Sources, Spatio-Temporal Distribution and Health Effects of Atmospheric Compositions (2nd Edition)

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

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 2911

Special Issue Editors

Shanghai Carbon Data Research Center, Key Laboratory of Low-Carbon Conversion Science & Engineering, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China
Interests: greenhouse gases; polycyclic aromatic hydrocarbons; black carbon; atmospheric monitoring
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Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China
Interests: air pollution; atmospheric chemical model; remote sensing; machine learning
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Guest Editor
School of Geography and Tourism, Zhengzhou Normal University, Zhengzhou 450011, China
Interests: environmental science; quaternary geology
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Guest Editor
Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China
Interests: air pollution; big data; microplastic transport
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Guest Editor
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: biomass burning; air pollution; global climate change; remote sensing; geospatial science

Special Issue Information

Dear Colleagues,

Climate change and air pollution are two global environmental issues. Anthropogenic activities lead to large amounts of greenhouse gases and air pollutants being released into the atmosphere, and therefore change the atmospheric composition. These anthropogenic atmospheric compositions can alter the Earth’s radiation balance, causing climate change on a macro scale; it can also have an effect on the human respiratory system, introducing health risks from the air pollutants on the micro scale. Therefore, it is necessary to monitor and simulate the spatio-temporal distribution of the atmospheric composition, identify emission sources and contributions, and assess the potential ecological risks and health effects, as well as exchanges with other environmental mediums.

This Special Issue aims to present the most recent and outstanding results of atmospheric composition studies. Topics of interest for this Special Issue cover different aspects of studies on atmospheric composition, including, but not limited to:

  • New technologies to monitor or measure atmospheric composition.
  • Temporal and spatial distribution of the constituents of atmospheric composition, including greenhouse gases and air pollutants.
  • Source appointment method to determine atmospheric composition.
  • Emission inventory of greenhouse gases and air pollutants.
  • Ecological and health risk assessment of atmospheric composition.
  • Exchange and transformation of atmospheric composition in different environmental mediums.

Dr. Chong Wei
Dr. Nan Li
Dr. Xingjun Xie
Dr. Xin Long
Dr. Shuai Yin
Guest Editors

Manuscript Submission Information

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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

  • air pollutants
  • greenhouse gases
  • atmospheric monitoring
  • atmospheric modeling
  • spatio-temporal distribution
  • source appointment
  • emission inventory
  • health risk assessment

Published Papers (4 papers)

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Research

21 pages, 20948 KiB  
Article
Analysis of Primary Air Pollutants’ Spatiotemporal Distributions Based on Satellite Imagery and Machine-Learning Techniques
by Yanyu Li, Meng Zhang, Guodong Ma, Haoyuan Ren and Ende Yu
Atmosphere 2024, 15(3), 287; https://doi.org/10.3390/atmos15030287 - 27 Feb 2024
Viewed by 571
Abstract
Accurate monitoring of air pollution is crucial to human health and the global environment. In this research, the various multispectral satellite data, including MODIS AOD/SR, Landsat 8 OLI, and Sentinel-2, together with the two most commonly used machine-learning models, viz. multi-layer backpropagation neural [...] Read more.
Accurate monitoring of air pollution is crucial to human health and the global environment. In this research, the various multispectral satellite data, including MODIS AOD/SR, Landsat 8 OLI, and Sentinel-2, together with the two most commonly used machine-learning models, viz. multi-layer backpropagation neural network (MLBPN) and random forest (RF), have been employed to analyze the spatiotemporal distributions of the primary air pollutant from 2019 to 2022 in Guanzhong Region, China. In the conducted experiments, the RF-based model, using the MODIS AOD data, has generally demonstrated the “optimal” estimation performance for the ground-surface concentrations of the primary air-pollutants. Then, the “optimal” estimation model has been employed to analyze the spatiotemporal distribution of the various air pollutants—in terms of temporal distribution, the annual average concentrations of PM2.5, PM10, NO2, and SO2 in the research area showed a decreasing trend from 2019 to 2022, while the annual average concentration of CO remained relatively stable and the annual average concentration of O3 slightly increased; in terms of the spatial distribution, the air pollution presents a gradual increase from west to east in the research area, with the distribution of higher concentrations in the center of the built-up areas and lower in the surrounding rural areas. The proposed estimation model and spatiotemporal analysis can provide reliable methodologies and data support for the further study of the air pollution characteristics in the research area. Full article
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15 pages, 6021 KiB  
Article
Temporal Dynamics of Negative Air Ion Concentrations in Nanjing Tulou Scenic Area
by Zhihui Li, Changshun Li, Bo Chen, Yu Hong, Lan Jiang, Zhongsheng He and Jinfu Liu
Atmosphere 2024, 15(3), 258; https://doi.org/10.3390/atmos15030258 - 21 Feb 2024
Viewed by 576
Abstract
Negative air ions (NAIs) are crucial for assessing the impact of forests on wellbeing and enhancing the physical and mental health of individuals. They serve as pivotal indicators for assessing air quality. Comprehensive research into the distribution patterns of NAI concentrations, especially the [...] Read more.
Negative air ions (NAIs) are crucial for assessing the impact of forests on wellbeing and enhancing the physical and mental health of individuals. They serve as pivotal indicators for assessing air quality. Comprehensive research into the distribution patterns of NAI concentrations, especially the correlation between NAI concentrations and meteorological elements in tourist environments, necessitates the accumulation of additional long-term monitoring data. In this paper, long-term on-site monitoring of NAI concentrations, air temperature, relative humidity, and other factors was conducted in real time over 24 h, from April 2020 to May 2022, to explore the temporal dynamic patterns of NAIs and their influencing factors. The results showed that (1) the daily dynamics of NAI concentrations followed a U-shaped curve. The peak concentrations usually occurred in the early morning (4:30–8:00) and evening (19:10–22:00), and the lowest concentrations usually occurred at noon (12:50–14:45). (2) At the monthly scale, NAI concentrations were relatively high in February, August, and September and low in January, June, and December. At the seasonal scale, NAI concentrations were significantly higher in winter than in other seasons, with higher concentrations occurring in the summer and autumn. (3) Relative humidity, air temperature, and air quality index (AQI) were the primary factors that influenced NAI concentrations. Relative humidity showed a significant positive correlation with NAI concentrations, while air temperature and AQI both exhibited a significant negative correlation with NAI concentrations. Higher air quality corresponds to higher NAI concentrations. Our research provides new insights into NAI temporal dynamics patterns and their driving factors, and it will aid in scheduling outdoor recreation and forest health activities. Full article
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17 pages, 29053 KiB  
Article
Wildfire CO2 Emissions in the Conterminous United States from 2015 to 2018 as Estimated by the WRF-Chem Assimilation System from OCO-2 XCO2 Retrievals
by Jiuping Jin, Qinwei Zhang, Chong Wei, Qianrong Gu and Yongjian Huang
Atmosphere 2024, 15(2), 186; https://doi.org/10.3390/atmos15020186 - 31 Jan 2024
Viewed by 631
Abstract
Wildfires are becoming more frequent due to the global climate change. Large amounts of greenhouse gases emitted by wildfires can lead to increases in extreme climate events. Accurately estimating the greenhouse gas carbon dioxide (CO2) emissions from wildfires is important for [...] Read more.
Wildfires are becoming more frequent due to the global climate change. Large amounts of greenhouse gases emitted by wildfires can lead to increases in extreme climate events. Accurately estimating the greenhouse gas carbon dioxide (CO2) emissions from wildfires is important for mitigation of climate change. In this paper, we develop a novel method to estimate wildfire CO2 emissions from the relationship between local CO2 emissions and XCO2 anomalies. Our method uses the WRF-Chem assimilation system from OCO-2 XCO2 retrievals which coupled with Data Assimilation Research Testbed (DART). To validate our results, we conducted three experiments evaluating the wildfire CO2 emissions over the conterminous United States. The four-month average wildfire emissions from July to October in 2015∼2018 were estimated at 4.408 Tg C, 1.784 Tg C, 1.514 Tg C and 2.873 Tg C, respectively. Compared to the average of established inventories CT2019B, FINNv1.5 and GFASv1.2 fire emissions, our estimates fall within one standard deviation, except for 2017 due to lacking of OCO-2 XCO2 retrievals. These results suggest that the regional carbon assimilation system, such as WRF-Chem/DART, using OCO-2 XCO2 retrievals has a great potential for accurately tracking regional wildfire emissions. Full article
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14 pages, 9408 KiB  
Article
Aircraft Measurements of Tropospheric CO2 in the North China Plain in Autumn and Winter of 2018–2019
by Hui Zhang, Qiang Yang, Hongjie Yuan, Dongliang Ma, Zhilei Liu, Jianguang Jia, Guan Wang, Nana Zhang, Hailiang Su, Youyu Shi, Yongjing Ma, Lindong Dai, Baojiang Li and Xiao Huang
Atmosphere 2023, 14(12), 1835; https://doi.org/10.3390/atmos14121835 - 18 Dec 2023
Cited by 1 | Viewed by 826
Abstract
Quantifying the level of CO2, the main greenhouse gas (GHG), is essential for research on regional and global climate change, especially in the densely populated North China Plain with its severe CO2 emissions. In this study, 12 airborne flights were [...] Read more.
Quantifying the level of CO2, the main greenhouse gas (GHG), is essential for research on regional and global climate change, especially in the densely populated North China Plain with its severe CO2 emissions. In this study, 12 airborne flights were managed and conducted during the autumn–winter period of 2018–2019 in downtown Shijiazhuang and its surrounding areas, which are representative of the typical urban conditions in the North China Plain, to explore the spatial and temporal distributions of CO2. The results showed that the measured columnar averages of CO2 ranged between 399.9 ± 1.5 and 443.8 ± 31.8 ppm; the average of the 12 flights was 412.1 ppm, slightly higher than the globally averaged 410.5 ± 0.20 ppm and the 2 background concentrations of 411.6 ± 2.1 ppm and 411.4 ± 0.2 ppm in low-latitude Mauna Loa and middle-latitude Waliguan in 2019, indicating the potential influences of anthropogenic activities. The typical stratification of the planetary boundary layer (PBLH), residual layer (RL), and elevated inversion layer (IL) was crucial in constraining the high CO2 concentrations. This illustrated that the warming effect of CO2 within the PBLH may also have some influences on regulating the thermal structure of the low troposphere. Based on a backward trajectory analysis, it was evidenced that there were three different categories of air masses for autumn and one category for winter. Both trajectories in the PBL, i.e., below 1000 m, from the local and southern areas with tremendous anthropogenic emissions (autumn) and from the western regions (winter) led to comparatively high levels of CO2, but the mid-tropospheric CO2 concentrations above 1000 m were commonly homogeneously distributed, with higher levels appearing in winter because the concentration in the free troposphere followed the global seasonal pattern, with a summer minimum and winter maximum as a result of the seasonality of the net CO2 exchange and the balance between photosynthesis and respiration. These results provide an in-depth understanding of the vertical concentrations of tropospheric CO2 in the North China Plain, which will offer scientific references for the evaluation of carbon accounting and carbon emissions. Full article
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