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This study leverages satellite remote sensing to investigate the impact of the coronavirus outbreak and the resulting lockdown of public venues on air pollution levels in East Asia. We analyze data from the Sentinel-5P and the Himawari-8 satellites to examine concentrations of NO 2 , HCHO, SO 2 , and CO, and the aerosol optical depth (AOD) over the BTH, Wuhan, Seoul, and Tokyo regions in February 2019 and February 2020. Results show that most of the concentrations of pollutants are lower than those of February 2019. Compared to other pollutants, NO 2 experienced the most significant reductions by almost 54%, 83%, 33%, and 19% decrease in BTH, Wuhan, Seoul, and Tokyo, respectively. The greatest reductions in pollutants occurred in Wuhan, with a decrease of almost 83%, 11%, 71%, and 4% in the column densities of NO 2 , HCHO, SO 2 , and CO, respectively, and a decrease of about 62% in the AOD. Although NO 2 , CO, and formaldehyde concentrations decreased in the Seoul and Tokyo metropolitan areas compared to the previous year, concentrations of SO 2 showed an increase in these two regions due to the effect of transport from polluted upwind regions. We also show that meteorological factors were not the main reason for the dramatic reductions of pollutants in the atmosphere. Moreover, an investigation of the HCHO/NO 2 ratio shows that in many regions of East China, particularly in Wuhan, ozone production in February 2020 is less NO X saturated during the daytime than it was in February 2019. With large reductions in the concentrations of NO 2 during lockdown situations, we find that significant increases in surface ozone in East China from February 2019 to February 2020 are likely the result of less reaction of NO and O 3 caused by significantly reduced NO X concentrations and less NO X saturation in East China during the daytime. Unlabelled Image • Remote sensing data, TROPOMI and AHI were used to quantify lockdown impact. • NO 2 experienced the highest decrease in East Asia due to lockdowns. • NO 2 (83%) and SO 2 (71%) decreased more than HCHO (11%) and CO (4%) in Wuhan. • SO 2 increased in Seoul and Tokyo due to polluted air transport in 2020. • HCHO to NO 2 ratio increased from below 1 to above 2 in East China in February 2020. [ABSTRACT FROM AUTHOR]

Covid-19 was first reported in Iraq on February 24, 2020. Since then, to prevent its propagation, the Iraqi government declared a state of health emergency. A set of rapid and strict countermeasures have taken, including locking down cities and limiting population's mobility. In this study, concentrations of four criteria pollutants, NO 2 , O 3 , PM 2.5 and PM 10 before the lockdown from January 16 to February 29, 2020, and during four periods of partial and total lockdown from March 1 to July 24, 2020, in Baghdad were analysed. Overall, 6, 8 and 15% decreases in NO 2 , PM 2.5 , and PM 10 concentrations, respectively in Baghdad during the 1st partial and total lockdown from March 1 to April 21, compared to the period before the lockdown. While, there were 13% increase in O 3 for same period. During the 2nd partial lockdown from June 14 to July 24, NO 2 and PM 2.5 decreases 20 and 2.5%, respectively. While, there were 525 and 56% increase in O 3 and PM 10 , respectively for same period. The air quality index (AQI) improved by 13% in Baghdad during the 1st partial lockdown from March 1 to April 21, compared to its pre-lockdown. The results of NO 2 tropospheric column extracted from the Sentinel-5P satellite shown the NO 2 emissions reduced up to 35 to 40% across Iraq, due to lockdown measures, between January and July, 2020, especially across the major cities such as Baghdad, Basra and Erbil. The lockdown due to COVID-19 has drastic effects on social and economic aspects. However, the lockdown also has some positive effect on natural environment and air quality improvement. Unlabelled Image • NO 2 concentrations reduced by 6, 7, 8 and 20%, respectively in Baghdad during the lockdown. • O 3 concentrations increased by 13%, 75%, 225% and 525%, for the same periods. • AQI improved in Baghdad by 13%, compared to the pre-lockdown. • NO 2 emissions reduced up to 35 to 40% in Iraq compared to the pre-lockdown. [ABSTRACT FROM AUTHOR]

The lockdown measures due to COVID-19 affected the industry, transportation and other human activities within China in early 2020, and subsequently the emissions of air pollutants. The decrease of atmospheric NO 2 due to the COVID-19 lockdown and other factors were quantitively analyzed based on the surface concentrations by in-situ observations, the tropospheric vertical column densities (VCDs) by different satellite retrievals including OMI and TROPOMI, and the model simulations by GEOS-Chem. The results indicated that due to the COVID-19 lockdown, the surface NO 2 concentrations decreased by 42% ± 8% and 26% ± 9% over China in February and March 2020, respectively. The tropospheric NO 2 VCDs based on both OMI and high quality (quality assurance value (QA) ≥ 0.75) TROPOMI showed similar results as the surface NO 2 concentrations. The daily variations of atmospheric NO 2 during the first quarter (Q1) of 2020 were not only affected by the COVID-19 lockdown, but also by the Spring Festival (SF) holiday (January 24–30, 2020) as well as the meteorology changes due to seasonal transition. The SF holiday effect resulted in a NO 2 reduction from 8 days before SF to 21 days after it (i.e. January 17 - February 15), with a maximum of 37%. From the 6 days after SF (January 31) to the end of March, the COVID-19 lockdown played an important role in the NO 2 reduction, with a maximum of 51%. The meteorology changes due to seasonal transition resulted in a nearly linear decreasing trend of 25% and 40% reduction over the 90 days for the NO 2 concentrations and VCDs, respectively. Comparisons between different datasets indicated that medium quality (QA ≥ 0.5) TROPOMI retrievals might suffer large biases in some periods, and thus attention must be paid when they are used for analyses, data assimilations and emission inversions. Image 1 • NO 2 in China decreased by 42% and 26% in February and March 2020 due to COVID-19. • The NO 2 VCDs by satellites were consistent with the surface NO 2 concentrations. • Spring Festival holiday led to a NO 2 decrease in 30 days with a maximum of 37%. • COVID-19 led to a NO 2 decrease from 6 days after SF with a maximum of 51%. • NO 2 concentrations and VCDs decreased by 25% and 40% in 90 days due to meteorology. [ABSTRACT FROM AUTHOR]

A lockdown was implemented in Canada mid-March 2020 to limit the spread of COVID-19. In the wake of this lockdown, declines in nitrogen dioxide (NO 2 ) were observed from the TROPOspheric Monitoring Instrument (TROPOMI). A method is presented to quantify how much of this decrease is due to the lockdown itself as opposed to variability in meteorology and satellite sampling. The operational air quality forecast model, GEM-MACH (Global Environmental Multi-scale - Modelling Air quality and CHemistry), was used together with TROPOMI to determine expected NO 2 columns that represents what TROPOMI would have observed for a non-COVID scenario. Applying this methodology to southern Ontario, decreases in NO 2 emissions due to the lockdown were seen, with an average 40 % (roughly 10 kt[NO 2 ]/yr) in Toronto and Mississauga and even larger declines in the city center. Natural and satellite sampling variability accounted for as much as 20–30%, which demonstrates the importance of taking meteorology into account. A model run with reduced emissions (from 65 kt[NO 2 ]/yr to 40 kt[NO 2 ]/yr in the Greater Toronto Area) based on emission activity data during the lockdown period was found to be consistent with TROPOMI NO 2 columns. [ABSTRACT FROM AUTHOR]

The unequal spatial distribution of ambient nitrogen dioxide ([Formula: see text]), an air pollutant related to traffic, leads to higher exposure for minority and low socioeconomic status communities. We exploit the unprecedented drop in urban activity during the COVID-19 pandemic and use high-resolution, remotely sensed [Formula: see text] observations to investigate disparities in [Formula: see text] levels across different demographic subgroups in the United States. We show that, prior to the pandemic, satellite-observed [Formula: see text] levels in the least White census tracts of the United States were nearly triple the [Formula: see text] levels in the most White tracts. During the pandemic, the largest lockdown-related [Formula: see text] reductions occurred in urban neighborhoods that have 2.0 times more non-White residents and 2.1 times more Hispanic residents than neighborhoods with the smallest reductions. [Formula: see text] reductions were likely driven by the greater density of highways and interstates in these racially and ethnically diverse areas. Although the largest reductions occurred in marginalized areas, the effect of lockdowns on racial, ethnic, and socioeconomic [Formula: see text] disparities was mixed and, for many cities, nonsignificant. For example, the least White tracts still experienced ∼1.5 times higher [Formula: see text] levels during the lockdowns than the most White tracts experienced prior to the pandemic. Future policies aimed at eliminating pollution disparities will need to look beyond reducing emissions from only passenger traffic and also consider other collocated sources of emissions such as heavy-duty vehicles., Competing Interests: The authors declare no competing interest., (Copyright © 2021 the Author(s). Published by PNAS.)

While multiple information sources exist concerning surface-level air pollution, no individual source simultaneously provides large-scale spatial coverage, fine spatial and temporal resolution, and high accuracy. It is, therefore, necessary to integrate multiple data sources, using the strengths of each source to compensate for the weaknesses of others. In this study, we propose a method incorporating outputs of NASA's GEOS Composition Forecasting model system with satellite information from the TROPOMI instrument and ground measurement data on surface concentrations. Although we use ground monitoring data from the Environmental Protection Agency network in the continental United States, the model and satellite data sources used have the potential to allow for global application. This method is demonstrated using surface measurements of nitrogen dioxide as a test case in regions surrounding five major US cities. The proposed method is assessed through cross-validation against withheld ground monitoring sites. In these assessments, the proposed method demonstrates major improvements over two baseline approaches which use ground-based measurements only. Results also indicate the potential for near-term updating of forecasts based on recent ground measurements., (© 2021. The Authors.)

Nitrogen dioxide (NO 2 ) is an important air pollutant and highly related to air quality, short- and long-term health effects, and even climate. A national model was developed using the extreme gradient boosting algorithm with high-resolution tropospheric vertical column NO 2 densities from the Sentinel-5 Precursor/Tropospheric Monitoring Instrument and general meteorological variables as input to generate daily mean surface NO 2 concentrations across mainland China. Model-derived daily NO 2 estimates were high accuracy with sample-based cross-validation coefficient of determination of 0.83, a root-mean-square error of 7.58 μg/m 3 , a mean prediction error of 5.56 μg/m 3 , and a mean relative prediction error of 18.08%. It has good performance in NO 2 estimations at both regional and individual site scale. The model also performed well in terms of estimating monthly, seasonal, and annual mean NO 2 concentrations across China. The model performance appears to better than or comparable to most previous related studies. The seasonal and annual spatial distributions of surface NO 2 across China and several regional NO 2 hotspots in 2019 were derived from the model and analyzed. Also evaluated were the population exposure levels of NO 2 for cities in and provinces of China. At the national scale, about 12% of the population experienced annual mean NO 2 concentrations exceeding the Chinese national air quality standard. The nationwide model with conventional predictors developed here can derive high-resolution surface NO 2 concentrations across China routinely, benefitting air epidemiological and environmental related studies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

In this paper, we present the Hefei EMI Cloud Retrieval Algorithm (HECORA), which uses information from the O2-O2 absorption band around 477 nm to retrieve effective cloud fraction and effective cloud pressure from satellite observations. The retrieved cloud information intends to improve the atmospheric trace gas products based on the Environment Monitoring Instrument (EMI) spectrometer. The HECORA method builds on OMCLDO2 and presents some evolutions. The Vector Linearized Discrete Ordinate Radiative Transfer (VLIDORT) model has been used to produce the Top of the Atmosphere (TOA) reflectance Look-up Tables (LUT) as a function of the cloud fraction and cloud pressure. Applying the Differential Optical Absorption Spectroscopy (DOAS) technique to the synthetic reflectance LUT, the reflectance spectra can be associated with O2-O2 geometrical vertical column densities (VCDgeo) and continuum reflectance. This is the core of the retrieval method, since there is a one-to-one relationship between O2-O2 VCDgeo and continuum reflectance, on the one hand, and effective cloud fraction and effective cloud pressure, on the other hand, for a given illumination and observing geometry and given surface height and surface albedo. We first used the VLIDORT synthetic spectra to verify the HECORA algorithm and obtained good results in both the Lambertian cloud model and the scattering cloud model. Secondly, HECORA is applied to OMI and TROPOMI and compared with OMCLDO2, FRESCO+, and OCRA/ROCINN cloud products. Later, the cloud pressure results from TROPOMI observations obtained using HECORA and FRESCO+ are compared with the CALIOP Cloud Layer product. HECORA is closer to the CALIOP results under low cloud conditions, while FRESCO+ is closer to high clouds due to the higher sensitivity of the O2 A-band to cloud vertical information. Finally, HECORA is applied to the TROPOMI NO2 retrieval. Validation of the tropospheric NO2 VCD with ground-based MAX-DOAS measurements shows that choosing HECORA cloud products to correct for photon path variations on the TROPOMI tropospheric NO2 VCD retrievals has better performance than using FRESCO+ under low cloud conditions. In conclusion, this paper shows that the HECORA cloud products are in good agreement with the well-established cloud products and that they are suitable for correcting the effect of cloud in trace gas retrievals. Therefore, HECORA has the potential to be applied to EMI. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO2) is an essential air pollutant related to adverse health effects. A space-time neural network model is developed for the estimation of ground-level NO2 in this study by integrating ground NO2 station measurements, satellite NO2 products, simulation data, and other auxiliary data. Specifically, a geographically and temporally weighted generalized regression neural network (GTW-GRNN) model is used with the advantage to consider the spatiotemporal variations of the relationship between NO2 and influencing factors in a nonlinear neural network framework. The case study across the Wuhan urban agglomeration (WUA), China, indicates that the GTW-GRNN model outperforms the widely used geographically and temporally weighted regression (GTWR), with the site-based cross-validation R2 value increasing by 0.08 (from 0.61 to 0.69). Besides, the comparison between the GTW-GRNN and original global GRNN models shows that considering the spatiotemporal variations in GRNN modeling can boost estimation accuracy. All these results demonstrate that the GTW-GRNN based NO2 estimation framework will be of great use for remote sensing of ground-level NO2 concentrations. [ABSTRACT FROM AUTHOR]

While solar-induced fluorescence (SIF) shows promise as a remotely-sensed measurement directly related to photosynthesis, interpretation and validation of satellite-based SIF retrievals remains a challenge. SIF is influenced by the fraction of absorbed photosynthetically-active radiation at the canopy level that depends upon illumination geometry as well as the escape of SIF through the canopy that depends upon the viewing geometry. Several approaches to estimate the effects of sun-sensor geometry on satellite-based SIF have been proposed, and some have been implemented, most relying upon satellite reflectance measurements and/or other ancillary data sets. These approaches, designed to ultimately estimate intrinsic or physiological components of SIF related to photosynthesis, have not generally been applied globally to satellite measurements. Here, we examine in detail how SIF and related reflectance-based indices from wide swath polar orbiting satellites in low Earth orbit vary systematically due to the host satellite orbital characteristics. We compare SIF and reflectance-based parameters from the Global Ozone Mapping Experiment 2 (GOME-2) on the MetOp-B platform and from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel 5 Precursor satellite with a focus on high northern latitudes in summer where observations at similar geometries and local times occur. We show that GOME-2 and TROPOMI SIF observations agree nearly to within estimated uncertainties when they are compared at similar observing geometries. We show that the cross-track dependence of SIF normalized by PAR and related reflectance-based indices are highly correlated for dense canopies, but diverge substantially as the vegetation within a field-of-view becomes more sparse. This has implications for approaches that utilize reflectance measurements to help account for SIF geometrical dependences in satellite measurements. To further help interpret the GOME-2 and TROPOMI SIF observations, we simulated cross-track dependences of PAR normalized SIF and reflectance-based indices with the one dimensional Soil-Canopy Observation Photosynthesis and Energy fluxes (SCOPE) canopy radiative transfer model at sun–satellite geometries that occur across the wide swaths of these instruments and examine the geometrical dependencies of the various components (e.g., fraction of absorbed PAR, SIF yield, and escape of SIF from the canopy) of the observed SIF signal. The simulations show that most of the cross-track variations in SIF result from the escape of SIF through the scattering canopy and not the illumination. [ABSTRACT FROM AUTHOR]

The new-generation sensor TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel 5 precursor (S5P) satellite is promising for monitoring air pollutants with greater spatial resolution, especially for China, which suffers from severe pollution. As tropospheric NO2 vertical column densities (VCDs) from TROPOMI have become available since February 2018, this study presents the comparisons of NO2 data measured by TROPOMI and its predecessor Ozone Monitoring Instrument (OMI) over China, together with validation against ground Multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements. At the nationwide scale, we used two different filters performed for the TROPOMI data (named TROPOMI50 and TROPOMI75), and the TROPOMI50 yielded larger values than TROPOMI75. The TROPOMI NO2 datasets from different filters show consistent spatial patterns with OMI, and the correlation coefficient values were both above 0.93. However, linear regression indicates that NO2 loadings in TROPOMI is about 2/3 to 4/5 of those in OMI, which is presumably due to a different cloud mask and uncertainties of air mass factors. The absolute difference is prominent over the high pollution areas such as Jing-Jin-Ji region and during winter and autumn, exceeding 0.6 × 1016 molecules cm−2 (molec cm−2). However, the NO2 concentrations retrieved from TROPOMI50 in the southern China may be somewhat higher than OMI. When it comes to the local-scale Jing-Jin-Ji hotspot, the analysis focuses on a comparison to TROPOMI75. TROPOMI manifests high quality and exhibits a significantly better performance of representing spatial variability. In contrast, OMI shows fewer effective pixels and does a poor job of capturing local details due to its row anomaly and low resolution. The absolute difference between two datasets shows the same seasonal behavior with NO2 variation, which is most striking in the winter (0.31 × 1016 molec cm−2) and is lowest in the summer (0.05 × 1016 molec cm−2). Furthermore, the ground MAX-DOAS instrument in Xianghe station, the representative site in Jing-Jin-Ji, is used to assess the skill of satellite retrievals. It turns out that both OMI and TROPOMI underestimate the observations, ranging from 30% to 50%, with OMI being less biased. In spite of the negative drift, the temporal structures of changes derived from OMI and TROPOMI closely match the ground-based records, since the correlation coefficients are above 0.8 and 0.95 for daily and monthly scales, respectively. Overall, TROPOMI NO2 retrievals are better suited for applications in China as well as the Jing-Jin-Ji hotspot due to its higher spatial resolution, although some improvements are also needed in the near future. [ABSTRACT FROM AUTHOR]

Solar-induced chlorophyll fluorescence (SIF) measured from space has been increasingly used to quantify plant photosynthesis at regional and global scales. Apparent canopy SIF yield (SIF yield apparent), determined by fluorescence yield (Φ F) and escaping ratio (fesc), together with absorbed photosynthetically active radiation (APAR), is crucial in driving spatio-temporal variability of SIF. While strong linkages between SIF yield apparent and plant physiological responses and canopy structure have been suggested, spatio-temporal variability of SIF yield apparent at regional scale remains largely unclear, which limits our understanding of the spatio-temporal variability of SIF and its relationship with photosynthesis. In this study, we utilized recent SIF data with high spatial resolution from two satellite instruments, OCO-2 and TROPOMI, together with multiple other datasets. We estimated SIF yield apparent across space, time, and different vegetation types in the U.S. Midwest during crop growing season (May to September) from 2015 to 2018. We found that SIF yield apparent of croplands was larger than non-croplands during peak season (July–August). However, SIF yield apparent between corn (C4 crop) and soybean (C3 crop) did not show a significant difference. SIF yield apparent of corn, soybean, forest, and grass/pasture show clear seasonal and spatial patterns. The spatial variability of precipitation during the growing season could explain the overall spatial pattern of SIF yield apparent. Further analysis by decomposing SIF yield apparent into Φ F and fesc using near-infrared reflectance of vegetation (NIR V) suggests that fesc may be the major driver of the observed variability of SIF yield apparent. • SIF yield apparent is interpreted using high spatial resolution satellite footprints. • Different spatio-temporal patterns of SIF yield apparent are revealed among vegetation types. • SIF yield apparent of croplands is larger than non-croplands in summer. • Escaping ratio largely explains the variations of SIF yield apparent in the Midwest. • Spatial variability of SIF yield apparent is correlated to precipitation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

U.S. oil and natural gas production volumes have grown by up to 100% in key production areas between January 2017 and August 2019. Here we show that recent trends are visible from space and can be attributed to drilling, production, and gas flaring activities. By using oil and gas activity data as predictors in a multivariate regression to satellite measurements of tropospheric NO2 columns, observed changes in NO2 over time could be attributed to NOx emissions associated with drilling, production and gas flaring for three select regions: the Permian, Bakken, and Eagle Ford basins. We find that drilling had been the dominant NOx source contributing around 80% before the downturn in drilling activity in 2015. Thereafter, NOx contributions from drilling activities and combined production and flaring activities are similar. Comparison of our top‐down source attribution with a bottom‐up fuel‐based oil and gas NOx emission inventory shows agreement within error margins. Plain Language Summary: U.S. oil and natural gas production volumes have grown by up to 100% in key production areas between January 2017 and August 2019. Here we show that recent trends are visible from space as increases in NO2, an air pollutant that is released from combustion engines associated with the oil and gas industry. For three select regions, the Permian (TX and NM), Bakken (ND), and Eagle Ford (TX) basins, we report that the trend in NO2 columns over time can be explained by a combination of drilling activity, production numbers, and flared gas volume, which allows us to quantify the contributions from these sources to the total NOx (= NO + NO2) emissions from these areas. We find that drilling had been the dominant NOx source contributing around 80% before the downturn in drilling activity in 2015. But now, NOx contributions from drilling activities and combined production and flaring activities are similar. Both Permian and Bakken oil and gas production volumes are at an all‐time high and if current growth rates continue in the Eagle Ford basin, maximum production volumes will be exceeded in about 1 year. Key Points: Recent increases in U.S. oil and gas production are seen from space as increased tropospheric NO2 columns and increased gas flaringChanges in NO2 over time can be attributed to oil and gas NOx emissions associated with drilling, production, and gas flaring activitiesTop‐down and bottom‐up source attributions agree that drilling and, to a lesser extent, production are the main sources of NOx emissions [ABSTRACT FROM AUTHOR]

Fine-scale nitrogen oxide (NOx) concentrations over South Korea are examined using surface observations, satellite data and high-resolution model simulations based on the latest emission inventory. While accurate information on NOx emissions in South Korea is crucial to understanding regional air quality in the region, consensus on the validation of NOx emissions is lacking. We investigate the spatial and temporal variation in fine-scale NOx emission sources over South Korea. Surface observations and newly available fine-scale satellite data (TROPOspheric Monitoring Instrument; TROPOMI; 3.5 × 7 km2) are compared with the community multiscale air quality (CMAQ) model based on the clean air policy support system (CAPSS) 2016 emission inventory. The results show that the TROPOMI NO2 column densities agree well with the CMAQ simulations based on CAPSS emissions (e.g., R = 0.96 for June 2018). The surface observations, satellite data and model are consistent in terms of their spatial distribution, the overestimation over the Seoul Metropolitan Area and major point sources; however, the model tends to underestimate the surface concentrations during the cold season. [ABSTRACT FROM AUTHOR]

Lockdown restrictions were implemented in Italy from 10 March 2020 to contain the COVID-19 pandemic. Our study aims to evaluate air pollution changes, with focus on nitrogen dioxide (NO 2 ), before and during the lockdown in Rome and in the surroundings. Significant NO 2 declines were observed during the COVID-19 pandemic with reductions of - 50%, - 34%, and - 20% at urban traffic, urban background, and rural background stations, respectively. Tropospheric NO 2 vertical column density (VCD) from the TROPOspheric Monitoring Instrument (TROPOMI) was used to evaluate the spatial-temporal variations of the NO 2 before and during the lockdown for the entire area where the surface stations are located. The evaluation is concerned with the pixels including one or more air quality stations to explore the capability of the unprecedented high spatial resolution to monitor urban and rural sites from space with relation to the surface measurements. Good agreement between surface concentration and TROPOMI VCD was obtained in Rome (R = 0.64 in 2019, R = 0.77 in 2020) and in rural sites (R = 0.71 in 2019). Inversely, a slight correlation (R = 0.20) was observed in rural areas during the lockdown due to very low levels of NO 2 . Finally, the TROPOMI VCD showed a sharp decline in NO 2 , larger in urban (- 43%) than in rural sites (- 17%) as retrieved with the concurrent surface measurements averaging all the traffic and urban background (- 44%) and all the rural background stations (- 20%). These results suggest air pollution improvement in Rome gained from implementing lockdown restrictions.

Satellite HCHO data are widely used as a reliable proxy of non-methane volatile organic compounds (NMVOCs) to constrain underlying emissions and chemistry. Here, we examine global significant changes in HCHO columns at the early stage of the COVID-19 pandemic (January-April 2020) compared with the same period in 2019 with observations from the TROPOspheric Monitoring Instrument (TROPOMI). HCHO columns decline (11.0%) in the Northern China Plain (NCP) because of a combination of meteorological impacts, lower HCHO yields as NO x emission plunges (by 36.0%), and reduced NMVOC emissions (by 15.0%) resulting from the lockdown. HCHO columns change near Beijing (+8.4%) due mainly to elevated hydroxyl radical as NO x emission decreases in a NO x -saturated regime. HCHO columns change in Australia (+17.5%), Northeastern Myanmar of Southeast Asia (+14.9%), Central Africa (+7.8%), and Central America (+18.9%), consistent with fire activities. Our work also points to other changes related to temperature and meteorological variations., (© 2021. The Authors.)

Nitrogen dioxide (NO 2 ) is an important air pollutant that causes direct harms to the environment and human health. Ground NO 2 mapping with high spatiotemporal resolution is critical for fine-scale air pollution and environmental health research. We thus developed a spatiotemporal regression kriging model to map daily high-resolution (3-km) ground NO 2 concentrations in China using the Tropospheric Monitoring Instrument (TROPOMI) satellite retrievals and geographical covariates. This model combined geographically and temporally weighted regression with spatiotemporal kriging and achieved robust prediction performance with sample-based and site-based cross-validation R 2 values of 0.84 and 0.79. The annual mean and standard deviation of ground NO 2 concentrations from June 1, 2018 to May 31, 2019 were predicted to be 15.05 ± 7.82 μg/m 3 , with that in 0.6% of China's area (10% of the population) exceeding the annual air quality standard (40 μg/m 3 ). The ground NO 2 concentrations during the coronavirus disease (COVID-19) period (January and February in 2020) was 14% lower than that during the same period in 2019 and the mean population exposure to ground NO 2 was reduced by 25%. This study was the first to use TROPOMI retrievals to map fine-scale daily ground NO 2 concentrations across all of China. This was also an early application to use the satellite-estimated ground NO 2 data to quantify the impact of the COVID-19 pandemic on the air pollution and population exposures. These newly satellite-derived ground NO 2 data with high spatiotemporal resolution have value in advancing environmental and health research in China., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Since its first confirmed case in December 2019, coronavirus disease 2019 (COVID-19) has become a worldwide pandemic with more than 90 million confirmed cases by January 2021. Countries around the world have enforced lockdown measures to prevent the spread of the virus, introducing a temporal change of air pollutants such as nitrogen dioxide (NO 2 ) that are strongly related to transportation, industry, and energy. In this study, NO 2 variations over regions with strong responses to COVID-19 are analysed using datasets from the Global Ozone Monitoring Experiment-2 (GOME-2) sensor aboard the EUMETSAT Metop satellites and TROPOspheric Monitoring Instrument (TROPOMI) aboard the EU/ESA Sentinel-5 Precursor satellite. The global GOME-2 and TROPOMI NO 2 datasets are generated at the German Aerospace Center (DLR) using harmonized retrieval algorithms; potential influences of the long-term trend and seasonal cycle, as well as the short-term meteorological variation, are taken into account statistically. We present the application of the GOME-2 data to analyze the lockdown-related NO 2 variations for morning conditions. Consistent NO 2 variations are observed for the GOME-2 measurements and the early afternoon TROPOMI data: regions with strong social responses to COVID-19 in Asia, Europe, North America, and South America show strong NO 2 reductions of ∼ 30-50% on average due to restriction of social and economic activities, followed by a gradual rebound with lifted restriction measures., Supplementary Information: The online version contains supplementary material available at 10.1007/s11869-021-01046-2., Competing Interests: Conflict of interestThe authors declare no competing interests., (© The Author(s) 2021.)

Methane emissions due to accidents in the oil and natural gas sector are very challenging to monitor, and hence are seldom considered in emission inventories and reporting. One of the main reasons is the lack of measurements during such events. Here we report the detection of large methane emissions from a gas well blowout in Ohio during February to March 2018 in the total column methane measurements from the spaceborne Tropospheric Monitoring Instrument (TROPOMI). From these data, we derive a methane emission rate of 120 ± 32 metric tons per hour. This hourly emission rate is twice that of the widely reported Aliso Canyon event in California in 2015. Assuming the detected emission represents the average rate for the 20-d blowout period, we find the total methane emission from the well blowout is comparable to one-quarter of the entire state of Ohio's reported annual oil and natural gas methane emission, or, alternatively, a substantial fraction of the annual anthropogenic methane emissions from several European countries. Our work demonstrates the strength and effectiveness of routine satellite measurements in detecting and quantifying greenhouse gas emission from unpredictable events. In this specific case, the magnitude of a relatively unknown yet extremely large accidental leakage was revealed using measurements of TROPOMI in its routine global survey, providing quantitative assessment of associated methane emissions.

In recent years, solar‐induced chlorophyll fluorescence (SIF) retrieved from spaceborne spectrometers has been extensively used as a proxy for terrestrial photosynthesis at relatively sparse temporal and spatial scales. The near‐infrared band of the recently launched TROPOspheric Monitoring Instrument (TROPOMI) features the required spectral resolution and signal‐to‐noise ratio to retrieve SIF in a spectral range devoid of atmospheric absorption features. We find that initial TROPOMI spectra meet high expectations for a substantially improved spatiotemporal resolution (up to 7‐km × 3.5‐km pixels with daily revisit), representing a step change in SIF remote sensing capabilities. However, interpretation requires caution, as the broad range of viewing‐illumination geometries covered by TROPOMI's 2,600‐km‐wide swath needs to be taken into account. A first intersensor comparison with OCO‐2 (Orbiting Carbon Observatory‐2) SIF shows excellent agreement, underscoring the high quality of TROPOMI's SIF retrievals and the notable radiometric performance of the instrument. Plain Language Summary: Photosynthesis is the most essential process for life on Earth, but gradually changing environmental conditions such as increasing concentrations of atmospheric trace gases, rising temperatures, or reduced water availability could adversely affect the photosynthetic productivity. The recently launched TROPOspheric Monitoring Instrument is designed to monitor atmospheric trace gases and air pollutants with an unprecedented resolution in space and time, while its radiometric performance also permits us to see a weak electromagnetic signal emitted by photosynthetically active vegetation—solar‐induced chlorophyll fluorescence (SIF). Mounting evidence suggests that SIF observations from satellite instruments augment our abilities to track the photosynthetic performance and carbon uptake of terrestrial vegetation. In this study, we present the first TROPOspheric Monitoring Instrument SIF retrievals, largely outperforming previous and existing capabilities for a spatial continuous monitoring of SIF from space. Key Points: We present the first solar‐induced chlorophyll fluorescence (SIF) observations from TROPOMI near‐infrared band measurementsTROPOMI enables unprecedented spatiotemporal resolution of global SIF mapsIntersensor comparison between TROPOMI and OCO‐2 SIF data shows excellent agreement [ABSTRACT FROM AUTHOR]