基于卫星高光谱遥感的2007年—2017年新疆地区大气NO<sub>2</sub>时空变化趋势分析

doi:10.3964/j.issn.1000-0593(2021)05-1631-08

摘要
参考文献
相关文章 (11)

全文: PDF (4325 KB)
输出: BibTeX | EndNote (RIS)

摘要：自2013年国务院颁布《大气污染防治行动计划》等大气综合治理政策以来，我国东部地区大气颗粒物等重要污染物得到了有效控制。伴随着我国能源政策的变化，西北地区在加大能源开发力度的同时，大气污染亦呈现加剧趋势，但在之前的研究中未得到重视。卫星遥感的监测手段较地面观测具有不受地域限制，观测时间长，观测污染物种类多等优势。其中，星载紫外-可见高光谱仪OMI自2005年在轨运行，在大气污染的时空变化趋势探测、排放源估计以及模式的同化和验证等科学应用中得到广泛应用。中国科学技术大学的卫星对流层NO₂柱浓度产品，通过对OMI原始测量光谱的二次标定和气体反演算法的关键优化，在与地基观测的结果对比验证中呈现良好的相关性，适合用于我国高气溶胶背景下的大气污染分析。结合中国科学技术大学OMI NO₂数据产品，我国新疆地区大气NO₂污染的时空分布特征得以表征。在2007年—2017年期间，新疆地区的NO₂污染集中分布于北疆，其中“乌鲁木齐—昌吉—石河子”城市群（乌昌石地区）与新疆总体NO₂水平月变化相关性很强（相关性系数r=0.942，p-value<0.01）。新疆NO₂年际变化存在明显阶段性特征，与相关政策以及能源行业排放变化相符：2007年—2010年变化趋势不明显，2014年较2010年总体年平均浓度增长18.5％，其中乌昌石地区增长41.3％；2017年较2014年总体平均浓度下降26.4%，乌昌石地区下降42.8%。乌昌石地区由于石油化工企业，经济开发区等分布密集，成为NO₂污染的聚集区，与乌鲁木齐市，昌吉市NO₂变化具有强相关性（r=0.982，p-value<0.01；r=0.951，p-value<0.01）。受采暖时期排放以及特殊气象条件控制，乌昌石地区NO₂污染峰值为12月，冬季污染尤其显著；在2007年—2016年采暖期间（每年10月—次年4月初），乌昌石地区NO₂水平有显著上升趋势（显著性水平α=0.01），在未来大气治理中需要格外关注。

关键词：卫星光谱遥感；新疆；NO₂污染；乌昌石地区

Abstract：Since the State Council promulgated the “Atmospheric Pollution Prevention Action Plan” and other comprehensive atmospheric control policies in 2013, the concentration of atmospheric pollutants and other important pollutants in eastern China has been effectively controlled. Along with the changes in China’s energy policy and increasing energy development in the northwest region, air pollution also shows an increasing trend, but it has not been paid attention to in previous studies. Compared with ground observation, the monitoring method of satellite remote sensing has the advantages of not being restricted by area, lasting long observation time and monitoring many types of pollutants simultaneously. The spaceborne ultraviolet-visible hyperspectrometer OMI has been in orbit since 2005 and has been widely used in scientific applications such as the detection of the temporal and spatial changes of atmospheric pollution, the estimation of emission sources, and the assimilation and verification of models. USTC’s tropospheric NO₂ column concentration product, through the secondary calibration of the OMI original measurement spectrum and the key optimization of the gas inversion algorithm, shows a good correlation in the comparison and verification with the results of ground-based observations. Analysis of air pollution in the background of sol. Combined with USTC’s OMI NO₂ data product, the spatial and temporal distribution characteristics of atmospheric NO₂ pollution in Xinjiang, China, can be characterized. From 2007 to 2017, NO₂ pollution in Xinjiang was concentrated in northern Xinjiang. Among them, the “Urumqi-Changi-Shihezi” urban agglomeration (“Wuchangshi” area) had a strong correlation with the monthly change of the overall NO₂ level in Xinjiang (correlation coefficient R=0.942, p-value<0.01). The inter-annual change of NO₂ in Xinjiang has obvious phase characteristics, which is consistent with the changes in relevant policies and energy industry emissions: the changing trend is not obvious from 2007 to 2010, the overall average concentration in 2014 increased by 18.5% compared with 2010, and the “Wuchangshi” area increased by 41.3%, in 2017, the overall average concentration of NO₂ decreased by 26.4% compared with 2014, and “Wuchangshi” decreased by 42.8%. Due to the dense distribution of petrochemical enterprises and economic development zones in the “Wuchangshi” area, it has become a NO₂ pollution gathering area, which has a strong correlation with Urumqi and Changi NO₂ changes (R=0.982, p-value<0.01; R=0.951, p-value<0.01). Controlled by the heating period’s emissions and the special meteorological conditions, the peak of NO₂ change in the “Wuchangshi” area in December, and the winter pollution is particularly significant. The heating period (from October to early April each year) has a significant upward trend from 2007 to 2016 (Significance level α=0.01), which requires special attention in future atmospheric governance.

Key words：Satellite spectrum remote sensing; Xinjiang Uygur autonomous region; NO₂ pollution; “Wuchangshi” area

收稿日期: 2020-05-20 修订日期: 2020-09-28

中图分类号:

X87

基金资助: 国家重点研发计划（2017YFC0210002，2018YFC0213104，2016YFC0200404，2016YFC0203302，2017YFC0212800，2019YFC0214702和2018YFC0213201），安徽省科技重大专项（18030801111），国家自然科学基金项目（41720501，51778596，41977184和41775025 ），美丽中国生态文明建设科技工程专项（XDA23020301），国家大气重污染成因与治理攻关项目（DQGG0102和DQGG0205），国家高分辨率对地观测重大科技专项项目“环境保护遥感动态监测信息服务系统”（二期，05-Y30B01-9001-19/20-3）资助

通讯作者: 张成歆 E-mail: zcx2011@mail.ustc.edu.cn

作者简介: 苏锦涛，1999年生，中国科学技术大学地球和空间科学学院本科生 e-mail：ca40@mail.ustc.edu.cn

引用本文:

苏锦涛，张成歆，胡启后，刘浩然，刘建国. 基于卫星高光谱遥感的2007年—2017年新疆地区大气NO₂时空变化趋势分析[J]. 光谱学与光谱分析, 2021, 41(05): 1631-1638.
SU Jin-tao, ZHANG Cheng-xin, HU Qi-hou, LIU Hao-ran, LIU Jian-guo. Analysis of Spatial and Temporal Change Trend of Xinjiang NO₂ in 2007—2017 Based on Satellite Hyperspectral Remote Sensing. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(05): 1631-1638.

链接本文:

https://www.gpxygpfx.com/CN/10.3964/j.issn.1000-0593(2021)05-1631-08 或 https://www.gpxygpfx.com/CN/Y2021/V41/I05/1631

[1] Jacob D J, Heikes E G, Fan S M, et al. Journal of Geophysical Research: Atmospheres, 1996, 101(D19): 24235.
[2] Duan L, Yu Q, Zhang Q, et al. Atmospheric Environment, 2016, 146: 55.
[3] Jacob D J. Introduction to Atmospheric Chemistry. Princeton University Press, 1999. 164.
[4] Boningari T, Smirniotis P G. Current Opinion in Chemical Engineering, 2016, 13: 133.
[5] Zhao B, Wang S X, Liu H, et al. Atmospheric Chemistry and Physics, 2013, 13(19): 9869.
[6] Ma Z, Deng J, Li Z, et al. Atmospheric Environment, 2016, 131(4): 164.
[7] Zhang Q, Geng G, Wang S, et al. Chinese Science Bulletin, 2012, 57(22): 2857.
[8] Liu F, Beirle S, Zhang Q, et al. Atmospheric Chemistry and Physics Discussions, 2015, 15(17): 24179.
[9] Ling Z, Huang T, Zhao Y, et al. Atmospheric Chemistry and Physics, 2017, 17(14): 9115.
[10] Zhang C, Liu C, Hu Q, et al. Light: Science & Applications, 2019, 8(1): 100.
[11] Dobber M, Dirksen R, Levelt P, et al. The International Society for Optical Engineering, 2006, 6296: 59621L-12.
[12] Miyazaki K, Eskes H J, Sudo K. Atmospheric Chemistry and Physics Discussions, 2011, 11(12): 31523.
[13] Levelt P, Joanna J, Tamminen J, et al. Atmospheric Chemistry and Physics, 2018, 18(8): 5699.
[14] Krotkov N A, Mclinden C A, Li C, et al. Atmospheric Chemistry and Physics, 2016, 16(7): 4605.
[15] Liu H, Liu C, Xie Z, et al. Scientific Reports, 2016, 6: 34408.
[16] Platt U, Stutz J. Differential Optical Absorption Spectroscopy. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. 135.
[17] Zhang C, Liu C, Wang Y, et al. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(6): 3323.
[18] Zhang C, Liu C, Chan K L, et al. Light: Science & Applications, 2020, 9(1): 66.
[19] Xing C, Liu C, Wang S, et al. Atmospheric Chemistry and Physics, 2017, 17(23): 14275.
[20] Tan W, Liu C, Wang S, et al. Atmospheric Chemistry and Physics, 2018, 18(20): 15387.
[21] Tan W, Zhao S, Liu C, et al. Atmospheric Environment, 2019, 200: 228.
[22] Chance K. Geophysical Research Letters, 1998, 25(17): 3335.
[23] Zhang Q, Streets D G, Carmichael G R, et al. Atmospheric Chemistry and Physics, 2009, 9(14): 5131.
[24] Li M, Zhang Q, Streets D G, et al. Atmospheric Chemistry and Physics, 2014, 14(11): 5617.
[25] Zheng B, Huo H, Zhang Q, et al. Atmospheric Chemistry and Physics, 2014, 14(18): 9787.