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Apparent temperature and air pollution vs. elderly population mortality in Metro Vancouver.

https://arctichealth.org/en/permalink/ahliterature130666
Source
PLoS One. 2011;6(9):e25101
Publication Type
Article
Date
2011
Author
Goran Krstic
Author Affiliation
Fraser Health, Environmental Health Services, New Westminster, British Columbia, Canada. Goran.Krstic@fraserhealth.ca
Source
PLoS One. 2011;6(9):e25101
Date
2011
Language
English
Publication Type
Article
Keywords
Air Pollution
Canada - epidemiology
Humans
Linear Models
Mortality
Particulate Matter - analysis
Temperature
Abstract
Meteorological conditions and air pollution in urban environments have been associated with general population and elderly mortality, showing seasonal variation.
This study is designed to evaluate the relationship between apparent temperature (AT) and air pollution (PM2.5) vs. mortality in elderly population of Metro Vancouver.
Statistical analyses are performed on moving sum daily mortality rates vs. moving average AT and PM2.5 in 1-, 2-, 3-, 5-, and 7-day models for all seasons, warm temperatures above 15?C, and cold temperatures below 10?C.
Approximately 37% of the variation in all-season mortality from circulatory and respiratory causes can be explained by the variation in 7-day moving average apparent temperature (r??=?0.37, p
Notes
Cites: Environ Monit Assess. 2006 Aug;119(1-3):425-3916741816
Cites: J Air Waste Manag Assoc. 2006 Jun;56(6):709-4216805397
Cites: Epidemiology. 2006 Nov;17(6):624-3117028505
Cites: Nature. 2006 Nov 16;444(7117):248-917108921
Cites: Nature. 2007 Jan 4;445(7123):2117203038
Cites: J Photochem Photobiol B. 2007 Mar 1;86(3):234-917142054
Cites: Environ Int. 2007 Apr;33(3):376-8417229464
Cites: Environ Health Perspect. 2007 Apr;115(4):524-717450219
Cites: Environ Health Perspect. 2008 Jan;116(1):64-918197301
Cites: Circulation. 2008 Jan 29;117(4):503-1118180395
Cites: Environ Health Perspect. 2008 Apr;116(4):A160-718414615
Cites: Am J Epidemiol. 2008 Dec 15;168(12):1397-40818952849
Cites: Rheumatology (Oxford). 2009 Mar;48(3):210-218930963
Cites: Clin Exp Immunol. 2009 Oct;158(1):20-519737226
Cites: Environ Health. 2010;9:1220219128
Cites: Int J Environ Res Public Health. 2010 Jun;7(6):2607-1920644691
Cites: Environ Health Perspect. 2000 Apr;108(4):347-5310753094
Cites: Am J Epidemiol. 2000 Sep 1;152(5):397-40610981451
Cites: Eur Respir J. 2000 Sep;16(3):391-611028649
Cites: Eur Respir J. 2001 May;17(5):1055-611488312
Cites: Epidemiology. 2001 Nov;12(6):662-711679794
Cites: Am J Epidemiol. 2002 Jan 1;155(1):80-711772788
Cites: Environ Health Perspect. 2003 Jan;111(1):45-5212515678
Cites: Epidemiol Rev. 2002;24(2):190-20212762092
Cites: Am J Public Health. 1994 Nov;84(11):1738-427977910
Cites: Environ Health Perspect. 1998 Dec;106(12):849-559831546
Cites: Environ Health Perspect. 2006 Jan;114(1):29-3316393654
Cites: Environ Health Perspect. 2006 Sep;114(9):1331-616966084
PubMed ID
21980381 View in PubMed
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The use of wide-band transmittance imaging to size and classify suspended particulate matter in seawater.

https://arctichealth.org/en/permalink/ahliterature282586
Source
Mar Pollut Bull. 2017 Feb 15;115(1-2):105-114
Publication Type
Article
Date
Feb-15-2017
Author
E J Davies
P J Brandvik
F. Leirvik
R. Nepstad
Source
Mar Pollut Bull. 2017 Feb 15;115(1-2):105-114
Date
Feb-15-2017
Language
English
Publication Type
Article
Keywords
Environmental monitoring
Mining
Norway
Particle Size
Particulate Matter - analysis
Seawater - analysis
Water Pollutants - analysis
Abstract
An in situ particle imaging system for measurement of high concentrations of suspended particles ranging from 30µm to several mm in diameter, is presented. The system obtains quasi-silhouettes of particles suspended within an open-path sample volume of up to 5cm in length. Benchmarking against spherical standards and the LISST-100 show good agreement, providing confidence in measurements from the system when extending beyond the size, concentration and particle classification capabilities of the LISST-100. Particle-specific transmittance is used to classify particle type, independent of size and shape. This is applied to mixtures of oil droplets, gas bubbles and oil-coated gas bubbles, to provide independent measures of oil and gas size distributions, concentrations, and oil-gas ratios during simulated subsea releases. The system is also applied to in situ measurements of high concentrations of large mineral flocs surrounding a submarine mine tailings placement within a Norwegian Fjord.
PubMed ID
27931867 View in PubMed
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Premature deaths attributed to ambient air pollutants: let us interpret the Robins-Greenland theorem correctly.

https://arctichealth.org/en/permalink/ahliterature291222
Source
Int J Public Health. 2017 04; 62(3):337-338
Publication Type
Letter
Comment
Date
04-2017
Author
Peter Morfeld
Thomas C Erren
Author Affiliation
Institute for Occupational Epidemiology and Risk Assessment (IERA) of Evonik Industries, Rellinghauser Str. 1-11, 45128, Essen, Germany. peter.morfeld@evonik.com.
Source
Int J Public Health. 2017 04; 62(3):337-338
Date
04-2017
Language
English
Publication Type
Letter
Comment
Keywords
Air Pollutants - analysis
Air Pollution
Animals
Environmental Exposure
Greenland
Humans
Mortality, Premature
Particulate Matter - analysis
Songbirds
Notes
CommentOn: Int J Public Health. 2016 Apr;61(3):387-8 PMID 27113708
CommentOn: Int J Public Health. 2016 Apr;61(3):383-4 PMID 27117686
Cites: Chem Biol Drug Des. 2012 Jan;79(1):84-91 PMID 21967481
Cites: Bioorg Med Chem. 2005 Jan 17;13(2):313-22 PMID 15598554
Cites: Bioorg Med Chem Lett. 2005 Jun 15;15(12):3119-25 PMID 15893927
Cites: Arch Biochem Biophys. 1989 Oct;274(1):155-60 PMID 2673042
Cites: J Chem Inf Model. 2009 May;49(5):1298-311 PMID 19413274
Cites: Chem Biol Drug Des. 2010 Mar;75(3):295-309 PMID 20331647
Cites: Am J Public Health. 1999 Aug;89(8):1166-9 PMID 10432900
Cites: J Chem Inf Model. 2008 Aug;48(8):1602-15 PMID 18642866
Cites: Int J Public Health. 2015 Jul;60(5):619-27 PMID 26024815
Cites: J Mol Graph Model. 2010 Nov;29(3):363-71 PMID 20863730
Cites: SAR QSAR Environ Res. 2013;24(7):519-51 PMID 23305412
Cites: Bioorg Med Chem Lett. 2010 Nov 15;20(22):6644-8 PMID 20888765
Cites: J Mol Graph Model. 2009 Feb;27(6):735-43 PMID 19117780
Cites: Bioorg Med Chem Lett. 2004 Feb 9;14(3):653-6 PMID 14741262
Cites: Eur J Med Chem. 2012 Oct;56:387-95 PMID 22907036
Cites: Lancet Infect Dis. 2003 Jul;3(7):432-42 PMID 12837348
Cites: J Am Chem Soc. 1988 Aug 1;110(18):5959-67 PMID 22148765
Cites: Nat Med. 2000 Dec;6(12):1327-9 PMID 11100115
Cites: Bioorg Med Chem Lett. 2011 Jan 1;21(1):456-62 PMID 21084193
Cites: Chem Biol Drug Des. 2010 Dec;76(6):511-7 PMID 21040497
Cites: Stat Med. 1991 Jan;10(1):79-93 PMID 2006358
Cites: Ann Epidemiol. 2015 Mar;25(3):155-61 PMID 25498918
Cites: Nat Struct Biol. 2000 Aug;7(8):663-8 PMID 10932251
Cites: Arch Biochem Biophys. 1990 May 1;278(2):373-80 PMID 2183722
Cites: Bioorg Med Chem. 2008 Apr 1;16(7):3675-86 PMID 18299198
Cites: Eur J Med Chem. 2011 Aug;46(8):3499-508 PMID 21621311
Cites: Nat Rev Microbiol. 2007 Jan;5(1):39-47 PMID 17160001
Cites: Epidemiol Perspect Innov. 2004 Dec 16;1(1):5 PMID 15601477
Cites: J Bacteriol. 1977 Jul;131(1):136-44 PMID 17593
Cites: Int J Public Health. 2016 Apr;61(3):383-4 PMID 27117686
Cites: J Antibiot (Tokyo). 1990 Oct;43(10):1240-4 PMID 2258323
Cites: Nature. 2015 Sep 17;525(7569):367-71 PMID 26381985
Cites: J Mol Graph Model. 2010 Aug 24;29(1):54-71 PMID 20471293
Cites: J Mol Graph Model. 2001;20(2):111-21 PMID 11774998
Cites: Int J Public Health. 2016 Apr;61(3):387-8 PMID 27113708
Cites: Health Phys. 1999 Mar;76(3):269-74 PMID 10025652
Cites: Nature. 2016 Feb 4;530(7588):27-9 PMID 26842041
Cites: Science. 2010 May 14;328(5980):852-6 PMID 20466922
Cites: Stat Med. 1989 Jul;8(7):845-59 PMID 2772444
Cites: Nat Med. 2005 Jun;11(6):638-44 PMID 15895072
Cites: Chemosphere. 2010 Jan;78(3):300-6 PMID 19914677
Cites: J Mol Graph Model. 2010 Apr;28(7):612-25 PMID 20083418
CommentIn: Int J Public Health. 2017 Apr;62(3):339-341 PMID 28299391
PubMed ID
27447726 View in PubMed
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Spatial & temporal variations of PM10 and particle number concentrations in urban air.

https://arctichealth.org/en/permalink/ahliterature80607
Source
Environ Monit Assess. 2007 Apr;127(1-3):477-87
Publication Type
Article
Date
Apr-2007
Author
Johansson Christer
Norman Michael
Gidhagen Lars
Author Affiliation
Department of Applied Environmental Science, Stockholm University, S-106 91, Stockholm, Sweden. christer.johansson@itm.su.se
Source
Environ Monit Assess. 2007 Apr;127(1-3):477-87
Date
Apr-2007
Language
English
Publication Type
Article
Keywords
Environmental Exposure
Environmental Monitoring - methods
Geography
Particulate Matter - analysis
Public Health
Sweden
Urban Population
Vehicle Emissions - analysis
Abstract
The size of particles in urban air varies over four orders of magnitude (from 0.001 microm to 10 microm in diameter). In many cities only particle mass concentrations (PM10, i.e. particles
PubMed ID
16983585 View in PubMed
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Free and combined L- and D-amino acids in Arctic aerosol.

https://arctichealth.org/en/permalink/ahliterature299446
Source
Chemosphere. 2019 Apr; 220:412-421
Publication Type
Journal Article
Date
Apr-2019
Author
Matteo Feltracco
Elena Barbaro
Torben Kirchgeorg
Andrea Spolaor
Clara Turetta
Roberta Zangrando
Carlo Barbante
Andrea Gambaro
Author Affiliation
Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Via Torino 155, 30172, Venice, Italy; Institute for the Dynamics of Environmental Processes CNR, Via Torino 155, 30172, Venice, Italy. Electronic address: matteo.feltracco@unive.it.
Source
Chemosphere. 2019 Apr; 220:412-421
Date
Apr-2019
Language
English
Publication Type
Journal Article
Keywords
Aerosols - analysis
Air Pollutants - analysis
Amino Acids - analysis
Arctic Regions
Environmental Monitoring - methods
Particulate Matter - analysis
Seasons
Abstract
Aerosol samples were collected with a high-volume cascade impactor with a 10 day sampling frequency at the Gruvebadet observatory, close to Ny-Ålesund (Svalbard Islands). A total of 42 filters were analyzed for free and combined amino acids, as they are key components of bio-aerosol. This article provides the first investigation of free and combined L- and d-amino acids in Arctic atmospheric particulate matter. The main aim of this study was to determine how these compounds are distributed in size-segregated aerosols after short-range and long-range atmospheric transport and understand the possible sources of amino acids. The total load of free amino acids ranged from 2.0 to 10.8?pmol?m-3, while combined amino acids ranged from 5.5 to 18.0?pmol?m-3. At these levels amino compounds could play a role in the chemistry of cloud condensation nuclei and fine particles, for example by influencing their buffering capacity and basicity. Free and combined amino acids were mainly found in the fine aerosol fraction (
PubMed ID
30597360 View in PubMed
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Galvanic manufacturing in the cities of Russia: potential source of ambient nanoparticles.

https://arctichealth.org/en/permalink/ahliterature264256
Source
PLoS One. 2014;9(10):e110573
Publication Type
Article
Date
2014
Author
Kirill S Golokhvast
Anna A Shvedova
Source
PLoS One. 2014;9(10):e110573
Date
2014
Language
English
Publication Type
Article
Keywords
Air Pollutants - analysis - chemistry
Metal Nanoparticles - analysis - chemistry - ultrastructure
Metallurgy
Metals - analysis - chemistry
Particulate Matter - analysis - chemistry
Siberia
Abstract
Galvanic manufacturing is widely employed and can be found in nearly every average city in Russia. The release and accumulation of different metals (Me), depending on the technology used can be found in the vicinities of galvanic plants. Under the environmental protection act in Russia, the regulations for galvanic manufacturing do not include the regulations and safety standards for ambient ultrafine and nanosized particulate matter (PM). To assess whether Me nanoparticles (NP) are among environmental pollutants caused by galvanic manufacturing, the level of Me NP were tested in urban snow samples collected around galvanic enterprises in two cities. Employing transmission electronic microscopy, energy-dispersive X-ray spectroscopy, and a laser diffraction particle size analyzer, we found that the size distribution of tested Me NP was within 10-120 nm range. This is the first study to report that Me NP of Fe, Cr, Pb, Al, Ni, Cu, and Zn were detected around galvanic shop settings.
Notes
Cites: Toxicol Sci. 2001 Dec;64(2):243-5211719707
Cites: Environ Toxicol. 2013 Feb;28(2):61-7521384495
Cites: Environ Res. 1985 Feb;36(1):111-373917912
Cites: Am J Epidemiol. 1998 Aug 1;148(3):241-89690360
Cites: J Hazard Mater. 2005 Apr 11;120(1-3):113-815811671
Cites: J Hazard Mater. 2006 Apr 17;131(1-3):210-616297539
Cites: Cell Biochem Biophys. 2013 Nov;67(2):461-7622669739
Cites: Adv Drug Deliv Rev. 2013 Dec;65(15):2070-723726945
Cites: Eur Heart J. 2014 Apr;35(13):861-824302272
Cites: Environ Pollut. 2014 Jun;189:208-1424682071
Cites: Occup Environ Med. 2008 Jul;65(7):458-6617989204
Cites: Inhal Toxicol. 2009 Jan;21(1):1-3118803063
Cites: Int J Environ Health Res. 2009 Jun;19(3):175-8520183191
Cites: Circulation. 2010 Jun 1;121(21):2331-7820458016
Cites: J Appl Toxicol. 2012 Jun;32(6):446-5322161551
Cites: Methods Enzymol. 2012;509:195-22422568907
Cites: J Med Toxicol. 2012 Jun;8(2):166-7522194192
Cites: PLoS One. 2012;7(9):e4628623029464
Cites: Circulation. 2004 Jun 1;109(21):2655-7115173049
PubMed ID
25329582 View in PubMed
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A combined Arctic-tropical climate pattern controlling the inter-annual climate variability of wintertime PM2.5 over the North China Plain.

https://arctichealth.org/en/permalink/ahliterature298557
Source
Environ Pollut. 2019 Feb; 245:607-615
Publication Type
Journal Article
Date
Feb-2019
Author
Kan Yi
Junfeng Liu
Xuejun Wang
Jianmin Ma
Jianying Hu
Yi Wan
Jiayu Xu
Haozhe Yang
Huazhen Liu
Songlin Xiang
Shu Tao
Author Affiliation
Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, 100871, Beijing, People's Republic of China.
Source
Environ Pollut. 2019 Feb; 245:607-615
Date
Feb-2019
Language
English
Publication Type
Journal Article
Keywords
Air Pollutants - analysis
Air Pollution - analysis
Beijing
China
Environmental Monitoring - methods
Meteorology
Particulate Matter - analysis
Seasons
Tropical Climate
Abstract
In recent years, the Chinese government has made tremendous efforts to reduce the emissions of atmospheric pollutants throughout the country. An apparent improvement in air quality was observed in Beijing and its adjacent region during the winter of 2017/2018. However, caution should be taken in directly attributing this improvement to air control actions without taking the effects of climate variability into account. Here, we develop a statistical prediction model that can successfully predict the variability of wintertime PM2.5 concentrations observed over these regions. Our analysis indicates that the remarkable decrease in PM2.5 concentrations over the North China Plain (NCP) observed during the winter of 2017/2018 can be largely explained by changes in meteorological conditions. To clarify which climate factors control the inter-annual variability of wintertime PM2.5 pollution over the NCP, we further reconstructed a 30-year time series of wintertime PM2.5 levels over the NCP over the period of 1988-2017 using our statistical model. Through our analysis, we found that the combined Arctic-tropical climate effects related to the ENSO and Arctic warming controlled the inter-annual variability of wintertime PM2.5 over the NCP. Specifically, the rapid warming of the Barents-Kara Sea region enhances the Siberian High and thus plays an important role in improving the air quality over the NCP during the 2017/2018 wintertime. These results help us understand the role of climate variability in modulating air quality, especially its contributions to the winter of 2017/2018. These results may assist in the evaluation of current air control actions and the revision of relevant policy for the future, which are urgently needed for China.
PubMed ID
30476890 View in PubMed
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Influence of contemporary carbon originating from the 2003 Siberian forest fire on organic carbon in PM2.5 in Nagoya, Japan.

https://arctichealth.org/en/permalink/ahliterature268221
Source
Sci Total Environ. 2015 Oct 15;530-531:403-10
Publication Type
Article
Date
Oct-15-2015
Author
Fumikazu Ikemori
Koji Honjyo
Makiko Yamagami
Toshio Nakamura
Source
Sci Total Environ. 2015 Oct 15;530-531:403-10
Date
Oct-15-2015
Language
English
Publication Type
Article
Keywords
Air Pollutants - analysis
Air Pollution - statistics & numerical data
Carbon - analysis
Environmental monitoring
Fires
Forests
Particulate Matter - analysis
Siberia
Abstract
In May 2003, high concentrations of organic carbon (OC) in PM2.5 were measured in Nagoya, a representative metropolitan area in Japan. To investigate the influence of possible forest fires on PM2.5 in Japan via long-range aerosol transport, the radiocarbon ((14)C) concentrations of PM2.5 samples from April 2003 to March 2004 were measured. (14)C concentrations in total carbon (TC) from May to early June showed higher values than those in other periods. The OC/elemental carbon (EC) ratios from May to early June were also significantly higher than the ones in other periods. In addition, OC concentrations from May to early June were typically high. These results indicate that the abundant OC fraction from May to early June in Nagoya consisted predominantly of contemporary carbon. Furthermore, simulations of diffusion and transport of organic matter (OM) in East Asia showed that abundant OM originating from East Siberia spread over East Asia and Japan in May and early June. Backward air mass trajectories from this time frame indicate that the air mass in Nagoya likely first passed through East Siberia where fire events were prevalent. However, the backward trajectories showed that the air mass after early June did not originate mainly from Siberia, and correspondingly, the (14)C and OC concentrations showed lower values than those from May to early June. Therefore, the authors conclude that contemporary carbon originating from the forest fire in East Siberia was transported to Nagoya, where it significantly contributed to the high observed concentrations of both OC and (14)C.
PubMed ID
26066482 View in PubMed
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Multi-element analysis of airborne particulate matter from different work tasks during subsea tunnel rehabilitation work.

https://arctichealth.org/en/permalink/ahliterature286365
Source
J Occup Environ Hyg. 2016 Oct;13(10):725-40
Publication Type
Article
Date
Oct-2016
Author
Hanne Weggeberg
Solveig Føreland
Morten Buhagen
Bjørn Hilt
Trond Peder Flaten
Source
J Occup Environ Hyg. 2016 Oct;13(10):725-40
Date
Oct-2016
Language
English
Publication Type
Article
Keywords
Construction Industry
Environmental monitoring
Humans
Hydrocarbons
Inhalation Exposure - analysis
Norway
Occupational Exposure - analysis
Particle Size
Particulate Matter - analysis
Workplace
Abstract
Tunnel rehabilitation work involves exposure to various air contaminants, including airborne particulate matter (APM). Little is known on the contents of different chemical components of APM generated during tunnel work. The objective of the present study was to characterize exposure to APM and various elements for different job categories in different size fractions of APM during a subsea tunnel rehabilitation project carried out in Western Norway. Personal as well as stationary samples of inhalable, thoracic and respirable dust were collected from workers divided into 11 different job categories based on work operations performed, and air concentrations of a range of elements were determined using high-resolution inductively coupled plasma-mass spectrometry (HR-ICP-MS). Overall, APM concentrations were low, but with some measurements exceeding the American Conference of Governmental Industrial Hygienists (ACGIH) Threshold Limit Value (TLV) for inhalable particles, and considerable proportions of respirable and especially inhalable APM exceeding 10% of the TLVs. For most elements, air concentrations measured were quite low, in the ng/m(3) range, except for the major crustal elements Si, Fe, Al, and Mg, which were found to be in the µg/m(3) range. Asphalt millers overall had the highest exposure levels for APM and most measured elements; for instance, mean concentrations of V, Rb, and Mn were 380, 210, and 2000 ng/m(3) in inhalable and 33, 44, and 310 ng/m(3) in respirable APM. Mounting PVC membrane seemed to generate elevated levels of Cr, Zn, Sn, Pb, Sb, As, Mn, Fe, and Ni, whereas typical bedrock elements were elevated during drilling activities compared to the low exposed categories lead car drivers, foremen/surveyors, drivers of heavy-duty vehicles, and electricians. Overall, stationary samples contained lower amounts of dust and elemental constituents compared to personal samples. Elemental air concentrations were highly variable with occasional elevated values for APM and certain elements, particularly Cr and Zn.
PubMed ID
27078031 View in PubMed
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Effective density and mixing state of aerosol particles in a near-traffic urban environment.

https://arctichealth.org/en/permalink/ahliterature260095
Source
Environ Sci Technol. 2014 Jun 3;48(11):6300-8
Publication Type
Article
Date
Jun-3-2014
Author
Jenny Rissler
Erik Z Nordin
Axel C Eriksson
Patrik T Nilsson
Mia Frosch
Moa K Sporre
Aneta Wierzbicka
Birgitta Svenningsson
Jakob Löndahl
Maria E Messing
Staffan Sjogren
Jette G Hemmingsen
Steffen Loft
Joakim H Pagels
Erik Swietlicki
Source
Environ Sci Technol. 2014 Jun 3;48(11):6300-8
Date
Jun-3-2014
Language
English
Publication Type
Article
Keywords
Aerosols - analysis - chemistry
Cities
Denmark
Environmental Monitoring - methods
Particle Size
Particulate Matter - analysis - chemistry
Time Factors
Vehicle Emissions - analysis
Abstract
In urban environments, airborne particles are continuously emitted, followed by atmospheric aging. Also, particles emitted elsewhere, transported by winds, contribute to the urban aerosol. We studied the effective density (mass-mobility relationship) and mixing state with respect to the density of particles in central Copenhagen, in wintertime. The results are related to particle origin, morphology, and aging. Using a differential mobility analyzer-aerosol particle mass analyzer (DMA-APM), we determined that particles in the diameter range of 50-400 nm were of two groups: porous soot aggregates and more dense particles. Both groups were present at each size in varying proportions. Two types of temporal variability in the relative number fraction of the two groups were found: soot correlated with intense traffic in a diel pattern and dense particles increased during episodes with long-range transport from polluted continental areas. The effective density of each group was relatively stable over time, especially of the soot aggregates, which had effective densities similar to those observed in laboratory studies of fresh diesel exhaust emissions. When heated to 300 °C, the soot aggregate volatile mass fraction was ~10%. For the dense particles, the volatile mass fraction varied from ~80% to nearly 100%.
PubMed ID
24798545 View in PubMed
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112 records – page 1 of 12.