The Thermal And Near infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observing SATellite (GOSAT) launched in Jan. 2009 has demonstrated accurate and precise CO2 and CH4 distribution measurements from space. The globally acquired data have contributed to reduce the uncertainties in global and regional flux inverse estimates. In response to the urgent need for monitoring carbon emissions from intense localized sources, such as cities and power plants, we have been developing a next generation instrument that should be able to detect and map plumes from the intense sources with a 1km spatial resolution. We design our system to implement both targeted observations for intense local sources with high spatial resolution and wide-swath observations for covering the earth’s entire surface with 2-axes pointing system and two telescopes. We have developed airborne imaging spectrometer suites with three imaging spectrometers: 0.47 µm for nitrogen dioxide (NO2), 0.76 µm for oxygen (O2) and solar-induced chlorophyll fluorescence (SIF) and 1.6 µm for carbon dioxide (CO2) and methane (CH4). The airplane observations successfully recorded CO2 and NO2 enhancements over a power plant in Greater Nagoya Area. In our presentation, we will present our first emission estimates based on the simultaneous CO2 and NO2 observation.

CO2 emissions from fossil fuel combustion (FFCO2) can be robustly estimated from fuel used (as activity data, AD) and CO2 emissions factor, due to the nature of FFCO2. Recent traffic emission changes under the impact of the COVID-19 pandemic have been estimated using emerging non-fuel consumption data, such as human mobility data that tech companies reported as AD, due to the unavailability of timely fuel statistics. The use of such unconventional activity data (UAD) might allow us to provide emission estimates in near-real time; however, the errors and uncertainties associated with such estimates are expected to be larger than those of common FFCO2 inventory estimates, and thus should be provided along with a thorough evaluation/validation of the methodology and the resulting estimates. Here, we show the impact of COVID-19 on traffic CO2 emissions over the first six months of 2020 in Japan. We calculated CO2 monthly emissions using fuel consumption data and assessed the emission changes relative to 2019. Regardless of Japan’s soft approach to COVID-19, traffic emissions significantly declined by 23.8% during the state of emergency in Japan (April-May). We also compared relative emission changes among different estimates available. Our analysis suggests that UAD-based emission estimates during April and May could be biased by -19.6% to 12.6%. We also used traffic count data for examining the performance of UAD as a proxy for traffic and/or CO2 emissions. We found traffic changes are not proportional enough to emission changes to allow emissions to be estimated with accuracy, and moreover, the traffic-based approach failed to capture emission seasonality. Our study highlighted the challenges and difficulties in the use of limited non-scientific data for modeling human activities and assessing the impact on the environment, and the importance of a thorough error and uncertainty assessment before using these data in policy applications.

The outbreak of coronavirus disease 2019 (COVID-19) has caused tremendous loss to human life and economic decline in China. A timely assessment of COVID-19’s impact on provincial CO emission reductions is crucial for accurately understanding the degree of reduction and its implications for mitigation measures. Here, we used gross domestic product (GDP) and an inventory (CEADs) to estimate the reductions in the first quarter (Q1) of 2020. We find a reduction of -257.7 Mt CO (-11.0%) over 2019 Q1. Secondary industry contributed 72.5% of the total reduction, due largely to lower coal consumption and cement reduction. At the provincial level, Hubei contributed the most to reductions. Transportation reduction also made a significant contribution. One policy implication is advocating working from home and holding teleconferences to reduce traffic emissions. We provide provincial reductions as spatial constraints for modeling studies and further support for both the carbon cycling scientists and policy makers.

Monitoring changes in greenhouse gas (GHG) emission is critical for assessing climate mitigation efforts towards the Paris Agreement goal. A crucial aspect of science-based GHG monitoring is to provide objective information for quality assurance and uncertainty assessment of the reported emissions. Emission estimates from combustion events (gas flaring and biomass burning) are often calculated based on activity data (AD) from satellite observations, such as those detected from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi-NPP and NOAA-20 satellites. These estimates are often incorporated into carbon models for calculating emissions and removals. Consequently, errors and uncertainties associated with AD propagate into these models and impact emission estimates. Deriving uncertainty of AD is therefore crucial for transparency of emission estimates but remains a challenge due to the lack of evaluation data or alternate estimates. This work proposes a new approach using machine learning (ML) for combustion detection from NASA’s Black Marble product suite and explores the assessment of potential uncertainties through comparison with existing datasets. We jointly characterize combustion using thermal and light emission signals, with the latter improving detection of probable weaker combustion with less distinct thermal signatures. Being methodologically independent, the differences in ML-derived estimates with existing approaches can indicate the potential uncertainties in detection. The approach was applied to detect gas flaring activities over the Eagle Ford Shale, Texas. We analyzed the spatio-temporal variations in detections and found that approximately 79.04% and 72.14% of the light emission-based detections are missed by ML-derived detections from VIIRS thermal bands and existing datasets, respectively. The region was impacted by the winter storm Uri which resulted in a significant reduction of flaring activities followed by a post-storm resumption. Our method is extendible to combustion events, such as biomass and waste burning, and can be scaled globally for transparent emission estimate reporting.