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Atmosphere
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Data Analysis of Atmospheric and Air Quality Process
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Data Analysis of Atmospheric and Air Quality Process

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

Deadline for manuscript submissions: closed (20 December 2023) | Viewed by 4752

Special Issue Editors

Dr. Ashok Kumar

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, The University of Toledo, Toledo, OH 43606, USA
Interests: air quality modeling; indoor air quality; environmental information technology
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dantong Liu

E-Mail Website
Guest Editor
Department of Atmospheric Sciences, School of Earth Sciences, Zhejiang University, Hangzhou 310027, China
Interests: atmospheric chemistry; aerosol; cloud
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Considerable amount of data are generated which relate to the atmosphere and air quality for protecting the health and well-being of the public. Many universities have introduced specific courses on these topics during the last 50 years to meet the demands of the governenment, industry, and consulting companies. Innovative data analysis techniques have been developed in recent years by professionals in the field to understand the cause and effect of atmospheric processes and the air quality. The application of data analysis to solve atmospheric problems has grown exponentially to eliminate the risk due to air contaminants. Data analysis is playing a critical role as a part of a larger strategy for resolving air issues in different countries. Bad atmospheric conditions and a poor air quality have been linked to health issues over the last several decades. This Special Issue aims to provide a comprehensive summary of case studies based on the current work being carried out in applying data analysis techniques to solve problems due to atmosperic processes. This issue invites the authors to submit papers that exploit the science and technology associated with data analysis, the atmosphere, and air quality. It is strongly recommended that the authors provide a detailed description of the relevant procedures adopted in their respective studies. The papers may range from a data collection to modeling to technology development.

This Special Issue on the Data Analyis of Atmospheric and Air Quality Processes invites you to submit papers across the broader spectrum of science and engineering (e.g., data analysis, databases, technology, atmospheric variables, measurement, air quality modelling, pollution control, risk, satellite data, and online learning). The submission of research work by interdisciplinary teams and multi-country groups is of significant interest.

Dr. Ashok Kumar
Prof. Dr. Dantong Liu
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

  • atmosphere
  • air quality
  • contaminant control
  • data analysis techniques
  • control technology
  • education
  • monitoring
  • databases
  • IT
  • satellite data
  • land use data

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Published Papers (2 papers)

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Research

21 pages, 6465 KiB  
Article
Temperature Inversion and Particulate Matter Concentration in the Low Troposphere of Cergy-Pontoise (Parisian Region)
by Souad Lagmiri and Salem Dahech
Atmosphere 2024, 15(3), 349; https://doi.org/10.3390/atmos15030349 - 12 Mar 2024
Cited by 2 | Viewed by 1697
Abstract
This study aims to elucidate the influence of meteorological conditions on particle levels in Cergy-Pontoise. It explores the temporal variability of PM10 pollution days by associating them with the vertical temperature profile derived from conventional radiosondes from 2013 to 2022 (regional station). The [...] Read more.
This study aims to elucidate the influence of meteorological conditions on particle levels in Cergy-Pontoise. It explores the temporal variability of PM10 pollution days by associating them with the vertical temperature profile derived from conventional radiosondes from 2013 to 2022 (regional station). The results indicate that nearly 80% of exceedance days were associated with thermal inversions, primarily observed in winter and typically lasting 1 to 3 days. Analysis of winter thermal inversion characteristics suggests that those linked to pollution primarily occur near the ground, with higher intensity in December (12.1 °C) and lower in February (10.3 °C). Persistent inversions (extended nocturnal by diurnal inversion) account for 91.4% of the total inversions associated with high concentrations. Captive balloon soundings and temperature measurements at different altitudes were conducted during the winter of 2022/2023 to clarify thermal inversion in the Oise Valley at the center of Cergy-Pontoise. The results highlight three nocturnal wind circulation mechanisms in the valley, including downslope flow, circulation influenced by an urban heat island, and mechanical air evacuation under an inversion layer towards the less steep East side of the valley. Analysis of PM with the temperature gradient in the Oise Valley shows a significant correlation, suggesting an increase in concentrations during locally detected inversions and a decrease during atmospheric disturbance. Full article
(This article belongs to the Special Issue Data Analysis of Atmospheric and Air Quality Process)
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21 pages, 3535 KiB  
Article
Prediction of PM10 Concentration in Malaysia Using K-Means Clustering and LSTM Hybrid Model
by Noratiqah Mohd Ariff, Mohd Aftar Abu Bakar and Han Ying Lim
Atmosphere 2023, 14(5), 853; https://doi.org/10.3390/atmos14050853 - 11 May 2023
Cited by 6 | Viewed by 2422
Abstract
Following the rapid development of various industrial sectors, air pollution frequently occurs in every corner of the world. As a dominant pollutant in Malaysia, particulate matter PM10 can cause highly detrimental effects on human health. This study aims to predict the daily [...] Read more.
Following the rapid development of various industrial sectors, air pollution frequently occurs in every corner of the world. As a dominant pollutant in Malaysia, particulate matter PM10 can cause highly detrimental effects on human health. This study aims to predict the daily average concentration of PM10 based on the data collected from 60 air quality monitoring stations in Malaysia. Building a forecasting model for each station is time-consuming and unrealistic; therefore, a hybrid model that combines the k-means clustering technique and the long short-term memory (LSTM) model is proposed to reduce the number of models and the overall model training time. Based on the training set, the stations were clustered using the k-means algorithm and an LSTM model was built for each cluster. Then, the prediction performance of the hybrid model was compared with the univariate LSTM model built independently for each station. The results show that the hybrid model has a comparable prediction performance to the univariate LSTM model, as it gives the relative percentage difference (RPD) less than or equal to 50% based on at least two accuracy metrics for 43 stations. The hybrid model can also fit the actual data trend well with a much shorter training time. Hence, the hybrid model is more competitive and suitable for real applications to forecast air quality. Full article
(This article belongs to the Special Issue Data Analysis of Atmospheric and Air Quality Process)
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