Reply on RC1

We present a comprehensive study integrating satellite observations of ozone pollution, in situ measurements and chemistry transport model simulations for quantifying the role of anthropogenic emission reductions during the COVID-19 lockdown in spring 2020 over Europe. Satellite observations are derived from the IASI+GOME2 multispectral synergism, which provides particularly enhanced sensitivity to near-surface ozone pollution. These observations are first analysed in terms of differences between the average on 1–15 April 2020, when the strictest lockdown restrictions took place, and the same period in 2019. They show clear enhancements of near-surface ozone in Central Europe and Northern Italy, and some other hotspots, which are typically characterized by VOC-limited chemical regimes. An overall reduction of ozone is observed elsewhere, where ozone chemistry is limited by the abundance of NOx. The spatial distribution of positive and negative ozone concentration anomalies observed from space is in relatively good quantitative agreement with surface in situ measurements over the continent (a correlation coefficient of 0.55, a root-mean-squared difference of 11 ppb and the same standard deviation and range of variability). An average bias of ∼8 ppb between the two observational datasets is remarked, which can partly be explained by the fact the satellite approach retrieves partial columns of ozone with a peak sensitivity above the surface (near 2 km of altitude). For assessing the impact of the reduction of anthropogenic emissions during the lockdown, we adjust the satellite and in situ surface observations for withdrawing the influence of meteorological conditions in 2020 and 2019. This adjustment is derived from the chemistry transport model simulations using the meteorological fields of each year and identical emission inventories. This observational estimate of the influence of lockdown emission reduction is consistent for both datasets. They both show lockdown-associated ozone enhancements in hotspots over Central Europe and Northern Italy, with a reduced amplitude with respect to the total changes observed between the two years, and an overall reduction elsewhere over Europe and the ocean. Satellite observations additionally highlight the ozone anomalies in the regions remote from in situ sensors, an enhancement over the Mediterranean likely associated with maritime traffic emissions and a marked large-scale reduction of ozone elsewhere over ocean (particularly over the North Sea), in consistency with previous assessments done with ozonesondes measurements in the free troposphere. These observational assessments are compared with model-only estimations, using the CHIMERE chemistry transport model. For analysing the uncertainty of the model estimates, we perform two sets of simulations with different setups, differing in the emission inventories, their modifications to account for changes in anthropogenic activities during the lockdown and the meteorological fields. Whereas a general qualitative consistency of positive and negative ozone anomalies is remarked between all model and observational estimates, significant changes are seen in their amplitudes. Models underestimate the range of variability of the ozone changes by at least a factor 2 with respect to the two observational data sets, both for enhancements and decreases of ozone, while the large-scale ozone decrease is not simulated. With one of the setups, the model simulates ozone enhancements a factor 3 to 6 smaller than with the other configuration. This is partly linked to the emission inventories of ozone precursors (at least a 30 % difference), but mainly to differences in vertical mixing of atmospheric constituents depending on the choice of the meteorological model.

We present a comprehensive study integrating satellite observations of ozone pollution, in situ measurements and chemistry transport model simulations for quantifying the role of anthropogenic emission reductions during the COVID-19 lockdown in spring 2020 over Europe. Satellite observations are derived from the IASI+GOME2 multispectral synergism, which provides particularly enhanced sensitivity to near-surface ozone pollution. These observations are first analysed in terms of differences between the average on 1–15 April 2020, when the strictest lockdown restrictions took place, and the same period in 2019. They show clear enhancements of near-surface ozone in Central Europe and Northern Italy, and some other hotspots, which are typically characterized by VOC-limited chemical regimes. An overall reduction of ozone is observed elsewhere, where ozone chemistry is limited by the abundance of NOx. The spatial distribution of positive and negative ozone concentration anomalies observed from space is in relatively good quantitative agreement with surface in situ measurements over the continent (a correlation coefficient of 0.55, a root-mean-squared difference of 11 ppb and the same standard deviation and range of variability). An average bias of ∼8 ppb between the two observational datasets is remarked, which can partly be explained by the fact the satellite approach retrieves partial columns of ozone with a peak sensitivity above the surface (near 2 km of altitude). For assessing the impact of the reduction of anthropogenic emissions during the lockdown, we adjust the satellite and in situ surface observations for withdrawing the influence of meteorological conditions in 2020 and 2019. This adjustment is derived from the chemistry transport model simulations using the meteorological fields of each year and identical emission inventories. This observational estimate of the influence of lockdown emission reduction is consistent for both datasets. They both show lockdown-associated ozone enhancements in hotspots over Central Europe and Northern Italy, with a reduced amplitude with respect to the total changes observed between the two years, and an overall reduction elsewhere over Europe and the ocean. Satellite observations additionally highlight the ozone anomalies in the regions remote from in situ sensors, an enhancement over the Mediterranean likely associated with maritime traffic emissions and a marked large-scale reduction of ozone elsewhere over ocean (particularly over the North Sea), in consistency with previous assessments done with ozonesondes measurements in the free troposphere. These observational assessments are compared with model-only estimations, using the CHIMERE chemistry transport model. For analysing the uncertainty of the model estimates, we perform two sets of simulations with different setups, differing in the emission inventories, their modifications to account for changes in anthropogenic activities during the lockdown and the meteorological fields. Whereas a general qualitative consistency of positive and negative ozone anomalies is remarked between all model and observational estimates, significant changes are seen in their amplitudes. Models underestimate the range of variability of the ozone changes by at least a factor 2 with respect to the two observational data sets, both for enhancements and decreases of ozone, while the large-scale ozone decrease is not simulated. With one of the setups, the model simulates ozone enhancements a factor 3 to 6 smaller than with the other configuration. This is partly linked to the emission inventories of ozone precursors (at least a 30 % difference), but mainly to differences in vertical mixing of atmospheric constituents depending on the choice of the meteorological model.

The influence of the Arctic Oscillation (AO) on the vertical distribution of stratospheric ozone in the Northern Hemisphere in winter is analyzed using observations and an offline chemical transport model. Positive ozone anomalies are found at low latitudes (0°–30°N) and there are three negative anomaly centers in the northern mid- and high latitudes during positive AO phases. The negative anomalies are located in the Arctic middle stratosphere (~30 hPa; 70°–90°N), Arctic upper troposphere–lower stratosphere (UTLS; 150–300 hPa, 70°–90°N), and midlatitude UTLS (70–300 hPa, 30°–60°N). Further analysis shows that anomalous dynamical transport related to AO variability primarily controls these ozone changes. During positive AO events, positive ozone anomalies between 0° and 30°N at 50–150 hPa are related to the weakened meridional transport of the Brewer–Dobson circulation (BDC) and enhanced eddy transport. The negative ozone anomalies in the Arctic middle stratosphere are also caused by the weakened BDC, while the negative ozone anomalies in the Arctic UTLS are caused by the increased tropopause height, weakened BDC vertical transport, weaker exchange between the midlatitudes and the Arctic, and enhanced ozone depletion via heterogeneous chemistry. The negative ozone anomalies in the midlatitude UTLS are mainly due to enhanced eddy transport from the midlatitudes to the latitudes equatorward of 30°N, while the transport of ozone-poor air from the Arctic to the midlatitudes makes a minor contribution. Interpreting AO-related variability of stratospheric ozone, especially in the UTLS, would be helpful for the prediction of tropospheric ozone variability caused by the AO.

Anthropogenic carbon dioxide (CO2) emissions from bottom-up inventories have high uncertainties due to the usage of proxy data in creating these inventories. To evaluate bottom-up inventories, satellite observations of atmospheric CO2 with continuously improved accuracies have shown great potential. In this study, we evaluate the consistency and uncertainty of four gridded CO2 emission inventories, including CHRED, PKU, ODIAC, and EDGAR that have been commonly used to study emissions in China, using GOSAT and OCO-2 satellite observations of atmospheric column-averaged dry-air mole fraction of CO2 (XCO2). The evaluation is carried out using two data-driven approaches: (1) quantifying the correlations of the four inventories with XCO2 anomalies derived from the satellite observations; (2) comparing emission inventories with emissions predicted by a machine learning-based model that considers the nonlinearity between emissions and XCO2. The model is trained using long-term datasets of XCO2 and emission inventories from 2010 to 2019. The result shows that the inconsistencies among these four emission inventories are significant, especially in areas of high emissions associated with large XCO2 values. In particular, EDGAR shows a larger difference to CHRED over super-emitting sources in China. The differences for ODIAC and EDGAR, when compared with the machine learning-based model, are higher in Asia than those in the USA and Europe. The predicted emissions in China are generally lower than the inventories, especially in megacities. The biases depend on the magnitude of inventory emissions with strong positive correlations with emissions (R2 is larger than 0.8). On the contrary, the predicted emissions in the USA are slightly higher than the inventories and the biases tend to be random (R2 is from 0.01 to 0.5). These results indicate that the uncertainties of gridded emission inventories of ODIAC and EDGAR are higher in Asian countries than those in European and the USA. This study demonstrates that the top-down approach using satellite observations could be applied to quantify the uncertainty of emission inventories and therefore improve the accuracy in spatially and temporally attributing national/regional totals inventories.

Exposure to air pollution, especially aerosols, can lead to adverse health effects. Particle toxicity has been generally linked to its mass concentration. However, toxicity is determined by the chemical composition of the aerosol, which varies greatly depending on pollution sources. Reactive oxygen species (ROS) can be formed in the lung when particles are inhaled and can trigger inflammatory processes. Transition metals strongly contribute to ROS, among which copper, iron and manganese. However, these metals are generally not included in current emission inventories and chemistry-transport models. Thus, to ultimately model aerosol toxicity, it would be necessary to add these three metals into a chemistry-transport model, and use it to provide inputs to a lung chemistry model (Lelieveld et al, 2021).Country-wise copper emissions are available from the EMEP database (https://www.ceip.at/) for the majority of European Union countries. Methods for evaluating emissions differ from country to country, and it is necessary to homogenize them to standardize emissions, at least for the main emitting countries around France which is the main target of the study. For iron and manganese, European scale inventories do not exist yet. Data from the French Citepa and ADEME, from EMEP reports, SPECIEUROPE and a bibliography of 16 references were used to build these two bottom-up inventories. Many sources have been studied: abrasion of tires, brakes and road for the road transport sector, wear of catenaries, brakes and rails for the rail sector, combustion of coal, biomass and of petroleum, the burning of motor oils, and the incineration of waste.Also, long term measurements from several dozens of rural, urban and traffic sites were collected to build a large database. These data come from EBAS website (https://ebas-data.nilu.no/), UKAIR website (https://uk-air.defra.gov.uk/data/), CARA program (Favez et al., 2021), the Spanish IDAEA and the French Atmo Auvergne-Rhône-Alpes and Marseille-Longchamp. Fe/Cu and Mn/Cu ratios were calculated for both the bottom-up emission inventories and measurements data. As these ratios turned out to be significantly lower in emission inventories, observed ratios were used to adjust Fe and Mn emissions from Cu ones.Using these two inventories, emission ratios with coarse particles for each country and sector were created and applied to the spatialized particles emission data. We then implemented the obtained spatialized Cu, Fe and Mg emission inventories into the CHIMERE chemistry-transport model, to simulate ambient copper, iron and manganese mass concentrations in Europe for the year 2014.

Hydrofluorocarbons (HFCs) have been increasingly replacing chlorofluorocarbons and hydrochlorofluorocarbons. Although their direct chemical ozone‐depleting potential is negligible, as potent greenhouse gases they modify atmospheric temperature and circulation patterns, thereby indirectly influencing stratospheric ozone recovery. Measurements and model projections must continue to evaluate HFC limitation measures and assess the long‐term impact of HFCs on the atmospheric radiation budget and stratospheric ozone. In this study, we present multi‐member ensemble simulations designed to estimate the impact of HFCs on stratospheric temperature, ozone and circulation changes at the end of the century. We compared simulations with and without HFCs for two three‐dimensional chemistry‐climate models that use the same chemistry module but different physical schemes. At low and mid‐latitudes, temperature and ozone responses were comparable for both models and in general agreement with previous studies. HFCs induced a marked temperature increase up to about 10–20 hPa and vertically alternating positive and negative ozone anomalies. We explained this pattern by competing effects of vertical motion (low and middle stratosphere) and temperature (upper stratosphere) anomalies. At northern high latitudes, there were strong discrepancies with previous studies and between the models themselves, attributed to differences in ozone anomalies caused by wave activity during winter. Quantitatively, we found a net positive, but small, HFC impact on total ozone amounts. Largest anomalies were less than 1% in the winter polar stratosphere. Our results indicate that increasing HFC amounts will likely have a limited impact on stratospheric ozone recovery within this century, with large uncertainty in the polar regions.

We evaluate the capability of 21 models from the new state-of-the-art Coupled Model Intercomparison Project, Phase 6 (CMIP6) in the representation of present-day precipitation characteristics and extremes along with their statistics in simulating daily precipitation during the West African Monsoon (WAM) period (June–September). The study uses a set of standard extreme precipitation indices as defined by the Expert Team on Climate Change Detection and Indices constructed using CMIP6 models and observational datasets for comparison. Three observations; Global Precipitation Climatology Project (GPCP), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), and Tropical Applications of Meteorology using SATellite and ground-based observation (TAMSAT) datasets are used for the validation of the model simulations. The results show that observed datasets present nearly the same spatial pattern but discrepancies in the magnitude of rainfall characteristics. The models show substantial discrepancies in comparison with the observations and among themselves. A number of the models depict the pattern of rainfall intensity as observed but some models overestimate the pattern over the coastal parts (FGOALS-f3-L and GFDL-ESM4) and western part (FGOALS-f3-L) of West Africa. All model simulations explicitly show the pattern of wet days but with large discrepancies in their frequencies. On extreme rainfall, half of the models express more intense extremes in the 95th percentiles while the other half simulate less intense extremes. All the models overestimate the mean maximum wet spell length except FGOALS-f3-L. The spatial patterns of the mean maximum dry spell length show a good general agreement across the different models, and the observations except for four models that show an overestimation in the Sahara subregion. INM-CM4-8 and INM-CM5-0 display smaller discrepancies from their long-term average rainfall characteristics, in terms of extreme rainfall estimates than the other CMIP6 datasets. For the frequency of heavy rainfall, TaiESM1 and IPSL-CMGA-LR perform better when compared with observational datasets. MIROC6 and GFDL-ESM4 displayed the largest error in representing the frequency of heavy rainfall and 95th percentile extremes, and therefore, cannot be reliable. The study has assessed how rainfall extremes are captured in both observation and the models. Though there are some discrepancies, it gives room for improvement of the models in the next version of CMIP.

Ultraviolet radiation and skin cancer of humans

Total ozone data for various regions of the world have been examined on a seasonal and annual basis. The increase in total ozone noted during the 1960's appears to have ended, and at United States and British stations there was a significant 2% reduction in ozone between 1970 and 1974, although the annual race of decrease diminished from 0.7% between 1971 and 1972 to 0.2% between 1973 and 1974. In the United States the 2% reduction returned the total ozone value nearly to its 1963 level, but in Great Britain (and at Huancayo, Peru) the decrease amounted to only one-third (one-fifth) of the increase during the 1960's. The existence of a quasi-biennial oscillation in total ozone, variable in phase, amplitude, and period, makes extremely difficult the detection of any effect on the ozone shield due to the large Russian nuclear explosions in 1961 and 1962. We fail to find an unambiguous effect, and suggest that, on a seasonal or annual basis, these nuclear tests could not have reduced the hemispheric total-ozone amount by more than 1–2%. There is no evidence of a decrease in total ozone over Japan during the passage of radioactive clouds following individual Chinese tests in 1968, 1969, and 1970. The coincidence of the breakdown of the quasi-biennial oscillation in total ozone at Aspendale and Brisbane in the exact season (spring, 1963) of the eruption of Mt. Agung (8°S) is noted. For whatever reason, it appears that the Southern Hemisphere ozone values were anomalously high (or indicated to be anomalously high) following the eruption, but the lengthening of the period of quasi-biennial oscillation during this time may also be influencing, or even dominating, the results. The sunspot-total ozone relation is reexamined using the available data. There is limited evidence of a nearly in-phase relationship; accordingly, the decrease in total ozone between 1970 and 1974 may be associated with the decrease in sunspot number following the sunspot maximum in 1969. There appears to have been some decrease in total ozone in north latitudes following intense solar flares (solar proton events) in 1960, 1966, and 1972, but the relationship is not conclusive.

Air quality impacts of fuel cell electric hydrogen vehicles with high levels of renewable power generation

Abstract. NH3 retrievals from the NASA Tropospheric Emission Spectrometer (TES), as well as surface and aircraft observations of NH3(g) and submicron NH4(p), are used to evaluate modeled concentrations of NH3(g) and NH4(p) from the Community Multiscale Air Quality (CMAQ) model in the San Joaquin Valley (SJV) during the California Research at the Nexus of Air Quality and Climate Change (CalNex) campaign. We find that simulations of NH3 driven with the California Air Resources Board (CARB) emission inventory are qualitatively and spatially consistent with TES satellite observations, with a correlation coefficient (r2) of 0.64. However, the surface observations at Bakersfield indicate a diurnal cycle in the model bias, with CMAQ overestimating surface NH3 at night and underestimating it during the day. The surface, satellite, and aircraft observations all suggest that daytime NH3 emissions in the CARB inventory are underestimated by at least a factor of 2, while the nighttime overestimate of NH3(g) is likely due to a combination of overestimated NH3 emissions and underestimated deposition.Running CMAQ v5.0.2 with the bi-directional NH3 scheme reduces NH3 concentrations at night and increases them during the day. This reduces the model bias when compared to the surface and satellite observations, but the increased concentrations aloft significantly increase the bias relative to the aircraft observations. We attempt to further reduce model bias by using the surface observations at Bakersfield to derive an empirical diurnal cycle of NH3 emissions in the SJV, in which nighttime and midday emissions differ by about a factor of 4.5. Running CMAQv5.0.2 with a bi-directional NH3 scheme together with this emissions diurnal profile further reduces model bias relative to the surface observations. Comparison of these simulations with the vertical profile retrieved by TES shows little bias except for the lowest retrieved level, but the model bias relative to flight data aloft increases slightly. Our results indicate that both diurnally varying emissions and a bi-directional NH3 scheme should be applied when modeling NH3(g) and NH4(p) in this region. The remaining model errors suggest that the bi-directional NH3 scheme in CMAQ v5.0.2 needs further improvements to shift the peak NH3 land–atmosphere flux to earlier in the day. We recommend that future work include updates to the current CARB NH3 inventory to account for NH3 from fertilizer application, livestock, and other farming practices separately; adding revised information on crop management practices specific to the SJV region to the bi-directional NH3 scheme; and top-down studies focused on determining the diurnally varying biases in the canopy compensation point that determines the net land–atmosphere NH3 fluxes.

Estimating 2013–2019 NO2 exposure with high spatiotemporal resolution in China using an ensemble model

BACKGROUND AND AIM: China faces serious air pollution problems, especially for traffic-related pollutants such as nitrogen dioxide (NO₂). Epidemiological studies in China were much less focused on NO₂ exposure and consequent health effects as compared to fine particles exposure at national scale, mainly due to lack of high-quality exposure model for accurate NO₂ predictions over a long period. We aim to develop a national model in estimating long-term NO₂ exposure in China with high spatiotemporal resolution. METHODS: We proposed an advanced modeling framework that incorporated multisource, high-quality predictors data (e.g., satellite observations [OMI NO₂, TROPOMI NO₂, and MAIAC AOD], chemical transport model simulations, high-resolution geographical variables) and three independent machine learning algorithms into an ensemble model. The model included three-stages: (1) filling missing satellite data; (2) building an ensemble model and predicting daily NO₂ levels from 2005 to 2019 across China at 1-km resolution; (3) downscaling the predictions to 100-meter resolution at city scale. RESULTS:Our model achieved a high performance with an overall cross-validated (CV) R² of 0.72 and a spatial R² of 0.85. The model has the potential to be extrapolated to previous years (2005-2012) or regions without monitoring data, with moderately good performance (CV R² 0.68). We identified a clear decreasing trend of NO₂ exposure from 2005 to 2019 across the country with the largest reduction in suburban and rural areas. Our downscaled model further improved the prediction ability by 4% in some megacities and captured substantial NO₂ variations within 1-km grids in urban areas especially near major roads. CONCLUSIONS:We developed a NO₂ national model with very high spatiotemporal resolution (daily, 1-km grid). Our model provided flexibility at both temporal and spatial scales that can be useful for exposure assessment and epidemiological studies with various study domains (e.g. national or citywide) and settings (e.g. long-term and short-term). KEYWORDS: air pollution, oxides of nitrogen, exposure assessment, long-term exposure, modeling

Abstract. This study investigates anomalous ozone distributions over cloudy areas in Nimbus-7 (N7) and Earth-Probe (EP) TOMS version-7 data and analyzes the causes for ozone anomaly formation. A 5°-longitude by 5°-latitude region is defined to contain a Positive Ozone Anomaly (POA) or Negative Ozone Anomaly (NOA) if the correlation coefficient between total ozone and reflectivity is > 0.5 or < -0.5. The average fractions of ozone anomalies among all cloud fields are 31.8 ± 7.7% and 35.8 ± 7.7% in the N7 and EP TOMS data, respectively. Some ozone anomalies are caused by ozone retrieval errors, and others are caused by actual geophysical phenomena. Large cloud-height errors are found in the TOMS version-7 algorithm in comparison to the Temperature Humidity Infrared Radiometer (THIR) cloud data. On average, cloud-top pressures are overestimated by ~200 hPa (THIR cloud-top pressure < 200 hPa) for high-altitude clouds and underestimated by ~150 hPa for low-altitude clouds (THIR cloud-top pressure > 750 hPa). Most tropical NOAs result from negative errors induced by large cloud-height errors, and most tropical POAs are caused by positive errors due to intra-cloud ozone absorption enhancement. However, positive and negative errors offset each other, reducing the ozone anomaly occurrence in TOMS data. Large ozone/reflectivity slopes for mid-latitude POAs show seasonal variation consistent with total ozone fluctuation, indicating that they result mainly from synoptic and planetary wave disturbances. POAs with an occurrence fraction of 30--60% occur in regions of marine stratocumulus off the west coast of South Africa and off the west coast of South America. Both fractions and ozone/reflectivity slopes of these POAs show seasonal variations consistent with that in the tropospheric ozone. About half the ozone/reflectivity slope can be explained by ozone retrieval errors over clear and cloudy areas. The remaining slope may result from there being more ozone production because of rich ozone precursors and higher photolysis rates over high-frequency, low-altitude clouds than in clear areas. Ozone anomalies due to ozone retrieval errors have important implications in TOMS applications such as tropospheric ozone derivation and analysis of ozone seasonal variation.

Satellite‐borne instruments (the Total Ozone Mapping Spectrometer and the Solar Backscattered Ultraviolet Instrument) show that, compared to 1979, total column ozone has a year‐round decrease of more than 5% in the neighborhood of 60°S. The meteorological conditions (warmer temperatures, the absence of polar stratospheric clouds) at these latitudes do not seem to favor heterogeneous chemistry as the direct cause for the observed year‐round ozone reduction. A mechanism involving the seasonal transport of ozone‐poor air from within the polar vortex to lower latitudes (the so‐called “dilution effect”) is proposed as a possible explanation for the observed year‐round ozone reduction in subpolar regions. A two‐dimensional model with an imposed springtime Antarctic ozone depletion is used to study the post‐ozone hole impact on the spatial and temporal distributions of column ozone at latitudes north of 60°S. It is found that the time constant associated with the dilution effect in the latitude region 40°–60°S is about 1 year, long enough to contribute to the observed year‐round decrease of total ozone in that region. Because of the relatively short ozone replacement time constant (∼1 month) at latitudes north of 30°S, the calculated dilution effect over these latitudes is small. Measurement strategies aimed at distinguishing various possible causes for the observed ozone reduction at middle to high latitudes in the southern hemisphere are proposed.