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 539

Special Issue Editors

College of New Energy and Environment, Jilin University, Changchun, China
Interests: environmental chemistry; environmental ecology; environmental modeling and management

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Guest Editor
College of New Energy and Environment, Jilin University, Changchun, China
Interests: atmospheric environmental chemistry; environmental pollution simulation; environmental modeling

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Guest Editor
College of New Energy and Environment, Jilin University, Changchun, China
Interests: atmospheric environmental chemistry; environmental process modeling and reconstruction; environmental management and environmental economics

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Guest Editor
Faculty of Science, Engineering and Built Environment, Deakin University, Burwood, VIC 3125, Australia
Interests: environmental management; pollution and contamination; environmental modeling and management
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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:

  1. Simulation of pollutant generation or transport processes in the atmosphere, soil, or water using environmental models.
  2. Application of environmental models in tracking and managing high-dynamic pollution events.
  3. 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

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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. 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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Published Papers (1 paper)

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Research

19 pages, 9490 KiB  
Article
Source Analysis of Ozone Pollution in Liaoyuan City’s Atmosphere Based on Machine Learning Models and HYSPLIT Clustering Method
by Xinyu Zou, Xinlong Li, Dali Wang and Ju Wang
Toxics 2025, 13(6), 500; https://doi.org/10.3390/toxics13060500 - 13 Jun 2025
Viewed by 327
Abstract
Firstly, this study investigates the spatiotemporal distribution characteristics of the ozone (O3) pollution in Liaoyuan City using monitoring data from 2015 to 2024. Then, three machine learning models (ML)—random forest (RF), support vector machine (SVM), and artificial neural network (ANN)—are employed [...] Read more.
Firstly, this study investigates the spatiotemporal distribution characteristics of the ozone (O3) pollution in Liaoyuan City using monitoring data from 2015 to 2024. Then, three machine learning models (ML)—random forest (RF), support vector machine (SVM), and artificial neural network (ANN)—are employed to quantify the influence of meteorological and non-meteorological factors on O3 concentrations. Finally, the HYSPLIT clustering method and CMAQ model are utilized to analyze inter-regional transport characteristics, identifying the causes of O3 pollution. The results indicate that O3 pollution in Liaoyuan exhibits a distinct seasonal pattern, with the highest concentrations found in spring and summer, peaking in the afternoon. Among the three ML models, the random forest model demonstrates the best predictive performance (R2 = 0.9043). Feature importance identifies NO2 as the primary driving factor, followed by meteorological conditions in the second quarter and land surface characteristics. Furthermore, regional transport significantly contributes to O3 pollution, with approximately 80% of air mass trajectories in heavily polluted episodes originating from adjacent industrial areas and the sea. The combined effects of transboundary precursors and O3 transport with local emissions and meteorological conditions further increase the O3 pollution level. This study highlights the need to strengthen coordinated NOX and VOCs emission reductions and enhance regional joint prevention and control strategies in China. Full article
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