OMI HCHO Research Articles

Remote Sensing Measurements at a Rural Site in China: Implications for Satellite NO2 and HCHO Measurement Uncertainty and Emissions From Fires

AbstractNitrogen dioxide (NO2) and formaldehyde (HCHO) play vital roles in atmospheric photochemical processes. Their tropospheric vertical column density (TVCD) distributions have been monitored by satellite instruments. Evaluation of these observations is essential for applying these observations to study photochemistry. Assessing satellite products using observations at rural sites, where local emissions are minimal, is particularly useful due in part to the spatial homogeneity of trace gases. In this study, we evaluate OMI and TROPOMI NO2 and HCHO TVCDs using multi‐axis differential optical absorption spectroscopy (MAX‐DOAS) measurements at a rural site in the east coast of the Shandong province, China in spring 2018 during the Ozone Photochemistry and Export from China Experiment (OPECE) measurement campaign. On days not affected by local burning, we found generally good agreement of NO2 data after using consistent a priori profiles in satellite and MAX‐DOAS retrievals and accounting for low biases in scattering weights in one of the OMI products. In comparison, satellite HCHO products exhibited weaker correlations with MAX‐DOAS data, in contrast to satellite NO2 products. However, TROPOMI HCHO products showed significantly better agreement with MAX‐DOAS measurements compared to OMI data. Furthermore, case studies of the vertical profiles measured by MAX‐DOAS on burning days revealed large enhancements of nitrous acid (HONO), NO2, and HCHO in the upper boundary layer, accompanied with considerable variability, particularly in HONO enhancements.

Satellite remote-sensing capability to assess tropospheric-column ratios of formaldehyde and nitrogen dioxide: case study during the Long Island Sound Tropospheric Ozone Study 2018 (LISTOS 2018) field campaign

Abstract. Satellite retrievals of tropospheric-column formaldehyde (HCHO) and nitrogen dioxide (NO2) are frequently used to investigate the sensitivity of ozone (O3) production to emissions of nitrogen oxides and volatile organic carbon compounds. This study inter-compared the systematic biases and uncertainties in retrievals of NO2 and HCHO, as well as resulting HCHO–NO2 ratios (FNRs), from two commonly applied satellite sensors to investigate O3 production sensitivities (Ozone Monitoring Instrument, OMI, and TROPOspheric Monitoring Instrument, TROPOMI) using airborne remote-sensing data taken during the Long Island Sound Tropospheric Ozone Study 2018 between 25 June and 6 September 2018. Compared to aircraft-based HCHO and NO2 observations, the accuracy of OMI and TROPOMI were magnitude-dependent with high biases in clean environments and a tendency towards more accurate comparisons to even low biases in moderately polluted to polluted regions. OMI and TROPOMI NO2 systematic biases were similar in magnitude (normalized median bias, NMB = 5 %–6 %; linear regression slope ≈ 0.5–0.6), with OMI having a high median bias and TROPOMI resulting in small low biases. Campaign-averaged uncertainties in the three satellite retrievals (NASA OMI; Quality Assurance for Essential Climate Variables, QA4ECV OMI; and TROPOMI) of NO2 were generally similar, with TROPOMI retrievals having slightly less spread in the data compared to OMI. The three satellite products differed more when evaluating HCHO retrievals. Campaign-averaged tropospheric HCHO retrievals all had linear regression slopes ∼0.5 and NMBs of 39 %, 17 %, 13 %, and 23 % for NASA OMI, QA4ECV OMI, and TROPOMI at finer (0.05∘×0.05∘) and coarser (0.15∘×0.15∘) spatial resolution, respectively. Campaign-averaged uncertainty values (root mean square error, RMSE) in NASA and QA4ECV OMI HCHO retrievals were ∼9.0×1015 molecules cm−2 (∼ 50 %–55 % of mean column abundance), and the higher-spatial-resolution retrievals from TROPOMI resulted in RMSE values ∼30 % lower. Spatially averaging TROPOMI tropospheric-column HCHO, along with NO2 and FNRs, to resolutions similar to the OMI reduced the uncertainty in these retrievals. Systematic biases in OMI and TROPOMI NO2 and HCHO retrievals tended to cancel out, resulting in all three satellite products comparing well to observed FNRs. However, while satellite-derived FNRs had minimal campaign-averaged median biases, unresolved errors in the indicator species did not cancel out in FNR calculations, resulting in large RMSE values compared to observations. Uncertainties in HCHO retrievals were determined to drive the unresolved biases in FNR retrievals.

Validation of satellite formaldehyde (HCHO) retrievals using observations from 12 aircraft campaigns

Abstract. Formaldehyde (HCHO) has been measured from space for more than 2 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 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 negative biases (−44.5 % to −21.7 %) under high-HCHO conditions, while it indicates high biases (+66.1 % to +112.1 %) under low-HCHO conditions. Under both conditions, HCHO a priori vertical profiles are likely not the main driver of the biases. By providing quick assessment of 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.

Inferring Changes in Summertime Surface Ozone-NOx-VOC Chemistry over U.S. Urban Areas from Two Decades of Satellite and Ground-Based Observations.

Urban ozone (O3) formation can be limited by NOx, VOCs, or both, complicating the design of effective O3 abatement plans. A satellite-retrieved ratio of formaldehyde to NO2 (HCHO/NO2), developed from theory and modeling, has previously been used to indicate O3 formation chemistry. Here, we connect this space-based indicator to spatiotemporal variations in O3 recorded by on-the-ground monitors over major U.S. cities. High-O3 events vary nonlinearly with OMI HCHO and NO2, and the transition from VOC-limited to NOx-limited O3 formation regimes occurs at higher HCHO/NO2 value (3 to 4) than previously determined from models, with slight intercity variations. To extend satellite records back to 1996, we develop an approach to harmonize observations from GOME and SCIAMACHY that accounts for differences in spatial resolution and overpass time. Two-decade (1996-2016) multisatellite HCHO/NO2 captures the timing and location of the transition from VOC-limited to NOx-limited O3 production regimes in major U.S. cities, which aligns with the observed long-term changes in urban-rural gradient of O3 and the reversal of O3 weekend effect. Our findings suggest promise for applying space-based HCHO/NO2 to interpret local O3 chemistry, particularly with the new-generation satellite instruments that offer finer spatial and temporal resolution.

Improved slant column density retrieval of nitrogen dioxide and formaldehyde for OMI and GOME-2A from QA4ECV: intercomparison, uncertainty characterisation, and trends

Abstract. Nitrogen dioxide (NO2) and formaldehyde (HCHO) column data from satellite instruments are used for air quality and climate studies. Both NO2 and HCHO have been identified as precursors to the ozone (O3) and aerosol essential climate variables, and it is essential to quantify and characterise their uncertainties. Here we present an intercomparison of NO2 and HCHO slant column density (SCD) retrievals from four different research groups (BIRA-IASB, IUP Bremen, and KNMI as part of the Quality Assurance for Essential Climate Variables (QA4ECV) project consortium, and NASA) and from the OMI and GOME-2A instruments. Our evaluation is motivated by recent improvements in differential optical absorption spectroscopy (DOAS) fitting techniques and by the desire to provide a fully traceable uncertainty budget for the climate data record generated within QA4ECV. The improved NO2 and HCHO SCD values are in close agreement but with substantial differences in the reported uncertainties between groups and instruments. To check the DOAS uncertainties, we use an independent estimate based on the spatial variability of the SCDs within a remote region. For NO2, we find the smallest uncertainties from the new QA4ECV retrieval (0.8 × 1015 molec. cm−2 for both instruments over their mission lifetimes). Relative to earlier approaches, the QA4ECV NO2 retrieval shows better agreement between DOAS and statistical uncertainty estimates, suggesting that the improved QA4ECV NO2 retrieval has reduced but not altogether eliminated systematic errors in the fitting approach. For HCHO, we reach similar conclusions (QA4ECV uncertainties of 8–12 × 1015 molec. cm−2), but the closeness between the DOAS and statistical uncertainty estimates suggests that HCHO uncertainties are indeed dominated by random noise from the satellite's level 1 data. We find that SCD uncertainties are smallest for high top-of-atmosphere reflectance levels with high measurement signal-to-noise ratios. From 2005 to 2015, OMI NO2 SCD uncertainties increase by 1–2 % year−1, which is related to detector degradation and stripes, but OMI HCHO SCD uncertainties are remarkably stable (increase < 1 % year−1) and this is related to the use of Earth radiance reference spectra which reduces stripes. For GOME-2A, NO2 and HCHO SCD uncertainties increased by 7–9 and 11–15 % year−1 respectively up until September 2009, when heating of the instrument markedly reduced further throughput loss, stabilising the degradation of SCD uncertainty to < 3 % year−1 for 2009–2015. Our work suggests that the NO2 SCD uncertainty largely consists of a random component ( ∼ 65 % of the total uncertainty) as a result of the propagation of measurement noise but also of a substantial systematic component ( ∼ 35 % of the total uncertainty) mainly from stripe effects. Averaging over multiple pixels in space and/or time can significantly reduce the SCD uncertainties. This suggests that trend detection in OMI, GOME-2 NO2, and HCHO time series is not limited by the spectral fitting but rather by the adequacy of assumptions on the atmospheric state in the later air mass factor (AMF) calculation step.

High-resolution inversion of OMI formaldehyde columns to quantify isoprene emission on ecosystem-relevant scales: application to the southeast US

Abstract. 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 1–3 ppb and decrease the isoprene contribution to organic aerosol from 40 to 20 %.

Evaluating a Space-Based Indicator of Surface Ozone-NO x -VOC Sensitivity Over Midlatitude Source Regions and Application to Decadal Trends.

Determining effective strategies for mitigating surface ozone (O3) pollution requires knowledge of the relative ambient concentrations of its precursors, NO x , and VOCs. The space-based tropospheric column ratio of formaldehyde to NO2 (FNR) has been used as an indicator to identify NO x -limited versus NO x -saturated O3 formation regimes. Quantitative use of this indicator ratio is subject to three major uncertainties: (1) the split between NO x -limited and NO x -saturated conditions may shift in space and time, (2) the ratio of the vertically integrated column may not represent the near-surface environment, and (3) satellite products contain errors. We use the GEOS-Chem global chemical transport model to evaluate the quantitative utility of FNR observed from the Ozone Monitoring Instrument over three northern midlatitude source regions. We find that FNR in the model surface layer is a robust predictor of the simulated near-surface O3 production regime. Extending this surface-based predictor to a column-based FNR requires accounting for differences in the HCHO and NO2 vertical profiles. We compare four combinations of two OMI HCHO and NO2 retrievals with modeled FNR. The spatial and temporal correlations between the modeled and satellite-derived FNR vary with the choice of NO2 product, while the mean offset depends on the choice of HCHO product. Space-based FNR indicates that the spring transition to NO x -limited regimes has shifted at least a month earlier over major cities (e.g., New York, London, and Seoul) between 2005 and 2015. This increase in NO x sensitivity implies that NO x emission controls will improve O3 air quality more now than it would have a decade ago.

OMI satellite observed formaldehyde column from 2006 to 2015 over Xishuangbanna, southwest China, and validation using ground based zenith-sky DOAS

Formaldehyde (HCHO) provides a proxy to reveal the isoprene and biogenic volatile organic compounds emission which plays important roles in atmospheric chemical process and climate change. The ground-based observation with zenith-sky DOAS is carried out in order to validate the HCHO columns from OMI. It has a good correlation of 0.71678 between the HCHO columns from two sources. Then we use the OMI HCHO columns from January 2006 to December 2015 to indicate the interannual variation and spatial distribution in Xishuangbanna. The HCHO concentration peaks appeared in March or April for each year significantly corresponding to the intensive fire counts at the same time, which illustrate that the high HCHO columns are strongly influenced by the biomass burning in spring. Temperature and precipitation are also the important influence factors in the seasonal variation when there is nearly no biomass burning. The spatial patterns over the past ten years strengthen the deduction from the temporal variation and show the relationship with land cover and land use, elevation and population density. It is concluded that the biogenic activity plays a role in controlling the background level of HCHO in Xishuangbanna, while biomass burning is the main driving force of high HCHO concentration. And forests are greater contributor to HCHO rather than rubber trees which cover over 20% of the land in the region. Moreover, uncertainties from HCHO slant column retrieval and AMFs calculation are discussed in detail.

Glyoxal yield from isoprene oxidation and relation to formaldehyde: chemical mechanism, constraints from SENEX aircraft observations, and interpretation of OMI satellite data

Abstract. 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 (GEOS-Chem) 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 isoprene 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.

Sensitivity of formaldehyde (HCHO) column measurements from a geostationary satellite to temporal variation of the air mass factor in East Asia

Abstract. 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.

EOF와 SVD을 이용한 아프리카 지역에서 관측된 OMI HCHO 자료의 검증

We have found an error in the operational OMI HCHO columns, and corrected it by applying a background parameterization derived on a 4th order polynomial fit to the time series of monthly average OMI HCHO data. The corrected OMI HCHO agrees with this understanding as well as with the other sensors measurements and has no unrealistic trends. A new scientific approach, statistical analyses with EOF and SVD, was adapted to reanalyze the consistency of the corrected OMI HCHO with other satellite measurements of HCHO, CO, NO 2 , and fire counts over Africa. The EOF and SVD analyses with MOPITT CO, OMI NO 2 , SCIAMAHCY, and OMI HCHO show the overall spatial and temporal pattern consistent with those of biomass burning over these regions. However, some discrepancies were observed from OMI HCHO over northern equatorial Africa during the northern biomass burning seasons: The maximum HCHO was found further downwind from where maximum fire counts occur and the minimum was found in January when biomass burning is strongest. The statistical analysis revealed that the influence of biogenic activity on HCHO wasn’t strong enough to cause the discrepancies, but it is caused by the error in OMI HCHO from using the wrong Air Mass Factor (AMF) associated with biomass burning aerosol. If the error is properly taken into consideration, the biomass burning is the strongest source of HCHO seasonality over the regions. This study suggested that the statistical tools are a very efficient method for evaluating satellite data.

Validation of OMI HCHO data and its analysis over Asia

OMI HCHO is validated over the continental US (CONUS), and used to analyze regional sources in Northeast Asia (NA) and Southeast Asia (SA). OMI HCHO Version 2.0 data show unrealistic trends, which prompted the production of a corrected OMI HCHO data set. EOF and SVD are utilized to compare the spatial and temporal variability between OMI HCHO against GOME and SCIAMACHY, and against GEOS-Chem. CONUS HCHO chemistry is well studied; its concentrations are greatest in the southeastern US with annual cycle maximums corresponding to the summer vegetation. The corrected OMI HCHO agrees with this understanding as well as with the other sensors measurements and has no unrealistic trends. In NA the annual cycle is super-posed by extremely large concentrations in polluted mega-cities. The other sensors generally agree with NA’s OMI HCHO regional distribution, but megacity signal is not seen in GEOS-Chem. Our study supports the findings proposed by others that the emission inventory used in GEOS-Chem significantly underestimates anthropogenic influence on HCHO emission over megacities. The persistent mega-city signal is also present in SA. In SA the spatial and temporal patterns of OMI HCHO show a maximum in the dry season. The patterns are in remarkably good agreement with fire counts, which illustrates that the variability of HCHO over SA is strongly influenced by biomass burning. The corrected OMI HCHO data has realistic trends, conforms to well-known sources over CONUS, and has shown a stationary large concentration over polluted Asian mega-cities, and a widespread biomass burning in SA.

통계적 방법을 이용한 동남아시아지역 위성 대기오염물질 분석과 검증

The statistical tools such as empirical orthogonal function (EOF), and singular value decomposition (SVD) have been applied to analyze the characteristic of air pollutant over southeast Asia as well as to evaluate Zimeke’s tropospheric column ozone (ZTO) determined by tropospheric residual method. In this study, we found that the EOF and SVD analyses are useful methods to extract the most significant temporal and spatial pattern from enormous amounts of satellite data. The EOF analyses with OMI NO2 and OMI HCHO over southeast Asia revealed that the spatial pattern showed high correlation with fire count (r=0.8) and the EOF analysis of CO (r=0.7). This suggests that biomass burning influences a major seasonal variability on NO2 and HCHO over this region. The EOF analysis of ZTO has indicated that the location of maximum ZTO was considerably shifted westward from the location of maximum of fire count and maximum month of ZTO occurred a month later than maximum month (March) of NO2, HCHO and CO. For further analyses, we have performed the SVD analyses between ZTO and ozone precursor to examine their correlation and to check temporal and spatial consistency between two variables. The spatial pattern of ZTO showed latitudinal gradient that could result from latitudinal gradient of stratospheric ozone and temporal maximum of ZTO in March appears to be associated with stratospheric ozone variability that shows maximum in March. These results suggest that there are some sources of error in the tropospheric residual method associated with cloud height error, low efficiency of tropospheric ozone, and low accuracy in lower stratospheric ozone.