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Emission inventories (EIs) are the fundamental tool to monitor compliance with greenhouse gas (GHG) emissions and emission reduction commitments. Inventory accounting guidelines provide the best practices to help EI compilers across different countries and regions make comparable, national emission estimates regardless of differences in data availability. However, there are a variety of sources of error and uncertainty that originate beyond what the inventory guidelines can define. Spatially explicit EIs, which are a key product for atmospheric modeling applications, are often developed for research purposes and there are no specific guidelines to achieve spatial emission estimates. The errors and uncertainties associated with the spatial estimates are unique to the approaches employed and are often difficult to assess. This study compares the global, high-resolution (1 km), fossil fuel, carbon dioxide (CO2), gridded EI Open-source Data Inventory for Anthropogenic CO2 (ODIAC) with the multi-resolution, spatially explicit bottom-up EI geoinformation technologies, spatio-temporal approaches, and full carbon account for improving the accuracy of GHG inventories (GESAPU) over the domain of Poland. By taking full advantage of the data granularity that bottom-up EI offers, this study characterized the potential biases in spatial disaggregation by emission sector (point and non-point emissions) across different scales (national, subnational/regional, and urban policy-relevant scales) and identified the root causes. While two EIs are in agreement in total and sectoral emissions (2.2% for the total emissions), the emission spatial patterns showed large differences (10~100% relative differences at 1 km) especially at the urban-rural transitioning areas (90–100%). We however found that the agreement of emissions over urban areas is surprisingly good compared with the estimates previously reported for US cities. This paper also discusses the use of spatially explicit EIs for climate mitigation applications beyond the common use in atmospheric modeling. We conclude with a discussion of current and future challenges of EIs in support of successful implementation of GHG emission monitoring and mitigation activity under the Paris Climate Agreement from the United Nations Framework Convention on Climate Change (UNFCCC) 21st Conference of the Parties (COP21). We highlight the importance of capacity building for EI development and coordinated research efforts of EI, atmospheric observations, and modeling to overcome the challenges.

Carbon accounting is important for quantifying the sources of greenhouse gas (GHG) emissions that are driving climate change, and is increasingly being used to guide policy, investment, business, and regulatory decisions. The current practice for accounting emissions from consumed electricity, guided by standards like the GHG protocol, uses annual-average grid emission factors, although previous studies have shown that grid carbon intensity varies across seasons and hours of the day. Previous case studies have shown that annual-average carbon accounting can bias emission inventories, but none have shown that this bias is substantial or widespread. This study addresses this gap by calculating emission inventories for thousands of residential, commercial, industrial, and agricultural facilities across the US, and explores the magnitude and direction of this bias compared to hourly accounting of emissions. Our results show that annual-average accounting can over- or under-estimate carbon inventories as much as 35% in certain settings but result in effectively no bias in others. Bias will be greater in regions with high variation in carbon intensity, and for end-users with high variation in their electricity consumption across hours and seasons. As variation in carbon intensity continues to grow with growing shares of variable and intermittent renewable generation, these biases will only continue to worsen in the future. In most cases, using monthly-average emission factors does not substantially reduce bias compared to annual averages. Thus, the authors recommend that hourly accounting be adopted as the best practice for emissions inventories of consumed electricity.

Global aviation emissions have been growing despite international efforts to limit climate change. Quantifying the status quo of domestic and international aviation emissions is necessary for establishing an understanding of current emissions and their mitigation. Yet, a majority of the United Nations framework convention on climate change (UNFCCC)-ratifying parties have infrequently disclosed aviation emissions within the international framework, if at all. Here, we present a set of national aviation emission and fuel burn inventories for these 197 individual parties, as calculated by the high-resolution aviation transport emissions assessment model (AviTeam) model. In addition to CO 2 emissions, the AviTeam model calculates pollutant emissions, including NO x , SO x , unburnt hydrocarbons, black carbon, and organic carbon. Emission inventories are created in aggregated and gridded format and rely on Automatic Dependent Surveillance–Broadcast combined with schedule data. The cumulative global fuel burn is estimated at 291 Tg for the year 2019. This corresponds to CO 2 emissions of 920 Tg, with 306 Tg originating from domestic aviation. We present emissions from 151 countries that have yet to report their emissions for 2019, which sum to 417 TgCO 2 . The improved availability of national emissions data facilitated by this inventory could support mitigation efforts in developed and developing countries and shows that such tools could bolster sector reporting to the UNFCCC.

The livestock sector is a major contributor to agricultural greenhouse gas (GHG) and nitrogen (N) emissions and efforts are being made to reduce these emissions. National emission inventories are the main tool for reporting emissions. They have to be consistent, comparable, complete, accurate and transparent. The quality of emission inventories is affected by the reporting methodology, emission factors and knowledge of individual sources. In this paper, we investigate the effects of moving from the 1996 IPCC Guidelines for National Greenhouse Gas Inventories to the 2006 IPCC Guidelines on the emission estimates from the livestock sector. With Austria as a case study, we estimated the emissions according to the two guidelines, revealing marked changes in emission estimates from different source categories resulting from changes in the applied methodology. Overall estimated GHG emissions from the livestock sector decreased when applying the IPCC 2006 methodology, except for emissions from enteric fermentation. Our study revealed shifts in the relative importance of main emission sources. While the share of CH 4 emissions from enteric fermentation and manure management increased, the share of N 2 O emissions from manure management and soils decreased. The most marked decrease was observed for the share of indirect N 2 O emissions. Our study reveals a strong relationship between the emission inventory methodology and mitigation options as mitigation measures will only be effective for meeting emission reduction targets if their effectiveness can be demonstrated in the national emission inventories. We include an outlook on the 2019 IPCC Refinement and its potential effects on livestock emissions estimates. Emission inventory reports are a potent tool to show the effect of mitigation measures and the methodology prescribed in inventory guidelines will have a distinct effect on the selection of mitigation measures.

Anthropogenic emissions of nitrogen oxides (NOx) can change rapidly due to economic growth or control measures. Bottom‐up emissions estimated using source‐specific emission factors and activity statistics require years to compile and can become quickly outdated. We present a method to use satellite observations of tropospheric NO2 columns to estimate changes in NOx emissions. We use tropospheric NO2 columns retrieved from the SCIAMACHY satellite instrument for 2003–2009, the response of tropospheric NO2 columns to changes in NOx emissions determined from a global chemical transport model (GEOS‐Chem), and the bottom‐up anthropogenic NOx emissions for 2006 to hindcast and forecast the inventories. We evaluate our approach by comparing bottom‐up and hindcast emissions for 2003. The two inventories agree within 6.0% globally and within 8.9% at the regional scale with consistent trends in western Europe, North America, and East Asia. We go on to forecast emissions for 2009. During 2006–2009, anthropogenic NOx emissions over land increase by 9.2% globally and by 18.8% from East Asia. North American emissions decrease by 5.7%.

Anthropogenic carbon dioxide (CO2) emissions from bottom-up inventories have high uncertainties due to the usage of proxy data in creating these inventories. To evaluate bottom-up inventories, satellite observations of atmospheric CO2 with continuously improved accuracies have shown great potential. In this study, we evaluate the consistency and uncertainty of four gridded CO2 emission inventories, including CHRED, PKU, ODIAC, and EDGAR that have been commonly used to study emissions in China, using GOSAT and OCO-2 satellite observations of atmospheric column-averaged dry-air mole fraction of CO2 (XCO2). The evaluation is carried out using two data-driven approaches: (1) quantifying the correlations of the four inventories with XCO2 anomalies derived from the satellite observations; (2) comparing emission inventories with emissions predicted by a machine learning-based model that considers the nonlinearity between emissions and XCO2. The model is trained using long-term datasets of XCO2 and emission inventories from 2010 to 2019. The result shows that the inconsistencies among these four emission inventories are significant, especially in areas of high emissions associated with large XCO2 values. In particular, EDGAR shows a larger difference to CHRED over super-emitting sources in China. The differences for ODIAC and EDGAR, when compared with the machine learning-based model, are higher in Asia than those in the USA and Europe. The predicted emissions in China are generally lower than the inventories, especially in megacities. The biases depend on the magnitude of inventory emissions with strong positive correlations with emissions (R2 is larger than 0.8). On the contrary, the predicted emissions in the USA are slightly higher than the inventories and the biases tend to be random (R2 is from 0.01 to 0.5). These results indicate that the uncertainties of gridded emission inventories of ODIAC and EDGAR are higher in Asian countries than those in European and the USA. This study demonstrates that the top-down approach using satellite observations could be applied to quantify the uncertainty of emission inventories and therefore improve the accuracy in spatially and temporally attributing national/regional totals inventories.

We present the comparison of source-partitioned CO2 flux measurements with a high-resolution urban CO2 emissions inventory (Hestia). Tower-based measurements of CO and 14C are used to partition net CO2 flux measurements into fossil and biogenic components. A flux footprint model is used to quantify spatial variation in flux measurements. We compare the daily cycle and spatial structure of Hestia and eddy-covariance derived fossil fuel CO2 emissions on a seasonal basis. Hestia inventory emissions exceed the eddy-covariance measured emissions by 0.36 µmol m−2 s−1 (3.2%) in the cold season and 0.62 µmol m−2 s−1 (9.1%) in the warm season. The daily cycle of fluxes in both products matches closely, with correlations in the hourly mean fluxes of 0.86 (cold season) and 0.93 (warm season). The spatially averaged fluxes also agree in each season and a persistent spatial pattern in the differences during both seasons that may suggest a bias related to residential heating emissions. In addition, in the cold season, the magnitudes of average daytime biological uptake and nighttime respiration at this flux site are approximately 15% and 27% of the mean fossil fuel CO2 emissions over the same time period, contradicting common assumptions of no significant biological CO2 exchange in northern cities during winter. This work demonstrates the effectiveness of using trace gas ratios to adapt eddy-covariance flux measurements in urban environments for disaggregating anthropogenic CO2 emissions and urban ecosystem fluxes at high spatial and temporal resolution.

China's implementation of stringent emission control measures during 2014–2020 has effectively reduced filterable particulate matter (FPM) emissions from stationary combustion sources, while increasing the contribution of condensable particulate matter (CPM) to total PM emissions. However, the lack of CPM emission inventories hinders the assessment of atmospheric impacts. This study developed a CPM emission inventory for China using field measurements from 148 typical industrial plants/processes. CPM's contribution to total PM emissions from stationary combustion sources had surged from 48.5% to 59.9% during 2014–2020, and will reach approximately 76% by 2030 under current emission control strategies in China. Furthermore, CPM constituted 14.5 ± 8.5% of ambient PM2.5 concentrations during January 2019 in China. Within this CPM contribution, 21.8% was contributed by sulfate/ammonium from coal combustion and ammonia slip in air pollution control devices. These findings call for establishing CPM‐specific emission standards and curbing ammonia slip for further improvements in air quality.

Volatile organic compounds (VOCs) in urban areas are of great interest due to their significant role in forming ground-level ozone and adverse public health effects. Emission inventories usually compile the outdoor VOCs emission sources (e.g., traffic and industrial emissions). However, considering emissions from volatile chemical products (e.g., solvents, printing ink, personal care products) is challenging because of scattered data and the lack of an effective method to estimate the VOCs emission rate from these chemical products. This paper aims to systematically analyse potential sources of VOCs emission in Canada’s built environment, including volatile chemical products. Also, spatial variation of VOCs level in the ambient atmosphere is examined to understand the VOC relationship with ozone and secondary organic aerosol formation. The study shows that VOCs level may vary among everyday microenvironments (e.g., residential areas, offices, and retail stores) depending on the frequency of product consumption, building age, ventilation condition, and background ambient concentration in the atmosphere. However, it is very difficult to establish VOC speciation and apportionment to different volatile chemical products that contribute most significantly to exposure and target subpopulations with elevated levels. Thus, tracer compounds can be used to identify inventory sources at the consumer end. A critical overview highlights the limitations of existing VOC estimation methods and possible approaches to control VOC emissions. The findings provide crucial information to establish an emission inventory framework for volatile chemical products at a national scale and enable policymakers to limit VOCs emission from various volatile chemical products.

Accurate emission inventories are essential for effective air quality prediction and management. Traditional bottom‐up methods and top‐down data assimilation approaches struggle to balance accuracy, timeliness, and usability. This study presents a novel inversion method combining deep learning (DL) and 3D‐Variational (3DVar) assimilation, termed DL‐3DVar. Based on the prior emissions and SO2 concentration observations, this method was applied to invert China's grided hourly SO2 emission inventory during the 2023 Chinese New Year (CNY). The results show that DL‐3DVar estimated an hourly SO2 emission increment of 1.95 × 106 kg at midnight on CNY's Eve relative to the prior emissions, primarily due to extensive fireworks. Forecast experiments showed that using DL‐3DVar inverted emissions reduced the root‐mean‐square error (RMSE) by 52.9% and increased the correlation coefficient (CORR) by 331% compared to the prior emissions. This high‐temporal‐resolution emission inventory can improve pollutant forecasting and provide robust support for air pollution control policymaking.