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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 NTRS2009

Air Quality Monitoring: Risk-Based Choices

Air monitoring is secondary to rigid control of risks to air quality. Air quality monitoring requires us to target the credible residual risks. Constraints on monitoring devices are severe. Must transition from archival to real-time, on-board monitoring. Must provide data to crew in a way that they can interpret findings. Dust management and monitoring may be a major concern for exploration class missions.

James, John T.↗
NASA NTRS2012

Global Air Quality and Climate

Emissions of air pollutants and their precursors determine regional air quality and can alter climate. Climate change can perturb the long-range transport, chemical processing, and local meteorology that influence air pollution. We review the implications of projected changes in methane (CH4), ozone precursors (O3), and aerosols for climate (expressed in terms of the radiative forcing metric or changes in global surface temperature) and hemispheric-to-continental scale air quality. Reducing the O3 precursor CH4 would slow near-term warming by decreasing both CH4 and tropospheric O3. Uncertainty remains as to the net climate forcing from anthropogenic nitrogen oxide (NOx) emissions, which increase tropospheric O3 (warming) but also increase aerosols and decrease CH4 (both cooling). Anthropogenic emissions of carbon monoxide (CO) and non-CH4 volatile organic compounds (NMVOC) warm by increasing both O3 and CH4. Radiative impacts from secondary organic aerosols (SOA) are poorly understood. Black carbon emission controls, by reducing the absorption of sunlight in the atmosphere and on snow and ice, have the potential to slow near-term warming, but uncertainties in coincident emissions of reflective (cooling) aerosols and poorly constrained cloud indirect effects confound robust estimates of net climate impacts. Reducing sulfate and nitrate aerosols would improve air quality and lessen interference with the hydrologic cycle, but lead to warming. A holistic and balanced view is thus needed to assess how air pollution controls influence climate; a first step towards this goal involves estimating net climate impacts from individual emission sectors. Modeling and observational analyses suggest a warming climate degrades air quality (increasing surface O3 and particulate matter) in many populated regions, including during pollution episodes. Prior Intergovernmental Panel on Climate Change (IPCC) scenarios (SRES) allowed unconstrained growth, whereas the Representative Concentration Pathway (RCP) scenarios assume uniformly an aggressive reduction, of air pollutant emissions. New estimates from the current generation of chemistry-climate models with RCP emissions thus project improved air quality over the next century relative to those using the IPCC SRES scenarios. These two sets of projections likely bracket possible futures. We find that uncertainty in emission-driven changes in air quality is generally greater than uncertainty in climate-driven changes. Confidence in air quality projections is limited by the reliability of anthropogenic emission trajectories and the uncertainties in regional climate responses, feedbacks with the terrestrial biosphere, and oxidation pathways affecting O3 and SOA.

ozone↗
NASA NTRS2024

Urban Air Quality Management at Low-Cost Using Micro Air Sensors: A Case Study From Accra, Ghana

Urban air quality management is dependent on the availability of local air pollution data. In many major urban centers of Africa, there is limited to non-existent information on air quality. This is gradually changing in part due to the increasing use of micro air sensors which have the potential to enable the generation of ground-based air quality data at fine scales for understanding local emission trends. Regional literature on the application of the high-resolution data for emission source identification in this region is limited. In this study a micro air sensor was co-located at the Physics Department, University of Ghana with a reference grade instrument to evaluate its performance for estimating PM2.5 pollution accurately at fine scales and the value of this data in identification of local sources and their behavior over time. For this study 15 weeks of data at hourly resolution with approximately 2500 data pairs are generated and analyzed (June 01, 2023, to September 15, 2023). For this time period a coefficient of determination (r 2 ) of 0.83 was generated with a mean absolute error (MAE) of 5.44 μgm -3 between the pre local calibration micro air sensor (i.e. out of box) and the reference-grade instrument. Following currently accepted best practice methods (see e.g., PAS4023) a domain specific (i.e. local) calibration factor was generated using a multi-linear regression model and when this factor is applied to the micro air sensor data, a reduction i.e., improvement in MAE to 1.43 μgm -3 was found. Daily variation was calculated, a receptor model was applied, and time series plots as a function of wind direction were generated, including PM2.5/PM10 ratio scatter and count plots to explore the utility of this observational approach for local source identification. The 3 data sets were compared (out of box, domain calibrated and reference-grade) and it was found that although there were variations in the data reported, source areas highlighted based on these data were similar, with input from local sources such as traffic emissions and biomass burning. As the temporal resolution of observational data associated with these micro air sensors is higher than for reference grade instruments (primarily due to costs and logistics limitations), they have the potential to provide insight into the complex, often hyper localized sources associated with urban areas, such as those found in major African cities.

Source apportionment↗
NASA NTRSDate not supplied

Multi-Decadal Nitrogen Dioxide and Derived Products from Satellites (MINDS) Datasets Released by NASA GES DISC and Their Applications for Air Quality

Nitrogen dioxide (NO2), a pervasive air pollutant, comes from vehicles, power plants, industrial emissions, and off-road sources such as construction or lawn and gardening equipment. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) curates many remote sensing datasets with NO2 retrievals, which have been utilized for air quality research and applications. The remotely-sensed datasets include those generated by the Ozone Monitoring Instrument (OMI) on the Aura satellite, the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Copernicus Sentinel-5 Precursor (S5P), and the Ozone Mapping and Profiling Suite (OMPS) Nadir-Mapper (NM) instrument on the Suomi National Polar-orbiting Partnership (S- NPP). In collaboration with the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Multi-Decadal Nitrogen Dioxide and Derived Products from Satellites (MINDS) project, the GES DISC recently released MINDS datasets. The NASA MEaSUREs MINDS project aims to develop long-term NO2 global data records by adapting a consistent retrieval algorithm to multiple instrument measurements. Long-term data records will be achieved by applying consistent retrieval approaches to multiple satellite instruments, including OMI (2004 - ); the Global Ozone Monitoring Experiment (GOME, 1995-2011) onboard the second European Remote Sensing satellite (ERS-2); the Scanning Imaging Spectrometer for Atmospheric Cartography (SCIAMACHY, 2002-2012) onboard the ENVIronmental SATellite (ENVISAT); GOME-2 on the Meteorological Operational satellites (MetOp-A and MetOp-B, 2006 - ); and TROPOMI onboard the Copernicus S5P (2017 - ). The long-term record (1995 to present) of MINDS datasets makes them very useful for air quality trend studies. Some MINDS datasets with high spatial resolution of only a few kilometers can be used for air quality research and applications at regional scales. In this presentation, we will introduce all of the MINDS products and services, and demonstrate use cases of MINDS data for studying air quality. We will also present a few other NO2 datasets acquired from NASA’s Health and Air Quality Applied Sciences Team (HAQAST), to be archived and distributed by the GES DISC, and highlight some of their applications for air quality and health.

Feng Ding↗
NASA NTRSDate not supplied

Integrating Low-Cost Sensor Systems and Networks to Enhance Air Quality Applications

Recent advances in low-cost air quality sensor systems are rapidly increasing the accessibility of air quality information around the world. At the same time, there are many technical challenges to appropriately and effectively using of the information they provide. A key opportunity to increase the applicability and actionability of low-cost sensor data is to use these devices at network scale and combine their information with insights from other air quality data sources such as numerical models and satellite remote sensing. When the limitations of low-cost sensors are understood and acknowledged, and appropriate complementary data are used to overcome these limitations, low cost sensors can support a variety of applications such as improving location-specific air quality forecasting and estimation, quantifying local source impacts, identifying air quality disparities, assessing the benefits of mitigation actions, and promoting community engagement with air quality issues. This presentation will first provide an overview and summarize key findings from a recently released World Meteorological Organization report discussing how networks of low-cost air quality sensor systems can be integrated to effectively support such applications. The presentation will also give a brief overview of an ongoing NASA-funded effort to create accessible tools for integrating multiple sources of air quality information, including global forecasting models, satellite remote sensing data, and in-situ information from both regulatory and low-cost monitors.

Carl Malings↗
NASA NTRS2010

Louisiana Air Quality - Using ASTER, Landsat 5, and MODIS to Assess the Impact of Sugar Cane and Marsh Burning Practices on Local Air Quality

Biomass burning is an event that occurs globally and encompasses both human-initiated and naturally-occurring fires. It is estimated that 3 billion metric tons of biomass are burned every year worldwide (Curtis 2002). Societies have used these burning techniques for cooking and heating, clearing land for agricultural use, and removing excess biomass from grazing and croplands (Levine 1991). Our study focuses on the state of Louisiana and its commonly occurring methods of sugarcane and marsh biomass burning (LSU Ag.Center 2000; Nyman and Chabreck 1995). Over the centuries, the sugarcane industry in this state has steadily grown to surpass all other agriculture commodities. To promote efficiency within this large industry, burning excess biomass takes place throughout the harvesting period (LSU Ag.Center 2000). In addition to sugarcane, Louisiana contains 30% of the total coastal marsh of the United States (LSU Ag.Center 2000). The periodic burning of such marshes is an ecologically important management tool that is practiced throughout the Atlantic and Gulf Coasts (Nyman and Chabreck 1995). In most biomass burning instances, the leading by-product is particulate matter that is less than 10 microns in diameter (PM10). Through past research, this fine material has been shown to have negative health effects on surrounding populations (Boopathy2001). While burning guidelines have been set into place by the Louisiana Department of Agriculture and Forestry (LDAF) to reduce health effects, the guidelines are voluntary (LDAF 2000). To help quantify emission estimates, we will focus on Iberia Parish for sugarcane burning and Cameron Parish for marsh burning. Through analysis of ASTER, Landsat 5 TM, and MODIS data, our goal is to determine the amount and location of land area burned for the years 2008 and 2009 due to these practices. With emissions algorithms from Seiler and Crutzen, 1980, total acreage burned can be used to estimate emissions. This information will help to document the impact of these smoke plumes on local populations for the improvement of biomass burning policies in Louisiana.

Clark, Robert↗
NASA NTRS2024

Leveraging Scientific Community Knowledge for Air Quality Model Chemistry Parameterizations

Air pollution contributes to adverse health outcomes. Approximately 121 million people in the United States—one third of the population—live where National Ambient Air Quality Standards (NAAQS) are violated. In most cases, the criteria pollutants exceeding standards are ozone (O 3 ) and fine particles (PM 2.5 ). In addition, 188 substances known or suspected to cause cancer or other serious health effects are designated as hazardous air pollutants (HAPs). Essentially, all O 3 and significant portions of PM 2.5 and HAPs are produced in the atmosphere through chemical and physical processes. In the case of PM 2.5 , subcomponents formed primarily from precursor gases—sulfate, nitrate, ammonium, and secondary organic aerosol (SOA)—account for 60% of the U.S. county-level annual mean concentration. In addition, 47% of the cancer risk and 25% of the noncancer risk from HAPs have been attributed to atmospheric chemistry rather than direct emissions. In this article, we introduce the role of chemical mechanisms in air quality models, a new atmospheric science community effort, and needs for further mechanism development.

Havala O.T. Pye↗
NASA NTRS2022

An Advanced Open-Source Platform for Air Quality Analysis, Visualization, and Prediction

Ambient air pollution is the largest environmental health risk factor, leading to several million premature deaths globally per year. The challenge of combating poor air quality is exacerbated by growing urban populations, changing emissions, and a warming climate. While there have been many advances monitoring and modeling of atmospheric composition, reflected in the dramatic increase in archived Earth Observations, there is no single measurement or method that alone can provide an accurate depiction of the entire atmosphere. The rapidly growing collections of observational and modeling data require us to be smarter about what data to include, and how such data is used. In recent years, NASA has invested significantly in advancing the concepts for Analytics Collaborative Framework (ACF) [5] and New Observing Strategies (NOS) [4] to tackle our software infrastructure need for harmonized data management and dynamic acquisition of diverse measurements for on-demand, interactive, multivariate analysis, and access [3]. It is not enough to have a big data, standalone analytics solution; it is critical that we start integrating data from remote sensing, modeling, and in-situ networks in a harmonized manner that enables timely and data-driven decision-making for air quality management. This work presents the design and development of an Air Quality Analytics Collaborative Framework (AQ ACF), as part of NASA’s Advanced Information Systems Technology (AIST) effort, to establish a data, machine-learning, and numerically driven platform for air quality analysis, visualization, and prediction.

Liu, Qian↗
NASA NTRSDate not supplied

Data Fusion With Uncertainty Quantification for Sub-City-Scale Air Quality Assessment and Forecasting

Many information sources can support air quality assessment and forecasting, including atmospheric chemistry model outputs, satellite retrievals of column chemical and aerosol constituents, and surface-based air quality monitoring data from both regulatory and low-cost instruments. Systematic integration of these data sources provides a major opportunity to improve understanding and management of air quality, but also presents technical barriers, especially in resource- and data-constrained settings in the Global South. This presentation describes a data fusion system, currently under development using the Google Earth Engine platform, which aims to integrate the information sources listed above to support comprehensive sub-city-scale assessment and management of air quality. Furthermore, the data fusion framework includes provisions for the quantification of uncertainties in the resulting fused estimates based on the variability of and among the input data sources. These capabilities will allow air quality managers to better understand their local air quality situation, including relative confidence in the fused estimates for different constituents, locations, and times, leading to better informed air quality management decisions. This presentation covers the underlying methodology of the data fusion and uncertainty quantification approaches, provides an update on the status of its implementation, and presents early qualitative and quantitative results.

Carl Malings↗
NASA NTRS2013

Downscaling a Global Climate Model to Simulate Climate Change Impacts on U.S. Regional and Urban Air Quality

Climate change can exacerbate future regional air pollution events by making conditions more favorable to form high levels of ozone. In this study, we use spectral nudging with WRF to downscale NASA earth system GISS modelE2 results during the years 2006 to 2010 and 2048 to 2052 over the continental United States in order to compare the resulting meteorological fields from the air quality perspective during the four seasons of five-year historic and future climatological periods. GISS results are used as initial and boundary conditions by the WRF RCM to produce hourly meteorological fields. The downscaling technique and choice of physics parameterizations used are evaluated by comparing them with in situ observations. This study investigates changes of similar regional climate conditions down to a 12km by 12km resolution, as well as the effect of evolving climate conditions on the air quality at major U.S. cities. The high resolution simulations produce somewhat different results than the coarse resolution simulations in some regions. Also, through the analysis of the meteorological variables that most strongly influence air quality, we find consistent changes in regional climate that would enhance ozone levels in four regions of the U.S. during fall (Western U.S., Texas, Northeastern, and Southeastern U.S), one region during summer (Texas), and one region where changes potentially would lead to better air quality during spring (Northeast). We also find that daily peak temperatures tend to increase in most major cities in the U.S. which would increase the risk of health problems associated with heat stress. Future work will address a more comprehensive assessment of emissions and chemistry involved in the formation and removal of air pollutants.

meteorological parameters↗
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 NTRS2012

NASA Earth Observation Systems and Applications for Public Health and Air Quality Models and Decisions Support

Health and Air Quality providers and researchers need environmental data to study and understand the geographic, environmental, and meteorological differences in disease. Satellite remote sensing of the environment offers a unique vantage point that can fill in the gaps of environmental, spatial, and temporal data for tracking disease. This presentation will demonstrate the need for collaborations between multi-disciplinary research groups to develop the full potential of utilizing Earth Observations in studying health. Satellite earth observations present a unique vantage point of the earth's environment from space, which offers a wealth of health applications for the imaginative investigator. The presentation is directly related to Earth Observing systems and Global Health Surveillance and will present research results of the remote sensing environmental observations of earth and health applications, which can contribute to the public health and air quality research. As part of NASA approach and methodology they have used Earth Observation Systems and Applications for Public Health and Air Quality Models to provide a method for bridging gaps of environmental, spatial, and temporal data for tracking disease. This presentation will provide an overview of projects dealing with infectious diseases, water borne diseases and air quality and how many environmental variables effect human health. This presentation will provide a venue where the results of both research and practice using satellite earth observations to study weather and it's role in public health research.

Estes, Sue↗
NASA NTRS2013

NASA Earth Observation Systems and Applications for Public Health and Air Quality Models and Decisions Support

Health and Air Quality providers and researchers need environmental data to study and understand the geographic, environmental, and meteorological differences in disease. Satellite remote sensing of the environment offers a unique vantage point that can fill in the gaps of environmental, spatial, and temporal data for tracking disease. This presentation will demonstrate the need for collaborations between multi-disciplinary research groups to develop the full potential of utilizing Earth Observations in studying health. Satellite earth observations present a unique vantage point of the earth's environment from space, which offers a wealth of health applications for the imaginative investigator. The presentation is directly related to Earth Observing systems and Global Health Surveillance and will present research results of the remote sensing environmental observations of earth and health applications, which can contribute to the public health and air quality research. As part of NASA approach and methodology they have used Earth Observation Systems and Applications for Public Health and Air Quality Models to provide a method for bridging gaps of environmental, spatial, and temporal data for tracking disease. This presentation will provide an overview of projects dealing with infectious diseases, water borne diseases and air quality and how many environmental variables effect human health. This presentation will provide a venue where the results of both research and practice using satellite earth observations to study weather and it's role in public health research.

Estes, Sue↗