Atmospheric Chemistry Transport Model Research Articles

Global tropospheric halogen (Cl, Br, I) chemistry and its impact on oxidants

Abstract. We present an updated mechanism for tropospheric halogen (Cl + Br + I) chemistry in the GEOS-Chem global atmospheric chemical transport model and apply it to investigate halogen radical cycling and implications for tropospheric oxidants. Improved representation of HOBr heterogeneous chemistry and its pH dependence in our simulation leads to less efficient recycling and mobilization of bromine radicals and enables the model to include mechanistic sea salt aerosol debromination without generating excessive BrO. The resulting global mean tropospheric BrO mixing ratio is 0.19 ppt (parts per trillion), lower than previous versions of GEOS-Chem. Model BrO shows variable consistency and biases in comparison to surface and aircraft observations in marine air, which are often near or below the detection limit. The model underestimates the daytime measurements of Cl2 and BrCl from the ATom aircraft campaign over the Pacific and Atlantic, which if correct would imply a very large missing primary source of chlorine radicals. Model IO is highest in the marine boundary layer and uniform in the free troposphere, with a global mean tropospheric mixing ratio of 0.08 ppt, and shows consistency with surface and aircraft observations. The modeled global mean tropospheric concentration of Cl atoms is 630 cm−3, contributing 0.8 % of the global oxidation of methane, 14 % of ethane, 8 % of propane, and 7 % of higher alkanes. Halogen chemistry decreases the global tropospheric burden of ozone by 11 %, NOx by 6 %, and OH by 4 %. Most of the ozone decrease is driven by iodine-catalyzed loss. The resulting GEOS-Chem ozone simulation is unbiased in the Southern Hemisphere but too low in the Northern Hemisphere.

Calibrating a global atmospheric chemistry transport model using Gaussian process emulation and ground-level concentrations of ozone and carbon monoxide

Abstract. Atmospheric chemistry transport models are important tools to investigate the local, regional and global controls on atmospheric composition and air quality. To ensure that these models represent the atmosphere adequately, it is important to compare their outputs with measurements. However, ground based measurements of atmospheric composition are typically sparsely distributed and representative of much smaller spatial scales than those resolved in models; thus, direct comparison incurs uncertainty. In this study, we investigate the feasibility of using observations of one or more atmospheric constituents to estimate parameters in chemistry transport models and to explore how these estimates and their uncertainties depend upon representation errors and the level of spatial coverage of the measurements. We apply Gaussian process emulation to explore the model parameter space and use monthly averaged ground-level concentrations of ozone (O3) and carbon monoxide (CO) from across Europe and the US. Using synthetic observations, we find that the estimates of parameters with greatest influence on O3 and CO are unbiased, and the associated parameter uncertainties are low even at low spatial coverage or with high representation error. Using reanalysis data, we find that estimates of the most influential parameter – corresponding to the dry deposition process – are closer to its expected value using both O3 and CO data than using O3 alone. This is remarkable because it shows that while CO is largely unaffected by dry deposition, the additional constraints it provides are valuable for achieving unbiased estimates of the dry deposition parameter. In summary, these findings identify the level of spatial representation error and coverage needed to achieve good parameter estimates and highlight the benefits of using multiple constraints to calibrate atmospheric chemistry transport models.

Quantifying Nitrate Formation Pathways in the Equatorial Pacific Atmosphere from the GEOTRACES Peru-Tahiti Transect

This study aims to better our collective understanding of the oxidative capacity and atmospheric chemistry over the equatorial Pacific. Bulk and size-segregated filter samples were collected during the GEOTRACES Eastern Tropical Pacific transect (4.1° S, 81.9° W to 10.5° S, 152.0° W; October–December 2013) and measured for aerosol concentration and complete isotopic composition of nitrate (δ15N, δ18O, Δ17O where Δ17O = δ17O – 0.52 × δ18O). Combined size-segregated filters produced data similar to that found in bulk filter samples, and notably neither δ15N nor δ18O showed any trends based on aerosol size. Similar to other studies, NO3– is concentrated (>80%) in the coarse size fractions (>1.5 μm). Bulk aerosol concentrations ranged from 6.6 to 89.8 nmol/m3. The bulk δ15N-, δ18O-, and Δ17O-nitrate ranged from −13.1 to −3.2‰, 68.5 to 79.3‰, and 23.5 to 28.4‰, respectively. Higher δ15N values near the coast are best explained by the influence of continental sources; lower δ15N values far from the coast may be associated with chemical fractionation during long-range transport or an oceanic source. Both Δ17O and δ18O are interpreted using kinetic analysis and gas concentrations from a global atmospheric chemical transport model (GEOS-Chem), which showed that nitrate production in this environment is dominated by OH oxidation (60%) and RONO2 hydrolysis (15%). To best match the δ18O and Δ17O observations in this study, the terminal oxygen isotopic values for ozone must be higher than those suggested by available observations and/or halogen-mediated chemistry must be more important than the models currently suggest.

The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies

Abstract. Atmospheric inversion approaches are expected to play a critical role in future observation-based monitoring systems for surface fluxes of greenhouse gases (GHGs), pollutants and other trace gases. In the past decade, the research community has developed various inversion software, mainly using variational or ensemble Bayesian optimization methods, with various assumptions on uncertainty structures and prior information and with various atmospheric chemistry–transport models. Each of them can assimilate some or all of the available observation streams for its domain area of interest: flask samples, in situ measurements or satellite observations. Although referenced in peer-reviewed publications and usually accessible across the research community, most systems are not at the level of transparency, flexibility and accessibility needed to provide the scientific community and policy makers with a comprehensive and robust view of the uncertainties associated with the inverse estimation of GHG and reactive species fluxes. Furthermore, their development, usually carried out by individual research institutes, may in the future not keep pace with the increasing scientific needs and technical possibilities. We present here the Community Inversion Framework (CIF) to help rationalize development efforts and leverage the strengths of individual inversion systems into a comprehensive framework. The CIF is primarily a programming protocol to allow various inversion bricks to be exchanged among researchers. In practice, the ensemble of bricks makes a flexible, transparent and open-source Python-based tool to estimate the fluxes of various GHGs and reactive species both at the global and regional scales. It will allow for running different atmospheric transport models, different observation streams and different data assimilation approaches. This adaptability will allow for a comprehensive assessment of uncertainty in a fully consistent framework. We present here the main structure and functionalities of the system, and we demonstrate how it operates in a simple academic case.

Hourly emission inventories for air toxic emissions for eastern Australian electricity generators derived from energy distribution data

Emission inventories are a fundamental input for atmospheric chemical transport models in order to accurately simulate air pollution and its impacts. In Australia, emission inventories are currently not available for electricity generators at high temporal and spatial resolutions. In this study, a new high spatial and temporal resolution inventory for Australian power plant emissions has been developed. The emissions from coal- and gas-fired power plants have been estimated using 5-min electricity generation time-series data and emission factors. The emissions are estimated from 2010 to 2019 for sulfur dioxide (\(\hbox {SO}_2\)), nitrogen oxide (\(\hbox {NO}_{2}\)), particulate matter (PM) and mercury (Hg). We estimate that, for 2019, the coal-fired power plants in Australia emitted significant amounts of \(\hbox {SO}_2\) (485.7 kTonne) and \(\hbox {NO}_{2}\) (452 kTonne), as there are no sulfur dioxide or nitrogen oxide controls in place. In contrast, PM emissions are mitigated by particulate matter control devices and coal-fired power plants emit an estimated 27.48 and 13.46 kTonne of \(\hbox {PM}_{10}\) and \(\hbox {PM}_{2.5}\), respectively. Coal-fired power plants were responsible for an estimated 3.13 tonnes of Hg emissions. Gas-fired plant emissions are significantly lower than coal-fired power emissions for all pollutants. Temporal analysis showed that coal- and gas-fired power plant emissions vary quite markedly during the day and over the week. There is a strong diurnal cycle in the gas-fired emissions, which, if ignored by assuming constant emissions (such as is routinely done), will lead to significant errors in modeled air pollution.

Source sector and fuel contributions to ambient PM2.5 and attributable mortality across multiple spatial scales

Ambient fine particulate matter (PM2.5) is the world’s leading environmental health risk factor. Reducing the PM2.5 disease burden requires specific strategies that target dominant sources across multiple spatial scales. We provide a contemporary and comprehensive evaluation of sector- and fuel-specific contributions to this disease burden across 21 regions, 204 countries, and 200 sub-national areas by integrating 24 global atmospheric chemistry-transport model sensitivity simulations, high-resolution satellite-derived PM2.5 exposure estimates, and disease-specific concentration response relationships. Globally, 1.05 (95% Confidence Interval: 0.74–1.36) million deaths were avoidable in 2017 by eliminating fossil-fuel combustion (27.3% of the total PM2.5 burden), with coal contributing to over half. Other dominant global sources included residential (0.74 [0.52–0.95] million deaths; 19.2%), industrial (0.45 [0.32–0.58] million deaths; 11.7%), and energy (0.39 [0.28–0.51] million deaths; 10.2%) sectors. Our results show that regions with large anthropogenic contributions generally had the highest attributable deaths, suggesting substantial health benefits from replacing traditional energy sources.

New approach to evaluate satellite-derived XCO<sub>2</sub> over oceans by integrating ship and aircraft observations

Abstract. Satellite observations provide spatially resolved global estimates of column-averaged mixing ratios of CO2 (XCO2) over the Earth's surface. The accuracy of these datasets can be validated against reliable standards in some areas, but other areas remain inaccessible. To date, limited reference data over oceans hinder successful uncertainty quantification or bias correction efforts and preclude reliable conclusions about changes in the carbon cycle in some regions. Here, we propose a new approach to analyze and evaluate seasonal, interannual, and latitudinal variations of XCO2 over oceans by integrating cargo-ship (Ship Of Opportunity – SOOP) and commercial aircraft (Comprehensive Observation Network for Trace gases by Airliner – CONTRAIL) observations with the aid of state-of-the art atmospheric chemistry-transport model calculations. The consistency of the “observation-based column-averaged CO2” dataset (obs. XCO2) with satellite estimates was analyzed over the western Pacific between 2014 and 2017, and its utility as a reference dataset evaluated. Our results demonstrate that the new dataset accurately captures seasonal and interannual variations of CO2. Retrievals of XCO2 over the ocean from GOSAT (Greenhouse Gases Observing Satellite: National Institute for Environmental Studies – NIES v02.75; Atmospheric CO2 Observation from Space – ACOS v7.3) and OCO-2 (Orbiting Carbon Observatory, v9r) observations show a negative bias of about 1 part per million (ppm) in northern midlatitudes, which was attributed to measurement uncertainties of the satellite observations. The NIES retrieval had higher consistency with obs. XCO2 at midlatitudes as compared to the other retrievals. At low latitudes, it shows many fewer valid data and high scatter, such that ACOS and OCO-2 appear to provide a better representation of the carbon cycle. At different times, the seasonal cycles of all three retrievals show positive phase shifts of 1 month relative to the observation-based data. The study indicates that even if the retrievals complement each other, remaining uncertainties limit the accurate interpretation of spatiotemporal changes in CO2 fluxes. A continuous long-term XCO2 dataset with wide latitudinal coverage based on the new approach has great potential as a robust reference dataset for XCO2 and can help to better understand changes in the carbon cycle in response to climate change using satellite observations.

Quantifying Contributions of Local Emissions and Regional Transport to NOX in Beijing Using TROPOMI Constrained WRF-Chem Simulation

Air quality is strongly influenced by both local emissions and regional transport. Atmospheric chemical transport models can distinguish between emissions and regional transport sources in air pollutant concentrations. However, quantifying model inventories is challenging due to emission changes caused by the recent strict control measures taken by the Chinese government. In this study, we use NO2 column observations from the Tropospheric Monitoring Instrument to retrieve top-down nitrogen oxide (NOX) emissions and quantify the contributions of local emissions and regional transport to NOx in Beijing (BJ), from 1 November 2018 to 28 February 2019 (W_2018) and 1 November 2019 to 29 February 2020 (W_2019). In W_2018 and W_2019, the BJ bottom-up NOX emissions from the multi-resolution emission inventory for China in 2017 were overestimated by 11.8% and 40.5%, respectively, and the input of NOX from other cities to BJ was overestimated by 10.9% and 51.6%, respectively. The simulation using our adjusted inventory exhibited a much higher spatial agreement (slope = 1.0, R2 = 0.79) and reduced a mean relative error by 45% compared to those of bottom-up NOX emissions. The top-down inventory indicated that (1) city boundary transport contributes approximately 40% of the NOX concentration in BJ; (2) in W_2019, NOX emissions and transport in BJ decreased by 20.4% and 17.2%, respectively, compared to those of W_2018; (3) in W_2019, NOX influx substantially decreased (−699 g/s) in BJ compared to that of W_2018 despite negative meteorological conditions that should have increased NOx influx by +503 g/s. Overall, the contribution of intercity input to NOx in BJ has declined with decreasing emissions in the surrounding cities due to regional cooperative control measures, and the role of local emissions in BJ NOx levels was more prominent. Our findings indicate that local emissions may play vital roles in regional center city air quality.

GOSAT CH4 Vertical Profiles over the Indian Subcontinent: Effect of a Priori and Averaging Kernels for Climate Applications

We examined methane (CH4) variability over different regions of India and the surrounding oceans derived from thermal infrared (TIR) band observations (TIR CH4) by the Thermal and Near-infrared Sensor for carbon Observation—Fourier Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observation SATellite (GOSAT) for the period 2009–2014. This study attempts to understand the sensitivity of the vertical profile retrievals at different layers of the troposphere and lower stratosphere, on the basis of the averaging kernel (AK) functions and a priori assumptions, as applied to the simulated concentrations by the MIROC4.0-based Atmospheric Chemistry-Transport Model (MIROC4-ACTM). We stress that this is of particular importance when the satellite-derived products are analyzed using different ACTMs other than those used as retrieved a priori. A comparison of modeled and retrieved CH4 vertical profiles shows that the GOSAT/TANSO-FTS TIR instrument has sufficient sensitivity to provide critical information about the transport of CH4 from the top of the boundary layer to the upper troposphere. The mean mismatch between TIR CH4 and model is within 50 ppb, except for the altitude range above 150 hPa, where the sensitivity of TIR CH4 observations becomes very low. Convolved model profiles with TIR CH4 AK reduces the mismatch to less than the retrieval uncertainty. Distinct seasonal variations of CH4 have been observed near the atmospheric boundary layer (800 hPa), free troposphere (500 hPa), and upper troposphere (300 hPa) over the northern and southern regions of India, corresponding to the southwest monsoon (July–September) and post-monsoon (October–December) seasons. Analysis of the transport and emission contributions to CH4 suggests that the CH4 seasonal cycle over the Indian subcontinent is governed by both the heterogeneous distributions of surface emissions and the influence of the global monsoon divergent wind circulations. The major contrast between monsoon, and pre- and post-monsoon profiles of CH4 over Indian regions are noticed near the boundary layer heights, which is mainly caused by seasonal change in local emission strength with a peak during summer due to increased emissions from the paddy fields and wetlands. A strong difference between seasons in the middle and upper troposphere is caused by convective transport of the emission signals from the surface and redistribution in the monsoon anticyclone of upper troposphere. TIR CH4 observations provide additional information on CH4 in the region compared to what is known from in situ data and total-column (XCH4) measurements. Based on two emission sensitivity simulations compared to TIR CH4 observations, we suggest that the emissions of CH4 from the India region were 51.2 ± 4.6 Tg year−1 during the period 2009–2014. Our results suggest that improvements in the a priori profile shape in the upper troposphere and lower stratosphere (UT/LS) region would help better interpretation of CH4 cycling in the earth’s environment.

Quantifying the high resolution seasonal emission of air pollutants from crop residue burning in India

Biomass burning, a recurring global phenomenon is also considered an environmental menace, making headlines every year in India with onset of autumn months. Agriculture is demographically the broadest economic sector and plays a significant role in the overall socio-economic fabric of India. Hence, disposal of crop residue is done mainly by burning leading to deterioration of air quality. Residue burning in parts of India is blamed for changing air quality in nearby cities. The spatial distribution of these emissions has always been a challenge due to various data constraints. We hereby present a comprehensive spatially resolved seasonal high resolution gridded (∼10 km × ∼10 km) emission inventory of major pollutants from crop residue burning source in India for the latest year 2018. The winter months contributes almost around ∼50% of total emission followed by summer (∼48%), which is the prime cause of changing air quality in nearby cities. Among all the crops; rice, wheat, maize and sugarcane accounts ∼90% of total PM10 load in the country. The estimated emission for PM2.5, PM10, BC and OC, CO, NOx, SO2, VOC, CH4 and CO2 are found to 990.68 Gg/yr, 1231.26 Gg/yr, 123.33 Gg/yr, 410.99 Gg/yr, 11208.18 Gg/yr, 484.55 Gg/yr, 144.66 Gg/yr, 1282.95 Gg/yr, 785.56 Gg/yr and 262051.06 Gg/yr respectively. The cropping pattern and its role in different geographic regions are analysed to identify all potential emission hotspots regions scattered across the country. The developed gridded emissions inventory is envisaged to serve as an important input to regional atmospheric chemistry transport model to better quantify its contribution in deteriorating air quality in various regions of India, paving the way to policy makers to better plan the mitigation and control strategies. The developed fundamental tool is likely to be useful for air quality management.

An enhanced integrated approach to knowledgeable high-resolution environmental quality assessment

Sustaining urban environmental quality requires effective policy measures that integrate local monitoring and contextualized high-resolution modelling with actionable scenarios. Knowledgeable decision making in this field can nowadays be supported by an array of atmospheric models, but their transfer into an Integrated Urban hydrometeorological, climate and environmental Services (IUS) remains challenging. Methodological aspects that are beyond pure technicalities of the model-to-model coupling are still poorly explored. Modeling downscaling chains lack their most user-relevant link – urban-to-neighborhood scale observations and models. This study looks at a socio-environmental context of the high-resolution atmospheric modeling in the case study of the Arctic urban cluster of Apatity and Kirovsk, Russia. We demonstrate that atmospheric dynamics of the lowermost, turbulent air layers is highly localized during the most influential episodes of atmospheric pollution. Urban micro-climates create strong circulations (winds) that are sensitive to the local environmental context. As the small-scale turbulence dynamics is not spatially resolved in meteorological downscaling or statistical modeling, capturing this local context requires specialized turbulence-resolving (large-eddy simulation) models. Societal acceptance of the urban modeling could be increased in the IUS with horizontally integrated modeling driven by localized scenarios. This study presents an enhanced integrated approach, which incorporates a large-eddy simulation model PALM into meteorological downscaling chains of a climate model (EC-EARTH), a numerical weather prediction – atmospheric chemical transport model (ENVIRO-HIRLAM) and a regional-scale meteorological model (COSMO-CLM). We discuss how this approach could be further developed into an environmental component of a digital “smart city”.

Disease burden and excess mortality from coal-fired power plant emissions in Europe

We studied the contribution of coal-fired power plant (CPP) emissions (SO2 and NOx) to air pollution levels and annual excess mortality by cardiovascular and respiratory diseases in Europe, based on fine particulate matter (PM2.5) concentrations computed with a regional atmospheric chemistry-transport model. The health burden of European CPP emission-induced PM2.5, estimated with the Global Exposure Mortality Model, amounts to at least 16 800 (CI95 14 800–18 700) excess deaths per year over the European domain. We identified an underestimation of the emissions magnitude and correcting for it doubles CPP-attributed annual excess mortality to 33 900 (CI95 33 000–37 600) per year. Due to the non-linearity of exposure-responses, especially at relatively low concentrations, these estimates represent lower limits of possible health benefits for the EU-28 states. CPP emission phase-out would avoid 18 400 (CI95 16 000–20 500) excess deaths annually assuming background PM2.5 levels of 10 μg m−3, 25 500 (CI95 22 600–28 200) per year if pollution levels from other sources are reduced by 50% in parallel, and 105 900 (CI95 89 900–121 700) deaths by drastically reducing anthropogenic pollution from other sources to 2.4 μg m−3 that represents the threshold for health impacts. Depending on the emission scenario, large health gains can be achieved from the phase-out of CPP emissions, which calls for coordinated air pollution control strategies at the European level.

The black carbon dispersion in the Southern Hemisphere and its transport and fate to Antarctica, an Anthropocene evidence for climate change policies.

Black carbon (BC) has been measured in Antarctica's air, and its global warming effect can potentially speed up the ice melting in the most solid water reservoir of the planet. However, the primary responsible sources are not well evidenced in this region. The dispersion of black carbon emissions from the Southern Hemisphere was conducting using atmospheric chemical transport model and we compared the results with satellite registries from March 1st to April 30th in 2014. The emission inventory considered the anthropogenic and biomass burning emissions from global datasets. The largest and most populated cities in Southern Hemisphere showed the higher emission of BC. As a result, the average daily concentrations of atmospheric BC were around 4ng/m3 in most regions of Antarctica according to its pristine characteristics. We analyzed fifteen relevant sites in coastal zones of Antartica and some peaks registered by the satellite records were not replicated by model outputs and it was mainly associated with the lack of emissions. Finally, we made simulations in the same period without biomass burning emissions and we observed decreased concentrations of BC in the range of 20-50%. As a result, we show that the black carbon transportation from the continental land to the polar region took place in 17-24days during the Austral summer and the biomass burning emissions were the primary source. Black Carbon deposition in Antarctica is not permanent, but the uncontrolled emissions from Southern Hemisphere can increase its transportation to the white continent and make its accumulation during the period when the weak polar vortex occurs.

Seasonal Variations of SF6, CO2, CH4, and N2O in the UT/LS Region due to Emissions, Transport, and Chemistry

AbstractDistributions of trace gases in the atmosphere provide key information on the transport and chemical properties of the Earth's environment. In this study, we examine the observed seasonal variability of SF6, CO2, CH4, and N2O from commercial aircraft‐based measurements in the upper troposphere and lower stratosphere (UT/LS) in combination with atmospheric chemistry‐transport model (ACTM) simulations. We used a dynamical tropopause defined based on maximum PV gradient that better allows for model‐observation comparisons in the UT and LS regions separately. The ACTM simulated the following observed features well: (1) smaller seasonality in SF6 in the UT than in the LS due to seasonal change in the transport processes, (2) large seasonality in CO2 in the UT, in phase with the source‐sink balance on the Earth's surface, (3) pronounced seasonality in CH4 in the UT with opposite phase in the subtropics and Eurasian high latitudes as contrasting phases of source and chemical loss seasonality develop, and (4) small N2O variability in the UT but stratospheric loss produces distinct seasonality in the LS. Vertical profile comparisons of all the species show that ACTM better simulated the photo‐chemically inert CO2 vertical gradients in all seasons, suggesting a realistic representation of the Brewer‐Dobson circulation in the model. The seasonal variabilities of the long‐lived species in the LS can be explained based on age of air derived from CO2 in the older air regime, except for the months of May‐July, when the model mixes UT and LS air more vigorously than suggested by the observations.

Photochemical environment over Southeast Asia primed for hazardous ozone levels with influx of nitrogen oxides from seasonal biomass burning

Abstract. Mainland and maritime Southeast Asia is home to more than 655 million people, representing nearly 10 % of the global population. The dry season in this region is typically associated with intense biomass burning activity, which leads to a significant increase in surface air pollutants that are harmful to human health, including ozone (O3). Latitude-based differences in the dry season and land use distinguish two regional biomass burning regimes: (1) burning on the peninsular mainland peaking in March and (2) burning across Indonesia peaking in September. The type and amount of material burned in each regime impact the emissions of nitrogen oxides (NOx = NO + NO2) and volatile organic compounds (VOCs), which combine to produce ozone. Here, we use the nested GEOS-Chem atmospheric chemistry transport model (horizontal resolution of 0.25∘ × 0.3125∘), in combination with satellite observations from the Ozone Monitoring Instrument (OMI) and ground-based observations from Malaysia, to investigate ozone photochemistry over Southeast Asia in 2014. Seasonal cycles of tropospheric ozone columns from OMI and GEOS-Chem peak with biomass burning emissions. Compared to OMI, the model has a mean annual bias of −11 % but tends to overestimate tropospheric ozone near areas of seasonal fire activity. We find that outside these burning areas, the underlying photochemical environment is generally NOx-limited and dominated by anthropogenic NOx and biogenic non-methane VOC emissions. Pyrogenic emissions of NOx play a key role in photochemistry, shifting towards more VOC-limited ozone production and contributing about 30 % of the regional ozone formation potential during both biomass burning seasons. Using the GEOS-Chem model, we find that biomass burning activity coincides with widespread ozone exposure at levels that exceed world public health guidelines, resulting in about 260 premature deaths across Southeast Asia in March 2014 and another 160 deaths in September. Despite a positive model bias, hazardous ozone levels are confirmed by surface observations during both burning seasons.

A decline in emissions of CFC-11 and related chemicals from eastern China.

Emissions of ozone-depleting substances, including trichlorofluoromethane (CFC-11), have decreased since the mid-1980s in response to the Montreal Protocol1,2. In recent years, an unexpected increase in CFC-11 emissions beginning in 2013 has been reported, with much of the global rise attributed to emissions from eastern China3,4. Here we use high-frequency atmospheric mole fraction observations from Gosan, South Korea and Hateruma, Japan, together with atmospheric chemical transport-model simulations, to investigate regional CFC-11 emissions from eastern China. We find that CFC-11 emissions returned to pre-2013 levels in 2019 (5.0±1.0 gigagramsper year in 2019, compared to 7.2±1.5gigagramsper year for 2008-2012, ±1standard deviation), decreasing by 10±3gigagramsper year since 2014-2017. Furthermore, we find that in this region, carbon tetrachloride (CCl4)and dichlorodifluoromethane(CFC-12) emissions-potentially associated with CFC-11 production-were higher than expected after 2013 and then declined one to two years before the CFC-11 emissions reduction. This suggests that CFC-11 production occurred in eastern China after the mandated global phase-out, and that there was a subsequent decline in production during 2017-2018. We estimate that the amount of the CFC-11 bank (the amount of CFC-11 produced, but not yet emitted) in eastern China is up to 112gigagrams larger in 2019 compared to pre-2013 levels, probably as a result of recent production. Nevertheless, it seems that any substantial delay in ozone-layer recovery has been avoided, perhaps owing to timely reporting3,4 and subsequent action by industry and government in China5,6.

Rain-fed pulses of methane from East Africa during 2018–2019 contributed to atmospheric growth rate

East Africa is a key location for wetland emissions of methane (CH4), driven by variations in rainfall that are in turn influenced by sea-surface temperature gradients over the Indian Ocean. Using satellite observations of CH4 and an atmospheric chemistry-transport model, we quantified East African CH4 emissions during 2018 and 2019 when there was 3-σ anomalous rainfall during the long rains (March–May) in 2018 and the short rains (October–December) in 2019. These rainfall anomalies resulted in CH4 emissions of 6.2 ± 0.3 Tg CH4 and 8.6 ± 0.3 Tg CH4, in each three month period, respectively, and represent a 10% and 37% increase compared to the equivalent season in the opposite year, when rainfall was close to the long-term seasonal mean. We find the additional short rains emissions were equivalent to over a quarter of the growth in global emissions in 2019, highlighting the disproportionate role of East Africa in the global CH4 budget.

Co-benefits of peaking carbon dioxide emissions on air quality and health, a case of Guangzhou, China

Cities play a key role in making carbon emission reduction targets achievable and tackling air pollution. Using Guangzhou city as a case, this paper explored the air quality and health co-benefits of peaking carbon dioxide emissions under three scenarios and developed an integrated assessment framework by combining a local air pollutant emission inventory, an atmospheric chemistry transport model, and a health assessment model. The results showed that SO2, PM10, and PM2.5 could achieve larger emission reductions than NH3, VOCs, and NOx among all the scenarios we examined. Under the enhanced peaking scenario with the most stringent mitigation strategies, Guangzhou could meet the local ambient air quality standard for PM2.5 (34 μg/m3), with the most reduction observed in the annual average PM2.5 concentration (28.4%) and related premature deaths (17.08%), compared with the base year 2015. We also identified hotspot grids, which were areas with high concentrations of carbon emissions, high concentrations of air pollution and poor air quality in Guangzhou. Our analysis highlighted the importance of promoting peaking carbon dioxide emission for the improvement of air quality and public health at the city level.

Life Course Air Pollution Exposure and Cognitive Decline: Modelled Historical Air Pollution Data and the Lothian Birth Cohort 1936.

Background:Air pollution has been consistently linked with dementia and cognitive decline. However, it is unclear whether risk is accumulated through long-term exposure or whether there are sensitive/critical periods. A key barrier to clarifying this relationship is the dearth of historical air pollution data.Objective:To demonstrate the feasibility of modelling historical air pollution data and using them in epidemiologicalmodels.Methods:Using the EMEP4UK atmospheric chemistry transport model, we modelled historical fine particulate matter (PM2.5) concentrations for the years 1935, 1950, 1970, 1980, and 1990 and combined these with contemporary modelled data from 2001 to estimate life course exposure in 572 participants in the Lothian Birth Cohort 1936 with lifetime residential history recorded. Linear regression and latent growth models were constructed using cognitive ability (IQ) measured by the Moray House Test at the ages of 11, 70, 76, and 79 years to explore the effects of historical air pollution exposure. Covariates included sex, IQ at age 11 years, social class, and smoking.Results:Higher air pollution modelled for 1935 (when participants would have been in utero) was associated with worse change in IQ from age 11–70 years (β = –0.006, SE = 0.002, p = 0.03) but not cognitive trajectories from age 70–79 years (p > 0.05). There was no support for other critical/sensitive periods of exposure or an accumulation of risk (all p > 0.05).Conclusion:The life course paradigm is essential in understanding cognitive decline and this is the first study to examine life course air pollution exposure in relation to cognitive health.

Improvements in air quality in the Netherlands during the corona lockdown based on observations and model simulations

The lockdown measures in response to the SARS-CoV-2 virus outbreak in 2020 have resulted in reductions in emissions of air pollutants and corresponding ambient concentrations. In the Netherlands, the most stringent lockdown measures were in effect from March to May 2020. These measures coincided with a period of unusual meteorological conditions with wind from the north-east and clear-sky conditions, which complicates the quantification of the effect of the lockdown measures on the air quality. Here we quantify the lockdown effects on the concentrations of nitrogen oxides (NOx and NO2), particulate matter (PM10 and PM2.5) and ozone (O3) in the Netherlands, by analyzing observations and simulations with the atmospheric chemistry-transport model EMEP/MSC-W in its EMEP4NL configuration, after eliminating the effects of meteorological conditions during the lockdown. Based on statistical analyses with a Random Forest method, we estimate that the lockdown reduced observed NO2 concentrations by 30% (95% confidence interval 25–35%), 26% (21–32%), and 18% (10–25%) for traffic, urban, and rural background locations, respectively. Slightly smaller reductions of 8–28% are found with the EMEP4NL simulations for urban and regional background locations based on estimates in reductions in economic activity and emissions of traffic and industry in the Netherlands and other European countries. Reductions in observed PM2.5 concentrations of about 20% (10–25%) are found for all locations, which is somewhat larger than the estimates of 5–16% based on the model simulations. A comparison of the calculated NO2 traffic contributions with observations shows a substantial drop of about 35% in traffic contributions during the lockdown period, which is similar to the estimated reductions in mobility data as reported by Apple and Google. Since the largest health effects related to air pollution in the Netherlands are associated with exposure to PM10 and PM2.5, the lockdown measures in spring of 2020 have temporarily improved the air quality in the Netherlands. The concentrations of the most health relevant compounds have only been reduced by about 10–25%.