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Soil emissions of nitrogen compounds, including NO and HONO, play a significant role in the atmospheric nitrogen budget. However, HONO has been overlooked in previous research on soil reactive nitrogen (Nr) emissions and their impacts on air quality in China. This study estimates soil NOx and soil HONO emissions (SNOx and SHONO) in North China during July 2018 using an updated soil Nr emissions scheme in a chemical transport model, Unified Inputs (initial and boundary conditions) for Weather Research and Forecasting coupled with Chemistry (UI-WRF-Chem). The effects of soil Nr emissions on O3 pollution, air quality and temperature rise are also studied, with a focus on two key regions, Beijing–Tianjin–Hebei (BTH) and the Fenwei Plain (FWP), known for high soil Nr and anthropogenic emissions. We find that the flux of SNOx is nearly double that of SHONO. The monthly contributions of SNOx and SHONO account, respectively, for 37.3 % and 13.5 % of anthropogenic NOx emissions in BTH and for 29.2 % and 19.2 % in the FWP during July 2018. Soil Nr emissions have a significant impact on surface O3 and nitrate, exceeding SNOx or SHONO effects alone. On average, soil Nr emissions increase maximum daily 8 h (MDA8) O3 by 16.9 % and nitrate concentrations by 42.4 % in BTH, and they increase MDA8 O3 by 17.2 % and nitrate concentrations by 42.7 % in the FWP. Reducing anthropogenic NOx emissions leads to a more substantial suppressive effect of soil Nr emissions on O3 mitigation, particularly in BTH. Soil Nr emissions, through their role as precursors for secondary inorganic aerosols, can result in a slower increase rate of surface air temperature in future emissions reduction scenarios. This study suggests that mitigating O3 pollution and addressing climate change in China should consider the role of soil Nr emissions and their regional differences.

This paper presents large-eddy simulations with atmospheric chemistry of four large point sources world-wide, focusing on the evaluation of NOx (NO + NO2) emissions with the TROPOspheric Monitoring Instrument (TROPOMI). We implemented a condensed chemistry scheme to investigate how the emitted NOx (95 % as NO) is converted to NO2 in the plume. To use NOx as a proxy for CO2 emission, information about its atmospheric lifetime and the fraction of NOx present as NO2 is required. We find that the chemical evolution of the plumes depends strongly on the amount of NOx that is emitted, as well as on wind speed and direction. For large NOx emissions, the chemistry is pushed in a high-NOx chemical regime over a length of almost 100 km downwind of the stack location. Other plumes with lower NOx emissions show a fast transition to an intermediate-NOx chemical regime, with short NOx lifetimes. Simulated NO2 columns mostly agree within 20 % with TROPOMI, signalling that the emissions used in the model were approximately correct. However, variability in the simulations is large, making a one-to-one comparison difficult. We find that temporal wind speed variations should be accounted for in emission estimation methods. Moreover, results indicate that common assumptions about the NO2 lifetime (≈ 4 h) and NOx:NO2 ratios (≈ 1.3) in simplified methods that estimate emissions from NO2 satellite data need revision.

Reliable and robust monitoring tools are crucial to assess the effectiveness of land mitigation techniques (LMTs) in enhancing carbon uptake, enabling informed decision making by policymakers. This study, addressing one of the scientific goals of the EU Horizon 2020 Land Use Based Mitigation for Resilient Climate Pathways (LANDMARC) project, examines the feasibility of using satellite solar-induced fluorescence (SIF) in combination with other satellite data as a monitoring proxy to evaluate the effects of LMTs on carbon uptake. Two distinct cases are explored: (1) instantaneous vegetation destruction caused by a 2019 eucalyptus wildfire in southeast Australia and (2) gradual forest gain resulting from reforestation efforts in northern China in 2007–2012. The cases are monitored using SIF from the TROPOspheric Monitoring Instrument (TROPOMI) and Global Ozone Monitoring Experiment-2A (GOME-2A), respectively. Comparing the temporal variability in SIF across the affected areas and nearby reference areas reveals that vegetation dynamics changed as a consequence of the land-use changes in both cases. Specifically, in the Australia case, TROPOMI demonstrated an immediate reduction in the SIF signal of 0.6 mW m−2 sr−1 nm−1 (−72 %) over the eucalypt forest right after the fire. Exploiting the strong correspondence between TROPOMI SIF and gross primary productivity (GPP) at the nearby representative eddy covariance Tumbarumba site and through the FluxSat product, we estimate that the Australian fire led to a loss in GPP of 130–200 GgC in the first 8 months after the fire. Over the northern Chinese provinces of Gansu, Shaanxi, Sichuan, Chongqing, and Shanxi, we report an increase in GOME-2A summertime SIF of 0.1–0.2 mW m−2 sr−1 nm−1, coinciding with reforestation efforts between 2007 and 2012. This increase in the SIF signal is likely driven by a combination of increasingly favourable natural conditions and by the reforestation effort itself. A multivariate model that takes into account growth factors such as water availability and maximum temperature as well as satellite-derived forest-cover data explains the observed variability in GOME-2A SIF in the Chinese case reasonably well (R2=0.72). The model suggests that increases in both forest cover and soil moisture have led, in step, to the observed increase in vegetation activity over northern China. In that region, for every 100 km2 of additional forest cover, SIF increases by 0.1 mW m−2 sr−1 nm−1 between 2007 and 2012. Our study highlights that the use of satellite-based SIF, together with supporting in situ, modelled, and satellite data, allows us to monitor the impact of LMT implementation on regional carbon uptake as long as the scale of the LMT is of sufficient spatial extent.

The sensitivity of satellites to air pollution close to the sea surface is decreased by the scattering of light in the atmosphere and low sea surface albedo. To reliably retrieve tropospheric nitrogen dioxide (NO2) columns using the TROPOspheric Monitoring Instrument (TROPOMI), it is therefore necessary to have good a priori knowledge of the vertical distribution of NO2. In this study, we use an aircraft of the Royal Belgian Institute of Natural Sciences equipped with a sniffer sensor system to measure NOx (= NO + NO2), CO2 and SO2. This instrumentation enabled us to evaluate vertical profile shapes from several chemical transport models and to validate TROPOMI tropospheric NO2 columns over the polluted North Sea in the summer of 2021. The aircraft sensor observes multiple clear signatures of ship plumes from seconds after emission to multiple kilometers downwind. Besides that, our results show that the chemical transport model Transport Model 5, Massively Parallel version (TM5-MP), which is used in the retrieval of the operational TROPOMI NO2 data, tends to underestimate surface level pollution – especially under conditions without land outflow – while overestimating NO2 at higher levels over the study region. The higher horizontal resolution in the regional CAMS (Copernicus Atmosphere Monitoring Service) ensemble mean and the LOTOS-EUROS (Long Term Ozone Simulation European Operational Smog) model improves the surface level pollution estimates. However, the models still systematically overestimate NO2 levels at higher altitudes, indicating exaggerated vertical mixing and overall too much NO2 in the models over the North Sea. When replacing the TM5 a priori NO2 profiles with the aircraft-measured NO2 profiles in the air mass factor (AMF) calculation, we find smaller recalculated AMFs. Subsequently, the retrieved NO2 columns increase by 20 %, indicating a significant negative bias in the operational TROPOMI NO2 data product (up to v2.3.1) over the North Sea. This negative bias has important implications for estimating emissions over the sea. While TROPOMI NO2 negative biases caused by the TM5 a priori profiles have also been reported over land, the reduced vertical mixing and smaller surface albedo over sea make this issue especially relevant over sea and coastal regions.

The Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) instrument, launched in October 2017, provides unique observations of atmospheric trace gases at a high resolution of about 5 km, with near-daily global coverage, resolving individual sources like thermal powerplants, industrial complexes, fires, medium-scale towns, roads, and shipping routes. Even though Sentinel-5P (S5P) is a global mission, these datasets are especially well suited to test high-resolution regional-scale air quality (AQ) models and provide valuable input for emission inversion systems. In Europe, the Copernicus Atmosphere Monitoring Service (CAMS) has implemented an operational regional AQ forecasting capability based on an ensemble of several European models, available at a resolution of 0.1∘ × 0.1∘. In this paper, we present comparisons between TROPOMI observations of nitrogen dioxide (NO2) and the CAMS AQ forecasts and analyses of NO2. We discuss the different ways of making these comparisons and present quantitative results in the form of maps for individual days, summer and winter months, and a time series for European subregions and cities between May 2018 and March 2021. The CAMS regional products generally capture the fine-scale daily and averaged features observed by TROPOMI in much detail. In summer, the comparison shows a close agreement between TROPOMI and the CAMS ensemble NO2 tropospheric columns with a relative difference of up to 15 % for most European cities. In winter, however, we find a significant discrepancy in the column amounts over much of Europe, with relative differences up to 50 %. The possible causes for these differences are discussed, focusing on the possible impact of retrieval and modeling errors. Apart from comparisons with the CAMS ensemble, we also present results for comparisons with the individual CAMS models for selected months. Furthermore, we demonstrate the importance of the free tropospheric contribution to the estimation of the tropospheric column and thus include profile information from the CAMS configuration of the ECMWF's (European Centre for Medium-Range Weather Forecasts) global integrated model above 3 km altitude in the comparisons. We also show that replacing the global 1∘ × 1∘ a priori information in the retrieval by the regional 0.1∘ × 0.1∘ resolution profiles of CAMS leads to significant changes in the TROPOMI-retrieved tropospheric column, with typical increases at the emission hotspots up to 30 % and smaller increases or decreases elsewhere. As a spinoff, we present a new TROPOMI NO2 level 2 (L2) data product for Europe, based on the replacement of the original TM5-MP generated global a priori profile by the regional CAMS ensemble profile. This European NO2 product is compared with ground-based remote sensing measurements of six Pandora instruments of the Pandonia Global Network and nine Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) instruments. As compared to the standard S5P tropospheric NO2 column data, the overall bias of the new product for all except two stations is 5 % to 12 % smaller, owing to a reduction in the multiplicative bias. Compared to the CAMS tropospheric NO2 columns, dispersion and correlation parameters with respect to the standard data are, however, superior.

Satellite-based retrievals of tropospheric NO2 columns are widely used to infer NOx (≡ NO + NO2) emissions. These retrievals rely on model information for the vertical distribution of NO2. The free tropospheric background above 2 km is particularly important because the sensitivity of the retrievals increases with altitude. Free tropospheric NOx also has a strong effect on tropospheric OH and ozone concentrations. Here we use observations from three aircraft campaigns (SEAC4RS, DC3, and ATom) and four atmospheric chemistry models (GEOS-Chem, GMI, TM5, and CAMS) to evaluate the model capabilities for simulating NOx in the free troposphere and attribute it to sources. NO2 measurements during the Studies of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys (SEAC4RS) and Deep Convective Clouds and Chemistry (DC3) campaigns over the southeastern U.S. in summer show increasing concentrations in the upper troposphere above 10 km, which are not replicated by the GEOS-Chem, although the model is consistent with the NO measurements. Using concurrent NO, NO2, and ozone observations from a DC3 flight in a thunderstorm outflow, we show that the NO2 measurements in the upper troposphere are biased high, plausibly due to interference from thermally labile NO2 reservoirs such as peroxynitric acid (HNO4) and methyl peroxy nitrate (MPN). We find that NO2 concentrations calculated from the NO measurements and NO–NO2 photochemical steady state (PSS) are more reliable to evaluate the vertical profiles of NO2 in models. GEOS-Chem reproduces the shape of the PSS-inferred NO2 profiles throughout the troposphere for SEAC4RS and DC3 but overestimates NO2 concentrations by about a factor of 2. The model underestimates MPN and alkyl nitrate concentrations, suggesting missing organic NOx chemistry. On the other hand, the standard GEOS-Chem model underestimates NO observations from the Atmospheric Tomography Mission (ATom) campaigns over the Pacific and Atlantic oceans, indicating a missing NOx source over the oceans. We find that we can account for this missing source by including in the model the photolysis of particulate nitrate on sea salt aerosols at rates inferred from laboratory studies and field observations of nitrous acid (HONO) over the Atlantic. The median PSS-inferred tropospheric NO2 column density for the ATom campaign is 1.7 ± 0.44 × 1014 molec. cm−2, and the NO2 column density simulated by the four models is in the range of 1.4–2.4 × 1014 molec. cm−2, implying that the uncertainty from using modeled NO2 tropospheric columns over clean areas in the retrievals for stratosphere–troposphere separation is about 1 × 1014 molec. cm−2. We find from GEOS-Chem that lightning is the main primary NOx source in the free troposphere over the tropics and southern midlatitudes, but aircraft emissions dominate at northern midlatitudes in winter and in summer over the oceans. Particulate nitrate photolysis increases ozone concentrations by up to 5 ppbv (parts per billion by volume) in the free troposphere in the northern extratropics in the model, which would largely correct the low model bias relative to ozonesonde observations. Global tropospheric OH concentrations increase by 19 %. The contribution of the free tropospheric background to the tropospheric NO2 columns observed by satellites over the contiguous U.S. increases from 25 ± 11 % in winter to 65 ± 9 % in summer, according to the GEOS-Chem vertical profiles. This needs to be accounted for when deriving NOx emissions from satellite NO2 column measurements.

Quantification and control of NOx and CO2 emissions are essential across the world to limit adverse climate change and improve air quality. We present a new top-down method, an improved superposition column model to estimate day-to-day NOx and CO2 emissions from the large city of Wuhan, China, located in a polluted background. The latest released version 2.3.1 TROPOMI (TROPOspheric Monitoring Instrument) NO2 columns and version 10r of the Orbiting Carbon Observatory-2 (OCO-2)-observed CO2 mixing ratio are employed. We quantified daily NOx and CO2 emissions from Wuhan between September 2019 and October 2020 with an uncertainty of 31 % and 43 %, compared to 39 % and 49 % with the earlier v1.3 TROPOMI data, respectively. Our estimated NOx and CO2 emissions are verified against bottom-up inventories with minor deviations ( % for the 2019 mean, ranging from −20 % to 48 % on a daily basis). Based on the estimated CO2 emissions, we also predicted daily CO2 column mixing ratio enhancements, which match well with OCO-2 observations ( % bias, within ±0.3 ppm). We capture the day-to-day variation of NOx and CO2 emissions from Wuhan in 2019–2020, which does not reveal a substantial “weekend reduction” but does show a clear “holiday reduction” in the NOx and CO2 emissions. Our method also quantifies the abrupt decrease and slow NOx and CO2 emissions rebound due to the Wuhan lockdown in early 2020. This work demonstrates the improved superposition model to be a promising new tool for the quantification of city NOx and CO2 emissions, allowing policymakers to gain real-time information on spatial–temporal emission patterns and the effectiveness of carbon and nitrogen regulation in urban environments.

A new method is presented for estimating urban hydroxyl radical (OH) concentrations using the downwind decay of the ratio of nitrogen dioxide over carbon monoxide column-mixing ratios (XNO2/XCO) retrieved from the Tropospheric Monitoring Instrument (TROPOMI). The method makes use of plumes simulated by the Weather Research and Forecast model (WRF-Chem) using passive-tracer transport, instead of the encoded chemistry, in combination with auxiliary input variables such as Copernicus Atmospheric Monitoring Service (CAMS) OH, Emission Database for Global Atmospheric Research v4.3.2 (EDGAR) NOx and CO emissions, and National Center for Environmental Protection (NCEP)-based meteorological data. NO2 and CO mixing ratios from the CAMS reanalysis are used as initial and lateral boundary conditions. WRF overestimates NO2 plumes close to the center of the city by 15 % to 30 % in summer and 40 % to 50 % in winter compared to TROPOMI observations over Riyadh. WRF-simulated CO plumes differ by 10 % with TROPOMI in both seasons. The differences between WRF and TROPOMI are used to optimize the OH concentration, NOx, CO emissions and their backgrounds using an iterative least-squares method. To estimate OH, WRF is optimized using (a) TROPOMI XNO2/XCO and (b) TROPOMI-derived XNO2 only. For summer, both the NO2/CO ratio optimization and the XNO2 optimization increase the prior OH from CAMS by 32 ± 5.3 % and 28.3 ± 3.9 %, respectively. EDGAR NOx and CO emissions over Riyadh are increased by 42.1 ± 8.4 % and 101 ± 21 %, respectively, in summer. In winter, the optimization method doubles the CO emissions while increasing OH by ∼ 52 ± 14 % and reducing NOx emissions by 15.5 ± 4.1 %. TROPOMI-derived OH concentrations and the pre-existing exponentially modified Gaussian function fit (EMG) method differ by 10 % in summer and winter, confirming that urban OH concentrations can be reliably estimated using the TROPOMI-observed NO2/CO ratio. Additionally, our method can be applied to a single TROPOMI overpass, allowing one to analyze day-to-day variability in OH, NOx and CO emission.

Nitrogen dioxide (NO2) is one of the main data products measured by the Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite, which combines a high signal-to-noise ratio with daily global coverage and high spatial resolution. TROPOMI provides a valuable source of information to monitor emissions from local sources such as power plants, industry, cities, traffic and ships, and variability of these sources in time. Validation exercises of NO2 v1.2–v1.3 data, however, have revealed that TROPOMI's tropospheric vertical column densities (VCDs) are too low by up to 50 % over highly polluted areas. These findings are mainly attributed to biases in the cloud pressure retrieval, the surface albedo climatology and the low resolution of the a priori profiles derived from global simulations of the TM5-MP chemistry model. This study describes improvements in the TROPOMI NO2 retrieval leading to version v2.2, operational since 1 July 2021. Compared to v1.x, the main changes are the following. (1) The NO2-v2.2 data are based on version-2 level-1b (ir)radiance spectra with improved calibration, which results in a small and fairly homogeneous increase in the NO2 slant columns of 3 % to 4 %, most of which ends up as a small increase in the stratospheric columns. (2) The cloud pressures are derived with a new version of the FRESCO cloud retrieval already introduced in NO2-v1.4, which led to a lowering of the cloud pressure, resulting in larger tropospheric NO2 columns over polluted scenes with a small but non-zero cloud coverage. (3) For cloud-free scenes a surface albedo correction is introduced based on the observed reflectance, which also leads to a general increase in the tropospheric NO2 columns over polluted scenes of order 15 %. (4) An outlier removal was implemented in the spectral fit, which increases the number of good-quality retrievals over the South Atlantic Anomaly region and over bright clouds where saturation may occur. (5) Snow/ice information is now obtained from ECMWF weather data, increasing the number of valid retrievals at high latitudes. On average the NO2-v2.2 data have tropospheric VCDs that are between 10 % and 40 % larger than the v1.x data, depending on the level of pollution and season; the largest impact is found at mid and high latitudes in wintertime. This has brought these tropospheric NO2 closer to Ozone Monitoring Instrument (OMI) observations. Ground-based validation shows on average an improvement of the negative bias of the stratospheric (from −6 % to −3 %), tropospheric (from −32 % to −23 %) and total (from −12 % to −5 %) columns. For individual measurement stations, however, the picture is more complex, in particular for the tropospheric and total columns.

TROPOMI (TROPOspheric Monitoring Instrument) measurements of tropospheric NO2 columns provide powerful information on emissions of air pollution by ships on open sea. This information is potentially useful for authorities to help determine the (non-)compliance of ships with increasingly stringent NOx emission regulations. We find that the information quality is improved further by recent upgrades in the TROPOMI cloud retrieval and an optimal data selection. We show that the superior spatial resolution of TROPOMI allows for the detection of several lanes of NO2 pollution ranging from the Aegean Sea near Greece to the Skagerrak in Scandinavia, which have not been detected with other satellite instruments before. Additionally, we demonstrate that under conditions of sun glint TROPOMI's vertical sensitivity to NO2 in the marine boundary layer increases by up to 60 %. The benefits of sun glint are most prominent under clear-sky situations when sea surface winds are low but slightly above zero (±2 m s−1). Beyond spatial resolution and sun glint, we examine for the first time the impact of the recently improved cloud algorithm on the TROPOMI NO2 retrieval quality, both over sea and over land. We find that the new FRESCO+ (Fast Retrieval Scheme for Clouds from the Oxygen A band) wide algorithm leads to 50 hPa lower cloud pressures, correcting a known high bias, and produces 1–4×1015 molec. cm−2 higher retrieved NO2 columns, thereby at least partially correcting for the previously reported low bias in the TROPOMI NO2 product. By training an artificial neural network on the four available periods with standard and FRESCO+ wide test retrievals, we develop a historic, consistent TROPOMI NO2 data set spanning the years 2019 and 2020. This improved data set shows stronger (35 %–75 %) and sharper (10 %–35 %) shipping NO2 signals compared to co-sampled measurements from OMI. We apply our improved data set to investigate the impact of the COVID-19 pandemic on ship NO2 pollution over European seas and find indications that NOx emissions from ships reduced by 10 %–20 % during the beginning of the COVID-19 pandemic in 2020. The reductions in ship NO2 pollution start in March–April 2020, in line with changes in shipping activity inferred from automatic identification system (AIS) data on ship location, speed, and engine.

Dry deposition is an important sink of tropospheric ozone that affects surface concentrations and impacts crop yields, the land carbon sink, and the terrestrial water cycle. Dry deposition pathways include plant uptake via stomata and non-stomatal removal by soils, leaf surfaces, and chemical reactions. Observational studies indicate that ozone deposition exhibits substantial temporal variability that is not reproduced by atmospheric chemistry models due to a simplified representation of vegetation uptake processes in these models. In this study, we explore the importance of stomatal and non-stomatal uptake processes in driving ozone dry deposition variability on diurnal to seasonal timescales. Specifically, we compare two land surface ozone uptake parameterizations – a commonly applied big leaf parameterization (W89; Wesely, 1989) and a multi-layer model (MLC-CHEM) constrained with observations – to multi-year ozone flux observations at two European measurement sites (Ispra, Italy, and Hyytiälä, Finland). We find that W89 cannot reproduce the diurnal cycle in ozone deposition due to a misrepresentation of stomatal and non-stomatal sinks at our two study sites, while MLC-CHEM accurately reproduces the different sink pathways. Evaluation of non-stomatal uptake further corroborates the previously found important roles of wet leaf uptake in the morning under humid conditions and soil uptake during warm conditions. The misrepresentation of stomatal versus non-stomatal uptake in W89 results in an overestimation of growing season cumulative ozone uptake (CUO), a metric for assessments of vegetation ozone damage, by 18 % (Ispra) and 28 % (Hyytiälä), while MLC-CHEM reproduces CUO within 7 % of the observation-inferred values. Our results indicate the need to accurately describe the partitioning of the ozone atmosphere–biosphere flux over the in-canopy stomatal and non-stomatal loss pathways to provide more confidence in atmospheric chemistry model simulations of surface ozone mixing ratios and deposition fluxes for large-scale vegetation ozone impact assessments.

Nitrogen oxides (NOx = NO + NO2) play a crucial role in the formation of ozone and secondary inorganic and organic aerosols, thus affecting human health, global radiation budget, and climate. The diurnal and spatial variations in NO2 are functions of emissions, advection, deposition, vertical mixing, and chemistry. Their observations, therefore, provide useful constraints in our understanding of these factors. We employ a Regional chEmical and trAnsport model (REAM) to analyze the observed temporal (diurnal cycles) and spatial distributions of NO2 concentrations and tropospheric vertical column densities (TVCDs) using aircraft in situ measurements and surface EPA Air Quality System (AQS) observations as well as the measurements of TVCDs by satellite instruments (OMI: the Ozone Monitoring Instrument; GOME-2A: Global Ozone Monitoring Experiment – 2A), ground-based Pandora, and the Airborne Compact Atmospheric Mapper (ACAM) instrument in July 2011 during the DISCOVER-AQ campaign over the Baltimore–Washington region. The model simulations at 36 and 4 km resolutions are in reasonably good agreement with the regional mean temporospatial NO2 observations in the daytime. However, we find significant overestimations (underestimations) of model-simulated NO2 (O3) surface concentrations during nighttime, which can be mitigated by enhancing nocturnal vertical mixing in the model. Another discrepancy is that Pandora-measured NO2 TVCDs show much less variation in the late afternoon than simulated in the model. The higher-resolution 4 km simulations tend to show larger biases compared to the observations due largely to the larger spatial variations in NOx emissions in the model when the model spatial resolution is increased from 36 to 4 km. OMI, GOME-2A, and the high-resolution aircraft ACAM observations show a more dispersed distribution of NO2 vertical column densities (VCDs) and lower VCDs in urban regions than corresponding 36 and 4 km model simulations, likely reflecting the spatial distribution bias of NOx emissions in the National Emissions Inventory (NEI) 2011.

The carbon cycle of tropical terrestrial vegetation plays a vital role in the storage and exchange of atmospheric CO2. But large uncertainties surround the impacts of land-use change emissions, climate warming, the frequency of droughts, and CO2 fertilization. This culminates in poorly quantified carbon stocks and carbon fluxes even for the major ecosystems of Africa (savannas and tropical evergreen forests). Contributors to this uncertainty are the sparsity of (micro-)meteorological observations across Africa's vast land area, a lack of sufficient ground-based observation networks and validation data for CO2, and incomplete representation of important processes in numerical models. In this study, we therefore turn to two remotely sensed vegetation products that have been shown to correlate highly with gross primary production (GPP): sun-induced fluorescence (SIF) and near-infrared reflectance of vegetation (NIRv). The former is available from an updated product that we recently published (Sun-Induced Fluorescence of Terrestrial Ecosystems Retrieval – SIFTER v2), which specifically improves retrievals in tropical environments. A comparison against flux tower observations of daytime-partitioned net ecosystem exchange from six major biomes in Africa shows that SIF and NIRv reproduce the seasonal patterns of GPP well, resulting in correlation coefficients of >0.9 (N=12 months, four sites) over savannas in the Northern and Southern hemispheres. These coefficients are slightly higher than for the widely used Max Planck Institute for Biogeochemistry (MPI-BGC) GPP products and enhanced vegetation index (EVI). Similarly to SIF signals in the neighboring Amazon, peak productivity occurs in the wet season coinciding with peak soil moisture and is followed by an initial decline during the early dry season, which reverses when light availability peaks. This suggests similar leaf dynamics are at play. Spatially, SIF and NIRv show a strong linear relation (R>0.9; N≥250 pixels) with multi-year MPI-BGC GPP even within single biomes. Both MPI-BGC GPP and the EVI show saturation relative to peak NIRv and SIF signals during high-productivity months, which suggests that GPP in the most productive regions of Africa might be larger than suggested.

Nitrogen oxides (NOx≡NO+NO2) in the NOx-limited upper troposphere (UT) are long-lived and so have a large influence on the oxidizing capacity of the troposphere and formation of the greenhouse gas ozone. Models misrepresent NOx in the UT, and observations to address deficiencies in models are sparse. Here we obtain a year of near-global seasonal mean mixing ratios of NO2 in the UT (450–180 hPa) at 1∘×1∘ by applying cloud-slicing to partial columns of NO2 from TROPOMI. This follows refinement of the cloud-slicing algorithm with synthetic partial columns from the GEOS-Chem chemical transport model. TROPOMI, prior to cloud-slicing, is corrected for a 13 % underestimate in stratospheric NO2 variance and a 50 % overestimate in free-tropospheric NO2 determined by comparison to Pandora total columns at high-altitude free-tropospheric sites at Mauna Loa, Izaña, and Altzomoni and MAX-DOAS and Pandora tropospheric columns at Izaña. Two cloud-sliced seasonal mean UT NO2 products for June 2019 to May 2020 are retrieved from corrected TROPOMI total columns using distinct TROPOMI cloud products that assume clouds are reflective boundaries (FRESCO-S) or water droplet layers (ROCINN-CAL). TROPOMI UT NO2 typically ranges from 20–30 pptv over remote oceans to >80 pptv over locations with intense seasonal lightning. Spatial coverage is mostly in the tropics and subtropics with FRESCO-S and extends to the midlatitudes and polar regions with ROCINN-CAL, due to its greater abundance of optically thick clouds and wider cloud-top altitude range. TROPOMI UT NO2 seasonal means are spatially consistent (R=0.6–0.8) with an existing coarser spatial resolution (5∘ latitude × 8∘ longitude) UT NO2 product from the Ozone Monitoring Instrument (OMI). UT NO2 from TROPOMI is 12–26 pptv more than that from OMI due to increase in NO2 with altitude from the OMI pressure ceiling (280 hPa) to that for TROPOMI (180 hPa), but possibly also due to altitude differences in TROPOMI and OMI cloud products and NO2 retrieval algorithms. The TROPOMI UT NO2 product offers potential to evaluate and improve representation of UT NOx in models and supplement aircraft observations that are sporadic and susceptible to large biases in the UT.

This paper reports on consolidated ground-based validation results of the atmospheric NO2 data produced operationally since April 2018 by the TROPOspheric Monitoring Instrument (TROPOMI) on board of the ESA/EU Copernicus Sentinel-5 Precursor (S5P) satellite. Tropospheric, stratospheric, and total NO2 column data from S5P are compared to correlative measurements collected from, respectively, 19 Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS), 26 Network for the Detection of Atmospheric Composition Change (NDACC) Zenith-Scattered-Light DOAS (ZSL-DOAS), and 25 Pandonia Global Network (PGN)/Pandora instruments distributed globally. The validation methodology gives special care to minimizing mismatch errors due to imperfect spatio-temporal co-location of the satellite and correlative data, e.g. by using tailored observation operators to account for differences in smoothing and in sampling of atmospheric structures and variability and photochemical modelling to reduce diurnal cycle effects. Compared to the ground-based measurements, S5P data show, on average, (i) a negative bias for the tropospheric column data, of typically −23 % to −37 % in clean to slightly polluted conditions but reaching values as high as −51 % over highly polluted areas; (ii) a slight negative median difference for the stratospheric column data, of about −0.2 Pmolec cm−2, i.e. approx. −2 % in summer to −15 % in winter; and (iii) a bias ranging from zero to −50 % for the total column data, found to depend on the amplitude of the total NO2 column, with small to slightly positive bias values for columns below 6 Pmolec cm−2 and negative values above. The dispersion between S5P and correlative measurements contains mostly random components, which remain within mission requirements for the stratospheric column data (0.5 Pmolec cm−2) but exceed those for the tropospheric column data (0.7 Pmolec cm−2). While a part of the biases and dispersion may be due to representativeness differences such as different area averaging and measurement times, it is known that errors in the S5P tropospheric columns exist due to shortcomings in the (horizontally coarse) a priori profile representation in the TM5-MP chemical transport model used in the S5P retrieval and, to a lesser extent, to the treatment of cloud effects and aerosols. Although considerable differences (up to 2 Pmolec cm−2 and more) are observed at single ground-pixel level, the near-real-time (NRTI) and offline (OFFL) versions of the S5P NO2 operational data processor provide similar NO2 column values and validation results when globally averaged, with the NRTI values being on average 0.79 % larger than the OFFL values.

This study investigates the use of co-located nitrogen dioxide (NO2) and carbon monoxide (CO) retrievals from the TROPOMI satellite to improve the quantification of burning efficiency and emission factors (EFs) over the megacities of Tehran, Mexico City, Cairo, Riyadh, Lahore, and Los Angeles. Efficient combustion is characterized by high NOx (NO+NO2) and low CO emissions, making the NO2∕CO ratio a useful proxy for combustion efficiency (CE). The local enhancement of CO and NO2 above megacities is well captured by TROPOMI at short averaging times compared with previous satellite missions. In this study, the upwind background and plume rotation methods are used to investigate the accuracy of satellite-derived ΔNO2∕ΔCO ratios. The column enhancement ratios derived using these two methods vary by 5 % to 20 % across the selected megacities. TROPOMI-derived column enhancement ratios are compared with emission ratios from the EDGAR v4.3.2 (Emission Database for Global Atmospheric Research v4.3.2) and the MACCity (Monitoring Atmospheric Chemistry and Climate and CityZen) 2018 emission inventories. TROPOMI correlates strongly (r=0.85 and 0.7) with EDGAR and MACCity, showing the highest emission ratio for Riyadh and lowest emission ratio for Lahore. However, inventory-derived emission ratios are 60 % to 85 % higher than TROPOMI column enhancement ratios across the six megacities. The short lifetime of NO2 and the different vertical sensitivity of TROPOMI NO2 and CO explain most of this difference. We present a method to translate TROPOMI-retrieved column enhancement ratios into corresponding emission ratios, thereby accounting for these influences. Except for Los Angeles and Lahore, TROPOMI-derived emission ratios are close (within 10 % to 25 %) to MACCity values. For EDGAR, however, emission ratios are ∼65 % higher for Cairo and 35 % higher for Riyadh. For Los Angeles, EDGAR and MACCity are a factor of 2 and 3 higher than TROPOMI respectively. The air quality monitoring networks in Los Angeles and Mexico City are used to validate the use of TROPOMI. For Mexico City and Los Angeles, these measurements are consistent with TROPOMI-derived emission ratios, demonstrating the potential of TROPOMI with respect to monitoring burning efficiency.

Solar-induced fluorescence (SIF) data from satellites are increasingly used as a proxy for photosynthetic activity by vegetation and as a constraint on gross primary production. Here we report on improvements in the algorithm to retrieve mid-morning (09:30 LT) SIF estimates on the global scale from the GOME-2 sensor on the MetOp-A satellite (GOME-2A) for the period 2007–2019. Our new SIFTER (Sun-Induced Fluorescence of Terrestrial Ecosystems Retrieval) v2 algorithm improves over a previous version by using a narrower spectral window that avoids strong oxygen absorption and being less sensitive to water vapour absorption, by constructing stable reference spectra from a 6-year period (2007–2012) of atmospheric spectra over the Sahara and by applying a latitude-dependent zero-level adjustment that accounts for biases in the data product. We generated stable, good-quality SIF retrievals between January 2007 and June 2013, when GOME-2A degradation in the near infrared was still limited. After the narrowing of the GOME-2A swath in July 2013, we characterised the throughput degradation of the level-1 data in order to derive reflectance corrections and apply these for the SIF retrievals between July 2013 and December 2018. SIFTER v2 data compare well with the independent NASA v2.8 data product. Especially in the evergreen tropics, SIFTER v2 no longer shows the underestimates against other satellite products that were seen in SIFTER v1. The new data product includes uncertainty estimates for individual observations and is best used for mostly clear-sky scenes and when spectral residuals remain below a certain spectral autocorrelation threshold. Our results support the use of SIFTER v2 data being used as an independent constraint on photosynthetic activity on regional to global scales.

We present a new product with explicit aerosol corrections, POMINO-TROPOMI, for tropospheric nitrogen dioxide (NO2) vertical column densities (VCDs) over East Asia, based on the newly launched TROPOspheric Monitoring Instrument with an unprecedented high horizontal resolution. Compared to the official TM5-MP-DOMINO (OFFLINE) product, POMINO-TROPOMI shows stronger concentration gradients near emission source locations and better agrees with MAX-DOAS measurements (R2=0.75; NMB=0.8 % versus R2=0.68, NMB=-41.9 %). Sensitivity tests suggest that implicit aerosol corrections, as in TM5-MP-DOMINO, lead to underestimations of NO2 columns by about 25 % over the polluted northern East China region. Reducing the horizontal resolution of a priori NO2 profiles would underestimate the retrieved NO2 columns over isolated city clusters in western China by 35 % but with overestimates of more than 50 % over many offshore coastal areas. The effect of a priori NO2 profiles is more important under calm conditions.

The Tropospheric Monitoring Instrument (TROPOMI), aboard the Sentinel-5 Precursor (S5P) satellite, launched on 13 October 2017, provides measurements of atmospheric trace gases and of cloud and aerosol properties at an unprecedented spatial resolution of approximately 7×3.5 km2 (approx. 5.5×3.5 km2 as of 6 August 2019), achieving near-global coverage in 1 d. The retrieval of nitrogen dioxide (NO2) concentrations is a three-step procedure: slant column density (SCD) retrieval, separation of the SCD in its stratospheric and tropospheric components, and conversion of these into vertical column densities. This study focusses on the TROPOMI NO2 SCD retrieval: the retrieval method used, the stability of the SCDs and the SCD uncertainties, and a comparison with the Ozone Monitoring Instrument (OMI) NO2 SCDs. The statistical uncertainty, based on the spatial variability of the SCDs over a remote Pacific Ocean sector, is 8.63 µmol m−2 for all pixels (9.45 µmol m−2 for clear-sky pixels), which is very stable over time and some 30 % less than the long-term average over OMI–QA4ECV data (since the pixel size reduction TROPOMI uncertainties are ∼8 % larger). The SCD uncertainty reported by the differential optical absorption spectroscopy (DOAS) fit is about 10 % larger than the statistical uncertainty, while for OMI–QA4ECV the DOAS uncertainty is some 20 % larger than its statistical uncertainty. Comparison of the SCDs themselves over the Pacific Ocean, averaged over 1 month, shows that TROPOMI is about 5 % higher than OMI–QA4ECV, which seems to be due mainly to the use of the so-called intensity offset correction in OMI–QA4ECV but not in TROPOMI: turning that correction off means about 5 % higher SCDs. The row-to-row variation in the SCDs of TROPOMI, the “stripe amplitude”, is 2.15 µmol m−2, while for OMI–QA4ECV it is a factor of ∼2 (∼5) larger in 2005 (2018); still, a so-called stripe correction of this non-physical across-track variation is useful for TROPOMI data. In short, TROPOMI shows a superior performance compared with OMI–QA4ECV and operates as anticipated from instrument specifications. The TROPOMI data used in this study cover 30 April 2018 up to 31 January 2020.

Ozone (O3) is a secondary air pollutant that negatively affects human and ecosystem health. Ozone simulations with regional air quality models suffer from unexplained biases over Europe, and uncertainties in the emissions of ozone precursor group nitrogen oxides (NOx=NO+NO2) contribute to these biases. The goal of this study is to use NO2 column observations from the Ozone Monitoring Instrument (OMI) satellite sensor to infer top-down NOx emissions in the regional Weather Research and Forecasting model with coupled chemistry (WRF-Chem) and to evaluate the impact on simulated surface O3 with in situ observations. We first perform a simulation for July 2015 over Europe and evaluate its performance against in situ observations from the AirBase network. The spatial distribution of mean ozone concentrations is reproduced satisfactorily. However, the simulated maximum daily 8 h ozone concentration (MDA8 O3) is underestimated (mean bias error of −14.2 µg m−3), and its spread is too low. We subsequently derive satellite-constrained surface NOx emissions using a mass balance approach based on the relative difference between OMI and WRF-Chem NO2 columns. The method accounts for feedbacks through OH, NO2's dominant daytime oxidant. Our optimized European NOx emissions amount to 0.50 Tg N (for July 2015), which is 0.18 Tg N higher than the bottom-up emissions (which lacked agricultural soil NOx emissions). Much of the increases occur across Europe, in regions where agricultural soil NOx emissions dominate. Our best estimate of soil NOx emissions in July 2015 is 0.1 Tg N, much higher than the bottom-up 0.02 Tg N natural soil NOx emissions from the Model of Emissions of Gases and Aerosols from Nature (MEGAN). A simulation with satellite-updated NOx emissions reduces the systematic bias between WRF-Chem and OMI NO2 (slope =0.98, r2=0.84) and reduces the low bias against independent surface NO2 measurements by 1.1 µg m−3 (−56 %). Following these NOx emission changes, daytime ozone is strongly affected, since NOx emission changes particularly affect daytime ozone formation. Monthly averaged simulated daytime ozone increases by 6.0 µg m−3, and increases of >10 µg m−3 are seen in regions with large emission increases. With respect to the initial simulation, MDA8 O3 has an improved spatial distribution, expressed by an increase in r2 from 0.40 to 0.53, and a decrease of the mean bias by 7.4 µg m−3 (48 %). Overall, our results highlight the dependence of surface ozone on its precursor NOx and demonstrate that simulations of surface ozone benefit from constraining surface NOx emissions by satellite NO2 column observations.

In this work, a ∼21-year global dataset from four different satellite sensors with a mid-morning overpass (GOME/ERS-2, SCIAMACHY/ENVISAT, GOME-2/Metop-A, and GOME-2/Metop-B) is compiled to study the long-term tropospheric NO2 patterns and trends. The Global Ozone Monitoring Experiment (GOME) and GOME-2 data are “corrected” relative to the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) data to produce a self-consistent dataset that covers the period April 1996–September 2017. The highest tropospheric NO2 concentrations are seen over urban, industrialized, and highly populated areas and over ship tracks in the oceans. Tropospheric NO2 has generally decreased during the last 2 decades over the industrialized and highly populated regions of the western world (a total decrease of the order of ∼49 % over the US, the Netherlands, and the UK; ∼36 % over Italy and Japan; and ∼32 % over Germany and France) and increased over developing regions (a total increase of ∼160 % over China and ∼33 % over India). It is suggested here that linear trends cannot be used efficiently worldwide for such long periods. Tropospheric NO2 is very sensitive to socioeconomic changes (e.g., environmental protection policies, economic recession, warfare, etc.) which may cause either short-term changes or even a reversal of the trends. The application of a method capable of detecting the year when a reversal of trends happened shows that tropospheric NO2 concentrations switched from positive to negative trends and vice versa over several regions around the globe. A country-level analysis revealed clusters of countries that exhibit similar positive-to-negative or negative-to-positive trend reversals, while 29 out of a total of 64 examined megacities and large urban agglomerations experienced a trend reversal at some point within the last 2 decades.

Satellite retrieval of vertical column densities (VCDs) of tropospheric nitrogen dioxide (NO2) is critical for NOx pollution and impact evaluation. For regions with high aerosol loadings, the retrieval accuracy is greatly affected by whether aerosol optical effects are treated implicitly (as additional “effective” clouds) or explicitly, among other factors. Our previous POMINO algorithm explicitly accounts for aerosol effects to improve the retrieval, especially in polluted situations over China, by using aerosol information from GEOS-Chem simulations with further monthly constraints by MODIS/Aqua aerosol optical depth (AOD) data. Here we present a major algorithm update, POMINO v1.1, by constructing a monthly climatological dataset of aerosol extinction profiles, based on level 2 CALIOP/CALIPSO data over 2007–2015, to better constrain the modeled aerosol vertical profiles. We find that GEOS-Chem captures the month-to-month variation in CALIOP aerosol layer height (ALH) but with a systematic underestimate by about 300–600 m (season and location dependent), due to a too strong negative vertical gradient of extinction above 1 km. Correcting the model aerosol extinction profiles results in small changes in retrieved cloud fraction, increases in cloud-top pressure (within 2 %–6 % in most cases), and increases in tropospheric NO2 VCD by 4 %–16 % over China on a monthly basis in 2012. The improved NO2 VCDs (in POMINO v1.1) are more consistent with independent ground-based MAX-DOAS observations (R2=0.80, NMB = −3.4 %, for 162 pixels in 49 days) than POMINO (R2=0.80, NMB = −9.6 %), DOMINO v2 (R2=0.68, NMB = −2.1 %), and QA4ECV (R2=0.75, NMB = −22.0 %) are. Especially on haze days, R2 reaches 0.76 for POMINO v1.1, much higher than that for POMINO (0.68), DOMINO v2 (0.38), and QA4ECV (0.34). Furthermore, the increase in cloud pressure likely reveals a more realistic vertical relationship between cloud and aerosol layers, with aerosols situated above the clouds in certain months instead of always below the clouds. The POMINO v1.1 algorithm is a core step towards our next public release of the data product (POMINO v2), and it will also be applied to the recently launched S5P-TROPOMI sensor.

Global observations of tropospheric nitrogen dioxide (NO2) columns have been shown to be feasible from space, but consistent multi-sensor records do not yet exist, nor are they covered by planned activities at the international level. Harmonised, multi-decadal records of NO2 columns and their associated uncertainties can provide crucial information on how the emissions and concentrations of nitrogen oxides evolve over time. Here we describe the development of a new, community best-practice NO2 retrieval algorithm based on a synthesis of existing approaches. Detailed comparisons of these approaches led us to implement an enhanced spectral fitting method for NO2, a 1°  ×  1° TM5-MP data assimilation scheme to estimate the stratospheric background and improve air mass factor calculations. Guided by the needs expressed by data users, producers, and WMO GCOS guidelines, we incorporated detailed per-pixel uncertainty information in the data product, along with easily traceable information on the relevant quality aspects of the retrieval. We applied the improved QA4ECV NO2 algorithm to the most current level-1 data sets to produce a complete 22-year data record that includes GOME (1995–2003), SCIAMACHY (2002–2012), GOME-2(A) (2007 onwards) and OMI (2004 onwards). The QA4ECV NO2 spectral fitting recommendations and TM5-MP stratospheric column and air mass factor approach are currently also applied to S5P-TROPOMI. The uncertainties in the QA4ECV tropospheric NO2 columns amount to typically 40 % over polluted scenes. The first validation results of the QA4ECV OMI NO2 columns and their uncertainties over Tai'an, China, in June 2006 suggest a small bias (−2 %) and better precision than suggested by uncertainty propagation. We conclude that our improved QA4ECV NO2 long-term data record is providing valuable information to quantitatively constrain emissions, deposition, and trends in nitrogen oxides on a global scale.

With the improved spatial resolution of the Ozone Monitoring Instrument (OMI) over earlier instruments and more than 10 years of service, tropospheric NO2 retrievals from OMI have led to many influential studies on the relationships between socioeconomic activities and NOx emissions. Previous studies have shown that the OMI NO2 data show different relative trends compared to in situ measurements. However, the sources of the discrepancies need further investigations. This study focuses on how to appropriately compare relative trends derived from OMI and in situ measurements. We retrieve OMI tropospheric NO2 vertical column densities (VCDs) and obtain the NO2 seasonal trends over the United States, which are compared with coincident in situ surface NO2 measurements from the Air Quality System (AQS) network. The Mann–Kendall method with Sen's slope estimator is applied to derive the NO2 seasonal and annual trends for four regions at coincident sites during 2005–2014. The OMI-based NO2 seasonal relative decreasing trends are generally biased low compared to the in situ trends by up to 3.7 % yr−1, except for the underestimation in the US Midwest and Northeast during December, January, and February (DJF). We improve the OMI retrievals for trend analysis by removing the ocean trend, using the Moderate Resolution Imaging Spectroradiometer (MODIS) albedo data in air mass factor (AMF) calculation. We apply a lightning flash filter to exclude lightning-affected data to make proper comparisons. These data processing procedures result in close agreement (within 0.3 % yr−1) between in situ and OMI-based NO2 regional annual relative trends. The remaining discrepancies may result from inherent difference between trends of NO2 tropospheric VCDs and surface concentrations, different spatial sampling of the measurements, chemical nonlinearity, and tropospheric NO2 profile changes. We recommend that future studies apply these procedures (ocean trend removal and MODIS albedo update) to ensure the quality of satellite-based NO2 trend analysis and apply the lightning filter in studying surface NOx emission changes using satellite observations and in comparison with the trends derived from in situ NO2 measurements. With these data processing procedures, we derive OMI-based NO2 regional annual relative trends using all available data for the US West (−2.0 % ± 0.3 yr−1), Midwest (−1.8 % ± 0.4 yr−1), Northeast (−3.1 % ± 0.5 yr−1), and South (−0.9 % ± 0.3 yr−1). The OMI-based annual mean trend over the contiguous United States is −1.5 % ± 0.2 yr−1. It is a factor of 2 lower than that of the AQS in situ data (−3.9 % ± 0.4 yr−1); the difference is mainly due to the fact that the locations of AQS sites are concentrated in urban and suburban regions.

Nitrogen dioxide (NO2) and formaldehyde (HCHO) column data from satellite instruments are used for air quality and climate studies. Both NO2 and HCHO have been identified as precursors to the ozone (O3) and aerosol essential climate variables, and it is essential to quantify and characterise their uncertainties. Here we present an intercomparison of NO2 and HCHO slant column density (SCD) retrievals from four different research groups (BIRA-IASB, IUP Bremen, and KNMI as part of the Quality Assurance for Essential Climate Variables (QA4ECV) project consortium, and NASA) and from the OMI and GOME-2A instruments. Our evaluation is motivated by recent improvements in differential optical absorption spectroscopy (DOAS) fitting techniques and by the desire to provide a fully traceable uncertainty budget for the climate data record generated within QA4ECV. The improved NO2 and HCHO SCD values are in close agreement but with substantial differences in the reported uncertainties between groups and instruments. To check the DOAS uncertainties, we use an independent estimate based on the spatial variability of the SCDs within a remote region. For NO2, we find the smallest uncertainties from the new QA4ECV retrieval (0.8 × 1015 molec. cm−2 for both instruments over their mission lifetimes). Relative to earlier approaches, the QA4ECV NO2 retrieval shows better agreement between DOAS and statistical uncertainty estimates, suggesting that the improved QA4ECV NO2 retrieval has reduced but not altogether eliminated systematic errors in the fitting approach. For HCHO, we reach similar conclusions (QA4ECV uncertainties of 8–12 × 1015 molec. cm−2), but the closeness between the DOAS and statistical uncertainty estimates suggests that HCHO uncertainties are indeed dominated by random noise from the satellite's level 1 data. We find that SCD uncertainties are smallest for high top-of-atmosphere reflectance levels with high measurement signal-to-noise ratios. From 2005 to 2015, OMI NO2 SCD uncertainties increase by 1–2 % year−1, which is related to detector degradation and stripes, but OMI HCHO SCD uncertainties are remarkably stable (increase −1) and this is related to the use of Earth radiance reference spectra which reduces stripes. For GOME-2A, NO2 and HCHO SCD uncertainties increased by 7–9 and 11–15 % year−1 respectively up until September 2009, when heating of the instrument markedly reduced further throughput loss, stabilising the degradation of SCD uncertainty to −1 for 2009–2015. Our work suggests that the NO2 SCD uncertainty largely consists of a random component ( ∼ 65 % of the total uncertainty) as a result of the propagation of measurement noise but also of a substantial systematic component ( ∼ 35 % of the total uncertainty) mainly from stripe effects. Averaging over multiple pixels in space and/or time can significantly reduce the SCD uncertainties. This suggests that trend detection in OMI, GOME-2 NO2, and HCHO time series is not limited by the spectral fitting but rather by the adequacy of assumptions on the atmospheric state in the later air mass factor (AMF) calculation step.

The angular distribution of the light reflected by the Earth's surface influences top-of-atmosphere (TOA) reflectance values. This surface reflectance anisotropy has implications for UV/Vis satellite retrievals of albedo, clouds, and trace gases such as nitrogen dioxide (NO2). These retrievals routinely assume the surface to reflect light isotropically. Here we show that cloud fractions retrieved from GOME-2A and OMI with the FRESCO and OMCLDO2 algorithms have an east–west bias of 10 % to 50 %, which are highest over vegetation and forested areas, and that this bias originates from the assumption of isotropic surface reflection. To interpret the across-track bias with the DAK radiative transfer model, we implement the bidirectional reflectance distribution function (BRDF) from the Ross–Li semi-empirical model. Testing our implementation against state-of-the-art RTMs LIDORT and SCIATRAN, we find that simulated TOA reflectance generally agrees to within 1 %. We replace the assumption of isotropic surface reflection in the equations used to retrieve cloud fractions over forested scenes with scattering kernels and corresponding BRDF parameters from a daily high-resolution database derived from 16 years' worth of MODIS measurements. By doing this, the east–west bias in the simulated cloud fractions largely vanishes. We conclude that across-track biases in cloud fractions can be explained by cloud algorithms that do not adequately account for the effects of surface reflectance anisotropy. The implications for NO2 air mass factor (AMF) calculations are substantial. Under moderately polluted NO2 and backward-scattering conditions, clear-sky AMFs are up to 20 % higher and cloud radiance fractions up to 40 % lower if surface anisotropic reflection is accounted for. The combined effect of these changes is that NO2 total AMFs increase by up to 30 % for backward-scattering geometries (and decrease by up to 35 % for forward-scattering geometries), which is stronger than the effect of either contribution alone. In an unpolluted troposphere, surface BRDF effects on cloud fraction counteract (and largely cancel) the effect on the clear-sky AMF. Our results emphasise that surface reflectance anisotropy needs to be taken into account in a coherent manner for more realistic and accurate retrievals of clouds and NO2 from UV/Vis satellite sensors. These improvements will be beneficial for current sensors, in particular for the recently launched TROPOMI instrument with a high spatial resolution.

On board the Copernicus Sentinel-5 Precursor (S5P) platform, the TROPOspheric Monitoring Instrument (TROPOMI) is a double-channel, nadir-viewing grating spectrometer measuring solar back-scattered earthshine radiances in the ultraviolet, visible, near-infrared, and shortwave infrared with global daily coverage. In the ultraviolet range, its spectral resolution and radiometric performance are equivalent to those of its predecessor OMI, but its horizontal resolution at true nadir is improved by an order of magnitude. This paper introduces the formaldehyde (HCHO) tropospheric vertical column retrieval algorithm implemented in the S5P operational processor and comprehensively describes its various retrieval steps. Furthermore, algorithmic improvements developed in the framework of the EU FP7-project QA4ECV are described for future updates of the processor. Detailed error estimates are discussed in the light of Copernicus user requirements and needs for validation are highlighted. Finally, verification results based on the application of the algorithm to OMI measurements are presented, demonstrating the performances expected for TROPOMI.

We provide a comprehensive description of the high-resolution version of the TM5-MP global chemistry transport model, which is to be employed for deriving highly resolved vertical profiles of nitrogen dioxide (NO2), formaldehyde (CH2O), and sulfur dioxide (SO2) for use in satellite retrievals from platforms such as the Ozone Monitoring Instrument (OMI) and the Sentinel-5 Precursor, and the TROPOspheric Monitoring Instrument (tropOMI). Comparing simulations conducted at horizontal resolutions of 3° × 2° and 1° × 1° reveals differences of ±20 % exist in the global seasonal distribution of 222Rn, being larger near specific coastal locations and tropical oceans. For tropospheric ozone (O3), analysis of the chemical budget terms shows that the impact on globally integrated photolysis rates is rather low, in spite of the higher spatial variability of meteorological data fields from ERA-Interim at 1° × 1°. Surface concentrations of O3 in high-NOx regions decrease between 5 and 10 % at 1° × 1° due to a reduction in NOx recycling terms and an increase in the associated titration term of O3 by NO. At 1° × 1°, the net global stratosphere–troposphere exchange of O3 decreases by ∼ 7 %, with an associated shift in the hemispheric gradient. By comparing NO, NO2, HNO3 and peroxy-acetyl-nitrate (PAN) profiles against measurement composites, we show that TM5-MP captures the vertical distribution of NOx and long-lived NOx reservoirs at background locations, again with modest changes at 1° × 1°. Comparing monthly mean distributions in lightning NOx and applying ERA-Interim convective mass fluxes, we show that the vertical re-distribution of lightning NOx changes with enhanced release of NOx in the upper troposphere. We show that surface mixing ratios in both NO and NO2 are generally underestimated in both low- and high-NOx scenarios. For Europe, a negative bias exists for [NO] at the surface across the whole domain, with lower biases at 1° × 1° at only ∼ 20 % of sites. For NO2, biases are more variable, with lower (higher) biases at 1° × 1° occurring at ∼ 35 % ( ∼ 20 %) of sites, with the remainder showing little change. For CH2O, the impact of higher resolution on the chemical budget terms is rather modest, with changes of less than 5 %. The simulated vertical distribution of CH2O agrees reasonably well with measurements in pristine locations, although column-integrated values are generally underestimated relative to satellite measurements in polluted regions. For SO2, the performance at 1° × 1° is principally governed by the quality of the emission inventory, with limited improvements in the site-specific biases, with most showing no significant improvement. For the vertical column, improvements near strong source regions occur which reduce the biases in the integrated column. For remote regions missing biogenic source terms are inferred.

Global surface emissions of nitrogen oxides (NOx) over a 10-year period (2005–2014) are estimated from an assimilation of multiple satellite data sets: tropospheric NO2 columns from Ozone Monitoring Instrument (OMI), Global Ozone Monitoring Experiment-2 (GOME-2), and Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY), O3 profiles from Tropospheric Emission Spectrometer (TES), CO profiles from Measurement of Pollution in the Troposphere (MOPITT), and O3 and HNO3 profiles from Microwave Limb Sounder (MLS) using an ensemble Kalman filter technique. Chemical concentrations of various species and emission sources of several precursors are simultaneously optimized. This is expected to improve the emission inversion because the emission estimates are influenced by biases in the modelled tropospheric chemistry, which can be partly corrected by also optimizing the concentrations. We present detailed distributions of the estimated emission distributions for all major regions, the diurnal and seasonal variability, and the evolution of these emissions over the 10-year period. The estimated regional total emissions show a strong positive trend over India (+29 % decade−1), China (+26 % decade−1), and the Middle East (+20 % decade−1), and a negative trend over the USA (−38 % decade−1), southern Africa (−8.2 % decade−1), and western Europe (−8.8 % decade−1). The negative trends in the USA and western Europe are larger during 2005–2010 relative to 2011–2014, whereas the trend in China becomes negative after 2011. The data assimilation also suggests a large uncertainty in anthropogenic and fire-related emission factors and an important underestimation of soil NOx sources in the emission inventories. Despite the large trends observed for individual regions, the global total emission is almost constant between 2005 (47.9 Tg N yr−1) and 2014 (47.5 Tg N yr−1).

We present the comparison of satellite-based OMI (Ozone Monitoring Instrument) NO2 products with ground-based observations in Helsinki. OMI NO2 total columns, available from NASA's standard product (SP) and KNMI DOMINO product, are compared with the measurements performed by the Pandora spectrometer in Helsinki in 2012. The relative difference between Pandora no. 21 and OMI SP total columns is 4 and −6 % for clear-sky and all-sky conditions, respectively. DOMINO NO2 retrievals showed slightly lower total columns with median differences about −5 and −14 % for clear-sky and all-sky conditions, respectively. Large differences often correspond to cloudy fall–winter days with solar zenith angles above 65°. Nevertheless, the differences remain within the retrieval uncertainties. The average difference values are likely the result of different factors partly canceling each other: the overestimation of the stratospheric columns causes a positive bias partly compensated by the limited spatial representativeness of the relatively coarse OMI pixel for sharp NO2 gradients. The comparison between Pandora and the new version (V3) of OMI NO2 retrievals shows a larger negative difference (about −30 %) than the current version (V2.1) because the revised spectral fitting procedure reduces the overestimation of the stratospheric column. The weekly and seasonal cycles from OMI, Pandora and NO2 surface concentrations are also compared. Both satellite- and ground-based data show a similar weekly cycle, with lower NO2 levels during the weekend compared to the weekdays as a result of reduced emissions from traffic and industrial activities. The seasonal cycle also shows a similar behavior, even though the results are affected by the fact that most of the data are available during spring–summer because of cloud cover in other seasons. This is one of few works in which OMI NO2 retrievals are evaluated in a urban site at high latitudes (60° N). Despite the city of Helsinki having relatively small pollution sources, OMI retrievals have proved to be able to describe air quality features and variability similar to surface observations. This adds confidence in using satellite observations for air quality monitoring also at high latitudes.

A variant of the limb–nadir matching technique for deriving tropospheric NO2 columns is presented in which the stratospheric component of the NO2 slant column density (SCD) measured by the Ozone Monitoring Instrument (OMI) is removed using non-coincident profiles from the Optical Spectrograph and InfraRed Imaging System (OSIRIS). In order to correct their mismatch in local time and the diurnal variation of stratospheric NO2, OSIRIS profiles, which were measured just after sunrise, were mapped to the local time of OMI observations using a photochemical box model. Following the profile time adjustment, OSIRIS NO2 stratospheric vertical column densities (VCDs) were calculated. For profiles that did not reach down to the tropopause, VCDs were adjusted using the photochemical model. Using air mass factors from the OMI Standard Product (SP), a new tropospheric NO2 VCD product – referred to as OMI-minus-OSIRIS (OmO) – was generated through limb–nadir matching. To accomplish this, the OMI total SCDs were scaled using correction factors derived from the next-generation SCDs that improve upon the spectral fitting used for the current operational products. One year, 2008, of OmO was generated for 60° S to 60° N and a cursory evaluation was performed. The OmO product was found to capture the main features of tropospheric NO2, including a background value of about 0.3 × 1015 molecules cm−2 over the tropical Pacific and values comparable to the OMI operational products over anthropogenic source areas. While additional study is required, these results suggest that a limb–nadir matching approach is feasible for the removal of stratospheric NO2 measured by a polar orbiter from a nadir-viewing instrument in a geostationary orbit such as Tropospheric Emissions: Monitoring of Pollution (TEMPO) or Sentinel-4.

The Ozone Monitoring Instrument (OMI) onboard NASA's Aura satellite has been providing global observations of the ozone layer and key atmospheric pollutant gases, such as nitrogen dioxide (NO2) and sulfur dioxide (SO2), since October 2004. The data products from the same instrument provide consistent spatial and temporal coverage and permit the study of anthropogenic and natural emissions on local-to-global scales. In this paper, we examine changes in SO2 and NO2 over some of the world's most polluted industrialized regions during the first decade of OMI observations. In terms of regional pollution changes, we see both upward and downward trends, sometimes in opposite directions for NO2 and SO2, for different study areas. The trends are, for the most part, associated with economic and/or technological changes in energy use, as well as regional regulatory policies. Over the eastern US, both NO2 and SO2 levels decreased dramatically from 2005 to 2015, by more than 40 and 80 %, respectively, as a result of both technological improvements and stricter regulations of emissions. OMI confirmed large reductions in SO2 over eastern Europe's largest coal-fired power plants after installation of flue gas desulfurization devices. The North China Plain has the world's most severe SO2 pollution, but a decreasing trend has been observed since 2011, with about a 50 % reduction in 2012–2015, due to an economic slowdown and government efforts to restrain emissions from the power and industrial sectors. In contrast, India's SO2 and NO2 levels from coal power plants and smelters are growing at a fast pace, increasing by more than 100 and 50 %, respectively, from 2005 to 2015. Several SO2 hot spots observed over the Persian Gulf are probably related to oil and gas operations and indicate a possible underestimation of emissions from these sources in bottom-up emission inventories. Overall, OMI observations have proved valuable in documenting rapid changes in air quality over different parts of the world during last decade. The baseline established during the first 11 years of OMI is indispensable for the interpretation of air quality measurements from current and future satellite atmospheric composition missions.

Ultraviolet–visible (UV–Vis) satellite retrievals of trace gas columns of nitrogen dioxide (NO2), sulfur dioxide (SO2), and formaldehyde (HCHO) are useful to test and improve models of atmospheric composition, for data assimilation, air quality hindcasting and forecasting, and to provide top-down constraints on emissions. However, because models and satellite measurements do not represent the exact same geophysical quantities, the process of confronting model fields with satellite measurements is complicated by representativeness errors, which degrade the quality of the comparison beyond contributions from modelling and measurement errors alone. Here we discuss three types of representativeness errors that arise from the act of carrying out a model–satellite comparison: (1) horizontal representativeness errors due to imperfect collocation of the model grid cell and an ensemble of satellite pixels called superobservation, (2) temporal representativeness errors originating mostly from differences in cloud cover between the modelled and observed state, and (3) vertical representativeness errors because of reduced satellite sensitivity towards the surface accompanied with necessary retrieval assumptions on the state of the atmosphere. To minimize the impact of these representativeness errors, we recommend that models and satellite measurements be sampled as consistently as possible, and our paper provides a number of recipes to do so. A practical confrontation of tropospheric NO2 columns simulated by the TM5 chemistry transport model (CTM) with Ozone Monitoring Instrument (OMI) tropospheric NO2 retrievals suggests that horizontal representativeness errors, while unavoidable, are limited to within 5–10 % in most cases and of random nature. These errors should be included along with the individual retrieval errors in the overall superobservation error. Temporal sampling errors from mismatches in cloud cover, and, consequently, in photolysis rates, are of the order of 10 % for NO2 and HCHO, and systematic, but partly avoidable. In the case of air pollution applications where sensitivity down to the ground is required, we recommend that models should be sampled on the same mostly cloud-free days as the satellite retrievals. The most relevant representativeness error is associated with the vertical sensitivity of UV–Vis satellite retrievals. Simple vertical integration of modelled profiles leads to systematically different model columns compared to application of the appropriate averaging kernel. In comparing OMI NO2 to GEOS-Chem NO2 simulations, these systematic differences are as large as 15–20 % in summer, but, again, avoidable.

Satellite retrievals of vertical column densities (VCDs) of tropospheric nitrogen dioxide (NO2) normally do not explicitly account for aerosol optical effects and surface reflectance anisotropy that vary with space and time. Here, we conduct an improved retrieval of NO2 VCDs over China, called the POMINO algorithm, based on measurements from the Ozone Monitoring Instrument (OMI), and we test the importance of a number of aerosol and surface reflectance treatments in this algorithm. POMINO uses a parallelized LIDORT-driven AMFv6 package to derive tropospheric air mass factors via pixel-specific radiative transfer calculations with no look-up tables, taking slant column densities from DOMINO v2. Prerequisite cloud optical properties are derived from a dedicated cloud retrieval process that is fully consistent with the main NO2 retrieval. Aerosol optical properties are taken from GEOS-Chem simulations constrained by MODIS aerosol optical depth (AOD) data. MODIS bi-directional reflectance distribution function (BRDF) data are used for surface reflectance over land. For the present analysis, POMINO level-2 data for 2012 are aggregated into monthly means on a 0.25° long. × 0.25° lat. grid. POMINO-retrieved annual mean NO2 VCDs vary from 15–25 × 1015 cm−2 over the polluted North China Plain (NCP) to below 1015 cm−2 over much of western China. Using POMINO to infer Chinese emissions of nitrogen oxides leads to annual anthropogenic emissions of 9.05 TgN yr−1, an increase from 2006 (Lin, 2012) by about 19 %. Replacing the MODIS BRDF data with the OMLER v1 monthly climatological albedo data affects NO2 VCDs by up to 40 % for certain locations and seasons. The effect on constrained NOx emissions is small. Excluding aerosol information from the retrieval process (this is the traditional "implicit" treatment) enhances annual mean NO2 VCDs by 15–40 % over much of eastern China. Seasonally, NO2 VCDs are reduced by 10–20 % over parts of the NCP in spring and over northern China in winter, despite the general enhancements in summer and fall. The effect on subsequently constrained annual emissions is between −5 and +30 % with large seasonal and spatial dependence. The implicit aerosol treatment also tends to exclude days with high pollution, since aerosols are interpreted as effective clouds and the respective OMI pixels are often excluded by cloud screening; this is a potentially important sampling bias. Therefore an explicit treatment of aerosols is important for space-based NO2 retrievals and emission constraints. A comprehensive independent measurement network with sufficient spatial and temporal representativeness is needed to further evaluate the different satellite retrieval approaches.

Biomass burning is an important and uncertain source of aerosols and NOx (NO + NO2) to the atmosphere. Satellite observations of tropospheric NO2 are essential for characterizing this emissions source, but inaccuracies in the retrieval of NO2 tropospheric columns due to the radiative effects of aerosols, especially light-absorbing carbonaceous aerosols, are not well understood. It has been shown that the O2–O2 effective cloud fraction and pressure retrieval is sensitive to aerosol optical and physical properties, including aerosol optical depth (AOD). Aerosols implicitly influence the tropospheric air mass factor (AMF) calculations used in the NO2 retrieval through the effective cloud parameters used in the independent pixel approximation. In this work, we explicitly account for the effects of biomass burning aerosols in the Ozone Monitoring Instrument (OMI) tropospheric NO2 AMF calculation for cloud-free scenes. We do so by including collocated aerosol extinction vertical profile observations from the CALIOP instrument, and aerosol optical depth (AOD) and single scattering albedo (SSA) retrieved by the OMI near-UV aerosol algorithm (OMAERUV) in the DISAMAR radiative transfer model. Tropospheric AMFs calculated with DISAMAR were benchmarked against AMFs reported in the Dutch OMI NO2 (DOMINO) retrieval; the mean and standard deviation of the difference was 0.6 ± 8 %. Averaged over three successive South American biomass burning seasons (2006–2008), the spatial correlation in the 500 nm AOD retrieved by OMI and the 532 nm AOD retrieved by CALIOP was 0.6, and 68 % of the daily OMAERUV AOD observations were within 30 % of the CALIOP observations. Overall, tropospheric AMFs calculated with observed aerosol parameters were on average 10 % higher than AMFs calculated with effective cloud parameters. For effective cloud radiance fractions less than 30 %, or effective cloud pressures greater than 800 hPa, the difference between tropospheric AMFs based on implicit and explicit aerosol parameters is on average 6 and 3 %, respectively, which was the case for the majority of the pixels considered in our study; 70 % had cloud radiance fraction below 30 %, and 50 % had effective cloud pressure greater than 800 hPa. Pixels with effective cloud radiance fraction greater than 30 % or effective cloud pressure less than 800 hPa corresponded with stronger shielding in the implicit aerosol correction approach because the assumption of an opaque effective cloud underestimates the altitude-resolved AMF; tropospheric AMFs were on average 30–50 % larger when aerosol parameters were included, and for individual pixels tropospheric AMFs can differ by more than a factor of 2. The observation-based approach to correcting tropospheric AMF calculations for aerosol effects presented in this paper depicts a promising strategy for a globally consistent aerosol correction scheme for clear-sky pixels.

An improved nitrogen dioxide (NO2) slant column density retrieval for the Ozone Monitoring Instrument (OMI) in the 405–465 nm spectral region is presented. Since the launch of OMI on board NASA's EOS-Aura satellite in 2004, differential optical absorption spectroscopy (DOAS) retrievals of NO2 slant column densities have been the starting point for the KNMI DOMINO and NASA SP NO2 vertical column data as well as the OMI NO2 data of some other institutes. However, recent intercomparisons between NO2 retrievals from OMI and other UV/Vis and limb spectrometers, as well as ground-based measurements, suggest that OMI stratospheric NO2 is biased high. This study revises and, for the first time, fully documents the OMI NO2 retrieval in detail. The representation of the OMI slit function to convolve high-resolution reference spectra onto the relevant spectral grid is improved. The window used for the wavelength calibration is optimised, leading to much-reduced fitting errors. Ozone and water vapour spectra used in the fit are updated, reflecting the recently improved knowledge of their absorption cross section in the literature. The improved spectral fit also accounts for absorption by the O2–O2 collision complex and by liquid water over clear-water areas. The main changes in the improved spectral fitting result from the updates related to the wavelength calibration: the RMS error of the fit is reduced by 23% and the NO2 slant column by 0.85 × 1015 molec cm−2, independent of latitude, solar zenith angle and NO2 value. Including O2–O2 and liquid water absorption and updating the O3 and water vapour cross-section spectra further reduces NO2 slant columns on average by 0.35 × 1015 molec cm−2, accompanied by a further 9% reduction in the RMS error of the fit. The improved OMI NO2 slant columns are consistent with independent NO2 retrievals from other instruments to within a range that can be explained by photochemically driven diurnal increases in stratospheric NO2 and by small differences in fitting window and approach. The revisions indicate that current OMI NO2 slant columns suffered mostly from an additive positive offset, which is removed by the improved wavelength calibration and representation of the OMI slit function. It is therefore anticipated that the improved NO2 slant columns are most important to retrievals of spatially homogeneous stratospheric NO2 rather than to heterogeneous tropospheric NO2.