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Journals
Atmosphere
Special Issues
Data Driven Analysis of Complex Atmospheric Environment
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Data Driven Analysis of Complex Atmospheric Environment

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

Deadline for manuscript submissions: closed (20 February 2023) | Viewed by 1982

Special Issue Editors

Prof. Dr. Kai Shi

E-Mail Website
Guest Editor
College of Environmental Sciences and Engineering, China West Normal University, Nanchong 637001, China
Interests: air quality assessment; atmospheric chemistry and physics; nonlinear modelling; machine learning; atmospheric modeling and simulation; climate change
Prof. Dr. Rui Zhao

E-Mail Website
Guest Editor
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
Interests: environmental systems engineering; environmental management; waste management and treatment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Reasonable decision-making plays a key role in aiding air quality improvement. This depends on reliable environmental data and model analysis. Atmospheric environmental data are growing in complexity, resolution and amount rapidly. The availability and accessibility of environmental datasets are promoting the growing interest in the field of modeling and quantitative assessment of atmospheric environmental problems.  Addressing the types of complex air pollution problems faced by today’s atmospheric environmental scientists requires the ability to synthesize and analyze heterogeneous data from multiple sources successfully. In the process of supporting holistic analyses and extraction of new knowledge, traditional analysis methods face limitations or challenges. Advanced data analysis methods have become indispensable tools to reveal hidden patterns in air pollution evolution.

The open-access journal Atmosphere is hosting a Special Issue to showcase the most recent findings related to data analysis and methods in atmospheric photochemistry, air quality evolution, meteorological change, greenhouse gases reduction, air quality management and air pollution health effect.

This Special Issue is an appropriate venue for papers that deal with complex atmospheric environment data analysis and methods. Any novel experimental and modeling studies that advance understanding of the complex atmospheric environmental problems are all welcome contributions. So the Special Issue aims to build a bridge among the atmospheric pollution emissions, atmospheric photochemistry, meteorological science, atmospheric models, quantitative assessment methods, environmental management and sustainable policy.

Prof. Dr. Kai Shi
Prof. Dr. Rui Zhao
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

  • complex atmospheric environment
  • meteorological science
  • atmospheric photochemistry
  • spatiotemporal evolution
  • data mining
  • modelling
  • assessment
  • atmospheric policy-making
  • greenhouse gases
  • emission reduction

Published Papers (1 paper)

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Research

20 pages, 3420 KiB  
Article
A Comparative Study of Deep Learning Models on Tropospheric Ozone Forecasting Using Feature Engineering Approach
by Reza Rezaei, Behzad Naderalvojoud and Gülen Güllü
Atmosphere 2023, 14(2), 239; https://doi.org/10.3390/atmos14020239 - 25 Jan 2023
Cited by 2 | Viewed by 1527
Abstract
This paper investigates the effect of the architectural design of deep learning models in combination with a feature engineering approach considering the temporal variation in the features in the case of tropospheric ozone forecasting. Although deep neural network models have shown successful results [...] Read more.
This paper investigates the effect of the architectural design of deep learning models in combination with a feature engineering approach considering the temporal variation in the features in the case of tropospheric ozone forecasting. Although deep neural network models have shown successful results by extracting features automatically from raw data, their performance in the domain of air quality forecasting is influenced by different feature analysis approaches and model architectures. This paper proposes a simple but effective analysis of tropospheric ozone time series data that can reveal temporal phases of the ozone evolution process and assist neural network models to reflect these temporal variations. We demonstrate that addressing the ozone evolution phases when developing the model architecture improves the performance of deep neural network models. As a result, we evaluated our approach on the CNN model and showed that not only does it improve the performance of the CNN model, but also that the CNN model in combination with our approach boosts the performance of the other deep neural network models such as LSTM. The development of the CNN, LSTM-CNN, and CNN-LSTM models using the proposed approach improved the prediction performance of the models by 3.58%, 1.68%, and 3.37%, respectively. Full article
(This article belongs to the Special Issue Data Driven Analysis of Complex Atmospheric Environment)
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