Revolutionary Air-Pollution Applications from Future Tropospheric Emissions: Monitoring of Pollution (TEMPO) Observations

Naeger, Aaron; Newchurch, M. J.; Moore, Tom; Chance, K.; Liu, Xueliang; Alexander, Susan; Murphy, Kelley M.; Wang, Bo

doi:10.1175/bams-d-21-0050.1

Cited by 10 publications

(5 citation statements)

References 9 publications

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“…One component of the pre-launch activities of the geostationary TEMPO satellite mission is to generate synthetic 240 retrieval data for end-user communities, which closely represents the planned operational products of the mission planned for launch in early 2023 (Naeger et al, 2021). The synthetic data products are provided daily and at the expected 2.0 km × 4.75 km (at nadir) spatial resolution of TEMPO.…”

Section: Tempo Synthetic Retrieval Productmentioning

confidence: 99%

Satellite remote-sensing capability to assess tropospheric column ratios of formaldehyde and nitrogen dioxide: case study during the LISTOS 2018 field campaign

Johnson

Philip

Kumar

et al. 2022

Preprint

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Abstract. Satellite retrievals of tropospheric column formaldehyde (HCHO) and nitrogen dioxide (NO2) are frequently used to investigate the sensitivity of ozone (O3) production to concentrations and emissions of nitrogen oxides (NOx) and volatile organic carbon compounds (VOCs). Space-based remote-sensing information of chemical proxies for NOx (i.e., NO2) and VOCs (i.e., HCHO), in particular the ratios of tropospheric column HCHO and NO2 (FNRs), provide insight into the non-linear relationship of O3 formation in the lower troposphere. Ultraviolet–visible (UV/VIS) satellite spectrometers such as the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) are capable of providing FNR information with high spatiotemporal coverage, yet a recent study suggested that the biases and noise of satellite retrievals are the largest source of uncertainty for applying satellite-derived FNRs to better understand O3 production sensitivities. To quantify, and inter-compare, the uncertainties in two of the most commonly-applied satellite sensors to investigate O3 production sensitivities, we evaluated OMI and TROPOMI retrievals of NO2 and HCHO tropospheric columns, and resulting FNRs, using Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) and GEO-CAPE Airborne Simulator (GCAS) airborne remote-sensing data taken during the Long Island Sound Tropospheric Ozone Study 2018 (LISTOS 2018). Compared to suborbital remote-sensing observations of tropospheric column NO2 and HCHO, the accuracy of OMI (using both the National Aeronautics and Space Administration (NASA) version 4 and the Quality Assurance for Essential Climate Variables (QA4ECV) retrieval algorithms) and TROPOMI were magnitude-dependent with high biases (i.e., satellite tropospheric columns > suborbital tropospheric columns) in clean/background environments and a tendency towards a low bias (i.e., satellite tropospheric columns < suborbital tropospheric columns) in moderate to polluted regions. Campaign-averaged NO2 median biases for OMI, using both the NASA and QA4ECV algorithms, were similar at 0.4±4.1 × 1015 molecules cm-2 (6.3 %) and 0.4±4.5 × 1015 molecules cm-2 (6.8 %), respectively. TROPOMI retrievals of NO2 had a campaign-averaged median bias of -0.3±3.7 × 1015 molecules cm-2 (-4.8 %) and 0.3±3.3 × 1015 molecules cm-2 (5.8 %) when averaged at finer (0.05° × 0.05°) and coarser (0.15° × 0.15°) spatial resolution. The three satellite products (NASA OMI, QA4ECV OMI, and TROPOMI) differed more when evaluating tropospheric column HCHO retrievals. Noise in the HCHO retrievals, likely due to low signal-to-noise ratios and the fact the UV/VIS measurement sensitivity at shorter wavelengths used in HCHO retrievals are low in the troposphere, resulted in low correlations and high oscillation/variability in bias (bias standard deviation) in all three satellite products, with campaign-averaged median biases of 5.1±7.8 × 1015 molecules cm-2 (38.7 %), 2.3±8.9 × 1015 molecules cm-2 (17.3 %), 1.9±6.7 × 1015 molecules cm-2 (12.9 %), and 2.9±4.9 × 1015 molecules cm-2 (23.1 %) for NASA OMI, QA4ECV OMI, and TROPOMI at finer and coarser spatial resolution, respectively. Spatially-averaging TROPOMI tropospheric column HCHO, along with NO2 and FNRs, to coarser resolutions similar to OMI native pixel size proved to reduce the bias standard deviation of the retrieval data. While large median biases, and enhanced variability in bias, were derived for HCHO, errors in both NO2 and HCHO tropospheric columns tended to offset as all three satellite products compared well to observed FNRs with campaign-averaged median biases from NASA OMI, QA4ECV OMI, and TROPOMI of 0.4±3.8 (11.0 %), -0.2±3.3 (-5.4 %), and 0.4±2.3 (13.0 %), respectively. While satellite-derived FNRs had minimal campaign-averaged median biases, the statistical analysis shows that all satellite FNR values still had large bias standard deviation due to unresolved errors in satellite retrievals of HCHO. This result is important as accurate retrievals (minimal median biases) of FNRs from satellites do not suggest the accuracy of the underlying proxy species. The reduction in noise in satellite retrievals of HCHO with additional calibration and improved sensor design and/or improved a priori information of the vertical profiles of HCHO in the troposphere to avoid the impact of the low measurement sensitivity in the shorter UV/VIS wavelengths used to retrieve HCHO is critical for reducing unresolved biases in satellite retrievals of FNRs. Furthermore, this work demonstrates the large impact of a) a priori vertical profiles of NO2 and HCHO for calculations of Air Mass Factors in tropospheric column trace gas retrievals in both OMI and TROPOMI, b) spatiotemporal averaging to increase signal-to-noise, and c) different retrieval algorithms on retrieval errors. Finally, the novel diurnal information of tropospheric FNRs that is expected to be provided by the upcoming NASA geostationary sensor Tropospheric Emissions: Monitoring of Pollution (TEMPO) is investigated and compared to low earth orbiting sensors currently applied to investigate tropospheric FNRs.

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Section: Tempo Synthetic Retrieval Productmentioning

confidence: 99%

Satellite remote-sensing capability to assess tropospheric column ratios of formaldehyde and nitrogen dioxide: case study during the LISTOS 2018 field campaign

Johnson

Philip

Kumar

et al. 2022

Preprint

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“…However, low data coverage from TropOMI could impact the identification of the observational hotspot, particularly in the winter when meteorological conditions are not conducive to TropOMI observations. This data scarcity highlights the utility of upcoming remote sensing technologies like TEMPO (Naeger et al 2021) that will provide higher spatiotemporal coverage, which in turn could better identify hotspots and help constrain NO x emission sources. Beyond emission uncertainties, the choice of model physics and parameterizations in WRF-CMAQ can bias simulated NO 2 concentrations due to poorly simulated meteorological processes and/or challenges associated with urban settings (Pleim et al 2014, Gilliam et al 2015.…”

Section: Discussionmentioning

confidence: 99%

Intraurban NO₂ hotspot detection across multiple air quality products

Montgomery,

Daepp,

Abdin

et al. 2023

Environ. Res. Lett.

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High-resolution air quality data products have the potential to help quantify inequitable environmental exposures over space and across time by enabling the identification of hotspots, or areas that consistently experience elevated pollution levels relative to their surroundings. However, when different high-resolution data products identify different hotspots, the spatial sparsity of “gold-standard” regulatory observations leaves researchers, regulators, and concerned citizens without a means to differentiate signal from noise. This study compares NO2 hotspots detected within the city of Chicago, IL, USA using three distinct high-resolution (1.3 km) air quality products: (1) an interpolated product from Microsoft Research’s Project Eclipse – a dense network of over 100 low-cost sensors; (2) a two-way coupled WRF-CMAQ simulation; and (3) a down-sampled product using TropOMI satellite instrument observations. We use the Getis-Ord Gi * statistic to identify hotspots of NO2 and stratify results into high-, medium-, and low-agreement hotspots, including one consensus hotspot detected in all three datasets. Interrogating medium- and low-agreement hotspots offers insights into dataset discrepancies, such as sensor placement and model physics considerations, data retrieval caveats, and the potential for missing emission inventories. When treated as complements rather than substitutes, our work demonstrates that novel air quality products can enable researchers to address discrepancies in data products and can help regulators evaluate confidence in policy-relevant insights.

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“…Synergistic AOD products developed from the ABI and upcoming NASA geostationary Tropospheric Emissions: Monitoring of Pollution (TEMPO) mission, planned for launch in 2023, will further enhance capabilities to predict and monitor PM 2.5 concentrations in the region. TEMPO will advance exposure science in North America, particularly by providing hourly observations of aerosols and gaseous pollutants for supporting air-pollution models [52,53].…”

Section: Discussionmentioning

confidence: 99%

Prediction of daily mean and one-hour maximum PM2.5 concentrations and applications in Central Mexico using satellite-based machine-learning models

Gutiérrez-Ávila

Arfer

Carrión

et al. 2022

J Expo Sci Environ Epidemiol

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Background Machine-learning algorithms are becoming popular techniques to predict ambient air PM2.5 concentrations at high spatial resolutions (1 × 1 km) using satellite-based aerosol optical depth (AOD). Most machine-learning models have aimed to predict 24 h-averaged PM2.5 concentrations (mean PM2.5) in high-income regions. Over Mexico, none have been developed to predict subdaily peak levels, such as the maximum daily 1-h concentration (max PM2.5). Objective Our goal was to develop a machine-learning model to predict mean PM2.5 and max PM2.5 concentrations in the Mexico City Metropolitan Area from 2004 through 2019. Methods We present a new modeling approach based on extreme gradient boosting (XGBoost) and inverse-distance weighting that uses AOD, meteorology, and land-use variables. We also investigated applications of our mean PM2.5 predictions that can aid local authorities in air-quality management and public-health surveillance, such as the co-occurrence of high PM2.5 and heat, compliance with local air-quality standards, and the relationship of PM2.5 exposure with social marginalization. Results Our models for mean and max PM2.5 exhibited good performance, with overall cross-validated mean absolute errors (MAE) of 3.68 and 9.20 μg/m3, respectively, compared to mean absolute deviations from the median (MAD) of 8.55 and 15.64 μg/m3. In 2010, everybody in the study region was exposed to unhealthy levels of PM2.5. Hotter days had greater PM2.5 concentrations. Finally, we found similar exposure to PM2.5 across levels of social marginalization. Significance Machine learning algorithms can be used to predict highly spatiotemporally resolved PM2.5 concentrations even in regions with sparse monitoring. Impact Our PM2.5 predictions can aid local authorities in air-quality management and public-health surveillance, and they can advance epidemiological research in Central Mexico with state-of-the-art exposure assessment methods.

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Revolutionary Air-Pollution Applications from Future Tropospheric Emissions: Monitoring of Pollution (TEMPO) Observations

Cited by 10 publications

References 9 publications

Satellite remote-sensing capability to assess tropospheric column ratios of formaldehyde and nitrogen dioxide: case study during the LISTOS 2018 field campaign

Satellite remote-sensing capability to assess tropospheric column ratios of formaldehyde and nitrogen dioxide: case study during the LISTOS 2018 field campaign

Intraurban NO₂ hotspot detection across multiple air quality products

Prediction of daily mean and one-hour maximum PM2.5 concentrations and applications in Central Mexico using satellite-based machine-learning models

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Revolutionary Air-Pollution Applications from Future Tropospheric Emissions: Monitoring of Pollution (TEMPO) Observations

Cited by 10 publications

References 9 publications

Satellite remote-sensing capability to assess tropospheric column ratios of formaldehyde and nitrogen dioxide: case study during the LISTOS 2018 field campaign

Satellite remote-sensing capability to assess tropospheric column ratios of formaldehyde and nitrogen dioxide: case study during the LISTOS 2018 field campaign

Intraurban NO2 hotspot detection across multiple air quality products

Prediction of daily mean and one-hour maximum PM2.5 concentrations and applications in Central Mexico using satellite-based machine-learning models

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Product

Resources

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Intraurban NO₂ hotspot detection across multiple air quality products