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Hydrogen cyanide (HCN) is an important tracer of biomass burning, but there are significant uncertainties in its atmospheric budget, especially its photochemical and ocean sinks. Here we use a tracer version of the TOMCAT global 3-D chemical transport model to investigate the physical and chemical processes driving the abundance of HCN in the troposphere and stratosphere over the period 2004–2016. The modelled HCN distribution is compared with version 4.1 of the Atmospheric Chemistry Experiment Fourier transform spectrometer (ACE-FTS) HCN satellite data, which provide profiles up to around 42 km, and with ground-based column measurements from the Network for the Detection of Atmospheric Composition Change (NDACC). The long-term ACE-FTS measurements reveal the strong enhancements in upper-tropospheric HCN due to large wildfire events in Indonesia in 2006 and 2015. Our 3-D model simulations confirm previous lower-altitude balloon comparisons that the currently recommended NASA Jet Propulsion Laboratory (JPL) reaction rate coefficient of HCN with OH greatly overestimates the HCN loss. The use of the rate coefficient proposed by Kleinböhl et al. (2006) in combination with the HCN oxidation by O(1D) gives good agreement between ACE-FTS observations and the model. Furthermore, the model photochemical loss terms show that the reduction in the HCN mixing ratio with height in the middle stratosphere is mainly driven by the O(1D) sink with only a small contribution from a reaction with OH. From comparisons of the model tracers with ground-based HCN observations we test the magnitude of the ocean sink in two different published schemes (Li et al., 2000, 2003). We find that in our 3-D model the two schemes produce HCN abundances which are very different to the NDACC observations but in different directions. A model HCN tracer using the Li et al. (2000) scheme overestimates the HCN concentration by almost a factor of 2, while a HCN tracer using the Li et al. (2003) scheme underestimates the observations by about one-third. To obtain good agreement between the model and observations, we need to scale the magnitudes of the global ocean sinks by factors of 0.25 and 2 for the schemes of Li et al. (2000) and Li et al. (2003), respectively. This work shows that the atmospheric photochemical sinks of HCN now appear well constrained but improvements are needed in parameterizing the major ocean uptake sink.

We present a novel approach to derive indirect global information on the hydroxyl radical (OH), one of the most important atmospheric oxidants, using state-of-art satellite trace gas observations (key sinks and sources of OH) and a steady-state approximation (SSA). This is a timely study as OH observations are predominantly from spatially sparse field and infrequent aircraft campaigns, so there is a requirement for further approaches to infer spatial and temporal information on OH and its interactions with important climate (e.g. methane, CH4) and air quality (e.g. nitrogen dioxide, NO2) trace gases. Due to the short lifetime of OH (~1.0 s), SSAs of varying complexities can be used to model its concentration and offer a tool to examine the OH budget in different regions of the atmosphere. Here, we use the well-evaluated TOMCAT three-dimensional chemistry transport model to identify atmospheric regions where different complexities of the SSAs are representative of OH. In the case of a simplified SSA (S-SSA), where we have observations of ozone (O3), carbon monoxide (CO), CH4 and water vapour (H2O) from the Infrared Atmospheric Sounding Interferometer (IASI) on-board ESA’s MetOp-A satellite, it is most representative of OH between 600 and 700 hPa (though suitable between 400–800 hPa) within ~20 % of TOMCAT modelled OH. The same S-SSA is applied to aircraft measurements from the Atmospheric Tomography Mission (ATom) and compares well with the observed OH concentrations within ~30 % yielding a correlation of 0.78. We apply the S-SSA to IASI data spanning 2008–2017 to explore the global long-term inter-annual variability of OH. Relative to the 10-year mean, we find that global annual mean OH anomalies ranged from −3.1 % to +4.4 %, with the largest spread in the tropics between −7.0 % and +7.7 %. Investigation of the individual terms in the S-SSA over this time period suggests that O3 and CO were the key drivers of variability in the production and loss of OH. For example, large enhancement in the OH sink during the positive 2015/2016 ENSO event was due to large scale CO emissions from drought induced wildfires in South East Asia). The methodology described here could be further developed as a constraint on the tropospheric OH distribution as further satellite data becomes available in the future.

We present a comparison of atmospheric transport models that simulate carbonyl sulfide (COS). This is part II of the ongoing Atmospheric Transport Model (ATM) Inter-comparison Project (TransCom–COS). Differently from part I, we focus on seven model intercomparison by transporting two recent COS inversions of NOAA surface data within TM5-4DVAR and LMDz models. The main goals of TransCom-COS part II are (a) to compare the COS simulations using the two sets of optimized fluxes with simulations that use a control scenario (part I) and (b) to evaluate the simulated tropospheric COS abundance with aircraft-based observations from various sources. The output of the seven transport models are grouped in terms of their vertical mixing strength: strong and weak mixing. The results indicate that all transport models capture the meridional distribution of COS at the surface well. Model simulations generally match the aircraft campaigns HIPPO and ATom. Comparisons to HIPPO and ATom demonstrate a gap between observed and modelled COS over the Pacific Ocean at 0–40$\degree$N, indicating a potential missing source in the free troposphere. The effects of seasonal continental COS uptake by the biosphere, observed on HIPPO and ATom over oceans, is well reproduced by the simulations. We found that the strength of the vertical mixing within the column as represented in the various atmospheric transport models explains much of the model to model differences. We also found that weak-mixing models transporting the optimized flux derived from the strong-mixing TM5 model show a too strong seasonal cycle at high latitudes.

Exposure to air pollution is a leading public health risk factor in India, especially over densely populated Delhi and the surrounding Indo-Gangetic Plain. During the post-monsoon months, the prevailing north-westerly winds are known to influence aerosol pollution events in Delhi, by advecting pollutants from agricultural fires as well as from local sources. Here we investigate the year-round impact of meteorology on gaseous nitrogen oxides (NOx = NO + NO2), a hazardous primary air pollutant for health, which can lead to the formation of secondary aerosols and ozone. We use bottom-up NOx emission inventories (anthropogenic and fire) and high-resolution satellite measurement based tropospheric column NO2 (TCNO2) data, from S5P on-board TROPOMI, alongside a back-trajectory model (ROTRAJ) to investigate the balance of local and external sources influencing air pollution changes in Delhi, with a focus on different emission sectors. Our analysis shows that accumulated emissions (i.e. integrated along the trajectory path, allowing for chemical loss) are highest under westerly, north-westerly and northerly flow during pre- (February–March) and post- (October–January) monsoon periods. During the pre-monsoon period, the residential and transport sectors together account for more than 50 % of the total accumulated emissions, which are dominated by local sources (90 %) under easterly winds and by non-local sources (> 70 %) under north-westerly winds. The high accumulated emissions estimated during the pre-monsoon season under north-westerly wind directions are likely to be driven by high NOx emissions locally and in nearby regions (since NOx lifetime is reduced and the boundary layer is relatively deeper in this period). During the post-monsoon period non-local (60 %) transport emissions are the largest contributor to the total accumulated emissions as high emissions, coupled with a relatively long NOx atmospheric lifetime and shallow boundary-layer aid the build-up of emissions along the trajectory path. Analysis of surface daily NO2 observations indicates that high pollution episodes (> 90th percentile) occur predominantly in the post-monsoon and more than 75 % of high pollution events are primarily caused by non-local sources. Overall, we find that in the post-monsoon period, there is a substantial import of NOx pollution into Delhi with a large contribution from the transport sector. This work indicates that the advection of highly polluted air originating from outside Delhi is of concern for the population and air quality mitigation strategies need to be adopted not only in Delhi but in the surrounding regions to successfully control this issue. In addition, our analysis suggests that the largest benefits to Delhi NOx air quality would be seen with targeted reductions in emissions from the transport sector, particularly during post-monsoon months.

Nitrogen oxides (NOx, NO + NO2) are potent air pollutants which directly impact on human health and which aid the formation of other hazardous pollutants such as ozone (O3) and particulate matter. In this study, we use satellite tropospheric column nitrogen dioxide (TCNO2) data to evaluate the spatiotemporal variability and magnitude of the United Kingdom (UK) bottom-up National Atmospheric Emissions Inventory (NAEI) NOx emissions. Although emissions and TCNO2 represent different quantities, for UK city sources we find a spatial correlation of ∼0.5 between the NAEI NOx emissions and TCNO2 from the high-spatial-resolution TROPOspheric Monitoring Instrument (TROPOMI), suggesting a good spatial distribution of emission sources in the inventory. Between 2005 and 2015, the NAEI total UK NOx emissions and long-term TCNO2 record from the Ozone Monitoring Instrument (OMI), averaged over England, show annually decreasing trends of 4.4 % and 2.2 %, respectively. Top-down NOx emissions were derived in this study by applying a simple mass balance approach to TROPOMI-observed downwind NO2 plumes from city sources. Overall, these top-down estimates were consistent with the NAEI, but for larger cities such as London and Birmingham the inventory is significantly (>25 %) less than the top-down emissions.

In the summer of 2018, Europe experienced an intense heat wave which coincided with several persistent large-scale ozone (O3) pollution episodes. Novel satellite data of lower tropospheric column O3 from the Global Ozone Monitoring Experiment-2 (GOME-2) and Infrared Atmospheric Sounding Interferometer (IASI) on the MetOp satellite showed substantial enhancements in 2018 relative to other years since 2012. Surface observations also showed ozone enhancements across large regions of continental Europe in summer 2018 compared to 2017. Enhancements to surface temperature and the O3 precursor gases carbon monoxide and methanol in 2018 were co-retrieved from MetOp observations by the same scheme. This analysis was supported by the TOMCAT chemistry transport model (CTM) to investigate processes driving the observed O3 enhancements. Through several targeted sensitivity experiments we show that meteorological processes, and emissions to a secondary order, were important for controlling the elevated O3 concentrations at the surface. However, mid-tropospheric (~500 hPa) O3 enhancements were dominated by meteorological processes. We find that contributions from stratospheric O3 intrusions ranged between 15–40 %. Analysis of back trajectories indicates that the import of O3-enriched air masses into Europe originated over the North Atlantic substantially increasing O3 in the 500 hPa layer during summer 2018.

International audience; We present a comparison of atmospheric transport model (ATM) simulations for carbonyl sulfide (COS), within the framework of the atmospheric tracer transport model intercomparison project "TransCom-COS." Seven ATMs participated in the experiment and provided simulations of COS mixing ratios over the years 2010-2018, using state-of-the-art surface fluxes for various components of the COS budget: biospheric sink, oceanic source, sources from fire and industry. The main goal of TransCom-COS is to investigate the impact of the transport uncertainty and emission distribution in simulating the spatio-temporal variability of tropospheric COS mixing ratios. A control case with seasonal surface fluxes of COS was constructed. The results indicate that the COS mixing ratios are underestimated by at least 50 parts per trillion (ppt) in the tropics, pointing to a missing tropical source. In summer, the mixing ratios are overestimated by at least 50 ppt above 40°N, pointing to a likely missing sink in the high northern latitudes. Regarding the latitudinal profile, the model spread is greater than 60 ppt above 40°N in boreal summer. Regarding the seasonal amplitude, the model spread reaches 50 ppt at 6 out of 15 sites, compared to an observed seasonal amplitude of 100 ppt. All models simulated a too late minimum by at least 2-3 months at two high northern-latitude sites, likely owing to errors in the seasonal cycle in the ocean emissions. This study highlighted the shortcomings in the COS global budget that need to be resolved before using COS as a photosynthesis tracer. Plain Language Summary In this study, we evaluate the state-of-the-art fluxes for various components of the carbonyl sulfide (COS) budget: biospheric sink, oceanic source, sources from fire and industry. A control case with seasonal surface fluxes of COS was constructed. Seven atmospheric transport models provided simulations of COS mixing ratios. Then, the simulated mixing ratios were evaluated against atmospheric measurements at several surface sites. Results show that all models fail to capture the observed latitudinal distribution and that the model spread is small compared to the model-observation mismatch. In summer, the overestimated mixing ratios above 40°N point to a likely missing sink in the high northern latitudes. The underestimated mixing ratios in the tropics point to a missing tropical source. This study highlighted the shortcomings in the COS global budget that need to be resolved before using COS as a photosynthesis tracer. REMAUD ET AL.

In this study we evaluate simulated surface SO2 and sulfate (SO42-) concentrations from the United Kingdom Earth System Model (UKESM1) against observations from ground-based measurement networks in the USA and Europe for the period 1987–2014. We find that UKESM1 captures the historical trend for decreasing concentrations of atmospheric SO2 and SO42- in both Europe and the USA over the period 1987–2014. However, in the polluted regions of the eastern USA and Europe, UKESM1 over-predicts surface SO2 concentrations by a factor of 3 while under-predicting surface SO42- concentrations by 25 %–35 %. In the cleaner western USA, the model over-predicts both surface SO2 and SO42- concentrations by factors of 12 and 1.5 respectively. We find that UKESM1’s bias in surface SO2 and SO42- concentrations is variable according to region and season. We also evaluate UKESM1 against total column SO2 from the Ozone Monitoring Instrument (OMI) using an updated data product. This comparison provides information about the model's global performance, finding that UKESM1 over-predicts total column SO2 over much of the globe, including the large source regions of India, China, the USA, and Europe as well as over outflow regions. Finally, we assess the impact of a more realistic treatment of the model's SO2 dry deposition parameterization. This change increases SO2 dry deposition to the land and ocean surfaces, thus reducing the atmospheric loading of SO2 and SO42-. In comparison with the ground-based and satellite observations, we find that the modified parameterization reduces the model's over-prediction of surface SO2 concentrations and total column SO2. Relative to the ground-based observations, the simulated surface SO42- concentrations are also reduced, while the simulated SO2 dry deposition fluxes increase.

We present a comparison of atmospheric transport model simulations for carbonyl sulfide (COS), within the framework of the ongoing atmospheric tracer transport model intercomparison project “TransCom”. Seven atmospheric transport models participated in the inter-comparison experiment and provided simulations of COS mixing ratios in the troposphere over a 9-year period (2010–2018), using prescribed state-of-the-art surface fluxes for various components of the atmospheric COS budget: biospheric sink, oceanic source, sources from fire and industry. Since the biosphere is the largest sink of COS, we tested sink estimates produced by two different biosphere models. The main goals of TransCom-COS are (a) to investigate the impact of the transport uncertainty and emission distribution in simulating the spatio-temporal variability of COS mixing ratios in the troposphere, and (b) to assess the sensitivity of simulated tropospheric COS mixing ratios to the seasonal and diurnal variability of the COS biosphere fluxes. To this end, a control case with state-of-the-art seasonal fluxes of COS was constructed. Models were run with the same fluxes and without chemistry to isolate transport differences. Further, two COS flux scenarios were compared: one using a biosphere flux with a monthly time resolution and the other using a biosphere flux with a three-hourly time resolution. In addition, we investigated the sensitivity of the simulated concentrations to different biosphere fluxes and to indirect oceanic emissions through dimethylsulfide (DMS) and carbon disulfide (CS2). The modelled COS mixing ratios were assessed against in-situ observations from surface stations and aircraft.

We have estimated the spatial changes in NO2 levels over different regions of India during the COVID-19 lockdown (25 March–3 May 2020) using the satellite-based tropospheric column NO2 observed by the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI), as well as surface NO2 concentrations obtained from the Central Pollution Control Board (CPCB) monitoring network. A substantial reduction in NO2 levels was observed across India during the lockdown compared to the same period during previous business-as-usual years, except for some regions that were influenced by anomalous fires in 2020. The reduction (negative change) over the urban agglomerations was substantial (∼ 20 %–40 %) and directly proportional to the urban size and population density. Rural regions across India also experienced lower NO2 values by ∼ 15 %–25 %. Localised enhancements in NO2 associated with isolated emission increase scattered across India were also detected. Observed percentage changes in satellite and surface observations were consistent across most regions and cities, but the surface observations were subject to larger variability depending on their proximity to the local emission sources. Observations also indicate NO2 enhancements of up to ∼ 25 % during the lockdown associated with fire emissions over the north-east of India and some parts of the central regions. In addition, the cities located near the large fire emission sources show much smaller NO2 reduction than other urban areas as the decrease at the surface was masked by enhancement in NO2 due to the transport of the fire emissions.

We use a regional chemistry transport model (Weather Research and Forecasting model coupled with chemistry, WRF-Chem) in conjunction with surface observations of tropospheric ozone and Ozone Monitoring Instrument (OMI) satellite retrievals of tropospheric column NO2 to evaluate processes controlling the regional distribution of tropospheric ozone over western Siberia for late spring and summer in 2011. This region hosts a range of anthropogenic and natural ozone precursor sources, and it serves as a gateway for near-surface transport of Eurasian pollution to the Arctic. However, there is a severe lack of in situ observations to constrain tropospheric ozone sources and sinks in the region. We show widespread negative bias in WRF-Chem tropospheric column NO2 when compared to OMI satellite observations from May–August, which is reduced when using ECLIPSE (Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants) v5a emissions (fractional mean bias (FMB) = −0.82 to −0.73) compared with the EDGAR (Emissions Database for Global Atmospheric Research)-HTAP (Hemispheric Transport of Air Pollution) v2.2 emissions data (FMB = −0.80 to −0.70). Despite the large negative bias, the spatial correlations between model and observed NO2 columns suggest that the spatial pattern of NOx sources in the region is well represented. Scaling transport and energy emissions in the ECLIPSE v5a inventory by a factor of 2 reduces column NO2 bias (FMB = −0.66 to −0.35), but with overestimates in some urban regions and little change to a persistent underestimate in background regions. Based on the scaled ECLIPSE v5a emissions, we assess the influence of the two dominant anthropogenic emission sectors (transport and energy) and vegetation fires on surface NOx and ozone over Siberia and the Russian Arctic. Our results suggest regional ozone is more sensitive to anthropogenic emissions, particularly from the transport sector, and the contribution from fire emissions maximises in June and is largely confined to latitudes south of 60∘ N. Ozone dry deposition fluxes from the model simulations show that the dominant ozone dry deposition sink in the region is to forest vegetation, averaging 8.0 Tg of ozone per month, peaking at 10.3 Tg of ozone deposition during June. The impact of fires on ozone dry deposition within the domain is small compared to anthropogenic emissions and is negligible north of 60∘ N. Overall, our results suggest that surface ozone in the region is controlled by an interplay between seasonality in atmospheric transport patterns, vegetation dry deposition, and a dominance of transport and energy sector emissions.

We use the TOMCAT 3-D chemical transport model with a balanced flux inventory to simulate the global distribution of atmospheric carbonyl sulfide (OCS). This is compared with limb-sounding satellite observations made by the Atmospheric Chemistry Experiment – Fourier Transform Spectrometer (ACE-FTS) and surface flask measurements made worldwide at 14 National Oceanic and Atmospheric Administration – Earth System Research Laboratory (NOAA-ESRL) sites. By scaling gross primary productivity (GPP) output from the Joint UK Land Environment Simulator (JULES), we provide a new estimation of global OCS vegetative uptake. This is calculated by scaling GPP according to a leaf relative uptake (LRU) term, yielding a global yearly atmospheric uptake of approximately 629 Gg S, which is toward the lower estimates from recent studies. To compensate for this larger vegetative sink, we scale oceanic emissions of OCS up to an annual mean of 689 Gg S, focused over the tropical ocean region. We combine our OCS fluxes to derive a new inventory which was used in a TOMCAT simulation from 2004–2018 to allow and investigate the annual distribution and seasonality of OCS as well as long term comparisons with available measurements. The simulation matches satellite and surface observations to within their uncertainties in most instances. When compared to co-located ACE-FTS OCS profiles between 5 km and 30 km, the simulation remains within 5 % of the measurements throughout the majority of this region and lies within the standard deviation of these measurements. At the surface, the model captures background concentrations at most of the surface sites to within the maximum and minimum of the seasonal measurements. Compared to a control TOMCAT simulation using the existing Kettle et al. (2002) benchmark flux inventory, errors in the surface comparisons are reduced by as much as 57 %. Our new inventory reduces the average difference in the modelled seasonal amplitude compared to the surface measurements from ±40 % to ±34 %. Other key improvements include better representation of OCS seasonality at North Hemisphere continental sites, as well as a better match in background concentration at tropical Hawaiin sites.

Atmospheric hydrogen cyanide (HCN) is one of the most abundant cyanides in the global atmosphere. Understanding its physical and chemical nature is important considering its influence on the nitrogen cycle. The key processes driving tropospheric HCN variability are biomass burning, as the main source, and ocean uptake, as the main tropospheric sink. In the upper troposphere and stratosphere, the main HCN loss mechanisms are oxidation by hydroxyl radicals (OH) and by reaction with O(1D). The resulting HCN lifetime varies from 2–5 months in the troposphere to several years in the stratosphere.Given its relatively long atmospheric lifetime, HCN is a good tracer of many biomass burning events. Peats contain a high concentration of partially decayed organic matter and once burned they can emit large quantities of carbon dioxide, particulate matter, and other trace gases, including HCN, which affect regional air quality. The widespread peatlands in Indonesia are seasonally drained and cleared of natural vegetation to prepare soil for agricultural activities, making them a prime fuel source and enhancing the potential for burning to occur. During 2015, one of the most intense and prolonged fire seasons in recent decades was observed in Indonesia due to the drought conditions by the abnormally strong 2015/2016 El Niño event.In this work, we use the TOMCAT three-dimensional (3-D) chemical transport model (CTM) to investigate the atmospheric response to the Indonesia 2015 peat fire season with a focus on HCN. The HCN concentrations over the Indonesian region have been modelled at a 2.8° × 2.8° spatial resolution from the surface to ~60 km. The modelled HCN distribution has been compared with the HCN observations over the Indonesia region measured by the Infrared Atmospheric Sounding Interferometer (IASI) instruments on-board the MetOp satellites. Retrievals of HCN columns from IASI measured radiances were made on an 8-layer equidistant altitude grid from 0 to 21 km using the optimal estimation method University of Leicester IASI Retrieval Scheme (ULIRS).Using IASI measurements, we are able to investigate the HCN plume propagation over the entire region and how the El Niño influenced the enhancement of the HCN concentration during the 2015 wildfire season, in particular, a large peak of HCN concentration was observed across the end of October and the beginning of November. We find that TOMCAT is able to simulate and reproduce the magnitude of the unprecedented HCN emissions observed by IASI instrument over Indonesia and the Indian Ocean. Emission factors for Indonesian peat have been derived from IASI satellite data and incorporated into the TOMCAT model. The results provided are comparable to the emission factors of peat derived from lab measurements of burning peat collected in other regions of the world. The implications of our results for understanding the HCN biomass burning emissions and its variability are then discussed.

The 2019/2020 Australian wildfires emitted large quantities of atmospheric pollutant gases and aerosols. Using state-of-the-art near-real-time satellite measurements of tropospheric composition, we present an analysis of several emitted trace gases and their long-range transport, and compare to the previous (2018/2019) fire season. Observations of carbon monoxide (CO) show that fire emissions were so intense that the distinct Australian fire plume managed to circumnavigate the Southern Hemisphere (SH) within a few weeks, with eastward propagation over the South Pacific, South America, the South Atlantic, Africa, and the Indian Ocean. Elevated atmospheric methane levels were also detected in January 2020 fire plumes over the Pacific, defined using CO as a plume tracer, even though sampling was restricted spatially by aerosols and clouds. Observations also show significant enhancements of methanol (CH3OH) from the fires, where CH3OH:CO enhancement ratios increased within the aged plume downwind over the South Pacific indicating secondary in-plume CH3OH formation.

Nitrogen oxides (NOx, NO+NO2) are potent air pollutants which directly impact on human health and which aid the formation of other hazardous pollutants such as ozone (O3) and particulate matter. In this study, we use satellite tropospheric column nitrogen dioxide (TCNO2) data to evaluate the spatiotemporal variability and magnitude of the United Kingdom (UK) bottom-up National Atmospheric Emissions Inventory (NAEI) NOx emissions. Although emissions and TCNO2 represent different quantities, for UK city sources we find a spatial correlation of ~0.5 between the NAEI NOx emissions and TCNO2 from the high-spatial-resolution TROPOspheric Monitoring Instrument (TROPOMI), suggesting a good spatial distribution of emission sources in the inventory. Between 2005 and 2015, the NAEI total UK NOx emissions and long-term TCNO2 record from the Ozone Monitoring Instrument (OMI), averaged over England, show decreasing trends of 4.4 % and 2.2 %, respectively. Top-down NOx emissions were derived in this study by applying a simple mass balance approach to TROPOMI observed downwind NO2 plumes from city sources. Overall, these top-down estimates were consistent with the NAEI, but for larger cities such as London and Manchester the inventory is significantly (> 25 %) less than the top-down emissions. This NAEI NOx emission underestimate is supported by comparing simulations from the GEOS-Chem atmospheric chemistry model, driven by the NAEI emissions, with satellite and surface NO2 observations over the UK. This yields substantial model negative biases, providing further evidence to demonstrate that the NAEI may be underestimating NOx emissions in London and Manchester.

New state‐of‐the‐art satellite measurements of tropospheric column NO2 from the TROPOMI instrument on‐board Sentinel‐5 Precursor (S5P), launched in October 2017, allow for an unprecedented high resolution (sub‐10km) assessment of UK air quality (AQ) from space. We present the first results from TROPOMI and compare them with its predecessor, the Ozone Monitoring Instrument (OMI), to quantify previously unresolved UK pollution hotspots (e.g. Bristol, Southampton and Liverpool). The TROPOMI tropospheric column NO2 data represents a powerful new tool to quantify UK AQ, evaluate atmospheric composition models and potentially derive new emission inventories from space.

The Australian 2019/2020 bushfires were unprecedented in their extent and intensity, causing a catastrophic loss of habitat, human and animal life across eastern‐Australia. We use a regional air quality model to assess the impact of the bushfires on particulate matter with a diameter less than 2.5 μm (PM2.5) concentrations and the associated health impact from short‐term population exposure to bushfire PM2.5. The mean population Air Quality Index (AQI) exposure between September and February in the fires and no fires simulations indicates an additional ∼437,000 people were exposed to “Poor” or worse AQI levels due to the fires. The AQ impact was concentrated in the cities of Sydney, Newcastle‐Maitland, Canberra‐Queanbeyan and Melbourne. Between October and February 171 (95% CI: 66–291) deaths were brought forward due to short‐term exposure to bushfire PM2.5. The health burden was largest in New South Wales (NSW) (109 (95% CI: 41–176) deaths brought forward), Queensland (15 (95% CI: 5–24)), and Victoria (35 (95% CI: 13–56)). This represents 38%, 13% and 30% of the total deaths brought forward by short‐term exposure to all PM2.5. At a city‐level 65 (95% CI: 24–105), 23 (95% CI: 9–38) and 9 (95% CI: 4–14) deaths were brought forward from short‐term exposure to bushfire PM2.5, accounting for 36%, 20%, and 64% of the total deaths brought forward from all PM2.5. Thus, the bushfires caused substantial AQ and health impacts across eastern‐Australia. Climate change is projected to increase bushfire risk, therefore future fire management policies should consider this., Key Points The fires led to widespread exposure to “Poor” or worse Air Quality Index levels across eastern‐AustraliaThe highest all‐cause, all‐age mortality from short‐term exposure to bushfire particulate matter with a diameter less than 2.5 μm (PM2.5) was seen in the states of New South Wales, Queensland, and VictoriaAll‐cause, all‐age mortality from short‐term exposure to bushfire PM2.5 was highest in the cities of Sydney, Melbourne, and Canberra

In this study we evaluate simulated surface SO2 and sulphate (SO42−) concentrations from the United Kingdom Earth System Model (UKESM1) against observations from ground based measurement networks in the USA and Europe for the period 1987 to 2014. We find that UKESM1 captures the historical trend for decreasing concentrations of atmospheric SO2 and SO42− in both Europe and the USA over the period 1987 to 2014. However, in the polluted regions of the eastern USA and Europe, UKESM1 over-predicts surface SO2 concentrations by a factor of 3, while under-predicting surface SO42− concentrations by 25–35 %. In the cleaner western USA, the model over-predicts both surface SO2 and SO42− concentrations by a factor of 12 and 1.5 respectively. We find that UKESM1’s bias in surface SO2 and SO42− concentrations is variable according to region and season. We also evaluate UKESM1 against total column SO2 from the Ozone Monitoring Instrument (OMI) using an updated data product. This comparison provides information about the model’s global performance, finding that UKESM1 over predicts total column SO2 over much of the globe, including the large source regions of India, China, the USA and Europe as well as over outflow regions. Finally, we assess the impact of a more realistic treatment of the model’s SO2 dry deposition parameterization. This change increases SO2 dry deposition to the land and ocean surfaces, thus reducing the atmospheric loading of SO2 and SO42− . In comparison with the ground-based and satellite observations, we find that the modified parameterization reduces the model’s over prediction of surface SO2 concentrations and total column SO2. Relative to the ground-based observations the simulated surface SO42− concentrations are also reduced, while the simulated SO2 dry deposition fluxes increase.

UKESM1 is the latest generation Earth system model to be developed in the UK. It simulates the core physical and dynamical processes of land, atmosphere, ocean and sea ice systems which are extended to incorporate key marine and terrestrial biogeochemical cycles. These include the carbon and nitrogen cycles and interactive stratosphere-troposphere trace gas chemistry. Feedbacks between these components that have an important amplifying or dampening effect on the physical climate, and/or change themselves in response to changes in the physical climate are also included. One focus for the future development of UKESM1 is improved treatment of sulphur processes, including emission, chemical processing and deposition in the aerosol-chemistry scheme, UKCA-Mode. These processes span land-atmosphere and ocean-atmosphere boundaries and can therefore impact feedbacks between these systems. Emissions of SO2 can be oxidised to form sulphate aerosol, which plays a key role in both acid deposition, atmospheric aerosol loading and cloud properties, thereby directly contributing to the Earth’s radiative balance. Good representation of sulphur processes in UKESM1 is therefore essential for constraining uncertainties associated with the impacts of aerosols on the Earth system and thus understanding the global climate. Here we challenge UKESM1 with observations of SO2 and sulphate from ground-based measurement networks in Europe and the USA, and of SO2 from the Ozone Monitoring Instrument (OMI). We use these to evaluate temporal and spatial biases in the model’s simulation of SO2 and sulphate. We find that UKESM1 captures the historical trend for decreasing concentrations of atmospheric SO2 and sulphate in both Europe and the USA over the period 1987 to 2014. However, in the polluted regions of the Eastern USA and Europe, UKESM1 over-predicts surface SO2 concentrations by an average of 320-340%, while under-predicting surface sulphate concentrations by 25-35%. In the cleaner Western USA, the model over-predicts both surface SO2 and sulphate concentrations by 1200% and 150% respectively. The variability in the direction of UKESM1’s bias according to species and region suggests that the model bias may be driven differently depending on species and region. These drivers likely result from uncertainty in aspects of the sulphur cycle, including SO2 emission, loss processes (oxidation and deposition) or transport. To evaluate UKESM1 at the global scale we use a newly available data product for total column SO2 (TCSO2) from OMI. We find that UKESM1 over-predicts TCSO2 over much of the globe, particularly the large source regions of India, China, the USA and Europe as well as over background regions, including much of the ocean. In this study we also assess changes to UKESM1’s SO2 dry deposition parameterization. These changes increase SO2 dry deposition to land and ocean surfaces, thus reducing atmospheric SO2 and sulphate concentrations, and ultimately reducing cold bias in UKESM1's simulation of mid 20th C global mean surface temperatures. In comparison with the ground based and satellite observations, we find that the changes reduce UKESM1's over prediction of surface SO2 concentrations and TCSO2

Hydrogen cyanide (HCN) is one of the most abundant cyanides present in the global atmosphere, and is a tracer of biomass burning, especially for peatland fires. The HCN lifetime is 2–5 months in the troposphere but several years in the stratosphere. Understanding the physical and chemical mechanisms of HCN variability is important due to its non-negligible role in the nitrogen cycle. The main source of tropospheric HCN is biomass burning with minor contributions from industry and transport. The main loss mechanism of atmospheric HCN is the reaction with the hydroxyl radical (OH). Ocean uptake is also important, while in the stratosphere oxidation by reaction with O(1D) needs to be considered.HCN variability can be investigated using chemical model simulations, such as three-dimensional (3-D) chemical transport models (CTMs). Here we use an adapted version of the TOMCAT 3-D CTM at a 1.2°x1.2° spatial resolution from the surface to ~60 km for 12 idealised HCN tracers which quantify the main loss mechanisms of HCN, including ocean uptake, atmospheric oxidation reactions and their combinations. The TOMCAT output of the HCN distribution in the period 2004-2020 has been compared with HCN profiles measured by the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) over an altitude grid from 6 to 42 km. HCN model data have also been compared with ground-based measurements of HCN columns from NDACC FTIR stations and with in-situ volume mixing ratios (VMRs) from NOAA ground-based measurement sites.The model outputs for the HCN tracer with full treatment of the loss processes generally agree well with ACE-FTS measurements, as long as we use recent laboratory values for the atmospheric loss reactions. Diagnosis of the individual loss terms shows that decay of the HCN profile in the upper stratosphere is due mainly to the O(1D) sink. In order to test the magnitude of the tropospheric OH sink and the magnitude of the ocean sink, we also show the comparisons of the model tracers with surface-based observations. The implications of our results for understanding HCN and its variability are then discussed.

The dramatic and sudden reduction in anthropogenic activity due to lockdown measures in the UK in response to the COVID-19 outbreak has resulted in a concerted effort to estimate local and regional changes in air quality, though changes in underlying emissions remain uncertain. Here we combine satellite observations of tropospheric NO2 from TROPOspheric Monitoring Instrument and the Goddard Earth Observing System (GEOS)-Chem 3D chemical transport model to estimate that NO x emissions declined nationwide by ∼20% during the lockdown (23 March to 31 May 2020). Regionally, these range from 22% to 23% in the western portion of the country to 29% in the southeast and Manchester, and >40% in London. We apply a uniform 20% lockdown period emission reduction to GEOS-Chem anthropogenic emissions over the UK to determine that decline in lockdown emissions led to a national decline in PM2.5 of 1.1 μg m−3, ranging from 0.6 μg m−3 in Scotland to 2 μg m−3 in the southwest. The decline in emissions in cities (>40%) is greater than the national average and causes an increase in ozone of ∼2 ppbv in London and Manchester. The change in ozone and PM2.5 concentrations due to emission reductions alone is about half the total change from 2019 to 2020. This emphasizes the need to account for emissions and other factors, in particular meteorology, in future air pollution abatement strategies and regulatory action.

We have estimated the spatial changes in NO2 levels over different regions of India during the COVID-19 lockdown (25th March–3rd May 2020) using the satellite-based tropospheric column NO2 observed by the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI), as well as surface NO2 concentrations obtained from the Central Pollution Control Board (CPCB) monitoring network. A substantial reduction in NO2 levels was observed across India during the lockdown compared to the same period during previous business-as-usual years, except for some regions that were influenced by anomalous fires in 2020. The reduction (negative change) over the urban agglomerations was substantial (∼ 20–40 %) and directly proportional to the urban size and population density. Rural regions across India also experienced lower NO2 values by ∼ 15–25 %. Localised enhancement of NO2 associated with isolated emission increase scattered across India, were also detected. Observed percentage changes in satellite and surface observations were consistent across most regions and cities, but the surface observations were subject to larger variability depending on their proximity to the local emission sources. Observations also indicate NO2 enhancements of up to ∼ 25 % during the lockdown associated with fire emissions over the north-east, and some parts of central regions. In addition, the cities located near the large fire emission sources show much smaller NO2 reduction than other urban areas as the decrease at the surface was masked by enhancement in NO2 due to the transport of the fire emissions.

Volcanic eruptions can cause significant disruption to society and numerical models are crucial for forecasting the dispersion of erupted material. Here we assess the skill and limitations of the Met Office’s Numerical Atmospheric-dispersion Modelling Environment (NAME) in simulating the dispersion of the sulfur dioxide (SO2) cloud from the 21–22 June 2019 eruption of the Raikoke volcano (48.3° N, 153.2° E). The eruption emitted around 1.5 ± 0.2 Tg of SO2, which represents the largest volcanic emission of SO2 into the stratosphere since the 2011 Nabro eruption. We simulate the temporal evolution of the volcanic SO2 cloud across the Northern Hemisphere (NH) and compare our model simulations to high-resolution SO2 measurements from the Tropospheric Monitoring Instrument (TROPOMI) and the Infrared Atmospheric Sounding Interferometer (IASI) satellite SO2 products. We show that NAME accurately simulates the observed location and horizontal extent of the SO2 cloud during the first 2–3 weeks after the eruption, but is unable, in its standard configuration, to capture the extent and precise location of very high-concentration regions within the volcanic cloud. Using the Fractional Skill Score as metric for model skill, NAME shows skill in simulating the horizontal extent of the cloud for 12–17 days after the eruption where vertical column densities (VCD) of SO2 (in Dobson Units, DU) are above 1 DU. For SO2 VCDs above 20 DU, which are predominantly observed as small-scale features within the SO2 cloud, the model shows skill on the order of 2–4 days only. The lower skill for these high-concentration regions is partly explained by the model-simulated SO2 cloud in NAME being too diffuse compared to TROPOMI retrievals. Reducing the standard diffusion parameters used in NAME by a factor of four results in a slightly increased model skill during the first five days of the simulation, but on longer timescales the simulated SO2 cloud remains too diffuse when compared to TROPOMI measurements. We find that the temporal evolution of the NH-mean SO2 mass burden simulated by NAME strongly depends on the fraction of SO2 mass emitted into the lower stratosphere, which is uncertain for the 2019 Raikoke eruption. When emitting 0.9–1.1 Tg of SO2 into the lower stratosphere (11–18 km) and 0.4–0.7 Tg into the upper troposphere (8–11 km), both NAME and TROPOMI show a similar peak in SO2 mass burden (1.4–1.6 Tg of SO2) with an average SO2 e-folding time of 14–15 days in the NH. Our work demonstrates the large potential of using high-resolution satellite retrievals to identify and rectify limitations in dispersion models like NAME, which will ultimately help to improve dispersion modelling efforts of volcanic SO2 clouds.

On June 24th 2018 one of the largest UK wildfires in recent history broke out on Saddleworth Moor, close to Manchester, in north-west England. Since wildfires close to large populations in the UK have been relatively small and rare in the past, there is little knowledge about the impacts. This has prevented the development of effective strategies to reduce them. This paper uses a high-resolution coupled atmospheric-chemistry model to assess the impact of the fires on particulate matter with a diameter less than 2.5 µm (PM2.5) across the region and the impact on health from short-term exposure. We find that the fires substantially degraded air quality. PM2.5 concentrations increased by more than 300% in Oldham and Manchester and up to 50% in areas up to 80 km away such as Liverpool and Wigan. This led to one quarter of the population (2.9 million people) in the simulation domain (−4.9–0.7°E and 53.0–54.4°N) being exposed to moderate PM2.5 concentrations on at least one day, according to the Daily Air Quality Index (36–53 µg m−3), between June 23rd and 30th 2018. This equates to 4.5 million people being exposed to PM2.5 above the WHO 24-hour guideline of 25 µg m−3 on at least one day. Using a concentration-response function we calculate the short-term health impact, which indicates that in total over the 7-day period 28 (95% CI: 14.1–42.1) deaths were brought forward, with a mean daily excess mortality of 3.5 deaths per day (95% CI: 1.8–5.3). The excess mortalities from PM2.5 from the fires represented up to 60% of the total excess mortality (5.7 of 9.5 excess deaths), representing an increase of 3.8 excess mortalities (165% increase) compared to if there were no fires. We find the impact of mortality due to PM2.5 from the fires on the economy was also substantial (£21.1 m).

The challenge in quantifying the sources and sinks of atmospheric carbon dioxide (CO2) is that the CO2 taken up by plants during photosynthesis cannot be distinguished from the CO2 released by plants and micro-organisms during respiration. It has been shown that carbonyl sulfide (OCS), the sulphur-containing analogue of CO2, can be used as a proxy for photosynthesis. The relationship between the vegetative flux of OCS and CO2 has been quantified for various species of plants and ecosystems, the results of which have been used in observing the relationship on a continental scale. The aim of this project is to both quantify the location and magnitude of the sources and sinks of atmospheric OCS, and to use these data to infer photosynthetic uptake of CO2 by vegetation on a global scale.A tracer version of the 3-dimensional chemical transport model TOMCAT has been adapted to include eleven different sources and sinks of OCS, including direct and indirect oceanic emissions, vegetative uptake and stratospheric photolysis. The modelled OCS (TOMCAT-OCS) distribution between 2004 and 2018 has been co-located spatially and temporally to OCS profiles measured by the Atmospheric Chemistry Experiment (ACE-FTS) over the 5 – 30 km altitude, showing generally good agreement. Furthermore, surface TOMCAT-OCS has been compared to OCS measurements made at twelve NOAA-ESRL sites, across both hemispheres, showing that the model captures the seasonal cycle at the surface.There have been several calls in recent years for a new satellite product of atmospheric OCS, which this project aims to satisfy. Work is ongoing to retrieve OCS total columns from measurements taken by the Infrared Atmospheric Sounding Interferometer (IASI) instruments on-board the MetOp satellites. The University of Leicester IASI Retrieval Scheme (ULIRS) has been adapted to retrieve OCS columns globally. Various case studies for different geographic regions and time periods will be presented and compared to other satellite observations.

The hydroxyl radical (OH) is one of the most important species in atmospheric chemistry. It plays a dominant role in the oxidation of many other species in the troposphere, such as anthropogenic pollutants. Direct in-situ and satellite measurements of OH are scarce due to its short lifetime (around 1 second) and low abundance. Other indirect methods of inferring global mean OH have been established, such as using methyl chloroform as a tracer. However because of its recent phase out there is a demand for another method of calculating the global OH abundance. It is therefore useful to explore indirect methods for calculating OH. In particular, global satellite data can provide a means for estimating mean OH within large atmospheric regions. An improved understanding of the global distribution of OH will allow a better understanding of atmospheric chemistry, especially the distributions of anthropogenic pollutants. Due to the short lifetime of OH, a steady-state approximation can be used to model its concentration. This allows the OH distribution to be calculated using a simple equation and the accuracy of the estimate depends on the number of source/sink terms which can be included in the equation. In this work, a steady state approximation has been applied to the global OH budget as defined in the TOMCAT 3-D model. The full steady-state equation (based on all reactions in the model) has been simplified in various ways to include only the major sources and sinks of OH that can be observed directly by satellite, such as carbon monoxide (CO), methane (CH4), water vapour (H2O) and ozone (O3). Recent satellite observations of these species is then applied to the steady-state approximation to derive an estimate of the global OH distribution. We use the 3-D model to determine where the simplified steady-state approximation is likely to be most valid. The overall potential of this method to calculate an accurate OH distribution, bearing in mind satellite observation errors, is discussed.

Volcanic eruptions pose a serious threat to the aviation industry causing widespread disruption. To identify any potential impacts, nine Volcanic Ash Advisory Centres (VAACs) provide global monitoring of all eruptions, informing stakeholders how each volcanic eruption might interfere with aviation. Numerical dispersion models represent a vital infrastructure when assessing and forecasting the atmospheric conditions from a volcanic plume.In this study we investigate the 2019 Raikoke eruption, which emitted approximately 1.5 Tg of sulfur dioxide (SO2) representing the largest volcanic emission of SO2 into the stratosphere since the Nabro eruption in 2011. Using the UK Met Office’s Numerical Atmospheric-dispersion Modelling Environment (NAME), we simulate the evolution of the volcanic gas and aerosol particle plumes (SO2 and sulfate, SO4) across the Northern Hemisphere between 21st June and 17th July. We evaluate the skills and limitations of NAME in terms of modelling volcanic SO2 plumes, by comparing our simulations to high-resolution measurements from the Tropospheric Monitoring Instrument (TROPOMI) on-board the European Space Agency (ESA)’s Sentinel 5 – Precursor (S5P) satellite.Our comparisons show that NAME accurately simulates the observed location and shape of the SO2 plume in the first few weeks after the eruption. NAME also reproduces the magnitude of the observed SO2 vertical column densities, when emitting 1.5 Tg of SO2, during the first 48 hours after the eruption. On longer timescales, we find that the model-simulated SO2 plume in NAME is more diffuse than in the TROPOMI measurements, resulting in an underestimation of the peak SO2 vertical column densities in the model. This suggests that the diffusion parameters used in NAME are too large in the upper troposphere and lower stratosphere.Finally, NAME underestimates the total mass of SO2 when compared to estimates from TROPOMI, however emitting 2 Tg of SO2 in the model improves the comparison, resulting in very good agreement with the satellite measurements.

The decline in Amazonian deforestation rates and biomass burning activity (2001–2012) has been shown to reduce air pollutant emissions (e.g., aerosols) and improve regional air quality. However, in the Cerrado region (savannah grasslands in northeastern Brazil), satellite observations reveal increases in fire activity and tropospheric column nitrogen dioxide (an ozone precursor) during the burning season (August‐October, 2005–2016), which have partially offset these air quality benefits. Simulations from a 3‐D global chemistry transport model (CTM) capture this increase in NO2 with a surface increase of ~1 ppbv per decade. As there are limited long‐term observational tropospheric ozone records, we utilize the well‐evaluated CTM to investigate changes in ozone. Here, the CTM suggests that Cerrado region surface ozone is increasing by ~10 ppbv per decade. If left unmitigated, these positive fire‐sourced ozone trends will substantially increase the regional health risks and impacts from expected future enhancements in South American biomass burning activity under climate change.

We use the ozone dataset from the Copernicus Climate Change Service (C3S) during 1979–2017 to investigate the long-term variations of the total column ozone (TCO) and the relative total ozone low (TOL) over the Tibetan Plateau (TP) during different seasons. Based on various regression models, the wintertime TCO over the TP decreases overall during 1979–2017 with ongoing decreases since 1997. We perform multivariate regression analysis to quantify the influence of dynamical and chemical processes responsible for the long-term TCO variability over the TP. We use both piecewise linear trend (PWLT) and equivalent effective stratospheric chlorine loading (EESC)-based regression models that include explanatory variables such as the 11-year solar cycle, quasi-biennial oscillation (QBO) at 30 hPa and 10 hPa and the geopotential height (GH) at 150 hPa. The 150 hPa GH is found to be a major dynamical contributor to the total ozone variability (8 %) over the TP in wintertime. We also find strong correlation between TCO in DJF and the following JJA, indicating that negative/positive anomalies in the wintertime build up persist into summer. We also use the TOMCAT/SLIMCAT 3-D chemical transport model to investigate the contributions of different factors to the ozone variations over the TP. Using identical regression model on simulated TCO time series, we obtain consistent results with C3S-based data. We perform two sensitivity experiments with repeating dynamics of 2004 and 2008 to further study the role that the GH at 150 hPa plays in the ozone variations over the TP. The GH differences between the two years show an obvious, negative centre near 150 hPa over the TP in DJF. Composite analysis show that GH fluctuations associated with Inter Tropical Convergence Zone, ENSO events or Walker circulation play a key role in controlling TCO variability in the lower stratosphere.

The growth rate of global methane (CH4) concentrations has a strong interannual variability which is believed to be driven largely by fluctuations in CH4 emissions from wetlands and wildfires, as well as changes to the atmospheric sink. The El Niño Southern Oscillation (ENSO) is known to influence fire occurrence, wetland emission and atmospheric transport, but there are still important uncertainties associated with the exact mechanism and magnitude of this influence. Here we use a modelling approach to investigate how fires and meteorology control the interannual variability of global carbon monoxide (CO), CH4 and ozone (O3) concentrations, particularly during large El Niño events. Using a three-dimensional chemical transport model (TOMCAT) coupled to a sophisticated aerosol microphysics scheme (GLOMAP) we simulate changes to CO, hydroxyl radical (OH) and O3 for the period 1997–2014. We then use an offline radiative transfer model to quantify the impact of changes to atmospheric composition as a result of specific drivers. During the El Niño event of 1997–1998, there were increased emissions from biomass burning globally. As a result, global CO concentrations increased by more than 40 %. This resulted in decreased global mass-weighted tropospheric OH concentrations of up to 9 % and a resulting 4 % increase in the CH4 atmospheric lifetime. The change in CH4 lifetime led to a 7.5 ppb yr−1 increase in global mean CH4 growth rate in 1998. Therefore biomass burning emission of CO could account for 72 % of the total effect of fire emissions on CH4 growth rate in 1998. Our simulations indicate variations in fire emissions and meteorology associated with El Niño have opposing impacts on tropospheric O3 burden. El Niño-related atmospheric transport changes decrease global tropospheric O3 concentrations leading to a −0.03 Wm−2 change in O3 radiative effect (RE). However, enhanced fire emission of precursors such as nitrous oxides (NOx) and CO increase O3 RE by 0.03 Wm−2. While globally the two mechanisms nearly cancel out, causing only a small change in global mean O3 RE, the regional changes are large   up to −0.33 Wm−2 with potentially important consequences for atmospheric heating and dynamics.

The June 2018 Saddleworth Moor fires were some of the largest UK wildfires on record and lasted for approximately three weeks. They emitted large quantities of smoke, trace gases and aerosols which were transported downwind over the highly populated regions of Manchester and Liverpool. Surface observations of PM2.5 indicate that concentrations were 4–5.5 times higher than the recent seasonal average. State-of-the-art satellite measurements of total column carbon monoxide (TCCO) from the TROPOMI instrument on the Sentinel 5—Precursor (S5P) platform, coupled with measurements from a flight of the UK BAe-146–301 research aircraft, are used to quantify the substantial enhancement in emitted trace gases. The aircraft measured plume enhancements with near-fire CO and PM2.5 concentrations >1500 ppbv and >125 μg m−3 (compared to ∼100 ppbv and ∼5 μg m−3 background concentrations). Downwind fire-plume ozone (O3) values were larger than the near-fire location, indicating O3 production with distance from source. The near-fire O3:CO ratio was (ΔO3/ΔCO) 0.001 ppbv/ppbv, increasing downwind to 0.060–0.105 ppbv/ppbv, suggestive of O3 production enhancement downwind of the fires. Emission rates of CO and CO2 ranged between 1.07 (0.07–4.69) kg s−1 and 13.7 (1.73–50.1) kg s−1, respectively, similar to values expected from a medium sized power station.

Each year more than 29,000 premature deaths in the UK are linked to long term-exposure to ambient particulate matter (PM) with a diameter less than 2.5 μm (PM2.5). Many studies have focused on the long-term impacts of exposure to PM, but short-term increases in pollution can also exacerbate health effects, leading to deaths brought forward within exposed populations. This study investigates the impact of different atmospheric circulation patterns on UK PM2.5 concentrations and the relative contribution of local and transboundary pollutants to variations in PM2.5 concentrations. Daily mean PM2.5 observations from 42 UK background sites indicate that easterly, south-easterly and southerly wind directions and anticyclonic circulation patterns enhance background concentrations of PM2.5 at all UK sites by up to 12 μg m-3. Results from back trajectory analysis and the European Monitoring and Evaluation Programme for UK model (EMEP4UK) show this is due to the transboundary transport of pollutants from continental Europe. While back trajectories indicate under easterly, south-easterly and southerly flow 25–50% of the total accumulated primary PM2.5 emissions originate outside of the UK, with a very polluted footprint (0.25–0.35 μg m-2). Anticyclonic conditions, which occur frequently (21%), also lead to increases in PM2.5 concentrations (UK multi-annual mean 14.7 μg m-3). EMEP4UK results indicate this is likely due the build-up of local emissions due to slack winds. Under westerly and north-westerly flow 15–30% of the total accumulated primary PM2.5 emissions originate outside of the UK, and are much less polluted (0.1 μg m-2) with model results indicating transport of clean maritime air masses from the Atlantic. Results indicate that both wind-direction and stability under anticyclonic conditions are important in controlling ambient PM2.5 concentrations across the UK. There is also a strong dependence of high PM2.5 Daily Air Quality Index (DAQI) values on easterly, south-easterly and southerly wind-directions, with >70% of occurrences of observed 48–71+ μg m-3 concentrations occurring under these wind directions. While north-westerly and cyclonic conditions reduce PM2.5 concentrations at all sites by up to 8 μg m-3. PM2.5 DAQI values are also lowest under these conditions, with >80% of 0–11 μg m-3 concentrations and >50% of 12–23 μg m-3 concentrations observed during westerly, north-westerly and northerly wind directions. Indicating that these conditions are likely to be associated with a reduction in the potential health effects from exposure to ambient levels of PM2.5. Keywords: LWT, PM2.5, long-range transport, Air quality, Emissions, AURN

We compare tropospheric column NO2 between the UK Met Office operational Air Quality in the Unified Model (AQUM) and satellite observations from the Ozone Monitoring Instrument (OMI) for 2006. Column NO2 retrievals from satellite instruments are prone to large uncertainty from random, systematic and smoothing errors. We present an algorithm to reduce the random error of time-averaged observations, once smoothing errors have been removed with application of satellite averaging kernels to the model data. This reduces the total error in seasonal mean columns by 10–70%, which allows critical evaluation of the model. The standard AQUM configuration evaluated here uses chemical lateral boundary conditions (LBCs) from the GEMS (Global and regional Earth-system Monitoring using Satellite and in situ data) reanalysis. In summer the standard AQUM overestimates column NO2 in northern England and Scotland, but underestimates it over continental Europe. In winter, the model overestimates column NO2 across the domain. We show that missing heterogeneous hydrolysis of N2O5 in AQUM is a significant sink of column NO2 and that the introduction of this process corrects some of the winter biases. The sensitivity of AQUM summer column NO2 to different chemical LBCs and NOx emissions data sets are investigated. Using Monitoring Atmospheric Composition and Climate (MACC) LBCs increases AQUM O3 concentrations compared with the default GEMS LBCs. This enhances the NOx–O3 coupling leading to increased AQUM column NO2 in both summer and winter degrading the comparisons with OMI. Sensitivity experiments suggest that the cause of the remaining northern England and Scotland summer column NO2 overestimation is the representation of point source (power station) emissions in the model.