Spatial Distribution Of Emission Research Articles

Characteristics of spatial and temporal distribution of carbon emissions and influencing factors in Hefei City based on nighttime lighting data

Urban areas are significant contributors to global carbon emissions. Investigating the spatiotemporal distribution characteristics of urban carbon emissions and their influencing factors is essential for formulating effective carbon reduction policies and enhancing urban environmental quality. This study utilized nighttime light data to analyse the spatiotemporal distribution of carbon emissions in Hefei City. We employed a Random Forest regression model to explore the impact of urban spatial morphology on carbon emissions. The findings indicated that carbon emissions in Hefei have shown a consistent annual increase without a clear spatial pattern. Regression analysis revealed a nonlinear positive correlation between carbon emissions and factors such as floor area ratio, building density, architectural volume and staggered degree. Conversely, the body mass index and enclosure factor demonstrated a nonlinear negative correlation with carbon emissions. Amongst them, the body mass index was shown to have the highest impact weight on urban carbon emission, which was 0.195, followed by architectural volume and floor area ratio, which were 0.155 and 0.145, respectively. This research highlights the complex influence of urban spatial morphology on carbon emissions and provides valuable insights for the development of urban carbon reduction strategies.

Abel transform of laser-induced plasma image: Overcoming challenges of noisy and insufficient data for reliable reconstruction

Reconstructing spatial distribution of emissivity is important for study of heterogeneous plasma sources. However, often used Radon and Abel transforms tend to amplify noise in the data. The small size and short lifetime of laser-induced plasma result in short exposures for signal acquisition, and, consequently limited number of points along the source available for measurements and noisy signals. We compared 14 Abel transform algorithms and various noise processing methods, focusing on both limited data points and high noise levels. All algorithms were presented in matrix form to ensure fair comparison, and we proposed a metric based on the largest singular value of matrix operator for error assessment. An error analysis is performed to show the contribution of approximation and noise errors to the overall reconstruction error. Our results indicate that at higher noise levels, the choice of noise reduction method is more critical than the inversion algorithm itself, with regularization proving to be the most effective for noise control. Given the minimal impact of algorithm choice on the final result, simple and easy-to-implement methods like “Onion Peeling” are recommended to be combined with regularization. Furthermore, transforms with regularization were the most stable for reconstructing complex profiles. Summarizing the results for all considered cases, “Hankel-Fourier” and “Cubic Spline” are the most accurate for noiseless data with limited number of spatial points, while for cases with a large amount of data, “Polynomial”, “Piessens-Verbaeten”, and “Minerbo-Levy” are better, especially for noisy data.

Assessing the Spatial Distribution of Carbon Emissions and Influencing Factors in the Yellow River Basin

The spatial distribution and trend of carbon emissions in the Yellow River Basin—an important ecological barrier and economic belt in China—directly affect the stability of the ecosystem and the sustainable development of the regional economy. Based on the data for carbon emissions in China’s counties from 1997 to 2017, this paper utilizes standard deviation ellipses, Theil index nested decomposition, and geographic detector models to make a comprehensive description of the spatial and temporal distribution and dynamic evolution characteristics of carbon emissions in the Yellow River Basin. Factors influencing carbon emissions are also analyzed from multiple dimensions. According to the findings, (1) carbon emissions at the county level show a clear upward trend without reaching a peak, exhibiting a spatial distribution of higher emissions in the east and lower in the west and higher in the south and lower in the north, with the mid-lower reaches being the center. The junction of the Shandong, Shaanxi, and Gansu provinces further exhibits a significant expansion, forming two core areas of carbon emissions. (2) Carbon emissions at the county level in the Yellow River Basin are influenced by both economic and geographic factors, exhibiting a significant high carbon spillover effect and a low carbon lock-in effect. The gravity center of the distribution has shifted towards the mid-lower reaches, with the upper reaches displaying dispersion tendencies. (3) Intra-regional disparities are the main source of the overall spatial differences in carbon emissions, with the largest disparities being observed in the upper reaches, followed by the middle reaches, and the smallest disparities being observed in the lower reaches. Further analysis shows that the level of economic development is the primary factor influencing the spatial variation of carbon emissions, and the combined effects of population size and industrial agglomeration are the key drivers of the annual growth in carbon emissions.

Evaluation of Spatial and Temporal Distribution of Carbon Emissions in Power Grid Based on Cloud Theory

In order to clearly determine the carbon emission distribution of regions, lines or nodes in the power grid, this paper applies cloud theory to the evaluation of the distribution of carbon emissions in the power grid. Based on the theory of carbon emission flow in the whole life cycle, five indicators that can reflect the spatial and temporal distribution of carbon emissions are constructed from the two dimensions of space and time. Cloud theory is used to establish the standard cloud of the carbon emission distribution level to quantify the randomness and fuzziness of the data to be evaluated. The bilateral constraint cloud theory and data-driven cloud transformation are combined to construct five comprehensive standard clouds of excellent, good, medium, poor and inferior, which are used as the evaluation interval of the evaluation index of carbon emission distribution. The reverse cloud is used to convert multiple sets of data into cloud droplets. Through the similarity measurement algorithm based on cloud model overlap, the comprehensive evaluation level of carbon emission distribution state in the time dimension is determined. Taking the IEEE 39 system as the research object, the spatial and temporal distribution of carbon emissions is evaluated, and the rationality and effectiveness of the proposed model are verified. Finally, the influence of the new energy penetration rate and power supply structure on the carbon emission distribution of the power grid is discussed by using cloud computing. Based on this, the targeted carbon reduction strategies for different types of nodes and the method of measuring the optimal new energy penetration rate are proposed and can provide a decision-making reference for optimizing the carbon emissions of the power grid.

Spatiotemporal variations of PM2.5 organic molecular markers in five central cities of the Yangtze River Delta, East China in autumn and winter: Implications for regional and local sources of organic aerosols

Information on the spatiotemporal variations in the composition and sources of organic aerosols (OA) is needed to identify regional influences and to establish effective control measures. Here, 23-h PM2.5 samples were collected in five central cities of the Yangtze River Delta in eastern China, including Nanjing, Suzhou, Wuxi, Changzhou, and Zhenjiang, every three days from 2020/09/01 to 2021/02/28. Each sample was analyzed for water-soluble inorganic ions, organic carbon (OC), elemental carbon (EC), and organic molecular markers (OMMs). Generally, the major components of PM2.5, including NH4+, SO42−, NO3−, OC, and EC, exhibited similar temporal patterns across the five cities. In all OMM groups, the concentrations of PAHs, oxygenated PAHs, and secondary products of isoprene showed strong correlations (r = 0.79 ± 0.050–0.93 ± 0.028) and low coefficient of divergence (COD = 0.22 ± 0.024–0.30 ± 0.033) between sampling sites, indicating a homogeneous spatial distribution of industrial emissions and biogenic secondary OA in autumn and winter. Other OMMs showed wider r (e.g., steranes and hopanes, 0.20–0.80) and COD (0.26–0.69) ranges for all site pairs, probably due to the influence of local emissions. Based on the source apportionment results using Positive matrix factorization, the biomass burning factor dominated the contribution to OC and EC in winter and showed strong correlations (r = 0.84 ± 0.063) between the sampling sites, indicating regional transport of emissions from biomass burning and fossil fuel combustion in the heating season. Traffic-related factors had the greatest spatial heterogeneity (r = 0.27 ± 0.19–0.51 ± 0.16) and contributed significantly to OC at their maximum levels.

Spatiotemporal Evolution of Territorial Spaces and Its Effect on Carbon Emissions in Qingdao City, China

Land use change has always been a significant factor affecting global carbon emissions. Dissecting the characteristics of territorial space evolution and its impact on carbon emissions is crucial for developing low-carbon-oriented territorial space optimization and governance strategies. This paper calculates the carbon emissions associated with territorial spaces in Qingdao from 2000 to 2020, utilizing land use data alongside various statistical data. Based on the accounting results, the evolution characteristics of territorial spaces and their corresponding carbon emissions, as well as the carbon transition dynamics resulting from space transfer, are analyzed. A carbon transition decomposition formula is then proposed to quantify the differential and spatially heterogeneous impacts of changes in space types and socio-economic development on emissions. The results indicate that: (1) the evolution of territorial spaces in Qingdao during 2000–2020 is characterized by an expansion of living space and a contraction of production and ecological spaces; (2) net carbon emissions rose from 313.98 × 104 tons to 1068.58 × 104 tons, with urban production space contributing the most (69.96% in 2020) due to its significantly high emission density. The spatial distribution of carbon emissions exhibited a stable “northwest–southeast” pattern, with increased dispersion and weakened directionality; (3) the transformation of territorial spaces promoted carbon emissions in Qingdao, with the conversion of urban production space to other uses yielding the most favorable carbon transitions, while the expropriation of agricultural production spaces for urban production and residents’ living has resulted in the most detrimental carbon transitions; (4) socio-economic development shapes the overarching pattern of regional emission density changes, whereas space transfers account for local variations. This paper also identifies priorities for spatial optimization and key sectors for emission reduction. The findings contribute to a deeper understanding of the carbon emission consequences of territorial space transformation in Qingdao, thereby providing valuable insights for regional spatial planning and optimization aimed at promoting low-carbon development.

Carbon footprints: Uncovering spatiotemporal dynamics of global container ship emissions during 2015–2021

The proposed “peak carbon” and “carbon neutral” targets have catapulted the reduction of carbon dioxide (CO2) emissions in the shipping industry into a global focal point. Given their pivotal role in global trade, the worldwide movements of container ships provide insights for decision-making in maritime shipping CO2 emission mitigation. This paper employs Automatic Identification System (AIS) trajectory data from 2015 to 2021 to estimate the global carbon emissions of container shipping through a bottom-up methodology, allocating emissions on a 1° × 1° grid. Additionally, it delves into the evolving trends in CO2 emissions from 2015 to 2021, conducts spatial autocorrelation analysis, identifies hot spots, examines the spatio-temporal distribution patterns of global container ship CO2 emissions, and analyzes the emission characteristics of major ocean shipping routes. The findings reveal a temporal pattern in global emissions from container shipping, characterized by an initial decrease followed by a rebound, notably declining from 217 million tons in 2016 to 148 million tons in 2020, and subsequently rising to 168 million tons in 2021. The spatial distribution of CO2 exhibits pronounced heterogeneity, marked by notable spatial dependence and aggregation tendencies. The spatial distribution of annual emissions from container shipping demonstrates a similar pattern, with peak emission clusters predominantly concentrated along the coastline and major ocean shipping routes. This study provides a comprehensive understanding of carbon emissions from container shipping with high spatial and temporal resolution, facilitating a detailed analysis of emission patterns and influencing factors. Furthermore, the outcomes offer a scientific foundation and decision-making support for environmentally sustainable practices in the global maritime shipping.

Assessment of VOCs emission inventory in Seoul through spatiotemporal observations using passive and online PAMS measurements

We have conducted a quantitative analysis of volatile organic compounds (VOCs) in Seoul from September 2022 to June 2023, employing passive air samplers (PAS) at 25 sites and continuous monitoring at five photochemical assessment monitoring stations (PAMS). Comparing VOCs concentrations and compositions from PAS and PAMS during the same time periods, we found that most VOCs showed no significant difference (p-value >0.05) between the methods, except for olefins, which were overestimated by 24 % in passive sampling, confirming PAS as an effective tool for assessing the spatial distribution of VOCs species over large areas. Throughout Seoul, median values of total volatile organic compounds (TVOCs) concentrations collected with passive samplers remained stable with a standard deviation of 1.22 ppbv, typically ranging from 11 to 13 ppbv, except for an increase during winter in the southwestern regions of the city, where intense industrial and vehicular emissions are reported in the current Clean Air Policy Support System (CAPSS) emissions inventory. Positive matrix factorization (PMF) analysis using the passive data revealed general consistency in the spatial distribution of area and road emissions when compared with CAPSS emission inventory, although localized discrepancies were observed. In quantitative assessments of TVOCs comparing PAS observations with emission-based models, modeled values were within 1.5 times the interquartile range of observed PAS concentrations over Seoul. However, the lack of detection of emission hotspots in southeastern Seoul in the PAS data, coupled with the omission of photochemical loss in our dispersion-only models, indicates that the current CAPSS emission inventory may significantly underestimate actual ambient VOCs levels, especially in summer.

Constructing a measurement-based spatially explicit inventory of US oil and gas methane emissions (2021)

Abstract. Accurate and comprehensive quantification of oil and gas methane emissions is pivotal in informing effective methane mitigation policies while also supporting the assessment and tracking of progress towards emissions reduction targets set by governments and industry. While national bottom-up source-level inventories are useful for understanding the sources of methane emissions, they are often unrepresentative across spatial scales, and their reliance on generic emission factors produces underestimations when compared with measurement-based inventories. Here, we compile and analyze previously reported ground-based facility-level methane emissions measurements (n=1540) in the major US oil- and gas-producing basins and develop representative methane emission profiles for key facility categories in the US oil and gas supply chain, including well sites, natural-gas compressor stations, processing plants, crude-oil refineries, and pipelines. We then integrate these emissions data with comprehensive spatial data on national oil and gas activity to estimate each facility's mean total methane emissions and uncertainties for the year 2021, from which we develop a mean estimate of annual national methane emissions resolved at 0.1° × 0.1° spatial scales (∼ 10 km × 10 km). From this measurement-based methane emissions inventory (EI-ME), we estimate total US national oil and gas methane emissions of approximately 16 Tg (95 % confidence interval of 14–18 Tg) in 2021, which is ∼ 2 times greater than the EPA Greenhouse Gas Inventory. Our estimate represents a mean gas-production-normalized methane loss rate of 2.6 %, consistent with recent satellite-based estimates. We find significant variability in both the magnitude and spatial distribution of basin-level methane emissions, ranging from production-normalized methane loss rates of < 1 % in the gas-dominant Appalachian and Haynesville regions to > 3 %–6 % in oil-dominant basins, including the Permian, Bakken, and the Uinta. Additionally, we present and compare novel comprehensive wide-area airborne remote-sensing data and results for total area methane emissions and the relative contributions of diffuse and concentrated methane point sources as quantified using MethaneAIR in 2021. The MethaneAIR assessment showed reasonable agreement with independent regional methane quantification results in sub-regions of the Permian and Uinta basins and indicated that diffuse area sources accounted for the majority of the total oil and gas emissions in these two regions. Our assessment offers key insights into plausible underlying drivers of basin-to-basin variabilities in oil and gas methane emissions, emphasizing the importance of integrating measurement-based data when developing high-resolution spatially explicit methane inventories in support of accurate methane assessment, attribution, and mitigation. The high-resolution spatially explicit EI-ME inventory is publicly available at https://doi.org/10.5281/zenodo.10734299 (Omara, 2024).

High-resolution mapping of carbon dioxide emissions in Guizhou Province and its scale effects

Accurate spatial distribution of carbon dioxide (CO2) emissions is essential information needed to peaking emissions and achieving carbon neutral in China. The aim of this study was to map CO2 emissions with high spatial resolution at provincial scale and then explore the scale effect on mapping results. As an example, the spatiotemporal pattern and factors influencing CO2 emissions were examined in Guizhou Province in Western China. With the proposed method, a reasonable spatial distribution of CO2 emissions with high spatial resolution was obtained, which had relatively accurate information on spatial details. The optimal resolution of CO2 emissions at the provincial scale under high spatial resolution was approximately 90 m and 1260 m. More detailed grid data can better reflect the spatial variability of CO2 emissions. Emissions of CO2 were spatially heterogeneous in Guizhou, with high emissions in centers of big cities that gradually spread and decreased from city centers. From 2009 to 2019, the spatial distribution of CO2 emissions developed from agglomeration to dispersion. Areas of high carbon emissions decreased, those of medium carbon emissions increased, and many areas changed from no emissions to carbon emissions. Industrial land had the highest emissions, followed by commercial and transportation lands. Over 10 years, changes occurred in the relation between interregional economic level of Guizhou and CO2 emissions, with the relation changing from linear into an inverted U-shaped relation. The effect of industrial structure on CO2 emissions decreased, and the linear increase between CO2 emissions and the urban scale became more evident. The results of this study will contribute to accurate monitoring and management of carbon emissions in Guizhou, as well as provide support to formulate policies related to controls on carbon emissions in different regions.

Variation and Spatial Distribution of Emissions from Livestock Manure Management in Iran: An Evaluation and Location Analysis

Variation and Spatial Distribution of Emissions from Livestock Manure Management in Iran: An Evaluation and Location Analysis

High-resolution carbon emission mapping and spatial-temporal analysis based on multi-source geographic data: A case study in Xi'an City, China

Cities, contributing over 70% of global emissions, are key areas for climate change mitigation. Heterogeneity within cities determines the need for spatialized urban emissions reduction policies. However, few studies have attempted to characterize the spatial distribution of carbon emissions at the urban scale. To address this issue, a novel mapping method was proposed, using Xi'an as an example to explore the spatial distribution of carbon emissions at the city scale. Firstly, multiple geospatial open-source data, such as point of interest (POI), road networks, and land use, were utilized to identify the locations of emission sources. High-resolution carbon emission distributions were then mapped by allocating emissions based on the Intergovernmental Panel on Climate Change (IPCC) methodology. The study employed Global Moran's I to analyze the changes in spatial heterogeneity at different resolutions. Additionally, the Local Indicators of Spatial Association index (LISA) and Standard Deviation Ellipses (SDE) were adopted to examine the spatiotemporal characteristics of carbon emissions in Xi'an. The results show that carbon emissions at Xi'an City rises from 45.112 million tons to 72.701 million tons between 2010 and 2021. The construction of multi-scale carbon emissions spatial distributions, with a resolution of up to 30 m, allowed for a more detailed characterization of carbon emissions, especially in urban fringe areas. In addition, the results indicate that urban carbon emissions exhibit the strongest spatial autocorrelation at a resolution of 350 m. The study can provide a reference for the development of regional carbon emission reduction policies and spatial planning. In addition, the proposed spatialized method of city carbon emissions depends on open-source data, which allows it to have the potential for application in other cities.

Mechanism underlying revetment effects on the spatial distribution of nitrogen removal and N2O emissions in riparian zones at summer

Study Region, Shanghai, China, Study FocusUrban riparian zones can mitigate or even control nitrogen pollution in rivers through nitrification and denitrification processes. However, these processes also produce N2O, a potent greenhouse gas. During urbanization, various types of revetments have been constructed in riparian zones. These revetments alter the exchange of materials and energy between the riparian zone and the river, which in turn affects the nitrification and denitrification processes in the riparian zone. However, the mechanisms by which revetments impact nitrogen removal and N2O emissions in riparian zones are not yet clear, and the ways in which revetments balance nitrogen removal and N2O emissions remain unknown. Therefore, in a single river section, natural (NR), permeable (PR), and impervious revetment (IR) types were replicated. Measurements are taken of nitrogen removal, N2O emissions, and related environmental indicators in both the river-riparian interface (RRI) and the riparian zone area outside the RRI (RZ). The study analyzes the differences in environmental factors, nitrification and denitrification potentials, and N2O emission rates among the different revetments to identify the main factors affecting nitrogen removal and N2O emissions in the riparian zone. It also explores the potential mechanisms by which revetments influence the spatial distribution of nitrogen removal and N2O emissions in the summer riparian zone.New Hydrological Insights for the Region: PR exhibit greater denitrification potential and lower N2O emission rates. The denitrification potential is higher in the RRI compared to RZ, while the N2O emission rate is lower than in RZ. nirK is the primary factor influencing denitrification potential, whereas nosZII predominantly affects N2O emissions. The lower soil carbon-to-nitrogen ratio in PR increases the abundance of nirK, thereby enhancing the riparian zone's denitrification potential. Moreover, greater aboveground vegetation biomass increases the abundance of nosZII, reducing the N2O emission rate. The study results could provide guidance for urban ecological restoration and greenhouse gas emission reduction.

Temporal and spatial characteristics of carbon emissions from cultivated land use and their influencing factors: A case study of the Yangtze River Delta region

This study focuses on the Yangtze River Delta region and employs spatial analysis and geographically and temporally weighted regression (GTWR) to explore the temporal and spatial characteristics of carbon emissions from cultivated land use and their influencing factors. The findings indicate that carbon emissions from cultivated land use in the Yangtze River Delta generally show a decreasing trend, but there has been a significant increase in Anhui Province, mainly due to government efforts to promote agricultural development. Additionally, the spatial distributions of carbon emissions from cultivated land use clearly differ in terms of proximity, with changing temporal and spatial patterns. (1) From 2005 to 2020, there were significant spatial differences in the carbon emission intensity from cultivated land use across the Yangtze River Delta. The emission intensities in the southern areas were generally higher than those in the northeastern and central regions, whereas the western areas presented lower emission intensities. (2) The carbon emission intensity from cultivated land use in the Yangtze River Delta exhibited significant spatial proximity, meaning that neighboring areas had similar emission intensities, presenting a continuous distribution pattern. Contiguous distributions were also observed in parts of northern Anhui, central Jiangsu, and southeastern Zhejiang. (3) The carbon emission intensity from cultivated land use in the Yangtze River Delta showed a trend of high-intensity reductions and low-intensity increases over time and space. The level of agricultural development, scale of farmland management, types of crops planted, income levels of rural residents, intensity of agricultural machinery input, and strength of agricultural financial support significantly impact carbon emissions from cultivated land use. The results provide important references and a theoretical basis for the management and regulation of agricultural carbon emissions in the Yangtze River Delta region.

Investigating spatio-temporal variations and contributing factors of land use-related carbon emissions in the Beijing-Tianjin-Hebei Region, China

The land use change is the primary factor in influencing the regional carbon emissions. Studying the effects of land use change on carbon emissions can provide supports for the development policies of carbon emission. Using land use and energy consumption data, this study measures carbon emissions from land use dynamics in the Beijing-Tianjin-Hebei region from 2000 to 2020. The standard deviation ellipse model is employed to investigate the distribution characteristics of the spatial patterns of carbon emissions, while the Geographically and Temporally Weighted Regression (GTWR) model is used to examine the contributing factors of carbon emissions and their spatial and temporal heterogeneity. Results indicate a consistently increasing trend in carbon emissions from land use in the Beijing-Tianjin-Hebei region from 2000 to 2020. Construction land is characterized with both the primary source and an increasing intensity of carbon emissions. Besides, the spatial distribution of carbon emissions from land use in the Beijing-Tianjin-Hebei region demonstrates an aggregation pattern from in the northeast-southwest direction towards the center, with a greater aggregation trend in the east–west direction compared to that in the south-north direction. During the study period, a positive correlation was documented between carbon emissions and factors including total population, economic development level, land use degree, and landscape patterns. This correlation showed a decreasing trend and reached a stable level at the end of the study period. Moreover, the analysis showed a negative correlation between industrial structure and carbon emissions, which showed an increasing trend and reached a relatively high level at the end of the study period.

Aircraft-derived CH4 emissions from surface and in-situ mining activities in the Alberta oil sands region

The identification and reduction of methane sources is considered an important part of the fight to stem greenhouse gas (GHG) emissions to the atmosphere. One of the largest industrial contributors to national GHG emissions in Canada is the Alberta Oil Sands Region. To quantify and investigate the spatial distribution and temporal variability of methane emissions from this region, airborne measurements were conducted in 2017 and 2018 with three aircraft. 59 flights were conducted in total to assess emissions for both open-pit and in-situ facilities, in both cold and warm seasons. Derived emission rates were higher than those reported in national inventories by 30%–96% depending on the facility. In-situ facilities had emission rates an order of magnitude lower than surface mining operations and differed significantly from inventory estimates. No statistical differences in CH4 emissions between cold and warm seasons were observed, substantiating the use of simple upscaling to annual emissions within inventories. Rather than confirming a reported decrease in emissions between 2013 and 2018, the measurements suggest essentially no change from the 18 t h−1 for the region observed in 2013. Overall, the results suggest that current methods of CH4 emission determination within the oil sands region, for use in reporting, require improvement.

Spatiotemporal variations of fossil fuel CO2 emissions in China: A sectoral allocation approach based on multi-source data

Fossil fuel (FF) CO2 emissions account for the largest portion of human-related CO2 emissions. It is essential to accurately understand the spatial distribution of high-resolution FFCO2 emissions to formulate different carbon emission reduction policies in different regions. Therefore, a sectoral allocation approach was proposed to estimate FFCO2 emissions in China from 2000 to 2021 based on multi-source data. Furthermore, the spatiotemporal characteristics of FFCO2 emissions in different sectors were analyzed at different scales, and the spatial correlation of FFCO2 emissions in the service sector and industrial sector was also evaluated through Moran's index. The results showed that the mean R2 value of the sectoral allocation approach (0.89) exceeds that of the approach using only nighttime light (0.72). Moreover, the calculated results were utilized to analyze the Spatiotemporal variation of FFCO2 emissions. The analysis revealed that China's FFCO2 emissions increased from 3173 Mt in 2000–10662 Mt in 2021. The high emissions of FFCO2 mainly come from the industrial sectors in North China and Central China, as well as the service sectors in the eastern coastal cities and other provincial capital cities. The spatial dependence of FFCO2 emissions in the industrial sector was stronger than that in the service sector, but the spatial dependence of FFCO2 emissions in the service sector showed an increasing trend from 2000 to 2021. These results have important references and implications for region-specific carbon emission reduction policies.

Bayesian inversion of HFC-134a emissions in southern China from a new AGAGE site: Results from an observing system simulation experiment

The abundance of hydrofluorocarbons (HFCs) in the atmosphere is increasing and is of significant importance to the Earth's system. It is thus crucial to investigate the spatial distribution of HFC emissions. However, inversion modeling, a commonly used approach, is susceptible to errors from a-priori emissions, inversion grids, observations, and other parameters. In this study, we conduct Observing System Simulation Experiment (OSSE) to evaluate the impact of inversion parameters on HFC emission estimates, focusing on HFC-134a at the newly established Xichong (XCG) AGAGE site in Shenzhen, China. We regard the EDGAR (v7) dataset as a reference for “true” emissions and simulate the “true” atmosphere using the GEOS-Chem model. Our OSSE indicates that conducting inversions in the medium-sensitivity region with source-receptor sensitivity larger than 6.0 (nmol mol−1)/(mol m−2 s−1) minimizes the bias between a-posteriori and “true” total emissions to 0.58 %–1.88 %. In the high-sensitivity region, enhancing the spatial resolution of inversion grids lowers this error from −27.07 % to +0.36 %. The accuracy of a-priori spatial distribution crucially influences that of the a-posteriori: the higher correlation coefficient between a-priori and “true” emissions, the better a-posteriori agree with the “true” emissions, as evidenced by a reduced root mean square error (RMSE) of the a-posteriori vs. “true” emissions from 1.44 × 10−9 g m−2 s−1 (GDP-based) to 8.78 × 10−10 g m−2 s−1 (VIIRS-based). Furthermore, selecting regularization parameters and balancing instrumental and a-priori errors are also important. Our OSSE setups allow for parameter testing during the inversion, offering a framework to assess the regional representativeness of future HFC measurement sites.

Global microplastic fiber pollution from domestic laundry

The rapid expansion of fast fashion has significantly increased microplastic fiber (MPF) release during laundry practices, accounting for approximately one-third of primary microplastics entering the ocean. Currently, a significant gap exists in global-scale research on the release of MPFs from washing textiles. This study introduces an innovative empirical model to assess the spatial distribution of MPF emissions. The model estimates an annual global emission of 5.69 million tons of MPFs from laundry. Of this total, machine washing accounts for the majority (93.7 %), with hand washing contributing the remaining 6.3 %. As the primary source of MPF pollution, Asia's emissions reach 3.71 million tons, far exceeding those of North America (1.18 million tons) and Europe (0.45 million tons). The primary issue is that wastewater management efficiency varies significantly worldwide. In Asia, there is persistently high discharge of MPFs into natural waters, and the removal efficiency of wastewater treatment plants is still comparatively low. In contrast, the United States and many European countries exhibit better MPF retention. The global nature of this challenge mandates international collaboration for comprehensive environmental conservation. Our study provides the first high-resolution global distribution map of MPF emissions and discharge into natural waters, establishing a data foundation for global and regional management of microplastics originating from household laundry sources.

Merging TROPOMI and eddy covariance observations to quantify 5-years of daily CH4 emissions over coal-mine dominated region

A simple and flexible mass balance approach was applied to observations of XCH4 from TROPOMI to estimate CH4 emissions over Shanxi Province, including the impacts of advective transport, pressure transport, and atmospheric diffusion. High-frequency eddy-covariance flux observations were used to constrain the driving terms of the mass balance equation. This equation was then used to calculate day-to-day and 5 km × 5 km grided CH4 emissions from May 2018 to July 2022 based on TROPOMI RPRO column CH4 observations. The Shanxi-wide emissions of CH4, 126 ± 58.8 ug/m2/s, shows a fat tail distribution and high variability on a daily time scale (the 90th percentile is 2.14 times the mean and 2.74 times the median). As the number of days in the rolling average increases, the change in the variation decreases to 128 ± 35.7 ug/m2/s at 10-day, 128 ± 19.8 ug/m2/s at 30-day and 127 ± 13.9 ug/m2/s at 90-day. The range of values of the annual mean emissions on coal mine grids within Shanxi for the years 2018 to 2022 was 122 ± 58.2, 131 ± 71.2, 111 ± 63.6, 129 ± 87.1, and 138 ± 63.4 ug/m2/s, respectively. The 5-year average emissions from TROPOMI are 131 ± 68.0 ug/m2/s versus 125 ± 94.6 ug/m2/s on the grids where the EDGAR bottom-up database also has data, indicating that those pixels with mines dominate the overall emissions in terms of both magnitude and variability. The results show that high-frequency observation-based campaigns can produce a less biased result in terms of both the spatial and temporal distribution of CH4 emissions as compared with approaches using either low-frequency data or bottom-up databases, that coal mines dominate the sources of CH4 in Shanxi, and that the observed fat tail distribution can be accounted for using this approach.