广州2014—2019年气象条件对O3污染影响的定量评估

doi:10.11755/j.issn.1006-7639(2022)-02-0266

摘要/Abstract

摘要：

O₃质量浓度受气象因子和污染源排放共同影响,为了定量化评估污染控制措施减排对广州市O₃质量浓度影响的效果,需将由污染源排放的O₃质量浓度数据分离出来。利用Kolmogorov-Zurbenko(KZ)滤波将广州市2014—2019年逐日O₃质量浓度数据和同期气象数据分解为长期分量、短期分量和季节分量,计算各分量方差对原始时间序列方差的贡献率;结合多元线性逐步回归方法建立O₃质量浓度各分量与相应尺度气象要素的线型回归模型,将气象因子和污染源排放对O₃质量浓度的贡献分离开来,得到仅由气象条件影响的O₃质量浓度贡献。分析结果表明:(1)广州O₃质量浓度长期序列总体呈波动上升趋势,季节分量和短期分量波动基本一致,季节分量表现为春末夏初O₃质量浓度出现高值,夏末秋初出现次高峰,冬季谷值;(2)分析各分量方差对O₃质量浓度原始序列总方差的贡献率发现,短期分量方差贡献率最大,其次为季节分量,长期分量方差贡献最小,表明广州O₃质量浓度的波动主要由前体物排放和气象条件的季节和短期变化引起,长期排放及气候条件的变化并不是引起监测波动的主要原因;(3)从解释方差来看,气象变量对O₃质量浓度长期分量的解释能力最高,季节分量其次;(4)经逐步回归消除气象条件影响的O₃质量浓度长期分量有波动下降趋势,结合2014—2019年O₃污染有所加重,表明这几年广州气象条件对O₃扩散不利。

关键词: KZ滤波, 广州, 逐步回归, O₃

Abstract:

The ozone mass concentration is affected by meteorological elements and emissions from air pollution sources. In order to quantitatively evaluate the effect of air pollution control measures, it is necessary to separate the contributions from air pollution sources. The Kolmogorov-Zurbenko filter was used to decompose the time series of daily ozone mass concentration during 2014-2019 as well as time series of meteorological factors in the same period in Guangzhou into long-term, short-term and seasonal components, and the variance contribution of each component to the total variance of the original ozone mass concentration data was calculated. Then multiple linear stepwise regression method was used to establish the relationship between ozone mass concentration data and meteorological variables for each time scale, the contributions of meteorological factors and pollutant emissions to ozone mass concentration were separated to obtain the contribution of ozone mass concentration influenced by meteorological conditions only. The results are as follows: (1) The long-term series of ozone mass concentration generally fluctuated and increased, the seasonal component showed a high value in late spring and early summer, a secondary peak in late summer and early autumn, and a trough in winter. (2) By analyzing the variance contribution rates of each component to the total variance of ozone mass concentration, the short-term component contributed the most, followed by the seasonal component, and the long-term component contributed the least, which indicated that the fluctuation of ozone mass concentration in Guangzhou was mainly caused by the short-term and seasonal changes of meteorological conditions and precursor emissions, and the long-term changes of emissions and climate conditions were not the main reasons for the fluctuation of ozone mass concentration. (3) In terms of explanatory variance, meteorological variables had the highest explanatory ability to the long-term component of ozone mass concentration, followed by the seasonal component. (4) The long-term component of ozone mass concentration which eliminated the influence of meteorological conditions by stepwise regression showed a fluctuating decreasing trend. Combined with the aggravation of ozone pollution from 2014 to 2019 in Guangzhou, the meteorological conditions were unfavorable for ozone diffusion in recent years.

Key words: KZ filter, Guangzhou, linear regression, ozone

中图分类号:

步巧利, 余乐福, 陈辰. 广州2014—2019年气象条件对O₃污染影响的定量评估[J]. 干旱气象, 2022, 40(2): 266-274.

BU Qiaoli, YU Lefu, CHEN Chen. Quantitative assessment of influence of meteorological conditions on ozone pollution in Guangzhou during 2014-2019[J]. Journal of Arid Meteorology, 2022, 40(2): 266-274.

图/表 9

图1 广州市环境监测站与气象站空间分布

Fig.1 The spatial distribution of environmental monitoring sites and meteorological stations in Guangzhou

图2 2014—2019年珠三角9市O3质量浓度日均值原始时间序列

Fig.2 The original time series of daily mean mass concentration of O3 in nine cities in the Pearl River Delta from 2014 to 2019

图3 2014—2019年珠三角9市O3质量浓度日均值长期分量时间序列

Fig.3 The long-term component time series of daily mean mass concentration of O3 in nine cities in the Pearl River Delta from 2014 to 2019

图4 2014—2019年珠三角9市O3质量浓度日均值短期分量时间序列

Fig.4 The short-term component time series of daily mean mass concentration of O3 in nine cities in the Pearl River Delta from 2014 to 2019

图5 2014—2019年珠三角9市O3质量浓度日均值季节分量时间序列

Fig.5 The seasonal component time series of daily mean mass concentration of O3 in nine cities in the Pearl River Delta from 2014 to 2019

表1 Variance contribution rate of each component of O3 mass concentration to the total variance 单位:%

Tab.1

站点	O₃质量浓度日均值序列各分量方差			方差贡献率合计
站点	长期	短期	季节	方差贡献率合计
东莞	6	42	23	71
惠州	4	31	15	50
江门	13	45	25	83
佛山	8	42	27	77
广州	7	41	25	73
肇庆	11	34	22	67
珠海	6	34	22	62
深圳	6	30	17	53
中山	10	41	25	76

表2 佛山与广州O3质量浓度日均值各分量的逐步回归模型及其解释方差

Tab.2 The stepwise regression equations for each component of daily mean mass concentration of O3 and their explained variance in Foshan and Guangzhou

	站名	逐步回归方程	解释方差/%
长期分量	佛山	$Y = - 1940.09 - 16.84 w s - 0.09 R + 5.38 T + 2 P + 5.51 S - 1.23 R H *$	72
	广州	$Y = - 2935.66 - 0.65 w s - 0.3 R + 4.26 T + 2.78 P + 7.36 S + 1.35 R H *$	55
短期分量	佛山	$Y = - 0.02 - 14.84 w s - 0.04 R + 2.97 T + 0.21 P + 3.46 S - 1.52 R H *$	45
	广州	$Y = - 0.01 + 4.18 w s - 0.07 R - 0.52 T + 0.13 P + 0.07 S + 0.35 R H *$	19
季节分量	佛山	$Y = 0.39 - 21.41 w s - 0.08 R + 3.82 T + 0.28 P + 1.2 S - 2.22 R H *$	69
	广州	$Y = - 1.26 - 8.45 w s - 0.31 R + 2.29 T + 0.25 P + 3.32 S - 0.76 R H *$	53

表2 佛山与广州O3质量浓度日均值各分量的逐步回归模型及其解释方差

Tab.2 The stepwise regression equations for each component of daily mean mass concentration of O3 and their explained variance in Foshan and Guangzhou

	站名	逐步回归方程	解释方差/%
长期分量	佛山	$Y = - 1940.09 - 16.84 w s - 0.09 R + 5.38 T + 2 P + 5.51 S - 1.23 R H *$	72
	广州	$Y = - 2935.66 - 0.65 w s - 0.3 R + 4.26 T + 2.78 P + 7.36 S + 1.35 R H *$	55
短期分量	佛山	$Y = - 0.02 - 14.84 w s - 0.04 R + 2.97 T + 0.21 P + 3.46 S - 1.52 R H *$	45
	广州	$Y = - 0.01 + 4.18 w s - 0.07 R - 0.52 T + 0.13 P + 0.07 S + 0.35 R H *$	19
季节分量	佛山	$Y = 0.39 - 21.41 w s - 0.08 R + 3.82 T + 0.28 P + 1.2 S - 2.22 R H *$	69
	广州	$Y = - 1.26 - 8.45 w s - 0.31 R + 2.29 T + 0.25 P + 3.32 S - 0.76 R H *$	53

图6 广州市气象条件调整前后O3质量浓度日均值长期分量变化趋势(a)及其差值变化(b)

Fig.6 Variations of long-term components of O3 daily mean mass concentration before and after meteorological adjustment (a) and their difference change (b) in Guangzhou

图7 2014—2019年广州O3质量浓度及O3综合指数年际变化

Fig.7 Interannual variation of O3 mass concentration and composite index of O3 in Guangzhou during 2014-2019

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广州2014—2019年气象条件对O₃污染影响的定量评估

Quantitative assessment of influence of meteorological conditions on ozone pollution in Guangzhou during 2014-2019

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