| 北京市2014年10月重霾污染特征及有机碳来源解析 |
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| 引用本文： | 方冬青,魏永杰,黄伟,蔡天骐,张阳,刘庆阳,张元勋.北京市2014年10月重霾污染特征及有机碳来源解析[J].环境科学研究,2016,29(1):12-19. |
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| 作者姓名： | 方冬青 魏永杰 黄伟 蔡天骐 张阳 刘庆阳 张元勋 |
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| 作者单位： | 1.中国科学院大学资源与环境学院, 北京 100049 |
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| 基金项目： | 国家自然科学基金项目(41375131) |
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| 摘 要： | 2014年10月北京市出现了多次重霾天气,与此同时,通过全国秸秆燃烧卫星遥感监测发现,北京周边河南、河北等地区恰存在一定规模的秸秆燃烧活动. 对2014年10月4—27日北京市大气PM2.5中的水溶性离子、金属、OC(有机碳)、EC(元素碳)和有机物示踪物等化学成分进行了分析,对霾天和非霾天PM2.5中化学成分进行了比较,并使用CMB(化学质量平衡)模型对PM2.5中有机物的来源进行了解析,采用后向轨迹模拟和卫星遥感图像定量评估生物质燃烧(秸秆燃烧等)对重霾污染的影响. 结果表明:霾天ρ(PM2.5)〔(229.0±96.3)μg/m3〕是非霾天的5.0倍,水溶性离子总质量浓度〔(125.3±59.3)μg/m3〕是非霾天的6.5倍,ρ(SO42-)、ρ(NO3-)和ρ(NH4+)分别是非霾天的6.1、8.6和7.1倍,ρ(OC)〔(81.8±39.5)μg/m3〕是非霾天的7.8倍,ρ(EC)〔(6.7±3.4)μg/m3〕是非霾天的4.2倍；霾天生物质燃烧的示踪物——左旋葡聚糖和K+的质量浓度平均值分别是非霾天的9.1和3.3倍. 生物质燃烧、机动车排放以及二次污染物对有机细颗粒物的贡献率分别为18.9%、36.9%和41.9%；二次细颗粒物质量浓度增加了1倍左右；气象条件同样在很大程度上促进了霾的形成. 常规的源解析方法仅可对生物质燃烧的一次污染贡献进行定量,但对重霾污染贡献的全面评价尚需进一步探讨.
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| 关 键 词： | 霾 生物质燃烧 PM2.5 左旋葡聚糖 源解析 |
| 收稿时间： | 2015/4/16 0:00:00 |
| 修稿时间： | 2015/8/24 0:00:00 |
Characterization and Source Apportionment of Organic Carbon during a Heavy Haze Episode in Beijing in October 2014 |
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| FANG Dongqing,WEI Yongjie,HUANG Wei,CAI Tianqi,ZHANG Yang,LIU Qingyang and ZHANG Yuanxun.Characterization and Source Apportionment of Organic Carbon during a Heavy Haze Episode in Beijing in October 2014[J].Research of Environmental Sciences,2016,29(1):12-19. |
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| Authors: | FANG Dongqing WEI Yongjie HUANG Wei CAI Tianqi ZHANG Yang LIU Qingyang and ZHANG Yuanxun |
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| Affiliation: | 1.College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China2.Chinese Research Academy of Environmental Sciences, Beijing 100012, China3.College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China ;Huairou Eco-Environmental Observatory, Chinese Academy of Sciences, Beijing 101407, China |
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| Abstract: | Several heavy haze episodes occurred in Beijing in October 2014 as a result of the large-scale straw burning that took place in the surrounding areas of Henan and Hebei provinces, as observed by the satellite remote sensing. To analyze the composition and investigate the source attribution of organic carbon (OC) as well as the relationship between biomass burning in northern China and the heavy haze episodes in Beijing during the month, airborne fine particles (PM2.5) were collected and analyzed. The measured chemical species included water-soluble ions, water-soluble metals, elemental and organic carbon (OC/EC), and organic tracers. The contribution of biomass burning to PM2.5 mass was estimated by comparing the results of a chemical mass balance (CMB) model between haze and non-haze days. Back trajectory model and satellite remote sensing data were also analyzed to assess the impact of biomass burning (such as straw burning) on the heavy haze episode. On the haze days, the average concentrations of PM2.5 and the total water-soluble ions were (229.0±96.3)μg/m3 and (125.3±59.3)μg/m3, which were 5.0 and 6.5 times higher than those on the non-haze days, respectively. The ρ(SO42-), ρ(NO3-) and ρ(NH4+) were 6.1,8.6 and 7.1 times higher than those on the non-haze days, respectively. The ρ(OC) and ρ(EC) were (81.8±39.5)μg/m3 and (6.7±3.4)μg/m3,7.8 and 4.2 times higher than those on the non-haze days, respectively. The concentration of levoglucosan and K+, the tracers of biomass burning, were 9.1 and 3.3 times higher on the haze days than on the non-haze days, respectively. Biomass burning, vehicle emissions and other sources contributed the largest fractions on both haze and non-haze days, contributing 18.9%, 36.9% and 41.9% to the total organic matter, respectively. The secondary aerosol concentrations on the haze days were twice those on non-haze days. Meteorological conditions were also considered to be important contributors to the haze episodes. Traditional source apportionment models can quantify the primary source contributions only; thus, further study is needed to quantify the total impact of biomass burning on the haze pollution. |
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| Keywords: | haze biomass burning PM2 5 levoglucosan source apportionment |
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