Articles | Volume 14, issue 9
https://doi.org/10.5194/amt-14-6023-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-14-6023-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Sep 2021
Research article |
| 15 Sep 2021
| 15 Sep 2021
A low-cost monitor for simultaneous measurement of fine particulate matter and aerosol optical depth – Part 3: Automation and design improvements
Eric A. Wendt
Department of Mechanical Engineering, Colorado State University, Fort Collins, CO 80523, USA
Casey Quinn
Department of Mechanical Engineering, Colorado State University, Fort Collins, CO 80523, USA
Christian L'Orange
Department of Mechanical Engineering, Colorado State University, Fort Collins, CO 80523, USA
Daniel D. Miller-Lionberg
Access Sensor Technologies, LLC, Fort Collins, CO 80523, USA
Bonne Ford
Department of Atmospheric Science, Colorado State University, Fort
Collins, CO 80523, USA
Jeffrey R. Pierce
Department of Atmospheric Science, Colorado State University, Fort
Collins, CO 80523, USA
John Mehaffy
Department of Mechanical Engineering, Colorado State University, Fort Collins, CO 80523, USA
Michael Cheeseman
Department of Atmospheric Science, Colorado State University, Fort
Collins, CO 80523, USA
Shantanu H. Jathar
Department of Mechanical Engineering, Colorado State University, Fort Collins, CO 80523, USA
David H. Hagan
QuantAQ, Inc., Somerville, MA 02143, USA
Zoey Rosen
Department of Journalism and Media Communication, Colorado State
University, Fort Collins, CO 80523, USA
Marilee Long
Department of Journalism and Media Communication, Colorado State
University, Fort Collins, CO 80523, USA
John Volckens
CORRESPONDING AUTHOR
Department of Mechanical Engineering, Colorado State University, Fort Collins, CO 80523, USA
Department of Environmental and Radiological Health Sciences, Colorado State University, Fort Collins, CO 80523, USA
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Atmos. Meas. Tech., 17, 4915–4939, https://doi.org/10.5194/amt-17-4915-2024, https://doi.org/10.5194/amt-17-4915-2024, 2024
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This study systematically characterizes the temperature enhancement in the lamp-enclosed oxidation flow reactor (OFR). The enhancement varied multiple dimensional factors, emphasizing the complexity of temperature inside of OFR. The effects of temperature on the flow field and gas- or particle-phase reaction inside OFR were also evaluated with experiments and model simulations. Finally, multiple mitigation strategies were demonstrated to minimize this temperature increase.
Linia Tashmim, William C. Porter, Qianjie Chen, Becky Alexander, Charles H. Fite, Christopher D. Holmes, Jeffrey R. Pierce, Betty Croft, and Sakiko Ishino
Atmos. Chem. Phys., 24, 3379–3403, https://doi.org/10.5194/acp-24-3379-2024, https://doi.org/10.5194/acp-24-3379-2024, 2024
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Dimethyl sulfide (DMS) is mostly emitted from ocean surfaces and represents the largest natural source of sulfur for the atmosphere. Once in the atmosphere, DMS forms stable oxidation products such as SO2 and H2SO4, which can subsequently contribute to airborne particle formation and growth. In this study, we update the DMS oxidation mechanism in the chemical transport model GEOS-Chem and describe resulting changes in particle growth as well as the overall global sulfur budget.
Benjamin N. Murphy, Darrell Sonntag, Karl M. Seltzer, Havala O. T. Pye, Christine Allen, Evan Murray, Claudia Toro, Drew R. Gentner, Cheng Huang, Shantanu Jathar, Li Li, Andrew A. May, and Allen L. Robinson
Atmos. Chem. Phys., 23, 13469–13483, https://doi.org/10.5194/acp-23-13469-2023, https://doi.org/10.5194/acp-23-13469-2023, 2023
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We update methods for calculating organic particle and vapor emissions from mobile sources in the USA. Conventionally, particulate matter (PM) and volatile organic carbon (VOC) are speciated without consideration of primary semivolatile emissions. Our methods integrate state-of-the-science speciation profiles and correct for common artifacts when sampling emissions in a laboratory. We quantify impacts of the emission updates on ambient pollution with the Community Multiscale Air Quality model.
Lixu Jin, Wade Permar, Vanessa Selimovic, Damien Ketcherside, Robert J. Yokelson, Rebecca S. Hornbrook, Eric C. Apel, I-Ting Ku, Jeffrey L. Collett Jr., Amy P. Sullivan, Daniel A. Jaffe, Jeffrey R. Pierce, Alan Fried, Matthew M. Coggon, Georgios I. Gkatzelis, Carsten Warneke, Emily V. Fischer, and Lu Hu
Atmos. Chem. Phys., 23, 5969–5991, https://doi.org/10.5194/acp-23-5969-2023, https://doi.org/10.5194/acp-23-5969-2023, 2023
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Air quality in the USA has been improving since 1970 due to anthropogenic emission reduction. Those gains have been partly offset by increased wildfire pollution in the western USA in the past 20 years. Still, we do not understand wildfire emissions well due to limited measurements. Here, we used a global transport model to evaluate and constrain current knowledge of wildfire emissions with recent observational constraints, showing the underestimation of wildfire emissions in the western USA.
Haihui Zhu, Randall V. Martin, Betty Croft, Shixian Zhai, Chi Li, Liam Bindle, Jeffrey R. Pierce, Rachel Y.-W. Chang, Bruce E. Anderson, Luke D. Ziemba, Johnathan W. Hair, Richard A. Ferrare, Chris A. Hostetler, Inderjeet Singh, Deepangsu Chatterjee, Jose L. Jimenez, Pedro Campuzano-Jost, Benjamin A. Nault, Jack E. Dibb, Joshua S. Schwarz, and Andrew Weinheimer
Atmos. Chem. Phys., 23, 5023–5042, https://doi.org/10.5194/acp-23-5023-2023, https://doi.org/10.5194/acp-23-5023-2023, 2023
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Particle size of atmospheric aerosol is important for estimating its climate and health effects, but simulating atmospheric aerosol size is computationally demanding. This study derives a simple parameterization of the size of organic and secondary inorganic ambient aerosol that can be applied to atmospheric models. Applying this parameterization allows a better representation of the global spatial pattern of aerosol size, as verified by ground and airborne measurements.
Nicole A. June, Anna L. Hodshire, Elizabeth B. Wiggins, Edward L. Winstead, Claire E. Robinson, K. Lee Thornhill, Kevin J. Sanchez, Richard H. Moore, Demetrios Pagonis, Hongyu Guo, Pedro Campuzano-Jost, Jose L. Jimenez, Matthew M. Coggon, Jonathan M. Dean-Day, T. Paul Bui, Jeff Peischl, Robert J. Yokelson, Matthew J. Alvarado, Sonia M. Kreidenweis, Shantanu H. Jathar, and Jeffrey R. Pierce
Atmos. Chem. Phys., 22, 12803–12825, https://doi.org/10.5194/acp-22-12803-2022, https://doi.org/10.5194/acp-22-12803-2022, 2022
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The evolution of organic aerosol composition and size is uncertain due to variability within and between smoke plumes. We examine the impact of plume concentration on smoke evolution from smoke plumes sampled by the NASA DC-8 during FIREX-AQ. We find that observed organic aerosol and size distribution changes are correlated to plume aerosol mass concentrations. Additionally, coagulation explains the majority of the observed growth.
Eric A. Wendt, Bonne Ford, and John Volckens
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-217, https://doi.org/10.5194/amt-2022-217, 2022
Publication in AMT not foreseen
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Outdoor air pollution poses global public health and environmental risks. One method to quantify outdoor air pollution is sun photometery, a technique that measures how much airborne particles affects sunlight intensity. Clouds obscuring the sun can bias sun photometer measurements. Here we propose an image-based deep learning framework for automatic quality control of sun photometer measurements. We show our algorithm is effective at classifying images of the sun as cloud-contaminated or not.
Junri Zhao, Weichun Ma, Kelsey R. Bilsback, Jeffrey R. Pierce, Shengqian Zhou, Ying Chen, Guipeng Yang, and Yan Zhang
Atmos. Chem. Phys., 22, 9583–9600, https://doi.org/10.5194/acp-22-9583-2022, https://doi.org/10.5194/acp-22-9583-2022, 2022
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Marine dimethylsulfide (DMS) emissions play important roles in atmospheric sulfur cycle and climate effects. In this study, DMS emissions were estimated by using the machine learning method and drove the global 3D chemical transport model to simulate their climate effects. To our knowledge, this is the first study in the Asian region that quantifies the combined impacts of DMS on sulfate, particle number concentration, and radiative forcings.
Ashley S. Bittner, Eben S. Cross, David H. Hagan, Carl Malings, Eric Lipsky, and Andrew P. Grieshop
Atmos. Meas. Tech., 15, 3353–3376, https://doi.org/10.5194/amt-15-3353-2022, https://doi.org/10.5194/amt-15-3353-2022, 2022
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Mathew Sebastian, Sobhan Kumar Kompalli, Vasudevan Anil Kumar, Sandhya Jose, S. Suresh Babu, Govindan Pandithurai, Sachchidanand Singh, Rakesh K. Hooda, Vijay K. Soni, Jeffrey R. Pierce, Ville Vakkari, Eija Asmi, Daniel M. Westervelt, Antti-Pekka Hyvärinen, and Vijay P. Kanawade
Atmos. Chem. Phys., 22, 4491–4508, https://doi.org/10.5194/acp-22-4491-2022, https://doi.org/10.5194/acp-22-4491-2022, 2022
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Characteristics of particle number size distributions and new particle formation in six locations in India were analyzed. New particle formation occurred frequently during the pre-monsoon (spring) season and it significantly modulates the shape of the particle number size distributions. The contribution of newly formed particles to cloud condensation nuclei concentrations was ~3 times higher in urban locations than in mountain background locations.
Michael Cheeseman, Bonne Ford, Zoey Rosen, Eric Wendt, Alex DesRosiers, Aaron J. Hill, Christian L'Orange, Casey Quinn, Marilee Long, Shantanu H. Jathar, John Volckens, and Jeffrey R. Pierce
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-751, https://doi.org/10.5194/acp-2021-751, 2021
Revised manuscript not accepted
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This article predicts concentrations of airborne particulate matter over wintertime Denver, CO, USA, using meteorological and geographic information, as well as low-cost aerosol optical depth (AOD) measurements captured by citizen scientists. Machine learning methods revealed that low boundary layer heights and stagnant air were the best predictors of poor air quality, while AOD provided little skill in predicting particulate matter for this location and time period.
Anna L. Hodshire, Emily Ramnarine, Ali Akherati, Matthew L. Alvarado, Delphine K. Farmer, Shantanu H. Jathar, Sonia M. Kreidenweis, Chantelle R. Lonsdale, Timothy B. Onasch, Stephen R. Springston, Jian Wang, Yang Wang, Lawrence I. Kleinman, Arthur J. Sedlacek III, and Jeffrey R. Pierce
Atmos. Chem. Phys., 21, 6839–6855, https://doi.org/10.5194/acp-21-6839-2021, https://doi.org/10.5194/acp-21-6839-2021, 2021
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Biomass burning emits particles and vapors that can impact both health and climate. Here, we investigate the role of dilution in the evolution of aerosol size and composition in observed US wildfire smoke plumes. Centers of plumes dilute more slowly than edges. We see differences in concentrations and composition between the centers and edges both in the first measurement and in subsequent measurements. Our findings support the hypothesis that plume dilution influences smoke aging.
Betty Croft, Randall V. Martin, Richard H. Moore, Luke D. Ziemba, Ewan C. Crosbie, Hongyu Liu, Lynn M. Russell, Georges Saliba, Armin Wisthaler, Markus Müller, Arne Schiller, Martí Galí, Rachel Y.-W. Chang, Erin E. McDuffie, Kelsey R. Bilsback, and Jeffrey R. Pierce
Atmos. Chem. Phys., 21, 1889–1916, https://doi.org/10.5194/acp-21-1889-2021, https://doi.org/10.5194/acp-21-1889-2021, 2021
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North Atlantic Aerosols and Marine Ecosystems Study measurements combined with GEOS-Chem-TOMAS modeling suggest that several not-well-understood key factors control northwest Atlantic aerosol number and size. These synergetic and climate-relevant factors include particle formation near and above the marine boundary layer top, particle growth by marine secondary organic aerosol on descent, particle formation/growth related to dimethyl sulfide, sea spray aerosol, and ship emissions.
Agnieszka Kupc, Christina J. Williamson, Anna L. Hodshire, Jan Kazil, Eric Ray, T. Paul Bui, Maximilian Dollner, Karl D. Froyd, Kathryn McKain, Andrew Rollins, Gregory P. Schill, Alexander Thames, Bernadett B. Weinzierl, Jeffrey R. Pierce, and Charles A. Brock
Atmos. Chem. Phys., 20, 15037–15060, https://doi.org/10.5194/acp-20-15037-2020, https://doi.org/10.5194/acp-20-15037-2020, 2020
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Tropical upper troposphere over the Atlantic and Pacific oceans is a major source region of new particles. These particles are associated with the outflow from deep convection. We investigate the processes that govern the formation of these particles and their initial growth and show that none of the formation schemes commonly used in global models are consistent with observations. Using newer schemes indicates that organic compounds are likely important as nucleating and initial growth agents.
David H. Hagan and Jesse H. Kroll
Atmos. Meas. Tech., 13, 6343–6355, https://doi.org/10.5194/amt-13-6343-2020, https://doi.org/10.5194/amt-13-6343-2020, 2020
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Assessing the error of low-cost particulate matter (PM) sensors has been difficult as each empirical study presents unique limitations. Here, we present a new, open-sourced, physics-based model (opcsim) and use it to understand how the properties of different particle sensors alter their accuracy. We offer a summary of likely sources of error for different sensor types, environmental conditions, and particle classes and offer recommendations for the choice of optimal calibrant.
Lawrence I. Kleinman, Arthur J. Sedlacek III, Kouji Adachi, Peter R. Buseck, Sonya Collier, Manvendra K. Dubey, Anna L. Hodshire, Ernie Lewis, Timothy B. Onasch, Jeffery R. Pierce, John Shilling, Stephen R. Springston, Jian Wang, Qi Zhang, Shan Zhou, and Robert J. Yokelson
Atmos. Chem. Phys., 20, 13319–13341, https://doi.org/10.5194/acp-20-13319-2020, https://doi.org/10.5194/acp-20-13319-2020, 2020
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Aerosols from wildfires affect the Earth's temperature by absorbing light or reflecting it back into space. This study investigates time-dependent chemical, microphysical, and optical properties of aerosols generated by wildfires in the Pacific Northwest, USA. Wildfire smoke plumes were traversed by an instrumented aircraft at locations near the fire and up to 3.5 h travel time downwind. Although there was no net aerosol production, aerosol particles grew and became more efficient scatters.
Chantelle R. Lonsdale, Matthew J. Alvarado, Anna L. Hodshire, Emily Ramnarine, and Jeffrey R. Pierce
Geosci. Model Dev., 13, 4579–4593, https://doi.org/10.5194/gmd-13-4579-2020, https://doi.org/10.5194/gmd-13-4579-2020, 2020
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The System for Atmospheric Modelling (SAM) has been coupled with the detailed gas/aerosol chemistry model, the Aerosol Simulation Program (ASP), to capture cross-plume concentration gradients as fire plumes evolve downwind. SAM-ASP v1.0 will lead to the development of parameterizations of near-source biomass burning chemistry that can be used to more accurately simulate biomass burning chemical and physical transformations of trace gases and aerosols within coarser chemical transport models.
Cited articles
Arku, R. E., Birch, A., Shupler, M., Yusuf, S., Hystad, P., and Brauer, M.:
Characterizing exposure to household air pollution within the Prospective
Urban Rural Epidemiology (PURE) study, Environ. Int., 114, 307–317,
https://doi.org/10.1016/j.envint.2018.02.033, 2018.
Badura, M., Sówka, I., Szymański, P., and Batog, P.: Assessing the
usefulness of dense sensor network for PM2.5 monitoring on an academic
campus area, Sci. Total Environ., 722, 137867,
https://doi.org/10.1016/j.scitotenv.2020.137867, 2020.
Bodhaine, B. A., Wood, N. B., Dutton, E. G., and Slusser, J. R.: On Rayleigh
Optical Depth Calculations, J. Atmos. Ocean. Tech., 16, 1854–1861, 1999.
Boersma, K. F. and de Vroom, J. P.: Validation of MODIS aerosol observations
over the Netherlands with GLOBE student measurements, J. Geophys. Res., 111,
D20311, https://doi.org/10.1029/2006JD007172, 2006.
Brauer, M., Freedman, G., Frostad, J., van Donkelaar, A., Martin, R. V.,
Dentener, F., Dingenen, R. van, Estep, K., Amini, H., Apte, J. S.,
Balakrishnan, K., Barregard, L., Broday, D., Feigin, V., Ghosh, S., Hopke,
P. K., Knibbs, L. D., Kokubo, Y., Liu, Y., Ma, S., Morawska, L., Sangrador,
J. L. T., Shaddick, G., Anderson, H. R., Vos, T., Forouzanfar, M. H.,
Burnett, R. T., and Cohen, A.: Ambient Air Pollution Exposure Estimation for
the Global Burden of Disease 2013, Environ. Sci. Technol., 50, 79–88,
https://doi.org/10.1021/acs.est.5b03709, 2016.
Brooks, D. R. and Mims, F. M.: Development of an inexpensive handheld
LED-based Sun photometer for the GLOBE program, J. Geophys. Res.-Atmos., 106, 4733–4740, https://doi.org/10.1029/2000JD900545, 2001.
Bulot, F. M. J., Johnston, S. J., Basford, P. J., Easton, N. H. C.,
Apetroaie-Cristea, M., Foster, G. L., Morris, A. K. R., Cox, S. J., and
Loxham, M.: Long-term field comparison of multiple low-cost particulate
matter sensors in an outdoor urban environment, Sci. Rep., 9, 7497,
https://doi.org/10.1038/s41598-019-43716-3, 2019.
Chadwick, E., Le, K., Pei, Z., Sayahi, T., Rapp, C., Butterfield, A. E., and
Kelly, K. E.: Technical note: Understanding the effect of COVID-19 on
particle pollution using a low-cost sensor network, J. Aerosol Sci., 155,
105766, https://doi.org/10.1016/j.jaerosci.2021.105766, 2021.
Diner, D. J., Beckert, J. C., Reilly, T. H., Bruegge, C. J., Conel, J. E.,
Kahn, R. A., Martonchik, J. V., Ackerman, T. P., Davies, R., Gerstl, S. A.
W., Gordon, H. R., Muller, J., Myneni, R. B., Sellers, P. J., Pinty, B., and
Verstraete, M. M.: Multi-angle Imaging SpectroRadiometer (MISR) instrument
description and experiment overview, IEEE Trans. Geosci. Remote Sens., 36,
1072–1087, https://doi.org/10.1109/36.700992, 1998.
Feng, S., Gao, D., Liao, F., Zhou, F., and Wang, X.: The health effects of
ambient PM2.5 and potential mechanisms, Ecotoxicol. Environ. Saf., 128,
67–74, https://doi.org/10.1016/j.ecoenv.2016.01.030, 2016.
Ford, B. and Heald, C. L.: Exploring the uncertainty associated with satellite-based estimates of premature mortality due to exposure to fine particulate matter, Atmos. Chem. Phys., 16, 3499–3523, https://doi.org/10.5194/acp-16-3499-2016, 2016.
Ford, B., Pierce, J. R., Wendt, E., Long, M., Jathar, S., Mehaffy, J., Tryner, J., Quinn, C., van Zyl, L., L'Orange, C., Miller-Lionberg, D., and Volckens, J.: A low-cost monitor for measurement of fine particulate matter and aerosol optical depth – Part 2: Citizen-science pilot campaign in northern Colorado, Atmos. Meas. Tech., 12, 6385–6399, https://doi.org/10.5194/amt-12-6385-2019, 2019.
Forouzanfar, M. H., Afshin, A., Alexander, L. T., et al.: Global, regional, and national comparative risk
assessment of 79 behavioural, environmental and occupational, and metabolic
risks or clusters of risks, 1990–2015: a systematic analysis for the Global
Burden of Disease Study 2015, Lancet, 388, 1659–1724,
https://doi.org/10.1016/S0140-6736(16)31679-8, 2016.
Garay, M. J., Kalashnikova, O. V., and Bull, M. A.: Development and assessment of a higher-spatial-resolution (4.4 km) MISR aerosol optical depth product using AERONET-DRAGON data, Atmos. Chem. Phys., 17, 5095–5106, https://doi.org/10.5194/acp-17-5095-2017, 2017.
Giles, D. M., Sinyuk, A., Sorokin, M. G., Schafer, J. S., Smirnov, A., Slutsker, I., Eck, T. F., Holben, B. N., Lewis, J. R., Campbell, J. R., Welton, E. J., Korkin, S. V., and Lyapustin, A. I.: 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, Atmos. Meas. Tech., 12, 169–209, https://doi.org/10.5194/amt-12-169-2019, 2019.
Griggs, M.: Absorption Coefficients of Ozone in the Ultraviolet and Visible
Regions, J. Chem. Phys., 49, 857–859, https://doi.org/10.1063/1.1670152,
1968.
Hammer, M. S., van Donkelaar, A., Li, C., Lyapustin, A., Sayer, A. M., Hsu,
N. C., Levy, R. C., Garay, M. J., Kalashnikova, O. V., Kahn, R. A., Brauer,
M., Apte, J. S., Henze, D. K., Zhang, L., Zhang, Q., Ford, B., Pierce, J.
R., and Martin, R. V.: Global Estimates and Long-Term Trends of Fine
Particulate Matter Concentrations (1998–2018), Environ. Sci. Technol., 54,
7879–7890, https://doi.org/10.1021/acs.est.0c01764, 2020.
Holben, B. N., Eck, T. F., Slutsker, I., Tanré, D., Buis, J. P., Setzer,
A., Vermote, E., Reagan, J. A., Kaufman, Y. J., Nakajima, T., Lavenu, F.,
Jankowiak, I., and Smirnov, A.: AERONET – A Federated Instrument Network and
Data Archive for Aerosol Characterization, Remote Sens. Environ., 66, 1–16,
https://doi.org/10.1016/S0034-4257(98)00031-5, 1998.
Janssen, N. A. H., Fischer, P., Marra, M., Ameling, C., and Cassee, F. R.:
Short-term effects of PM2.5, PM10 and PM2.5–10 on daily mortality in the Netherlands, Sci. Total Environ., 463–464, 20–26,
https://doi.org/10.1016/j.scitotenv.2013.05.062, 2013.
Jin, X., Fiore, A. M., Curci, G., Lyapustin, A., Civerolo, K., Ku, M., van Donkelaar, A., and Martin, R. V.: Assessing uncertainties of a geophysical approach to estimate surface fine particulate matter distributions from satellite-observed aerosol optical depth, Atmos. Chem. Phys., 19, 295–313, https://doi.org/10.5194/acp-19-295-2019, 2019.
Kelleher, S., Quinn, C., Miller-Lionberg, D., and Volckens, J.: A low-cost particulate matter (PM2.5) monitor for wildland fire smoke, Atmos. Meas. Tech., 11, 1087–1097, https://doi.org/10.5194/amt-11-1087-2018, 2018.
Kelly, K. E., Whitaker, J., Petty, A., Widmer, C., Dybwad, A., Sleeth, D.,
Martin, R., and Butterfield, A.: Ambient and laboratory evaluation of a
low-cost particulate matter sensor, Environ. Pollut., 221, 491–500,
https://doi.org/10.1016/j.envpol.2016.12.039, 2017.
Kim, I., Lee, K., Lee, S., and Kim, S. D.: Characteristics and health
effects of PM2.5 emissions from various sources in Gwangju, South Korea,
Sci. Total Environ., 696, 133890,
https://doi.org/10.1016/j.scitotenv.2019.133890, 2019.
Lee, C.: Impacts of multi-scale urban form on PM2.5 concentrations using
continuous surface estimates with high-resolution in U.S. metropolitan
areas, Landsc. Urban Plan., 204, 103935,
https://doi.org/10.1016/j.landurbplan.2020.103935, 2020.
Levy Zamora, M., Xiong, F., Gentner, D., Kerkez, B., Kohrman-Glaser, J., and
Koehler, K.: Field and Laboratory Evaluations of the Low-Cost Plantower
Particulate Matter Sensor, Environ. Sci. Technol., 53, 838–849,
https://doi.org/10.1021/acs.est.8b05174, 2019.
Li, J., Liu, H., Lv, Z., Zhao, R., Deng, F., Wang, C., Qin, A., and Yang,
X.: Estimation of PM2.5 mortality burden in China with new exposure
estimation and local concentration-response function, Environ. Pollut., 243,
1710–1718, https://doi.org/10.1016/j.envpol.2018.09.089, 2018.
Li, J., Zhang, H., Chao, C.-Y., Chien, C.-H., Wu, C.-Y., Luo, C. H., Chen,
L.-J., and Biswas, P.: Integrating low-cost air quality sensor networks with
fixed and satellite monitoring systems to study ground-level PM2.5, Atmos. Environ., 223, 117293, https://doi.org/10.1016/j.atmosenv.2020.117293, 2020.
Lin, C., Labzovskii, L. D., Leung Mak, H. W., Fung, J. C. H., Lau, A. K. H.,
Kenea, S. T., Bilal, M., Vande Hey, J. D., Lu, X., and Ma, J.: Observation
of PM2.5 using a combination of satellite remote sensing and low-cost sensor
network in Siberian urban areas with limited reference monitoring, Atmos.
Environ., 227, 117410, https://doi.org/10.1016/j.atmosenv.2020.117410, 2020.
Liu, Y., Sarnat, J. A., Kilaru, V., Jacob, D. J., and Koutrakis, P.:
Estimating Ground-Level PM 2.5 in the Eastern United States Using
Satellite Remote Sensing, Environ. Sci. Technol., 39, 3269–3278,
https://doi.org/10.1021/es049352m, 2005.
Lu, X., Lin, C., Li, W., Chen, Y., Huang, Y., Fung, J. C. H., and Lau, A. K.
H.: Analysis of the adverse health effects of PM2.5 from 2001 to 2017 in
China and the role of urbanization in aggravating the health burden, Sci.
Total Environ., 652, 683–695,
https://doi.org/10.1016/j.scitotenv.2018.10.140, 2019.
Lu, Y., Giuliano, G., and Habre, R.: Estimating hourly PM2.5 concentrations at the neighborhood scale using a low-cost air sensor network: A Los Angeles case study, Environ. Res., 195, 110653,
https://doi.org/10.1016/j.envres.2020.110653, 2021.
Myhre, G., Shindell, D., Bréon, F.-M., Collins, W., Fuglestvedt, J., Huang, J., Koch, D., Lamarque, J.-F., Lee, D., Mendoza, B., Nakajima, T., Robock, A., Stephens, G., Takemura, T., and Zhang, H. : Anthropogenic and Natural Radiative Forcing, in: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, Cambridge, UK and New York, NY, USA, 2013.
Pillarisetti, A., Carter, E., Rajkumar, S., Young, B. N., Benka-Coker, M.
L., Peel, J. L., Johnson, M., and Clark, M. L.: Measuring personal exposure
to fine particulate matter (PM2.5) among rural Honduran women: A field
evaluation of the Ultrasonic Personal Aerosol Sampler (UPAS), Environ. Int.,
123, 50–53, https://doi.org/10.1016/j.envint.2018.11.014, 2019.
Pope, C. A. and Dockery, D. W.: Health Effects of Fine Particulate Air
Pollution: Lines that Connect, J. Air Waste Manag. Assoc., 56, 709–742,
https://doi.org/10.1080/10473289.2006.10464485, 2006.
Reda, I. and Andreas, A.: Solar Position Algorithm for Solar Radiation
Applications (Revised), Technical Report, National Renewable Energy Lab. (NREL), Golden, CO, USA, https://doi.org/10.2172/15003974, 2008.
Salomonson, V. V., Barnes, W. L., Maymon, P. W., Montgomery, H. E., and
Ostrow, H.: MODIS: advanced facility instrument for studies of the Earth as
a system, IEEE Trans. Geosci. Remote Sens., 27, 145–153,
https://doi.org/10.1109/36.20292, 1989.
Sayahi, T., Butterfield, A., and Kelly, K. E.: Long-term field evaluation of
the Plantower PMS low-cost particulate matter sensors, Environ. Pollut.,
245, 932–940, https://doi.org/10.1016/j.envpol.2018.11.065, 2019.
Sayer, A. M., Hsu, N. C., Bettenhausen, C., Jeong, M.-J., Holben, B. N., and Zhang, J.: Global and regional evaluation of over-land spectral aerosol optical depth retrievals from SeaWiFS, Atmos. Meas. Tech., 5, 1761–1778, https://doi.org/10.5194/amt-5-1761-2012, 2012.
Smirnov, A., Holben, B. N., Eck, T. F., Dubovik, O., and Slutsker, I.:
Cloud-Screening and Quality Control Algorithms for the AERONET Database,
Remote Sens. Environ., 73, 337–349,
https://doi.org/10.1016/S0034-4257(00)00109-7, 2000.
Snider, G., Weagle, C. L., Martin, R. V., van Donkelaar, A., Conrad, K., Cunningham, D., Gordon, C., Zwicker, M., Akoshile, C., Artaxo, P., Anh, N. X., Brook, J., Dong, J., Garland, R. M., Greenwald, R., Griffith, D., He, K., Holben, B. N., Kahn, R., Koren, I., Lagrosas, N., Lestari, P., Ma, Z., Vanderlei Martins, J., Quel, E. J., Rudich, Y., Salam, A., Tripathi, S. N., Yu, C., Zhang, Q., Zhang, Y., Brauer, M., Cohen, A., Gibson, M. D., and Liu, Y.: SPARTAN: a global network to evaluate and enhance satellite-based estimates of ground-level particulate matter for global health applications, Atmos. Meas. Tech., 8, 505–521, https://doi.org/10.5194/amt-8-505-2015, 2015.
Tryner, J., L'Orange, C., Mehaffy, J., Miller-Lionberg, D., Hofstetter, J.
C., Wilson, A., and Volckens, J.: Laboratory evaluation of low-cost
PurpleAir PM monitors and in-field correction using co-located portable
filter samplers, Atmos. Environ., 220, 117067,
https://doi.org/10.1016/j.atmosenv.2019.117067, 2020.
van Donkelaar, A., Martin, R. V., and Park, R. J.: Estimating ground-level
PM 2.5 using aerosol optical depth determined from satellite remote
sensing, J. Geophys. Res., 111, D21201,
https://doi.org/10.1029/2005JD006996, 2006.
van Donkelaar, A., Martin, R. V., Brauer, M., Kahn, R., Levy, R., Verduzco,
C., and Villeneuve, P. J.: Global Estimates of Ambient Fine Particulate
Matter Concentrations from Satellite-Based Aerosol Optical Depth:
Development and Application, Environ. Health Perspect., 118, 847–855,
https://doi.org/10.1289/ehp.0901623, 2010.
van Donkelaar, A., Martin, R. V., Pasch, A. N., Szykman, J. J., Zhang, L.,
Wang, Y. X., and Chen, D.: Improving the Accuracy of Daily Satellite-Derived
Ground-Level Fine Aerosol Concentration Estimates for North America,
Environ. Sci. Technol., 46, 11971–11978, https://doi.org/10.1021/es3025319,
2012.
van Donkelaar, A., Martin, R. V., Brauer, M., Hsu, N. C., Kahn, R. A., Levy,
R. C., Lyapustin, A., Sayer, A. M., and Winker, D. M.: Global Estimates of
Fine Particulate Matter using a Combined Geophysical-Statistical Method with
Information from Satellites, Models, and Monitors, Environ. Sci. Technol.,
50, 3762–3772, https://doi.org/10.1021/acs.est.5b05833, 2016.
van Donkelaar, A., Martin, R. V., Li, C., and Burnett, R. T.: Regional
Estimates of Chemical Composition of Fine Particulate Matter Using a
Combined Geoscience-Statistical Method with Information from Satellites,
Models, and Monitors, Environ. Sci. Technol., 53, 2595–2611,
https://doi.org/10.1021/acs.est.8b06392, 2019.
Van Heuklon, T. K.: Estimating atmospheric ozone for solar radiation models,
Sol. Energy, 22, 63–68, https://doi.org/10.1016/0038-092X(79)90060-4, 1979.
Vohra, K., Vodonos, A., Schwartz, J., Marais, E. A., Sulprizio, M. P., and
Mickley, L. J.: Global mortality from outdoor fine particle pollution
generated by fossil fuel combustion: Results from GEOS-Chem, Environ. Res.,
195, 110754, https://doi.org/10.1016/j.envres.2021.110754, 2021.
Volckens, J., Quinn, C., Leith, D., Mehaffy, J., Henry, C. S., and
Miller-Lionberg, D.: Development and evaluation of an ultrasonic personal
aerosol sampler, Indoor Air, 27, 409–416,
https://doi.org/10.1111/ina.12318, 2017.
Wendt, E. A.: Data set associated with “A low-cost monitor for simultaneous measurement of fine particulate matter and aerosol optical depth – Part 3: Automation and design improvements”, Colorado State University [data set], https://doi.org/10.25675/10217/225291, 2021.
Wendt, E. A., Quinn, C. W., Miller-Lionberg, D. D., Tryner, J., L'Orange, C., Ford, B., Yalin, A. P., Pierce, J. R., Jathar, S., and Volckens, J.: A low-cost monitor for simultaneous measurement of fine particulate matter and aerosol optical depth – Part 1: Specifications and testing, Atmos. Meas. Tech., 12, 5431–5441, https://doi.org/10.5194/amt-12-5431-2019, 2019.
Young, A. T.: Air mass and refraction, Appl. Opt., 33, 1108,
https://doi.org/10.1364/AO.33.001108, 1994.
Zheng, T., Bergin, M. H., Johnson, K. K., Tripathi, S. N., Shirodkar, S., Landis, M. S., Sutaria, R., and Carlson, D. E.: Field evaluation of low-cost particulate matter sensors in high- and low-concentration environments, Atmos. Meas. Tech., 11, 4823–4846, https://doi.org/10.5194/amt-11-4823-2018, 2018.
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Short summary
Fine particulate matter air pollution is one of the leading contributors to adverse health outcomes on the planet. Here, we describe the design and validation of a low-cost, compact, and autonomous instrument capable of measuring particulate matter levels directly, via mass sampling, and optically, via mass and sunlight extinction measurements. We demonstrate the instrument's accuracy relative to reference measurements and its potential for community-level sampling.
Fine particulate matter air pollution is one of the leading contributors to adverse health...
