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As part of the Pandonia Global Network (PGN), Pandora spectrometers are widely deployed around the world. These ground-based, remote-sensing instruments are federated such that they employ a common algorithm and data protocol for reporting on trace gas column densities and lower atmospheric profiles using two modes based on direct-sun and sky-scan observations. To aid users in the analysis of Pandora observations, the PGN standard quality assurance procedure assigns flags to the data indicating high, medium, and low quality. This work assesses the suitability of these data quality flags for filtering data in the scientific analysis of nitrogen dioxide (NO2) and formaldehyde (HCHO), two critical precursors controlling tropospheric ozone production. Pandora data flagged as high quality assures scientifically valid data and is often more abundant for direct-sun NO2 columns. For direct-sun HCHO and sky-scan observations of both molecules, large amounts of data flagged as low quality also appear to be valid. Upon closer inspection of the data, independent uncertainty is shown to be a better indicator of data quality than the standard quality flags. After applying an independent uncertainty filter, Pandora data flagged as medium or low quality in both modes can be demonstrated to be scientifically useful. Demonstrating the utility of this filtering method is enabled by correlating contemporaneous but independent direct-sun and sky-scan observations. When evaluated across 15 Pandora sites in North America, this new filtering method increased the availability of scientifically useful data by as much as 90 % above that tagged as high quality. A method is also developed for combining the direct-sun and sky-scan observations into a single dataset by accounting for biases between the two observing modes and differences in measurement integration times. This combined data provides a more continuous record useful for interpreting Pandora observations against other independent variables such as hourly observations of surface ozone. When Pandora HCHO columns are correlated with surface ozone measurements, data filtered by independent uncertainty exhibits similarly strong and more robust relationships than high-quality data alone. These results suggest that Pandora data users should carefully assess data across all quality flags and consider their potential for useful application to scientific analysis. The present study provides a method for maximizing use of Pandora data with expectation of more robust satellite validation and comparisons with ground-based observations in support of air quality studies. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO2) is a precursor of ozone (O3) and fine particulate matter (PM2.5) – two pollutants that are above regulatory guidelines in many cities. Bringing urban areas into compliance of these regulatory standards motivates an understanding of the distribution and sources of NO2 through observations and simulations. The TRACER-AQ campaign, conducted in Houston, TX in September 2021, provided a unique opportunity to compare observed NO2 columns from ground-, airborne-, and satellite-based spectrometers. In this study, we investigate how these observational datasets compare, and simulate column NO2 using WRF-CAMx with fine resolution (444 x 444 m2) comparable to the airborne column measurements. We find that observations from the GEOCAPE Airborne Simulator (GCAS) instrument were strongly correlated (r2=0.80) to observations from Pandora spectrometers with a negligible bias (NMB=0.1 %). Remote-sensing observations from the TROPOMI instrument were generally well correlated with Pandora observations (r2=0.73) with a negative bias (NMB=-22.8 %). We intercompare different versions of TROPOMI data and find similar correlations across three versions but slightly different biases (from -22.8 % in v2.4.0 to -18.2 % in the NASA MINDS product). Compared to Pandora observations, the WRF-CAMx simulation had reduced correlation (r2=0.34) and a low bias (-25.5 %) over the entire study region. We find particularly poor agreement between simulated NO2 columns and GCAS-observed NO2 columns in downtown Houston an area of high population and roadway densities. These findings point to a potential underestimate of vehicle NOX emissions in the WRF-CAMx simulation driven by the Texas state inventory; and further investigation is recommended. [ABSTRACT FROM AUTHOR]

Sub-grid variability (SGV) of atmospheric trace gases within satellite pixels is a key issue in satellite design, and interpretation and validation of retrieval products. However, characterizing this variability is challenging due to the lack of independent high-resolution measurements. Here we use tropospheric NO2 vertical column (VC) measurements from the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument with a spatial resolution of about 250 m 250 m to quantify the normalized SGV (i.e., the standard deviation of the sub-grid GeoTASO values within the sampled satellite pixel divided by their mean of the sub-grid GeoTASO values within the sampled satellite pixel) for different satellite pixel sizes. We use the GeoTASO measurements over the Seoul Metropolitan Area (SMA) and Busan region of South Korea during the 2016 KORUS‐AQ field campaign, and over the Los Angeles Basin, USA during the 2017 SARP field campaign. We find that the normalized SGV of NO2 VC increases with increasing satellite pixel sizes (from ~10 % for 0.5 km × 0.5 km pixel size to ~35 % for 25 km × 25 km pixel size), and this relationship holds for the three study regions, which are also within the domains of upcoming geostationary satellite air quality missions. We also quantify the temporal variability of the retrieved NO2 VC within the same satellite pixels (represented by the difference of retrieved values at two different times of a day). For a given satellite pixel size, the temporal variability within the same satellite pixels increases with the sampling time difference over SMA. For a given small (e.g., <= 4 hours) sampling time difference within the same satellite pixels, the temporal variability of the retrieved NO2 VC increases with the increasing spatial resolution over the SMA, Busan region, and the Los Angeles basin. The results of this study have implications for future satellite design and retrieval interpretation, and validation when comparing pixel data with local observations. In addition, the analyses presented in this study are equally applicable in model evaluation when comparing model grid values to local observations. Results from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model indicate that the normalized satellite SGV of tropospheric NO2 VC calculated in this study could serve as an upper bound to the satellite SGV of other species (e.g., CO and SO2) that share common source(s) with NO2 but have relatively longer lifetime. [ABSTRACT FROM AUTHOR]

Abundant NO[sub 2] column measurements from airborne and ground-based Pandora spectrometers were collected as part of the 2018 Long Island Sound Tropospheric Ozone Study (LISTOS) in the New York City/Long Island Sound region and coincided with early measurements from the Sentinel-5P TROPOMI instrument. Both airborne- and ground-based measurements are used to evaluate the TROPOspheric Monitoring Instrument (TROPOMI) NO[sub 2] Tropospheric Vertical Column (TrVC) product v1.2 in this region, which has high spatial and temporal heterogeneity in NO[sub 2]. 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[sup 2]=0.92 and slope of 1.03) with the biggest 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 representivity. Pandora spectrometers can provide continuous long-term measurements but may lack areal representivity 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 250 m × 250 m over the entire TROPOMI pixels they overfly. Thus, airborne data are more correlated with TROPOMI measurements (r[sup 2]=0.96) than Pandora measurements are with TROPOMI (r[sup 2]=0.84). The largest outliers between TROPOMI and the reference measurements are caused by errors in the TROPOMI retrieval of cloud pressure impacting the calculation of tropospheric air mass factors in cloud-free scenes. 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 TM5-MP model; replacing these profiles with those from a 12 km 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. [ABSTRACT FROM AUTHOR]

NASA deployed an airborne UV/Visible spectrometer, GeoTASO, in May-June 2017 to produce high resolution (approximately 250 x 250 m), gapless NO2 datasets over the western shore of Lake Michigan and over the Los Angeles Basin. Results 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 also 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 is degraded as localized polluted plumes observed by Pandora are spatially averaged over larger areas (aircraft-to-Pandora slope: TEMPO scale = 0.88; TROPOMI scale = 0.77; OMI scale = 0.57). This behavior suggests that satellite products are representative of individual Pandora observations up to a certain pollution scale that depends on satellite spatial resolution. In these two regions, Pandora and TEMPO or TROPOMI have the potential to compare well up to pollution scales of 30 x 1015 molecules cm-2. Two publicly available OMI tropospheric NO2 retrievals are both found to be biased low with respect to Pandora observations (NASA V3 Standard Product slope = 0.18 and Berkeley High Resolution Product slope = 0.30). However, the agreement improves when higher resolution a priori inputs are used for the tropospheric air mass factor calculation. Overall, this work explores best practices for satellite validation strategies by showing the sensitivity to product spatial resolution and demonstrates how the high spatial resolution NO2 data retrieved from airborne spectrometers, such as GeoTASO, can be used with high temporal resolution surface observations to evaluate the influence of spatial heterogeneity on validation results. [ABSTRACT FROM AUTHOR]

In Houston, Texas, nitrogen dioxide (NO 2 ) air pollution disproportionately affects Black, Latinx, and Asian communities, and high ozone (O 3 ) days are frequent. There is limited knowledge of how NO 2 inequalities vary in urban air quality contexts, in part from the lack of time-varying neighborhood-level NO 2 measurements. First, we demonstrate that daily TROPOspheric Monitoring Instrument (TROPOMI) NO 2 tropospheric vertical column densities (TVCDs) resolve a major portion of census tract-scale NO 2 inequalities in Houston, comparing NO 2 inequalities based on TROPOMI TVCDs and spatiotemporally coincident airborne remote sensing (250 m × 560 m) from the NASA TRacking Aerosol Convection ExpeRiment-Air Quality (TRACER-AQ). We further evaluate the application of daily TROPOMI TVCDs to census tract-scale NO 2 inequalities (May 2018-November 2022). This includes explaining differences between mean daily NO 2 inequalities and those based on TVCDs oversampled to 0.01° × 0.01° and showing daily NO 2 column-surface relationships weaken as a function of observation separation distance. Second, census tract-scale NO 2 inequalities, city-wide high O 3 , and mesoscale airflows are found to covary using principal component and cluster analysis. A generalized additive model of O 3 mixing ratios versus NO 2 inequalities reproduces established nonlinear relationships between O 3 production and NO 2 concentrations, providing observational evidence that neighborhood-level NO 2 inequalities and O 3 are coupled. Consequently, emissions controls specifically in Black, Latinx, and Asian communities will have co-benefits, reducing both NO 2 disparities and high O 3 days city wide., Competing Interests: The authors declare no competing financial interest., (© 2024 The Authors. Published by American Chemical Society.)