Enhancement of the polynomial functions response surface model for real-time analyzing ozone sensitivity

Highlights

• The calculation process and algorithm of response surface model (RSM) were enhanced.

• The prediction errors of RSM in the margin and transition areas were greatly reduced.

• The enhanced RSM was able to analyze O₃-NO_x-VOC sensitivity in real-time.

• The O₃ formations were mainly sensitive to VOC, for the two case study regions.

Abstract

Quantification of the nonlinearities between ambient ozone (O₃) and the emissions of nitrogen oxides (NO_x) and volatile organic compound (VOC) is a prerequisite for an effective O₃ control strategy. An Enhanced polynomial functions Response Surface Model (Epf-RSM) with the capability to analyze O₃-NO_x-VOC sensitivities in real time was developed by integrating the hill-climbing adaptive method into the optimized Extended Response Surface Model (ERSM) system. The Epf-RSM could single out the best suited polynomial function for each grid cell to quantify the responses of O₃ concentrations to precursor emission changes. Several comparisons between Epf-RSM and pf-ERSM (polynomial functions based ERSM) were performed using out-of-sample validation, together with comparisons of the spatial distribution and the Empirical Kinetic Modeling Approach diagrams. The comparison results showed that Epf-RSM effectively addressed the drawbacks of pf-ERSM with respect to over-fitting in the margin areas and high biases in the transition areas. The O₃ concentrations predicted by Epf-RSM agreed well with Community Multi-scale Air Quality simulation results. The case study results in the Pearl River Delta and the north-western area of the Shandong province indicated that the O₃ formations in the central areas of both the regions were more sensitive to anthropogenic VOC in January, April, and October, while more NO_x-sensitive in July.

Graphical abstract

Keywords

Response surface model / Hill-climbing algorithm / Ozone pollution / Precursor emissions / Control strategy

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Acknowledgements

This work was supported by the Science and Technology Program of Guangzhou, China (No. 202002030188), the National Key Research and Development Program of China (No.2016YFC0207606), US EPA Emission, Air quality, and Meteorological Modeling Support (No. EP-D-12-044), the National Natural Science Foundation of China (Grant No. 21625701), the Fundamental Research Funds for the Central Universities (Nos. D2160320, D6180330, and D2170150) and the Natural Science Foundation of Guangdong Province, China (No. 2017A030310279).

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11783-020-1323-0 and is accessible for authorized users.

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