Classical Environmental Process Modeling, Interpretable Machine Learning, and Their Integrative Innovations—Targeting Atmospheric, Water, and Soil
A special issue of Toxics (ISSN 2305-6304).
Deadline for manuscript submissions: 26 September 2025 | Viewed by 129
Special Issue Editors
Interests: environmental chemistry; environmental ecology; environmental modeling and management
Interests: atmospheric environmental chemistry; environmental pollution simulation; environmental modeling
Interests: atmospheric environmental chemistry; environmental process modeling and reconstruction; environmental management and environmental economics
Interests: environmental management; pollution and contamination; environmental modeling and management
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In environmental science, environmental models are critical for studying pollutants. Many complex processes—such as microscopic pollutant transport in soil or hidden contaminant transformation in ecosystems—cannot be directly observed. To address this, environmental models combine theoretical assumptions, data-driven methods, and process simulations. These tools provide a unique opportunity to uncover the underlying mechanisms of such processes. In complex media like the atmosphere and water bodies, environmental models excel at reconstructing and predicting pollutant dynamics. These dynamics include enrichment, transport, transformation, and attenuation. By integrating principles from physics, chemistry, and biology, the models build mathematical frameworks. These frameworks then quantify how pollutants evolve over time. Recent advances in machine learning—especially deep learning and reinforcement learning—have opened new frontiers. Emerging techniques offer powerful capabilities, including nonlinear fitting, automated feature extraction, and self-learning. When hybridized with classical models, they significantly improve accuracy, generalization, and adaptability. This synergy is reshaping research on environmental pollutants.
This Special Issue invites contributions on the following:
- Simulation of pollutant generation or transport processes in the atmosphere, soil, or water using environmental models.
- Application of environmental models in tracking and managing high-dynamic pollution events.
- Innovations in environmental modeling methodologies and synergistic paradigms integrating traditional models with machine learning.
Dr. Anyi Niu
Prof. Dr. Chunsheng Fang
Prof. Dr. Ju Wang
Prof. Dr. Chu Xia Lin
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. Toxics 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 2600 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
- environmental models
- environmental simulation
- environmental management
- machine learning
- deep learning
- source apportionment
- atmosphere
- soil
- water
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