High resolution temporal profiles in the Emissions Database for Global Atmospheric Research

Trends and drivers of anthropogenic NOx emissions in China since 2020

Nitrogen oxides (NOx), significant contributors to air pollution and climate change, form aerosols and ozone in the atmosphere. Accurate, timely, and transparent information on NOx emissions is essential for decision-making to mitigate both haze and ozone pollution. However, a comprehensive understanding of the trends and drivers behind anthropogenic NOx emissions from China-the world's largest emitter-has been lacking since 2020 due to delays in emissions reporting. Here we show a consistent decline in China's NOx emissions from 2020 to 2022, despite increased fossil fuel consumption, utilizing satellite observations as constraints for NOx emission estimates through atmospheric inversion. This reduction is corroborated by data from two independent spaceborne instruments: the TROPOspheric Monitoring Instrument (TROPOMI) and the Ozone Monitoring Instrument (OMI). Notably, a reduction in transport emissions, largely due to the COVID-19 lockdowns, slightly decreased China's NOx emissions in 2020. In subsequent years, 2021 and 2022, reductions in NOx emissions were driven by the industry and transport sectors, influenced by stringent air pollution controls. The satellite-based inversion system developed in this study represents a significant advancement in the real-time monitoring of regional air pollution emissions from space.

Expanding carbon neutrality strategies: Incorporating out-of-boundary emissions in city-level frameworks

Cities are increasingly vital in global carbon mitigation efforts, yet few have specifically tailored carbon neutrality pathways. Furthermore, out-of-boundary indirect greenhouse gas (GHG) emissions, aside from those related to electricity and heat imports, are often overlooked in existing pathways, despite their significance in comprehensive carbon mitigation strategies. Addressing this gap, here we introduce an integrated analysis framework focusing on both production and consumption-related GHG emissions. Applied to Wuyishan, a service-oriented city in Southern China, this framework provides a holistic view of a city's carbon neutrality pathway, from a full-scope GHG emission perspective. The findings reveal the equal importance of carbon reduction within and outside the city's boundaries, with out-of-boundary emissions accounting for 42% of Wuyishan's present total GHG emissions. This insight highlights the necessity of including these external factors in GHG accounting and mitigation strategy development. This framework serves as a practical tool for cities, particularly in developing countries, to craft effective carbon neutrality roadmaps that encompass the full spectrum of GHG emissions.

Long-term variations of air pollutants and public exposure in China during 2000-2020

Since 2000, China has faced severe air pollution challenges，prompting the initiation of comprehensive emission control measures post-2013. The subsequent implementation of these measures has led to remarkable enhancements in air quality. This study aims to enhance our understanding of the long-term trends in fine particulate matter (PM2.5) and gaseous pollutants of ozone (O3) and nitrogen dioxide (NO2) across China from 2000 to 2020. Utilizing the Community Multiscale Air Quality (CMAQ) model, we conducted a nationwide analysis of air quality, systematically quantifying model predictions against observations for pollutants. The CMAQ model effectively captured the trends of air pollutants, meeting recommended performance benchmarks. The findings reveal variations in pollutant concentrations, with initial increases in PM2.5 followed by a decline after 2013. The proportion of the population living in high PM2.5 concentrations (>75 μg/m3) decreased to <5 % after 2015. However, during the period from 2017 to 2020, around 40 % of the population continued to live in regions that did not meet the criteria for Chinese air quality standards (35 μg/m3). From 2000 to 2019, fewer than 20 % of the population met the WHO standard (100 μg/m3) for MDA8 O3. In 2000, 77 % of the population met the NO2 standard (<20 μg/m3), a figure that declined to 60 % between 2005 and 2014, nearly reaching 70 % in 2020. This study offers a comprehensive analysis of the changes in pollutants and public exposure in 2000-2020. It serves as a foundational resource for future efforts in air pollution control and health research.

Recent decline in carbon monoxide levels observed at an urban site in Ahmedabad, India

Carbon monoxide (CO) is a prominent air pollutant in cities, with far-reaching implications for both local air quality and global atmospheric chemistry. The long-term change in atmospheric CO levels at a specific location is influenced by a complex interplay of local emissions, atmospheric transport, and photochemical processes, making it a subject of considerable interest. This study presents an 8-year analysis (2014-2021) of in situ CO observations using a cutting-edge laser-based analyzer at an urban site in Ahmedabad, western India. The long-term observations reveal a subtle trend in CO levels, masked by contrasting year-to-year variations, particular after 2018, across distinct diurnal time windows. Mid-afternoon (12:00-16:00 h) CO levels, reflecting background and regional conditions, remained relatively stable over the study period. In contrast, evening (18:00-21:00 h) CO levels, influenced by local emissions, exhibited substantial inter-annual variability without discernible trends from 2014 to 2018. However, post-2018, evening CO levels showed a consistent decline, predating COVID-19 lockdown measures. This decline coincided with the nationwide adoption of Bharat stage IV emission standards and other measures aimed at reducing vehicular emissions. The COVID-19 lockdown in 2020 further resulted in a noteworthy 29% reduction in evening CO levels compared to the pre-lockdown (2014-2019) period, highlighting the potential for substantial CO reduction through stringent vehicular emission controls. The observed long-term changes in CO levels do not align with the decreasing emission estimated by various inventories from 2014 to 2018, suggesting a need for improved emission statistics in Indian urban regions. This study underscores the importance of ongoing continuous CO measurements in urban areas to inform policy efforts aimed at controlling atmospheric pollutants.

How Human Activities Affect Groundwater Storage

Despite the recognized influence of natural factors on groundwater, the impact of human activities remains less explored because of the challenges in measuring such effects. To address this gap, our study proposes an approach that considers carbon emissions as an indicator of human activity intensity and quantifies their impact on groundwater storage. The combination of carbon emission data and groundwater storage data for 17,152 grid cells over 16 years in 4 typical basins shows that they were generally negatively correlated, whereas both agriculture and aviation had positive impacts on groundwater storage. The longest impact from aviation and agriculture can even persist for 7 years. Furthermore, an increase of 1 Yg CO2/km2 per second in emissions from petroleum processing demonstrates the most pronounced loss of groundwater storage in the Yangtze River Basin (approximately 4.1 mm). Moreover, regions characterized by high-quality economic development tend to have favorable conditions for groundwater storage. Overall, our findings revealed the substantial role of human activities in influencing groundwater dynamics from both temporal and spatial aspects. This study fills a crucial gap by exploring the relationship between human activities and groundwater storage through the introduction of a quantitative modeling framework based on carbon emissions. It also provides insights for facilitating empirical groundwater management planning and achieving optimal emission reduction levels.

Predicting particulate matter, nitrogen dioxide, and ozone across Great Britain with high spatiotemporal resolution based on random forest models

In Great Britain, limited studies have employed machine learning methods to predict air pollution especially ozone (O3) with high spatiotemporal resolution. This study aimed to address this gap by developing random forest models for four key pollutants (fine and inhalable particulate matter [PM2.5 and PM10], nitrogen dioxide [NO2] and O3) by integrating multiple-source predictors at a daily level and 1-km resolution. The out-of-bag R2 (root mean squared error, RMSE) between predictions from models and measurements from monitoring stations in 2006-2013 was 0.85 (3.63 μg/m3) for PM2.5, 0.77 (6.00 μg/m3) for PM10, 0.85 (9.71 μg/m3) for NO2, and 0.85 (9.39 μg/m3) for maximum daily 8-h average (MDA8) O3 at daily level, and the predicting accuracy was higher at monthly and annual level. The high-resolution predictions captured characterized spatiotemporal patterns of the four pollutants. Higher concentrations of PM2.5, PM10, and NO2 were distributed in densely populated southern regions of Great Britain while O3 showed an inverse spatial pattern in general, which could not be fully depicted by monitoring stations. Therefore, predictions produced in this study could improve exposure assessment with less exposure misclassification and flexible exposure windows for future epidemiological studies to investigate the impact of air pollution across Great Britain.

China's Fossil Fuel CO2 Emissions Estimated Using Surface Observations of Coemitted NO2

Accurate estimates of fossil fuel CO2 (FFCO2) emissions are of great importance for climate prediction and mitigation regulations but remain a significant challenge for accounting methods relying on economic statistics and emission factors. In this study, we employed a regional data assimilation framework to assimilate in situ NO2 observations, allowing us to combine observation-constrained NOx emissions coemitted with FFCO2 and grid-specific CO2-to-NOx emission ratios to infer the daily FFCO2 emissions over China. The estimated national total for 2016 was 11.4 PgCO2·yr-1, with an uncertainty (1σ) of 1.5 PgCO2·yr-1 that accounted for errors associated with atmospheric transport, inversion framework parameters, and CO2-to-NOx emission ratios. Our findings indicated that widely used "bottom-up" emission inventories generally ignore numerous activity level statistics of FFCO2 related to energy industries and power plants in western China, whereas the inventories are significantly overestimated in developed regions and key urban areas owing to exaggerated emission factors and inexact spatial disaggregation. The optimized FFCO2 estimate exhibited more distinct seasonality with a significant increase in emissions in winter. These findings advance our understanding of the spatiotemporal regime of FFCO2 emissions in China.

Regional sources of NH3, SO2 and CO in the Third Pole

The Third Pole (TP) is a high mountain region in the world, and is well-known for its pristine environment, but recent development activities in the region have degraded its air quality. Here, we investigate the spatial and temporal changes of the air pollutants ammonia (NH₃), sulphur dioxide (SO₂) and carbon monoxide (CO) in TP, and reveal their sources using satellite measurements and emission inventory. We observe a clear seasonal cycle of NH3 in TP, with high values in summer and low values in winter. The intense agriculture activities in the southern TP are the cause of high NH₃ (6-8 × 1016 molec./cm2) there. Similarly, CO shows a distinct seasonal cycle with high values in spring in the southeast TP due to biomass burning. In addition, the eastern boundary of TP in the Sichuan and Qinghai provinces also show high values of CO (about 1.5 × 1018 mol/cm2), primarily owing to the industrial activities. There is no seasonal cycle found for SO₂ distribution in TP, but relatively high values (8-10 mg/m2) are observed in its eastern boundary. The high-altitude pristine regions of inner TP are also getting polluted because of increased human activities in and around TP, as we estimate positive trends in CO (0.5-1.5 × 1016 mol/cm2/yr) there. In addition, positive trends are also found in NH₃ (0.025 × 1016 molec./cm2/yr) during 2008-2020 in most regions of TP and SO₂ (about 0.25-0.75 mg/m2/yr) in the Sichuan and Qinghai region during 2000-2020. As revealed by the emission inventory, there are high anthropogenic emissions of NH3, SO2 and CO within TP. There are emissions of pollutants from energy sectors, oil and refinery, agriculture waste burning and manure management within TP. These anthropogenic activities accelerate the ongoing development in TP, but severely erode its environment.

Direct and indirect monitoring methods for nitrous oxide emissions in full-scale wastewater treatment plants: A critical review

Mitigation of nitrous oxide (N2O) emissions in full-scale wastewater treatment plant (WWTP) has become an irreversible trend to adapt the climate change. Monitoring of N2O emissions plays a fundamental role in understanding and mitigating N2O emissions. This paper provides a comprehensive review of direct and indirect N2O monitoring methods. The techniques, strengths, limitations, and applicable scenarios of various methods are discussed. We conclude that the floating chamber technique is suitable for capturing and interpreting the spatiotemporal variability of real-time N2O emissions, due to its long-term in-situ monitoring capability and high data acquisition frequency. The monitoring duration, location, and frequency should be emphasized to guarantee the accuracy and comparability of acquired data. Calculation by default emission factors (EFs) is efficient when there is a need for ambiguous historical N2O emission accounts of national-scale or regional-scale WWTPs. Using process-specific EFs is beneficial in promoting mitigation pathways that are primarily focused on low-emission process upgrades. Machine learning models exhibit exemplary performance in the prediction of N2O emissions. Integrating mechanistic models with machine learning models can improve their explanatory power and sharpen their predictive precision. The implementation of the synergy of nutrient removal and N2O mitigation strategies necessitates the calibration and validation of multi-path mechanistic models, supported by long-term continuous direct monitoring campaigns.

Spatiotemporal investigation of near-surface CH4 and factors influencing CH4 over South, East, and Southeast Asia

Methane (CH4) is the second most abundant greenhouse gas after CO2, which plays the most important role in global and regional climate change. To explore the long-term spatiotemporal variations of near-surface CH4, datasets were extracted from Greenhouse gases Observing SATellite (GOSAT), and the Copernicus Atmospheric Monitoring Service (CAMS) reanalyzed datasets from June 2009 to September 2020 over South, East, and Southeast Asia. The accuracy of near-surface CH4 from GOSAT and CAMS was verified against surface observatory stations available in the study region to confirm both dataset applicability and results showed significant correlations. Temporal plots revealed continuous inflation in the near-surface CH4 with a significant seasonal and monthly variation in the study region. To explore the factors affecting near-surface CH4 distribution, near-surface CH4 relationship with anthropogenic emission, NDVI data, wind speed, temperature, precipitation, soil moisture, and relative humidity were investigated. The results showed a significant contribution of anthropogenic emissions with near-surface CH4. Regression and correlation analysis showed a significant positive correlation between NDVI data and near-surface CH4 from GOSAT and CAMS, while a significant negative correlation was found between wind and near-surface CH4. In the case of temperature, soil moisture, and near-surface CH4 from GOSAT and CAMS over high CH4 regions of the study area showed a significant positive correlation. However significant negative correlations were found between precipitation and relative humidity with GOSAT and CAMS datasets over high CH4 regions in South, East, and Southeast Asia. Moreover, these climatic factors showed no significant correlation within the low near-surface CH4 areas in our study region. Our study results showed that anthropogenic emissions, NDVI data, wind speed, temperature, precipitation, soil moisture, and humidity could significantly affect the near-surface CH4 over South, East, and Southeast Asia.

Fires as a source of annual ambient PM2.5 exposure and chronic health impacts in Europe

Chronic exposure to ambient PM2.5 is the largest environmental health risk in Europe. We used a chemical transport model and recent exposure response functions to simulate ambient PM2.5, contribution from fires and related health impacts over Europe from 1990 to 2019. Our estimation indicates that the excess death burden from exposure to ambient PM2.5 declined across Europe at a rate of 10,000 deaths per year, from 0.57 million (95 % confidence intervals: 0.44-0.75 million) in 1990 to 0.28 million (0.19-0.42 million) in the specified period. Among these excess deaths, approximately 99 % were among adults, while only around 1 % occurred among children. Our findings reveal a steady increase in fire mortality fractions (excess deaths from fires per 1000 deaths from ambient PM2.5) from 2 in 1990 to 13 in 2019. Notably, countries in Eastern Europe exhibited significantly higher fire mortality fractions and experienced more pronounced increases compared to those in Western and Central Europe. We performed sensitivity analyses by considering fire PM2.5 to be more toxic as compared to other sources, as indicated by recent studies. By considering fire PM2.5 to be more toxic than other PM2.5 sources results in an increased relative contribution of fires to excess deaths, reaching 2.5-13 % in 2019. Our results indicate the requirement of larger mitigation and adaptation efforts and more sustainable forest management policies to avert the rising health burden from fires.

Nitrogen and phosphorus trends in lake sediments of China may diverge

The brief history of monitoring nutrient levels in Chinese lake waters limits our understanding of the causes and the long-term trends of their eutrophication and constrains effective lake management. We therefore synthesize nutrient data from lakes in China to reveal the historical changes and project their future trends to 2100 using models. Here we show that the average concentrations of nitrogen and phosphorus in lake sediments have increased by 267% and 202%, respectively since 1850. In the model projections, 2030-2100, the nitrogen concentrations in the studied lakes in China may decrease, for example, by 87% in the southern districts and by 19% in the northern districts. However, the phosphorus concentrations will continue to increase by an average of 25% in the Eastern Plain, Yunnan-Guizhou Plateau, and Xinjiang. Based on this differentiation, we suggest that nitrogen and phosphorus management in Chinese lakes should be carried out at the district level to help develop rational and sustainable environmental management strategies.

Unveiling the importance of VOCs from pesticides applicated in main crops for elevating ozone concentrations in China

Volatile organic compounds (VOCs) are considered as important precursors of ozone in the air, while the contribution of VOCs from pesticide application (PVOCs) to ozone production is unknown. Utilizing data from the Ministry of Agriculture and Rural Affairs of the People's Republic of China and ChinaCropPhen1km, this paper developed PVOC emission inventories with a resolution of 1 km for the main crops (rice, maize, and wheat) from 2012 to 2019 in China. The results revealed that pesticide application is an important VOC emission source in China. Specially, the PVOC emissions from the major grain-producing regions in June accounted for approximately 30% of the annual total PVOC emissions in the local regions. The simulation with the Weather Research and Forecasting Community Multiscale Air Quality model (WRF-CMAQ) indicated that the PVOC emissions increased the mean maximum daily 8-hour average (MDA8) ozone concentration across China by 2.5 ppb in June 2019. During the same period, PVOCs in the parts of North China Plain contributed 10% of the ozone formation. Under the comprehensive emission reduction scenario, it is anticipated that by 2025, the joint implementation of measures including reducing pesticide application, improving pesticide utilization efficiency and promoting solvent substitution will decrease PVOC emissions by 60% compared with 2019, thereby mitigating ozone pollution.

Modelling monthly-gridded carbon emissions based on nighttime light data

Timely and accurate implementation of carbon emissions (CE) analysis and evaluation is necessary for policymaking and management. However, previous inventories, most of which are yearly, provincial or city, and incomplete, have failed to reflect the spatial variations and monthly trends of CE. Based on nighttime light (NTL) data, statistical data, and land use data, in this study, a high-resolution (1 km × 1 km) monthly inventory of CE was developed using back propagation neural network, and the spatiotemporal variations and impact factors of CE at multiple administrative levels was evaluated using spatial autocorrelation model and spatial econometric model. As a large province in terms of both economy and population, Guangdong is facing the severe emission reduction challenges. Therefore, in this study, Guangdong was taken as a case study to explain the method. The results revealed that CE increased unsteadily in Guangdong from 2013 to 2022. Spatially, the high CE areas were distributed in the Pearl River Delta region such as Guangzhou, Shenzhen, and Dongguan, while the low CE areas were distributed in West and East Guangdong. The Global Moran's I decreased from 2013 to 2022 at the city and county levels, suggesting that the inequality of CE in Guangdong steadily decreased at these two administrative levels. Specifically, at the city level, the Global Moran's I gradually decreased from 0.4067 in 2013 to 0.3531 in 2022. In comparison, at the county level, the trend exhibited a slower decline, from 0.3647 in 2013 to 0.3454 in 2022. Furthermore, the analysis of the impact factors revealed that the relationship between CE and gross domestic product was an inverted U-shaped, suggesting the existence of the inverted U-shaped Environmental Kuznets Curve for CE in Guangdong. In addition, the industrial structure had larger positive impact on CE at the different levels. The method developed in this study provides a perspective for establishing high spatiotemporal resolution CE evaluation through NTL data, and the improved inventory of CE could help understand the spatial-temporal variations of CE and formulate regional-monthly-specific emission reduction policies.

China's anthropogenic N2O emissions with analysis of economic costs and social benefits from reductions in 2022

We assess China's overall anthropogenic N2O emissions via the official guidebook published by Chinese government. Results show that China's overall anthropogenic N2O emissions in 2022 were around 1593.1 (1508.7-1680.7) GgN, about 47.0 %, 27.0 %, 13.4 %, 4.9 %, and 7.7 % of which were caused by agriculture, industry, energy utilization, wastewater, and indirect sources, respectively. Maximum reduction rate for N2O emissions from agriculture, industry, energy utilization, wastewater, and indirect sources can achieve 69 %, 99 %, 79 %, 86 %, and 48 %, respectively, in 2022. However, given current global scenarios with a rapidly changing population and geopolitical and energy tension, the emission reduction may not be fully fulfilled. Without compromising yields, China's theoretical minimum anthropogenic N2O emissions would be 600.6 (568.8-633.6) GgN. In terms of the economic costs for reducing one kg of N2O-N emissions, the price ranged from €12.9 to €81.1 for agriculture, from €0.08 to €0.16 for industry, and from €104.8 to €1571.5 for energy utilization. We acknowledge the emission reduction rates may not be completely realistic for large-scale application in China. The social benefits gained from reducing one kg of N2O-N emissions in China was about €5.2, indicating anthropogenic N2O emissions caused a loss 0.03 % of China's GDP, but only justifying reduction in industrial N2O emissions from the economic perspective. We perceive that the present monetized values will be trustworthy for at least three to five years, but later the numerical monetized values need to be considered in inflation and other currency-dependent conditions.

Insights into the source contributions to the elevated fine particulate matter in Nigeria using a source-oriented chemical transport model

In 2021, Nigeria was ranked by the World Health Organization (WHO) as one of the top countries with highly deteriorating air quality in the world. To date, no study has elucidated the sources of elevated fine particulate matter (PM2.5) concentrations over the entire Nigeria. In this study, the Community Multiscale Air Quality (CMAQ) model was applied to quantify the contributions of seven emissions sectors to PM2.5 and its components in Nigeria in 2021. Residential, industry, and agriculture were the major sources of primary PM (PPM) during the four seasons, elemental carbon (EC) and primary organic carbon (POC) were dominated by residential and industry, while residential, industry, transportation, and agriculture were the important sources of secondary inorganic aerosols (SIA) and its components in most regions. PM2.5 was up to 150 μg/m3 in the north in all the seasons, while it reached ∼80 μg/m3 in the south in January. Residential contributed most to PM2.5 (∼80 μg/m3), followed by industry (∼40 μg/m3), transportation (∼20 μg/m3), and agriculture (∼15 μg/m3). The large variation in the sources of PM2.5 and its components across Nigeria suggests that emissions control strategies should be separately designed for different regions. The results imply that urgent control of PM2.5 pollution in Nigeria is highly necessitated.

Health effects of carbonaceous PM2.5 compounds from residential fuel combustion and road transport in Europe

Exposure to fine particulate matter (PM2.5) is associated with an increased risk of morbidity and mortality. In Europe, residential fuel combustion and road transport emissions contribute significantly to PM2.5. Toxicological studies indicate that PM2.5 from these sources is relatively more hazardous, owing to its high content of black and organic carbon. Here, we study the contribution of the emissions from these sectors to long-term exposure and excess mortality in Europe. We quantified the impact of anthropogenic carbonaceous aerosols on excess mortality and performed a sensitivity analysis assuming that they are twice as toxic as inorganic particles. We find that total PM2.5 from residential combustion leads to 72,000 (95% confidence interval: 48,000-99,000) excess deaths per year, with about 40% attributed to carbonaceous aerosols. Similarly, road transport leads to about 35,000 (CI 23,000-47,000) excess deaths per year, with 6000 (CI 4000-9000) due to carbonaceous particles. Assuming that carbonaceous aerosols are twice as toxic as other PM2.5 components, they contribute 80% and 37%, respectively, to residential fuel combustion and road transport-related deaths. We uncover robust national variations in the contribution of each sector to excess mortality and emphasize the importance of country-specific emission reduction policies based on national characteristics and sectoral shares.

The Multi-temporal and Multi-dimensional Global Urban Centre Database to Delineate and Analyse World Cities

Monitoring sustainable urban development requires comparable geospatial information on cities across several thematic domains. Here we present the first global database combining such information with city extents. The Global Human Settlement Urban Centre Database (GHS-UCDB) is produced by geospatial data integration to characterise more than 10,000 urban centres worldwide. The database is multi-dimensional and multi-temporal, containing 28 variables across five domains and having multitemporal attributes for one or more epochs when the UC are delineated (1975-1990-2000-2015). Delineation of urban centres for the year 2015 is performed via a logic of grid cell population density, population size, and grid cell contiguity defined by the Degree of Urbanisation method. Each of the urban centres has 160 attributes, including a validation assessment. The novel aspects of this database concern the thematic richness and temporal depth of the variables (across geography, socio-economic, environmental, disaster risk reduction, and sustainable development domains) and the type of geo-information provided (location and extent), featuring an overall consistency that allows comparative analyses across locations and time.

Investigation of Plant-Level Volatile Organic Compound Emissions from Chemical Industry Highlights the Importance of Differentiated Control in China

The chemical industry is a significant source of nonmethane volatile organic compounds (NMVOCs), pivotal precursors to ambient ozone (O3), and secondary organic aerosol (SOA). Despite their importance, precise estimation of these emissions remains challenging, impeding the implementation of NMVOC controls. Here, we present the first comprehensive plant-level assessment of NMVOC emissions from the chemical industry in China, encompassing 3461 plants, 127 products, and 50 NMVOC compounds from 2010 to 2019. Our findings revealed that the chemical industry in China emitted a total of 3105 (interquartile range: 1179-8113) Gg of NMVOCs in 2019, with a few specific products accounting for the majority of the emissions. Generally, plants engaged in chemical fibers production or situated in eastern China pose a greater risk to public health due to their higher formation potentials of O3 and SOA or their proximity to residential areas or both. We demonstrated that targeting these high-risk plants for emission reduction could enhance health benefits by 7-37% per unit of emission reduction on average compared to the current situation. Consequently, this study provides essential insights for developing effective plant-specific NMVOC control strategies within China's chemical industry.

Current investigations on global N2O emissions and reductions: Prospect and outlook

Global nitrous oxide (N2O) emissions merit scrutiny, because N2O is the third most important greenhouse gas for global warming and the predominant ozone-depleting substance in this century. Here we recapitulate global natural and anthropogenic N2O sources, comprehensively depict global sectoral human-induced N2O emissions by country, thoroughly survey all existing approaches for mitigating human-induced N2O emissions, preview the economic costs and social benefits from abating N2O emissions, and summarize roadblocks for achieving its emission reductions. From 1970 to 2018, the annual global anthropogenic N2O emissions increased by 64%-about 3.6 teragrams (Tg); agricultural sources primarily accounted for 78% of this increment. We find the social benefits from reducing N2O emissions override the economic costs for abatements, only except precision farming for agricultural sources and replacement by Xe for anesthetic, thus justifying the motivation for crafting policies to limit its emissions. Net zero N2O emissions cannot be achieved via applying current technologies and breeding N2O-reducing microbes is a potential method to accrue N2O sinks.

Tracing sources of atmospheric methane using clumped isotopes

We apply a recently developed measurement technique for methane (CH4) isotopologues* (isotopic variants of CH4-13CH4, 12CH3D, 13CH3D, and 12CH2D2) to identify contributions to the atmospheric burden from fossil fuel and microbial sources. The aim of this study is to constrain factors that ultimately control the concentration of this potent greenhouse gas on global, regional, and local levels. While predictions of atmospheric methane isotopologues have been modeled, we present direct measurements that point to a different atmospheric methane composition and to a microbial flux with less clumping (greater deficits relative to stochastic) in both 13CH3D and 12CH2D2 than had been previously assigned. These differences make atmospheric isotopologue data sufficiently sensitive to variations in microbial to fossil fuel fluxes to distinguish between emissions scenarios such as those generated by different versions of EDGAR (the Emissions Database for Global Atmospheric Research), even when existing constraints on the atmospheric CH4 concentration profile as well as traditional isotopes are kept constant.

Trends in air pollutants emissions in the Qinghai-Tibet Plateau and its surrounding areas under different socioeconomic scenarios

The Qinghai-Tibetan Plateau (QTP) and its surrounding areas are undergoing rapid changes in socioeconomic conditions, activity sectors, and emission levels. These changes underscore the significance of conducting local environmental assessments in the future and generating air pollutant emission forecasts necessary for effective evaluation. Current pollutants emissions pathways exhibit regional limitation since their based historical inventory could not accurately reflect the emission characteristics in QTP. This study constructed a high spatial resolution (0.1° × 0.1°) atmospheric pollutant emissions dataset in the Qinghai-Tibet Plateau and its surrounding Areas (QTPA) based on updated emission inventory and various socioeconomic scenarios. We found that the pollutant emissions levels are distinct among different social development scenarios, with SSP3-7.0 demonstrating the highest magnitude of emissions. Regional and sectoral contributions exhibit substantial variations. Notably, solid fuel combustion originating from residential sectors in Northeast India and open fires in Myanmar are identified as high-density sources of PM2.5 emissions. Current pollutant emission patterns in the QTPA are more akin to SSP2-4.5, however, specific regions such as Qinghai and Tibet have exhibited more pronounced trends of emission reduction. The comparison with previous datasets reveals that the predicted pollutant emissions in this study are lower than Scenario Model Intercomparison Project (SMIP) dataset but higher than Asian-Pacific Integrated Model (AIM) dataset due to the revised inventory data and model variations, in which the latter might be the main obstacle to accurate emissions prediction.

The spatial and temporal disaggregation models of high-accuracy vehicle emission inventory

A high-accuracy gridding vehicle emission inventory is not only the foundation for developing refined emission control strategies but a necessary input to air quality model as well. An accurate approach to the spatiotemporal disaggregation is the key step to improving the accuracy of gridding emission inventories. The existing spatial disaggregation method considers relatively fewer impact factors, lacking adequate correlation analysis among impact factors. Additionally, the existing temporal disaggregation method does not correspond with the actual travel behavior of residents. This paper proposes a multi-factor spatial disaggregation model by principal component analysis (PCAM), based on a correlation analysis of the main impact factors. Further, a new temporal disaggregation model is proposed based on the congestion delay index combined with the traffic flow fundamental model (CDITF). The results from a case study in Jinan show that the square of correlation coefficients (RSQ) between the model- disaggregated NO2 emissions based on PCAM and the monitored NO2 concentration increased by 34.4% compared to the traditional disaggregation model based on the standard road length, and the RSQ for CO increased by 13%; the NMD and NME of the simulation results based on CMAQ model compared to standard road length model decrease by approximately 33.7% and 35.5%, respectively. The trend of the monthly, daily, and hourly variations of NO2 and CO emissions disaggregated by the proposed temporal disaggregation model is quite consistent with that of the monitored concentration data. The PCAM method and the CDITF proposed in this paper are more in line with the actual situation using the cumulative emissions on road sections. The vehicle emissions in Jinan are found to be concentrated in the center of each district and county and near high-grade roads. The disaggregation results in areas with large road slopes are more realistic for considering road slope factors. The trend of the monthly, daily, and hourly variations of NO2 and CO emissions disaggregated by the proposed temporal disaggregation model is quite consistent with that of the monitored concentration data, however, the monitored concentration data presents a certain degree of time lag. The proposed spatiotemporal disaggregation model in this paper improves the accuracy of gridding vehicle emission inventory, which is of a great significance for developing precise control strategies of vehicle emissions and improving the urban air quality in general.

Mapping the heterogeneity of global methane footprint in China at the subnational level

Achieving the ambitious Global Methane Pledge announced in the Glasgow Climate Pact requires collaborative efforts from both the signatory countries and China which serves as the world's largest emitter. Considering the heterogeneity of economic structures within China and the relocation of emissions between regions via the global economic network, it is vital to investigate how China's methane emissions at the subnational level are linked to global final consumption. In this paper, we mapped global methane footprint in China from 2007 to2015 at the subnational level, by nesting China's interprovincial input-output tables into global multiregional input-output accounts and upscaling grid-level methane emission data of the Edgar database to the provincial level. Our results suggested that global methane footprint in China shifted westward, and the United States, European Union, Japan, and Hong Kong were the main drivers of China's local methane emissions. By illustrating the international and interprovincial trade flows of methane emissions, this study demonstrated that southeast coastal provinces were the hotspots for global methane footprint while middle inland provinces were the emission hotspots for China's domestic demands. We also showed how China's methane emissions were distributed through the nested global economic network to different economic agents. Moreover, emission trends of key exporting sectors for China's eight economic zones were detailed discussed. The outcome of this study may be fully supportive for identifying the heterogeneous effects of global methane footprint in China and implicative for interprovincial and international collaborations towards methane emission mitigation.

Subways and CO2 emissions: A global analysis with satellite data

This paper estimates a global CO2 emissions model using satellite data at 25 km resolution. The model incorporates industrial sources (including power, steel, cement, and refineries), fires, and non-industrial population-related factors associated with household incomes and energy requirements. It also tests the impact of subways in the 192 cities where they operate. We find highly significant effects with the expected signs for all model variables, including subways. In a counterfactual exercise estimating CO2 emissions with and without subways, we find they have reduced population-related CO2 emissions by about 50 % for the 192 cities and about 11 % globally. Extending the analysis to future subways for other cities, we estimate the magnitude and social value of CO2 emissions reductions with conservative assumptions about population and income growth and a range of values for the social cost of carbon and investment costs. Even under pessimistic assumptions for these costs, we find that hundreds of cities realize a significant climate co-benefit, along with benefits from reduced traffic congestion and local air pollution, which have traditionally motivated subway construction. Under more moderate assumptions, we find that, on climate grounds alone, hundreds of cities realize high enough social rates of return to warrant subway construction.

Continuous atmospheric in-situ measurements of the CH4/CO ratio at the Mt. Cimone station (Italy, 2165 m a.s.l.) and their possible use for estimating regional CH4 emissions

Methane (CH₄) is an important climate forcer, contributing about 17% of the total radiative forcing by long living greenhouse gases. The Po basin is one of the most polluted and densely populated areas in Europe representing an important source region for CH₄. The aim of this work was to test an inter-species correlation approach to derive estimates of anthropogenic CH₄ emissions for the period 2015-2019 from the Po basin by combining CO bottom-up inventory data and continuous CH₄ and CO observations from a mountain site in the northern Italy. The tested methodology suggested lower emissions in respect to EDGAR (-17%) and the Italian National Inventory (-40%) for the Po basin. However, despite the two bottom-up inventories, the emissions derived from the atmospheric observations reported an increasing tendency from 2015 to 2019 for the CH₄ emissions. A sensitivity study revealed that using different subsets of the atmospheric observations implied a difference of 26% in the CH₄ emission estimates. The highest agreement with two bottom-up CH₄ inventories (EDGAR and the Italian national inventory) were obtained when atmospheric data were strictly selected for periods representative of air mass transport from the Po basin. Our study identified various challenges when using this methodology as a benchmark to verify bottom-up CH₄ inventories. Issues could be attributed to the annual aggregation of the proxies used to derive the emission amounts, to the CO bottom-up inventory used as input information and to the relatively high sensitivity of the results to the different subsets of the atmospheric observations. However, the use of different bottom-up inventories as input data for CO emissions can potentially provide information that should be carefully considered for the purpose of integrating CH₄ bottom-up inventories.

Carbon Monitor Europe near-real-time daily CO2 emissions for 27 EU countries and the United Kingdom

With the urgent need to implement the EU countries pledges and to monitor the effectiveness of Green Deal plan, Monitoring Reporting and Verification tools are needed to track how emissions are changing for all the sectors. Current official inventories only provide annual estimates of national CO₂ emissions with a lag of 1+ year which do not capture the variations of emissions due to recent shocks including COVID lockdowns and economic rebounds, war in Ukraine. Here we present a near-real-time country-level dataset of daily fossil fuel and cement emissions from January 2019 through December 2021 for 27 EU countries and UK, which called Carbon Monitor Europe. The data are calculated separately for six sectors: power, industry, ground transportation, domestic aviation, international aviation and residential. Daily CO₂ emissions are estimated from a large set of activity data compiled from different sources. The goal of this dataset is to improve the timeliness and temporal resolution of emissions for European countries, to inform the public and decision makers about current emissions changes in Europe.

An integrated view of correlated emissions of greenhouse gases and air pollutants in China

BACKGROUND

Air pollution in China has raised great concerns due to its adverse effects on air quality, human health, and climate. Emissions of air pollutants (APs) are inherently linked with CO₂ emissions through fossil-energy consumption. Knowledge of the characteristics of APs and CO₂ emissions and their relationships is fundamentally important in the pursuit of co-benefits in addressing air quality and climate issues in China. However, the linkages and interactions between APs and CO₂ in China are not well understood.

RESULTS

Here, we conducted an ensemble study of six bottom-up inventories to identify the underlying drivers of APs and CO₂ emissions growth and to explore their linkages in China. The results showed that, during 1980-2015, the power and industry sectors contributed 61-79% to China's overall emissions of CO₂, NO_x, and SO₂. In addition, the residential and industrial sectors were large emitters (77-85%) of PM₁₀, PM_2.5, CO, BC, and OC. The emissions of CH₄, N₂O and NH₃ were dominated by the agriculture sector (46-82%) during 1980-2015, while the share of CH₄ emissions in the energy sector increased since 2010. During 1980-2015, APs and greenhouse gases (GHGs) emissions from residential sources generally decreased over time, while the transportation sector increased its impact on recent emissions, particularly for NO_x and NMVOC. Since implementation of stringent pollution control measures and accompanying technological improvements in 2013, China has effectively limited pollution emissions (e.g., growth rates of -10% per year for PM and -20% for SO₂) and slowed down the increasing trend of carbon emissions from the power and industrial sectors. We also found that areas with high emissions of CO, NO_x, NMVOC, and SO₂ also emitted large amounts of CO₂, which demonstrates the possible common sources of APs and GHGs. Moreover, we found significant correlations between CO₂ and APs (e.g., NO_x, CO, SO₂, and PM) emissions in the top 5% high-emitting grid cells, with more than 60% common grid cells during 2010-2015.

CONCLUSIONS

We found significant correlation in spatial and temporal aspects for CO₂, and NO_x, CO, SO₂, and PM emissions in China. We targeted sectorial and spatial APs and GHGs emission hot-spots, which help for management and policy-making of collaborative reductions of them. This comprehensive analysis over 6 datasets improves our understanding of APs and GHGs emissions in China during the period of rapid industrialization from 1980 to 2015. This study helps elucidate the linkages between APs and CO₂ from an integrated perspective, and provides insights for future synergistic emissions reduction.

Advances in Simulating the Global Spatial Heterogeneity of Air Quality and Source Sector Contributions: Insights into the Global South

High-resolution simulations are essential to resolve fine-scale air pollution patterns due to localized emissions, nonlinear chemical feedbacks, and complex meteorology. However, high-resolution global simulations of air quality remain rare, especially of the Global South. Here, we exploit recent developments to the GEOS-Chem model in its high-performance implementation to conduct 1-year simulations in 2015 at cubed-sphere C360 (∼25 km) and C48 (∼200 km) resolutions. We investigate the resolution dependence of population exposure and sectoral contributions to surface fine particulate matter (PM2.5) and nitrogen dioxide (NO2), focusing on understudied regions. Our results indicate pronounced spatial heterogeneity at high resolution (C360) with large global population-weighted normalized root-mean-square difference (PW-NRMSD) across resolutions for primary (62-126%) and secondary (26-35%) PM2.5 species. Developing regions are more sensitive to spatial resolution resulting from sparse pollution hotspots, with PW-NRMSD for PM2.5 in the Global South (33%), 1.3 times higher than globally. The PW-NRMSD for PM2.5 for discrete southern cities (49%) is substantially higher than for more clustered northern cities (28%). We find that the relative order of sectoral contributions to population exposure depends on simulation resolution, with implications for location-specific air pollution control strategies.

Civil aviation emissions in Argentina

The impact of aviation on climate change is reflected in increasing emissions of CO2 and other pollutants from fuel burning emitted at high altitudes, representing 2.9 % of total Greenhouse gases (GHG) emissions in 2019. However, mitigations options for decarbonization of aviation are difficult to implement given operational safety, technology maturity, energy density and other constraints. One alternative for mitigation is the use of certified sustainable aviation fuel (SAF) with lower carbon intensity than conventional jet fuel (CJF). This research presents an inventory of Argentine civil aviation emissions for its domestic and international flights, and analyzes the possibility of supplying SAF as a mitigation strategy given its abundant biomass production. Argentine aviation activity is presented as a monthly 4D (latitude, longitude, altitude and time) spatial inventory for the interval 2001-2021, based on origin and destination city pairs, aircraft types and airlines. Fuel consumption and pollutant emissions were calculated for landing-and-take-off and cruise phases. Monthly domestic ranged from 67 to 179 kt CO_2eq (2001-2019). Annual peak values occurred in 2019 consuming 560 kt CJF and direct emitting of 1.77 Mt CO_2eq. While Revenue-Passenger-Kilometer (RPK) grew almost 4 times (4.18 × 10⁹ in 2001 to 16.42 × 10⁹ in 2019), the number of flights changed only 1.5 times (from 98,000 in 2002 to 152,000 in 2019). The main efficiency indexes varied from 97 t CJF/RPK, 308 gCO_2eq/RPK to 34 t CJF/RPK, 107 gCO_2eq/RPK between 2001 and 2019, respectively, showing an average annual improvement of 3.5 % due to partial fleet renewal, especially from 2015 onwards. Emissions of other pollutants for 2019 reached total values of CO 14.14 kt; NOx 6.77 kt; PM tot 55.12 kt. For the period 2001-2019, international aviation consumed between 1 Mt - 1.5 Mt CJF, directly emitting between 3.30 and 4.80 Mt of CO_2eq; RPKs went from 6.234 × 10⁹ to 20.524 × 10⁹; the efficiency indices ranged from 529 to 240 gCO_2eq/RPK. The most important changes occurred with an optimization of routes and number of flights and the replacement of the four-engines (B747, A380) by more efficient twin-engines (B777, A330) aircraft. Argentina is not required to any offsetting regulatory program due to its small aviation market (approx. 0.22 % global market in 2019), nor has to date certified SAF production pathways, nevertheless it has potential for SAF availability based on actual biofuels production (ethanol, biodiesel and soybean oil) and biomass feedstock's existences. In this sense this studies proposes that 2019 domestic fuel consumption could be supplied using 79 % exportable amounts of sugarcane ethanol (257 ± 53 kt) (by Ethanol to Jet ETJ) and 34 % of exportable soybean oil (1079 ± 160 kt) (by hydroprocessed esters and fatty acids- HEFA) pathways. For this scenario average GHG emissions reached 1.321 ± 0.115 Mt CO2eq; which would imply a 62 % of the current emission value using CJF (2.17Mt CO2eq), or savings of about 838 kt CO2eq (38 %). At the 2019 level of harvest and biofuel production, up to 1.4 Mt of SAF could be produced from sugarcane ethanol/ETJ and soybean oil/HEFA mitigating up to 1.8 MtCO2eq. A 35 kt CO_2eq annual sectoral national mitigation strategy could be reached by using 14 kt of SAF.

Improved estimation of particulate matter in China based on multisource data fusion

Particulate matter (PM) is a global health concern and causes millions of premature deaths worldwide annually. High-resolution and full-coverage PM datasets are essential to support the accurate assessment of PM exposure. Here, a three-stage model framework is developed based on the Community Multiscale Air Quality (CMAQ) simulations (12 km) and multisource data fusion to estimate 1 km daily PM concentrations across China in 2015, including PM_2.5 (<2.5 μm) and PM₁₀ (<10 μm). The three-stage model performs well with cross-validation coefficient of determination (R²) of 0.91 and 0.87, and root mean square error (RMSE) of 17.3 μg/m³ and 27.2 μg/m³ for PM_2.5 and PM₁₀, respectively. After data fusion from multiple sources, the concentrations of PM_2.5 and PM₁₀ are in better agreement with ground observations compared to the CMAQ simulation with RMSE reduced by 72 % and 67 %. High PM_2.5 events mainly occur in the North China Plain, Yangtze River Delta, and Sichuan Basin, and PM₁₀ show similar spatial patterns to PM_2.5 in eastern China. These full-coverage PM datasets enable in-depth analysis of PM pollution over small areas and support future epidemiological studies and health assessments.

National contributions to climate change due to historical emissions of carbon dioxide, methane, and nitrous oxide since 1850

Anthropogenic emissions of carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) have made significant contributions to global warming since the pre-industrial period and are therefore targeted in international climate policy. There is substantial interest in tracking and apportioning national contributions to climate change and informing equitable commitments to decarbonisation. Here, we introduce a new dataset of national contributions to global warming caused by historical emissions of carbon dioxide, methane, and nitrous oxide during the years 1851–2021, which are consistent with the latest findings of the IPCC. We calculate the global mean surface temperature response to historical emissions of the three gases, including recent refinements which account for the short atmospheric lifetime of CH4. We report national contributions to global warming resulting from emissions of each gas, including a disaggregation to fossil and land use sectors. This dataset will be updated annually as national emissions datasets are updated.

Interactive effects of irrigation system and level on grain yield, crop water use, and greenhouse gas emissions of summer maize in North China Plain

Irrigation management is one of most critical factors influencing soil N₂O and CO₂ emissions in dryland agriculture. To explore the effects of irrigation systems and levels on the mitigation of N₂O and CO₂ emissions from maize fields and to determine the balance among greenhouse gases (GHG) emission, water-saving and grain yield, a two-year field experiment was conducted in the North China Plain (NCP) during the growing seasons of 2018 and 2019. Two irrigation systems (i.e., flood irrigation, FI, and drip irrigation, DI) were adopted with four irrigation levels in each system, including 65 mm/event (sufficient irrigation, CK), 50 mm/event (decreased by 23 %), 35 mm/event (by 46 %) and 20 mm/event (by 69 %), respectively. The results showed that both irrigation systems and levels had significant effects on soil N₂O and CO₂ emissions (P < 0.05). Nitrous oxide (N₂O) and CO₂ emissions peaked following irrigation or irrigation + fertilization events during sowing to early filling stage (R1), with the peak values increasing with irrigation levels. Meanwhile, peak values from FI were higher than those from DI at 50 mm and 65 mm irrigation levels. The average cumulative N₂O and CO₂ emissions of DI treatments were 14.9 % and 6.23 % lower than those of FI treatments (P < 0.05), respectively. Soil moisture was identified as one of the most crucial factors influencing N₂O and CO₂ fluxes. Deficit irrigation efficiently deceased cumulative N₂O and CO₂ emissions, but moderate to severe deficit irrigation brought significant reduction in grain yield. Drip irrigation with a slight deficit irrigation level (decreased by 23 %) obtained the best economic and environmental benefits, which achieved the dual goal of lower GHG emissions but higher WUE without sacrificing grain yield.

Effect of biomass burning on premature mortality associated with long-term exposure to PM2.5 in Equatorial Asia

The health burden from exposure to ambient fine particulates (PM_2.5) in Equatorial Asia is substantially affected by the peatland fires in Indonesia, but the long-term health effect of the fires on local inhabitants is unclear. In this study, PM_2.5-associated excess mortality in Equatorial Asia over the past 30 years (1990-2019) was estimated and then the health effect of biomass burning was identified. The PM_2.5-related death in Equatorial Asia almost tripled from 113 (95% confidence interval, 100-125) thousand in 1990 to 337 (300-373) thousand in 2019, with a rate of increase of 6.4 (6.2-6.9) thousand/yr. The intense biomass burning between 1990 and 2019 was estimated to have induced 317 (282-348) thousand excess deaths in the study regions, with excess deaths mainly occurring in the El Niño years, such as in 1997, 2006, 2015 and 2019. Although the remote sensing data and emission inventories both reveal that the effective control measures have reduced biomass burning intensity in Equatorial Asia (especially in Sumatra and Borneo), the corresponding health benefit has been offset by variations in demographic factors, i.e., population and age structure. Over the same period, fossil fuel emissions continued to increase rapidly. Thus, more stringent and ambitious policies are required to reduce the health burden from biomass burning and anthropogenic emissions simultaneously to maximize the health benefits from government measures and policies.

Portable TDLAS Sensor for Online Monitoring of CO2 and H2O Using a Miniaturized Multi-Pass Cell

We designed a tunable diode laser absorption spectroscopy (TDLAS) sensor for the online monitoring of CO₂ and H₂O concentrations. It comprised a small self-design multi-pass cell, home-made laser drive circuits, and a data acquisition circuit. The optical and electrical parts and the gas circuit were integrated into a portable carrying case (height = 134 mm, length = 388 mm, and width = 290 mm). A TDLAS drive module (size: 90 mm × 45 mm) was designed to realize the function of laser current and temperature control with a temperature control accuracy of ±1.4 mK and a current control accuracy of ±0.5 μA, and signal acquisition and demodulation. The weight and power consumption of the TDLAS system were only 5 kg and 10 W, respectively. Distributed feedback lasers (2004 nm and 1392 nm) were employed to target CO₂ and H₂O absorption lines, respectively. According to Allan analysis, the detection limits of CO₂ and H₂O were 0.13 ppm and 3.7 ppm at an average time of 18 s and 35 s, respectively. The system response time was approximately 10 s. Sensor performance was verified by measuring atmospheric CO₂ and H₂O concentrations for 240 h. Experimental results were compared with those obtained using a commercial instrument LI-7500, which uses non-dispersive infrared technology. Measurements of the developed gas analyzer were in good agreement with those of the commercial instrument, and its accuracy was comparable. Therefore, the TDLAS sensor has strong application prospects in atmospheric CO₂ and H₂O concentration detection and ecological soil flux monitoring.

An acid rain-friendly NH3 control strategy to maximize benefits toward human health and nitrogen deposition

Ammonia (NH₃) abatement remains controversial in China owing to its effectiveness in reducing PM_2.5 pollution and nitrogen deposition but with the potential risk of promoting acid rain formation, necessitating scientific guidance. Here, we propose a novel method for designing an NH₃ control strategy to mitigate both air pollution and nitrogen deposition without significantly exacerbating acid rain. This method involves extending the response surface model (RSM) to deposition using a delicately developed polynomial response function of deposition (i.e., dep-RSM). The Yangtze River Delta (YRD) dep-RSM application reveals that 16 out of 41 cities have NH₃ control potentials from 15 % to 71 %. Excellent NH₃ control potentials have been noted between April and June (78 %-92 %). From 2013 to 2017, the effective SO₂ and NO_x control significantly reduced wet sulfur and oxidized nitrogen deposition, providing considerable NH₃ abatement potentials (15 %-24 %) to further reduce PM_2.5 and nitrogen deposition by up to 2 % and 9 %, respectively, without acid rain exacerbation (the wet neutralization factor was maintained). Additionally, 57 % and 73 % NH₃ emission reduction potentials were obtained under acid rain constraints with 75 % and 86 % reductions in the other precursors to reduce the average PM_2.5 concentration below 25 and 15 μg/m³, and an additional 8408 and 14,459 premature deaths could only be avoided at an extra cost of 8.7 and 19.7 billion CNY, respectively. Meanwhile, the N deposition considerably reduced by 10 and 13 kgN/ha·yr. However, the YRD region could still simultaneously obtain substantial amounts of PM_2.5 and N deposition mitigation using the strategy proposed herein. The expanded optimization system can be directly adopted by policymakers to implement coordinated control in regions or countries facing the same NH₃ control conundrum.

Dynamic harmonization of source-oriented and receptor models for source apportionment

Millions of premature mortalities are caused by the air pollution of fine particulate matter with aerodynamic diameters less than 2.5 μm (PM_2.5) globally per year. To effectively control the dominant emission sources and abate air pollution, source apportionment of PM_2.5 is normally conducted to quantify the contributions of various sources, but the results of different methods might be inconsistent. In this study, we dynamically harmonized the results from the two dominant source apportionment methods, the source-oriented and receptor models, by updating the emission inventories of primary PM_2.5 from the major sectors based on the Bayesian Inference. An adjoint model was developed to efficiently construct the source-receptor sensitivity matrix, which was the critical information for the updates, and depicted the response of measurements to the changes in the emissions of various sources in different regions. The harmonized method was applied to a measurement campaign in Beijing from January to February 2021. The results suggested a significant reduction of primary PM_2.5 emissions in Beijing. Compared with the baseline emission inventory of 2017, the primary PM_2.5 emissions from the local residential combustion and industry in Beijing had significantly declined by about 90 % during the investigated period of the year, and the traffic emission decreased by about 50 %. The proposed methods successfully identified the temporally dynamic changes in the emissions induced by the Spring Festival. The methods could be a promising pathway for the harmonization of source-oriented and receptor source apportionment models.

New emission inventory reveals termination of global dioxin declining trend

Accurate estimates of spatiotemporally resolved Polychlorinated dibenzo-p-dioxins (PCDD/Fs, or dioxins) emissions are critical for understanding their environmental fate and associated health risks. In this study, by utilizing an empirical regression model for PCDD/Fs emissions, we developed a global emission inventory for 17 toxic PCDD/Fs congeners from 8 source sectors with a spatial resolution of 1° × 1° from 2002 to 2018. The results show that PCDD/Fs emissions decreased by 25.7 % (12.5 kg TEQ) between 2002 and 2018, mostly occurring in upper- and lower-middle income countries. Globally, open-burning processes, waste incineration, ferrous and nonferrous metal production sectors and heat and power generation were the major source sectors of PCDD/Fs. Spatially, high PCDD/Fs emissions were mainly identified in East and South Asia, Southeast Asia, and part of Sub-Saharan Africa. We find that the declining trend of dioxin emissions over the past decades terminated from the early 2010s due to increasing significance of wildfire induced emissions in the total emission. The PCDD/Fs emission inventory developed in the present study was verified by inputting the inventory as initial conditions into an atmospheric transport model, the Canadian Model for Environmental Transport of Organochlorine Pesticides (CanMETOP), to simulate PCDD/Fs concentrations in air and soil. The predicted concentrations were compared to field sampling data. The good agreement between the modeled and measured concentrations demonstrates the reliability of the inventory.

Smoke-weather interaction affects extreme wildfires in diverse coastal regions

Extreme wildfires threaten human lives, air quality, and ecosystems. Meteorology plays a vital role in wildfire behaviors, and the links between wildfires and climate have been widely studied. However, it is not fully clear how fire-weather feedback affects short-term wildfire variability, which undermines our ability to mitigate fire disasters. Here, we show the primacy of synoptic-scale feedback in driving extreme fires in Mediterranean and monsoon climate regimes in the West Coast of the United States and Southeastern Asia. We found that radiative effects of smoke aerosols can modify near-surface wind, air dryness, and rainfall and thus worsen air pollution by enhancing fire emissions and weakening dispersion. The intricate interactions among wildfires, smoke, and weather form a positive feedback loop that substantially increases air pollution exposure.

Wet depositions of cations in forests across NADP, EMEP, and EANET monitoring networks over the last two decades

Studies focused on emissions and acid deposition of sulfur (S) and nitrogen (N) and the consequent precipitation acidity have a long history. However, atmospheric depositions of cations play a critical role in buffering precipitation acidity, and providing cationic nutrients for vegetation growth lacks sufficient studies equally. The spatiotemporal patterns of cation depositions and their neutralization potential across broad scales remain unclear. Through synthesizing the long-term data in forest sites (n = 128) derived from three monitoring networks (NADP in Northern America, EMEP in Europe, and EANET in East Asia) on wet deposition of cations (Na+, NH4-N, K+, Mg2+, and Ca2+), this study assesses the temporal changes and spatial patterns of cation depositions and their neutralization potential over the last two decades. The results showed that the depositions of cationic nutrients were considerably higher in EANET compared to NADP and EMEP. The depositions of sea salt-associated sodium exhibited a significant transition from marine (> 15 kg ha-1 year-1) to inland (< 3.0 kg ha-1 year-1) forest sites attributable to the precipitation quantity and influences of sea spray. The higher emissions of NH3 and particulate matter in East Asia explained the higher cation depositions in EANET than NADP and EMEP. The annual trends of cations revealed that only 20-30% of the forest sites showed significant changing trends and the sites widely spread across the three networks. Possibly, base cation (BC) deposition has reached a low and stable condition in NADP and EMEP, while it has high spatial heterogeneity in the temporal change in EANET. The difference in BC deposition among the three networks reflects their distinct development of economy. Our synthesis indicates that the annual trends of neutralization factor (NF) in NADP can be explained by the declining of acid potential (AP), not by neutralization potential (NP) as BC deposition has been stably low over the past two decades. Whereas, the concurrent decreases of AP and NP in EMEP or plateau period of both AP and NP in EANET have come to a standstill of acid neutralizing capacity.

Dramatic decline of observed atmospheric CO2 and CH4 during the COVID-19 lockdown over the Yangtze River Delta of China

The temporal variation of greenhouse gas concentrations in China during the COVID-19 lockdown in China is analyzed in this work using high resolution measurements of near surface △CO₂, △CH₄ and △CO concentrations above the background conditions at Lin'an station (LAN), a regional background station in the Yangtze River Delta region. During the pre-lockdown observational period (IOP-1), both △CO₂ and △CH₄ exhibited a significant increasing trend relative to the 2011-2019 climatological mean. The reduction of △CO₂, △CH₄ and △CO during the lockdown observational period (IOP-2) (which also coincided with the Chinese New Year Holiday) reached up to 15.0 ppm, 14.2 ppb and 146.8 ppb, respectively, and a reduction of △CO₂/△CO probably due to a dramatic reduction from industrial emissions. △CO₂, △CH₄ and △CO were observed to keep declining during the post-lockdown easing phase (IOP-3), which is the synthetic result of lower than normal CO₂ emissions from rural regions around LAN coupled with strong uptake of the terrestrial ecosystem. Interestingly, the trend reversed to gradual increase for all species during the later easing phase (IOP-4), with △CO₂/△CO constantly increasing from IOP-2 to IOP-3 and finally IOP-4, consistent with recovery in industrial emissions associated with the staged resumption of economic activity. On average, △CO₂ declined sharply throughout the days during IOP-2 but increased gradually throughout the days during IOP-4. The findings showcase the significant role of emission reduction in accounting for the dramatic changes in measured atmospheric △CO₂ and △CH₄ associated with the COVID-19 lockdown and recovery.

Future emissions of greenhouse gases, particulate matter and volatile organic compounds from municipal solid waste burning in India

Waste generation projections for the 21st century are important for the investigation of long-term global environmental problems, and greenhouse gas emissions associated with waste management. This paper presents future waste generation and open waste burning projections for India, which are consistent with the scenarios in the shared socio-economic pathways (SSPs) database. India's waste generation will increase to 547 Tgy^-1 and 828 Tgy^-1, by 2030 and 2050, respectively, if India's waste generation rates converge to those of developed economies under the fossil fuel based economic growth projections of SSP5. This will increase open waste burning emissions by 140 % and 110 % over 2015 levels by 2030 and 2050, respectively. Business-as-usual projections predict a waste generation of 268 ± 14 Tgy^-1 by 2030 and 356 ± 34 Tgy^-1 by 2050 and elimination of waste burning other than landfill fires by the mid-2040s. Aggressive promotion of source segregation and treatment of biodegradable waste under a sustainable development scenario (SSP1) can advance this transition despite higher income growth and reduce waste burning from 68 (45-105) Tgy^-1 in 2015 to 21-48 Tgy^-1 and 2-22 Tgy^-1 of waste burning by 2030 and 2050, respectively. The failure of programs targeted at this waste component would result in 31-60 Tgy^-1 and 26-108 Tgy^-1 of waste burning by 2030 and 2050, respectively. For the SSP5 income trajectory a failure to successfully source segregate and treat biodegradable waste would almost double open waste burning by 2050.

China's process-related greenhouse gas emission dataset 1990-2020

China's industrial process-related Greenhouse Gas (GHG) emissions are growing rapidly and are already equivalent to 13-19% of energy-related emissions in the past three decades. Previous studies mainly focused on emissions from fossil fuel combustion, however, there are a broad range of misconceptions regarding the trend and source of process-related emissions. To effectively implement emission reduction policies, it is necessary to compile an accurate accounting of process-related GHG emissions. However, the incompleteness in scope, unsuitable emission factor, and delay in updates in the current emission inventory have led to inaccurate emission estimates and inefficient mitigation actions. Following the methodology provided by Intergovernmental Panel on Climate Change (IPCC), we constructed a time series inventory of process-related GHG emissions for 15 industrial products from 1990-2020 in China. This emission inventory covers more than 90% of China's process-related GHG emissions. In our study, emission factors were adjusted to refer to the industrial production process, technology, and raw material structure in China, which has led to increased accuracy of emission accounting. The dataset can help identify the sources of process-related GHG emissions in China and provide a data base for further policy implications.

A new method of hotspot analysis on the management of CO2 and air pollutants, a case study in Guangzhou city, China

Emission inventory plays an important role in designing effective emission control strategies. Currently, there is unbalanced development of CO₂ and air pollutant emission inventories in China and the spatial information of both cannot be obtained simultaneously, which prevents a collaborative control strategy. In this study, we developed a unified emission inventory including both CO₂ and air pollutants, then utilized spatial mapping methods to identify the co-hotspots of both CO₂ and air pollutants at a high spatial resolution (1 × 1 km²). We applied Guangzhou city as a case study to illustrate the method. The results showed that CO₂ and air pollutants were mainly emitted from the stationary combustion sector and the transportation sector. These two sectors contributed 95 %, 67 %, and 93 % to total CO₂, SO₂, and NO_x emissions, respectively. Up to 86 %, 86 %, 66 %, and 72 % of total CO₂, SO₂, NO_x, and PM_2.5 emissions were attributed to the top 10 % emission grids with 1 × 1 km² resolution. However, our results showed high emission grids were not surrounded by other high emissions grids for all types of emissions analyzed in this study. The co-hotspot analysis enables accurate identification of high-emission grids, which helps environment managers to prioritize resource allocation when designing control strategies. Our study underscores the importance of managing CO₂ and air pollutants simultaneously at the city level.

Atmospheric observations suggest methane emissions in north-eastern China growing with natural gas use

The dramatic increase of natural gas use in China, as a substitute for coal, helps to reduce CO₂ emissions and air pollution, but the climate mitigation benefit can be offset by methane leakage into the atmosphere. We estimate methane emissions from 2010 to 2018 in four regions of China using the GOSAT satellite data and in-situ observations with a high-resolution (0.1° × 0.1°) inverse model and analyze interannual changes of emissions by source sectors. We find that estimated methane emission over the north-eastern China region contributes the largest part (0.77 Tg CH₄ yr^-1) of the methane emission growth rate of China (0.87 Tg CH₄ yr^-1) and is largely attributable to the growth in natural gas use. The results provide evidence of a detectable impact on atmospheric methane observations by the increasing natural gas use in China and call for methane emission reductions throughout the gas supply chain and promotion of low emission end-use facilities.

Near-real-time daily estimates of fossil fuel CO2 emissions from major high-emission cities in China

Cities in China are on the frontline of low-carbon transition which requires monitoring city-level emissions with low-latency to support timely climate actions. Most existing CO₂ emission inventories lag reality by more than one year and only provide annual totals. To improve the timeliness and temporal resolution of city-level emission inventories, we present Carbon Monitor Cities-China (CMCC), a near-real-time dataset of daily CO₂ emissions from fossil fuel and cement production for 48 major high-emission cities in China. This dataset provides territory-based emission estimates from 2020-01-01 to 2021-12-31 for five sectors: power generation, residential (buildings and services), industry, ground transportation, and aviation. CMCC is developed based on an innovative framework that integrates bottom-up inventory construction and daily emission estimates from sectoral activities and models. Annual emissions show reasonable agreement with other datasets, and uncertainty ranges are estimated for each city and sector. CMCC provides valuable daily emission estimates that enable low-latency mitigation monitoring for cities in China.

Measurement(s)	carbon dioxide emissions
Technology Type(s)	fossil fuel consumption

Emission estimates and air quality simulation on Lombardy during lockdown

This paper illustrates the study carried out by ARPA Lombardia to quantify the variation in daily emissions of the main pollutants and their impacts on air quality in Lombardy during the anti-COVID-19 lockdown between the end of February and the end of May 2020. A methodology for emission estimates was developed over Lombardy for this purpose and later was extended to larger areas: the Po-basin, (LIFE PREPAIR 2020) and the entire Italy (PULVIRUS 2021). In this study, the daily emissions estimates were derived by combining data from air emission inventory of Lombardy and a set of indicators that allowed to update the estimates and describe the temporal and spatial variations of the emission sources. The calculation of emission variation was conducted for all the main pollutants (PM₁₀, NH₃, NO_x, SO₂, NMVOC) and the greenhouse gases; then, the impact on air quality concentrations was simulated by the chemical and transport model FARM, that also allows to track secondary particulate and its variability in time and space on the basis of nonlinear processes and weather conditions. The estimated emission reduction, compared to the expected average value in the absence of anti-COVID-19 measures, daily varies depending on pollutants and is mainly affected by reductions in road traffic emissions and an estimated increase in domestic heating emissions. Simulations confirm strong reductions of NO₂ atmospheric average concentrations, slightly variations of PM₁₀ averages and a potential growth of tropospheric ozone.

Methane sources from waste and natural gas sectors detected in Pune, India, by concentration and isotopic analysis

Methane (CH₄) is a potent greenhouse gas and also plays a significant role in tropospheric chemistry. High-frequency (sub-hourly) measurements of CH₄ and carbon isotopic ratio (δ¹³CH₄) have been conducted at Pune (18.53°N, 73.80°E), an urban environment in India, during 2018-2020. High CH₄ concentrations were observed, with a mean of 2100 ± 196 ppb (1844-2749 ppb), relative to marine background concentrations. The δ¹³CH₄ varied between -45.11 and -50.03 ‰ for the entire study period with an average of -47.41 ± 0.94 ‰. The diurnal variability of CH₄ typically showed maximum values in the morning (08:00-09:00 local time) and minimum in the afternoon (15:00 local time). The deepest diurnal amplitude of ~500 ppb was observed during winter (December-February), which was reduced to less than half, ~200 ppb, during the summer (March-May). CH₄ concentration at Pune showed a strong seasonality (470 ppb), much higher than that at Mauna Loa, Hawaii. On the other hand, δ¹³CH₄ records did not show distinct seasonality at Pune. The δ¹³CH₄ values revealed that the significant sources of CH₄ in Pune were from the waste sector (enhanced during the monsoon season; signature of depleted δ¹³CH₄), followed by the natural gas sector with a signature of enriched δ¹³CH₄. Our analysis of Covid-19 lockdown (April to May 2020) effect on the CH₄ variability showed no signal in the CH₄ variability; however, the isotopic analysis indicated a transient shift in the CH₄ source to the waste sector (early summer of 2020).

An operational urban air quality model ENFUSER, based on dispersion modelling and data assimilation

An operational urban air quality modelling system ENFUSER is presented with an evaluation against measured data. ENFUSER combines several dispersion modelling approaches, uses data assimilation, and continuously extracts information from online, global open-access sources. The modelling area is described with a combination of geographic datasets. These GIS datasets are globally available with open access, and therefore the model can be applied worldwide. Urban scale dispersion is addressed with a combination of Gaussian puff and Gaussian plume modelling, and long-range transport of pollutants is accounted for via a separate regional model. The presented data assimilation method, which supports the use of AQ sensors and incorporates a longer-term learning mechanism, adjusts emission factors and the regional background values on an hourly basis. The model can be used with reasonable accuracy also in urban areas, for which detailed emissions inventories would not be available, due to the data assimilation capabilities.

Carbon Monitor Cities near-real-time daily estimates of CO2 emissions from 1500 cities worldwide

Building on near-real-time and spatially explicit estimates of daily carbon dioxide (CO2) emissions, here we present and analyze a new city-level dataset of fossil fuel and cement emissions, Carbon Monitor Cities, which provides daily estimates of emissions from January 2019 through December 2021 for 1500 cities in 46 countries, and disaggregates five sectors: power generation, residential (buildings), industry, ground transportation, and aviation. The goal of this dataset is to improve the timeliness and temporal resolution of city-level emission inventories and includes estimates for both functional urban areas and city administrative areas that are consistent with global and regional totals. Comparisons with other datasets (i.e. CEADs, MEIC, Vulcan, and CDP-ICLEI Track) were performed, and we estimate the overall annual uncertainty range to be ±21.7%. Carbon Monitor Cities is a near-real-time, city-level emission dataset that includes cities around the world, including the first estimates for many cities in low-income countries.