Validation of OMI HCHO Products Using MAX-DOAS observations from 2010 to 2016 in Xianghe, Beijing: Investigation of the Effects of Aerosols on Satellite Products

Wang, Yapeng; Wang, Zifeng; Yu, Chao; Zhu, Shaojiang; Cheng, Liangxiao; Zhang, Ying; Chen, Liangfu

doi:10.3390/rs11020203

Cited by 12 publications

(9 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We see in Table 5 that at the cleanest site (Uccle) the effect of the smoothing is small, while at the more polluted sites Xianghe and UNAM, the biases are strongly reduced by about 20%. This result is in agreement with previous MAX-DOAS validation studies (De Smedt et al, 2015;Wang et al, 2019b), but also with aircraft and regional model comparisons (Zhu et al, 2020;Su et al, 2020). The effect of the smoothing is also clearly seen in Figure 17 where the scatter plots of daily comparisons between TROPOMI and MAX-DOAS are shown before and after vertical smoothing.…”

Section: Effect Of Vertical Smoothingsupporting

confidence: 90%

Comparative assessment of TROPOMI and OMI formaldehyde observations against MAX-DOAS network column measurements

Smedt

Pinardi

Vigouroux

et al. 2021

Preprint

View full text Add to dashboard Cite

Abstract. The TROPOspheric Monitoring Instrument (TROPOMI), launched in October 2017 on board the Sentinel-5 Precursor (S5P) satellite, monitors the composition of the Earth's atmosphere at an unprecedented horizontal resolution as fine as 3.5 × 5.5 km2. This paper assess the performances of the TROPOMI formaldehyde (HCHO) operational product compared to its predecessor, the OMI HCHO QA4ECV product, at different spatial and temporal scales. The parallel development of the two algorithms favored the consistency of the products, which facilitates the production of long-term combined time series. The main difference between the two satellite products is related to the use of different cloud algorithms, leading to a positive bias of OMI compared to TROPOMI of up to 30 % in Tropical regions. We show that after switching off the explicit correction for cloud effects, the two datasets come into an excellent agreement. For medium to large HCHO vertical columns (larger than 5 × 1015 molec.cm−2) the median bias between OMI and TROPOMI HCHO columns is not larger than 10 % (< 0.4 × 1015 molec.cm−2). For lower columns, OMI observations present a remaining positive bias of about 20 % (< 0.8 × 1015 molec.cm−2) compared to TROPOMI in mid-latitude regions. Here, we also use a global network of 18 MAX-DOAS instruments to validate both satellite sensors for a large range of HCHO columns. This work complements the study by Vigouroux et al. (2020) where a global FTIR network is used to validate the TROPOMI HCHO operational product. Consistent with the FTIR validation study, we find that for elevated HCHO columns, TROPOMI data are systematically low (−25 % for HCHO columns larger than 8 × 1015 molec.cm−2), while no significant bias is found for medium range column values. We further show that OMI and TROPOMI data present equivalent biases for large HCHO levels. However, TROPOMI significantly improves the precision of the HCHO observations at short temporal scales, and for low HCHO columns. We show that compared to OMI, the precision of the TROPOMI HCHO columns is improved by 25 % for individual pixels, and up to a factor 3 when considering daily averages in 20 km-radius circles. The validation precision obtained with daily TROPOMI observations is comparable to the one obtained with monthly OMI observations. To illustrate the improved performances of TROPOMI in capturing weak HCHO signals, we present clear detection of HCHO column enhancements related to shipping emissions in the Indian Ocean. This is achieved by averaging data over a much shorter period (3 months) than required with previous sensors, and opens new perspectives to study shipping emissions of VOCs and related atmospheric chemical interactions.

show abstract

Section: Effect Of Vertical Smoothingsupporting

confidence: 90%

Comparative assessment of TROPOMI and OMI formaldehyde observations against MAX-DOAS network column measurements

Smedt

Pinardi

Vigouroux

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Because of its short lifetime (of the order of a few hours), HCHO in the boundary layer can be related to the release of a large number of short-lived volatile hydrocarbons. Furthermore, HCHO observations provide information on the chemical oxidation processes in the atmosphere, including CO chemical production from CH 4 and NMVOCs, the oxidation of isoprene into HCHO, which allows the quantification of midday OH (Wells et al, 2020), and the tropospheric ozone production regimes that depend on the HCHO to NO 2 ratios (Jin et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…They are used in many studies related to air quality and climate change (e.g. Stavrakou et al, 2014Stavrakou et al, , 2015Stavrakou et al, , 2016Stavrakou et al, , 2018Fortems-Cheiney et al, 2012;Marais et al, 2012;Mahajan et al, 2015;Choi and Souri, 2015;Zhu et al, 2016;Chan Miller et al, 2017;Jin et al, 2017;Barkley et al, 2017;Cao et al, 2018;Khan et al, 2018;Surl et al, 2018;Shen et al, 2019;Su et al, 2019;Zyrichidou et al, 2019;Jin et al, 2020;Souri et al, 2020;Wells et al, 2020;Franco et al, 2021;Opacka et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Comparative assessment of TROPOMI and OMI formaldehyde observations and validation against MAX-DOAS network column measurements

et al. 2021

View full text Add to dashboard Cite

Abstract. The TROPOspheric Monitoring Instrument (TROPOMI), launched in October 2017 on board the Sentinel-5 Precursor (S5P) satellite, monitors the composition of the Earth's atmosphere at an unprecedented horizontal resolution as fine as 3.5 × 5.5 km2. This paper assesses the performances of the TROPOMI formaldehyde (HCHO) operational product compared to its predecessor, the OMI (Ozone Monitoring Instrument) HCHO QA4ECV product, at different spatial and temporal scales. The parallel development of the two algorithms favoured the consistency of the products, which facilitates the production of long-term combined time series. The main difference between the two satellite products is related to the use of different cloud algorithms, leading to a positive bias of OMI compared to TROPOMI of up to 30 % in tropical regions. We show that after switching off the explicit correction for cloud effects, the two datasets come into an excellent agreement. For medium to large HCHO vertical columns (larger than 5 × 1015 molec. cm−2) the median bias between OMI and TROPOMI HCHO columns is not larger than 10 % (< 0.4 × 1015 molec. cm−2). For lower columns, OMI observations present a remaining positive bias of about 20 % (< 0.8 × 1015 molec. cm−2) compared to TROPOMI in midlatitude regions. Here, we also use a global network of 18 MAX-DOAS (multi-axis differential optical absorption spectroscopy) instruments to validate both satellite sensors for a large range of HCHO columns. This work complements the study by Vigouroux et al. (2020), where a global FTIR (Fourier transform infrared) network is used to validate the TROPOMI HCHO operational product. Consistent with the FTIR validation study, we find that for elevated HCHO columns, TROPOMI data are systematically low (−25 % for HCHO columns larger than 8 × 1015 molec. cm−2), while no significant bias is found for medium-range column values. We further show that OMI and TROPOMI data present equivalent biases for large HCHO levels. However, TROPOMI significantly improves the precision of the HCHO observations at short temporal scales and for low HCHO columns. We show that compared to OMI, the precision of the TROPOMI HCHO columns is improved by 25 % for individual pixels and by up to a factor of 3 when considering daily averages in 20 km radius circles. The validation precision obtained with daily TROPOMI observations is comparable to the one obtained with monthly OMI observations. To illustrate the improved performances of TROPOMI in capturing weak HCHO signals, we present clear detection of HCHO column enhancements related to shipping emissions in the Indian Ocean. This is achieved by averaging data over a much shorter period (3 months) than required with previous sensors (5 years) and opens new perspectives to study shipping emissions of VOCs (volatile organic compounds) and related atmospheric chemical interactions.

show abstract

“…37.208.233:20035). Satellite remote sensing monitoring has the advantage of wide coverage and can use large-scale satellite remote sensing to scan the distribution of atmospheric pollutants and determine the overall diffusion trend of pollutants [43][44][45]. According to the backward trajectory model and monitoring results, the pollution source in Wuqing District could be determined as an extraterritorial pollution source or a pollution source within the domain.…”

Section: Discussionmentioning

confidence: 99%

Regional Atmospheric Aerosol Pollution Detection Based on LiDAR Remote Sensing

Wang

Han

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Atmospheric aerosol is one of the major factors that cause environmental pollution. Light detection and ranging (LiDAR) is an effective remote sensing tool for aerosol observation. In order to provide a comprehensive understanding of the aerosol pollution from the physical perspective, this study investigated regional atmospheric aerosol pollution through the integration of measurements, including LiDAR, satellite, and ground station observations and combined the backward trajectory tracking model. First, the horizontal distribution of atmospheric aerosol wa obtained by a whole-day working scanning micro-pulse LiDAR placed on a residential building roof. Another micro-pulse LiDAR was arranged at a distance from the scanning LiDAR to provide the vertical distribution information of aerosol. A new method combining the slope and Fernald methods was then proposed for the retrieval of the horizontal aerosol extinction coefficient. Finally, whole-day data, including the LiDAR data, the satellite remote sensing data, meteorological data, and backward trajectory tracking model, were selected to reveal the vertical and horizontal distribution characteristics of aerosol pollution and to provide some evidence of the potential pollution sources in the regional area. Results showed that the aerosol pollutants in the district on this specific day were mainly produced locally and distributed below 2.0 km. Six areas with high aerosol concentration were detected in the scanning area, showing that the aerosol pollution was mainly obtained from local life, transportation, and industrial activities. Correlation analysis with the particulate matter data of the ground air quality national control station verified the accuracy of the LiDAR detection results and revealed the effectiveness of LiDAR detection of atmospheric aerosol pollution.

show abstract

Validation of OMI HCHO Products Using MAX-DOAS observations from 2010 to 2016 in Xianghe, Beijing: Investigation of the Effects of Aerosols on Satellite Products

Cited by 12 publications

References 50 publications

Comparative assessment of TROPOMI and OMI formaldehyde observations against MAX-DOAS network column measurements

Comparative assessment of TROPOMI and OMI formaldehyde observations against MAX-DOAS network column measurements

Comparative assessment of TROPOMI and OMI formaldehyde observations and validation against MAX-DOAS network column measurements

Regional Atmospheric Aerosol Pollution Detection Based on LiDAR Remote Sensing

Contact Info

Product

Resources

About