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纸质出版日期： 2023-07-07 ，
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杨东上，罗宇涵，曾议，周海金，司福祺，窦科，詹锴，刘文清.2023.基于MAX-DOAS和TROPOMI对北京冬季对流层NO2污染监测和对比分析.遥感学报，27（7）： 1680-1690
Yang D S，Luo Y H，Zeng Y，Zhou H J，Si F Q，Dou K，Zhan K and Liu W Q. 2023. Monitoring and comparative analysis of NO2 pollution in the troposphere in winter over Beijing based on MAX-DOAS and TROPOMI. National Remote Sensing Bulletin， 27（7）：1680-1690
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—2019年2月北京站点冬季的对流层NO
2
垂直柱浓度，总结了北京冬季NO
2
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NO
2
垂直柱浓度，与TROPOMI过境时刻的NO
2
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NO
2
浓度变化的影响。结果显示北京地区11月的对流层NO
2
平均浓度高于冬季其他月份，最大时均浓度可达到4.04×10
16
molec·cm
-2
，且冬季各月份下午对流层平均NO
2
浓度明显高于上午。利用TROPOMI和MAX-DOAS获得的对流层NO
2
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NO
2
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NO
2
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，通过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NO
2
浓度变化的重要因素之一。
Satellite- and ground-based remote sensing methods have unique advantages in monitoring atmospheric pollutants. The comparison and verification of different remote sensing data and collaborative observations use different monitoring platforms
which play a major role in accurately assessing changes in atmospheric pollution. In this study
the tropospheric NO
2
vertical column amounts in the winter from November 2018 to February 2019 at the Beijing site were retrieved using the MAX-DOAS spectrometer deployed at the Beijing site. Moreover
the daily and monthly changes in NO
2
in Beijing were summarized. The MAX-DOAS spectrometer was also used with TROPOMI’s products to analyze the NO
2
pollution during winter in Beijing.
The MAX-DOAS measurement spectrum combined with the DOAS inversion algorithm was used to obtain the vertical column amounts of tropospheric NO
2
at different times and compare the changes and correlation of the NO
2
columns obtained by TROPOMI at the time of satellite overpass. It was also used to analyze the sensitivity of the NO
2
columns of ground-based and spaceborne observations at different sampling times and the average sampling distance between the satellite and the ground site at the time of passing territory. Moreover
we counted wind fields in winter weather conditions. The influence of the wind field on the changes in NO
2
in Beijing was also analyzed. The two-factor analysis of variance was applied to evaluate the influence of the wind field on the change in the regional NO
2
amounts.
The results show that the average columns of NO
2
in the troposphere in Beijing in November are higher than those in other months in winter. The maximum hourly average columns can reach 4.04 × 10
16
molec·cm
-2
. The average amounts of NO
2
in the troposphere in the afternoon of each winter month are significantly higher than those in the morning. The tropospheric NO
2
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 NO
2
amounts of TROPOMI are overestimated to varying degrees relative to the ground-based MAX-DOAS observation results. Moreover
the sensitivity of satellite-ground comparison shows that within a certain sampling range
the correlation appears to increase significantly with the increase in average time and average distance. The correlation is sensitive to the sampling distance
whereas the relative column deviation is sensitive to the sampling time. This finding provides a reference for the selection of reasonable sampling intervals during data comparison. In addition
wind field analysis indicates that wind speed and the interaction between wind speed and wind direction are the main factors leading to changes in NO
2
in Beijing.
The results of monitoring NO
2
in winter on different platforms indicate that the NO
2
in the Beijing area has obvious monthly and diurnal changes. This monitoring is vital for establishing pollution forecasting models and analyzing pollution causes. The comparative observation and sampling sensitivity analysis of the two different observation platforms also provide important reference and data support for the reliability of NO
2
inversion on the spaceborne platform.
NO2TROPOMIMAX-DOAS双因素方差分析变化趋势遥感DOAS对比验证
NO2TROPOMIMAX-DOASTwo-way ANOVAchanging trendremote sensingDOAScomparison and validation
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