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Frontiers of Environmental Science & Engineering >> 2021, Volume 15, Issue 2 doi: 10.1007/s11783-020-1323-0

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

1. Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution Control, College of Environment and Energy, South China University of Technology, Guangzhou Higher Education Mega Center, Guangzhou 510006, China
2. Southern Marine Science and Engineering Guangdong Laboratory, Sun Yat-Sen University, Zhuhai 519000, China
3. State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Tsinghua University, Beijing 100084, China
4. Graduate Institute of Environmental Engineering, Taiwan University, Taipei 10673, China
5. Carbon Cycle Research Center, Taiwan University, Taipei 10672, China

Available online: 2020-09-14

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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-NOx-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 (NOx) 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-NOx-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 NOx-sensitive in July.

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