Akihiko Kuze

and 8 more

Takahiro Kawashima

and 8 more

In almost Japanese megacities, various CO2 and CH4 emission source like industrial activity (power plant, landfills, gas factory, water processing plants), and agricultural activity (rice cultivation, pig farm) are concentrated within a few tens kilometers region. In order to estimate CO2 and CH4 emission rate for above various different sources, we newly developed airborne Imaging-spectrometer suites which consist of NIR spectrometer for O2-A band measurement and SWIR spectrometer for CO2/CH4 measurement. We also developed quick algorithm based on nonlinear fitting of synthetic spectrum to observation spectrum by optimization of column density of CO2 / CH4 and instrumental characteristic parameter simultaneously. The algorithm takes less than 20 second per 1 retrieval by using laptop computer, and we will challenge further acceleration by more than tens of times in order to realize real-time observation. For the first flight, we selected the eastern part of the Nagoya urban area, in which there are large CO2 emission sources, including a coal power plant and the transportation sector, and possible CH4 sources from agriculture, energy manufacturing, and waste that are geographically mixed. The results of observing the Hekinan power plant (coal-fired power generation) over Aichi Prefecture on Feb. 16, 2018 are shown in Figure 1. At the Hekinan Power Station, enhancement of CO2 column-averaged mole fractions are observed, and it can be seen that the high concentration area extends toward the downwind side. The accuracy of column density calculated by the quick algorithm will be validated with ground observation data. We estimated emission rate of CO2 of Hekinan Power plant.

Akihiko Kuze

and 7 more

The Thermal And Near infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observing SATellite (GOSAT) was launched in Jan. 2009 to monitor global CO2 and CH4 distribution from space. The wide-spectral-range data by FTS can measure the partial-column density of the lower troposphere using sun-light reflected from the surface and thermal emission from the atmosphere. In addition to nominal global grid-observation, TANSO-FTS has an agile pointing system to target various CH4 point-sources as well as reference points every three days over years, and can capture the entire flux emitted vertically and horizontally from the source. We demonstrated the monitoring capability by using the natural-gas blowout event at Aliso Canyon, CA and tried to estimate the CH4 flux from a dairy farm in Chino, CA with the Weather Research and Forecasting model. The GOSAT footprint, which is much larger than the point-source area, reduces the enhancement of the retrieved column density close to the detection level. As the single-pixel data and acquisition time of 4 s by FTS limits the number of sampling points near the emission source, careful screening with wind speed and direction is required to acquire reference and source dataset for analysis. The largely fluctuated single data requires to be averaged to improve the precision, but the GOSAT data is spatially too sparse. We calculated the local CH4 flux from Chino using the correlation between wind speed and density, but the lack of a proper reference result in large errors. To solve the above-mentioned issues, we manufactured airborne imaging-spectrometer suites comprising two bands to measure CH4 and CO2 at 1.6 μm and O2 at 0.76 μm. They have 4,000 times more data than GOSAT, increase enhancement with higher spatial resolution, and select proper upwind reference with imaging capability. In Feb. 2018, we flew over greater Nagoya with the mixture of possible emission sources such as energy production, waste water, dairy farm, and agriculture. The spectral image of spatial resolution <100 m has clearly detected enhancement from individual local sources of CH4 and CO2. We add one more spectrometer to measure short-lived NO2 to detect plume orientation. Our goal is to individually estimate city-level CH4 flux from different source sectors.

Akihiko Kuze

and 5 more

GOSAT and GOSAT-2 have simultaneously observed both reflected SWIR solar light and TIR emissions with a single FTS mechanism with the Thermal And Near-infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) and TANSO-FTS-2 since 2009 and 2018, respectively. Their linear polarization bands provide information on light-path modification and make accurate remote sensing possible, even under aerosol and thin-cloud contaminated conditions. We can retrieve the difference between the partial column-averaged dry-air mole fractions of the two individual layers of lower and upper troposphere (LT and UT) by combining TIR and SWIR spectra data simultaneously, thereby constraining the accurate total column density of XCO2 and XCH4. TANSO-FTS has a two-axis agile pointing system, which allows cross-track and along-track motions It was originally designed for grid scan observations and viewing onboard calibration sources. After the pointing mechanism was switched from primary to secondary on 26 January 2015, We decided to make more frequent target observations, by uploading AT and CT pointing angles and observation timing as commands from the ground every day About 1000 locations are allocated to target observations such as calibration and validation site, megacities, or large emission sources. We define vertical layers of LT and UT not by temperature, but by the retrieved Psurf from individual O2 A band data. The pressure-height ranges of the LT and UT were taken as 0.6–1 Psurf and 0.2–0.6 Psurf, respectively. As the LT includes the entire boundary layer, analysis using XCO2 (LT) and XCH4 (LT) can double the signal of local emissions and remove the effects of CO2 and CH4 variability in the UT, which typically extends over a much wider area. We have targeted intense measurements over mega cities since 2016. We assume the average density of the upper troposphere is a background. We define XCO2 anomalies XCO2(LT)-XCO2(UT average), which show enhancement caused by local anthropogenic emissions. In 2020, we detected lower anomalies than previous years over mega cities such as Tokyo, Beijing, and New York.