Surrogate parameters are measurable or calculable quantities closely related, directly, or indirectly, to conventional direct measurements of pollutants.
The surrogate parameters in monitoring are used as models, with strong tendency to use statistical learning which should ensure the quality needed. Their application is entirely in environmental permitting.
The focus is on models combining more surrogate parameters could model emissions and on valuation of the results of such models according to the requirement of legislation. Certain number of models on surrogate parameters is already set in permits (neural networks, mass balance-based models and regression models). Models using other techniques of statistical learning as on Bayesian statistics, requirements of process control etc. should be considered for further development of models and progress.
The challenge is to investigate indirect measurement and monitoring that could be clearly comparable with direct measurement of emissions and, in a scientific sense of view, to justify arguments for such claims. The significance of such approach is in great number of parameters for use together with progress in learning models compared to previously used indirect measurement models (mass balance models, process models, emission factors models).
The primary goal of this special issue is to strength the existing knowledge and experience and actual practice in using surrogate parameters, additionally strengthened by administrative and legal basis which now, if not yet encouraging the indirect measurement of emission, gives them broader space in environmental legislation then before. We invite contributions that explore indirect measurement and monitoring of emissions, with particular interest in surrogate parameters: models and methods, implementation, economic and legal issues.
Through this special issue, we aim to share the knowledge and to encourage the future work in the field of special issue. We welcome researchers from various disciplines to provide interdisciplinary perspectives on the theme- indirect measurement and monitoring of the emissions. Your contributions will play a crucial role in advancing knowledge in this field.
Original research articles, review articles, case studies, etc. are welcomed.
Potential topics include, but are not limited to:
- Predictive surrogates : Models and methods
- Other surrogates (indicative, qualitative etc.) - models (including the complex models with various type of surrogates) and methods
- Soft sensors – inferential models using system identification and machine learning methods
- Implementation, costs and comparison with direct measurement methods
- Legal environmental issues – relationship between technical and legal issues