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.

Satellite NO2 Retrieval Complicated by Aerosol Composition over Global Urban Agglomerations: Seasonal Variations and Long-Term Trends (2001-2018)

Tropospheric nitrogen dioxide (NO2) poses a serious threat to the environmental quality and public health. Satellite NO2 observations have been continuously used to monitor NO2 variations and improve model performances. However, the accuracy of satellite NO2 retrieval depends on the knowledge of aerosol optical properties, in particular for urban agglomerations accompanied by significant changes in aerosol characteristics. In this study, we investigate the impacts of aerosol composition on tropospheric NO2 retrieval for an 18 year global data set from Global Ozone Monitoring Experiment (GOME)-series satellite sensors. With a focus on cloud-free scenes dominated by the presence of aerosols, individual aerosol composition affects the uncertainties of tropospheric NO2 columns through impacts on the aerosol loading amount, relative vertical distribution of aerosol and NO2, aerosol absorption properties, and surface albedo determination. Among aerosol compositions, secondary inorganic aerosol mostly dominates the NO2 uncertainty by up to 43.5% in urban agglomerations, while organic aerosols contribute significantly to the NO2 uncertainty by -8.9 to 37.3% during biomass burning seasons. The possible contrary influences from different aerosol species highlight the importance and complexity of aerosol correction on tropospheric NO2 retrieval and indicate the need for a full picture of aerosol properties. This is of particular importance for interpreting seasonal variations or long-term trends of tropospheric NO2 columns as well as for mitigating ozone and fine particulate matter pollution.

Space-Based Observations of Ozone Precursors within California Wildfire Plumes and the Impacts on Ozone-NOx-VOC Chemistry

The frequency of wildfires in the western United States has escalated in recent decades. Here we examine the impacts of wildfires on ground-level ozone (O3) precursors and the O3-NOx-VOC chemistry from the source to downwind urban areas. We use satellite retrievals of nitrogen dioxide (NO2) and formaldehyde (HCHO, an indicator of VOC) from the Tropospheric Monitoring Instrument (TROPOMI) to track the evolution of O3 precursors from wildfires over California from 2018 to 2020. We improved these satellite retrievals by updating the a priori profiles and explicitly accounting for the effects of smoke aerosols. TROPOMI observations reveal that the extensive and intense fire smoke in 2020 led to an overall increase in statewide annual average HCHO and NO2 columns by 16% and 9%. The increase in the level of NO2 offsets the anthropogenic NOx emission reduction from the COVID-19 lockdown. The enhancement of NO2 within fire plumes is concentrated near the regions actively burning, whereas the enhancement of HCHO is far-reaching, extending from the source regions to urban areas downwind due to the secondary production of HCHO from longer-lived VOCs such as ethene. Consequently, a larger increase in NOx occurs in NOx-limited source regions, while a greater increase in HCHO occurs in VOC-limited urban areas, both contributing to more efficient O3 production.

Investigating Changes in Ozone Formation Chemistry during Summertime Pollution Events over the Northeastern United States

Understanding the local-scale spatial and temporal variability of ozone formation is crucial for effective mitigation. We combine tropospheric vertical column densities (VCDTrop) of formaldehyde (HCHO) and nitrogen dioxide (NO2), referred to as HCHO-VCDTrop and NO2-VCDTrop, retrieved from airborne remote sensing and the TROPOspheric Monitoring Instrument (TROPOMI) with ground-based measurements to investigate changes in ozone precursors and the inferred chemical production regime on high-ozone days in May-August 2018 over two Northeast urban domains. Over New York City (NYC) and Baltimore/Washington D.C. (BAL/DC), HCHO-VCDTrop increases across the domain, but higher NO2-VCDTrop occurs mainly in urban centers on ozone exceedance days (when maximum daily 8 h average (MDA8) ozone exceeds 70 ppb at any monitor in the region). The ratio of HCHO-VCDTrop to NO2-VCDTrop, proposed as an indicator of the sensitivity of local surface ozone production rates to its precursors, generally increases on ozone exceedance days, implying a transition toward a more NOx-sensitive ozone production regime that should lead to higher efficacy of NOx controls on the highest ozone days in NYC and BAL/DC. Warmer temperatures and enhanced influence from emissions in the local boundary layer on the high-ozone days are accompanied by slower wind speeds in BAL/DC but stronger, southwesterly winds in NYC.

Combining Machine Learning and Satellite Observations to Predict Spatial and Temporal Variation of near Surface OH in North American Cities

The hydroxyl radical (OH) is the primary cleansing agent in the atmosphere. The abundance of OH in cities initiates the removal of local pollutants; therefore, it serves as the key species describing the urban chemical environment. We propose a machine learning (ML) approach as an efficient alternative to OH simulation using a computationally expensive chemical transport model. The ML model is trained on the parameters simulated from the WRF-Chem model, and it suggests that six predictive parameters are capable of explaining 76% of the OH variability. The parameters are the tropospheric NO₂ column, the tropospheric HCHO column, J(O¹D), H₂O, temperature, and pressure. We then use observations of the tropospheric NO₂ column and HCHO column from OMI as input to the ML model to enable measurement-based prediction of daily near surface OH at 1:30 pm local time across 49 North American cities over the course of 10 years between 2005 and 2014. The result is validated by comparing the OH predictions to measurements of isoprene, which has a source that is uncorrelated with OH and is removed rapidly and almost exclusively by OH in the daytime. We demonstrate that the predicted OH is, as expected, anticorrelated with isoprene. We also show that this ML model is consistent with our understanding of OH chemistry given the solely data-driven nature.

TROPOspheric Monitoring Instrument observations of total column water vapour: Algorithm and validation

In this paper, we present the total column water vapour (TCWV) retrieval for the TROPOspheric Monitoring Instrument (TROPOMI) observations in the visible blue spectral band. The TROPOMI TCWV algorithm is being optimized and validated in the framework of the Sentinel 5 Precursor Product Algorithm Laboratory (S5P-PAL) project from the European Space Agency (ESA). The retrieval was first developed to retrieve TCWV from the Global Ozone Monitoring Experiment 2 (GOME-2). We have optimized the settings of the retrieval to adapt it for TROPOMI observations. The TROPOMI TCWV algorithm follows the typical two step approach, using spectral fit retrieval of slant columns, and conversion of the slant columns to vertical columns using air mass factors (AMFs). An iterative optimization algorithm is developed to dynamically find the optimal a priori water vapour profile for the AMF calculation. Further optimizations on the spectral retrieval, air mass factor calculations as well as a new surface albedo retrieval approach are implemented. The TCWV retrieval algorithm is applied to TROPOMI observations from May 2018 to May 2021. The results are validated by comparing them to ERA5 reanalysis data, GOME-2, MODerate resolution Imaging Spectroradiometer (MODIS) and Special Sensor Microwave Imager Sounder (SSMIS) satellite observations. TCWV derived from TROPOMI observations agree well with the other data sets with Pearson correlation coefficient (R) ranging from 0.96 to 0.99. The mean bias between TROPOMI and ERA5 data is -1.24 kg m^-2 for measurements over land and 0.73 kg m^-2 for measurements over water. The comparison to MODIS observations show similar results with small dry bias of 1.51,kg m^-2 for measurements over land and a small wet bias of 1.25 kg m^-2 for measurements over water. Slightly larger dry bias of 1.98 kg m^-2 for measurements over land and 1.74 kg m^-2 for measurements over water are found when compared to GOME-2 obserations. Compared to SSMIS data over water, TROPOMI observations are bias low by 3.25 kg m^-2. The small discrepancies found between TROPOMI and reference data sets are related to the differences in measurement technique, measurement time, surface albedo issue, as well as cloud and aerosol contamination. This study demonstrates that the algorithm can provide stable and consistent results on a global scale and can be applied to generate operational TCWV products from TROPOMI and the forthcoming Copernicus missions Sentinel-4 and Sentinel-5. We have also demonstrated the capability of retrieving fine scale water vapour structures in a case study over the Amazon. This indicates that the TROPOMI data set is also useful for local and regional climate studies.

An Inversion Framework for Optimizing Non-Methane VOC Emissions Using Remote Sensing and Airborne Observations in Northeast Asia During the KORUS-AQ Field Campaign

We aim to reduce uncertainties in CH₂O and other volatile organic carbon (VOC) emissions through assimilation of remote sensing data. We first update a three-dimensional (3D) chemical transport model, GEOS-Chem with the KORUSv5 anthropogenic emission inventory and inclusion of chemistry for aromatics and C₂H₄, leading to modest improvements in simulation of CH₂O (normalized mean bias (NMB): -0.57 to -0.51) and O₃ (NMB: -0.25 to -0.19) compared against DC-8 aircraft measurements during KORUS-AQ; the mixing ratio of most VOC species are still underestimated. We next constrain VOC emissions using CH₂O observations from two satellites (OMI and OMPS) and the DC-8 aircraft during KORUS-AQ. To utilize data from multiple platforms in a consistent manner, we develop a two-step Hybrid Iterative Finite Difference Mass Balance and four-dimensional variational inversion system (Hybrid IFDMB-4DVar). The total VOC emissions throughout the domain increase by 47%. The a posteriori simulation reduces the low biases of simulated CH₂O (NMB: -0.51 to -0.15), O₃ (NMB: -0.19 to -0.06), and VOCs. Alterations to the VOC speciation from the 4D-Var inversion include increases of biogenic isoprene emissions in Korea and anthropogenic emissions in Eastern China. We find that the IFDMB method alone is adequate for reducing the low biases of VOCs in general; however, 4D-Var provides additional refinement of high-resolution emissions and their speciation. Defining reasonable emission errors and choosing optimal regularization parameters are crucial parts of the inversion system. Our new hybrid inversion framework can be applied for future air quality campaigns, maximizing the value of integrating measurements from current and upcoming geostationary satellite instruments.

Satellite Formaldehyde to Support Model Evaluation

Formaldehyde (HCHO), a known carcinogen classified as a hazardous pollutant by the United States Environmental Protection Agency (U.S. EPA), is measured through monitoring networks across the U.S. Since these data are limited in spatial and temporal extent, model simulations from the U.S. EPA Community Multiscale Air Quality (CMAQ) model are used to estimate ambient HCHO exposure for the EPA National Air Toxics Assessment (NATA). Here, we employ satellite HCHO retrievals from the Ozone Monitoring Instrument (OMI)-the NASA retrieval developed by the Smithsonian Astrophysical Observatory (SAO), and the European Union Quality Assurance for Essential Climate Variables (QA4ECV) retrieval-to evaluate three CMAQ configurations, spanning the summers of 2011 and 2016, with differing biogenic emissions inputs and chemical mechanisms. These CMAQ configurations capture the general spatial and temporal behavior of both satellite retrievals, but underestimate column HCHO, particularly in the western U.S. In the southeastern U.S., the comparison with OMI HCHO highlights differences in modeled meteorology and biogenic emissions even with differences in satellite retrievals. All CMAQ configurations show low daily correlations with OMI HCHO (r = 0.26 - 0.38), however, we find higher monthly correlations (r = 0.52 - 0.73), and the models correlate best with the OMI-QA4ECV product. Compared to surface observations, we find improved agreement over a 24-hour period compared to afternoon-only, suggesting daily HCHO amounts are captured with more accuracy than afternoon amounts. This work highlights the potential for synergistic improvements in modeling and satellite retrievals to support near-surface HCHO estimates for the NATA and other applications.

Assessing the Impact of Corona-Virus-19 on Nitrogen Dioxide Levels over Southern Ontario, Canada

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 (NO2) 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 NO2 columns that represents what TROPOMI would have observed for a non-COVID scenario. Applying this methodology to southern Ontario, decreases in NO2 emissions due to the lockdown were seen, with an average 40% (roughly 10 kt[NO2]/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[NO2]/yr to 40 kt[NO2]/yr in the Greater Toronto Area) based on emission activity data during the lockdown period was found to be consistent with TROPOMI NO2 columns.

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.

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.

SO2 and HCHO over the major cities of Kazakhstan from 2005 to 2016: influence of political, economic and industrial changes

Satellite observations of the Ozone Monitoring Instrument (OMI) for tropospheric sulfur dioxide (SO₂) and formaldehyde (HCHO) column mass densities (CMD) are analyzed for the period 2005-2016 over the atmosphere of Kazakhstan. Regarding SO₂ the major hot spots relate to regions with high population and large industrial facilities. Such an example is the city of Ekibastuz that hosts the biggest thermal power plants in the country and exhibits the higher SO₂ CMD at national level. The annual average CMD in Ekibastuz reaches 2.5 × 10^-5 kg/m², whereas for the rest of the country respective values are 6 times lower. Other hotspots, mostly urban conglomerates such as Almaty and Nur-Sultan, experience high CMDs of SO₂ in particular years, such as 2008. One of the main reasons for this behavior is the financial crisis of 2008, forcing the application of alternate heating sources based on cheap low-quality coal. Regarding HCHO, an oxygenated Volatile Organic Compound (VOC), the main hot spot is noticed over the city Atyrau, the oil capital of the country where two massive oil fields are located. The highest HCHO CMD (9 × 10¹⁵ molecules/cm²) appears in the summertime due to secondary production as a result of the photo-oxidation of VOCs emitted by industrial sectors, oil refinery plants and vehicles. Strongly elevated HCHO amounts are also observed in Nur-Sultan in 2012 that could be due to the residential coal combustion and vehicle exhaust under poor winter dispersion conditions. Significant reductions in HCHO observed between 2012 and 2015 can be attributed to two significant measures implemented in the country in 2013 that aimed at the improvement of air quality: the introduction of the emission trading system (ETS) for greenhouse gases and Euro-4 standards for new vehicles entering the national vehicle fleet.

First observation of tropospheric nitrogen dioxide from the Environmental Trace Gases Monitoring Instrument onboard the GaoFen-5 satellite

The Environmental Trace Gases Monitoring Instrument (EMI) is the first Chinese satellite-borne UV-Vis spectrometer aiming to measure the distribution of atmospheric trace gases on a global scale. The EMI instrument onboard the GaoFen-5 satellite was launched on 9 May 2018. In this paper, we present the tropospheric nitrogen dioxide (NO2) vertical column density (VCD) retrieval algorithm dedicated to EMI measurement. We report the first successful retrieval of tropospheric NO2 VCD from the EMI instrument. Our retrieval improved the original EMI NO2 prototype algorithm by modifying the settings of the spectral fit and air mass factor calculations to account for the on-orbit instrumental performance changes. The retrieved EMI NO2 VCDs generally show good spatiotemporal agreement with the satellite-borne Ozone Monitoring Instrument and TROPOspheric Monitoring Instrument (correlation coefficient R of ~0.9, bias < 50%). A comparison with ground-based MAX-DOAS (Multi-Axis Differential Optical Absorption Spectroscopy) observations also shows good correlation with an R of 0.82. The results indicate that the EMI NO2 retrieval algorithm derives reliable and precise results, and this algorithm can feasibly produce stable operational products that can contribute to global air pollution monitoring.

Evaluating the impact of spatial resolution on tropospheric NO2 column comparisons within urban areas using high-resolution airborne data

NASA deployed the GeoTASO airborne UV-Visible spectrometer in May-June 2017 to produce high resolution (approximately 250 × 250 m) gapless NO₂ datasets over the western shore of Lake Michigan and over the Los Angeles Basin. The results collected show that the airborne tropospheric vertical column retrievals compare well with ground-based Pandora spectrometer column NO₂ observations (r²=0.91 and slope of 1.03). Apparent disagreements between the two measurements can be sensitive to the coincidence criteria and are often associated with large local variability, including rapid temporal changes and spatial heterogeneity that may be observed differently by the sunward viewing Pandora observations. The gapless mapping strategy executed during the 2017 GeoTASO flights provides data suitable for averaging to coarser areal resolutions to simulate satellite retrievals. As simulated satellite pixel area increases to values typical of TEMPO, TROPOMI, and OMI, the agreement with Pandora measurements degraded, particularly for the most polluted columns as localized large pollution enhancements observed by Pandora and GeoTASO are spatially averaged with nearby less-polluted locations within the larger area representative of the satellite spatial resolutions (aircraft-to-Pandora slope: TEMPO scale=0.88; TROPOMI scale=0.77; OMI scale=0.57). In these two regions, Pandora and TEMPO or TROPOMI have the potential to compare well at least up to pollution scales of 30×10¹⁵ molecules cm^-2. Two publicly available OMI tropospheric NO₂ retrievals are both found to be biased low with respect to these Pandora observations. However, the agreement improves when higher resolution a priori inputs are used for the tropospheric air mass factor calculation (NASA V3 Standard Product slope = 0.18 and Berkeley High Resolution Product slope=0.30). Overall, this work explores best practices for satellite validation strategies with Pandora direct-sun observations by showing the sensitivity to product spatial resolution and demonstrating how the high spatial resolution NO₂ data retrieved from airborne spectrometers, such as GeoTASO, can be used with high temporal resolution ground-based column observations to evaluate the influence of spatial heterogeneity on validation results.

Constraining Emissions of Volatile Organic Compounds Over the Indian Subcontinent Using Space-Based Formaldehyde Measurements

India is an air pollution mortality hot spot, but regional emissions are poorly understood. We present a high-resolution nested chemical transport model (GEOS-Chem) simulation for the Indian subcontinent and use it to interpret formaldehyde (HCHO) observations from two satellite sensors (OMI and GOME-2A) in terms of constraints on regional volatile organic compound (VOC) emissions. We find modeled biogenic VOC emissions to be overestimated by ~30-60% for most locations and seasons, and derive a best estimate biogenic flux of 16 Tg C/year subcontinent-wide for year 2009. Terrestrial vegetation provides approximately half the total VOC flux in our base-case inversions (full uncertainty range: 44-65%). This differs from prior understanding, in which biogenic emissions represent >70% of the total. Our derived anthropogenic VOC emissions increase slightly (13-16% in the base case, for a subcontinent total of 15 Tg C/year in 2009) over RETRO year 2000 values, with some larger regional discrepancies. The optimized anthropogenic emissions agree well with the more recent CEDS inventory, both subcontinent-wide (within 2%) and regionally. An exception is the Indo-Gangetic Plain, where we find an underestimate for both RETRO and CEDS. Anthropogenic emissions thus constitute 37-50% of the annual regional VOC source in our base-case inversions and exceed biogenic emissions over the Indo-Gangetic Plain, West India, and South India, and over the entire subcontinent during winter and post-monsoon. Fires are a minor fraction (<7%) of the total regional VOC source in the prior and optimized model. However, evidence suggests that VOC emissions in the fire inventory used here (GFEDv4) are too low over the Indian subcontinent.

High resolution mapping of nitrogen dioxide with TROPOMI: First results and validation over the Canadian oil sands

TROPOMI, on-board the Sentinel-5 Precursor satellite is a nadir-viewing spectrometer measuring reflected sunlight in the ultraviolet, visible, near-infrared, and shortwave infrared spectral range. From these spectra several important air quality and climate-related atmospheric constituents are retrieved at an unprecedented high spatial resolution, including nitrogen dioxide (NO₂). We present the first retrievals of TROPOMI NO₂ over the Canadian Oil Sands, contrasting them with observations from the OMI satellite instrument, and demonstrate its ability to resolve individual plumes and highlight its potential for deriving emissions from individual mining facilities. Further, the first TROPOMI NO₂ validation is presented, consisting of aircraft and surface in-situ NO₂ observations, as well as ground-based remote-sensing measurements between March and May 2018. Our comparisons show that the TROPOMI NO₂ vertical column densities are highly correlated with the aircraft and surface in-situ NO₂ observations, and the ground-based remote-sensing measurements with a low bias (15-30 %) over the Canadian Oil Sands.

PLAIN LANGUAGE SUMMARY

Nitrogen dioxide (NO₂) is a pollutant that is linked to respiratory health issues and has negative environmental impacts such as soil and water acidification. Near the surface the most significant sources of NO₂ are fossil fuel combustion and biomass burning. With a recently launched satellite instrument (TROPOspheric Monitoring Instrument; TROPOMI) NO₂ can be measured with an unprecedented combination of accuracy, spatial coverage, and resolution. This work presents the first TROPOMI NO₂ measurements near the Canadian Oil Sands and shows that these measurements have an outstanding ability to detect NO₂ on a very high horizontal resolution that is unprecedented for satellite NO₂ observations. Further, these satellite measurements are in excellent agreement with aircraft and ground-based measurements.

Improved Satellite Retrieval of Tropospheric NO2 Column Density via Updating of Air Mass Factor (AMF): Case Study of Southern China

Improving air quality and reducing human exposure to unhealthy levels of airborne chemicals are important global missions, particularly in China. Satellite remote sensing offers a powerful tool to examine regional trends in NO2, thus providing a direct measure of key parameters that strongly affect surface air quality. To accurately resolve spatial gradients in NO2 concentration using satellite observations and thus understand local and regional aspects of air quality, a priori input data at sufficiently high spatial and temporal resolution to account for pixel-to-pixel variability in the characteristics of the land and atmosphere are required. In this paper, we adapt the Berkeley High Resolution product (BEHR-HK) and meteorological outputs from the Weather Research and Forecasting (WRF) model to describe column NO2 in southern China. The BEHR approach is particularly useful for places with large spatial variabilities and terrain height differences such as China. There are two major objectives and goals: (1) developing new BEHR-HK v3.0C product for retrieving tropospheric NO2 vertical column density (TVCD) within part of southern China, for four months of 2015, based upon satellite datasets from Ozone Monitoring Instrument (OMI); and (2) evaluating BEHR-HK v3.0C retrieval result through validation, by comparing with MAX-DOAS tropospheric column measurements conducted in Guangzhou. Results show that all BEHR-HK retrieval algorithms (with R-value of 0.9839 for v3.0C) are of higher consistency with MAX-DOAS measurements than OMI-NASA retrieval (with R-value of 0.7644). This opens new windows into research questions that require high spatial resolution, for example retrieving NO2 vertical column and ground pollutant concentration in China and other countries.

Assessing snow extent data sets over North America to inform and improve trace gas retrievals from solar backscatter

Accurate representation of surface reflectivity is essential to tropospheric trace gas retrievals from solar backscatter observations. Surface snow cover presents a significant challenge due to its variability and thus snow-covered scenes are often omitted from retrieval data sets; however, the high reflectance of snow is potentially advantageous for trace gas retrievals. We first examine the implications of surface snow on retrievals from the upcoming TEMPO geostationary instrument for North America. We use a radiative transfer model to examine how an increase in surface reflectivity due to snow cover changes the sensitivity of satellite retrievals to NO2 in the lower troposphere. We find that a substantial fraction (>50%) of the TEMPO field of regard can be snow covered in January, and that the average sensitivity to the tropospheric NO2 column substantially increases (doubles) when the surface is snow covered. We then evaluate seven existing satellite-derived or reanalysis snow extent products against ground station observations over North America to assess their capability of informing surface conditions for TEMPO retrievals. The Interactive Multisensor Snow and Ice Mapping System (IMS) had the best agreement with ground observations (accuracy of 93%, precision of 87%, recall of 83%). Multiangle Implementation of Atmospheric Correction (MAIAC) retrievals of MODIS-observed radiances had high precision (90% for Aqua and Terra), but underestimated the presence of snow (recall of 74% for Aqua, 75% for Terra). MAIAC generally outperforms the standard MODIS products (precision of 51%, recall of 43% for Aqua; precision of 69%, recall of 45% for Terra). The Near-real-time Ice and Snow Extent (NISE) product had good precision (83%) but missed a significant number of snow-covered pixels (recall of 45%). The Canadian Meteorological Centre (CMC) Daily Snow Depth Analysis Data set had strong performance metrics (accuracy of 91%, precision of 79%, recall of 82%). We use the F score, which balances precision and recall, to determine overall product performance (F = 85%, 82(82)%, 81%, 58%, 46(54)% for IMS, MAIAC Aqua(Terra), CMC, NISE, MODIS Aqua(Terra) respectively) for providing snow cover information for TEMPO retrievals from solar backscatter observations. We find that using IMS to identify snow cover and enable inclusion of snow-covered scenes in clear-sky conditions across North America in January can increase both the number of observations by a factor of 2.1 and the average sensitivity to the tropospheric NO2 column by a factor of 2.7.

The Dawn of Geostationary Air Quality Monitoring: Case Studies from Seoul and Los Angeles

With the near-future launch of geostationary pollution monitoring satellite instruments over North America, East Asia, and Europe, the air quality community is preparing for an integrated global atmospheric composition observing system at unprecedented spatial and temporal resolutions. One of the ways that NASA has supported this community preparation is through demonstration of future space-borne capabilities using the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument. This paper integrates repeated high-resolution maps from GeoTASO, ground-based Pandora spectrometers, and low Earth orbit measurements from the Ozone Mapping and Profiler Suite (OMPS), for case studies over two metropolitan areas: Seoul, South Korea on June 9th, 2016 and Los Angeles, California on June 27th, 2017. This dataset provides a unique opportunity to illustrate how geostationary air quality monitoring platforms and ground-based remote sensing networks will close the current spatiotemporal observation gap. GeoTASO observes large differences in diurnal behavior between these urban areas, with NO2 accumulating within the Seoul Metropolitan Area through the day but NO2 peaking in the morning and decreasing throughout the afternoon in the Los Angeles Basin. In both areas, the earliest morning maps exhibit spatial patterns similar to emission source areas (e.g., urbanized valleys, roadways, major airports). These spatial patterns change later in the day due to boundary layer dynamics, horizontal transport, and chemistry. The nominal resolution of GeoTASO is finer than will be obtained from geostationary platforms, but when NO2 data over Los Angeles are up-scaled to the expected resolution of TEMPO, spatial features discussed are conserved. Pandora instruments installed in both metropolitan areas capture the diurnal patterns observed by GeoTASO, continuously and over longer time periods, and will play a critical role in validation of the next generation of satellite measurement.. These case studies demonstrate that different regions can have diverse diurnal patterns and that day-to-day variability due to meteorology or anthropogenic patterns such as weekday/weekend variations in emissions is large. Low Earth orbit measurements, despite their inability to capture the diurnal patterns at fine spatial resolution, will be essential for intercalibrating the geostationary radiances and cross-validating the geostationary retrievals in an integrated global observing system.

Validation of integrated water vapor from OMI satellite instrument against reference GPS data at the Iberian Peninsula

This paper shows the validation of integrated water vapor (IWV) measurements retrieved from the Ozone Monitoring Instrument (OMI), using as reference nine ground-based GPS stations in the Iberian Peninsula. The study period covers from 2007 to 2009. The influence of two factors, - solar zenith angle (SZA) and IWV -, on OMI-GPS differences was studied in detail, as well as the seasonal dependence. The pseudomedian of the relative differences is -1 ± 1% and the inter-quartile range (IQR) is 41%. Linear regressions calculated over each station show an acceptable agreement (R² up to 0.77). The OMI-GPS differences display a clear dependence on IWV values. Hence, OMI substantially overestimates the lower IWV data recorded by GPS (∼ 40%), while underestimates the higher IWV reference values (∼ 20%). In connection to this IWV dependence, the relative differences also show an evident SZA dependence when the whole range of IWV values are analyzed (OMI overestimates for high SZA values while underestimates for low values). Finally, the seasonal variation of the OMI-GPS differences is also associated with the strong IWV dependence found in this validation exercise.

Tropospheric Emissions: Monitoring of Pollution (TEMPO)

TEMPO was selected in 2012 by NASA as the first Earth Venture Instrument, for launch between 2018 and 2021. It will measure atmospheric pollution for greater North America from space using ultraviolet and visible spectroscopy. TEMPO observes from Mexico City, Cuba, and the Bahamas to the Canadian oil sands, and from the Atlantic to the Pacific, hourly and at high spatial resolution (~2.1 km N/S×4.4 km E/W at 36.5°N, 100°W). TEMPO provides a tropospheric measurement suite that includes the key elements of tropospheric air pollution chemistry, as well as contributing to carbon cycle knowledge. Measurements are made hourly from geostationary (GEO) orbit, to capture the high variability present in the diurnal cycle of emissions and chemistry that are unobservable from current low-Earth orbit (LEO) satellites that measure once per day. The small product spatial footprint resolves pollution sources at sub-urban scale. Together, this temporal and spatial resolution improves emission inventories, monitors population exposure, and enables effective emission-control strategies. TEMPO takes advantage of a commercial GEO host spacecraft to provide a modest cost mission that measures the spectra required to retrieve ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), formaldehyde (H2CO), glyoxal (C2H2O2), bromine monoxide (BrO), IO (iodine monoxide),water vapor, aerosols, cloud parameters, ultraviolet radiation, and foliage properties. TEMPO thus measures the major elements, directly or by proxy, in the tropospheric O3 chemistry cycle. Multi-spectral observations provide sensitivity to O3 in the lowermost troposphere, substantially reducing uncertainty in air quality predictions. TEMPO quantifies and tracks the evolution of aerosol loading. It provides these near-real-time air quality products that will be made publicly available. TEMPO will launch at a prime time to be the North American component of the global geostationary constellation of pollution monitoring together with the European Sentinel-4 (S4) and Korean Geostationary Environment Monitoring Spectrometer (GEMS) instruments.

Observing atmospheric formaldehyde (HCHO) from space: validation and intercomparison of six retrievals from four satellites (OMI, GOME2A, GOME2B, OMPS) with SEAC4RS aircraft observations over the Southeast US

Formaldehyde (HCHO) column data from satellites are widely used as a proxy for emissions of volatile organic compounds (VOCs) but validation of the data has been extremely limited. Here we use highly accurate HCHO aircraft observations from the NASA SEAC⁴RS campaign over the Southeast US in August-September 2013 to validate and intercompare six retrievals of HCHO columns from four different satellite instruments (OMI, GOME2A, GOME2B and OMPS) and three different research groups. The GEOS-Chem chemical transport model is used as a common intercomparison platform. All retrievals feature a HCHO maximum over Arkansas and Louisiana, consistent with the aircraft observations and reflecting high emissions of biogenic isoprene. The retrievals are also interconsistent in their spatial variability over the Southeast US (r=0.4-0.8 on a 0.5°×0.5° grid) and in their day-to-day variability (r=0.5-0.8). However, all retrievals are biased low in the mean by 20-51%, which would lead to corresponding bias in estimates of isoprene emissions from the satellite data. The smallest bias is for OMI-BIRA, which has high corrected slant columns relative to the other retrievals and low scattering weights in its air mass factor (AMF) calculation. OMI-BIRA has systematic error in its assumed vertical HCHO shape profiles for the AMF calculation and correcting this would eliminate its bias relative to the SEAC⁴RS data. Our results support the use of satellite HCHO data as a quantitative proxy for isoprene emission after correction of the low mean bias. There is no evident pattern in the bias, suggesting that a uniform correction factor may be applied to the data until better understanding is achieved.

Why do Models Overestimate Surface Ozone in the Southeastern United States?

Ozone pollution in the Southeast US involves complex chemistry driven by emissions of anthropogenic nitrogen oxide radicals (NO_x ≡ NO + NO₂) and biogenic isoprene. Model estimates of surface ozone concentrations tend to be biased high in the region and this is of concern for designing effective emission control strategies to meet air quality standards. We use detailed chemical observations from the SEAC⁴RS aircraft campaign in August and September 2013, interpreted with the GEOS-Chem chemical transport model at 0.25°×0.3125° horizontal resolution, to better understand the factors controlling surface ozone in the Southeast US. We find that the National Emission Inventory (NEI) for NO_x from the US Environmental Protection Agency (EPA) is too high. This finding is based on SEAC⁴RS observations of NO_x and its oxidation products, surface network observations of nitrate wet deposition fluxes, and OMI satellite observations of tropospheric NO₂ columns. Our results indicate that NEI NO_x emissions from mobile and industrial sources must be reduced by 30-60%, dependent on the assumption of the contribution by soil NO_x emissions. Upper tropospheric NO₂ from lightning makes a large contribution to satellite observations of tropospheric NO₂ that must be accounted for when using these data to estimate surface NO_x emissions. We find that only half of isoprene oxidation proceeds by the high-NO_x pathway to produce ozone; this fraction is only moderately sensitive to changes in NO_x emissions because isoprene and NO_x emissions are spatially segregated. GEOS-Chem with reduced NO_x emissions provides an unbiased simulation of ozone observations from the aircraft, and reproduces the observed ozone production efficiency in the boundary layer as derived from a regression of ozone and NO_x oxidation products. However, the model is still biased high by 8±13 ppb relative to observed surface ozone in the Southeast US. Ozonesondes launched during midday hours show a 7 ppb ozone decrease from 1.5 km to the surface that GEOS-Chem does not capture. This bias may reflect a combination of excessive vertical mixing and net ozone production in the model boundary layer.

Unusually high soil nitrogen oxide emissions influence air quality in a high-temperature agricultural region

Fertilized soils have large potential for production of soil nitrogen oxide (NO_x=NO+NO₂), however these emissions are difficult to predict in high-temperature environments. Understanding these emissions may improve air quality modelling as NO_x contributes to formation of tropospheric ozone (O₃), a powerful air pollutant. Here we identify the environmental and management factors that regulate soil NO_x emissions in a high-temperature agricultural region of California. We also investigate whether soil NO_x emissions are capable of influencing regional air quality. We report some of the highest soil NO_x emissions ever observed. Emissions vary nonlinearly with fertilization, temperature and soil moisture. We find that a regional air chemistry model often underestimates soil NO_x emissions and NO_x at the surface and in the troposphere. Adjusting the model to match NO_x observations leads to elevated tropospheric O₃. Our results suggest management can greatly reduce soil NO_x emissions, thereby improving air quality.

Soil NO_x emissions can significantly impact air quality in agricultural regions, particularly high temperature fertilized systems. Here, the authors investigate NO_x emissions in one such system in California and suggest that the NO_x emissions are the highest ever observed, with implications for air quality.

Validation of OMI HCHO data and its analysis over Asia

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

Scaling relationship for NO2 pollution and urban population size: a satellite perspective

Concern is growing about the effects of urbanization on air pollution and health. Nitrogen dioxide (NO2) released primarily from combustion processes, such as traffic, is a short-lived atmospheric pollutant that serves as an air-quality indicator and is itself a health concern. We derive a global distribution of ground-level NO2 concentrations from tropospheric NO2 columns retrieved from the Ozone Monitoring Instrument (OMI). Local scaling factors from a three-dimensional chemistry-transport model (GEOS-Chem) are used to relate the OMI NO2 columns to ground-level concentrations. The OMI-derived surface NO2 data are significantly correlated (r = 0.69) with in situ surface measurements. We examine how the OMI-derived ground-level NO2 concentrations, OMI NO2 columns, and bottom-up NOx emission inventories relate to urban population. Emission hot spots, such as power plants, are excluded to focus on urban relationships. The correlation of surface NO2 with population is significant for the three countries and one continent examined here: United States (r = 0.71), Europe (r = 0.67), China (r = 0.69), and India (r = 0.59). Urban NO2 pollution, like other urban properties, is a power law scaling function of the population size: NO2 concentration increases proportional to population raised to an exponent. The value of the exponent varies by region from 0.36 for India to 0.66 for China, reflecting regional differences in industrial development and per capita emissions. It has been generally established that energy efficiency increases and, therefore, per capita NOx emissions decrease with urban population; here, we show how outdoor ambient NO2 concentrations depend upon urban population in different global regions.

Quantitative and qualitative sensing techniques for biogenic volatile organic compounds and their oxidation products

The physiological production mechanisms of some of the organics in plants, commonly known as biogenic volatile organic compounds (BVOCs), have been known for more than a century. Some BVOCs are emitted to the atmosphere and play a significant role in tropospheric photochemistry especially in ozone and secondary organic aerosol (SOA) productions as a result of interplays between BVOCs and atmospheric radicals such as hydroxyl radical (OH), ozone (O3) and NOX (NO + NO2). These findings have been drawn from comprehensive analysis of numerous field and laboratory studies that have characterized the ambient distribution of BVOCs and their oxidation products, and reaction kinetics between BVOCs and atmospheric oxidants. These investigations are limited by the capacity for identifying and quantifying these compounds. This review highlights the major analytical techniques that have been used to observe BVOCs and their oxidation products such as gas chromatography, mass spectrometry with hard and soft ionization methods, and optical techniques from laser induced fluorescence (LIF) to remote sensing. In addition, we discuss how new analytical techniques can advance our understanding of BVOC photochemical processes. The principles, advantages, and drawbacks of the analytical techniques are discussed along with specific examples of how the techniques were applied in field and laboratory measurements. Since a number of thorough review papers for each specific analytical technique are available, readers are referred to these publications rather than providing thorough descriptions of each technique. Therefore, the aim of this review is for readers to grasp the advantages and disadvantages of various sensing techniques for BVOCs and their oxidation products and to provide guidance for choosing the optimal technique for a specific research task.

Isoprene emissions in Africa inferred from OMI observations of formaldehyde columns

We use 2005-2009 satellite observations of formaldehyde (HCHO) columns from the OMI instrument to infer biogenic isoprene emissions at monthly 1 × 1° resolution over the African continent. Our work includes new approaches to remove biomass burning influences using OMI absorbing aerosol optical depth data (to account for transport of fire plumes) and anthropogenic influences using AATSR satellite data for persistent small-flame fires (gas flaring). The resulting biogenic HCHO columns (Ω_HCHO) from OMI follow closely the distribution of vegetation patterns in Africa. We infer isoprene emission (E _ISOP) from the local sensitivity S = ΔΩ_HCHO / ΔE _ISOP derived with the GEOS-Chem chemical transport model using two alternate isoprene oxidation mechanisms, and verify the validity of this approach using AMMA aircraft observations over West Africa and a longitudinal transect across central Africa. Displacement error (smearing) is diagnosed by anomalously high values of S and the corresponding data are removed. We find significant sensitivity of S to NO_x under low-NO_x conditions that we fit to a linear function of tropospheric column NO₂. We estimate a 40% error in our inferred isoprene emissions under high-NO_x conditions and 40-90% under low-NO_x conditions. Our results suggest that isoprene emission from the central African rainforest is much lower than estimated by the state-of-the-science MEGAN inventory.

Formaldehyde columns from the Ozone Monitoring Instrument: Urban versus background levels and evaluation using aircraft data and a global model

[1] We combine aircraft measurements (Second Texas Air Quality Study, Megacity Initiative: Local and Global Research Observations, Intercontinental Chemical Transport Experiment: Phase B) over the United States, Mexico, and the Pacific with a 3-D model (GEOS-Chem) to evaluate formaldehyde column (Ω_HCHO) retrievals from the Ozone Monitoring Instrument (OMI) and assess the information they provide on HCHO across local to regional scales and urban to background regimes. OMI Ω_HCHO correlates well with columns derived from aircraft measurements and GEOS-Chem (R = 0.80). For the full data ensemble, OMI's mean bias is -3% relative to aircraft-derived Ω_HCHO (-17% where Ω_HCHO > 5 × 10¹⁵ molecules cm^-2) and -8% relative to GEOS-Chem, within expected uncertainty for the retrieval. Some negative bias is expected for the satellite and model, given the plume sampling of many flights and averaging over the satellite and model footprints. Major axis regression for OMI versus aircraft and model columns yields slopes (95% confidence intervals) of 0.80 (0.62-1.03) and 0.98 (0.73-1.35), respectively, with no significant intercept. Aircraft measurements indicate that the normalized vertical HCHO distribution, required by the satellite retrieval, is well captured by GEOS-Chem, except near Mexico City. Using measured HCHO profiles in the retrieval algorithm does not improve satellite-aircraft agreement, suggesting that use of a global model to specify shape factors does not substantially degrade retrievals over polluted areas. While the OMI measurements show that biogenic volatile organic compounds dominate intra-annual and regional Ω_HCHO variability across the United States, smaller anthropogenic Ω_HCHO gradients are detectable at finer spatial scales (∼20-200 km) near many urban areas.

Bichromatic method for identification of clear-sky scenarios over ground pixel viewed from space

We propose a method for identifying clear-sky scenarios from a measurement time series over satellite-observed ground pixels of unknown surface albedo and aerosol type. The lack of a general monotonic relationship between aerosol loading and observed reflectance encumbers the ordering of the observation time series according to aerosol loading. This problem is ameliorated by using two wavelengths at which the surface albedos are known to differ. Treating an observation as being cloud/aerosol free allows for the determination of the corresponding Lambertian equivalent albedo, the relative contrast of which at the two wavelengths varies monotonically with respect to aerosol-loading, clear-sky and completely clouded scenarios representing the extreme cases. Applying this method to the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography measurements over a nondark surface, we validate it by comparing measured against modeled O(2) A- and B-band absorption at the retrieved albedo in an aerosol-free atmosphere.

Interpreting satellite column observations of formaldehyde over tropical South America

Space-borne column measurements of formaldehyde (HCHO), a high-yield oxidation product of volatile organic compounds (VOCs), represent important constraints for quantifying net regional fluxes of VOCs. Here, we interpret observed distributions of HCHO columns from the Global Ozone Monitoring Experiment (GOME) over tropical South America during 1997-2001. We present the first comparison of year-long in situ isoprene concentrations and fire-free GOME HCHO columns over a tropical ecosystem. GOME HCHO columns and in situ isoprene concentrations are elevated in the wet and dry seasons, with the highest values in the dry season. Previous analysis of the in situ data highlighted the possible role of drought in determining the elevated concentrations during the dry season, inferring the potential of HCHO columns to provide regional-scale constraints for estimating the role of drought on isoprene emissions. The agreement between the observed annual cycles of GOME HCHO columns and Along-Track Scanning Radiometer firecount data over the Amazon basin (correlations typically greater than 0.75 for a particular year) illustrates the potential of HCHO column to provide quantitative information about biomass burning emissions.