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NASA NTRS2004

The Average Quality Factors by TEPC for Charged Particles

The quality factor used in radiation protection is defined as a function of LET, Q(sub ave)(LET). However, tissue equivalent proportional counters (TEPC) measure the average quality factors as a function of lineal energy (y), Q(sub ave)(Y). A model of the TEPC response for charged particles considers energy deposition as a function of impact parameter from the ion s path to the volume, and describes the escape of energy out of sensitive volume by delta-rays and the entry of delta rays from the high-density wall into the low-density gas-volume. A common goal for operational detectors is to measure the average radiation quality to within accuracy of 25%. Using our TEPC response model and the NASA space radiation transport model we show that this accuracy is obtained by a properly calibrated TEPC. However, when the individual contributions from trapped protons and galactic cosmic rays (GCR) are considered; the average quality factor obtained by TEPC is overestimated for trapped protons and underestimated for GCR by about 30%, i.e., a compensating error. Using TEPC's values for trapped protons for Q(sub ave)(y), we obtained average quality factors in the 2.07-2.32 range. However, Q(sub ave)(LET) ranges from 1.5-1.65 as spacecraft shielding depth increases. The average quality factors for trapped protons on STS-89 demonstrate that the model of the TEPC response is in good agreement with flight TEPC data for Q(sub ave)(y), and thus Q(sub ave)(LET) for trapped protons is overestimated by TEPC. Preliminary comparisons for the complete GCR spectra show that Q(sub ave)(LET) for GCR is approximately 3.2-4.1, while TEPC measures 2.9-3.4 for QQ(sub ave)(y), indicating that QQ(sub ave)(LET) for GCR is underestimated by TEPC.

Kim, Myung-Hee Y.↗
NASA NTRS2007

Cabin Air Quality On Board Mir and the International Space Station: A Comparison

The maintenance of the cabin atmosphere aboard spacecraft is critical not only to its habitability but also to its function. Ideally, air quality can be maintained by striking a proper balance between the generation and removal of contaminants. Both very dynamic processes, the balance between generation and removal can be difficult to maintain and control because the state of the cabin atmosphere is in constant evolution responding to different perturbations. Typically, maintaining a clean cabin environment on board crewed spacecraft and space habitats is the central function of the environmental control and life support (ECLS) system. While active air quality control equipment is deployed on board every vehicle to remove carbon dioxide, water vapor, and trace chemical components from the cabin atmosphere, perturbations associated with logistics, vehicle construction and maintenance, and ECLS system configuration influence the resulting cabin atmospheric quality. The air-quality data obtained from the International Space Station (ISS) and NASA-Mir programs provides a wealth of information regarding the maintenance of the cabin atmosphere aboard long-lived space habitats. A comparison of the composition of the trace chemical contaminant load is presented. Correlations between ground-based and in-flight operations that influence cabin atmospheric quality are identified and discussed, and observations on cabin atmospheric quality during the NASA-Mir expeditions and the International Space Station are explored.

Macatangay, Ariel↗
NASA NTRS2007

Air Quality Research and Applications Using AURA OMi Data

The Ozone Monitoring Instrument (OMI) on EOS Aura is a new generation of satellite remote sensing instrument designed to measure trace gas and aerosol absorption at the UV and blue wavelengths. These measurements are made globally at urban scale resolution with no inter-orbital gaps that make them potentially very useful for air quality research, such as the determination of the sources and processes that affect global and regional air quality, and to develop applications such as air quality forecast. However, the use of satellite data for such applications is not as straight forward as satellite data have been for stratospheric research. There is a need for close interaction between the satellite product developers, in-situ measurement programs, and the air quality research community to overcome some of the inherent difficulties in interpreting data from satellite-based remote sensing instruments. In this talk we will discuss the challenges and opportunities in using OMI products for air quality research and applications. A key conclusion of this work is that to realize the full potential of OMI measurements it will be necessary to combine OMI data with data from instruments such as MLS, MODIS, AIRS, and CALIPSO that are currently flying in the "A-train" satellite constellation. In addition similar data taken by satellites crossing the earth at different local times than the A-train (e.g., the recently MetOp satellite) would need to be processed in a consistent manner to study diurnal variability, and to capture the effects on air quality of rapidly changing events such as wild fires.

Bhartia, P.K.↗
NASA NTRS2011

Orion Handling Qualities During ISS Proximity Operations and Docking

NASA's Orion spacecraft is designed to autonomously rendezvous and dock with many vehicles including the International Space Station. However, the crew is able to assume manual control of the vehicle s attitude and flight path. In these instances, Orion must meet handling qualities requirements established by NASA. Two handling qualities assessments were conducted at the Johnson Space Center to evaluate preliminary designs of the vehicle using a six degree of freedom, high-fidelity guidance, navigation, and control simulation. The first assessed Orion s handling qualities during the last 20 ft before docking, and included both steady and oscillatory motions of the docking target. The second focused on manual acquisition of the docking axis during the proximity operations phase and subsequent station-keeping. Cooper-Harper handling qualities ratings, workload ratings and comments were provided by 10 evaluation pilots for the docking study and 5 evaluation pilots for the proximity operations study. For the docking task, both cases received 90% Level 1 (satisfactory) handling qualities ratings, exceeding NASA s requirement. All ratings for the ProxOps task were Level 1. These evaluations indicate that Orion is on course to meet NASA's handling quality requirements for ProxOps and docking.

Stephens, John-Paul↗
NASA NTRS2011

An Investigation of Large Tilt-Rotor Hover and Low Speed Handling Qualities

A piloted simulation experiment conducted on the NASA-Ames Vertical Motion Simulator evaluated the hover and low speed handling qualities of a large tilt-rotor concept, with particular emphasis on longitudinal and lateral position control. Ten experimental test pilots evaluated different combinations of Attitude Command-Attitude Hold (ACAH) and Translational Rate Command (TRC) response types, nacelle conversion actuator authority limits and inceptor choices. Pilots performed evaluations in revised versions of the ADS-33 Hover, Lateral Reposition and Depart/Abort MTEs and moderate turbulence conditions. Level 2 handling qualities ratings were primarily recorded using ACAH response type in all three of the evaluation maneuvers. The baseline TRC conferred Level 1 handling qualities in the Hover MTE, but there was a tendency to enter into a PIO associated with nacelle actuator rate limiting when employing large, aggressive control inputs. Interestingly, increasing rate limits also led to a reduction in the handling qualities ratings. This led to the identification of a nacelle rate to rotor longitudinal flapping coupling effect that induced undesired, pitching motions proportional to the allowable amount of nacelle rate. A modification that counteracted this effect significantly improved the handling qualities. Evaluation of the different response type variants showed that inclusion of TRC response could provide Level 1 handling qualities in the Lateral Reposition maneuver by reducing coupled pitch and heave off axis responses that otherwise manifest with ACAH. Finally, evaluations in the Depart/Abort maneuver showed that uncertainty about commanded nacelle position and ensuing aircraft response, when manually controlling the nacelle, demanded high levels of attention from the pilot. Additional requirements to maintain pitch attitude within 5 deg compounded the necessary workload.

Malpica, Carlos A.↗
NASA NTRS2012

Handling Qualities Implications for Crewed Spacecraft Operations

Abstract Handling qualities embody those qualities or characteristics of an aircraft that govern the ease and precision with which a pilot is able to perform the tasks required in support of an aircraft role. These same qualities are as critical, if not more so, in the operation of spacecraft. A research, development, test, and evaluation process was put into effect to identify, understand, and interpret the engineering and human factors principles which govern the pilot-vehicle dynamic system as they pertain to space exploration missions and tasks. Toward this objective, piloted simulations were conducted at the NASA Langley Research Center and Ames Research Center for earth-orbit proximity operations and docking and lunar landing. These works provide broad guidelines for the design of spacecraft to exhibit excellent handling characteristics. In particular, this work demonstrates how handling qualities include much more than just stability and control characteristics of a spacecraft or aircraft. Handling qualities are affected by all aspects of the pilot-vehicle dynamic system, including the motion, visual and aural cues of the vehicle response as the pilot performs the required operation or task. A holistic approach to spacecraft design, including the use of manual control, automatic control, and pilot intervention/supervision is described. The handling qualities implications of design decisions are demonstrated using these pilot-in-the-loop evaluations of docking operations and lunar landings.

Bailey, Randall E.↗
NASA NTRS2014

A Systems Engineering Approach to Quality Assurance for Aerospace Testing

On the surface, it appears that AS9100 has little to say about how to apply a Quality Management System (QMS) to major aerospace test programs (or even smaller ones). It also appears that there is little in the quality engineering Body of Knowledge (BOK) that applies to testing, unless it is nondestructive examination (NDE), or some type of lab or bench testing associated with the manufacturing process. However, if one examines: a) how the systems engineering (SE) processes are implemented throughout a test program; and b) how these SE processes can be mapped to the requirements of AS9100, a number of areas for involvement of the quality professional are revealed. What often happens is that quality assurance during a test program is limited to inspections of the test article; what could be considered a manufacturing al fresco approach. This limits the quality professional and is a disservice to the programs and projects, since there are a number of ways that quality can enhance critical processes, and support efforts to improve risk reduction, efficiency and effectiveness. The Systems Engineering (SE) discipline is widely used in aerospace to ensure the progress from Stakeholder Expectations (the President, Congress, the taxpayers) to a successful, delivered product or service. Although this is well known, what is not well known is that these same SE processes are implemented in varying complexity, to prepare for and implement test projects that support research, development, verification and validation, qualification, and acceptance test projects. Although the test organization's terminology may vary from the SE terminology, and from one test service provider to another, the basic process is followed by successful, reliable testing organizations. For this analysis, NASA Procedural Requirements (NPR) 7123.1, NASA Systems Engineering Processes and Requirements is used to illustrate the SE processes that are used for major aerospace testing. Many of these processes are also implemented for smaller test projects, and this set of processes will also look familiar to those who have participated in launch site activation and flight demonstrations.

Shepherd, Christena C.↗
NASA NTRS2014

Satellite Data of Atmospheric Pollution for U.S. Air Quality Applications: Examples of Applications, Summary of Data End-User Resources, Answers to FAQs, and Common Mistakes to Avoid

Satellite data of atmospheric pollutants are becoming more widely used in the decision-making and environmental management activities of public, private sector and non-profit organizations. They are employed for estimating emissions, tracking pollutant plumes, supporting air quality forecasting activities, providing evidence for "exceptional event" declarations, monitoring regional long-term trends, and evaluating air quality model output. However, many air quality managers are not taking full advantage of the data for these applications nor has the full potential of satellite data for air quality applications been realized. A key barrier is the inherent difficulties associated with accessing, processing, and properly interpreting observational data. A degree of technical skill is required on the part of the data end-user, which is often problematic for air quality agencies with limited resources. Therefore, we 1) review the primary uses of satellite data for air quality applications, 2) provide some background information on satellite capabilities for measuring pollutants, 3) discuss the many resources available to the end-user for accessing, processing, and visualizing the data, and 4) provide answers to common questions in plain language.

Duncan, Bryan↗
NASA NTRS2015

A Systems Engineering Approach to Quality Assurance for Aerospace Testing

On the surface, it appears that AS91001 has little to say about how to apply a Quality Management System (QMS) to major aerospace test programs (or even smaller ones). It also appears that there is little in the quality engineering Body of Knowledge (BOK)2 that applies to testing, unless it is nondestructive examination (NDE), or some type of lab or bench testing associated with the manufacturing process. However, if one examines: a) how the systems engineering (SE) processes are implemented throughout a test program; and b) how these SE processes can be mapped to the requirements of AS9100, a number of areas for involvement of the quality professional are revealed. What often happens is that quality assurance during a test program is limited to inspections of the test article; what could be considered a manufacturing al fresco approach. This limits the quality professional and is a disservice to the programs and projects, since there are a number of ways that quality can enhance critical processes, and support efforts to improve risk reduction, efficiency and effectiveness.

Shepherd, Christena C.↗
NASA NTRS2017

Importance of a Priori Vertical Ozone Profiles for TEMPO Air Quality Retrievals

Ozone (O3) is a toxic pollutant which plays a major role in air quality. Typically, monitoring of surface air quality and O3 mixing ratios is conducted using in situ measurement networks. This is partially due to high-quality information related to air quality being limited from space-borne platforms due to coarse spatial resolution, limited temporal frequency, and minimal sensitivity to lower tropospheric and surface-level O3. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite is designed to address the limitations of current space-based platforms and to improve our ability to monitor North American air quality. TEMPO will provide hourly data of total column and vertical profiles of O3 with high spatial resolution to be used as a near-real-time air quality product. TEMPO O3 retrievals will apply the Smithsonian Astrophysical Observatory profile algorithm developed based on work from GOME (Global Ozone Monitoring Experiment), GOME-2, and OMI (Ozone Monitoring Instrument). This algorithm is suggested to use a priori O3 profile information from a climatological data-base developed from long-term ozone-sonde measurements (tropopause-based (TB-Clim) O3 climatology). This study evaluates the TB-Clim dataset and model simulated O3 profiles, which could potentially serve as a priori O3 profile information in TEMPO retrievals, from near-real-time data assimilation model products (NASA GMAO's (Global Modeling and Assimilation Office) operational GEOS-5 (Goddard Earth Observing System, Version 5) FP (Forecast Products) model and reanalysis data from MERRA2 (Modern-Era Retrospective analysis for Research and Applications, Version 2)) and a full chemical transport model (CTM), GEOS-Chem. In this study, vertical profile products are evaluated with surface (0-2 kilometers) and tropospheric (0-10 kilometers) TOLNet (Tropospheric Ozone Lidar Network) observations and the theoretical impact of individual a priori profile sources on the accuracy of TEMPO O3 retrievals in the troposphere and at the surface are presented. Results indicate that while the TB-Clim climatological dataset can replicate seasonally-averaged tropospheric O3 profiles, model-simulated profiles from a full CTM resulted in more accurate tropospheric and surface-level O3 retrievals from TEMPO when compared to hourly and daily-averaged TOLNet observations. Furthermore, it is shown that when large surface O3 mixing ratios are observed, TEMPO retrieval values at the surface are most accurate when applying CTM a priori profile information compared to all other data products.

Priori↗
NASA NTRS2018

Cost of Quality for Government

The American society for Quality (ASQ) has recently published ASQ TR2:2018: Cost of Quality: Guidelines for Development, Implementation and Monitoring to Improve Quality and Performance.1 Efforts were made during the Technical Report drafting process to ensure that examples were provided which could apply to government services, rather than just private sector production. The concept of "Cost of Quality" was first described by Joseph Juran in 1951 and Armand Feigenbaum expanded on the topic in 1956.2 Since that time, systems have been developed by the private sector to monitor and measure these costs. The concept is often described as "cost of poor quality". ASQ sets forth guidance for development of a Cost of Quality Program in TR2:2018.

Shepherd, Christena C.↗
NASA NTRS2019

Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements

The Aerosol Robotic Network (AERONET) has provided highly accurate, ground-truth measurements of the aerosol optical depth (AOD) using Cimel Electronique Sun–sky radiometers for more than 25 years. In Version 2 (V2) of the AERONET database, the near-real-time AOD was semiautomatically quality controlled utilizing mainly cloud-screening methodology, while additional AOD data contaminated by clouds or affected by instrument anomalies were removed manually before attaining quality-assured status (Level 2.0). The large growth in the number of AERONET sites over the past 25 years resulted in significant burden to the manual quality control of millions of measurements in a consistent manner. The AERONET Version 3 (V3) algorithm provides fully automatic cloud screening and instrument anomaly quality controls. All of these new algorithm updates apply to near-real-time data as well as post-field-deployment processed data, and AERONET reprocessed the database in 2018. A full algorithm redevelopment provided the opportunity to improve data inputs and corrections such as unique filter-specific temperature characterizations for all visible and near-infrared wavelengths, updated gaseous and water vapor absorption coefficients, and ancillary data sets. The Level 2.0 AOD quality-assured data set is now available within a month after post-field calibration, reducing the lag time from up to several months. Near-real-time estimated uncertainty is determined using data qualified as V3 Level 2.0 AOD and considering the difference between the AOD computed with the pre-field calibration and AOD computed with pre-field and post-field calibration. This assessment provides a near-real-time uncertainty estimate for which average differences of AOD suggest a +0.02 bias and one sigma uncertainty of 0.02, spectrally, but the bias and uncertainty can be significantly larger for specific instrument deployments. Long-term monthly averages analyzed for the entire V3 and V2 databases produced average differences (V3–V2) of +0.002 with a ±0.02 SD (standard deviation), yet monthly averages calculated using time-matched observations in both databases were analyzed to compute an average difference of −0.002 with a ±0.004 SD. The high statistical agreement in multiyear monthly averaged AOD validates the advanced automatic data quality control algorithms and suggests that migrating research to the V3 database will corroborate most V2 research conclusions and likely lead to more accurate results in some cases.

David M. Giles↗
NASA NTRSDate not supplied

pyQuARC: Open Source Library for Earth Observation Metadata Quality Assessment

Metadata quality is essential to effective data discovery and has become increasingly vital as more Earth Science data sets become available. The Common Metadata Repository (CMR) hosts metadata describing NASA’s Earth Observation data products, which are archived across 12 Distributed Active Archive Centers (DAACs). The Analysis and Review of CMR (ARC) Team, located at Marshall Space Flight Center, conducts metadata quality assessments to ensure that these data products are discoverable, accessible, and usable. To achieve these goals, the ARC team has developed a metadata quality assessment framework to evaluate metadata completeness, correctness, and consistency. ARC uses a combination of manual and automated methods to assess these three components and identify areas of improvement; the team then collaborates with the DAACs to resolve any findings. To streamline this process, ARC is currently developing a host of scripts, known as pyQuARC, to automate metadata quality assessments as much as possible. pyQuARC is an open source library for Earth Observation Metadata Quality Assessment, and the tool utilizes ARC’s metadata quality assessment framework to make basic validation checks, pinpoint inconsistencies between dataset-level (i.e. collection) and file-level (i.e. granule) metadata, and identify opportunities for more descriptive and robust information. Since pyQuARC is also customizable, other users can make modifications as needed, and future metadata standards can also be implemented. Once pyQuARC is fully developed, it will support multiple schema types to serve the broader EOSDIS metadata community. This presentation will provide an overview of pyQuARC and its process of development while showcasing the tool’s valuable features and uses.

Jenny Wood↗
NASA NTRSDate not supplied

Making Dataset Quality Information FAIR: Supporting Open-Source Science and Enhancing (Re)Use and Trustworthiness of Scientific Data

- Quality information should be documented and readily shared within and across domains. - Sharing of dataset quality information supports open science and trustworthiness of scientific data. - Dataset quality is more than data quality. - Quality tends to be domain-specific and context-dependent. - Community guidelines provide practical steps towards FAIR dataset quality information.

Ge Peng↗
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

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

Data Fusion for Urban Air Quality Assessment & Forecasting

This presentation provides an overview for our funded project with NASA's Health and Air Quality Applied Sciences Program. The project will expand an existing air quality data fusion tool implemented in Google Earth Engine (GEE) by our project team members at Sonoma Technology, Inc. (STI), a private air quality data company. We will expand the capabilities of this tool using new methods developed by the NASA GMAO which will give it the capability of providing sub-city scale resolution and hourly frequency estimates and forecasts of three key air quality indicators: surface-level particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3). We will combine a variety of Earth Observations including satellite data, global air quality forecasts, and local data from regulatory-grade monitors and/or low cost sensors. We will implement the new data fusion capabilities into the existing GEE tool in consultation with our end-users to best address their needs for sub-city scale air quality estimates and forecasts.

K Emma Knowland↗
NASA NTRSDate not supplied

A NASA Airborne Lens Into Air Quality Field Studies of the Last Decade

Field studies add an enhanced perspective to our everyday observing system for air quality with the goals of better understanding the air we breathe and identifying solutions toward a healthier future. Over the last decade, over 10 air quality field studies were conducted around the US with other ranging internationally with support through large agency-led efforts down to the grass-roots collaborative style. This presentation will highlight how NASA airborne observations have fit as one piece of the integrated observing system for air quality during these field studies. Research topics to be discussed are centered around the idea of the transition to geostationary air quality satellite observations. Specific topics include how temporally and spatially resolved measurements help us learn about knowledge gaps in NOx emissions, satellite-proxies for surface air quality, as well as evaluating state-of-the-art chemical transport models and using the enhanced observations as tools for understand what models/satellites can and cannot resolve. Each research topic will aim to discuss how the field measurement strategies that made this work possible as well as specific challenges that still exist to take these results further. Lastly, this presentation will discuss strategies to be carried forward as well as new ones as a peek into future air quality airborne field work.

Laura Judd↗