Identification and Parameter Estimation of Multi-Scale Environmental Pollution Sources

Dear Colleagues,

Identifying pollution sources and estimating parameters in multi-scale environments are critical due to their direct impact on public health and industrial product quality, especially in the case of atmospheric pollution. The accurate identification of pollution sources enables a more precise forecasting of potential hazards and a better implementation of protective measures. However, despite its importance, research in this area faces several challenges. Most studies rely on numerical simulations, which often struggle with real-world applications due to the noise in sensor measurements and uncertainty in computational models. Overcoming these challenges is essential; thus, they should be studied alongside the integration of novel measurement techniques, computational methods, source identification, and estimation algorithms. Concentrating and publishing relevant research will foster a systematic understanding of these technologies, encouraging further advancements of pollution source identification and estimation.

To promote insights into the application and progress of pollution source identification and parameter estimation in multi-scale environments concerning different spatial scales (e.g., local and regional pollution), temporal scales (e.g., short-term and long-term pollution), and environmental media (e.g., air, soil, and water), this Special Issue aims to provide a platform for researchers to address the outlined challenges and explore innovative solutions. We invite high-quality research and review articles that shed light on various aspects of pollution source identification and parameter estimation, fostering a comprehensive understanding and facilitating its widespread adoption in multi-scale environments. Studies related to atmospheric and extreme environments are particularly welcome. The scope of this Special Issue includes, but is not limited to, the following topics:

1. Advanced algorithms for atmospheric pollution source identification and parameter estimation;
2. Integration of robust computational methods with real-time sensor technologies for enhanced source detection;
3. Novel measurement techniques and experimental investigations on atmospheric pollution dispersion and source detection;
4. Forecasting and risk assessment supported by accurate pollution source identification;
5. Regulatory frameworks and comprehensive reviews related to pollution source identification methodologies;
6. Low-cost sensor networks or approaches for real-world multi-scale pollution detection.

Dr. Fei Li
Prof. Dr. Zhuangbo Feng
Prof. Dr. Hao Cai
Guest Editors

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