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Atmosphere
Special Issues
New Insights in Air Quality Assessment: Forecasting and Monitoring
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New Insights in Air Quality Assessment: Forecasting and Monitoring

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

Deadline for manuscript submissions: 30 April 2024 | Viewed by 760

Special Issue Editors

Dr. Qingyan Fu

E-Mail Website
Guest Editor
Shanghai Academy of Environmental Sciences, Shanghai 200233, China
Interests: atmospheric environment monitoring; forecast of ambient air quality; information construction of environmental monitoring, emission inventory and simulation of air pollutants, emission verification of industrial areas; emission monitoring of pollution sources and research on composite air pollution
Dr. Jianjun Li

E-Mail Website
Guest Editor
China National Environmental Monitoring Center, Beijing 100012, China
Interests: air quality monitoring; air quality forecast; environmental monitoring

Special Issue Information

Dear Colleagues,

Air quality monitoring is an important means with which to evaluate ambient air status and human health exposure, while air quality forecasting is used to predict the change trend in air quality in the future. Air quality forecasting is usually based on historical data and monitoring data, using statistical methods, numerical models, artificial intelligence algorithms, expert experience comprehensive judgment, etc., which can be divided into short- and medium-/long-term prediction. Short-term forecasts are usually based on weather forecasts and air quality models, while medium-/long-term forecasts take into account more factors, such as seasonal changes, economic development, changes in pollutant emissions, and even climate change uncertainties. In addition, according to the results of air quality forecasting, different countries and regions set up different air pollution warnings. Early warnings can help the public or government departments to take appropriate measures to protect themselves or reduce the impact of pollution, such as reducing outdoor activities and limiting traffic emissions, etc. In recent years, with the rapid development of air quality monitoring and forecasting technology, the guarantee of major activities and the continuous improvement of air quality have gradually embarked on the road of fine management and regulation.

However, different from air quality monitoring and evaluation, different countries and regions in the world have different forecasting methods, time cycles, and evaluation methods. Being complex and important work, information exchange and standard rules of air quality forecasting are particularly important. The purpose of this Special Issue is to promote the continuous improvement of ambient air quality and the protection of human health in countries around the world by sharing and exchanging the latest ambient air quality monitoring technology, analyses of pollution causes, and practices of forecasting and warning.

Dr. Qingyan Fu
Dr. Jianjun 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

  • air monitoring
  • forecasting
  • emission inventory
  • numerical model
  • pollution warnings
  • O3
  • PM2.5

Published Papers (2 papers)

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Research

23 pages, 6024 KiB  
Article
Air Quality Class Prediction Using Machine Learning Methods Based on Monitoring Data and Secondary Modeling
by Qian Liu, Bingyan Cui and Zhen Liu
Atmosphere 2024, 15(5), 553; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos15050553 (registering DOI) - 30 Apr 2024
Abstract
Addressing the constraints inherent in traditional primary Air Quality Index (AQI) forecasting models and the shortcomings in the exploitation of meteorological data, this research introduces a novel air quality prediction methodology leveraging machine learning and the enhanced modeling of secondary data. The dataset [...] Read more.
Addressing the constraints inherent in traditional primary Air Quality Index (AQI) forecasting models and the shortcomings in the exploitation of meteorological data, this research introduces a novel air quality prediction methodology leveraging machine learning and the enhanced modeling of secondary data. The dataset employed encompasses forecast data on primary pollutant concentrations and primary meteorological conditions, alongside actual meteorological observations and pollutant concentration measurements, spanning from 23 July 2020 to 13 July 2021, sourced from long-term air quality projections at various monitoring stations within Jinan, China. Initially, through a rigorous correlation analysis, ten meteorological factors were selected, comprising both measured and forecasted data across five categories each. Subsequently, the significance of these ten factors was assessed and ranked based on their impact on different pollutant concentrations, utilizing a combination of univariate and multivariate significance analyses alongside a random forest approach. Seasonal characteristic analysis highlighted the distinct seasonal impacts of temperature, humidity, air pressure, and general atmospheric conditions on the concentrations of six key air pollutants. The performance evaluation of various machine learning-based classification prediction models revealed the Light Gradient Boosting Machine (LightGBM) classifier as the most effective, achieving an accuracy rate of 97.5% and an F1 score of 93.3%. Furthermore, experimental results for AQI prediction indicated the Long Short-Term Memory (LSTM) model as superior, demonstrating a goodness-of-fit of 91.37% for AQI predictions, 90.46% for O3 predictions, and a perfect fit for the primary pollutant test set. Collectively, these findings affirm the reliability and efficacy of the employed machine learning models in air quality forecasting. Full article
(This article belongs to the Special Issue New Insights in Air Quality Assessment: Forecasting and Monitoring)
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28 pages, 13221 KiB  
Article
Machine Learning Forecast of Dust Storm Frequency in Saudi Arabia Using Multiple Features
by Reem K. Alshammari, Omer Alrwais and Mehmet Sabih Aksoy
Atmosphere 2024, 15(5), 520; https://0-doi-org.brum.beds.ac.uk/10.3390/atmos15050520 (registering DOI) - 24 Apr 2024
Viewed by 294
Abstract
Dust storms are significant atmospheric events that impact air quality, public health, and visibility, especially in arid Saudi Arabia. This study aimed to develop dust storm frequency predictions for Riyadh, Jeddah, and Dammam by integrating meteorological and environmental variables. Our models include multiple [...] Read more.
Dust storms are significant atmospheric events that impact air quality, public health, and visibility, especially in arid Saudi Arabia. This study aimed to develop dust storm frequency predictions for Riyadh, Jeddah, and Dammam by integrating meteorological and environmental variables. Our models include multiple linear regression, support vector machine, gradient boosting regression tree, long short-term memory (LSTM), and temporal convolutional network (TCN). This study highlights the effectiveness of LSTM and TCN models in capturing the complex temporal dynamics of dust storms and demonstrates that they outperform traditional methods, as evidenced by their lower mean absolute error (MAE) and root mean square error (RMSE) values and higher R2 score. In Riyadh, the TCN model demonstrates its remarkable performance, with an R2 score of 0.51, an MAE of 2.80, and an RMSE of 3.48, highlighting its precision, adaptability, and responsiveness to changes in dust storm frequency. Conversely, in Dammam, the LSTM model proved to be the most accurate, achieving an MAE of 3.02, RMSE of 3.64, and R2 score of 0.64. In Jeddah, the LSTM model also exhibited an MAE of 2.48 and an RMSE of 2.96. This research shows the potential of using deep learning models to improve the accuracy and reliability of dust storm frequency forecasts. Full article
(This article belongs to the Special Issue New Insights in Air Quality Assessment: Forecasting and Monitoring)
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