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Atmosphere
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Study of Mitigation of PM2.5 and Surface Ozone Pollution
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Study of Mitigation of PM2.5 and Surface Ozone Pollution

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 (18 December 2021) | Viewed by 20660

Special Issue Editor

Dr. Ying Chen

E-Mail Website
Guest Editor
Lancaster Environment Centre, Lancaster LA1 4YQ, UK
Interests: air pollution and mitigation strategy; aerosol-cloud-climate interaction; aerosol hygroscopicity; heterogeneous chemical processes

Special Issue Information

Dear Colleagues,

Atmosphere is an open access journal featuring a new Special Issue, the scope of which is focused on studies of air quality issues. Particulate matter pollution and surface ozone pollution are the primary focus, but submissions are not limited to these. The manuscript types considered are review articles, research articles, letters, technical notes, and commentaries/replies. We welcome original studies bringing new insights in emission sources, atmospheric components, chemical and physical processes, impacts on public health, climate, economy, and ecosystems, mitigation strategies and policy relevant science. We encourage submissions investigating regulation measures and indoor air quality. To be considered for publication in Atmosphere, manuscripts should clearly show how this research advances current understanding or improve investigation methods.

Dr. Ying Chen
Guest Editor

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

  • air quality
  • mitigation strategy
  • particulate matter
  • ozone.

Published Papers (7 papers)

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Research

13 pages, 4748 KiB  
Article
Classification of Urban Pollution Levels Based on Clustering and Spatial Statistics
by Ziyi Xu, Zhixin Liu, Jiawei Tian, Yan Liu, Hongling Pan, Shan Liu, Bo Yang, Lirong Yin and Wenfeng Zheng
Atmosphere 2022, 13(3), 494; https://doi.org/10.3390/atmos13030494 - 19 Mar 2022
Cited by 7 | Viewed by 1822
Abstract
In recent years, the occurrence and frequency of haze are constantly increasing, severely threatening people’s daily lives and health and bringing enormous losses to the economy. To this end, we used cluster analysis and spatial autocorrelation methods to discuss the spatial and temporal [...] Read more.
In recent years, the occurrence and frequency of haze are constantly increasing, severely threatening people’s daily lives and health and bringing enormous losses to the economy. To this end, we used cluster analysis and spatial autocorrelation methods to discuss the spatial and temporal distribution characteristics of severe haze in China and to classify regions of China. Furthermore, we analyzed the interaction between haze pollution and the influence of economy and energy structure in 31 provinces in China, providing references for the prevention and treatment of haze pollution. The processed data mainly include API, meteorological station data, and PM 2.5 concentration distribution vector graph. The results show the yearly haze pattern from 2008 to 2012, and present a strong pattern of pollution concentrated around Beijing–Tianjin, the Yangtze River Delta, southwest China, and central China. The overall spatial pattern of decreasing from north to south is relatively constant over the study period. Full article
(This article belongs to the Special Issue Study of Mitigation of PM2.5 and Surface Ozone Pollution)
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12 pages, 5386 KiB  
Article
Haze Prediction Model Using Deep Recurrent Neural Network
by Kailin Shang, Ziyi Chen, Zhixin Liu, Lihong Song, Wenfeng Zheng, Bo Yang, Shan Liu and Lirong Yin
Atmosphere 2021, 12(12), 1625; https://doi.org/10.3390/atmos12121625 - 06 Dec 2021
Cited by 77 | Viewed by 3742
Abstract
In recent years, haze pollution is frequent, which seriously affects daily life and production process. The main factors to measure the degree of smoke pollution are the concentrations of PM2.5 and PM10. Therefore, it is of great significance to study the prediction of [...] Read more.
In recent years, haze pollution is frequent, which seriously affects daily life and production process. The main factors to measure the degree of smoke pollution are the concentrations of PM2.5 and PM10. Therefore, it is of great significance to study the prediction of PM2.5/PM10 concentration. Since PM2.5 and PM10 concentration data are time series, their time characteristics should be considered in their prediction. However, the traditional neural network is limited by its own structure and has some weakness in processing time related data. Recurrent neural network is a kind of network specially used for sequence data modeling, that is, the current output of the sequence is correlated with the historical output. In this paper, a haze prediction model is established based on a deep recurrent neural network. We obtained air pollution data in Chengdu from the China Air Quality Online Monitoring and Analysis Platform, and conducted experiments based on these data. The results show that the new method can predict smog more effectively and accurately, and can be used for social and economic purposes. Full article
(This article belongs to the Special Issue Study of Mitigation of PM2.5 and Surface Ozone Pollution)
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15 pages, 3220 KiB  
Article
Grey Correlation Analysis of Haze Impact Factor PM2.5
by Jiayi Xu, Zhixin Liu, Lirong Yin, Yan Liu, Jiawei Tian, Yang Gu, Wenfeng Zheng, Bo Yang and Shan Liu
Atmosphere 2021, 12(11), 1513; https://doi.org/10.3390/atmos12111513 - 16 Nov 2021
Cited by 21 | Viewed by 2034
Abstract
In recent years, frequent severe haze weather has formed in China, including some of the most populated areas. We found that these smog-prone areas are often relatively a “local climate” and aim to explore this series of scientific problems. This paper uses remote [...] Read more.
In recent years, frequent severe haze weather has formed in China, including some of the most populated areas. We found that these smog-prone areas are often relatively a “local climate” and aim to explore this series of scientific problems. This paper uses remote sensing and data mining methods to study the correlation between haze weather and local climate. First, we select Beijing, China and its surrounding areas (East longitude 115°20′11″–117°40′35″, North latitude 39°21′11″–41°7′51″) as the study area. We collected data from meteorological stations in Beijing and Xianghe from March 2014 to February 2015, and analyzed the meteorological parameters through correlation analysis and a grey correlation model. We study the correlation between the six influencing factors of temperature, dew point, humidity, wind speed, air pressure and visibility and PM2.5, so as to analyze the correlation between haze weather and local climate more comprehensively. The results show that the influence of each index on PM2.5 in descending order is air pressure, wind speed, humidity, dew point, temperature and visibility. The qualitative analysis results confirm each other. Among them, air pressure (correlation 0.771) has the greatest impact on haze weather, and visibility (correlation 0.511) is the weakest. Full article
(This article belongs to the Special Issue Study of Mitigation of PM2.5 and Surface Ozone Pollution)
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11 pages, 2452 KiB  
Article
A Haze Prediction Method Based on One-Dimensional Convolutional Neural Network
by Ziyan Zhang, Jiawei Tian, Weizheng Huang, Lirong Yin, Wenfeng Zheng and Shan Liu
Atmosphere 2021, 12(10), 1327; https://doi.org/10.3390/atmos12101327 - 11 Oct 2021
Cited by 81 | Viewed by 4056
Abstract
In recent years, more and more people are paying close attention to the environmental problems in metropolitan areas and their harm to the human body. Among them, haze is the pollutant that people are most concerned about. The demand for a method to [...] Read more.
In recent years, more and more people are paying close attention to the environmental problems in metropolitan areas and their harm to the human body. Among them, haze is the pollutant that people are most concerned about. The demand for a method to predict the haze level for the public and academics keeps rising. In order to predict the haze concentration on a time scale in hours, this study built a haze concentration prediction method based on one-dimensional convolutional neural networks. The gated recurrent unit method was used for comparison, which highlights the training speed of a one-dimensional convolutional neural network. In summary, the haze concentration data of the past 24 h are used as input and the haze concentration level on the next moment as output such that the haze concentration level on the time scale in hours can be predicted. Based on the results, the prediction accuracy of the proposed method is over 95% and can be used to support other studies on haze prediction. Full article
(This article belongs to the Special Issue Study of Mitigation of PM2.5 and Surface Ozone Pollution)
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17 pages, 3256 KiB  
Article
Prediction of PM2.5 Concentration Based on the LSTM-TSLightGBM Variable Weight Combination Model
by Xuchu Jiang, Yiwen Luo and Biao Zhang
Atmosphere 2021, 12(9), 1211; https://doi.org/10.3390/atmos12091211 - 16 Sep 2021
Cited by 15 | Viewed by 2997
Abstract
PM2.5 is one of the main pollutants that cause air pollution, and high concentrations of PM2.5 seriously threaten human health. Therefore, an accurate prediction of PM2.5 concentration has great practical significance for air quality detection, air pollution restoration, and human [...] Read more.
PM2.5 is one of the main pollutants that cause air pollution, and high concentrations of PM2.5 seriously threaten human health. Therefore, an accurate prediction of PM2.5 concentration has great practical significance for air quality detection, air pollution restoration, and human health. This paper uses the historical air quality concentration data and meteorological data of the Beijing Olympic Sports Center as the research object. This paper establishes a long short-term memory (LSTM) model with a time window size of 12, establishes a T-shape light gradient boosting machine (TSLightGBM) model that uses all information in the time window as the next period of prediction input, and establishes a LSTM-TSLightGBM model pair based on an optimal weighted combination method. PM2.5 hourly concentration is predicted. The prediction results on the test set show that the mean squared error (MAE), root mean squared error (RMSE), and symmetric mean absolute percentage error (SMAPE) of the LSTM-TSLightGBM model are 11.873, 22.516, and 19.540%, respectively. Compared with LSTM, TSLightGBM, the recurrent neural network (RNN), and other models, the LSTM-TSLightGBM model has a lower MAE, RMSE, and SMAPE, and higher prediction accuracy for PM2.5 and better goodness-of-fit. Full article
(This article belongs to the Special Issue Study of Mitigation of PM2.5 and Surface Ozone Pollution)
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18 pages, 6296 KiB  
Article
Atmospheric PM2.5 Prediction Using DeepAR Optimized by Sparrow Search Algorithm with Opposition-Based and Fitness-Based Learning
by Feng Jiang, Xingyu Han, Wenya Zhang and Guici Chen
Atmosphere 2021, 12(7), 894; https://doi.org/10.3390/atmos12070894 - 09 Jul 2021
Cited by 12 | Viewed by 2527
Abstract
There is an important significance for human health in predicting atmospheric concentration precisely. However, due to the complexity and influence of contingency, atmospheric concentration prediction is a challenging topic. In this paper, we propose a novel hybrid learning method to make point and [...] Read more.
There is an important significance for human health in predicting atmospheric concentration precisely. However, due to the complexity and influence of contingency, atmospheric concentration prediction is a challenging topic. In this paper, we propose a novel hybrid learning method to make point and interval predictions of PM2.5 concentration simultaneously. Firstly, we optimize Sparrow Search Algorithm (SSA) by opposition-based learning, fitness-based learning, and Lévy flight. The experiments show that the improved Sparrow Search Algorithm (FOSSA) outperforms SSA-based algorithms. In addition, the improved Sparrow Search Algorithm (FOSSA) is employed to optimize the initial weights of probabilistic forecasting model with autoregressive recurrent network (DeepAR). Then, the FOSSA–DeepAR learning method is utilized to achieve the point prediction and interval prediction of PM2.5 concentration in Beijing, China. The performance of FOSSA–DeepAR is compared with other hybrid models and a single DeepAR model. Furthermore, hourly data of PM2.5 and O3 concentration in Taian of China, O3 concentration in Beijing, China are used to verify the effectiveness and robustness of the proposed FOSSA–DeepAR learning method. Finally, the empirical results illustrate that the proposed FOSSA–DeepAR learning model can achieve more efficient and accurate predictions in both interval and point prediction. Full article
(This article belongs to the Special Issue Study of Mitigation of PM2.5 and Surface Ozone Pollution)
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17 pages, 2959 KiB  
Article
Different Characteristics of PM2.5 Measured in Downtown and Suburban Areas of a Medium-Sized City in South Korea
by Sung-Won Park, Su-Yeon Choi, Jin-Yeo Byun, Hekap Kim, Woo-Jin Kim, Pyung-Rae Kim and Young-Ji Han
Atmosphere 2021, 12(7), 832; https://doi.org/10.3390/atmos12070832 - 28 Jun 2021
Cited by 6 | Viewed by 2262
Abstract
Chuncheon, a medium-sized city in South Korea, frequently shows high PM2.5 concentrations despite scarce anthropogenic emission sources. To identify factors increasing PM2.5 concentrations, PM2.5 and its major chemical components were concurrently measured at two different sites, namely, downtown and suburban [...] Read more.
Chuncheon, a medium-sized city in South Korea, frequently shows high PM2.5 concentrations despite scarce anthropogenic emission sources. To identify factors increasing PM2.5 concentrations, PM2.5 and its major chemical components were concurrently measured at two different sites, namely, downtown and suburban areas. The average PM2.5 concentrations at the two sites were similar, but the daily and monthly variations in PM2.5 and its components were significantly larger at the suburban site. NH4+ was significantly higher at the suburban site than at the downtown site, whereas organic carbon (OC) showed the opposite trend. Several PM2.5 samples showed an abrupt increase during winter at the suburban site, along with an increase in the amount of OC, NH4+, and K+, and the correlations between water-soluble OC, K+, and NH4+ were considerably strong, implying that local biomass burning in the suburban site was an important source of high PM2.5 episodes. Secondary OC (SOC) concentration was generally lower at the suburban site than at the downtown site, but its contribution to OC increased during winter with an increase in relative humidity, indicating the significance of heterogeneous SOC formation reactions at the suburban site. These results indicate that relevant local measures can be put into place to alleviate the occurrence of high PM2.5 concentration episodes even in medium-sized residential cities where medium-and long-range transport is anticipated to be significant. Full article
(This article belongs to the Special Issue Study of Mitigation of PM2.5 and Surface Ozone Pollution)
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