Regional uncertainty of GOSAT XCO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; retrievals in China: quantification and attribution

Bie, Nian; Lei, Liping; Zeng, Zhao‐Cheng; Cai, Bofeng; Yang, Shenglin; He, Zhongshi; Wu, Chenjie; Nassar, Ray

doi:10.5194/amt-11-1251-2018

Cited by 20 publications

(17 citation statements)

References 66 publications

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“…From Figure 8a, it can be found that the dXCO 2 in western area, the desert area of Xinjiang, shows high values even if there are much less anthropogenic emission over this area as shown in Figure 4b. This is likely resulted in the uncertainty of ACOS XCO 2 retrievals in desert which has been indicated by Bie et al [34].…”

Section: Resultsmentioning

confidence: 95%

An Assessment of Anthropogenic CO2 Emissions by Satellite-Based Observations in China

Yang

Lei

Baker

et al. 2019

Sensors

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Carbon dioxide (CO2) is the most important anthropogenic greenhouse gas and its concentration in atmosphere has been increasing rapidly due to the increase of anthropogenic CO2 emissions. Quantifying anthropogenic CO2 emissions is essential to evaluate the measures for mitigating climate change. Satellite-based measurements of greenhouse gases greatly advance the way of monitoring atmospheric CO2 concentration. In this study, we propose an approach for estimating anthropogenic CO2 emissions by an artificial neural network using column-average dry air mole fraction of CO2 (XCO2) derived from observations of Greenhouse gases Observing SATellite (GOSAT) in China. First, we use annual XCO2 anomalies (dXCO2) derived from XCO2 and anthropogenic emission data during 2010–2014 as the training dataset to build a General Regression Neural Network (GRNN) model. Second, applying the built model to annual dXCO2 in 2015, we estimate the corresponding emission and verify them using ODIAC emission. As a results, the estimated emissions significantly demonstrate positive correlation with that of ODIAC CO2 emissions especially in the areas with high anthropogenic CO2 emissions. Our results indicate that XCO2 data from satellite observations can be applied in estimating anthropogenic CO2 emissions at regional scale by the machine learning. This developed method can estimate carbon emission inventory in a data-driven way. In particular, it is expected that the estimation accuracy can be further improved when combined with other data sources, related CO2 uptake and emissions, from satellite observations.

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Section: Resultsmentioning

confidence: 95%

An Assessment of Anthropogenic CO2 Emissions by Satellite-Based Observations in China

Yang

Lei

Baker

et al. 2019

Sensors

Self Cite

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“…However, the study periods or spatial scales are limited [34,35]. In addition, most scholars focus their research on accuracy/uncertainty analysis [5,36,37], and some studies compared the difference of CO 2 concentration in the same area from different satellites [31,37] or investigated the effect of a single carbon source/sink on the concentration of CO 2 ; for example, the studies of CO 2 emissions from anthropogenic activities [32,38], the impact of vegetable absorption [14,39] and the estimated CO 2 emissions from forest fires [10,40]. However, these studies lack comprehensive consideration of multiple factors.…”

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Variations of Atmospheric CO2 Concentration in China and Its Influencing Factors

Shi

Zang

et al. 2020

Atmosphere

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Over the past few decades, concentrations of carbon dioxide (CO2), a key greenhouse gas, have risen at a global rate of approximately 2 ppm/a. China is the largest CO2 emitter and is the principle contributor to the increase in global CO2 levels. Based on a satellite-retrieved atmospheric carbon dioxide column average dry air mixing ratio (XCO2) dataset, derived from the greenhouse gas observation satellite (GOSAT), this paper evaluates the spatial and temporal variations of XCO2 characteristics in China during 2009–2016. Moreover, the factors influencing changes in XCO2 were investigated. Results showed XCO2 concentrations in China increased at an average rate of 2.28 ppm/a, with significant annual seasonal variations of 6.78 ppm. The rate of change of XCO2 was greater in south China compared to other regions across China, with clear differences in seasonality. Seasonal variations in XCO2 concentrations across China were generally controlled by vegetation dynamics, characterized by the Normalized Difference Vegetation Index (NDVI). However, driving factors exhibited spatial variations. In particular, a distinct belt (northeast–southwest) with a significant negative correlation (r < −0.75) between XCO2 and NDVI was observed. Furthermore, in north China, human emissions were identified as the dominant influencing factor of total XCO2 variations (r > 0.65), with forest fires taking first place in southwest China (r > 0.47). Our results in this study can provide us with a potential way to better understand the spatiotemporal changes of CO2 concentration in China with NDVI, human activity and biomass burning, and could have an enlightening effect on slowing the growth of CO2 concentration in China.

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“…The parameters in Eq. (1) are detailed in the study of Bie et al (2018). 0 1 2 3 4 5 (t) A A t A sin 2 t A cos 2 t A sin 4 t A cos 4 t…”

Section: Time Series and Seasonal Trend Of Nh3mentioning

confidence: 99%

Spatial distribution and seasonal variability in atmospheric ammonia measured from ground-based FTIR observations at Hefei, China

Wang

Liu

Clarisse

et al. 2020

Preprint

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Abstract. Atmospheric ammonia (NH3) plays an important role in the formation of fine particulate matter, leading to severe environmental degradation and human health issues. In this work, ground-based FTIR observations are used to obtain the total columns and vertical profiles of atmospheric NH3 at a measurement site in Hefei, China, from December 2016 to November 2018. The spatial distribution and temporal variation, seasonal trend, emission sources and potential sources of NH3 are analyzed. The time series of ammonia columns show that FTIR observations captured the seasonal cycle of NH3 over the two years of measurement, with a 22.14 % yr-1 annual increase rate over the Hefei site. We used IASI satellite data to compare with the FTIR data, and the correlation coefficients (R) between the two datasets are 0.86 and 0.78 for IASI-A and IASI-B, respectively. The results demonstrate the IASI data are in broad agreement with our FTIR data. To examine the contribution of traffic to NH3 columns, we analyze the relationship of NH3 columns with CO surface concentrations. NH3 columns show high correlation (R = 0.77) with CO concentrations in summer, indicating that the elevated NH3 columns are partly caused by urban emissions from vehicles. Further, high correlation of NH3 columns with air temperature is obvious from their diurnal variation during the observation period. In addition, the clear correlation between NH3 columns and air temperature in spring and autumn over Hefei, suggests that agriculture was indeed the main source of ammonia in spring and autumn. Furthermore, the back trajectories of air masses calculated by the HYSPLIT model confirmed that agriculture was the dominant source of ammonia in spring, autumn and winter, while urban anthropogenic emissions contributed to the high level of NH3 in summer over the Hefei site. The potential source areas influencing the NH3 columns were distributed in the local area of Hefei, the northern part of Anhui province, as well as Shangdong, Jiangsu and Henan provinces. This study helps to identify the emission sources and potential sources that contribute to NH3 columns over Hefei, a highly populated and polluted area. This is the first time that ground-based FTIR remote sensing of NH3 columns and comparison with satellite data are reported in China.

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Regional uncertainty of GOSAT XCO<sub>2</sub> retrievals in China: quantification and attribution

Cited by 20 publications

References 66 publications

An Assessment of Anthropogenic CO2 Emissions by Satellite-Based Observations in China

An Assessment of Anthropogenic CO2 Emissions by Satellite-Based Observations in China

Spatial and Temporal Variations of Atmospheric CO2 Concentration in China and Its Influencing Factors

Spatial distribution and seasonal variability in atmospheric ammonia measured from ground-based FTIR observations at Hefei, China

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