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NASA NTRS2021

Global Impact of COVID-19 Restrictions on the Atmospheric Concentrations of Nitrogen Dioxide and Ozone

Social-distancing to combat the COVID-19 pandemic has led to widespread reductions in air pollutant emissions. Quantifying these changes requires a business-as-usual counterfactual that accounts for the synoptic and seasonal variability of air pollutants. We use a machine learning algorithm driven by information from the NASA GEOS-CF model to assess changes in nitrogen dioxide (NO2) and ozone (O3) at 5,756 observation sites in countries from January through June 2020. Reductions in NO2 coincide with timing and intensity of COVID-19 restrictions, ranging from 60% in severely affected cities (e.g., Wuhan, Milan) to little change (e.g., Rio de Janeiro, Taipei). On average, NO2 concentrations were (13-23) % lower than business as usual from February 2020 onward. China experienced the earliest and steepest decline, but concentrations since April have mostly recovered and remained within 5% to the business-as-usual estimate. NO2 reductions in Europe and the US have been more gradual with a halting recovery starting in late March. We estimate that the global NOx (NO+NO2) emission reduction during the first 6 months of 2020 amounted to 3.1 (2.6-3.6) TgN, equivalent to 5.5(4.7-6.4) % of the annual anthropogenic total. The response of surface O3 is complicated by competing influences of non-linear atmospheric chemistry. While surface O3 increased by up to 50% in some locations, we find the overall net impact on daily average O3 between February -June 2020 to be small. However, our analysis indicates a flattening of the O3 diurnal cycle with an increase in nighttime ozone due to reduced titration and a decrease in daytime ozone, reflecting a reduction in photochemical production.

Christoph A Keller↗
NASA NTRS2021

Ozone Production and Precursor Emission from Wildfires in Africa

Tropospheric ozone (O3) negatively impacts human health and is also a greenhouse gas. It is formed photochemically by reactions of nitrogen oxides (NOx) and volatile organic compounds (VOCs), of which wildfires are an important source. This study presents data from research flights sampling wildfires in West and Central African savannah regions, both close to the fires and after the emissions had been transported several days over the tropical North Atlantic Ocean. Emission factors (EFs) in g kg-1 for NOx (as NO), six VOCs and formaldehyde were calculated from enhancement to mole fractions in data taken close to the fires. For NOx, the emission factor was calculated as 2.05±0.43 g kg-1 for Senegal and 1.20±0.28 g kg-1 for Uganda, both higher than the average value of 1.13±0.6 g kg-1 for previous studies of African savannah regions. For most VOCs (except acetylene), EFs in Uganda were lower by factors of 20-50% compared to Senegal, with almost all the values below those in the literature. O3 enhancement in the fire plumes was investigated by examining the ΔO3/ΔCO enhancement ratio, with values ranging from 0.07 - 0.14 close to the fires up to 0.25 for measurements taken over the Atlantic Ocean up to 200 hours downwind. In addition, measurements of O3 and its precursors were compared to the output of a global chemistry transport model (GEOS-CF) for the flights over the Atlantic Ocean. Normalised mean bias (NMB) comparison between the measured and modelled data was good outside of the fire plumes, with CO showing a model under-prediction of 4.6% and O3 a slight over-prediction of 0.7% (both within the standard deviation of the data). For NOx the agreement was poorer, with an under-prediction of 9.9% across all flights. Inside the fire plumes the agreement between modelled and measured values is worse, with the model being biased significantly lower for all three species. In total across all flights, there was an under-prediction of 29.4%, 16.5% and 37.5% for CO, O3 and NOx respectively. Finally, the measured ΔO3/ΔCO enhancement ratios were compared those in the model for the equivalent flight data, with the model showing a lower value of 0.17±0.03 compared to an observed value of 0.29±0.05. The results detailed here show that the O3 burden to the North Atlantic Ocean from African wildfires may be underestimated and that further study is required to better study the O3 precursor emissions and chemistry.

James D Lee↗
NASA NTRSDate not supplied

Sub-City-Scale Air Quality Forecasts Combining Models, Satellites, and Surface Measures

Poor air quality is a global major concern, especially in cities with their higher emissions and large numbers of exposed people. Air quality monitoring has traditionally relied on ground-based measurements from a few accurate but expensive regulatory-grade monitors, leading to limited spatial data coverage. More recently, these have been supplemented with other data sources, including satellite observations of pollutants, atmospheric chemistry model simulations, and low-cost monitors allowing for denser spatial data collection at the expense of lower accuracy compared to regulatory-grade monitors. Each of these air quality data sources have their own benefits and drawbacks, and so there is an opportunity to combine these data together while respecting their relative strengths and weaknesses in order to generate a more comprehensive and detailed picture of local air quality. I will present our current work towards such a combination, with a focus on producing higher spatial resolution estimates and near-term forecasts of Nitrogen Dioxide in urban areas in the United States. Our method combines data from the GEOS Composition Forecasting (GEOS-CF) model system, TROPOMI tropospheric NO2 satellite data products, and ground measurements from the EPA regulatory monitoring network using a combination of simple intuitive relationships and machine learning techniques. I will show the performance of this proposed method in several urban areas in the United States, comparing it with baseline approaches using single data sources separately. Overall, we find that combining these disparate datasets together leads to more accurate air quality forecasts in the target areas than is possible using each data source separately.

Air quality↗
NASA NTRSDate not supplied

Air Quality Forecasting at Sub-City Scale by Combining Models, Satellites, and Surface Measures

While there are a variety of sources for air quality information, no one source simultaneously allows for high accuracy, low bias, fine spatial resolution, wide spatial coverage, high temporal frequency, and the capability for near-term forecasting of air quality. Global models, like the NASA’s Goddard Earth Observing System -Composition Forecasting (GEOS-CF) model, provide global coverage and forecasting capabilities, but operate at relatively coarse spatial resolution and require ground-truthing with in-situ data. Polar-orbiting satellite data products, like those of the ESA TROPOspheric Monitoring Instrument (TROPOMI), provide higher-spatial-resolution remote sensing of atmospheric composition, but are limited by cloud cover and overpass times and report column-integrated quantities. Surface measurements, both from regulatory-grade monitors and low-cost networks, measure “nose-level” air quality, but may not represent concentration variability across large spatial domains, and (in the case of low-cost sensors) are subject to interference and biases. There exists a great potential to combine these diverse data sources together, using the strengths of some to offset the weaknesses of others to build a more comprehensive picture of air quality. This presentation will summarize results from ongoing efforts to produce such a combined forecast, with application case studies for surface-level Nitrogen Dioxide forecasting in several major US cities. Furthermore, we will examine the relative impacts and benefits of different data sources on the forecasting accuracy at different spatial and temporal scales. Finally, we will examine the potential for integrating low-cost sensors into such a system, both in terms of using these integrated air quality estimates as a baseline from which to calibrate networks of low-cost sensors in the field, and in terms of using dense networks of low-cost sensors to refine the spatial resolution of integrated air quality forecasts.

Air Quality↗
NASA NTRSDate not supplied

Model Evaluation and Intercomparison Using Data Collected by the Langley Mobile Ozone Lidar in Hampton, Virginia

Throughout the year 2022, the Langley Mobile Ozone Lidar (LMOL) has been frequently collecting ozone and aerosol measurements in the lower troposphere. The lidar is located in the parking lot behind the Atmospheric Sciences building at NASA Langley in Hampton, Virginia, approximately 37.095 N, -76.389 W. The data was collected during a range of several distinct atmospheric conditions, including stratospheric intrusions, surface frontal passages, and long-range transported wildfire smoke plumes. We use this data to evaluate and intercompare the forecast accuracy of two atmospheric chemistry models: the GEOS Composition Forecasting model (GEOS-CF) and the Weather Research and Forecasting model with Chemistry (WRF-Chem). Both models make daily three-dimensional forecasts of many trace gases and species for the contiguous United States. Over a range of ten distinct collection events, each spanning 36 hours to nearly seven days, the forecast models predict tropospheric ozone at NASA Langley with reasonable accuracy. The models best predict the timing and shape of the observed stratospheric intrusions in the middle troposphere but often vary in the magnitude of the ozone mixing ratios. For other atmospheric conditions, there is more variability in the model accuracy. Here, we summarize the accuracy of the models for all events and investigate reasons for differences between the models and the lidar.

Daniel B. Phoenix↗
NASA NTRSDate not supplied

NASA TROPOMI Aerosol Products: Algorithmic Upgrades and Preliminary Evaluation

This poster presentation describes an expanded NASA TROPOMI (Tropospheric Monitoring Instrument)aerosol algorithm (N-TROPOMAER) that takes advantage of TROPOMI observations in the ultraviolet and visible spectral regions. The availability of the Oxygen B-band observations, and the unprecedentedly high spatial resolution (3.5 km X 5.5 km) for a hyper-spectral sensor are significant improvements for aerosol properties retrieval. The heritage N-TROPOMAER aerosol algorithm uses near-ultraviolet radiances at 354 nm and 388 nm from Sentinel 5 Precursor-TROPOMI for simultaneously retrieving aerosol optical depth (AOD), single-scattering albedo (SSA), aerosol absorption optical depth (AAOD), and above-cloud aerosol optical depth(ACAOD) at 388 nm, along with the qualitative UV aerosol index (UVAI). We have expanded the inversion capability beyond the UV, to retrieve AOD at 466 nm and 680 nm. Surface reflectance effects at466 nm are accounted for using a recently developed geometry-dependent surface Lambertian-equivalent reflectivity (GLER) product, which is derived from the top-of-atmosphere radiance computed with Rayleigh scattering and surface bidirectional reflectance distribution function (BRDF) for the exact viewing geometry at the sensor’s spatial resolution. Aerosol layer height (ALH) and 680 nm AOD are simultaneously derived from observations at 680 nm and at the Oxygen-B band (688 nm). Another important upgrade is the use of time averaged total column carbon monoxide from the NASA GEOS-CF(Global Earth Observing System Composition Forecast) as a tracer of carbonaceous aerosols.

TROPOMI↗
NASA NTRSDate not supplied

Towards A Flexible Data Fusion Tool Incorporating Model, Satellite, Regulatory Monitor and Low-Cost Sensor Data for Air Quality Estimation and Forecasting

Air quality managers, researchers, and concerned community scientists around the world have a variety of sources for air quality information, ranging from traditional regulatory monitoring networks and atmospheric chemistry models to remote sensing data products and low-cost sensor networks. However, the ability to incorporate data from these disparate sources and synthesize a comprehensive overview of the local air quality situation remains a considerable barrier for many end-users. This presentation will outline a tool, currently in development, which will address this need using a flexible data fusion approach. The tool will make use of air quality forecast model outputs (primarily from the NASA GEOS-CF composition forecast modeling system), satellite remote sensing data (from instruments including MODIS, VIIRS, TROPOMI, plus TEMPO for the US when available), and in-situ data from official regulatory and/or low-cost networks where these are available. The ability to incorporate data from low-cost sensor networks will be a key feature of the tool; it will make use of other available data sources to calibrate the low-cost sensor data on a regional scale, then use these calibrated low-cost sensor data for localized updating to resolve finer-scale air quality patterns. Development of this tool is taking place with the help of national and international partners and end-user groups, coordinated through the US EPA and the United Nations Environment Programme (UNEP). The tool is being developed on the Google Earth Engine cloud computing platform to facilitate integration of diverse data sources and free access by a broad community of end-users. Stewardship of the tool will be passed to US EPA and UNEP to support future activities with end-users in the US and around the world, and the tool itself will remain freely accessible. We hope that this tool will lower the barrier to entry for various user groups worldwide, including community scientists, who struggle to integrate disparate data sources to gain insight into their local air quality situations. This presentation will cover the early stages of the development of the tool, including the underlying methods and some pilot case studies in integrating low-cost sensor data.

global models↗
NASA NTRSDate not supplied

Analysis of Atmospheric Conditions Responsible for an Ozone Exceedance Event in Southeast Virginia on June 15, 2022

On June 15, 2022, the Virginia Department of Environmental Quality monitoring site at Suffolk/Holland, a rural site in southeast Virginia, recorded its first ozone exceedance since 2016. An ozone exceedance day occurs when the daily maximum 8-hour average surface ozone concentration is greater than 70 ppb. On this day, it was 75 ppb. This event is also noteworthy due to the rapid increase in surface ozone between 6 am and 9 am EDT as well as the hourly maximum ozone concentration of 82 ppb that was measured at 1 pm EDT. In this analysis, we utilize various observational and model data to determine the atmospheric conditions responsible for this ozone exceedance event. The analysis is conducted in two parts: (1) an evaluation of the accuracy of the GEOS-CF and WRF-Chem model forecasts and (2) an investigation of the mechanisms responsible for the high surface ozone at the Virginia DEQ Suffolk/Holland monitoring site. Comparisons of model forecasted ozone time-height cross sections with measured ozone by lidars from the Tropospheric Ozone Lidar Network (TOLNet) at NASA Langley and NASA Goddard reveal that both models forecasted ozone reasonably well near the surface. Model and observational data reveal that an upper-level ridge was present over much of the eastern United States, with a broad anticyclonic circulation near the surface, resulting in north-northeasterly flow over southeast Virginia. Back trajectory calculations using the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) and model data suggest that a low-level plume rich in ozone and precursor species originating from the New York City and New Jersey region was transported along the east coast between June 14 and June 15, arriving in southeast Virginia around 6 am EDT on June 15. In the early morning hours, this plume mixed down to the surface, elevating the background ozone mixing ratio as well as the mixing ratios of several precursor species. Other potential contributions are also explored and discussed. Lastly, the authors note that this work represents the analysis of the authors and not the Virginia Department of Environmental Quality.

Daniel B Phoenix↗
NASA NTRSDate not supplied

Global Burden of Tropospheric Ozone: Effects of Particulate Nitrate Photolysis and Assimilation of NO2 Measurements

Tropospheric ozone is a major pollutant, a greenhouse gas, and the primary source of the hydroxyl radical. It is largely produced in situ from the oxidation of CO and volatile organic compounds in the presence of nitrogen oxides (NOx=NO+NO2). Global atmospheric chemistry models generally underestimate NOx and CO concentrations in the northern hemisphere away from source regions, which would lead to an underestimate in the tropospheric ozone burden. Here we evaluate the tropospheric ozone simulation in the GEOS-Chem model (version 14) using observations from satellite, aircraft, and ozonesondes. We include GEOS-Chem simulations conducted in the standard offline mode, as well as in the online mode within the NASA GEOS earth system model which forms the basis for the GEOS Composition Forecasts (GEOS-CF). In both configurations, GEOS-Chem underestimates ozone concentrations in the northern midlatitude free troposphere in spring and summer. We find that we can improve the ozone simulation by including an additional NOx source over the oceans from particulate nitrate photolysis on sea salt aerosols at rates inferred from field studies over the Atlantic Ocean. We investigate whether further improvements in the ozone simulation can be achieved by assimilating satellite measurements of NO2 from the Ozone Monitoring Instrument (OMI) and CO from the Measurement of Pollution in the Troposphere (MOPITT) instruments.

tropospheric ozone↗
NASA NTRSDate not supplied

NASA GEOS Forecasting Capabilities for Air Quality

An overview of GEOS Forecasting capabilities for Air Quality, showing the evolution from GEOS with GOCART to GEOS with GEOS-Chem. Ways to access GEOS for research scientists and engaged community members will be given, as well as examples on how the GEOS-CF forecasts can be bias-corrected and downscaled for decision making processes.

K Emma Knowland↗
NASA NTRSDate not supplied

Near Real Time Air Quality Forecasts Using the NASA GEOS Model

This presentation provides an overview of NASA's Global Modeling and Assimilation Office (GMAO) high-resolution global forecast and reanalysis products for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model radiatively coupled to GOCART aerosol module assimilates 2-dimensional column-integrated aerosol optical depth (AOD) at one wavelength (550 nm) in order to constrain the model's background AOD in order to have the best historical estimate and forecasts of AOD and particulate matter. Furthermore, the GEOS model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5).

K Emma Knowland↗
NASA NTRSDate not supplied

Near Real Time Air Quality Forecasts Using the NASA GEOS Model

This presentation provides an overview of NASA's Global Modeling and Assimilation Office (GMAO) high-resolution global forecast and reanalysis products for weather, aerosols, and air quality. The NASA Global Earth Observing System (GEOS) model radiatively coupled to GOCART aerosol module assimilates 2-dimensional column-integrated aerosol optical depth (AOD) at one wavelength (550 nm) in order to constrain the model's background AOD in order to have the best historical estimate and forecasts of AOD and particulate matter. Furthermore, the GEOS model has been expanded to provide global near-real-time 5-day forecasts of atmospheric composition at unprecedented horizontal resolution of 0.25 degrees (~25 km). This composition forecast system (GEOS-CF) combines the operational GEOS weather forecasting model with the state-of-the-science GEOS-Chem chemistry module (version 12) to provide detailed analysis of a wide range of air pollutants such as ozone, carbon monoxide, nitrogen oxides, and fine particulate matter (PM2.5).

K Emma Knowland↗
NASA NTRSDate not supplied

NASA GEOS Forecasting Capabilities for Air Quality

An overview of GEOS Forecasting capabilities for Air Quality, showing the evolution from GEOS with GOCART to GEOS with GEOS-Chem. Ways to access GEOS for research scientists and engaged community members will be given, as well as examples on how the GEOS-CF forecasts can be bias-corrected and downscaled for decision making processes.

K Emma Knowland↗
NASA NTRSDate not supplied

Tropospheric Ozone in the NASA GEOS Model: Effects of Satellite NO2 Data Assimilation and Improvements in Background NOx Chemistry

Tropospheric ozone is a major atmospheric oxidant, the primary source of the hydroxyl radical, and a greenhouse gas. In the troposphere, it is produced from the oxidation of carbon monoxide and volatile organic compounds in the presence of nitrogen oxides (NOx=NO+NO2) or has its origins in the stratosphere. The tropospheric ozone burdens in global atmospheric chemistry models disagree by a factor of 1.5, indicating an incomplete understanding of ozone sources and sinks. Here we examine the tropospheric ozone simulation in NASA Global Modeling and Assimilation Office’s (GMAO’s) GEOS model with the GEOS-Chem chemistry mechanism (version 14.2), using observations from satellites, aircraft, ozonesondes, and surface sites. This modeling framework is used to produce GMAO’s GEOS Composition Forecasts (GEOS-CF), and it has been updated extensively with new emission inventories, improved model physics, satellite data assimilation capability, and an up-to-date chemical mechanism that includes tropospheric halogen chemistry and NOx recycling from particulate nitrate. Earlier versions of the model using GEOS-Chem version 12.0 showed an underestimate in tropospheric ozone in the northern hemisphere. We evaluate the ozone simulation in the updated model, focusing on the effects of assimilating satellite NO2 data from the Ozone Monitoring Instrument (OMI), constraining stratospheric ozone to satellite data, and updates to the NOx chemistry. We will examine changes in the vertical profiles of tropospheric ozone over the US in support of the Tropospheric Emissions: Monitoring of Pollution (TEMPO), and discuss implications for the tropospheric ozone budget.

V. Shah↗
NASA NTRS2023

Public Health Data Applications Using the CDC Tracking Network: Augmenting Environmental Hazard Information with Lower-latency NASA Data

Exposure to environmental hazards is an important determinant of health, and the frequency and severity of exposures is expected to be impacted by climate change. Through a partnership with the U.S. National Aeronautics and Space Administration, the U.S. Centers for Disease Control and Prevention’s National Environmental Public Health Tracking Network is integrating timely observations and model data of priority environmental hazards into its publicly accessible Data Explorer (https://ephtracking.cdc.gov/DataExplorer/). Newly integrated datasets over the contiguous U.S. (CONUS) include: daily 5-day forecasts of air quality based on the Goddard Earth Observing System Composition Forecast (GEOS-CF), daily historical (1980-present) concentrations of speciated PM2.5 based on the Modern Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), and Moderate Resolution Imaging Spectroradiometer (MODIS) daily near real-time maps of flooding (MCDWD). Data integrated into the CDC Tracking Network are broadly intended to improve community health through action by informing both research and early warning activities, including (1) describing temporal and spatial trends in disease and potential environmental exposures, (2) identifying populations most affected, (3) generating hypotheses about associations between health and environmental exposures, and (4) developing, guiding, and assessing environmental public health policies and interventions aimed at reducing or eliminating health outcomes associated with environmental factors.

air quality↗
NASA NTRSDate not supplied

Exploring Anomalous PM 2.5 from Wildfires and Dust Storms using Data and Services at NASA GES DISC

The presence of fine particles in the atmosphere with a diameter of less than 2.5 µm, called particulate matter 2.5 (PM 2.5 ), poses a significant threat to human health as a criteria air pollutant. Fortunately, NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC) provides easy access to several PM 2.5 concentration products. These datasets include the reanalysis of global hourly and monthly aerosol components including PM 2.5 data from the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), as well as 3-hourly real-time ensemble forecasts of PM 2.5 from the Hazardous Air Quality Ensemble System (HAQES). The HAQES products are developed by the George Mason University Air Quality Laboratory as part of NASA's Health Air Quality Applied Science Team (HAQAST). The GES DISC is actively collaborating with scientists in the HAQAST program to further expand air quality data collections. Two new datasets are currently being archived: one is the machine learning-based global hourly PM 2.5 derived from MERRA-2; the other is the localized data (NO 2 , O 3 , and PM 2.5 ) time series derived from NASA's GEOS Composition Forecasting (GEOS-CF) system. In this presentation, we will explore the spatial patterns and long-distance transport characteristics of elevated PM 2.5 during extreme pollution events, such as the June 2023 Canadian wildfires, which are still active at the time of writing; and severe spring dust storms in 2023 over Asia. To gain comprehensive insights, we will utilize various PM 2.5 data in conjunction with satellite-observed aerosol data from TROPOspheric Monitoring Instrument (TROPOMI) on Sentinel-5P. The primary focus of this presentation will be to demonstrate effective use of data tools and services to visualize and explore extreme air pollution phenomena. Additionally, we will provide guidance on how users can download specific data of interest, facilitating further analysis and research in this critical area.

air quality↗
NASA NTRSDate not supplied

Tropospheric Ozone in the NASA GEOS Model: Effects of Satellite Data Assimilation and Improvements in Background NO x Chemistry

Tropospheric ozone is a major atmospheric oxidant, the primary source of the hydroxyl radical, and a greenhouse gas. In the troposphere, it is produced from the oxidation of carbon monoxide and volatile organic compounds in the presence of nitrogen oxides (NO x =NO+NO 2 ) or has its origins in the stratosphere. The tropospheric ozone burdens in global atmospheric chemistry models disagree by a factor of 1.5, indicating an incomplete understanding of ozone sources and sinks. Here we examine the tropospheric ozone simulation in NASA Global Modeling and Assimilation Office’s (GMAO’s) GEOS model with the GEOS-Chem chemistry mechanism (version 14.2), using observations from satellites, aircraft, ozonesondes, and surface sites. This modeling framework is used to produce GMAO’s GEOS Composition Forecasts (GEOS-CF), and it has been updated extensively with new emission inventories, improved model physics, satellite data assimilation capability, and an up-to-date chemical mechanism that includes tropospheric halogen chemistry and NOx recycling from particulate nitrate. Earlier versions of the model using GEOS-Chem version 12.0 showed an underestimate in tropospheric ozone in the northern hemisphere. We evaluate the ozone simulation in the updated model, focusing on the effects of assimilating satellite NO 2 data from the Ozone Monitoring Instrument (OMI), constraining stratospheric ozone to satellite data, and updates to the NOx chemistry. We will examine changes in the vertical profiles of tropospheric ozone over the US in support of the Tropospheric Emissions: Monitoring of Pollution (TEMPO), and discuss implications for the tropospheric ozone budget.

Viral Shah↗