Tropospheric Column Research Articles

Rapid O3 assimilations – Part 1: Background and local contributions to tropospheric O3 changes in China in 2015–2020

Abstract. A single ozone (O3) tracer mode was developed in this work to build the capability of the Goddard Earth Observing System model with Chemistry (GEOS-Chem) for rapid O3 simulation. The single O3 tracer simulation demonstrates consistency with the GEOS-Chem full chemistry simulation, with dramatic reductions in computational costs of approximately 91 %–94 %. The single O3 tracer simulation was combined with surface and Ozone Monitoring Instrument (OMI) O3 observations to investigate the changes in tropospheric O3 over eastern China in 2015–2020. The assimilated O3 concentrations demonstrate good agreement with O3 observations because surface O3 concentrations are 43.2, 41.8, and 42.1 ppb and tropospheric O3 columns are 37.1, 37.9, and 38.0 DU in the simulations, assimilations, and observations, respectively. The assimilations indicate rapid rises in surface O3 concentrations by 1.60 (spring), 1.16 (summer), 1.47 (autumn), and 0.80 ppb yr−1 (winter) over eastern China in 2015–2020, and the increasing trends are underestimated by the a priori simulations. More attention is suggested to the rapid increases in the O3 pollution in spring and autumn. We find stronger rises in tropospheric O3 columns over highly polluted areas due to larger local contributions, for example, 0.12 DU yr−1 (North China Plain) in contrast to −0.29 (Sichuan Basin) and −0.25 DU yr−1 (southern China). Furthermore, our analysis demonstrated noticeable contributions of the interannual variability in background O3 to the trends in surface O3 (particularly in the summer) and tropospheric O3 columns over eastern China in 2015–2020. This work highlights the importance of rapid simulations and assimilations to extend and interpret atmospheric O3 observations.

To new heights by flying low: comparison of aircraft vertical NO2 profiles to model simulations and implications for TROPOMI NO2 retrievals

Abstract. 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 Role of Local and Remote Processes for Wintertime Surface Energy Budget Extremes over Arctic Sea Ice

Abstract Arctic warm extremes and anomalous sea ice melting have been linked to episodic injections of warm and moist air from midlatitudes, as well as airmass transformations inside the Arctic. However, the relative importance of remote and local processes for such events remains unclear. Here, we focus on events with extreme positive daily-mean surface energy budget (SEB) anomalies over Arctic sea ice in ERA5 data during extended winters (November–March during 1979–2020). Kinematic backward trajectories from the tropospheric column collocated with the SEB anomalies show that near-surface air is of Arctic origin, whereas air farther aloft is transported poleward from the midlatitudes and ascends. Despite the different origin of the air, the entire tropospheric column shows pronounced potential temperature anomalies (on the order of 10 K) building up along air-parcel trajectories over 2–4 days. Quantifying the contributions of horizontal and vertical transport as well as diabatic processes to the generation of these potential temperature anomalies emphasizes the relevance of horizontal advection across the climatological potential temperature gradient for the generation of the anomalies at all levels. Anomalies aloft are further enhanced by latent heating and those near the surface by subsidence, respectively. Surface heat fluxes over subpolar and polar oceans are key for warming and moistening the near-surface air of predominantly Arctic origin and maintaining a positive potential temperature anomaly, which due to its proximity to the surface unfolds the strongest impact on the SEB. This suggests that Arctic airmasses and their local transformations are crucial for generating the most extreme SEB anomalies.

A simplified non-linear chemistry transport model for analyzing NO2 column observations: STILT–NOx

Abstract. Satellites monitoring air pollutants (e.g., nitrogen oxides; NOx = NO + NO2) or greenhouse gases (GHGs) are widely utilized to understand the spatiotemporal variability in and evolution of emission characteristics, chemical transformations, and atmospheric transport over anthropogenic hotspots. Recently, the joint use of space-based long-lived GHGs (e.g., carbon dioxide; CO2) and short-lived pollutants has made it possible to improve our understanding of emission characteristics. Some previous studies, however, lack consideration of the non-linear NOx chemistry or complex atmospheric transport. Considering the increase in satellite data volume and the demand for emission monitoring at higher spatiotemporal scales, it is crucial to construct a local-scale emission optimization system that can handle both long-lived GHGs and short-lived pollutants in a coupled and effective manner. This need motivates us to develop a Lagrangian chemical transport model that accounts for NOx chemistry and fine-scale atmospheric transport (STILT–NOx) and to investigate how physical and chemical processes, anthropogenic emissions, and background may affect the interpretation of tropospheric NO2 columns (tNO2). Interpreting emission signals from tNO2 commonly involves either an efficient statistical model or a sophisticated chemical transport model. To balance computational expenses and chemical complexity, we describe a simplified representation of the NOx chemistry that bypasses an explicit solution of individual chemical reactions while preserving the essential non-linearity that links NOx emissions to its concentrations. This NOx chemical parameterization is then incorporated into an existing Lagrangian modeling framework that is widely applied in the GHG community. We further quantify uncertainties associated with the wind field and chemical parameterization and evaluate modeled columns against retrieved columns from the TROPOspheric Monitoring Instrument (TROPOMI v2.1). Specifically, simulations with alternative model configurations of emissions, meteorology, chemistry, and inter-parcel mixing are carried out over three United States (US) power plants and two urban areas across seasons. Using the U.S. Environmental Protection Agency (EPA)-reported emissions for power plants with non-linear NOx chemistry improves the model–data alignment in tNO2 (a high bias of ≤ 10 % on an annual basis), compared to simulations using either the Emissions Database for Global Atmospheric Research (EDGAR) model or without chemistry (bias approaching 100 %). The largest model–data mismatches are associated with substantial biases in wind directions or conditions of slower atmospheric mixing and photochemistry. More importantly, our model development illustrates (1) how NOx chemistry affects the relationship between NOx and CO2 in terms of the spatial and seasonal variability and (2) how assimilating tNO2 can quantify systematic biases in modeled wind directions and emission distribution in prior inventories of NOx and CO2, which laid a foundation for a local-scale multi-tracer emission optimization system.

Satellite unravels recent changes in atmospheric nitrogen oxides emissions from global ocean shipping

Nitrogen dioxide (NO2) in the lower marine atmosphere, mainly emitted by maritime shipping, plays a crucial role in air pollution formation and global human health. However, few measurements of marine atmospheric NO2 hinder knowledge of trace gas trends and atmospheric chemistry evolution due to shipping emissions. In this study, we use long-term satellite observations of tropospheric NO2 column from the European TROPOspheric Monitoring Instrument (TROPOMI) and the Chinese Environmental trace gases Monitoring Instrument (EMI) to analyze marine atmospheric NO2 variations, especially during the global COVID-19 pandemic and escalating geopolitical crises. First, we demonstrate the detection of NO2 enhancements along shipping routes, including the North Atlantic route, the North Pacific route, and the Cape route, indicating significant emissions of atmospheric NO2 from on-ocean shipping. Second, we observe and quantify the response of marine atmospheric NO2 concentrations to major shipping events, such as the Suez Canal blockage, the Los Angeles-Long Beach port congestion, and the Russia-Ukraine war, resulting in local NO2 concentration variations of approximately 40% decrease to 70% increase. Long-term analysis reveals reduced NO2 concentrations in most coastal ports and maritime shipping routes during the COVID-19 lockdown, with reductions exceeding 50% or durations lasting up to 200 days. However, some rapidly developing ports, such as Beibu Gulf (China) and Dakar (Senegal), did not experience a decrease in NO2 concentrations, suggesting that local authorities need to pay more attention to these fast-growing yet underestimated emission sources. In addition, by excluding the impact of meteorology using statistical models, we find that the current Emission Control Area (ECA) policies have effectively reduced NO2 concentrations in Chinese coastal ports. These results contribute to understanding spatiotemporal characteristics of marine atmospheric NO2, including ports and open-sea shipping routes, and guide further ECA policies to control marine NO2 pollution.

Ground-based Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) observations of NO2 and H2CO at Kinshasa and comparisons with TROPOMI observations

Abstract. We present a database of MAX-DOAS (Multi-AXis Differential Optical Absorption Spectroscopy) ground-based observations of NO2 and H2CO tropospheric vertical column densities (VCDtropo) performed for the first time in the city of Kinshasa. These measurements were conducted between November 2019 and July 2021 and processed using the standardized inversion tools developed in the ESA FRM4DOAS (Fiducial Reference Measurements for Ground-Based DOAS Air-Quality Observations) project. The retrieved geophysical quantities are used to validate column observations from the TROPOspheric Monitoring Instrument (TROPOMI) over Kinshasa. In the validation, we experiment with three different comparison cases of increasing complexity. In the first case, a direct comparison between MAX-DOAS observations (hourly average of MAX-DOAS VCDtropo at overpass) and TROPOMI shows an underestimation of TROPOMI with a median bias of −38 % for NO2 and −39 % for H2CO based on monthly comparison. The second case takes into account the different vertical sensitivities of the two instruments and the a priori profile. We note significant changes in the median bias for both compounds: −12 % for NO2 and +11 % for H2CO. The third case builds on the second case by considering also the direction of sight of the MAX-DOAS. For this third case, we find a median bias of +44 % for NO2 and a median bias of +4 % for H2CO. However this case is impacted by low sampling and is considered less reliable. The findings from this study underscore the significance of employing a realistic a priori profile in TROPOMI column extraction, particularly within heavily polluted urban zones like Kinshasa. The investigation also highlights the necessity for prudence when integrating the MAX-DOAS line of sight due to the noise generated during subsampling and the limited horizontal sensitivity of MAX-DOAS observations. Importantly, the study further reveals the pronounced pollution levels of NO2, H2CO and aerosols in both the city of Kinshasa and its adjacent regions, underscoring the imperative for consistent monitoring and effective regulatory measures by local authorities.

Estimate of near-surface NO2 concentrations in Fenwei Plain, China, based on TROPOMI data and random forest model.

Nitrogen dioxide (NO2) concentration is a crucial indicator of ground-level air quality, and elevated concentrations can adversely affect human health and the atmospheric environment. In this study, we utilized Tropospheric Monitoring Instrument (TROPOMI) tropospheric NO2 vertical column density data (VCD) and multi-source geographic data to establish a random forest regression (RF) model that accurately estimates NO2 concentrations near the ground in the Fenwei Plain. The model addresses the inherent limitations of traditional ground-based monitoring and provides data support for analyzing regional pollution spatial and temporal characteristics. (1) The RF model based on TROPOMI and geographic data demonstrates high estimation accuracy, with monthly average RF model fit and validation coefficient of determination (R2) reaching 0.949 and 0.875, respectively. (2) A complex nonlinear relationship exists between near-surface NO2 concentration and multi-source geographic data. The RF model's estimations reveal clear seasonal and regional variations in near-surface NO2 concentration. Concentrations are generally highest in winter, followed by spring and autumn, and lowest in summer. The high NO2 concentrations are primarily mainly distributed in the plains and river valleys with low elevation and dense population density. The model estimation results also indicate that the estimated effect is better when the NO2 concentration fluctuates less and anthropogenic emission reduction measures significantly impact the NO2 concentration near the ground. (3) The population exposure risk results indicate that most cities in the Fenwei Plain face varying exposure risks. These findings offer valuable insights for regional NO2 pollution management.

Possible factors responsible for the withdrawal process of the South Asian high during summer-to-winter transition

This study investigates the possible factors responsible for interannual variations in the withdrawal process of the South Asian high (SAH) during its summer-to-winter transition. The SAH is more obviously intensified from July to September and lasts longer over the Tibetan Plateau in late-withdrawal years (L_SAH) than early-withdrawal years (E_SAH). The L_SAH tends to appear during the stage when an El Niño has faded away and a La Niña begins to emerge, with warm SST anomalies occurring over the northern Indian Ocean and tropical northwestern Pacific, and cold anomalies existing across the equatorial central eastern Pacific. The influences of these anomalous SST variations on the SAH's withdrawal process take place primarily through two pathways. On the one hand, warm SST anomalies over the northern Indian Ocean and tropical western Pacific can warm the local tropospheric column above via a Matsunuo-Gill response to moisture adjustment and subsequently lead to an intensified and later withdrawal of the SAH. On the other hand, accompanied by warm SST anomalies, an abnormally intensified anticyclone is observed over the northwestern Pacific that shifts southwestwards and brings abundant rainfall to the region between the Yellow River and Yangtze River in July and August via southwesterly water vapor transport. Consequently, latent heating will generate positive geopotential height anomalies on its northwest side in the upper troposphere and ultimately result in an intensified and later withdrawal of the SAH. In addition, warm surface air temperature over the Tibetan Plateau in September also plays a significant role in lengthening the maintenance of the SAH over the Tibetan Plateau. However, the E_SAH tends be weak and lasts for a shorter period over the Tibetan Plateau. The E_SAH tends to occur together with a developing El Niño event, with cold SST anomalies existing over the northern Indian Ocean and tropical northwestern Pacific. Negative SST anomalies, together with a weak and northeastward-shifting subtropical western Pacific high, result in a weak SAH and quicken its withdrawal process from the Tibetan Plateau via cooling the local tropospheric column and reducing rainfall over East Asia, respectively.

Air quality simulation with WRF-Chem over southeastern Brazil, part I: Model description and evaluation using ground-based and satellite data

A comprehensive Weather Research and Forecasting with Chemistry (WRF-Chem) model evaluation is conducted using ground-based and total column observational data from air quality stations and satellite retrievals. Fine particles (PM2.5; ≤ 2.5 μm in aerodynamic diameter), nitrogen oxides (NOx, NO + NO2), carbon monoxide (CO), tropospheric ozone (O3) concentrations and AOD values over southeastern Brazil were analyzed to assess the model's capability in reproducing atmospheric observation. The model simulations were performed over simple one domain at grid resolution of 10 km over southeastern Brazil. This spatial resolution was chosen due to a previous evaluation between five MODIS AOD products with AERONET estimates, resulting in Dark Target at 10 km of spatial resolution the best product to represent the AOD values over our study domain. Model input emissions comprise vehicular emissions derived from a bottom-up emission model, as well as on-line calculations of biogenic and fire emission rates. Given that the atmospheric state affects air pollution dispersion, a model evaluation on the meteorological conditions was carried out to better evaluate the model performance in reproducing the pollutant concentrations. Good agreement between model simulations and observations for air temperature and relative humidity at 2 m height was found, with correlation coefficients higher than 0.85 in most periods. Expected benchmarks for wind speed and direction at 10 m height were also found in this analysis, though with larger uncertainties. Underestimation occurred for daily accumulated precipitation due to the limitations of the cloud microphysics scheme or cumulus parameterization. Model simulations of PM2.5, NOx, CO and O3 agreed well with ground-based observations in terms of temporal variations and trends, with model-observation discrepancies due to uncertainties in the emission inventories. O3 was the better simulated pollutant in terms of temporal variability, with the characteristic large and small amplitudes observed over urban and rural areas being well represented by the model. High O3 concentrations were observed at the Botucatu station, due to transport of pollutants generated in the Metropolitan Area of São Paulo, and were also represented by the model, indicating the need of more active air quality monitoring stations over inland regions in southeastern Brazil. Moderate and high correlation coefficients (ranging 0.46–0.81) were found for tropospheric NO2 VCD and CO column, and AOD at 550 nm due to uncertainties in the emission inventories and aerosol model simplifications. Both the model and satellite captured higher values in similar regions over our study domain. This work represents a first effort, in southeastern Brazil, that combines numerical modeling, remote sensing and ground-based stations to analyze and understand the impact exerted by the emissions of urban pollution over surrounding areas. A more in-depth analysis of the impact of emissions transport to inland regions from urban areas in southeastern Brazil will be discussed in the second part of this work.

Retrieval of Tropospheric NO2 Vertical Column Densities from Ground-Based MAX-DOAS Measurements in Lhasa, a City on the Tibetan Plateau

In order to investigate the abundance of and temporal variation in nitrogen dioxide (NO2) in the troposphere and validate the corresponding satellite products during a normal year and the lockdown period of coronavirus disease 2019 (COVID-19) in Lhasa, a city on the Tibetan Plateau (TP), ground-based remote-sensing measurements captured by applying multi-axis differential optical absorption spectroscopy (MAX-DOAS) were recorded from August 2021 to March 2023 at the Lhasa site (91.14°E, 29.66°N; 3552.5 m altitude). The NO2 differential slant column densities (dSCDs) were retrieved from the spectra of scattered solar light at different elevation angles. Then, the tropospheric NO2 vertical column densities (VCDs) were calculated with the geometric approximation method. Based on the retrieved tropospheric NO2 VCDs, we found that the pattern of monthly variation in tropospheric NO2 VCDs in Lhasa presented two peaks, one in winter and one around May. According to the monthly means of tropospheric NO2 VCDs during the COVID-19 lockdown, the NO2 background level in Lhasa was estimated to be 0.53 × 1015 molecules·cm−2. For diurnal variations in tropospheric NO2 VCDs, the morning and evening peaks disappeared during the COVID-19 lockdown period. The east–west direction (i.e., along the river valley) was the main path of NO2 transport and dispersion in Lhasa, but the tropospheric NO2 VCDs were little dependent on the wind direction or wind speed during the COVID-19 lockdown. The correlation coefficient of tropospheric NO2 VCDs was R = 0.33 (R = 0.43), with the averaged relative deviation of −28% (99%) for the TROPOMI (GEMS) relative to ground-based MAX-DOAS. The monthly deviations of tropospheric NO2 VCDs between ground-based MAX-DOAS and the satellite showed a dependence on NO2 abundance, with the maxima of the monthly positive deviations during the COVID-19 lockdown period. The GEMS could not capture the strong and systematic diurnal variation in tropospheric NO2 VCDs in the “normal” year well. During the COVID-19 lockdown, the GEMS (>2 × 1015 molecules·cm−2) overestimated the hourly levels measured by ground-based MAX-DOAS (<1.6 × 1015 molecules·cm−2). As a whole, these results are beneficial to understanding the influences of anthropogenic activities on NO2 background levels in Lhasa and to learning the accuracy of satellite products over the TP, with its high altitude and complex terrain.

A regional modelling study of halogen chemistry within a volcanic plume of Mt Etna's Christmas 2018 eruption

Abstract. Volcanoes are known to be important emitters of atmospheric gases and aerosols, which for certain volcanoes can include halogen gases and in particular HBr. HBr emitted in this way can undergo rapid atmospheric oxidation chemistry (known as the bromine explosion) within the volcanic emission plume, leading to the production of bromine oxide (BrO) and ozone depletion. In this work, we present the results of a modelling study of a volcanic eruption from Mt Etna that occurred around Christmas 2018 and lasted 6 d. The aims of this study are to demonstrate and evaluate the ability of the regional 3D chemistry transport model Modèle de Chimie Atmosphérique de Grande Echelle (MOCAGE) to simulate the volcanic halogen chemistry in this case study, to analyse the variability of the chemical processes during the plume transport, and to quantify its impact on the composition of the troposphere at a regional scale over the Mediterranean basin. The comparison of the tropospheric SO2 and BrO columns from 25 to 30 December 2018 from the MOCAGE simulation with the columns derived from the TROPOspheric Monitoring Instrument (TROPOMI) satellite measurements shows a very good agreement for the transport of the plume and a good consistency for the concentrations if considering the uncertainties in the flux estimates and the TROPOMI columns. The analysis of the bromine species' partitioning and of the associated chemical reaction rates provides a detailed picture of the simulated bromine chemistry throughout the diurnal cycle and at different stages of the volcanic plume's evolution. The partitioning of the bromine species is modulated by the time evolution of the emissions during the 6 d of the eruption; by the meteorological conditions; and by the distance of the plume from the vent, which is equivalent to the time since the emission. As the plume travels further from the vent, the halogen source gas HBr becomes depleted, BrO production in the plume becomes less efficient, and ozone depletion (proceeding via the Br+O3 reaction followed by the BrO self-reaction) decreases. The depletion of HBr relative to the other prevalent hydracid HCl leads to a shift in the relative concentrations of the Br− and Cl− ions, which in turn leads to reduced production of Br2 relative to BrCl. The MOCAGE simulations show a regional impact of the volcanic eruption on the oxidants OH and O3 with a reduced burden of both gases that is caused by the chemistry in the volcanic plume. This reduction in atmospheric oxidation capacity results in a reduced CH4 burden. Finally, sensitivity tests on the composition of the emissions carried out in this work show that the production of BrO is higher when the volcanic emissions of sulfate aerosols are increased but occurs very slowly when no sulfate and Br radicals are assumed to be in the emissions. Both sensitivity tests highlight a significant impact on the oxidants in the troposphere at the regional scale of these assumptions. All the results of this modelling study, in particular the rapid formation of BrO, which leads to a significant loss of tropospheric ozone, are consistent with previous studies carried out on the modelling of volcanic halogens.

Satellites capture socioeconomic disruptions during the 2022 full-scale war in Ukraine

Since February 2022, the full-scale war in Ukraine has been strongly affecting society and economy in Ukraine and beyond. Satellite observations are crucial tools to objectively monitor and assess the impacts of the war. We combine satellite-based tropospheric nitrogen dioxide (NO2) and carbon dioxide (CO2) observations to detect and characterize changes in human activities, as both are linked to fossil fuel combustion processes. We show significantly reduced NO2 levels over the major Ukrainian cities, power plants and industrial areas: the NO2 concentrations in the second quarter of 2022 were 15–46% lower than the same quarter during the reference period 2018–2021, which is well below the typical year-to-year variability (5–15%). In the Ukrainian capital Kyiv, the NO2 tropospheric column monthly average in April 2022 was almost 60% smaller than 2019 and 2021, and about 40% smaller than 2020 (the period mostly affected by the COVID-19 restrictions). Such a decrease is consistent with the essential reduction in population and corresponding emissions from the transport and commercial/residential sectors over the major Ukrainian cities. The NO2 reductions observed in the industrial regions of eastern Ukraine reflect the decline in the Ukrainian industrial production during the war (40–50% lower than in 2021), especially from the metallurgic and chemical industry, which also led to a decrease in power demand and corresponding electricity production by thermal power plants (which was 35% lower in 2022 compared to 2021). Satellite observations of land properties and thermal anomalies indicate an anomalous distribution of fire detections along the front line, which are attributable to shelling or other intentional fires, rather than the typical homogeneously distributed fires related to crop harvesting. The results provide timely insights into the impacts of the ongoing war on the Ukrainian society and illustrate how the synergic use of satellite observations from multiple platforms can be useful in monitoring significant societal changes. Satellite-based observations can mitigate the lack of monitoring capability during war and conflicts and enable the fast assessment of sudden changes in air pollutants and other relevant parameters.

Estimating daily air temperature and pollution in Catalonia: A comprehensive spatiotemporal modelling of multiple exposures

Environmental epidemiology studies require models of multiple exposures to adjust for co-exposure and explore interactions. We estimated spatiotemporal exposure to surface air temperature and pollution (PM2.5, PM10, NO2, O3) at high spatiotemporal resolution (daily, 250 m) for 2018–2020 in Catalonia. Innovations include the use of TROPOMI products, a data split for remote sensing gap-filling evaluation, estimation of prediction uncertainty, and use of explainable machine learning.We compiled meteorological and air quality station measurements, climate and atmospheric composition reanalyses, remote sensing products, and other spatiotemporal data. We performed gap-filling of remotely-sensed products using Random Forest (RF) models and validated them using Out-Of-Bag (OOB) samples and a structured data split. The exposure modelling workflow consisted of: 1) PM2.5 station imputation with PM10 data; 2) quantile RF (QRF) model fitting; and 3) geostatistical residual spatial interpolation. Prediction uncertainty was estimated using QRF. SHAP values were used to examine variable importance and the fitted relationships. Model performance was assessed via nested CV at the station level.Evaluation of the gap-filling models using the structured split showed error underestimation when using OOB. Temperature models had the best performance (R2 =0.98) followed by the gaseous air pollutants (R2 =0.81 for NO2 and 0.86 for O3), while the performance of the PM2.5 and PM10 models was lower (R2 =0.57 and 0.63 respectively). Predicted exposure patterns captured urban heat island effects, dust advection events, and NO2 hotspots. SHAP values estimated a high importance of TROPOMI tropospheric NO2 columns in PM and NO2 models, and confirmed that the fitted associations conformed to prior knowledge.Our work highlights the importance of correctly validating gap-filling models and the potential of TROPOMI measurements. Moderate performance in PM models can be partly explained by the poor station coverage. Our exposure estimates can be used in epidemiological studies potentially accounting for exposure uncertainty.

Spatial and Temporal Variations in Ozone Pollution and Sensitivity Characteristics in Hainan Island

Based on ambient air quality data, meteorological observation data, and satellite remote sensing data, the temporal and spatial variations in ozone (O3) pollution, the sensitivity of O3, and its relationship with meteorological factors in Hainan Island were analyzed in this study. The results showed that the maximum daily 8-h moving mean (O3-8h) in western and northern cities in Hainan Island was higher than that in the central, eastern, and southern cities. O3-8h was the highest in 2015, and O3-8h exceeding the standard proportion was the largest in 2019. In addition, O3-8h was positively correlated with average temperature (P<0.1), sunshine duration (P<0.01), total solar radiation (P<0.01), atmospheric pressure, and average wind speed and was negatively correlated with precipitation (P<0.05) and relative humidity. The satellite remote sensing data showed that the tropospheric NO2 column concentration (NO2-OMI) and HCHO column concentration (HCHO-OMI) displayed opposite trends in Hainan Island from 2015 to 2020. Compared with those in 2015, NO2-OMI increased by 7.74% and HCHO-OMI decreased by 10.2% in 2020. Moreover, Hainan Island belongs to the NOx control area, and the FNR value exhibited a fluctuating downward trend in the past 6 years, with a trend coefficient and climatic trend rate of -0.514 and -0.123 a-1, respectively. A strong correlation was observed between meteorological factors and the FNR value of Hainan Island.

Rapid O3 assimilations – Part 2: Tropospheric O3 changes accompanied by declining NOx emissions in the USA and Europe in 2005–2020

Abstract. Tropospheric nitrogen dioxide (NO2) concentrations have declined dramatically over the United States (USA) and Europe in recent decades. Here we investigate the changes in surface and free-tropospheric O3 accompanied by NO2 changes over the USA and Europe in 2005–2020 by assimilating the Ozone Monitoring Instrument (OMI) and U.S. Air Quality System (AQS) and European AirBase network O3 observations. The assimilated O3 concentrations demonstrate good agreement with O3 observations. Surface O3 concentrations are 41.4, 39.5, and 39.5 ppb (parts per billion; USA) and 35.3, 32.0, and 31.6 ppb (Europe) and tropospheric O3 columns are 35.5, 37.0, and 36.8 DU (USA) and 32.8, 35.3, and 36.4 DU (Europe) in the simulations, assimilations, and observations, respectively. We find overestimated summertime surface O3 concentrations over the USA and Europe, which resulted in a surface O3 maximum in July–August in the simulations, which is in contrast to April in the observations. Furthermore, our analysis exhibits limited changes in surface O3 concentrations; i.e., they decreased by −6 % over the USA and increased by 1.5 % over Europe in 2005–2020. The surface observation-based assimilations suggest insignificant changes in tropospheric O3 columns, namely −3.0 % (USA) and 1.5 % (Europe) in 2005–2020. While the OMI-based assimilations exhibit larger decreases in tropospheric O3 columns, with −12.0 % (USA) and −15.0 % (Europe) in 2005–2020, the decreases mainly occurred in 2010–2014, corresponding to the reported slower decline in free-tropospheric NO2 since 2010. Our analysis thus suggests that there are limited impacts of the decline in local emissions on tropospheric O3 over the USA and Europe and advises more efforts to evaluate the possible contributions of natural sources and transport. The discrepancy in assimilated tropospheric O3 columns further indicates the possible uncertainties in the derived tropospheric O3 changes.

Long-term spatiotemporal variations in surface NO2 for Beijing reconstructed from surface data and satellite retrievals

Remote sensing data from the Ozone Monitoring Instrument (OMI) and the TROPOspheric Monitoring Instrument (TROPOMI) play important roles in estimating surface nitrogen dioxide (NO2), but few studies have compared their differences for application in surface NO2 reconstruction. This study aims to explore the effectiveness of incorporating the tropospheric NO2 vertical column density (VCD) from OMI and TROPOMI (hereafter referred to as OMI and TROPOMI, respectively, for conciseness) for deriving surface NO2 and to apply the resulting data to revisit the spatiotemporal variations in surface NO2 for Beijing over the 2005–2020 period during which there were significant reductions in nitrogen oxide emissions. In the OMI versus TROPOMI performance comparison, the cross-validation R2 values were 0.73 and 0.72, respectively, at 1 km resolution and 0.69 for both at 100 m resolution. The comparisons between satellite data sources indicate that even though TROPOMI has a finer resolution it does not improve upon OMI for deriving surface NO2 at 1 km resolution, especially for analyzing long-term trends. In light of the comparison results, we used a hybrid approach based on machine learning to derive the spatiotemporal distribution of surface NO2 during 2005–2020 based on OMI. We had novel, independent passive sampling data collected weekly from July to September of 2008 for hindcasting validation and found a spatiotemporal R2 of 0.46 (RMSE = 7.0 ppb). Regarding the long-term trend of surface NO2, the level in 2008 was obviously lower than that in 2007 and 2009, as expected, which was attributed to pollution restrictions during the Olympic Games. The NO2 level started to steadily decline from 2015 and fell below 2008's level after 2017. Based on OMI, a long-term and fine-resolution surface NO2 dataset was developed for Beijing to support future environmental management questions and epidemiological research.

Assessing the impact of urban form and urbanization process on tropospheric nitrogen dioxide pollution in the Yangtze River Delta, China

Optimizing urban form through urban planning and management policies can improve air quality and transition to demand-side control. Nitrogen dioxide (NO2) in the urban atmosphere, mainly emitted by anthropogenic sources such as industry and vehicles, is a key precursor of fine particles and ozone pollution. Both NO2 and its secondary pollutants pose health risks for humans. Here we assess the interactions between urban forms and airborne NO2 pollution in different cities with various stages of urbanization in the Yangtze River Delta (YRD) in China, by using the machine learning and geographical regression model. The results reveal a strong correlation between urban fragmentation and tropospheric NO2 vertical column density (TVCD) in YRD cities in 2020, particularly those with lower or higher levels of urbanization. The correlation coefficients (R2) between NO2 TVCD and the largest patch index (a metric of urban fragmentation) in different cities are greater than 0.8. For cities at other urbanization stages, population and road density are strongly correlated with NO2 TVCD, with an R2 larger than 0.61. This study highlights the interdependence among urbanization, urban forms, and air pollution, emphasizing the importance of customized urban landscape management strategies for mitigating urban air pollution.

Photochemical model assessment of single source NO2 and O3 plumes using field study data

Single source contribution to ambient O3 and PM2.5 has been estimated with photochemical grid models to support policy demonstrations for National Ambient Air Quality Standards, regional haze, and permit related programs. Limited field data exists to evaluate model representation of the spatial extent and chemical composition of plumes emitted by specific facilities. New tropospheric column measurements of NO2 and in-plume chemical measurements downwind of specific facilities allows for photochemical model evaluation of downwind plume extent, grid resolution impacts on plume concentration gradients, and source attribution methods. Here, photochemical models were applied with source sensitivity and source apportionment approaches to differentiate single source impacts on NO2 and O3 and compare with field study measurements. Source sensitivity approaches (e.g., brute-force difference method and decoupled direct method (DDM)) captured the spatial extent of NO2 plumes downwind of three facilities and the transition of near-source O3 titration to downwind production. Source apportionment approaches showed variability in terms of attributing the spatial extent of NO2 plumes and downwind O3 production. Each of the Community Multiscale Air Quality (CMAQ) source apportionment options predicted large O3 contribution from a large industrial facility in the flight transects nearest the facility when measurements and source sensitivity approaches suggest titration was outpacing production. In general, CMAQ DDM tends to attribute more O3 to boundary inflow and less to within-domain NOX and VOC sources compared to CMAQ source apportionment. The photochemical modeling system was able to capture single source plumes using 1 to 12 km grid resolution with best representation of plume extent and magnitude at the finer resolutions. When modeled at 1 to 12 km grid resolution, primary and secondary PM2.5 impacts were highest at the source location and decrease as distance increases downwind. The use of coarser grid resolution for single source attribution resulted in predicted impacts highest near the source but lower peak source specific concentrations compared to finer grid resolution simulations because impacts were spread out over a larger area.

Changes in Ozone Pollution Trend Characteristics and Sensitivity in Jinan from 2015 to 2020

The surface ozone (O3) spatiotemporal distribution, variations, and its causes in Ji'nan from 2015 to 2020 were revealed based on the air quality monitoring network data and satellite retrievals from the Ozone Monitoring Instrument (OMI). The results showed that the ozone concentration in Ji'nan gradually increased from 2015 to 2020. The annual 90th percentile of the daily maximum 8-h average (MDA8) O3(namely the annual evaluation value) and the MDA8 O3(April-September) increased by 4.8 μg·(m3·a)-1 and 3.8 μg·(m3·a)-1, respectively. The trend of the ozone levels in the high-concentration range increased faster than that in the low-concentration range. The MDA8 in June increased by 7.4 μg·(m3·a)-1, and the rate range of increases was 2.6-3.9 μg·(m3·a)-1 in the cool seasons (December-February); thus, the O3 control in winter cannot be ignored. It is apparent from the diurnal variations in ozone from 2015 to 2020 in April-September that the average ozone levels have risen in recent years. The growth rate in the daytime was higher than that at night. The capacity of photochemical production has been increasing, especially in recent years. Additionally, it is noteworthy that the peak time for ozone levels occurred approximately 1-2 h earlier. The disparity of ozone concentrations among different stations gradually decreased in recent years. Compared with that in 2015, the range of areas with high O3 concentrations in 2019-2020 was further expanded. The significant positive trends in MDA8-90th and MDA8 (April-September) were observed in 16.1% and 22.6% of the monitoring sites in Ji'nan (P<0.05), most of which were located in urban areas and the suburbs close to urban areas. The temporal and spatial changes in ozone in Jinan had been affected by the changes in VOCs and NOx emissions since 2015. Satellite remote sensing data from 2015 to 2020 revealed that the NO2 tropospheric columns (April-September) showed reductions of 20.6%, with a decreasing rate of 0.3×1015 mole·(cm2·a)-1, especially in the urban areas and suburbs. The detected variation trends of tropospheric HCHO were weak and insignificant, which suggested that the decrease in NOx emissions was much greater than the decrease in VOCs emissions, and the gap had become more obvious in the urban areas. With responses to precursor emissions, the chemical sensitivity of O3 formation had been changing. The VOCs-limited regimes continuously decreased, and the mixed NOx/VOCs-sensitive regimes and NOx-limited regimes increased. In general, such an extremely inappropriate control ratio of ozone precursor NOx/VOCs led to an overall trend of slow increasing fluctuations of O3 in Ji'nan. The findings clearly indicate that the reduction of VOCs in Ji'nan was far from sufficient, and strengthening the current control of VOCs emissions is an effective measure to control the growth trend of O3 pollution in Ji'nan in the near future, especially in urban and surrounding suburban areas.

High Spatial Resolution Ozone Profiles Retrieved from the First Chinese Ultraviolet–Visible Hyperspectral Satellite Instrument

Understanding the vertical distribution of ozone is crucial when assessing both its horizontal and vertical transport, as well as when analyzing the physical and chemical properties of the atmosphere. One of the most effective ways to obtain high spatial resolution ozone profiles is through satellite observations. The Environmental Trace Gases Monitoring Instrument (EMI) deployed on the Gaofen-5 satellite is the first Chinese ultraviolet–visible hyperspectral spectrometer. However, retrieving ozone profiles using backscattered radiance values measured by the EMI is challenging due to unavailable measurement errors and a low signal-to-noise ratio. The algorithm developed for the Tropospheric Monitoring Instrument did not allow us to retrieve 87% of the EMI pixels. Therefore, we developed an algorithm specific to the characteristics of the EMI. The fitting residuals are smaller than 0.3% in most regions. The retrieved ozone profiles were in good agreement with ozonesonde data, with maximum mean biases of 20% at five latitude bands. By applying EMI averaging kernels to the ozonesonde profiles, the integrated stratospheric column ozone and tropospheric column ozone also showed excellent agreement with ozonesonde data. The lower layers (0–7.5 km) of the EMI ozone profiles reflected the seasonal variation in surface ozone derived from the China National Environmental Monitoring Center (CNEMC). However, the upper layers (9.7–16.7 km) of the ozone profiles show different trends, with the ozone peak occurring at an altitude of 9.7–16.7 km in March, 2019. A stratospheric intrusion event in central China from August 11 to 15, 2019, is captured using the EMI ozone profiles, potential vorticity data, and relative humidity data. The increase in the CNEMC ozone concentration showed that downward transport enhanced surface ozone pollution.