济南市PM2.5中二次组分的时空变化特征及其影响因素

doi:10.16258/j.cnki.1674-5906.2022.01.012

生态环境学报 ›› 2022, Vol. 31 ›› Issue (1): 100-109.DOI: 10.16258/j.cnki.1674-5906.2022.01.012

济南市PM_2.5中二次组分的时空变化特征及其影响因素

李圣增¹(), 郝赛梅², 谭路遥², 张怀成¹, 徐标³, 谷树茂³, 潘光³, 王淑妍², 闫怀忠¹, 张桂芹²^,^*()

1.山东省济南生态环境监测中心,山东济南 250101
2.山东建筑大学市政与环境工程学院,山东济南 250101
3.山东省生态环境监测中心,山东济南 250101

收稿日期:2021-04-22 出版日期:2022-01-18 发布日期:2022-03-10
通讯作者: *张桂芹（1969年生）女,教授,博士,硕士研究生导师,主要从事大气污染控制理论与技术研究。E-mail: zhangguiqin320@163.com
作者简介:李圣增（1971年生）,男,高级工程师,主要从事环境监测工作。E-mail: LSZ0928@163.com
基金资助:
济南市政府空气质量分析保障项目(GK2020-0002);济南市高校创新团队项目(2020GXRC008);济南市大气颗粒物来源解析研究项目(Z21001Z)

Characteristics of Spatiotemporal Variation, and Factors Influencing Secondary Components in PM_2.5 in Ji'nan

LI Shengzeng¹(), HAO Saimei², TAN Luyao², ZHANG Huaicheng¹, XU Biao³, GU Shumao³, PAN Guang³, WANG Shuyan², YAN Huaizhong¹, ZHANG Guiqin²^,^*()

1. Ji'nan Ecological Environment Monitoring Center of Shandong Province, Ji'nan 250101, P. R. China
2. School of Municipal and Environmental Engineering, Shandong Jianzhu University, Ji'nan 250101, P. R. China
3. Shandong Provincial Environmental Monitoring Center, Ji'nan 250101, P. R. China

Received:2021-04-22 Online:2022-01-18 Published:2022-03-10

摘要/Abstract

摘要：

为分析济南市PM_2.5中二次组分的时空变化和影响因素,对济南市春季（2019年5月16—25日）、秋季（2019年10月15—24日）和冬季（2019年12月17—2020年1月16日）4个典型点位的PM_2.5样品进行连续采样,并测定了PM_2.5中水溶性离子、有机碳（OC）和元素碳（EC）的含量。结果表明：物流交通区的二次组分质量浓度最高（56.13 μg∙m^-3）,钢铁工业区的二次组分浓度比城市市区高,但是二次组分占比较城市市区低,清洁对照点的浓度和占比最低;济南市4个功能区SO₄^2-和NO₃^-转化率均高于0.1,除清洁对照点外,城市市区、钢铁工业区和物流交通区的SO₄^2-转化率明显高于NO₃^-转化率;济南市春季、秋季和冬季的ρ_(NO3-)/ρ_(SO42-)分别为0.67、2.57和1.98,春季PM_2.5浓度以固定源贡献为主,秋季和冬季以移动源贡献为主;运用ISORROPIA热力学模型分析了含水量和pH对二次组分生成的影响,含水量会随着污染增大而增大,酸度和含水量对二次无机组分的转化机理产生影响,酸度会抑制二次无机组分的生成,而含水量会促进二次组分的生成;后向轨迹聚类分析结果表明,占比最高的轨迹（29.2%）来自东北方向的滨州和东营,基于潜在源贡献因子（WPSCF）和浓度权重轨迹（WCWT）分析PM_2.5中二次组分质量浓度的潜在污染源区域,SO₄^2-的主要贡献源区在济南市区北部的济阳区和东北方向的滨州、东营等,NO₃^-和NH₄⁺的主要贡献源区在济南市区北方向的济阳区、东北方向的章丘区和南方向的莱芜区等。该研究结果可为中国北方城市细颗粒物进一步的治理和防控提供数据支撑和理论依据。

关键词: PM_2.5, 二次组分, 时空变化, 污染特征, 影响因素

Abstract:

To analyze temporal and spatial variations and factors influencing secondary components of Ji’nan’s M_2.5, continuous PM_2.5 sampling was conducted in spring (May 16-25, 2019), autumn (October 15-24, 2019) and winter (December 17-January 16, 2020) at four typical sites in Ji’nan. Water-soluble ion, organic carbon (OC), and elemental carbon (EC) content in PM_2.5 were determined. The results showed that the concentration of secondary components was the highest (56.13 μg·m^-3) in logistics and transportation areas. Concentrations of secondary components in iron and steel industrial areas were higher than those in urban areas, but the proportions of secondary components were lower. The cleaning control sites had the lowest concentrations and proportions. SO₄^2- and NO₃^- conversion rates in the four functional areas of Ji’nan were all higher than 0.1, and SO₄^2- conversion rates were significantly higher than NO₃^- conversion rates in urban areas, iron and steel industrial areas, and logistics and transportation areas, but not at the cleaning control sites. In Ji’nan, ρ_(NO3-)/ρ_(SO42-) values in spring, autumn and winter were 0.67, 2.57 and 1.98, respectively. In spring, the PM_2.5 concentration was mainly determined by fixed sources, while in autumn and winter, it was mainly attributed to mobile sources. The ISORROPIA thermodynamic model was used to analyze the effects of water content and pH on the secondary component formation. The water content increased as pollution rose. Both the acidity and the water content affected the secondary inorganic component transformation mechanism. Acidity inhibited secondary inorganic component formation, whereas water content promoted it. The back trajectory cluster analysis results showed that the track with the highest proportion (29.2%) came from Binzhou and Dongying in the northeast. Based on the weight potential source contribution factor (WPSCF) and weight-analyzed concentration weight trajectory (WCWT), the potential pollution source area of the secondary component mass concentration in PM_2.5 was analyzed. SO₄^2- was the main contribution of the source area in Jiyang area in the north of Ji’nan city, and the northeast of Binzhou and Dongying. The main contribution sources of NO₃^- and NH₄⁺ are Jiyang District in the north of Jinan City, Zhangqiu District in the northeast, and Laiwu District in the south. The results of this study can provide data support and theoretical basis for further governance and prevention of fine particulate matter in northern Chinese cities..

Key words: PM_2.5, secondary component, spatiotemporal variation, pollution characteristics, influencing factors

中图分类号:

李圣增, 郝赛梅, 谭路遥, 张怀成, 徐标, 谷树茂, 潘光, 王淑妍, 闫怀忠, 张桂芹. 济南市PM_2.5中二次组分的时空变化特征及其影响因素[J]. 生态环境学报, 2022, 31(1): 100-109.

LI Shengzeng, HAO Saimei, TAN Luyao, ZHANG Huaicheng, XU Biao, GU Shumao, PAN Guang, WANG Shuyan, YAN Huaizhong, ZHANG Guiqin. Characteristics of Spatiotemporal Variation, and Factors Influencing Secondary Components in PM_2.5 in Ji'nan[J]. Ecology and Environment, 2022, 31(1): 100-109.

图/表 11

参考文献 36

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采样点 The sampling site	代表功能区 Representative functional area	经度 Longitude	纬度 Latitude	采样高度 Height of sampling/m	采样日期 Sampling date	样品数量 The sample quantity
跑马岭 Paomaling	清洁对照点	117°13'23"E	36°25'57"N	山顶	春季: 2019-05-16-25 秋季: 2019-10-15-24 冬季: 2019-12-17-2020-01-16	50
蓝翔技校 Lanxiang vocational school	物流交通区	116°56'53"E	36°42'51"N	18		50
技术学院 Institute of technology	钢铁工业区	117°40'47"E	36°13'44"N	18		50
建筑大学 Jianzhu university	城市市区	117°11'6"E	36°40'31"N	18		50
龙环大厦 Long Huan building	城市市区	117°9'4"E	36°39'10"N	7	2019-12-01-31, 2020-03-01-03-31	在线监测小时数据

采样点 The sampling site	代表功能区 Representative functional area	经度 Longitude	纬度 Latitude	采样高度 Height of sampling/m	采样日期 Sampling date	样品数量 The sample quantity
跑马岭 Paomaling	清洁对照点	117°13'23"E	36°25'57"N	山顶	春季: 2019-05-16-25 秋季: 2019-10-15-24 冬季: 2019-12-17-2020-01-16	50
蓝翔技校 Lanxiang vocational school	物流交通区	116°56'53"E	36°42'51"N	18		50
技术学院 Institute of technology	钢铁工业区	117°40'47"E	36°13'44"N	18		50
建筑大学 Jianzhu university	城市市区	117°11'6"E	36°40'31"N	18		50
龙环大厦 Long Huan building	城市市区	117°9'4"E	36°39'10"N	7	2019-12-01-31, 2020-03-01-03-31	在线监测小时数据

采样点 Sampling site	季节 Sampling periods	温度 Temperature/ ℃	湿度 Humidity/ %	风速 Wind velocity/ (m∙s^-1)
清洁对照点 (跑马岭) Cleaning control site	春季秋季冬季	20 15 -2.3	44 66 72	4.0 2.3 2.8
物流交通区 (蓝翔技校) Logistics and transportation area	春季秋季冬季	27.5 17 1.4	31 58 70	1.4 0.8 0.8
钢铁工业区 (技术学院) Iron and steel area	春季秋季冬季	25 15.5 1.5	32 57 58	1.0 0.5 0.7
城市市区 (建筑大学) Urban areas	春季秋季冬季	27.5 16.9 1.2	31 57 67	1.4 1.4 1.4

采样点 Sampling site	季节 Sampling periods	温度 Temperature/ ℃	湿度 Humidity/ %	风速 Wind velocity/ (m∙s^-1)
清洁对照点 (跑马岭) Cleaning control site	春季秋季冬季	20 15 -2.3	44 66 72	4.0 2.3 2.8
物流交通区 (蓝翔技校) Logistics and transportation area	春季秋季冬季	27.5 17 1.4	31 58 70	1.4 0.8 0.8
钢铁工业区 (技术学院) Iron and steel area	春季秋季冬季	25 15.5 1.5	32 57 58	1.0 0.5 0.7
城市市区 (建筑大学) Urban areas	春季秋季冬季	27.5 16.9 1.2	31 57 67	1.4 1.4 1.4

功能区 Functional areas	SO₂/ ₍μg∙m^-3)	NO₂/ (μg∙m^-3)	SOR	NOR
城市市区 Urban areas	14.68±6.72	69.33±16.81	0.42±0.14	0.24±0.10
钢铁工业区 Iron and steel area	21.59±8.72	46.12±13.93	0.35±0.12	0.29±0.12
物流交通区 Logistics and transportation area	29.37±17.10	63.46±15.41	0.34±0.13	0.29±0.11
清洁对照点 Cleaning control site	12.33±4.41	19.24±7.32	0.33±0.10	0.36±0.16

济南市PM_2.5中二次组分的时空变化特征及其影响因素

Characteristics of Spatiotemporal Variation, and Factors Influencing Secondary Components in PM_2.5 in Ji'nan

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不同污染等级 Different pollution levels	含水量 The water content/%	酸度 pH
优 Excellent	4.16±0.76	4.73±0.30
良 Good	11.77±22.79	4.33±0.96
轻度污染 Light pollution	28.29±50.33	4.49±1.17

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济南市PM2.5中二次组分的时空变化特征及其影响因素

Characteristics of Spatiotemporal Variation, and Factors Influencing Secondary Components in PM2.5 in Ji'nan

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摘要/Abstract

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参考文献 36

相关文章 15

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济南市PM_2.5中二次组分的时空变化特征及其影响因素

Characteristics of Spatiotemporal Variation, and Factors Influencing Secondary Components in PM_2.5 in Ji'nan