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Atmosphere
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Physical Models and Statistical Methods in Atmospheric Environment
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Physical Models and Statistical Methods in Atmospheric Environment

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (17 June 2022) | Viewed by 4999

Special Issue Editors

Dr. Rui Li

E-Mail Website
Guest Editor
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
Interests: air quality forecast; machine learning; field measurement
Dr. Qingyang Xiao

E-Mail Website
Guest Editor
School of Environment, Tsinghua University, Beijing 100084, China
Interests: remote sensing; air pollution; environmental health; exposure assessment
Dr. Yawen Kong

E-Mail Website
Guest Editor
Department of Earth System Science, Tsinghua University, Beijing 100084, China
Interests: atmospheric model; atmospheric environment; remote sensing; data assimilation

Special Issue Information

Dear Colleagues,

Along with the rapid economic development and urbanization, air pollution has become a hot topic, especially in developing countries such as China. Accurate air quality forecast is very important for air pollution mitigation. Chemical transport models and statistical methods are typical tools to predict air pollutant concentrations. In recent years, machine learning algorithms have also been proven to be a robust tool to simulate air quality with the advent of the big data era. The combination of data-driven methods and physical models promotes the high-quality advancement of this discipline. We invite manuscripts regarding the application of statistical models (machine learning) and chemical transport models (earth system models) in atmospheric environments. Topics of particular interest include (1) the application of physical models and machine learning models in air quality simulation, (2) the development of physical models and data-driven methods, and (3) the statistical models in data analysis of air pollutants in the field measurement. The Special Issue is not limited to the topics mentioned above.

Dr. Rui Li
Dr. Qingyang Xiao
Dr. Yawen Kong
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • chemical transport model
  • air quality forecast
  • atmospheric environment
  • field measurement

Published Papers (3 papers)

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Research

10 pages, 2553 KiB  
Article
Source Apportionment of Ambient Aerosols during a Winter Pollution Episode in Yinchuan by Using Single-Particle Mass Spectrometry
by Kangning Li, Liukun Li, Bin Huang and Zengyu Han
Atmosphere 2022, 13(8), 1174; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13081174 - 25 Jul 2022
Cited by 2 | Viewed by 1190
Abstract
For a winter pollution episode in Yinchuan, a city in Northwestern China, ambient aerosols were characterized using a real-time single-particle aerosol mass spectrometer (SPAMS). More than 160,000 individual particles analyzed with the SPAMS were classified into eight major categories on the basis of [...] Read more.
For a winter pollution episode in Yinchuan, a city in Northwestern China, ambient aerosols were characterized using a real-time single-particle aerosol mass spectrometer (SPAMS). More than 160,000 individual particles analyzed with the SPAMS were classified into eight major categories on the basis of their mass spectral patterns: traffic emissions, biomass burning, dust, coal burning, industrial emissions, secondary inorganic, cooking, and others, all of which contribute to fine particles. The results revealed that coal burning (29.6%) and traffic emissions (23.4%) were the main sources during the monitoring period. Industrial emissions and secondary inorganic aerosols accounted for 16.6% and 14.0%, respectively. The SPAMS data indicated that the number concentration of the eight types of particles was markedly different in the different pollution cases, and higher number concentrations were discovered more often during pollution episodes. The three pollution cases were mainly caused by the accumulation of fine particles, mainly from traffic emissions, industrial emissions, and increased secondary inorganic conversion. Full article
(This article belongs to the Special Issue Physical Models and Statistical Methods in Atmospheric Environment)
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13 pages, 1776 KiB  
Article
Influence of Meteorological Factors and Chemical Processes on the Explosive Growth of PM2.5 in Shanghai, China
by Wenwen Sun, Juntao Huo, Qingyan Fu, Yuxin Zhang and Xiangde Lin
Atmosphere 2022, 13(7), 1068; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13071068 - 06 Jul 2022
Cited by 8 | Viewed by 1557
Abstract
In order to explore the mechanism of haze formation, the meteorological effect and chemical reaction process of the explosive growth (EG) of PM2.5 were studied. In this study, the level of PM2.5, water-soluble inorganic ions, carbonaceous aerosols, gaseous precursors, and [...] Read more.
In order to explore the mechanism of haze formation, the meteorological effect and chemical reaction process of the explosive growth (EG) of PM2.5 were studied. In this study, the level of PM2.5, water-soluble inorganic ions, carbonaceous aerosols, gaseous precursors, and meteorological factors were analyzed in Shanghai in 2018. The EG event is defined by a net increase of PM2.5 mass concentration greater than or equal to 100 μg m−3 within 3, 6, or 9 h. The results showed that the annual average PM2.5 concentration in Shanghai in 2018 was 43.2 μg m−3, and secondary inorganic aerosols and organic matter (OM) accounted for 55.8% and 20.1% of PM2.5, respectively. The increase and decrease in the contributions of sulfate, nitrate, ammonium (SNA), and elemental carbon (EC) to PM2.5 from clean days to EG, respectively, indicated a strong, secondary transformation during EG. Three EG episodes (Ep) were studied in detail, and the PM2.5 concentration in Ep3 was highest (135.7 μg m−3), followed by Ep2 (129.6 μg m−3), and Ep1 (82.3 μg m−3). The EG was driven by stagnant conditions and chemical reactions (heterogeneous and gas-phase oxidation reactions). This study improves our understanding of the mechanism of haze pollution and provides a scientific basis for air pollution control in Shanghai. Full article
(This article belongs to the Special Issue Physical Models and Statistical Methods in Atmospheric Environment)
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8 pages, 235 KiB  
Article
Comparisons of Combined Oxidant Capacity and Redox-Weighted Oxidant Capacity in Their Association with Increasing Levels of COVID-19 Infection
by Huibin Guo, Yidan Wang, Kaixing Yao, Liu Yang and Shiyu Cheng
Atmosphere 2022, 13(4), 569; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos13040569 - 01 Apr 2022
Cited by 1 | Viewed by 1515
Abstract
Background: Ozone (O3) and nitrogen dioxide (NO2) are substances with oxidizing ability in the atmosphere. Only considering the impact of a single substance is not comprehensive. However, people’s understanding of “total oxidation capacity” (Ox) and “weighted average [...] Read more.
Background: Ozone (O3) and nitrogen dioxide (NO2) are substances with oxidizing ability in the atmosphere. Only considering the impact of a single substance is not comprehensive. However, people’s understanding of “total oxidation capacity” (Ox) and “weighted average oxidation” (Oxwt) is limited. Objectives: This investigation aims to assess the impact of Ox and Oxwt on the novel coronavirus disease (COVID-19). We also compared the relationship between the different calculation methods of Ox and Oxwt and the COVID-19 infection rate. Method: We recorded confirmed COVID-19 cases and daily pollutant concentrations (O3 and NO2) in 34 provincial capital cities in China. The generalized additive model (GAM) was used to analyze the nonlinear relationship between confirmed COVID-19 cases and Ox and Oxwt. Result: Our results indicated that the correlation between Ox and COVID-19 was more sensitive than Oxwt. The hysteresis effect of Ox and Oxwt decreased with time. The most obvious statistical data was observed in Central China and South China. A 10 µg m−3 increase in mean Ox concentrations were related to a 23.1% (95%CI: 11.4%, 36.2%) increase, and a 10 µg m−3 increase in average Oxwt concentration was related to 10.7% (95%CI: 5.2%, 16.8%) increase in COVID-19. In conclusion, our research results show that Ox and Oxwt can better replace the single pollutant research on O3 and NO2, which is used as a new idea for future epidemiological research. Full article
(This article belongs to the Special Issue Physical Models and Statistical Methods in Atmospheric Environment)
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