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Wildfires are becoming more frequent due to the global climate change. Large amounts of greenhouse gases emitted by wildfires can lead to increases in extreme climate events. Accurately estimating the greenhouse gas carbon dioxide (CO2) emissions from wildfires is important for mitigation of climate change. In this paper, we develop a novel method to estimate wildfire CO2 emissions from the relationship between local CO2 emissions and XCO2 anomalies. Our method uses the WRF-Chem assimilation system from OCO-2 XCO2 retrievals which coupled with Data Assimilation Research Testbed (DART). To validate our results, we conducted three experiments evaluating the wildfire CO2 emissions over the conterminous United States. The four-month average wildfire emissions from July to October in 2015∼2018 were estimated at 4.408 Tg C, 1.784 Tg C, 1.514 Tg C and 2.873 Tg C, respectively. Compared to the average of established inventories CT2019B, FINNv1.5 and GFASv1.2 fire emissions, our estimates fall within one standard deviation, except for 2017 due to lacking of OCO-2 XCO2 retrievals. These results suggest that the regional carbon assimilation system, such as WRF-Chem/DART, using OCO-2 XCO2 retrievals has a great potential for accurately tracking regional wildfire emissions. [ABSTRACT FROM AUTHOR]

This paper attempts to develop a calculation model to estimate the carbon dioxide (CO2) emissions during the mixing process of asphalt mixtures and explore energy-saving and emission reduction technologies. Based on a comprehensive analysis of the mixer's working mechanism, mixing quality requirement, and theoretical deductions, a CO2 emission model for the mixing process of asphalt mixtures is established. The model highlights the significant impact of mixing time on both mixing quality and carbon emissions. The model demonstrates that the mixing quality improves with an increase in mixing time, but the degree of improvement diminishes after an initial significant enhancement, eventually stabilizing. Importantly, excessive mixing time does not significantly improve the mixing quality; conversely, an extended mixing time has a notable impact on carbon emissions. Results show that when the deviation of the asphalt content is changed from 0.3% to 0.2% for a 5% asphalt content mixture, the mixing time and resulting CO2 emissions increase by 14%; similarly, when the deviation is 0.1%, the mixing time and resulting CO2 emissions increase by nearly 40%. Additionally, the agitator's capacity also significantly influences the CO2 emissions. For a project of a given scale, increasing the agitator capacity leads to a reduction in total carbon emissions during the mixing process. Compared to a type 1500 agitator, employing agitators of types 3000, 4000, and 5000 can achieve reductions in total CO2 emissions by 26.3%, 32.9%, and 36.8%, respectively. Therefore, for large-scale engineering projects aiming to minimize CO2 emissions during the mixing process, it is essential to determine the optimal mixing time to avoid excessive mixing and select a larger capacity agitator, preferably type 4000 or higher. These findings could support the development of effective emission reduction measures in the field of road construction, thereby contributing to the achievement of emission reduction targets and promoting the advancement of sustainable road development. [ABSTRACT FROM AUTHOR]

China's economy has made significant strides in the past three decades. As a direct result of China's "one belt, one road" (OBOR) initiative, the country's rate of industrialization and urbanization is currently the fastest in the entire world. This rapid development is largely dependent on the enormous amounts of energy currently being consumed and forms the foundation of the world's high levels of carbon emissions. It is generally agreed that the production of greenhouse gases, particularly carbon dioxide, is the primary contributor to the current state of climate change. In this paper, a CO2 emission prediction model based on Bi-LSTM is constructed. In order to conduct empirical tests on the model, this study uses data from South Asian countries and China from 2001 to 2020. China's CO2 emissions from 2022 to 2030 were predicted along with those of other countries in order to study the combined effects of the scientific and technological progress, industrial structures, and energy structure factors affecting CO2 emissions. When compared with the LSTM and GRU methods, the Bi-LSTM model's results produced lower MAE, MSE, and MAPE values, indicating that it performs better. According to the findings, carbon emissions represent a significant problem that will become much worse in the future due to China and India's high emissions, particularly in the next 10 years, if the government does not implement policies that help reduce those emissions. [ABSTRACT FROM AUTHOR]

The paper utilized a panel dataset to investigate the effects of the impact factors, namely, economic growth, energy intensity, energy consumption, urbanisation, passenger vehicles and transport infrastructure investments on carbon emissions (CO2-eq). The transport sector of the main buoyant economic provinces of South Africa for a consecutive period of five years was investigated using STIRPAT models. Carbon emissions coefficient method, (CECM) is made applicable to determine the quantification of the forms of fossil fuels utilized in the transportation sector. The characterized parametric panel dataset for Gauteng, KwaZulu-Natal and Western Cape were assessed from 2016 to 2020 to make objective function determinations. The results indicate that GDP and passenger vehicles contributed largely to CO2-eq emissions, however, the impact varies across the three provinces. Energy intensity and the approach to energy consumption are significant in mitigating carbon emissions, which is due to the possibilities of high fuel efficiency and pollution decrease. The outcome of the research advances climate change mitigation strategy and proffers the required attention to policy makers in South Africa and Africa as a continent. [ABSTRACT FROM AUTHOR]

Microalgae-mediated CO2 sequestration has been a subject of numerous research works and has become one of the most promising strategies to mitigate carbon dioxide emissions. However, feeding flue and exhaust gas into algae-based systems has been shown to destroy chloroplasts, as well as disrupt photosynthesis and other metabolic processes in microalgae, which directly limits CO2 uptake. CO2 biosequestration in existing photobioreactors (PBRs) is also limited by the low biomass concentration in the growth medium. Therefore, there is a real need to seek alternative solutions that would be competitive in terms of performance and cost-effectiveness. The present paper reports the results of experiments aimed to develop an innovative trickle bed reactor that uses immobilized algae to capture CO2 from flue and exhaust gas (IMC-CO2PBR). In the experiment, ambient air enriched with technical-grade CO2 to a CO2 concentration of 25% v/v was used. The microalgae immobilization technology employed in the experiment produced biomass yields approximating 100 g DM/dm3. A relationship was found between CO2 removal rates and gas volume flux: almost 40% of CO2 was removed at a feed of 25 dm3 of gas per hour, whereas in the 200 dm3/h group, the removal efficiency amounted to 5.9%. The work includes a determination of basic process parameters, presentation of a developed functional model and optimized lighting system, proposals for components to be used in the system, and recommendations for an automation and control system for a full-scale implementation. [ABSTRACT FROM AUTHOR]