Spatial Distribution Of Emission Research Articles

Quantification of methane emissions in Hamburg using a network of FTIR spectrometers and an inverse modeling approach

Abstract. Methane (CH4) is a potent greenhouse gas, and anthropogenic CH4 emissions contribute significantly to global warming. In this study, the CH4 emissions of the second most populated city in Germany, Hamburg, were quantified with measurements from four solar-viewing Fourier transform infrared (FTIR) spectrometers, mobile in situ measurements, and an inversion framework. For source type attribution, an isotope ratio mass spectrometer was deployed in the city. The urban district hosts an extensive industrial and port area in the south as well as a large conglomerate of residential areas north of the Elbe River. For emission modeling, the TNO GHGco (Netherlands Organisation for Applied Scientific Research greenhouse gas and co-emitted species emission database) inventory was used as a prior for the inversion. In order to improve the inventory, two approaches were followed: (1) the addition of a large natural CH4 source, the Elbe River, which was previously not included in the inventory, and (2) mobile measurements were carried out to update the spatial distribution of emissions in the TNO GHGco gridded inventory and derive two updated versions of the inventory. The addition of the river emissions improved model performance, whereas the correction of the spatial distribution with mobile measurements did not have a significant effect on the total emission estimates for the campaign period. A comparison of the updated inventories with emission estimates from a Gaussian plume model (GPM) showed that the updated versions of the inventory match the GPM emissions estimates well in several cases, revealing the potential of mobile measurements to update the spatial distribution of emission inventories. The mobile measurement survey also revealed a large and, at the time of the study, unknown point source of thermogenic origin with a magnitude of 7.9 ± 5.3 kg h−1 located in a refinery. The isotopic measurements show strong indications that there is a large biogenic CH4 source in Hamburg that produced repeated enhancements of over 1 ppm which correlated with the rising tide of the river estuary. The CH4 emissions (anthropogenic and natural) of the city of Hamburg were quantified as 1600 ± 920 kg h−1, 900 ± 510 kg h−1 of which is of anthropogenic origin. This study reveals that mobile street-level measurements may miss the majority of total methane emissions, potentially due to sources located within buildings, including stoves and boilers operating on natural gas. Similarly, the CH4 enhancements recorded during the mobile survey from large-area sources, such as the Alster lakes, were too small to generate GPM emission estimates with confidence, but they could nevertheless influence the emission estimates based on total column measurements.

Surface Properties of Global Land Surface Microwave Emissivity Derived from FY-3D/MWRI Measurements

Land surface microwave emissivity is crucial to the accurate retrieval of surface and atmospheric parameters and the assimilation of microwave data into numerical models over land. The microwave radiation imager (MWRI) sensors aboard on Chinese FengYun-3 (FY-3) series satellites provide valuable measurements for the derivation of global microwave physical parameters. In this study, an approximated microwave radiation transfer equation was used to estimate land surface emissivity from MWRI by using brightness temperature observations along with corresponding land and atmospheric properties obtained from ERA-Interim reanalysis data. Surface microwave emissivity at the 10.65, 18.7, 23.8, 36.5, and 89 GHz vertical and horizontal polarizations was derived. Then, the global spatial distribution and spectrum characteristics of emissivity over different land cover types were investigated. The seasonal variations of emissivity for different surface properties were presented. Furthermore, the error source was also discussed in our emissivity derivation. The results showed that the estimated emissivity was able to capture the major large-scale features and contains a wealth of information regarding soil moisture and vegetation density. The emissivity increased with the increase in frequency. The smaller surface roughness and increased scattering effect may result in low emissivity. Desert regions showed high emissivity microwave polarization difference index (MPDI) values, which suggested the high contrast between vertical and horizontal microwave signals in this region. The emissivity of the deciduous needleleaf forest in summer was almost the greatest among different land cover types. There was a sharp decrease in the emissivity at 89 GHz in the winter, possibly due to the influence of deciduous leaves and snowfall. The land surface temperature, the radio-frequency interference, and the high-frequency channel under cloudy conditions may be the main error sources in this retrieval. This work showed the potential capabilities of providing continuous and comprehensive global surface microwave emissivity from FY-3 series satellites for a better understanding of its spatiotemporal variability and underlying processes.

Simulation of the influence of residential biomass burning on air quality in an urban area

Residential biomass burning is often one of the dominant local sources of air pollution in urban areas during the winter months. However, the corresponding particle emissions are quite uncertain and chemical transport models (CTMs) often have difficulties reproducing the observed winter particulate matter (PM) concentrations. In this work, we combine measurements from a low-cost PM sensor network and the CTM PMCAMx (Particulate Matter Comprehensive Air quality Modeling with extensions) to estimate the spatial and temporal distribution of biomass burning emissions in an urban area.An estimate of 8100 kg d−1 (40 g d−1 per person) PM2.5 emissions from residential biomass burning was calculated for the city of Patras including organic compounds with effective saturation concentrations C* up to 104 μg m−3. The spatial distribution of the emissions was based on the density of fireplaces in the city. The temporal distribution of emissions was based on measurements of the biomass burning organic aerosol (bbOA) and are higher from 18:00 to 22:00 LT peaking at 21:00 LT. The nighttime (18:00-22:00 LT) bbOA emissions were 4 times higher than the corresponding morning (8:00-13:00 LT) ones. Estimated biomass burning emissions vary from day to day based on ambient temperature, with higher emissions during the colder days. The Volatility Basis Set (VBS) was used to simulate bbOA that is treated as semi-volatile and chemically reactive.PMCAMx predicts that bbOA concentration reaches on average 15 μg m−3 in the high-density bbOA emission area during the simulated period, while the corresponding peak hourly concentrations are higher than 40 μg m−3 most of the nights. The average predicted bbOA concentration in the city center is 3–6 μg m−3 and is lower than 1 μg m−3 at the suburbs. The model predicts that bbOA concentrations peak at 9:00 LT in the morning and at 21:00 LT during the nighttime, reproducing the bbOA measurements.

The spatial distribution of agricultural emissions in the United States: The role of organic farming in mitigating climate change

Agriculture is a significant contributor to greenhouse gas (GHG) emissions and organic farming practices can potentially offset some of these emissions. However, previous research on the environmental impact of organic agriculture has provided mixed or contradictory results. This study aims to analyze the role that organic farming can play in mitigating agricultural emissions across the United States. Using panel U.S. state-level data, we find evidence that farming activities increase GHG emissions. However, we also find that dedicating a larger share of farmland to organic and pasture farming reduces GHG emissions. A spatial analysis of agricultural emissions that accounts for the role of organic farming provides three key insights. First, the spatial distribution of agricultural GHG emissions in the United States is uneven. Second, agriculture is a significant contributor to GDP within high-emissions states where organic agriculture represents only a small proportion of total farmland. Third, agriculture has a substantially lower contribution to GDP within low-emissions states where organic agriculture represents a large proportion of total farmland. These findings suggest that reducing GHG emissions effectively may necessitate creating and implementing policies and initiatives tailored to specific regions rather than relying on general recommendations. Thus, low-emissions states should be explored as examples of sustainable agricultural practices that could set the stage for scaling up organic farming practices across the country.

ANN-based prediction of ammonia nitrogen for wastewater discharge indicators under carbon neutral trend

IntroductionWith the rapid development of society and urbanization, greenhouse gas emissions have increased, leading to environmental problems such as global warming. The rise in urban water consumption has also resulted in increased sewage discharge, exacerbating freshwater scarcity and water pollution. Understanding the current status and spatial distribution of greenhouse gas emissions in China's sewage treatment industry is crucial for emission reduction measures and controlling ammonia nitrogen pollution.MethodsThis study comprehensively investigates greenhouse gas emissions from sewage treatment plants, analyzing influencing factors and predicting future spatial and temporal distributions. The uncertainty of ammonia nitrogen emissions is calculated using the IPCC's error propagation method, considering uncertainty ranges of variables. Additionally, an artificial neural network is employed to predict ammonia nitrogen content in sewage discharge, aiming to prevent excessive levels in wastewater.Results and discussionThe proposed model outperforms others with an R-Squared score of 0.926, demonstrating its superior accuracy in predicting ammonia content in wastewater. These findings contribute to better emission reduction strategies and control of ammonia nitrogen emissions. This model can effectively prevent excessive ammonia nitrogen content in discharged wastewater, contributing to water pollution control. In conclusion, this study highlights the importance of understanding greenhouse gas emissions from sewage treatment plants and their impact on water pollution. The research provides valuable insights into emission reduction measures, emission prediction, and technological innovations suitable for China's specific conditions. By effectively managing ammonia nitrogen emissions and adopting the proposed predictive model, the goals of carbon neutrality and environmental sustainability can be better achieved.

Spatial and temporal characteristics of carbon emission and sequestration of terrestrial ecosystems and their driving factors in mainland China—a case study of 352 prefectural administrative districts

IntroductionGlobal climate change, increase in human activities, and prominence of ecological issues have led to uneven quantitative and spatial distributions of carbon emission and sequestration of terrestrial ecosystems. Such uneven distributions can lead to more negative impacts on the natural environment and human living conditions.MethodsTherefore, based on the carbon neutralization policy, we conducted geographically weighted regression (GWR) modeling in this study using panel data from 352 Chinese prefectural administrative districts in 2000, 2005, 2010, and 2017 to analyze and determine the impact factors and their spatial distribution for carbon emission and sequestration of terrestrial ecosystems.ResultsOur results showed that total population (TP), per capita gross domestic product (GDP) (PCG), proportion of secondary industry output (PSIO), scale of urban built-up area (SUB), green space proportion in city areas (GSP), normalized difference vegetation index (NDVI), and temperature (TEM) are factors driving carbon sequestration and carbon emission. The spatial distribution of these driving factors in mainland China is: (1) TP showed a negative correlation to carbon emission in most areas, while it exhibited a positive correlation to carbon sequestration in the southern, southwestern, and western parts of northwest China; however, in all other areas, TP showed a negative correlation with carbon sequestration; (2) PCG was positively correlated to carbon emission in most areas of China and to carbon sequestration in southwest, south, central, and northeast China; however, PCG demonstrated a negative correlation to carbon sequestration in the remaining areas; (3) PSIO and SUB presented a positive correlation to carbon emission and a negative correlation to carbon sequestration in most areas; (3) In contrast, GSP showed a negative correlation to carbon emission and a positive correlation to carbon sequestration in most areas; (5)NDVI showed a negative correlation to carbon emission and carbon sequestration in most areas toward the east of the “Heihe-Tengchong Line”; NDVI was positively correlated to both carbon emission and sequestration toward the west of this line; (6)TEM was positively correlated to carbon emission and sequestration in most parts of China.DiscussionBased on these results, we further divided the Chinese cities into 6 groups: (1) Groups 1, 2, 3, and 6 are areas where carbon emission and sequestration are governed by both socioeconomic and natural ecological factors. The major driving factors of carbon emission and carbon sequestration in group 1 are PSIO, GSP, and NDVI; the driving factors of group 2 are SUB and NDVI. Meanwhile, carbon emission and sequestration in group 3 are governed by PCG, GSP, and NDVI; for group 6, carbon emission and sequestration are controlled by PCG, SUB, GSP, and NDVI; (2) Group 4 represents areas where carbon emission and sequestration are majorly impacted by PCG and SUB, thereby rendering socioeconomic factors as the major driving forces. Group 5 represents areas where carbon emission and sequestration are sensitive to the natural environment, with GSP and NDVI being the driving factors. Considering the uneven distribution of carbon sequestration and emission and the diverse driving factors in different areas of China, we provided guidance for future environmental policies aimed at reducing the uneven distribution of carbon sequestration and emission in different areas to achieve carbon neutralization.

Improved Anthropogenic Mercury Emission Inventories for China from 1980 to 2020: Toward More Accurate Effectiveness Evaluation for the Minamata Convention.

Anthropogenic mercury (Hg) emission inventories are crucial for the effectiveness evaluation of the Minamata Convention on Mercury. In this study, we developed an integrated Dynamic Inventory for Mercury Emission (DIME) model and improved the accuracy of emission estimates for primary sources in China. Long-term historical speciated Hg emission inventories for China were established. The total Hg emissions increased from 217.0 t in 1980 to 357.8 t in 2020 with a peak value of 506.6 t in 2010. Three stages with distinct leading drivers were identified. At Stage 1 (1980-1997), Hg emissions doubled with the rapid growth of economy; the driver was offset by the increase of dust and SO2 control measures at Stage 2 (1997-2010) except for cement production; and co-benefits from strict control measures induced the decoupling of Hg emissions from the economy at Stage 3 (2010-2020). The ultralow emission (ULE) retrofits in key industries had pronounced Hg removal efficiencies. Large emission reduction potential still exists in the cement industry. The improved emission estimation methods for key sectors, the consistency in methodology for historical Hg emission inventories, and the more accurate spatial distribution of speciated Hg emissions in this study provide a practical toolkit for the effectiveness evaluation of the Minamata Convention.

Permafrost Effect on the Spatial Distribution of CO2 Emission in the North of Western Siberia (Russia)

The landscapes in the discontinuous permafrost area of Western Siberia are unique objects for assessing the direct and indirect impact of permafrost on greenhouse gas fluxes. The aim of this study was to identify the influence of permafrost on the CO2 emission at the landscape and local levels. The CO2 emission from the soil surface with the removed vegetation cover was measured by the closed chamber method, with simultaneous measurements of topsoil temperature and moisture and thawing depth in forest, palsa, and bog ecosystems in August 2022. The CO2 emissions from the soils of the forest ecosystems averaged 485 mg CO2 m−2 h−1 and was 3–3.5 times higher than those from the peat soils of the palsa mound and adjacent bog (on average, 150 mg CO2 m−2 h−1). The high CO2 emission in the forest was due to the mild soil temperature regime, high root biomass, and good water–air permeability of soils in the absence of permafrost. A considerable warming of bog soils, and the redistribution of CO2 between the elevated palsa and the bog depression with water flows above the permafrost table, equalized the values of CO2 emissions from the palsa and bog soils. Soil moisture was a significant factor of the spatial variability in the CO2 emission at all levels. The temperature affected the CO2 emission only at the sites with a shallow thawing depth.

Study on the influence of coal fire on the temporal and spatial distribution of CO2 and CH4 gas emissions.

In order to study the impact of gas released from coal fire combustion on the spatial-temporal distribution of CO2 and CH4 and other greenhouse gas emissions, the impact of regional coal fire on CO2 and CH4 emission flux was comprehensively evaluated using Landsat 8 and GOSAT satellite data in Xinjiang. In addition, typical fire areas are selected, a single-channel algorithm is used to invert the surface temperature of the coal field, the spatial distribution of the coal fire area is extracted by setting the threshold, and the influence law of CO2 and CH4 emissions in the typical fire area is accurately analyzed. The results show that during 2017-2018, CO2 and CH4 emissions in Xinjiang were generally dispersed and locally concentrated, while CO2-O and CH4-O were at low levels in most regions, fluctuating in the ranges of 0.01 ~ 0.14g·m-2·day-1 and 0.001 ~ 0.003g·m-2·day-1, respectively. However, the emission intensity of CO2-O and CH4-O in coal fire concentrated areas is higher, which are 1.6 ~ 3.8g·m-2day-1 and 0.013 ~ 0.026g·m-2·day-1, respectively. CO2-F and CH4-ag have similar laws. The fire area of Daquan Lake is scattered, and there are four areas with the surface temperature over 35°C: A, B, C, and D, respectively. The Sandaoba fire area is more concentrated, and only two areas are E and F when the surface temperature exceeds 35°C. CO2 and CH4 released by burning in Daquan Lake and Sandaoba fire areas increased CO2-F and CH4-ag by 2.08 and 0.89 times, respectively. The results provide a reference for coal fire control and carbon emission reduction.

Analysis of regional differences and spatial spillover effects of agricultural carbon emissions in China

In order to realize “double carbon” target in agriculture and high-quality development of the rural economy in China, it is crucial to study the regional differences and spatial spillover effects of agricultural carbon emissions (ACE). This paper measures ACE using panel data of 31 Chinese provinces from 2005 to 2020, examines the spatio-temporal evolution characteristics，the convergence of agricultural carbon emissions, compares and analyzes regional differences, and investigates the spatial correlation and spatial spillover effects. The study found that: (1) Total agricultural carbon emissions over the research period exhibit a rising and then reducing trend, the spatial distribution of total agricultural carbon emissions is described as high in east-central and low in west. The gap of agricultural carbon emissions is gradually declining in the east, and will eventually reach their respective steady-state levels in the west and northeast. (2) There is a strong spatial interprovincial link of ACE, which has a beneficial knock-on effect on the convergence of adjacent provinces. (3) Agricultural industrial structure, urbanization level, the size of the agricultural labor force, and the intensity of the agricultural machinery input all directly affect ACE in this province and indirectly affect ACE in adjacent provinces, with the exception of the negligible coefficient of economic development level on ACE. Hence, pertinent policy suggestions are put out to serve as a guide for reducing ACE.

Decomposition of drivers and identification of decoupling states for the evolution of carbon emissions from energy consumption in China.

As the largest energy consumer, China's control of carbon emissions from energy consumption plays a pivotal role in world climate governance. However, few studies have been conducted to explore the emission reduction pathways that promote a high level of synergy between China's economic growth and the " carbon peaking and carbon neutrality " goal from the perspective of energy consumption. Based on the measurement of energy consumption carbon emissions, this paper reveals the spatial and temporal distribution and evolution trends of carbon emissions in China at the national-provincial level. The multi-dimensional socio-economic factors such as R&D and urbanization are taken into account, and the LMDI model is used to decompose the driving effects of energy consumption carbon emissions at the national-provincial levels. Further, this paper combines the Tapio decoupling index with the LMDI model to decompose the decoupling states of China year by year and at the provincial level in four periods to explore the reasons for the change of carbon decoupling states. The results show that: (1) China's energy consumption carbon emissions grew at a high rate before 2013, and slowed down after that. There are significant differences in the scale and growth rate of carbon emissions among provinces, which can be classified into four types accordingly. (2) The R&D scale effect, urbanization effect, and population scale effect are the factors driving the growth of China's carbon emissions; while the energy structure effect, energy consumption industry structure effect, energy intensity effect, and R&D efficiency effect inhibit the growth of China's carbon emissions. (3) Weak decoupling is the most dominant decoupling state in China from 2003 to 2020, and the decoupling state varies significantly among provinces. According to the conclusions, this paper proposes targeted policy recommendations based on China's energy endowment.

Temporal and Spatial Differences in CO2 Equivalent Emissions and Carbon Compensation Caused by Land Use Changes and Industrial Development in Hunan Province

The differences in CO2 equivalent emissions and carbon compensation due to land use changes can provide a basis for formulating low-carbon development policies in various regions according to net CO2 emissions. Based on the land use and energy consumption data of Hunan Province from 2000 to 2020, the calculation model is constructed to calculate the CO2 equivalent emissions and carbon compensation values for different cities in different periods. The results showed that: (1) From 2000 to 2020, there was a significant growth trend in the area of built-up land in Hunan Province, mainly from the forest and cropland, while the area of forest is relatively stable. (2) The net CO2 equivalent emissions from land use changes in Hunan Province shows a trend of increasing first and then decreasing with an initially fast and then slowed growth rate. Built-up land is the main carbon source, and its CO2 equivalent emissions increased by 26.78 million tons, while the forest is the main carbon sink, and its carbon absorption decreased by 4.11 × 104 tons. (3) The carbon sink areas are mainly located in Zhangjiajie and Xiangxi in western Hunan, and the CO2 equivalent emission intensity of other carbon source areas is gradually increasing from eastern Hunan to southern Hunan. (4) The carbon compensation value is very similar to the spatial distribution of CO2 equivalent emissions in different cities. The high carbon compensation areas are mainly Yueyang, Loudi, and Xiangtan due to their backward energy structure, un-upgraded industrial structure, and large net CO2 equivalent emissions, while the high carbon compensation areas are mainly Changsha, due to its high economic development level, great technological progress, and small net CO2 equivalent emissions. To achieve regional coordination and low-carbon development, it is necessary to continually improve the carbon compensation mechanism and to build on carbon compensation to promote regional low-carbon coordinated development from a low-carbon level. Meanwhile, the government should rank and direct the transformation and development of different types of cities, to build a low-carbon land development model and achieve the goal of developing carbon neutrality.

Spatial Modeling of Airport Surface Fuel Burn for Environmental Impact Analyses

The assessment of the fuel burn and emissions impact of airport surface operations is a key part of understanding the environmental impacts of aviation. These assessments are needed at two levels: the analysis of inventories (the total amount of fuel burned and emissions discharged over some period of time), and the analysis of spatial distributions (the amount of emissions experienced at a particular location within or near the airport). Although the availability of taxi times for the operations of interest is sufficient for inventory analysis, the analysis of spatial distributions requires estimates of where on the airport surface an aircraft is located as it consumes fuel. In this paper, we show how a data-driven queuing network model can be developed in order to estimate the time that an aircraft spends at different congested locations on the airport surface. These models are useful both in spatial distribution analysis and in accurately predicting taxi times in the absence of measurements (for example, for projected demand sets). We use measurements of ultrafine particles at Los Angeles International Airport to demonstrate that the proposed model can help predict the measured emissions at different monitoring sites located in the vicinity of the airport. In the process, we show how one could develop a machine learning model of the spatial distribution of airport surface emissions given the pollutant measurements, air traffic demand, and prevailing weather conditions. Finally, we develop a clustering-based method to evaluate the generalizability of our surface operations modeling framework.

Hybrid method of mapping urban residential carbon emissions with high-spatial resolution: A case study of Suzhou, China

Implementing carbon mitigation through urban spatial optimisation is a possible solution for alleviating global warming. However, the relationship between urban carbon emissions and urban spatial structure has not been well clarified, as adequate mapping of high-spatial-resolution urban carbon emissions from different sectors (particularly residential sectors), a precondition to solving the problem, has yet to be achieved. This study proposes a hybrid method of mapping the spatial distribution of urban residential carbon emissions on a 1 km × 1 km scale using multi-source data and exemplifies it via a case study of the Chinese city of Suzhou. The purpose of using this method is to differentiate residential carbon emissions by commuter population and home-based population, as the time they spend at home differs. The mobile signalling data of Suzhou were used to identify commuter and home-based populations. The number and spatial distribution of these two groups were then calibrated by referring to land use and O-D data. Using calibrated data, the proportion of electricity consumed by the two groups in different residential districts across the city was calculated. Total urban residential carbon emissions were then proportionally allocated to 1 km × 1 km grids. By validating estimated data against the data from the Statistical Yearbook, we found that the proximity level is higher than 93%. Furthermore, comparing these outcomes against the results estimated by using NTL data and the size of the identified population as the proxy data, it was observed that the results estimated by the hybrid method are of higher accuracy and stability.

The influence of vegetation drought stress on formaldehyde and ozone distributions over a central European city

To estimate the effect of vegetation stress and changes in biogenic volatile organic compound (BVOC) emissions on urban ozone (O3) levels we perform a systematic, observation-based analysis of the relationship between formaldehyde (HCHO) mixing ratios, meteorological parameters, measurement-based drought indicators and O3 over the central European city of Vienna, Austria. In addition, numerical models SURface EXternalisée (SURFEX), Model of Emissions of Gases and Aerosols from Nature (MEGAN) Vers.2.1 and 3 and MOdèle de Chimie A Grande Echelle (MOCAGE) are combined to estimate the soil moisture, the spatial distribution and drought response of isoprene emissions, and the resulting distribution of HCHO in the atmosphere. To analyse the effect of drought during spring and summer we contrast observations during dry and reference years. Our results show that the observed HCHO can be explained using the simulated isoprene emissions as well as observed and simulated vegetation drought responses. HCHO mixing ratios differ strongly between dry and reference seasons. Spring-time precipitation deficits facilitate reduced HCHO mixing ratios due to delayed and weakened plant growth. In consequence also O3 burdens are lowered due to reduced BVOC precursor emissions. These reductions occur despite radiation levels being higher than during the reference year, illustrating the strong potential of spring-time BVOC emissions to modulate urban O3 burdens. Conversely, during summer elevated O3 levels occur during local drought conditions. These are driven by advected isoprene originating from nearby forest areas, which are not affected by drought. Our results regarding elevated summer-time O3 burdens under vegetation heat and drought stress are in good agreement with previous work.

Simulation of Anthropogenic CO2 Emissions in the Yangtze River Delta Based on Different Emission Inventories

Nowadays, great uncertainty still exists on the urban- and regional-scale anthropogenic CO2 emission estimation based on emission inventories. In order to achieve the carbon peaking and neutrality targets for China, it is urgent to accurately estimate anthropogenic CO2 emissions at regional scales, especially in large urban agglomerations. Using two inventories (EDGAR v6.0 inventory and a modified inventory combining EDGAR v6.0 with GCG v1.0) as prior anthropogenic CO2 emission datasets andtaking themas input data respectively, this study utilized the WRF-STILT atmospheric transport model to simulate atmospheric CO2 concentration in the Yangtze River Delta region from December 2017 to February 2018. The simulated atmospheric CO2 concentrations were further improved by referencing atmospheric CO2 concentration observation at a tall tower in Quanjiao County of Anhui Province and using the scaling factors obtained from the Bayesian inversion method. An estimation of anthropogenic CO2 emission flux in the Yangtze River Delta regionwas finally accomplished. The results indicated that:①in winter, in comparison to the atmospheric CO2 concentration simulated based on EDGAR v6.0, the atmospheric CO2 concentration simulated based on the modified inventory was more consistent with observed values. ②The simulated atmospheric CO2 concentration was higher than observation at night and lower than observation during the daytime. The CO2 emission data of emission inventories could not fully reflect the diurnal variation in anthropogenic emissions, andtheoverestimation, caused by the simulated low-atmospheric boundary layer height at night, of the contribution from point sources with higher emission height near the observation station were the main reasons. ③The simulation performance on atmospheric CO2 concentration was greatly affected by the emission bias of the EDGAR grid points that significantly contributed to concentrations of the observation station, and this indicated that the uncertainty in the spatial distribution in EDGAR emission was the main factor influencing the simulation accuracy. ④The posterior anthropogenic CO2 emission flux in the Yangtze River Delta from December 2017 to February 2018 was around (0.184±0.006) mg·(m2·s)-1and (0.183±0.007) mg·(m2·s)-1 based on EDGAR and the modified inventory, respectively. It is suggested that the inventories with higher temporal and spatial resolutions and more accurate spatial emission distribution should be selected as the prior emissions to obtain a more accurate estimation of the regional anthropogenic CO2 emissions.

Global methyl halide emissions from biomass burning during 2003-2021.

Methyl halides (CH3Cl, CH3Br, and CH3I) are ozone-depleting substances. Biomass burning (BB) is an important source of methyl halides. The temporal variations and global spatial distribution of BB methyl halide emissions are unclear. Thus, global methyl halide emissions from BB during 2003-2021 were estimated based on satellite data. A significant decreasing trend (p<0.01) in global methyl halide emissions from BB was found between 2003 and 2021, with CH3Cl emissions decreasing from 302 to 220Ggyr-1, CH3Br emissions decreasing from 16.5 to 11.7Ggyr-1, and CH3I emissions decreasing from 8.9 to 6.1Ggyr-1. From a latitudinal perspective, the northern high-latitude region (60-90° N) was the only latitude zone with significant increases in BB methyl halide emissions (p<0.01). Based on an analysis of the drivers of BB methyl halide emissions, emissions from cropland, grassland, and shrubland fires were more correlated with the burned area, while BB emissions from forest fires were more correlated with the emissions per unit burned area. The non-BB emissions of CH3Cl increased from 4749Ggyr-1 in 2003 to 4882Ggyr-1 in 2020, while those of CH3Br decreased from 136Ggyr-1 in 2003 to 118Ggyr-1 in 2020 (global total CH3I emissions are not available). The finding indicates that global CH3Cl and CH3Br emissions from sources besides BB increased and decreased during 2003-2020. Based on our findings, not only searching for unknown sources is important, but also re-evaluating known sources is necessary for addressing methyl halide emissions.

The Impacts of Road Traffic on Urban Carbon Emissions and the Corresponding Planning Strategies

Carbon emissions increase the risk of climate change. As one of the primary sources of carbon emissions, road traffic faces a significant challenge in terms of reducing carbon emissions. Many studies have been conducted to examine the impacts of cities on carbon emissions from the perspectives of urbanization, population size, and economics. However, a detailed understanding of the relationship between road traffic and urban carbon emissions is lacking due to the lack of a reasonable set of road traffic metrics. Furthermore, there have been fewer studies that have conducted cluster analyses of the impact factors, which will be supplemented in this research. We established 10 impact metrics, including the highway network system, city road network system, public transit system, and land use system of streets and transportation, using 117 county-level cities in Hebei Province as the study area, which is one of the regions in China with the most acute conflicts between economic development and the environment. We built an ordinary least squares (OLS) model, a spatial lag model (SLM), a spatial error model (SEM), a spatial Durbin model (SDM), and a geographically weighted regression (GWR) model, and performed a cluster analysis on the key metrics. The results are as follows: (1) The difference in spatial distribution of urban land-average carbon emissions is obvious, highly concentrated in the areas surrounding Beijing and Tianjin. (2) The GWR model has a higher R2 and a lower AICc than global models (OLS, SLM, SEM, and SDM) and performs better when analyzing the impact mechanism. (3) Highway network density, city road length, and density of the public transit network have significant effects on urban land-average carbon emissions, whereas the street and transportation land use systems have no significant effect, which indicates that the highway network and public transit systems should be prioritized. (4) The GWR model results show that the impact of the four metrics on the urban land-average carbon emissions exhibits clear spatial heterogeneity with a significant piecewise spatial distribution pattern. The highway network density has a relatively large impact on the northern region. The northwest is more affected by the density of the public transit network. The southwest is most impacted by the length of city roads. (5) The study area is divided into four distinct characteristic areas: the highway network dominant impact area, the public transit dominant impact area, the city road network dominant impact area, and the multi-factor joint impact area. Different traffic optimization strategies are proposed for different areas.

Estimates of Dust Emissions and Organic Carbon Losses Induced by Wind Erosion in Farmland Worldwide from 2017 to 2021

Wind erosion can cause high dust emissions from agricultural land and can lead to a significant loss of carbon and nutrients from the soil. The carbon balance of farmland soil is an integral part of the carbon cycle, especially under the current drive to develop carbon-neutral practices. However, the amount of global carbon lost due to the wind erosion of farmland is unknown. In this study, global farmland dust emissions were estimated from a dust emission inventory (0.1° × 0.1°, daily) built using the improved Community Multiscale Air Quality Modeling System–FENGSHA (CMAQ-FENGSHA), and global farmland organic carbon losses were estimated by combining this with global soil organic carbon concentration data. The average global annual dust emissions from agricultural land from 2017 to 2021 were 1.75 × 109 g/s. Global dust emissions from agricultural land are concentrated in the UK, Ukraine, and Russia in Europe; in southern Canada and the central US in North America; in the area around Buenos Aires, the capital of Argentina, in South America; and in northeast China in Asia. The global average annual organic carbon loss from agricultural land was 2970 Gg for 2017–2021. The spatial distribution of emissions is roughly consistent with that of dust emissions, which are mainly concentrated in the world’s four major black soil regions. These estimates of dust and organic carbon losses from agricultural land are essential references that can inform the global responses to the carbon cycle, dust emissions, and black soil conservation.

Impact of interannual weather variation on ammonia emissions and concentrations in Germany

Ammonia is one of the most impactful pollutants emitted from agricultural activities, harming human health and contributing to biodiversity loss. In ammonia emission inventories, the spatial distribution of annual emissions is mostly approximated by constant empirical emission fractions, which do not account for spatial variability, nor for temporal variability within a year or between years caused by weather variations. Besides, factors like manure properties, soil properties, and manure application techniques also lead to differences in the amount of ammonia emitted into the atmosphere. By not or only partly accounting for these factors, significant uncertainties are introduced into ammonia emission estimates at regional and national scales. In this study, we applied the empirical ALFAM2 model to derive spatially explicit slurry application emission fractions from cropland for use in the large-scale INTEGRATOR model, using the information on slurry properties (dry matter content and pH), manure application rate, application technique, incorporation time, air temperature, wind speed, and rainfall rate. In addition, the impact of weather on the ammonia emissions from animal housing and manure storage systems was included through a temperature-dependent scaling. We applied the method to investigate the year-to-year spatio-temporal variabilities of ammonia emissions and modeled concentrations across Germany from 2015 to 2018. Through the comparison with in situ measurements and satellite-derived observations, we studied how surface concentrations and total columns relate to local meteorology. We found that the spatio-temporal variability in emission fractions improves the ability to reproduce the interannual variability observed in ammonia concentration and total column measurements. This study shows that the developed approach to derive spatially explicit emission fractions can significantly improve ammonia emission modeling and is of great importance for studying the temporal variability between years.