Search

The northern high latitudes (50–90° N, mostly including boreal forest and tundra ecosystem) has been undergoing rapid climate and ecological changes over recent decades, leading to significant variations in volatile organic compounds (VOCs) emissions from biogenic and biomass burning sources. HCHO, a widely used indicator of VOC emission, exhibits high climate sensitivity. However, the interannual variability of HCHO and its main drivers over the region remain unclear. In this study, we use the GEOS-Chem chemical transport model and satellite retrievals from Ozone Monitoring Instrument (OMI) and Ozone Mapping and Profiler Suite (OMPS) to examine HCHO vertical column density (VCD) interannual variations in summertime of 2005–2019. Our results show that wildfires heavily influence interannual variability of HCHO VCD over Siberia, Alaska, and North Canada, while biogenic emissions and background methane oxidation are the predominant drivers of HCHO interannual variability over East Europe. Solar-induced chlorophyll fluorescence (SIF) from Orbiting Carbon Observatory-2 (OCO-2) provides additional evaluation for HCHO interannual variability from biogenic emission, showing potential of constraining biogenic emission in northern high latitudes. [ABSTRACT FROM AUTHOR]

The northern high latitudes (50-90°N, mostly including boreal forest and tundra ecosystem) has been undergoing rapid climate and ecological changes over recent decades, leading to significant variations in volatile organic compounds (VOCs) emissions from biogenic and biomass burning sources. HCHO, a widely used indicator of VOC emission, exhibits high climate sensitivity. However, the interannual variability of HCHO and its main drivers over the region remain unclear. In this study, we use the GEOS-Chem chemical transport model and satellite retrievals from Ozone Monitoring Instrument (OMI) and Ozone Mapping and Profiler Suite (OMPS) to examine HCHO vertical column density (VCD) interannual variations in summertime of 2005-2019. Our results show that wildfires heavily influence interannual variability of HCHO VCD over Siberia, Alaska, and North Canada, while biogenic emissions and background methane oxidation are the predominant drivers of HCHO interannual variability over East Europe. Solarinduced chlorophyll fluorescence (SIF) from Orbiting Carbon Observatory-2 (OCO-2) provides additional evaluation for HCHO interannual variability from biogenic emission, showing potential of constraining biogenic emission in northern high latitudes. [ABSTRACT FROM AUTHOR]

We report the development of an algorithm for the retrieval of Total Column Water Vapor (TCWV) from blue spectra obtained by satellite instruments such as the Ozone Monitoring Instrument (OMI). The algorithm is implemented in an automatic processing pipeline and will be used to generate a long-term data record as part of a MEaSUREs project. TCWV is calculated as the ratio between the Slant Column Density (SCD) and Air Mass Factor (AMF). Both these factors are improved upon previous work by incorporating more constraints or physical processes. For the SCD, we have optimized the retrieval window to 432 - 466 nm, performed a temperature correction, and employed a new stripe-removal post-processing routine. The use of OMI collection 4 spectra reduces the SCD fitting uncertainty by ~9% with respect to collection 3. For the AMF, we perform on-line radiative transfer using VLIDORT. Over land surfaces, we use bi directional reflectances based on MODIS products. Over the oceans, we consider surface roughness and water-leaving radiance, and we find that water-leaving radiance is important for avoiding large TCWV biases over the oceans. Under relatively clear conditions, the MEaSUREs OMI data are well correlated with the reference datasets, having correlation coefficients of r ~0.9. Over the oceans, MEaSUREs-AMSR_E has an overall mean (median) of ~ 1 mm (0.6 mm) with a standard deviation of a ~6.5 mm, though large systematic differences in certain regions are also found. Over land surfaces, MEaSUREs-GPS has an overall mean (median) of -0.7 mm (-0.8 mm) with a ~5.7 mm. Even a small amount of cloud can introduce large bias and scatter; thus, without further correction, strict data filtering criteria are required. However, the MEaSUREs TCWV data can be corrected through machine learning when accurate measurements are abundant. In this regard, under all-sky conditions, the mean bias of MEaSUREs over the oceans reduces from 4.5 mm (without correction) to -0.3 mm (with correction using LightGBM models), and the standard deviation decreases from 11.8 mm to 3.8 mm. We also examined the representation error of the GPS stations using the dense GEONET data. The within-pixel variance of TCWV varies with grid size following a power law dependence. At 0.25°x0.25° resolution, the derived representation error is about 1.4 mm. [ABSTRACT FROM AUTHOR]

Quantifying the global bromine monoxide (BrO) budget is essential to understand ozone chemistry better. In particular, the tropospheric BrO budget has not been well characterized. Here, we retrieve nearly a decade (February 2012–July 2021) of stratospheric and tropospheric BrO vertical columns from the Ozone Mapping and Profiling Suite Nadir Mapper (OMPS-NM) onboard the Suomi National Polar-orbiting Partnership (Suomi-NPP) satellite. To address the mismatch between a priori profiles and column retrievals in the stratosphere-troposphere separation, for each OMPS-NM pixel, we save two types of BrO vertical profiles and use the appropriate one based on whether a tropospheric enhancement is detected. Total ozone columns observed from OMPS-NM are used to identify tropospheric BrO enhancements. We demonstrate good agreement for both the stratosphere (r = 0.81–0.83) and the troposphere (r = 0.50–0.69) by comparing monthly mean BrO vertical columns from OMPS-NM with ground-based observations from three stations (Lauder, Utqiag ̇vik, and Harestua). The OMPS-NM BrO retrievals successfully capture tropospheric enhancements not only in the polar but also in the extrapolar regions (the Rann of Kutch and the Great Salt Lake). We also estimate random uncertainties in the retrievals pixel by pixel, which can assist in quantitative applications of the OMPS-NM BrO dataset. Our BrO retrieval algorithm is designed for cross-sensor applications and can be adapted to other space-borne ultraviolet spectrometers, contributing to the creation of continuous long-term satellite BrO observation records. [ABSTRACT FROM AUTHOR]

Abundant NO[sub 2] column measurements from airborne and ground-based Pandora spectrometers were collected as part of the 2018 Long Island Sound Tropospheric Ozone Study (LISTOS) in the New York City/Long Island Sound region and coincided with early measurements from the Sentinel-5P TROPOMI instrument. Both airborne- and ground-based measurements are used to evaluate the TROPOspheric Monitoring Instrument (TROPOMI) NO[sub 2] Tropospheric Vertical Column (TrVC) product v1.2 in this region, which has high spatial and temporal heterogeneity in NO[sub 2]. First, airborne and Pandora TrVCs are compared to evaluate the uncertainty of the airborne TrVC and establish the spatial representativeness of the Pandora observations. The 171 coincidences between Pandora and airborne TrVCs are found to be highly correlated (r[sup 2]=0.92 and slope of 1.03) with the biggest individual differences being associated with high temporal and/or spatial variability. These reference measurements (Pandora and airborne) are complementary with respect to temporal coverage and spatial representivity. Pandora spectrometers can provide continuous long-term measurements but may lack areal representivity when operated in direct-sun mode. Airborne spectrometers are typically only deployed for short periods of time, but their observations are more spatially representative of the satellite measurements with the added capability of retrieving at subpixel resolutions of 250 m × 250 m over the entire TROPOMI pixels they overfly. Thus, airborne data are more correlated with TROPOMI measurements (r[sup 2]=0.96) than Pandora measurements are with TROPOMI (r[sup 2]=0.84). The largest outliers between TROPOMI and the reference measurements are caused by errors in the TROPOMI retrieval of cloud pressure impacting the calculation of tropospheric air mass factors in cloud-free scenes. This factor causes a high bias in TROPOMI TrVCs of 4-11 %. Excluding these cloud-impacted points, TROPOMI has an overall low bias of 19-33% during the LISTOS timeframe of June-September 2018. Part of this low bias is caused by coarse a priori profile input from TM5-MP model; replacing these profiles with those from a 12 km NAMCMAQ analysis results in a 12-14 % increase in the TrVCs. Even with this improvement, the TROPOMI-NAMCMAQ TrVCs have a 7-19 % low bias, indicating needed improvement in a priori assumptions in the air mass factor calculation. Future work should explore additional impacts of a priori inputs to further assess the remaining low biases in TROPOMI using these datasets. [ABSTRACT FROM AUTHOR]

Formaldehyde (HCHO) has been measured from space for more than two decades. Owing to its short atmospheric lifetime, satellite HCHO data are used widely as a proxy of volatile organic compounds (VOCs; please refer to Appendix A for abbreviations and acronyms), providing constraints on underlying emissions and chemistry. However, satellite HCHO products from different satellite sensors using different algorithms have received little validation so far. The accuracy and consistency of HCHO retrievals remain largely unclear. Here we develop a global validation platform for satellite HCHO retrievals using in situ observations from 12 aircraft campaigns with a chemical transport model (GEOS-Chem) as the intercomparison method. Application to the NASA operational OMI HCHO product indicates slight biases (-30.9 % to +16.0 %) under high-HCHO conditions partially caused by a priori shape factors used in the retrievals, while high biases (+113.9 % to +194.6 %) under low-HCHO conditions due mainly to slant column fitting and radiance reference sector correction. By providing quick assessment to systematic biases in satellite products over large domains, the platform facilitates, in an iterative process, optimization of retrieval settings and the minimization of retrieval biases. It is also complementary to localized validation efforts based on ground observations and aircraft spirals. [ABSTRACT FROM AUTHOR]

The GEOstationary Coastal and Air Pollution Events (GEO-CAPE) Airborne Simulator (GCAS) was developed in support of NASA's decadal survey GEO-CAPE geostationary satellite mission. GCAS is an airborne pushbroom remote sensing instrument, consisting of two channels which make hyperspectral measurements in the ultraviolet/visible (optimized for air quality observations) and the visible/near-infrared (optimized for ocean color observations). The GCAS instrument participated in its first intensive field campaign during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) in Texas in September 2013. During this campaign, the instrument flew on a King Air B-200 aircraft during 21 flights on 11 days to make air quality observations over Houston, Texas. We present GCAS trace gas retrievals of nitrogen dioxide (NO2) and formaldehyde (CH2O), and compare these results with trace gas columns derived from coincident in situ profile measurements of NO2 and CH2O made by instruments on a P-3B aircraft, and with NO2 observations from ground-based Pandora spectrometers operating in direct sun and scattered light modes. GCAS tropospheric column measurements correlate well spatially and temporally with columns estimated from the P-3B measurements for both NO2 (r2=0.89) and CH2O (r2=0.54) and with Pandora direct sun (r2=0.85) and scattered light (r2=0.94) observed NO2 columns. Coincident GCAS columns agree in magnitude with NO2 and CH2O P-3B-observed columns to within 10%, but are larger than scattered light Pandora tropospheric NO2 columns by 33% and direct sun Pandora NO2 columns by 50%. [ABSTRACT FROM AUTHOR]

This paper presents the retrieval algorithm for the operational Ozone Monitoring Instrument (OMI) total bromine monoxide (BrO) data product (OMBRO) developed at the Smithsonian Astrophysical Observatory (SAO), and shows some validation with correlative measurements and retrieval results. The algorithm is based on direct nonlinear least squares fitting of radiances from the spectral range 319.0-347.5 nm. Radiances are modeled from the solar irradiance, attenuated by contributions from BrO and interfering gases, and including rotational Raman scattering, additive and multiplicative closure polynomials, correction for Nyquist undersampling, and the average fitting residual spectrum. The retrieval uses albedo- and wavelength-dependent air mass factors (AMFs), which have been pre-computed using a single mostly stratospheric BrO profile. The BrO cross sections are multiplied by the wavelength- dependent AMFs before fitting so that the vertical column densities (VCDs) are retrieved directly. The fitting uncertainties of BrO VCDs typically vary between 4 and 7 x 1012 molecules cm-2 (~ 10-20 % of the measured BrO VCDs). The retrievals agree well with GOME-2 observations at simultaneous nadir overpasses and ground-based zenith-sky measurements at 25 Harestua, Norway, with mean biases less than 0.12 ± 0.76 x 1013 molecules cm-2 (3.2 ± 16.3 %). Global distribution and seasonal variation of OMI BrO are generally consistent with previous satellite observations. The retrievals show enhancement of BrO at US Great Salt Lake. It also shows significant BrO enhancement from the eruption of the Eyjafjallajökull volcano, although the BrO retrievals can be affected under high SO2 loading conditions by the sub-optimum choice of SO2 cross sections. [ABSTRACT FROM AUTHOR]

Isoprene emissions from vegetation have a large effect on atmospheric chemistry and air quality. "Bottom-up" isoprene emission inventories used in atmospheric models are based on limited vegetation information and uncertain land cover data, leading to potentially large errors. Satellite observations of atmospheric formaldehyde (HCHO), a high-yield isoprene oxidation product, provide "top-down" information to evaluate isoprene emission inventories through inverse analyses. Past inverse analyses have however been hampered by uncertainty in the HCHO satellite data, uncertainty in the time- and NOx-dependent yield of HCHO from isoprene oxidation, and coarse resolution of the atmospheric models used for the inversion. Here we demonstrate the ability to use HCHO satellite data from OMI in a high-resolution inversion to constrain isoprene emissions on ecosystem-relevant scales. The inversion uses the adjoint of the GEOS-Chem chemical transport model at 0.25°×0.3125° horizontal resolution to interpret observations over the Southeast US in August–September 2013. It takes advantage of concurrent NASA SEAC4RS aircraft observations of isoprene and its oxidation products including HCHO to validate the OMI HCHO data over the region, test the GEOS-Chem isoprene oxidation mechanism and NOx environment, and independently evaluate the inversion. This evaluation shows in particular that local model errors in NOx concentrations propagate to biases in inferring isoprene emissions from HCHO data. It is thus essential to correct model NOx biases, which was done here using SEAC4RS observations but can be done more generally using satellite NO2 data concurrently with HCHO. We find in our inversion that isoprene emissions from the widely-used MEGAN v2.1 inventory are biased high over the Southeast US by 40% on average, although the broad-scale distributions are correct including maximum emissions in Arkansas/Louisiana and high base emission factors in the oak-covered Ozarks of Southeast Missouri. A particularly large discrepancy is in the Edwards Plateau of Central Texas where MEGAN v2.1 is too high by a factor of 3, possibly reflecting errors in land cover. The lower isoprene emissions inferred from our inversion, when implemented into GEOS-Chem, decrease surface ozone over the Southeast US by 1–3ppb and decrease the isoprene contribution to organic aerosol from 40% to 20%. [ABSTRACT FROM AUTHOR]

The Ozone Monitoring Instrument (OMI) has been successfully measuring the Earth's atmospheric composition since 2004, but the on-orbit behavior of its slit functions has not been thoroughly characterized. Preflight measurements of slit functions have been used as a static input in many OMI retrieval algorithms. This study derives on-orbit slit functions from the OMI irradiance spectra assuming various function forms, including standard and super-Gaussian functions and a stretch to the preflight slit functions. The on-orbit slit functions in the UV bands show U-shaped cross-track dependences that cannot be fully represented by the preflight ones. The full widths at half maximum (FWHM) of the stretched preflight slit functions for detector pixels at large viewing angles are up to 30% larger than the nadir pixels for the UV1 band, 5% larger for the UV2 band, and practically flat in the VIS band. Nonetheless, the on-orbit changes of OMI slit functions are found to be insignificant over time after accounting for the solar activity, despite of the decaying of detectors and the occurrence of OMI row anomaly. Applying the derived on-orbit slit functions to ozone-profile retrieval shows substantial improvements over the preflight slit functions based on comparisons with ozonesonde validations. [ABSTRACT FROM AUTHOR]

Glyoxal (CHOCHO) is produced in the atmosphere by the oxidation of volatile organic compounds (VOCs). Like formaldehyde (HCHO), another VOC oxidation product, it is measurable from space by solar backscatter. Isoprene emitted by vegetation is the dominant source of CHOCHO and HCHO in most of the world. We use aircraft observations of CHOCHO and HCHO from the SENEX campaign over the southeast US in summer 2013 to better understand the CHOCHO time-dependent yield from isoprene oxidation, its dependence on nitrogen oxides (NOx ≡NO + NO2), the behavior of the CHOCHO-HCHO relationship, the quality of OMI CHOCHO satellite observations, and the implications for using CHOCHO observations from space as constraints on isoprene emissions. We simulate the SENEX and OMI observations with the Goddard Earth Observing System chemical transport model (GEOSChem) featuring a new chemical mechanism for CHOCHO formation from isoprene. The mechanism includes prompt CHOCHO formation under low-NOx conditions following the isomerization of the isoprene peroxy radical (ISOPO2). The SENEX observations provide support for this prompt CHOCHO formation pathway, and are generally consistent with the GEOS-Chem mechanism. Boundary layer CHOCHO and HCHO are strongly correlated in the observations and the model, with some departure under low-NOx conditions due to prompt CHOCHO formation. SENEX vertical profiles indicate a free-tropospheric CHOCHO background that is absent from the model. The OMI CHOCHO data provide some support for this free-tropospheric background and show southeast US enhancements consistent with the iso-prene source but a factor of 2 too low. Part of this OMI bias is due to excessive surface reflectivities assumed in the retrieval. The OMI CHOCHO and HCHO seasonal data over the southeast US are tightly correlated and provide redundant proxies of isoprene emissions. Higher temporal resolution in future geostationary satellite observations may enable detection of the prompt CHOCHO production under low-NOx conditions apparent in the SENEX data. [ABSTRACT FROM AUTHOR]

We examine upcoming geostationary satellite observations of formaldehyde (HCHO) vertical column densities (VCDs) in East Asia and the retrieval sensitivity to the temporal variation of air mass factors (AMFs) considering the presence of aerosols. Observation system simulation experiments (OSSE) were conducted using a combination of a global 3-D chemical transport model (GEOS-Chem), a radiative transfer model (VLIDORT), and a HCHO retrieval algorithm developed for the Geostationary Environment Monitoring Spectrometer (GEMS), which will be launched in 2019. Application of the retrieval algorithm to simulated hourly radiances yields the retrieved HCHO VCDs, which are then compared with the GEOS-Chem HCHO VCDs as true values for the evaluation of the retrieval algorithm. In order to examine the retrieval sensitivity to the temporal variation of AMF, we examine three AMF specifications, AMFm, AMFh, and AMFmh, using monthly, hourly, and monthly mean hourly input data for their calculation, respectively.We compare the retrieved HCHO VCDs using those three AMFs and find that the HCHO VCDs with AMFh are in better agreement with the true values than the results using AMFmh and AMFm. AMFmh reflects diurnal variation of planetary boundary layer and other meteorological parameters, so that the results with AMFmh show a better performance than those with AMFm. The differences between AMFh and AMFm range from -0.76 to 0.74 in absolute value and are mainly caused by temporal changes in aerosol chemical compositions and aerosol vertical distributions, which result in -27 to 58 and -34 to 43% changes in HCHO VCDs over China, respectively, compared to HCHO VCDs using AMFm. We apply our calculated AMF table together with OMI aerosol optical properties to OMI HCHO products in March 2006, when Asian dust storms occurred, and find -32 to 47% changes in the retrieved HCHO columns due to temporal changes in aerosol optical properties in East Asia. The impact of aerosol temporal variability cannot be neglected for future geostationary observations. [ABSTRACT FROM AUTHOR]

Using Ozone Monitoring Instrument (OMI) trace gas vertical column observations of nitrogen dioxide (NO2), formaldehyde (HCHO), sulfur dioxide (SO2), and glyoxal (CHOCHO), we have conducted a robust and detailed time series analysis to assess changes in local air quality for over 1000 locations (focussing on urban, oil refinery, oil port, and power plant targets) over the Middle East for 2005-2014. Apart from NO2, which is highest over urban locations, average tropospheric column levels of these trace gases are highest over oil ports and refineries. The highest average pollution levels over urban settlements are typically in Bahrain, Kuwait, Qatar, and the United Arab Emirates. We detect 278 statistically significant and real linear NO2 trends in total. Over urban areas NO2 increased by up to 12%yr-1, with only two locations showing a decreasing trend. Over oil refineries, oil ports, and power plants, NO2 increased by about 2-9%yr-1. For HCHO, 70 significant and real trends were detected, with HCHO increasing by 2-7%yr-1 over urban settlements and power plants and by about 2-4%yr-1 over refineries and oil ports. Very few SO2 trends were detected, which varied in direction and magnitude (23 increasing and 9 decreasing). Apart from two locations where CHOCHO is decreasing, we find that glyoxal tropospheric column levels are not changing over the Middle East. Hence, for many locations in the Middle East, OMI observes a degradation in air quality over 2005-2014. This study therefore demonstrates the capability of OMI to generate long-term air-quality monitoring at local scales over this region. [ABSTRACT FROM AUTHOR]

Formaldehyde (HCHO) column data from satellites are widely used as a proxy for emissions of volatile organic compounds (VOCs), but validation of the data has been extremely limited. Here we use highly accurate HCHO aircraft observations from the NASA SEAC4RS (Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys) campaign over the southeast US in August-September 2013 to validate and intercompare six retrievals of HCHO columns from four different satellite instruments (OMI, GOME2A, GOME2B and OMPS; for clarification of these and other abbreviations used in the paper, please refer to Appendix A.) and three different research groups. The GEOS-Chem chemical transport model is used as a common intercomparison platform. All retrievals feature a HCHO maximum over Arkansas and Louisiana, consistent with the aircraft observations and reflecting high emissions of biogenic isoprene. The retrievals are also interconsistent in their spatial variability over the southeast US (r = 0.4- 0.8 on a 0.5° × 0.5° grid) and in their day-to-day variability (r = 0.5-0.8). However, all retrievals are biased low in the mean by 20-51%, which would lead to corresponding bias in estimates of isoprene emissions from the satellite data. The smallest bias is for OMI-BIRA, which has high corrected slant columns relative to the other retrievals and low scattering weights in its air mass factor (AMF) calculation. OMIBIRA has systematic error in its assumed vertical HCHO shape profiles for the AMF calculation, and correcting this would eliminate its bias relative to the SEAC4RS data. Our results support the use of satellite HCHO data as a quantitative proxy for isoprene emission after correction of the low mean bias. There is no evident pattern in the bias, suggesting that a uniform correction factor may be applied to the data until better understanding is achieved. [ABSTRACT FROM AUTHOR]

This paper presents our new formaldehyde (H2CO) retrievals, obtained from spectra recorded by the nadir instrument of the Ozone Mapping and Profiler Suite (OMPS) flown on board NASA's Suomi National Polar-orbiting Partnership (SUOMI-NPP) satellite. Our algorithm is similar to the one currently in place for the production of NASA's Ozone Monitoring Instrument (OMI) operational H2CO product. We are now able to produce a set of long-term data from two different instruments that share a similar concept and a similar retrieval approach. The ongoing overlap period between OMI and OMPS offers a perfect opportunity to study the consistency between both data sets. The different spatial and spectral resolution of the instruments is a source of discrepancy in the retrievals despite the similarity of the physic assumptions of the algorithm. We have concluded that the reduced spectral resolution of OMPS in comparison with OMI is not a significant obstacle in obtaining good-quality retrievals. Indeed, the improved signal-to-noise ratio of OMPS with respect to OMI helps to reduce the noise of the retrievals performed using OMPS spectra. However, the size of OMPS spatial pixels imposes a limitation in the capability to distinguish particular features of H2CO that are discernible with OMI. With root mean square (RMS) residuals  ∼ 5 × 10−4 for individual pixels we estimate the detection limit to be about 7.5 × 1015 molecules cm−2. Total vertical column density (VCD) errors for individual pixels range between 40 % for pixels with high concentrations to 100 % or more for pixels with concentrations at or below the detection limit. We compare different OMI products (SAO OMI v3.0.2 and BIRA OMI v14) with our OMPS product using 1 year of data, between September 2012 and September 2013. The seasonality of the retrieved slant columns is captured similarly by all products but there are discrepancies in the values of the VCDs. The mean biases among the two OMI products and our OMPS product are 23 % between OMI SAO and OMPS SAO and 28 % between OMI BIRA and OMPS SAO for eight selected regions. [ABSTRACT FROM AUTHOR]

The Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument is a test bed for upcoming air quality satellite instruments that will measure backscattered ultraviolet, visible and near-infrared light from geostationary orbit. GeoTASO flew on the NASA Falcon aircraft in its first intensive field measurement campaign during the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) Earth Venture Mission over Houston, Texas, in September 2013. Measurements of backscattered solar radiation between 420 and 465 nm collected on 4 days during the campaign are used to determine slant column amounts of NO2 at 250m?250m spatial resolution with a fitting precision of 2.2?1015 moleculescm-2. These slant columns are converted to tropospheric NO2 vertical columns using a radiative transfer model and trace gas profiles from the Community Multiscale Air Quality (CMAQ) model. Total column NO2 from GeoTASO is well correlated with ground-based Pandora observations (r = 0:90 on the most polluted and cloud-free day of measurements and r = 0:74 overall), with GeoTASO NO2 slightly higher for the most polluted observations. Surface NO2 mixing ratios inferred from GeoTASO using the CMAQ model show good correlation with NO2 measured in situ at the surface during the campaign (r = 0:85). NO2 slant columns from GeoTASO also agree well with preliminary retrievals from the GEO-CAPE Airborne Simulator (GCAS) which flew on the NASA King Air B200 (r = 0:81, slope = 0.91). Enhanced NO2 is resolvable over areas of traffic NOx emissions and near individual petrochemical facilities. [ABSTRACT FROM AUTHOR]

Space-borne ozone (O3) measurements have indicated consistent positive trends across the entire Asia–Pacific region despite the considerable reduction of NOx since 2000s. The rate of increase in O3 derived from lower free tropospheric column measurements was observed to be 0.21 ± 0.05 DU/decade during 2005–2018. Our space-borne-based diagnosis of the nonlinear photochemical formation regimes, NOx-limited and NOx-saturated, show that O3 chemistry is undergoing a transitional process to the NOx-limited regime throughout most of the Asian region. Nevertheless, NOx-saturated conditions persist at present in and over eight major megacities. These NOx-saturated conditions in megacities contribute to the increased O3 due to NOx reduction, which could also affect the enhanced O3 concentrations throughout the Asia–Pacific region via long-range transport. This indicates that VOC limits along with NOx reductions are needed in megacities in the short term to reduce O3 levels. Moreover, NOx saturation in major megacities will continue until 2025, according to the forecast emission scenarios from the Intergovernmental Panel on Climate Change (IPCC). These scenarios gradually shift nearly all cities to the NOx-limited regime by 2050 with the exception of few cities under IPCC RCP8.5. Thus, continued reductions in NOx will be a key factor in reducing O3 in the long term. [ABSTRACT FROM AUTHOR]

Questions about how emissions are changing during the COVID-19 lockdown periods cannot be answered by observations of atmospheric trace gas concentrations alone, in part due to simultaneous changes in atmospheric transport, emissions, dynamics, photochemistry, and chemical feedback. A chemical transport model simulation benefiting from a multi-species inversion framework using well-characterized observations should differentiate those influences enabling to closely examine changes in emissions. Accordingly, we jointly constrain NO x and VOC emissions using well-characterized TROPOspheric Monitoring Instrument (TROPOMI) HCHO and NO 2 columns during the months of March, April, and May 2020 (lockdown) and 2019 (baseline). We observe a noticeable decline in the magnitude of NO x emissions in March 2020 (14 %-31 %) in several major cities including Paris, London, Madrid, and Milan, expanding further to Rome, Brussels, Frankfurt, Warsaw, Belgrade, Kyiv, and Moscow (34 %-51 %) in April. However, NO x emissions remain at somewhat similar values or even higher in some portions of the UK, Poland, and Moscow in March 2020 compared to the baseline, possibly due to the timeline of restrictions. Comparisons against surface monitoring stations indicate that the constrained model underrepresents the reduction in surface NO 2 . This underrepresentation correlates with the TROPOMI frequency impacted by cloudiness. During the month of April, when ample TROPOMI samples are present, the surface NO 2 reductions occurring in polluted areas are described fairly well by the model (model: -21 ± 17 %, observation: -29 ± 21 %). The observational constraint on VOC emissions is found to be generally weak except for lower latitudes. Results support an increase in surface ozone during the lockdown. In April, the constrained model features a reasonable agreement with maximum daily 8 h average (MDA8) ozone changes observed at the surface ( r = 0.43), specifically over central Europe where ozone enhancements prevail (model: +3.73 ± 3.94 %, + 1.79 ppbv, observation: +7.35 ± 11.27 %, +3.76 ppbv). The model suggests that physical processes (dry deposition, advection, and diffusion) decrease MDA8 surface ozone in the same month on average by -4.83 ppbv, while ozone production rates dampened by largely negative J NO 2 [ NO 2 ] - k NO + O 3 [ NO ] [ O 3 ] become less negative, leading ozone to increase by +5.89 ppbv. Experiments involving fixed anthropogenic emissions suggest that meteorology contributes to 42 % enhancement in MDA8 surface ozone over the same region with the remaining part (58 %) coming from changes in anthropogenic emissions. Results illustrate the capability of satellite data of major ozone precursors to help atmospheric models capture ozone changes induced by abrupt emission anomalies., Competing Interests: Competing interests. The contact author has declared that neither they nor their co-authors have any competing interests.

Airborne and ground-based Pandora spectrometer NO 2 column measurements were collected during the 2018 Long Island Sound Tropospheric Ozone Study (LISTOS) in the New York City/Long Island Sound region, which coincided with early observations from the Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) instrument. Both airborne- and ground-based measurements are used to evaluate the TROPOMI NO 2 Tropospheric Vertical Column (TrVC) product v1.2 in this region, which has high spatial and temporal heterogeneity in NO 2 . First, airborne and Pandora TrVCs are compared to evaluate the uncertainty of the airborne TrVC and establish the spatial representativeness of the Pandora observations. The 171 coincidences between Pandora and airborne TrVCs are found to be highly correlated ( r 2 =0.92 and slope of 1.03), with the largest individual differences being associated with high temporal and/or spatial variability. These reference measurements (Pandora and airborne) are complementary with respect to temporal coverage and spatial representativity. Pandora spectrometers can provide continuous long-term measurements but may lack areal representativity when operated in direct-sun mode. Airborne spectrometers are typically only deployed for short periods of time, but their observations are more spatially representative of the satellite measurements with the added capability of retrieving at subpixel resolutions of 250m×250m over the entire TROPOMI pixels they overfly. Thus, airborne data are more correlated with TROPOMI measurements ( r 2 = 0.96) than Pandora measurements are with TROPOMI ( r 2 = 0.84). The largest outliers between TROPOMI and the reference measurements appear to stem from too spatially coarse a priori surface reflectivity (0.5°) over bright urban scenes. In this work, this results during cloud-free scenes that, at times, are affected by errors in the TROPOMI cloud pressure retrieval impacting the calculation of tropospheric air mass factors. This factor causes a high bias in TROPOMI TrVCs of 4%-11%. Excluding these cloud-impacted points, TROPOMI has an overall low bias of 19%-33% during the LISTOS timeframe of June-September 2018. Part of this low bias is caused by coarse a priori profile input from the TM5-MP model; replacing these profiles with those from a 12 km North American Model-Community Multiscale Air Quality (NAMCMAQ) analysis results in a 12%-14% increase in the TrVCs. Even with this improvement, the TROPOMI-NAMCMAQ TrVCs have a 7%-19% low bias, indicating needed improvement in a priori assumptions in the air mass factor calculation. Future work should explore additional impacts of a priori inputs to further assess the remaining low biases in TROPOMI using these datasets., Competing Interests: Competing interests. The authors declare that they have no conflict of interest.