Ozone Monitoring Instrument Observations Research Articles

Modeling the atmospheric dispersion of SO2 from Mount Nyiragongo

Mount Nyiragongo, an active volcano, is the most dominant natural source of sulphur dioxide (SO2) in Africa. While a number of studies have employed atmospheric models to simulate the dispersion of SO2 from this mountain, prior to this study, no attempt has been made to use deep learning to bias correct the model's estimates. Here, the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) was used to simulate massive SO2 plumes degassed from this mountain between September 2014 and August 2015. Satellite observations by the Ozone Monitoring Instrument (OMI) showed that the SO2 spread to over 500 km from the volcano site. A deep convolutional autoencoder algorithm (WRF-DCA) was then applied to reduce the bias that WRF-Chem showed against the OMI observations. Finally, the correction performance of WRF-DCA was compared with a conventional bias correction method, linear scaling (WRF-LS). The performance of WRF-Chem, WRF-DCA, and WRF-LS was analyzed using three metrics, that is, the normalized mean bias (NMB), the root mean square error (RMSE), and Pearson's correlation coefficient (R). The results showed that WRF-Chem overestimated SO2 at locations near the volcano site and underestimated SO2 at locations further away from the volcano site. It generated an overall average NMB of −0.61 against the OMI observations. Respectively, WRF-DCA and WRF-LS reduced this bias by an average of 0.25 (40.9%) and 0.21 (34.4%). Furthermore, although both methods also reduced the RMSE and improved the correlation, WRF-DCA consistently performed better than WRF-LS. This study demonstrates the advantage that deep learning can provide in estimating volcanic SO2 emissions.

Analysis of global trends of total column water vapour from multiple years of OMI observations

Abstract. In this study, we investigate trends in total column water vapour (TCWV) retrieved from measurements of the Ozone Monitoring Instrument (OMI) for the time range between January 2005 to December 2020. The trend analysis reveals, on global average, an annual increase in the TCWV amount of approximately +0.054 kg m−2 yr−1 or +0.21 % yr−1. After the application of a Z test (to the significance level of 5 %) and a false discovery rate (FDR) test to the results of the trend analysis, mainly positive trends remain, in particular over the northern subtropics in the eastern Pacific. Combining the relative TCWV trends with trends in air temperature, we also analyse trends in relative humidity (RH) on the local scale. This analysis reveals that the assumption of temporally invariant RH is not always fulfilled, as we obtain increasing and decreasing RH trends over large areas of the ocean and land surface and also observe that these trends are not limited to arid and humid regions, respectively. For instance, we find decreasing RH trends over the (humid) tropical Pacific Ocean in the region of the Intertropical Convergence Zone. Interestingly, these decreasing RH trends in the tropical Pacific Ocean coincide well with decreasing trends in precipitation. Moreover, by combining the trends of TCWV, surface temperature, and precipitation, we derive trends for the global water vapour turnover time (TUT) of approximately +0.02 d yr−1. Also, we obtain a TUT rate of change of around 8.4 % K−1, which is 2 to 3 times higher than the values obtained in previous studies.

Comparative assessment of TROPOMI and OMI formaldehyde observations and validation against MAX-DOAS network column measurements

Abstract. The TROPOspheric Monitoring Instrument (TROPOMI), launched in October 2017 on board the Sentinel-5 Precursor (S5P) satellite, monitors the composition of the Earth's atmosphere at an unprecedented horizontal resolution as fine as 3.5 × 5.5 km2. This paper assesses the performances of the TROPOMI formaldehyde (HCHO) operational product compared to its predecessor, the OMI (Ozone Monitoring Instrument) HCHO QA4ECV product, at different spatial and temporal scales. The parallel development of the two algorithms favoured the consistency of the products, which facilitates the production of long-term combined time series. The main difference between the two satellite products is related to the use of different cloud algorithms, leading to a positive bias of OMI compared to TROPOMI of up to 30 % in tropical regions. We show that after switching off the explicit correction for cloud effects, the two datasets come into an excellent agreement. For medium to large HCHO vertical columns (larger than 5 × 1015 molec. cm−2) the median bias between OMI and TROPOMI HCHO columns is not larger than 10 % (< 0.4 × 1015 molec. cm−2). For lower columns, OMI observations present a remaining positive bias of about 20 % (< 0.8 × 1015 molec. cm−2) compared to TROPOMI in midlatitude regions. Here, we also use a global network of 18 MAX-DOAS (multi-axis differential optical absorption spectroscopy) instruments to validate both satellite sensors for a large range of HCHO columns. This work complements the study by Vigouroux et al. (2020), where a global FTIR (Fourier transform infrared) network is used to validate the TROPOMI HCHO operational product. Consistent with the FTIR validation study, we find that for elevated HCHO columns, TROPOMI data are systematically low (−25 % for HCHO columns larger than 8 × 1015 molec. cm−2), while no significant bias is found for medium-range column values. We further show that OMI and TROPOMI data present equivalent biases for large HCHO levels. However, TROPOMI significantly improves the precision of the HCHO observations at short temporal scales and for low HCHO columns. We show that compared to OMI, the precision of the TROPOMI HCHO columns is improved by 25 % for individual pixels and by up to a factor of 3 when considering daily averages in 20 km radius circles. The validation precision obtained with daily TROPOMI observations is comparable to the one obtained with monthly OMI observations. To illustrate the improved performances of TROPOMI in capturing weak HCHO signals, we present clear detection of HCHO column enhancements related to shipping emissions in the Indian Ocean. This is achieved by averaging data over a much shorter period (3 months) than required with previous sensors (5 years) and opens new perspectives to study shipping emissions of VOCs (volatile organic compounds) and related atmospheric chemical interactions.

Variability of NO<sub>2</sub> concentrations over China and effect on air quality derived from satellite and ground-based observations

Abstract. The variation of NO2 concentrations in mainland China is analyzed on different timescales, from decadal to weekly, using both satellite data and data from ground-based monitoring networks. TROPOMI (TROPOspheric Monitoring Instrument) data were used to study the spatial variations of tropospheric NO2 vertical column densities (TVCDs) over the study area during 16–20 weeks after the Chinese Spring Festival (25 January 2020). These data were used to select 11 regions for more detailed analysis of the variation of NO2 TVCDs on a decadal timescale. In this analysis, monthly and annual averaged NO2 TVCDs derived from OMI (Ozone Monitoring Instrument) observations were used for the years 2011 to 2019. The results show the NO2 TVCD trends for different regions, all decreasing in response to emission reduction policies but with a different onset and a possible halt of the decrease in recent years; trends and period in the south of the study area are different from those in the north. Variations of NO2 TVCDs on shorter timescales, monthly and weekly, were analyzed using TROPOMI data. In addition, the variations of weekly-averaged ground-based NO2 concentrations in 11 major cities were analyzed together with those for O3 and PM2.5. In particular these data were used to determine their effect on the air quality as expressed by the air quality index (AQI). For quantitative estimates, the use of weekly concentrations is more accurate than the use of monthly values, and the effects of long-term trends and their reversal needs to be taken into account for the separation of effects of the lockdown and the Spring Festival. Neglecting the possible reversal of the trends leads to overestimation of the lockdown effect in the south and underestimation in the north. The ground-based data confirm earlier reports, based on satellite observations, that the expected improvement of air quality due to the reduction of NO2 concentrations was offset by the increase of the concentrations of O3 and the different effects of the lockdown measures on PM2.5, as well as effects of meteorological influences and heterogeneous chemistry. The AQI seems to be mostly influenced by PM2.5 rather than NO2. A qualitative comparison between time series of satellite and ground-based NO2 observations shows both similarities and differences. The study further shows the different behaviors in city clusters in the north and south of China, as well as inland in the Sichuan and Guanzhong basins. Effects of other holidays and events are small, except in Beijing where the air quality in 2020 was notably better than in previous years. This study was undertaken for China, but the methodology and results have consequences for air quality studies in other areas, and part of the conclusions are generally applicable.

Observational evidence of energetic particle precipitation NO<sub><i>x</i></sub> (EPP-NO<sub><i>x</i></sub>) interaction with chlorine curbing Antarctic ozone loss

Abstract. We investigate the impact of the so-called energetic particle precipitation (EPP) indirect effect on lower stratospheric ozone, ClO, and ClONO2 in the Antarctic springtime. We use observations from the Microwave Limb Sounder (MLS) and Ozone Monitoring Instrument (OMI) on Aura, the Atmospheric Chemistry Experiment – Fourier Transform Spectrometer (ACE-FTS) on SCISAT, and the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) on Envisat, covering the period from 2005 to 2017. Using the geomagnetic activity index Ap as a proxy for EPP, we find consistent ozone increases with elevated EPP during years with an easterly phase of the quasi-biennial oscillation (QBO) in both OMI and MLS observations. While these increases are the opposite of what has previously been reported at higher altitudes, the pattern in the MLS O3 follows the typical descent patterns of EPP-NOx. The ozone enhancements are also present in the OMI total O3 column observations. Analogous to the descent patterns found in O3, we also found consistent decreases in springtime MLS ClO following winters with elevated EPP. To verify if this is due to a previously proposed mechanism involving the conversion of ClO to the reservoir species ClONO2 in reaction with NO2, we used ClONO2 observations from ACE-FTS and MIPAS. As ClO and NO2 are both catalysts in ozone destruction, the conversion to ClONO2 would result in an ozone increase. We find a positive correlation between EPP and ClONO2 in the upper stratosphere in the early spring and in the lower stratosphere in late spring, providing the first observational evidence supporting the previously proposed mechanism relating to EPP-NOx modulating Clx-driven ozone loss. Our findings suggest that EPP has played an important role in modulating ozone depletion in the last 15 years. As chlorine loading in the polar stratosphere continues to decrease in the future, this buffering mechanism will become less effective, and catalytic ozone destruction by EPP-NOx will likely become a major contributor to Antarctic ozone loss.

MAX-DOAS measurements of tropospheric NO<sub>2</sub> and HCHO in Munich and the comparison to OMI and TROPOMI satellite observations

Abstract. We present two-dimensional scanning Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) observations of nitrogen dioxide (NO2) and formaldehyde (HCHO) in Munich. Vertical columns and vertical distribution profiles of aerosol extinction coefficient, NO2 and HCHO are retrieved from the 2D MAX-DOAS observations. The measured surface aerosol extinction coefficients and NO2 mixing ratios derived from the retrieved profiles are compared to in situ monitoring data, and the surface NO2 mixing ratios show a good agreement with in situ monitoring data with a Pearson correlation coefficient (R) of 0.91. The aerosol optical depths (AODs) show good agreement as well (R = 0.80) when compared to sun photometer measurements. Tropospheric vertical column densities (VCDs) of NO2 and HCHO derived from the MAX-DOAS measurements are also used to validate Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) satellite observations. Monthly averaged data show a good correlation; however, satellite observations are on average 30 % lower than the MAX-DOAS measurements. Furthermore, the MAX-DOAS observations are used to investigate the spatiotemporal characteristic of NO2 and HCHO in Munich. Analysis of the relations between aerosol, NO2 and HCHO shows higher aerosol-to-HCHO ratios in winter, which reflects a longer atmospheric lifetime of secondary aerosol and HCHO during winter. The analysis also suggests that secondary aerosol formation is the major source of these aerosols in Munich.

Long-term ambient SO2 concentration and its exposure risk across China inferred from OMI observations from 2005 to 2018

Long-term trends in the ambient SO2 concentrations across China from 2005 to 2018 were estimated using Ozone Monitoring Instrument (OMI) SO2 columns and an atmospheric SO2 profile simulated by the Model for Ozone And Related Chemical Tracers (MOZART)-4. The estimated ground-level SO2 concentrations were in strong agreement with the ground observations (R = 0.86, root-mean-square error = 10.49 μg/m3, relative prediction error = 19%). From 2005 to 2018, the average ground-level SO2 concentration ranged from 0.97 to 106.41 μg/m3 across China, with higher values in eastern China than in western China. Considering the uneven distribution of SO2 concentration and population across China, the population-weighted SO2 concentration was estimated to be 20.30 μg/m3 on a national scale. The population-weighted SO2 levels showed decreasing trends, and the absolute values of the decreasing trend peaked in winter (1.41 μg/m3 per year), followed by those in autumn (0.92 μg/m3 per year), spring (0.74 μg/m3 per year) and summer (0.75 μg/m3 per year) from 2005 to 2018. Although the SO2 exposure risk in 2018 decreased significantly from 2005 on a national scale, 80% and 17% of people still faced a high SO2 exposure risk (higher than 20 μg/m3) in the North China Plain and Northwest China, respectively.

Impact of Coronavirus Outbreak on NO2 Pollution Assessed Using TROPOMI and OMI Observations.

Spaceborne NO2 column observations from two high‐resolution instruments, Tropospheric Monitoring Instrument (TROPOMI) on board Sentinel‐5 Precursor and Ozone Monitoring Instrument (OMI) on Aura, reveal unprecedented NO2 decreases over China, South Korea, western Europe, and the United States as a result of public health measures enforced to contain the coronavirus disease outbreak (Covid‐19) in January–April 2020. The average NO2 column drop over all Chinese cities amounts to −40% relative to the same period in 2019 and reaches up to a factor of ~2 at heavily hit cities, for example, Wuhan, Jinan, while the decreases in western Europe and the United States are also significant (−20% to −38%). In contrast with this, although Iran is also strongly affected by the disease, the observations do not show evidence of lower emissions, reflecting more limited health measures.

Changes in Atmospheric, Meteorological, and Ocean Parameters Associated with the 12 January 2020 Taal Volcanic Eruption

The Taal volcano erupted on 12 January 2020, the first time since 1977. About 35 mild earthquakes (magnitude greater than 4.0) were observed on 12 January 2020 induced from the eruption. In the present paper, we analyzed optical properties of volcanic aerosols, volcanic gas emission, ocean parameters using multi-satellite sensors, namely, MODIS (Moderate Resolution Imaging Spectroradiometer), AIRS (Atmospheric Infrared Sounder), OMI (Ozone Monitoring Instrument), TROPOMI (TROPOspheric Monitoring Instrument) and ground observations, namely, Argo, and AERONET (AErosol RObotic NETwork) data. Our detailed analysis shows pronounced changes in all the parameters, which mainly occurred in the western and south-western regions because the airmass of the Taal volcano spreads westward according to the analysis of airmass trajectories and wind directions. The presence of finer particles has been observed by analyzing aerosol properties that can be attributed to the volcanic plume after the eruption. We have also observed an enhancement in SO2, CO, and water vapor, and a decrease in Ozone after a few days of the eruption. The unusual variations in salinity, sea temperature, and surface latent heat flux have been observed as a result of the ash from the Taal volcano in the south-west and south-east over the ocean. Our results demonstrate that the observations combining satellite with ground data could provide important information about the changes in the atmosphere, meteorology, and ocean parameters associated with the Taal volcanic eruption.

MAX-DOAS measurements of tropospheric NO<sub>2</sub> and HCHO in Nanjing and a comparison to ozone monitoring instrument observations

Abstract. In this paper, we present long-term observations of atmospheric nitrogen dioxide (NO2) and formaldehyde (HCHO) in Nanjing using a Multi-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) instrument. Ground-based MAX-DOAS measurements were performed from April 2013 to February 2017. The MAX-DOAS measurements of NO2 and HCHO vertical column densities (VCDs) are used to validate ozone monitoring instrument (OMI) satellite observations over Nanjing. The comparison shows that the OMI observations of NO2 correlate well with the MAX-DOAS data with Pearson correlation coefficient (R) of 0.91. However, OMI observations are on average a factor of 3 lower than the MAX-DOAS measurements. Replacing the a priori NO2 profiles by the MAX-DOAS profiles in the OMI NO2 VCD retrieval would increase the OMI NO2 VCDs by ∼30 % with correlation nearly unchanged. The comparison result of MAX-DOAS and OMI observations of HCHO VCD shows a good agreement with R of 0.75 and the slope of the regression line is 0.99. An age-weighted backward-propagation approach is applied to the MAX-DOAS measurements of NO2 and HCHO to reconstruct the spatial distribution of NO2 and HCHO over the Yangtze River Delta during summer and winter time. The reconstructed NO2 fields show a distinct agreement with OMI satellite observations. However, due to the short atmospheric lifetime of HCHO, the backward-propagated HCHO data do not show a strong spatial correlation with the OMI HCHO observations. The result shows that the MAX-DOAS measurements are sensitive to the air pollution transportation in the Yangtze River Delta, indicating the air quality in Nanjing is significantly influenced by regional transportation of air pollutants. The MAX-DOAS data are also used to evaluate the effectiveness of air pollution control measures implemented during the Youth Olympic Games 2014. The MAX-DOAS data show a significant reduction of ambient aerosol, NO2 and HCHO (30 %–50 %) during the Youth Olympic Games. Our results provide a better understanding of the transportation and sources of pollutants over the Yangtze River Delta as well as the effect of emission control measures during large international events, which are important for the future design of air pollution control policies.

SO2 Emission Estimates Using OMI SO2 Retrievals for 2005-2017.

SO2 column densities from Ozone Monitoring Instrument provide important information on emission trends and missing sources, but there are discrepancies between different retrieval products. We employ three Ozone Monitoring Instrument SO2 retrieval products (National Aeronautics and Space Administration (NASA) standard (SP), NASA prototype, and BIRA) to study the magnitude and trend of SO2 emissions. SO2 column densities from these retrievals are most consistent when viewing angles and solar zenith angles are small, suggesting more robust emission estimates in summer and at low latitudes. We then apply a hybrid 4D‐Var/mass balance emission inversion to derive monthly SO2 emissions from the NASA SP and BIRA products. Compared to HTAPv2 emissions in 2010, both posterior emission estimates are lower in United States, India, and Southeast China, but show different changes of emissions in North China Plain. The discrepancies between monthly NASA and BIRA posterior emissions in 2010 are less than or equal to 17% in China and 34% in India. SO2 emissions increase from 2005 to 2016 by 35% (NASA)–48% (BIRA) in India, but decrease in China by 23% (NASA)–33% (BIRA) since 2008. Compared to in situ measurements, the posterior GEOS‐Chem surface SO2 concentrations have reduced NMB in China, the United States, and India but not in South Korea in 2010. BIRA posteriors have better consistency with the annual growth rate of surface SO2 measurement in China and spatial variability of SO2 concentration in China, South Korea, and India, whereas NASA SP posteriors have better seasonality. These evaluations demonstrate the capability to recover SO2 emissions using Ozone Monitoring Instrument observations.

A geometry-dependent surface Lambertian-equivalent reflectivity product for UV–Vis retrievals – Part 1: Evaluation over land surfaces using measurements from OMI at 466 nm

Abstract. The anisotropy of the Earth's surface reflection has implications for satellite-based retrieval algorithms that utilize climatological surface reflectivity databases that do not depend upon the observation geometry. This is the case for most of the current ultraviolet and visible (UV–Vis) cloud, aerosol, and trace-gas algorithms. The illumination–observation dependence of surface reflection is described by the bidirectional reflectance distribution function (BRDF). To account for the BRDF effect, we use the concept of geometry-dependent surface Lambertian-equivalent reflectivity (GLER), which is derived from the top-of-atmosphere (TOA) radiance computed with Rayleigh scattering and surface BRDF for the exact geometry of a satellite-based pixel. We present details on the implementation of land and water surface BRDF models, and we evaluate our GLER product over land surfaces using observed Sun-normalized radiances at 466 nm. The input surface BRDF parameters for computing TOA radiance are derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observations. The observed TOA radiance for comparison is from the Ozone Monitoring Instrument (OMI). The comparison shows good agreement between observed and calculated OMI reflectivity in 2006 in typical geographical regions, with correlation coefficients greater than 0.8 for some regions. Seasonal variations of clear-sky OMI reflectivity (i.e., with minimum clouds and aerosols) closely follow those computed using MODIS-derived GLER over land. GLER also captures the cross-track dependence of OMI-derived LER, though the latter is slightly higher than the former presumably owing to residual cloud and aerosol (nonabsorbing) contamination, particularly over dark surfaces (heavily vegetated regions such as mixed forest, croplands, and grasslands). Calibration differences between OMI and MODIS may also be responsible for some of this bias. The standard OMI climatological surface reflectivity database predicts higher radiances than GLER and OMI observations with different seasonal variation over most regions and does not have any angular-dependent variation. Overall, our evaluation demonstrates that the GLER product adequately accounts for surface BRDF effects while at the same time simplifying the surface BRDF implementation within the existing OMI retrieval infrastructure; use of our GLER product requires changes only to the input surface reflectivity database.

Surface erythemal UV irradiance in the continental United States derived from ground-based and OMI observations: quality assessment, trend analysis and sampling issues

Abstract. Surface full-sky erythemal dose rate (EDR) from the Ozone Monitoring Instrument (OMI) at both satellite overpass time and local noon time is evaluated against ground measurements at 31 sites from the US Department of Agriculture's (USDA) UV-B Monitoring and Research Program (UVMRP) over the period of 2005–2017. We find that both OMI overpass and solar noon time EDR are highly correlated with the measured counterparts (with a linear correlation coefficient of 0.90 and 0.88, respectively). Although the comparison statistics are improved with a longer time window (0.5–1.0 h) for pairing surface and OMI measurements, both OMI overpass and local noon time EDRs have 7 % overestimation that is larger than 6 % uncertainty in the ground measurements and show different levels of dependence on solar zenith angle (SZA) and to lesser extent on cloud optical depth. The ratio of EDR between local noon and OMI overpass time is often (95 % in frequency) larger than 1 with a mean of 1.18 in the OMI product; in contrast, the same ratio from surface observation is normally distributed with 22 % of the times less than 1 and a mean of 1.38. This contrast in part reflects the deficiency in the OMI surface UV algorithm that assumes constant atmospheric conditions between overpass and noon time. The probability density functions (PDFs) for both OMI and ground measurements of noontime EDR are in statistically significant agreement, showing dual peaks at ∼20 and ∼200 mW m−2, respectively; the latter is lower than 220 mW m−2, the value at which the PDF of daily EDR from ground measurements peaks, and this difference indicates that the largest EDR value for a given day may not often occur at local noon. Lastly, statistically significant positive trends of EDR are found in the northeastern US in OMI data, but opposite trends are found within ground-based data (regardless of sampling for either noontime or daily averages). While positive trends are consistently found between OMI and surface data for EDR over the southern Great Plains (Texas and Oklahoma), their values are within the uncertainty of ground measurements. Overall, no scientifically sound trends can be found among OMI data for aerosol total and absorbing optical depth, cloud optical depth and total ozone to explain coherently the surface UV trends revealed either by OMI or ground-based estimates; these data also cannot reconcile trend differences between the two estimates (of EDR from OMI and surface observations). Future geostationary satellites with better spatiotemporal resolution data should help overcome spatiotemporal sampling issues inherent in OMI data products and therefore improve the estimates of surface UV flux and EDR from space.

Tropospheric NO2 vertical column densities retrieved from ground-based MAX-DOAS measurements at Shangdianzi regional atmospheric background station in China

Ground-basedMulti-AXis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements were performed at Shangdianzi (SDZ) regional atmospheric background station in northern China from March 2009 to February 2011. The tropospheric NO2 vertical column densities (VCDs) were retrieved to investigate the background condition of the Beijing–Tianjin–Hebei developed economic circle in China. The seasonal variation of mean NO2 tropospheric VCDs (VCDTrop) at SDZ is apparent, with the maximum (1.3 × 1016 molec/cm2) in February and the minimum (3.5 × 1015 molec/cm2) in August, much lower than those observed at the Beijing city center. The average daytime diurnal variations of NO2 VCDTrop are rather consistent for all four seasons, presenting the minimum at noon and the higher values in the morning and evening. The largest and lowest amplitudes of NO2 VCDTrop diurnal variation appear in winter and in summer, respectively. The diurnal pattern at SDZ station is similar to those at other less polluted stations, but distinct from the ones at the urban or polluted stations. Tropospheric NO2 VCDs at SDZ are strongly dependent on the wind, with the higher values being associated with the pollution plumes from Beijing city. Tropospheric NO2 VCDs derived from ground-based MAX-DOAS at SDZ show to be well correlated with corresponding OMI (Ozone Monitoring Instrument) satellite products with a correlation coefficient R = 0.88. However, the OMI observations are on average higher than MAX-DOAS NO2 VCDs by a factor of 28%, probably due to the OMI grid cell partly covering the south of SDZ which is influenced more by the pollution plumes from the urban areas.

A Retrieval of Glyoxal from OMI over China: Investigation of the Effects of Tropospheric NO2

East China is the ‘hotspot’ of glyoxal (CHOCHO), especially over the Pearl River Delta (PRD) region, where glyoxal is yielded from the oxidation of aromatics. To better understand the glyoxal spatial-temporal characteristics over China and evaluate the effectiveness of atmospheric prevention efforts on the reduction of volatile organic compound (VOC) emissions, we present an algorithm for glyoxal retrieval using the Ozone Monitoring instrument (OMI) over China. The algorithm is based on the differential optical absorption spectroscopy (DOAS) and accounts for the interference of the tropospheric nitrogen dioxide (NO2) spatial-temporal distribution on glyoxal retrieval. We conduct a sensitively test based on a synthetic spectrum to optimize the fitting parameters set. It shows that the fitting interval of 430–458 nm and a 4th order polynomial are optimal for glyoxal retrieval when using the daily mean value of the earthshine spectrum in the Pacific region as a reference. In addition, tropospheric NO2 pre-fitted during glyoxal retrieval is first proposed and tested, which shows a ±10% variation compared with the reference scene. The interference of NO2 on glyoxal was further investigated based on the OMI observations, and the spatial distribution showed that changes in the NO2 concentration can affect the glyoxal result depending on the NO2 spatial distribution. A method to prefix NO2 during glyoxal retrieval is proposed in this study and is referred to as OMI-CAS. We perform an intercomparison of the glyoxal from the OMI-CAS with the seasonal datasets provided by different institutions for North China (NC), South China (SC), the Yangtze River Delta (YRD) and the ChuanYu (CY) region in southwestern China in the year 2005. The results show that our algorithm can obtain the glyoxal spatial and temporal variations in different regions over China. OMI-CAS has the best correlations with other datasets in summer, with the correlations between OMI-CAS and OMI-Harvard, OMI-CAS and OMI-IUP, and OMI-CAS and Sciamachy-IUP being 0.63, 0.67 and 0.67, respectively. Autumn results followed, with the correlations of 0.58, 0.36 and 0.48, respectively, over China. However, the correlations are less or even negative for spring and winter. From the regional perspective, SC has the best correlation compared with other regions, with R reaching 0.80 for OMI-CAS and OMI-IUP in summer. The discrepancies between different glyoxal datasets can be attributed to the fitting parameters and larger glyoxal retrieval uncertainties. Finally, useful recommendations are given based on the results comparison according to region and season.

A new global anthropogenic SO<sub>2</sub> emission inventory for the last decade: a mosaic of satellite-derived and bottom-up emissions

Abstract. Sulfur dioxide (SO2) measurements from the Ozone Monitoring Instrument (OMI) satellite sensor have been used to detect emissions from large point sources. Emissions from over 400 sources have been quantified individually based on OMI observations, accounting for about a half of total reported anthropogenic SO2 emissions. Here we report a newly developed emission inventory, OMI-HTAP, by combining these OMI-based emission estimates and the conventional bottom-up inventory, HTAP, for smaller sources that OMI is not able to detect. OMI-HTAP includes emissions from OMI-detected sources that are not captured in previous leading bottom-up inventories, enabling more accurate emission estimates for regions with such missing sources. In addition, our approach offers the possibility of rapid updates to emissions from large point sources that can be detected by satellites. Our methodology applied to OMI-HTAP can also be used to merge improved satellite-derived estimates with other multi-year bottom-up inventories, which may further improve the accuracy of the emission trends. OMI-HTAP SO2 emissions estimates for Persian Gulf, Mexico, and Russia are 59 %, 65 %, and 56 % larger than HTAP estimates in 2010, respectively. We have evaluated the OMI-HTAP inventory by performing simulations with the Goddard Earth Observing System version 5 (GEOS-5) model. The GEOS-5 simulated SO2 concentrations driven by both HTAP and OMI-HTAP were compared against in situ measurements. We focus for the validation on 2010 for which HTAP is most valid and for which a relatively large number of in situ measurements are available. Results show that the OMI-HTAP inventory improves the agreement between the model and observations, in particular over the US, with the normalized mean bias decreasing from 0.41 (HTAP) to −0.03 (OMI-HTAP) for 2010. Simulations with the OMI-HTAP inventory capture the worldwide major trends of large anthropogenic SO2 emissions that are observed with OMI. Correlation coefficients of the observed and modeled surface SO2 in 2014 increase from 0.16 (HTAP) to 0.59 (OMI-HTAP) and the normalized mean bias dropped from 0.29 (HTAP) to 0.05 (OMI-HTAP), when we updated 2010 HTAP emissions with 2014 OMI-HTAP emissions in the model.

A 12-year long global record of optical depth of absorbing aerosols above the clouds derived from the OMI/OMACA algorithm

Abstract. Aerosol–cloud interaction continues to be one of the leading uncertain components of climate models, primarily due to the lack of adequate knowledge of the complex microphysical and radiative processes of the aerosol–cloud system. Situations when light-absorbing aerosols such as carbonaceous particles and windblown dust overlay low-level cloud decks are commonly found in several regions of the world. Contrary to the known cooling effects of these aerosols in cloud-free scenario over darker surfaces, an overlapping situation of the absorbing aerosols over the cloud can lead to a significant level of atmospheric absorption exerting a positive radiative forcing (warming) at the top of the atmosphere. We contribute to this topic by introducing a new global product of above-cloud aerosol optical depth (ACAOD) of absorbing aerosols retrieved from the near-UV observations made by the Ozone Monitoring Instrument (OMI) onboard NASA's Aura platform. Physically based on an unambiguous “color ratio” effect in the near-UV caused by the aerosol absorption above the cloud, the OMACA (OMI above-cloud aerosols) algorithm simultaneously retrieves the optical depths of aerosols and clouds under a prescribed state of the atmosphere. The OMACA algorithm shares many similarities with the two-channel cloud-free OMAERUV algorithm, including the use of AIRS carbon monoxide for aerosol type identification, CALIOP-based aerosol layer height dataset, and an OMI-based surface albedo database. We present the algorithm architecture, inversion procedure, retrieval quality flags, initial validation results, and results from a 12-year long OMI record (2005–2016) including global climatology of the frequency of occurrence, ACAOD, and aerosol-corrected cloud optical depth. A comparative analysis of the OMACA-retrieved ACAOD, collocated with equivalent accurate measurements from the HSRL-2 lidar for the ORACLES Phase I operation (August–September 2016), revealed a good agreement (R = 0.77, RMSE = 0.10). The long-term OMACA record reveals several important regions of the world, where the carbonaceous aerosols from the seasonal biomass burning and mineral dust originated over the continents are found to overlie low-level cloud decks with moderate (0.3 < ACAOD < 0.5, away from the sources) to higher levels of ACAOD (> 0.8 in the proximity to the sources), including the southeastern Atlantic Ocean, southern Indian Ocean, Southeast Asia, the tropical Atlantic Ocean off the coast of western Africa, and northern Arabian sea. No significant long-term trend in the frequency of occurrence of aerosols above the clouds and ACAOD is noticed when OMI observations that are free from the “row anomaly” throughout the operation are considered. If not accounted for, the effects of aerosol absorption above the clouds introduce low bias in the retrieval of cloud optical depth with a profound impact on increasing ACAOD and cloud brightness. The OMACA aerosol product from OMI presented in this paper offers a crucial missing piece of information from the aerosol loading above cloud that will help us to quantify the radiative effects of clouds when overlaid with aerosols and their resultant impact on cloud properties and climate.

Application of satellite-based sulfur dioxide observations to support the cleantech sector: Detecting emission reduction from copper smelters

Abstract In this study, we present the result of the application of space-based sulfur dioxide (SO2) observations to evaluate the efficacy of cleantech solutions in reducing air polluting emissions from metal smelting. We analyze the Ozone Monitoring Instrument (OMI) satellite-based SO2 observations over Tsumeb (Namibia) and Bor (Serbia) copper smelters, where two sulfur-capture plants, designed to transform gaseous SO2 emissions into sulfuric acid, were implemented in 2015. We observe a reduction in the annual SO2 emissions by up to 90% after 2015 at both smelters, as a result of the implementation of the sulfuric acid plants. The OMI-based emission estimates are mostly in line with those reported at facility-level and reproduce the same year-to-year variability. This variability is driven by the changes in the copper production, the sulfur-to-copper ratio and by the technology employed to reduce the SO2 emissions. OMI observations are directly used by the company operating the sulfuric acid plants to confirm the efficacy of the employed technology using independent satellite-based observations. The results demonstrate how satellite-based observations are able to detect relative changes in SO2 emissions and can be used to verify and complete existing emission informations. The approach presented here can be applied to other sources on global scale to support cleantech companies as well as decision-makers involved in environmental policies and sustainable development.

Estimation of Power Plant Emissions With Unscented Kalman Filter

Emissions from power plants constitute a major part of air pollution and should be adequately estimated. In this paper, we consider the problem of estimating nitrogen dioxide (NO $_X$ ) emission of power plants by developing an inverse method to integrate satellite observations of atmospheric pollutant column concentrations with species concentrations and direct sensitivities predicted by a regional air quality model, in order to discern biases in the emissions of the pollutant precursors. Using this method, the emission fields are analyzed using a “bottom-up” approach, with an inversion performed by an unscented Kalman filter (UKF) to improve estimation profiles from emissions inventories data for the Sydney metropolitan area. The idea is to integrate information from the original inventories with tropospheric nitrogen dioxide (NO $_2$ ) emissions estimated during one month from the air pollution model-chemical transport model, and then, for validation, to compare the resulting model with satellite retrievals from the ozone monitoring instrument (OMI) above the region. The UKF-based estimation of NO $_2$ emissions shows better agreement with OMI observations, implying a significant improvement in accuracy as compared with the original inventories. Therefore, the proposed method is a promising tool for estimation of air emissions in urban areas.

Impact of the ozone monitoring instrument row anomaly on the long-term record of aerosol products

Abstract. Since about three years after the launch the Ozone Monitoring Instrument (OMI) on the EOS-Aura satellite, the sensor's viewing capability has been affected by what is believed to be an internal obstruction that has reduced OMI's spatial coverage. It currently affects about half of the instrument's 60 viewing positions. In this work we carry out an analysis to assess the effect of the reduced spatial coverage on the monthly average values of retrieved aerosol optical depth (AOD), single scattering albedo (SSA) and the UV Aerosol Index (UVAI) using the 2005–2007 three-year period prior to the onset of the row anomaly. Regional monthly average values calculated using viewing positions 1 through 30 were compared to similarly obtained values using positions 31 through 60, with the expectation of finding close agreement between the two calculations. As expected, mean monthly values of AOD and SSA obtained with these two scattering-angle dependent subsets of OMI observations agreed over regions where carbonaceous or sulphate aerosol particles are the predominant aerosol type. However, over arid regions, where desert dust is the main aerosol type, significant differences between the two sets of calculated regional mean values of AOD were observed. As it turned out, the difference in retrieved desert dust AOD between the scattering-angle dependent observation subsets was due to the incorrect representation of desert dust scattering phase function. A sensitivity analysis using radiative transfer calculations demonstrated that the source of the observed AOD bias was the spherical shape assumption of desert dust particles. A similar analysis in terms of UVAI yielded large differences in the monthly mean values for the two sets of calculations over cloudy regions. On the contrary, in arid regions with minimum cloud presence, the resulting UVAI monthly average values for the two sets of observations were in very close agreement. The discrepancy under cloudy conditions was found to be caused by the parameterization of clouds as opaque Lambertian reflectors. When properly accounting for cloud scattering effects using Mie theory, the observed UVAI angular bias was significantly reduced. The analysis discussed here has uncovered important algorithmic deficiencies associated with the model representation of the angular dependence of scattering effects of desert dust aerosols and cloud droplets. The resulting improvements in the handling of desert dust and cloud scattering have been incorporated in an improved version of the OMAERUV algorithm.