Tropospheric vertical column densities (VCDs) of NO₂, SO₂ and HCHO derived from the Ozone Monitoring Instrument (OMI) on AURA and the Global Ozone Monitoring Experiment 2 aboard METOP-A (GOME-2A) and METOP-B (GOME-2B) are widely used to characterize the global distributions, trends and dominating sources of these trace gases. They are also useful for the comparison with chemical transport models (CTMs). We use tropospheric VCDs and vertical profiles of NO₂, SO₂ and HCHO derived from MAX-DOAS measurements from 2011 to 2014 in Wuxi, China, to validate the corresponding products (daily and bi-monthly-averaged data) derived from OMI and GOME-2A/B by different scientific teams. Prior to the comparison, the spatial and temporal coincidence criteria for MAX-DOAS and satellite data are determined by a sensitivity study using different spatial and temporal averaging conditions. Cloud effects on both MAX-DOAS and satellite observations are also investigated. Our results indicate that the discrepancies between satellite and MAX-DOAS results increase with increasing effective cloud fraction and are dominated by the effects of clouds on the satellite products. In comparison with MAX-DOAS, we found a systematic underestimation of all SO₂ (40 to 57 %) and HCHO products (about 20 %), and an overestimation of the GOME-2A/B NO₂ products (about 30 %), but good consistency with the DOMINO version 2 NO₂ product. To better understand the reasons for these differences, we evaluated the a priori profile shapes used in the OMI retrievals (derived from CTM) by comparison with those derived from the MAX-DOAS observations. Significant differences are found for the SO₂ and HCHO profile shapes derived from the IMAGES model, whereas on average good agreement is found for the NO₂ profile shapes derived from the TM4 model. We also applied the MAX-DOAS profile shapes to the satellite retrievals and found that these modified satellite VCDs agree better with the MAX-DOAS VCDs than the VCDs from the original data sets by up to 10, 47 and 35 % for NO₂, SO₂ and HCHO, respectively. Furthermore, we investigated the effect of aerosols on the satellite retrievals. For OMI observations of NO₂, a systematic underestimation is found for large AOD, which is mainly attributed to effect of the aerosols on the cloud retrieval and the subsequent application of a cloud correction scheme (implicit aerosol correction). In contrast, the effect of aerosols on the clear-sky air mass factor (explicit aerosol correction) has a smaller effect. For SO₂ and HCHO observations selected in the same way, no clear aerosol effect is found, probably because for the considered data sets no cloud correction is applied (and also because of the larger scatter). From our findings we conclude that for satellite observations with cloud top pressure (CTP) > 900 hPa and effective cloud fraction (eCF) < 10 % the application of a clear-sky air mass factor might be a good option if accurate aerosol information is not available. Another finding of our study is that the ratio of morning-to-afternoon NO₂ VCDs can be considerably overestimated if results from different sensors and/or retrievals (e.g. OMI and GOME-2) are used, whereas fewer deviations for HCHO and SO₂ VCDs are found.

In order to promote the development of the passive DOAS technique the Multi Axis DOAS – Comparison campaign for Aerosols and Trace gases (MAD-CAT) was held at the Max Planck Institute for Chemistry in Mainz, Germany, from June to October 2013. Here, we systematically compare the differential slant column densities (dSCDs) of nitrous acid (HONO) derived from measurements of seven different instruments. We also compare the tropospheric difference of SCDs (delta SCD) of HONO, namely the difference of the SCDs for the non-zenith observations and the zenith observation of the same elevation sequence. Different research groups analysed the spectra from their own instruments using their individual fit software. All the fit errors of HONO dSCDs from the instruments with cooled large-size detectors are mostly in the range of 0.1 to 0.3 × 10¹⁵ molecules cm⁻² for an integration time of 1 min. The fit error for the mini MAX-DOAS is around 0.7 × 10¹⁵ molecules cm⁻². Although the HONO delta SCDs are normally smaller than 6 × 10¹⁵ molecules cm⁻², consistent time series of HONO delta SCDs are retrieved from the measurements of different instruments. Both fits with a sequential Fraunhofer reference spectrum (FRS) and a daily noon FRS lead to similar consistency. Apart from the mini-MAX-DOAS, the systematic absolute differences of HONO delta SCDs between the instruments are smaller than 0.63 × 10¹⁵ molecules cm⁻². The correlation coefficients are higher than 0.7 and the slopes of linear regressions deviate from unity by less than 16 % for the elevation angle of 1°. The correlations decrease with an increase in elevation angle. All the participants also analysed synthetic spectra using the same baseline DOAS settings to evaluate the systematic errors of HONO results from their respective fit programs. In general the errors are smaller than 0.3 × 10¹⁵ molecules cm⁻², which is about half of the systematic difference between the real measurements. The differences of HONO delta SCDs retrieved in the selected three spectral ranges 335–361, 335–373 and 335–390 nm are considerable (up to 0.57 × 10¹⁵ molecules cm⁻²) for both real measurements and synthetic spectra. We performed sensitivity studies to quantify the dominant systematic error sources and to find a recommended DOAS setting in the three spectral ranges. The results show that water vapour absorption, temperature and wavelength dependence of O₄ absorption, temperature dependence of Ring spectrum, and polynomial and intensity offset correction all together dominate the systematic errors. We recommend a fit range of 335–373 nm for HONO retrievals. In such fit range the overall systematic uncertainty is about 0.87 × 10¹⁵ molecules cm⁻², much smaller than those in the other two ranges. The typical random uncertainty is estimated to be about 0.16 × 10¹⁵ molecules cm⁻², which is only 25 % of the total systematic uncertainty for most of the instruments in the MAD-CAT campaign. In summary for most of the MAX-DOAS instruments for elevation angle below 5°, half daytime measurements (usually in the morning) of HONO delta SCD can be over the detection limit of 0.2 × 10¹⁵ molecules cm⁻² with an uncertainty of ∼ 0.9 × 10¹⁵ molecules cm⁻².

研究了多轴差分吸收光谱技术(MAX-DOAS)的气溶胶消光系数垂直廓线反演算法. 该算法应用非线性最优估算法, 通过MAX-DOAS测量的氧的二聚体(O4), 反演气溶胶消光系数垂直廓线和光学厚度(AOD). 首先研究了非线性最优估算法中权重函数、先验廓线协方差矩阵、测量不确定度协方差矩阵的计算方法, 针对中国气溶胶浓度较高且变化剧烈的特征, 设计了非线性迭代方案. 然后在低气溶胶、高气溶胶和抬高型气溶胶三种状态下, 通过计算机仿真模拟验证了MAX-DOAS气溶胶消光系数垂直分布反演算法, 讨论了误差来源. 之后在合肥地区开展了连续观测实验, 并将反演的AOD与CE318太阳光度计对比, 两者的相关性系数达到了0.94. AOD反演的相对误差约为20%. 又将反演的最低层(0–0.3 km)气溶胶消光系数与能见度仪对比, 两者的相关性系数为0.65. 近地面气溶胶消光系数反演的总相对误差约为10%. 模拟验证和对比实验均说明本文研究的气溶胶消光系数垂直廓线反演算法可以较好地获取对流层的气溶胶状态.

The tropospheric vertical column density of NO₂ (Trop NO₂ VCD) can be obtained using satellite remote sensing, but it has been discovered that the Trop NO₂ VCD is affected by uncertainties such as the cloud fraction, terrain reflectivity, and aerosol optical depth. A certain error occurs in terms of data inversion accuracy, necessitating additional ground observation verification. This study uses surface NO₂ mass concentrations from the China National Environmental Monitoring Center (CNEMC) sites in Jiangsu Province, China in 2019 and the Trop NO₂ VCD measured by MAX-DOAS, respectively, to verify the Trop NO₂ VCD product (daily and monthly average data), that comes from the TROPOspheric Monitoring Instrument (TROPOMI) and Ozone Monitoring Instrument (OMI). The results show that the spatial distributions of NO₂ in TROPOMI and OMI exhibit a similar tendency and seasonality, showing the characteristics of being high in spring and winter and low in summer and autumn. On the whole, the concentration of NO₂ in the south of Jiangsu Province is higher than that in the north. The Pearson correlation coefficient (r) between the monthly average TROPOMI VCD NO₂ and the CNEMC NO₂ mass concentration is 0.9, which is greater than the r (0.78) between OMI and CNEMC; the r (0.69) between TROPOMI and the MAX-DOAS VCD NO₂ is greater than the r (0.59) between OMI and the MAX-DOAS. As such, the TROPOMI is better than the previous generation of OMI at representing the spatio-temporal distribution of NO₂ in the regional scope. On the other hand, the uncertainties of the satellite products provided in this study can constrain regional air quality forecasting models and top-down emission inventory estimation.

NO_X (NO_X = NO + NO₂) emissions measurements in Beijing are of great significance because they can aid in understanding how NO_X pollution develops in mega-cities throughout China. However, NO_X emissions in mega-cities are difficult to measure due to changes in wind patterns and moving sources on roads during measurement. To obtain good spatial coverage on different ring roads in Beijing over a short amount of time, two mobile differential optical absorption spectroscopy (DOAS) instruments were used to measure NO_X emission flux from April 18th to 26th, 2018. In addition, a wind profile radar provided simultaneous wind field measurements for altitudes between 50 m and 1 km for each ring road measurement. We first determined NO_X emission flux of different ring roads using wind field averages from measured wind data. The results showed that the NO_X emission flux of Beijing’s fifth ring road, which represented the urban part, varied from (19.29 ± 5.26) × 10²⁴ molec./s to (36.46 ± 12.86) × 10²⁴ molec./s. On April 20th, NO_X emission flux for the third ring was slightly higher than the fourth ring because the two ring roads were measured at different time periods. We then analyzed the NO_X emission flux error budget and error sensitivity. The main error source was the wind field uncertainty. For some measurements, the main emission flux error source was either wind speed uncertainty or wind direction uncertainty, but not both. As Beijing’s NO_X emissions came from road vehicle exhaust, we found that emission flux error had a more diverse sensitivity to wind direction uncertainty, which improved our knowledge on this topic. The NO_X emission flux error sensitivity study indicated that more accurate measurements of the wind field are crucial for effective NO_X emission flux measurements in Chinese mega-cities. Obtaining actual time and high resolved wind measurements is an advantage for mega-cities’ NO_X emission flux measurements. The emission flux errors caused by wind direction and wind speed uncertainties were clearly distinguished. Other sensitivity studies indicated that NO_X/NO₂ ratio uncertainty dominated flux errors when the NO_X/NO₂ ratio uncertainty was >0.4. Using two mobile-DOAS and wind profile radars to measure NOx emission flux improved the quality of the emission flux measuring results. This approach could be applied to many other mega-cities in China and in others countries.