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Atmosphere
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Ozone Pollution: Modeling, Observations, and Impacts
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Ozone Pollution: Modeling, Observations, and Impacts

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 (31 July 2024) | Viewed by 5231

Special Issue Editors

Dr. Le Cao

E-Mail Website
Guest Editor
Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: air pollution simulation; polar air quality; machine learning; atmospheric physics; atmospheric chemistry; computational fluid dynamics
Dr. Xuewei Hou

E-Mail Website
Guest Editor
School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing, China
Interests: the source, sink and transport of tropospheric ozone; the impact of meteorological parameters on air quality
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the atmosphere, there exist many “criteria air pollutants” (e.g., ozone, PM2.5), which are harmful to the health of human beings and the environment under a high-concentration condition. Among them, ozone is a unique trace gas in the atmosphere. Unlike the role it has in the stratosphere protecting the biosphere of the earth, ozone in the troposphere is a kind of pollutant, which directly endangers the ecological environment and human health. Ozone in the troposphere also participates in photochemical reactions and thus dominates the oxidation ability of the atmosphere.

Although there exist many studies on ozone pollution, it was found in previous model studies that the simulation results frequently deviate from observations, which might be caused by: (i) an incomplete description of the ozone chemistry by chemical mechanisms; (ii) uncertainties in emission inventories; (iii) inaccurate treatment of the interaction between ozone and other atmospheric constituents such as PM2.5; and (iv) the influence of complex terrain and underlying surface properties.

Original papers (including review articles) investigating ozone pollution on the topics discussed above and many other relevant topics, focusing on ozone and its precursors in urban, rural, and background environments, are welcome for this Special Issue.

Dr. Le Cao
Dr. Xuewei Hou
Guest Editors

Manuscript Submission Information

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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

  • ozone pollution
  • modeling
  • chemical mechanism
  • emission inventory
  • PM2.5
  • complex terrain

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Published Papers (3 papers)

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Research

18 pages, 11110 KiB  
Article
Quantifying the Impact of Multiple Factors on Air Quality Model Simulation Biases Using Machine Learning
by Chunying Fan, Ruilin Wang, Ge Song, Mengfan Teng, Maolin Zhang, Huangchuan Liu, Zhujun Li, Siwei Li and Jia Xing
Atmosphere 2024, 15(11), 1337; https://doi.org/10.3390/atmos15111337 - 7 Nov 2024
Viewed by 384
Abstract
Accurate air pollutant prediction is essential for addressing environmental and public health concerns. Air quality models like WRF-CMAQ provide simulations, but often show significant errors compared to observed concentrations. To identify the sources of these model biases, we applied the XGBoost machine learning [...] Read more.
Accurate air pollutant prediction is essential for addressing environmental and public health concerns. Air quality models like WRF-CMAQ provide simulations, but often show significant errors compared to observed concentrations. To identify the sources of these model biases, we applied the XGBoost machine learning algorithm to assess the performance of WRF-CMAQ in predicting air pollutants across two regions in China. XGBoost models trained with observations achieved high accuracy (R > 0.95), indicating that the selected features effectively capture pollutant variations. When trained on WRF-CMAQ inputs, XGBoost still improved performance but revealed biases linked to both model inputs (10–60%) and mechanisms (1–30%). Analysis identified previous-hour pollutant levels as the largest bias contributor, followed by meteorological variables. The study highlights the need for improving both model inputs and mechanisms to enhance future air quality predictions and support pollution control strategies. Full article
(This article belongs to the Special Issue Ozone Pollution: Modeling, Observations, and Impacts)
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19 pages, 1353 KiB  
Article
Representing Ozone Formation from Volatile Chemical Products (VCP) in Carbon Bond (CB) Chemical Mechanisms
by Greg Yarwood and Katie Tuite
Atmosphere 2024, 15(2), 178; https://doi.org/10.3390/atmos15020178 - 31 Jan 2024
Cited by 1 | Viewed by 1812
Abstract
Volatile organic compound (VOC) emissions to the atmosphere cause air pollution associated with adverse health outcomes. Volatile chemical products (VCPs) have emerged as a VOC emission category that is poorly characterized by air pollution models. VCPs are present throughout developed economies in manufactured [...] Read more.
Volatile organic compound (VOC) emissions to the atmosphere cause air pollution associated with adverse health outcomes. Volatile chemical products (VCPs) have emerged as a VOC emission category that is poorly characterized by air pollution models. VCPs are present throughout developed economies in manufactured products that include paints, cleaning agents, printing inks, adhesives and pesticides. Air quality models must accurately represent the atmospheric chemistry of VCPs to develop reliable air quality plans. We develop a chemical mechanism for oxidant formation by VCP compounds that is compatible with version 6 of the Carbon Bond (CB6) mechanism. We analyzed a recent U.S. VCP emission inventory and found that ~67% of the emissions mass can be well-represented by existing CB6 mechanism species but ~33% could be better represented by adding 16 emitted VCP species including alcohols, ethers, esters, alkanes and siloxanes. For larger alkanes, an important VCP category, our mechanism explicitly represents temperature-dependent organic nitrate formation and autoxidation via 1,6 H-shift reactions consistent with current knowledge. We characterized the ozone forming potential of each added VCP species and compared it to the current practice of representing VCP species by surrogate species. Nine of the sixteen added VCP species are less reactive than the current practice, namely i-propanol, dimethyl ether, methyl formate, ethyl formate, methyl acetate, larger esters, i-butane, large alkanes and siloxanes. These less reactive VCP species are characterized by having OH-reactions that form un-reactive products. A total of 7 of the 16 VCP species are more reactive than current practice, namely n-propanol, ethylene glycol, propylene glycol, larger alcohols, diethyl ether, larger ethers and ethyl acetate. These more reactive VCP species are characterized as containing functional groups that promote faster OH-reaction. The VCP chemical mechanism for CB6 can improve how VCP impacts to oxidants are represented and will be incorporated to CB7. Changes in oxidant formation resulting from the mechanism update will depend on how VCP emissions are speciated for modeling, which is uncertain, and impacts may go in opposite directions for specific categories of VCP emissions that have unique chemical speciation characteristics. We provide guidance to help modelers implement the VCP mechanism update. Full article
(This article belongs to the Special Issue Ozone Pollution: Modeling, Observations, and Impacts)
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14 pages, 2998 KiB  
Article
Simulations of Summertime Ozone and PM2.5 Pollution in Fenwei Plain (FWP) Using the WRF-Chem Model
by Yuxi Wang, Le Cao, Tong Zhang and Haijiang Kong
Atmosphere 2023, 14(2), 292; https://doi.org/10.3390/atmos14020292 - 1 Feb 2023
Cited by 4 | Viewed by 2252
Abstract
In recent years, ozone and PM2.5 pollution has often occured in the Fenwei Plain due to heavy emission and favorable geographical conditions. In this study, we used the weather research and forecasting/chemistry (WRF-Chem) model to reproduce the complex air pollution of the [...] Read more.
In recent years, ozone and PM2.5 pollution has often occured in the Fenwei Plain due to heavy emission and favorable geographical conditions. In this study, we used the weather research and forecasting/chemistry (WRF-Chem) model to reproduce the complex air pollution of the ozone and PM2.5 in the Fenwei Plain (FWP) from 20 May to 29 May 2015. By comparing the simulation results with the observed data, we found that although in some cities there was a bias between the simulated values and observed data, the model captured the trend of pollutants generally. Moreover, according to the assessment parameters, we validated that the deviations are acceptable. However, according to these parameters, we found that the WRF-Chem performed better on ozone simulation rather than PM2.5. Based on the validation, we further analyzed the pollutant distribution during the contaminated period. Generally speaking, the polluted area is mainly located in the cities of the Shanxi province and Henan province. Moreover, in this time period, pollution mainly occurred on 27 May and 28 May. In addition, due to different formation conditions of ozone and PM2.5 pollution, the distribution characteristics of these two pollutants were also found to be different. Ozone pollution mainly occurred north of FWP due to the prevailing wind and the chemistry of ozone production. As for PM2.5, the pollution occurred at night and the polluted area was located in the FWP. Furthermore, high PM2.5 areas were closed to emission sources in the FWP, showing a high correlation with primary emissions. Full article
(This article belongs to the Special Issue Ozone Pollution: Modeling, Observations, and Impacts)
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