Plume detection and estimate emissions for biomass burning plumes from TROPOMI Carbon monoxide observations using APE v1.0

Abstract

Abstract. The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor (S-5P) satellite, launched in 2017, measures the total column concentration of the trace gas Carbon Monoxide (CO) daily on a global scale and at a high spatial resolution of 7 x 7 km2, improved to 5.5 x 7 km2 in August 2019. The TROPOMI observations show plumes of CO due to localized CO emissions from industrial sources and biomass burning. In this paper, we quantify these CO emissions for biomass burning by an automated algorithm, APE, to detect plumes and quantify the CO emission rate using cross-sectional flux method. Furthermore, the influence of a constant and a varying plume height in downwind direction on emissions is investigated and algorithm uncertainties are quantified. The VIIRS active fire data in conjunction with the TROPOMI CO datasets is used to identify fires and the fire locations. Then, an automated plume detection algorithm using traditional image processing algorithms is developed and utilized to identify plumes. For these plumes, the emission rate is estimated by the cross-section flux method at three different plume heights. The first two are constant plume heights at a 100 m and an IS4FIRES injection height from Global Fire Assimilation System. And the last one is a varying plume height in downwind direction. A 3D Lagrangian model is used to simulate tracer particles where the source locations for the simulation are based on the VIIRS fire counts and IS4FIRES injection height. 3D velocities at 137 model levels (ERA5) are utilized to simulate tracer particles. We demonstrate the quality and validity of our automated approach by investigating biomass burning events and their emissions for Australia on Oct 2019 and the US on Sept 2020. A total of 110 and 31 individual fire plumes in Australia and the US, respectively were detected and their emissions estimated. The emissions were severely under-predicted and negative for 11 cases when based on constant plume height of 100 m compared to emissions based on varying plume height. Furthermore, the effect of the changing plume height in downwind direction on the emission estimate compared to emissions from constant IS4FIRES plume height was minor as 124 cases are found to have emission variation less than 10 %. However, we were able to identify several cases where the flux estimates become more reliable with varying plume height. Thus, the varying plume height in downwind direction is considered for the automated algorithm. The cross-section flux method is found to have an uncertainty of 38 % in one of the idealized cases. However, overall uncertainty of the algorithm is difficult to quantify as conditions for each fire are unique.

Comparing space-based to reported carbon monoxide emission estimates for Europe’s iron & steel plants

“…Gloudemans et al (2006); Khlystova et al (2009); Worden et al (2010); Girach and Nair (2014); van der Velde et al ( 2021)). Additionally, the resolution of TROPOMI, down to 7 x 5.5 km 2 (across x along track), has been shown to be sufficiently high to study individual cities and CO point sources (Tian et al, 2022;Plant et al, 2022;Leguijt et al, 2023;Goudar et al, 2023;Schneising et al, 2024). The coverage of polar-orbiting satellites like TROPOMI allows for consistent investigation of regions all over the world rather than being confined to places with good reporting infrastructure.…”

RICHARD 1.0 – Routine for the Isolation of Chemical Hotspots in Atmospheric Research Data

“…For example, Goudar et al (2022) explores carbon monoxide from a multi-annual data record of TROPOMI in a well-targeted proof of concept study. In contrast, the study by van Damme et al (2018) uses an averaged ammonia data set from the Infrared Atmospheric Sounding Interferometer (IASI) and Tu et al (2021) uses a single year of cloud-free methane total column data measured by TROPOMI as a case study.…”