基于MODIS的长三角地区气溶胶时空变化规律及其气象解释

陈裕迪; 王洁; 陈唯天; 马燮铫; 胡晓东

doi:10.13205/j.hjgc.202112018

基于MODIS的长三角地区气溶胶时空变化规律及其气象解释

doi: 10.13205/j.hjgc.202112018

1. 南京信息工程大学水文与水资源工程学院, 南京 210044;
2. 中山大学大气科学学院, 广州 528400;
3. 江苏省水利科学研究院, 南京 210017

基金项目:

江苏省水利科技项目(2020040)

河北省重点研发计划项目(19275408D)

淮河流域气象开放基金(HRM201703)

国家自然科学基金面上项目(41671022,41877158)。

详细信息

作者简介:
陈裕迪(1997-),女,硕士,主要研究方向为大气环境与大气污染。chenyudi2019@163.com

通讯作者:
王洁(1984-),男,副教授,主要研究方向为大气环境与大气污染。wangjie0775@163.com

计量
- 文章访问数: 220
- HTML全文浏览量: 32
- PDF下载量: 10
- 被引次数: 0
出版历程
- 收稿日期: 2020-12-28
- 网络出版日期: 2022-03-30
- 刊出日期: 2022-03-30

SPATIAL AND TEMPORAL CHANGES OF AEROSOL IN YANGTZE RIVER DELTA AND ITS METEOROLOGICAL INTERPRETATION

1. School of Hydrology and Water Resources, Nanjing University of Information Science & Technology, Nanjing 210044, China;
2. School of Atmaspheric Sciences, Sun Yat-Sen University, Guangzhou 528400, China;
3. Jiangsu Water Conservancy Research Institute, Nanjing 210017, China

摘要

摘要: 基于2008—2017年的MODIS气溶胶光学厚度(AOD)数据、实测气象观测数据,探究了长三角地区的AOD时空分布规律,并分析了AOD与多个气象要素的相关性,以对AOD的时空变化作出合理的气象解释。结果表明:1)从时间分布来看,长三角地区年均AOD呈周期性波动变化趋势,2011年出现峰值0.83,2014年AOD开始迅速下降,至2017年达到最低,较2014年相比下降22.8%,这与政府实施的固体颗粒物控制排放政策有关;每年夏季(6,7月)AOD出现最大值,这主要是海洋上大量的海盐气溶胶颗粒和水汽扩散到内陆地区造成的。2)从空间分布来看,长三角AOD高值区均分布在江苏南部以及徐州一带,2014年以来AOD高值范围逐步缩小;浙江地区AOD明显低于苏沪地区,这与浙江地势高起伏较大密切相关。3)从相关性方面来看,AOD变化与气温、相对湿度变化之间呈较好的正相关性,而与风速的相关性较复杂,这可能受风向的不确定影响;夏季气温高、湿度大,因此出现大范围的AOD高值区;冬季气温低、空气中水汽含量低,固体颗粒物对于AOD贡献率较大,因此冬季AOD变化能够在一定程度上反映空气污染状况。该研究结果可为长三角地区气溶胶评估、空气质量归因分析、空气质量改善等相关研究提供参考。
- 气溶胶光学厚度 /
- MODIS /
- 长江三角洲 /
- 时空变化 /
- 气象解释
Abstract: Based on MODIS aerosol optical depth(AOD) data and meteorological observed data from 2008 to 2017, the spatial and temporal distribution of AOD in the Yangtze River Delta area was summarized, which was then explained by analyzing the correlation between AOD and meteorological factors. The results showed that: 1) the annual average AOD fluctuated periodically, which showed its peak value of 0.83 in 2011, and declined rapidly from 2014, with an obvious decrease of 22.8% in 2017 compared with 2014. In addition, the peak value of monthly mean AOD appeared in summer, which was mainly caused by the diffusion of large amount of sea salt aerosol particles and water vapor from the ocean in summer; 2) in terms of spatial distribution, the AOD high value in Yangtze River Delta mainly distributed in southern Jiangsu and Xuzhou area, and the area with AOD high value had gradually narrowed since 2014; the AOD of Zhejiang Province was significantly lower than the other areas, which was closely related to the high topography of Zhejiang Province; 3) the change of AOD had a good positive correlation with the temperature and relative humidity, but the correlation with wind speed was poor and complex, which may be affected by the uncertainty of wind direction. In summer, due to high temperature and humidity, most areas showed high AOD values, and the low temperature and air moisture content in winter caused low AOD. However, the contribution of solid particles to AOD was large in winter, so the change of AOD may reflect the air pollution in winter. The results can provide a reference for aerosol assessment, air quality attribution analysis and air quality improvement in the Yangtze River Delta in future.
- aerosol optical depth(AOD) /
- MODIS /
- Yangtze River Delta /
- spatial and temporal changes /
- meteorological interpretation

HTML全文

参考文献(25)

[1]	陆天蔚,张晶,乔岩,等.长三角地区气溶胶对辐射和降水影响的分析[J].北京师范大学学报(自然科学版),2019,55(1):135-144.
[2]	吴国雄,李占清,符淙斌,等.气溶胶与东亚季风相互影响的研究进展[J].中国科学:地球科学,2015,45(11):1609-1627.
[3]	刘平平,刘鑫,刘琼,等.新疆地区气溶胶对地表太阳辐射的影响初探[J].环境科学学报,2020,40(9):3155-3164.
[4]	华珊.青藏高原气溶胶-云相互作用及云辐射效应研究[D].兰州:兰州大学,2020.
[5]	徐小红,余兴,朱延年,等.气溶胶对中国中纬度夏季低层风速的影响[J/OL].高原气象:1-7[2020-12-27 ].http://kns.cnki.net/kcms/detail/62.1061.P.20201012.1558.006.html.
[6]	汤宏山.东北地区气溶胶光学厚度与大气污染物相关性分析[D].大连:辽宁师范大学,2020.
[7]	施益强,陈坰烽,王坚,等.厦门市MODIS气溶胶光学厚度与PM_2.5的时空特征及其相关性[J].大气与环境光学学报,2020,15(5):334-346.
[8]	李慧娟,胡列群,李帅.北疆地区MODIS 3 km气溶胶光学厚度与PM₁₀质量浓度的相关性分析[J].环境科学学报,2018,38(3):1109-1116.
[9]	付泽宇,马晓燕,王静,等.南京和北京气溶胶光学厚度与PM_2.5质量浓度关系对比分析[J].环境科学学报,2020,40(8):2911-2918.
[10]	岳辉,刘英,张元敏.基于MODIS数据的中国地区气溶胶光学厚度时空变化特征[J].环境污染与防治,2020,42(1):89-93.
[11]	李忠宾,王楠,张自力,等.中国地区MODIS气溶胶光学厚度产品综合验证及分析[J].中国环境科学,2020,40(10):4190-4204.
[12]	陈辉,厉青,王中挺,等.一种基于FY3D/MERSI2的AOD遥感反演方法[J].地球信息科学学报,2020,22(9):1887-1896.
[13]	祝善友,李佳敏,向嘉敏,等.基于FY4A AGRI数据的气溶胶光学厚度反演研究[J].地理与地理信息科学,2020,36(4):26-32.
[14]	马小雨,陈正华,宿鑫,等.GF-4增强型地表反射率库支持法的气溶胶光学厚度反演[J].遥感学报,2020,24(5):578-595.
[15]	牛林芝,王旭红,韩海青,等.中亚五国气溶胶光学厚度时空分布特征研究[J/OL].环境科学学报:1-13[2020-12-25 ].https://doi.org/10.13671/j.hjkxxb.2020.0256.
[16]	刘雨华,郑小慎.环渤海地区气溶胶光学厚度数据选取及时空特征分析[J].环境科学学报,2020,40(5):1621-1628.
[17]	韩锋,徐峻,党鸿雁,等.基于CALIOP探测的京津冀地区气溶胶垂直分布特征[J].环境工程,2017,35(6):108-113.
[18]	王跃思,辛金元,李占清,等.中国地区大气气溶胶光学厚度与Angstrom参数联网观测(2004-08—2004-12)[J].环境科学,2006,27(9):1703-1711.
[19]	关于2015年夏季秸秆焚烧污染防控工作情况的通报[EB/OL].http://www.mee.gov.cn/gkml/hbb/bgth/201510/t20151012_314885. htm.
[20]	LEE H J,LIU Y,COULL B A,et al.A novel calibration approach of MODIS AOD data to predict PM_2.5 concentrations[J].Atmospheric Chemistry and Physics,2011,11:7991-8002.
[21]	段婧,毛节泰.长江三角洲大气气溶胶光学厚度分布和变化趋势研究[J].环境科学学报,2007,27(4):537-543.
[22]	王月华,汤莉莉,邹强,等.苏州地区黑碳气溶胶季节变化研究[J].环境工程,2014,32(增刊):544-546.
[23]	白淑英,史建桥,卜军,等.近年来长江流域气溶胶光学厚度时空变化特征分析[J].生态坏境学报,2012,21(9):1567-1573.
[24]	张颖蕾,崔希民.基于MODIS_C061的长三角地区AOD与Angstrom指数时空变化分析[J].环境科学,2020,41(6):2617-2624.
[25]	肖宇,王茜,赵倩彪,等.降雨对长三角区域PM_2.5浓度的影响[J].中国环境监测,2020,36(2):109-115.