Anthropogenic Emission Inventory Research Articles

The greenhouse gas observation mission with Global Observing SATellite for Greenhouse gases and Water cycle (GOSAT-GW): objectives, conceptual framework and scientific contributions

The Japanese Global Observing SATellite for Greenhouse gases and Water cycle (GOSAT-GW) will be an Earth-observing satellite to conduct global observations of atmospheric carbon dioxide (CO2), methane (CH4), and nitrogen dioxide (NO2) simultaneously from a single platform. GOSAT-GW is the third satellite in the series of the currently operating Greenhouse gases Observing SATellite (GOSAT) and GOSAT-2. It will carry two sensors, the Total Anthropogenic and Natural emissions mapping SpectrOmeter-3 (TANSO-3) and the Advanced Microwave Scanning Radiometer 3 (AMSR3), with the latter dedicated to the observation of physical parameters related to the water cycle. TANSO-3 is a high-resolution grating spectrometer designed to measure reflected sunlight in the visible to short-wave infrared spectral ranges. It aims to retrieve the column-averaged dry-air mole fractions of CO2 and CH4 (denoted as XCO2 and XCH4, respectively), as well as the vertical column density of tropospheric NO2. The TANSO-3 sensor onboard GOSAT-GW will utilize the wavelength bands of 0.45, 0.76, and 1.61 µm for NO2, O2, and CO2 and CH4 retrievals, respectively. GOSAT-GW will fly in a sun-synchronous orbit with a local overpass time of approximately 13:30 and a 3-day ground-track repeat cycle. The TANSO-3 sensor has two observation modes in the push-broom operation: Wide Mode, which provides globally covered maps with a 10-km spatial resolution within 3 days, and Focus Mode, which provides snapshot maps over targeted areas with a high spatial resolution of 1–3 km. The objectives of the GOSAT-GW mission include (1) monitoring atmospheric global-mean concentrations of greenhouse gasses (GHGs), (2) verifying national anthropogenic GHG emissions inventories, and (3) detecting GHG emissions from large sources, such as megacities and power plants. A comprehensive validation exercise will be conducted to ensure that the sensor products’ quality meets the required precision to achieve the above objectives. With a projected operational lifetime of seven years, GOSAT-GW will provide vital space-based constraints on both anthropogenic and natural GHG emissions. These measurements will contribute significantly to climate change mitigation efforts, particularly by supporting the Global Stocktake (GST) mechanism, a key element of the Paris Agreement.

Satellite quantification of methane emissions from South American countries: a high-resolution inversion of TROPOMI and GOSAT observations

Abstract. We use 2021 TROPOMI and GOSAT satellite observations of atmospheric methane in an analytical inversion to quantify national methane emissions from South America at up to 25 km × 25 km resolution. From the inversion, we derive optimal posterior estimates of methane emissions, adjusting a combination of national anthropogenic emission inventories reported by individual countries to the United Nations Framework Convention on Climate Change (UNFCCC), the UNFCCC-based Global Fuel Exploitation Inventory (GFEIv2), and the Emissions Database for Global Atmospheric Research (EDGARv7) as prior estimates. We also evaluate two alternative wetland emission inventories (WetCHARTs and LPJ-wsl) as prior estimates. Our best posterior estimates for wetland emissions are consistent with previous inventories for the Amazon but lower for the Pantanal and higher for the Paraná. Our best posterior estimate of South American anthropogenic emissions is 48 (41–56) Tg a−1, where numbers in parentheses are the range from our inversion ensemble. This is 55 % higher than our prior estimate and is dominated by livestock (65 % of anthropogenic total). We find that TROPOMI and GOSAT observations can effectively optimize and separate national emissions by sector for 10 of the 13 countries and territories in the region, 7 of which account for 93 % of continental anthropogenic emissions: Brazil (19 (16–23) Tg a−1), Argentina (9.2 (7.9–11) Tg a−1), Venezuela (7.0 (5.5–9.9) Tg a−1), Colombia (5.0 (4.4–6.7) Tg a−1), Peru (2.4 (1.6–3.9) Tg a−1), Bolivia (0.96 (0.66–1.2) Tg a−1), and Paraguay (0.93 (0.88–1.0) Tg a−1). Our estimates align with the prior estimates for Brazil, Bolivia, and Paraguay but are significantly higher for other countries. Emissions in all countries are dominated by livestock (mainly enteric fermentation) except for oil–gas in Venezuela and landfills in Peru. Methane intensities from the oil–gas industry are high in Venezuela (33 %), Colombia (6.5 %), and Argentina (5.9 %). The livestock sector shows the largest difference between our top-down estimate and the UNFCCC prior estimates, and even countries using complex bottom-up methods report UNFCCC emissions significantly lower than our posterior estimate. These discrepancies could stem from underestimations in IPCC-recommended bottom-up calculations or uncertainties in the inversion from aggregation error and the prior spatial distribution of emissions.

ПОТЕНЦИАЛ И ВОЗМОЖНОСТИ АГРОЛЕСОВОДСТВА НА НЕИСПОЛЬЗУЕМЫХ ЗЕМЛЯХ СЕЛЬСКОХОЗЯЙСТВЕННОГО НАЗНАЧЕНИЯ

According to the National Inventory of Anthropogenic Emissions, agriculture is one of the largest sources of greenhouse gas emissions, while the proper use of its potential can help mitigate climate change. The article provides a scientific basis for the models of climate projects that can be implemented on agricultural lands. It has been determined that the greatest complementarity to the baseline is provided by such climate solutions on agricultural lands as agroforestry, zero-tillage technologies, and restoration of degraded pastures. It has been established that among all resource-saving methods of managing agricultural lands, agroforestry has the greatest sequestration potential. A quantitative assessment of the areas on agricultural lands in the constituent entities of the Russian Federation suitable for the implementation of climate projects has made it possible to determine that the greatest potential for the implementation of climate projects on agroforestry will be in areas of arable land that have not been used for their main purpose for a long time, including those overgrown with trees and shrubs. A classification of the subjects of the federal districts of the Russian Federation was carried out from the position of favorable and unsuitable conditions formed in them on unused agricultural lands for the implementation of climate projects.

Effect of Animal and Poultry Manure Processing Technologies on Greenhouse Gas Emissions

Introduction. The agricultural sector is one of the most important sources of anthropogenic emissions. The correct accounting of greenhouse gas emissions in this sector depends on technologies used to process animal/poultry manure. To date, there is a lack of research to adjust the methane and nitrous oxide emissions from existing animal/poultry manure sto­rages, because of the variety of technologies used. This is why the methodological approach developed to estimate the annual emissions of methane and nitrous oxide is so important.Aim of the Study. The study is aimed at determining the impact of manure and litter processing technologies on annual emissions of methane and nitrous oxide.Materials and Methods. To determine the emission of nitrous oxide and methane, there were calculated mass of animal/poultry manure and its total nitrogen and carbon content; there were analysed manure processing technologies such as long-term manure storing, passive and active composting, biofermentation, drying and granulation, incineration. There were performed calculations for two options: 1) according to the National Inventory of Anthropogenic Emissions, taking into account the share of manure processed with the use of each technology in 2022; 2) according to the actual data of technology distribution in 2022. The predictive estimate for the period up to 2030 was made for regions in the North-Western Federal District of the Russian Federation.Results. The study analysed animal and poultry housing technologies at three types of enterprises: agricultural organizations, peasant (private) farms, and household farms. There was calculated the animal/poultry manure mass generated at each type of enterpri­ses and determined the share of manure processed with the use of each technology. Based on the data of the North-Western Federal District, there were calculated methane and nitrous oxide emissions in 2022 in CO2-eq.Discussion and Conclusions. There was estimated the effect of manure collection and sto­rage technologies on methane and nitrous oxide emissions. The obtained data on emissions exceeded by 35.6% (methane) and 14.2% (nitrous oxide) those calculated by the methods used in National Inventory indicating their refinement expediency. Categorization of agricultural enterprises makes calculations simpler for regional and national assessments. The refined data on manure collection and storing technologies and the emissions specific for these technologies will help to perform prediction calculations and determine options for technological upgrading to mitigate GHG emissions.

Soil Emissions of Reactive Oxidized Nitrogen Reduce the Effectiveness of Anthropogenic Source Control in China.

Nitrogen dioxide (NO2) has decreased by ∼33% across over 1200 monitoring sites in China during 2015-2023, following a series of clean air policies. However, most of these sites are located in or near cities, leading to uncertainties in NO2 trends beyond urban regions due to limited observations. Here, we used satellite measurements to examine the differences in NO2 trends between urban and rural China. In urban areas, NO2 columns decreased by 4.0% per annum (a-1) during summer 2011-2023, consistent with bottom-up anthropogenic emission inventory and in situ measurements. In contrast, rural NO2 columns showed a slower than expected reduction (-2.6 to -0.0% a-1) during the same period. Model simulations with updates in the soil reactive oxidized nitrogen (Nr) scheme indicated that increasing soil Nr emissions can be an important factor contributing to the observed slow NO2 decrease in rural areas. This unregulated source increased summertime pollutant levels, partially offsetting the national efforts to mitigate NO2, ozone (O3), and particulate nitrate (NO3-) levels by 20.9%, 15.4%, and 4.7%, respectively, from 2011 to 2020. In the agriculture-intensive North China Plain, the increase in soil Nr emissions offset 46.6% of the NO2 reductions achieved by clean air policies. Our results highlight the increasing significance of soil emissions and the need to control them in future air-quality policies.

Advanced method for compiling a high-resolution gridded anthropogenic CO2 emission inventory at a regional scale

ABSTRACT As global climate change accelerates, the need for precise and high-resolution carbon emission inventories becomes increasingly urgent, particularly in rapidly developing regions. This study introduces an advanced methodology for constructing regional-scale gridded anthropogenic CO2 emission inventories, integrating the latest energy consumption data and remote sensing technologies. By systematically calculating emissions across various sectors and energy types, and utilizing Geographic Information System (GIS) for spatial distribution, we have generated a high-resolution emission map with a 1 km × 1 km grid. The accuracy of the map was validated through regression analysis with the Multi-resolution Emission Inventory for China (MEIC) dataset, ensuring its reliability. This work provides critical insights for targeted emission reduction strategies, contributing to the formulation of sustainable development policies that align with global climate objectives.

ACEIC: a comprehensive anthropogenic chlorine emission inventory for China

Abstract. Chlorine species play a crucial role as precursors to Cl radicals, which can significantly impact the atmospheric oxidation capacity and influence the levels of trace gases related to climate and air quality. Several studies have established a chlorine emission inventory in China in recent years, but the emission remains uncertain and requires further investigation. The Anthropogenic Chlorine Emission Inventory for China (ACEIC) was the first chlorine emission inventory for China based on local data developed in our previous study, which only includes the emissions from coal combustion and waste incineration. In this study, we updated this inventory to include data for a more recent year (2019) and expanded the range of species considered (HCl, fine particulate Cl−, Cl2, and hypochlorous acid (HOCl)) and the number of anthropogenic sources (41 specific sources). Compared with previous studies, this updated inventory considered more anthropogenic sources, used more localized emission factors, and adopted more refined estimation methods. The total emissions of HCl, fine particulate Cl−, Cl2, and HOCl in mainland China for the year 2019 were estimated to be 361 (−18 % to 27 %), 174 (−27 % to 59 %), 18 (−10 % to 15 %), and 79 (−12 % to 18 %) Gg, respectively. To facilitate analysis, we aggregated the chlorine emissions from various sources into five economic sectors: power, industry, residential, agriculture, and biomass burning. HCl emissions were primarily derived from the industry (43 %), biomass burning (38 %), and residential (13 %) sectors. The biomass burning and industry sectors accounted for 74 % and 19 % of the fine particulate Cl− emissions, respectively. Residential and industry sectors contributed 61 % and 29 % of the total Cl2 emissions. HOCl emissions were predominantly from the residential sector, constituting 90 % of the total emissions. Notably, the usage of chlorine-containing disinfectants was identified as the most significant source of Cl2 and HOCl emissions in the residential sector. Geographically, regions with high HCl and fine particulate Cl− emissions were found in the North China Plain, northeastern China, central China, and the Yangtze River Delta, whereas the Pearl River Delta, Yangtze River Delta, and Beijing–Tianjin–Hebei regions exhibited elevated levels of Cl2 and HOCl emissions. Regarding monthly variation, emissions of HCl and fine particulate Cl− were higher during early spring (February to April) and winter (December to January) due to intensified agricultural activities, while Cl2 and HOCl emissions were higher in the summer months due to increased demand for water disinfection. We incorporated this emission inventory into the chemical transport model and found the simulated concentrations of chlorine species agreed reasonably well with the observations, which suggested the relatively faithful estimations of their emissions. This updated inventory contributes to a better understanding of anthropogenic sources of chlorine species and can aid in the formulation of emission control strategies to mitigate secondary pollution in China.

Unexpected anthropogenic emission decreases explain recent atmospheric mercury concentration declines.

Anthropogenic activities emit ~2,000 Mg y-1 of the toxic pollutant mercury (Hg) into the atmosphere, leading to long-range transport and deposition to remote ecosystems. Global anthropogenic emission inventories report increases in Northern Hemispheric (NH) Hg emissions during the last three decades, in contradiction with the observed decline in atmospheric Hg concentrations at NH measurement stations. Many factors can obscure the link between anthropogenic emissions and atmospheric Hg concentrations, including trends in the reemissions of previously released anthropogenic ("legacy") Hg, atmospheric sink variability, and spatial heterogeneity of monitoring data. Here, we assess the observed trends in gaseous elemental mercury (Hg0) in the NH and apply biogeochemical box modeling and chemical transport modeling to understand the trend drivers. Using linear mixed effects modeling of observational data from 51 stations, we find negative Hg0 trends in most NH regions, with an overall trend for 2005 to 2020 of -0.011 ± 0.006 ng m-3 y-1 (±2 SD). In contrast to existing emission inventories, our modeling analysis suggests that annual NH anthropogenic emissions must have declined by at least 140 Mg between the years 2005 and 2020 to be consistent with observed trends. Faster declines in 95th percentile Hg0 values than median values in Europe, North America, and East Asian measurement stations corroborate that the likely cause is a decline in nearby anthropogenic emissions rather than background legacy reemissions. Our results are relevant for evaluating the effectiveness of the Minamata Convention on Mercury, demonstrating that existing emission inventories are incompatible with the observed Hg0 declines.

Local and regional enhancements of CH4, CO, and CO2 inferred from TCCON column measurements

Abstract. In this study, we demonstrate the utility of available correlative measurements of carbon species to identify regional and local air mass characteristics as well as their associated source types. In particular, we combine different regression techniques and enhancement ratio algorithms with carbon monoxide (CO), carbon dioxide (CO2), and methane (CH4) total column abundance from 11 sites of the Total Carbon Column Observing Network (TCCON) to infer relative contributions of regional and local sources to each of these sites. The enhancement ratios provide a viable alternative to univariate measures of relationships between the trace gases that are insufficient in capturing source-type and transport signatures. Regional enhancements are estimated from the difference between bivariate regressions across a specific time window of observed total abundance of these species (BERr for bulk enhancement regression ratio) and inferred anomalies (AERr for anomaly enhancement regression ratio) associated with a site-specific background. Since BERr and AERr represent the bulk and local species enhancement ratio, respectively, its difference simply represents the site-specific regional component of these ratios. We can then compare these enhancements for CO2 and CH4 with CO to differentiate between combustion and non-combustion air masses. Our results show that while the regional and local influences in enhancements vary across sites, dominant characteristics are found to be consistent with previous studies over these sites and with bottom-up anthropogenic and fire emission inventories. The site in Pasadena shows a dominant local influence (> 60 %) across all species enhancement ratios, which appear to come from a mixture of biospheric and combustion activities. In contrast, Anmyeondo shows more regionally influenced (> 60 %) air masses associated with high-temperature and/or biofuel combustion activities. Ascension Island appears to only show a large regional influence (> 80 %) on CO / CO2 and CO / CH4, which is indicative of transported and combustion-related CO from the nearby African region, consistent with a sharp rise in column CO (3.51 ± 0.43 % ppb yr−1) at this site. These methods have important applications to source analysis using spaceborne column retrievals of these species.

On the Biases of MERRA-2 Reanalysis and Ground-Based Measurements of Black Carbon Aerosols Over India

The precise measurement of black carbon (BC) and assessment of involved biases are very important considering its serious health and climatic effects. MERRA-2 reanalysis data (modern-era retrospective analysis for research and applications, version 2) of surface black carbon (SBC) and ground measurements by optical photometers (mostly Aethalometer) have been used widely to study the temporal and spatial variation of BC aerosols over India. However, a significant bias (more than 70%) between MERRA-2 SBC concentration and ground measurements of BC (by Aethalometer, referred as MBC (AE)) was observed by Malik and Aggarwal, (2021). In the current study, the possible reasons for this bias are further evaluated by comparing the MERRA-2 SBC and Aethalometer measurements against a reference photometer, i.e., continuous soot monitoring system (COSMOS). Three-year (2020-2022) SBC concentrations of MERRA-2 reanalysis data are compared against simultaneous measurements of BC by COSMOS (MBC (COSMOS)). Additionally, the biases in ground measurements are further investigated by comparing one-month (March 2023) observations of Aethalometer (MBC (AE)). These comparison results revealed a 40% underestimation in MERRA-2 reanalysis during 2020-2022. The higher underestimation (50%) of SBC during increased biomass emissions months (October to May), in contrast to the 25% underestimation during conventional emissions months (June to September), emphasizes the underestimation of biomass emissions inputs in MERRA-2 SBC reanalysis. The initial large bias between raw data of Aethalometer (MBC (AE-raw) = 1.92-1.98 times MBC (COSMOS)) get reduced significantly when correction due to multiple scattering in the filter media and mass absorption cross-section (MAC) are incorporated to correct the Aethalometer data (MBC (AE-corr) = 1.15-1.18 times MBC (COSMOS)). The findings of this study underscore the need to harmonize the correction methods for multiple scattering and redefine the MAC values for Aethalometer measurements as well as updating the anthropogenic emission inventories in MERRA-2 SBC reanalysis.

Source attribution of carbon monoxide over Northern India during crop residue burning period over Punjab

National Capital Territory of Delhi and its satellite cities suffer from poor air quality during the post-monsoon months of October–November. In this study, a novel attempt is made to estimate the contribution of different emission sources (industrial, residential, power generation, transportation, biomass burning, photochemical production, lateral transport, etc.) towards the criteria air pollutant carbon monoxide (CO) concentration over North India. Multiple simulations of the WRF-Chem model with a tagged tracer approach with different inputs (6 anthropogenic emission inventories and 3 biomass burning emission inventories) were used. The model performance was evaluated against the MOPITT retrieved CO surface concentration. Analysis of model simulated CO over North India suggests that anthropogenic emissions contribute around 32–49% to surface CO concentration while crop residue burning contributes 27–44% of which 80% originates from Punjab. For Delhi, the contribution from anthropogenic sources is dominant (53–77%) of which 10–28% is from the domestic sector and 14–55% is from the transport sector. Agricultural waste burning contributes about 15–30% to Delhi's surface CO concentration (of which 75% originates from Punjab). Crop residue burning emission is a chief source of CO over Punjab with a contribution of about 56–76%. The results suggest that industrial, transport, and domestic sector activities are more responsible for increased CO levels over New Delhi and surrounding regions than crop residue burning over Punjab. Furthermore, critical meteorological parameters like 10 m wind speed, boundary layer height, 2 m temperature, total precipitation, and relative humidity were evaluated against CO concentration to understand their impact on CO distribution. Results conclude that deteriorating air quality over the North Indian region is caused by a combination of prevailing meteorological factors (such as slow winds, shallow mixing layer, and cold temperatures) and man-made emissions.

Evaluation of WRF-Chem PM2.5 simulations in Thailand with different anthropogenic and biomass-burning emissions

Thailand experiences severe air quality issues, predominantly due to PM2.5 pollution that surpasses WHO guidelines. The main sources are attributed to energy production, industrial activities, vehicular emissions, agricultural burning, and transboundary transport of pollutants. Understanding the transport and transformation of these pollutants is necessary for addressing air quality issues. The Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) provides information about meteorology, chemical reactions, and transport of trace gases and aerosols. The accuracy of WRF-Chem simulations greatly depends on the choice of anthropogenic and biomass burning emissions inventories. This study provides a detailed evaluation of these inventories to model PM2.5 concentrations in Thailand during both haze and off-haze seasons in 2019. We evaluated WRF-Chem using four anthropogenic emission inventories—CAMS-GLOB-ANT, ECLIPSE, HTAP, and REAS—and four biomass burning emissions inventories—FINN1.5, FINN2.5 MOD, FINN2.5 MODVAR, and QFED—using data from ground-based air quality stations, MODIS AOD, and MOPITT CO satellite data. Our findings suggest CAMS-GLOB-ANT performs optimally for North Thailand, while HTAP and REAS are more effective in Eastern Thailand. For biomass burning, FINN1.5 shows superior performance. The study also highlights the challenge in capturing PM2.5 diurnal variability, particularly due to inaccuracies in simulating the planetary boundary layer height during nighttime in complex terrains. Moreover, our analysis exhibits moderate model performances during the off-haze season while using global and regional anthropogenic emissions in Thailand, emphasizing the need for improving anthropogenic inventories for reliable air quality prediction. For biomass burning emissions, updating emission factors to reflect Thailand's specific vegetation types is recommended to improve WRF-Chem's representation of PM2.5 levels.

Evaluating CHASER V4.0 global formaldehyde (HCHO) simulations using satellite, aircraft, and ground-based remote-sensing observations

Abstract. Formaldehyde (HCHO), a precursor to tropospheric ozone, is an important tracer of volatile organic compounds (VOCs) in the atmosphere. Two years (2019–2020) of HCHO simulations obtained from the global chemistry transport model CHASER at a horizontal resolution of 2.8° × 2.8° have been evaluated using the Tropospheric Monitoring Instrument (TROPOMI) and multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations. In situ measurements from the Atmospheric Tomography Mission (ATom) in 2018 were used to evaluate the HCHO simulations for 2018. CHASER reproduced the TROPOMI-observed global HCHO spatial distribution with a spatial correlation (r) of 0.93 and a negative bias of 7 %. The model showed a good capability to reproduce the observed magnitude of the HCHO seasonality in different regions, including the background conditions. The discrepancies between the model and satellite in the Asian regions were related mainly to the underestimated and missing anthropogenic emission inventories. The maximum difference between two HCHO simulations based on two different nitrogen oxide (NOx) emission inventories was 20 %. TROPOMI's finer spatial resolution than that of the Ozone Monitoring Instrument (OMI) sensor reduced the global model–satellite root-mean-square error (RMSE) by 20 %. The OMI- and TROPOMI-observed seasonal variations in HCHO abundances were consistent. The simulated seasonality showed better agreement with TROPOMI in most regions. The simulated HCHO and isoprene profiles correlated strongly (R=0.81) with the ATom observations. However, CHASER overestimated HCHO mixing ratios over dense vegetation areas in South America and the remote Pacific region (background condition), mainly within the planetary boundary layer (< 2 km). The simulated seasonal variations in the HCHO columns showed good agreement (R>0.70) with the MAX-DOAS observations and agreed within the 1σ standard deviation of the observed values. However, the temporal correlation (R∼0.40) was moderate on a daily scale. CHASER underestimated the HCHO levels at all sites, and the peak occurrences in the observed and simulated HCHO seasonality differed. The coarseness of the model's resolution could potentially lead to such discrepancies. Sensitivity studies showed that anthropogenic emissions were the highest contributor (up to ∼ 35 %) to the wintertime regional HCHO levels.

Evaluation and prediction of anthropogenic impacts on long-term multimedia fate and health risks of PFOS and PFOA in the Elbe River Basin

Perfluorooctane sulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) have aroused great concern owing to their widespread occurrence and toxic effects. However, their long-term trends and multimedia fate remain largely unknown. Here, we investigate the spatiotemporal characteristics and periodic oscillations of PFOS and PFOA in the Elbe River between 2010 and 2021. Anthropogenic emission inventories and multimedia fugacity model were developed to analyse their historical and future transport fates and quantify related human risks in each medium for the three age groups. The results show that average PFOS and PFOA concentrations in the Elbe River were 4.08 and 3.41 ng/L, declining at the annual rate of 7.36% and 4.98% during the study period, respectively. Periodic oscillations of their concentrations and mass fluxes were most pronounced at 40–60 and 20–40 months. The multimedia fugacity model revealed that higher concentrations occurred in fish (PFOS: 14.29, PFOA: 0.40 ng/g), while the soil was their dominant sink (PFOS: 179, PFOA: 95 tons). The exchange flux between water and sediment was the dominant pathway in multimedia transportation (397 kg/year). Although PFOS and PFOA concentrations are projected to decrease by 22.41% and 50.08%, respectively, from 2021 to 2050, the hazard quotient of PFOS in fish is a low hazard. This study provides information for the assessment of PFOS and PFOA pollution in global watersheds and the development of related mitigation policies, such as banning fish predation in polluted rivers, to mitigate their risks.

Light oxygenated volatile organic compound concentrations in an Eastern Mediterranean urban atmosphere rivalling those in megacities

Highly resolved measurements of primary and secondary oxygenated volatile organic compounds (OVOCs) by proton-transfer-reaction mass spectrometry (PTR-MS) and the AMOVOC sampler (Airborne Measurements Of VOC) were performed in Beirut, Lebanon, during the ECOCEM (Emissions and Chemistry of Organic Carbon in the East Mediterranean) experiments. The OVOC concentrations (0.15–7.0 ppb) rival those reported for international megacities like Paris, Tokyo, or São Paulo (0.3–6.5 ppb). This study highlights the seasonal variability of OVOCs, the potential role of background pollution on OVOC concentrations, traffic emissions of OVOCs, and the secondary production of OVOCs during both summer and winter.The primary and secondary OVOC fractions were estimated using two methods based on the night-time emission ratio and photochemical age. Our calculations coupled with a correlation analysis revealed the following: firstly, background concentrations contributed significantly, especially for longer-lived OVOCs, such as methanol and acetone (30%–80%). Secondly, secondary production in summer increased up to 60%, except for methanol and isoprene oxidation products, i.e., for methacrolein and methyl vinyl ketone. Thirdly, the secondary production in the Eastern Mediterranean persisted in winter, and finally, strong primary traffic emissions dominated the primary biogenic emissions.Finally, the emission ratios were used to evaluate the global anthropogenic emission inventories downscaled to Lebanon. Although limited to two individual non-lumped species (formaldehyde and acetone), the emission ratios compared well, within a factor of 2. However, the emissions of aldehydes and ketones from the CAMS, Edgar, and MACCITY inventories showed discrepancies of up to three orders of magnitude. This demonstrates a need for improved OVOC representation in emission inventories, considering the atmospheric relevance and abundance of OVOCs and their use in volatile chemical products.

Improvement of the anthropogenic emission rate estimate in Ulaanbaatar, Mongolia, for 2020–21 winter

Ulaanbaatar (UB), the fast-growing capital of Mongolia, is known for its world's worst level of particulate matter (PM) concentrations in winter. However, current anthropogenic emission inventories over the UB are based on data from more than fifteen years ago, and satellite observations are scarce because UB is in high latitudes. During the winter of 2020–21, the first period of the Fine Particle Research Initiative in East Asia considering the National Differences (FRIEND), several times higher concentrations of PM in UB compared to other urban sites in East Asia were observed but not reproduced with a chemical transport model mainly due to the underestimated anthropogenic emissions. Therefore, we devised a method for sequentially adjusting emissions based on the reactivity of PM precursors using ground observations. We scaled emission rates for the inert species (CO, elemental carbon (EC), and organic carbon (OC)) to reproduce their observed ambient concentrations, followed by SO2 to reproduce the concentration of SO42−, which was examined to have the least uncertainty based on the abundance of observed NH3, and finally NO and NH3 for NO3−, and NH4+. This improved estimation is compared to regional inventories for Asia and suggests more than an order of magnitude increase in anthropogenic emissions in UB. Using the improved emission inventory, we were able to successfully reproduce independent observation data on PM2.5 concentrations in UB in December 2021 from the U.S. Embassy. During the campaign period, we found more than 50% of the SO42−, NO3−, and NH4+ increased in UB due to the improvement could travel to Beijing, China (BJ), and about 20% of the SO42− could travel to Noto, Japan (NT), more than 3000 km away. Also, the anthropogenic emissions in UB can effectively increase OC, NO3−, and NH4+ concentrations in BJ when Gobi dust storms occur.

Detection of Chinese Spring Festival in Beijing using in-situ CO2 observations and atmospheric inversion

Anthropogenic CO2 emissions inventories in urban areas are highly uncertain and lack fine-scale temporal information. Here, we use atmospheric CO2 observations from 6 sites and an ensemble-based "top-down" inversion system to estimate the spatiotemporal dynamics of CO2 emissions in the world's largest urban agglomeration of Beijing-Tianjin-Hebei (JJJ). Relative to the period before the Chinese Spring Festival (CSF) in 2019, we showed, for the first time, a 13.9% reduction and a 14.3% increase in emissions during and after the CSF, respectively, which is missed by a monthly emissions inventory. Moreover, the emissions reduction occurred in most of the JJJ areas. The exact timing of emissions dynamics inferred from the CO2 observations is consistent with a NO2 observation and a near-real-time daily “bottom-up” estimate. Here, we showed the advantages of "top-down" inversion on a fine temporal scale (e.g., daily to weekly) emissions assessment and its potential applications in refined emissions management and reductions.

Coordinated Control of Carbon Emission Reduction and Air Quality Improvement in the Industrial Sector in Hunan Province

Climate warming and air pollution are the main environmental problems in China. This study used China's Carbon Accounting Database, energy economic model, and air quality model to analyze the potential carbon emission peaking path and synergistic air quality improvement gain in the industrial sector in Hunan Province. Based on China's Carbon Accounting Database and the local industry/energy statistical yearbooks in Hunan, the total CO2 emissions in Hunan Province in 2019 were 310.6 Mt, of which the industrial sector accounted for over 70% of the emissions, mainly from the production and supply of electricity, steam, and heat; the production of non-metallic minerals; and the smelting and pressing of ferrous metals. Three potential industrial carbon emission peaking scenarios were analyzed using the LEAP energy economic model, including the business-as-usual scenario (peaking by 2030), moderate emission reduction scenario (peaking by 2028), and aggressive emission reduction scenario (peaking by 2025), by employing different economic growth rates, energy technology progress, and energy structures of the industrial sector. Furthermore, by combining the anthropogenic air pollutant emission inventory and the regional air quality model WRF-Chem, we analyzed the air quality improvement associated with various carbon emission peak paths. The results showed that the annual mean concentrations of major air pollutants had decreased in the three scenarios, especially in the Chang-Zhu-Tan Region. The aggressive emission reduction scenario was the most effective scenario, followed by the moderate emission reduction scenario and the business-as-usual scenario. Manufacturing was the sector with the most significant synergistic effect of pollution and carbon reduction. When carbon emission peaks were achieved, the annual average concentrations of PM2.5 and PM10 in Hunan Province could be synergistically reduced by 0.6-1.8 μg·m-3 and 1.8-8.9 μg·m-3, respectively. Our findings offer important insights into carbon emission peaking and can provide useful information for potential mitigation actions.

Optimising urban measurement networks for CO2 flux estimation: a high-resolution observing system simulation experiment using GRAMM/GRAL

Abstract. To design a monitoring network for estimating CO2 fluxes in an urban area, a high-resolution observing system simulation experiment (OSSE) is performed using the transport model Graz Mesoscale Model (GRAMMv19.1) coupled to the Graz Lagrangian Model (GRALv19.1). First, a high-resolution anthropogenic emission inventory which is considered as the truth serves as input to the model to simulate CO2 concentration in the urban atmosphere on 10 m horizontal resolution in a 12.3 km × 12.3 km domain centred in Heidelberg, Germany. By sampling the CO2 concentration at selected stations and feeding the measurements into a Bayesian inverse framework, CO2 fluxes on a neighbourhood scale are estimated. Different configurations of possible measurement networks are tested to assess the precision of posterior CO2 fluxes. We determine the trade-off between the quality and quantity of sensors by comparing the information content for different set-ups. Decisions on investing in a larger number or in more precise sensors can be based on this result. We further analyse optimal sensor locations for flux estimation using a Monte Carlo approach. We examine the benefit of additionally measuring carbon monoxide (CO). We find that including CO as tracer in the inversion enables the disaggregation of different emission sectors. Finally, we quantify the benefit of introducing a temporal correlation into the prior emissions. The results of this study have implications for an optimal measurement network design for a city like Heidelberg. The study showcases the general usefulness of the inverse framework developed using GRAMM/GRAL for planning and evaluating measurement networks in an urban area.

OMI-based emission source classification in East China and its spatial redistribution in view of pollution control measures

This study aims to generate a satellite-based qualitative emission source characterization for the heavily polluted eastern part of China in the 2010–2016 time period. The applied source identification technique relies on satellite-based NOx and SO2 emission estimates by OMI, their SO2:NOx ratio, and the MIX anthropogenic emission inventory to distinguish emissions from different emission categories (urban, industrial, natural) and characterize the dominant source per 0.25° × 0.25° grid cell in East China. Overall, we find good agreement between the satellite- and emission inventory–based spatiotemporal distribution and characterization of the dominant emission sources in East China in 2010–2016. In 2010, the satellite measurements suggest an emission distribution less dominated by industrial areas, a somewhat larger role for urban/transportation areas and agricultural activities, and more natural emissions in the southern part compared to the bottom-up emission categorization. In 2016, more than half of the classified emission categories over East China have remained the same. At the same time, there is a notable increase of agricultural lands and decrease of areas dominated by industry/transportation in 2016, suggestive of an overall decrease in heavy air pollution in East China over the course of 7 years. This is likely attributed to the sustained efforts of the Chinese government to drastically improve the air quality, especially since 2013 when the National Air Pollution Prevention and Control Action Plan was enacted. However, signs of urban expansion (urbanization) and rural–urban migration (“Go West” motion) stemmed from China’s rapid economic growth and labour demand are evident; escalating industrialization (even with cleaner means) and the urban population growth in East China resulted in stronger emissions from sources representing consumption and transportation which are strongly related to NO2 and PM10 pollution (rather than SO2) and are directly influenced by the population size. This resulted to a shift of the emissions from the east mainly to the north and northwest of East China. Overall, although the effectiveness of the Chinese environmental control policies has been successful, the air pollution problem remains an important concern.