Pinpointing nitrogen oxide emissions from space

Hyperspectral remote sensing for air pollutants: Stereoscopic monitoring, source localization & warning, and a dynamic emission inventory concept

With the continuous improvement of air quality in China, the characteristics of emission sources of pollutants have changed significantly, from their distribution to emitted atmospheric species and the corresponding emission concentrations and source localization has become increasingly challenging. The localization uncertainties of in situ observations are further amplified when combined with model simulations, which seriously restricts the realization of China's strategic goal of "reducing pollution and carbon." In this study, we established a localization and emission warning scheme for emission sources based on various hyperspectral remote sensing techniques with different observation spatial resolutions. These include satellite remote sensing, horizontal remote sensing, Unmanned Aerial Vehicle (UAV) remote sensing, and imaging. Based on this study, we aimed to locate high-concentration emission sources of NO2 (coal-fired power plants), HCHO (chemical and coking industries), and CH2CCH3CHO (metallurgical and material synthesis industries) and provide excess emission warnings for these species. Moreover, hyperspectral imaging remote sensing technology provides a possible method to obtain a dynamic emission inventory of pollutants, and the emission concentrations of NO2, SO2, HCHO, CHOCHO, and CH2CCH3CHO emitted from the coking industry at different timescales were obtained. The localization and emission warning scheme of pollutants established based on stereoscopic remote sensing, as well as the dynamic emission inventory established based on hyperspectral imaging remote sensing, provides technical and data support for air pollution control efforts.

Transforming CCTV cameras into NO2 sensors at city scale for adaptive policymaking

Air pollution in cities, especially NO2, is linked to numerous health problems, ranging from mortality to mental health challenges and attention deficits in children. While cities globally have initiated policies to curtail emissions, real-time monitoring remains challenging due to limited environmental sensors and their inconsistent distribution. This gap hinders the creation of adaptive urban policies that respond to the sequence of events and daily activities affecting pollution in cities. Here, we demonstrate how city CCTV cameras can act as a pseudo-NO2 sensors. Using a predictive graph deep model, we utilised traffic flow from London's cameras in addition to environmental and spatial factors, generating NO2 predictions from over 133 million frames. Our analysis of London's mobility patterns unveiled critical spatiotemporal connections, showing how specific traffic patterns affect NO2 levels, sometimes with temporal lags of up to 6 h. For instance, if trucks only drive at night, their effects on NO2 levels are most likely to be seen in the morning when people commute. These findings cast doubt on the efficacy of some of the urban policies currently being implemented to reduce pollution. By leveraging existing camera infrastructure and our introduced methods, city planners and policymakers could cost-effectively monitor and mitigate the impact of NO2 and other pollutants.

Mapping NO x emissions in Cyprus using TROPOMI observations: evaluation of the flux-divergence scheme using multiple parameter sets

The production of nitrogen oxides (NOx = NO + NO2 ) is substantial in urban areas and from fossil fuel-fired power plants, causing both local and regional pollution, with severe consequences for human health. To estimate their emissions and implement air quality policies, authorities often rely on reported emission inventories. The island of Cyprus is de facto divided into two different political entities, and as a result, such emissions inventories are not systematically available for the whole island. We map NOx emissions in Cyprus for two 6-month periods in 2021 and 2022 with a flux-divergence scheme, using spaceborne retrievals of nitrogen dioxide (NO2 ) columns at high spatial resolution from the TROPOMI instrument, as well as horizontal wind data to derive advection and concentrations of OH, NO, and NO2 to derive chemical processes. Emissions are estimated under three different sets of parameters using ECMWF data and WRF-Chem simulations. These sets are chosen for their differences in spatial resolution and representation of wind and air composition. Exploiting the low emissions in Cyprus, we show that the flux-divergence method is limited by the resolution of wind and hydroxyl radical, the signal-to-noise ratio of the observed tropospheric column densities, and the NOx :NO2 ratio above the main pollution sources. Such limitations lead to large discrepancies in the emissions calculated with the three different sets of parameters, making it difficult to estimate NOx emissions for the five power plants of the island without high uncertainties. Nevertheless, the obtained emissions display a higher seasonality than reported or inventory emissions. For the two power plants in the south, the different mean daytime output estimates appear to be significantly higher than the bottom-up estimates. They are also higher than those from the power plants in the south combined, despite a much lower production capacity, illustrating the application of different environmental norms and the use of different technologies and fuels in the two parts of Cyprus.

Quantifying NO x Emission Sources in Houston, Texas Using Remote Sensing Aircraft Measurements and Source Apportionment Regression Models

Air quality managers in areas exceeding air pollution standards are motivated to understand where there are further opportunities to reduce NO x emissions to improve ozone and PM2.5 air quality. In this project, we use a combination of aircraft remote sensing (i.e., GCAS), source apportionment models (i.e., CAMx), and regression models to investigate NO x emissions from individual source-sectors in Houston, TX. In prior work, GCAS column NO2 was shown to be close to the "truth" for validating column NO2 in model simulations. Column NO2 from CAMx was substantially low biased compared to Pandora (-20%) and GCAS measurements (-31%), suggesting an underestimate of local NO x emissions. We applied a flux divergence method to the GCAS and CAMx data to distinguish the linear shape of major highways and identify NO2 underestimates at highway locations. Using a multiple linear regression (MLR) model, we isolated on-road, railyard, and "other" NO x emissions as the likeliest cause of this low bias, and simultaneously identified a potential overestimate of shipping NO x emissions. Based on the MLR, we modified on-road and shipping NO x emissions in a new CAMx simulation and increased the background NO2, and better agreement was found with GCAS measurements: bias improved from -31% to -10% and r2 improved from 0.78 to 0.80. This study outlines how remote sensing data, including fine spatial information from newer geostationary instruments, can be used in concert with chemical transport models to provide actionable information for air quality managers to identify further opportunities to reduce NO x emissions.

Estimating global 0.1° scale gridded anthropogenic CO2 emissions using TROPOMI NO2 and a data-driven method

Satellite remote sensing is a promising approach for monitoring global CO2 emissions. However, existing satellite-based CO2 observations are too coarse to meet the requirements of fine-scale global mapping. We propose a novel data-driven method to estimate global anthropogenic CO2 emissions at a 0.1° scale, which integrates emissions inventories and satellite data while bypassing the inadequate accuracy of CO2 observations. Due to the co-emitted anthropogenic emissions of nitrogen oxides (NOx = NO + NO2) and CO2, high-resolution NO2 measurements from the TROPOspheric Monitoring Instrument (TROPOMI) are employed to map the global anthropogenic emissions at a global 0.1° scale. We construct the driving features from NO2 data and also incorporate gridded CO2/NOx emission ratios and NOx/NO2 conversion ratios as driving data to describe co-emissions. Both ratios are predicted using a long short-term memory (LSTM) neural network (with an R2 of 0.984 for the CO2/NOx emission ratio and an R2 of 0.980 for the NOx/NO2 conversion ratio). The data-driven model for estimating anthropogenic CO2 emissions is implemented by random forest regression (RFR) and trained using the Emissions Database for Global Atmospheric Research (EDGAR). The satellite-based anthropogenic CO2 emission dataset at a global 0.1° scale agrees well with the national CO2 emission inventories (an R2 of 0.998 with Global Carbon Budget (GCB) and an R2 of 0.996 with EDGAR) and consistent with city-level emission estimates from Carbon Monitor Cities (CMC) with the R2 of 0.824. This data-driven method based on satellite-observed NO2 provides a new perspective for fine-resolution anthropogenic CO2 emissions estimation.

Xerogel mesoporous materials based on ultrastable Blatter radicals for efficient sorption of nitric oxide

The reduction of hazardous nitric oxide emissions remains a significant ecological challenge. Despite the variety of possibilities, sorbents able to capture low concentrations of NO from flue gas with high selectivity are still in demand. In this work a new type of mesoporous xerogel material highly loaded with ultrastable Blatter radicals (BTR, >60 % by mass) that act as selective NO sorption sites is developed. Electron Paramagnetic Resonance (EPR) spectroscopy evidences reversible NO sorption in nanometer-scale pores of BTR-based xerogels and indicates the high NO capacity of such radical-rich sorbent. Efficient NO capture from model flue gas mixture is also evidenced in experiments with a fixed bed reactor. Such advanced properties of new materials as selectivity, strong binding with NO and an ability for mild regeneration via thermodesorption promote them for future ecological applications.

Quantifying instantaneous nitrogen oxides emissions from power plants based on space observations

Thermal power plants are significant contributors to nitrogen oxides (NOx), impacting global atmospheric conditions and human health. Satellite observations, known for their continuity and global coverage, have become an effective means of quantifying power plant emissions. Previous studies, often accumulating long temporal data into integrated plumes, resulted in substantial errors in annual emissions at the individual power plant level due to neglecting variations in emissions and diffusion conditions. This study presents, for the first time, the quantification of instantaneous NOx emissions based on single overpass observations from the Tropospheric Monitoring Instrument (TROPOMI) aboard the Sentinel-5 Precursor satellite. By addressing the temporal variability of power plant emissions, it effectively reduces annual estimation errors. Comparative analysis between the Exponentially-Modified Gaussian (EMG) and Gaussian Plume Model (GPM) simulations demonstrates the capability of EMG to provide instantaneous emission estimates based on actual plumes, exhibiting closer proximity to actual monitoring values than GPM. Applying the EMG method, we quantify the instantaneous emission rates of six power plants in the United States. Comparing annual emission estimations at individual power plants with traditional integrated plume results, our method demonstrates a 63.7 % improvement in annual emission estimations. This study offers more detailed data on power plant emissions, providing a new avenue for better understanding the emission behavior of thermal power plants.

Air pollution impacts from warehousing in the United States uncovered with satellite data

Regulators, environmental advocates, and community groups in the United States (U.S.) are concerned about air pollution associated with the proliferating e-commerce and warehousing industries. Nationwide datasets of warehouse locations, traffic, and satellite observations of the traffic-related pollutant nitrogen dioxide (NO2) provide a unique capability to evaluate the air quality and environmental equity impacts of these geographically-dispersed emission sources. Here, we show that the nearly 150,000 warehouses in the U.S. worsen local traffic-related air pollution with an average near-warehouse NO2 enhancement of nearly 20% and are disproportionately located in marginalized and minoritized communities. Near-warehouse truck traffic and NO2 significantly increase as warehouse density and the number of warehouse loading docks and parking spaces increase. Increased satellite-observed NO2 near warehouses underscores the need for indirect source rules, incentives for replacing old trucks, and corporate commitments towards electrification. Future ground-based monitoring campaigns may help track impacts of individual or small clusters of facilities.

Quantifying NOx point sources with Landsat and Sentinel-2 satellite observations of NO2 plumes

We show that the Landsat and Sentinel-2 satellites can detect NO2 plumes from large point sources at 10 to 60 m pixel resolution in their blue and ultrablue bands. We use the resulting NO2 plume imagery to quantify nitrogen oxides (NOx) emission rates for several power plants in Saudi Arabia and the United States, including a 13-y analysis of 132 Landsat plumes from Riyadh power plant 9 from 2009 through 2021. NO2 in the plumes initially increases with distance from the source, likely reflecting recovery from ozone titration. The fine pixel resolutions of Landsat and Sentinel-2 enable separation of individual point sources and stacks, including in urban background, and the long records enable examination of multidecadal emission trends. Our inferred NOx emission rates are consistent with previous estimates to within a precision of about 30%. Sources down to ~500 kg h-1 can be detected over bright, quasi-homogeneous surfaces. The 2009 to 2021 data for Riyadh power plant 9 show a strong summer peak in emissions, consistent with increased power demand for air conditioning, and a marginal slow decrease following the introduction of Saudi Arabia's Ambient Air Standard 2012.

Identification of NOx emissions and source characteristics by TROPOMI observations - A case study in north-central Henan, China

With the development of industries, air pollution in north-central Henan is becoming increasingly severe. The TROPOspheric Monitoring Instrument (TROPOMI) provides nitrogen dioxide (NO2) column densities with high spatial resolution. Based on TROPOMI, in this study, the nitrogen oxides (NOx) emissions in north-central Henan are derived and the emission hotspots are identified with the flux divergence method (FDM) from May to September 2021. The results indicate that Zhengzhou has the highest NOx emissions in north-central Henan. The most prominent hotspots are in Guancheng Huizu District (Zhengzhou) and Yindu District (Anyang), with emissions of 448.4 g/s and 300.3 g/s, respectively. The Gaussian Mixture Model (GMM) is applied to quantify the characteristics of emission hotspots, including the diameter, eccentricity, and tilt angle, among which the tilt angle provides a novel metric for identifying the spatial distribution of pollution sources. Furthermore, the results are compared with the CAMS global anthropogenic emissions (CAMS-GLOB-ANT) and Multi-resolution Emission Inventory model for Climate and air pollution research (MEIC), and they are generally in good agreement. However, some point sources, such as power plants, may be missed by both inventories. It is also found that for emission hotspots near transportation hubs, CAMS-GLOB-ANT may not have fully considered the actual traffic flow, leading to an underestimation of transportation emissions. These findings provide key information for the accurate implementation of pollution prevention and control measures, as well as references for future optimization of emission inventories. Consequently, deriving NOx emissions from space, quantifying the characteristics of emission hotspots, and combining them with bottom-up inventories can provide valuable insights for targeted emission control.

Advancing shipping NOx pollution estimation through a satellite-based approach

Estimating shipping nitrogen oxides (NOx) emissions and their associated ambient NO2 impacts is a complex and time-consuming task. In this study, a satellite-based ship pollution estimation model (SAT-SHIP) is developed to estimate regional shipping NOx emissions and their contribution to ambient NO2 concentrations in China. Unlike the traditional bottom-up approach, SAT-SHIP employs satellite observations with varying wind patterns to improve the top-down emission inversion methods for individual sectors amidst irregular emission plume signals. Through SAT-SHIP, shipping NOx emissions for 17 ports in China are estimated. The results show that SAT-SHIP performed comparably with the bottom-up approach, with an R2 value of 0.8. Additionally, SAT-SHIP reveals that the shipping sector in port areas contributes ∼21 and 11% to NO2 concentrations in the Yangtze River Delta and Pearl River Delta areas of China, respectively, which is consistent with the results from chemical transportation model simulations. This approach has practical implications for policymakers seeking to identify pollution sources and develop effective strategies to mitigate air pollution.

NO2 emissions from oil refineries in the Mississippi Delta

Of the >17,943 thousand barrels per calendar day (bbl/d) of oil refining capacity located in the US, the Petroleum Administration for Defense District 3 (PADD-3) region has the largest number of refineries and accounts for >53 % (or 9607 tbbl/d) of all US oil refining capacity. Processing facilities in this area are mainly located on the Gulf of Mexico coast in Texas and Louisiana. This study selected a sub-region for analysis within the Mississippi River delta in the state of Louisiana between the cities of New Orleans and Baton Rouge. This region is characterized by intensive industrial activity connected with oil refining and related activities. The TROPOspheric Monitoring Instrument (TROPOMI) detected highly localized NO2 vertical column densities (VCDs) over the two largest US refineries in Baton Rouge (503,000 bbl/d) and Garyville (578,000 bbl/d). TROPOMI NO2 VCD over these stations were 100 μmol/m2 and 80 μmol/m2, respectively. A high correlation coefficient (r = 0.65, p < 0.05) was also found between TROPOMI NO2 and population density. Data from the National Emissions Inventory (NEI) showed high NOx emissions from refineries and other industries including coal-fired power generation, chemical, and aluminum processing plants. The results of the NO2 analysis are of practical interest for a comparative assessment of air pollution, as well as for the exchange of best practices in the field of low-waste fuel combustion technologies.

Satellites capture socioeconomic disruptions during the 2022 full-scale war in Ukraine

Since February 2022, the full-scale war in Ukraine has been strongly affecting society and economy in Ukraine and beyond. Satellite observations are crucial tools to objectively monitor and assess the impacts of the war. We combine satellite-based tropospheric nitrogen dioxide (NO2) and carbon dioxide (CO2) observations to detect and characterize changes in human activities, as both are linked to fossil fuel combustion processes. We show significantly reduced NO2 levels over the major Ukrainian cities, power plants and industrial areas: the NO2 concentrations in the second quarter of 2022 were 15-46% lower than the same quarter during the reference period 2018-2021, which is well below the typical year-to-year variability (5-15%). In the Ukrainian capital Kyiv, the NO2 tropospheric column monthly average in April 2022 was almost 60% smaller than 2019 and 2021, and about 40% smaller than 2020 (the period mostly affected by the COVID-19 restrictions). Such a decrease is consistent with the essential reduction in population and corresponding emissions from the transport and commercial/residential sectors over the major Ukrainian cities. The NO2 reductions observed in the industrial regions of eastern Ukraine reflect the decline in the Ukrainian industrial production during the war (40-50% lower than in 2021), especially from the metallurgic and chemical industry, which also led to a decrease in power demand and corresponding electricity production by thermal power plants (which was 35% lower in 2022 compared to 2021). Satellite observations of land properties and thermal anomalies indicate an anomalous distribution of fire detections along the front line, which are attributable to shelling or other intentional fires, rather than the typical homogeneously distributed fires related to crop harvesting. The results provide timely insights into the impacts of the ongoing war on the Ukrainian society and illustrate how the synergic use of satellite observations from multiple platforms can be useful in monitoring significant societal changes. Satellite-based observations can mitigate the lack of monitoring capability during war and conflicts and enable the fast assessment of sudden changes in air pollutants and other relevant parameters.

Quantification and evaluation of atmospheric emissions from crop residue burning constrained by satellite observations in China during 2016-2020

In rural regions of China, crop residue burning (CRB) is the major biomass burning activity, which can result in massive emissions of atmospheric particulate, greenhouse gas, and trace gas pollutants. Based on Himawari-8 satellite fire radiative power and agricultural statistics data, we implemented a daily inventory of agricultural fire emissions in 2016-2020 with a 2-km spatial resolution, including atmospheric pollutants such as CO₂, CH₄, CO, N₂O, NO_X, NH₃, SO₂, PM₁₀, PM_2.5, Hg, OC, EC, and NMVOCs. Our inventory constrained by geostationary satellite monitoring is more consistent with the actual CRB emissions in China, as many flaring events occur surreptitiously in the early morning and late evening to avoid regulation, which may be overlooked by polar-orbiting satellites. The spatiotemporal characterizations of various CRB emissions are found to be consistent with multiple satellite trace gas retrievals. We also assessed the effectiveness of field burning bans in China. Combined with other relevant datasets, it was found that although China has been advocating for a long time not to burn straw in the open field, CRB emissions was not successfully controlled nationwide until 2016. We estimated that the cumulative reduction of CO₂ CRB emissions alone amounts to 809 ± 651 (2σ) teragram (Tg) during the 13th Five-Year Plan period (2016-2020), with an average value equivalent to 1.2 times the total annual territorial CO₂ emissions by fossil fuels from Germany in 2021 (675 Tg, ranked 1st in EU27 and 7th in the world). Our inventory also suggests that continuous, long-term controls are necessary. Otherwise, CRB emissions may only be delayed on a seasonal scale, rather than reduced.

Widespread missing super-emitters of nitrogen oxides across China inferred from year-round satellite observations

Nitrogen oxides (NO_x ≡ NO + NO₂) play a central role in air pollution and are targeted for emission mitigation by environmental protection agencies globally. Unique challenges for mitigation are presented by super-emitters, typically with the potential to dominate localized NO_x budgets. Nevertheless, identifying super-emitters still challenges emission mitigation, while the spatial resolution of emission monitoring rises continuously. Here we develop an efficient, super-resolution (1 × 1 km²) inverse model based on year-round TROPOMI satellite observations over China. Consequently, we resolve hundreds of super-emitters in virtually every corner of China, even in remote and mountainous areas. They are attributed to individual plants or parks, mostly associated with industrial sectors, like energy, petrochemical, and iron and steel industries. State-of-the-art bottom-up emission estimates (i.e., MEICv1.3 and HTAPv2), as well as classic top-down inverse methods (e.g., a CTM coupled with the Ensemble Kalman Filter), do not adequately identify these super-emitters. Remarkably, more than one hundred super-emitters are unambiguously missed, while the establishments or discontinuations of the super-emitters potentially lead to under- or over-estimates, respectively. Moreover, evidence shows that these super-emitters generally dominate the NO_x budget in a localized area (e.g., equivalent to a spatial scale of a medium-sized county). Although our dataset is incomplete nationwide due to the undetectable super-emitters on top of high pollution, our results imply that super-emitters contribute significantly to national NO_x budgets and thus suggest the necessity to address the NO_x budget by revisiting super-emitters on a large scale. Integrating the results we obtain here with a multi-tiered observation system can lead to identification and mitigation of anomalous NO_x emissions.

Widespread Frequent Methane Emissions From the Oil and Gas Industry in the Permian Basin

Emissions of methane (CH₄) in the Permian basin (USA) have been derived for 2019 and 2020 from satellite observations of the Tropospheric Monitoring Instrument (TROPOMI) using the divergence method, in combination with a data driven method to estimate the background column densities. The resulting CH₄ emission data, which have been verified using model data with known emissions, have a spatial resolution of approximately 10 km. The CH₄ emissions show moderate spatial correlation with the locations of oil and gas production and drilling activities in the Permian basin, as well as with emissions of nitrogen oxides (NO_x). Analysis of the emission maps and time series indicates that a significant fraction of methane emissions in the Permian basin is from frequent widespread emissions sources, rather than from a few infrequent very large unplanned releases, which is important considering possible CH₄ emission mitigation strategies. In addition to providing spatially resolved emissions, the divergence method also provides the total emissions of the Permian basin and its main sub-basins. The total CH₄ emission of the Permian is estimated as 3.0 ± 0.7 Tg yr^-1 for 2019, which agrees with other independent estimates based on TROPOMI data. For the Delaware sub-basin, it is estimated as 1.4 ± 0.3 Tg yr^-1 for 2019, and for the Midland sub-basin 1.2 ± 0.3 Tg yr^-1. In 2020 the emissions are 9% lower compared to 2019 in the entire Permian basin, and respectively 19% and 27% for the Delaware and Midland sub-basins.

Quantifying emission fluxes of atmospheric pollutants from mobile differential optical absorption spectroscopic (DOAS) observations

This study demonstrates a mobile passive differential optical absorption spectroscopy (DOAS) based remote sensing method for quantifying the emission fluxes of soot pollutants. First, the mobile DOAS system scans the plume emitted from urban sources. Then, the DOAS method retrieves the total columns of pollutant gases along the measurement path. Combining the longitude, latitude, and mobile speed recorded by vehicle GPS, the net emission fluxes of NO₂ and SO₂ in the measurement area are calculated by coupling with the wind field data. The NO₂ flux in the region is combined with the NO to NO₂ concentration ratio in the Copernicus Atmospheric Monitoring Service (CAMS) model to calculate NO_x net emission flux in the measurement period. We conducted the mobile DOAS measurements in the coal production area and obtained the distribution of pollutant gases along the measurement path. Meanwhile, the NO₂ concentration distribution of the city and surrounding areas were reconstructed by using TROPOMI satellite data. During the mobile measurement, the net NO₂ emission flux measured by mobile DOAS are in good agreement with satellite observations (R² = 0.66). This study verified that the flux calculation method based on mobile DOAS can be used to detect urban soot pollutant gas emissions.

New sights on the impact of spatial composition of production factors for socioeconomic recovery in the post-epidemic era: a case study of cities in central and eastern China

The COVID-19 pandemic led to a sharp economic contraction. A comprehensive understanding of the relationship between the spatial composition of production factor (SCPF) and socioeconomic recovery is still missing. Here, we applied the contrasting status of nitrogen dioxide (NO₂) concentrations in cities in central and eastern China as natural laboratories. From the perspective of the spatial composition of land (SCL) and the dependence on the inflow population (DIP), four quantifiable indicators (resilience, impact, sensitivity, recovery speed) were used to analyze the adaptability of SCPF to the epidemic lockdown. The results indicate that appropriate SCPF is a prerequisite for a complete "land-population-industry" nexus. The built-up area proportion is below 74.38%, with higher adaptability to epidemic shocks. The range of rural built-up proportion conducive to economic recovery is 10.18%-15.18%. The proportions of various land types inside the city's defense unit should also be constrained. Similarly, DIP is advocated to be maintained below 17.5%. For urban-rural fringe areas, the response to epidemic prevention and socioeconomic recovery are rapid. This observation-driven study indicated that COVID-19 is a shocking reminder for policymakers, to improve the socioeconomic recovery ability from the spatial composition of production factor perspective in the post-COVID-19 era.

NO2 retrievals from NOAA-20 OMPS: Algorithm, evaluation, and observations of drastic changes during COVID-19

We present the first NO2 measurements from the Nadir Mapper of Ozone Mapping and Profiler Suite (OMPS) instrument aboard the NOAA-20 satellite. NOAA-20 OMPS was launched in November 2017, with a nadir resolution of 17 × 13 km2 similar to the Ozone Monitoring Instrument (OMI). The retrieval of NOAA-20 NO2 vertical columns were achieved through the Direct Vertical Column Fitting (DVCF) algorithm, which was uniquely designed and successfully used to retrieve NO2 from OMPS aboard Suomi National Polar-orbiting Partnership (SNPP) spacecraft, predecessor to NOAA-20. Observations from NOAA-20 reveal a 20-40% decline in regional tropospheric NO2 in January-April 2020 due to COVID-19 lockdown, consistent with the findings from other satellite observations. The NO2 retrievals are preliminarily validated against ground-based Pandora spectrometer measurements over the New York City area as well as other U.S. Pandora locations. It shows OMPS total columns tend to be lower in polluted urban regions and higher in clean areas/episodes associated with relatively small NO2 total columns, but generally the agreement is within ±2.5 × 1015 molecules/cm2. Comparisons of stratospheric NO2 columns exhibit the excellent agreement between OMPS and OMI, validating OMPS capability in capturing the stratospheric background accurately. These results demonstrate the high sensitivity of OMPS to tropospheric NO2 and highlight its potential use for extending the long-term global NO2 record.

Rapid Inference of Nitrogen Oxide Emissions Based on a Top-Down Method with a Physically Informed Variational Autoencoder

Accurate timely estimation of emissions of nitrogen oxides (NO_x) is a prerequisite for designing an effective strategy for reducing O₃ and PM_2.5 pollution. The satellite-based top-down method can provide near-real-time constraints on emissions; however, its efficiency is largely limited by efforts in dealing with the complex emission-concentration response. Here, we propose a novel machine-learning-based method using a physically informed variational autoencoder (VAE) emission predictor to infer NO_x emissions from satellite-retrieved surface NO₂ concentrations. The computational burden can be significantly reduced with the help of a neural network trained with a chemical transport model, allowing the VAE emission predictor to provide a timely estimation of posterior emissions based on the satellite-retrieved surface NO₂ concentration. The VAE emission predictor successfully corrected the underestimation of NO_x emissions in rural areas and the overestimation in urban areas, resulting in smaller normalized mean biases (reduced from -0.8 to -0.4) and larger R² values (increased from 0.4 to 0.7). The interpretability of the VAE emission predictor was investigated using sensitivity analysis by modulating each feature, indicating that NO₂ concentration and planetary boundary layer (PBL) height are important for estimating NO_x emissions, which is consistent with our common knowledge. The advantages of the VAE emission predictor in efficiency, flexibility, and accuracy demonstrate its great potential in estimating the latest emissions and evaluating the control effectiveness from observations.

Considerable Unaccounted Local Sources of NOx Emissions in China Revealed from Satellite

High-resolution (e.g., 5 km) emission data of nitrogen oxides (NO_x = NO + NO₂) provide localized knowledge of pollution sources for targeted regulations, yet such data are lacking or inaccurate over most regions at present. Here we improve our PHLET-based inversion method to derive NO_x emissions in China at a 5-km resolution in summer 2019, based on the TROPOMI-POMINO satellite product of nitrogen dioxide (NO₂) columns. With low computational costs, our inversion explicitly accounts for the effects of horizontal transport and nonlinear chemistry. We find numerous small-to-medium sources related to minor roads and small human settlements at relatively low affluence levels, in addition to clear emission signals along major transportation lines, consistent with road line density and Tencent location data. Many small-to-medium sources and transportation emissions are unclear or missing in the spatial distributions of four widely used emission inventories. Our emissions offer a unique reference for targeted emission control.

Declines and peaks in NO2 pollution during the multiple waves of the COVID-19 pandemic in the New York metropolitan area

The COVID-19 pandemic created an extreme natural experiment in which sudden changes in human behavior and economic activity resulted in significant declines in nitrogen oxide (NO _x ) emissions, immediately after strict lockdowns were imposed. Here we examined the impact of multiple waves and response phases of the pandemic on nitrogen dioxide (NO₂) dynamics and the role of meteorology in shaping relative contributions from different emission sectors to NO₂ pollution in post-pandemic New York City. Long term (> 3.5 years), high frequency measurements from a network of ground-based Pandora spectrometers were combined with TROPOMI satellite retrievals, meteorological data, mobility trends, and atmospheric transport model simulations to quantify changes in NO₂ across the New York metropolitan area. The stringent lockdown measures after the first pandemic wave resulted in a decline in top-down NO _x emissions by approx. 30% on top of long-term trends, in agreement with sector-specific changes in NO _x emissions. Ground-based measurements showed a sudden drop in total column NO₂ in spring 2020, by up to 36% in Manhattan and 19%-29% in Queens, New Jersey (NJ), and Connecticut (CT), and a clear weakening (by 16%) of the typical weekly NO₂ cycle. Extending our analysis to more than a year after the initial lockdown captured a gradual recovery in NO₂ across the NY/NJ/CT tri-state area in summer and fall 2020, as social restrictions eased, followed by a second decline in NO₂ coincident with the second wave of the pandemic and resurgence of lockdown measures in winter 2021. Meteorology was not found to have a strong NO₂ biassing effect in New York City after the first pandemic wave. Winds, however, were favorable for low NO₂ conditions in Manhattan during the second wave of the pandemic, resulting in larger column NO₂ declines than expected based on changes in transportation emissions alone. Meteorology played a key role in shaping the relative contributions from different emission sectors to NO with low-speed (< 5 ms^-1) SW-SE winds enhancing contributions from the high-emitting power-generation sector in NJ and Queens and driving particularly high NO₂ pollution episodes in Manhattan, even during - and despite - the stringent early lockdowns. These results have important implications for air quality management in New York City, and highlight the value of high resolution NO₂ measurements in assessing the effects of rapid meteorological changes on air quality conditions and the effectiveness of sector-specific NO _x emission control strategies.

Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants

Effective and precise monitoring is a prerequisite to control human emissions and slow disruptive climate change. To obtain the near-real-time status of power plant emissions, we built machine learning models and trained them on satellite observations (Sentinel 5), ground observed data (EPA eGRID), and meteorological observations (MERRA) to directly predict the NO2 emission rate of coal-fired power plants. A novel approach to preprocessing multiple data sources, coupled with multiple neural network models (RNN, LSTM), provided an automated way of predicting the number of emissions (NO2, SO2, CO, and others) produced by a single power plant. There are many challenges on overfitting and generalization to achieve a consistently accurate model simply depending on remote sensing data. This paper xaddresses the challenges using a combination of techniques, such as data washing, column shifting, feature sensitivity filtering, etc. It presents a groundbreaking case study on remotely monitoring global power plants from space in a cost-wise and timely manner to assist in tackling the worsening global climate.

Satellite observations for monitoring atmospheric NO2 in correlation with the existing pollution sources under arid environment

Monitoring of air pollutants using satellite data has been largely improved over the past few decades, which can provide deeper insights into the effects of anthropogenic activities on the air quality. The observations and measurements of atmospheric NO₂ are poorly investigated in North Africa, therefore, the current study applied a multi-proxy approach to better understand of the ambient environment. This approach is based on satellite observations, chemical and biological analyses, and investigative information during fieldworks. The Aura satellite provides the basic data for the current study with fine resolution of atmospheric NO₂ and O₃ concentrations. The obtained results reveal noticeable increases of atmospheric NO₂ values since the 2011, where its emission reaches the peak during summer season that is characterized by high anthropogenic activities. The study area has many sources for NO₂ emissions, such as the urban region, traffic, as well as the NH₃ emission that is in turn converted to NO_2. Although the discharged and spreading wastewater (80,000 m³/day in summer) has a limited role in NO₂ emissions, it represents an indicator of the anthropogenic activities. The wastewater analyses confirm the occurrence of nitrate (NO₃⁻), nitrite (NO₂⁻), and ammonia (NH₄⁺), which provide an appropriate condition for NO₂ release. The analyses of multi-climate datasets (previous records and the expected scenarios) reveal an increase of temperature accompanied by decrease of precipitation which confirmed the existence of climate change. Therefore, the study presents a set of suggestions to mitigate the release of NOx gases and achieve Net-Zero emissions.

Sector-Based Top-Down Estimates of NO x , SO2, and CO Emissions in East Asia

Top-down estimates using satellite data provide important information on the sources of air pollutants. We develop a sector-based 4D-Var framework based on the GEOS-Chem adjoint model to address the impacts of co-emissions and chemical interactions on top-down emission estimates. We apply OMI NO₂, OMI SO₂, and MOPITT CO observations to estimate NO _x , SO₂, and CO emissions in East Asia during 2005-2012. Posterior evaluations with surface measurements show reduced normalized mean bias (NMB) by 7% (NO₂)-15% (SO₂) and normalized mean square error (NMSE) by 8% (SO₂)-9% (NO₂) compared to a species-based inversion. This new inversion captures the peak years of Chinese SO₂ (2007) and NO _x (2011) emissions and attributes their drivers to industry and energy activities. The CO peak in 2007 in China is driven by residential and industry emissions. In India, the inversion attributes NO _x and SO₂ trends mostly to energy and CO trend to residential emissions.

Tropospheric NO2 and O3 Response to COVID-19 Lockdown Restrictions at the National and Urban Scales in Germany

This study estimates the influence of anthropogenic emission reductions on nitrogen dioxide (N O 2 ) and ozone ( O 3 ) concentration changes in Germany during the COVID-19 pandemic period using in-situ surface and Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) satellite column measurements and GEOS-Chem model simulations. We show that reductions in anthropogenic emissions in eight German metropolitan areas reduced mean in-situ (& column)N O 2 concentrations by 23 % (& 16 % ) between March 21 and June 30, 2020 after accounting for meteorology, whereas the corresponding mean in-situ O 3 concentration increased by 4 % between March 21 and May 31, 2020, and decreased by 3 % in June 2020, compared to 2019. In the winter and spring, the degree ofN O X saturation of ozone production is stronger than in the summer. This implies that future reductions inN O X emissions in these metropolitan areas are likely to increase ozone pollution during winter and spring if appropriate mitigation measures are not implemented. TROPOMIN O 2 concentrations decreased nationwide during the stricter lockdown period after accounting for meteorology with the exception of North-West Germany which can be attributed to enhancedN O X emissions from agricultural soils.

Nitrogen dioxide decline and rebound observed by GOME-2 and TROPOMI during COVID-19 pandemic

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 (NO2) that are strongly related to transportation, industry, and energy. In this study, NO2 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 NO2 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 NO2 variations for morning conditions. Consistent NO2 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 NO2 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.

SAMIRA-SAtellite Based Monitoring Initiative for Regional Air Quality

The satellite based monitoring initiative for regional air quality (SAMIRA) initiative was set up to demonstrate the exploitation of existing satellite data for monitoring regional and urban scale air quality. The project was carried out between May 2016 and December 2019 and focused on aerosol optical depth (AOD), particulate matter (PM), nitrogen dioxide (NO2), and sulfur dioxide (SO2). SAMIRA was built around several research tasks: 1. The spinning enhanced visible and infrared imager (SEVIRI) AOD optimal estimation algorithm was improved and geographically extended from Poland to Romania, the Czech Republic and Southern Norway. A near real-time retrieval was implemented and is currently operational. Correlation coefficients of 0.61 and 0.62 were found between SEVIRI AOD and ground-based sun-photometer for Romania and Poland, respectively. 2. A retrieval for ground-level concentrations of PM2.5 was implemented using the SEVIRI AOD in combination with WRF-Chem output. For representative sites a correlation of 0.56 and 0.49 between satellite-based PM2.5 and in situ PM2.5 was found for Poland and the Czech Republic, respectively. 3. An operational algorithm for data fusion was extended to make use of various satellite-based air quality products (NO2, SO2, AOD, PM2.5 and PM10). For the Czech Republic inclusion of satellite data improved mapping of NO2 in rural areas and on an annual basis in urban background areas. It slightly improved mapping of rural and urban background SO2. The use of satellites based AOD or PM2.5 improved mapping results for PM2.5 and PM10. 4. A geostatistical downscaling algorithm for satellite-based air quality products was developed to bridge the gap towards urban-scale applications. Initial testing using synthetic data was followed by applying the algorithm to OMI NO2 data with a direct comparison against high-resolution TROPOMI NO2 as a reference, thus allowing for a quantitative assessment of the algorithm performance and demonstrating significant accuracy improvements after downscaling. We can conclude that SAMIRA demonstrated the added value of using satellite data for regional- and urban-scale air quality monitoring.

Spatiotemporal Patterns in Data Availability of the Sentinel-5P NO2 Product over Urban Areas in Norway

Due to its comparatively high spatial resolution and its daily repeat frequency, the tropospheric nitrogen dioxide product provided by the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor platform has attracted significant attention for its potential for urban-scale monitoring of air quality. However, the exploitation of such data in, for example, operational assimilation of local-scale dispersion models is often complicated by substantial data gaps due to cloud cover or other retrieval limitations. These challenges are particularly prominent in high-latitude regions where significant cloud cover and high solar zenith angles are often prevalent. Using the example of Norway as a representative case for a high-latitude region, we here evaluate the spatiotemporal patterns in the availability of valid data from the operational TROPOMI tropospheric nitrogen dioxide (NO2) product over five urban areas (Oslo, Bergen, Trondheim, Stavanger, and Kristiansand) and a 2.5 year period from July 2018 through November 2020. Our results indicate that even for relatively clean environments such as small Norwegian cities, distinct spatial patterns of tropospheric NO2 are visible in long-term average datasets from TROPOMI. However, the availability of valid data on a daily level is limited by both cloud cover and solar zenith angle (during the winter months), causing the fraction of valid retrievals in each study site to vary from 20% to 50% on average. A temporal analysis shows that for our study sites and the selected period, the fraction of valid pixels in each domain shows a clear seasonal cycle reaching a maximum of 50% to 75% in the summer months and 0% to 20% in winter. The seasonal cycle in data availability shows the inverse behavior of NO2 pollution in Norway, which typically has its peak in the winter months. However, outside of the mid-winter period we find the TROPOMI NO2 product to provide sufficient data availability for detailed mapping and monitoring of NO2 pollution in the major urban areas in Norway and see potential for the use of the data in local-scale data assimilation and emission inversions applications.

Impacts of Soil NOx Emission on O3 Air Quality in Rural California

Nitrogen oxides (NO_x) are a key precursor in O₃ formation. Although stringent anthropogenic NO_x emission controls have been implemented since the early 2000s in the United States, several rural regions of California still suffer from O₃ pollution. Previous findings suggest that soils are a dominant source of NO_x emissions in California; however, a statewide assessment of the impacts of soil NO_x emission (SNO_x) on air quality is still lacking. Here we quantified the contribution of SNO_x to the NO_x budget and the effects of SNO_x on surface O₃ in California during summer by using WRF-Chem with an updated SNO_x scheme, the Berkeley Dalhousie Iowa Soil NO Parameterization (BDISNP). The model with BDISNP shows a better agreement with TROPOMI NO₂ columns, giving confidence in the SNO_x estimates. We estimate that 40.1% of the state's total NO_x emissions in July 2018 are from soils, and SNO_x could exceed anthropogenic sources over croplands, which accounts for 50.7% of NO_x emissions. Such considerable amounts of SNO_x enhance the monthly mean NO₂ columns by 34.7% (53.3%) and surface NO₂ concentrations by 176.5% (114.0%), leading to an additional 23.0% (23.2%) of surface O₃ concentration in California (cropland). Our results highlight the cobenefits of limiting SNO_x to help improve air quality and human health in rural California.

The Korea-United States Air Quality (KORUS-AQ) field study

The Korea-United States Air Quality (KORUS-AQ) field study was conducted during May-June 2016. The effort was jointly sponsored by the National Institute of Environmental Research of South Korea and the National Aeronautics and Space Administration of the United States. KORUS-AQ offered an unprecedented, multi-perspective view of air quality conditions in South Korea by employing observations from three aircraft, an extensive ground-based network, and three ships along with an array of air quality forecast models. Information gathered during the study is contributing to an improved understanding of the factors controlling air quality in South Korea. The study also provided a valuable test bed for future air quality-observing strategies involving geostationary satellite instruments being launched by both countries to examine air quality throughout the day over Asia and North America. This article presents details on the KORUS-AQ observational assets, study execution, data products, and air quality conditions observed during the study. High-level findings from companion papers in this special issue are also summarized and discussed in relation to the factors controlling fine particle and ozone pollution, current emissions and source apportionment, and expectations for the role of satellite observations in the future. Resulting policy recommendations and advice regarding plans going forward are summarized. These results provide an important update to early feedback previously provided in a Rapid Science Synthesis Report produced for South Korean policy makers in 2017 and form the basis for the Final Science Synthesis Report delivered in 2020.

Nitrogen oxides concentration and emission change detection during COVID-19 restrictions in North India

COVID-19 related restrictions lowered particulate matter and trace gas concentrations across cities around the world, providing a natural opportunity to study effects of anthropogenic activities on emissions of air pollutants. In this paper, the impact of sudden suspension of human activities on air pollution was analyzed by studying the change in satellite retrieved NO2 concentrations and top-down NOx emission over the urban and rural areas around Delhi. NO2 was chosen for being the most indicative of emission intensity due to its short lifetime of the order of a few hours in the planetary boundary layer. We present a robust temporal comparison of Ozone Monitoring Instrument (OMI) retrieved NO2 column density during the lockdown with the counterfactual baseline concentrations, extrapolated from the long-term trend and seasonal cycle components of NO2 using observations during 2015 to 2019. NO2 concentration in the urban area of Delhi experienced an anomalous relative change ranging from 60.0% decline during the Phase 1 of lockdown (March 25-April 13, 2020) to 3.4% during the post-lockdown Phase 5. In contrast, we find no substantial reduction in NO2 concentrations over the rural areas. To segregate the impact of the lockdown from the meteorology, weekly top-down NOx emissions were estimated from high-resolution TROPOspheric Monitoring Instrument (TROPOMI) retrieved NO2 by accounting for horizontal advection derived from the steady state continuity equation. NOx emissions from urban Delhi and power plants exhibited a mean decline of 72.2% and 53.4% respectively in Phase 1 compared to the pre-lockdown business-as-usual phase. Emission estimates over urban areas and power-plants showed a good correlation with activity reports, suggesting the applicability of this approach for studying emission changes. A higher anomaly in emission estimates suggests that comparison of only concentration change, without accounting for the dynamical and photochemical conditions, may mislead evaluation of lockdown impact. Our results shall also have a broader impact for optimizing bottom-up emission inventories.

TROPOMI NO2 in the United States: A Detailed Look at the Annual Averages, Weekly Cycles, Effects of Temperature, and Correlation With Surface NO2 Concentrations

Observing the spatial heterogeneities of NO2 air pollution is an important first step in quantifying NOX emissions and exposures. This study investigates the capabilities of the Tropospheric Monitoring Instrument (TROPOMI) in observing the spatial and temporal patterns of NO2 pollution in the continental United States. The unprecedented sensitivity of the sensor can differentiate the fine-scale spatial heterogeneities in urban areas, such as emissions related to airport/shipping operations and high traffic, and the relatively small emission sources in rural areas, such as power plants and mining operations. We then examine NO2 columns by day-of-the-week and find that Saturday and Sunday concentrations are 16% and 24% lower respectively, than during weekdays. We also analyze the correlation of daily maximum 2-m temperatures and NO2 column amounts and find that NO2 is larger on the hottest days (>32°C) as compared to warm days (26°C-32°C), which is in contrast to a general decrease in NO2 with increasing temperature at moderate temperatures. Finally, we demonstrate that a linear regression fit of 2019 annual TROPOMI NO2 data to annual surface-level concentrations yields relatively strong correlation (R 2 = 0.66). These new developments make TROPOMI NO2 satellite data advantageous for policymakers and public health officials, who request information at high spatial resolution and short timescales, in order to assess, devise, and evaluate regulations.

Variation Characteristics and Transportation of Aerosol, NO2, SO2, and HCHO in Coastal Cities of Eastern China: Dalian, Qingdao, and Shanghai

This paper studied the method for converting the aerosol extinction to the mass concentration of particulate matter (PM) and obtained the spatio-temporal distribution and transportation of aerosol, nitrogen dioxide (NO2), sulfur dioxide (SO2), and formaldehyde (HCHO) based on multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations in Dalian (38.85°N, 121.36°E), Qingdao (36.35°N, 120.69°E), and Shanghai (31.60°N, 121.80°E) from 2019 to 2020. The PM2.5 measured by the in situ instrument and the PM2.5 simulated by the conversion formula showed a good correlation. The correlation coefficients R were 0.93 (Dalian), 0.90 (Qingdao), and 0.88 (Shanghai). A regular seasonality of the three trace gases is found, but not for aerosols. Considerable amplitudes in the weekly cycles were determined for NO2 and aerosols, but not for SO2 and HCHO. The aerosol profiles were nearly Gaussian, and the shapes of the trace gas profiles were nearly exponential, except for SO2 in Shanghai and HCHO in Qingdao. PM2.5 presented the largest transport flux, followed by NO2 and SO2. The main transport flux was the output flux from inland to sea in spring and winter. The MAX-DOAS and the Copernicus Atmosphere Monitoring Service (CAMS) models’ results were compared. The overestimation of NO2 and SO2 by CAMS is due to its overestimation of near-surface gas volume mixing ratios.

Satellite-based estimates of decline and rebound in China's CO2 emissions during COVID-19 pandemic

Changes in CO₂ emissions during the COVID-19 pandemic have been estimated from indicators on activities like transportation and electricity generation. Here, we instead use satellite observations together with bottom-up information to track the daily dynamics of CO₂ emissions during the pandemic. Unlike activity data, our observation-based analysis deploys independent measurement of pollutant concentrations in the atmosphere to correct misrepresentation in the bottom-up data and can provide more detailed insights into spatially explicit changes. Specifically, we use TROPOMI observations of NO₂ to deduce 10-day moving averages of NO _x and CO₂ emissions over China, differentiating emissions by sector and province. Between January and April 2020, China's CO₂ emissions fell by 11.5% compared to the same period in 2019, but emissions have since rebounded to pre-pandemic levels before the coronavirus outbreak at the beginning of January 2020 owing to the fast economic recovery in provinces where industrial activity is concentrated.

Evaluating Sentinel-5P TROPOMI tropospheric NO2 column densities with airborne and Pandora spectrometers near New York City and Long Island Sound

Airborne and ground-based Pandora spectrometer NO₂ column measurements were collected during the 2018 Long Island Sound Tropospheric Ozone Study (LISTOS) in the New York City/Long Island Sound region, which coincided with early observations from the Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) instrument. Both airborne- and ground-based measurements are used to evaluate the TROPOMI NO₂ Tropospheric Vertical Column (TrVC) product v1.2 in this region, which has high spatial and temporal heterogeneity in NO₂. First, airborne and Pandora TrVCs are compared to evaluate the uncertainty of the airborne TrVC and establish the spatial representativeness of the Pandora observations. The 171 coincidences between Pandora and airborne TrVCs are found to be highly correlated (r ² =0.92 and slope of 1.03), with the largest individual differences being associated with high temporal and/or spatial variability. These reference measurements (Pandora and airborne) are complementary with respect to temporal coverage and spatial representativity. Pandora spectrometers can provide continuous long-term measurements but may lack areal representativity when operated in direct-sun mode. Airborne spectrometers are typically only deployed for short periods of time, but their observations are more spatially representative of the satellite measurements with the added capability of retrieving at subpixel resolutions of 250m×250m over the entire TROPOMI pixels they overfly. Thus, airborne data are more correlated with TROPOMI measurements (r ² = 0.96) than Pandora measurements are with TROPOMI (r ² = 0.84). The largest outliers between TROPOMI and the reference measurements appear to stem from too spatially coarse a priori surface reflectivity (0.5°) over bright urban scenes. In this work, this results during cloud-free scenes that, at times, are affected by errors in the TROPOMI cloud pressure retrieval impacting the calculation of tropospheric air mass factors. This factor causes a high bias in TROPOMI TrVCs of 4%-11%. Excluding these cloud-impacted points, TROPOMI has an overall low bias of 19%-33% during the LISTOS timeframe of June-September 2018. Part of this low bias is caused by coarse a priori profile input from the TM5-MP model; replacing these profiles with those from a 12 km North American Model-Community Multiscale Air Quality (NAMCMAQ) analysis results in a 12%-14% increase in the TrVCs. Even with this improvement, the TROPOMI-NAMCMAQ TrVCs have a 7%-19% low bias, indicating needed improvement in a priori assumptions in the air mass factor calculation. Future work should explore additional impacts of a priori inputs to further assess the remaining low biases in TROPOMI using these datasets.

Nitrogen Dioxide (NO2) Pollution Monitoring with Sentinel-5P Satellite Imagery over Europe during the Coronavirus Pandemic Outbreak

Nitrogen dioxide (NO2) is one of the main air quality pollutants of concern in many urban and industrial areas worldwide, and particularly in the European region, where in 2017 almost 20 countries exceeded the NO2 annual limit values imposed by the European Commission Directive 2008/50/EC (EEA, 2019). NO2 pollution monitoring and regulation is a necessary task to help decision makers to search for a sustainable solution for environmental quality and population health status improvement. In this study, we propose a comparative analysis of the tropospheric NO2 column spatial configuration over Europe between similar periods in 2019 and 2020, based on the ESA Copernicus Sentinel-5P products. The results highlight the NO2 pollution dynamics over the abrupt transition from a normal condition situation to the COVID-19 outbreak context, characterized by a short-time decrease of traffic intensities and industrial activities, revealing remarkable tropospheric NO2 column number density decreases even of 85% in some of the European big cities. The validation approach of the satellite-derived data, based on a cross-correlation analysis with independent data from ground-based observations, provided encouraging values of the correlation coefficients (R2), ranging between 0.5 and 0.75 in different locations. The remarkable decrease of NO2 pollution over Europe during the COVID-19 lockdown is highlighted by S-5P products and confirmed by the Industrial Production Index and air traffic volumes.

What does success look like for air quality policy? A perspective

This paper explores the drivers and role of science in air quality policy over the last 100 years or so. Case studies on the smogs of Los Angeles and London, acid rain, health impacts of particulate matter, diesel and lead in fuel are used to explore the drivers and models for the interaction of science, evidence and air quality policy. It suggests there are two phases to air quality mitigation, the first driven by the air quality emergency as the pollution is visible and the effects can be relatively obvious and the second driven by science that is directed towards continuous improvement. A critical element of the 'science phase' is the evidence base, the models of evidence-based and -informed policy-making are explored with the conclusion that it is optimal when guided by the ideal of co-creation of knowledge and policy options between scientists and policy-makers. The future and wider drivers for air quality are detailed with a number of key areas for 'success' indicated as important for air quality policy development such as continuous improvement. Overall, we find there is tension between two factors: the ambition to reduce emissions, improve air quality and reduce the impacts on public health and the environment on one hand, and questions of cost, technical feasibility and societal acceptability on the other. This article is part of a discussion meeting issue 'Air quality, past present and future'.

Disentangling the Impact of the COVID-19 Lockdowns on Urban NO2 From Natural Variability

TROPOMI satellite data show substantial drops in nitrogen dioxide (NO₂) during COVID-19 physical distancing. To attribute NO₂ changes to NO _x emissions changes over short timescales, one must account for meteorology. We find that meteorological patterns were especially favorable for low NO₂ in much of the United States in spring 2020, complicating comparisons with spring 2019. Meteorological variations between years can cause column NO₂ differences of ~15% over monthly timescales. After accounting for solar angle and meteorological considerations, we calculate that NO₂ drops ranged between 9.2% and 43.4% among 20 cities in North America, with a median of 21.6%. Of the studied cities, largest NO₂ drops (>30%) were in San Jose, Los Angeles, and Toronto, and smallest drops (<12%) were in Miami, Minneapolis, and Dallas. These normalized NO₂ changes can be used to highlight locations with greater activity changes and better understand the sources contributing to adverse air quality in each city.

NOx Emission Flux Measurements with Multiple Mobile-DOAS Instruments in Beijing

NOX (NOX = NO + NO2) emissions measurements in Beijing are of great significance because they can aid in understanding how NOX pollution develops in mega-cities throughout China. However, NOX emissions in mega-cities are difficult to measure due to changes in wind patterns and moving sources on roads during measurement. To obtain good spatial coverage on different ring roads in Beijing over a short amount of time, two mobile differential optical absorption spectroscopy (DOAS) instruments were used to measure NOX emission flux from April 18th to 26th, 2018. In addition, a wind profile radar provided simultaneous wind field measurements for altitudes between 50 m and 1 km for each ring road measurement. We first determined NOX emission flux of different ring roads using wind field averages from measured wind data. The results showed that the NOX emission flux of Beijing’s fifth ring road, which represented the urban part, varied from (19.29 ± 5.26) × 1024 molec./s to (36.46 ± 12.86) × 1024 molec./s. On April 20th, NOX emission flux for the third ring was slightly higher than the fourth ring because the two ring roads were measured at different time periods. We then analyzed the NOX emission flux error budget and error sensitivity. The main error source was the wind field uncertainty. For some measurements, the main emission flux error source was either wind speed uncertainty or wind direction uncertainty, but not both. As Beijing’s NOX emissions came from road vehicle exhaust, we found that emission flux error had a more diverse sensitivity to wind direction uncertainty, which improved our knowledge on this topic. The NOX emission flux error sensitivity study indicated that more accurate measurements of the wind field are crucial for effective NOX emission flux measurements in Chinese mega-cities. Obtaining actual time and high resolved wind measurements is an advantage for mega-cities’ NOX emission flux measurements. The emission flux errors caused by wind direction and wind speed uncertainties were clearly distinguished. Other sensitivity studies indicated that NOX/NO2 ratio uncertainty dominated flux errors when the NOX/NO2 ratio uncertainty was >0.4. Using two mobile-DOAS and wind profile radars to measure NOx emission flux improved the quality of the emission flux measuring results. This approach could be applied to many other mega-cities in China and in others countries.

An Estimation of Top-Down NOx Emissions from OMI Sensor Over East Asia

This study focuses on the estimation of top-down NOx emissions over East Asia, integrating information on the levels of NO2 and NO, wind vector, and geolocation from Ozone Monitoring Instrument (OMI) observations and Weather Research and Forecasting (WRF)-Community Multiscale Air Quality (CMAQ) model simulations. An algorithm was developed based on mass conservation to estimate the 30 km × 30 km resolved top-down NOx emissions over East Asia. In particular, the algorithm developed in this study considered two main atmospheric factors—(i) NOx transport from/to adjacent cells and (ii) calculations of the lifetimes of column NOx (τ). In the sensitivity test, the analysis showed the improvements in the top-down NOx estimation via filtering the data (τ ≤ 2 h). The best top-down NOx emissions were inferred after the sixth iterations. Those emissions were 11.76 Tg N yr−1 over China, 0.13 Tg N yr−1 over North Korea, 0.46 Tg N yr−1 over South Korea, and 0.68 Tg N yr−1 over Japan. These values are 34%, 62%, 60%, and 47% larger than the current bottom-up NOx emissions over these countries, respectively. A comparison between the CMAQ-estimated and OMI-retrieved NO2 columns was made to confirm the accuracy of the newly estimated NOx emission. The comparison confirmed that the estimated top-down NOx emissions showed better agreements with observations (R2 = 0.88 for January and 0.81 for July).