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At least 451 records · Page 25
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NASA NTRS2016

Why Do Models Overestimate Surface Ozone in the Southeast United States?

Ozone pollution in the Southeast US involves complex chemistry driven by emissions of anthropogenic nitrogen oxide radicals (NO(x) triple bond NO + NO2) and biogenic isoprene. Model estimates of surface ozone concentrations tend to be biased high in the region and this is of concern for designing effective emission control strategies to meet air quality standards. We use detailed chemical observations from the SEAC(exp 4)RS aircraft campaign in August and September 2013, interpreted with the GEOS-Chem chemical transport model at 0.25 deg x 0.3125 deg horizontal resolution, to better understand the factors controlling surface ozone in the Southeast US. We find that the National Emission Inventory (NEI) for NO(x) from the US Environmental Protection Agency (EPA) is too high. This finding is based on SEAC(exp 4)RS observations of NO(x) and its oxidation products, surface network observations of nitrate wet deposition fluxes, and OMI satellite observations of tropospheric NO2 columns. Our results indicate that NEI NO(x) emissions from mobile and industrial sources must be reduced by 30-60%, dependent on the assumption of the contribution by soil NO(x) emissions. Upper-tropospheric NO2 from lightning makes a large contribution to satellite observations of tropospheric NO2 that must be accounted for when using these data to estimate surface NO(x) emissions. We find that only half of isoprene oxidation proceeds by the high-NO(x) pathway to produce ozone; this fraction is only moderately sensitive to changes in NO(x) emissions because isoprene and NO(x) emissions are spatially segregated. GEOS-Chem with reduced NO(x) emissions provides an unbiased simulation of ozone observations from the aircraft and reproduces the observed ozone production efficiency in the boundary layer as derived from a regression of ozone and NO(x) oxidation products. However, the model is still biased high by 6 plus or minus 14 ppb relative to observed surface ozone in the Southeast US. Ozonesondes launched during midday hours show a 7 ppb ozone decrease from 1.5 km to the surface that GEOS-Chem does not capture. This bias may reflect a combination of excessive vertical mixing and net ozone production in the model boundary layer.

Oxidation↗
NASA NTRS2017

GEER Status Update

History: The Glenn Extreme Environments Rig (GEER) first became operational in the early part of 2015. Since that time GEER has completed a number of scientific tests and has undergone improvements in the chemical delivery system and analytics following a year of operations experience. Recent Updates: In June 2016, the GEER process system was rebuilt to provide a more robust system, higher accuracy and new capabilities. New insulation was installed on the exterior of the pressure vessel and gas lines. The newly revamped GEER plumbing system can provide extremely precise custom gas mixtures using any gas desired by the investigator in any combination. GEER can heat the resulting mixture up to 500 deg C and 1500 psia. The 304 stainless steel vessel walls were polished to reduce corrosion rate and reduce unwanted chemical reactions. The process lines were replaced with high purity Sulfinert coated tubing. The GEER team added the ability to individually boost specialty gases to GEER thus allowing operators to make very precise changes to the gas chemistry inside of GEER during a test while at high temperature and pressure. High accuracy mass flow meters were added to further improve gas mixing accuracy and precision. An in-line, integrated Inficon MicroGC Fusion was added for real time gas analysis along with a high purity gas sampling system, providing fully automated, real time analysis of the gas chemistry inside of GEER in minutes. This complements a co-located mass spectrometer and both are used for regular monitoring of the vessel chemistry. All internal vessel components were replaced with polished 304SS equivalents. Hot vent down capability was increased. Finally, an automated liquid injection system was added and is rated for max vessel operating conditions of (1500 psia, 500 C). Recent Results and Publications: In May 2016, GEER completed a test that exposed high temperature electronics to Venus surface conditions for 21.5 days. This demonstrated the potential for operating robotic spacecraft in the Venus environment without the need for thermal or environmental protection. Results from this test were published in December 2016 and received national media attention. In April 2017 GEER implemented an 80 day test at Venus surface conditions to simulate chemical weathering of expected Venus minerals. This test supported a ROSES award to a team led by Prof. Ralph Harvey of Case Western Reserve University. The test concluded in July 2017 and nearly doubled previous operation record of 42 days at Venus surface conditions. Preliminary results of these and previous experiments were presented at the recent Venus Modeling Workshop. In June 2017, NASA TM2017-219437 "Chemical and Microstructural Changes in Metallic and Ceramic Materials Exposed to Venusian Surface Conditions" was published. This report provides an extensive and valuable resource detailing the behavior of a variety of engineering materials at Venus surface conditions. Community Involvement: An external science advisory panel has been formed.

Kremic, Tibor↗
NASA NTRS2016

Airborne Measurements of Ozone and Other Trace Gases Captured by the Alpha Jet Atmospheric eXperiment (AJAX) During the 2016 California Baseline Ozone Transport Study (CABOTS)

In October 2015, the Environmental Protection Agency lowered the National Ambient Air Quality Standard for ozone (O3) from 75 ppbv to 70 ppbv. However, meeting the stricter air standards is a challenge for certain areas of California, like the San Joaquin Valley (SJV), where O3 levels are typically high due to topography, meteorology, and local emissions. Another factor potentially contributing to increased surface O3 is the trans-Pacific transport of O3 from Asia. The extent of which O3stems from local emissions or is transported across the Pacific, however, is unclear. The California Ozone Transport Study (CABOTS), a joint effort between the California Air Resource Board, the National Oceanic and Atmospheric Administration, and San Jose State University, was conducted during the spring and summer of 2016 in an attempt to answer this question.Nearly 10 science flights were carried out by the Alpha Jet Atmospheric eXperiment (AJAX) between June and August 2016, based out of the NASA Ames Research Center. A summary of airborne O3, CO2, CH4, H2O, formaldehyde (HCHO), and 3D wind measurements will be presented. AJAX flights connect the fixed-location measurements at Visalia (TOPAZ ozone lidar) and Bodega Bay (ozonesondes), while exploring the spatial heterogeneity of O3 concentrations across California and at various offshore locations. Preliminary analyses of these flights will investigate connections between offshore O3 and Central Valley O3. Vertical profiles, time series, and tracer-tracer correlations will be employed to identify the sources of O3 during these flights.

Iraci, Laura T.↗
NASA NTRS2019

Improving the Nuclear Launch Approval Process; Progress and Plans

Launches involving radioisotope power systems (RPS) or radioisotope heater units (RHU’s) must comply with a number of different statutory, regulatory, and administrative requirements. While some of these are well defined, others have been carried out on the basis of past practice rather than a set of formal standards. In addition, some of the requirements reference outdated standards and are in need of updates. The overall process is also time consuming and expensive. This paper describes efforts by NASA, the Department of Energy (DOE) and others to make improvements to the process while maintaining safety and environmental protection.

McCallum, Peter↗
NASA NTRSDate not supplied

TPSAS-NF1676L-35847-DND

Aerosols, especially particulate matter with aerodynamic diameters smaller than 2.5 ?m (PM2.5), contribute to air pollution and negatively impact human health. Past studies have estimated PM2.5 concentrations through the use of aerosol optical thickness (AOT) datasets from passive satellite sensors like MODIS and MISR. However, a major limitation of using passive AOTs for PM2.5 applications is that they are column-integrated, while PM2.5 is a surface measurement. In this study, we employ a bulk-mass-modeling-based method to directly derive PM2.5 concentrations over the contiguous United States (CONUS) using two years (2008-2009) of daytime and nighttime near-surface aerosol extinction retrievals from the NASA Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument, bulk mass extinction efficiencies, and model-based hygroscopicity. Results reveal that CALIOP-derived PM2.5 agrees reasonably well with ground-based PM2.5 observations from the U.S. Environmental Protection Agency (EPA), implying this method exhibits some merit in monitoring PM2.5 concentrations from CALIOP data. The newly developed method is then applied to CALIOP aerosol extinction retrievals using nearly the entire CALIOP data record (2007-2018), and an initial trend analysis is conducted. Results from various sensitivity studies are also shown, including those of surface layer height and assumed aerosol type.

Travis D Toth↗
NASA NTRS2020

Washington Health & Air Quality: Quantifying Air Quality Parameters and Validating Air Pollution Sources Impacting the Health of Puget Sound Residents Through the Use of NASA and ESA Remote Sensing Data

In the Puget Sound region of Washington, high levels of air pollutants put residents’ health at risk by increasing their likelihood of developing critical respiratory conditions. This project used remotely-sensed data to investigate aerosol optical depth (AOD) from NASA satellite sensors including the Terra and Aqua MODerate Resolution Imaging Spectroradiometer (MODIS) and European Space Agency Copernicus Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI). The team visualized the most recent data in Google Earth Engine (GEE) API to display air pollution trends in Washington State, which will support the Puget Sound Clean Air Agency’s (PSCAA) decision-making processes. The team performed linear regressions using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to form a relationship between ground-level microscopic particles (PM2.5) and AOD in the Puget Sound region, validating the relationship using concentration readings taken from Environmental Protection Agency (EPA) air quality monitors. The team utilized estimated PM2.5 and other satellite data to produce a web-based tool and to evaluate the effectiveness of using such a tool for near real-time air quality monitoring within a particular region. The team found that the tool provides useful supplementary data that fills in the gaps of the PSCAA’s air monitoring network.

Health & Air Quality↗
NASA NTRS2020

Contractors Road Heavy Equipment Area (SWMU055) Hot Spot 1 Bioremediation Interim Measure Performance Monitoring And Site-Wide Interim Groundwater Monitoring Report

This document presents a summary of activities completed from January through December 2019 at the Contractors Road Heavy Equipment (CRHE) area, located at John F. Kennedy Space Center (KSC), Florida. The activities summarized include: 1) Annual Hot Spot 1 (HS1) eastern bioremediation interim measure (IM) performance monitoring (December 2019); 2) Annual HS1 western bioremediation IM performance monitoring (December 2019); 3) Annual Interim Groundwater Monitoring (IGM) activities (December 2019); 4) Sub-slab soil gas (SSSG) sampling to investigate potential vapor intrusion (VI) (June 2019). Bioremediation IM activities were implemented in two phases at the CRHE. The first event was implemented in November and December 2016 in the eastern portion of HS1. In April and May 2018, bioremediation IM was implemented in the western portion of HS1. The bioremediation IM targeted chlorinated volatile organic compound (CVOC) concentrations greater than 10 times their Florida Department of Environmental Protection (FDEP) Natural Attenuation Default Criteria (NADC). Bioremediation IM activities included the injection of a diluted solution of SRS®-SD (electron donor) and microbial culture (KB-1®) into the subsurface via direct push technology (DPT) injection down to 50 feet below land surface (ft bls).

Andrew Scott Starr↗
NASA NTRS2020

Washington Health & Air Quality: Quantifying Air Quality Parameters and Validating Air Pollution Sources Impacting the Health of Puget Sound Residents Through the Use of NASA and ESA Remote Sensing Data

In the Puget Sound region of Washington, high levels of air pollutants put residents’ health at risk by increasing their likelihood of developing critical respiratory conditions. This project used remotely-sensed data to investigate aerosol optical depth (AOD) from NASA satellite sensors including the Terra and Aqua MODerate resolution Imaging Spectroradiometer (MODIS) and European Space Agency Copernicus Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI). The team visualized the most recent data in Google Earth Engine (GEE) API to display air pollution trends from Northern California to British Columbia, which will support the Puget Sound Clean Air Agency’s (PSCAA) decision-making processes. The team performed linear regressions using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to form a relationship between ground-level microscopic particles (PM2.5) and AOD in the Puget Sound region, validating the relationship using concentration readings taken from Environmental Protection Agency (EPA) air quality monitors. The team utilized estimated PM2.5 and other satellite data to produce a web-based tool and to evaluate the effectiveness of using such a tool for near real-time air quality monitoring within a particular region. The team found that the tool provides useful supplementary data that fills in the gaps of the PSCAA’s air monitoring network.

Health & Air Quality↗
NASA NTRS2021

Sub-city Scale Hourly Air Quality Forecasting by Combining Models, Satellite Observations, and Ground Measurements

While multiple information sources exist concerning surface-level air pollution, no individual source simultaneously provides large-scale spatial coverage, fine spatial and temporal resolution, and high accuracy. It is, therefore, necessary to integrate multiple data sources, using the strengths of each source to compensate for the weaknesses of others. In this study, we propose a method incorporating outputs of NASA’s GEOS Composition Forecasting model system with satellite information from the TROPOMI instrument and ground measurement data on surface concentrations. Although we use ground monitoring data from the Environmental Protection Agency network in the continental United States, the model and satellite data sources used have the potential to allow for global application. This method is demonstrated using surface measurements of nitrogen dioxide as a test case in regions surrounding five major US cities. The proposed method is assessed through cross-validation against withheld ground monitoring sites. In these assessments, the proposed method demonstrates major improvements over two baseline approaches which use ground-based measurements only. Results also indicate the potential for near-term updating of forecasts based on recent ground measurements.

C. Malings↗
NASA NTRS2021

Components Cleaning Facility, SWMU 030 Eastern High-Concentration Plume Area Implementation Work Plan Kennedy Space Center, Florida

This Implementation Work Plan (IWP) presents detailed design elements and coordination specifics to implement an air sparging (AS) Interim Measure (IM) to remediate groundwater at the Components Cleaning Facility (CCF), Eastern High Concentration Plume (HCP) Area located at Kennedy Space Center (KSC), Florida. The objective of the CCF East IM is to remediate groundwater where trichloroethene, cis-1,2-dichloroethene, and vinyl chloride concentrations exceed Florida Department of Environmental Protection Natural Attenuation Default Criteria (NADC) (identified as the HCP) via AS and to transition to monitored natural attenuation. CCF has been designated Solid Waste Management Unit 030 under KSC’s Resource Conservation and Recovery Act Corrective Action Program.

James Lloyd↗
NASA NTRSDate not supplied

NASA Advanced Space Suit xEMU Development Report – Components

For the past several years, the Exploration Extra-Vehicular Mobility Unit (xEMU) team at NASA’s Johnson Space Center has focused on the development and detailed design of the xEMU to support missions to the International Space Station (ISS) and a moon landing in 2024. In that context, this paper examines the development and baseline detailed design of the xEMU helmet, extra-vehicular visor assembly (EVVA), hard upper torso (HUT), shoulders, liquid cooling and ventilation garment (LCVG), boots, waist brief hip (WBH), ancillary hardware, and environmental protection garment (EPG) . This paper will outline the challenging technical requirements, significant architectural trades, technical solutions required to overcome these challenges, and a current status of the detailed design. The preliminary results of Design Verification Testing (DVT) as it relates specifically to these components will also be provided, along with a forward strategy for final maturation into a flight-ready design.

Shane McFarland↗
NASA NTRS2021

C-5 Electrical Substation (SWMU 066) Performance Monitoring Report

The C-5 Electrical Substation (C5ES) is a National Aeronautics and Space Administration (NASA) operated electrical power substation located at the John F. Kennedy Space Center (KSC), Florida. This facility has been designated Solid Waste Management Unit (SWMU) 066 under KSC’s Resource Conservation and Recovery Act (RCRA) Corrective Action program. This Performance Monitoring Report (PMR) presents the Interim Measure (IM) activities, including baseline sampling, air sparge system construction and operation, and performance monitoring, that were completed to remediate chlorinated volatile organic compounds (CVOC) in groundwater exceeding the Florida Department of Environmental Protection (FDEP) natural attenuation default concentrations (NADC).

Andrew Scott Starr↗
NASA NTRSDate not supplied

A Novel Machine Learning Method for Surface PM2.5 Estimations from Geostationary Satellites

Particulate matter (PM) with a diameter of less or equal to 2.5 μm, known as PM , affects human health as it penetrates the respiratory system. The Environmental Protection Agency (EPA) measures the atmospheric concentration of PM using air quality monitors stationed throughout the Continental United States (CONUS). Such measurements are points on a spatial domain and therefore, might not be representative of the air quality at nearby areas considering that the composition of the atmosphere is highly variable from place to place. Satellite based AOD permits a spatially uniform means of estimating PM and new geostationary satellites provide high temporal and spatial resolution estimation of AOD. However, the concentration of PM is non-linearly dependent on other atmospheric parameters that include relative humidity, temperature, and height of the planetary boundary layer. This information may be estimated at similar spatial and temporal resolutions as AOD from numerical modeling such as from the National Oceanic and Atmospheric Administration’s (NOAA) High Resolution Rapid Refresh (HRRR) model which resolves near real-time atmospheric conditions over the CONUS. The estimation of PM concentration is a multi-parametric problem that considers the effect of temporal dependencies among the different parameters. Deep learning approaches are appropriate for such complex estimation problems as they intrinsically capture relations among multiple non-linear parameters. This study compares deep-learning methods to traditional regression analysis to demonstrate the capabilities of these methods in predicting PM2.5 concentrations. Additionally, a novel ensemble learning approach is employed to identify scientific processes that could further improve the estimation of PM concentration. Utilizing Long Short-Term Memory (LSTM) neural networks, which are suitable for multivariate time series estimation problems as they are capable of learning long-term dependencies, individual models are created for each EPA station and trained on the aforementioned dataset collocated over each station. Individual station models are merged if the model's performance is improved by reducing the root mean squared error (RMSE) metric. This ensemble training method ultimately reduces the RMSE value. Evaluation of these results provide insights into physical processes and related observable parameters that may contribute to PM concentrations. Identified parameters evaluated to be statistically different between the merged and unmerged models are expected to improve overall performance. These new parameters are then utilized for reevaluation of the deep learning methods with an extreme gradient boosting model with an RMSE of 5.5 providing the best results.

George Priftis↗
NASA NTRS2021

St. Joseph Peninsula Disasters: Using NASA Earth Observations to Investigate Land Cover, Shoreline Change, and Sediment Transport in St. Joseph Peninsula after Hurricane Michael

T.H. Stone Memorial St. Joseph Peninsula State Park experienced significant damages from Hurricane Michael in 2018, the first Category 5 hurricane to hit the contiguous United States since 1992. These damages included a 300-meter-wide and 10-meter-deep breach in the peninsula, habitat disruption, and a forced closure of over half of the total park area. These damages, coupled with restricted visitor access, resulted in a significant loss of revenue for the park. NASA DEVELOP partnered with the Florida Department of Environmental Protection (DEP) to determine the overall impact of Hurricane Michael on land cover and shoreline change by using NASA Earth observations including Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI), Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), and the European Space Agency’s Sentinel-2 Multispectral Instrument (MSI) to analyze sediment transport and climatology to further understand the lasting impacts of hurricanes on the ecosystems of the park. The DEVELOP team’s analyses showed that chlorophyll-a concentrations, sea surface temperature, and precipitation are increasing over time. The sediment transport analysis showed dynamic movement across the peninsula, with the greatest erosion occurring within the bay and along the length of the peninsula. These results are supported by evidence of declining seagrass abundances and seasonal turbidity patterns within those areas. Providing these analyses for the partner allows for a greater understanding of how best to proceed with restoration efforts, which may include rebuilding camping services, expanding fishing recreation, and conserving habitats for endangered species.

Erica Kriner↗
NASA NTRS2021

Air Pollution Scenario over Pakistan: Characterization and Ranking of Extremely Polluted Cities using Long-Term Concentrations of Aerosols and Trace Gases

Pakistan ranks third in the world in terms of mortality attributable to air pollution, with aerosol mass concentrations (PM2.5) consistently well above WHO (World Health Organization) air quality guidelines (AQG). However, regulation is dependent on a sparse network of air quality monitoring stations and insufficient ground data. This study utilizes long-term observations of aerosols and trace gases to characterize and rank the air pollution scenarios and pollution characteristics of 80 selected cities in Pakistan. Datasets used include (1) the Aqua and Terra (AquaTerra) MODIS (Moderate Resolution Imaging Spectroradiometer) Level 2 Collection 6.1 merged Dark Target and Deep Blue (DTB) aerosol optical depth (AOD) retrieval products; (2) the CAMS (Copernicus Atmosphere Monitoring Service) reanalysis PM1, PM2.5, and PM10 data; (3) the MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) reanalysis PM2.5 data, (4) the OMI (Ozone Monitoring Instrument) tropospheric vertical column density (TVCD) of nitrogen dioxide (NO2), and VCD of sulfur dioxide (SO2) in the Planetary Boundary Layer (PBL), (5) the VIIRS (Visible Infrared Imaging Radiometer Suite) Nighttime Lights data, (6) MODIS Collection 6 Version 2 global monthly fire location data (MCD14ML), (7) population density, (8) MODIS Level 3 Collection 6 land cover types, (9) AERONET (AErosol RObotic NETwork) Version 3 Level 2.0 data, and (10) ground-based PM2.5 concentrations from air quality monitoring stations. Potential Source Contribution Function (PSCF) analyses were performed by integrating with ground-based PM2.5 concentrations and the NOAA (National Oceanic and Atmospheric Administration) HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) air parcel back trajectories to identify potential pollution source areas which are responsible for extreme air pollution in Pakistan. Results show that the ranking of the top polluted cities depends on the type of pollutant considered and the metric used. For example, Jhang, Multan, and Vehari were characterized as the top three polluted cities in Pakistan when considering AquaTerra DTB AOD products; for PM1, PM2.5, and PM10 Lahore, Gujranwala, and Okara were the top three; for tropospheric NO2 VCD Lahore, Rawalpindi, and Islamabad and for PBL SO2 VCD Lahore, Mirpur, and Gujranwala. The results demonstrate that Pakistan’s entire population has been exposed to high PM2.5 concentrations for many years, with a mean annual value of 54.7 μg/cu. m, over all Pakistan from 2003 to 2020. This value exceeds Pakistan’s National Environmental Quality Standards (Pak-NEQS, i.e., <15 μg/cu. m annual mean) for ambient air defined by the Pakistan Environmental Protection Agency (Pak-EPA) as well as the WHO Interim Target-1 (i.e., mean annual PM2.5 <35 μg/cu. m). The spatial analyses of the concentrations of aerosols and trace gases in terms of population density, nighttime lights, land cover types, and fire location data, and the PSCF analysis indicate that Pakistan’s air quality is strongly affected by anthropogenic sources inside of Pakistan, with contributions from surrounding countries. Statistically significant positive (increasing) trends in PM1, PM2.5, PM10, tropospheric NO2 VCD, and SO2 VCD were observed in ~89%, ~67%, ~48%, 91%, and ~88% of the Pakistani cities (80 cities), respectively. This comprehensive analysis of aerosol and trace gas levels, their characteristics in spatio-temporal domains, and their trends over Pakistan, is the first of its kind. Results will be helpful to the Ministry of Climate Change (Government of Pakistan), Pak-EPA, SUPARCO (Pakistan Space and Upper Atmosphere Research Commission), policymakers, and the local research community to mitigate air pollution and its effects on human health.

Muhammad Bilal↗
NASA NTRSDate not supplied

2020 Wildfire Plumes Observed by Satellite and Ground Sensors at San Francisco Bay

The main purpose of this research is to study the spreading of the 2020 wildfire plumes in the San Francisco Bay Area. Last year’s fire plumes have caused severe impact on regional air quality and public health over large part of the west coast. We studied fire plumes with two datasets: aerosol optical depth (AOD) retrieved from MODIS sensor onboard two NASA satellites (Terra and Aqua), and surface PM2.5 measurements from US Environmental Protection Agency (EPA). Satellite can monitor fire plumes from a top-down view, including active fire location, emission amount, spreading of the fire plume in both horizontal and vertical directions. EPA records air quality using the ground network of in-situ sensors. In general, the two points of view are consistent with each other. But in the peak of the 2020 fire season, we found an episode where the AOD and PM2.5 are out-of-phase for two days, in addition data from NASA satellite AERONET was use to corroborate AOD information for the episode, yielding similar results. Thereafter, we explored the possible mechanism for this shift with available meteorological measurements, including both ground measurements and sounding data. By tracking the evolution of the sounding data, we concluded a heated near surface inversion layer might shield the region from aloft fire plumes for two days before they touch down and severely downgraded the air quality over the whole Bay area.

Hazem Mahmoud↗
NASA NTRS2022

Retrieving Particulate Matter Concentrations over the Contiguous United States Using CALIOP Observations

Using twelve years (2007-2018) of NASA Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) near-surface 532 nm aerosol extinction retrievals, multi-year mean and trends of particulate matter (PM) concentrations are derived over the contiguous United States (CONUS). Different from past studies that use column integrated aerosol optical thickness, here only near-surface CALIOP aerosol extinction is used for deriving near-surface PM with aerodynamic diameters less than 2.5 µm (PM2.5) concentrations using an innovative, bulk-mass-modeling-based method. Compared against ground based PM2.5 measurements from the U.S. Environmental Protection Agency (EPA), an encouraging relationship between CALIOP-derived PM2.5 and EPA-observed PM2.5 (Deming slope = 0.89; RMSE = 3.42 µg/m3; mean bias = -1.00 µg/m3) is found using combined daytime/nighttime CALIOP data. Also, comparable trends in PM2.5 concentrations from the EPA and daytime and nighttime CALIOP data are found for most of the eastern CONUS and imply that air quality is generally improving over this region for the study period. Over the western CONUS, a seasonal analysis reveals that PM2.5 trends are positive during the more active wildfire season (June through November) but negative for other months. This study suggests that lidar data show promise in their use for obtaining PM2.5 estimates and provides motivation to further explore aerosol extinction-based PM concentration retrievals in anticipation of future space-based lidar missions.

CALIOP↗