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arXiv 2021年

作者： Tricarico, Pasquale Scheeres, D.J. French, A.S. McMahon, J.W. Brack, D.N. Leonard, J.M. Antreasian, P. Chesley, S.R. Farnocchia, D. Takahashi, Y. Mazarico, E.M. Rowlands, D. Highsmith, D. Getzandanner, K. Moreau, M. Johnson, C.L. Philpott, L. Bierhaus, E.B. Walsh, K.J. Barnouin, O.S. Palmer, E.E. Weirich, J.R. Gaskell, R.W. Daly, M.G. Seabrook, J.A. Nolan, M.C. Lauretta, D.S. Planetary Science Institute TucsonAZ United States Independent Researcher Beaucouzé France Smead Department of Aerospace Engineering Sciences University of Colorado BoulderCO United States KinetX Aerospace Inc. Simi ValleyCA United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States NASA Goddard Spaceflight Center GreenbeltMD United States Department of Earth Ocean and Atmospheric Sciences University of British Columbia VancouverBC Canada Lockheed Martin Space LittletonCO United States Southwest Research Institute BoulderCO United States Johns Hopkins University Applied Physics Laboratory LaurelMD United States Centre for Research in Earth and Space Science York University TorontoON Canada Lunar and Planetary Laboratory University of Arizona TucsonAZ United States

Exploration of asteroid (101955) Bennu by the OSIRIS-REx mission has provided an in-depth look at this rubble-pile near-Earth asteroid. In particular, the measured gravity field and the detailed shape model of Bennu indicate significant heterogeneities in its interior structure, compatible with a lower density at its center. Here we combine gravity inversion methods with a statistical rubble-pile model to determine the density and size-frequency distribution (SFD) index of the rubble that constitutes Bennu. The best-fitting models indicate that the SFD of the interior is consistent with that observed on the surface, with a cumulative SFD index of approximately −2.9. The rubble bulk density is approximately 1.35 g/cm3, corresponding to a 12% macro-porosity. We find the largest rubble particle to be approximately 145 m, whereas the largest void is approximately 10 m. © 2021, CC BY.

关键词： Asteroids

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A Machine Learning Approach to Produce a Continuous Solar-Induced Chlorophyll Fluorescence Over the Arctic Ocean

Journal of Geophysical Research: Machine Learning and Comput...

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Journal of Geophysical Research: Machine Learning and Computation 2024年第4期1卷

作者： Nima Madani Nicholas C. Parazoo Manfredi Manizza Abhishek Chatterjee Dustin Carroll Dimitris Menemenlis Vincent Le Fouest Atsushi Matsuoka Kelly M. Luis Camila Serra-Pompei Charles E. Miller UCLA Joint Institute for Regional Earth System Science and Engineering Los Angeles CA USA Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA Geosciences Research Division Scripps Institution of Oceanography University of California San Diego La Jolla CA USA Moss Landing Marine Laboratories San José State University Moss Landing CA USA Littoral Environnement et Sociétés UMR 7266 Université de La Rochelle La Rochelle France Institute for the Study of Earth Oceans and Space University of New Hampshire Durham NH USA Center for Climate Change Science Massachusetts Institute of Technology Cambridge MA USA

Phytoplankton primary production is a crucial component of Arctic Ocean (AO) biogeochemistry, playing a pivotal role in carbon cycling by supporting higher trophic levels and removing atmospheric carbon dioxide. The advent of satellite observations measuring chlorophyll a concentration (Chl_a) has provided unprecedented insights into the distribution of AO phytoplankton, enhancing our ability to assess oceanic net primary production (NPP). However, the optical properties of AO waters differ significantly from those of the lower-latitude waters, complicating remotely sensed Chl_a retrievals. To mitigate these deficiencies, solar-induced chlorophyll fluorescence (SIF) has emerged as a valuable tool for gaining physiological insights into the direct photosynthetic processes of the AO. However, the temporal coverage of satellite SIF data makes long-term analysis of Chl_a photosynthetic activity challenging. In this study, we leverage satellite-based SIF measurements from 2018 to 2021 to assess their correlation with a set of predictive factors influencing phytoplankton photosynthesis. Generally, observed SIF over the AO showed a higher correlation with normalized fluorescence line height (NFLH) compared to Chl_a. We extended the temporal coverage of the original SIF data to encompass the period from 2004 to 2020. The extended record revealed noticeable differences between SIF, and satellite-based Chl_a, and NFLH observations. Our novel data set offers a pathway forward to monitor the physiological interactions of phytoplankton with climate changes, promising to significantly improve our understanding of Arctic waters productivity. The application of this data is expected to provide new insights into how phytoplankton respond to environmental shifts, contributing to a more nuanced understanding of their role in high-latitude marine ecosystems. We extrapolated Arctic Ocean red SIF over the 2004–2020 period using a set of predictive variables that impact marine photosynthe

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Unsupervised Learning for Subterranean Junction Recognition Based on 2D Point Cloud

arXiv

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arXiv 2020年

作者： Mansouri, Sina Sharif Pourkamali-Anaraki, Farhad Arranz, Miguel Castao Agha-mohammadi, Ali-akbar Burdick, Joel Nikolakopoulos, George Robotics and AI Team Department of Computer Electrical and Space Engineering Luleå University of Technology LuleåSE-97187 Sweden Department of Computer Science University of Massachusetts Lowell MA United States Department of Civil Environmental and Natural Resources Engineering Luleå University of Technology LuleåSE-97187 Sweden Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Division of Engineering and Applied Sciences California Institute of Technology PasadenaCA United States

This article proposes a novel unsupervised learning framework for detecting the number of tunnel junctions in subterranean environments based on acquired 2D point clouds. The implementation of the framework provides valuable information for high level mission planners to navigate an aerial platform in unknown areas or robot homing missions. The framework utilizes spectral clustering, which is capable of uncovering hidden structures from connected data points lying on non-linear manifolds. The spectral clustering algorithm computes a spectral embedding of the original 2D point cloud by utilizing the eigen decomposition of a matrix that is derived from the pairwise similarities of these points. We validate the developed framework using multiple data-sets, collected from multiple realistic simulations, as well as from real flights in underground environments, demonstrating the performance and merits of the proposed methodology. Copyright © 2020, The Authors. All rights reserved.

关键词： Antennas

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The ZTF Source Classification Project: III. A Catalog of Variable Sources

arXiv

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arXiv 2023年

作者： Healy, Brian F. Coughlin, Michael W. Mahabal, Ashish A. Laz, Theophile Jegou du Drake, Andrew Graham, Matthew J. Hillenbrand, Lynne A. van Roestel, Jan Szkody, Paula Zielske, LeighAnna Guiga, Mohammed Hassan, Muhammad Yusuf Hughes, Jill L. Nir, Guy Parikh, Saagar Park, Sungmin Purohit, Palak Rebbapragada, Umaa Reed, Draco Warshofsky, Daniel Wold, Avery Bloom, Joshua S. Masci, Frank J. Riddle, Reed Smith, Roger School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States Division of Physics Mathematics and Astronomy California Institute of Technology PasadenaCA91125 United States Center for Data Driven Discovery California Institute of Technology PasadenaCA91125 United States Department of Astronomy California Institute of Technology PasadenaCA91125 United States Anton Pannekoek Institute for Astronomy University of Amsterdam Amsterdam1090 GE Netherlands Department of Astronomy University of Washington SeattleWA98195 United States Department of Physics Pacific Lutheran University TacomaWA98447 United States Department of Computer Science and Engineering University of Minnesota MinneapolisMN55455 United States Department of Electrical Engineering Indian Institute of Technology Gandhinagar Gujarat 382055 India Department of Astronomy University of California BerkeleyCA94720 United States Lawrence Berkeley National Laboratory 1 Cyclotron Road MS 50B-4206 BerkeleyCA94720 United States Department of Electrical and Computer Engineering Carnegie Mellon University PittsburghPA15213 United States Computer Vision Imaging and Graphics Lab Indian Institute of Technology Gujarat Gandhinagar382055 India School of Statistics University of Minnesota MinneapolisMN55455 United States Division of Engineering and Applied Science California Institute of Technology PasadenaCA91125 United States Jet Propulsion Laboratory California Institute of Technology PasadenaCA91125 United States IPAC California Institute of Technology 1200 E. California Blvd. PasadenaCA91125 United States Caltech Optical Observatories California Institute of Technology PasadenaCA91125 United States

The classification of variable objects provides insight into a wide variety of astrophysics ranging from stellar interiors to galactic nuclei. The Zwicky Transient Facility (ZTF) provides time-series observations that record the variability of more than a billion sources. The scale of these data necessitates automated approaches to make a thorough analysis. Building on previous work, this paper reports the results of the ZTF Source Classification Project (SCoPe), which trains neural network and XGBoost machine-learning (ML) algorithms to perform dichotomous classification of variable ZTF sources using a manually constructed training set containing 170,632 light curves. We find that several classifiers achieve high precision and recall scores, suggesting the reliability of their predictions for 209,991,147 light curves across 77 ZTF fields. We also identify the most important features for XGB classification and compare the performance of the two ML algorithms, finding a pattern of higher precision among XGB classifiers. The resulting classification catalog is available to the public, and the software developed for SCoPe is open-source and adaptable to future time-domain surveys. Copyright © 2023, The Authors. All rights reserved.

关键词： Classification (of information)

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Trends in Silicates in the β Pictoris Disk

arXiv

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arXiv 2022年

作者： Lu, Cicero X. Chen, Christine H. Sargent, B.A. Watson, Dan M. Lisse, Carey M. Green, Joel D. Sitko, Michael L. Mittal, Tushar Lebouteiller, V. Sloan, G.C. Rebollido, Isabel Hines, Dean C. Girard, Julien H. Werner, Michael W. Stapelfeldt, Karl R. Wu, Winston Worthen, Kadin Department of Physics and Astronomy The Johns Hopkins University 3400 N. Charles Street BaltimoreMD21218 United States Space Telescope Science Institute 3700 San Martin Dr. BaltimoreMD21218 United States Department of Physics and Astronomy University of Rochester 500 Wilson Blvd RochesterNY14627 United States Johns Hopkins University Applied Physics Laboratory 11100 Johns Hopkins Rd LaurelMD20723 United States Department of Physics University of Cincinnati CincinnatiOH45221 United States Space Science Institute BoulderCO80301 United States Mit Department of Earth Atmospheric and Planetary Sciences Green Bldg The 77 Massachusetts Ave CambridgeMA02139 United States Aim Cea Cnrs Université Paris-Saclay Université Paris Diderot Sorbonne Paris Cité Gif-sur-YvetteF-91191 France Department of Physics and Astronomy University of North Carolina Chapel HillNC27599-3255 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91107 United States Center for Language and Speech Processing Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States Computer Science and Engineering University of Michigan Ann ArborMI48109 United States

While β Pic is known to host silicates in ring-like structures, whether the properties of these silicate dust vary with stellocentric distance remains an open question. We re-analyze the β Pictoris debris disk spectrum from the Spitzer Infrared Spectrograph (IRS) and a new IRTF/SpeX spectrum to investigate trends in Fe/Mg ratio, shape, and crystallinity in grains as a function of wavelength, a proxy for stellocentric distance. By analyzing a recalibrated and re-extracted spectrum, we identify a new 18 μm forsterite emission feature and recover a 23 μm forsterite emission feature with a substantially larger line-to-continuum ratio than previously reported. We find that these prominent spectral features are primarily produced by small submicron-sized grains, which are continuously generated and replenished from planetesimal collisions in the disk and can elucidate their parent bodies' composition. We discover three trends about these small grains: as stellocentric distance increases, (1) small silicate grains become more crystalline (less amorphous), (2) they become more irregular in shape, and (3) for crystalline silicate grains, the Fe/Mg ratio decreases. Applying these trends to β Pic's planetary architecture, we find that the dust population exterior to the orbits of β Pic b and c differs substantially in crystallinity and shape. We also find a tentative 3-5 μm dust excess due to spatially unresolved hot dust emission close to the star. From our findings, we infer that the surfaces of large planetesimals are more Fe-rich and collisionally-processed closer to the star but more Fe-poor and primordial farther from the star. © 2022, CC BY.

关键词： Silicates

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81-beam supra-THz local oscillator by a phase grating and a quantum cascade laser 30

81-beam supra-THz local oscillator by a phase grating and a ...

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2019 30th International Symposium on Space Terahertz Technology, ISSTT 2019

作者： Gan, Y. Mirzaei, B. Silva, J.R.G. Khalatpour, A. Hu, Qing Groppi, C. Siles, J.V. Gao, J.R. SRON Netherlands Institute for Space Research Groningen/Utrecht Netherlands Kapteyn Astronomical Institute University of Groningen Groningen9747 AD Netherlands Faculty of Applied Science Delft University of Technology Delft Netherlands Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States School of Earth and Space Exploration Arizona State University AZ United States NASA Jet Propulsion Laboratory PasadenaCA United States

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Momentum Transfer from the DART Mission Kinetic Impact on Asteroid Dimorphos

arXiv

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arXiv 2023年

作者： Cheng, Andrew F. Agrusa, Harrison F. Barbee, Brent W. Meyer, Alex J. Farnham, Tony L. Raducan, Sabina D. Richardson, Derek C. Dotto, Elisabetta Zinzi, Angelo Della Corte, Vincenzo Statler, Thomas S. Chesley, Steven Naidu, Shantanu P. Hirabayashi, Masatoshi Li, Jian-Yang Eggl, Siegfried Barnouin, Olivier S. Chabot, Nancy L. Chocron, Sidney Collins, Gareth S. Daly, R. Terik Davison, Thomas M. DeCoster, Mallory E. Ernst, Carolyn M. Ferrari, Fabio Graninger, Dawn M. Jacobson, Seth A. Jutzi, Martin Kumamoto, Kathryn M. Luther, Robert Lyzhoft, Joshua R. Michel, Patrick Murdoch, Naomi Nakano, Ryota Palmer, Eric Rivkin, Andrew S. Scheeres, Daniel J. Stickle, Angela M. Sunshine, Jessica M. Trigo-Rodriguez, Josep M. Vincent, Jean-Baptiste Walker, James D. Wünnemann, Kai Zhang, Yun Amoroso, Marilena Bertini, Ivano Brucato, John R. Capannolo, Andrea Cremonese, Gabriele Dall’Ora, Massimo Deshapriya, Prasanna J.D. Gai, Igor Hasselmann, Pedro H. Ieva, Simone Impresario, Gabriele Ivanovski, Stavro L. Lavagna, Michèle Lucchetti, Alice Epifani, Elena M. Modenini, Dario Pajola, Maurizio Palumbo, Pasquale Perna, Davide Pirrotta, Simone Poggiali, Giovanni Rossi, Alessandro Tortora, Paolo Zannoni, Marco Zanotti, Giovanni Johns Hopkins University Applied Physics Laboratory United States Department of Astronomy University of Maryland College ParkMD United States NASA/Goddard Space Flight Center United States Smead Department of Aerospace Engineering Sciences University of Colorado BoulderCO United States Space Research and Planetary Sciences Physikalisches Institut University of Bern Switzerland INAF-Osservatorio Astronomico di Roma Italy ASI-Space Science Data Center Roma Italy INAF-Istituto di Astrofisica e Planetologia Spaziali Roma Italy Planetary Defense Coordination Office and Planetary Science Division NASA Headquarters 300 Hidden Figures Way SW WashingtonDC20546 United States Jet Propulsion Laboratory California Institute of Technology United States Auburn University AuburnAL United States Planetary Science Institute United States Department of Aerospace Engineering University of Illinois at Urbana-Champaign IL United States Southwest Research Institute United States Imperial College London United Kingdom Department of Aerospace Science and Technology Politecnico di Milano Italy Michigan State University East LansingMI United States Lawrence Livermore National Laboratory LivermoreCA94550 United States Museum für Naturkunde Leibniz Institute for Evolution and Biodiversity Science Berlin Germany Université Côte d'Azur Observatoire de la Côte d'Azur CNRS Laboratoire Lagrange Nice France Université de Toulouse Toulouse France Spain DLR Institute of Planetary Research Rutherfordstrasse 2 Berlin12489 Germany Freie Universität Berlin Germany Department of Aerospace Engineering University of Maryland College ParkMD United States Italian Space Agency - ASI Italy Department of Science and Technology University of Naples "Parthenope" Naples Italy INAF Rome Italy INAF-Osservatorio Astrofisico di Arcetri Firenze Italy Politecnico di Milano Dipartimento di Scienze e Tecnologie Aerospaziali Milano Italy INAF-Osservatorio Astronomico di Padova Padova Italy I

The NASA Double Asteroid Redirection Test (DART) mission performed a kinetic impact on asteroid Dimorphos, the satellite of the binary asteroid (65803) Didymos, at 23:14 UTC on September 26, 2022 as a planetary defense test1. DART was the first hypervelocity impact experiment on an asteroid at size and velocity scales relevant to planetary defense, intended to validate kinetic impact as a means of asteroid deflection. Here we report the first determination of the momentum transferred to an asteroid by kinetic impact. Based on the change in the binary orbit period2, we find an instantaneous reduction in Dimorphos’s along-track orbital velocity component of 2.70 ± 0.10 mm s–1, indicating enhanced momentum transfer due to recoil from ejecta streams produced by the impact3,4. For a Dimorphos bulk density range of 1,500 to 3,300 kg m–3, we find that the expected value of the momentum enhancement factor, β, ranges between 2.2 and 4.9, depending on the mass of Dimorphos. If Dimorphos and Didymos are assumed to have equal densities of 2,400 kg m–3, β = 3. 61−0.25+0.19(1σ). These β values indicate that significantly more momentum was transferred to Dimorphos from the escaping impact ejecta than was incident with DART. Therefore, the DART kinetic impact was highly effective in deflecting the asteroid Dimorphos. © 2023, CC BY.

关键词： Asteroids

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Quantified uncertainty in thermodynamic modeling for materials design

arXiv

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arXiv 2019年

作者： Paulson, Noah H. Bocklund, Brandon J. Otis, Richard A. Liu, Zi-Kui Stan, Marius Applied Materials Division Argonne National Laboratory ArgonneIL60439 United States Department of Material Science and Engineering Pennsylvania State University University ParkPA16802 United States Engineering and Science Directorate Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States

Phase fractions, compositions and energies of the stable phases as a function of macroscopic composition, temperature, and pressure (X-T-P) are the principle correlations needed for the design of new materials and improvement of existing materials. They are the outcomes of thermodynamic modeling based on the CALculation of PHAse Diagrams (CALPHAD) approach. The accuracy of CALPHAD predictions vary widely in X-T-P space due to experimental error, model inadequacy and unequal data coverage. In response, researchers have developed frameworks to quantify the uncertainty of thermodynamic property model parameters and propagate it to phase diagram predictions. In previous studies, uncertainty was represented as intervals on phase boundaries (with respect to composition) or invariant reactions (with respect to temperature) and was unable to represent the uncertainty in eutectoid reactions or in the stability of phase regions. In this work, we propose a suite of tools that leverages samples from the multivariate model parameter distribution to represent uncertainty in forms that surpass previous limitations and are well suited to materials design. These representations include the distribution of phase diagrams and their features, as well as the dependence of phase stability and the distributions of phase fraction, composition activity and Gibbs energy on X-T-P location - irrespective of the total number of components. Most critically, the new methodology allows the material designer to interrogate a certain composition and temperature domain and get in return the probability of different phases to be stable, which can positively impact materials design. Copyright © 2019, The Authors. All rights reserved.

关键词： Metastable phases

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Temporal super-resolution of microwave remote sensing images 15

Temporal super-resolution of microwave remote sensing images

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15th Specialist Meeting on Microwave Radiometry and Remote Sensing of the Environment, MicroRad 2018

作者： Yanovsky, Igor Lambrigtsen, Bjorn Jet Propulsion Laboratory California Institute of Technology PasadenaCA91109 United States Joint Institute for Regional Earth System Science and Engineering University of California Los AngelesCA90095 United States

ISBN: (纸本)9781538650158

We develop an approach for increasing the temporal resolution of a temporally blurred sequence of observations. Super-resolution is performed in time using a variational approach. By temporal super-resolution, we mean recovering rapidly evolving events that were corrupted by the induced blur of the sensor. A blurred sequence of observations is assumed to have been generated by convolution of a physical scene with a temporal rectangular convolution kernel whose support is the sensor exposure time. We solve the deconvolution problem using the Split-Bregman method. Such methodology is based on current research in sparse optimization and compressed sensing, which lead to unprecedented efficiencies for solving image reconstruction problems. We test our method using a simulated temporally blurred and noisy temporal precipitation sequence and show that our method significantly reduces the errors in the corrupted sequence. © 2018 IEEE.

关键词： Remote sensing

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Elemental Characterization of Ambient Particulate Matter for a Globally Distributed Monitoring Network: Methodology and Implications

ACS ES&T air

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ACS ES&T air 2024年第4期1卷 283-293页

作者： Xuan Liu Jay R Turner Christopher R Oxford Jacob McNeill Brenna Walsh Emmie Le Roy Crystal L Weagle Emily Stone Haihui Zhu Wenyu Liu Zilin Wei Nicole P Hyslop Jason Giacomo Ann M Dillner Abdus Salam Al-Amin Hossen Zubayer Islam Ihab Abboud Clement Akoshile Omar Amador-Muñoz Nguyen Xuan Anh Araya Asfaw Rajasekhar Balasubramanian Rachel Ying-Wen Chang Craig Coburn Sagnik Dey David J Diner Jinlu Dong Tareq Farrah Paterne Gahungu Rebecca M Garland Michel Grutter de la Mora Sina Hasheminassab Juanette John Jhoon Kim Jong Sung Kim Kristy Langerman Pei-Chen Lee Puji Lestari Yang Liu Tesfaye Mamo Mathieu Martins Olga L Mayol-Bracero Mogesh Naidoo Sang Seo Park Yoav Schechner Robyn Schofield Sachchida N Tripathi Eli Windwer Ming-Tsang Wu Qiang Zhang Michael Brauer Yinon Rudich Randall V Martin Department of Energy Environmental & Chemical Engineering Washington University in St. Louis St. Louis Missouri 63130 United States. Air Quality Research Center University of California Davis Davis California 95616 United States. Department of Chemistry University of Dhaka Dhaka 1000 Bangladesh. Air Quality Research Division Environment and Climate Change Canada Toronto Ontario M3H 5T4 Canada. Department of Physics University of Ilorin Ilorin 240003 Nigeria. Instituto de Ciencias de la Atmósfera y Cambio Climático Universidad Nacional Autónoma de México Mexico City 04510 Mexico. Institute of Geophysics Vietnam Academy of Science and Technology Hanoi 11307 Vietnam. Institute of Geophysics and Space Science Addis Ababa University Addis Ababa 1176 Ethiopia. Department of Civil and Environmental Engineering National University of Singapore Singapore 117576 Singapore. Department of Physics and Atmospheric Science Dalhousie University Halifax Nova Scotia B3H 4R2 Canada. Department of Geography and Environment University of Lethbridge Lethbridge Alberta T1K 3M4 Canada. Centre for Atmospheric Sciences Indian Institute of Technology Delhi New Delhi 110016 India. Jet Propulsion Laboratory California Institute of Technology Pasadena California 91109 United States. School of Environment Tsinghua University Beijing 100084 People's Republic of China. Research Laboratories Khalifa University Abu Dhabi 127788 United Arab Emirates. Institute of Applied Statistics University of Burundi Bujumbura BP1550 Burundi. Council for Scientific and Industrial Research Pretoria 0001 South Africa. Unit for Environmental Sciences and Management North-West University Potchefstroom 2531 South Africa. Department of Geography Geo-Informatics and Meteorology University of Pretoria Pretoria 0002 South Africa. Department of Atmospheric Sciences Yonsei University Seoul 03722 Republic of Korea. Department of Community Health and Epidemiology Dalhousie University Halifax Nova Scotia B3

Global ground-level measurements of elements in ambient particulate matter (PM) can provide valuable information to understand the distribution of dust and trace elements, assess health impacts, and investigate emission sources. We use X-ray fluorescence spectroscopy to characterize the elemental composition of PM samples collected from 27 globally distributed sites in the Surface PARTiculate mAtter Network (SPARTAN) over 2019-2023. Consistent protocols are applied to collect all samples and analyze them at one central laboratory, which facilitates comparison across different sites. Multiple quality assurance measures are performed, including applying reference materials that resemble typical PM samples, acceptance testing, and routine quality control. Method detection limits and uncertainties are estimated. Concentrations of dust and trace element oxides (TEO) are determined from the elemental dataset. In addition to sites in arid regions, a moderately high mean dust concentration (6 μg/m) in PM is also found in Dhaka (Bangladesh) along with a high average TEO level (6 μg/m). High carcinogenic risk (>1 cancer case per 100000 adults) from airborne arsenic is observed in Dhaka (Bangladesh), Kanpur (India), and Hanoi (Vietnam). Industries of informal lead-acid battery and e-waste recycling as well as coal-fired brick kilns likely contribute to the elevated trace element concentrations found in Dhaka.

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