空气质量数值预报优化方法研究

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Abstract In this study, the forecast values of hourly PM_2.5、PM₁₀、O₃、SO₂、NO₂、CO concentrations at 13 environmental monitoring stations in Xuzhou city during December 2016 were corrected using nudging scheme and XGBoost algorithm, and improvement model prediction before and after correction were analyzed. A method combining nudging scheme and IDW interpolation algorithm was adopted by modifying the forecast values of SO₂、NO₂、CO concentrations, results showed that the correlation coefficient between the predicted concentration and the observation simulated by the assimilation source increased by 0.06~0.27, and the mean absolute error and the root mean square error decreased obviously, the average relative deviation (MFB) and average relative error (MFE) were within the ideal range, had best effect on NO₂ followed by SO₂ and CO. The part of statistical revision which based on XGBoost algorithm, by introducing WRF meteorological forecast elements established a statistical regression model, which could be used for modifying the forecast values of PM_2.5、PM₁₀、O₃、SO₂、NO₂、CO concentrations. Results showed that lower or higher than normal conditions were greatly improved,with the exception of SO₂, the correlation coefficient increased to about 0.6~0.7, the reduction of the error of statistical indicators was very obvious.

Key words： Models-3/CMAQ “nudging&rdquo scheme statistical revision optimization of numerical air quality prediction

Received: 21 November 2017

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Corresponding Authors: 肖昕,副教授,passerxx@sina.com E-mail: passerxx@sina.com

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	Articles by authors
	ZHAO Jun-ri
	XIAO Xin
	WU Tao
	LI Yan-peng
	JIA Hong-xia

Cite this article:

ZHAO Jun-ri,XIAO Xin,WU Tao等. A revised approach to air quality forecast based on Models-3/CMAQ[J]. CHINA ENVIRONMENTAL SCIENCECE, 2018, 38(6): 2047-2054.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2018/V38/I6/2047

[1]	唐孝炎,张远航,邵敏.大气环境化学-第2版[M]. 北京:高等教育出版社, 2006:447-449.
[2]	王占山,李晓倩,王宗爽,等.空气质量模型CMAQ的国内外研究现状[J]. 环境科学与技术, 2013,(S1):386-391.
[3]	薛文博,王金南,杨金田,等.国内外空气质量模型研究进展[J]. 环境与可持续发展, 2013,38(3):14-20.
[4]	Yumimoto K, Uno I. Adjoint inverse modeling of CO emissions over Eastern Asia using four-dimensional variational data assimilation[J]. Atmospheric Environment, 2006,40(35):6836-6845.
[5]	孟凯,程兴宏,徐祥德,等.基于CMAQ源同化反演方法的京津冀局地污染源动态变化特征模拟研究[J]. 环境科学学报, 2017,37(1):52-60.
[6]	Xu X, Xie L, Cheng X, et al. Application of an adaptive nudging scheme in air quality forecasting in China[J]. Journal of Applied Meterology and Climatology, 2008,47(8):2105-2114.
[7]	靳璐滨,臧增亮,潘晓滨,等.PM2.5和PM2.5~10资料同化及在南京青奥会期间的应用试验[J]. 中国环境科学, 2016,36(2):331-341.
[8]	Dennis R L, Byun D W, Novak J H, et al. The next generation of integrated air quality modeling:EPA's models-3[J]. Atmospheric Environment, 1996,30(12):1925-1938.
[9]	谢敏,钟流举,陈焕盛,等.CMAQ模式及其修正预报在珠三角区域的应用检验[J]. 环境科学与技术, 2012,35(2):96-101.
[10]	Djalalova I, Monache L D, Wilczak J. PM2.5 analog forecast and Kalman filter post-processing for the Community Multiscale Air Quality (CMAQ) model[J]. Atmospheric Environment, 2015,119:431-442.
[11]	许建明,徐祥德,刘煜,等.CMAQ-MOS区域空气质量统计修正模型预报途径研究[J]. 中国科学, 2005,35(z1):131-144.
[12]	尤佳红,束炯,陈亦君,等.基于MOS的杭州秋冬季空气污染预报和霾诊断[J]. 中国环境科学, 2014,34(7):1660-1666.
[13]	薛文博,许艳玲,唐晓龙,等.中国氨排放对PM2.5污染的影响[J]. 中国环境科学, 2016,36(12):3531-3539.
[14]	胡向军,陶健红,郑飞,等.WRF模式物理过程参数化方案简介[J]. 甘肃科技, 2008,24(20):73-75.
[15]	Hu X M, Nielsengammon J W, Zhang F. Evaluation of Three Planetary Boundary Layer Schemes in the WRF Model[J]. Journal of Applied Meteorology & Climatology, 2010,49(9):1831-1844.
[16]	Grell G A, Peckham S E, Schmitz R, et al. Fully coupled "online" chemistry in the WRF model[J]. Atmospheric Environment, 2005, 39(37):6957-6975.
[17]	王刚.杭州市二次形成PM2.5的研究[D]. 浙江大学, 2008.
[18]	He K. Multi-resolution Emission Inventory for China (MEIC):model framework and 1990-2010 anthropogenic emissions[C]//AGU Fall Meeting, 2012.
[19]	孟智勇,徐祥德,陈联寿.卫星亮温资料四维同化方案及其对"7·20"武汉特大暴雨的模拟试验[J]. 大气科学, 2002, 26(5):663-676.
[20]	余小东,武莹,何腊梅.反距离加权网格化插值算法的改进及比较[J]. 工程地球物理学报, 2013,10(6):900-904.
[21]	Chen T, Guestrin C. XGBoost:A Scalable Tree Boosting System[C]. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016:785-794.
[22]	张钰,陈珺,王晓峰,等.Xgboost在滚动轴承故障诊断中的应用[J]. 噪声与振动控制, 2017,37(4):166-170.
[23]	Boylan J W, Russell A G. PM and light extinction model performance metrics, goals, and criteria for three-dimensional air quality models[J]. Atmospheric Environment, 2006,40(26):4946-4959.
[24]	程兴宏,刁志刚,胡江凯,等.基于CMAQ模式和自适应偏最小二乘回归法的中国地区PM2.5浓度动力-统计预报方法研究[J]. 环境科学学报, 2016,36(8):2771-2782.
[25]	陆维青,江峰琴,刘丽霞,等.江苏省空气质量预报与实测结果比对研究[J]. 环境监控与预警, 2017,9(1):10-14.