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引用本文:

DOI:

10.11834/jrs.20211002

收稿日期:

2021-01-03

修改日期:

2021-04-25

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基于MAX-DOAS和TROPOMI对北京冬季对流层NO2污染监测和对比分析
杨东上, 罗宇涵, 曾议, 周海金, 司福祺, 窦科, 詹锴, 刘文清
中国科学院合肥物质科学研究院安徽光机所环境光学与技术重点实验室
摘要:

利用不同监测平台对大气环境污染进行监测时,不同遥感数据之间的对比验证以及协同观测是准确评估大气污染变化的关键。本文利用北京站点布置的多轴差分吸收光谱仪(MAX-DOAS)光谱仪,反演了2018年11月至2019年2月北京站点冬季的对流层NO2垂直柱浓度,总结了北京冬季NO2的日变化和月变化规律。研究中首先利用MAX-DOAS测量光谱结合DOAS反演算法获取不同时刻对流层NO2垂直柱浓度,与TROPOMI过境时刻的NO2遥测数据的变化趋势和相关性进行比较,并对卫星过境时地基站点不同的数据平均时间,和星-地间平均采样距离进行敏感性分析,同时将双因素方差分析方法(Two-way ANOVA)应用于评估风场对区域NO2浓度变化的影响。结果显示北京地区11月的对流层NO2平均浓度高于冬季其他月份,最大时均浓度可达到4.04×1016 molec.cm-2,且冬季各月份下午对流层平均NO2浓度明显高于上午。利用TROPOMI和MAX-DOAS获得的对流层NO2具有较好的相关性(r=0.88),其中2018年12月星-地观测相关性可达到0.96,但TROPOMI的NO2浓度结果相对于地基MAX-DOAS观测结果均有不同程度的高估。同时星-地对比敏感性表明,在一定的采样范围内,随着平均时间和平均距离的增大,星-地NO2浓度检测相关性均表现为明显的增高,其中相关性对采样距离的敏感性较大,而浓度相对偏差对采样时间敏感性较大,这为数据对比时的合理采样间隔选择提供参考。此外,风场分析发现风速及风速和风向的交互作用是导致北京地区NO2浓度变化的重要因素之一。

Monitoring and comparative analysis of NO2 pollution in the troposphere in winter over Beijing based on MAX-DOAS and TROPOMI
Abstract:

Objective: Satellite-based and ground-based remote sensing methods have unique advantages in monitoring atmospheric pollutants. The comparison and verification of different remote sensing data and collaborative observation using different monitoring platforms, which play a major role in assessing changes accurately in atmospheric pollution. In this paper, using the MAX-DOAS spectrometer deployed at the Beijing site, the tropospheric NO2 vertical column amounts in the winter from November 2018 to February 2019 at the Beijing site was retrieved, and the daily and monthly changes of NO2 in Beijing were summarized. Additionaly, it was used together with TROPOMI"s products to analyze the NO2 pollution in winter in Beijing. Method: The MAX-DOAS measurement spectrum combined with the DOAS inversion algorithm was used to obtain the vertical column amounts of tropospheric NO2 at different times, and compared with the changes and correlation of the NO2 columns obtained by TROPOMI at the time of satellite overpass. It also analyzes the sensitivity of the NO2 columns of ground-based and space-borne observations at different sampling times and the average sampling distance between the satellite and the ground site at the time of passing territory. In addition, we counted wind field in the winter weather conditions, and analyzed the influence of the wind field on the changes in NO2 in Beijing. The two-factor analysis of variance was applied to evaluate the influence of the wind field on the change of the regional NO2 amounts. Result: The results show that the average columns of NO2 in the troposphere in Beijing in November is higher than that in other months in winter, and the maximum hourly average columns can reach 4.04×1016 molec.cm-2, and the average amounts of NO2 in the troposphere in the afternoon of each winter month is significantly higher than that in the morning. The tropospheric NO2 obtained by TROPOMI and MAX-DOAS has a good correlation (r=0.88). The correlation between satellite-ground-based observations in December 2018 can reach 0.96. However, the NO2 amounts of TROPOMI are overestimated to varying degrees relative to the ground-based MAX-DOAS observation results. At the same time, the sensitivity of satellite-ground comparison shows that within a certain sampling range, with the increase of average time and average distance, the correlation appears to increase significantly. Among them, the correlation is more sensitive to the sampling distance, and the relative columns deviation is more sensitive to the sampling time, which provides a reference for the selection of reasonable sampling interval during data comparison. In addition, wind field analysis found that wind speed and the interaction between wind speed and wind direction are the main factors leading to changes in NO2 in Beijing. Conclusion: Through the monitoring of NO2 in winter on different platforms, we found that the NO2 in Beijing area has obvious monthly and diurnal changes. This is of great significance for establishing pollution forecasting models and analyzing the causes of pollution. The comparative observation and sampling sensitivity analysis of the two different observation platforms also provide important reference and data support for the reliability of NO2 inversion on the spaceborne platform.

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