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Chatzidiakou L, Krause A, Popoola OAM, Di Antonio A, Kellaway M, Han Y, Squires FA, Wang T, Zhang H, Wang Q, Fan Y, Chen S, Hu M, Quint JK, Barratt B, Kelly FJ, Zhu T, Jones RL. Characterising low-cost sensors in highly portable platforms to quantify personal exposure in diverse environments. Atmos Meas Tech 2019; 12:4643-4657. [PMID: 31534556 PMCID: PMC6751078 DOI: 10.5194/amt-12-1-2019] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The inaccurate quantification of personal exposure to air pollution introduces error and bias in health estimations, severely limiting causal inference in epidemiological research worldwide. Rapid advancements in affordable, miniaturised air pollution sensor technologies offer the potential to address this limitation by capturing the high variability of personal exposure during daily life in large-scale studies with unprecedented spatial and temporal resolution. However, concerns remain regarding the suitability of novel sensing technologies for scientific and policy purposes. In this paper we characterise the performance of a portable personal air quality monitor (PAM) that integrates multiple miniaturised sensors for nitrogen oxides (NO x ), carbon monoxide (CO), ozone (O3) and particulate matter (PM) measurements along with temperature, relative humidity, acceleration, noise and GPS sensors. Overall, the air pollution sensors showed high reproducibility (meanR ¯ 2 = 0.93, min-max: 0.80-1.00) and excellent agreement with standard instrumentation (meanR ¯ 2 = 0.82, min-max: 0.54-0.99) in outdoor, indoor and commuting microenvironments across seasons and different geographical settings. An important outcome of this study is that the error of the PAM is significantly smaller than the error introduced when estimating personal exposure based on sparsely distributed outdoor fixed monitoring stations. Hence, novel sensing technologies such as the ones demonstrated here can revolutionise health studies by providing highly resolved reliable exposure metrics at a large scale to investigate the underlying mechanisms of the effects of air pollution on health.
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Affiliation(s)
- Lia Chatzidiakou
- Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | - Anika Krause
- Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | | | - Andrea Di Antonio
- Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
| | | | - Yiqun Han
- MRC-PHE Centre for Environment & Health, Imperial College London and King’s College London, London, W2 1PG, UK
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
- Department of Analytical, Environmental and Forensic Sciences, King’s College London, London, SE1 9NH, UK
| | | | - Teng Wang
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
- The Beijing Innovation Center for Engineering Science and Advanced Technology, Peking University, Beijing, 100871, China
| | - Hanbin Zhang
- MRC-PHE Centre for Environment & Health, Imperial College London and King’s College London, London, W2 1PG, UK
- Department of Analytical, Environmental and Forensic Sciences, King’s College London, London, SE1 9NH, UK
- NIHR Health Protection Research Unit in Health Impact of Environmental Hazards, King’s College London, London, SE1 9NH, UK
| | - Qi Wang
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
- The Beijing Innovation Center for Engineering Science and Advanced Technology, Peking University, Beijing, 100871, China
| | - Yunfei Fan
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
- The Beijing Innovation Center for Engineering Science and Advanced Technology, Peking University, Beijing, 100871, China
| | - Shiyi Chen
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
| | - Min Hu
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
- The Beijing Innovation Center for Engineering Science and Advanced Technology, Peking University, Beijing, 100871, China
| | - Jennifer K. Quint
- National Heart and Lung Institute, Imperial College London, SW3 6LR, UK
| | - Benjamin Barratt
- MRC-PHE Centre for Environment & Health, Imperial College London and King’s College London, London, W2 1PG, UK
- Department of Analytical, Environmental and Forensic Sciences, King’s College London, London, SE1 9NH, UK
- NIHR Health Protection Research Unit in Health Impact of Environmental Hazards, King’s College London, London, SE1 9NH, UK
| | - Frank J. Kelly
- MRC-PHE Centre for Environment & Health, Imperial College London and King’s College London, London, W2 1PG, UK
- Department of Analytical, Environmental and Forensic Sciences, King’s College London, London, SE1 9NH, UK
- NIHR Health Protection Research Unit in Health Impact of Environmental Hazards, King’s College London, London, SE1 9NH, UK
| | - Tong Zhu
- College of Environmental Sciences and Engineering, Peking University, Beijing, 100871, China
- The Beijing Innovation Center for Engineering Science and Advanced Technology, Peking University, Beijing, 100871, China
| | - Roderic L. Jones
- Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK
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2
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Tackett JL, Winker DM, Getzewich BJ, Vaughan MA, Young SA, Kar J. CALIPSO lidar level 3 aerosol profile product: version 3 algorithm design. Atmos Meas Tech 2018; 11:4129-4152. [PMID: 33510819 PMCID: PMC7840064 DOI: 10.5194/amt-11-4129-2018] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
The CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) level 3 aerosol profile product reports globally gridded, quality-screened, monthly mean aerosol extinction profiles retrieved by CALIOP (the Cloud-Aerosol Lidar with Orthogonal Polarization). This paper describes the quality screening and averaging methods used to generate the version 3 product. The fundamental input data are CALIOP level 2 aerosol extinction profiles and layer classification information (aerosol, cloud, and clear-air). Prior to aggregation, the extinction profiles are quality-screened by a series of filters to reduce the impact of layer detection errors, layer classification errors, extinction retrieval errors, and biases due to an intermittent signal anomaly at the surface. The relative influence of these filters are compared in terms of sample rejection frequency, mean extinction, and mean aerosol optical depth (AOD). The "extinction QC flag" filter is the most influential in preventing high-biases in level 3 mean extinction, while the "misclassified cirrus fringe" filter is most aggressive at rejecting cirrus misclassified as aerosol. The impact of quality screening on monthly mean aerosol extinction is investigated globally and regionally. After applying quality filters, the level 3 algorithm calculates monthly mean AOD by vertically integrating the monthly mean quality-screened aerosol extinction profile. Calculating monthly mean AOD by integrating the monthly mean extinction profile prevents a low bias that would result from alternately integrating the set of extinction profiles first and then averaging the resultant AOD values together. Ultimately, the quality filters reduce level 3 mean AOD by -24 and -31% for global ocean and global land, respectively, indicating the importance of quality screening.
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Affiliation(s)
- Jason L. Tackett
- Science Systems and Applications, Inc., Hampton, VA, USA
- NASA Langley Research Center, Hampton, VA, USA
| | | | - Brian J. Getzewich
- Science Systems and Applications, Inc., Hampton, VA, USA
- NASA Langley Research Center, Hampton, VA, USA
| | | | | | - Jayanta Kar
- Science Systems and Applications, Inc., Hampton, VA, USA
- NASA Langley Research Center, Hampton, VA, USA
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3
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Ning G, Wang S, Ma M, Ni C, Shang Z, Wang J, Li J. Characteristics of air pollution in different zones of Sichuan Basin, China. Sci Total Environ 2018; 612:975-984. [PMID: 28892849 DOI: 10.1016/j.scitotenv.2017.08.205] [Citation(s) in RCA: 73] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Revised: 08/17/2017] [Accepted: 08/20/2017] [Indexed: 06/07/2023]
Abstract
Sichuan Basin, located in southwest China, has been ranked as the fourth of heavily air polluted regions in China partly due to its deep mountain-basin topography. However, spatial-temporal distribution of air pollution over the basin is still unclear due to the lack of monitoring data and poor knowledge. Since January 2015, six criteria air pollutants began to be monitored in 20 cities across the basin. The measured data enable us to analyze the basin-wide spatial-temporal distribution characteristics of these air pollutants. Results revealed heavy air pollution in the bottom zone, medium in the slope zone, and light pollution in the edge zone of the Basin in terms of the altitudes of air quality monitoring stations across the Basin. The average concentrations of PM2.5 and PM10 were 55.87μg/m3 and 86.49μg/m3 in the bottom, 33.76μg/m3 and 63.33μg/m3 in the slope, and 19.71μg/m3 and 35.06μg/m3 in the edge, respectively. In the bottom and slope of the basin, high PM2.5 concentration events occurred most frequently in winter. While in summer, ozone became primary pollutant. Among the six air pollutants, concentrations of PM2.5 and PM10 decrease dramatically with increasing altitude which was fitted by a nonlinear relationship between particulate matter (PM) concentrations and altitude. This relationship was validated by extinction coefficient profiles from CALIPSO observations and EV-lidar data, and hence used to reflect vertical distribution of air PM concentrations. It has been found that the thickness of higher PM concentrations is less than 500m in the basin. In the bottom of the basin, PM concentrations exhibited stronger horizontal homogeneities as compared with those in the North China Plain and Yangtze River Delta. However, gaseous pollutants seemed not to show clear relationships between their concentrations and altitudes in the basin. Their horizontal homogeneities were less significant compared to PM.
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Affiliation(s)
- Guicai Ning
- The Gansu Key Laboratory of Arid Climate Change and Reducing Disaster, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China.
| | - Shigong Wang
- Mountain Environment and Meteorology Key Laboratory of Education Bureau of Sichuan Province, College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China; The Gansu Key Laboratory of Arid Climate Change and Reducing Disaster, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China.
| | - Minjin Ma
- The Gansu Key Laboratory of Arid Climate Change and Reducing Disaster, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China.
| | - Changjian Ni
- Mountain Environment and Meteorology Key Laboratory of Education Bureau of Sichuan Province, College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China
| | - Ziwei Shang
- The Gansu Key Laboratory of Arid Climate Change and Reducing Disaster, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
| | - Jiaxin Wang
- Mountain Environment and Meteorology Key Laboratory of Education Bureau of Sichuan Province, College of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225, China
| | - Jingxin Li
- Institute of Climate System, Chinese Academy of Metrological Sciences, Beijing 100081, China.
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4
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Kim MH, Omar AH, Tackett JL, Vaughan MA, Winker DM, Trepte CR, Hu Y, Liu Z, Poole LR, Pitts MC, Kar J, Magill BE. The CALIPSO Version 4 Automated Aerosol Classification and Lidar Ratio Selection Algorithm. Atmos Meas Tech 2018; 11:6107-6135. [PMID: 31921372 PMCID: PMC6951257 DOI: 10.5194/amt-11-6107-2018] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) version 4.10 (V4) level 2 aerosol data products, released in November 2016, include substantial improvements to the aerosol subtyping and lidar ratio selection algorithms. These improvements are described along with resulting changes in aerosol optical depth (AOD). The most fundamental change in V4 level 2 aerosol products is a new algorithm to identify aerosol subtypes in the stratosphere. Four aerosol subtypes are introduced for the stratospheric aerosols: polar stratospheric aerosol (PSA), volcanic ash, sulfate/other, and smoke. The tropospheric aerosol subtyping algorithm was also improved by adding the following enhancements: (1) all aerosol subtypes are now allowed over polar regions, whereas the version 3 (V3) algorithm allowed only clean continental and polluted continental aerosols; (2) a new "dusty marine" aerosol subtype is introduced, representing mixtures of dust and marine aerosols near the ocean surface; and (3) the "polluted continental" and "smoke" subtypes have been renamed "polluted continental/smoke" and "elevated smoke", respectively. V4 also revises the lidar ratios for clean marine, dust, clean continental, and elevated smoke subtypes. As a consequence of the V4 updates, the mean 532 nm AOD retrieved by CALIOP has increased by 0.044 (0.036) or 52 % (40 %) for nighttime (daytime). Lidar ratio revisions are the most influential factor for AOD changes from V3 to V4, especially for cloud-free skies. Preliminary validation studies show that the AOD discrepancies between CALIOP and AERONET/MODIS (ocean) are reduced in V4 compared to V3.
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Affiliation(s)
- Man-Hae Kim
- NASA Postdoctoral Program (USRA), Hampton, VA, USA
| | - Ali H. Omar
- NASA Langley Research Center, Hampton, VA, USA
| | | | | | | | | | | | - Zhaoyan Liu
- Science Systems and Applications, Inc., Hampton, VA, USA
| | | | | | - Jayanta Kar
- Science Systems and Applications, Inc., Hampton, VA, USA
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5
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Kim MH, Omar AH, Tackett JL, Vaughan MA, Winker DM, Trepte CR, Hu Y, Liu Z, Poole LR, Pitts MC, Kar J, Magill BE. The CALIPSO Version 4 Automated Aerosol Classification and Lidar Ratio Selection Algorithm. Atmos Meas Tech 2018; 11:6107-6135. [PMID: 31921372 DOI: 10.1175/2009jtecha1231.1] [Citation(s) in RCA: 101] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) version 4.10 (V4) level 2 aerosol data products, released in November 2016, include substantial improvements to the aerosol subtyping and lidar ratio selection algorithms. These improvements are described along with resulting changes in aerosol optical depth (AOD). The most fundamental change in V4 level 2 aerosol products is a new algorithm to identify aerosol subtypes in the stratosphere. Four aerosol subtypes are introduced for the stratospheric aerosols: polar stratospheric aerosol (PSA), volcanic ash, sulfate/other, and smoke. The tropospheric aerosol subtyping algorithm was also improved by adding the following enhancements: (1) all aerosol subtypes are now allowed over polar regions, whereas the version 3 (V3) algorithm allowed only clean continental and polluted continental aerosols; (2) a new "dusty marine" aerosol subtype is introduced, representing mixtures of dust and marine aerosols near the ocean surface; and (3) the "polluted continental" and "smoke" subtypes have been renamed "polluted continental/smoke" and "elevated smoke", respectively. V4 also revises the lidar ratios for clean marine, dust, clean continental, and elevated smoke subtypes. As a consequence of the V4 updates, the mean 532 nm AOD retrieved by CALIOP has increased by 0.044 (0.036) or 52 % (40 %) for nighttime (daytime). Lidar ratio revisions are the most influential factor for AOD changes from V3 to V4, especially for cloud-free skies. Preliminary validation studies show that the AOD discrepancies between CALIOP and AERONET/MODIS (ocean) are reduced in V4 compared to V3.
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Affiliation(s)
- Man-Hae Kim
- NASA Postdoctoral Program (USRA), Hampton, VA, USA
| | - Ali H Omar
- NASA Langley Research Center, Hampton, VA, USA
| | | | | | | | | | | | - Zhaoyan Liu
- Science Systems and Applications, Inc., Hampton, VA, USA
| | - Lamont R Poole
- Science Systems and Applications, Inc., Hampton, VA, USA
| | | | - Jayanta Kar
- Science Systems and Applications, Inc., Hampton, VA, USA
| | - Brian E Magill
- Science Systems and Applications, Inc., Hampton, VA, USA
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6
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Xu X, Wang J, Wang Y, Zeng J, Torres O, Yang Y, Marshak A, Reid J, Miller S. Passive remote sensing of altitude and optical depth of dust plumes using the oxygen A and B bands: first results from EPIC/DSCOVR at Lagrange-1 point. Geophys Res Lett 2017; 44:7544-7554. [PMID: 32661445 PMCID: PMC7357207 DOI: 10.1002/2017gl073939] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
We presented an algorithm for inferring aerosol layer height (ALH) and optical depth (AOD) over ocean surface from radiances in oxygen A and B bands measured by the Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory orbiting at Lagrangian-1 point. The algorithm was applied to EPIC imagery of a two-day dust outbreak over the North Atlantic Ocean. Retrieved ALHs and AODs were evaluated against counterparts observed by Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), Moderate Resolution Imaging Spectroradiometer (MODIS), and Aerosol Robotic Network. The comparisons showed 71.5% of EPIC-retrieved ALHs were within ±0.5 km of those determined from CALIOP and 74.4% of EPIC AOD retrievals fell within a ±(0.1+10%) envelope of MODIS retrievals. This study demonstrates the potential of EPIC measurements for retrieving global aerosol height multiple times daily, which are essential for evaluating aerosol profile simulated in climate models and for better estimating aerosol radiative effects.
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Affiliation(s)
- Xiaoguang Xu
- Department of Chemical and Biochemical Engineering, Center for Global and Regional Environmental Studies, and Informatics Initiative, The University of Iowa, Iowa City, IA 52242
| | - Jun Wang
- Department of Chemical and Biochemical Engineering, Center for Global and Regional Environmental Studies, and Informatics Initiative, The University of Iowa, Iowa City, IA 52242
| | - Yi Wang
- Department of Chemical and Biochemical Engineering, Center for Global and Regional Environmental Studies, and Informatics Initiative, The University of Iowa, Iowa City, IA 52242
| | - Jing Zeng
- Department of Chemical and Biochemical Engineering, Center for Global and Regional Environmental Studies, and Informatics Initiative, The University of Iowa, Iowa City, IA 52242
| | - Omar Torres
- Atmospheric Chemistry and Dynamics Laboratory, NASA Goddard Space Flight Center, 8800 Greenbelt Rd, Greenbelt, MD 20771 USA
| | - Yuekui Yang
- Climate and Radiation Laboratory, NASA Goddard Space Flight Center, 8800 Greenbelt Rd, Greenbelt, MD 20771 USA
| | - Alexander Marshak
- Climate and Radiation Laboratory, NASA Goddard Space Flight Center, 8800 Greenbelt Rd, Greenbelt, MD 20771 USA
| | - Jeffrey Reid
- Marine Meteorology Division, Naval Research Laboratory, 7 Grace Hopper Ave, Stop 2, Monterey, CA 93943, USA
| | - Steve Miller
- Cooperative Institute for Research in the Atmosphere, Colorado State University, 1375 Campus Delivery, Fort Collins, CO 80523, USA
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7
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Kim MH, Omar AH, Vaughan MA, Winker DM, Trepte CR, Hu Y, Liu Z, Kim SW. Quantifying the low bias of CALIPSO's column aerosol optical depth due to undetected aerosol layers. J Geophys Res Atmos 2017; 122:1098-1113. [PMID: 31534879 PMCID: PMC6749610 DOI: 10.1002/2016jd025797] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
The CALIOP data processing scheme only retrieves extinction profiles in those portions of the return signal where cloud or aerosol layers have been identified by the CALIOP layer detection scheme. In this study we use two years of CALIOP and MODIS data to quantify the aerosol optical depth of undetected weakly backscattering layers. Aerosol extinction and column-averaged lidar ratio is retrieved from CALIOP Level 1B (Version 4) profile using MODIS AOD as a constraint over oceans from March 2013 to February 2015. To quantify the undetected layer AOD (ULA), an unconstrained retrieval is applied globally using a lidar ratio of 28.75 sr estimated from constrained retrievals during the daytime over the ocean. We find a global mean ULA of 0.031 ± 0.052. There is no significant difference in ULA between land and ocean. However, the fraction of undetected aerosol layers rises considerably during daytime, when the large amount of solar background noise lowers the signal to noise ratio (SNR). For this reason, there is a difference in ULA between day (0.036 ± 0.066) and night (0.025 ± 0.021). ULA is larger in the northern hemisphere and relatively larger at high latitudes. Large ULA for the Polar Regions is strongly related to the cases where the CALIOP Level 2 Product reports zero AOD. This study provides an estimate of the complement of AOD that is not detected by lidar, and bounds the CALIOP AOD uncertainty to provide corrections for science studies that employ the CALIOP Level 2 AOD.
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Affiliation(s)
- Man-Hae Kim
- NASA Langley Research Center, Hampton, VA, USA
- Universities Space Research Association, Columbia, Maryland, USA
| | - Ali H. Omar
- NASA Langley Research Center, Hampton, VA, USA
| | | | | | | | | | - Zhaoyan Liu
- NASA Langley Research Center, Hampton, VA, USA
- Science Systems and Applications, Inc., Hampton, VA, USA
| | - Sang-Woo Kim
- School of Earth and Environmental Sciences, Seoul National University, Seoul, Korea
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8
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Anderson RF, Cheng H, Edwards RL, Fleisher MQ, Hayes CT, Huang KF, Kadko D, Lam PJ, Landing WM, Lao Y, Lu Y, Measures CI, Moran SB, Morton PL, Ohnemus DC, Robinson LF, Shelley RU. How well can we quantify dust deposition to the ocean? Philos Trans A Math Phys Eng Sci 2016; 374:rsta.2015.0285. [PMID: 29035251 DOI: 10.1098/rsta.2015.02852016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Accepted: 08/10/2016] [Indexed: 05/25/2023]
Abstract
Deposition of continental mineral aerosols (dust) in the Eastern Tropical North Atlantic Ocean, between the coast of Africa and the Mid-Atlantic Ridge, was estimated using several strategies based on the measurement of aerosols, trace metals dissolved in seawater, particulate material filtered from the water column, particles collected by sediment traps and sediments. Most of the data used in this synthesis involve samples collected during US GEOTRACES expeditions in 2010 and 2011, although some results from the literature are also used. Dust deposition generated by a global model serves as a reference against which the results from each observational strategy are compared. Observation-based dust fluxes disagree with one another by as much as two orders of magnitude, although most of the methods produce results that are consistent with the reference model to within a factor of 5. The large range of estimates indicates that further work is needed to reduce uncertainties associated with each method before it can be applied routinely to map dust deposition to the ocean. Calculated dust deposition using observational strategies thought to have the smallest uncertainties is lower than the reference model by a factor of 2-5, suggesting that the model may overestimate dust deposition in our study area.This article is part of the themed issue 'Biological and climatic impacts of ocean trace element chemistry'.
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Affiliation(s)
- R F Anderson
- Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA
- Department of Earth and Environmental Sciences, Columbia University, New York, NY 10027, USA
| | - H Cheng
- Department of Earth Sciences, University of Minnesota, Minneapolis, MN 55455, USA
- Institute of Global Environmental Change, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - R L Edwards
- Department of Earth Sciences, University of Minnesota, Minneapolis, MN 55455, USA
| | - M Q Fleisher
- Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA
| | - C T Hayes
- Department of Marine Science, University of Southern Mississippi, Stennis Space Center, MS 39529, USA
| | - K-F Huang
- Institute of Earth Sciences, Academia Sinica, Taipei, Taiwan, Republic of China
| | - D Kadko
- Applied Research Center, Florida International University, Miami, FL 33174, USA
| | - P J Lam
- Department of Ocean Sciences, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - W M Landing
- Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, FL 32306, USA
| | - Y Lao
- Department of Laboratory Services, Massachusetts Water Resources Authority, 190 Tafts Avenue, Winthrop, MA 02152, USA
| | - Y Lu
- Earth Observatory of Singapore, 50 Nanyang Avenue, Singapore 639798, Republic of Singapore
| | - C I Measures
- Department of Oceanography, University of Hawaii, Honolulu, HI 96822, USA
| | - S B Moran
- College of Fisheries and Ocean Sciences, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
| | - P L Morton
- Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, FL 32306, USA
| | - D C Ohnemus
- Bigelow Laboratory for Ocean Sciences, East Boothbay, ME 04544, USA
| | - L F Robinson
- School of Earth Sciences, University of Bristol, Queens Road, Bristol BS8 1RJ, UK
| | - R U Shelley
- LEMAR/UMR CNRS 6539/IUEM, Technopôle Brest-Iroise, Place Nicolas Copernic, Plouzané 29280, France
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9
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Anderson RF, Cheng H, Edwards RL, Fleisher MQ, Hayes CT, Huang KF, Kadko D, Lam PJ, Landing WM, Lao Y, Lu Y, Measures CI, Moran SB, Morton PL, Ohnemus DC, Robinson LF, Shelley RU. How well can we quantify dust deposition to the ocean? Philos Trans A Math Phys Eng Sci 2016; 374:20150285. [PMID: 29035251 PMCID: PMC5069522 DOI: 10.1098/rsta.2015.0285] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 08/10/2016] [Indexed: 05/09/2023]
Abstract
Deposition of continental mineral aerosols (dust) in the Eastern Tropical North Atlantic Ocean, between the coast of Africa and the Mid-Atlantic Ridge, was estimated using several strategies based on the measurement of aerosols, trace metals dissolved in seawater, particulate material filtered from the water column, particles collected by sediment traps and sediments. Most of the data used in this synthesis involve samples collected during US GEOTRACES expeditions in 2010 and 2011, although some results from the literature are also used. Dust deposition generated by a global model serves as a reference against which the results from each observational strategy are compared. Observation-based dust fluxes disagree with one another by as much as two orders of magnitude, although most of the methods produce results that are consistent with the reference model to within a factor of 5. The large range of estimates indicates that further work is needed to reduce uncertainties associated with each method before it can be applied routinely to map dust deposition to the ocean. Calculated dust deposition using observational strategies thought to have the smallest uncertainties is lower than the reference model by a factor of 2-5, suggesting that the model may overestimate dust deposition in our study area.This article is part of the themed issue 'Biological and climatic impacts of ocean trace element chemistry'.
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Affiliation(s)
- R F Anderson
- Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA
- Department of Earth and Environmental Sciences, Columbia University, New York, NY 10027, USA
| | - H Cheng
- Department of Earth Sciences, University of Minnesota, Minneapolis, MN 55455, USA
- Institute of Global Environmental Change, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - R L Edwards
- Department of Earth Sciences, University of Minnesota, Minneapolis, MN 55455, USA
| | - M Q Fleisher
- Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY 10964, USA
| | - C T Hayes
- Department of Marine Science, University of Southern Mississippi, Stennis Space Center, MS 39529, USA
| | - K-F Huang
- Institute of Earth Sciences, Academia Sinica, Taipei, Taiwan, Republic of China
| | - D Kadko
- Applied Research Center, Florida International University, Miami, FL 33174, USA
| | - P J Lam
- Department of Ocean Sciences, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - W M Landing
- Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, FL 32306, USA
| | - Y Lao
- Department of Laboratory Services, Massachusetts Water Resources Authority, 190 Tafts Avenue, Winthrop, MA 02152, USA
| | - Y Lu
- Earth Observatory of Singapore, 50 Nanyang Avenue, Singapore 639798, Republic of Singapore
| | - C I Measures
- Department of Oceanography, University of Hawaii, Honolulu, HI 96822, USA
| | - S B Moran
- College of Fisheries and Ocean Sciences, University of Alaska Fairbanks, Fairbanks, AK 99775, USA
| | - P L Morton
- Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, FL 32306, USA
| | - D C Ohnemus
- Bigelow Laboratory for Ocean Sciences, East Boothbay, ME 04544, USA
| | - L F Robinson
- School of Earth Sciences, University of Bristol, Queens Road, Bristol BS8 1RJ, UK
| | - R U Shelley
- LEMAR/UMR CNRS 6539/IUEM, Technopôle Brest-Iroise, Place Nicolas Copernic, Plouzané 29280, France
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Koffi B, Schulz M, Bréon FM, Dentener F, Steensen BM, Griesfeller J, Winker D, Balkanski Y, Bauer SE, Bellouin N, Berntsen T, Bian H, Chin M, Diehl T, Easter R, Ghan S, Hauglustaine DA, Iversen T, Kirkevåg A, Liu X, Lohmann U, Myhre G, Rasch P, Seland Ø, Skeie RB, Steenrod SD, Stier P, Tackett J, Takemura T, Tsigaridis K, Vuolo MR, Yoon J, Zhang K. Evaluation of the aerosol vertical distribution in global aerosol models through comparison against CALIOP measurements: AeroCom phase II results. J Geophys Res Atmos 2016; 121:7254-7283. [PMID: 32818126 PMCID: PMC7430518 DOI: 10.1002/2015jd024639] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
The ability of 11 models in simulating the aerosol vertical distribution from regional to global scales, as part of the second phase of the AeroCom model intercomparison initiative (AeroCom II), is assessed and compared to results of the first phase. The evaluation is performed using a global monthly gridded data set of aerosol extinction profiles built for this purpose from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) Layer Product 3.01. Results over 12 subcontinental regions show that five models improved, whereas three degraded in reproducing the interregional variability in Z α0-6 km, the mean extinction height diagnostic, as computed from the CALIOP aerosol profiles over the 0-6 km altitude range for each studied region and season. While the models' performance remains highly variable, the simulation of the timing of the Z α0-6 km peak season has also improved for all but two models from AeroCom Phase I to Phase II. The biases in Z α0-6 km are smaller in all regions except Central Atlantic, East Asia, and North and South Africa. Most of the models now underestimate Z α0-6 km over land, notably in the dust and biomass burning regions in Asia and Africa. At global scale, the AeroCom II models better reproduce the Z α0-6 km latitudinal variability over ocean than over land. Hypotheses for the performance and evolution of the individual models and for the intermodel diversity are discussed. We also provide an analysis of the CALIOP limitations and uncertainties contributing to the differences between the simulations and observations.
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Affiliation(s)
- Brigitte Koffi
- European Commission, Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy
| | | | - François-Marie Bréon
- Laboratoire des Sciences du Climat et de l'Environnement, Gif-sur-Yvette, France
| | - Frank Dentener
- European Commission, Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy
| | | | | | - David Winker
- NASA Langley Research Center, MS/475, Hampton, Virginia, USA
| | - Yves Balkanski
- Laboratoire des Sciences du Climat et de l'Environnement, Gif-sur-Yvette, France
| | - Susanne E Bauer
- Center for Climate Systems Research, Columbia University, New York, New York, USA
- NASA Goddard Institute for Space Studies, New York, New York, USA
| | | | - Terje Berntsen
- Department of Geosciences, University of Oslo, Oslo, Norway
- Center for International Climate and Environmental Research-Oslo (CICERO), Oslo, Norway
| | - Huisheng Bian
- NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
- Joint Center for Earth Systems Technology, University of Maryland Baltimore County, Baltimore Country, Maryland, USA
| | - Mian Chin
- NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
| | - Thomas Diehl
- European Commission, Joint Research Centre, Institute for Environment and Sustainability, Ispra, Italy
| | - Richard Easter
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Steven Ghan
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | | | - Trond Iversen
- Norwegian Meteorological Institute, Oslo, Norway
- Department of Geosciences, University of Oslo, Oslo, Norway
| | - Alf Kirkevåg
- Norwegian Meteorological Institute, Oslo, Norway
| | - Xiaohong Liu
- Pacific Northwest National Laboratory, Richland, Washington, USA
- Now at University of Wyoming, Laramie, Wyoming, USA
| | | | - Gunnar Myhre
- Center for International Climate and Environmental Research-Oslo (CICERO), Oslo, Norway
| | - Phil Rasch
- NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
| | | | - Ragnhild B Skeie
- Center for International Climate and Environmental Research-Oslo (CICERO), Oslo, Norway
| | | | - Philip Stier
- Department of Physics, University of Oxford, Oxford, UK
| | - Jason Tackett
- Science Systems and Applications, Inc., Hampton, Virginia, USA
| | - Toshihiko Takemura
- Research Institute for Applied Mechanics, Kyushu University, Fukuoka, Japan
| | - Kostas Tsigaridis
- Center for Climate Systems Research, Columbia University, New York, New York, USA
- NASA Goddard Institute for Space Studies, New York, New York, USA
| | - Maria Raffaella Vuolo
- Laboratoire des Sciences du Climat et de l'Environnement, Gif-sur-Yvette, France
- Now at National Institute for Agronomic Research, Thiverval-Grignon, France
| | - Jinho Yoon
- Pacific Northwest National Laboratory, Richland, Washington, USA
- Now at Gwangju Institute of Science and Technology, Gwangju, Korea
| | - Kai Zhang
- Pacific Northwest National Laboratory, Richland, Washington, USA
- Max Planck Institute for Meteorology, Hamburg, Germany
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Hodnebrog Ø, Myhre G, Forster PM, Sillmann J, Samset BH. Local biomass burning is a dominant cause of the observed precipitation reduction in southern Africa. Nat Commun 2016; 7:11236. [PMID: 27068129 DOI: 10.1038/ncomms11236] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2015] [Accepted: 03/04/2016] [Indexed: 11/24/2022] Open
Abstract
Observations indicate a precipitation decline over large parts of southern Africa since the 1950s. Concurrently, atmospheric concentrations of greenhouse gases and aerosols have increased due to anthropogenic activities. Here we show that local black carbon and organic carbon aerosol emissions from biomass burning activities are a main cause of the observed decline in southern African dry season precipitation over the last century. Near the main biomass burning regions, global and regional modelling indicates precipitation decreases of 20–30%, with large spatial variability. Increasing global CO2 concentrations further contribute to precipitation reductions, somewhat less in magnitude but covering a larger area. Whereas precipitation changes from increased CO2 are driven by large-scale circulation changes, the increase in biomass burning aerosols causes local drying of the atmosphere. This study illustrates that reducing local biomass burning aerosol emissions may be a useful way to mitigate reduced rainfall in the region. Black carbon aerosols in the atmosphere absorb solar radiation and affect the hydrological cycle. Here, the authors show that local aerosol emissions from biomass burning activities are a main cause of observed decline in southern African dry season precipitation over the last century.
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12
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Winker D, Kato S, Tackett J. Global Aerosol Direct Radiative Effect from CALIOP and C3M. EPJ Web of Conferences 2016. [DOI: 10.1051/epjconf/201611921001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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