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The papers by Yang et al. [[5]] and Wang et al. [[6]] mainly studied the corrosion of CO SB 2 sb on gathering pipelines and the monitoring of the corrosion rate. The paper by Hou et al. [[1]] investigated the microscopic adsorption pattern of CH SB 4 sb and the competitive adsorption behavior of CH SB 4 sb and CO SB 2 sb in sodium-based montmorillonite using molecular simulation methods. The CO SB 2 sb pre-pad energized fracturing technology can not only further increase oil production, but also effectively realize the geological storage and utilization of CO SB 2 sb , thus greatly reducing the greenhouse effect. [Extracted from the article]

As the second most predominant greenhouse gas, methane-targeted emission mitigation holds the potential to decelerate the pace of global warming. Satellite remote sensing is an important monitoring tool, and we review developments in the satellite detection of methane. This paper provides an overview of the various types of satellites, including the various instrument parameters, and describes the different types of satellite retrieval algorithms. In addition, the currently popular methane point source quantification method is presented. Based on existing research, we delineate the classification of methane remote sensing satellites into two overarching categories: area flux mappers and point source imagers. Area flux mappers primarily concentrate on the assessment of global or large-scale methane concentrations, with a further subclassification into active remote sensing satellites (e.g., MERLIN) and passive remote sensing satellites (e.g., TROPOMI, GOSAT), contingent upon the remote sensing methodology employed. Such satellites are mainly based on physical models and the carbon dioxide proxy method for the retrieval of methane. Point source imagers, in contrast, can detect methane point source plumes using their ultra-high spatial resolution. Subcategories within this classification include multispectral imagers (e.g., Sentinel-2, Landsat-8) and hyperspectral imagers (e.g., PRISMA, GF-5), contingent upon their spectral resolution disparities. Area flux mappers are mostly distinguished by their use of physical algorithms, while point source imagers are dominated by data-driven methods. Furthermore, methane plume emissions can be accurately quantified through the utilization of an integrated mass enhancement model. Finally, a prediction of the future trajectory of methane remote sensing satellites is presented, in consideration of the current landscape. This paper aims to provide basic theoretical support for subsequent scientific research. [ABSTRACT FROM AUTHOR]

Using an aerosol–climate coupled model, this paper has investigated the changes in distributions of anthropogenic and natural particles due to 4 × CO2-induced global warming, under the low emission scenario of Representative Concentration Pathway 4.5 (RCP4.5). Special attention is paid to the seasonal variations of aerosol size modes. With rising CO2 levels, surface warming, and changes in atmospheric circulations and hydrologic cycles are found during both summer (JJA) and winter (DJF). For anthropogenic particles, changes in fine anthropogenic particulate matter (PM2.5, particles with diameters smaller than 2.5 μm) decrease over high-latitude regions and increase over the tropics in both DJF and JJA. Global mean column concentrations of PM2.5 decrease by approximately 0.19 mg m−2, and concentrations of coarse anthropogenic particles (CPM, particles with diameters larger than 2.5 μm) increase by 0.005 mg m−2 in JJA. Changes in anthropogenic particles in DJF are similar to those in JJA, but the magnitudes of maximum regional changes are much smaller than those in JJA. The coarse anthropogenic particles (CPM, particles with diameters larger than 2.5 μm) increase over northern Africa and the Arabian Peninsula during JJA, whereas changes in anthropogenic CPM during DJF are minimal. During both JJA and DJF, changes in anthropogenic CPM are about two orders of magnitude smaller than those of anthropogenic PM2.5. Enhanced wet deposition by large-scale precipitation under rising CO2-induced surface warming is the critical factor affecting changes in anthropogenic particles. For natural particles, the distribution of change in the natural PM2.5 burden is similar to that of natural CPM, but much larger than natural CPM during each season. Both natural PM2.5 and CPM burdens increase over northern Africa and the Arabian Peninsula during JJA, but decrease over most of the continental regions during DJF. Changes in surface wind speed, divergence/convergence of surface wind, and precipitation are primary reasons for the variation of natural particles. [ABSTRACT FROM AUTHOR]

Increased warming in the Arctic is of great concern. This is particularly due to permafrost degradation, which is expected to accelerate microbial breakdown of soil organic carbon, with its further release into the atmosphere as carbon dioxide (CO2). The fine-scale variability of CO2 fluxes across highly mosaic Arctic tundra landscapes can provide us with insights into the diverse responses of individual plant communities to environmental change. In the paper, we contribute to filling existing gaps by investigating the variability of CO2 flux rates within different landscape units for dominant vegetation communities and plant species across typical tundra of the southern part of the Taimyr Peninsula, Siberia. In general, the variability of soil CO2 flux illustrates a four-fold increase from non-vascular vegetation, mainly lichens and mosses (1.05 ± 0.36 µmol m−2 s−1), towards vascular plants (3.59 ± 0.51 µmol m−2 s−1). Barren ground ("frost boils") shows the lowest value of 0.79 ± 0.21 µmol m−2 s−1, while considering the Arctic "browning" phenomenon, a further substantial increase of CO2 flux can be expected with shrub expansion. Given the high correlation with top soil temperature, well-drained and relatively dry habitats such as barren ground and non-vascular vegetation are expected to be the most sensitive to the observed and projected temperature growth in the Arctic. For mixed vegetation and vascular species that favor wetter conditions, soil moisture appears to play a greater role. Based on the modeled seasonal pattern of soil CO2 flux and precipitation records, and applying the rainfall simulations in situ we outlined the role of precipitation across enhanced CO2 emissions (i.e., the "Birch" effect). We found that a pulse-like growth of soil CO2 fluxes, observed within the first few minutes after rainfall on vegetated plots, reaches 0.99 ± 0.48 µmol m−2 s−1 per each 1 mm of precipitation, while barren ground shows 55–70% inhibition of CO2 emission during the first several hours. An average additive effect of precipitation on soil CO2 flux may achieve 7–12% over the entire growing season, while the projected increased precipitation regime in the Arctic may strengthen the total CO2 release from the soil surface to the atmosphere during the growing season. [ABSTRACT FROM AUTHOR]

The cooking oil fume particulate (COFP) produced by indoor cooking can harm human health seriously, and therefore requires urgent monitoring and optimization. In this paper, the kitchen cooking simulation process was established by using computational fluid dynamics (CFD) based on the fluid dynamics theory. Combined with the user defined function (UDF), the spatial and temporal distributions of COFP and carbon dioxide (CO2) during the cooking process were simulated and analyzed, respectively. Both simulation results were verified using experimental data. Moreover, this paper introduces a COFP concentration correlation function that utilizes the spatiotemporal correlation between COFP and CO2 concentrations during the cooking process. The function is based on the spatiotemporal distribution of CO2 concentration. By comparing it with traditional calculations, the proposed function is shown to achieve a remarkable 70% improvement in efficiency and maintain an accuracy rate exceeding 90%. This enables the rapid analysis and control of COFP concentration through monitoring and analyzing CO2 levels in the kitchen. [ABSTRACT FROM AUTHOR]

The main source of production in the middle and late stages of shale gas extraction is the adsorbed gas in shale, and the adsorbed gas in shale mainly comes from organic matter casein and clay minerals in shale; therefore, this paper uses sodium-based montmorillonite to characterize the clay minerals in shale and study the CH4 adsorption law in clay minerals, and this study has certain guiding significance for shale gas extraction. In addition, this paper also conducts a study on the competitive adsorption law of CH4 and CO2, and at the same time, predicts the theoretical sequestration of CO2 in shale clay minerals, which is a reference value for the study of CO2 burial in shale and is beneficial to the early realization of carbon neutral. In this paper, the slit model of sodium-based montmorillonite and the fluid model of CH4 and CO2 were constructed using Materials Studio software, and the following two aspects were studied based on the Monte Carlo method: Firstly, the microscopic adsorption behavior of CH4 in sodium-based montmorillonite was studied, and the simulations showed that the adsorption capacity of montmorillonite decreases with increasing temperature, increases and then decreases with increasing pressure, and decreases with increasing pore size. CH4 has two states of adsorption and free state in the slit. The adsorption type of CH4 in montmorillonite is physical adsorption. Secondly, the competitive adsorption of CH4 and CO2 in sodium-based montmorillonite was studied, and the simulations showed that the CO2 repulsion efficiency increased with increasing CO2 injection pressure, and the CO2/CH4 competitive adsorption ratio decreased with increasing pressure. The amount of CO2 storage decreased with increasing temperature and increased with increasing CO2 injection pressure. [ABSTRACT FROM AUTHOR]

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 review paper illustrates the recommended monitoring technologies for the detection of various greenhouse gaseous emissions for solid waste thermochemical reactions, including incineration, pyrolysis, and gasification. The illustrated gas analyzers are based on the absorption principle, which continuously measures the physicochemical properties of gaseous mixtures, including oxygen, carbon dioxide, carbon monoxide, hydrogen, and methane, during thermochemical reactions. This paper illustrates the recommended gas analyzers and process control tools for different thermochemical reactions and aims to recommend equipment to increase the sensitivity, linearity, and dynamics of various thermochemical reactions. The equipment achieves new levels of on-location, real-time analytical capability using FTIR analysis. The environmental assessment study includes inventory analysis, impact analysis, and sensitivity analysis to compare the mentioned solid waste chemical recycling methods in terms of greenhouse gaseous emissions, thermal efficiency, electrical efficiency, and sensitivity analysis. The environmental impact assessment compares each technology in terms of greenhouse gaseous emissions, including CO2, NOx, NH3, N2O, CO, CH4, heat, and electricity generation. The conducted environmental assessment compares the mentioned technologies through 15 different emission-related impact categories, including climate change impact, ecosystem quality, and resource depletion. The continuously monitored process streams assure the online monitoring of gaseous products of thermochemical processes that enhance the quality of the end products and reduce undesired products, such as tar and char. This state-of-the-art monitoring and process control framework provides recommended analytical equipment and monitoring tools for different thermochemical reactions to optimize process parameters and reduce greenhouse gaseous emissions and undesired products. [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]

The geological storage of CO2 is a critical technique for reducing emissions, which significantly contributes to the mitigation of the greenhouse effect. Currently, CO2 is often geologically stored in coal seams, hydrocarbon reservoirs, and saline aquifers in order to store CO2 and improve the oil and gas recovery simultaneously. Shale formations, as candidates for CO2 storage, are drawing more attention because of their rich volumes. CO2 storage through shale formations in the Sichuan Basin, China, has tremendous potential because of the readily available CO2 injection equipment, such as abandoned shale gas wells. Therefore, we review the potential of using these wells to store CO2 in this paper. Firstly, we review the status of the geological storage of CO2 and discuss the features and filed applications for the most studied storage techniques. Secondly, we investigate the formation properties, shale gas field development process, and characteristics of the abandoned wells in the Sichuan Basin. Additionally, after carefully studying the mechanism and theoretical storage capacity, we evaluate the potential of using these abandoned wells to store CO2. Lastly, recommendations are proposed based on the current technologies and government policies. We hope this paper may provide some insights into the development of geological CO2 storage using unconventional reservoirs. [ABSTRACT FROM AUTHOR]

Observations of the atmospheric sources and sinks of carbon dioxide (CO2) and methane (CH4) in the pan-Arctic domain are extremely scarce, limiting our knowledge of carbon turnover in this climatically sensitive environment and the fate of the enormous carbon reservoirs conserved in the permafrost. Especially critical are the gaps in the high latitudes of Siberia, covered by the vast permafrost underlain tundra, where only several atmospheric monitoring sites are operational. This paper presents the first two years (September 2018–January 2021) of accurate continuous observations of atmospheric CO2 and CH4 dry mole fractions at the recently deployed tower-based measurement station "DIAMIS" (73.5068° N, 80.5198° E) located on the southwestern coast of the Taimyr Peninsula, Siberia, at the Gulf of the Yenisei River that opens to the Kara Sea (Arctic Ocean). In this paper, we summarized the scientific rationale of the site, examined the seasonal footprint of the station with an analysis of terrestrial vegetation and maritime sector contributing to the captured atmospheric signal, and illustrated temporal patterns of CO2 and CH4 for the daytime mixed atmospheric layer over the continent–sea interface. Along with the temporal variations reflecting a signal caused pan-Arctic and not very much influenced by the local processes, we analyzed the spatiotemporal distribution of the synoptic anomalies representing the atmospheric signatures of regional sources and sinks of CO2 and CH4 for the studied high-arctic Siberian domain of ~625 thousand km2, with nearly equal capturing the land surface (54%) and the ocean (46%) throughout the year. Both for CO2 and CH4, we have observed a sea–continent declining trend, presuming a larger depletion of trace gases in the maritime air masses compared to the continental domain. So far, over the Kara Sea, we have not detected any prominent signals of CH4 that might have indicated processes of subsea permafrost degradation and occurrence of cold seeps–still mainly observed in the eastern Arctic Seas—The Laptev Sea and the East-Siberian Sea. [ABSTRACT FROM AUTHOR]

Methane (CH4) is the second-largest greenhouse gas emitted by human activity and natural sources after carbon dioxide (CO2). Its relatively short lifetime in the atmosphere (about 12 years) means that we can mitigate the human impacts of climate change in a relatively short period of time by reducing CH4 emissions. The creation of CH4 emissions management policies can be based on the distribution maps of surface CH4 concentration that are in large-scale and at high-resolution. The estimate of CH4 emissions with broad coverage are provided by currently extensively used satellite data supplemented with data from model simulations. However, it is at low spatial resolution. In this paper, through the combination of atmospheric CH4 observations from the TROPOMI sensor and wind data from the ECMWF global reanalysis, a straightforward divergence method is proposed to estimate the surface CH4 emissions in China from March 2019 to September 2022 at a resolution of 7 km × 7 km. This method was compared with the average annual CH4 emissions of Emissions Database for Global Atmospheric Research (EDGARv7.0), and the Root Mean Square Error (RMSE) is 2.53 kg/km2/h and within error envelop (EE) is 72.93%, which represents the proportion of reliable values under certain uncertain conditions. We estimated that the average annual CH4 emissions in China from 2019 to 2022 is 81 Tg, with the lowest emissions in 2021 (75 Tg) due to the impact of COVID-19. In 2021, the largest anthropogenic emissions in China are from agriculture, energy activities and livestock, accounting for 28% (20.8 Tg), 25% (18.9 Tg) and 19% (13.9 Tg) of total emissions, respectively, while wetlands, as the largest natural source, produce 14% (10.5 Tg) of CH4 emissions. [ABSTRACT FROM AUTHOR]

With the proposal of China's national "double carbon" strategic goal, carbon capture, utilization and storage (CCUS) technology has attracted more and more attention. Due to the high cost, high energy consumption and high risk of CCUS technology, this technology is still in the initial stage of development in China. Among them, CO2 geological storage is one of the risks, and the environmental monitoring technology of CO2 storage leakage is particularly important in the large-scale popularization and application of CCUS technology in China. On the basis of extensive research on the related literature concerning CO2 storage and leakage, this paper begins with the types and mechanisms of CO2 storage, analyzes the ways and risks of CO2 storage and leakage and then summarizes the existing environmental monitoring technologies of CO2 geological storage and leakage. In the future, China can promote the progress of CO2 geological storage monitoring technology and help achieve the goal of "double carbon" by strengthening the research on CO2 storage mechanism and main control factors, perfecting the risk assessment method of CO2 storage, constructing the monitoring technology system of the CO2 storage life cycle, and standardizing the CO2 storage risk response system. [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]

The carbon dioxide (CO2) differential absorption lidar echo signal is susceptible to noise and must satisfy the high demand for signal-retrieval precision. Thus, a proper de-noising method should be selected to improve the inversion result. In this paper, we simultaneously decompose three signal pairs into different intrinsic mode functions (IMFs) using the method of ensemble empirical mode decomposition (EEMD). Further, the correlation coefficients of the IMFs with the same temporal scale are regarded as the criterion to determine the components that need removal. This method not only retains the useful information effectively but also removes the noise component. A significant improvement in the R2 of the differential absorption optical depth (DAOD) of the de-noised signals is obtained. The results of the simulated and observed analysis signal demonstrated improvement both in the SNR and in the retrieval precision. [ABSTRACT FROM AUTHOR]

The air environment (e.g., high concentration of carbon dioxide) in a pig house will affect the health conditions and growth performance of the pigs, and the quality of pork as well. In order to reduce the cumulative concentration of carbon dioxide in the pig house, the prediction model was established by the deep learning method to predict the changes of the carbon dioxide cumulative concentration in a pig house. This model will also be used for the real-time monitoring and adjustment of the concentration of carbon dioxide of the pig house. The experiment was designed to collect environmental parameters (e.g., temperature, humidity, wind speed, and carbon dioxide concentration) data in the pig house for several months. The ensemble empirical mode decomposition–gated recurrent unit (EEMD–GRU) prediction model was established in the prediction of carbon dioxide concentration in the pig house. The results show that compared with the other models, the prediction accuracy of the EEMD–GRU model is the highest, and the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and r-squared (R2) of carbon dioxide concentration in autumn and winter are 123.2 ppm, 88.3 ppm, 3.2%, and 0.99, respectively. The RMSE, MAE, MAPE, and R2 for carbon dioxide concentration are 129.1 ppm, 93.2 ppm, 5.9%, and 0.76 in spring and summer. The prediction model proposed in this paper can effectively predict the concentration of carbon dioxide in the pig house and provide effective help for the precise control of the pig house environment. [ABSTRACT FROM AUTHOR]

Walnut shell is a very potential biochar precursor because of its wide source, low cost, and easy structure modification. In this paper, the co-activation method of FeCl3, ZnCl2 and H2O(g) was adopted to prepare walnut shell-based biochar with high microporosity and the effect of pore structure on CO2 adsorption performance at different temperatures was investigated. The prepared biochar had a larger specific surface area (2647.8 m2 g−1), satisfactory micropore area (2008.7 m2 g−1) and high total pore volume (2.58 cm3 g−1). At 273 K and 298 K, its CO2 adsorption capacity was 4.79 mmol g−1 and 3.20 mmol g−1, respectively. Particularly, CO2 adsorbed uptake on biochar was strongly sensitive to their narrow micropore volume, instead of the total specific surface area, total pore volume, and micropore specific surface area. The optimal pore size beneficial for CO2 adsorption was 0.33–0.82 nm at 273 K, but the optimal pore size was 0.33–0.39 nm at 298 K. It provides theoretical guidance for future material preparation and selection, and FeCl3, ZnCl2 and H2O(g) may be effective biochar activators. [ABSTRACT FROM AUTHOR]

This paper presents the comparison of two CO2 datasets from the National Institute for Environmental Studies (NIES) of Japan and the Atmospheric CO2 Observations from Space (ACOS) of NASA for three years (2010 to 2012). Both CO2 datasets are retrieved from the Greenhouse gases Observing SATellite (GOSAT) short-wavelength infrared spectra over High gain surface land. In this three-year period, the yield of the NIES CO2 column averaged dry air mole fractions (XCO2) is about 71% of ACOS retrievals. The overall bias is 0.21 ± 1.85 ppm and -0.69 ± 2.13 ppm for ACOS and NIES XCO2, respectively, when compared with ground-based Fourier Transform Spectrometer (FTS) observations from twelve Total Carbon Column Observing Network (TCCON) sites. The differences in XCO2 three-year means and seasonal means are within about 1 to 2 ppm. Strong consistency is obtained for the ACOS and NIES XCO2 monthly averages time series over different regions, with the greatest mean difference of ACOS to NIES monthly means over China (1.43 ± 0.60 ppm) and the least over Brazil (-0.03 ± 0.64 ppm). The intercomparison between the two XCO2 datasets indicates that the ACOS XCO2 is globally higher than NIES by about 1 ppm and has smaller bias and better consistency than NIES data. [ABSTRACT FROM AUTHOR]

The cooking oil fume particulate (COFP) produced by indoor cooking can harm human health seriously, and therefore requires urgent monitoring and optimization. In this paper, the kitchen cooking simulation process was established by using computational fluid dynamics (CFD) based on the fluid dynamics theory. Combined with the user defined function (UDF), the spatial and temporal distributions of COFP and carbon dioxide (CO2) during the cooking process were simulated and analyzed, respectively. Both simulation results were verified using experimental data. Moreover, this paper introduces a COFP concentration correlation function that utilizes the spatiotemporal correlation between COFP and CO2 concentrations during the cooking process. The function is based on the spatiotemporal distribution of CO2 concentration. By comparing it with traditional calculations, the proposed function is shown to achieve a remarkable 70% improvement in efficiency and maintain an accuracy rate exceeding 90%. This enables the rapid analysis and control of COFP concentration through monitoring and analyzing CO2 levels in the kitchen.

The regional differences in carbon dioxide (CO2) variations from the Orbiting Carbon Observatory-2 (OCO-2) over the Beijing-Tianjin-Hebei (Jing-Jin-Ji) region from 2015 to 2021 are analyzed in this study. This study shows an annual increase and a seasonal cycle; the CO2 annual growth rate was about 2.63 ppm year−1, with the highest value being in spring and the lowest in summer. The spatial distribution is unbalanced, regional differences are prominent, and the CO2 concentration is lower in the north of the Jing-Jin-Ji region (like Zhangjiakou, Chengde, and Qinhuangdao). Land-type structures and population economy distributions are the key factors affecting CO2 concentration. By analyzing the land-type structures over Jing-Jin-Ji in 2020, we find that cropland, woodland, and grassland (CWG) are the main land cover types in Jing-Jin-Ji; the proportion of these three types is about 83.3%. The woodland areas in Zhangjiakou, Chengde, and Qinhuangdao account for about 65% of the total woodland areas in Jing-Jin-Ji; meanwhile, the grassland areas in these three regions account for 62% of the total grassland areas in Jing-Jin-Ji. CO2 concentration variation shows a high negative correlation with CWG land areas (coefficient of determination (R2) > 0.76). The regions with lower population and GDP secondary industry (SI) density also have lower CO2 concentration (like Zhangjiakou, Chengde, and Qinhuangdao), and the regions with higher population and GDP SI density also have higher CO2 concentration (like the southeast of Jing-Jin-Jin). [ABSTRACT FROM AUTHOR]

Storing CO2 in oil and gas reservoirs offers a dual benefit: it reduces atmospheric CO2 concentration while simultaneously enhancing oil displacement efficiency and increasing crude oil production. This is achieved by injecting CO2 into producing oil and gas wells. Employing particle jet technology at the bottom of CO2 injection wells significantly expands the bottom hole diameter, thereby improving CO2 injection efficiency and storage safety. To further investigate the rock-breaking characteristics and efficiency, a finite element model for particle jet rock breaking is established by utilizing the smoothed particle hydrodynamics (SPH) method. Specifically, this new model considers the high temperature and confining pressure conditions present at the bottom hole. The dynamic response and fracturing effects of rock subjected to a particle jet are also revealed. The results indicate that particle jet impact rebound significantly influences the size of the impact crater, with the maximum first principal stress primarily concentrated on the crater's surface. The impact creates a "v"-shaped crater on the rock surface, with both depth and volume increasing proportionally to jet inlet velocity and particle diameter. However, beyond a key particle concentration of 3%, the increase in depth and volume becomes less pronounced. Confining pressure is found to hinder particle impact rock-breaking efficiency, while high temperatures contribute to larger impact depths and breaking volumes. This research can provide theoretical support and parameter guidance for the practical application of particle impact technology in enhancing CO2 injection efficiency at the bottom hole. [ABSTRACT FROM AUTHOR]

This review paper illustrates the recommended monitoring technologies for the detection of various greenhouse gaseous emissions for solid waste thermochemical reactions, including incineration, pyrolysis, and gasification. The illustrated gas analyzers are based on the absorption principle, which continuously measures the physicochemical properties of gaseous mixtures, including oxygen, carbon dioxide, carbon monoxide, hydrogen, and methane, during thermochemical reactions. This paper illustrates the recommended gas analyzers and process control tools for different thermochemical reactions and aims to recommend equipment to increase the sensitivity, linearity, and dynamics of various thermochemical reactions. The equipment achieves new levels of on-location, real-time analytical capability using FTIR analysis. The environmental assessment study includes inventory analysis, impact analysis, and sensitivity analysis to compare the mentioned solid waste chemical recycling methods in terms of greenhouse gaseous emissions, thermal efficiency, electrical efficiency, and sensitivity analysis. The environmental impact assessment compares each technology in terms of greenhouse gaseous emissions, including CO2, NOx, NH3, N2O, CO, CH4, heat, and electricity generation. The conducted environmental assessment compares the mentioned technologies through 15 different emission-related impact categories, including climate change impact, ecosystem quality, and resource depletion. The continuously monitored process streams assure the online monitoring of gaseous products of thermochemical processes that enhance the quality of the end products and reduce undesired products, such as tar and char. This state-of-the-art monitoring and process control framework provides recommended analytical equipment and monitoring tools for different thermochemical reactions to optimize process parameters and reduce greenhouse gaseous emissions and undesired products.

For greenhouse gas control and environment protection, CO2 emission reduction has become a hot spot in global research. CO2 injection in developed oil reservoirs to enhance oil recovery is widely regarded as one of the most economical and promising measures for reducing anthropogenic CO2 emissions into the atmosphere. In this paper, a three-dimensional embedded discrete fracture model is proposed and applied to simulate CO2 flooding and geological storage processes in hydraulically fractured reservoirs. The gas injection is simplified as a two-phase two-component mathematical model with the varying pressure-related fluid physical properties, and its accuracy is verified by commercial software tNavigator®. The advantage of this model is that it can deal with the complex geological conditions of three-dimensional arbitrary-inclined fracture networks and accurately assess the effects of CO2-EOR and geological sequestration in real reservoirs. Two application cases of CO2 huff-and-puff with a single well and inter-fracture asynchronous injection and production are demonstrated and explained in detail. The optimized technological parameters and CO2 saturation distribution can provide key technical parameters for field operations. [ABSTRACT FROM AUTHOR]

In the CO2 differential absorption lidar (DIAL) system, signals are simultaneously collected through analog detection (AD) and photon counting (PC). These two kinds of signals have their own characteristics. Therefore, a combination of AD and PC signals is of great importance to improve the detection capability (detection range and accuracy) of CO2-DIAL. The traditional signal splicing algorithm cannot meet the accuracy requirements of CO2 inversion due to unreasonable data fitting. In this paper, a piecewise least square splicing algorithm is developed to make signal splicing more flexible and efficient. First, the lidar signal is segmented, and according to the characteristics of each signal, the best fitting parameters are obtained by using the least square fitting with different steps. Then, all the segmented and fitted signals are integrated to realize the effective splicing of the near-field AD signal and the far-field PC signal. A weight gradient strategy is also adopted in signal splicing, and the weights of the AD and PC signals in the spliced signal change with the height. The splicing effect of the improved algorithm is evaluated by the measured signal, which are obtained in Wuhan, China, and the splice of the AD and PC signals in the range of 800–1500 m are completed. Compared with the traditional method, the evaluation parameter R2 and the residual sum of squares of the spliced signal are greatly improved. The linear relationship between the AD and PC signals is improved, and the fitting R2 of differential absorption optical depth reaches 0.909, indicating that the improved signal splicing algorithm can well splice the near-field AD signal and the far-field PC signal. [ABSTRACT FROM AUTHOR]

The microscopic pore structure of tight sandstone reservoirs significantly influences the characteristics of CO2 flooding after water flooding. This research was conducted using various techniques such as casting thin sections, high-pressure mercury injection, scanning electron microscopy, nuclear magnetic resonance (NMR) testing, and a self-designed high-temperature and high-pressure microscopic visualization displacement system. Three types of cores with different pore structures were selected for the flooding experiments and the microscopic visualization displacement experiments, including CO2 immiscible flooding, near-miscible flooding, and miscible flooding after conventional water flooding. The characteristics of CO2 flooding and the residual oil distribution after water flooding were quantitatively analyzed and evaluated. The results show the following: (1) During the water flooding process, the oil produced from type I and type III samples mainly comes from large and some medium pores. Oil utilization of all pores is significant for type II samples. The physical properties and pore types have a greater impact on water flooding. Type I and II samples are more suitable for near-miscible flooding after water flooding. Type III samples are more suitable for miscible flooding after water flooding. (2) In CO2 flooding, oil recovery increases gradually with increasing pressure for all three types of samples. Type II core samples have the highest recovery. Before miscibility, the oil recovered from type I and type II samples is primarily from large pores; however, oil recovery mainly comes from medium pores when reaching miscibility. As for the type III samples, the oil produced in the immiscible state mainly comes from large and medium pores, and the enhanced oil recovery mainly comes from medium and small pores after reaching the near-miscible phase. (3) It can be seen from the microscopic residual oil distribution that oil recovery will increase as the petrophysical properties of the rock model improve. The oil recovery rate of near-miscible flooding after water flooding using the type II model is up to 68.11%. The oil recovery of miscible flooding after water flooding with the type III model is the highest at 74.57%. With increasing pressure, the proportion of flake residual oil gradually decreases, while the proportion of droplet-like and film-like residual oil gradually increases. Type II samples have a relatively large percentage of reticulated residual oil in the near-miscible stage. [ABSTRACT FROM AUTHOR]

The large amounts of greenhouse gases, such as carbon dioxide, produced by severe forest fires not only seriously affect the ecosystems in the area where the fires occur but also cause a greenhouse effect that has a profound impact on the natural environment in other parts of the world. Numerical simulations of greenhouse gas transport processes are often affected by uncertainties in the location and timing of the emission sources and local meteorological conditions, and it is difficult to obtain accurate and credible predictions by combining remote sensing satellite data with given meteorological forecasts or reanalyses. To study the regional transport processes and impacts of greenhouse gases produced by sudden large-scale forest fires, this study applies the Lagrangian particle dispersion model Massive-Parallel Trajectory Calculations (MPTRAC) to conduct forward simulations of the CO2 transport process of greenhouse gases emitted from forest fires in the central region of Saskatchewan, Canada, during the period of 17 May to 25 May 2021. The simulation results are validated with the Orbiting Carbon Observatory-2 Goddard Earth Observing System (OCO-2 GEOS) Level 3 daily gridded CO2 product over the study area. In order to leverage the high computational costs of the numerical simulations of the model, we implement the forward simulations on the Tianhe-2 supercomputer platform and the JUWELS HPC system, which greatly improves the computational efficiency through parallel computation and makes near-real-time predictions of atmospheric transport processes feasible. [ABSTRACT FROM AUTHOR]

The monitoring of anthropogenic CO2 emissions, which increase the atmospheric CO2 concentration, plays the most important role in the management of emission reduction and control. With the massive increase in satellite-based observation data related to carbon emissions, a data-driven machine learning method has great prospects for predicting anthropogenic CO2 emissions. Training samples, which are used to model predictions of anthropogenic CO2 emissions through machine learning algorithms, play a key role in obtaining accurate predictions for the spatial heterogeneity of anthropogenic CO2 emissions. We propose an approach for predicting anthropogenic CO2 emissions using the training datasets derived from the clustering of the atmospheric CO2 concentration and the segmentation of emissions to resolve the issue of the spatial heterogeneity of anthropogenic CO2 emissions in machine learning modeling. We assessed machine learning algorithms based on decision trees and gradient boosting (GBDT), including LightGBM, XGBoost, and CatBoost. We used multiple parameters related to anthropogenic CO2-emitting activities as predictor variables and emission inventory data from 2019 to 2021, and we compared and verified the accuracy and effectiveness of different prediction models based on the different sampling methods of training datasets combined with machine learning algorithms. As a result, the anthropogenic CO2 emissions predicted by CatBoost modeling from the training dataset derived from the clustering analysis and segmentation method demonstrated optimal prediction accuracy and performance for revealing anthropogenic CO2 emissions. Based on a machine learning algorithm using observation data, this approach for predicting anthropogenic CO2 emissions could help us quickly obtain up-to-date information on anthropogenic CO2 emissions as one of the emission monitoring tools. [ABSTRACT FROM AUTHOR]

As global warming intensifies, monitoring carbon dioxide (CO2) has increasingly become a focal point of research. Investigating urban XCO2 emission systems holds paramount importance, given the pivotal role of cities as major contributors to carbon emissions. Consequently, this study centers on urban locales, employing Shanghai as a case study for a comprehensive evaluation of regional XCO2 levels. We utilized high spatial resolution imagery from the PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite to conduct an XCO2 assessment over the Baoshan District with a 30 m spatial resolution from April 2021 to October 2022. Our XCO2 analysis was conducted in two steps. Firstly, we conducted a sensitivity analysis on key parameters in the inversion process, where cloud cover severely interfered with inversion accuracy. Therefore, we developed the Fmask 4.0 cloud removal and iterative maximum a posteriori differential optical absorption spectroscopy (FIMAP-DOAS) algorithm. This novel integration eliminated cloud interference during the inversion process, achieving high-precision CO2 detection in the region. Secondly, we compared the XCO2 of the region with Level-2 data from carbon monitoring satellites such as OCO-2. The comparison results showed a strong consistency, with a root mean squared error (RMSE) of 0.75 ppm for Shanghai XCO2 data obtained from the PRISMA satellite compared to OCO-2 Level-2 data and an RMSE of 1.49 ppm compared to OCO-3. This study successfully established a high-accuracy and high-spatial-resolution XCO2 satellite monitoring system for the Shanghai area. The efficacy of the FIMAP-DOAS algorithm has been demonstrated in CO2 monitoring and inversion within urban environments, with potential applicability to other cities. [ABSTRACT FROM AUTHOR]

This paper aims to study the characteristics of greenhouse gas (GHG) emissions from heavy-duty trucks in the Beijing-Tianjin-Hebei (BTH) region, which is located in Northern China. The multiyear emissions of GHG (CO2, CH4 and N2O) from heavy-duty trucks fueled by diesel and natural gas during the period of 2006-2015 were compared and analyzed. The results show that the GHG emissions from heavy-duty trucks increase with time, which is consistent with the trend of the population growth. The total amount of carbon dioxide equivalence (CO2e) emissions in the BTH region was about 5.12 x 106 t in 2015. Among the three sub-regions, Hebei possesses the largest number of heavy-duty trucks due to the size of its heavy-duty industries. As a consequence, the GHG emissions are about 10 times compared to Beijing and Tianjin. Tractor trailers account for the major proportion of heavy-duty trucks and hence contribute to about 74% of GHG emissions. Diesel- and liquefied natural gas (LNG)-powered heavy-duty trucks can reduce GHG emissions more effectively under current national standard IV than can the previous standard. The widespread utilization of the alternative fuel of LNG to mitigate emissions must be accompanied with engine technology development in China. This study has provided new insight on management methods and the policy-making as regards trucks in terms of environmental demand. [ABSTRACT FROM AUTHOR]

Observations of the atmospheric sources and sinks of carbon dioxide (CO2) and methane (CH4) in the pan-Arctic domain are extremely scarce, limiting our knowledge of carbon turnover in this climatically sensitive environment and the fate of the enormous carbon reservoirs conserved in the permafrost. Especially critical are the gaps in the high latitudes of Siberia, covered by the vast permafrost underlain tundra, where only several atmospheric monitoring sites are operational. This paper presents the first two years (September 2018–January 2021) of accurate continuous observations of atmospheric CO2 and CH4 dry mole fractions at the recently deployed tower-based measurement station “DIAMIS” (73.5068° N, 80.5198° E) located on the southwestern coast of the Taimyr Peninsula, Siberia, at the Gulf of the Yenisei River that opens to the Kara Sea (Arctic Ocean). In this paper, we summarized the scientific rationale of the site, examined the seasonal footprint of the station with an analysis of terrestrial vegetation and maritime sector contributing to the captured atmospheric signal, and illustrated temporal patterns of CO2 and CH4 for the daytime mixed atmospheric layer over the continent–sea interface. Along with the temporal variations reflecting a signal caused pan-Arctic and not very much influenced by the local processes, we analyzed the spatiotemporal distribution of the synoptic anomalies representing the atmospheric signatures of regional sources and sinks of CO2 and CH4 for the studied high-arctic Siberian domain of ~625 thousand km2, with nearly equal capturing the land surface (54%) and the ocean (46%) throughout the year. Both for CO2 and CH4, we have observed a sea–continent declining trend, presuming a larger depletion of trace gases in the maritime air masses compared to the continental domain. So far, over the Kara Sea, we have not detected any prominent signals of CH4 that might have indicated processes of subsea permafrost degradation and occurrence of cold seeps–still mainly observed in the eastern Arctic Seas—The Laptev Sea and the East-Siberian Sea.

Given the global pressure of climate change and ecological equilibrium, there is an urgent need to develop effective carbon dioxide (CO2) capture technology. Due to its comprehensiveness and flexibility, Direct Air Capture (DAC) technology has emerged as a vital supplement to traditional emission reduction methods. This study aims to innovate Direct Air Capture (DAC) technology by utilizing the ultrasonic impregnation method to load Tetraethylenepentamine (TEPA) onto alumina (Al2O3) as the adsorbent. Furthermore, high gravity adsorption technology is integrated to significantly enhance the efficiency of DAC. Characterization tests, including BET, FTIR, TG, XRD, and SEM-EDS, confirm the structural stability and high capture capacity of the adsorbent. Additionally, this study demonstrates the rapid and efficient capture of CO2 from the air using TEPA-Al2O3 adsorbent under high gravity conditions for the first time. Under optimal conditions with TEPA loading at 15.06%, a high gravity factor of 2.67, and a gas flow rate of 30 L/min, TEPA-Al2O3 achieves a CO2 adsorption capacity of 48.5 mg/g in RAB, which is an improvement of 15.56 mg/g compared to traditional fixed-bed technology. Moreover, it reaches adsorption saturation faster under high gravity conditions, exhibiting a significantly higher adsorption rate compared to traditional fixed-bed systems. Furthermore, the adsorption process better conforms to the Avrami model. Steam stripping regeneration is utilized to regenerate the adsorbent, demonstrating excellent regeneration performance and stable adsorption capacity, thereby proving its feasibility and economic benefits as a DAC technology. [ABSTRACT FROM AUTHOR]

In this study, the trajectory mapping domain-filling technology, which can provide more reliable statistical estimates of long-lived gas concentrations in a broader geographical area based on limited station data, is used to map the CO2 concentration data of six ground observation stations to the entire Shanxi Province. The technology combines a dynamical model of the atmosphere with trace gas observations, combining forward and backward trajectories to greatly expand the information on long-lived CO2 gas concentrations over a trajectory path. The mapped results show good agreement with the observation results, which reveals the generalizability of the trajectory mapping domain-filling technology. The results show that the spatio-temporal distribution characteristics of CO2 concentration in the entire Shanxi region is significant: during the five years, the provincial average CO2 concentration exhibits an overall increasing trend. The CO2 concentration increases from the north to the south across the province. Influenced by the economic growth rate and COVID-19, there are differences in the annual variation characteristics of the CO2 concentration across the entire province, with the highest year-on-year growth in 2019 and a year-on-year decrease in 2020. The increasing rate of the CO2 concentration in the northern low-value areas is faster than that in the southern high-value areas. Overall, there is a decreasing trend in the CO2 concentration growth from the north to the south in the entire province. There are seasonal differences in the CO2 concentration distribution across the entire province. The CO2 concentration and amplitude are higher in autumn and winter than they are in spring and summer. This study can provide scientific support and methodological reference for the spatio-temporal distribution characteristics analysis of GHGs at the provincial–regional scale, as well as at the national and global scales. [ABSTRACT FROM AUTHOR]

By using space-based measurements of the column-averaged dry air mole fraction of carbon dioxide (XCO2) from the Orbiting Carbon Observatory-2 (OCO-2) and CO and NO2 from the Tropospheric Monitoring Instrument (TROPOMI), this study investigates the seasonal variation in the characteristics of CO2, CO, and NO2 across major states in the United States. Beyond correlating these trends with natural factors, significant emphasis is placed on human activities, including heating demands, energy usage, and the impacts of the COVID-19 pandemic. Concentration enhancements in observations influenced by anthropogenic emissions from urban regions relative to background values are calculated to estimate gas emissions. Our investigation reveals a strong correlation between NO2 and CO2 emissions, as evidenced by a correlation coefficient (r) of 0.75. Furthermore, we observe a correlation of 0.48 between CO2 and CO emissions and a weaker correlation of 0.37 between CO and NO2 emissions. Notably, we identify the NO2 concentration as a reliable indicator of CO2 emission levels, in which a 1% increase in NO2 concentration corresponds to a 0.8194% (±0.0942%) rise in annual mean CO2 emissions. Enhancement ratios among NO2, CO, and XCO2 are also calculated, uncovering that high ΔNO2: ΔXCO2 ratios often signify outdated industrial structures and production technologies, while low ΔCO: ΔXCO2 ratios are linked to states that utilize clean energy sources. This approach offers a deeper understanding of the effect of human activities on atmospheric gas concentrations, paving the way for more effective environmental monitoring and policy-making. [ABSTRACT FROM AUTHOR]

Welding is an important and widely used process in the manufacturing and maintenance of various works involving metals and alloys. While welding has broad applications, the welding fume generated during the process has impacts on workers' health, which needs to be addressed. One of the major steps that can be undertaken to take care of this issue is the use of ventilation, which requires knowledge of characteristics and dispersion of the welding fume in the workers' breathing zone. It is difficult to assess welding fume dispersion from manual measurement due to numerous welding processes and sufficient data requirement. Numerical prediction of welding fume is dubious due to several errors. This paper considers the use of numerically predicted CO2 concentrations to indirectly predict welding fume distribution in workshops. This is based on the assumption that if the particles are sufficiently small size, they follow the diffusion pattern of gases. Experiments are carried out in a room with an opening and a welding fume generation system for measurement of CO2 and fume diffusion. The results show high possibility of predicting welding fume concentration based on Computational Fluid Dynamics (CFD) simulated CO2 concentration with a correlation coefficient of 0.74. [ABSTRACT FROM AUTHOR]

Increased warming in the Arctic is of great concern. This is particularly due to permafrost degradation, which is expected to accelerate microbial breakdown of soil organic carbon, with its further release into the atmosphere as carbon dioxide (CO2). The fine-scale variability of CO2 fluxes across highly mosaic Arctic tundra landscapes can provide us with insights into the diverse responses of individual plant communities to environmental change. In the paper, we contribute to filling existing gaps by investigating the variability of CO2 flux rates within different landscape units for dominant vegetation communities and plant species across typical tundra of the southern part of the Taimyr Peninsula, Siberia. In general, the variability of soil CO2 flux illustrates a four-fold increase from non-vascular vegetation, mainly lichens and mosses (1.05 ± 0.36 µmol m−2 s−1), towards vascular plants (3.59 ± 0.51 µmol m−2 s−1). Barren ground (“frost boils”) shows the lowest value of 0.79 ± 0.21 µmol m−2 s−1, while considering the Arctic “browning” phenomenon, a further substantial increase of CO2 flux can be expected with shrub expansion. Given the high correlation with top soil temperature, well-drained and relatively dry habitats such as barren ground and non-vascular vegetation are expected to be the most sensitive to the observed and projected temperature growth in the Arctic. For mixed vegetation and vascular species that favor wetter conditions, soil moisture appears to play a greater role. Based on the modeled seasonal pattern of soil CO2 flux and precipitation records, and applying the rainfall simulations in situ we outlined the role of precipitation across enhanced CO2 emissions (i.e., the “Birch” effect). We found that a pulse-like growth of soil CO2 fluxes, observed within the first few minutes after rainfall on vegetated plots, reaches 0.99 ± 0.48 µmol m−2 s−1 per each 1 mm of precipitation, while barren ground shows 55–70% inhibition of CO2 emission during the first several hours. An average additive effect of precipitation on soil CO2 flux may achieve 7–12% over the entire growing season, while the projected increased precipitation regime in the Arctic may strengthen the total CO2 release from the soil surface to the atmosphere during the growing season.

A barn's climate is vital for animal welfare and emissions control, including greenhouse gases like methane (CH4) and carbon dioxide (CO 2 ) and environmental gases like ammonia (NH 3 ). The goal of this study was to investigate a developed low-cost online tool for monitoring barn climate and air pollutant emissions (OTICE) in naturally ventilated barns. OTICE employed a wireless sensor network with low-cost sensors for gases and climate variables, allowing scalable use across multiple barns. We evaluated the sensors for CO 2 , NH 3 , and CH4 for accuracy, both in controlled lab conditions and in a dairy barn in Germany, where measurements were carried out continuously for a duration of 12 days. For the averaged concentration levels over the measurement period, the low-cost sensors agreed well with the reference system, with relative deviations lower than 7% for all three gases, with maximum peak deviations up to 32% for CO 2 , 67% for NH 3 , and 65% for CH 4 , with strong Spearman correlations for CO 2 and NH 3 ( ρ C O 2 = 0.8, ρ N H 3 = 0.68) and a rather weak correlation for CH4 with ρ C H 4 = 0.24. Further calibration and stability investigations are required, especially for CH4 sensing. However, the overall good results for NH 3 and especially CO 2 measurements indicate a huge potential of the low-cost system as a valuable tool for monitoring relative NH 3 emission levels and the measurement of air exchange rates in naturally ventilated barns. [ABSTRACT FROM AUTHOR]

In natural ventilation system-enabled dairy buildings (NVDB), achieving accurate gas emission values is highly complicated. The external weather affects measurements of the gas concentration of pollutants ( c P ) and volume flow rate (Q) due to the open-sided design. Previous research shows that increasing the number of sensors at the side opening is not cost-effective. However, accurate measurements can be achieved with fewer sensors if an optimal sampling position is identified. Therefore, this study attempted to calibrate the outlet of an NVDB for the direct emission measurement method. Our objective was to investigate the c P gradients, in particular, for ammonia ( c N H 3 ), carbon dioxide ( c C O 2 ), and methane ( c C H 4 ) considering the wind speed (v) and their mixing ratios ([ c CH 4 / c NH 3 ¯ ]) at the outlet, and assess the effect of sampling height (H). The deviations in each c P at six vertical sampling points were recorded using a Fourier-transform infrared (FTIR) spectrometer. Additionally, wind direction and speed were recorded at the gable height (10 m) by an ultrasonic anemometer. The results indicated that, at varied heights, the average c N H 3 (p < 0.001), c C O 2 (p < 0.001), and (p < 0.001) were significantly different and mostly concentrated at the top (H = 2.7). Wind flow speed information revealed drastic deviations in c P , for example up to + 105.1 % higher c N H 3 at the top (H = 2.7) compared to the baseline (H = 0.6), especially during low wind speed (v < 3 m s − 1 ) events. Furthermore, [ c CH 4 / c NH 3 ¯ ] exhibited significant variation with height, demonstrating instability below 1.5 m, which aligns with the average height of a cow. In conclusion, the average c C O 2 , c C H 4 , and c N H 3 measured at the barn's outlet are spatially dispersed vertically which indicates a possibility of systematic error due to the sensor positioning effect. The outcomes of this study will be advantageous to locate a representative gas sampling position when measurements are limited to one constant height, for example using open-path lasers or low-cost devices. [ABSTRACT FROM AUTHOR]

The synergetic reduction of CO2 and PM2.5 emissions has received much attention in China in recent years. A comprehensive evaluation of the synergy between CO2 emission reduction (CER) and PM2.5 emission reduction (PER) would provide valuable information for developing synergetic control policies. Thus, we constructed a comprehensive CO2-PM2.5-emission-reduction index system and evaluated the synergy between CER and PER, using the coupling coordination degree (CCD) and relative development degree (RDD) model in China's 329 cities from 2003 to 2017. The spatiotemporal characteristics of the CCD were analyzed on the national, regional, and urban scales. Furthermore, we used the spatial autocorrelation analysis, kernel density estimation, and Dagum Gini coefficient to investigate the spatial autocorrelation, evolutionary characteristics, and regional differences of the CCD. The results indicate that (1) the synergy between CO2 and PM2.5 emissions' reductions showed an upward trend, and the lowest CCD values occurred in NW and Shanghai on the regional and urban scales, respectively; (2) the CCD showed obvious spatial clustering characteristics, with 75% of the cities located in the "High–High" or "Low–Low" clustering zones in the Moran scatter plots in 2017; (3) the polarization of CCD in SC, MYR, and SW showed intensified trends; (4) and the hypervariable density was the largest contributor to the overall difference in the CCD. Our findings suggest that more attention should be paid to the top-level design of the policies, technological innovation, and cross-regional or intercity cooperation. [ABSTRACT FROM AUTHOR]

Sustainable building design, such as the Passivhaus standard, seeks to minimise energy consumption, while improving indoor environmental comfort. Very few studies have studied the indoor air quality (IAQ) in Passivhaus homes outside of Europe. This paper presents the indoor particulate matter (PM2.5), carbon dioxide (CO2), and total volatile organic compounds (tVOC) measurements of the first residential Passivhaus in Latin America. It compares them to a standard home in Mexico City. Low-cost monitors were installed in the bedroom, living room, and kitchen spaces of both homes, to collect data at five-minute intervals for one year. The physical measurements from each home were also compared to the occupants' IAQ perceptions. The measurements demonstrated that the Passivhaus CO2 and tVOC annual average levels were 143.8 ppm and 81.47 μg/m3 lower than the standard home. The PM2.5 in the Passivhaus was 11.13 μg/m3 lower than the standard home and 5.75 μg/m3 lower than outdoors. While the results presented here cannot be generalised, the results suggest that Passivhaus dwellings can provide better and healthier indoor air quality in Latin America. Further, large-scale studies should look at the indoor environmental conditions, energy performance, and dwelling design of Passivhaus dwellings in Latin America. [ABSTRACT FROM AUTHOR]

Nature-based solutions such as green infrastructure present an opportunity to reduce air pollutant concentrations and greenhouse gas emissions. This paper presents new findings from a controlled field study in Ontario, Canada, evaluating the impact of productive applications of green infrastructure on air pollution and carbon dioxide concentrations across different agricultural morphologies compared to other non-productive applications. This study demonstrates that productive green infrastructure applications are as beneficial as non-productive applications in reducing ozone, nitrogen dioxide, and carbon dioxide concentrations. Nature-based solutions present an opportunity to build climate resilience into agricultural systems through supply-side mitigation and adaptation. The implementation of productive green infrastructure could be a viable agricultural practice to address multiple climate change impacts. [ABSTRACT FROM AUTHOR]

Numerous technologies have been investigated for mitigating air pollutant emissions from swine barns. Among them, algal photobioreactors (PBRs) can remove and utilize air pollutants such as CO2 and NH3 from barn exhaust. However, a challenge to PBR operation is that it involves multiple system input parameters and output goals. A key question is then how to determine the appropriate CO2 and NH3 concentrations in this case. Conventional statistical methods are inadequate for handling this complex problem. Multi-criteria decision-making (MCDM) emerges as a practical methodology for comparison and can be utilized to rank different CO2–NH3 interactions based on their environmental and biological performance. By employing MCDM methods, producers can effectively control the ratio of CO2 and NH3 concentrations, enabling them to identify the optimal range of operating parameters for various housing types, ensuring efficient pollutant mitigation. In this study, a multi-criteria decision-making (MCDM) approach was employed to support operation management. Specifically, influent CO2 and NH3 concentrations were optimized for three scenarios (the best biological, environmental, and overall performance), using a combination of two MCDM techniques. This study is anticipated to facilitate the system analysis and optimization of algae-based phytoremediation processes. [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]

Atmospheric observations of sources and sinks of carbon dioxide (CO2) and methane (CH4) in the pan-Arctic domain are highly sporadic, limiting our understanding of carbon turnover in this climatically sensitive environment and the fate of enormous carbon reservoirs buried in permafrost. Particular gaps apply to the Arctic latitudes of Siberia, covered by the vast tundra ecosystems underlain by permafrost, where only few atmospheric sites are available. The paper presents the first results of continuous observations of atmospheric CO2 and CH4 dry mole fractions at a newly operated station "DIAMIS" (73.506828° N, 80.519869° E) deployed on the edge of the Dikson settlement on the western coast of the Taimyr Peninsula. Atmospheric mole fractions of CO2, CH4, and H2O are measured by a CRDS analyzer Picarro G2301-f, which is regularly calibrated against WMO-traceable gases. Meteorological records permit screening of trace gas series. Here, we give the scientific rationale of the site, describe the instrumental setup, analyze the local environments, examine the seasonal footprint, and show CO2 and CH4 fluctuations for the daytime mixed atmospheric layer that is representative over a vast Arctic domain (~500–1000 km), capturing both terrestrial and oceanic signals. [ABSTRACT FROM AUTHOR]

In recent decades, the massive emission of greenhouse gases, such as carbon dioxide and methane, into the atmosphere has had a serious impact on the ecological environment. The dry reforming of carbon dioxide and methane to syngas cannot only realize the resource utilization of methane and carbon dioxide but also reduce global climate change. It is of great significance in carbon emission reduction. Owing to the dry reforming of methane (DRM) being a strongly endothermic reaction, it needs to be carried out under high-temperature conditions. It makes the catalyst have problems of the sintering of metal, carbon deposition, and poisoning. This article revolves around the problem of catalyst deactivation during the DRM reaction. It expands upon the thermodynamics and mechanisms of the DRM reaction, analyzes the causes of metal catalyst deactivation due to carbon deposition, sintering, and poisoning, and summarizes how the active components, supports, and additives of metal catalysts restrain the DRM catalyst deactivation during the reaction. The analysis revealed that changing the type and size of the active metal, adjusting the properties of the support, and adding additives can further regulate the dispersion of the active component, the interaction between the active component and the support, the oxygen vacancies of the support, and the acidity and basicity of the catalyst surface, ultimately achieving control over the metal catalyst's resistance to sintering, carbon deposition, and sulfur poisoning. In addition, it discusses the application of metal catalysts in photothermal and plasma-catalyzed DRM. Finally, it outlines the prospects for research on metal catalysts for the DRM. [ABSTRACT FROM AUTHOR]

Understanding greenhouse gas–climate processes and feedbacks is a fundamental step in understanding climate variability and its links to greenhouse gas fluxes. Chemical transport models are the primary tool for linking greenhouse gas fluxes to their atmospheric abundances. Hence, accurate simulations of greenhouse gases are essential. Here, we present a new simulation in the GEOS-Chem chemical transport model that couples the two main greenhouse gases—carbon dioxide (CO 2 ) and methane (CH 4 )—along with the indirect greenhouse gas carbon monoxide (CO) based on their chemistry. Our updates include the online calculation of the chemical production of CO from CH 4 and the online production of CO 2 from CO, both of which were handled offline in the previous versions of these simulations. In the newly developed coupled (online) simulation, we used consistent hydroxyl radical (OH) fields for all aspects of the simulation, resolving biases introduced by inconsistent OH fields in the currently available uncoupled (offline) CH 4 , CO and CO 2 simulations. We compare our coupled simulation with the existing v12.1.1 GEOS-Chem uncoupled simulations run the way they are currently being used by the community. We discuss differences between the uncoupled and coupled calculation of the chemical terms and compare our results with surface measurements from the NOAA Global Greenhouse Gas Reference Network (NOAA GGGRN), total column measurements from the Total Carbon Column Observing Network (TCCON) and aircraft measurements from the Atmospheric Tomography Mission (ATom). Relative to the standard uncoupled simulations, our coupled results suggest a stronger CO chemical production from CH 4 , weaker production of CO 2 from CO and biases in the OH fields. However, we found a significantly stronger chemical production of CO 2 in tropical land regions, especially in the Amazon. The model–measurement differences point to underestimated biomass burning emissions and secondary production for CO. The new self-consistent coupled simulation opens new possibilities when identifying biases in CH 4 , CO and CO 2 source and sink fields, as well as a better understanding of their interannual variability and co-variation. [ABSTRACT FROM AUTHOR]

Remote Emission Sensing (RES) is a measurement method based on absorption spectroscopy for the determination of pollutant concentrations. The absorption of the exhaust plume of a vehicle is measured from the roadside without intervention by means of a light/laser barrier during a short measurement (∼0.5 s) and concentration ratios of pollutants (e.g., NO x to CO 2 ) are estimated. Unsteady Reynolds-Averaged Navier-Stokes (URANS) simulations of exhaust plumes in vehicle wakes are performed using the k- ω SST turbulence model with focus on pollutant dispersion. The simulation setup has been validated by a comparison with experimentally obtained drag coefficients. The resulting concentration fields represent the pollutants available for measurements by a RES device. The influence of the characteristics of the RES device on the measurement is assessed. In addition, investigations involve several environmental and vehicle related parameters. The results demonstrate that due to strong turbulence, mixing is enhanced and the exhaust plumes rapidly disperse in the near vehicle wakes. Results show that emission characteristics of a vehicle are contained downstream for approximately half the vehicle length, regardless of different vehicle configurations, driving and ambient parameters. Further downstream dispersion of pollutants results in concentrations that are less than 1 / 100 of the pollutant concentration in the vehicle's exhaust tailpipe implying that RES devices have to measure at a high sampling frequency. Therefore, reliable determination of the concentration ratios of pollutant at high vehicle velocities requires the RES device to operate in the order of 1000 Hz sampling frequency. Ultimately, the numerical simulations not only help to understand exhaust plume dispersion, but provide a very useful tool to minimize RES uncertainties. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the emission factor of N2O, CH4, and the volatilization of NH3 for the combination of feces or urine with increasing doses of ammonium nitrate in tropical palisade grass pastures. The emission of greenhouse gases was assessed in eight treatments combining feces and urine with doses (75 and 150 kg of N ha−1) of ammonium nitrate, (32% N). The emission factor of N2O was 0.11, 0.19, and 0.17% for feces, urine, and 75 kg N ha−1 year−1 (as ammonium nitrate) and showed an additive linear effect when feces or urine were combined with increasing doses of N fertilizer. The emission factor of CH4 of feces (0.18 kg CH4 animal−1 year−1) was similar irrespective of combination with ammonium nitrate. The N loss by volatilized NH3 has a decreasing linear effect (p < 0.05) for the combination of feces or urine with ammonium nitrate. We concluded that N2O and CH4 emission factors of feces and urine in tropical climate conditions are lower than those reported by the IPCC. However, their N2O emission factors are sharply enhanced when combined with ammonium nitrate. These results may contribute to improvements in national and regional greenhouse gas inventories of livestock production. [ABSTRACT FROM AUTHOR]

Research on carbon management is fueled by the growing concern over rising carbon dioxide (CO2) levels in the atmospheric air and its possible impacts on the climate. In this study, we proposed a method of CO2 separation from atmospheric air. This study aimed to investigate the effects of CO2 enrichment on the air temperature inside a greenhouse using a hollow fiber (HF) membrane system. The experiment was conducted over a period of 30 days in two experimental conditions: 15 days without CO2 enrichment (WCS) and 15 days with CO2 enrichment (CS). Results showed that the mean CO2 concentration and air temperature were highest inside the greenhouse during the CS period, with values of 1120 ppm and 37.42 °C, respectively. Regression analysis revealed a positive correlation between CO2 concentration and temperature during the CS period (R2 = 0.628). The HF membrane system was found to be effective in increasing both the CO2 concentration and air temperature inside the greenhouse. However, the system also has limitations, including the cost, maintenance, and suitability for all types of crops. Further experiments are needed to address these limitations and determine the optimal CO2 concentration for different kinds of crops growing in greenhouses. [ABSTRACT FROM AUTHOR]

For catalytic converter-equipped light-duty gasoline vehicles (LDGV), the hot-stabilized tailpipe emissions for pollutants such as carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxides (NOx) are well controlled. However, there are few reported real-world measurements of cold starts. Idling cold start and hot-stabilized trip exhaust emissions were measured for 37 LDGVs using a portable emissions measurement system (PEMS). Five vehicles were also measured for transient driving cold starts. On average, it took approximately 400, 150, 330, and 120 s to accumulate 90 percent of the idle cold start increments for fuel use, CO, HC, and NOx, respectively. Driving cold start increments were substantially higher than idling cold start increments, whereas cold start duration was typically shorter. For example, driving cold start contributed approximately 64%, 68%, 58%, and 4.5% of the trip total CO, HC, NOx, and carbon dioxide (CO2), respectively. This study is unique in quantifying the cold start contribution on a trip basis with real-world data. Although the cold start increment is sensitive to driving compared to idling, in either case, cold starts contribute substantially to total exhaust mass emissions. Furthermore, driver decisions regarding driving versus idle can substantially affect the contribution of cold starts, especially for CO and NOx. [ABSTRACT FROM AUTHOR]