Special Issue Novel Insights into Air Pollution over East Asia)

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13 pages, 4718 KiB

Open AccessArticle

Seasonal and Emission Characteristics of PAHs in the Ambient Air of Industrial Complexes

by Yong-koo Lee, Ji-hwan Lee, Nam-gwon Beak, Kyoung-chan Kim and Jin-seok Han

Atmosphere 2024, 15(1), 30; https://doi.org/10.3390/atmos15010030 - 27 Dec 2023

Cited by 1 | Viewed by 1140

Abstract

Particulate and gaseous polycyclic aromatic hydrocarbon (PAHs) samples (n = 108) were measured every six days from January to December 2022 at a representative point in the Korean Banwol National Industrial Complex. The measurement results revealed that the concentration of particulate Σ18 [...] Read more.

Particulate and gaseous polycyclic aromatic hydrocarbon (PAHs) samples (n = 108) were measured every six days from January to December 2022 at a representative point in the Korean Banwol National Industrial Complex. The measurement results revealed that the concentration of particulate Σ18 PAHs was 7.92 ± 4.04 ng/Sm³ in winter, 1.83 ± 1.99 ng/Sm³ in spring, 1.43 ± 0.95 ng/Sm³ in summer, and 2.58 ± 2.14 ng/Sm³ in autumn. The concentration of gaseous Σ18 PAHs was 3.32 ± 3.72 ng/Sm³ in winter, 6.34 ± 5.95 ng/Sm³ in spring, 8.33 ± 8.13 ng/Sm³ in summer, and 3.88 ± 1.71 ng/Sm³ in autumn. The results of the correlation analysis showed that particulate PAHs have positive relationships with PM₁₀ and PM_2.5 and negative relationships with temperature and O₃. The diagnostic ratio and PAHs component slope showed that the emission source characteristics of the Banwol National Industrial Complex were dominated by biomass coal combustion over four seasons; however, the influence of petroleum combustion (automobile emissions) was not negligible. As for coal combustion, bituminous coal was the most influential, and lignite was relevant in summer and autumn. Full article

(This article belongs to the Special Issue Novel Insights into Air Pollution over East Asia)

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Review

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28 pages, 6428 KiB

Open AccessReview

Evolution and Control of Air Pollution in China over the Past 75 Years: An Analytical Framework Based on the Multi-Dimensional Urbanization

by Zhaopeng Li, Kai Zhao, Xiaoling Yuan, Yinan Zhou, Li Yang and Hanyu Geng

Atmosphere 2024, 15(9), 1093; https://doi.org/10.3390/atmos15091093 - 8 Sep 2024

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Abstract

China’s approach to air pollution control has been shown successful in East Asian countries and even elsewhere in the world. The analysis of the evolution and control of air pollution in China over the past 75 years can be used as a reference [...] Read more.

China’s approach to air pollution control has been shown successful in East Asian countries and even elsewhere in the world. The analysis of the evolution and control of air pollution in China over the past 75 years can be used as a reference for developing countries suffering from air pollution resulting from urbanization. Based on the sorting and mining of relevant indicators, data and policy texts from the areas of population, economy, space and social urbanization, the findings suggest that the presence of air pollution and its changing forms indeed have complex interactive relationships with the process of urbanization. Specifically: (1) the feature of air pollution has changed from “single pollutant and pollution source to multiple pollutants and pollution source, local pollution to regional pollution, light pollution to heavy compound pollution” as a result of urbanization, the emphasizing of construction and the neglect of governance, the emphasizing of economics and the neglect of ecology, and the emphasizing of immediate interests over long-term interests; (2) the interactive relationship between air pollution and urbanization has also gone through three stages from being irrelevant each other to “urbanization determines air pollution” and then “air pollution restricts urbanization”; (3) this has forced air pollution control to shift from the traditional “treating symptoms” to “high-quality urbanization”, thus promoting air pollution and urbanization to move “from confrontation to unification”. Therefore, air pollution control is not a simple technical issue; one of the keys lies in exploring how to adjust the urbanization model, so as to achieve the “win–win” of urbanization and air pollution control. Full article

(This article belongs to the Special Issue Novel Insights into Air Pollution over East Asia)

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