scholarly journals Neural-network-based estimation of regional-scale anthropogenic CO<sub>2</sub> emissions using an Orbiting Carbon Observatory-2 (OCO-2) dataset over East and West Asia

2021 ◽  
Vol 14 (11) ◽  
pp. 7277-7290
Author(s):  
Farhan Mustafa ◽  
Lingbing Bu ◽  
Qin Wang ◽  
Na Yao ◽  
Muhammad Shahzaman ◽  
...  
Keyword(s):  
Neural Network ◽  
Carbon Dioxide ◽  
Co2 Emissions ◽  
Co2 Emission ◽  
Net Primary Productivity ◽  
Regional Scale ◽  
Mitigation Strategies ◽  
Generalized Regression Neural Network ◽  
Anthropogenic Co2 ◽  
East And West

Abstract. Atmospheric carbon dioxide (CO2) is the most significant greenhouse gas, and its concentration is continuously increasing, mainly as a consequence of anthropogenic activities. Accurate quantification of CO2 is critical for addressing the global challenge of climate change and for designing mitigation strategies aimed at stabilizing CO2 emissions. Satellites provide the most effective way to monitor the concentration of CO2 in the atmosphere. In this study, we utilized the concentration of the column-averaged dry-air mole fraction of CO2, i.e., XCO2 retrieved from a CO2 monitoring satellite, the Orbiting Carbon Observatory-2 (OCO-2), and the net primary productivity (NPP) provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate the anthropogenic CO2 emissions using the Generalized Regression Neural Network (GRNN) over East and West Asia. OCO-2 XCO2, MODIS NPP, and the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) CO2 emission datasets for a period of 5 years (2015–2019) were used in this study. The annual XCO2 anomalies were calculated from the OCO-2 retrievals for each year to remove the larger background CO2 concentrations and seasonal variability. The XCO2 anomaly, NPP, and ODIAC emission datasets from 2015 to 2018 were then used to train the GRNN model, and, finally, the anthropogenic CO2 emissions were estimated for 2019 based on the NPP and XCO2 anomalies derived for the same year. The estimated and the ODIAC CO2 emissions were compared, and the results showed good agreement in terms of spatial distribution. The CO2 emissions were estimated separately over East and West Asia. In addition, correlations between the ODIAC emissions and XCO2 anomalies were also determined separately for East and West Asia, and East Asia exhibited relatively better results. The results showed that satellite-based XCO2 retrievals can be used to estimate the regional-scale anthropogenic CO2 emissions, and the accuracy of the results can be enhanced by further improvement of the GRNN model with the addition of more CO2 emission and concentration datasets.

scholarly journals Neural Network Based Estimation of Regional Scale Anthropogenic CO<sub>2</sub> Emissions Using OCO-2 Dataset Over East and West Asia

2021 ◽  
Author(s):  
Farhan Mustafa ◽  
Lingbing Bu ◽  
Qin Wang ◽  
Na Yao ◽  
Muhammad Shahzaman ◽  
...  
Keyword(s):  
Neural Network ◽  
Carbon Dioxide ◽  
Co2 Emissions ◽  
Co2 Emission ◽  
Anthropogenic Activities ◽  
Regional Scale ◽  
Mitigation Strategies ◽  
Generalized Regression Neural Network ◽  
Anthropogenic Co2 ◽  
East And West

Abstract. Atmospheric carbon dioxide (CO2) is the most significant greenhouse gas and its concentration is continuously increasing mainly as a consequence of anthropogenic activities. Accurate quantification of CO2 is critical for addressing the global challenge of climate change and designing mitigation strategies aimed at stabilizing the CO2 emissions. Satellites provide the most effective way to monitor the concentration of CO2 in the atmosphere. In this study, we utilized the concentration of column-averaged dry-air mole fraction of CO2 i.e., XCO2 retrieved from a CO2 monitoring satellite, the Orbiting Carbon Observatory 2 (OCO-2) to estimate the anthropogenic CO2 emissions using Generalized Regression Neural Network over East and West Asia. OCO-2 XCO2 and the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) CO2 emission datasets for a period of 5 years (2015–2019) were used in this study. The annual XCO2 anomalies were calculated from the OCO-2 retrievals for each year to remove the larger background CO2 concentrations and seasonal variabilities. Then the XCO2 anomaly and ODIAC emission datasets from 2015 to 2018 were used to train the GRNN model, and finally, the anthropogenic CO2 emissions were estimated for 2019 based on the XCO2 anomalies derived for the same year. The XCO2-based estimated and the ODIAC actual CO2 emissions were compared and the results showed a good agreement in terms of spatial distribution. The CO2 emissions were estimated separately over East and West Asia. In addition, correlations between the ODIAC emissions and XCO2 anomalies were also determined separately for East and West Asia, and East Asia exhibited relatively better results. The results showed that satellite-based XCO2 retrievals can be used to estimate the regional scale anthropogenic CO2 emissions and the accuracy of the results can be enhanced by further improvement of the GRNN model with the addition of more CO2 emission and concentration datasets.

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

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1118 ◽  
Author(s):  
Shaoyuan Yang ◽  
Liping Lei ◽  
Zhaocheng Zeng ◽  
Zhonghua He ◽  
Hui Zhong
Keyword(s):  
Neural Network ◽  
Greenhouse Gases ◽  
Co2 Emissions ◽  
Regional Scale ◽  
Satellite Observations ◽  
General Regression Neural Network ◽  
Training Dataset ◽  
Estimation Accuracy ◽  
Co2 Uptake ◽  
Anthropogenic Co2

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.

scholarly journals Climate benefits of proposed carbon dioxide mitigation strategies for international shipping and aviation

2019 ◽  
Vol 19 (23) ◽  
pp. 14949-14965 ◽  
Author(s):  
Catherine C. Ivanovich ◽  
Ilissa B. Ocko ◽  
Pedro Piris-Cabezas ◽  
Annie Petsonk
Keyword(s):  
Carbon Dioxide ◽  
Co2 Emissions ◽  
Mitigation Strategies ◽  
Mitigation Measures ◽  
Civil Aviation ◽  
Economic Sectors ◽  
International Shipping ◽  
Nationally Determined Contributions ◽  
Reduce Carbon Dioxide ◽  
Carbon Dioxide Co2

Abstract. While individual countries work to achieve and strengthen their nationally determined contributions (NDCs) to the Paris Agreement, the growing emissions from two economic sectors remain largely outside most countries' NDCs: international shipping and international aviation. Reducing emissions from these sectors is particularly challenging because the adoption of any policies and targets requires the agreement of a large number of countries. However, the International Maritime Organization (IMO) and the International Civil Aviation Organization (ICAO) have recently announced strategies to reduce carbon dioxide (CO2) emissions from their respective sectors. Here we provide information on the climate benefits of these proposed measures, along with related potential measures. Given that the global average temperature has already risen 1 ∘C above preindustrial levels, there is only 1.0 or 0.5 ∘C of additional “allowable warming” left to stabilize below the 2 or 1.5 ∘C thresholds, respectively. We find that if no actions are taken, CO2 emissions from international shipping and aviation may contribute roughly equally to an additional combined 0.12 ∘C to global temperature rise by end of century – which is 12 % and 24 % of the allowable warming we have left to stay below the 2 or 1.5 ∘C thresholds (1.0 and 0.5 ∘C), respectively. However, stringent mitigation measures may avoid over 85 % of this projected future warming from the CO2 emissions from each sector. Quantifying the climate benefits of proposed mitigation pathways is critical as international organizations work to develop and meet long-term targets.

scholarly journals Carbon dioxide emissions in Northern China based on atmospheric observations from 2005 to 2009

2019 ◽  
Author(s):  
Archana Dayalu ◽  
J. William Munger ◽  
Yuxuan Wang ◽  
Steven C. Wofsy ◽  
Yu Zhao ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Observational Data ◽  
Co2 Emissions ◽  
Carbon Dioxide Emissions ◽  
Spatial Scales ◽  
Northern China ◽  
High Temporal Resolution ◽  
Anthropogenic Co2 ◽  
Time Period ◽  
Data Limitations

Abstract. China has pledged reduction of carbon dioxide emissions per unit GDP by 60–65 % relative to 2005 levels, and to peak carbon emissions overall by 2030. However, disagreement among available inventories makes it difficult for China to track progress toward these goals and evaluate the efficacy of control measures. In this study, we demonstrate an approach based on a long time series of surface CO2 observations to evaluate regional CO2 emissions rates in northern China estimated by three anthropogenic CO2 inventories – two of which are subsets from global inventories, and one of which is China-specific. Comparison of CO2 observations to CO2 predicted from accounting for global background concentration and atmospheric mixing of emissions suggests potential biases in the inventories. The period analyzed focuses on the key commitment period for the Paris accords (2005) and the Beijing Olympics (2008). Model-observation mismatch in concentration units is translated to mass units and is displayed against the original inventories in the measurement influence region, largely corresponding to northern China. Owing to limitations from having a single site, addressing the significant uncertainty stemming from transport error and error in spatial allocation of the emissions remains a challenge. Our analysis uses observations to support and justify increased use and development of China-specific inventories in tracking China's progress as a whole towards reducing emissions. Here we are restricted to a single measurement site; effectively evaluating and constraining inventories at relevant spatial scales requires multiple stations of high-temporal resolution observations. At this stage and with observational data limitations, we emphasize that this work is intended to be a comparison of a subset of anthropogenic CO2 emissions rates from inventories that were readily available at the time this research began. For this study's analysis time period, there was not enough spatially distinct observational data to conduct an optimization of the inventories. Rather, our analysis provides an important quantification of model-observation mismatch. In the northern China evaluation region, emission rates from the China-specific inventory produce the lowest model-observation mismatch at all timescales from daily to annual. Additionally, we note that averaged over the study time period, the unscaled China-specific inventory has substantially larger annual emissions for China as a whole (20 % higher) and the northern China evaluation region (30 %) than the unscaled global inventories. Our results lend support the rates and geographic distribution in the China-specific inventory. However, exploring this discrepancy for China as a whole requires a denser observational network in future efforts to measure and verify CO2 emissions for China both regionally and nationally. This study provides a baseline analysis for a small but import region within China, as well a guide for determining optimal locations for future ground-based measurement sites.

scholarly journals Detecting the Responses of CO2 Column Abundances to Anthropogenic Emissions from Satellite Observations of GOSAT and OCO-2

2021 ◽  
Vol 13 (17) ◽  
pp. 3524
Author(s):  
Mengya Sheng ◽  
Liping Lei ◽  
Zhao-Cheng Zeng ◽  
Weiqiang Rao ◽  
Shaoqing Zhang
Keyword(s):  
Co2 Emissions ◽  
Urban Areas ◽  
Atmospheric Transport ◽  
Southern Oscillation ◽  
Regional Scale ◽  
Co2 Concentration ◽  
Anthropogenic Emissions ◽  
Urban Agglomerations ◽  
Anthropogenic Co2

The continuing increase in atmospheric CO2 concentration caused by anthropogenic CO2 emissions significantly contributes to climate change driven by global warming. Satellite measurements of long-term CO2 data with global coverage improve our understanding of global carbon cycles. However, the sensitivity of the space-borne measurements to anthropogenic emissions on a regional scale is less explored because of data sparsity in space and time caused by impacts from geophysical factors such as aerosols and clouds. Here, we used global land mapping column averaged dry-air mole fractions of CO2 (XCO2) data (Mapping-XCO2), generated from a spatio-temporal geostatistical method using GOSAT and OCO-2 observations from April 2009 to December 2020, to investigate the responses of XCO2 to anthropogenic emissions at both global and regional scales. Our results show that the long-term trend of global XCO2 growth rate from Mapping-XCO2, which is consistent with that from ground observations, shows interannual variations caused by the El Niño Southern Oscillation (ENSO). The spatial distributions of XCO2 anomalies, derived from removing background from the Mapping-XCO2 data, reveal XCO2 enhancements of about 1.5–3.5 ppm due to anthropogenic emissions and seasonal biomass burning in the wintertime. Furthermore, a clustering analysis applied to seasonal XCO2 clearly reveals the spatial patterns of atmospheric transport and terrestrial biosphere CO2 fluxes, which help better understand and analyze regional XCO2 changes that are associated with atmospheric transport. To quantify regional anomalies of CO2 emissions, we selected three representative urban agglomerations as our study areas, including the Beijing-Tian-Hebei region (BTH), the Yangtze River Delta urban agglomerations (YRD), and the high-density urban areas in the eastern USA (EUSA). The results show that the XCO2 anomalies in winter well capture the several-ppm enhancement due to anthropogenic CO2 emissions. For BTH, YRD, and EUSA, regional positive anomalies of 2.47 ± 0.37 ppm, 2.20 ± 0.36 ppm, and 1.38 ± 0.33 ppm, respectively, can be detected during winter months from 2009 to 2020. These anomalies are slightly higher than model simulations from CarbonTracker-CO2. In addition, we compared the variations in regional XCO2 anomalies and NO2 columns during the lockdown of the COVID-19 pandemic from January to March 2020. Interestingly, the results demonstrate that the variations of XCO2 anomalies have a positive correlation with the decline of NO2 columns during this period. These correlations, moreover, are associated with the features of emitting sources. These results suggest that we can use simultaneously observed NO2, because of its high detectivity and co-emission with CO2, to assist the analysis and verification of CO2 emissions in future studies.

scholarly journals A Study of Carbon Dioxide Emission in Different Types of Peatland Use in Kalimantan

2015 ◽  
Vol 18 (1) ◽  
pp. 9
Author(s):  
Nyahu Rumbang
Keyword(s):  
Carbon Dioxide ◽  
Co2 Emissions ◽  
Co2 Emission ◽  
Sweet Corn ◽  
Aloe Vera ◽  
Groundwater Table ◽  
West Kalimantan ◽  
Central Kalimantan ◽  
Different Types ◽  
Corn Plants

Study of carbon dioxide emissions in different types of peatlands use in Central and West Kalimantan has been conducted in January-June 2006 and January-April 2007. The study represents 4 types of land use in Central Kalimantan as treatment: 5 years for chinesse cabbage, 10 years for chinesse cabbage, 5 years for sweet corns, and 10 years for sweet corns. As for the treatments in West Kalimantan, they include corn field, Aloe vera field, oil palm plantation, and rubber plantation. Carbon dioxide was measured using infrared gas analysis (model EGM-4, PP systems, Hitchin, UK). In Central Kalimantan, the highest CO2 is emitted from sweet corn plants (arable land for 10 years) by 0.79 g CO2/m2/hour, chinesse cabbage plants (for 5 years) by 0.73 g CO2/m2/hour, chinesse cabbage plants (for 10 years) by 0.67 g CO2/m2/hour and, the least, sweet corn plants (for 5 years) by 0.41 g CO2/m2/hour. The highest CO2 emission from West Kalimantan is released from rubber plants at 1.22 g CO2/m2/hour, followed by palm oil plants by 0.96 g CO2/m2/hour, Aloe vera plants by 0.68 g CO2/m2/hour and corn plants by 0.35 g CO2/m2/hour. Groundwater table depth are the most important factors among other factors that influence CO2 emissions. Groundwater table depth indicated a positive correlation with CO2 emissions in all types of peatlands use. C-organic production of sweet corn plants at 11.66 t C/ha/year is higher than that of chinesse cabbage plants at 1.64 t C/ha /year. Corn plants produce organic-C was 11.66 t C/ha/year, equivalent to the amount of loss of C through CO2 emissions by 11.29 t C/ha/year.Keywords: peat, types of land use, carbon, CO2 emission

Anthropogenic CO2 emission uncertainties

2020 ◽  
Author(s):  
Margarita Choulga ◽  
Greet Janssens-Maenhout ◽  
Gianpaolo Balsamo ◽  
Joe McNorton ◽  
Efisio Solazzo ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Co2 Emission ◽  
Fossil Fuel ◽  
Greenhouse Gas Inventories ◽  
Global Emission ◽  
Anthropogenic Co2 ◽  
Ipcc Guidelines ◽  
First Results ◽  
Inverse Transport ◽  
The Impact

&lt;p&gt;The CO2 Human Emissions (CHE) project has been tasked by the European Commission to prepare the development of a European capacity to monitor anthropogenic CO2 emissions. The monitoring of fossil fuel CO2 emissions has to come with a sufficiently low uncertainty in order to be useful for policymakers. In this context, the main approaches to estimate fossil fuel emissions, apart from bottom-up inventories, are based on inverse transport&lt;br&gt;modeling either on its own or within a coupled carbon cycle fossil fuel data assimilation system. Both approaches make use of atmospheric CO2 and other tracers (e.g., CO and NOx) and rely on the availability of prior fossil fuel CO2 emission estimates and uncertainties (as well as biogenic fluxes for the transport inverse modeling). For a robust estimate of the uncertainty, information from different sources needs to be brought together.&lt;br&gt;A methodology to calculate yearly and monthly anthropogenic CO2 emission uncertainties based on IPCC guidelines (2006 IPCC Guidelines for National Greenhouse Gas Inventories + its 2019 Refinements) has been developed. Emission uncertainties are calculated for all world countries, under the assumption of two categories of world countries, depending on whether the country&amp;#8217;s statistical infrastructure is well or less developed. For well-developed statistical infrastructure, emission uncertainties are lower, while less developed statistical infrastructure countries have higher emission uncertainties. A sensitivity analysis is investigating the impact of the well or less developed infrastructure assumption for several countries on the global emission uncertainty. Sensitivity experiments with different anthropogenic CO2 sources distributions, as well as the first results on using these prior anthropogenic CO2 uncertainties in ensemble perturbation runs will be presented.&lt;/p&gt;

scholarly journals Establishment of the Predicting Models of the Dyeing Effect in Supercritical Carbon Dioxide Based on the Generalized Regression Neural Network and Back Propagation Neural Network

Processes ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 1631 ◽  
Author(s):  
Zhuo Zhang ◽  
Fayu Sun ◽  
Qingling Li ◽  
Weiqiang Wang ◽  
Dedong Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Generalized Regression Neural Network ◽  
Data Sets ◽  
Generalized Regression ◽  
Supercritical Carbon

With the growing demand of supercritical carbon dioxide (SC-CO2) dyeing, it is important to precisely predict the dyeing effect of supercritical carbon dioxide. In this work, Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) models have been employed to predict the dyeing effect of SC-CO2. These two models have been constructed based on published experimental data and calculated values. A total of 386 experimental data sets were used in the present work. In GRNN and BPNN models, two input parameters, such as temperature, pressure, dye stuff types, carrier types and dyeing time, were selected for the input layer and one variable, K/S value or dye-uptake, was used in the output layer. It was found that the values of mean-relative-error (MRE) for BPNN model and for GRNN model are 3.27–6.54% and 1.68–3.32%, respectively. The results demonstrate that both BPNN and GPNN models can accurately predict the effect of supercritical dyeing but the former is better than the latter.

scholarly journals Social and Economic Factors of Industrial Carbon Dioxide in China: From the Perspective of Spatiotemporal Transition

2021 ◽  
Vol 13 (8) ◽  
pp. 4268
Author(s):  
Jingyuan Li ◽  
Jinhua Cheng ◽  
Beidi Diao ◽  
Yaqi Wu ◽  
Peiqi Hu ◽  
...  
Keyword(s):  
Carbon Dioxide ◽  
Co2 Emissions ◽  
Spatial Data ◽  
Emission Reduction ◽  
Co2 Emission ◽  
Carbon Dioxide Emission ◽  
Spatial Data Analysis ◽  
Co2 Emission Reduction ◽  
Intensity Effect ◽  
Industrial Carbon

The reduction of CO2 emission has become one of the significant tasks to control climate change in China. This study employs Exploratory Spatial Data Analysis (ESDA) to identify the provinces in China with different types of spatiotemporal transition, and applies the Logarithmic Mean Divisia Index (LMDI) to analyze the influencing factors of industrial CO2 emissions. Spatial autocorrelation of provincial industrial CO2 emissions from 2003 to 2017 has been demonstrated. The results are as follows: (1) 30 provinces in China are categorized into 8 types of spatiotemporal transition, among which 24 provinces are characterized by stable spatial structure and 6 provinces show significant spatiotemporal transition; (2) For all types of spatiotemporal transition, economic scale effect is mostly contributed to industrial CO2 emission, while energy intensity effect is the most crucial driving force to reduce industrial carbon dioxide emission; (3) provinces of type HH-HH, HL-HL and HL-HH are most vital for CO2 emission reduction, while the potential CO2 emission increase of developing provinces in LL-LL, LH-LH and LL-LH should also be taken into account. Specific measures for CO2 emission reduction are suggested accordingly.

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