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

Abstract

• 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.

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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