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Atmosphere
Special Issues
Air Pollution Modeling and Observations in Asian Megacities
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Air Pollution Modeling and Observations in Asian Megacities

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

Deadline for manuscript submissions: closed (30 September 2024) | Viewed by 2764

Special Issue Editor

Dr. Mengmeng Li

E-Mail Website
Guest Editor
School of Atmospheric Science, Nanjing University, Nanjing 21009, China
Interests: air quality modeling; urbanization; extreme weather; air pollution

Special Issue Information

Dear Colleagues,

Intense human activity associated with urbanization has caused serious air pollution in Asian megacities, posing great threats to human health and ecosystem. The growing public awareness of environmental improvements has increased the importance of related research. New techniques for observations and modeling are urgently needed to better understand the characteristics, formation mechanisms, source apportionments, and impacts of air pollution in megacities. This Special Issue aims to present innovative reearch articles and reviews in characterizing air pollution in Asian megacities, including both experimental, monitoring, and numerical modeling studies. Papers that discuss the impacts of air pollution on human health and ecosystem are also welcomed.

Dr. Mengmeng Li
Guest Editor

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.

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Keywords

  • air quality modelling
  • formation mechanism
  • data assimilation
  • source apportionment
  • remote sensing
  • human health
  • urbanization

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Published Papers (3 papers)

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Research

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11 pages, 3322 KiB  
Article
Comparative Study of O3 Forecast Performance Using Multiple Models in Beijing–Tianjin–Hebei and Surrounding Regions
by Lili Zhu, Wei Wang, Huihui Zheng, Xiaoyan Wang, Yonghai Huang and Bing Liu
Atmosphere 2024, 15(3), 300; https://doi.org/10.3390/atmos15030300 - 28 Feb 2024
Viewed by 901
Abstract
In order to systematically understand the operational forecast performance of current numerical, statistical, and ensemble models for O3 in Beijing–Tianjin–Hebei and surrounding regions, a comprehensive evaluation was conducted for the 30 model sets regarding O3 forecasts in June–July 2023. The evaluation [...] Read more.
In order to systematically understand the operational forecast performance of current numerical, statistical, and ensemble models for O3 in Beijing–Tianjin–Hebei and surrounding regions, a comprehensive evaluation was conducted for the 30 model sets regarding O3 forecasts in June–July 2023. The evaluation parameters for O3 forecasts in the next 1–3 days were found to be more reasonable and practically meaningful than those for longer lead times. When the daily maximum 8 h average concentration of O3 was below 100 μg/m3 or above 200 μg/m3, a significant decrease in the percentage of accurate models was observed. As the number of polluted days in cities increased, the overall percentage of accurate models exhibited a decreasing trend. Statistical models demonstrated better overall performance in terms of metrics such as root mean square error, standard mean bias, and correlation coefficient compared to numerical and ensemble models. Numerical models exhibited significant performance variations, with the best-performing numerical model reaching a level comparable to that of statistical models. This finding suggests that the continuous tuning of operational numerical models has a more pronounced practical effect. Although the best statistical model had higher accuracy than numerical and ensemble models, it showed a significant overestimation when O3 concentrations were low and a significant underestimation when concentrations were high. In particular, the underestimation rate for heavy polluted days was significantly higher than that for numerical and ensemble models. This implies that statistical models may be more prone to missing high-concentration O3 pollution events. Full article
(This article belongs to the Special Issue Air Pollution Modeling and Observations in Asian Megacities)
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17 pages, 1277 KiB  
Article
Resilience Assessment and Improvement Strategies for Urban Haze Disasters Based on Resident Activity Characteristics: A Case Study of Gaoyou, China
by Yang Cao, Tingting Yang, Hao Wu, Shuqi Yan, Huadong Yang, Chengying Zhu and Yan Liu
Atmosphere 2024, 15(3), 289; https://doi.org/10.3390/atmos15030289 - 27 Feb 2024
Viewed by 1132
Abstract
The popularisation of mobile information technology has provided access to the living habits and activity trajectories of residents and enabled the accurate measurement of the impact of urban haze disasters on residents’ lives, supporting urban haze risk response. Using the main urban area [...] Read more.
The popularisation of mobile information technology has provided access to the living habits and activity trajectories of residents and enabled the accurate measurement of the impact of urban haze disasters on residents’ lives, supporting urban haze risk response. Using the main urban area of Gaoyou City as a case study, this study identifies the spatial range and trajectory characteristics of the daily activities of residents in a haze disaster environment, based on air pollution monitoring and resident travel positioning data. We constructed an evaluation index system to measure the corresponding relationship between residential activities and haze disasters. The results indicate that the interference with residential activities and the adaptability of built environments are key indicators for evaluating urban resilience in haze environments, with weights of 0.57 and 0.43, and correlation indices of 0.67 and 0.81, respectively. The interference with residential activities and the adaptability of built environments exhibit spatial characteristics of cold and hot ‘multi-core’ agglomeration and ‘strip’ agglomeration, respectively. Specific indicators show that the residential activity exposure index is significantly influenced by the built environment factor index, with the vegetation coverage index showing a significant positive correlation (0.837) and the public transportation facility accessibility index showing a significant negative correlation (−1.242). Planning should focus on improving the adaptability of the built environment or reducing the interference with residential activities and enhancing the matching degree of the two at the spatial facility level. Full article
(This article belongs to the Special Issue Air Pollution Modeling and Observations in Asian Megacities)
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Review

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20 pages, 717 KiB  
Review
Deep Learning-Based Atmospheric Visibility Detection
by Yawei Qu, Yuxin Fang, Shengxuan Ji, Cheng Yuan, Hao Wu, Shengbo Zhu, Haoran Qin and Fan Que
Atmosphere 2024, 15(11), 1394; https://doi.org/10.3390/atmos15111394 - 19 Nov 2024
Viewed by 254
Abstract
Atmospheric visibility is a crucial meteorological element impacting urban air pollution monitoring, public transportation, and military security. Traditional visibility detection methods, primarily manual and instrumental, have been costly and imprecise. With advancements in data science and computing, deep learning-based visibility detection technologies have [...] Read more.
Atmospheric visibility is a crucial meteorological element impacting urban air pollution monitoring, public transportation, and military security. Traditional visibility detection methods, primarily manual and instrumental, have been costly and imprecise. With advancements in data science and computing, deep learning-based visibility detection technologies have rapidly emerged as a research hotspot in atmospheric science. This paper systematically reviews the applications of various deep learning models—Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and Transformer networks—in visibility estimation, prediction, and enhancement. Each model’s characteristics and application methods are discussed, highlighting the efficiency of CNNs in spatial feature extraction, RNNs in temporal tracking, GANs in image restoration, and Transformers in capturing long-range dependencies. Furthermore, the paper addresses critical challenges in the field, including dataset quality, algorithm optimization, and practical application barriers, proposing future research directions, such as the development of large-scale, accurately labeled datasets, innovative learning strategies, and enhanced model interpretability. These findings highlight the potential of deep learning in enhancing atmospheric visibility detection techniques, providing valuable insights into the literature and contributing to advances in the field of meteorological observation and public safety. Full article
(This article belongs to the Special Issue Air Pollution Modeling and Observations in Asian Megacities)
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