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Atmosphere
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Asian/Pacific Air Pollution and Environment
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Asian/Pacific Air Pollution and Environment

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 51608

Special Issue Editor

Dr. Ja-Ho Koo

E-Mail Website
Guest Editor
Department of Atmospheric Sciences, Yonsei University, Seoul, Korea
Interests: atmospheric chemistry; air pollution; air quality; polar environment; meteorology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are planning a Special Issue that focuses on various issues of the atmospheric pollution, environment, and chemistry in the Asia and Pacific region. Asia is known to experience the most serious air pollution in the world. Therefore, air pollutants are not only limited in a certain local region, but also widely transported to other areas. Trans-Pacific transport is one representative example that has been investigated for some time. In spite of numeous findings, there is still a need for more accurate evaluation of the influence of long-range transboundary transport.

Over the Asia and Pacific area, many kinds of teleconnections (climate variability) have been discovered so far. These teleconnections may relate to the long-term variation or extreme pattern of air pollutants, however, our understanding regarding this connection remains very poor. Due to the recent increasing severity of global climate change, climate variability is expected to have a greater effect on the variation in the Asia and Pacific region.

Previously, regional air pollution research has mostly been confined to the atmosphere. However, the importance of the interaction between atmosphere and ocean is now recognized. Nowadays, in terms of the Asia and Pacific air pollution and environment, new ideas have been suggested regarding the ocean effect to the atmosphere, expanding our view when interpreting the properties of air pollution. Biogenic emissions from the ocean is one such example, and this novel idea will help us to better determine the unknown features of atmospheric chemistry in the Asia and Pacific region.

Prof. Ja-Ho Koo
Guest Editor

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Keywords

  • long-range transport (Trans-Pacific transport)
  • blocking effect to the air quality
  • influence of climate variability
  • air–sea interaction
  • biogenic emissions from the ocean
  • arious observation and modeling results

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Published Papers (6 papers)

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Research

21 pages, 3300 KiB  
Article
Assessment of the Components and Sources of Acid Deposition in Northeast Asia: A Case Study of the Coastal and Metropolitan Cities in Northern Taiwan
by Hung-Yu Chen, Ling-Fei Hsu, Shih-Zhe Huang and Long Zheng
Atmosphere 2020, 11(9), 983; https://doi.org/10.3390/atmos11090983 - 14 Sep 2020
Cited by 10 | Viewed by 4634
Abstract
In this study, temporal variations, major ion reference indexes, correlation analysis, and statistical data were used to investigate the chemical characteristics of the atmospheric pollutants in wet deposition and reasons for their formation, and further insight into the impact of local and regional [...] Read more.
In this study, temporal variations, major ion reference indexes, correlation analysis, and statistical data were used to investigate the chemical characteristics of the atmospheric pollutants in wet deposition and reasons for their formation, and further insight into the impact of local and regional atmospheric pollutant distributions on urban and coastal area environments. From November 2014 to October 2015, 158 rainwater samples were collected in coastal Wanli and urban Banqiao of southern Northeast Asia (northern Taiwan). The mean pH of the coastal and urban was 4.63 and 4.58, respectively, lower than the mean (5.31) of 10 East Asia regions during the year of 2015. This was possibly because the concentration of the combined SO42− and NO3− in the study area were greater than the mean of the 10 East Asian regions. This is verified by the calculation of sea-salt fraction (SSF) and non-SSF fraction (NSSF) in study areas, which indicated that Na+ and Cl− accounted for over 85% of the SSF, without Na+ in Banqiao, were mainly due to marine sources. For the NSSF, in addition to SO42− in Wanli, nearly 90% of wet disposition was from SO42− and NO3−, which were emitted from human activities. Furthermore, the analysis of fractional acidity (FA), neutralization factors (NF), neutralization potential (NP), and acidification potential (AP) revealed that acidified precipitation was caused by a lack of neutralizing compounds, which resulted in less neutralization of acidic precipitation. Finally, the results of correlation and principal component analysis (PCA) indicated that because coastal area were geographically closer to the ocean, wet deposition mainly comes from marine sources. However, in urban with a high population density and high traffic quantity, the ions in wet deposition primarily come from anthropogenic activities, such as industrial combustion and vehicle emissions. Full article
(This article belongs to the Special Issue Asian/Pacific Air Pollution and Environment)
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21 pages, 5939 KiB  
Article
Air Quality Modeling Study on the Controlling Factors of Fine Particulate Matter (PM2.5) in Hanoi: A Case Study in December 2010
by Thanh Hung Nguyen, Tatsuya Nagashima and Quang-Van Doan
Atmosphere 2020, 11(7), 733; https://doi.org/10.3390/atmos11070733 - 10 Jul 2020
Cited by 10 | Viewed by 4832
Abstract
Meteorology and emission sources are the two main factors determining concentrations of air pollutants, including fine particulate matter. A regional air quality modeling system was used to analyze the sources of fine-particulate air pollution in Hanoi, Vietnam, in December 2010. The impacts of [...] Read more.
Meteorology and emission sources are the two main factors determining concentrations of air pollutants, including fine particulate matter. A regional air quality modeling system was used to analyze the sources of fine-particulate air pollution in Hanoi, Vietnam, in December 2010. The impacts of precipitation and winds on PM2.5 concentrations was investigated. Precipitation was negatively correlated with PM2.5 concentrations. However, winds showed both positive and negative correlations with PM2.5 concentrations, depending on wind direction (WD) and the level of upwind concentrations. Sensitivity simulations were conducted to investigate the contribution of local and non-local emissions sources on total PM2.5 by perturbing the emission inputs of the model. Overall, local and non-local sources contributed equally to the total PM2.5 in Hanoi. Local emission sources comprised 57% of the total PM2.5 concentrations for the high PM2.5 pollution levels, while only comprising 42% of the total PM2.5 for low levels of PM2.5 concentrations. In Hanoi’s urban areas, local sources contributed more to the total PM2.5 than non-local sources. In contrast, non-local sources were the main contributors to the PM2.5 in Hanoi’s rural areas. Additional sensitivity simulations were conducted to identify the main local emission sources of PM2.5 concentrations in December 2010. The industrial and residential sectors collectively comprised 79% of the total PM2.5 concentrations while the transport and power sectors comprised only 2% and 3%, respectively. This is the first case study which used a regional air quality modeling system to provide new and informative insights into PM2.5 air pollution in Hanoi by estimating the contributions of local and non-local emissions sources, as well as the contribution of local emission sectors to PM2.5 concentrations in Hanoi. Full article
(This article belongs to the Special Issue Asian/Pacific Air Pollution and Environment)
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12 pages, 2224 KiB  
Article
Quantifying the Influences of PM2.5 and Relative Humidity on Change of Atmospheric Visibility over Recent Winters in an Urban Area of East China
by Xiaoyun Sun, Tianliang Zhao, Duanyang Liu, Sunling Gong, Jiaping Xu and Xiaodan Ma
Atmosphere 2020, 11(5), 461; https://doi.org/10.3390/atmos11050461 - 2 May 2020
Cited by 30 | Viewed by 4599
Abstract
Fine particulate matters (PM2.5) and relative humidity (RH) in the ambient atmosphere are the leading anthropogenic and natural factors changing atmospheric horizontal visibility. Based on the analysis of environmental and meteorological data observed over 2013–2019 in Nanjing, an urban area in [...] Read more.
Fine particulate matters (PM2.5) and relative humidity (RH) in the ambient atmosphere are the leading anthropogenic and natural factors changing atmospheric horizontal visibility. Based on the analysis of environmental and meteorological data observed over 2013–2019 in Nanjing, an urban area in East China, this study investigated the influences of PM2.5 and RH on atmospheric visibility changes over recent years. The visibility had significantly negative correlations with the PM2.5 concentrations and RH changes. The nonlinear relationships existed between PM2.5 concentrations and visibility, as well as between RH and visibility, with the inflection points in the atmospheric visibility changes. The PM2.5 inflection concentrations were 81.0 μg m−3, 76.0 μg m−3, 49.0 μg m−3, and 33.0 μg m−3, respectively, for the RH ranges of RH < 60%, 60% ≤ RH < 80%, 80% ≤ RH < 90%, and RH ≥ 90%, indicating that the improvement of visibility with reducing PM2.5 concentrations could be more difficult under the humid meteorological condition. The visibility changes were most sensitive to PM2.5 concentrations in the RH range of 60–80% in this urban area of East China. The relative contributions of natural factor RH and anthropogenic factor PM2.5 to variations of wintertime atmospheric visibility were quantified with 54.3% and 45.7%, respectively, revealing an important role of natural factor RH in the change of atmospheric visibility in the urban area of East Asian monsoon region. Full article
(This article belongs to the Special Issue Asian/Pacific Air Pollution and Environment)
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15 pages, 5931 KiB  
Article
NOx Emission Reduction and Recovery during COVID-19 in East China
by Ruixiong Zhang, Yuzhong Zhang, Haipeng Lin, Xu Feng, Tzung-May Fu and Yuhang Wang
Atmosphere 2020, 11(4), 433; https://doi.org/10.3390/atmos11040433 - 24 Apr 2020
Cited by 193 | Viewed by 25471
Abstract
Since its first confirmed case at the end of 2019, COVID-19 has become a global pandemic in three months with more than 1.4 million confirmed cases worldwide, as of early April 2020. Quantifying the changes of pollutant emissions due to COVID-19 and associated [...] Read more.
Since its first confirmed case at the end of 2019, COVID-19 has become a global pandemic in three months with more than 1.4 million confirmed cases worldwide, as of early April 2020. Quantifying the changes of pollutant emissions due to COVID-19 and associated governmental control measures is crucial to understand its impacts on economy, air pollution, and society. We used the WRF-GC model and the tropospheric NO2 column observations retrieved by the TROPOMI instrument to derive the top-down NOx emission change estimation between the three periods: P1 (January 1st to January 22nd, 2020), P2 (January 23rd, Wuhan lockdown, to February 9th, 2020), and P3 (February 10th, back-to-work day, to March 12th, 2020). We found that NOx emissions in East China averaged during P2 decreased by 50% compared to those averaged during P1. The NOx emissions averaged during P3 increased by 26% compared to those during P2. Most provinces in East China gradually regained some of their NOx emissions after February 10, the official back-to-work day, but NOx emissions in most provinces have not yet to return to their previous levels in early January. NOx emissions in Wuhan, the first epicenter of COVID-19, had no sign of emission recovering by March 12. A few provinces, such as Zhejiang and Shanxi, have recovered fast, with their averaged NOx emissions during P3 almost back to pre-lockdown levels. Full article
(This article belongs to the Special Issue Asian/Pacific Air Pollution and Environment)
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19 pages, 7491 KiB  
Article
A Hybrid Deep Learning Model to Forecast Particulate Matter Concentration Levels in Seoul, South Korea
by Guang Yang, HwaMin Lee and Giyeol Lee
Atmosphere 2020, 11(4), 348; https://doi.org/10.3390/atmos11040348 - 31 Mar 2020
Cited by 75 | Viewed by 7708
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Asian/Pacific Air Pollution and Environment)
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17 pages, 3729 KiB  
Article
Regional Differences of Primary Meteorological Factors Impacting O3 Variability in South Korea
by Yeomin Jeong, Hwa Woon Lee and Wonbae Jeon
Atmosphere 2020, 11(1), 74; https://doi.org/10.3390/atmos11010074 - 8 Jan 2020
Cited by 12 | Viewed by 3206
Abstract
Surface ozone (O3) is a harmful pollutant and effective strategies must be developed for its reduction. In this study, the impact of meteorological factors on the annual O3 variability for South Korea were analyzed. In addition, the regional differences of [...] Read more.
Surface ozone (O3) is a harmful pollutant and effective strategies must be developed for its reduction. In this study, the impact of meteorological factors on the annual O3 variability for South Korea were analyzed. In addition, the regional differences of meteorological factors in six air quality regions in South Korea (Seoul Metropolitan Area, SMA; Central region, CN; Honam, HN; Yeongnam, YN; Gangwon, GW; Jeju, JJ) were compared. The analysis of ground observation data from 2001 to 2017 revealed that the long-term variability of O3 concentration in South Korea continuously increased since 2001, and the upward trend in 2010 to 2017 (Period 2, PRD2) was 29.8% higher than that in 2001 to 2009 (Period 1, PRD1). This was because the meteorological conditions during PRD2 became relatively favorable for high O3 concentrations compared to conditions during PRD1. In particular, the increase in the solar radiation (SR) and maximum temperature (TMAX) and the decrease in the precipitation (PRCP) and wind speed (WS) of South Korea in PRD2 were identified as the main causes for the rise in O3 concentrations. When meteorological factors and O3 variability were compared among the six air quality regions in South Korea during PRD1 and PRD2, significant differences were observed. This indicated that different meteorological changes occurred in South Korea after 2010 due to the different topographical characteristics of each region; thus, O3 variability also changed differently in each region. Interestingly, for the regions with almost similar meteorological changes after 2010, the O3 concentration changed differently depending on the difference in the distribution of emissions. These results indicate that the O3–meteorology relationship shows spatiotemporal differences depending on the topographical and emission distribution characteristics of each area and implies that it is necessary to fully consider such differences for efficient O3 reduction. Full article
(This article belongs to the Special Issue Asian/Pacific Air Pollution and Environment)
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