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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Harmonizing solar induced fluorescence across spatial scales, instruments, and extraction methods using proximal and airborne remote sensing: A multi-scale study in a soybean field

Solar-induced chlorophyll fluorescence (SIF) has been widely used to track vegetation photosynthesis at different scales ranging from in-situ measurements to satellite products. Airborne platforms sample SIF data at a spatial scale intermediate between in-situ and satellite, matching that of ground measurement (e.g. flux tower footprints and other field sampling), enabling us to explore causes of SIF variation and validate satellite-based SIF products. However, harmonizing SIF across sensors and platforms (correcting for systematic errors to yield a consistent, comparable SIF product) is challenging because SIF can be retrieved in different absorption windows, with different instruments and methods complicating the comparison between different observational levels (i.e., ground, airborne, satellites) and between sites equipped with different instruments with varying optical properties (spectral resolution and sampling intervals, spatial resolution). Additionally, the spatial and temporal variability of atmospheric properties can influence the retrieval of the weak SIF signal. Because of these complications, direct comparisons of airborne and ground SIF across scales are rarely attempted. Here, in this study, we combined airborne SIF data with simultaneous ‘ground truth’ data collected by stationary and mobile platforms in a soybean field in Nebraska, USA. In this effort, we tested several SIF extraction methods, including Fraunhofer Line Discrimination (FLD), improved Fraunhofer Line Discrimination (iFLD), Spectral Fitting Method (SFM), SpecFit, and a Singular Vector Decomposition (SVD) method. The SpecFit method was sensitive to the 715–740 nm water bands and removing the water bands in the fitting process yielded better agreement between the airborne and ground SIF spectra. Accurate estimation of the ground level downwelling irradiance obtained by ground measurements over a calibration target improved agreement between airborne and ground SIF retrievals at the O 2 A band, and allowed us to derive a SIF dataset with improved agreement across platforms and sampling scales. This experimental approach provided a method for generating comparable SIF signals across instruments, methods and platforms, which is critical to understanding the SIF-GPP relationship at different scales and to cross-validate the diversity of platforms used for satellite products calibration and validation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A missing layer in COVID-19 studies: Transmission of enveloped viruses in mucus-rich droplets

Here we evaluate the influence of mucus layers on the evaporation time and transport of enveloped viruses, including SARS-CoV-2. Enveloped viruses must remain moist to be fully infective. Yet, the Wells model based on water droplets divides respiratory droplets into either quickly evaporated aerosolized particles termed droplet nuclei (<10 s) or liquid droplets that fall to the nearest surface, leaving no physical mechanism for airborne transmission of fully infective enveloped viruses over large distances (greater than a few meters). Yet, the role of mucus layers on evaporation times has not been considered even though the formation of mucus shells around liquid cores of respiratory droplets has been shown experimentally. Here we show that mucus shells increase the drying time by orders of magnitude so that enveloped virions may remain well hydrated and, thus, fully infective at substantial distances. Further, this provides a mechanism by which infective enveloped virus particles can transmit as aerosols within buildings and between buildings over extended distances. This analysis is important because public health agencies typically follow the Wells model to establish health policies including social/physical distancing guidelines.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Cross-scale sensing of field-level crop residue cover: Integrating field photos, airborne hyperspectral imaging, and satellite data

Conservation tillage practices can bring benefits to agricultural sustainability. Accurate spatial and temporal resolved information of field-scale crop residue cover, which reflects tillage intensity, is highly valuable for evaluating the outcomes of government conservation programs and voluntary ecosystem service markets, as well as facilitating agroecosystem modeling to quantify cropland biogeochemical processes. Remote sensing has the potential to cost-effectively detect crop residue cover, however, existing regional-scale studies were limited by insufficient ground truth data, scale mismatch between coarse satellite pixels and ground data, and the lack of key spectral data for detecting crop residues. Therefore, this study developed an innovative cross-sensing framework to integrate proximal sensing, airborne hyperspectral imaging, and satellite Earth Observation through deep learning to quantify field-level crop residue cover fractions at the regional scale. Specifically, we have collected intensive ground orthographic photos and conducted airborne hyperspectral surveys at corn and soybean fields of Champaign and nearby counties in Illinois, the heartland of the U.S. Corn Belt. Through semi-automatic labeling aided by ResNet-50 and superpixel image segmentation, we obtained 6719 records of ground residue fractions. With these ground data, we developed the 1-dimensional convolution neural network (CNN) model using airborne hyperspectral reflectance, which has 0.5m spatial resolution and 3–5 nm spectral resolution from 400 to 2400 nm, to predict residue fractions. By applying the CNN model to airborne pixels, we augmented “ground truth” data of crop residues and further combined them with Harmonized Landsat and Sentinel-2 (HLS) satellite data to quantify regional residue fractions at 30 m resolution. Results show that airborne hyperspectral imagery with CNN can accurately detect residue fractions (R 2 = 0.82, relative RMSE = 11.73%) to effectively generate quasi “ground truth” data to support satellite upscaling to all fields. With independent ground data for testing, we found that the ground-airborne-satellite integrative framework achieved better predictions in estimating crop residue cover (R 2 = 0.67, relative RMSE = 17.53%) than the conventional ground-satellite upscaling (R 2 = 0.22, relative RMSE = 32.09%). Here we also found that the shortwave infrared wavelengths, particularly 2100–2300 nm, are vital for predicting crop residue cover. Sentinel-2 and Landsat-8 data have a comparable capability to track residue fractions due to similar shortwave infrared wavelengths. This study highlights the high accuracy of hyperspectral imaging to detect agroecosystem tillage management practices and the advantages of cross-scale sensing to cost-effectively integrate multi-source data to quantify field-level agroecosystem variables across scales.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

LiDAR-based aboveground biomass changes data of tropical montane forest in Borneo

Anthropogenic activities are increasingly impacting the carbon storage of tropical montane forests. We employed airborne LiDAR data to estimate the aboveground biomass (AGB) changes at high resolution in a human-modified tropical montane forest in northern Borneo. The data package consists of LiDAR-based AGB changes at 1m resolution in GeoTIFF format, a table of field and estimated AGB changes at plot level and a shape file of the plots’ coordinates. The raster data (GeoTIFF) at 1 m resolution covers two study sites (site 1: 2.67 km x 8 km; site 2: 2 km x 8 km). The GeoTIFF and shape files can be read using any GIS or image processing software. The dataset can be utilized to deepen our understanding of the carbon storage of tropical montane forests, establish reference values for Southeast Asian tropical montane forests, guide forest resource managers on rehabilitation strategies and further research on the impacts of anthropogenic land use activities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Post survey report for AOP Assignable Asset collection of Crested Butte, CO

This report contains details of the National Ecological Observatory Network Airborne Observation Platform (NEON AOP) assignable asset (AA) flight over Crested Butte, Colorado (CO). The report is being prepared for Lawrence Berkeley National Lab (LBNL), the primary clients for the flights who contracted the NEON AOP for flights during June 2018. The report includes a summary of the acquired flight lines over the planned area, results of calibration surveys, and quality assurance (QA) results of the acquired data. Detailed reports on the acquired data and processing results were also delivered with the data. LBNL contracted the AA NEON AOP flights to observe watersheds of interest at Crested Butte with remotely sensed data including high resolution LiDAR, imaging spectroscopy, and high-resolution camera imagery. The remote sensing imagery acquired by the AOP intendeds to support LBNL on an interdisciplinary project on hydrology, biogeochemistry, and ecosystem functioning in the area surrounding the Rocky Mountain Biological Laboratory (RMBL), one of the most prominent ecological study sites in the United States.

2018 NEON and 2025 CHESS Campaigns↗
DOE OSTI2023

Effects of forest degradation classification on the uncertainty of aboveground carbon estimates in the Amazon

Tropical forests are critical for the global carbon budget, yet they have been threatened by deforestation and forest degradation by fire, selective logging, and fragmentation. Existing uncertainties on land cover classification and in biomass estimates hinder accurate attribution of carbon emissions to specific forest classes. In this study, we used textural metrics derived from PlanetScope images to implement a probabilistic classification framework to identify intact, logged and burned forests in three Amazonian sites. We also estimated biomass for these forest classes using airborne lidar and compared biomass uncertainties using the lidar-derived estimates only to biomass uncertainties considering the forest degradation classification as well. Our classification approach reached overall accuracy of 0.86, with accuracy at individual sites varying from 0.69 to 0.93. Logged forests showed variable biomass changes, while burned forests showed an average carbon loss of 35%. We found that including uncertainty in forest degradation classification significantly increased uncertainty and decreased estimates of mean carbon density in two of the three test sites. Our findings indicate that the attribution of biomass changes to forest degradation classes needs to account for the uncertainty in forest degradation classification. By combining very high-resolution images with lidar data, we could attribute carbon stock changes to specific pathways of forest degradation. This approach also allows quantifying uncertainties of carbon emissions associated with forest degradation through logging and fire. Both the attribution and uncertainty quantification provide critical information for national greenhouse gas inventories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Measured influence of overhead HVAC on exposure to airborne contaminants from simulated speaking in a meeting and a classroom

Tracer gas experiments were conducted in a 158 m 3 room with overhead supply diffusers to study dispersion of contaminants from simulated speaking in physically distanced meeting and classroom configurations. The room was contained within a 237 m 3 cell with open plenum return to the HVAC system. Heated manikins at desks and a researcher operating the tracer release apparatus presented 8-9 thermal plumes. Experiments were conducted under conditions of no forced air and neutral, cooled, or heated air supplied at 980-1100 cmh, and with/out 20% outdoor air. CO 2 was released at the head of one manikin in each experiment to simulate small (< 5 µm diameter) respiratory aerosols. The metric of exposure relative to perfectly mixed (ERM) is introduced to quantify impacts, based on measurements at manikin heads and at three heights in the center and corners of the room. Chilled or neutral supply air provided good mixing with ERMs close to one. Thermal stratification during heating produced higher ERMs at most manikins: 25% were ≥ 2.5 and the highest were > 5× perfectly mixed conditions. Operation of two within-zone air cleaners together moving ≥400 cmh vertically in the room provided enough mixing to mitigate elevated exposure variations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Modeling and mitigating airborne pathogen risk factors in school buses

Computational fluid dynamics (CFD) models were developed to simulate the impact of different ventilation scenarios on airborne exposure risks in a 72-passenger school bus. Here, scenarios and factors that were investigated included a moving vs. stationary bus, impacts of a heating unit within the bus, and impacts of alternative ventilation scenarios with different combinations of openings (e.g., windows, door, emergency hatch). Results of the simulations showed that when the bus was stationary, use of the heater increased receptor concentrations unless there was another opening. When the bus was moving, simulations with at least two sets of openings separated from each other in the forward and aft directions produced a through-flow condition that reduced concentrations via dilution from outside air by a factor of ten or more. A single opening in a moving bus generally increased concentrations throughout the cabin due to increased mixing with minimal ventilation. The cumulative exposure risk (time-averaged concentrations) was found to be inversely correlated to the air exchange rate. Stationary and moving-bus scenarios that yielded above ~20 air changes per hour resulted in the lowest cumulative exposures. Recommendations from this study were implemented in new safety and operating procedures by the Albuquerque Public Schools Transportation Center.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Towards mapping biodiversity from above: Can fusing lidar and hyperspectral remote sensing predict taxonomic, functional, and phylogenetic tree diversity in temperate forests?

Abstract Aim Rapid global change is impacting the diversity of tree species and essential ecosystem functions and services of forests. It is therefore critical to understand and predict how the diversity of tree species is spatially distributed within and among forest biomes. Satellite remote sensing platforms have been used for decades to map forest structure and function but are limited in their capacity to monitor change by their relatively coarse spatial resolution and the complexity of scales at which different dimensions of biodiversity are observed in the field. Recently, airborne remote sensing platforms making use of passive high spectral resolution (i.e., hyperspectral) and active lidar data have been operationalized, providing an opportunity to disentangle how biodiversity patterns vary across space and time from field observations to larger scales. Most studies to date have focused on single sites and/or one sensor type; here we ask how multiple sensor types from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) perform across multiple sites in a single biome at the NEON field plot scale (i.e., 40 m × 40 m). Location Eastern USA. Time period 2017–2018. Taxa studied Trees. Methods With a fusion of hyperspectral and lidar data from the NEON AOP, we assess the ability of high resolution remotely sensed metrics to measure biodiversity variation across eastern US temperate forests. We examine how taxonomic, functional, and phylogenetic measures of alpha diversity vary spatially and assess to what degree remotely sensed metrics correlate with in situ biodiversity metrics. Results Models using estimates of forest function, canopy structure, and topographic diversity performed better than models containing each category alone. Our results show that canopy structural diversity, and not just spectral reflectance, is critical to predicting biodiversity. Main conclusions We found that an approach that jointly leverages spectral properties related to leaf and canopy functional traits and forest health, lidar derived estimates of forest structure, fine‐resolution topographic diversity, and careful consideration of biogeographical differences within and among biomes is needed to accurately map biodiversity variation from above.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Determining Unabated Airborne Radionuclide Emissions Monitoring Requirements Using Inventory-Based Methods

Compliance with the airborne radionuclide emission monitoring requirements in the National Emission Standards for Hazardous Air Pollutants (NESHAP; Title 40 of the U.S. Code of Federal Regulations Part 61, Subpart H) and State requirements in Washington Administrative Code 246–247: Radiation Protection – Air Emissions and 173-480: Ambient Air Quality Standards and Emission Limits for Radionuclides were evaluated for Pacific Northwest National Laboratory (PNNL) operations. Additional guidance may be found in the U.S. Department of Energy Handbook, Environmental Radiological Effluent Monitoring and Environmental Surveillance. To meet regulatory requirements, reviews of planned or proposed projects and activities provide the basis for implementing necessary monitoring adjustments or for implementing changes to projects and activities in a timely manner. Potential unabated off-site doses were evaluated for emission locations managed by PNNL and licensed to the Department of Energy. These locations were at facilities in Richland, Washington (i.e., the Hanford Site and PNNL–Richland campus) and in Sequim, Washington, (PNNL–Sequim campus). This report describes the inventory-based methods and provides the results for the NESHAP assessment performed in January 2024 for calendar year ending 2023.

40 CFR 61 Subpart H↗
DOE OSTI2022

Airborne Lidar Measurements of XCO 2 in Synoptically Active Environment and Associated Comparisons With Numerical Simulations

Frontal boundaries have been shown to cause large changes in CO 2 mole-fractions, but clouds and the complex vertical structure of fronts make these gradients difficult to observe. It remains unclear how the column average CO 2 dry air mole-fraction (XCO 2 ) changes spatially across fronts, and how well airborne lidar observations, data assimilation systems, and numerical models without assimilation capture XCO 2 frontal contrasts (ΔXCO 2 , i.e., warm minus cold sector average of XCO 2 ). We demonstrated the potential of airborne Multifunctional Fiber Laser Lidar (MFLL) measurements in heterogeneous weather conditions (i.e., frontal environment) to investigate the ΔXCO 2 during four seasonal field campaigns of the Atmospheric Carbon and Transport-America (ACT-America) mission. Most frontal cases in summer (winter) reveal higher (lower) XCO 2 in the warm (cold) sector than in the cold (warm) sector. During the transitional seasons (spring and fall), no clear signal in ΔXCO 2 was observed. Intercomparison among the MFLL, assimilated fields from NASA's Global Modeling and Assimilation Office (GMAO), and simulations from the Weather Research and Forecasting-—Chemistry (WRF-Chem) showed that (a) all products had a similar sign of ΔXCO 2 though with different levels of agreement in ΔXCO 2 magnitudes among seasons; (b) ΔXCO 2 in summer decreases with altitude; and (c) significant challenges remain in observing and simulating XCO 2 frontal contrasts. A linear regression analyses between ΔXCO 2 for MFLL versus GMAO, and MFLL versus WRF-Chem for summer-2016 cases yielded a correlation coefficient of 0.95 and 0.88, respectively. The reported ΔXCO 2 variability among four seasons provide guidance to the spatial structures of XCO 2 transport errors in models and satellite measurements of XCO 2 in synoptically-active weather systems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Airborne hyperspectral imaging of nitrogen deficiency on crop traits and yield of maize by machine learning and radiative transfer modeling

Nitrogen is an essential nutrient that directly affects plant photosynthesis, crop yield, and biomass production for bioenergy crops, but excessive application of nitrogen fertilizers can cause environmental degradation. To achieve sustainable nitrogen fertilizer management for precision agriculture, there is an urgent need for nondestructive and high spatial resolution monitoring of crop nitrogen and its allocation to photosynthetic proteins as that changes over time. Here, we used visible to shortwave infrared (400–2400 nm) airborne hyperspectral imaging with high spatial (0.5 m) and spectral (3–5 nm) resolutions to accurately estimate critical crop traits, i.e., nitrogen, chlorophyll, and photosynthetic capacity (CO 2 -saturated photosynthesis rate, V max,27 ), at leaf and canopy scales, and to assess nitrogen deficiency on crop yield. We conducted three airborne campaigns over a maize (Zea mays L.) field during the growing season of 2019. Physically based soil-canopy Radiative Transfer Modeling (RTM) and data-driven approaches i.e. Partial-Least Squares Regression (PLSR) were used to retrieve crop traits from hyperspectral reflectance, with ground truth of leaf nitrogen, chlorophyll, V max,27 , Leaf Area Index (LAI), and harvested grain yield. To improve computational efficiency of RTMs, Random Forest (RF) was used to mimic RTM simulations to generate machine learning surrogate models RTM-RF. The results show that prior knowledge of soil background and leaf angle distribution can significantly reduce the ill-posed RTM retrieval. RTM-RF achieved a high accuracy to predict leaf chlorophyll content (R 2 = 0.73) and LAI (R 2 = 0.75). Meanwhile, PLSR exhibited better accuracy to predict leaf chlorophyll content (R 2 = 0.79), nitrogen concentration (R 2 = 0.83), nitrogen content (R 2 = 0.77), and V max,27 (R 2 = 0.69) but required measured traits for model training. We also found that canopy structure signals can enhance the use of spectral data to predict nitrogen related photosynthetic traits, as combining RTM-RF LAI and PLSR leaf traits well predicted canopy-level traits (leaf traits × LAI) including canopy chlorophyll (R 2 = 0.80), nitrogen (R 2 = 0.85) and V max,27 (R 2 = 0.82). Compared to leaf traits, we further found that canopy-level photosynthetic traits, particularly canopy V max,27 , have higher correlation with maize grain yield. This study highlights the potential for synergistic use of process-based and data-driven approaches of hyperspectral imaging to quantify crop traits that facilitate precision agricultural management to secure food and bioenergy production.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Investigation of potential aerosol transmission and infectivity of SARS-CoV-2 through central ventilation systems

Here we evaluate the concentrations and probabilities of infection for both building interior and exterior exposure sources using a well-mixed model in a connected multizone building. As a central hub of many community and economic activities, buildings provide social connectivity, but the COVID-19 pandemic has reduced social connectivity due to concerns of viral spread within buildings. Although single zone models of infectious spread are well studied, the impact of aerosolized spread of SARS-CoV-2 via air handling systems in multizone buildings remains unexplored. Here we evaluate the influence of filtration, air exchange rates, and the fraction of outdoor air on the probability of infection using the well-known well-mixed modeling approach for a multizone. We find filtration lowers the concentration and probability of infection in both source and connected rooms provided at least some air is recirculated, but that probability is not zero. Filtration has no influence without recirculation or unless the outdoor air contains virus. We find that increasing the air exchange rate removes virus from the source room faster but also increases the rate of exposure to connected rooms. Therefore, slower air exchange rates reduce infectivity in connected rooms at shorter durations, but higher air exchange rates reduce infectivity at longer durations. We further find that when outdoor air is virus free, increasing the fraction of outdoor air is helpful, but, when outdoor air is infective, pathogen exposure inside can persist for hours after a short-term release.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Size dependent infectivity of SARS-CoV-2 via respiratory droplets spread through central ventilation systems

In this work, we evaluate the transport of respiratory droplets that carry SARS-CoV-2 through central air handling systems in multiroom buildings. Respiratory droplet size modes arise from the bronchioles representing the lungs and lower respiratory tract, the larynx representing the upper respiratory tract including vocal cords, or the oral cavity. The size distribution of each mode remains largely conserved, although the magnitude of each droplet mode changes as infected individuals breathe, speak, sing, laugh, cough, and sneeze. Here we evaluate how each type of respiratory droplet transits through central ventilation systems and the implications thereof for infectivity of COVID-19. We find that while larger oral droplets can transmit through the air handling systems, their size and concentration are greatly reduced with but few oral droplets leaving the source room. In contrast, the smaller droplets that originate from the bronchioles and larynx are much more effective in transiting through the air handling system into connected rooms. This suggests that the ratio of lower respiratory or deep lung infections may increase relative to upper respiratory infections in rooms connected by central air handling systems. Also, increasing the temperature and humidity in the range considered after the droplets have achieved an “equilibrium” size reduces the probability of infection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Cloud Type and Life Stage Dependency of Liquid–Ice Mass Partitioning in Mixed-Phase Clouds

This paper analyzes the temperature, cloud type, and life stage dependencies of phase partitioning in mixed-phase clouds spanning tropics, midlatitudes, and the Arctic, using data from ground-based remote sensing measurements in Alaska and aircraft measurements from three field campaigns. The results show: (1) The liquid fraction in Arctic stratiform clouds decreased from 1 to 0.6 between 0 °C and −30 °C and was lower in spring because of the higher dust occurrence in Barrow, Alaska; (2) In wintertime orographic clouds, the liquid fraction was greater than 0.8; (3) Phase partitioning in convective clouds varied significantly with life stages. In the developing stage, it decreased from 1 to 0.3 between −5 °C and −15 °C, indicating rapid ice generation, while at the mature and dissipating stages, the liquid fractions were lower; (4) The stratiform regions of mesoscale convective systems were dominated by ice, with liquid fractions lower than 0.2; and (5) The variability of phase partitioning varied for different cloud types. In stratiform clouds, liquid dominated at warm temperatures. As the temperature decreased, an ice-dominated region was more frequently observed, while the occurrence of the mixed-phase region remained low. For convective clouds, the variability of phase partitioning was controlled by continuous glaciation with decreasing temperature and life cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Vulnerability of Wetlands Due to Projected Sea-Level Rise in the Coastal Plains of the South and Southeast United States

Coastal wetlands are vulnerable to accelerated sea-level rise, yet knowledge about their extent and distribution is often limited. We developed a land cover classification of wetlands in the coastal plains of the southern United States along the Gulf of Mexico (Texas, Louisiana, Mississippi, Alabama, and Florida) using 6161 very-high (2 m per pixel) resolution WorldView-2 and WorldView-3 satellite images from 2012 to 2015. Area extent estimations were obtained for the following vegetated classes: marsh, scrub, grass, forested upland, and forested wetland, located in elevation brackets between 0 and 10 m above sea level at 0.1 m intervals. Sea-level trends were estimated for each coastal state using tide gauge data collected over the period 1983–2021 and projected for 2100 using the trend estimated over that period. These trends were considered conservative, as sea level rise in the region accelerated between 2010 and 2021. Estimated losses in vegetation area due to sea level rise by 2100 are projected to be at least 12,587 km 2 , of which 3224 km 2 would be coastal wetlands. Louisiana is expected to suffer the largest losses in vegetation (80%) and coastal wetlands (75%) by 2100. Such high-resolution coastal mapping products help to guide adaptation plans in the region, including planning for wetland conservation and coastal development.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1998

Organics, Meteoritic Material, and other Elements in High Altitude Aerosols

Recent in situ measurements of the chemical composition of single aerosol particles at altitudes up to 19 km have revealed a number of surprising features about ambient particles. Upper tropospheric aerosols in the study region often contained more organic material than sulfate.

atmospheric aerosols atmospheric mercury airborne ↗