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Frontiers of Environmental Science & Engineering

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Front. Environ. Sci. Eng.    2021, Vol. 15 Issue (2) : 31    https://doi.org/10.1007/s11783-020-1323-0
RESEARCH ARTICLE
Enhancement of the polynomial functions response surface model for real-time analyzing ozone sensitivity
Jiangbo Jin1, Yun Zhu1,2(), Jicheng Jang1, Shuxiao Wang3, Jia Xing3, Pen-Chi Chiang4,5, Shaojia Fan2, Shicheng Long1
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
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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.
Keywords Response surface model      Hill-climbing algorithm      Ozone pollution      Precursor emissions      Control strategy     
Corresponding Author(s): Yun Zhu   
Issue Date: 14 September 2020
 Cite this article:   
Jiangbo Jin,Yun Zhu,Jicheng Jang, et al. Enhancement of the polynomial functions response surface model for real-time analyzing ozone sensitivity[J]. Front. Environ. Sci. Eng., 2021, 15(2): 31.
 URL:  
https://academic.hep.com.cn/fese/EN/10.1007/s11783-020-1323-0
https://academic.hep.com.cn/fese/EN/Y2021/V15/I2/31
Fig.1  Key operation process for the development and application of an Enhanced polynomial functions Response Surface Model (Epf-RSM) (Note: HSS: the Hammersley quasi-random Sequence Sample; WRF-CMAQ: the Weather Research and Forecasting coupled with Community Multi-scale Air Quality).
Fig.2  The schematic diagrams of the hill-climbing adaptive method (HCAM) (Note: F(a, b, c, d) represents a polynomial function; a, b, c, and d represent the nonnegative integer exponents of E NOX, E SO2, E NH3, and E AVOC, respectively; the red and blue lines represent either adding 1 to a or d, respectively).
Short name Objective Control factor Number of cases
Baseline Baseline case – 1
RSM RSM method, to create single regional RSM in seven regions separately Four precursors including NOx, SO2, NH3, AVOC in each of the seven regions For the PRD: 41 samples for each region in addition to the baseline case (total 288, 41 × 7+ 1= 288), Hamersley quasi-random Sequence Sampling between 0.0 to 1.5 a, the control matrix is shown in Table S5
For the NWShD: 41 samples for each region in addition to the baseline case (total 288, 41 × 7+ 1= 288), Hamersley quasi-random Sequence Sampling between 0.0 to 2.0 a, the control matrix is shown in Table S6
RSMTT Using RSM method, to create multiple regional RSM in seven regions Four precursors including NOx, SO2, NH3, AVOC in each of the seven regions For the PRD: 41 samples for seven regions together in addition to the baseline case (total 42), Hamersley quasi-random Sequence Sampling between 0.0 to 1.5 a, the control matrix is shown in Table S5
For NWShD: 41 samples for seven regions together in addition to the baseline case (total 42), Hamersley quasi-random Sequence Sampling between 0.0 to 2.0 a, the control matrix is shown in Table S6
OOS Out-of-sample validation Four precursors including NOx, SO2, NH3, AVOC in each of the seven regions For the PRD: 10 samples for seven regions together, Hamersley quasi-random Sequence Sampling between 0.0 to 1.5 a, the control matrix is shown in Table S7
For the NWShD: 15 samples for seven regions together, Hamersley quasi-random Sequence Sampling between 0.0 to 2.0 a, the control matrix is shown in Table S8
Tab.1  Scenarios for Response Surface Modeling design
Fig.3  Comparison of O3 concentrations simulated by CMAQ with (a) Epf-RSM-predicted in the PRD, (b) pf-ERSM-predicted in the PRD, (c) Epf-RSM-predicted in the NWShD, (d) pf-ERSM-predicted in the NWShD, respectively (monthly averaged daily 1 h maxima O3; unit: mg/m3) (Note: the percent of data point represents the density of points falling at each grid cell on the plot with a resolution of 1 mg/m3 × 1 mg/m3. The red lines are the one-to-one lines indicating perfect agreement, and the dashed lines are the best-fit lines described by slope and intercept).
Fig.4  Spatial distribution of CMAQ-simulated, Epf-RSM-predicted, and pf-ERSM-predicted O3 responses, along with corresponding errors under (a) moderate control and (b) strict control scenarios of the PRD (monthly averaged daily 1 h maxima O3 in 2015, unit: mg/m3).
Fig.5  Spatial distribution of CMAQ-simulated, Epf-RSM-predicted, and pf-ERSM-predicted O3 responses, along with corresponding errors under (a) moderate control and (b) strict control scenarios of the NWShD (monthly averaged daily 1 h maxima O3 in 2017, unit: mg/m3).
Fig.6  Comparison of the EKMA diagrams as derived from the (a) Epf-RSM for the cPRD, (b) pf-ERSM for the cPRD, (c) Epf-RSM for the cNWShD, and (d) pf-ERSM for the cNWShD (monthly averaged daily 1 h maxima O3; unit: mg/m3) (Note: the x- and y-axis represent the ratios of current NOx and AVOC emissions to base emissions respectively; the blue lines are ridgelines).
Region Jan. Apr. Jul. Oct.
PRD Shunde 0.35 0.83 >1.50 0.69
Foshan (excluding Shunde) 0.37 0.86 >1.50 0.72
Guangzhou 0.31 0.74 1.22 0.56
Zhongshan 0.40 0.81 >1.50 0.74
Jiangmen 0.41 0.86 >1.50 0.73
Dongguan & Shenzhen 0.49 0.74 1.37 0.87
cPRD 0.41 0.83 1.37 0.73
NWShD Jinan <0.00 0.19 1.65 0.28
Dezhou <0.00 0.16 >2.00 0.27
Binzhou <0.00 0.19 >2.00 0.32
Liaocheng <0.00 0.15 1.75 0.23
Taian <0.00 0.19 1.81 0.25
Zibo <0.00 0.17 1.23 0.23
cNWShD <0.00 0.16 1.46 0.27
Tab.2  The domain-averaged PR of each region
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