Ozone Monitoring Instrument Data Research Articles

Bias correction of OMI HCHO columns based on FTIR and aircraft measurements and impact on top-down emission estimates

Abstract. Spaceborne formaldehyde (HCHO) measurements constitute an excellent proxy for the sources of non-methane volatile organic compounds (NMVOCs). Past studies suggested substantial overestimations of NMVOC emissions in state-of-the-art inventories over major source regions. Here, the QA4ECV (Quality Assurance for Essential Climate Variables) retrieval of HCHO columns from OMI (Ozone Monitoring Instrument) is evaluated against (1) FTIR (Fourier-transform infrared) column observations at 26 stations worldwide and (2) aircraft in situ HCHO concentration measurements from campaigns conducted over the USA during 2012–2013. Both validation exercises show that OMI underestimates high columns and overestimates low columns. The linear regression of OMI and aircraft-based columns gives ΩOMI=0.651Ωairc+2.95×1015 molec.cm-2, with ΩOMI and Ωairc the OMI and aircraft-derived vertical columns, whereas the regression of OMI and FTIR data gives ΩOMI=0.659ΩFTIR+2.02×1015 molec.cm-2. Inverse modelling of NMVOC emissions with a global model based on OMI columns corrected for biases based on those relationships leads to much-improved agreement against FTIR data and HCHO concentrations from 11 aircraft campaigns. The optimized global isoprene emissions (∼445Tgyr-1) are 25 % higher than those obtained without bias correction. The optimized isoprene emissions bear both striking similarities and differences with recently published emissions based on spaceborne isoprene columns from the CrIS (Cross-track Infrared Sounder) sensor. Although the interannual variability of OMI HCHO columns is well understood over regions where biogenic emissions are dominant, and the HCHO trends over China and India clearly reflect anthropogenic emission changes, the observed HCHO decline over the southeastern USA remains imperfectly elucidated.

Assessing the Impacts of COVID-19 on SO2, NO2, and CO Trends in Durban Using TROPOMI, AIRS, OMI, and MERRA-2 Data

This research report investigated the impacts of the COVID-19 lockdown restrictions on CO, SO2, and NO2 trends in Durban from 2019 to 2021. The COVID-19 lockdown restrictions proved to decrease greenhouse gas (GHG) emissions globally; however, the decrease in GHG emissions was for a short period only. Space-borne technology has been used by researchers to understand the spatial and temporal trends of GHGs. This study used Sentinel-5P to map the spatial distribution of CO, SO2, and NO2. Use was also made of the Atmospheric Infrared Sounder (AIRS), Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), and the Ozone Monitoring Instrument (OMI) to understand the temporal trends of CO, SO2, and NO2, respectively. To validate the results of this study, we used the Sequential Mann–Kendall (SQMK) test. This study indicated that there were no significant changes in all the investigated gases. Therefore, this study failed to reject the null hypothesis of the SQMK test that there was no significant trend for all investigated gasses. Increasing trends were observed for CO, SO2, and NO2 trends during winter months throughout the study period, whereas a decreasing trend was observed in all investigated gases during the spring months. This shows that meteorological factors play a significant role in the accumulation of air pollutants in the atmosphere. Most importantly, this study has noted that there was an inverse relationship between the trends of all investigated gases and the COVID-19 lockdown restrictions.

Seasonal, interannual and decadal variability of tropospheric ozone in the North Atlantic: comparison of UM-UKCA and remote sensing observations for 2005–2018

Abstract. Tropospheric ozone is an important component of the Earth system as it can affect both climate and air quality. In this work, we use observed tropospheric column ozone derived from the Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) OMI-MLS, in addition to OMI ozone retrieved in discrete vertical layers, and compare it to tropospheric ozone from UM-UKCA simulations (which utilize the Unified Model, UM, coupled to UK Chemistry and Aerosol, UKCA). Our aim is to investigate recent changes (2005–2018) in tropospheric ozone in the North Atlantic region, specifically its seasonal, interannual and decadal variability, and to understand what factors are driving such changes. The model exhibits a large positive bias (greater than 5 DU or ∼ 50 %) in the tropical upper troposphere: through sensitivity experiments, time series correlation, and comparison with the Lightning Imaging Sensor and Optical Transient Detector lightning flash dataset, the model positive bias in the tropics is attributed to shortcomings in the convection and lightning parameterizations, which overestimate lightning flashes in the tropics relative to mid-latitudes. Use of OMI data, for which vertical averaging kernels and a priori information are available, suggests that the model negative bias (6–10 DU or ∼ 20 %) at mid-latitudes, relative to OMI-MLS tropospheric column, could be the result of vertical sampling. Ozone in the North Atlantic peaks in spring and early summer, with generally good agreement between the modelled and observed seasonal cycle. Recent trends in tropospheric ozone were investigated: whilst both observational datasets indicate positive trends of ∼ 5 % and ∼ 10 % in North Atlantic ozone, the modelled ozone trends are much closer to zero and have large uncertainties. North Atlantic ozone interannual variability (IAV) in the model was found to be correlated to the IAV of ozone transported to the North Atlantic from the stratosphere (R=0.77) and emission of NOx from lightning in the tropics (R=0.72). The discrepancy between modelled and observed trends for 2005–2018 could be linked to the model underestimating lower stratospheric ozone trends and associated stratosphere to troposphere transport. Modelled tropospheric ozone IAV is driven by IAV of tropical emissions of NOx from lightning and IAV of ozone transport from the stratosphere; however, the modelled and observed IAV differ. To understand the IAV discrepancy we investigated how modelled ozone and its drivers respond to large-scale modes of variability. Using OMI height-resolved data and model idealized tracers, we were able to identify stratospheric transport of ozone into the troposphere as the main driver of the dynamical response of North Atlantic ozone to the Arctic Oscillation (AO) and the North Atlantic Oscillation (NAO). Finally, we found that the modelled ozone IAV is too strongly correlated to the El Niño–Southern Oscillation (ENSO) compared to observed ozone IAV. This is again linked to shortcomings in the lightning flashes parameterization, which underestimates (overestimates) lightning flash production in the tropics during positive (negative) ENSO events.

Biogenic isoprene emissions, dry deposition velocity, and surface ozone concentration during summer droughts, heatwaves, and normal conditions in southwestern Europe

Abstract. At high concentrations, tropospheric ozone (O3) deteriorates air quality, inducing adverse effects on human and ecosystem health. Meteorological conditions are key to understanding the variability in O3 concentration, especially during extreme weather events. In addition to modifying photochemistry and atmospheric transport, droughts and heatwaves affect the state of vegetation and thus the biosphere–troposphere interactions that control atmospheric chemistry, namely biogenic emissions of precursors and gas dry deposition. A major source of uncertainty and inaccuracy in the simulation of surface O3 during droughts and heatwaves is the poor representation of such interactions. This publication aims at quantifying the isolated and combined impacts of both extremes on biogenic isoprene (C5H8) emissions, O3 dry deposition, and surface O3 in southwestern Europe. First, the sensitivity of biogenic C5H8 emissions, O3 dry deposition, and surface O3 to two specific effects of droughts, the decrease in soil moisture and in biomass, is analysed for the extremely dry summer 2012 using the biogenic emission model MEGANv2.1 and the chemistry transport model CHIMEREv2020r1. Despite a significant decrease in biogenic C5H8 emissions and O3 dry deposition velocity, characterized by a large spatial variability, the combined effect on surface O3 concentration remains limited (between +0.5 % and +3 % over the continent). The variations in simulated biogenic C5H8 emissions, O3 dry deposition, and surface O3 during the heatwaves and agricultural droughts are then analysed for summer 2012 (warm and dry), 2013 (warm), and 2014 (relatively wet and cool). We compare the results with large observational data sets, namely O3 concentrations from Air Quality (AQ) e-Reporting (2000–2016) and total columns of formaldehyde (HCHO, which is used as a proxy for biogenic emissions of volatile organic compounds) from the Ozone Monitoring Instrument (OMI) of the Aura satellite (2005–2016). Based on a cluster approach using the percentile limit anomalies indicator, we find that C5H8 emissions increase by +33 % during heatwaves compared to normal conditions, do not vary significantly during all droughts (either accompanied or not by a heatwave), and decrease by −16 % during isolated droughts. OMI data confirm an average increase in HCHO during heatwaves (between +15 % and +31 % depending on the product used) and decrease in HCHO (between −2 % and −6 %) during isolated droughts over the 2005–2016 summers. Simulated O3 dry deposition velocity decreases by −25 % during heatwaves and −35 % during all droughts. Simulated O3 concentrations increase by +7 % during heatwaves and by +3 % during all droughts. Compared to observations, CHIMERE tends to underestimate the daily maximum O3. However, similar sensitivity to droughts and heatwaves are obtained. The analysis of the AQ e-Reporting data set shows an average increase of +14 % during heatwaves and +7 % during all droughts over the 2000–2016 summers (for an average daily concentration value of 69 µg m−3 under normal conditions). This suggests that identifying the presence of combined heatwaves is fundamental to the study of droughts on surface–atmosphere interactions and O3 concentration.

Absorbing Aerosol Optical Depth From OMI/TROPOMI Based on the GBRT Algorithm and AERONET Data in Asia

Quantifying the concentration of absorbing aerosol is essential for pollution tracking and calculation of atmospheric radiative forcing. To quickly obtain absorbing aerosol optical depth (AAOD) with high-resolution and high-accuracy, the gradient boosted regression trees (GBRT) method based on the joint data from Ozone Monitoring Instrument (OMI), Moderate Resolution Imaging Spectro-Radiometer (MODIS), and AErosol RObotic NETwork (AERONET) is used for TROPOspheric Monitoring Instrument (TROPOMI). Compared with the ground-based data, the correlation coefficient of the results is greater than 0.6 and the difference is generally within ±0.04. Compared with OMI data, the underestimation has been greatly improved. By further restricting the impact factors, three valid conclusions can be drawn: 1) the model with more spatial difference information achieves better results than the model with more temporal difference information; 2) the training dataset with a high cloud fraction (0.1–0.4) can partly improve the performance of GBRT results; and 3) when aerosol optical depth (AOD) is less than 0.3, the perform of retrieved AAODs is still good by comparing with ground-based measurements. The novel finding is expected to contribute to regional and even urban anthropogenic pollution research.

Spatiotemporal Variations in the Air Pollutant NO2 in Some Regions of Pakistan, India, China, and Korea, before and after COVID-19, Based on Ozone Monitoring Instrument Data

In 2020, COVID-19 was proclaimed a pandemic by the World Health Organization, prompting several nations throughout the world to block their borders and impose a countrywide lockdown, halting all major manmade activities and thus leaving a beneficial impact on the natural environment. We investigated the influence of a sudden cessation of human activity on tropospheric NO2 concentrations to understand the resulting changes in emissions, particularly from the power-generating sector, before (2010–2019) and during the pandemic (2020). NO2 was chosen because of its short lifespan in the Earth’s atmosphere. Using daily tropospheric NO2 column concentrations from the Ozone Monitoring Instrument, the geographic and temporal characteristics of tropospheric NO2 column were investigated across 12 regions in India, Pakistan, China, and South Korea (2010–2020). We analyzed weekly, monthly, and annual trends and found that the NO2 concentrations were decreased in 2020 (COVID-19 period) in the locations investigated. Reduced anthropogenic activities, including changes in energy production and a reduction in fossil fuel consumption before and during the COVID-19 pandemic, as well as reduced traffic and industrial activity in 2020, can explain the lower tropospheric NO2 concentrations. The findings of this study provide a better understanding of the process of tropospheric NO2 emissions over four nations before and after the coronavirus pandemic for improving air quality modeling and management approaches.

Synergy of Using Nadir and Limb Instruments for Tropospheric Ozone Monitoring (SUNLIT)

Abstract. Satellite measurements in nadir and limb viewing geometry provide a complementary view of the atmosphere. An effective combination of the limb and nadir measurements can give new information about atmospheric composition. In this work, we present tropospheric ozone column datasets that have been created using a combination of total ozone columns from OMI (Ozone Monitoring Instrument) and TROPOMI (TROPOspheric Monitoring Instrument) with stratospheric ozone column datasets from several available limb-viewing instruments: MLS (Microwave Limb Sounder), OSIRIS (Optical Spectrograph and InfraRed Imaging System), MIPAS (Michelson Interferometer for Passive Atmospheric Sounding), SCIAMACHY (SCanning Imaging Spectrometer for Atmospheric CHartographY), OMPS-LP (Ozone Mapping and Profiles Suite – Limb Profiler), and GOMOS (Global Ozone Monitoring by Occultation of Stars). We have developed further the methodological aspects of the assessment of tropospheric ozone using the residual method supported by simulations with the chemistry transport model SILAM (System for Integrated modeLling of Atmospheric coMposition). It has been shown that the accurate assessment of ozone in the upper troposphere and the lower stratosphere (UTLS) is of high importance for detecting the ground-level ozone patterns. The stratospheric ozone column is derived from a combination of ozone profiles from several satellite instruments in limb-viewing geometry. We developed a method for the data homogenization, which includes the removal of biases and a posteriori estimation of random uncertainties, thus making the data from different instruments compatible with each other. The high-horizontal- and vertical-resolution dataset of ozone profiles is created via interpolation of the limb profiles from each day to a 1∘×1∘ horizonal grid. A new kriging-type interpolation method, which takes into account data uncertainties and the information about natural ozone variations from the SILAM-adjusted ozone field, has been developed. To mitigate the limited accuracy and coverage of the limb profile data in the UTLS, a smooth transition to the model data is applied below the tropopause. This allows for the estimation of the stratospheric ozone column with full coverage of the UTLS. The derived ozone profiles are in very good agreement with collocated ozonesonde measurements. The residual method was successfully applied to OMI and TROPOMI clear-sky total ozone data in combination with the stratospheric ozone column from the developed high-resolution limb profile dataset. The resulting tropospheric ozone column is in very good agreement with other satellite data. The global distributions of tropospheric ozone exhibit enhancements associated with the regions of high tropospheric ozone production. The main datasets created are (i) a monthly 1∘×1∘ global tropospheric ozone column dataset (from ground to 3 km below the tropopause) using OMI and limb instruments, (ii) a monthly 1∘×1∘ global tropospheric ozone column dataset using TROPOMI and limb instruments, and (iii) a daily 1∘×1∘ interpolated stratospheric ozone column from limb instruments. Other datasets, which are created as an intermediate step of creating the tropospheric ozone column data, are (i) a daily 1∘×1∘ clear-sky and total ozone column from OMI and TROPOMI, (ii) a daily 1∘×1∘ homogenized and interpolated dataset of ozone profiles from limb instruments, and (iii) a daily 1∘×1∘ dataset of ozone profiles from SILAM simulations with adjustment to satellite data. These datasets can be used in various studies related to variability and trends in ozone distributions in both the troposphere and the stratosphere. The datasets are processed from the beginning of OMI and TROPOMI measurements until December 2020 and are planned to be regularly extended in the future.

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.

Long-Term Variation in the Tropospheric Nitrogen Dioxide Vertical Column Density over Korea and Japan from the MAX-DOAS Network, 2007–2017

We investigated long-term observations of the tropospheric nitrogen dioxide vertical column density (NO2 TropVCD) from the Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) network in Russia and ASia (MADRAS) from 2007 to 2017 at urban (Yokosuka and Gwangju) and remote (Fukue and Cape Hedo) sites in East Asia. The monthly mean in the NO2 TropVCD from MAX-DOAS measured at ~13:30 local time, which is the Ozone Monitoring Instrument (OMI) overpass time, shows good agreement with OMI data during summer, but differences between the two datasets increase in winter. The Theil-Sen slope of the long-term trend indicate a relatively rapid and gradual reduction in NO2 at Yokosuka and two remote sites (Fukue and Cape Hedo), respectively, regardless of the season except for fall at Fukue, but significant changes in NO2 are not observed at Gwangju, Korea. In contrast, OMI satellite data reveal an increase in the NO2 TropVCD at all sites except for Yokosuka, where a decreasing trend common to MAX-DOAS is found, suggesting that the results from satellites need to be cautiously used for investigating long-term trends in less polluted or remote areas. Using backward trajectories, potential source regions are identified for the two urban sites. The spatial distribution from OMI data shows good agreement with the potential source regions at Yokosuka. The potential source regions in Gwangju are identified as the National Industrial Complex in Yeosu and Gwangyang, while the transport route is not clearly visible with OMI data because of their low sensitivity in less polluted areas. The proposed approach is suitable for identifying potential source areas that might not be recognized by satellite observations.

Synergistic Use of Hyperspectral UV-Visible OMI and Broadband Meteorological Imager MODIS Data for a Merged Aerosol Product

The retrieval of optimal aerosol datasets by the synergistic use of hyperspectral ultraviolet (UV)–visible and broadband meteorological imager (MI) techniques was investigated. The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) was used as a proxy for hyperspectral UV–visible instrument data to which the Geostationary Environment Monitoring Spectrometer (GEMS) aerosol algorithm was applied. Moderate-Resolution Imaging Spectroradiometer (MODIS) L1B and dark target aerosol Level 2 (L2) data were used with a broadband MI to take advantage of the consistent time gap between the MODIS and the OMI. First, the use of cloud mask information from the MI infrared (IR) channel was tested for synergy. High-spatial-resolution and IR channels of the MI helped mask cirrus and sub-pixel cloud contamination of GEMS aerosol, as clearly seen in aerosol optical depth (AOD) validation with Aerosol Robotic Network (AERONET) data. Second, dust aerosols were distinguished in the GEMS aerosol-type classification algorithm by calculating the total dust confidence index (TDCI) from MODIS L1B IR channels. Statistical analysis indicates that the Probability of Correct Detection (POCD) between the forward and inversion aerosol dust models (DS) was increased from 72% to 94% by use of the TDCI for GEMS aerosol-type classification, and updated aerosol types were then applied to the GEMS algorithm. Use of the TDCI for DS type classification in the GEMS retrieval procedure gave improved single-scattering albedo (SSA) values for absorbing fine pollution particles (BC) and DS aerosols. Aerosol layer height (ALH) retrieved from GEMS was compared with Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data, which provides high-resolution vertical aerosol profile information. The CALIOP ALH was calculated from total attenuated backscatter data at 1064 nm, which is identical to the definition of GEMS ALH. Application of the TDCI value reduced the median bias of GEMS ALH data slightly. The GEMS ALH bias approximates zero, especially for GEMS AOD values of >~0.4 and GEMS SSA values of <~0.95. Finally, the AOD products from the GEMS algorithm and MI were used in aerosol merging with the maximum-likelihood estimation method, based on a weighting factor derived from the standard deviation of the original AOD products. With the advantage of the UV–visible channel in retrieving aerosol properties over bright surfaces, the combined AOD products demonstrated better spatial data availability than the original AOD products, with comparable accuracy. Furthermore, pixel-level error analysis of GEMS AOD data indicates improvement through MI synergy.

Anthropogenic and volcanic point source SO<sub>2</sub> emissions derived from TROPOMI on board Sentinel-5 Precursor: first results

Abstract. The paper introduces the first TROPOMI-based sulfur dioxide (SO2) emissions estimates for point sources. A total of about 500 continuously emitting point sources releasing about 10 kt yr−1 to more than 2000 kt yr−1 of SO2, previously identified from Ozone Monitoring Instrument (OMI) observations, were analyzed using TROPOMI (TROPOspheric Monitoring Instrument) measurements for 1 full year from April 2018 to March 2019. The annual emissions from these sources were estimated and compared to similar estimates from OMI and Ozone Mapping Profiling Suite (OMPS) measurements. Note that emissions from many of these 500 sources have declined significantly since 2005, making their quantification more challenging. We were able to identify 274 sources where annual emissions are significant and can be reliably estimated from TROPOMI. The standard deviations of TROPOMI vertical column density data, about 1 Dobson unit (DU, where 1 DU =2.69×1016 molecules cm−2) over the tropics and 1.5 DU over high latitudes, are larger than those of OMI (0.6–1 DU) and OMPS (0.3–0.4 DU). Due to its very high spatial resolution, TROPOMI produces 12–20 times more observations over a certain area than OMI and 96 times more than OMPS. Despite higher uncertainties of individual TROPOMI observations, TROPOMI data averaged over a large area have roughly 2–3 times lower uncertainties compared to OMI and OMPS data. Similarly, TROPOMI annual emissions can be estimated with uncertainties that are 1.5–2 times lower than the uncertainties of annual emissions estimates from OMI. While there are area biases in TROPOMI data over some regions that have to be removed from emission calculations, the absolute magnitude of these are modest, typically within ±0.25 DU, which can be comparable with SO2 values over large sources.

Satellite verification of ultra-low emission reduction effect of coal-fired power plants

In this paper, satellite data from five isolated power plants were used to explore the change local emissions levels belonged to the vicinity of the power plant before and after ultra-low emissions (ULE) retrofitted. Sulphur dioxide (SO2) and nitrogen dioxide (NO2) observations from Ozone monitoring Instrument (OMI) satellite data were used to evaluate the emission changes of the power plants retrofitted by ULE technology. Meanwhile, the actual emission reduction effect of isolated power plants was evaluated by the OMI data. It was observed that the isolated power plants have shown obvious point source characteristics. The average annual SO2 emission reduction ratio of SZ, ZD, JJ, ND and BD power plants after ultra-low transformation is 43.7%, 56.9%, 29.0%, 34.6% and 26.8%, respectively, compared with that before the transformation, while the corresponding PBL-SO2 column concentration reduction ratio is 27.0%, 14.3%, 15.6%, 26.6% and 4.2%, respectively. The average annual NOx emission reduction ratio of the power plants after ultra-low transformation is 44.7%,79.3%,29.0%,73.2% and 29.8%, respectively, compared with that before the transformation, while the corresponding NO2 column concentration reduction ratio is 7.0%,27.2%,25.7%,8.9% and 5.6%, respectively. The trend of emission reduction before and after the ULE retrofitted of SO2 and NO2 column concentration by satellite observation is consistent with the calculated trend of emission reduction. Satellite observations have confirmed the emission reduction effect of ULE technology.

Ozone trends on equatorial and tropical regions of South America using Dobson spectrophotometer, TOMS and OMI satellites instruments

The ozone layer filters solar ultraviolet radiation and is affected by several photochemical and dynamical processes in the atmosphere. Stratospheric ozone formation takes place primarily in tropical regions and it is the consequence of reactions between atmospheric oxygen and the ultraviolet solar radiation. For the purpose of this paper, the atmospheric TOC (Total Ozone Content) measurements are calculated using Dobson spectrophotometers, in conjunction of satellite data of TOMS (Total Ozone Mapping Spectrometer), and OMI (Ozone Monitoring Instrument), at Cachoeira Paulista (22.7oS, 45.0oW) (CP) and Natal (5.8oS, 35.2oW) (NT), Brazil. The time series considered extend from 1974 to 2013 for CP and from 1978 to 2013 for NT. The mean differences in ozone values between Dobson and TOMS, and between Dobson and OMI, are about 3% and 2%, respectively. The correlations of the Dobson spectrophotometers with TOMS are inferior to the correlations of Dobson to OMI data, due to the improved retrieval and algorithm of the more recent OMI instrument. The observed long-term ozone trends obtained for CP and NT with Dobsons, are: −2.76%, for the period of 1974–2013, and -0.78%, for the period of 1978–2013, respectively. The trend in TOC per decade is of −0.71 ± 0.4% at CP, and of −0.22 ± 0.2%, at NT. The estimated seasonal trends for CP in June-July-August are −3.3%, compared to NT, which yields −1% in the same period. NT seasonal trend for December-January-February is −1.3%. This shows that there is a more significant decrease at CP than at NT, which agrees with the fact that the most active ozone production occurs near the equator. The average ozone amounts were 268.3 ± 13.1 DU for CP and 265.9 ± 10.9 DU for NT, and a seasonal cycle with a minimum of 256.0 DU at CP and NT in autumn and maximum of 283 DU at CP and 279 DU at NT in spring, of southern hemisphere.

Analysis of tropospheric NO2 over Iraq using OMI satellite measurements

Tropospheric nitrogen dioxide (NO2) is a trace gas with important impact on atmospheric chemistry, human health and a key pollutant in particular cities, measured from space since the mid-1990s by the GOME, SCIAMACHY, OMI, and GOME-2 instruments. This study present ten years (monthly and yearly averaged) dataset from Ozone Monitoring Instrument (OMI) used to investigate tropospheric NO2 characteristics and variations over Iraq during 2005–2014. Annual NO2 shows an elevation from the northern to the southern and highest values was at central parts of Iraq. Monthly distributions revels higher values NO2 in winter and summer than spring and autumn seasons, and rising NO2 throughout study period over industrial and crowded urban zones. The trend analysis over Baghdad shows a linear growth rate 9.8% per year with an annual average (5.6·1015 molecules per 1 cm2). The air mass trajectory analysis as hotspot regions shows seasonal fluctuations between winter and summer seasons depend on weather conditions and topography. The increased NO2 values in winter are due to anthropogenic emissions and subsequent plumes from Europe. In addition, in summer because of hot weather and large paddy fields emissions. The lowest NO2 value was at monsoon period mostly linked to the rains. The OMI data and satellite information are able to observe the troposphere NO2 elevation at different regions.

Analysis of tropospheric NO2 over Iraq using OMI satellite measurements

Tropospheric nitrogen dioxide (NO2) is a trace gas with important impact on atmospheric chemistry, human health and a key pollutant in particular cities, measured from space since the mid-1990s by the GOME, SCIAMACHY, OMI, and GOME-2 instruments. This study present ten years (monthly and yearly averaged) dataset from Ozone Monitoring Instrument (OMI) used to investigate tropospheric NO2 characteristics and variations over Iraq during 2005–2014. Annual NO2 shows an elevation from the northern to the southern and highest values was at central parts of Iraq. Monthly distributions revels higher values NO2 in winter and summer than spring and autumn seasons, and rising NO2 throughout study period over industrial and crowded urban zones. The trend analysis over Baghdad shows a linear growth rate 9.8% per year with an annual average (5.6·1015 molecules per 1 cm2). The air mass trajectory analysis as hotspot regions shows seasonal fluctuations between winter and summer seasons depend on weather conditions and topography. The increased NO2 values in winter are due to anthropogenic emissions and subsequent plumes from Europe. In addition, in summer because of hot weather and large paddy fields emissions. The lowest NO2 value was at monsoon period mostly linked to the rains. The OMI data and satellite information are able to observe the troposphere NO2 elevation at different regions.

The New Potential of Deep Convective Clouds as a Calibration Target for a Geostationary UV/VIS Hyperspectral Spectrometer

As one of geostationary earth orbit constellation for environmental monitoring over the next decade, the Geostationary Environment Monitoring Spectrometer (GEMS) has been designed to observe the Asia-Pacific region to provide information on atmospheric chemicals, aerosols, and cloud properties. In order to continuously monitor sensor performance after its launch in early 2020, we suggest in this paper deep convective clouds (DCCs) as a possible target for the vicarious calibration of the GEMS, the first ultraviolet and visible hyperspectral sensor onboard a geostationary satellite. The Tropospheric Monitoring Instrument (TROPOMI) and the Ozone Monitoring Instrument (OMI) are used as a proxy for GEMS, and a conventional DCC-detection approach applying a thermal threshold test is used for DCC detection based on collocations with the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite. DCCs are frequently detected over the GEMS observation area at an average of over 200 pixels within a single observation scene. Considering the spatial resolution of the GEMS (3.5 × 8 km2), which is similar to the TROPOMI and its temporal resolution (eight times a day), the availability of DCCs is expected to be sufficient for the vicarious calibration of the GEMS. Inspection of the DCC reflectivity spectra estimated from OMI and TROPOMI data also shows promising results. The estimated DCC spectra are in good agreement within a known uncertainty range with comparable spectral features even with different observation geometries and sensor characteristics. When DCC detection is improved further by applying both visible and infrared tests, the variability of DCC reflectivity from TROPOMI data is reduced from 10% to 5%. This is mainly due to the efficient screening out of cold, thin cirrus clouds in the visible test and of bright, warm clouds in the infrared test. Precise DCC detection is also expected to contribute to the accurate characterization of cloud reflectivity, which will be investigated further in future research.

Intra-annual variations of regional total column ozone, aerosol optical depth, and water vapor from ground-based, satellite-based and model-based observations

Abstract The Microtops II ozonometer has been deployed to continuously measure the total column ozone (TCO), precipitable water content (PWC) and aerosol optical thickness (AOT) at Atigre village (16.74° N, 74.37° E, 604 m above sea level, masl) located on the southeastern slope of Indian western Ghats. The Microtops II ozonometer measurements during the period from September 2017 to June 2018 were analyzed along with the retrieved products of Ozone Monitoring Instrument (OMI) and Moderate Resolution Imaging Spectroradiometer (MODIS) onboard NASA's Terra satellites and also the ERA-interim reanalysis model. We found that the TCO (or PWC) data from the OMI (or MODIS) and the ERA reanalysis model data products are in good agreement. The measurements showed strong seasonal variability such that TCO was lowest in the winter season but increased in the summer season, and both PWC and AOT were lowest in the post-monsoon season and increased in the summer season. In continuous ERA-interim reanalysis TCO observations, we found the three types of periodicities in the daily mean TCO as well as PWC (i) The weekly (7 days), (ii) quasi-biweekly (14 to 16 days), (iii) Madden Julian Oscillations (MJO) (30–60 days) oscillations. These oscillations depend on the periodic weather changes in the troposphere. We also observe that the average day to day variability in daily mean TCO observations in the observational period is about 1.4%, 1.1%, 2.62% in the Microtops II ozonometer, ERA-interim reanalysis, and OMI data respectively. We found the positive correlation between daily mean AOT and PWC in all seasons (except monsoon), which is maximum in the winter season, showing the hygroscopic nature of aerosols. The sources of water vapor and aerosol at our location are also studied using back-trajectory analysis.

Impact of COVID-19 Containment Measures on Air Pollution in California

This study used space- and ground-based sensors in conjunction meteorological and traffic information to evaluate the impact of the COVID-19 containment measures on air pollution in California by comparing data from March–April 2020 to the similar period in 2019. Although significantly lower pollution levels were observed throughout the COVID-19 containment period in 2020 compared to 2019, our meteorological analysis found that periods of enhanced precipitation likely contributed to the cleaner environment over the Central Valley and southern California. Therefore, we focused our assessment on a 19-day period of drier conditions across the region. During this period, TROPOspheric Monitoring Instrument (TROPOMI) data revealed strong reductions in tropospheric NO2 of 40% in Los Angeles, 38% in Fresno, and about 20% in Bakersfield and San Francisco when compared to 2019. The reductions were mostly within about 10% of the decrease in vehicle miles traveled (VMT), which indicates that the decrease in traffic-related NOx due to the COVID-19 lockdown was an important driver of the NO2 reduction. Ozone Monitoring Instrument (OMI) data showed similar NO2 reductions to TROPOMI over Los Angeles during the lockdown, but drastically different results over the other cities where little to no reductions were observed. The close agreement between ground-based and TROPOMI observations indicated that a more accurate assessment of the impacts from the COVID-19 lockdown can be accomplished using TROPOMI rather than OMI data, which is attributed to its improved resolution and sensitivity that can better characterize NO2 pollution associated with fine-scale emissions. Altogether, the space- and ground-based observations provide strong evidence that the containment measures led to NO2 reductions of around 35% in Los Angeles and Fresno and 25% in San Francisco and Bakersfield relative to 2019, along with decreases in PM2.5 and improved air quality at the surface.

Air quality in the Galapagos Islands: A baseline view from remote sensing and in situ measurements

AbstractA characterization of ambient air levels of PM2.5, O3, SO2, NO2 and CO in the Galapagos Islands of Ecuador is presented from in situ and remote sensing observations. PM2.5 was derived from aerosol optical depth (AOD; AERONET) measured at the Universidad San Francisco de Quito, Galapagos Campus (2017–2019). Boundary layer (BL) ozone was obtained from Southern Hemisphere Additional Ozonesondes (SHADOZ) profiles (1998–2016). Background SO2 and pollution events during volcanic eruptions (2005–2018) were estimated from Ozone Monitoring Instrument (OMI) total column measurements through a well‐mixed volume approach. Similarly, ambient NO2 was estimated from OMI data (2012–2019). CO was obtained from Measurement Of Pollution In The Troposphere (MOPITT) observations (2012–2017). The study was complemented using Modern‐Era Retrospective Analysis for Research and Applications (MERRA‐2) reanalysis products and backward trajectory model runs. From the results, baseline levels of the analysed species (PM2.5 = 3.8 μg·m−3, O3 = 17 ppbv, SO2 = 3.6 ppbv, CO = 80 ppbv, NO2 in populated islands = 23 pptv as one year averages) are comparable with other pristine regions, but some factors can cause increased concentrations. First, high tourism seasons (February–April and July–September) raise background PM2.5, NO2 and CO. Furthermore, signals in July–September can be augmented by transport from biomass‐burning regions in the Amazon. This latter factor episodically causes ozone to increase up to 45–75 ppbv (1 hr mean). Lastly, volcanic eruptions raise SO2 up to almost 700 ppbv (24 hr mean) and increase PM2.5 to 29.5 μg·m−3 (1 hr mean). The present study provides for the first time baseline levels of air contaminants in the Galapagos, and identifies specific sources whose effect in time is necessary to monitor given global conditions of vulnerable environmental quality.

Impact of natural gas production on nitrogen dioxide and sulphur dioxide over Northeast British Columbia, Canada

Northeast British Columbia (BC) Canada, is a region in which natural gas production has undergone rapid development since 2007. We used nitrogen dioxide (NO2) and sulphur dioxide (SO2) data products of the Ozone Monitoring Instrument (OMI) to assess the impact of natural gas development activity on air quality in this region from 2005 to 2018. We noticed that values of both pollutants were elevated in the immediate vicinity of large emission sources within the Montney formation and Horn River Basin – regions, which have experienced an increase in unconventional natural gas activities. Places with elevated NO2 Vertical Column Densities (VCDs) are Fort St. John, Taylor, and Dawson Creek located in the Montney formation, and higher SO2 VCDs are found near the Fort Nelson gas plant situated in the Horn River and Liard Basin areas. Although all the OMI data products consistently reported relatively high NO2 VCDs in the same areas, VCD values vary substantially with data products largely due to differences in Air Mass Factor (AMF) calculations. The rate of increase in NO2 VCDs and mass between 2005 and 2018 in the Dawson Creek area was assessed at 2.34% yr−1 and 4.32% yr−1, respectively, and these rates of change are statistically significant. Although we obtained an overall increasing trend for NO2 in Northeast BC, we also noticed a decreasing trend in the period of 2011–2013, which may be attributed, in part, to compliance and enforcement of regulations concerning flaring activities from oil and gas activities in Northeast BC, or due to less development activities. From our analysis, we suggest that the current air quality monitoring network in Northeast BC should be expanded to capture the spatial distribution of SO2 by deploying one additional station near Fort Nelson equipped with meteorology and SO2 monitoring systems.