长株潭城市群气溶胶时空分布与传输规律

摘要
图/表
参考文献(32)
相关文章 (15)

全文: PDF (3213 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要

为研究长株潭城市群大气污染时空演化特征及潜在传输规律，采用2008~2016年中分辨率成像光谱仪（MODIS）MAIAC气溶胶光学厚度（AOD）数据，分析长株潭城市群近10a来AOD演化特征.在此基础上，利用拉格朗日混合型单粒子轨迹模式（HYSPLIT）及全球资料同化系统（GDAS）气象要素数据研究大气污染物潜在传输规律.结果表明，长株潭城市群AOD呈现下降趋势，并以春、夏季下降幅度最为显著.空间上，AOD总体呈北高南低、西高东低分布特征，并与经济发展、城市化水平等因素密切相关.此外，长株潭城市群大气污染物向外长距离传输（>1500km）、中距离传输（500~1500km）以及局地传输（0~500km）比率分别为17.89%、36.45%和45.66%，主要影响湖北、江西、安徽、广东、广西、江苏和浙江等地区.研究结果有助于理解长株潭城市群大气污染的时空变化规律，同时为区域“联防联控”、建设“美丽中国”提供科学的辅助依据.

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	丁莹
	冯徽徽
	邹滨
	刘宁
	叶书朝

关键词 ：气溶胶光学厚度, 时空分布, 前向轨迹, 传输路径, 长株潭城市群

Abstract：

In order to capture the spatial-temporal variations and regional transport characteristics of air pollution in Changsha- Zhuzhou-Xiangtan urban agglomeration, the spatial-temporal variations of aerosol optical depth (AOD) were analyzed using the MODIS MAIAC data from 2008 to 2016. On this basis, the potential transport characteristics of air pollutant were studied using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and global data assimilation system (GDAS) meteorological element data. Results showed that AOD showed a downward trend, with the most significant decline in spring and summer. The spatial distribution of AOD was mainly characterized by the high in west and north while low in east and south. In the meantime, it was closely related to economic development and urbanization. In addition, the long-distance transportation (>1500km), medium-distance transportation (500~1500km) and local transportation (0~500km) rates of air pollution were 16.56%, 30.7% and 52.74%, respectively. The potential diffusion of air pollutant mainly affected Hubei, Jiangxi, Anhui, Guangdong, Guangxi, Jiangsu and Zhejiang. This study not only helps to understand the spatial-temporal variations of air pollution in the Changsha-Zhuzhou- Xiangtan urban agglomeration, but also provides a scientific auxiliary basis for the regional joint prevention and control and the beautiful China construction.

Key words： aerosol optical depth spatial-temporal distribution forward trajectory transport path Changsha-Zhuzhou-Xiangtan urban agglomeration

收稿日期: 2019-10-15

PACS:

P407

基金资助:

国家自然科学基金资助项目（41871317）；湖南省自然科学基金资助项目（2018JJ2498）

通讯作者: 冯徽徽,副教授,hhfeng@csu.edu.cn E-mail: hhfeng@csu.edu.cn

作者简介: 丁莹(1996-),女,新疆乌鲁木齐人,中南大学硕士研究生,主要研究方向为资源环境遥感.发表论文1篇.

引用本文:

丁莹, 冯徽徽, 邹滨, 刘宁, 叶书朝. 长株潭城市群气溶胶时空分布与传输规律[J]. 中国环境科学, 2020, 40(5): 1906-1914. DING Ying, FENG Hui-hui, ZOU Bin, LIU Ning, YE Shu-chao. Spatial-temporal distribution and transport characteristic of aerosol in Changsha-Zhuzhou-Xiangtan urban agglomeration. CHINA ENVIRONMENTAL SCIENCECE, 2020, 40(5): 1906-1914.

链接本文:

http://www.zghjkx.com.cn/CN/ 或 http://www.zghjkx.com.cn/CN/Y2020/V40/I5/1906

[1]	Yang X, Zhao C F, Zhou L J, et al. Distinct impact of different types of aerosols on surface solar radiation in China[J]. Journal of Geophysical Research:Atmospheres, 2016,121(11):6459-6471.
[2]	Feng H H, Zou B. Satellite-based estimation of the aerosol forcing contribution to the global land surface temperature in the recent decade[J]. Remote Sensing of Environment, 2019,232(111299):1-10.
[3]	Butt E W, Rap A, Schmidt A, et al. The impact of residential combustion emissions on atmospheric aerosol, human health and climate[J]. Atmospheric Chemistry & Physics, 2016,15(14):20449-20520.
[4]	张喆,丁建丽,王瑾杰.中亚沙尘气溶胶时空分布特征及潜在扩散特性分析[J]. 地理学报, 2017,72(3):507-520. Zhang Z, Ding J L, Wang J J. Spatio-temporal variation and potential diffusion characteristics of dust aerosol originating from Central Asia[J]. Acta Geographica Sinca, 2017,72(3):507-520.
[5]	Zhang Z Y, Wu W L, Wei J, et al. Aerosol optical depth retrieval from visibility in China during 1973~2014[J]. Atmospheric Environment, 2017,171(2017):38-48.
[6]	Holben B N, Eck T F, Slutsker I, et al. AERONET:A federated instrument network and Data archive for aerosol characterization[J]. Remote Sensing of Environment, 1998,66(1):1-16.
[7]	Che H Z, Zhang X Y, Chen H B, et al. Instrument calibration and aerosol optical depth validation of the China Aerosol Remote Sensing Network[J]. Journal of Geophysical Research, 2009,114(D03206):1-12.
[8]	贺欣,周茹,姚媛,等.基于AERONET的中国地区典型站点气溶胶类型变化特征[J]. 中国环境科学, 2020,40(2):485-496. He X, Zhou R, Yao Y, et al. The spatiotemporal variations of aerosol types in representative sites of China basing on the Aerosol Robotic Network (AERONET)[J]. China Environment Science, 2020,40(2):485-496.
[9]	Qin W, Liu Y, Wang L, et al. Characteristic and driving factors of aerosol optical depth over mainland China during 1980~2017[J]. Remote Sensing, 2018,10(7):1064-1089.
[10]	Rupakheti D, Kang S C, Bilal M, et al. Aerosol optical depth climatology over Central Asian countries based on Aqua-MODIS Collection 6.1data:Aerosol variations and sources[J]. Atmospheric Environment, 2019,207(2019):205-214.
[11]	王银牌,喻鑫,谢广奇.中国近15年气溶胶光学厚度时空分布特征[J]. 中国环境科学, 2018,38(2):426-434. Wang Y, Yu X, Xie G Q. Spatial distribution and temporal variation of aerosol optical depth over China in the past 15 years[J]. China Environment Science, 2018,38(2):426-434.
[12]	Ramanathan V, Feng Y. Air pollution, greenhouse gases and climate change:Global and regional perspectives[J]. Atmospheric Environment, 2009,43(1):37-50.
[13]	Wang Y, Zhang X, Arimoto R, et al. The transport pathways and sources of PM10pollution in Beijing during spring 2001, 2002 and 2003[J]. Geophysical Research Letters, 2004,31(L14110):1-4.
[14]	任浦慧,解静芳,姜洪进,等.太原市大气PM2.5季节传输路径和潜在源分析[J]. 中国环境科学, 2019,39(8):3144-3151. Ren P H, Xie J F, Jiang H D, et al. Transport pathways and potential sources of PM2.5 in different seasons in Taiyuan city[J]. China Environment Science, 2019,39(8):3144-3151.
[15]	任传斌,吴立新,张媛媛,等.北京城区PM2.5输送途径与潜在源区贡献的四季差异分析[J]. 中国环境科学, 2016,36(9):2591-2598. Ren C B, Wu L X, Zhang Y Y, et al. Analyze to the seasonal differences of transport pathways and potential source-zones of Beijing Urban PM2.5[J]. China Environment Science, 2016,36(9):2591-2598.
[16]	Stein A F, Draxler R R, Rolph G D, et al. NOAA's HYSPLIT atmospheric transport and dispersion modeling system[J]. Bulletin of the American Meteorological Society, 2016,96(12):2059-2077.
[17]	杨忠霖,游介文,邹滨,等.两型模式下长株潭主城区PM2.5时空变化特征分析[J]. 测绘与空间地理信息, 2019,42(5):65-72. Yang Z, You J W, Zou B, et al. Spatial-temporal variation of PM2.5 concentrations in Chang-Zhu-Tan agglomeration under resource-conserving and environment-friendly development mode[J]. Geomatics & Spatial Information Technology, 2019,42(5):65-72.
[18]	Zou B, Xu S, Sternberg T, et al. Effect of land use and cover change on air quality in urban Sprawl[J]. Sustainability, 2016,8(677):1-14.
[19]	曾晶,陈浩,樊奕茜,等.基于MODIS影像的长株潭地区气溶胶光学厚度反演[J]. 资源信息与工程, 2019,34(1):176-178. Zeng J, Chen H, Fan Y Q, et al. Inversion of aerosol optical thickness in Chang-Zhu-Tan area based on MODIS image[J]. Resource Information and Engineering, 2019,34(1):176-178.
[20]	胡晨霞,邹滨,李沈鑫,等.城市微环境PM2.5浓度空间分异特征分析[J]. 中国环境科学, 2018,38(3):910-916. Hu C X, Zou B, Li S X, et al. Spatial heterogeneity analysis of PM2.5 concentrations in intra-urban microenvironments[J]. China Environmental Science, 2018,38(3):910-916.
[21]	Lyapustin A I, Wang Y, Laszlo I, et al. Multi-angle implementation of atmospheric correction for MODIS (MAIAC):3. Atmospheric correction[J]. Remote Sensing of Environment, 2012,127(12):385-393.
[22]	Lyapustin A, Wang Y, Korkin S, et al. MODIS collection 6MAIAC algorithm[J]. Atmospheric Measurement Techniques, 2018,11(10):5741-5765.
[23]	Zhang Z, Wu W, Fan M, et al. Evaluation of MAIAC aerosol retrievals over China[J]. Atmospheric Environment, 2019,202(2019):8-16.
[24]	Liu N, Zou B, Feng H, et al. Evaluation and comparison of multiangle implementation of the atmospheric correction algorithm, dark target and deep blue aerosol products over China[J]. Atmospheric Chemistry Physics Discussion, 2019,19(2019):8243-8268.
[25]	Van Donkelaar A, Martin R V, Brauer M, et al. Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors[J]. Environmental Science&Technology, 2016,50(7):3762-3772.
[26]	Stein A F, Draxler R R, Rolph G D, et al. NOAA's HYSPLIT atmospheric transport and dispersion modeling system[J]. Bulletin of the American Meteorological Society, 2016,96(12):2059-2077.
[27]	Rolph G, Stein A, Stunder B. Real-time environmental applications and display system:READY[J]. Environmental Modelling & Software, 2017,95(2017):210-228.
[28]	黄柏良.城市通风及其影响城市热岛效应与空气质量研究[D]. 长沙:中南大学, 2011. Huang B L. Urban ventilation and its impact on urban heat island and air quality[D]. Changsha:Central South University, 2011.
[29]	郑小波,罗宇翔,赵天良,等.中国气溶胶分布的地理学和气候学特征[J]. 地理科学, 2012,32(3):265-272. Zheng X B, Luo Y X, Zhao T L, et al. Geographical climatological characterization of aerosol distribution in China[J]. Scientia Geographica Sinica, 2012,32(3):265-272.
[30]	Qu W, Wang J, Zhang X, et al. Influence of relative humidity on aerosol composition:Impacts on light extinction and visibility impairment at two sites in coastal area of China[J]. Atmospheric Research, 2016,153(2015):500-511.
[31]	Zhang L, Liao H, Li J. Impacts of Asian summer monsoon on seasonal and interannual variations of aerosols over eastern China[J]. Journal of Geophysical Research Atmospheres, 2010,115(D00K05):1-20.
[32]	刘浩,高小明,谢志英,等.京津冀晋鲁区域气溶胶光学厚度的时空特征[J]. 环境科学学报, 2015,35(5):1506-1511. Liu H, Gao X M, Xie Z Y, et al. Spatio-temporal characteristics of aerosol optical depth over Beijing-Tianjin-Hebei-Shanxi-Shandong region during 2000~2013[J]. Acta Scientiae Circumstantiae, 2015, 35(5):1506-1511.