海口市区臭氧污染变化特征及潜在源区分析

doi:10.16258/j.cnki.1674-5906.2023.02.013

生态环境学报 ›› 2023, Vol. 32 ›› Issue (2): 331-340.DOI: 10.16258/j.cnki.1674-5906.2023.02.013

海口市区臭氧污染变化特征及潜在源区分析

符传博¹^,³(), 丹利²^,^*(), 佟金鹤¹^,³, 陈红⁴

1.海南省气象科学研究所，海南海口 570203
2.中国科学院大气物理研究所东亚区域气候-环境重点实验室，北京 100029
3.海南省南海气象防灾减灾重点实验室，海南海口 570203
4.海南省气象台，海南海口 570203

收稿日期:2022-05-28 出版日期:2023-02-18 发布日期:2023-05-11
通讯作者: *丹利，研究员，博士研究生导师。E-mail: danli@tea.ac.cn
作者简介:符传博（1985年生），男，正高级工程师，主要从事大气环境与大气污染方面的研究。E-mail: hnfuchuanbo@163.com
基金资助:
国家自然科学基金项目(42065010);国家自然科学基金项目(42141017);海南省重大科技计划项目(ZDKJ202007);海南省自然科学基金项目(422RC802);海南省自然科学基金项目(421QN0967);海南省院士创新平台科研项目(YSPTZX202143)

Characteristics and Potential Source Analysis of Ozone pollution in Haikou City

FU Chuanbo¹^,³(), DAN Li²^,^*(), TONG Jinhe¹^,³, CHEN Hong⁴

1. Hainan Institute of Meteorological Science, Haikou 570203, P. R. China
2. Key Laboratory of Regional Climate-Environment Research for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, P. R. China
3. Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province, Haikou 570203, P. R. China
4. Hainan Meteorological Observatory, Haikou 570203, P. R. China

Received:2022-05-28 Online:2023-02-18 Published:2023-05-11

摘要/Abstract

摘要：

海口市作为我国著名的热带滨海城市之一，地理环境特殊，气候资源独特。为深入认识海口市臭氧（O₃）污染变化规律及污染物潜在贡献源区，也为进一步开展O₃污染预报预警和区域联防联控提供技术支撑，利用海口市区4个环境监测数据，结合气象观测资料，采用后向轨迹模拟、聚类分析、多元回归分析法、潜在源区贡献因子算法和权重轨迹方法分析了海口市最大8 h平均（O₃-8h）质量浓度年际变化、月际变化、日变化及与气象影响因子的关系，影响O₃-8h质量浓度的主控因子，并探讨了O₃传输路径和潜在贡献源区。结果表明，2013-2020年海口市区4个站点O₃-8h质量浓度均出现不同程度的上升，其中龙华站的趋势系数达到了0.929，通过了99.9 %的信度检验；O₃-8h质量浓度月际变化呈“V”型分布，最大值出现在10月；日变化呈单峰型，峰值出现在15:00附近；平均气温在18-28 ℃之间，相对湿度位于65%-80%，太阳辐射日总量在6-23 MJ·m^-2之间，日照时数位于4-10 h·d^-1，受4-6 m·s^-1之间的东北风影响时，海口市O₃-8h质量浓度容易超标。多元回归分析表明，10 m平均风速、相对湿度和大气压是主控因子；后向轨迹和潜在源区分析发现，秋季的内陆中短距离气流和沿海长距离气流，春季的沿海中短距离气流和冬季内陆短距离气流容易造成O₃-8h质量浓度超标，其中江西省、浙江省、福建省和广东省是O₃污染的主要贡献源区。

关键词: 臭氧（O₃）, 气象因子, 后向轨迹, 潜在源区, 海口市

Abstract:

Haikou City is one of the well-known tropical coastal cities in China, which has a special geographical environment and unique climate resources. In order to have a further understanding of ozone (O₃) pollution characteristics and potential source regions of pollutants in Haikou City, and technological support for carrying out warning, forecast, management and regional joint prevention of ozone pollution, four environmental monitoring data and meteorological observational data in Haikou City were used; interannual variation, monthly change, diurnal variation and meteorological influence of the O₃-8h (defined as the maximum 8 h average result) were analyzed using the backward trajectory simulation, cluster analysis, multiple regression analysis, PSCF (Potential source analysis function) and CWT (Concentration weight trajectory) methods. The results showed that the O₃-8h from all the four stations in Haikou City showed an upward trend from 2013 to 2020. The trend coefficient of Longhua Station reached 0.929, which passed the 99.9% significant test. The monthly variation of O₃-8h showed a “V”-shaped distribution, with the maximum value occurred in October. The daily variation was unimodal, with a peak value around 15:00. The exceedance probability of O₃-8h concentrations was relatively high when the average temperature was around 18-28 ℃, relative humidity was between 65% and 80%, the total solar radiation ranged 6-23 MJ·m^-2, sunshine duration was between 4 h·d^-1 and 10 h·d^-1, and wind speed of the northeast wind was between 4 to 6 m·s^-1. Multiple regression analysis showed that 10 m average wind speed, relative humidity and atmosphere pressure were the main dominate factors on O₃-8h. Backward trajectory and potential source analysis revealed that the inland short-distance air flow and coastal long-distance air flow in autumn, the medium and short-distance air flow along the coast in spring and the inland short-distance air flow in winter are likely to cause the above-normal O₃-8h concentration. Jiangxi, Zhejiang, Fujian and Guangdong provinces are the main sources of O₃ pollution in Haikou City.

Key words: ozone (O₃), meteorological factor, backward trajectory, potential source, Haikou City

中图分类号:

符传博, 丹利, 佟金鹤, 陈红. 海口市区臭氧污染变化特征及潜在源区分析[J]. 生态环境学报, 2023, 32(2): 331-340.

FU Chuanbo, DAN Li, TONG Jinhe, CHEN Hong. Characteristics and Potential Source Analysis of Ozone pollution in Haikou City[J]. Ecology and Environment, 2023, 32(2): 331-340.

图/表 11

参考文献 46

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年份	小时数据个数				小时数据有效率/%
年份	海大站	海师站	龙华站	秀英站	海大站	海师站	龙华站	秀英站
2013	7799	8440	8153	7688	89.03	96.35	93.07	87.76
2014	8124	8151	8074	8243	92.74	93.05	92.17	94.10
2015	8681	8631	8595	8720	99.10	98.53	98.12	99.54
2016	8716	8712	8696	8727	99.23	99.18	99.00	99.35
2017	8671	8603	8757	8666	98.98	98.21	99.97	98.93
2018	8600	8642	8748	8634	98.17	98.65	99.86	98.56
2019	8521	8452	8495	8534	97.27	96.48	96.97	97.42
2020	8089	8667	8486	8541	92.09	98.67	96.61	97.23

年份	小时数据个数				小时数据有效率/%
年份	海大站	海师站	龙华站	秀英站	海大站	海师站	龙华站	秀英站
2013	7799	8440	8153	7688	89.03	96.35	93.07	87.76
2014	8124	8151	8074	8243	92.74	93.05	92.17	94.10
2015	8681	8631	8595	8720	99.10	98.53	98.12	99.54
2016	8716	8712	8696	8727	99.23	99.18	99.00	99.35
2017	8671	8603	8757	8666	98.98	98.21	99.97	98.93
2018	8600	8642	8748	8634	98.17	98.65	99.86	98.56
2019	8521	8452	8495	8534	97.27	96.48	96.97	97.42
2020	8089	8667	8486	8541	92.09	98.67	96.61	97.23

站名	纬度/(°)	经度/(°)	平均值/(μg·m^-3)	标准差/(μg·m^-3)	趋势系数	气候倾向率/(μg·m^-3·a^-1)	信度/%
海大站	20.0596	110.3190	80.08	6.46	0.509	1.43	不显著
海师站	19.9969	110.3376	68.87	12.15	0.785	4.16	90
龙华站	20.0357	110.3303	68.46	9.92	0.929	4.02	99.9
秀英站	20.0053	110.2832	66.25	10.09	0.834	3.67	99

站名	纬度/(°)	经度/(°)	平均值/(μg·m^-3)	标准差/(μg·m^-3)	趋势系数	气候倾向率/(μg·m^-3·a^-1)	信度/%
海大站	20.0596	110.3190	80.08	6.46	0.509	1.43	不显著
海师站	19.9969	110.3376	68.87	12.15	0.785	4.16	90
龙华站	20.0357	110.3303	68.46	9.92	0.929	4.02	99.9
秀英站	20.0053	110.2832	66.25	10.09	0.834	3.67	99

站点	O₃-8h与NO₂及气象因子的拟合关系	相关系数	信度/%
海大站	ρ_(O3-8h)=0.18ρ_(NO2)+0.14φ₍_P₎+1.80φ_(Pr)- 2.13φ_(RH)-4.15φ_(W10)+1.06φ_(SD)- 0.06φ₍_t₎-1.36φ_(Tr)-1533.28	0.557	99.9
海师站	ρ_(O3-8h)=0.56ρ_(NO2)+0.14φ₍_P₎+2.54φ_(Pr)- 2.16φ_(RH)-3.32φ_(W10)+1.44φ_(SD)+ 1.88φ₍_t₎-2.16φ_(Tr)-2323.14	0.575	99.9
龙华站	ρ_(O3-8h)=0.28ρ_(NO2)+0.19φ₍_P₎+2.46φ_(Pr)- 2.12φ_(RH)-4.66φ_(W10)-0.55φ_(SD)+ 1.59φ₍_t₎-0.54φ_(Tr)-2247.42	0.527	99.9
秀英站	ρ_(O3-8h)=0.43ρ_(NO2)+0.12φ₍_P₎+2.25φ_(Pr)- 2.11φ_(RH)-2.66φ_(W10)+0.19φ_(SD)+ 1.37φ₍_t₎-1.18φ_(Tr)-2037.06	0.566	99.9

海口市区臭氧污染变化特征及潜在源区分析

Characteristics and Potential Source Analysis of Ozone pollution in Haikou City

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季节	轨迹编号	比例/ %	O₃-8h质量浓度/(μg·m^-3)	O₃-8h超标频率/%	超标时段O₃-8h 质量浓度/(μg·m^-3)
春季	1	39	56.10	0.00	-
	2	37	76.31	1.49	162.72
	3	24	66.11	0.00	-
夏季	1	31	50.50	0.00	-
	2	49	56.60	0.00	-
	3	20	58.14	0.00	-
秋季	1	18	53.33	0.00	-
	2	24	60.19	0.00	-
	3	21	87.88	3.85	169.70
	4	37	113.47	14.29	176.00
冬季	1	47	85.23	1.19	177.95
	2	14	52.25	0.00	-
	3	34	76.08	0.00	-
	4	6	97.60	0.00	-

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