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Atmosphere
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Atmospheric Pollutants: Characteristics, Sources and Transport (2nd Edition)
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Atmospheric Pollutants: Characteristics, Sources and Transport (2nd Edition)

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

Deadline for manuscript submissions: closed (28 May 2024) | Viewed by 820

Special Issue Editors

Dr. Chengzhi Xing

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Guest Editor
Key Lab of Environmental Optics & Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China
Interests: stereoscopic remote sensing; instrument technology; aerosol; nitrous acid; ozone; source analysis; atmospheric oxidation capacity
Special Issues, Collections and Topics in MDPI journals
Dr. Yan Xiang

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Guest Editor
Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China
Interests: stereoscopic observation; lidar; atmospheric chemistry model; data assimilation; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Qihua Li

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Guest Editor
Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China
Interests: MAX-DOAS; air pollutant profile; optical remote sensing; neural network; downscaling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a follow-up of the first Special Issue, entitled “Atmospheric Pollutants: Characteristics, Sources and Transport” (https://www.mdpi.com/journal/atmosphere/special_issues/3EA1WFWT8J) published in Atmosphere.

Air pollution sources can be roughly classified into direct emissions, secondary production, and transport. Transportation can directly deteriorate the environment through the production and emission of a large number of pollutants. The movement of warm and humid air masses likely increases secondary aerosol formation by aggravating aqueous and heterogeneous reactions. Moreover, variations in atmospheric oxidation capacity could also deeply influence several pollution processes; therefore, it is also critical to understand the source, distribution, and transport process of atmospheric oxidants. In addition, considering their health risk to humans, it is also necessary to study the human health effects of different air pollutants. Field observations and model simulations are two important methods with which to understand the characteristics, physicochemical processes, and transport processes of air pollutants. Thus, we also strongly encourage authors to use advanced observation technologies (satellite remote sensing, Lidar, MAX-DOAS, etc.), analysis schemes (e.g., big data and machine learning), instruments, and models during their studies.

Solicited contributions include, but are not limited to, studies on the characteristics, sources, and transport analysis of air pollutants through measurements and simulations. Research on environmental monitoring instruments and models is also encouraged. We invite authors to submit original research or to review previous work and summarize the current state of the art. Submissions of research work by multi-country groups are of significant interest.

Dr. Chengzhi Xing
Dr. Yan Xiang
Dr. Qihua Li
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

  • stereoscopic observation
  • model
  • remote sensing
  • source analysis
  • transport
  • data assimilation
  • machine learning
  • aerosol
  • trace gases
  • atmospheric oxidation capacity
  • human health

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Published Papers (1 paper)

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Research

14 pages, 5468 KiB  
Article
Spatiotemporal Modeling of Carbon Fluxes over Complex Underlying Surfaces along the North Shore of Hangzhou Bay
by Kaidi Zhang, Min Zhao, Zhenyu Zhao, Xucheng Shen, Yanyu Lu and Jun Gao
Atmosphere 2024, 15(6), 727; https://doi.org/10.3390/atmos15060727 - 17 Jun 2024
Viewed by 525
Abstract
Urban areas contribute to over 80% of carbon dioxide emissions, and considerable efforts are being undertaken to characterize spatiotemporal variations of CO2 (carbon dioxide) at a city, regional, and national level, aiming at providing pipelines for carbon mission reduction. The complex underlying [...] Read more.
Urban areas contribute to over 80% of carbon dioxide emissions, and considerable efforts are being undertaken to characterize spatiotemporal variations of CO2 (carbon dioxide) at a city, regional, and national level, aiming at providing pipelines for carbon mission reduction. The complex underlying surface composition of urban areas makes process-based and physiology-based models inadequate for simulating carbon flux in this context. In this study, long short-term memory (LSTM), support vector machine (SVM), random forest (RF), and artificial neural network (ANN) were employed to develop and investigate their viability in estimating carbon flux at the ecosystem level. All the data used in our study were derived from the long-term chronosequence observations collected from the flux towers within urban complex underlying surface, along with meteorological reanalysis datasets. To assess the generalization ability of these models, the following statistical metrics were utilized: coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Our analysis revealed that the RF model performed the best in simulating carbon flux over long time series, with the highest R2 values reaching up to 0.852, and exhibiting the smallest RMSE and MAE values at 0.293 μmol·m−2·s−1 and 0.157 μmol·m−2·s−1. As a result, the RF model was chosen for simulating carbon flux at spatial scale and assessing the impact of urban impervious surfaces in the simulation. The results showed that the RF model performs well in simulating carbon flux at the spatial scale. The input of impervious surface area index can improve the performance of the RF model in simulating carbon flux, with R2 values of 84.46% (with the impervious surface area index in) and 83.74% (without the impervious surface area index in). Furthermore, the carbon flux in Fengxian District, Shanghai, exhibited significant spatial heterogeneity: the CO2 flux in the western part of Fengxian District was less than in the eastern part, and the CO2 flux gradually increased from the west to the east. In addition, we creatively introduced the diurnal impervious surface area index based on the Kljun model, and clarified the influence of impervious surface on the spatiotemporal simulation of CO2 flux over the complex urban underlying surface. Based on these findings, we conclude that the RF models can be effectively applied for estimating carbon flux on the complex underlying urban surface. The results of our study reduce the uncertainty in modeling carbon cycling in terrestrial ecosystems, and make the variety of models for the carbon cycling of terrestrial ecosystems more diverse. Full article
(This article belongs to the Special Issue Atmospheric Pollutants: Characteristics, Sources and Transport (2nd Edition))
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