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This poster was presented at the American Geophysical Union (AGU) 2024 Fall Conference. The poster describes the latest developments through collaboration with Carnegie Mellon University on point-source emissions impact modeling using the latest high-resolution reduced-form air pollution model, AP4.
Wildfires in Chile in the last decade were the worst on record, destroying homes and livelihoods, polluting the air, and displacing whole towns. To predict locations where wildfires were likely to start, the Corporación Nacional Forestal (CONAF) created a wildfire risk model within ArcGIS Pro and Google Earth Engine (GEE) that utilized the NOAA Global Forecast System (GFS) and the NASA Shuttle Radar Topography Mission (STRM) 90-meter datasets. The previous CONAF model was very resource-heavy and time-intensive to run. NASA DEVELOP, in partnership with CONAF, automated the previous model and transferred it fully into GEE where all Earth observation datasets could be used without downloading. The new model substantially reduced the runtime. The final model was used to create a near real-time wildfire monitoring application as well as fire severity maps. The end products will be used by CONAF for wildfire prediction and management to prevent more destruction in the future.
The Atlanta Urban Heat Island and Air Quality Project had its genesis in Project ATLANTA (ATlanta Land use Analysis: Temperature and Air quality) that began in 1996. Project ATLANTA examined how high-spatial resolution thermal remote sensing data could be used to derive better measurements of the Urban Heat Island effect over Atlanta. We have explored how these thermal remote sensing, as well as other imaged datasets, can be used to better characterize the urban landscape for improved air quality modeling over the Atlanta area. For the air quality modeling project, the National Land Cover Dataset and the local scale Landpro99 dataset at 30m spatial resolutions have been used to derive land use/land cover characteristics for input into the MM5 mesoscale meteorological model that is one of the foundations for the Community Multiscale Air Quality (CMAQ) model to assess how these data can improve output from CMAQ. Additionally, land use changes to 2030 have been predicted using a Spatial Growth Model (SGM). SGM simulates growth around a region using population, employment and travel demand forecasts. Air quality modeling simulations were conducted using both current and future land cover. Meteorological modeling simulations indicate a 0.5 C increase in daily maximum air temperatures by 2030. Air quality modeling simulations show substantial differences in relative contributions of individual atmospheric pollutant constituents as a result of land cover change. Enhanced boundary layer mixing over the city tends to offset the increase in ozone concentration expected due to higher surface temperatures as a result of urbanization.
Sixteen remote sensing applications or groups of related applications judged to be most important of any in the forestry and range disciplines were evaluated. In one application, major land classification, large amounts of useful data are anticipated to be contributed by space sensors in 1980. In four applications moderate amounts are anticipated to be so contributed. These are timber inventory, range inventory, fire weather forecasting, and monitoring snowfields. In the following seven applications small but significant amounts of data are anticipated to be contributed by space sensors: (1) detailed land classification; (2) inventory of wildlife habitat; (3) recreation resource inventory; (4) detecting stresses on the vegetation (5) monitoring air pollution caused by wildfires and prescribed burning; (6) monitoring water cycle, (7) pollution and erosion; and (8) evaluating damage to forests and ranges.
MISTiC(TM) Winds is an approach to improve short-term weather forecasting based on a miniature high resolution, wide field, thermal emission spectrometry instrument that will provide global tropospheric vertical profiles of atmospheric temperature and humidity at high (3-4 km) horizontal and vertical ( 1 km) spatial resolution. MISTiCs extraordinarily small size, payload mass of less than 15 kg, and minimal cooling requirements can be accommodated aboard a 27U-class CubeSat or an ESPA-Class micro-satellite. Low fabrication and launch costs enable a LEO sunsynchronous sounding constellation that would collectively provide frequent IR vertical profiles and vertically resolved atmospheric motion vector wind observations in the troposphere. These observations are highly complementary to present and emerging environmental observing systems, and would provide a combination of high vertical and horizontal resolution not provided by any other environmental observing system currently in operation. The spectral measurements that would be provided by MISTiC Winds are similar to those of NASA's AIRS that was built by BAE Systems and operates aboard the AQUA satellite. These new observations, when assimilated into high resolution numerical weather models, would revolutionize short-term and severe weather forecasting, save lives, and support key economic decisions in the energy, air transport, and agriculture arenasat much lower cost than providing these observations from geostationary orbit. In addition, this observation capability would be a critical tool for the study of transport processes for water vapor, clouds, pollution, and aerosols. Key remaining technical risks are being reduced through laboratory and airborne testing under NASA's Instrument Incubator Program.
During winter 2018, the 16 prefecture-level cities in Anhui Province, Western Yangtze River Delta region, China had very high PM2.5 concentrations and prolonged pollution days. The impact of regional transport in the formation, accumulation, as well as dispersion of fine particulate matter (PM2.5) in Anhui Province was very significant. This study quantified and analyzed the vertical transport of PM2.5 in three major cities (Hefei, Fuyang, and Suzhou) of Anhui Province in January and July 2018 using the Weather Research and Forecasting (WRF) model coupled with the Community Multiscale Air Quality (CMAQ) model. The results of the inter-regional transport of PM2.5 revealed the dominant transport pathways for the three cities. The flux mainly flowed into Fuyang from Henan (2.23 and 1.42 kt/day in January and July, respectively) and Bozhou (1.96 and 1.21 kt/day in January and July, respectively), while the main flux from Fuyang flowed into Henan (-2.15 kt/day) and Lu'an (-1.91 kt/day) in January and Henan (-0.34 kt/day) and Bozhou (-0.29 kt/day) in July. In addition, the dominant transport pathways and the heights at which they occurred were identified: the northwest-southeast and northeast-south pathways in both winter and summer at both lower (?300 m) and higher (=300 m) levels for Fuyang; the northwest-south and northeast-southwest pathways in winter (at both lower and upper levels) and northwest-east and northeast-southwest pathways in summer at lower and upper levels for Hefei; and the northwest-southeast and northeast-south pathways in both winter (from 50 m up to the top level) and summer (between 100 and 300 m) for Suzhou. Furthermore, the intensities of daily PM2.5 transport fluxes in Fuyang during the atmospheric pollution episode (APE1) were stronger than the monthly average. These results show that joint emission controls across multiple cities along the identified pathways are urgently needed to reduce winter episodes.
Observations have well established that aerosols from various sources in Asia, Europe, and Africa can travel across the Pacific and reach the contiguous United States (U.S.) at least on episodic bases throughout a year, with a maximum import in spring. The imported aerosol not only can serve as an additional source to regional air pollution (e.g., direct input), but also can influence regional air quality through the aerosol-cloud-radiation (ACR) interactions that change local and regional meteorology. This study assessed impacts of the transpacific aerosol on air quality, focusing on surface ozone and PM2.5, over the U.S. using the NASA Unified Weather Research Forecast model. Based on the results of 3- month (April to June of 2010) simulations, the impact of direct input (as an additional source) of transpacific aerosol caused an increase of surface PM2.5 concentration by approximately 1.5 micro-g/cu m over the west coast and about 0.5 micro-g/cu m over the east coast of the U.S. By influencing key meteorological processes through the ACR interactions, the transpacific aerosol exerted a significant effect on both surface PM2.5 (+/-6 micro-g/cu m3) and ozone (+/-12 ppbv) over the central and eastern U.S. This suggests that the transpacific transport of aerosol could either improve or deteriorate local air quality and complicate local effort toward the compliance with the U.S. National Ambient Air Quality Standards.
We use a machine learning algorithm combining information from the NASA GEOS composition forecast (GEOS-CF) model and surface observations of nitrogen dioxide (NO2) and ozone (O3) at more than 5,000 observation sites to assess the impact of COVID-19 restrictions on surface air quality in 46 countries. Our methodology removes the compounding impacts of meteorology, seasonality and atmospheric chemistry on air pollution, thus allowing for a quantitative estimate of the change in surface air quality following COVID-19 containment measures. Compared to GEOS-CF model predictions that do not include emission reductions related to COVID-19 restrictions, surface observations show a drop in surface NO2 of up to 60% after the implementation of lockdowns. Average NO2 concentrations between February 2020 to June 2020 were 18% lower than business as usual. The earliest and strongest declines are observed over China, followed by Europe and the US. While NO2 concentrations over China recovered within 2 months, the recovery has been slower over Europe and the US. The impact of COVID-19 restrictions on O3 is complicated by non-linear atmospheric chemistry. Locally, O3 can show a short-term increase of up to 50% as a result of the decrease in NO2, which leads to a reduction in night time titration. However, this effect is offset by a decrease in photochemical production during the day. Our results indicate that these two competing processes resulted in a net zero change in average surface ozone during the first 5 months of the pandemic. The results also indicate that the reduced photochemical production becomes increasingly important over time. Our analysis is based on surface observations and model simulations available in near real-time, and we will present an up-to-date view of the short and medium-term impacts of COVID-19 restrictions on air quality around the world.
Background: Individual components of the ambient environment, such as temperature and air pollution, exist as part of a complex mixture and have been associated with suicide; however, their interactive effects remain poorly understood. This study examined the independent and interactive effects of wet bulb globe temperature (WBGT), nitrogen dioxide (NO 2 ), and fine particulate matter (PM 2.5 ) on suicide mortality. Methods: We identified 7,551 suicide cases in Utah, USA, from 2000 to 2016 and assigned exposure to daily maximum WBGT (sourced from the European Center for Medium-Range Weather Forecasts) and PM 2.5 and NO 2 concentrations (sourced from a national spatiotemporal ensemble model) using decedent’s residential address at the time of death. A case-crossover design with conditional logistic regression was used to estimate the independent and interactive effects of WBGT max , PM 2.5 , and NO 2 on suicide. For exposure windows, we considered single days preceding suicide (lag 0 to 6) and their averages across preceding days (lag 0–1, 0–3, and 0–6). Analyses were stratified by season. Results: We identified a significant association between WBGT max and suicide across all seasons (odds ratio [OR] = 1.05, 95% confidence interval [CI]: 1.01, 1.10; per 5 °C increase on lag 0–3 days). The associations were stronger in the warm season (March 22 to September 21), with ORs and 95% CIs ranging from 1.08 (1.02, 1.15) to 1.20 (1.10, 1.30) per 5 °C increase depending on the lag periods. We observed synergistic interactions between WBGT max and PM 2.5 and NO 2 in the warm season, associated with higher odds of suicide. The associations of WBGT max with suicide were most pronounced at high NO 2 levels. Conclusions: We found evidence of synergistic interactions between WBGT max and PM 2.5 and NO 2 on suicide in the warm season, emphasizing the need for considering the combined effects of heat stress and air pollution in suicide prevention strategies.
Ozone (O3) dry deposition is a major O3 sink. As a follow-up study of Huang et al. (2021), we quantify the impact of satellite soil moisture (SM) on model representations of this process when different dry-deposition parameterizations are implemented, based on which the implications for interpreting O3 air pollution levels and assessing the O3 impacts on human and ecosystem health are provided. The SM data from NASA's Soil Moisture Active Passive mission are assimilated into the Noah-Multiparameterization (Noah-MP) land surface model within the NASA Land Information System framework, semicoupled with Weather Research and Forecasting model with online Chemistry (WRF-Chem) regional-scale simulations covering the southeastern US. Major changes in the modeling system used include enabling the dynamic vegetation option, adding the irrigation process, and updating the scheme for the surface exchange coefficient. Two dry-deposition schemes are implemented, i.e., the Wesely scheme and a “dynamic” scheme, in the latter of which dry-deposition parameterization is coupled with photosynthesis and vegetation dynamics. It is demonstrated that, when the dynamic scheme is applied, the simulated O3 dry-deposition velocities vd and their stomatal and cuticular portions, as well as the total O3 fluxes Ft, are larger overall; vd and Ft are 2–3 times more sensitive to the SM changes due to the data assimilation (DA). Further, through case studies at two forested sites with different soil types and hydrological regimes, we highlight that, applying the Community Land Model type of SM factor controlling stomatal resistance (i.e., β factor) scheme in replacement of the Noah-type β factor scheme reduced the v(d) sensitivity to SM changes by ∼75 % at one site, while it doubled this sensitivity at the other site. Referring to multiple evaluation datasets, which may be associated with variable extents of uncertainty, the model performance of vegetation, surface fluxes, weather, and surface O3 concentrations shows mixed responses to the DA, some of which display land cover dependency. Finally, using model-derived concentration- and flux-based policy-relevant O3 metrics as well as their matching exposure–response functions, the relative biomass/crop yield losses for several types of vegetation/crops are estimated to be within a wide range of 1 %–17 %. Their sensitivities to the model's dry-deposition scheme and the implementation of SM DA are discussed.
We give an overview of the NASA Global Earth Observing System Composition Forecast model (GEOS-CF), a high-resolution (roughly 25-km) global composition model developed by the NASA Global Modeling and Assimilation Office (GMAO). This system combines the GEOS weather and aerosol model with the GEOS-Chem chemistry module to provide a holistic view of atmospheric composition that captures a wide range of air pollutants such as ozone,nitrogen oxides, volatile organic compounds, and fine particulate matter.Given the global extent of the model, GEOS-CF captures large-scale processes such as long-range transport of air pollutants, across ocean basins and continents.
We will give an overview of the NASA Global Earth Observing System Composition Forecast system (GEOS-CF), a high-resolution (0.25 degree) global composition model developed by the NASA Global Modeling and Assimilation Office (GMAO). This system combines the GEOS weather and aerosol model with the GEOS-Chem chemistry module (version 12) to provide a holistic view of atmospheric composition that captures a wide range of air pollutants such as ozone, nitrogen oxides, volatile organic compounds, and fine particulate matter. The spatial resolution of 0.25 degrees (approx. 25 km) is fine enough to resolve local features such as nighttime ozone titration previously resolved only by urban or regional models. Furthermore, since there are no boundary conditions for a global model, the GEOS-CF captures large-scale processes such as long-range transport of air pollutants from forest fires. Comparisons against surface observations highlight the model’s overall capability to reproduce the diurnal variability of air pollutants under a variety of meteorological conditions. In addition, we show how machine learning techniques can be used to correct for sub-grid variability, which further improves model estimates at a given surface observation site. The GEOS-CF system offers a new tool for scientists and the public health community alike and is being developed jointly with several government and non-profit partners. As an example, we will show the use of GEOS-CF during the Satellite Coastal and Oceanic Atmospheric Pollution Experiment (SCOAPE). The campaign, conducted in collaboration between NASA and the Bureau of Ocean Energy Management (BOEM), aims to investigate the response of onshore air quality to Outer Continental Shelf (OCS) oil and gas exploration, development and production. Detailed gas-phase chemistry, as provided by GEOS-CF, is critical to understand the formation of air pollution related to hydrocarbon emissions from offshore oil and gas activities. The accuracy of GEOS-CF can be further improved by incorporating detailed offshore emissions compiled by BOEM.
Abstract Coal is declining in the U.S. as part of the clean energy transition, resulting in remarkable air pollution benefits for the American public and significant costs for the industry. Using the AP3 integrated assessment model, we estimate that fewer emissions of sulfur dioxide, nitrogen oxides, and primary fine particulate matter driven by coal’s decline led to $300 billion in benefits from 2014 to 2019. Conversely, we find that job losses driven by less coal plant and mining activity resulted in $7.84 billion in foregone wages over the same timeframe. While the benefits were greatly distributed (mostly throughout the East), costs were highly concentrated in coal communities. Transferring a small fraction of the benefits to workers could cover these costs while maintaining societal net benefits. Forecasting coal fleet damages from 2020 to 2035, we find that buying out or replacing these plants would result in $589 billion in air quality benefits, which considerably outweigh the costs. The return on investment increases when policy targets the most damaging capacity, and net benefits are maximized when removing just facilities where marginal benefits exceed marginal costs. Evaluating competitive reverse auction policy designs akin to Germany’s Coal Exit Act, we find that adjusting bids based on monetary damages rather than based only on carbon dioxide emissions – the German design – provides a welfare advantage. Our benefit–cost analyses clearly support policies that drive a swift and just transition away from coal, thereby clearing the air while supporting communities needing assistance.
Topics covered include: Image-Capture Devices Extend Medicine's Reach; Medical Devices Assess, Treat Balance Disorders; NASA Bioreactors Advance Disease Treatments; Robotics Algorithms Provide Nutritional Guidelines; "Anti-Gravity" Treadmills Speed Rehabilitation; Crew Management Processes Revitalize Patient Care; Hubble Systems Optimize Hospital Schedules; Web-based Programs Assess Cognitive Fitness; Electrolyte Concentrates Treat Dehydration; Tools Lighten Designs, Maintain Structural Integrity; Insulating Foams Save Money, Increase Safety; Polyimide Resins Resist Extreme Temperatures; Sensors Locate Radio Interference; Surface Operations Systems Improve Airport Efficiency; Nontoxic Resins Advance Aerospace Manufacturing; Sensors Provide Early Warning of Biological Threats; Robot Saves Soldier's Lives Overseas (MarcBot); Apollo-Era Life Raft Saves Hundreds of Sailors; Circuits Enhance Scientific Instruments and Safety Devices; Tough Textiles Protect Payloads and Public Safety Officers; Forecasting Tools Point to Fishing Hotspots; Air Purifiers Eliminate Pathogens, Preserve Food; Fabrics Protect Sensitive Skin from UV Rays; Phase Change Fabrics Control Temperature; Tiny Devices Project Sharp, Colorful Images; Star-Mapping Tools Enable Tracking of Endangered Animals; Nanofiber Filters Eliminate Contaminants; Modeling Innovations Advance Wind Energy Industry; Thermal Insulation Strips Conserve Energy; Satellite Respondent Buoys Identify Ocean Debris; Mobile Instruments Measure Atmospheric Pollutants; Cloud Imagers Offer New Details on Earth's Health; Antennas Lower Cost of Satellite Access; Feature Detection Systems Enhance Satellite Imagery; Chlorophyll Meters Aid Plant Nutrient Management; Telemetry Boards Interpret Rocket, Airplane Engine Data; Programs Automate Complex Operations Monitoring; Software Tools Streamline Project Management; Modeling Languages Refine Vehicle Design; Radio Relays Improve Wireless Products; Advanced Sensors Boost Optical Communication, Imaging; Tensile Fabrics Enhance Architecture Around the World; Robust Light Filters Support Powerful Imaging Devices; Thermoelectric Devices Cool, Power Electronics; Innovative Tools Advance Revolutionary Weld Technique; Methods Reduce Cost, Enhance Quality of Nanotubes; Gauging Systems Monitor Cryogenic Liquids; Voltage Sensors Monitor Harmful Static; and Compact Instruments Measure Heat Potential.
Provides a final report on the work accomplished by several meteorological scientists under a NASA grant in conjunction with the DC-8 component of Pacific Exploratory Mission (PEM)-Tropics B. The responsibilities of the principal investigator included collaboration with the Science Team on flight planning, presentation of forecasts, and the preparation of map discussions for each flight. In a published manuscript, the principal investigator summarized the meteorological conditions during PEM-TB which included mean flow patterns, subtropical anticyclones, the South Pacific Convergence Zone (SPCZ), and the Intertropical Convergence Zone (ITCZ). Methodologies used included streamlines, ten day backward trajectories, thermodynamic soundings, and satellite imagery. Other interests included air sampling for the purpose of determining pollution levels.
In this study we explore the potential applications of MODIS (Moderate Resolution Imaging Spectroradiometer) -like satellite sensors in air quality research for some Asian regions. The MODIS aerosol optical thickness (AOT), NCEP global reanalysis meteorological data, and daily surface PM(sub 10) concentrations over China and Thailand from 2001 to 2009 were analyzed using simple and multiple regression models. The AOT-PM(sub 10) correlation demonstrates substantial seasonal and regional difference, likely reflecting variations in aerosol composition and atmospheric conditions, Meteorological factors, particularly relative humidity, were found to influence the AOT-PM(sub 10) relationship. Their inclusion in regression models leads to more accurate assessment of PM(sub 10) from space borne observations. We further introduced a simple method for employing the satellite data to empirically forecast surface particulate pollution, In general, AOT from the previous day (day 0) is used as a predicator variable, along with the forecasted meteorology for the following day (day 1), to predict the PM(sub 10) level for day 1. The contribution of regional transport is represented by backward trajectories combined with AOT. This method was evaluated through PM(sub 10) hindcasts for 2008-2009, using ohservations from 2005 to 2007 as a training data set to obtain model coefficients. For five big Chinese cities, over 50% of the hindcasts have percentage error less than or equal to 30%. Similar performance was achieved for cities in northern Thailand. The MODIS AOT data are responsible for at least part of the demonstrated forecasting skill. This method can be easily adapted for other regions, but is probably most useful for those having sparse ground monitoring networks or no access to sophisticated deterministic models. We also highlight several existing issues, including some inherent to a regression-based approach as exemplified by a case study for Beijing, Further studies will be necessa1Y before satellite data can see more extensive applications in the operational air quality monitoring and forecasting.
We quantify both the spatial and temporal variability of column integrated O3, NO2, CO, SO2, and HCHO over the Baltimore / Washington, DC area using output from the Weather Research and Forecasting model with on-line chemistry (WRF/Chem) for the entire month of July 2011, coinciding with the first deployment of the NASA Earth Venture program mission Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ). Using structure function analyses, we find that the model reproduces the spatial variability observed during the campaign reasonably well, especially for O3. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument will be the first NASA mission to make atmospheric composition observations from geostationary orbit and partially fulfills the goals of the Geostationary Coastal and Air Pollution Events (GEO-CAPE) mission. We relate the simulated variability to the precision requirements defined by the science traceability matrices of these space-borne missions. Results for O3 from 0- 2 km altitude indicate that the TEMPO instrument would be able to observe O3 air quality events over the Mid-Atlantic area, even on days when the violations of the air quality standard are not widespread. The results further indicated that horizontal gradients in CO from 0-2 km would be observable over moderate distances (≥ 20 km). The spatial and temporal results for tropospheric column NO2 indicate that TEMPO would be able to observe not only the large urban plumes at times of peak production, but also the weaker gradients between rush hours. This suggests that the proposed spatial and temporal resolutions for these satellites as well as their prospective precision requirements are sufficient to answer the science questions they are tasked to address.
Ozone concentrations at the Earth's surface are controlled by meteorological and chemical processes and are a function of advection, entrainment, deposition, and net chemical production/loss. The relative contributions of these processes vary in time and space. Understanding the relative importance of these processes controlling surface ozone concentrations is an essential component for designing effective regulatory strategies. Here we focus on the diurnal cycle of entrainment through atmospheric boundary layer (ABL) growth in the Colorado Front Range. Aircraft soundings and surface observations collected in July/August 2014 during the DISCOVER-AQ/FRAPPÉ (Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality/Front Range Air Pollution and Photochemistry Éxperiment) campaigns and equivalent data simulated by a regional chemical transport model are analyzed. Entrainment through ABL growth is most important in the early morning, fumigating the surface at a rate of ~5 ppbv/h. The fumigation effect weakens near noon and changes sign to become a small dilution effect in the afternoon on the order of -1 ppbv/h. The chemical transport model WRF-Chem (Weather Research and Forecasting Model with chemistry) underestimates ozone at all altitudes during this study on the order of 10-15 ppbv. The entrainment through ABL growth is overestimated by the model in the order of 0.6-0.8 ppbv/h. This results from differences in boundary layer growth in the morning and ozone concentration jump across the ABL top in the afternoon. This implicates stronger modeled fumigation in the morning and weaker modeled dilution after 11:00 LT.