Examining the Role of the Main Terrestrial Factors Won the Seasonal Distribution of Atmospheric Carbon Dioxide Concentration over Iran

Mousavi, Seyed Mohsen; Dinan, Naghmeh Mobarghaee; Ansarifard, Saeed; Borhani, Faezeh; Ezimand, Keyvan; Naghibi, Amir

doi:10.1007/s12524-022-01650-4

Cited by 11 publications

(3 citation statements)

References 50 publications

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“…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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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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“…In the process of sustaining population growth and meeting the rising demand for materials and energy during rapid urbanization [10][11][12], there have been increasing conflicts between resource supply and demand, along with an accumulation of pollutants and a rise in waste emissions [13,14]. Additionally, solid waste accumulation has contributed to the deterioration of air and water quality [15], and increased urbanization has further enhanced greenhouse gas emissions [16]. Environmental damage will hinder not only the economic development of China's cities but also their sustainable development [17,18].…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Mapping of Urban Residential Building Stock Using Multisource Geographic Data

Shen,

Wang,

Yang

et al. 2024

Buildings

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The rapid pace of urbanization and the increasing concentration of populations in urban areas have generated a substantial demand for architectural structures, resulting in a significant increase in building stock and continuous material flows that interact with the environment. This study emphasizes the importance of high-spatial-resolution mapping of residential building stock for effective urban-construction resource management, planning, and waste management. Focusing on Xi’an as a case study, the research develops a comprehensive framework for mapping urban residential building stock by integrating diverse data dimensions, including temporal, spatial, network, and multi-attribute aspects. The findings indicate that between 1990 and 2020, approximately 4758 residential communities were established in central Xi’an. The analysis of seven key residential construction materials revealed that the building stock escalated from 1.53 million tons to 731.12 million tons, with a steady spatial expansion of material distribution. The study attributes this growth to factors such as population increase, economic advancement, and policy initiatives, which, in turn, have driven the demand for residential building materials and reinforced the interdependence between urban expansion and residential construction development. Remarkably, from 1990 to 2020, the population surged by 2.1-fold, the economy by 66-fold, and the stock of residential building materials by 477-fold, indicating that the growth rate of material stock consistently outpaced that of both population and economic growth. Over the past three decades, the rapid expansion of residential buildings has led to the encroachment of urban ecological spaces by concrete structures. The methodology proposed in this study for quantifying building material offers valuable insights for policymakers and urban and environmental planners to foster responsible resource consumption and supports component-level circularity in the built environment.

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“…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 . Among them, Kriging interpolation, as a classic geostatistical method, is widely used for estimating spatially continuous sur-faces [39][40][41]. Spatial regression analysis combines statistical regression methods with geographic information, thus effectively considering spatial correlations, and provides a more accurate model for interpolation.…”

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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Examining the Role of the Main Terrestrial Factors Won the Seasonal Distribution of Atmospheric Carbon Dioxide Concentration over Iran

Cited by 11 publications

References 50 publications

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

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

High-Resolution Mapping of Urban Residential Building Stock Using Multisource Geographic Data

XCO2 Super-Resolution Reconstruction Based on Spatial Extreme Random Trees

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