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Atmosphere
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Rainwater Chemistry and Atmospheric Pollutants
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Rainwater Chemistry and Atmospheric Pollutants

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

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 8534

Special Issue Editors

Prof. Dr. Qixin Wu

E-Mail Website
Guest Editor
Key Laboratory of Karst Georesources and Environment, Ministry of Education, College of Resources and Environmental Engineering, Guizhou University, Guiyang 550025, China
Interests: global climate change; atmospheric wet deposition; CO2 emission; environmental geochemistry; heavy metal pollution
Dr. Caiqing Qin

E-Mail Website
Guest Editor
Institute of Global Environmental Change, Department of Earth and Environmental Science, School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an 710100, China
Interests: wet deposition; global carbon cycle; greenhouse gases; climate change and anthropogenic disturbances; isotope geochemistry
Special Issues, Collections and Topics in MDPI journals
Jie Zeng

E-Mail Website
Guest Editor Assistant
Institute of Earth Sciences, China University of Geosciences (Beijing), No.29, Xueyuan Road, Haidian District, 100083 Beijing, China.
Interests: rainwater chemistry; atmospheric pollution and source appointment; environmental isotope geochemistry; earth surface processes; circulation of materials

Special Issue Information

Dear Colleagues,

The contradiction between human's requirement for clean air resources and atmospheric pollution due to rapid social-economic development and urbanization has become one of the most vital limiting factors for regional and even global sustainable development. Rainwater (wet deposition) is a primary sink of air pollutants (e.g., soluble gases and particulate matter), which can remove the air pollutants by both in-cloud (dissolution) and below-cloud (scour) processes, and further influenced rainwater chemical compositions and earth-surface ecosystem. Therefore, the rainwater chemical characteristics are also the reflection of air pollution and air quality. Generally, anions, cations, heavy metals, rare earth elements, carbon species are the important components of rainwater chemicals, which mainly originated from anthropogenic origin, terrestrial source, and sea-salt source. In addition, the rainwater chemistry is also influenced by several factors, such as meteorology, geography, and environmental protection policies. In the context of the globalization of environmental change and the high frequency of extreme rainfall events, how to identify the rainwater quality and atmospheric contamination (including pollution levels, sources of pollutants, and influencing factors) is most important to realize high-efficiency atmospheric environmental management and sustainable development. However, the rainwater chemistry and air pollutants study in different environments or ecosystems remain many challenges over the world. Accurate assessment of sources, transformation, and migration of pollutants in rainwater and air is a critical challenge due the different strength influence of anthropogenic and natural processes on various environmental systems. In this Special Issue, we plan to promote the publication of papers dealing rainwater chemistry and air pollution under the different environmental systems, mainly focus on the compositions, evolution, deposition fluxes, risk assessment of rainwater chemicals, the relationship between rainwater chemistry and air pollutants and the related new technologies/models. This special issue aim to publish the new/fresh/innovative ideas from different perspectives over the world, in the field of rainwater chemistry and atmospheric pollutants. This topic could be addressed from several different perspectives (Including but not limited):

1) The chemical compositions and evolution of rainwater in different terrestrial environment and ecosystem impacted by both anthropogenic and natural processes.

2) Wet deposition fluxes of typical pollutants (e.g., sulfate, nitrate, ammonia, heavy metals, rare earth elements, organic matter) and their environmental effects (risk assessment).

3) Multi-methods based identification and quantification of sources of rainwater ions and air pollutants.

4) Linkage between the rainwater chemistry and air pollutants, and its controlling factors.

5)  New technologies/models on observation and study of rainwater chemistry and air pollutants.

6) The responses of rainwater chemistry and air pollution to the environmental policy.

7) Source-sink of greenhouse gases in atmosphere and its reaction between water-air interface.

Prof. Dr. Qixin Wu
Dr. Caiqing Qin
Jie Zeng
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.

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Keywords

  • rainwater chemical compositions
  • extreme rainfall event
  • air pollution
  • wet deposition
  • atmospheric materials circulation
  • source appointment
  • atmospheric isotope geochemistry
  • rainwater risk assessment
  • throughfall
  • risk evaluation
  • greenhouse gases
  • particulate matter

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Published Papers (3 papers)

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Research

14 pages, 2099 KiB  
Article
Impacts of Soil Moisture and Fertilizer on N2O Emissions from Cornfield Soil in a Karst Watershed, SW China
by Lai Wei, Xiaolong Liu, Caiqing Qin, Wencong Xing, Yongbo Gu, Xiaoxia Wang, Li Bai and Jun Li
Atmosphere 2022, 13(8), 1200; https://doi.org/10.3390/atmos13081200 - 29 Jul 2022
Cited by 7 | Viewed by 2192
Abstract
Incubation experiments using a typical cornfield soil in the Wujiang River watershed, SW China, were conducted to examine the impacts of soil moisture and fertilizer on N2O emissions and production mechanisms. According to the local fertilizer type, we added NH4 [...] Read more.
Incubation experiments using a typical cornfield soil in the Wujiang River watershed, SW China, were conducted to examine the impacts of soil moisture and fertilizer on N2O emissions and production mechanisms. According to the local fertilizer type, we added NH4NO3 (N) and glucose (C) during incubation to simulate fertilizer application in the cornfield soil. The results showed that an increase in soil moisture and fertilizer significantly stimulated N2O emissions in cornfield soil in the karst area, and it varied with soil moisture. The highest N2O emission fluxes were observed in the treatment with nitrogen and carbon addition at 70% water-filled pore space (WFPS), reaching 6.6 mg kg−1 h−1, which was 22,310, 124.9, and 1.4 times higher than those at 5%, 40%, and 110% WFPS, respectively. The variations of nitrogen species indicated that the production of extremely high N2O at 70% WFPS was dominated by nitrifier denitrification and denitrification, and N2O was the primary form of soil nitrogen loss when soil moisture was >70% WFPS. This study provides a database for estimating N2O emissions in cropland soil in the karst area, and further helped to promote proper soil nitrogen assessment and management of agricultural land of the karst watersheds. Full article
(This article belongs to the Special Issue Rainwater Chemistry and Atmospheric Pollutants)
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18 pages, 5128 KiB  
Article
Measuring the Critical Influence Factors for Predicting Carbon Dioxide Emissions of Expanding Megacities by XGBoost
by Jianxun Zhang, He Zhang, Rui Wang, Mengxiao Zhang, Yazhe Huang, Jiahui Hu and Jingyi Peng
Atmosphere 2022, 13(4), 599; https://doi.org/10.3390/atmos13040599 - 8 Apr 2022
Cited by 9 | Viewed by 2410
Abstract
CO2 is the main greenhouse gas. Urban spatial development, land use, and so on may be affected by CO2 and climate change. The main questions studied in this paper are as follows: What are the drivers of CO2 emissions of [...] Read more.
CO2 is the main greenhouse gas. Urban spatial development, land use, and so on may be affected by CO2 and climate change. The main questions studied in this paper are as follows: What are the drivers of CO2 emissions of expanding megacities? How can they be analyzed from different perspectives? Do the results differ for megacities at different stages of development? Based on the XGBoost model, this paper explored the complex factors affecting CO2 emissions by using data of four Chinese megacities, Beijing, Tianjin, Shanghai, and Chongqing, from 2003 to 2017. The main findings are as follows: The XGBoost model has better applicability and accuracy in predicting carbon emissions of expanding megacities, with root mean square error (RMSE) as low as 0.036. Under the synergistic effect of multiple factors, population, land size, and gross domestic product are still the primary driving forces of CO2 emissions. Population density and population become more important in the single-factor analysis. The key drivers of CO2 emissions in megacities at respective developmental stages are different. This paper provides methods and tools for accurately predicting CO2 emissions and measuring the critical drivers. Furthermore, it could provide decision support for megacities to make targeted carbon-emission-reduction strategies based on their own developmental stages. Full article
(This article belongs to the Special Issue Rainwater Chemistry and Atmospheric Pollutants)
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20 pages, 6741 KiB  
Article
Regional VOCs Gathering Situation Intelligent Sensing Method Based on Spatial-Temporal Feature Selection
by Hongbin Dai, Guangqiu Huang, Jingjing Wang, Huibin Zeng and Fangyu Zhou
Atmosphere 2022, 13(3), 483; https://doi.org/10.3390/atmos13030483 - 16 Mar 2022
Cited by 8 | Viewed by 2733
Abstract
As VOCs pose a threat to human health, it is important to accurately capture changes in VOCs concentrations and sense VOCs concentrations in relevant areas. Therefore, it is necessary to improve the accuracy of VOCs concentration prediction and realise the VOCs aggregation situation [...] Read more.
As VOCs pose a threat to human health, it is important to accurately capture changes in VOCs concentrations and sense VOCs concentrations in relevant areas. Therefore, it is necessary to improve the accuracy of VOCs concentration prediction and realise the VOCs aggregation situation sensing. Firstly, on the basis of regional grid division, the inverse distance spatial interpolation method is used for spatial interpolation to collect regional VOCs data information. Secondly, extreme gradient boosting (XGBoost) is used for spatio-temporal feature selection, combined with graph convolutional neural network (GCN) to construct regional spatial relationships of VOCs, and multiple linear regression (MLR) to process VOCs time series data and predict the VOCs concentration in the grid. Finally, the aggregation potential values of VOCs are calculated based on the prediction results, and the potential perception results are visualised. A VOCs aggregation perception method based on concentration prediction is proposed, using the XGBoost-GCN-MLR method with a scenario-aware approach for VOCs to perceive the VOCs aggregation in the relevant region. VOCs concentration prediction and VOCs aggregation trend perception were carried out in Xi’an, Baoji, Tongchuan, Weinan and Xianyang. The results show that compared with the GCN model, XGBoost model, MLR model and GCN-MLR model, the XGBoost-GCN-MLR model reduces the input variables, achieves the optimisation of the input parameters of the VOCs concentration prediction model, reduces the complexity of the prediction model and improves the prediction accuracy. Intelligent sensing of VOCs aggregation can visualise the regional VOCs. The intelligent sensing of VOCs aggregation can visualise the development trend and status of regional VOCs aggregation and convey more information, which has practical value. Full article
(This article belongs to the Special Issue Rainwater Chemistry and Atmospheric Pollutants)
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