CO2 Concentration, A Critical Factor Influencing the Relationship between Solar-induced Chlorophyll Fluorescence and Gross Primary Productivity

Hot spots and anomalies of CO2 over eastern Amazonia, Brazil: A time series from 2015 to 2018

The easternmost Amazon, located in the Maranhão State, in Brazil, has suffered massive deforestation in recent years, which has devastated almost 80% of the original vegetation. We aim to characterize hot spots, hot moments, atmospheric carbon dioxide anomalies (Xco₂, ppm), and their interactions with climate and vegetation indices in eastern Amazon, using data from NASA's Orbiting Carbon Observatory-2 (OCO-2). The study covered the period from January 2015 to December 2018. The data were subjected to regression, correlation, and temporal analysis, identifying the spatial distribution of hot/cold moments and hot/cold spots. In addition, anomalies were calculated to identify potential CO₂ sources and sinks. Temporal changes indicate atmospheric Xco₂ in the range from 362.2 to 403.4 ppm. Higher Xco₂ values (hot moments) were concentrated between May and September, with some peaks in December. The lowest values (cold moments) were concentrated from November to April. SIF 771 W m^-2 sr^-1 μm^-1 explained the temporal changes of Xco₂ in 58% (R² adj = 0.58; p < 0.001) and precipitation in 27% (R² adj = 0.27; p ≤ 0.001). Spatial hot spots with 90% confidence were more representative in 2016. The maximum and minimum Xco₂ (ppm) anomalies were 6.19 ppm (source) and -6.29 ppm (sink), respectively. We conclude that the hot moments of Xco₂ in the eastern Amazon rainforest are concentrated in the dry season of the year. Xco₂ spatial hot spots and anomalies are concentrated in the southern region and close to protected areas of the Amazon rainforest.

Investigating the Performance of Red and Far-Red SIF for Monitoring GPP of Alpine Meadow Ecosystems

Alpine meadow ecosystems are extremely vulnerable to climate change and serve an essential function in terrestrial carbon sinks. Accurately estimating their gross primary productivity (GPP) is essential for understanding the global carbon cycle. Solar-induced chlorophyll fluorescence (SIF), as a companion product directly related to plant photosynthesis process, has become an attractive pathway for estimating GPP accurately. To date, the quantitative SIF-GPP relationship in terrestrial ecosystems is not yet clear. Especially, red SIF and far-red SIF present differences in their ability to track GPP under different environmental conditions. In this study, we investigated the performance of SIF at both red and far-red band in monitoring the GPP of an alpine meadow ecosystem based on continuous tower-based observations in 2019 and 2020. The results show that the canopy red SIF (SIFRed) and far-red SIF (SIFFar-red) were both strongly correlated with GPP. SIFRed was comparable to SIFFar-red for monitoring GPP based on comparisons of both half-hourly averaged and daily averaged datasets. Moreover, the relationship between SIFRed and GPP was linearly correlated, while the relationship between SIFFar-red and GPP tended to be nonlinear. At a diurnal scale, dramatic changes in photosynthetically active radiation (PAR), air temperature (Ta), and vapor pressure deficit (VPD) all had effects on the slope of the linear fitted line with zero intercept for SIFRed-GPP and SIFFar-red-GPP, and the effect on the slope of the linear fitted line with zero intercept for SIFFar-red-GPP was obviously stronger than that for SIFRed-GPP. PAR was the dominant factor among the three environmental factors in determining the diurnal variation of the slope of SIF-GPP. At a seasonal scale, the SIFFar-red/GPP was susceptible to PAR, Ta, and VPD, while the SIFRed/GPP remained relatively stable at different levels of Ta and VPD, and it was only weakly affected by PAR, suggesting that SIFRed was more consistent than SIFFar-red with GPP in response to seasonal variations in environmental factors. These results indicate that SIFRed has more potential than SIFFar-red for monitoring the GPP of alpine meadow ecosystems and can also assist researchers in gaining a more comprehensive understanding of the diversity of SIF-GPP relationships in different ecosystems.

Variation patterns and driving factors of regional atmospheric CO2 anomalies in China

Atmospheric CO₂ anomaly (△XCO₂) is essential in evaluating regional carbon balance. However, it is difficult to understand △XCO₂ variation characteristics due to regional differences. This paper explored the inter-annual and inter-monthly variation patterns of △XCO₂ in different regions of China based on satellite observations. The relation model between regional △XCO₂ and anthropogenic emissions, gross primary productivity (GPP), wind speed, upwind region's emission, and upwind region's CO₂ concentration was established. Results show that the annual average △XCO₂ in the northwest and southeast regions is stable at around 0 and 1-2 ppm, respectively. Some municipalities directly under the central government and the southern coastal areas showed relatively intense inter-annual fluctuations. Four inter-monthly △XCO₂ variation patterns were observed: the northern region has a stable change, the northeast region has the lowest in summer, the southwest region has the highest in summer, and the central region has no obvious change rule. Furthermore, △XCO₂ in most areas can be explained by the emission-absorption-transportation model. Significant positive △XCO₂ in the southern coastal region in summer may be related to the stable GPP seasonal variation and increased power generation. In the southwestern plateau region, it may be related to the low wind speed and increased soil emission with rising temperature. The stability of the plateau carbon sink and inter-regional cooperation cannot be ignored for improving regional atmospheric environments.

Response of major air pollutants to COVID-19 lockdowns in China

COVID-19 suddenly struck Wuhan at the end of 2019 and soon spread to the whole country and the rest of world in 2020. To mitigate the pandemic, China authority has taken unprecedentedly strict measures across the country. That provides a precious window to study how the air quality response to quick decline of anthropogenic emissions in terms of national scale, which would be critical basis to make atmospheric governance policies in the future. In this work, we utilized observations from both remote sensing and in-situ measurements to investigate impacts of COVID-19 lockdown on different air pollutions in different regions of China. It is witnessed that the PM_2.5 concentrations exhibited distinct trends in different regions, despite of plunges of NO₂ concentrations over the whole country. The steady HCHO concentration in urban area provides sufficient fuels for generations of tropospheric O₃, leading to high concentrations of O₃, especially when there is not enough NO to consume O₃ via the titration effect. Moreover, the SO₂ concentration kept steady at a low level regardless of cities. As a conclusion, the COVID-19 lockdown indeed helped reduce NO₂ concentration. However, the atmospheric quality in urban areas of China has not improved overall due to lockdown measures. It underscores the significance of comprehensive control of atmospheric pollutants in cleaning air. Reducing VOCs (volatile organic compounds) concentrations in urban areas would be a critical mission for better air quality in the future.

Obtaining Gradients of XCO2 in Atmosphere Using the Constrained Linear Least-Squares Technique and Multi-Wavelength IPDA LiDAR

Integrated-path differential absorption (IPDA) LiDAR is a promising means of measuring the global distributions of the column weighted xCO2 (dry-air mixing ratio of CO2) with adequate accuracy and precision. Most IPDA LiDARs are incapable of discerning the vertical information of CO2 diffusion, which is of great significance for studies on the carbon cycle and climate change. Hence, we developed an inversion method using the constrained linear least-squares technique for a pulsed direct-detection multi-wavelength IPDA LiDAR to obtain sliced xCO2. In the proposed inversion method, the atmosphere is sliced into three different layers, and the xCO2 of those layers is then retrieved using the constrained linear least-squares technique. Assuming complete knowledge of the water vapor content, the accuracy of the retrieved sliced xCO2 could be as high as 99.85% when the signal-to-noise ratio of central wavelength retrievals is higher than 25 (with a log scale). Further experiments demonstrated that different carbon characteristics can be identified by the sign of the carbon gradient of the retrieved xCO2 between the ABL (atmospheric boundary layer) and FT (free troposphere). These results highlight the potential applications of multiple wavelength IPDA LiDAR.