深圳市城区冬季大气PM<sub>2.5</sub>在线来源解析

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摘要利用颗粒物同步混合实时监测仪、气溶胶化学组分监测仪(ACSM)、大气多金属元素在线监测仪、黑碳仪等在线仪器于2020年12月27日~2021年1月31日在深圳观测了PM_2.5及其化学组分.结果显示,深圳市PM_2.5在观测期间平均浓度为(32.2 ±17.0)μg/m³.其中,有机物在PM_2.5中浓度最高,均值为(15.4 ±9.5)μg/m³,其次是NO₃^-、SO₄^2-、BC、NH₄⁺和元素,浓度分别为(4.3 ±3.9),(3.8 ±2.1),(2.7±1.6),(2.5 ±1.7)和(1.9±1.2)μg/m³.本研究将ACSM获取的有机质谱信息(m/z 44)作为二次有机气溶胶(SOA)的示踪物纳入PMF (正交矩阵因子分解)模型,成功地识别了SOA.源解析结果显示,SOA对深圳市冬季PM_2.5贡献了23.8%,机动车排放、二次硝酸盐、二次硫酸盐、生物质燃烧和扬尘分别贡献了21.7%、15.3%、15.2%、8.2%和5.7%,船舶排放、工业排放和燃煤对PM_2.5的贡献在1.6%~3.3%.污染源的日变化分析和潜在来源分析结果表明,SOA、二次硝酸盐、机动车、扬尘、工业排放等污染源受本地排放的影响更突出,二次硫酸盐、生物质燃烧、燃煤、船舶排放受区域传输的影响更突出.结果表明未来深圳市PM_2.5污染防治应强化机动车、扬尘、工业等本地污染源的排放控制,同时针对燃煤、生物质燃烧和船舶排放要加强与周边城市大气污染联防联控工作.

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	林楚雄
	姚沛廷
	彭杏
	古添发
	孙天乐
	云龙
	何凌燕
	黄晓锋

关键词 ： PM_2.5, 在线来源解析, PMF模型, 日变化, 潜在来源

Abstract：This work conducted high time resolution observations of PM_2.5 and its chemical composition during December 27^th in 2020 to January 31^th in 2021 in Shenzhen using a hybrid synchronous mixing real-time environmental particulate matter monitor, aerosol chemical speciation monitor (ACSM), aethalometer, and automated multi-metals monitor. During the observational period, the average concentrations of PM_2.5 was 32.2 ±17.0μg/m³. Organic matter was the most abundant component of PM_2.5, with average concentration of 15.4±9.5μg/m³, followed by NO₃^-(4.3±3.9μg/m³), SO₄^2-(3.8±2.1μg/m³), BC (2.7±1.6μg/m³), NH₄⁺(2.5±1.7μg/m³), and elements (1.9±1.2μg/m³). The mass spectra information (m/z 44) obtained from ACSM, as the tracer of the secondary organic aerosol (SOA), was introduced into PMF (Positive Matrix Factorization) model to identify SOA. PMF results showed that PM_2.5 during winter in Shenzhen was dominated by SOA, vehicle emissions, secondary nitrate, secondary sulfate, biomass burning, and fugitive dust, which were accounting for 23.8%, 21.7%, 15.3%, 15.2%, 8.2%, and 5.7% of PM_2.5 mass concentrations, respectively. In addition, ship emissions, industrial emissions, and coal combustion had relatively small contributions, ranging from 1.6% to 3.3% of PM_2.5. The diurnal variations of each source and the potential source area were analyzed and found that local emissions played an important role for SOA, secondary nitrate, vehicle emissions, fugitive dust, and industrial emissions, while regional transmission played an important role for secondary sulfate, biomass burning, coal combustion, and ship emissions. The findings in this work highlight that further decreasing PM_2.5 level in Shenzhen needs to control the local emissions (e.g. vehicle emissions, fugitive dust, industrial emissions) and joint prevent coal combustion, biomass burning, and ship emissions.

Key words： PM_2.5 online source apportionment PMF model diurnal variation potential source area

收稿日期: 2022-06-21

PACS:

X513

基金资助:深圳市科技计划项目(GXWD20201231165807007-202008-08165742001)

通讯作者: 彭杏,助理研究员,nkpx0125@hotmail.com E-mail: nkpx0125@hotmail.com

作者简介: 林楚雄(1968-),男,籍贯广东揭阳人,本科,高级工程师,主要研究方向为大气环境监测.发表论文3篇.

引用本文:

林楚雄, 姚沛廷, 彭杏, 古添发, 孙天乐, 云龙, 何凌燕, 黄晓锋. 深圳市城区冬季大气PM_2.5在线来源解析[J]. 中国环境科学, 2023, 43(2): 506-513. LIN Chu-xiong, YAO Pei-ting, PENG Xing, GU Tian-fa, SUN Tian-le, YUN Long, HE Ling-yan, HUANG Xiao-feng. Online source apportionment of PM_2.5 during winter at urban site in Shenzhen. CHINA ENVIRONMENTAL SCIENCECE, 2023, 43(2): 506-513.

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http://www.zghjkx.com.cn/CN/ 或 http://www.zghjkx.com.cn/CN/Y2023/V43/I2/506

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