Analyzing spatio-temporal patterns in atmospheric carbon dioxide concentration across Iran from 2003 to 2020

Mousavi, Seyed Mohsen; Dinan, Naghmeh Mobarghaee; Ansarifard, Saeed; Sonnentag, Oliver

doi:10.1016/j.aeaoa.2022.100163

Cited by 13 publications

(10 citation statements)

References 85 publications

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“…Air currents have an important role in order to transfer high amounts of CO 2 to the east and southeast of Iran 33 . Mousavi et al 59 found the negative correlation between XCO 2 and precipitation over Iran from 2003 to 2020 in different months. They observed the same correlation between atmospheric CO 2 and vegetation density.…”

Section: Resultsmentioning

confidence: 98%

Satellite observation of atmospheric CO2 and water storage change over Iran

Safaeian

Falahatkar

Tourian

2023

Sci Rep

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Like many other Middle East countries, Iran has been suffering from severe water shortages over the last two decades, as evidenced by significant decline in surface water and groundwater levels. The observed changes in water storage can be attributed to the mutually reinforcing effects of human activities, climatic variability, and of course the climate change. The objective of this study is to analyze the dependency of atmospheric CO2 increase on the water shortage of Iran, for which we investigate the spatial relationship between water storage change and CO2 concentration using large scale satellite data. We conduct our analysis using water storage change data from GRACE satellite and atmospheric CO2 concentration from GOSAT and SCIAMACHY satellites during 2002–2015. To analyze the long-term behavior of time series we benefit from Mann-Kendal test and for the investigation of the relationship between atmospheric CO2 concentration and total water storage we use Canonical Correlation Analysis (CCA) and Regression model. Our Results show that the water storage change anomaly and CO2 concentration are negatively correlated especially in northern, western, southwest (Khuzestan province), and also southeast (Kerman, Hormozgan, Sistan, and Baluchestan provinces) of Iran. CCA results reveal that in the most of northern regions, the decrease in water storage is significantly influenced by the increase of CO2 concentration. The results further show that precipitation in the highland and peaks does not seem to be influenced by the long and short-term variation in CO2 concentration. Besides, our results show that the CO2 concentration is slightly correlated with a weak positive trend in evapotranspiration over agricultural areas. Thus, the indirect effect of CO2 on increasing evapotranspiration is observed spatially in the whole of Iran. The results of the regression model between total water storage change and carbon dioxide (R2 = 0.91)/water discharge/water consumption show that carbon dioxide has the highest effect on total water storage change at large scale. The results of this study will contribute to both water resource management and mitigation plans to achieve the goal of CO2 emission reduction.

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Section: Resultsmentioning

confidence: 98%

Satellite observation of atmospheric CO2 and water storage change over Iran

Safaeian

Falahatkar

Tourian

2023

Sci Rep

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“…As a successor mission of GOSAT, GOSAT-2 equipped with the improved TANSO-FTS-2 featuring enhanced signal-to-noise ratio (SNR), was launched in October 2018. Both GOSAT and GOSAT-2 employ non-consecutive sampling, cross-track mechanical pointing, which restrict their observational spatial coverage (swath 790 km, 10.5 km at sub-satellite point) [ 13 ]. Launched in July 2014, Orbiting Carbon Observatory-2 (OCO-2) is an Earth observing satellite mission owned and operated by National Aeronautics and Space Administration (NASA).…”

Section: Introductionmentioning

confidence: 99%

“…By using long time-series XCO 2 monitored by these satellites, it can offer insights into continental-scale CO 2 budgets and initial understanding of the carbon flux in land surface systems [ 17 , 18 , 19 , 20 ]. Atmospheric CO 2 concentration has been examined in several studies from different perspectives, such as spatial distribution and relation with different variables and so on [ 13 , 21 , 22 , 23 ]. While the detection accuracy of these satellites has improved to a certain level, their overall capabilities remain limited [ 6 , 24 ], particularly in terms of coverage range and spatial-temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Spatial Resolution and XCO2 Precision on Satellite Capability for CO2 Plumes Detection

Li,

Fan,

Tao

et al. 2024

Sensors

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Greenhouse gas satellites can provide consistently global CO2 data which are important inputs for the top-down inverse estimation of CO2 emissions and their dynamic changes. By tracking greenhouse gas emissions, policymakers and businesses can identify areas where reductions are needed most and implement effective strategies to reduce their impact on the environment. Monitoring greenhouse gases provides valuable data for scientists studying climate change. The requirements for CO2 emissions monitoring and verification support capacity drive the payload design of future CO2 satellites. In this study, we quantitatively evaluate the performance of satellite in detecting CO2 plumes from power plants based on an improved Gaussian plume model, with focus on impacts of the satellite spatial resolution and the satellite-derived XCO2 precision under different meteorological conditions. The simulations of CO2 plumes indicate that the enhanced spatial resolution and XCO2 precision can significantly improve the detection capability of satellite, especially for small-sized power plants with emissions below 6 Mt CO2/yr. The satellite-detected maximum of XCO2 enhancement strongly varies with the wind condition. For a satellite with a XCO2 precision of 0.7 ppm and a spatial resolution of 2 km, it can recognize a power plant with emissions of 2.69 Mt CO2/yr at a wind speed of 2 m/s, while its emission needs be larger than 5.1 Mt CO2/yr if the power plant is expected to be detected at a wind speed of 4 m/s. Considering the uncertainties in the simulated wind field, the satellite-derived XCO2 measurements and the hypothesized CO2 emissions, their cumulative contribution to the overall accuracy of the satellite’s ability to identify realistic enhancement in XCO2 are investigated in the future. The uncertainties of ΔXCO2 caused by the uncertainty in wind speed is more significant than those introduced from the uncertainty in wind direction. In the case of a power plant emitting 5.1 Mt CO2/yr, with the wind speed increasing from 0.5 m/s to 4 m/s, the simulated ΔXCO2 uncertainty associated with the wind field ranges from 3.75 ± 2.01 ppm to 0.46 ± 0.24 ppm and from 1.82 ± 0.95 ppm to 0.22 ± 0.11 ppm for 1 × 1 km2 and 2 × 2 km2 pixel size, respectively. Generally, even for a wind direction with a higher overall uncertainty, satellite still has a more effective capability for detecting CO2 emission on this wind direction, because there is more rapid growth for simulated maximal XCO2 enhancements than that for overall uncertainties. A designed spatial resolution of satellite better than 1 km and a XCO2 precision higher than 0.7 ppm are suggested, because the CO2 emission from small-sized power plants is much more likely be detected when the wind speed is below 3 m/s. Although spatial resolution and observed precision parameters are not sufficient to support the full design of future CO2 satellites, this study still can provide valuable insights for enhancing satellite monitoring of anthropogenic CO2 emissions.

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“…However, currently, remote sensing monitoring data with high-precision spatiotemporal resolution are not available. In order to obtain high-precision, high-coverage, and high spatiotemporal resolution CO 2 data products, numerous scholars have conducted research on the high-resolution reconstruction of CO 2 column concentration (XCO 2 ) using methods such as physical data fusion [34], interpolation, and geostatistics [35][36][37][38]. These interpolation and geostatistical methods model the correlation between known CO 2 points to estimate the concentration at unknown points, providing powerful tools for understanding the spatiotemporal distribution of CO 2 .…”

Section: Introductionmentioning

confidence: 99%

XCO2 Super-Resolution Reconstruction Based on Spatial Extreme Random Trees

Li,

Jiang,

Wang

et al. 2024

Atmosphere

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Carbon dioxide (CO2) is currently the most harmful greenhouse gas in the atmosphere. Obtaining long-term, high-resolution atmospheric column CO2 concentration (XCO2) datasets is of great practical significance for mitigating the greenhouse effect, identifying and controlling carbon emission sources, and achieving carbon cycle management. However, mainstream satellite observations provide XCO2 datasets with coarse spatial resolution, which is insufficient to support the needs of higher-precision research. To address this gap, in this study, we integrate spatial information with the extreme random trees model and develop a new machine learning model called spatial extreme random trees (SExtraTrees) to reconstruct a 1 km spatial resolution XCO2 dataset for China from 2016 to 2020. The results indicate that the predictive ability of spatial extreme random trees is more stable and has higher fitting accuracy compared to other methods. Overall, XCO2 in China shows an increasing trend year by year, with the spatial distribution revealing significantly higher XCO2 levels in eastern coastal regions compared to western inland areas. The contributions of this study are primarily in the following areas: (1) Considering the spatial heterogeneity of XCO2 and combining spatial features with the advantages of machine learning, we construct the spatial extreme random trees model, which is verified to have high predictive accuracy. (2) Using the spatial extreme random trees model, we reconstruct high-resolution XCO2 datasets for China from 2016 to 2020, providing data support for carbon emission reduction and related decision making. (3) Based on the generated dataset, we analyze the spatiotemporal distribution patterns of XCO2 in China, thereby improving emission reduction policies and sustainable development measures.

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Analyzing spatio-temporal patterns in atmospheric carbon dioxide concentration across Iran from 2003 to 2020

Cited by 13 publications

References 85 publications

Satellite observation of atmospheric CO2 and water storage change over Iran

Satellite observation of atmospheric CO2 and water storage change over Iran

Impacts of Spatial Resolution and XCO2 Precision on Satellite Capability for CO2 Plumes Detection

XCO2 Super-Resolution Reconstruction Based on Spatial Extreme Random Trees

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